UNIVERSITY OF CALGARY

UNIVERSITY OF CALGARY
Quality of Life After Prostate Cancer Diagnosis: A Longitudinal Prospective Cohort Study in
Alberta, Canada
by
Megan Farris
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
GRADUATE PROGRAM IN COMMUNITY HEALTH SCIENCES
CALGARY, ALBERTA
SEPTEMBER, 2016
© Megan Farris 2016
Abstract
OBJECTIVES: First, we examined the associations of post-diagnosis physical activity
and change in pre-diagnosis physical activity on quality of life (QoL) in prostate cancer
survivors. Then, we identified post-prostate cancer diagnosis QoL trajectories over time
in the population.
METHODS: 830 prostate cancer survivors were derived from a prior case-control study
where information at diagnosis was collected, then survivors were re-consented into a
follow-up study. Three repeated measurements of physical activity and QoL were
undertaken post-diagnosis.
RESULTS: We observed improvements in physical QoL in prostate cancer survivors
who maintained or adopted higher levels of physical activity pre- and post-diagnosis,
according to the cancer prevention physical activity guidelines compared to those who
were non-exercisers. In the trajectory analysis, three physical and three mental trajectory
groups were identified.
CONCLUSION: With additional research, these established trajectory groups may help
healthcare professionals in improving treatment and follow-up for this population of
prostate cancer survivors.
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Acknowledgements
I would like to genuinely thank everyone involved in this Master’s thesis degree.
Specifically, all of the staff in the Department of Cancer Epidemiology and Prevention
Research at Alberta Health Services and the Department of Community Health Sciences
at the University of Calgary. Foremost, I would like to thank my supervisor, Dr. Christine
Friedenreich, for her constant support and mentorship throughout my Master's training.
Without her careful guidance and vote of confidence, this thesis would not be the caliber
it is today. I truly appreciate her time and willingness to help me achieve my goals and I
would not be where I am today without her. In addition, I would like to extend a special
thank you to my committee members: Dr. Karen Kopciuk for her careful guidance and
eagerness to pursue new statistical methods, Dr. Kerry Courneya for his expertise, speedy
reviews and generous praise and Dr. Elizabeth McGregor for her thorough remarks,
epidemiological capabilities and genuine support. I would also like to recognize the
contributions of my co-author, Qinggang Wang, on both manuscripts for his constructive
feedback and interest in my work and data management of the study's datasets.
I would like to thank Dr. Tavis Campbell, Professor, Department of Psychology at the
University of Calgary for agreeing to serve as my internal-external examiner with the
Department of Community Health Sciences.
A special thank you is also necessary to all of the participants in the Prostate Cancer
Cohort Study who generously gave their time to participate in the study making this
project possible. I would like to thank all past and present study staff from the
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Department of Cancer Epidemiology and Prevention Research who made this project
possible. Thank you to my colleagues and peers who supported, mentored and kept me
level-headed through this process: Eileen Shaw, Abbey Poirier, Trisha Kelly, Sarah
MacLaughlin, Pamela Round and Dr. Darren Brenner. A special thanks to my peer
mentor, Eileen Shaw (BB), I could not have done this without you! To my dear
classmates and now friends, Kathryn Wiens, Lauren Hiersch, Amanda Barberio, Chelsea
Doktorchik and my office mates Stephanie Gill and Alexis Mickle, thank you for being
there for all of the ups and downs, for guidance and keeping me grounded, you truly
made this experience wonderful.
On a more personal note, I want to send my utmost thanks and praise to my loving
parents. Without their support, willingness to listen to my problems and for providing
dinner at the end of a long day, this degree would not have been finished on time. I would
also like to thank my dearest boyfriend Troy Bozarth, for his patience and unconditional
support of all my endeavors in every possible way; my two brothers, Mitchell Farris and
Blake Farris for their support and occasional comic relief in times of stress; and my
childhood friend Kathryn Saretsky for always being there for me.
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Table of Contents
Abstract ................................................................................................................................2
Acknowledgements ..............................................................................................................3
Table of Contents .................................................................................................................5
List of Tables .......................................................................................................................8
List of Figures ....................................................................................................................12
List of Symbols, Abbreviations and Nomenclature ...........................................................13
CHAPTER ONE: INTRODUCTION..................................................................... 15
1.1 Background ..............................................................................................................15
1.1.1 Burden of prostate cancer ................................................................................15
1.1.2 Importance of quality of life in prostate cancer...............................................16
1.1.2.1 Prevention of quality of life reductions through physical activity.........17
1.1.2.2 High-risk groups for low QoL in prostate cancer populations ..............18
1.2 Aims and hypotheses ...............................................................................................18
1.3 Ethics approval ........................................................................................................20
1.4 References ................................................................................................................21
CHAPTER TWO: LITERATURE REVIEW .......................................................... 25
2.1 Preamble ..................................................................................................................25
2.2 Part 1: Physical activity and prostate cancer ...........................................................25
2.2.1 Physical activity and prostate cancer risk ........................................................26
2.2.2 Physical activity and prostate cancer prognosis and survivorship ..................27
2.2.3 Physical activity and quality of life in prostate cancer survivors ....................28
2.3 Part 2: Change in quality of life after prostate cancer .............................................33
2.3.1 Long-term change in quality of life after prostate cancer: epidemiological
evidence ...........................................................................................................34
2.3.2 Growth modelling and prostate cancer ............................................................37
2.4 References ................................................................................................................40
2.5 Tables .......................................................................................................................50
CHAPTER THREE: ASSOCIATION OF POST-DIAGNOSIS PHYSICAL
ACTIVITY AND CHANGE IN PRE-DIAGNOSIS PHYSICAL ACTIVITY
WITH QUALITY OF LIFE IN PROSTATE CANCER SURVIVORS ............. 62
3.1 Preamble ..................................................................................................................62
3.2 Abstract ....................................................................................................................64
3.3 Introduction ..............................................................................................................66
3.4 Materials and methods .............................................................................................67
3.4.1 Study design ....................................................................................................67
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3.4.2 Data collection .................................................................................................68
3.4.3 Physical activity assessment ............................................................................69
3.4.4 Quality of life assessment ................................................................................70
3.4.5 Statistical analysis ...........................................................................................71
3.5 Results ......................................................................................................................72
3.5.1 Descriptive statistics ........................................................................................72
3.5.2 Post-diagnosis physical activity and quality of life at first follow-up .............73
3.5.3 Change in physical activity over the diagnostic period and quality of life .....74
3.6 Discussion ................................................................................................................75
3.7 Conclusion ...............................................................................................................79
3.8 Manuscript acknowledgements................................................................................80
3.9 References ................................................................................................................81
3.10 Tables and figures ..................................................................................................87
3.11 Additional analyses and results: Lasso and elastic-net regularized generalized
linear model (GLMNET package) covariate selection methods............................96
CHAPTER FOUR: IDENTIFICATION AND PREDICTION OF QUALITY OF
LIFE TRAJECTORIES AFTER A PROSTATE CANCER DIAGNOSIS ..... 103
4.1 Preamble ................................................................................................................103
4.2 Abstract ..................................................................................................................105
4.3 Introduction ............................................................................................................107
4.4 Materials and methods ...........................................................................................108
4.4.1 Study sample .................................................................................................108
4.4.2 Data collection ...............................................................................................109
4.4.3 Statistical analysis .........................................................................................110
4.4.3.1 Identifying trajectory groups ...............................................................110
4.4.3.2 Characteristics of trajectory groups .....................................................112
4.4.3.3 Sensitivity analyses ..............................................................................113
4.5 Results ....................................................................................................................113
4.5.1 Sample ...........................................................................................................113
4.5.2 Trajectory group selection and evaluation ....................................................114
4.5.3 Characteristics of trajectory groups ...............................................................116
4.5.4 Dropout within trajectory groups ..................................................................118
4.5.5 Sensitivity analyses .......................................................................................119
4.6 Discussion ..............................................................................................................120
4.7 Conclusion .............................................................................................................124
4.8 Manuscript acknowledgements..............................................................................124
4.9 References ..............................................................................................................125
4.10 Tables and Figures ...............................................................................................129
4.11 Additional analyses and results: Sensitivity analysis tables and figures .............143
4.11.1 Dropout within trajectory groups ................................................................143
4.11.2 Sensitivity analyses: Complete case analysis for physical and mental
QoL trajectories .............................................................................................150
4.11.3 Sensitivity analyses: Two complete assessments analysis for physical
and mental QoL trajectories ...........................................................................158
4.11.4 Sensitivity analysis: Not modelling dropout on covariate significance
analysis ...........................................................................................................166
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4.11.5 Sensitivity analyses: Dropout switchers between trajectory groups with
and without modelling dropout ......................................................................170
4.11.6 Sensitivity analysis: Time lagged model for previous quality of life score
analysis ...........................................................................................................173
CHAPTER FIVE: DISCUSSION........................................................................ 177
5.1 Summary of findings .............................................................................................177
5.2 Limitations .............................................................................................................178
5.2.1 Internal validity .............................................................................................179
5.2.1.1 Survivorship bias due to missing data and dropout .............................179
5.2.1.2 Measurement error with self-administered questionnaires ..................181
5.2.1.3 Confounding ........................................................................................183
5.2.1.4 Precision and issues with multiple statistical tests...............................185
5.2.1.5 Causal inference ...................................................................................187
5.2.2 External validity ............................................................................................187
5.3 Public health implications ......................................................................................188
5.4 Future directions ....................................................................................................190
5.5 Conclusion .............................................................................................................191
5.6 References ..............................................................................................................193
Appendices A: Lifetime Physical Activity Questionnaire ...............................................195
Appendices B: Past-Year Physical Activity Questionnaire .............................................204
Appendices C: Well-being Questionnaire .......................................................................213
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List of Tables
Table 2.1. Physical activity and SF-36 quality of life studies in prostate cancer
populations. ............................................................................................................... 50
Table 2.2. Prostate cancer cohort studies examining changes in QoL post-diagnosis
using the SF-36 published between 2011-2016. ....................................................... 57
Table 3.1. Characteristics for prostate cancer survivors (n=817), in the Prostate
Cancer Cohort Study, Alberta, Canada, 1997-2014.................................................. 87
Table 3.2. Associations between post-diagnosis first follow-up physical activity and
post-diagnosis first follow-up quality of life in prostate cancer survivors in
Alberta, Canada in 1997-2002. ................................................................................. 90
Table 3.3. Associations between physical activity change (pre-diagnosis minus
average post-diagnosis scores) over the diagnosis period and average postdiagnosis quality of life in prostate cancer survivors in Alberta, Canada in 19972007........................................................................................................................... 92
Table 3.4. Change in moderate-to-vigorous recreational physical activity based on
meeting the cancer prevention guidelines (150 minutes/week) over the diagnostic
period (pre- and average post-diagnosis) and average post-diagnosis quality of
life in prostate cancer survivors in Alberta, Canada in 1997-2007. .......................... 94
Table 3.5. Associations between post-diagnosis physical activity on first follow-up
post-diagnosis QoL using lasso and elastic-net regularized generalized linear
model (GLMNET package) methods in prostate cancer survivors in Alberta,
Canada in 1997-2002 (n=817). ................................................................................. 98
Table 4.1. Model selection to determine number of groups for physical quality of life
post-diagnosis in prostate cancer survivors (n = 817) in Alberta, Canada (19972007). ...................................................................................................................... 129
Table 4.2. Model selection for determining linear or quadratic structure of trajectories
for physical quality of life post-diagnosis in prostate cancer survivors (n = 817)
in Alberta, Canada (1997-2007). ............................................................................ 130
Table 4.3. Model selection to determine number of groups for mental quality of life
post-diagnosis in prostate cancer survivors (n = 817) in Alberta, Canada (19972007). ...................................................................................................................... 131
Table 4.4. Model selection for determining linear or quadratic structure of trajectories
for mental quality of life post-diagnosis in prostate cancer survivors (n = 817) in
Alberta, Canada (1997-2007).................................................................................. 132
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Table 4.5. Descriptive characteristics by physical quality of life trajectory group of
prostate cancer survivors (n = 817) in Alberta, Canada (1997-2007)..................... 133
Table 4.6. Descriptive characteristics by mental quality of life trajectory group of
prostate cancer survivors (n = 817) in Alberta, Canada (1997-2007)..................... 135
Table 4.7. Multinomial logistic regression models of influential factors on physical
and mental QoL trajectory group membership relative to the averagemaintaining/increasing QoL group in prostate cancer survivors (n = 817) in
Alberta, Canada (1997-2007).................................................................................. 137
Table 4.8. Maximum likelihood estimates for the mean coefficients and
corresponding standard errors for physical and mental quality of life trajectory
groups from final adjusted group-based trajectory models including physical
activity as a time-varying covariate in prostate cancer survivors (n = 817) in
Alberta, Canada (1997-2007). ................................................................................. 139
Table 4.9. Logistic regression models assessing the relationship between dropping out
and covariates within the very low-maintaining physical QoL trajectory group. ... 144
Table 4.10. Logistic regression models assessing the relationship between dropping
out and covariates within the low-declining physical QoL trajectory group. ......... 145
Table 4.11. Logistic regression models assessing the relationship between dropping
out and covariates within the average-maintaining physical QoL trajectory
group. ...................................................................................................................... 146
Table 4.12. Logistic regression models assessing the relationship between dropping
out and covariates within the low-increasing mental QoL trajectory group. .......... 147
Table 4.13. Logistic regression models assessing the relationship between dropping
out and covariates within the above average-declining mental QoL trajectory
group. ...................................................................................................................... 148
Table 4.14. Logistic regression models assessing the relationship between dropping
out and covariates within the average-maintaining mental QoL trajectory group. . 149
Table 4.15 Model selection to determine number of groups for physical quality of life
post-diagnosis in prostate cancer survivors (n = 454) in Alberta, Canada (19972007). ...................................................................................................................... 151
Table 4.16. Model selection for determining linear or quadratic structure of
trajectories for physical quality of life post-diagnosis in prostate cancer survivors
(n = 454) in Alberta, Canada (1997-2007).............................................................. 152
Table 4.17. Model selection to determine number of groups for mental quality of life
post-diagnosis in prostate cancer survivors (n = 454) in Alberta, Canada (19972007). ...................................................................................................................... 153
9
Table 4.18. Model selection for determining linear or quadratic structure of
trajectories for mental quality of life post-diagnosis in prostate cancer survivors
(n = 454) in Alberta, Canada (1997-2007).............................................................. 154
Table 4.19. Multinomial logistic regression models of influential factors on physical
and mental QoL trajectory group membership relative to the averagemaintaining/increasing QoL group in prostate cancer survivors (n = 454) in
Alberta, Canada (1997-2007).................................................................................. 155
Table 4.20. Maximum likelihood estimates for the mean coefficients and
corresponding standard errors for physical and mental quality of life trajectory
groups from final adjusted group-based trajectory models including physical
activity as a time-varying covariate in prostate cancer survivors (n = 454) in
Alberta, Canada (1997-2007). ................................................................................. 157
Table 4.21. Model selection to determine number of groups for physical quality of
life post-diagnosis in prostate cancer survivors (n = 636) in Alberta, Canada
(1997-2007). ............................................................................................................ 159
Table 4.22. Model selection for determining linear or quadratic structure of
trajectories for physical quality of life post-diagnosis in prostate cancer survivors
(n = 636) in Alberta, Canada (1997-2007).............................................................. 160
Table 4.23. Model selection to determine number of groups for mental quality of life
post-diagnosis in prostate cancer survivors (n = 636) in Alberta, Canada (19972007). ...................................................................................................................... 161
Table 4.24. Model selection for determining linear or quadratic structure of
trajectories for mental quality of life post-diagnosis in prostate cancer survivors
(n = 636) in Alberta, Canada (1997-2007).............................................................. 162
Table 4.25. Multinomial logistic regression models of influential factors on physical
and mental QoL trajectory group membership relative to the averagemaintaining/increasing QoL group in prostate cancer survivors (n = 636) in
Alberta, Canada (1997-2007).................................................................................. 163
Table 4.26. Maximum likelihood estimates for the mean coefficients and
corresponding standard errors for physical and mental quality of life trajectory
groups from final adjusted group-based trajectory models including physical
activity as a time-varying covariate in prostate cancer survivors (n = 636) in
Alberta, Canada (1997-2007). ................................................................................. 165
Table 4.27. Multinomial logistic regression models of influential factors on physical
and mental QoL trajectory group membership relative to the averagemaintaining/increasing QoL group without modelling dropout in prostate cancer
survivors (n = 817) in Alberta, Canada (1997-2007). ............................................. 167
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Table 4.28. Maximum likelihood estimates for the mean coefficients and
corresponding standard errors for physical and mental quality of life trajectory
groups from final adjusted group-based trajectory models including physical
activity as a time-varying covariate and not modelling dropout in prostate cancer
survivors (n = 817) in Alberta, Canada (1997-2007). ............................................. 169
Table 4.29. Participants who switched physical quality of life trajectory groups when
modelling dropout vs. not modelling dropout in prostate cancer survivors (n =
817) in Alberta, Canada (1997-2007). .................................................................... 171
Table 4.30. Participants who switched mental quality of life trajectory groups when
modelling dropout vs. not modelling dropout in prostate cancer survivors (n =
817) in Alberta, Canada (1997-2007). .................................................................... 172
Table 4.31. Time-lagged multinomial logistic regression models of influential factors
on physical and mental QoL trajectory group membership relative to the
average-maintaining/increasing QoL group in prostate cancer survivors (n =
817) in Alberta, Canada (1997-2007). .................................................................... 174
Table 4.32. Maximum likelihood estimates for the mean coefficients and
corresponding standard errors for physical and mental quality of life trajectory
groups from final adjusted group-based trajectory time-lagged models including
physical activity as a time-varying covariate in prostate cancer survivors (n =
817) in Alberta, Canada (1997-2007). .................................................................... 176
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List of Figures
Figure 3.1. Change in moderate-to-vigorous recreational physical activity guideline
adherence groups over the diagnostic period in prostate cancer survivors in
Alberta, Canada (2000-2007).................................................................................... 95
Figure 3.2. Total physical activity and PCS score output using GLMNET
regularization. ........................................................................................................... 99
Figure 3.3. Total physical activity and MCS score output using GLMNET
regularization. ......................................................................................................... 100
Figure 3.4. Recreational, occupational and household physical activity (mutually
adjusted) and PCS score output using GLMNET regularization. ........................... 101
Figure 3.5. Recreational, occupational and household physical activity (mutually
adjusted) and MCS score output using GLMNET regularization........................... 102
Figure 4.1. Repeated measurements and cohort timeline. Past two years of physical
activity were recalled and past four weeks of QoL (N = complete QoL
measurements at each time point). Abbreviations: PA = physical activity, QoL =
quality of life, Dx = diagnosis................................................................................. 140
Figure 4.2. Final adjusted model selected for physical QoL trajectory groups (black
lines are trajectory groups, grey lines are 95% confidence and dot symbols are
observed group means at each time-point) in cohort of prostate cancer survivors
in Alberta, Canada (n=817). Abbreviations: QoL=quality of life, DP=dropout
probability. .............................................................................................................. 141
Figure 4.3. Final adjusted model selected for mental QoL trajectory groups (black
lines are trajectory groups, grey lines are 95% confidence intervals and dot
symbols are observed group means at each time-point) in cohort of prostate
cancer survivors in Alberta, Canada (n=817). Abbreviations: QoL=quality of
life, DP=dropout probability. .................................................................................. 142
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List of Symbols, Abbreviations and Nomenclature
Symbol
>
<
Definition
Greater than Less than ≥
≤
±
Δ

ACSM
ADT
BMI
CaPSURE
Greater than or equal to Less than or equal to
Plus or minus
Change
Beta coefficient
American College of Sports Medicine
Androgen Deprivation Therapy
Body Mass Index
Cancer of the Prostate Strategic Urologic Research
Endeavour
Conjoint Health Research Ethics Board
Confidence Interval
European Organization for Research and Treatment of
Cancer Core Quality of Life Questionnaire
European Organization for Research and Treatment of
Cancer Quality of Life Questionnaire-Prostate 25
Expanded Prostate Index Composite
Functional Assessment of Cancer TherapyProstate/Breast/General
High-Intensity Focused Ultrasound
Hazard Ratio
hour
International Physical Activity Questionnaire
International Prostate Symptom Score
Incidence Rate Ratio
Lifetime Physical Activity Questionnaire
Mental Component Summary
Medical Subject Heading
Metabolic Equivalent
Nerve Sparing Radical Prostatectomy
Physical Component Summary
Patient-Oriented Prostate Utility Scale
Prostate Specific Antigen
Past Year Total Physical Activity Questionnaire
Quality of Life
Relative Risk
Standard Deviation
Short Form-36
Specific, Measurable, Achievable, Realistic and Timebound
CHREB
CI
EORTC QLQ-C30
EORTC QLQ-PR25
EPIC
FACT-P/B/G
HIFU
HR
hr
IPAQ
IPSS
IRR
LTPAQ
MCS
MeSH
MET
NSRP
PCS
PORPUS
PSA
PYTPAQ
QoL
RR
SD
SF-36
SMART
13
SMD
UCLA-PCI
USA
Standard Mean Difference
University of California, Los Angeles-Prostate Cancer
Index
United States of America
14
Chapter One: INTRODUCTION
1.1 Background
1.1.1 Burden of prostate cancer
Prostate cancer is one of the most prevalent cancers among Canadian men (1, 2).
In 2015 an estimated 24,000 new cases and 4,100 deaths from prostate cancer occurred in
Canada accounting for 23.9% cancer diagnoses and 10.1% cancer deaths in men (3).
Early detection and improved treatment have resulted in a decrease in mortality rates and
greater numbers of prostate cancer survivors (4). In 2015 there were 176,365 prevalent
prostate cancer cases in Canada and five-year relative survival rates were approximately
93% (3).
One major contributing factor to the increased prevalence of prostate cancer in
developed countries was the introduction of the Prostate Specific Antigen (PSA) testing
in the early 1990s (5, 6). PSA is a protein produced in the prostate gland and the PSA test
measures the level of PSA in blood (7). Until recently, if elevated levels of PSA were
detected (>4.0 ng/mL), it was recommended to monitor and potentially biopsy for
prostate cancer (8). However, benign medical conditions such as prostatitis or urinary
tract infection may also result in increased PSA levels (9). Further, recent studies have
also found that prostate cancer patients can have low PSA levels while others might have
high PSA levels and therefore, putting into question the sensitivity and specificity of the
test (10). While early detection of prostate cancer can lead to decreased mortality, overdetection of low grade non-threatening prostate cancers can negatively affect the health
of individuals, as well as the healthcare system. This increase of detection of low-grade
prostate cancers has resulted in over-treatment with a consequent increase in the side
effects related to prostate cancer treatments. These side effects may include reduced
15
physical functioning, a compromised mental state and overall reduced quality of life
(QoL) (11-15). As a result, PSA testing are under scrutiny to provide more accurate
thresholds and recommendations for better detection methods of prostate cancer (16). In
addition, there has been an increased awareness of the need for “watchful waiting” rather
than active treatment for some early stage prostate cancers (17).
1.1.2 Importance of quality of life in prostate cancer
High priorities in prostate cancer control involve improving treatment and care of
prostate cancer and reducing the burdens of living beyond prostate cancer which includes
improving QoL and addressing the specific needs of prostate cancer survivors (18). Soon
after prostate cancer diagnosis, patients undergo different treatment regimens that include
surgery, hormone therapy and radiation therapy that have been shown to improve prostate
cancer survival (19). However, living longer with prostate cancer may be associated with
factors that impact QoL and survivorship including: treatment side effects, co-morbidities
(including related causes of death, such as coronary heart disease), age, race and lifestyle
behavioural factors (20). These factors may be more detrimental than the diagnosis itself
and also lead to a decreased QoL post-diagnosis. QoL assessments in cancer patients
measure physical, mental, social, and spiritual well-being. Physical well-being generally
refers to functional ability in daily living and is well-recognized from a public health
perspective. It is measured through objective tests like muscular strength, endurance, and
independence (21). Specifically for prostate cancer, sufficient function of the lower
extremities is very important since the cancer may produce disease-specific deficiencies
such as sexual dysfunction, impaired bowel function and urinary incontinence (22-25).
Prostate cancer patients suffer psychological and sexual problems especially post16
treatment, that have major impacts on QoL (26). Alternatively, patient feedback can
measure aspects of disease and treatment including: various symptoms, symptom
severity, symptom frequency, nature of disability that are specific to the patient, impacts
on daily life, and perceptions or feelings of the patient towards their conditions (27). The
patients' concerns are important to examine since not all physical, physiological or
biological exams are able to capture this type of information on patients' overall wellbeing. Therefore, patients may need their treatment regimens individualized to account
for individual patient differences and to deliver optimal care after prostate cancer
diagnosis.
1.1.2.1 Prevention of quality of life reductions through physical activity
While different treatments negatively affect QoL after prostate cancer diagnosis
(28, 29), long-term effects of diagnosis and treatment remain poorly documented and
understood (15). Therefore, healthcare systems are now focusing on preventing
reductions of QoL after prostate cancer diagnosis through different mechanisms.
Prognostic factors such as age, stage of cancer, PSA levels, and co-morbidities are nonmodifiable (4, 30), whereas dietary intake and physical activity are modifiable lifestyle
factors that may be of importance for maintaining QoL post-diagnosis (22, 31). There is a
wealth of literature focusing on physical activity interventions and the impacts of
treatments related to prostate cancer, including androgen deprivation therapy (ADT),
which indicate improvements in QoL in prostate cancer populations (32-37). However,
most studies focus on exercise interventions directly after prostate cancer diagnosis,
therefore, long term QoL has not been assessed. There is a need for more research
17
focusing on different types of physical activity, as well as documented change in physical
activity behaviours across the diagnostic period and the impacts on QoL.
1.1.2.2 High-risk groups for low QoL in prostate cancer populations
Another target of prevention and/or intervention is to identify subgroups in the
population that are at higher risk for prolonged suffering after prostate cancer diagnosis
(38). In this case, higher risk would be defined as those with low or declining QoL after
prostate cancer diagnosis. Identifying those who experience reduced QoL after diagnosis
of prostate cancer is crucial to improving care. Change in QoL has generally been
examined as a single or average pattern after diagnosis of prostate cancer. There is no
substantial evidence behind why there may only be one pattern of QoL or if there are
indeed differences between prostate cancer survivors’ QoL after diagnosis. There may be
subtle differences between prostate cancer survivors that are missed by modelling the
average QoL over time after diagnosis. Therefore, a need exists to test whether or not
there are different trajectory patterns of QoL long term after prostate cancer diagnosis
among prostate cancer survivors. Health professions need to be informed of different
trajectory patterns in order to target specific QoL prostate cancer survivors to prevent any
declines in health, QoL and survival.
1.2 Aims and hypotheses
The aim of this thesis was to examine different aspects of prostate cancer
survivors’ QoL after diagnosis. This was accomplished by examining, first, how different
types of post-diagnosis physical activity as well as changes from pre- to post-diagnosis
physical activity influenced QoL post-diagnosis among a cohort of prostate cancer
18
survivors. Secondly, we investigated if there were different QoL trajectories within the
study population and characterized these trajectory groups systematically. Specifically,
the study objectives were as follows:
1) To investigate the association between post-diagnosis total, recreational,
occupational and household physical activity and QoL post-diagnosis in a cohort
of prostate cancer survivors in Alberta. We hypothesize that a per unit increase in
post-diagnosis physical activity will be associated with positive QoL scores in a
cohort of prostate cancer survivors.
2) To investigate the association between the change in pre- and post-diagnosis total,
recreational, occupational and household physical activity and QoL postdiagnosis in a cohort of prostate cancer survivors. Further, to investigate the
association between adherence to cancer prevention physical activity guidelines
pre- and post-diagnosis and QoL post-diagnosis in prostate cancer survivors in
Alberta. We hypothesize that a per unit increase of physical activity change
scores will be positively associated with QoL scores in a cohort of prostate
cancer survivors. Secondly, we hypothesized that meeting cancer prevention
physical activity guidelines pre- and post-diagnosis would result in higher postdiagnosis QoL scores in prostate cancer survivors compared to those who did not
meet guidelines.
3) To examine post-diagnosis QoL trajectory groups in a cohort of prostate cancer
survivors in Alberta, during the follow-up period. We hypothesize that there
19
would be more than one and up to four different trajectories of QoL in prostate
cancer survivors over time.
1.3 Ethics approval
The University of Calgary Conjoint Health Research Ethics Board (CHREB) has
approved the protocol for the “Cohort Study of Physical Activity and Prostate Cancer
Survival” (16369) project and approved the addition of me as a study investigator to
undertake the work for this thesis project, September 25, 2015.
20
1.4 References
1.
Siegel R, DeSantis C, Virgo K, Stein K, Mariotto A, Smith T, et al. Cancer
treatment and survivorship statistics, 2012. CA Cancer J Clin. 2012;62(4):220-41.
2.
Torre LA, Siegel RL, Ward EM, Jemal A. Global Cancer Incidence and Mortality
Rates and Trends-An Update. Cancer Epidemiol Biomarkers Prev. 2016;25(1):16-27.
3.
Canadian Cancer Society's Advisory Committee on Cancer S. Canadian Cancer
Statistics 2015. Toronto, ON: Canadian Cancer Society, 2015.
4.
Aziz NM. Cancer survivorship research: challenge and opportunity. J Nutr.
2002;132:3494S-503S.
5.
Ferlay J SI, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM,
Forman D, Bray, F. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality
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24
Chapter Two: LITERATURE REVIEW
2.1 Preamble
This chapter reviews two separate bodies of literature: 1) the scientific evidence
regarding the association between physical activity and prostate cancer risk, mortality and
QoL, and; 2) the evidence regarding how QoL changes after prostate cancer diagnosis.
2.2 Part 1: Physical activity and prostate cancer
There is accumulating epidemiologic evidence that physical activity is associated
with both cancer risk and recently also with cancer survival. To summarize this
literature, two separate searches of the MEDLINE database using PubMed, up to June 3rd,
2016, were conducted on the association between physical activity and prostate cancer
risk and survival. Keywords and medical subject heading (MeSH) terms (exploded) were
used. For prostate cancer risk, the search terms were: ((“physical activity” OR “motor
activity” OR exercise) AND (“prostate cancer” OR “prostatic cancer”) AND (risk OR
“risk factors” OR “risk factor”)), including Boolean operating terms. This search
rendered 465 results, of which, the most recent select studies and review paper are
reported to briefly review this literature. For prostate cancer survival, the search terms
were: ((“physical activity” OR “motor activity” OR exercise) AND (“prostate cancer”
OR “prostatic cancer”) AND (survivor* OR survivorship OR patients)), including
Boolean operating terms. This second search rendered 467 results and no language, date,
or geographical restrictions were applied. Abstracts and unpublished results were not
included. Overall, only four studies fit the criteria examining how physical activity
impacts survival in a prostate cancer population. All other search results were irrelevant
in regards to study population, exposure assessment, outcome assessment or otherwise
25
off topic. The four relevant publications including observational studies examining
physical activity as an exposure and prostate cancer as an outcome, or prostate cancer as
the population of interest with mortality or progression as the outcome, are discussed
below.
2.2.1 Physical activity and prostate cancer risk
Physical activity has been identified as a possible protective behaviour for several
cancer sites (1). There is plausible evidence for an etiologic role of physical activity in
breast (2), colorectal (3), and endometrial cancers (4). However, there is inconsistent
evidence that physical activity is associated with prostate cancer risk (1, 5, 6). To date,
more than 55 retrospective and prospective epidemiological studies have examined the
association between physical activity and prostate cancer risk (7, 8). Of these studies,
most demonstrate a slight protective effect (7, 9-11), while others support a null
hypothesis (12, 13) and increased risk (14, 15). These differences in the literature may be
attributable to physical activity measurement error, long latency periods and slow tumour
growth for prostate cancer (7). Investigations into this association often experience
difficulties comparing prostate cancer cases and healthy controls, given the high
probability of undetected latent prostate cancer in the general population. Further, there
may be unmeasured competing risks or confounding variables potentially generating
differences in results between studies.
Although the evidence is inconsistent for a role of physical activity in prostate
cancer risk overall, there is some evidence of a protective effect on the risk of advanced
prostate cancer (6, 16). The Health Professionals Follow-up Study (17) identified no
association with observed total prostate cancer cases and vigorous physical activity
26
(relative risk (RR) = 1.09, 95% confidence interval (CI): 0.94-1.28) but supported a
reduced risk in the highest intensity of vigorous activity and advanced or fatal prostate
cancer cases in men older than 65 years of age (RR = 0.33, 95% CI: 0.17-0.62).
Furthermore, the American Cancer Society Cancer Prevention Study II Nutrition Cohort
(18) collected information on recreational physical activity, reporting no association in
overall prostate cancer risk (RR = 0.90, 95% CI: 0.78-1.04), but a potential association of
reduced risk of aggressive prostate cancer (RR = 0.69, 95% CI: 0.52-0.92). On the
contrary, the European Prospective Investigation into Cancer Cohort Study (19) reported
no association between vigorous recreational physical activity and advanced prostate
cancer risk (incidence rate ratio (IRR) = 1.23, 95% CI: 0.91-1.65), but an inverse
association between occupational vigorous physical activity and advanced prostate cancer
risk (IRR = 0.75, 95% CI: 0.53-1.05). These inconsistencies may be due to measurement
error associated with the assessment of physical activity. Across studies, there is not a
uniform definition of the highest levels of physical activity. Nonetheless, there remains a
potential for physical activity being an etiologically relevant lifestyle factor for prostate
cancer, particularly given its long latency period (20).
2.2.2 Physical activity and prostate cancer prognosis and survivorship
Prostate cancer has a promising prognosis (21), with the five-year survival rate of
approximately 93% in Canada (22). There is now emerging evidence regarding a
potentially promising role for physical activity as a protective agent to reduce side effects
of cancer, other comorbid conditions and causes of death. To date, few prospective
epidemiologic studies have examined the role of increased regular physical activity in
improving prostate cancer survival (23-26). Three of these studies examined the
27
association between post-diagnosis physical activity on all-cause and cancer-specific
mortality (23, 24, 26). The first study examining the association between total physical
activity and prostate cancer prognosis was published in 2011 (23), with 33-35% risk
reductions observed in both overall and prostate cancer-specific mortality. Two other
studies (24, 26) found risk reductions similar in magnitude, however, their lowest risk
reductions were attributed to different amounts of recreational physical activity rather
than total activity. Richman and colleagues (25), found a reduced risk of prostate cancer
progression with increased walking pace regardless of walking duration.
It has been hypothesized that men may reduce their physical activity as their
health decreases after prostate cancer. Therefore, the focus of this study on the
progression of disease rather than mortality reduces any influence of reverse causation
due to declined health. There is some suggestion of consistency in the literature that
physical activity improves prostate cancer survival (23-26), however, the evidence is
limited to few studies.
2.2.3 Physical activity and quality of life in prostate cancer survivors
A separate search of the literature of the MEDLINE database using the PubMed
up to August 16th, 2016 was conducted to review the literature regarding physical activity
and QoL in prostate cancer survivors. The search included keywords and MeSH terms
(exploded) as follows: ((“physical activity” OR “motor activity” OR exercise) AND
(“prostate cancer” OR “prostatic cancer”) AND (“quality of life” OR QoL) AND
(survivor* OR survivorship OR patients)) including Boolean operating terms. No date,
language, or geographical restrictions were applied. Abstracts and unpublished results
were not included in this review. This search rendered 192 results, of which 39 original
28
research articles and five review articles were found to be relevant and discussed below.
Other studies were excluded due to irrelevant study population, physical activity or an
exercise intervention was not the exposure or QoL was not an outcome.
To date, the association between physical activity and QoL in prostate cancer
survivors has been limited to few long-term epidemiological studies and exercise
interventions. Five systematic review articles have attempted to summarize these
associations in different capacities and have a common message; physical activity
improves QoL (27-31). Two systematic reviews (28, 30) limited their inclusion criteria to
prostate cancer survivors who underwent an exercise intervention and received ADT.
Others restricted their inclusion criteria to exercise interventions at least four weeks long
(27) or resistance exercise interventions (29). Two reviews (30, 31) were able to perform
a meta-analysis. Teleni et al. (2015) included five intervention studies with common
measurements of QoL and found statistically significant pooled standard mean
differences (SMD) of 0.29 (95% CI: 0.10-0.49) related to health-related QoL and 0.36
(95% CI: 0.11-0.61) related to disease-specific QoL. While only a fraction of exercise
interventions were included in this meta-analysis, heterogeneity was minimal (I2% = 0).
Alternatively, Bourke et al., 2016 included seven exercise randomized controlled trials in
their meta-analysis and found no significant improvements in cancer-specific QoL with
SMD = 0.13 (95% CI: -0.08-0.34). However, in a sensitivity analysis excluding studies
they deemed to be low quality (predominantly due to low adherence rates), they did find
a statistically significant improvement in QoL (SMD = 0.33, 95% CI: 0.08-0.58), based
on three studies (32-34).
29
A majority of the epidemiological studies that have assessed the association
between physical activity and QoL involve measures of QoL at one point in time postdiagnosis. Ten cross-sectional studies (35-44) were identified from the literature, of
which, seven (35, 37-39, 41, 42, 44) found some statistically significant associations
between physical activity and QoL. However, cross-sectional studies may be susceptible
to potential measurement error in physical activity exposure measurement. Measurement
of physical activity is difficult to capture accurately through self-reported questionnaires
at a single time-point. Seven studies measured physical activity through validated
questionnaires (38-44), such as the International Physical Activity Questionnaire (IPAQ),
and utilized cut-offs according to pre-defined guidelines (40, 42-44). One study only
assessed vigorous physical activity with a single question (37), which may lead to
measurement error. Methods used to collect information on physical activity and QoL in
these studies was very heterogeneous including: different questionnaires, QoL outcomes
and time periods of exposure and outcome assessment. For these reasons, it may be
difficult to glean meaningful conclusions from these cross-sectional studies in addition to
the recognized methodologic limitations for cross-sectional study evidence, including
lack of temporality.
Two prospective cohort studies (45, 46) and one retrospective cohort study (47)
have been conducted on prostate cancer survivors, addressing the association between
physical activity and QoL. All three cohort studies found some significant associations
between physical activity and QoL. However, improvements in QoL were not consistent
across studies. For example, from the Health Professionals Follow-up Study (45), the
only significant improvements of QoL identified were found in the vitality domain (p <
30
0.001) with increased total physical activity (>10 hours/week compared to <1
hour/week). Further, with increased weekly walking time in hours, there appeared to be
significant interactions with time since treatment in years (p = 0.03), Gleason score (p =
0.04) and dichotomized presence of comorbidities (p = 0.02).
On a separate note, two studies (46, 47) obtained repeated measurements of QoL
from participants. Both repeated measures studies measured health-related QoL using the
Patient-Oriented Prostate Utility Scale (PORPUS) and found significant associations with
meeting cancer prevention physical activity guidelines pre- and post-operatively (47) and
with an increase in different types of past-year physical activity (46). Overall, there is
clear evidence that physical activity can improve QoL in prostate cancer survivors
consistently. There remain gaps in the evidence, however, from long-term, large scale
follow-up studies on how QoL is influenced by different types of physical activity,
different doses/volumes of activity, specifically, if meeting cancer prevention physical
activity guidelines is associated with QoL and if these associations are influenced by any
important effect modifiers.
A large number of experimental exercise intervention studies have evaluated the
relation between physical activity and different aspects of QoL in prostate cancer
survivors (32-34, 48-71). Most exercise intervention studies examine treatment-related
outcomes as a main objective and QoL as a secondary analysis, since the detrimental side
effects of prostate cancer treatments are of concern. Regardless, most studies (32, 34, 48,
50, 54, 56, 59, 61, 62, 65-69) found statistically significant improvements in some aspect
of QoL during or after the exercise interventions. Some possible explanations for these
inconsistencies between studies include: duration of exercise interventions, diversity of
31
different exercise interventions undertaken and adequacy of study sample size to examine
subgroup effects. Exercise interventions range in duration between eight weeks (54) to as
long as 15 months (69). Prostate cancer survivors, on average, survive much longer than
15 months, especially those with low-grade or slow progressing disease. Therefore, these
interventions do not address the effects of exercise on QoL long-term after diagnosis of
prostate cancer. Further, 17 studies (33, 48, 49, 51, 53-55, 59-62, 64, 67-71) had less than
100 prostate cancer survivors included in their exercise interventions, and therefore, these
studies may have had limited power to detect statistically significant differences in QoL
and type II error may be present. This hypothesis is plausible since six studies with larger
sample sizes (32, 34, 50, 56, 65, 66), did find significant results in QoL outcomes.
Moreover, there is a chance these exercise interventions are too heterogeneous, making
direct comparisons across studies difficult. There were exercise interventions that
involved resistance exercise only interventions (50, 55, 61, 62, 70), aerobic exercise only
interventions (32, 48, 54, 60, 71), a mixture of resistance and aerobic exercise
interventions (33, 34, 59, 67, 69), home-based/telephone exercise interventions (52, 53,
56, 58, 63-65, 68), supervised and home-based exercise interventions (51, 66) or partnerbased resistance training (49). With this variety in exercise interventions examining QoL
in prostate cancer survivors, caution in the interpretation of this literature as a whole is
warranted. As Bourke et al. (2016) noted, most exercise randomized controlled trials
included in their meta-analysis had a high risk of bias, due to poor intervention adherence
(31). This issue is particularly important to recognize when examining QoL because there
is a possibility that lack of exercise adherence may be due to reduced QoL and thereby
biasing the results towards the null.
32
Twelve studies examined the relationship between physical activity or an exercise
intervention and QoL in prostate cancer survivors using the Short Form-36 (SF-36),
presented in Table 2.1. All of these studies were completed in developed countries
including: the United States of American (USA) (37, 40, 49, 51, 52, 56, 65), Australia
(59, 67), Korea (61) and Canada (63, 70). Ten of the 12 studies were randomized
controlled trials (49, 51, 52, 56, 59, 61, 63, 65, 67, 70), while only half of the studies (37,
56, 59, 61, 65, 67) found statistically significant associations between physical activity or
an exercise intervention and QoL. Therefore, the level of evidence surrounding studies
utilizing the SF-36 as a measurement of QoL are less consistent and research is needed in
this area to confirm if physical activity is associated with QoL in prostate cancer
survivors.
In summary, there is evidence that physical activity interventions benefit prostate
cancer survivors’ QoL after diagnosis. However, long-term effects of QoL are not well
documented and this research is needed to understand fully the role of physical activity
and QoL in prostate cancer.
2.3 Part 2: Change in quality of life after prostate cancer
The importance of QoL after prostate cancer diagnosis is evident with a growing
body of literature that has elucidated the change of QoL after diagnosis in epidemiologic
cohort studies. Here we summarize this literature by systematically searching the
MEDLINE database up to August 16th, 2016 using the following search strategy:
(("Quality of life" OR "QoL") AND ("prostate cancer" OR "prostatic cancer" OR
"prostate neoplasm") AND ("change" OR "over time" OR "long term" OR development
OR trajector*)). This search was limited to articles written in English, adult humans with
33
no date or country restrictions. This search returned 648 hits with 70 articles fitting the
criteria of examining repeated measurements of QoL in a prostate cancer study
population over time. Other studies were excluded due to only one measurement of QoL
or irrelevant study population. The following relevant articles are reviewed below.
2.3.1 Long-term change in quality of life after prostate cancer: epidemiological
evidence
A plethora of research has been published on changes in QoL after prostate cancer
diagnosis. Due to the vast extant literature to review, this section is focused on the most
recent five years (2011-present) of research in the field. The search of the literature
identified 33 cohort studies (72-104) examining QoL changes after prostate cancer
diagnosis. Twenty of these studies (73-79, 86, 87, 89-97, 101, 104) examined a particular
group of prostate cancer survivors on the same treatment regimen and their QoL over
time. These treatment regimens included anything from patients who were treated with a
radical prostatectomy (78, 89, 90, 96), or specifically patients treated with helical
tomotherapy in a hypo-fractionated radiation schedule with long-term androgen
suppressions (101). Given these studies were in different populations assessing QoL
before and after a specific treatment regiments, it may be difficult to determine a
hierarchy of treatments most beneficial for patients diagnosed with prostate cancer.
Other studies have appraised the impacts of different prostate cancer treatments
on QoL (81, 84, 85, 88, 98-100, 102), of which, most studies compared QoL before and
after different treatment regimens with up to 10 years of follow-up (81). Included studies
assessed differences between types of surgeries (88, 99, 102), radical prostatectomy
versus hormone/radiation therapies (84, 98), doses/presence of ADT (85, 100) or all
34
treatments (including nerve-sparing radical prostatectomy, non-nerve-sparing radical
prostatectomy, brachytherapy, external beam radiation therapy, ADT or active
surveillance) (81) and QoL after diagnosis. Generally, the more invasive the treatments,
the lower the score and the greater the decline in QoL observed.
A few additional studies examined pre-defined strata of prostate cancer survivors
and changes in QoL. Specifically, two studies (82, 83) considered age groups, while one
of the same studies (82) also stratified on the number of co-morbidities to examine the
impact on changes in QoL. Further, one study stratified prostate cancer survivors by
incident diabetes, prevalent diabetes or no diabetes (103) to determine QoL differences;
while others assessed predictors including illness uncertainty, anxiety and fear of
progression (72) and patient-physician communication (80) on changes in QoL after
diagnosis of prostate cancer. These studies investigated more than one average pattern of
QoL after prostate cancer diagnosis on pre-specified criteria. It may be difficult to
hypothesize potential effect modifiers through which prostate cancer survivors can be
stratified by a priori to determine different QoL patterns after diagnosis. Therefore, this
research of studying different patterns of QoL based on strata determined a priori is
limited to prior knowledge of these potential subgroups, which is lacking in the literature.
Repeated measurements of QoL ranged from two assessments (73, 79, 82, 83, 88,
91, 104) to 11 assessments (93) over different post-prostate cancer diagnosis periods.
Eight studies examined disease-specific QoL (88, 90, 92-94, 99, 101, 102) compared to
four studies that measured general QoL (74, 76, 78-80, 82, 85) or 15 studies that
examined a combination of disease-specific and general QoL outcomes (72, 73, 75, 77,
81, 83, 84, 86, 87, 89, 91, 95-98, 100, 103, 104). Overall, there is abundant literature
35
examining QoL after prostate cancer. Several of the aforementioned studies, have
different study populations, different objectives and different follow-up time frames,
making direct comparisons across studies difficult. In general, these studies have found
non-statistically significant changes in QoL (72, 73, 82, 89, 94), improvements in QoL
over time (74, 102), declines in QoL over time (75-78, 83, 88, 91, 92, 96, 98, 100, 103,
104), initial declines with no recovery or partial recovery of QoL (81, 84, 86, 87, 90, 93,
99, 101), increases and decreases of QoL throughout the follow-up (80, 97) or a mixture
of decreases and increases between the different domains of QoL examined (79, 85, 95).
One of the most comprehensive studies examining QoL changes after prostate cancer
diagnosis by Punnen et al. (2015) publishing results from the Cancer of the Prostate
Strategic Urologic Research Endeavor (CaPSURE), which included a large sample of
prostate cancer survivors (n=3,274) and is one of most definitive studies on this topic.
The CaPSURE study population underwent different kinds of treatments and were
followed for disease-specific and general QoL outcomes up to 10 years after diagnosis.
Most QoL measures declined early with partial recovery 1-2 years after diagnosis,
followed by a plateau in QoL. Surgery had the largest impact on sexual function/bother
and urinary function. Radiation affected bowel function, while ADT had the largest
impacts on physical function. Their results provided insights on long-term QoL from a
general prostate cancer study population and are, therefore, of particular relevance for
this thesis project.
There were ten studies (76-79, 81, 83, 85, 89, 98, 103) examining changes in QoL
after prostate cancer diagnosis using the SF-36 questionnaire from 2011-2016 (Table
2.2). Similar to the physical activity and QoL studies, all 10 studies were conducted in
36
developed countries including the USA (79, 81, 83, 98, 103), Japan (78, 89), The
Netherlands (77) and Canada (76, 85). Change in general QoL (as measured by the SF36) after prostate cancer diagnosis either declined (77-79, 85, 98), remained consistent
(76, 83, 89, 103) or partially recovered (81) in these studies. Although the SF-36 is a
valid and widely used questionnaire in healthy and diseased populations (105), there are
several factors that may contribute to differences in results seen among the 10 studies.
Some of these potentially contributing factors involve the recruitment of different
prostate cancer populations due to inclusion criteria, selection and participation rates, the
length of follow-up and number of repeated measurements of QoL after prostate cancer
diagnosis in each study.
Literature is lacking on whether or not examining average QoL for a study
population actually reflects the entire populations QoL experience after prostate cancer
diagnosis. Furthermore, a majority of these studies, unlike Punnen et al. (81), examined
QoL for short periods of time after prostate cancer diagnosis, given the relative survival
rates for prostate cancer are well over 90% (22). There is a need to examine QoL patterns
for long periods of time after diagnosis to capture the entire survival experience of
prostate cancer survivors to promote wellbeing beyond cancer.
2.3.2 Growth modelling and prostate cancer
Modelling change in QoL over time is important from a healthcare delivery
perspective and for patients and caregivers to understand how QoL can be either
maintained or improved after prostate cancer diagnosis. To date, previous research (as
reviewed in section 2.3.1), examined only single patterns of QoL for the entire study
population or predefined subgroups based on potential effect modifiers. It is unknown if
37
applying a single QoL trajectory for an entire population is appropriate or if the
subgroups defined a priori are sensitive enough to capture QoL differences. There are
inherent assumptions made when examining an average trajectory of QoL, including that
every participant in a study may be represented by the average change or maintenance of
QoL overtime post-diagnosis. Since prostate cancer does not affect a homogeneous group
of individuals, it may be inaccurate to assume everyone follows an average trajectory of
QoL. Therefore, attempting to distinguish separate QoL patterns into distinct subgroup
trajectories is a knowledge gap that needs to be addressed.
The modelling technique used to fulfill the third objective of my thesis is Groupbased Trajectory Modelling (GBTM), which has been used in several areas of literature
including more recently medicine (106). Most studies that use this technique come from
the other areas in the literature, but recently GBTM has gained attention in specific
medical populations, such as cancer (106). To our knowledge, only two studies (107,
108) have considered these methods when determining different aspects of health-related
QoL in a prostate cancer population. The first study (108), published in 2004, determined
different recovery patterns in sexual function after radical prostatectomy. This study
found four different trajectories over a five-year period. There were some limitations to
this study, specifically in regards to generalizability, due to inclusion criteria and missing
data. The second study (107), examined urinary function up to 12 months postprostatectomy and found three distinct subgroups in a prostate cancer study population.
This study attempted to systematically characterize the subgroups by age, comorbidities,
past or current smoking, PSA level and prostate volume. In both studies, there were low
and high groups of QoL observed. Therefore, the rationale for applying these methods to
38
general physical and mental QoL has never been applied and is warranted in this
population.
39
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2.5 Tables
Table 2.1. Physical activity and SF-36 quality of life studies in prostate cancer populations.
Author,
year,
country
Physical activity
assessment
methods
QoL
assessment
methods
688
One item to assess
vigorous physical
activity (average
continuous
vigorous exercise
for at least 20
minutes, three or
more times per
week).
SF-36
physical
function
subscale
modified to
only include
vigorous
activity not
moderate.
Positive association
between vigorous
exercise and physical
function score for
breast and prostate
cancer patients
(p<0.0001)
Age, race,
gender, time
since diagnosis,
and concurrent
health
behaviours
93
12-month
intervention
included a plantbased vegan diet,
three hours per
week of moderate
exercise and one
hour a day of
stress
management.
SF-36: eight
domains, PCS
and MCS
scores.
Intervention vs
controls on PCS
scores: (time effect
p=0.45, group effect
p=0.18, time x group
p=0.28)
MCS scores: (time
effect p=0.82, time
effect p=0.01, time x
group p=0.50)
-
Study Design and
population
Sample
size
DemarkWahnefried
et al., 2004,
USA (1)
Cross-sectional:
participants were 60+
years old, no more than
18 months beyond
diagnosis of early stage
(I-II) breast and
prostate cancer
identified through
cancer registries,
private practices and
self-referrals.
Daubenmier
et al., 2006,
USA (2)
Randomized controlled
trial: prostate cancer
patients undergoing
active surveillance,
with PSA levels of 4-10
ng/mL and Gleason
scores <7 randomly
assigned to a lifestyle
intervention or usual
care control group.
50
Adjustment
for
confounding
Analytic
methods/Results
DemarkWahnefried
et al., 2006,
USA (3)
Randomized controlled
trial: prostate cancer
and breast cancer
survivors aged >65
years old and within 18
months of diagnosis,
ascertained from 13
hospitals in North
Carolina, able to
participate in
intervention and
without limiting
comorbidities.
182
Treatment arm
received telephone
counseling and
tailored print
materials aimed at
increased exercise
and improved
overall diet, as
well as 12
bimonthly 20-30
min sessions over
a six-month
period.
Functional
Assessment of
Cancer
TherapyProstate/Breas
t/ General
(FACTP/B/G) and
SF-36:
physical
function
subscale.
Intervention vs control
on physical function
score at six months:
p=0.23 and 12
months: p=0.48
Blanchard et
al., 2008,
USA (4)
Cross-sectional:
participants were from
the American Cancer
Society’s Study of
Cancer Survivors-II,
aged 18+ years,
diagnosed with local,
regional or distant
cancer, a resident of the
state at diagnosis, and
diagnosed between 2-10
years before sampling.
36,372
Godin LeisureTime Exercise
Questionnaire,
dichotomized as
did or did not meet
American College
of Sports
Medicine (ACSM)
recommendations.
SF-36: eight
domain scores
and a global
health
composite
score.
Met physical activity
guidelines:
mean=53.6, SD=10
Did not meet PA
guidelines:
mean=49.8, SD=10.9
(d=0.3)
Race, stage,
marital status,
education, age
and total
number of
comorbidities
Mosher et
al., 2009,
USA (5)
Randomized controlled
trial: prostate, breast
and colorectal cancer
survivors, >65 years, >5
753
The intervention
consisted of two
45-60 min
telephone surveys
SF-36: eight
domains
scores, PCS
and MCS
Correlations between
moderate-to-vigorous
physical activity
min/week and:
Age, level of
education,
number of
comorbidities
51
Baseline value
of the outcome,
baseline martial
status, smoking
status, age at
diagnosis, sex,
Intervention vs control race, education
on FACT-G QOL at
attainment, and
six months: p=0.38 at social
12 months: p=0.97
desirability
score.
years post-diagnosis,
approval from
physician, able to speak
and write in English, no
medical conditions,
residence within the
community, overweight
but not obese, and not
adhering to physical
activity guidelines
randomized to a homebased diet and exercise
intervention of tailored
mailed materials and
telephone counseling.
Galvao et al., Randomized controlled
2011,
trial: prostate cancer
Australia (6) patients aged 55-84
years, undergoing
androgen suppression
therapy for non-bone
metastatic prostate
cancer completed a
progressive resistance
and cardiovascular
exercise program for 12
weeks.
50
(two days to three scores.
weeks apart) based
on the Community
Healthy Activities
Models Program
and 24-hour diet
recalls adapted for
telephone
administered use.
Physical QoL:
(p<0.0001)
Mental QoL: (p=0.94)
Pain: (p=0.02)
Health perceptions:
(p=0.01)
Physical functioning:
(p<0.0001)
Role-physical:
(p=0.053)
Vitality: (p=0.0001)
Mental health:
(p=0.19)
Social functioning:
(p=0.025)
Role-emotional:
(p=0.65)
Participants
performed
combined
progressive
resistance (last
approx. 40 mins)
and aerobic
training (15-20
mins) twice
weekly for 12
weeks.
In chronic androgen
suppression therapy
patients, there was an
increased QoL in
physical function
(p=0.031), general
health (p=0.012) and
vitality (p=0.027) and
the physical health
composite score
(p=0.016)
52
SF-36: eight
domains
scores.
Age and
baseline values
(non-specified)
Park et al.,
2012, Korea
(7)
Randomized controlled
trial: prostate cancer
patients undergoing
laparoscopic radical
prostatectomy, aged
>65 years, localized
disease, Eastern
Cooperative Oncology
Group performance
score of 0 or 1, written
informed consent and
randomized to
intervention or control
groups.
66
Intervention
included
resistance, pelvic
and Kegel
exercises
performed two
times/week for 60
mins, conducted
three weeks postoperatively for 12
weeks.
SF-36: PCS
and MCS
scores.
Pre to post- exercise
intervention vs control
groups and PCS score:
(p<0.05)
MCS score: (p<0.05)
-
McGowan et
al., 2013,
Canada (8)
Randomized controlled
trial: random sample of
prostate cancer
survivors 18+ years old,
diagnosed between
2005-2009, were
randomized to standard
physical activity
recommendations, selfadministered
implementation
intention, or a
telephone-assisted
implementation
intention.
423
All intervention
groups received a
fact sheet on
physical activity
guidelines for
Americans as well
as information
created by the
researchers, the
implementation
intention group
also included
Specific,
Measureable,
Achievable,
Realistic and
FACT-P and
SF-36: PCS
and MCS
scores.
No significant
improvements in
FACT-P QoL, generic
QoL PCS or MCS in
any intervention
groups.
Baseline value
of the outcome,
age, disease
stage, surgery,
radiation,
hormone
therapy,
chemotherapy,
and disease
status.
53
Time-bound
(SMART) goal
setting principles
and additionally
telephone calls
once a week in the
third group.
Winger et
al., 2014,
USA (9)
Randomized controlled
trial: prostate, breast
and colorectal cancer
survivors, >65 years, >5
years post-diagnosis,
approval from
physician, able to speak
and write in English, no
medical conditions,
residence within the
community, overweight
but not obese, and not
adhering to physical
activity guidelines
randomized to a homebased diet and exercise
intervention of tailored
mailed materials and
telephone counseling.
641
Participants
reported exercise
and dietary
behaviors during
sessions 2-15 of
the 15 telephone
counseling
sessions, a selfreported behavior
log (reporting
frequency and
duration of
exercise in past
week), during a
year-long
intervention
period.
SF-36: MCS
scores.
Correlations indicated
Strength exercise was
associated with the
MCS (p<0.01)
-
Cormie et
al., 2015,
Australia
Randomized controlled
trial: prostate cancer
patients scheduled to
63
Intervention was
three months, with
twice weekly
European
Organization
for Research
Mean adjusted
difference between
exercise group and
Baseline values
54
(10)
receive ADT were
randomly assigned to a
supervised exercise
program involving
aerobic and resistance
exercise sessions
commenced within 10
days of their first ADT
injection or usual care.
Norris et al.,
2015,
Canada (11)
Randomized controlled
trial: patients with nonmetastatic prostate
cancer, between the
ages of 18-80 years of
age and were not
currently performing
resistance training >= 2
days a week.
30
supervised
exercise sessions
lasting
approximately one
hour. There were
moderate-vigorous
aerobic and
resistance exercise
components with
appropriate warm
up and cool down
periods.
and Treatment
of Cancer
Quality of
Life
Questionnaire
-Prostate 25
(EORTC
QLQ-PR25)
and SF-36:
eight domains
scores.
usual care revealed
social functioning
(3.8, 1.1-6.5,
p=0.015), mental
health (3.8, 1.1-6.5,
p=0.006), mental
health composite (3.6,
0.5-6.6, p=0.022),
sexual function (15.2,
1.9-28.4, p=0.028)
were significantly
different
Pilot- two-arm
trial comparing
three versus two
days/week of
resistance training
for 12 weeks.
SF-36: PCS
and MCS
scores.
Trend favoring three
days over two days
(p<0.1) and not
statistically
significant. Trends
toward improvements
of PCS scores with
three days of
resistance training and
MCS scores for two
days of resistance
training.
55
Baseline value
of the outcome,
current
hormone
therapy and
previous
treatments
WintersStone et al.,
2016, USA
(12)
Randomized controlled
trial: patients who had
received treatment for
prostate cancer, not
currently undergoing
radiation or
chemotherapy, 60+
years of age, residing
with a spouse willing to
participate, not
currently exercising two
or more times/week and
physician clearance.
64
couples
Intervention
compared
Exercising
Together to usual
care. Exercising
Together includes
one hour
resistance training
sessions modified
to implement
spousal support
and motivation
done twice a week
for six months.
56
SF-36:
physical
function,
vitality
subscales,
PCS and MCS
scores.
For prostate cancer
survivors, adjusted
group difference in
mean slope of PCS
(p=0.99), MCS
(p=0.39), physical
function (0.72) or
vitality (p=0.98) did
not statistically
significantly differ
over the six months.
Age, time since
diagnosis
Table 2.2. Prostate cancer cohort studies examining changes in QoL post-diagnosis using the SF-36 published between 20112016.
Author,
year,
country
Study characteristics/
Inclusion criteria &
Number of participants
Thong et
al., 2011,
USA (13)
1,811 prostate cancer
patients with nonmetastatic cancer
enrolled in Prostate
Cancer Outcomes Study.
Diabetes
Brassell et
al., 2013,
USA (14)
595 prostate cancer
patients enrolled from
2003-2010 before
prostate biopsy and
followed for QoL
outcomes.
-
Determinants/
exposures
Number of
QoL
measure(s) and
tool
Results
Adjustment
variables
Four
measurements
at: six
(baseline), 12,
24 and 60
months after
initial diagnosis
using SF-36 and
modified
questionnaire
for urinary,
sexual and
bowel function.
Men with prevalent diabetes
had the poorest QoL scores and
non-diabetic best scores,
independent of treatment. Men
with prevalent diabetes had the
lowest urinary control and
sexual function scores over
time while men without
diabetes had the highest scores.
Men with incident diabetes had
intermediate scores.
Pre-treatment
urinary sexual and
bowel functioning,
age, marital status,
educational status,
annual income,
employment status,
cancer stage,
baseline PSA,
baseline Gleason
score.
Eight
measurements
at: baseline, 3, 6,
9, 12, 18, 24 and
30 months using
Expanded
Prostate Cancer
Index
Composite
(EPIC) and SF-
Radical prostatectomy led to
the greatest decline in urinary
function. Bowel function
significantly worsened with
addition of hormone therapy to
external beam radiotherapy.
Sexual bother and function
declined in all active treatment
options. SF-36 domains were
not affected by radical
Age, race, smoking
history, education,
marital status,
income, PSA at
diagnosis, T stage,
and Gleason score.
57
36
questionnaires.
prostatectomy, but radiotherapy
and radiotherapy plus hormone
therapy declined SF-36
domains. Radiotherapy showed
worsened results by the 12-24
time-period for all domains.
Namiki et
al., 2014,
Japan (15)
91 prostate cancer
patients undergoing
radical prostatectomy
followed for QoL
outcomes in prospective
cohort study conducted
between 2002-2005.
-
Seven
measurements
at: baseline, 1, 2,
3, 4, 5 and 10
years’ postsurgery using
SF-36 and
University of
California, Los
AngelesProstate Cancer
Index (UCLAPCI).
Mental and role composite
summary scores remained
stable throughout the follow-up
period and other domains
remained constant after the first
year period where sexual
function declined.
Peters et al.,
2014, The
Netherlands
(16)
20 patients treated with
focal salvage
brachytherapy with
biochemical failure
greater than two years
after primary treatment,
unilateral biopsy proven
recurrence after
systematic transrectal
biopsies of both prostate
-
Four
measurements
at: baseline, 1, 6
months and 3
years using SF36, European
Organization for
Research and
Treatment of
Cancer Core
After three years, physical
functioning was statistically
significantly different
(decreased), while no other SF36 scores showed any changes.
Global health, general physical
discomfort, fatigue and urinary
symptoms significantly
changed at different timepoints during follow-up.
58
-
lobes, no extra-capsular
extension or seminal
vesicle involvement and
no ADT at time of
salvage were recruited
between 2009-2012.
Quality of Life
Questionnaire
(EORTC QLQC30), and
EORTC QLQPR25.
Alibhai et
87 patients on ADT, 87
al., 2015,
prostate cancer controls,
Canada (17) and 87 healthy controls
(matched on age,
education and baseline
function).
ADT
Eight
measurements
at: baseline, 3,
6,12, 18, 24, 30
and 36 months
using the SF-36.
PCS score declined in ADT
group (p<0.001), but remained
stable. MCS scores remained
stable over time in all three
cohorts with no significant
differences between groups.
Hampson et
al., 2015,
USA (18)
5,362 prostate cancer
patients who underwent
treatments related to
prostate cancer from the
CaPSURE prospective
cohort study.
Age
Two
measurements
at: baseline and
2 years using
SF-36 and
UCLA-PCI.
Older men had lower mean
QoL scores than younger men
in all domains. Sexual function,
sexual bother and urinary
function showed declines over
time. More men <60 years vs
>70 years, experienced declines
in urinary function and sexual
bother at two years.
Miyake et
al., 2015,
Japan (19)
81 patients who
underwent radical
prostatectomy and
salvage radiotherapy for
biochemical recurrence
for prostate cancer.
-
Seven
measurements:
prior to, during
and immediately
after salvage
radiotherapy and
1, 3, 6, 12
PCS, role physical problems,
role emotional problems and
general health domains scores
were statistically significantly
different (lower) immediately
after completion of treatment
compared to baseline (p<0.05).
59
Age, education,
body mass index,
and Charlson
comorbidity index
score.
-
months post
treatment using
the SF-36.
Punnen et
al., 2015,
USA (20)
3,294 underwent
different forms of
prostate cancer treatment
were captured from the
CaPSURE registry
prospective cohort study.
Treatments:
nerve-sparing
radical
prostatectomy
(NSRP), nonNSRP,
brachytherapy,
external beam
radiotherapy,
ADT, active
surveillance.
Four
measurements
at: baseline, 2, 5
and 10 years
using SF-36 and
UCLA-PCI.
Most QoL measures resulted in
early declines with partial
recovery the first 1-2 years then
a plateau. Surgery had largest
impact on sexual function and
bother, and urinary function.
Radiation had the largest
impacts on bowel function,
ADT on physical function.
Age, year of
treatment, number
of comorbidities,
medical insurance
status, cancer
progression risk at
diagnosis, receipt
of any secondary
treatments
Holliday et
al., 2016,
USA (21)
28 men undergoing
radiotherapy for early
stage prostate cancer.
Fatigue
Two
measurements:
prior to
treatment and at
the end of
treatment using
SF-36.
Vitality and social functioning
significantly decreased over
treatment.
-
60
Siddiqui et 81 patients accrued from
al., 2016,
the phase II study, with
Canada (22) stage cT1c-T2 cancer,
who received radionucleotide bone scan and
computed tomography of
the abdomen and pelvis
to confirm cancer and
high-intensity focused
ultrasound (HIFU)
therapy.
-
Four
measurements
at: pre-HIFU,
45, 90 and 180
months postHIFU using SF36.
61
Global SF-36 score remained
constant across all time-points.
-
Chapter Three: ASSOCIATION OF POST-DIAGNOSIS PHYSICAL ACTIVITY
AND CHANGE IN PRE-DIAGNOSIS PHYSICAL ACTIVITY WITH QUALITY
OF LIFE IN PROSTATE CANCER SURVIVORS
3.1 Preamble
This manuscript addresses objectives 1 and 2 of the thesis through cross-sectional
analyses of different types of physical activity, change in physical activity and QoL. It
was submitted to a peer-review journal, Cancer Epidemiology, Biomarkers and
Prevention on June 1, 2016.
This work was also accepted as a poster presentation at the following conference:
Farris MS, Courneya KS, Kopciuk KA, McGregor SE, Friedenreich CM. The impact and
change in lifetime and post-diagnosis physical activity on quality of life in prostate
cancer survivors. Charbonneau 2nd Annual Cancer Research Symposium, Calgary, AB;
February 5, 2016.
Running title: Physical activity and QoL in prostate cancer survivors
Keywords: physical activity, quality of life, prostate cancer, guideline adherence, cancer
survivorship
Financial support: Dr. Friedenreich held career awards from Alberta Innovates, Health
Solutions and Alberta Cancer Foundation, Dr. Courneya was supported by the Canada
62
Research Chairs Program and Dr. McGregor by the Alberta Heritage Foundation for
Medical Research.
63
3.2 Abstract
Introduction: This prospective study examined the associations between post-diagnosis physical
activity and change from pre-diagnosis physical activity with quality of life (QoL) in prostate
cancer survivors.
Materials and Methods: Prostate cancer survivors (n=830) who participated in a case-control
study with invasive stage ≥II disease were followed up to 2007 to capture QoL outcomes. At
baseline and three time points post-diagnosis (2000-2007), interviews/questionnaires were used
to collect data on physical activity, general QoL measured by the SF-36 and other
treatment/lifestyle factors. Multivariable linear regression was used to test the relation between
post-diagnosis physical activity and QoL as well as the change in physical activity over the
diagnostic period and QoL.
Results: Both total and recreational physical activities were positively associated with physical
QoL. Further, when comparing changes in physical activity levels from pre- to post-diagnosis,
men who consistently met physical activity guidelines had significantly higher physical (β =
6.01, 95% CI: 4.15-7.86) and mental (β = 2.32, 95% CI: 0.29-4.34) QoL scores compared to
those who did not meet guidelines pre- or post-diagnosis. Further, those who adopted and met
guidelines had increased QoL, while those who relapsed experienced decreased QoL.
Conclusions: Post-diagnosis recreational physical activity is associated with better physical QoL
in prostate cancer survivors. Moreover, prostate cancer survivors who maintain or adopt physical
64
activity after diagnosis report substantially higher QoL than men who never exercised or stopped
exercising after diagnosis.
Impact: Future intervention studies should focus on achieving and maintaining adherence to
physical activity guidelines post-diagnosis in prostate cancer survivors.
65
3.3 Introduction
Prostate cancer is the second most common cancer in men worldwide (1, 2). Early
detection and improved treatment options have increased prevalence and decreased the mortality
rate associated with prostate cancer (3). Due to the increase in five-year survival rates, currently
approximately 93% in Canada (4), men diagnosed with low-grade (stage I-II) prostate cancer die,
more likely than not from other causes, particularly after age 75 (5). In addition, Prostate
Specific Antigen (PSA) testing often over detect low grade cancers, which for the most part, are
indolent for long periods of time (6). In turn, overtreatment of potentially non-life threatening
disease has burdened prostate cancer survivors and the healthcare system (7, 8). Consequently,
priorities in prostate cancer control involve reducing the burdens of living beyond cancer
diagnosis, improving health, overall functioning, care, quality of life (QoL) (9) and addressing
the specific needs of prostate cancer survivors (10).
Prostate cancer survivors who undergo surgery (11, 12), receive treatments such as
androgen deprivation therapy (13, 14) or radiation therapy (11, 15, 16) often experience worse
QoL. Physical activity is an inexpensive, modifiable behaviour that has been shown to improve
QoL after diagnosis and may be associated with survival (17-20). Observational epidemiologic
research (21-32) and randomized controlled exercise intervention trials (33-40) have shown that
physical activity can improve QoL in prostate cancer survivors. No study to date, however, has
examined the associations between changes in physical activity across the diagnostic period and
QoL in prostate cancer survivors. Further, only one study (27) explicitly examined different
types of physical activity including household and occupational physical activity and QoL.
Moreover, previous observational research has had methodologic limitations associated with
66
inadequate control for confounding and an inability to examine effect modification because of
the small sample sizes.
This study was primarily designed to examine the associations of physical activity and
survival in prostate cancer survivors (17). In this current analysis, we examined the role of both
total post-diagnosis physical activity and each type of activity including recreational,
occupational and household activity and changes in physical activity over the diagnostic period
on physical and mental QoL in prostate cancer survivors. This study is the first with repeated
measurements of physical activity and QoL with a large sample size that can explore multiple
subgroup analyses.
3.4 Materials and methods
3.4.1 Study design
A prospective cohort of prostate cancer survivors that originated from a population-based
case-control study in Alberta, Canada, was conducted between November 1997 and December
2000 (41). All cases from the original case-control study were re-contacted and followed up for
post-diagnosis measurements and mortality outcomes to 2014. Details regarding this study have
been published previously (17). Briefly, prostate cancer cases were histologically confirmed,
invasive cases of stage II or greater prostate cancer and identified from the Alberta Cancer
Registry, a population-based cancer registry. The prostate cancer survivors were <80 years of
age, English speaking with no previous cancer diagnosis except for non-melanoma skin cancer.
Permission to contact prostate cancer cases for in-person interviews was obtained through the
referring urologist. All surviving cases from the case-control study were re-contacted by
telephone for voluntary recruitment and provided written informed consent for the prostate
67
cancer cohort follow-up study (2% rate of refusal). The cohort study began in 2000, to permit
follow-up and further data collection from the cases. In addition, referring urologists were recontacted for additional missing medical data. This study received ethics approval from the
Alberta Cancer Research Ethics Board and the Conjoint Health Research Ethics Board at the
University of Calgary.
3.4.2 Data collection
Through in-person interviews, conducted in the original case-control study, participants
reported on their personal health history, prostate cancer screening history, prostate conditions,
surgery history, family history of cancer, lifetime physical activity patterns prior to diagnosis,
dietary intake during the reference year (year prior to cancer diagnosis), lifetime alcohol
consumption history, smoking habits, current social support and motivation for lifestyle change,
demographic characteristics and adult height and weight at each decade from age 20-60 years.
Anthropometric measurements were measured directly at the interview. Additionally, as a part of
the cohort follow-up study, survivors were interviewed to assess their physical activity behaviour
within the first two years after diagnosis. At the cohort follow-up interview, self-reported
questionnaires on current QoL were completed by participants. At a second and third follow-ups,
approximately two years apart, self-administered questionnaires asking participants about their
past two years of physical activity behaviour as well as their current QoL were mailed to
participants to complete. Further, medical chart abstractions and vital status assessments were
completed throughout the follow-up to capture updated medical information, treatments and
outcomes.
68
3.4.3 Physical activity assessment
Physical activity was assessed using the Lifetime Total Physical Activity Questionnaire
(LTPAQ) and the Past Year Total Physical Activity Questionnaire (PYTPAQ) previously tested
for reliability and validity (42, 43). Both the LTPAQ and the PYTPAQ record all types of
physical activity (occupational, household and recreational) as well as all parameters of activity
(frequency, duration, intensity) from childhood to the time of diagnosis. The LTPAQ was
interview-administered as part of the baseline case-control study and the results from that study
have been published (41). The participants were re-interviewed between 2000-2002 using the
LTPAQ restricted to their activity done since the first interview which had occurred between
1997 and 2000. Two additional assessments of physical activity were obtained through the selfadministered PYTPAQ between 2002-2004 and 2004-2007. Hence, continuous levels of physical
activity throughout the participant’s life up until 2007 (when active data collection ceased) were
obtained.
All physical activities (pre- and post-diagnosis) were assigned a Metabolic Equivalent
(MET) value defined as a ratio of the associated metabolic rate for a specific activity as
compared with the resting metabolic rate. These MET values were based on the Compendium of
Physical Activities (44) and used with the reported frequency and duration of activity to derive
the MET-hours/week/year for each type of activity. Moderate-to-vigorous recreational activity
was defined as any activity >3 METs according to the Compendium of Physical Activities
categorization (44). Moderate-to-vigorous total and recreational physical activity
(hour/week/year) were derived to examine activity duration similar to general cancer prevention
physical activity guidelines (45, 46).
69
To assess the change in physical activity over the diagnosis period, a change score was
derived using the difference between pre-diagnosis lifetime and average post-diagnosis physical
activity. The average post-diagnosis physical activity variables contained up to three postdiagnosis time-points. For those with missing assessments, because of non-response or death, the
average physical activity levels were derived from available complete assessments. Additionally,
adherence to physical activity guidelines for cancer prevention and survivorship (150 minutes of
moderate or 75 minutes of vigorous recreational physical activity) (45, 46) over the diagnosis
period was assessed and behaviours were categorized as follows: consistently not meeting preand post-diagnosis (non-exercisers); not meeting pre-diagnosis to meeting guidelines postdiagnosis (adopters); meeting pre-diagnosis to not meeting guidelines post-diagnosis (relapsers);
or consistently meeting guidelines pre- and post-diagnosis (maintainers). Active data collection
ended in 2007; therefore we utilized the physical activity cancer prevention and survivorship
guidelines specific to aerobic physical activity.
3.4.4 Quality of life assessment
QoL assessments at up to three follow-ups were obtained through a single questionnaire,
the RAND 36-Item Short Form Health Survey (SF-36) version 1.0. The SF-36 has been used in
several settings, is one of the most widely used QoL questionnaires (47), and was validated in
colorectal cancer survivors (48). The SF-36 addresses eight domains of health status that have
been summarized into a physical component summary (PCS) score and a mental component
summary (MCS) score. Both scores range from 0-100; higher scores indicate better QoL.
Clinically meaningful QoL score differences reflect >5 point changes (49).
70
3.4.5 Statistical analysis
Descriptive statistics were performed on all variables to report study characteristics.
Next, multivariable least squares linear regression was used in all analyses (all statistical linear
regression assumptions, including distributional, homogeneity of variances, were verified). A
priori variables deemed to be predictors of QoL were forced into the models: prostatectomy
surgery (yes, no), post-diagnosis Charlson co-morbidity score (50) (0, 1, 2, ≥3), aggressive
(Gleason score >8 or stage >II cancer (51)) or non-aggressive disease, type of treatment (none,
hormone, radiation therapy, both hormone and radiation therapy), and age at diagnosis (years).
The aforementioned variables were first tested for interaction. Further, we tested the following
covariates for potential confounding first, with univariate analyses (p<0.2) and secondly through
backward elimination: pre-diagnosis lifetime physical activity (MET-hour/week/year) to adjust
for baseline differences, nonlinearity of age at diagnosis, family history of cancer (yes, no), PSA
score at diagnosis (≤4, >4 and ≤10, >10 and ≤20, >20), smoking status at diagnosis (never,
former, current), total alcohol consumption at diagnosis (grams/year), region of residence (urban,
rural), ethnicity (Caucasian, other), education (less than high school, high school, trade school,
other non-university degree, university degree), marital status (married, other), weight at
diagnosis (kg), Body Mass Index (BMI) (kg/m2), daily caloric intake at diagnosis (kcal/day),
lifestyle change post-diagnosis (yes, no), family support (yes, no), friend support (yes, no) and
ever joined a support group (yes, no). In addition, recreational, occupational and household
physical activity types and other physical activity intensities were mutually adjusted to control
for the effects of one type/intensity on another. All quantitative variables were centered at the
median for ease of interpretation.
71
To investigate the associations between post-diagnosis physical activity and QoL, the
first follow-up data (collected 2000-2002) for both physical activity and QoL were used. To
determine changes in pre- and post-diagnosis physical activity and the influence on QoL, lifetime
pre-diagnosis physical activity and average (over all available time-points) post-diagnosis
physical activity data were used to derive change scores and adherence groups. Additionally,
average (over all available time-points) post-diagnosis QoL scores were derived for these
analyses.
Sensitivity analyses were performed by removing survivors diagnosed with stage III/IV
and IV metastatic cancer to account for disease severity differences. In addition, cases who died
prior to completing all three post-diagnosis follow-ups were excluded in the physical activity
change over the diagnostic period analysis. Estimated rates of change of QoL scores, per unit of
physical activity values and their corresponding 95% confidence intervals (CI) were reported in
multivariable adjusted models. A priori hypotheses were assessed with a significance level of
p<0.05, using the statistical package Stata v.13 (College Town, Texas).
3.5 Results
3.5.1 Descriptive statistics
There were initially 830 men who survived up until the first follow-up time-point (20002002), eight of those participants were missing first follow-up QoL data, five participants were
missing first follow-up lifetime physical activity data and therefore, the analytic sample was 817
for these analyses. The majority of the study participants were diagnosed with stage II cancer,
married and had at least one co-morbidity post-diagnosis (Table 3.1). The mean age of the
participants at start of follow-up was 67.3 years. The sample had a post-diagnosis first follow-up
72
total physical activity median of 78.7 MET-hour/week/year, however, the recreational physical
activity median was only 11.1 MET-hour/week/year. Further, their first follow-up mean PCS
score was 40.8 (Standard Deviation (SD)=12.4) and MCS score was 50.8 (SD=11.8), which were
approximately symmetrically distributed.
3.5.2 Post-diagnosis physical activity and quality of life at first follow-up
Final multivariable adjusted models found statistically significant associations between
three post-diagnosis types of physical activity in relation to first follow-up PCS score (Table
3.2). Only one type of physical activity was significantly associated with MCS score.
Recreational physical activity revealed the strongest associations (relative to the other types)
with an estimated increase of 0.16 (95% CI: 0.12-0.20, p<0.001) in PCS score and 0.05 (95% CI:
0.01-0.09, p=0.024) for MCS score per MET hour/week/year increase in physical activity
volume. While other physical activity types were not significantly associated with MCS score,
associations appeared to be consistent in direction with estimated PCS score, except household
physical activity, which was slightly negative. Further, sensitivity analyses excluding stage III/IV
and IV cancers (n=130) did not appear to substantially influence these results.
Analyses restricted to duration (hour/week/year) of moderate-to-vigorous total and
recreational physical activity are presented in Table 3.2. Recreational moderate-to-vigorous
activity was positively associated with both PCS and MCS scores with estimated increases of
0.68 (95% CI: 0.50-0.85, p<0.001) and 0.22 (95% CI: 0.03-0.40, p=0.021) per hour of
recreational activity respectively. Total moderate-to-vigorous physical activity was also
positively associated with PCS score with an estimated increase of 0.09 (95% CI: 0.05-0.13,
p<0.001) but not with MCS score ( = -0.01 (95% CI: -0.05-0.04, p=0.76)). Interestingly, the
73
sensitivity analysis excluding stage III/IV and IV cancers increased the recreational moderate-tovigorous physical activity associations, but not those for total activity.
3.5.3 Change in physical activity over the diagnostic period and quality of life
The analysis evaluating changes of physical activity volume (MET hour/week/year)
between lifetime (pre-diagnosis) and post-diagnosis activity on average post-diagnosis QoL for
up to three follow-ups, revealed similar results to the post-diagnosis physical activity analysis
(Table 3.3). While recreational physical activity change was consistently associated with PCS
score ( = 0.15, 95% CI: 0.11-0.19, p<0.001 and MCS score  = 0.06, 95% CI: 0.02-0.11,
p=0.006), total and occupational activity change were only associated with PCS score. Similarly,
household activity did not impact the PCS or MCS scores significantly. Excluding stage III/IV
and IV cancers or those who died before completing all three follow-ups, did not materially
change results.
Table 3.3 also presents physical activity change focused on moderate-to-vigorous total
and recreational activity, which indicated associations with recreational activity and both PCS
and MCS QoL scores with estimated increases of 0.72 (95% CI: 0.54-0.90, p<0.001) and 0.35
(95% CI: 0.15-0.55, p=0.001) per hour of activity performed. Total physical activity was once
again statistically significantly associated with PCS score ( = 0.11, 95% CI, 0.07-0.16,
p<0.001) but not MCS score ( = 0.04, 95% CI: -0.01-0.09, p=0.15). Nonetheless, in the
sensitivity analysis excluding participants diagnosed with stage III/IV and IV cancers,
recreational activity and PCS QoL score associations were slightly heightened, however, this
finding was not seen in the total activity analysis, nor in the analysis excluding those who did not
complete all three follow-ups.
74
Finally, the change in behaviour over the diagnostic period according to the cancer
prevention physical activity guidelines (46) relative to PCS and MCS scores is presented. This
analysis provides more definitive physical activity change according to cancer prevention
guidelines in existence at the time (2007). First, the median pre-diagnosis and post-diagnosis
physical activity levels for each cancer prevention guideline adherence group were examined
(Figure 3.1). Maintainers and adopters increased physical activity levels, while relapsers and
non-exercisers reduced physical activity levels from pre-diagnosis to post-diagnosis. In
multivariable analyses (Table 3.4), prostate cancer survivors who were characterized as
adopters, experienced an increase of PCS score by 4.80 (95% CI: 2.82-6.78, p<0.001) and MCS
score by 2.26 (95% CI: 0.09-4.43, p=0.041) relative to non-exercisers. Further, maintainers
showed the strongest results with increased PCS score of 6.01 (95% CI: 4.15-7.86, p<0.001) and
MCS score of 2.32 (95% CI: 0.29-4.34, p=0.025) relative to non-exercisers. Interestingly,
relapsers had non-statistically significant worse scores. Slight attenuations were shown in the
analysis excluding stage III/IV and IV cancers and participants that did not complete all followups due to death, specifically in MCS scores for adopters and maintainers, however, point
estimates and CI’s remained fairly unchanged. Evidence of statistical interactions were not
present in these analyses, therefore, estimated PCS and MCS scores for the entire study
population were reported.
3.6 Discussion
This study found that post-diagnosis physical activity was associated with PCS scores in
prostate cancer survivors. Specifically, the positive association between recreational physical
activity and PCS score was consistent across analyses, however, the magnitude of the association
75
of physical activity on PCS score were relatively small (49). Total, occupational and household
activities were not associated with QoL at the first follow-up. It is also interesting to note that
mean first follow-up PCS scores were 10 points lower than MCS scores. Adherence to cancer
prevention physical activity guidelines resulted in five and six-point increases in average PCS
scores for prostate cancer survivors who were adopters and maintainers relative to nonexercisers. These results were clinically relevant. Both relapsers and non-exercisers had reduced
physical activity levels post-diagnosis (Figure 3.1). Future interventions should consider
strategies to improve adherence to current exercise and cancer guidelines prior to and after
prostate cancer diagnosis.
Our results illustrated a positive association between physical activity and QoL in
prostate cancer survivors, similar to other cohort studies (26, 27). However, the studies differed
in their methods for physical activity and QoL assessment therefore, comparisons between
studies are somewhat difficult. For example, in the Health Professionals Follow-up Study (24),
prostate cancer-specific QoL was measured and physical activity was significantly associated
with vitality/hormonal functioning scores. On the other hand, four exercise intervention studies
(52-55) measured generic QoL using PCS and MCS scores. One of these interventions (55)
found clinically relevant effects with PCS score (similar to our study), however, three studies
(52, 53, 55) found significant associations with MCS score. On another note, to our knowledge,
our study is the first to measure change in lifetime physical activity prior to and after prostate
cancer diagnosis in relation to QoL.
The lack of association in total activity may be attributed to the increased duration of
occupational and household physical activities in the average day for those who were employed
post-diagnosis. Retired men who had an occupational score of zero (Tables 3.2 & 3.3), would
76
not have contributed to these associations and therefore, may have spent more time performing
other types of activities. We also considered the possibility that cancer survivors with
progressing disease or diminishing health would experience a decrease in their QoL and in turn,
exercise less. To minimize this bias, we performed sensitivity analyses excluding men with stage
III/IV and IV cancer at diagnosis, as well as those who died before completing all post-diagnosis
assessments. Regardless, we continued to observe positive associations between recreational
physical activity and PCS scores.
Reverse causation was of less concern with the change in physical activity analysis. Men
who were maintainers and adopters had the highest QoL scores. Further, slightly stronger effects
were observed for analyses restricted to moderate-vigorous recreational physical activity.
Nonetheless, given that a majority of our sample had stage II prostate cancer at diagnosis and
only a small portion of the sample were confirmed to be stage IV (n=55), we cannot be certain if
disease severity did not have an impact on the physical activity and QoL relationship.
There are multiple proposed mechanisms by which physical activity has been linked to
physical and mental QoL. First, in prostate cancer exercise intervention studies, fatigue (56, 57),
cardiorespiratory fitness (57), walking speed (56), upper body strength (56) and lower body
functional performance (58) has been identified as possible mediators between exercise
intervention and different aspects of QoL. Further, marital status, time since diagnosis and use of
bisphosphonates (58) have been described as moderators. In epidemiological studies, physical
activity has been shown to reduce adiposity (27, 59) and inflammation (60), which can account
for increased physical functioning. Further, physical activity increases secretion of insulin (61),
enhances sex hormone regulation (59) and in turn, decreases insulin resistance and QoL (62).
Specifically, improved sex hormone regulation may lead to decreased erectile dysfunction and
77
improved martial relations (62-64). Therefore, modifiable behaviours like physical activity can
reduce these burdens. With respect to mental QoL, physical activity has been associated with
increased regulation of neurotransmitters associated with mood (65), improved self-esteem, selfefficacy and reduced anxiety (66); all aspects of psychological functioning. More work is needed
to confirm these mechanisms specific to changes in recreational physical activity over the course
of the diagnostic period in prostate cancer survivors.
There are inherent limitations that need to be considered when interpreting these results.
First, this was an observational study and therefore, subject to bias. Measurement error in
physical activity assessment may exist, which could attenuate the results. However, these data
were collected first, by in-person interviews and additionally by self-reported questionnaires,
therefore, measurement bias was minimized. Also, measurement and adjustment for sedentary
behaviour in all analyses would strengthen these results by further isolating physical activity
levels. Further, in the physical activity change analysis, we used a change score and are limited
in our generalizability of interpretations to individual participants. Change scores are not related
to individual baseline values, although we did adjust for pre-diagnosis physical activity in
general, we cannot differentiate between participant specific changes in physical activity levels.
However, the analysis examining adherence to physical activity cancer prevention guidelines
addresses these concerns by evaluating physical activity against a standard. Another limitation
arises from missing data due to mortality. However, sensitivity analyses excluding those who
died before the third follow-up did not materially change the results. Finally, we restricted our
study sample to ≥ stage II cancers aged < 80 years of age, therefore we can only generalize our
results to these prostate cancer populations.
78
There are implicit strengths that should also be noted. First, this study included a large
population-based sample of prostate cancer survivors with a follow-up of 10 years for QoL
outcomes. Four repeated physical activity and three QoL measurements were collected. The
assessment of physical activity was comprehensive since all parameters of physical activity (i.e.,
frequency, duration and intensity) of three separate physical activity types (i.e., recreational,
occupational and household) were captured. Further, using the PCS and MCS scores, rather than
individual QoL domains reduced the issue of multiple testing while providing summary scores of
physical and mental QoL. Moreover, the pre- and post-diagnosis physical activity measurements
enabled us to examine associations with change in physical activity over the diagnostic period in
prostate cancer survivors. Finally, several potential effect modifiers and confounding variables
were considered for this analysis to reduce the chance of spurious results and to identify potential
sub-groups within the population.
3.7 Conclusion
This study provided insights regarding the types of physical activity that impact QoL in
prostate cancer survivors. Objective measurements of physical activity by accelerometry or
fitness trackers are warranted in this population to reduce any suspicions of measurement error
and further confirm the benefits of recreational physical activity on QoL. Health professionals
should emphasize the importance of recreational physical activity to their patients, as a
modifiable and a possible behavioural change for most prostate cancer survivors. Interpretations
may need to be tailored to pre-diagnosis physical activity level to ensure prostate cancer
survivors can achieve recommended physical activity thresholds. Future research should focus
79
on sedentary behaviour to determine efficacy of increased physical activity post-diagnosis in a
phase III trial or health promotion program.
3.8 Manuscript acknowledgements
Study coordination was done by Aleata Ryhorchuk and Sana Fakih. Interviewers for this
study were Jodi Parrotta, Linda Davison, Pearl Cooke, Nicole Slot, Carol-Anne Zawalykut,
Catherine Munro. Data entry was done by Carla Quesnel and chart abstraction by Yvonne
LeBlanc and Sarah MacLaughlin. Dr. Steven J. Angyalfi provided clinical expertise regarding
prostate cancer treatment and care.
80
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Khera M. Male hormones and men's quality of life. Curr Opin Urol. 2016;26(2):152-7.
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Pereira RF, Daibs YS, Tobias-Machado M, Pompeo AC. Quality of life, behavioral
problems, and marital adjustment in the first year after radical prostatectomy. Clin Genitourin
Cancer. 2011;9(1):53-8.
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Ramsey SD, Zeliadt SB, Blough DK, Moinpour CM, Hall IJ, Smith JL, et al. Impact of
prostate cancer on sexual relationships: a longitudinal perspective on intimate partners'
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Erickson KI, Miller DL, Roecklein KA. The aging hippocampus: interactions between
exercise, depression, and BDNF. Neuroscientist. 2012;18(1):82-97.
66.
Phillips SM, McAuley E. Physical activity and quality of life in breast cancer survivors:
the role of self-efficacy and health status. Psychooncology. 2014;23(1):27-34.
86
3.10 Tables and figures
Table 3.1. Characteristics for prostate cancer survivors (n=817), in the Prostate Cancer
Cohort Study, Alberta, Canada, 1997-2014.
Study Characteristics a
Gleason score at diagnosis
≤7
>7
PSA value at diagnosis
<4
4-10
>10-20
>20
Stage of cancer
II (T1/T2, N0, M0)
III (T3, N0, M0)
III/IV (T3, NX, MX)
IV b
Primary Treatment c
Prostatectomy
Hormone therapy d
Radiation therapy
Relationship status
Married/ common law
Other
Education level
University degree
Other non-university degree
Trade degree
High school diploma
Less than High school diploma
Race
Caucasian
Other
Region of residence
Urban
Rural
Post-diagnosis Charlson comorbidity score
0
1
2
3+
First degree family history of prostate cancer
Yes
n (%)
312 (38.2%)
505 (61.8%)
75 (9.2%)
297 (36.4%)
193 (23.6%)
252 (30.8%)
630 (77.1%)
57 (7.0%)
75 (9.2%)
55 (6.7%)
240 (29.4%)
517 (63.3%)
359 (43.9%)
689 (84.3%)
128 (15.7%)
140 (17.2%)
170 (20.8%)
166 (20.3%)
86 (10.5%)
255 (31.2%)
780 (95.5%)
37 (4.5%)
482 (59.0%)
335 (41.0%)
71 (8.7%)
218 (26.7%)
214 (26.2%)
314 (38.4%)
166 (20.3%)
87
No
Smoking status
Current smoker
Former smoker
Never smoker
651 (79.7%)
114 (14.0%)
465 (56.9%)
238 (29.1%)
Median (Q1, Q3)
e
Total lifetime alcohol intake (g/year)
1909.2 (588.4, 4507.3)
Dietary caloric intake (kcal/day)
2008.3 (1644.4, 2474.5)
Pre-diagnosis lifetime total physical activity f
143.7 (98.1, 198.3)
f
Pre-diagnosis lifetime recreational physical activity
12.4 (6.7, 21.3)
Pre-diagnosis lifetime occupational physical activity f
107.9 (58.0, 160.4)
Pre-diagnosis lifetime household physical activity f
17.8 (9.2, 29.9)
f
First follow-up total physical activity
78.7 (44.2, 126.9)
First follow-up recreational physical activity f
11.1 (3.0, 25.1)
f
First follow-up occupational physical activity
2.9 (0.0, 50.0)
First follow-up household physical activity f
33.1 (12.5, 56.0)
Second follow-up total physical activity f
71.6 (35.1, 132.9)
Second follow-up recreational physical activity f
13.5 (2.0, 32.6)
f
Second follow-up occupational physical activity
0.0 (0.0, 41.8)
Second follow-up household physical activity f
25.9 (7.6, 49.7)
Third follow-up total physical activity f
62.0 (28.3, 106.7)
Third follow-up recreational physical activity f
13.5 (0.0, 32.9)
Third follow-up occupational physical activity f
0.0 (0.0, 12.9)
f
Third follow-up household physical activity
21.9 (5.8, 47.5)
Mean (SD)
Age at diagnosis (years)
67.3 (7.4)
Body mass index (kg/m2)
28.0 (3.8)
First follow-up Physical component summary score
40.8 (12.4)
First follow-up Mental component summary score
50.8 (11.8)
Second follow-up Physical component summary score
40.8 (12.2)
Second follow-up Mental component summary score
50.5 (11.7)
Third follow-up Physical component summary score
40.1 (12.0)
Third follow-up Mental component summary score
51.0 (11.6)
a
Baseline (captured at diagnosis) characteristics unless otherwise stated.
b
IV included: (T4, N0, M0), (T4 N0/NX, M0/MX), (any T, N1, M0/MX), (any T, any N, M1),
(any T, NX, MX).
c
Not mutually exclusive (could have more than one treatment).
d
Included bilateral orchiectomy, luteinizing hormone-releasing hormone agonists, nonsteroidal
antiandrogens.
88
e
66 never-drinkers.
f
Units: MET-hour/week/year.
89
Table 3.2. Associations between post-diagnosis first follow-up physical activity and post-diagnosis first follow-up quality of life
in prostate cancer survivors in Alberta, Canada in 1997-2002.
Type of physical activity
Physical
activity
median
(min, max)
Physical component summary score a
Mental component summary score b
β
95% CI
P-value
β
95% CI
P-value
0.04
0.16
0.02
0.01
0.02, 0.05
0.12, 0.20
0.01, 0.03
-0.01, 0.03
<0.0001
<0.0001
0.001
0.14
0.01
0.05
0.01
-0.01
0, 0.02
0.01, 0.09
-0.01, 0.02
-0.03, 0.01
0.19
0.024
0.22
0.26
0.03
0.16
0.02
0.01
0.02, 0.04
0.12, 0.20
0, 0.03
-0.01, 0.03
<0.0001
<0.0001
0.023
0.20
0.01
0.05
0.01
-0.01
-0.01, 0.02
0.01, 0.10
-0.01, 0.02
-0.04, 0.01
0.20
0.024
0.24
0.33
0.09
0.68
0.05, 0.13
0.50, 0.85
<0.0001
<0.0001
-0.01
0.22
-0.05, 0.04
0.03, 0.40
0.76
0.021
0.08
0.71
0.04, 0.13
0.53, 0.88
<0.0001
<0.0001
-0.01
0.25
-0.06, 0.04
0.05, 0.45
0.78
0.015
All activity (MET-hour/week/year)
Total sample (n=817)
Total
79 (0, 491)
Recreational c
11 (0, 120)
c
Occupational
3 (0, 445)
Household c
33 (0, 314)
Excluding stage III/IV and IV (n=687) d
Total
79 (0, 491)
Recreational c
11 (0, 120)
c
Occupational
3 (0, 445)
Household c
33 (0, 314)
Moderate-to-vigorous activity
(hour/week/year)
Total sample (n=817)
Total
28 (0, 113)
c
Recreational
2 (0, 28)
Excluding stage III/IV and IV (n=687) d
Total
28 (0, 113)
c
Recreational
2 (0, 28)
90
All models adjusted for: age at diagnosis, aggressive versus non-aggressive prostate cancer, prostate cancer treatment (none, hormone
only, radiation therapy only or both hormone and radiation therapy), prostatectomy, post-diagnosis Charlson co-morbidity score, level
of education and pre-diagnosis lifetime physical activity.
a
Additionally adjusted for: smoking status.
b
Additionally adjusted for: whether or not they joined a support group post-diagnosis and whether they had family support or not.
c
Mutually adjusted for other physical activity types and intensities (for moderate-vigorous activity analysis).
d
Stage IV cancers: 55; stage III/IV cancers: 75.
91
Table 3.3. Associations between physical activity change (pre-diagnosis minus average post-diagnosis scores) over the
diagnosis period and average post-diagnosis quality of life in prostate cancer survivors in Alberta, Canada in 1997-2007.
Type of physical activity
Physical activity
change score:
Median
(min, max)
All activity (MET hour/week/year)
Total sample (n=817)
Total
-60 (-425, 246)
Recreational b
0 (-83, 120)
b
Occupational
-76 (-390, 258)
Household b
10 (-87, 279)
Excluding stage III/IV and IV (n=687) c
Total
-60 (-426, 246)
b
Recreational
0 (-83, 120)
Occupational b
-76 (-390, 258)
Household b
10 (-87, 279)
Excluding those who died before 3rd follow-up
(n=559) d
Total
-56 (-426, 246)
Recreational b
0 (-83, 120)
b
Occupational
-72 (-390, 258)
Household b
10 (-66, 222)
Physical component summary score a
Mental component summary score
β
95% CI
P-value
β
95% CI
P-value
0.04
0.15
0.04
0.02
0.03, 0.05
0.11, 0.19
0.02, 0.05
0, 0.04
<0.0001
<0.0001
<0.0001
0.08
0.01
0.06
0.02
0
0, 0.03
0.02, 0.11
0, 0.03
-0.03, 0.02
0.07
0.006
0.05
0.51
0.04
0.16
0.03
0.02
0.02, 0.05
0.12, 0.20
0.01, 0.04
0, 0.05
<0.0001
<0.0001
0.001
0.05
0.01
0.09
0.02
-0.02
0, 0.03
0.04, 0.14
0, 0.04
-0.05, 0.01
0.15
<0.0001
0.09
0.16
0.04
0.14
0.03
0.03
0.02, 0.05
0.10, 0.18
0.01, 0.04
0, 0.05
<0.001
<0.001
0.001
0.027
0.02
0.08
0.02
0
0, 0.04
0.03, 0.13
0, 0.04
-0.03, 0.03
0.019
0.001
0.07
0.94
0.11
0.72
0.07, 0.16
0.54, 0.90
<0.0001
<0.0001
0.04
0.35
-0.01, 0.09
0.15, 0.55
0.15
0.001
0.10
0.81
0.05, 0.15
0.62, 1.00
<0.0001
<0.0001
0.02
0.45
-0.03, 0.08
0.23, 0.68
0.38
<0.0001
Moderate-to-vigorous activity (hour/week/year)
Total sample (n=817)
Total
-17 (-83, 60)
Recreational b
0 (-20, 21)
Excluding stage III/IV and IV (n=687) c
Total
-17 (-83, 60)
Recreational b
0 (-20, 21)
92
Excluding those who died before 3rd follow-up
(n=559) d
Total
-16 (-83, 60)
0.11
0.06, 0.16
<0.0001
0.05
-0.01, 0.11
0.08
b
Recreational
0 (-20, 21)
0.70
0.51, 0.89
<0.0001
0.43
0.21, 0.66
<0.0001
All models adjusted for: age at diagnosis, aggressive versus non-aggressive prostate cancer, prostate cancer treatment, prostatectomy,
PSA value at diagnosis, post-diagnosis Charlson co-morbidity score and pre-diagnosis lifetime physical activity.
a
Additionally adjusted for BMI and smoking status.
b
Mutually adjusted for other physical activity types and intensities (for moderate-vigorous activity analysis).
c
Stage IV cancers: 55; stage III/IV cancers: 75.
d
152 participants died before second follow-up; 106 participants died between second and third follow-up.
93
Table 3.4. Change in moderate-to-vigorous recreational physical activity based on meeting the cancer prevention guidelines
(150 minutes/week) over the diagnostic period (pre- and average post-diagnosis) and average post-diagnosis quality of life in
prostate cancer survivors in Alberta, Canada in 1997-2007.
Moderate-to-vigorous recreational
physical activity guideline adherence
(hour/week/year)
No. in
group
Physical component summary score
β
95% CI
P-value
Mental component summary score
β
95% CI
P-value
Total sample (n=817)
Non-exercisers
272
Referent
Referent
Adopters
173
4.80
2.82, 6.78
<0.0001
2.26
0.09, 4.43
0.041
Relapsers
149
-0.04
-2.10, 2.01
0.97
-0.35
-2.60, 1.89
0.76
Maintainers
223
6.01
4.15, 7.86
<0.0001
2.32
0.29, 4.34
0.025
Excluding stage III/IV and IV cases (n=687) a
Non-exercisers
224
Referent
Referent
Adopters
142
4.71
2.60, 6.81
<0.0001
2.12
-0.30, 4.54
0.09
Relapsers
125
-0.28
-2.46, 1.89
0.80
-0.64
-3.13, 1.86
0.62
Maintainers
196
6.03
4.09, 7.96
<0.0001
2.34
0.12, 4.56
0.039
Excluding those who died before 3rd follow-up (n=559) c
Non-exercisers
151
Referent
Referent
Adopters
128
4.75
2.54, 7.00
<0.0001
1.89
-0.66, 4.46
0.15
Relapsers
99
0
-2.37, 2.36
0.99
-0.41
-3.15, 2.33
0.77
Maintainers
181
5.76
3.74, 7.78
<0.0001
2.19
-0.15, 4.52
0.07
All models adjusted for: age at diagnosis, aggressive versus non-aggressive prostate cancer, prostate cancer treatment, prostatectomy,
PSA value at diagnosis and post-diagnosis Charlson co-morbidity score.
a
Stage IV cancers: 55; stage III/IV cancers: 75.
c
152 participants died before second follow-up; 106 participants died between second and third follow-up.
94
Figure 3.1. Change in moderate-to-vigorous recreational physical activity guideline
adherence groups over the diagnostic period in prostate cancer survivors in Alberta,
Canada (2000-2007).
95
3.11 Additional analyses and results: Lasso and elastic-net regularized generalized linear
model (GLMNET package) covariate selection methods
The lasso and elastic-net regularized generalized linear model (GLMNET) package in R,
is an efficient method to test all possible interactions between multiple covariates in a single step.
GLMNET produces output and a figure indicating influential covariates whose coefficients
deviate from zero. From there, a cut-off of the lambda value was determined. As the lambda
value (a measure between 0-1) increases, the model is more conservative on covariate inclusion
in the model. For this analysis, our selection was very lenient and we tested all covariates
deviating from the zero line. The objective of these analyses was to determine if the GLMNET
method and the backwards elimination statistical modelling covariate selection methods came to
the same conclusions when examining the association between post-diagnosis physical activity
and physical and mental QoL scores.
Models fit with all terms and plotted visually using the GLMNET approach found few
important covariates for further testing (Figures 3.2-3.5). Specifically, for total physical activity
and PSC score, only one covariate deviated from zero. However, all covariates were fit in
multivariable liner regression models and did appear to substantially confound the relationship
between post-diagnosis physical activity and physical and mental QoL. Final models were
adjusted only for a priori covariates (age at diagnosis, prostate cancer treatment, Gleason score,
Charlson post-diagnosis co-morbidity index score, prostatectomy and stage of cancer) and results
were very similar to the traditional backwards elimination method results for the association
between post-diagnosis physical activity and physical and mental QoL (Table 3.5). Statistical
significance and magnitude in the physical activity and QoL relationships did not notably differ
between the GLMNET approach and the traditional approach. However, the final models were
adjusted for additional variables in the traditional approach.
96
A key strength of the GLMNET approach for epidemiological research is the elimination
of the issue of multiple testing. Most epidemiological studies seek to test multiple covariates in
analyses to determine the best model fit and biological relevance of their data. The art of
covariate selection requires multiple tests, elaborate planning and transparency and might not
always be feasible with the traditional methods of backwards elimination. GLMNET is able to
take into account all combinations of included covariates and may be more sensitive to subtle
changes in covariate interactions. Implications of comparisons between the two model covariate
selection methods that need to be considered include that these analyses were descriptive and
very preliminary. A more in-depth analysis may be undertaken in future studies to determine
specific advantages and disadvantages in choosing which method may be more beneficial when
modelling epidemiological associations.
97
Table 3.5. Associations between post-diagnosis physical activity on first follow-up post-diagnosis QoL using lasso and elasticnet regularized generalized linear model (GLMNET package) methods in prostate cancer survivors in Alberta, Canada in
1997-2002 (n=817).
Physical Component Score a
Type of physical activity
β
95% CI
(MET-hour/week/year)
Total
-0.02
-0.03, -0.008
Recreational*
0.08
0.02, 0.14
Occupational*
-0.02
-0.03, -0.009
Household*
-0.03
-0.08, 0.02
*Mutually adjusted for other physical activity types.
a
Mental Component Score a
P-value
β
95% CI
P-value
0.001
0.009
<0.001
0.192
-0.004
0.05
-0.003
-0.03
-0.02, 0.008
-0.02, 0.12
-0.02, 0.009
-0.08, 0.03
0.536
0.1156
0.580
0.360
All models adjusted for: age at diagnosis, Gleason score, stage of prostate cancer, prostate cancer treatment, prostatectomy and post-
diagnosis Charlson co-morbidity score.
98
Figure 3.2. Total physical activity and PCS score output using GLMNET
regularization.
99
Figure 3.3. Total physical activity and MCS score output using GLMNET
regularization.
100
Figure 3.4. Recreational, occupational and household physical activity (mutually
adjusted) and PCS score output using GLMNET regularization.
101
Figure 3.5. Recreational, occupational and household physical activity (mutually
adjusted) and MCS score output using GLMNET regularization.
102
Chapter Four: IDENTIFICATION AND PREDICTION OF QUALITY OF LIFE
TRAJECTORIES AFTER A PROSTATE CANCER DIAGNOSIS
4.1 Preamble
This manuscript addresses objective three of this thesis through a group-based trajectory
modelling longitudinal analysis determining trajectory groups of QoL. The manuscript
was submitted for publication on June 24, 2016 and is currently under review.
This work was also accepted as an oral/poster presentation at the following conferences:
Farris MS, Kopciuk KA, Courneya KS, McGregor SE, Friedenreich CM. Quality of life
trajectories after prostate cancer diagnosis: the role of physical activity and prognostic
factors. Canadian Society of Epidemiology and Biostatistics (CSEB) Student Conference,
Winnipeg, MA June 8-10, 2016. (Oral presentation)
Farris MS, Kopciuk KA, Courneya KS, McGregor SE, Friedenreich CM. Quality of life
trajectories after prostate cancer diagnosis: the role of physical activity and prognostic
factors. 2016 Epidemiology Congress of the Americas, Miami, Florida June 21-24, 2016.
(Poster presentation)
Running title: Trajectories of QoL in prostate cancer survivors
Keywords: group-based trajectory, quality of life, prostate cancer, mixed model
103
Financial support: Research relating to this manuscript was supported by the Canadian
Institute of Health Research [2004-2007; grant number MOP-67217]; the National
Cancer Institute of Canada with funds from the Canadian Cancer Society [2000-2004;
grant number 011004]; and the Alberta Cancer Board-Research Initiative Program [20042007; grant number 4570)]. Christine M. Friedenreich held career awards from Alberta
Innovates-Health Solutions and the Alberta Cancer Foundation, Kerry S. Courneya was
supported by the Canada Research Chairs Program and S. Elizabeth McGregor by the
Alberta Heritage Foundation for Medical Research.
104
4.2 Abstract
Introduction: The aim of this study was to identify physical and mental quality of life
(QoL) trajectories after a prostate cancer diagnosis and systematically characterize these
trajectories by behaviours and prognostic factors.
Materials and methods: Prostate cancer survivors diagnosed between 1997-2000 were
recruited between 2000-2002 into a prospective cohort study with repeated
measurements. Behavioural/prognostic data were collected through in-person interviews
and questionnaires. QoL was collected at three post-diagnosis time-points, approximately
two years apart using the Short Form (SF)-36 validated questionnaire. To identify
physical and mental QoL trajectories, group-based trajectory modelling was undertaken.
Differences between groups were evaluated by assessing potentially influential dropouts
(through mortality or poor health), behavioural/prognostic factors at diagnosis or during
the follow-up.
Results: Three trajectories of physical QoL were identified including: averagemaintaining QoL (32.2%), low-declining QoL (40.5%) and very low-maintaining QoL
(27.3%). In addition, three trajectories for mental QoL were identified: averagemaintaining QoL (66.5%), above average-declining QoL (19.7%) and low-increasing
QoL (13.8%). In both physical and mental QoL, dropout (due to morality/loss to followup) was different between trajectories, thus confirming QoL and mortality were closely
related. Furthermore, increased Charlson comorbidity index score was consistently
associated with physical and mental QoL group membership relative to average
105
maintaining groups, while behaviours such as time-varying physical activity was only
associated with physical QoL trajectories, not mental QoL trajectories.
Conclusion: It was possible to define three trajectories of prostate cancer survivors
related to physical and mental QoL. These data provide insights regarding means for
identifying subgroups of prostate cancer survivors with lower or declining QoL after
diagnosis who could be targeted for further interventions aimed at improving QoL.
106
4.3 Introduction
Globally the burden of prostate cancer is on the rise, with an estimated 1.1 million
new cases of prostate cancer (1) and an estimated 3.9 million prevalent cases of prostate
cancer worldwide estimated in 2012 (2). Screening programs largely driven by the
Prostate-Specific Antigen (PSA) testing introduced in the early 1990s have increased
detection of prostate cancer, specifically in developed countries (1, 3). Early detection
was believed to reduce prostate cancer mortality (4). However, early detection may also
result in over-diagnosis and over-treatment of potentially non-life threatening prostate
cancer (5). The consequences of treating men with non-life threatening prostate cancer
are serious since long-term residual side effects, reduced functioning, a compromised
mental state and overall reduced Quality of Life (QoL) (6-10) often occur in this
population.
While QoL outcomes have been well documented after diagnosis of prostate
cancer, most research studies focus on impacts of different treatments (11) or supportive
interventions (12) on QoL over time. Moreover, studies have examined QoL changes
with repeated measurements long-term after diagnosis of prostate cancer (13-17).
However, to our knowledge, studies that examined physical and mental QoL changes in
prostate cancer survivors have made assumptions that the whole study population can be
represented by a single pattern of QoL, by measuring average changes in QoL.
Nonetheless, it is unknown if there are distinct subgroups of prostate cancer populations
with different QoL patterns following diagnosis. Subtle differences may be missed when
examining average patterns of QoL over time. Group-based trajectory modelling
(GBTM) is an innovative analytic method that allows researchers to determine
longitudinal subgroups within a population without making distribution assumptions (18).
107
To date, only two studies have utilized GBTM to evaluate surgical outcomes following
prostatectomy in prostate cancer survivors including urinary function (19) and sexual
function (20). To our knowledge, GBTM has never been applied to physical and mental
QoL in a prostate cancer population.
The primary objective of this prospective cohort study was to determine QoL
trajectory patterns in prostate survivors. We hypothesized that there would be more than
one and up to four trajectory groups of physical and mental QoL in our study population
up to 10 years following prostate cancer diagnosis. A secondary objective and
exploratory analysis was to characterize trajectory groups systematically by analyzing
common prognostic and behavioral factors associated with prostate cancer. We
hypothesized that factors associated with negative prognosis would be associated with
low QoL groups relative to any high QoL group.
4.4 Materials and methods
4.4.1 Study sample
This prostate cancer cohort study in Alberta, Canada has been previously
published in detail elsewhere (21). Briefly, initial recruitment of prostate cancer cases
(through the Alberta Cancer Registry) was carried out in a population-based case-control
study (1997-2000) and participants were re-contacted for consent to enroll in the
prospective cohort study between the years 2000 and 2002. Telephone interviews were
completed at the first follow-up to obtain physical activity data and a questionnaire was
mailed to participants to obtain QoL data. Subsequently, at second and third follow-ups,
self-administered questionnaires were sent through the mail to obtain updated physical
activity and QoL data. Final mailed questionnaire assessments were completed in 2007
108
and participants were followed for cancer outcomes until 2014 by medical chart
abstractions, vital statistics updates and medical record checks. Eligibility criteria for
prostate cancer cases included: histologically confirmed invasive stage II prostate cancer
or greater, English speaking, <80 years of age and no previous diagnosis of cancer
(excluding non-melanoma skin cancer). Ethics approval was obtained through the Alberta
Cancer Research Ethics Board and the Conjoint Health Research Ethics Board at the
University of Calgary.
4.4.2 Data collection
The Short Form (SF)-36 , previously tested for reliability and validity in several
domains (22), was used to evaluate the prostate cancer survivors QoL at three separate
time-points. Composite summary scores of physical and mental QoL were calculated.
Each participant needed to have all eight individual SF-36 scale scores to be considered
eligible to calculate summary scores. Higher composite summary scores related to higher
QoL and possible scores ranged from 0-100. Clinically meaningful differences between
QoL scores were ≥5 points (23).
Vital status updates of the survivors was completed on a monthly basis, and
further updated at each follow-up time-point by study staff. We assumed that participants
who could not be contacted because of death, refusal or loss to follow-up were likely in
poor health and which consequently influenced their ability to participate in the cohort
follow-ups that occurred at three separate time points between 2000 and 2007. Medical
data and cancer progressions, recurrences and new primary cancer outcomes were
obtained through clinical chart abstractions done by qualified health record technicians
working with the Alberta Cancer Registry.
109
Other behavioural and prognostic information was obtained from the prior casecontrol study (24) through in-person interviews. Variables obtained at prostate cancer
diagnosis and used in this current study included: average total energy intake
(grams/day), average alcohol intake (grams/year), smoking status, Body Mass Index
(BMI) (kg/m2), age in years, aggressive versus non-aggressive disease (> stage II or
Gleason score ≥8 (25)), whether they had a prostatectomy, radiation therapy, hormone
therapy and number of co-morbidities based on the Charlson co-morbidity index score
(26). In addition, past year physical activity (MET-hours/week/year) was also collected
post-diagnosis at three separate follow-ups (21).
4.4.3 Statistical analysis
4.4.3.1 Identifying trajectory groups
The analysis utilized GBTM (Traj for Stata®) (27), which uses finite mixture
modelling to approximate unknown distributions of trajectories across a study population.
Censored normal or tobit models were used to estimate trajectories of physical and
mental QoL over the post-diagnosis period (2000-2007 for active data collection) in this
cohort of prostate cancer survivors. First follow-up data were collected between 20002002, second follow-up data in 2002-2004 and third follow-up in 2004-2007 with
approximately two years between measurements for each participant (Figure 4.1). In the
model selection process, the Bayesian Information Criterion (BIC) was utilized to
determine the best model underlying the group selection and functional form (18). The
BIC was used for model selection within a finite set of models and is based on the
likelihood function plus a penalty term for the number of parameters in the model (28).
BIC values balance model fit with model complexity and the closer the negative BIC
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value is to zero, the better is the fit of the model. Supplemental non-statistical criteria
were also implemented for model selection including: (1) all trajectory groups
encompassed >10% of the total sample, (2) all trajectories were distinct from one another
(visual assessment of trajectory figures looking for non-overlapping confidence intervals
(CI)) and (3) posterior probabilities of group membership were >0.7 (18). Posterior
probabilities represent the average probability that the trajectory group each participant
was assigned to was the most appropriate group selection and model fit. Model selection
is a multi-step procedure. First, we determined the most appropriate number of
trajectories for the physical and mental QoL data by fitting models with up to four
trajectory groups. A priori, we hypothesized that four groups would be the maximum
number of groups tested for model fit according to the sample size and to avoid overfitting the data. Second, the functional form of the trajectory groups (linear or quadratic)
was evaluated to obtain a base model for physical and mental QoL trajectory groups
separately. Once the model trajectory groups were established, testing for influential
observations and determining characteristics of trajectory groups followed (18).
There was a substantial decrease in the sample size during the follow-up period
that extended up to 10 years since diagnosis because of mortality, poor health or loss to
follow-up, so differential dropout assessment between trajectory groups was performed.
Dropout probabilities for each trajectory group were estimated to substantiate differences
between groups (29). Differential dropout was evaluated based on arbitrary differences
between any two trajectory groups of >10%. If there were substantial differences (>10%),
dropout was considered influential and this process included/maintained in the final
models.
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4.4.3.2 Characteristics of trajectory groups
Descriptive characteristics for each physical and mental QoL trajectory group
were produced and assessed for initial differences between groups. Then, as an
exploratory analysis, we assessed behavioural and prognostic factors to characterize
physical and mental QoL trajectory group membership using multivariable multinomial
logistic regression. Multivariable models included the following prognostic and
behavioural risk factors: age at diagnosis (years), aggressive versus non-aggressive
disease at diagnosis (Stage >II or Gleason score ≥8 (25)), prostatectomy, radiation
therapy, hormone therapy, Charlson co-morbidity index score (26) post-diagnosis,
approximate daily energy intake (kcal/day), average alcohol intake (grams/year),
smoking status (current versus former/never (30)) and BMI at diagnosis (kg/m2). Timevarying physical activity was measured up to three time-points (MET-hours/week/year)
and evaluated for associations on the groups trajectories. The trajectory group with the
highest QoL was used as the reference category. Relative risk ratios (RRRs) and 95% CIs
were produced based on the final adjusted models to represent the multinomial logistic
regression comparisons. In addition, models produced estimated mean QoL coefficients
(intercepts), slope values and physical activity as a time-varying covariate for each
trajectory group. Additionally, we assessed if there were any within trajectory group
dropout characteristic differences. We included a logistic regression model examining the
odds of dropping out within each trajectory group that could depend on the two previous
responses in the final adjusted models.
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4.4.3.3 Sensitivity analyses
Sensitivity analyses were performed on the complete case sample (all three
follow-ups completed) and two complete assessments sample (two of the three followups completed), to determine differences in model selection, dropout probabilities and
prognostic/behavioural risk factor associations with trajectory group membership.
Further, to determine the impact of modelling dropout, multivariable multinomial logistic
regression excluding dropout were performed. Then we examined participants who
switched trajectory groups with and without modelling dropout to substantiate any shift
in trajectory group membership. Finally, to explore the possibility of reverse causality,
time-lagged models were fit based on previous physical or mental QoL scores.
Final trajectory group figures were produced post-model selection. All statistical tests
were two-sided and evaluated at an alpha level of 0.05, using the statistical package Stata
v.13 (College Town, Texas).
4.5 Results
4.5.1 Sample
There were 830 prostate cancer survivors who provided consent to participate in
this cohort study. Trajectory models for both physical and mental QoL included 817
cancer survivors who had complete first follow-up data. Of these, 636 and 454
participants had complete data at the second and the third follow-up time-points,
respectively.
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4.5.2 Trajectory group selection and evaluation
Three physical QoL trajectory groups were identified from the GBTM analysis
over the post-diagnosis follow-up (Table 4.1). Moreover, when assessing functional
form, the BIC was closest to zero when all groups were linear, thus the linear function in
all groups was considered the most advantageous model fit (Table 4.2). All groups
included at least 100 survivors (which would equate to 12.2% of the total sample (n =
817)).
After fitting the base model, we fit models to determine if dropout, behavioural
and prognostic factors were associated with model fit and group membership. First, the
model including dropout did not improve model fit according to the BIC values. The base
model BIC = -7228 and the dropout model BIC = -7558, in which case, the base model
BIC value was closer to zero, thus a better fit. However, when we estimated dropout
probabilities for each trajectory group, they were substantially different. The probability
of dropping out was more than double (0.42) in the very low-maintaining QoL group,
compared to 0.15 in the low-declining group and 0.21 in the average-maintaining QoL
group. Therefore, the attrition process or probability of “dropout” that was based on the
two previous outcomes was maintained in the model to allow for this variation across
groups. Next, models were fit with all behavioural and prognostic factors in a
multivariable multinomial logistic regression analysis. The BIC improved substantially
with inclusion of these factors (dropout model without factors=-7558 versus with factors
-7453).
A plot/figure of the fully adjusted model was produced (Figure 4.2). Based on the
visual assessment of this plot, there was a “very low-maintaining” group with physical
QoL mean scores consistently at 24 throughout the follow-up, a “low-declining” group
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with physical QoL scores starting at approximately 42 and declining to 36 by the third
follow-up and a “average-maintaining” group, with physical QoL mean scores
consistently at approximately 50. Posterior group membership probabilities were also
sufficiently high with: 0.86 for the very low-maintaining group, 0.79 for the lowdeclining group and 0.86 for the average-maintaining group. In addition, groups were
well separated with clear differences of >5 points between the groups.
Three mental QoL trajectory groups also emerged from the GBTM analysis. The
BIC value was the closest to zero in the model with three groups and became unstable
with four groups (Table 4.3). Linear functions were most appropriate for each trajectory
group model fit (Table 4.4). Further, all groups included more than 100 prostate cancer
survivors. The BIC associated with the model including dropout for mental QoL
trajectories did not achieve a better fit (base model = -7089 versus dropout model = 7442). However, like physical QoL, the low-increasing mental QoL group had
statistically significantly higher dropout probabilities (0.38) compared to above averagedeclining (0.27) and average-maintaining (0.20) groups and therefore, the attrition
process or probability of “dropout” that was also based on the two previous outcomes
was maintained in the model. Similar to physical QoL again, the BIC value in the mental
QoL multinomial logistic model including prognostic/behavioural factors substantially
improved when they were included (dropout model=-7442 versus adjusted model=7425).
A final figure from the fully adjusted model was produced, see Figure 4.3.
According to the figure, there was a “low-increasing” QoL group that started at 26 and
increased in QoL score to 36, a “above average-declining” QoL group starting at 57 and
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declined substantially to mean scores of 38 over the follow-up and a “averagemaintaining” QoL group starting at 55 and slightly increasing to 57 in mental QoL score.
Posterior group membership probabilities were 0.91, 0.80 and 0.87 for low-increasing,
above average-declining and average-maintaining QoL groups, respectively. There was
separation (generally >5 points) between the trajectory groups with none of the groups
overlapping.
4.5.3 Characteristics of trajectory groups
Descriptive characteristics of physical QoL trajectory groups are found in Table
4.5. The low-declining QoL group appeared to have the highest number of survivors who
had aggressive disease (n=70) and radiation therapy (n=168). The average-maintaining
QoL group had the largest proportion of survivors who did not receive hormone therapy
(50%), did not have a prostatectomy (46%) and were former/never smokers (90%).
Median age at diagnosis, Charlson co-morbidity index score and BMI increased across
average-maintaining, low-declining and very low-maintaining QoL groups. Alternatively,
median physical activity at every time-point decreased across QoL groups with the lowest
levels in the very low-maintaining QoL group and the highest levels in the averagemaintaining QoL group.
Table 4.6 displays descriptive characteristics of mental QoL trajectory groups. A
large proportion of the participants were in the average-maintaining QoL group (568 out
of 817), therefore, it is difficult to glean meaningful differences between the QoL groups.
The above average-declining QoL group had the highest proportion of participants who
received hormone therapy (79%) and had a prostatectomy (91%). Interestingly, the lowincreasing group had the lowest proportion of aggressive disease (11%). The above
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average-declining QoL group had the highest median age at diagnosis (72 years), highest
Charlson co-morbidity index score and the lowest physical activity levels at the first
follow-up (68 MET-hour/week/year). BMI, average alcohol consumption and total
energy intake did not substantially differ between trajectory groups.
When examining the multinomial logistic regression models, a higher Charlson
co-morbidity index score was statistically significantly associated with both very lowmaintaining physical QoL (RRR = 2.04, 95% CI:1.66-2.51) and low-declining physical
QoL (RRR = 1.59, 95% CI: 1.29-1.95) group membership, relative to the averagemaintaining QoL group (Table 4.7). Similarly, BMI was statistically significantly
associated with very low-maintaining and low-declining physical QoL group membership
with an increase in risk per kg/m2 (very low-maintaining: RRR = 1.18, 95% CI: 1.091.27; low-declining: RRR = 1.09, 95% CI: 1.02-1.17), relative to the average-maintaining
physical QoL group. Other statistically significant associations were found with hormone
therapy with very low-maintaining and low-declining physical QoL group membership
while prostatectomy, smoking status and age at diagnosis were only associated with very
low-maintaining physical QoL group membership.
Results from mental QoL group membership multinomial logistic regression were
slightly attenuated compared to physical QoL trajectory group results (Table 4.7, lower).
Charlson comorbidity index score was statistically significantly associated with lowincreasing (RRR = 1.18, 95% CI: 1.03-1.36) and above average-declining (RRR = 1.32,
95% CI: 1.12-1.56) mental QoL group membership, relative to the average-maintaining
mental QoL group. Interestingly, aggressiveness of disease (RRR = 0.40, 95% CI: 0.190.86) and receiving radiation therapy (RRR = 0.50, 95% CI: 0.28-0.89) were statistically
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significantly associated with only the low-increasing mental QoL group membership
relative to the average-maintaining mental QoL group. Having a prostatectomy was
statistically significantly associated with a three-fold risk of above average-declining
QoL group membership (RRR = 3.32, 95% CI: 1.23-8.94) but was not significantly
associated with low-increasing QoL group membership (RRR = 1.73, 95% CI: 0.893.36).
Table 4.8 displays the mean coefficients for values from the fully adjusted
models in physical and mental QoL trajectory groups. In physical QoL groups, baseline
intercept coefficient QoL scores were estimated as follows: average-maintaining (49.64),
low-declining (41.68) and very low-maintaining (24.37). Total physical activity measured
in MET-hours/week/year was significantly and positively associated with all trajectory
groups (estimated slopes of 0.02 to 0.03). The mean baseline intercept coefficients for the
mental QoL trajectory group scores were: average-maintaining (54.59), above averagedeclining (57.32) and low-increasing (25.99). The above average-declining group had a
negative estimated slope of -6.98, also evident in Figure 4.3. However, physical activity
over time was not associated with any mental QoL trajectory groups.
4.5.4 Dropout within trajectory groups
An exploratory analysis in physical QoL trajectory groups suggested that BMI
was statistically significantly protective against dropping out in the very low-maintaining
QoL group (OR = 0.91, 95% CI: 0.85-0.98) but not the low-declining or averagemaintaining QoL groups (data not shown). Having radiation therapy was statistically
significantly protective against dropping out in the low-declining (OR = 0.52, 95% CI:
0.30-0.91) QoL group while, prostatectomy was adversely associated with dropping out
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in the low-declining QoL group (OR = 2.30, 95% CI: 1.16-4.57) and average-maintaining
QoL group (OR = 2.48, 95% CI: 1.11-5.55).
In mental QoL trajectory groups, radiation therapy was statistically significantly
protective against dropping out in the average-maintaining QoL group (OR = 0.50, 95%
CI: 0.32-0.78). Having a prostatectomy was adversely significantly associated with
dropping out in the average-maintaining QoL group (OR = 2.12, 95% CI: 1.26-3.58). In
addition, current smokers had a three-fold significant increase in dropping out in the lowincreasing QoL group (OR = 3.48, 95% CI: 1.11-10.88) and almost two-fold significant
increase in the average-maintaining QoL group (OR = 1.91, 95% CI: 1.09-3.35). No other
significant associations were found and results were interpreted with caution due to the
small sample sizes.
4.5.5 Sensitivity analyses
In sensitivity analyses comparing survivors with complete follow-up data and
those with only two complete follow-up assessments, the model selection and evaluation
process did not differ for physical or mental trajectory groups established using all the
data and for the final adjusted model fit. Multinomial logistic regression results were very
similar for physical and mental QoL trajectories (data not shown). Further, when
modelling dropout versus not, the multinomial logistic regression comparisons relative to
the high QoL group were not different for physical or mental QoL trajectory groups.
Additionally, we compared participants who switched trajectory groups when modelling
dropout versus not and there were only 53 “switchers” in the physical QoL analysis and
58 “switchers” in the mental QoL analysis. Only one survivor in the mental QoL
trajectory group analysis moved across two groups (low-increasing QoL group when
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modelling dropout then switched to the average-maintaining QoL group without
modelling dropout).
Finally, we fit time-lagged models and found that modelling previous QoL score
had a more advantageous fit with BIC values of -7168 versus -7453 for physical QoL
trajectories and -7106 versus -7425 for mental QoL trajectories. However, multinomial
logistic regression results did not substantially differ between the final adjusted models
and the time-lagged models (data not shown).
4.6 Discussion
When utilizing GBTM we identified three physical QoL trajectories that were
meaningfully different according to clinically relevant thresholds (≥5 points) (23) up to
10 years following prostate cancer diagnosis. The average-maintaining and very lowmaintaining physical QoL group scores were relatively consistent across time-points. On
the other hand, the low-declining physical QoL group scores declined by the third timepoint by approximately five QoL points. Prevalence in each of these groups was fairly
equal, with the highest prevalence in the low-declining group (40.5%). We also
discovered three mental QoL trajectories in our study population of prostate cancer
survivors. The above average-declining mental QoL trajectory had substantial declines
over the course of the study follow-up, whereas the low-increasing and averagemaintaining QoL trajectories experienced increases in QoL scores by the third time-point.
Prevalence of the mental QoL trajectories was highly concentrated in the averagemaintaining groups with 66.5% of the population falling into this group relative to the
above average-declining (19.7%) and low-increasing (13.8%) groups. In addition, we
discovered that the probability of dropout was substantially different in the low physical
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and mental QoL groups compared to the high and medium groups. Further, Charlson
comorbidity index score was consistently associated with low and medium QoL group
membership relative to the high QoL groups in both physical and mental trajectories.
Other studies have focused on disease specific QoL in prostate cancer survivors.
One study examining urinary function in prostate cancer survivors after radical
prostatectomy found three trajectories of QoL post-surgery (19). Although this study
focused on recovery patterns with a follow-up up to 12-months post-prostatectomy, age
and number of co-morbidities were also associated with lower QoL group outcomes,
similar to our study. The Prostate Cancer Outcomes Study, examined sexual function
following radical prostatectomy (20). Interestingly, their GBTM analysis fit four distinct
sexual function trajectories, with age, ethnicity, baseline sexual function and having had
nerve-sparing surgery associated with the low QoL group. While these studies
investigated different QoL outcomes, there are some similarities with regard to
identifying high QoL and low QoL sub-populations, similar to our study.
Other previous research a priori stratified prostate cancer cohorts based on age
groups (31) and number of comorbidities (32). Age groups characterize differences in
QoL similar to this study, yet, unlike our results, QoL scores post-diagnosis did not differ
by number of co-morbidities. Further, despite treatment differences observed in this study
between trajectory groups, mechanisms may be influenced by age of participants and
length of follow-up, that cannot be teased apart in observational studies (33).
Additionally, we found BMI, smoking and physical activity to be associated with
medium and low QoL groups to different degrees. For example, time-varying physical
activity and BMI were associated with physical QoL group trajectories but not mental
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QoL trajectories. Moreover, smoking was associated with low groups in both physical
and mental QoL trajectories but not medium QoL groups, additionally characterizing
these QoL groups from one another. The ability to target prostate cancer survivors based
on these characteristics may lead to enhanced QoL and survival outcomes.
Our analysis that modelled dropout produced significantly higher dropout
probabilities in both the physical and mental low QoL groups relative to the other groups.
This finding supported the mechanism that QoL may be a predictor of morbidity and
mortality (34). Clinical implications of these findings support interventions for these
specific groups of prostate cancer survivors. Specifically, for the very low-maintaining
physical QoL group, scores were consistently below 30, which is well below the mean
score of 50 (35). The very low-maintaining physical QoL group was older, more likely to
be on hormone therapy, have undergone a prostatectomy, have co-morbidities, higher
BMI and more likely to be current smokers compared to the average-maintaining QoL
group. These differences characterize the very low-maintaining QoL group and can make
it easier for physicians to target this population for further intervention.
There are limitations that should be noted in this study. First, this study was
observational, and therefore, we cannot rule out the possibility of alternative explanations
of our results. Confounding by indication can occur in observational studies where other
factors, in this case co-morbid conditions, general health and age, may alter survivors’
baseline risk, thus explaining their QoL group membership, rather than the diagnosis of
prostate cancer itself. For example, older prostate cancer survivors are more likely to
have co-morbid conditions and less overall general health, resulting in a lower baseline
for QoL scores after prostate cancer diagnosis. We had sufficient power to identify
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trajectory groups using GBTM methods, however, when examining prognostic and
behavioural associations between groups, precision was lacking and therefore, chance of
type II error is present. This situation may be especially relevant in the low-increasing
(13.8%) and above average-declining (19.7%) mental QoL trajectory groups,
representing <35% of the study population. Moreover, our study only had three repeated
measurements over the post-diagnosis period of two and up to 10 years post-diagnosis.
We made the assumption that QoL remained consistent between follow-up time-points of
up to two years post-diagnosis. In between follow-up time-points participants may
experience response shift as perceptions and priorities of QoL change over time (36, 37).
However, time-lagged exploratory models found that previous QoL score did predict
future QoL scores and sensitivity analyses did not materially change results, therefore,
these concerns are attenuated. Finally, our results may only apply to prostate cancer
survivors who survived at least two and up to three years’ post-diagnosis and those with
stage T2 or higher disease. Although this issue limits our generalizability, it was
necessary to define a homogeneous population.
While it is important to take into account limitations, our study strengths need
also be recognized when interpreting these results. We utilized an innovative analysis
technique which in theory does make one distributional assumption based on the ceiling
and floor effects of the tobit distribution of QoL scores over time. This study is the first to
identify separate groups of prostate cancer survivors following different post-diagnosis
physical and mental QoL trajectories. In addition, we addressed biases inherent in longterm prospective cohort studies by identifying the role of dropping out or mortality
during active data collection. Dropout proved to be important in this study population.
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While modelling dropout, we attempted to systematically characterize the trajectory
groups by several behavioural and prognostic factors measured at diagnosis or over the
diagnosis period. Future research will be able to capitalize on these findings in order to
target at-risk groups of prostate cancer survivors with low and declining QoL trajectories.
4.7 Conclusion
Based on these analyses there were three distinct trajectories of physical and
mental QoL up to 10 years post-prostate cancer diagnosis. Confirmation of these findings
is warranted in this population to determine which prostate cancer survivors are truly
experiencing low and/or declining QoL relative to average-maintaining QoL. Active
surveillance may be an option to minimize aggressive treatment regiments.
Characteristics of QoL trajectories will lead to a better understanding of differences
between groups and how health professionals and researchers can use this information to
plan future interventions to specifically target low/medium and/or declining QoL
populations.
4.8 Manuscript acknowledgements
Study coordination was done by Aleata Ryhorchuk and Sana Fakih. Interviewers
for this study were Jodi Parrotta, Linda Davison, Pearl Cooke, Nicole Slot, Carol-Anne
Zawalykut, Catherine Munro. Data entry was done by Carla Quesnel and chart
abstraction by Yvonne LeBlanc and Sarah MacLaughlin. Dr. Steven J. Angyalfi provided
clinical expertise regarding prostate cancer treatment and care.
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Health-related quality of life after radical prostatectomy depends on patient's age but not
on comorbidities. Urol Oncol. 2015;33(6):266.e1-7.
33.
Kendal WS. Age Bias in Time From Diagnosis Comparisons of Prostate Cancer
Treatment. Am J Clin Oncol. 2016;[Epub ahead of print].
34.
Sullivan PW, Nelson JB, Mulani PM, Sleep D. Quality of life as a potential
predictor for morbidity and mortality in patients with metastatic hormone-refractory
prostate cancer. Qual LIfe Res. 2006;15(8):1297-306.
35.
Ware JE, Jr., Gandek B, Kosinski M, Aaronson NK, Apolone G, Brazier J, et al.
The equivalence of SF-36 summary health scores estimated using standard and countryspecific algorithms in 10 countries: results from the IQOLA Project. International Quality
of Life Assessment. J Clin Epidemiol. 1998;51(11):1167-70.
36.
Serda IFBC, Valle AD, Marcos-Gragera R. Prostate cancer and quality of life:
analysis of response shift using triangulation between methods. J Gerontol Nurs.
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127
37.
Gerlich C, Schuler M, Jelitte M, Neuderth S, Flentje M, Graefen M, et al. Prostate
cancer patients' quality of life assessments across the primary treatment trajectory: 'True'
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128
4.10 Tables and Figures
Table 4.1. Model selection to determine number of groups for physical quality of life postdiagnosis in prostate cancer survivors (n = 817) in Alberta, Canada (1997-2007).
Number
of groups
BIC
Base
model
Estimated % in each group
BIC
1
1
-7498.93
100
2
-7277.90
1
-221.03
42.09
3a
-7238.69
2
-39.21
25.61
4
-7239.99
3
1.3
6.36
Abbreviation: BIC = Bayesian Information Criterion.
a
BIC value closest to zero.
129
2
3
4
57.91
40.19
27.17
34.20
35.95
30.51
Table 4.2. Model selection for determining linear or quadratic structure of trajectories for
physical quality of life post-diagnosis in prostate cancer survivors (n = 817) in Alberta,
Canada (1997-2007).
Model
BIC
Base model
a
(1 1 1)
-7228.18
(1 1 2)
-7231.79
1
(1 2 1)
-7231.23
2
(2 1 1)
-7231.95
3
(1 2 2)
-7234.93
4
(2 1 2)
-7235.56
5
(2 2 1)
-7235.00
6
(2 2 2)
-7238.69
7
Abbreviation: BIC = Bayesian Information Criterion.
a
BIC value closest to zero.
130
BIC
3.61
3.05
3.05
3.77
7.38
6.82
10.51
Table 4.3. Model selection to determine number of groups for mental quality of life
post-diagnosis in prostate cancer survivors (n = 817) in Alberta, Canada (19972007).
Number
of groups
BIC
Base
model
BIC
Estimated % in each group
1
1
-7410.63
100
2
-7123.37
1
-287.26
20.17
3a
-7098.90
2
-24.47
14.51
4b
-7114.01
3
15.11
14.51
Abbreviation: BIC = Bayesian Information Criterion.
2
3
4
79.83
9.69
9.69
75.80
75.80
0
a
BIC value closest to zero.
b
Four groups warning: variance matrix is non-symmetric or highly singular.
131
Table 4.4. Model selection for determining linear or quadratic structure of
trajectories for mental quality of life post-diagnosis in prostate cancer survivors (n =
817) in Alberta, Canada (1997-2007).
Model
BIC
Base model
a
(1 1 1)
-7088.81
(1 1 2)
-7091.94
1
(1 2 1)
-7092.03
2
(2 1 1)
-7092.58
3
(1 2 2)
-7095.62
4
(2 1 2)
-7095.71
5
(2 2 1)
-7095.21
6
(2 2 2)
-7098.90
7
Abbreviation: BIC = Bayesian Information Criterion.
a
BIC value closest to zero.
132
BIC
3.13
3.22
3.77
6.81
6.9
6.4
10.09
Table 4.5. Descriptive characteristics by physical quality of life trajectory group of prostate cancer survivors (n = 817) in
Alberta, Canada (1997-2007).
Characteristics
Number of participants at first follow-up
Number of participants at second follow-up
Number of participants at third follow-up
Type of Cancer
Aggressive
Non-aggressive
Hormone therapy a
Yes
No/missing
Radiation Therapy
Yes
No
Prostatectomy
Yes
No
Smoking status
Current
Former/never
Physical Quality of Life Trajectory Groups
Average-maintaining
Low-declining
Very low-maintaining
259 (32%)
208 (35%)
174 (35%)
N (%)
330 (40%)
242 (41%)
217 (44%)
228 (28%)
145 (24%)
104 (21%)
36 (14%)
224 (86%)
70 (21%)
264 (79%)
49 (22%)
174 (78%)
131 (50%)
129 (50%)
227 (68%)
107 (32%)
159 (71%)
64 (29%)
93 (36%)
167 (64%)
168 (50%)
166 (50%)
98 (44%)
125 (56%)
140 (54%)
120 (46%)
247 (74%)
87 (26%)
190 (85%)
33 (15%)
26 (10%)
234 (90%)
53 (16%)
281 (84%)
35 (16%)
188 (84%)
Median (Q1-Q3)
Age at diagnosis
Post-diagnosis Charlson co-morbidity score
Pre-diagnosis lifetime PA b
PA at first follow-up b
64 (59-70)
1 (0.5-2)
172 (121-225)
98 (58-140)
133
68 (63-73)
2 (1-3)
138 (98-193)
77 (46-124)
72 (66-75)
3 (2-4)
126 (84-171)
64 (29-107)
PA at second follow-up b
89 (46-143)
PA at third follow-up b
75 (45-124)
BMI
27 (25-29)
Average total alcohol intake (g/year) c
1747 (451-4368)
Average total energy intake (kcal/day)
2033 (1623-2465)
Abbreviations: BMI = body mass index; PA = physical activity.
74 (40-134)
57 (25-99)
28 (26-31)
2010 (695-4679)
1998 (1659-2475)
43 (15-91)
42 (15-90)
29 (26-31)
1758 (572-4259)
1970 (1644-2493)
a
Included bilateral orchiectomy, luteinizing hormone-releasing agonists, nonsteroidal antiandrogens, steroidal antiandrogens.
b
Physical activity measurements are: total MET-hour/week/year.
c
66 never-drinkers.
134
Table 4.6. Descriptive characteristics by mental quality of life trajectory group of prostate cancer survivors (n = 817) in
Alberta, Canada (1997-2007).
Mental QoL Trajectory Groups
Characteristics
Number of participants at first follow-up
Number of participants at second follow-up
Number of participants at third follow-up
Type of Cancer
Aggressive
Non-aggressive
Hormone therapy a
Yes
No/missing
Radiation Therapy
Yes
No
Prostatectomy
Yes
No
Smoking status
Current
Former/never
Average-maintaining
Above average-declining
Low-increasing
568 (70%)
411 (69%)
368 (74%)
N (%)
134 (16%)
108 (18%)
75 (15%)
115 (14%)
76 (13%)
52 (11%)
112 (20%)
451 (80%)
30 (22%)
107 (88%)
13 (11%)
104 (89%)
339 (60%)
224 (40%)
108 (79%)
29 (21%)
70 (60%)
47 (40%)
253 (45%)
310 (55%)
63 (46%)
74 (54%)
43 (37%)
74 (63%)
371 (66%)
192 (34%)
125 (91%)
12 (9%)
81 (69%)
36 (31%)
64 (11%)
499 (89%)
25 (18%)
112 (82%)
25 (21%)
92 (79%)
Median (Q1-Q3)
Age at diagnosis
Post-diagnosis Charlson co-morbidity score
PA at first follow-up b
Pre-diagnosis lifetime PA b
67 (61-72)
2 (1-3)
80 (47-128)
161 (111-207)
135
72 (67-76)
3 (2-4)
68 (36-112)
155 (111-210)
67 (61-73)
2 (1-3)
79 (43-130)
137 (95-190)
PA at second follow-up b
77 (44-140)
PA at third follow-up b
69 (37-111)
BMI
28 (25-30)
Average total alcohol intake (g/year) c
1835 (529-4430)
Average total energy intake (kcal/day)
2009 (1644-2460)
Abbreviations: BMI = body mass index; PA = physical activity.
40 (16-97)
35 (6-92)
28 (25-31)
1858 (628-4294)
1990 (1596-2374)
62 (17-111)
34 (16-78)
28 (26-31)
2105 (822-5137)
2013 (1673-2669)
a
Included bilateral orchiectomy, luteinizing hormone-releasing agonists, nonsteroidal antiandrogens, steroidal antiandrogens.
b
Physical activity measurements are: total MET-hour/week/year.
c
66 never-drinkers.
136
Table 4.7. Multinomial logistic regression models of influential factors on physical and mental QoL trajectory group
membership relative to the average-maintaining/increasing QoL group in prostate cancer survivors (n = 817) in Alberta,
Canada (1997-2007).
Prognostic/behavioural factors
Low/above average-declining versus
average-maintaining Quality of Life
RRR a
Very low/low-maintaining/increasing versus
average-maintaining Quality of Life
95% CI
RRR a
95% CI
1.11
1.50
0.64
1.99
2.29
2.04
1.18
2.77
1.06-1.16
0.76-2.95
0.35-1.17
1.14-3.48
1.07-4.89
1.66-2.51
1.09-1.27
1.23-6.26
1.00
0.40
0.50
1.01
1.73
1.18
1.04
2.35
0.97-1.04
0.19-0.86
0.28-0.89
0.61-1.67
0.89-3.36
1.03-1.36
0.97-1.10
1.27-4.36
Physical QoL trajectories
Age at diagnosis (years)
1.04
0.99-1.08
b
Aggressiveness of disease
1.41
0.72-2.79
b
Radiation therapy
1.10
0.57-2.12
b
Hormone therapy
1.76
1.02-3.02
b
Prostatectomy
1.25
0.58-2.68
Charlson co-morbidity score
1.59
1.29-1.95
BMI (kg/m2)
1.09
1.02-1.17
b
Smoking status
1.71
0.75-3.90
Mental QoL trajectories
Age at diagnosis (years)
1.06
1.01-1.11
b
Aggressiveness of disease
0.89
0.44-1.81
Radiation therapy b
0.57
0.30-1.10
Hormone therapy b
1.61
0.82-3.16
b
Prostatectomy
3.32
1.23-8.94
Charlson co-morbidity score
1.32
1.12-1.56
2
BMI (kg/m )
1.04
0.96-1.12
Smoking status b
1.79
0.75-4.24
Abbreviations: BMI = body mass index; RRR = relative risk ratio; CI = confidence interval.
a
All models were adjusted for time varying total physical activity, dropout probabilities and all other factors in table.
137
b
Dichotomized variables.
138
Table 4.8. Maximum likelihood estimates for the mean coefficients and corresponding standard errors for physical and mental
quality of life trajectory groups from final adjusted group-based trajectory models including physical activity as a timevarying covariate in prostate cancer survivors (n = 817) in Alberta, Canada (1997-2007).
Trajectory groups
Physical quality of life trajectories
Average-maintaining
Low-declining
Very low-maintaining
Mental quality of life trajectories
Average-maintaining
Above average-declining
Low-increasing
a
p-value < 0.001.
b
Estimated mean coefficients (Standard Error)
Baseline intercept
Slope
Physical activity slope
49.64 (1.38) a
41.68 (1.16) a
24.37 (1.28) a
-0.03 (0.49)
-1.85 (0.46) a
0.19 (0.55)
0.02 (0.01) b
0.02 (0.01) a
0.03 (0.01) b
54.59 (0.76) a
57.32 (2.37) a
25.99 (1.97) a
0.45 (0.30)
-6.98 (1.13) a
2.62 (0.84) b
0.01 (0.01)
0.01 (0.01)
0.01 (0.01)
p-value < 0.01.
139
Figure 4.1. Repeated measurements and cohort timeline. Past two years of physical
activity were recalled and past four weeks of QoL (N = complete QoL measurements
at each time point). Abbreviations: PA = physical activity, QoL = quality of life, Dx
= diagnosis.
140
Figure 4.2. Final adjusted model selected for physical QoL trajectory groups (black
lines are trajectory groups, grey lines are 95% confidence and dot symbols are
observed group means at each time-point) in cohort of prostate cancer survivors in
Alberta, Canada (n=817). Abbreviations: QoL=quality of life, DP=dropout
probability.
141
Figure 4.3. Final adjusted model selected for mental QoL trajectory groups (black
lines are trajectory groups, grey lines are 95% confidence intervals and dot symbols
are observed group means at each time-point) in cohort of prostate cancer survivors
in Alberta, Canada (n=817). Abbreviations: QoL=quality of life, DP=dropout
probability.
142
4.11 Additional analyses and results: Sensitivity analysis tables and figures
4.11.1 Dropout within trajectory groups
As an exploratory and sensitivity analysis, we examined the association between the
estimated log odds of dropping out on behavioural/prognostic factors within each
trajectory group. This analysis was performed to determine if certain factors were
associated with dropping out in each trajectory group. These results are described in the
manuscript.
143
Table 4.9. Logistic regression models assessing the relationship between dropping
out and covariates within the very low-maintaining physical QoL trajectory group.
Characteristics
OR
95% CI
Physical activity at first follow-up
1.00
0.99, 1.00
Age at diagnosis (years)
0.98
0.92, 1.02
a
1.92
0.89, 4.12
Aggressiveness of disease
0.55
0.29, 1.04
Radiation therapy a
a
1.10
0.56, 2.15
Hormone therapy
a
1.46
0.51, 4.17
Prostatectomy
Charlson co-morbidity score
0.96
0.83, 1.12
2
BMI (kg/m )
0.91
0.85, 0.98
3.89
1.39, 10.90
Smoking status a
Note: All analyses are mutually adjusted for other variables in table.
P-value
0.24
0.54
0.10
0.07
0.78
0.48
0.63
0.013
0.010
Abbreviations: BMI= body mass index; OR= odds ratio; CI= confidence interval.
a
Dichotomous variables.
144
Table 4.10. Logistic regression models assessing the relationship between dropping
out and covariates within the low-declining physical QoL trajectory group.
Characteristics
OR
95% CI
Physical activity at first follow-up
1.02
0.99, 1.00
Age at diagnosis (years)
1.02
0.98, 1.06
a
1.25
0.71, 2.21
Aggressiveness of disease
a
0.52
0.30, 0.91
Radiation therapy
1.35
0.80, 2.28
Hormone therapy a
a
2.30
1.16, 4.57
Prostatectomy
Charlson co-morbidity score
0.87
0.74, 1.02
BMI (kg/m2)
0.99
0.93, 1.05
a
1.77
0.94, 3.36
Smoking status
Note: All analyses are mutually adjusted for other variables in table.
P-value
0.78
0.25
0.43
0.022
0.27
0.018
0.08
0.67
0.08
Abbreviations: BMI= body mass index; OR= odds ratio; CI= confidence interval.
a
Dichotomous variables.
145
Table 4.11. Logistic regression models assessing the relationship between dropping
out and covariates within the average-maintaining physical QoL trajectory group.
Characteristics
OR
95% CI
Physical activity at first follow-up
1.00
0.99, 1.00
Age at diagnosis (years)
1.02
0.98, 1.07
a
0.95
0.43, 2.07
Aggressiveness of disease
0.59
0.29, 1.23
Radiation therapy a
a
0.91
0.50, 1.65
Hormone therapy
a
2.48
1.11, 5.55
Prostatectomy
Charlson co-morbidity score
0.73
0.57, 0.93
2
BMI (kg/m )
1.05
0.96, 1.14
2.99
1.23, 7.31
Smoking status a
Note: All analyses are mutually adjusted for other variables in table.
P-value
0.98
0.28
0.89
0.16
0.77
0.028
0.012
0.31
0.016
Abbreviations: BMI= body mass index; OR= odds ratio; CI= confidence interval.
a
Dichotomous variables.
146
Table 4.12. Logistic regression models assessing the relationship between dropping
out and covariates within the low-increasing mental QoL trajectory group.
Characteristics
OR
95% CI
Physical activity at first follow-up
0.99
0.99, 1.00
Age at diagnosis (years)
1.04
0.97, 1.12
2.38
0.56, 10.06
Aggressiveness of disease a
0.86
0.30, 2.45
Radiation therapy a
a
0.80
0.32, 1.97
Hormone therapy
2.36
0.64, 8.75
Prostatectomy a
Charlson co-morbidity score
0.79
0.62, 1.02
BMI (kg/m2)
0.95
0.85, 1.07
a
3.48
1.11, 10.88
Smoking status
Note: All analyses are mutually adjusted for other variables in table.
P-value
0.18
0.25
0.24
0.78
0.62
0.20
0.07
0.38
0.032
Abbreviations: BMI= body mass index; OR= odds ratio; CI= confidence interval.
a
Dichotomous variables.
147
Table 4.13. Logistic regression models assessing the relationship between dropping
out and covariates within the above average-declining mental QoL trajectory group.
Characteristics
OR
95% CI
Physical activity at first follow-up
0.99
0.99, 1.00
Age at diagnosis (years)
1.02
0.95, 1.10
1.32
0.52, 3.34
Aggressiveness of disease a
a
0.63
0.28, 1.40
Radiation therapy
a
1.30
0.51, 3.28
Hormone therapy
2.46
0.59, 10.31
Prostatectomy a
Charlson co-morbidity score
0.93
0.77, 1.12
BMI (kg/m2)
0.97
0.89, 1.06
a
2.88
0.93, 8.88
Smoking status
Note: All analyses are mutually adjusted for other variables in table.
P-value
0.20
0.52
0.56
0.25
0.58
0.22
0.45
0.50
0.07
Abbreviations: BMI= body mass index; OR= odds ratio; CI= confidence interval.
a
Dichotomous variables.
148
Table 4.14. Logistic regression models assessing the relationship between dropping out and
covariates within the average-maintaining mental QoL trajectory group.
Characteristics
OR
95% CI
Physical activity at first follow-up
1.00
0.99, 1.00
Age at diagnosis (years)
1.02
0.99, 1.05
a
1.38
0.89, 2.15
Aggressiveness of disease
a
0.50
0.32, 0.78
Radiation therapy
a
1.26
0.85, 1.86
Hormone therapy
a
2.12
1.26, 3.58
Prostatectomy
Charlson co-morbidity score
0.93
0.82, 1.05
2
BMI (kg/m )
1.00
0.95, 1.05
1.91
1.09, 3.35
Smoking status a
Note: All analyses are mutually adjusted for other variables in table.
P-value
0.91
0.23
0.15
0.002
0.26
0.005
0.22
0.99
0.024
Abbreviations: BMI= body mass index; OR= odds ratio; CI= confidence interval.
a
Dichotomous variables.
149
4.11.2 Sensitivity analyses: Complete case analysis for physical and mental QoL
trajectories
Another sensitivity analysis was done to determine if only including prostate
cancer survivors with complete data at all three time-points influenced the model
selection, dropout probabilities and behavioural/prognostic factors. While the number and
function of trajectories were similar, it is interesting to note that the function form was
better fit with a quadratic function in the low-declining group of physical QoL. The
quadratic term was not a significantly better fit as the change in BIC was <2, however, it
is interesting to point out that there may even be more variation in that particular
trajectory. Further, smoking status was no longer associated with the very lowmaintaining or low-increasing groups. This might be attributed to the fact that men who
were current smokers were more likely to dropout/die during active data collection, as
smokers tend to have other poor health habits that may increase their mortality. Other
associations were attenuated compared to the total sample (n=817), however, no other
substantial changes were noted.
150
Table 4.15 Model selection to determine number of groups for physical quality of
life post-diagnosis in prostate cancer survivors (n = 454) in Alberta, Canada (19972007).
Number
of groups
BIC
1
-5278.86
2
-5096.88
1
a
3
-5057.43
2
4
-5062.24
3
a
BIC value closest to zero.
Base
model
BIC
-181.98
-39.45
4.81
151
Estimated % in each group
1
100
38.32
19.70
3.75
2
3
4
61.68
42.45
21.19
37.85
39.81
35.26
Table 4.16. Model selection for determining linear or quadratic structure of
trajectories for physical quality of life post-diagnosis in prostate cancer survivors (n
= 454) in Alberta, Canada (1997-2007).
Model
(1 1 1) a
(1 1 2)
(1 2 1) a
(2 1 1)
(1 2 2)
(2 1 2)
(2 2 1)
(2 2 2)
a
BIC value closest to zero.
BIC
-5050.82
-5054.31
-5050.64
-5054.30
-5054.24
-5057.79
-5053.83
-5057.43
Base model
1
2
3
4
5
6
7
152
BIC
3.49
-0.18
3.48
3.42
6.97
3.01
6.61
Table 4.17. Model selection to determine number of groups for mental quality of life
post-diagnosis in prostate cancer survivors (n = 454) in Alberta, Canada (19972007).
Number
of groups
BIC
1
-5259.52
2
-5027.17
1
a
3
-5001.75
2
4
-5016.19
3
a
BIC value closest to zero.
Base
model
BIC
-232.35
-25.42
14.44
153
Estimated % in each group
1
100
18.22
8.72
8.72
2
3
4
81.78
19.11
19.12
72.17
72.17
<0.01
Table 4.18. Model selection for determining linear or quadratic structure of
trajectories for mental quality of life post-diagnosis in prostate cancer survivors (n =
454) in Alberta, Canada (1997-2007).
Model
(1 1 1) a
(1 1 2)
(1 2 1)
(2 1 1)
(1 2 2)
(2 1 2)
(2 2 1)
(2 2 2)
a
BIC value closest to zero.
BIC
-4991.66
-4994.83
-4995.25
-4994.98
-4998.35
-4998.15
-4998.59
-5001.75
Base model
1
2
3
4
5
6
7
154
BIC
3.17
3.59
3.32
6.69
6.49
6.93
10.09
Table 4.19. Multinomial logistic regression models of influential factors on physical and mental QoL trajectory group
membership relative to the average-maintaining/increasing QoL group in prostate cancer survivors (n = 454) in Alberta,
Canada (1997-2007).
Prognostic/behavioural
factors
Low/above average-declining versus
Average-maintaining Quality of Life
RRR a
95% CI
Very low/low-maintaining/increasing versus
Average-maintaining Quality of Life
RRR a
Physical QoL trajectories
Age at diagnosis (years)
1.06
1.01, 1.11
1.21
b
Aggressiveness of disease
1.02
0.45, 2.33
0.62
b
Radiation therapy
1.38
0.62, 3.05
0.79
Hormone therapy b
1.54
0.83, 2.85
1.54
b
Prostatectomy
1.16
0.49, 2.75
2.63
Charlson co-morbidity score
1.48
1.19, 1.84
1.98
2
BMI (kg/m )
1.14
1.04, 1.25
1.36
Smoking status b
2.01
0.74, 5.46
1.91
Mental QoL trajectories
Age at diagnosis (years)
1.04
0.98, 1.10
0.97
b
Aggressiveness of disease
0.68
0.28, 1.65
0.24
Radiation therapy b
0.57
0.26, 1.22
0.46
b
Hormone therapy
1.40
0.68, 2.89
1.18
Prostatectomy b
2.31
0.86, 6.21
1.16
Charlson co-morbidity score
1.47
1.22, 1.77
1.32
BMI (kg/m2)
1.04
0.95, 1.13
1.06
b
Smoking status
0.99
0.31, 3.19
0.98
Abbreviations: BMI = body mass index; RRR = relative risk ratio; CI = confidence interval.
a
95% CI
1.12, 1.30
0.22, 1.76
0.33, 1.88
0.70, 3.35
0.89, 7.78
1.51, 2.58
1.21, 1.54
0.44, 8.24
0.92, 1.03
0.05, 1.09
0.16, 1.30
0.54, 2.59
0.39, 3.48
1.05, 1.66
0.96, 1.17
0.26, 3.71
All models were adjusted for time varying total physical activity, dropout probabilities and all other factors in table.
155
b
Dichotomized variables.
156
Table 4.20. Maximum likelihood estimates for the mean coefficients and corresponding standard errors for physical and
mental quality of life trajectory groups from final adjusted group-based trajectory models including physical activity as a
time-varying covariate in prostate cancer survivors (n = 454) in Alberta, Canada (1997-2007).
Trajectory groups
Physical quality of life trajectories
Average-maintaining
Low-declining
Very low-maintaining
Mental quality of life trajectories
Average-maintaining
Above average-declining
Low-increasing
***p-value<0.001
Estimated mean coefficients (Standard Error)
Baseline intercept
Slope
Physical activity slope
51.16 (1.24)***
43.37 (1.22)***
29.56 (1.66)***
-0.37 (0.46)
-2.19 (0.48)***
-1.65 (0.66)*
0.01 (0.01)*
0.02 (0.01)*
0.02 (0.01)**
54.85 (0.83)***
49.84 (2.39)***
22.32 (3.20)***
0.49 (0.31)
-3.90 (1.04)***
3.54 (1.17)**
0.01 (0.01)
0.01 (0.01)
0.01 (0.01)
**p-value<0.01
*p-value<0.05
157
4.11.3 Sensitivity analyses: Two complete assessments analysis for physical and mental
QoL trajectories
This second sensitivity analysis was completed for similar purposes of the
complete-case sample. These results had little to no effects on the model selection,
dropout probabilities nor the associations of behavioural/prognostic factors with group
membership. The complete case analysis and the two complete assessments analysis
speak to the robustness of the GBTM modelling strategy to effectively detect meaningful
subgroups in a study population.
158
Table 4.21. Model selection to determine number of groups for physical quality of
life post-diagnosis in prostate cancer survivors (n = 636) in Alberta, Canada (19972007).
Number
of groups
BIC
1
-6761.16
2
-6543.64
1
a
3
-6505.65
2
4
-6506.51
3
a
BIC value closest to zero.
Base
model
BIC
-217.52
-37.99
0.86
159
Estimated % in each group
1
100
40.50
22.96
4.49
2
3
4
59.50
41.30
25.90
35.74
37.53
32.09
Table 4.22. Model selection for determining linear or quadratic structure of
trajectories for physical quality of life post-diagnosis in prostate cancer survivors (n
= 636) in Alberta, Canada (1997-2007).
Model
(1 1 1) a
(1 1 2)
(1 2 1)
(2 1 1)
(1 2 2)
(2 1 2)
(2 2 1)
(2 2 2)
a
BIC value closest to zero.
BIC
-6495.42
-6499.08
-6498.63
-6498.91
-6502.34
-6502.57
-6501.94
-6505.65
Base model
1
2
3
4
5
6
7
160
BIC
3.66
3.21
3.49
6.92
7.15
6.52
10.23
Table 4.23. Model selection to determine number of groups for mental quality of life
post-diagnosis in prostate cancer survivors (n = 636) in Alberta, Canada (19972007).
Number
of groups
BIC
1
-6700.98
2
-6429.25
1
a
3
-6404.44
2
4b
-6419.35
3
a
BIC value closest to zero.
b
Base
model
BIC
-271.73
-24.81
14.91
Estimated % in each group
1
100
19.32
11.57
11.57
2
3
4
80.67
11.86
11.89
76.57
76.57
<0.01
Four groups warning: variance matrix is non-symmetric or highly singular.
161
Table 4.24. Model selection for determining linear or quadratic structure of
trajectories for mental quality of life post-diagnosis in prostate cancer survivors (n =
636) in Alberta, Canada (1997-2007).
Model
(1 1 1) a
(1 1 2)
(1 2 1)
(2 1 1)
(1 2 2)
(2 1 2)
(2 2 1)
(2 2 2)
a
BIC value closest to zero.
BIC
-6394.25
-6397.35
-6397.45
-6397.97
-6400.86
-6401.07
-6400.81
-6404.44
Base model
1
2
3
4
5
6
7
162
BIC
3.1
3.2
3.72
6.61
6.82
6.56
10.19
Table 4.25. Multinomial logistic regression models of influential factors on physical and mental QoL trajectory group
membership relative to the average-maintaining/increasing QoL group in prostate cancer survivors (n = 636) in Alberta,
Canada (1997-2007).
Prognostic/behavioural
factors
Low/above average-declining versus
Average-maintaining Quality of Life
RRR a
95% CI
Very low/low-maintaining/increasing
versus Average-maintaining Quality of Life
RRR a
Physical QoL trajectories
Age at diagnosis (years)
1.04
0.99, 1.09
1.16
b
Aggressiveness of disease
1.17
0.57, 2.41
0.84
b
Radiation therapy
1.13
0.58, 2.23
0.70
Hormone therapy b
1.74
1.00, 3.03
2.03
b
Prostatectomy
1.06
0.49, 2.29
2.23
Charlson co-morbidity score
1.56
1.27, 1.92
2.15
2
BMI (kg/m )
1.11
1.03, 1.20
1.25
Smoking status b
1.97
0.81, 4.78
2.33
Mental QoL trajectories
Age at diagnosis (years)
1.06
1.01, 1.11
0.98
Aggressiveness of disease b
0.93
0.45, 1.92
0.48
Radiation therapy b
0.54
0.28, 1.02
0.47
b
Hormone therapy
1.56
0.80, 3.04
1.01
Prostatectomy b
3.57
1.34, 9.46
1.42
Charlson co-morbidity score
1.33
1.13, 1.57
1.28
BMI (kg/m2)
1.04
0.96, 1.12
1.03
b
Smoking status
1.83
0.78, 4.33
2.07
Abbreviations: BMI = body mass index; RRR = relative risk ratio; CI = confidence interval.
a
95% CI
1.09, 1.23
0.37, 1.91
0.34, 1.41
1.07, 3.88
0.89, 5.55
1.71, 2.70
1.14, 1.36
0.83, 6.52
0.94, 1.03
0.20, 1.13
0.23, 0.96
0.56, 1.81
0.64, 3.13
1.08, 1.51
0.96, 1.11
0.97, 4.43
All models were adjusted for time varying total physical activity, dropout probabilities and all other factors in table.
163
b
Dichotomized variables.
164
Table 4.26. Maximum likelihood estimates for the mean coefficients and corresponding standard errors for physical and
mental quality of life trajectory groups from final adjusted group-based trajectory models including physical activity as a
time-varying covariate in prostate cancer survivors (n = 636) in Alberta, Canada (1997-2007).
Trajectory groups
Physical quality of life trajectories
Average-maintaining
Low-declining
Very low-maintaining
Mental quality of life trajectories
Average-maintaining
Above average-declining
Low-increasing
***p-value<0.001
Estimated mean coefficients (Standard Error)
Baseline intercept
Slope
Physical activity slope
49.85 (1.34)***
42.34 (1.19)***
25.75 (1.41)***
-0.13 (0.48)
-2.04 (0.47)***
-0.39 (0.60)
0.01 (0.01)**
0.02 (0.01)*
0.03 (0.01)**
54.59 (0.79)***
57.35 (2.27)***
24.13 (2.32)***
0.44 (0.31)
-7.12 (1.07)***
3.20 (0.94)***
0.01 (0.01)
0.01 (0.01)
0.01 (0.01)
**p-value<0.01
*p-value<0.05
165
4.11.4 Sensitivity analysis: Not modelling dropout on covariate significance analysis
Similar to the above, this analysis was used as a tool to test the GBTM analysis
for robustness by modelling behavioural/prognostic factors with and without the logistic
regression dropout model that was based on previous outcome. Associations of groupmembership in regards to behavioural/prognostic factors did not change statistically or
meaningfully between this model and the fully adjusted model including dropout.
166
Table 4.27. Multinomial logistic regression models of influential factors on physical and mental QoL trajectory group
membership relative to the average-maintaining/increasing QoL group without modelling dropout in prostate cancer
survivors (n = 817) in Alberta, Canada (1997-2007).
Prognostic/behavioural
factors
Low/above average-declining versus
Average-maintaining Quality of Life
RRR a
95% CI
Very low/low-maintaining/increasing
versus average-maintaining Quality of Life
RRR a
Physical QoL trajectories
Age at diagnosis (years)
1.06
1.01, 1.10
1.12
b
Aggressiveness of disease
1.34
0.69, 2.60
1.47
b
Radiation therapy
1.02
0.53, 1.94
0.56
b
Hormone therapy
1.53
0.90, 2.60
2.01
Prostatectomy b
1.52
0.74, 3.15
2.49
Charlson co-morbidity score
1.49
1.23, 1.82
1.95
2
BMI (kg/m )
1.08
1.01, 1.16
1.18
b
Smoking status
1.99
0.91, 4.34
3.55
Mental QoL trajectories
Age at diagnosis (years)
1.05
1.00, 1.10
0.99
Aggressiveness of disease b
0.92
0.44, 1.90
0.33
b
Radiation therapy
0.79
0.41, 1.52
0.53
Hormone therapy b
1.40
0.70, 2.79
0.98
Prostatectomy b
2.40
0.89, 6.45
1.55
Charlson co-morbidity score
1.35
1.15, 1.59
1.18
BMI (kg/m2)
1.03
0.96, 1.12
1.04
b
Smoking status
1.17
0.46, 3.01
2.18
Abbreviations: BMI = body mass index; RRR = relative risk ratio; CI = confidence interval.
a
All models were adjusted for time varying total physical activity and all other factors in table.
167
95% CI
1.07, 1.17
0.74, 2.90
0.30, 1.04
1.14, 3.57
1.17, 5.33
1.60, 2.39
1.09, 1.27
1.60, 7.88
0.95, 1.02
0.14, 0.79
0.29, 0.97
0.58, 1.66
0.78, 3.07
1.02, 1.36
0.97, 1.10
1.18, 4.04
b
Dichotomized variables.
168
Table 4.28. Maximum likelihood estimates for the mean coefficients and corresponding standard errors for physical and
mental quality of life trajectory groups from final adjusted group-based trajectory models including physical activity as a
time-varying covariate and not modelling dropout in prostate cancer survivors (n = 817) in Alberta, Canada (1997-2007).
Trajectory groups
Physical quality of life trajectories
Average-maintaining
Low-declining
Very low-maintaining
Mental quality of life trajectories
Average-maintaining
Above average-declining
Low-increasing
***p-value<0.001
Estimated mean coefficients (Standard Error)
Baseline intercept
Slope
Physical activity slope
50.59 (1.17)***
41.63 (1.14)***
24.32 (1.30)***
-0.37 (0.43)
-1.90 (0.43)***
-0.01 (0.57)
0.01 (0.01)*
0.02 (0.01)**
0.03 (0.01)***
54.62 (0.72)***
52.28 (2.83)***
24.64 (2.17)***
0.41 (0.28)
-5.39 (1.27)***
3.04 (0.91)***
0.01 (0.01)
0.01 (0.01)
0.01 (0.01)
**p-value<0.01
*p-value<0.05
169
4.11.5 Sensitivity analyses: Dropout switchers between trajectory groups with and
without modelling dropout
These sensitivity analyses were elaborated on in the manuscript and did not
substantially contribute to the interpretations of these data as there was a small number of
prostate cancer survivors who switched trajectory groups with and without modelling
dropout. These results speak to the resiliency of the GBTM method and its utility to
define trajectories in a study population.
170
Table 4.29. Participants who switched physical quality of life trajectory groups when modelling dropout vs. not modelling
dropout in prostate cancer survivors (n = 817) in Alberta, Canada (1997-2007).
With dropout
Very low-maintaining
Low-declining
Average-maintaining
Total
Very low-maintaining
3
0
3
Without dropout
Low-declining
19
23
42
171
Total
Average-maintaining
0
8
8
19
11
23
53
Table 4.30. Participants who switched mental quality of life trajectory groups when modelling dropout vs. not modelling
dropout in prostate cancer survivors (n = 817) in Alberta, Canada (1997-2007).
With dropout
Low-increasing
Above average-declining
Average-maintaining
Total
Low-increasing
0
0
0
Without dropout
Above averagedeclining
13
9
22
172
Total
Average-maintaining
1
35
36
14
35
9
58
4.11.6 Sensitivity analysis: Time lagged model for previous quality of life score analysis
Time-lagged GBTM was used in this analysis to determine if modelling the
previous physical and mental QoL score had any effect on the trajectory groups observed,
the model fit and characterization of trajectory groups. Briefly, the physical and mental
time-lagged GBTM analysis found three trajectory groups similar to the non-time-lagged
models shown in the main body of the manuscript. As noted in the manuscript, the timelagged models had a more advantageous fit of the data. Further, differences between
characterization of trajectory groups were not substantial and these differences are
hypothesized to be due to chance or spurious in nature. Further, mean coefficient
intercepts, slope values and physical activity as a time-varying covariate maintained
similar statistical significance between time-lagged models and models presented in the
manuscript.
These analyses rule out the possibility of response shift, which was noted as a
limitation of this study. Our results suggest that future research replicating these results
should plan a priori to test time-lagged models as their main modelling strategy to fit
trajectories in prostate cancer populations.
173
Table 4.31. Time-lagged multinomial logistic regression models of influential factors on physical and mental QoL trajectory
group membership relative to the average-maintaining/increasing QoL group in prostate cancer survivors (n = 817) in
Alberta, Canada (1997-2007).
Prognostic/behavioural
factors
Low/above average-declining versus
Average-maintaining Quality of Life
RRR a
95% CI
Very low/low-maintaining/increasing versus
Average-maintaining Quality of Life
RRR a
Physical QoL trajectories
Age at diagnosis (years)
1.06
1.01, 1.10
1.12
b
Aggressiveness of disease
1.34
0.69, 2.60
1.47
b
Radiation therapy
1.02
0.53, 1.94
0.56
b
Hormone therapy
1.53
0.90, 2.61
2.01
Prostatectomy b
1.52
0.73, 3.16
2.49
Charlson co-morbidity score
1.49
1.23, 1.82
1.95
2
BMI (kg/m )
1.08
1.01, 1.16
1.18
b
Smoking status
1.99
0.91, 4.35
3.55
Mental QoL trajectories
Age at diagnosis (years)
1.05
1.00, 1.10
0.99
Aggressiveness of disease b
0.92
0.44, 1.90
0.33
b
Radiation therapy
0.79
0.41, 1.52
0.53
Hormone therapy b
1.40
0.70, 2.80
0.98
Prostatectomy b
2.40
0.89, 6.47
1.55
Charlson co-morbidity score
1.35
1.15, 1.59
1.18
BMI (kg/m2)
1.03
0.96, 1.12
1.04
b
Smoking status
1.17
0.46, 3.02
2.18
Abbreviations: BMI = body mass index; RRR = relative risk ratio; CI = confidence interval.
a
All models were adjusted for time varying total physical activity and all other factors in table.
174
95% CI
1.07, 1.17
0.74, 2.91
0.30, 1.04
1.13, 3.57
1.16, 5.34
1.60, 2.39
1.09, 1.27
1.60, 7.91
0.95, 1.02
0.14, 0.79
0.29, 0.97
0.58, 1.66
0.78, 3.07
1.02, 1.36
0.97, 1.11
1.18, 4.05
b
Dichotomized variables.
175
Table 4.32. Maximum likelihood estimates for the mean coefficients and corresponding standard errors for physical and
mental quality of life trajectory groups from final adjusted group-based trajectory time-lagged models including physical
activity as a time-varying covariate in prostate cancer survivors (n = 817) in Alberta, Canada (1997-2007).
Trajectory groups
Physical quality of life trajectories
Average-maintaining
Low-declining
Very low-maintaining
Mental quality of life trajectories
Average-maintaining
Above average-declining
Low-increasing
***p-value<0.001
Estimated mean coefficients (Standard Error)
Baseline intercept
Slope
Physical activity slope
50.59 (1.18)***
41.63 (1.15)***
24.32 (1.30)***
-0.37 (0.43)
-1.90 (0.43)***
-0.04 (0.57)
0.01 (0.01)*
0.02 (0.01)**
0.03 (0.01)***
54.62 (0.72)***
52.28 (2.84)***
24.64 (2.18)***
0.41 (0.28)
-5.39 (1.27)***
3.04 (0.91)***
0.01 (0.01)
0.01 (0.01)
0.01 (0.01)
**p-value<0.01
*p-value<0.05
176
Chapter Five: DISCUSSION
5.1 Summary of findings
The overall aim of this thesis was to explore long-term QoL in prostate cancer survivors’
post-diagnosis. This aim was achieved by first investigating associations between physical
activity and QoL in a cohort of prostate cancer survivors to determine options for tertiary
prevention and future intervention studies in this population. Subsequently, long-term trajectories
of QoL using GBTM were used to determine if there were distinct subgroups of prostate cancer
survivors who follow different patterns of QoL after diagnosis. When examining physical
activity as a modifiable behaviour that can improve QoL, we found that recreational physical
activity improved physical QoL after diagnosis. Specifically, those who maintained physical
activity according to cancer prevention physical activity guidelines across the diagnostic period
improved physical and mental QoL to the greatest extent compared to non-exercisers. In
examining trajectories of QoL, we discovered there were indeed three different subgroups of
physical and mental QoL trajectories present in our study population. These trajectory groups
ranged in prevalence, with the highest prevalence in the low-declining physical QoL trajectory
and the average-maintaining mental QoL trajectory. This finding denotes a greater reported
problem for physical QoL over mental QoL in this population. In addition, there were several
different patterns of QoL scores among the trajectory groups. For physical QoL, both high and
low trajectory groups maintained their QoL scores, while the medium group declined over the
follow-up. However, in mental QoL trajectories, high and low groups increased in QoL scores by
the third time-point while again, the medium group declined. The mental above averagedeclining QoL group declined substantially. This result may be concerning to healthcare
professionals since the mean score reduction was more than 10 points which represents a
177
substantial clinically relevant decrease in QoL (1). Dropout probabilities were the highest in the
very low-maintaining physical, the low-increasing and above average-declining mental groups.
Finally, we systematically characterized the trajectory groups and found multiple factors
associated with low and medium groups compared to the corresponding high group for physical
and mental QoL. An increase in Charlson comorbidity index score was consistently associated
with both low and medium physical and mental QoL group membership relative to the
corresponding high group. Hormone therapy and increased BMI were related to both very lowmaintaining and low-declining physical QoL group memberships, while age at diagnosis, having
a prostatectomy and being a current smoker were related to very low-maintaining physical QoL
group membership. Additionally, aggressiveness of disease, radiation therapy and being a current
smoker were related to low-increasing mental QoL group membership relative to the averagemaintaining group. Finally, age at diagnosis and having a prostatectomy were associated with the
above average-declining mental QoL group membership. Together, these observations, provide
relevant evidence to inform future research to further develop and validate these findings.
5.2 Limitations
The limitations specific to each analysis have been highlighted in chapters three and four.
This section will discuss, in depth, potential threats to the internal and external validity of this
study and the magnitude of these concerns when communicating results to other researchers,
health professionals, patients and other stakeholders. The main threats to internal and external
validity that will be discussed below are: survivorship bias due to missing data and dropout,
reporting bias with respect to self-administered questionnaires, potential confounding, potential
178
lack of precision based on sample size, causal inference and generalizability of results to the
greater prostate cancer survivor population.
5.2.1 Internal validity
Internal validity refers to the method by which a study is conducted and if all of the
components accurately represented the true association present. This topic is most important
when making causal inferences about study results. There are two threats to internal validity:
systematic error and random error. Systematic error is a fundamental flaw in the study design,
implementation, measurement, analysis or interpretation of the study that may distort the true
association between the exposure and the outcome. The most common types of systematic error
are selection, measurement and confounding biases. While systematic error cannot be resolved
by increasing the sample size, random error is variability based on the role of chance. Random
error may dilute an association of interest if there is a lack of precision due to small sample sizes
(2). Each one of these biases pertaining to internal validity will be discussed below.
5.2.1.1 Survivorship bias due to missing data and dropout
Survivorship bias is a type of selection bias that can occur when the selection
probabilities are related to the exposure and the outcome; this bias is a main concern in
prospective cohort studies with long follow-up periods (3). The prostate cancer cohort study was
derived from a prior population-based case-control study. Cases were derived from the Alberta
Cancer Registry which is a province-wide cancer registry with an ascertainment rate of
approximately 95% (4). From the prior case-control study (5), 75.7% of eligible men with
prostate cancer were included in the initial case-control study. The remaining cases from the
179
prior case-control study who were not included in the case-control sample were 265 men who
refused (19.8%), 34 who had incomplete interviews at post-diagnosis (2.5%), and 24 who were
unsuitable for an interview at first follow-up (1.8%). From there, men re-consented to participate
in the prospective cohort study, in which there were three who refused (2% refusal rate), one
man whose primary cancer turned out to not be prostate cancer, and 154 who died before the first
follow-up (14% of the original case-control study sample). Consequently, survivorship bias may
be present if the reason why prostate cancer survivors did not re-consent to the prospective
cohort study was related to their physical activity levels and QoL scores. A hypothetical example
of this mechanism for the association between physical activity and QoL may arise if prostate
cancer survivors who did not consent to participate to the prospective cohort study had very low
levels of physical activity and QoL scores. The prostate cancer survivors who were not included
in this cohort study might have experienced several treatment side effects, may not have adjusted
well to their disease, might have been ashamed of their lifestyle, could have had potentially
higher cancer stages or lethal prostate cancer and died early after diagnosis and would not be
included in our study. This would lead to an overestimation of the association between physical
activity and QoL present in these results. When examining the stage distribution between the
prior case-control study and the prospective cohort study, there were substantially more (n=39,
25%) stage IV cancers in the 157 men who did not re-consent to the cohort study compared to
those who did re-consent to the cohort study (n=55, 5.7%). However, because there was <20% of
the original case-control study population who did not consent to the study, these concerns are
somewhat mitigated.
There were also prostate cancer survivors who dropped out, were lost to follow-up or
died during active data collection, which were other possible sources of survivorship bias. In our
180
analyses we tried to substantiate the magnitude of this potential bias by conducting sensitivity
analyses and specifically, in the QoL GBTM analysis, by accounting for dropout in our final
models. In the physical activity and QoL analyses, sensitivity analyses ruled out the possibility of
advanced cancer stage (III/IV and IV) or mortality explaining these results, which reduces the
likelihood of survivorship bias. In addition, sensitivity analyses were performed in the GBTM
analyses to dismiss the role of missing data on model selection and characteristics of trajectory
groups. While significance of some characteristics may have changed slightly in these sensitivity
analyses, meaningful clinically relevant differences were not observed. On another note, in the
QoL trajectory analysis, we determined that there were differential probabilities of dropping out
due to mortality and being lost to follow-up between the trajectory groups. Therefore, in the very
low-maintaining physical QoL group and the low-increasing mental QoL group, the patterns of
maintenance or increasing QoL score may be superficial, since men with declining QoL could
have dropped out of the study. It is possible that those who had started with low baseline QoL
values have poorer general health. This possibility was supported in the analyses characterizing
the trajectory groups. Nonetheless, from these analyses we were able to determine that dropout
probabilities do differ between trajectory groups and therefore, there is a need to examine these
very low/low-maintaining/increasing groups further to tease apart the mechanisms by which
dropout is affecting the QoL patterns over time.
5.2.1.2 Measurement error with self-administered questionnaires
Recall error is a type of measurement error that arises from erroneous recollection of
information, usually associated with exposure status (2). These errors may impact study results
depending on the magnitude of the misreporting of exposure information. In this study, all
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physical activity and QoL data were obtained through either interviewer-administered or selfadministered questionnaires. The differences between interviewer-administered and self-reported
data are minimal with respect to recollection of information (6). However, since interviews and
self-administered questionnaires both rely on the participant to recall their exposure or outcome
information, these methods of data ascertainment may be prone to recall error. Recollection of
physical activity behaviours may be difficult for some participants, especially if these activities
are not part of a consistent routine. In this case, participants may over- or under-estimate their
physical activity levels leading to errors in physical activity exposure assessment. These errors
may be especially susceptible to the longer exposure periods being collected, specifically, the
lifetime physical activity measurement. Further, for older participants, a lifetime measurement of
physical activity would encompass many decades of behaviour to recall, which may be more
difficult than for the younger participants. To mitigate these issues of recall error, several
methods were employed in this study including: collection of lifetime physical activity using a
reliable questionnaire (7), cognitive interviewing methods (8), a recall calendar sent to the
participants prior to their interview and different quality control mechanisms within the study
design and staff. Moreover, post-prostate cancer diagnosis, another three repeated measurements
of past one to two years of physical activity were collected from the participants. The past-year
physical activity measurements may also be subject to the same errors. However, to reduce
errors, the questionnaire used was tested for validity and reliability (9), repeated measurements
were taken from the same participants at up to three time-points and similar quality control
methods were implemented as with the lifetime physical activity assessment. The detailed and
careful collection of physical activity behaviour in this study may alleviate concerns regarding
the possibility of potential bias introduced by the aforementioned recall error. Notwithstanding,
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objective measurements of physical activity are recommended for increased accuracy in
estimating physical activity levels in future research.
In respect to measurement of QoL information, these data were collected through selfadministered questionnaires. Since QoL is a patient reported outcome and very personal to each
individual, it may be difficult to determine any substantial measurement errors from
misreporting. However, the subjectivity of an individual’s own statement of how he/she feels
may introduce measurement and recollection errors (10). While these measures can bridge the
gap of quality of care between health professionals and patients, there may be additional
complications in interpreting these data. In this study, the SF-36 questionnaire was used to
collect QoL information. The SF-36 is the most commonly used health status measure and has
been validated in cancer populations (11, 12). Therefore, systematic error due to lack of
reliability and validity is unlikely from these measurements. However, as is common with most
types of self-reported QoL data, the SF-36 may be subject to non-differential misclassification
bias that would attenuate statistical associations (10). This bias may lead to QoL scores that are
very similar without any variation. However, when utilizing the component summary scores for
physical and mental QoL, the distributions approximated a normal distribution with enough
variation to analyze these data. When interpreting these data, it is important to consider the
magnitude of potential errors and how future research may improve methods to reduce
measurement error when examining these associations.
5.2.1.3 Confounding
Confounding is the intermixing of an extraneous variable on the association of interest.
This extraneous variable is an independent risk factor of the outcome, associated with the
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exposure and not on the causal pathway (2, 3). In this thesis, several potential confounding
variables were considered in the analyses (chapters three and four). Multiple steps were taken to
appropriately test for potential confounding by factors that were hypothesized to possibly act as
confounders. In addition, for analyses presented in chapter three, two different model selection
methods were used; first, backward elimination (presented in the manuscript) and second,
GLMNET (presented in the additional results sections). Both of these model-generating methods
found similar conclusions, with only slight differences in the final covariates that were included
in the final models as confounders.
While our analyses did control for several confounders, there is still a possibility of
residual confounding due to unmeasured factors that could not be considered in these models.
This issue was specifically of concern in chapter four, when dichotomizing smoking status and
treatment variables. The GBTM methods cannot model categorical variables with more than two
levels. In this case, we made assumptions when dichotomizing some variables, which may result
in residual confounding. First, never and former smokers have been documented in the literature
to have similar QoL (13), however, this similarity may not exist or be relevant when identifying
QoL trajectories. In addition, other variables such as race/ethnicity, marital status and
socioeconomic status were not included in the GBTM analyses, partially because our study
population was very homogeneous with respect to these factors. There is evidence supporting the
different associations between race/ethnicity and disadvantaged/low socioeconomic status and
QoL after prostate cancer diagnosis (14, 15). Further, research has also indicated that having a
partner may be an indicator of better QoL after prostate cancer diagnosis (16). These distinctions
may need to be teased apart to clarify our results and to identify features of this population to
consider for future research.
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Confounding by indication was potentially present in the analyses presented in chapter
four. The definition is as follows: when an extraneous variable is an independent risk factor for
QoL in the non-exposed population, is associated with the exposure of interest as well as not on
the causal pathway. In this case, in chapter four, a brief discussion of the situation when it is not
the prostate cancer diagnosis that is driving the QoL patterns, but perhaps the increase in age,
number of co-morbidities or lack of general health that may have created a heightened baseline
risk for some survivors. In this situation, our results cannot disentangle the difference between
the diagnoses of prostate cancer versus the potential decline of health that is associated with
aging. It is possible that low, medium and high QoL patterns exist in healthy populations as
well. Since this research area is very novel and similar analyses have not been carried out in
healthy populations, it is unknown whether or not these groups (if they exist) would be
comparable to the trajectory groups of physical and mental QoL found in this thesis. Likewise, it
is unknown, if other diseased populations within this age range and progression would follow
similar trajectory patterns. It is possible that these trajectory patterns are specific to prostate
cancer survivors, however, research thus far using these methodologies has been limited and we
were unable to account for this potential confounding by indication bias. Ultimately, to address
this limitation, future research including prostate cancer survivors and healthy men in the same
age range and demographics would need to be conducted to determine the impact of prostate
cancer on QoL trajectory groups after diagnosis.
5.2.1.4 Precision and issues with multiple statistical tests
Although random error is a function of the precision of the estimates produced and
observed, multiple statistical tests may lead to type I error. Type I error occurs when the
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observed significant result occurred by chance, which can occur when multiple testing is done
and the likelihood of a significant result increases (2). In our physical activity and QoL study
(presented in chapter three), several analyses were run. The majority of the associations were
proposed a priori based on gaps in the literature, the research question and available data. These
analyses were expanded to include more in-depth examination of duration (hours/week) and
recreational physical activity based on cancer prevention physical activity guideline adherence.
In addition, our sample size allowed us to test multiple potential effect modifiers and
confounders, which further lends to the issue of multiple testing. However, meaningful results
came from this testing and we were able to fully adjust the models for potential influential
variables. In the GBTM analysis (chapter four), the precision of the trajectory groups found was
evident as the CI surrounding the trajectory patterns (Figures 4.2 & 4.3) were quite narrow. This
analysis was sufficiently powered to determine groups within the sample, as shown by our
results. However, the multinomial logistic regression models were not powered to detect
differences between trajectory groups. The CIs were very wide and therefore, these results
should be interpreted with caution. The issue of multiple testing is less of a concern in the QoL
trajectory analysis since characterizing the trajectory groups was an exploratory analysis. On
another note, in both chapters three and four, we utilized the component summary scores of
physical and mental QoL, which are overall measures of general QoL. Using the component
summary scores, rather than the eight individual domain scores, allowed for more direction in the
analyses, ease of interpretation and a reduction in statistical testing compared to analyzing all
eight domains separately. While issues of precision and multiple testing were present in this
thesis, nonetheless, meaningful investigation of potential modification, confounding, dropout,
trajectory characteristics and sensitivity analyses will lead to more focused research in the future.
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5.2.1.5 Causal inference
Causation between an exposure and an outcome may be evaluated through the Bradford
Hill Criteria (2). First of all, with the design of the study and methods, temporality of the
association between physical activity and QoL and QoL trajectories was established. These
participants were enrolled in the study and followed over time. In addition, the association
between physical activity and QoL has been examined in several different populations including
several prostate cancer study populations. The literature generally supports this association
alluding to a level of consistency between these results. However, the strength of the association
may be questionable. Only the adherence to the cancer prevention physical activity guidelines
analysis resulted in clinically relevant QoL score differences. With respect to the QoL trajectory
analysis (chapter four), there have only been two previous studies that examined post-prostate
cancer diagnosis disease-specific QoL (urinary function (17) sexual function (18)) trajectories
using GBTM methods. As noted in chapter four, these studies also found different trajectories of
prostate cancer survivors. However, the time frame in which these studies followed participants
for QoL measurements were shorter and the population restrictions were different than for this
study. The associations determining QoL trajectories in prostate cancer survivors is not
supported by a large body of literature and therefore, more research are needed to confirm these
findings for potential future policy initiative or change in standard care to be implemented in this
population.
5.2.2 External validity
External validity is the extent to which study results can be generalized to other, more
broad populations (2). If internal validity is not met, external validity cannot be assessed or
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considered. Threats to external validity may involve very rigorous inclusion criteria, thus
resulting in population specific estimates not applicable to other sources. While this study
population came from a prior population-based case-control study and cases were collected from
a population registry with an ascertainment rate of 94.5%, there are inherent limitations to the
generalizability of the results. First of all, re-consent into the prospective cohort study was not
considered until two to three years post-prostate cancer diagnosis. Therefore, only prostate
cancer cases who survived to that point could participate and the results can only be generalized
to this specific group of survivors. While this issue may be of concern, there was only a 2%
refusal rate and approximately 82% of the original case-control study participants did participate
in the cohort follow-up, therefore, the threats to generalizability may be modest. Furthermore,
due to our inclusion criteria, these results can only be generalized to men with stage T2 or greater
prostate cancer, who were less than 80 years at diagnosis. Although these inclusion criteria may
limit generalizability of the results, they may also rule out other competing risks, confounding
and other alternative explanations of these results, thus, strengthening the internal validity.
Further research representing all prostate cancer survivors followed for QoL trajectories directly
after prostate cancer diagnosis is warranted.
5.3 Public health implications
This thesis has provided new empirical evidence regarding the changes in QoL after
prostate cancer diagnosis and the role of physical activity in these temporal changes. This
research has implications for changes to clinical practice and future public health policy
initiatives. Specifically, the results observed in chapter three build stronger evidence towards
recommending recreational physical activity and specifically moderate-vigorous recreational
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physical activity to improve physical QoL after prostate cancer diagnosis regardless of age at
diagnosis (<80 years), aggressiveness of cancer, treatment type (hormone or radiation therapy),
prostatectomy or degree of comorbidity. In addition, we also found that among prostate cancer
survivors who either maintained high levels of physical activity from pre to post-diagnosis or
who increased their levels of activity to those recommended for cancer prevention, there were
higher levels of QoL. The fact that both maintainers and adopters benefitted almost equally in
QoL improvements, suggests that adopting a physically active lifestyle after prostate cancer
diagnosis may be equally beneficial as maintaining a physically active lifestyle throughout the
cancer diagnosis and survival experience. To further emphasize this point, we found that men
who relapsed in their levels of activity experienced declines in QoL which might be preventable
with regular physical activity. Improvements in physical QoL were found to be of clinical
relevance in both analyses and therefore, health professionals should be encouraged to
recommend recreational moderate-vigorous physical activity as a tertiary prevention method to
prevent declines and/or achieve increases in QoL after prostate cancer diagnosis. This type of
evidence is of relevance for organizations such as Exercise is Medicine®, an initiative started by
the American College of Sports Medicine that seeks to provide evidence for primary care health
providers that can be used to prescribe exercise as a means of treating their patient population for
a wider range of chronic disease conditions. The core objectives of Exercise is Medicine®
include: the initiation and collaboration of research projects to enhance implementation of
physical activity in all domains of healthcare, to serve as a coordinating centre for these
implementation projects and to collaborate on developing models to educate healthcare
professionals in community and clinical settings to effectively promote physical activity
behaviour change. Specifically, exercise-referral schemes, training for healthcare professionals in
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physical activity prescription and integration of wearable technology in promotion of physical
activity may be appropriate next research steps for prostate cancer QoL and survival research in
order to reduce overall burden of disease (19).
In chapter four, we identified trajectory patterns of QoL after prostate cancer diagnosis
that are also of relevance for clinical practice. We identified different and potentially “at-risk”
low or declining QoL subgroups within the larger population of prostate cancer survivors who
would warrant closer follow-up and possibly clinical interventions. Our research will require
further confirmation in future studies. If these subgroups are found in future research, tertiary
prevention intervention studies could focus on at-risk groups, who would benefit the most from
these interventions, to help improve or maintain QoL after prostate cancer diagnosis and overall,
reduce the burden of prostate cancer after diagnosis. This research is of particular relevance for
public health, given that prostate cancer patients are surviving longer and a proportion of them
may be experiencing declines in their QoL long after their diagnosis. If subgroups of these
individuals at risk for such QoL declines can be identified early after prostate cancer diagnosis,
there is the possibility of improving their long-term QoL through interventions targeting their
activity levels.
5.4 Future directions
Several areas for future research on this topic can be considered given the substantial
body of evidence that has clearly demonstrated improvements in QoL associated with physical
activity done after prostate cancer diagnosis. Ideally, these include studies that examine different
exercise interventions aimed at delineating the most effective, population-based approaches that
will be feasible, sustainable and have maximal adherence for different populations of prostate
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cancer survivors. Efficacy studies will need to investigate how these interventions can be
personalized to ensure behavioural change at the individual level and effectiveness trials will
need to examine various interventions and how they can be best implemented at the population
level. Another topic area for future research is education for healthcare professionals to
incorporate recommendations on physical activity after prostate cancer diagnosis in their
interactions with this clinical population. Any cancer diagnosis is stressful and overwhelming,
therefore, research that investigates the most effective timing and type of intervention with this
population is needed to ensure lifestyle changes can be made to maintain or improve QoL after
prostate cancer diagnosis.
The research evidence to date on QoL trajectories after prostate cancer diagnosis remains
limited, hence, additional research is essential to replicate our results to determine if the three
subgroups of physical and mental QoL that we identified here are present in other prostate cancer
populations. It may be feasible in the future, once sufficient confirmatory evidence has
accumulated, to target research studies on the high risk sub-groups who have low and/or
declining QoL with a focus on improving QoL in the low groups and preventing reductions in
QoL over time in the declining QoL groups.
5.5 Conclusion
This thesis adds to the literature providing potential methods of improving the QoL of
prostate cancer survivors after diagnosis. QoL is a central component to most patients’ lives that
should be addressed when considering the healthcare needs of prostate survivors after diagnosis.
Our research examining how physical activity improves QoL found moderate-to-vigorous
recreational physical activity was the most beneficial for physical QoL. In addition, adhering to
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cancer prevention physical activity guidelines both pre- and post-diagnosis or just post-diagnosis
was positively related to physical QoL, compared to those who did not meet guidelines at either
time point. These results were clinically meaningful and should be taken into consideration when
healthcare professionals are giving prostate cancer survivors advice on how to improve their
QoL. In addition, our analyses identified three physical and three mental QoL trajectories up to
10 years after diagnosis of prostate cancer. These groups had very different QoL patterns
providing evidence that there may indeed be different sub-populations experiencing very
different QoL after prostate cancer diagnosis. Our results have provided insights for future
interventions to improve and/or maintain QoL long-term in this population.
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Appendices A: Lifetime Physical Activity Questionnaire
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Appendices B: Past-Year Physical Activity Questionnaire
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Appendices C: Well-being Questionnaire
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