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Physical Activity and Sleep Quality in Breast Cancer Survivors

A Randomized Trial


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Medicine & Science in Sports & Exercise: October 2017 - Volume 49 - Issue 10 - p 2009-2015
doi: 10.1249/MSS.0000000000001327
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The cancer survivorship journey begins for an individual at the time of diagnosis and continues until the end of life. This journey may involve detrimental effects on normal activities, including but not limited to sleep. The term “sleep quality” encompasses perceived and/or objective measures of sleep aspects (e.g., onset) and effects (e.g., tiredness during the day) (17). Persistent poor sleep quality plagues nearly a third of breast cancer survivors with such symptoms being of substantial clinical significance (8). Specifically, poor sleep is associated with greater breast cancer mortality (18,19), and being able to sleep is among the top 5 highest ranked patient-reported outcomes of importance (i.e., ranked as “important or very important” by 96% of breast cancer survivors) (13). Physical activity is one potential nonpharmacological intervention for poor sleep quality (20,24), yet a recent meta-analysis reported no benefits of exercise interventions on sleep quality in breast cancer survivors (39).

The inability of this meta-analysis to detect sleep quality benefits is not unexpected given that currently published randomized controlled trials of aerobic exercise (e.g., home-based walking and supervised aerobic exercise) in breast cancer survivors specifically have not consistently reported beneficial intervention effects on sleep (10,22,27,30–32,34,37). In the seven trials measuring self-report, significant intervention effects were reported for overall sleep quality in four studies (two occurred during chemotherapy/radiation) (10,22,27,37), efficiency in one study (during chemotherapy) (10), latency in two studies (one during chemotherapy) (10,31), and sleep duration in one (after chemotherapy/radiation) (30). Another after chemotherapy/radiation trial reported beneficial effects on accelerometer measured efficiency, latency, and awake time (34). The three trials using both accelerometer and self-report measures did not occur during chemotherapy or radiation and reported no effect on accelerometer efficiency and latency even when global self-report sleep quality benefits were found (27,30,31). Importantly, five of the eight trials available in breast cancer survivors, to date, were post–primary treatment (i.e., participants had completed surgery, chemotherapy, and/or radiation) yet enrolled ≤46 participants per trial reducing their study power (27,30–32,34). The only large trial (301 participants) was conducted during chemotherapy (limiting generalizability to post–primary treatment survivors) and did not include an accelerometer or objective measure of sleep quality (10). Published scientific reviews including trials enrolling mixed or cancer types other than breast and testing a variety of exercise modes (e.g., resistance, yoga) also support inconsistencies in exercise effects on sleep among cancer survivors and the infrequent use of objective sleep measures (20,21,39).

Given the importance of sleep quality after breast cancer diagnosis, the lack of agreement between accelerometer and self-report outcomes, and the generally accepted use of physical activity for treatment of poor sleep without consistent scientific data to support its use, further testing in larger trials is needed to better describe physical activity effects on sleep quality. As previously reported, our 3-month physical activity behavior change intervention significantly increased the odds of meeting physical activity recommendations (primary outcome for this trial) (28). Measurement of sleep outcomes as a secondary study outcome provides the opportunity to further address the current knowledge gap by testing sleep outcomes in a larger trial (33). Therefore, the primary purpose of this report was to compare the effects of a physical activity behavior change intervention with usual care (written materials) on self-report and accelerometer-measured sleep quality outcomes. Our primary outcome for this report was perceived sleep quality (Pittsburgh Sleep Quality Index [PSQI] global score; lower scores indicate better sleep quality). We hypothesized that, when compared with usual care, the physical activity intervention would result in significant improvement in self-report and accelerometer-measured sleep quality outcomes immediately postintervention (month 3; M3) and 3 months postintervention (month 6; M6).


Study design, setting, participants, and randomization

A multicenter, randomized controlled trial enrolling 222 breast cancer survivors was conducted at three U.S. academic institutions with study design and sample size justification previously described ( identifier NCT00929617) (33). Inclusion criteria included women, age 18–70 yr, history of ductal carcinoma in situ or stage I–IIIA breast cancer, post–primary treatment (i.e., no longer on radiation or chemotherapy but could be on longer term treatments such as anti-estrogen therapy), ≥ 8 wk postsurgery, English speaking, physician clearance received, and reporting ≤30 min of vigorous physical activity or ≤60 min of moderate activity per week, on average, during the past 6 months. Exclusion criteria included dementia, inability to ambulate, unable to fully participate in study activities, anticipated surgery during the intervention, anticipated out of town travel in the first 4 wk and travel ≥1 wk in the last 8 wk of the intervention, physical activity contraindication, metastatic or recurrent breast cancer, and current participation in another exercise trial. The trial received institutional review board approval, and all participants provided written informed consent before initiating study activities. Computer-generated numbers in blocks of four and stratified by study site were used for randomization (33). Randomization occurred in the order of participant completion of baseline testing with study staff kept blinded to group allocation until the baseline assessment was complete.

Study group allocations

All participants received written materials regarding physical activity for cancer survivors publically available from the American Cancer Society. Half were randomized to also receive the 3-month, social cognitive theory-based Better Exercise Adherence after Treatment for Cancer (BEAT Cancer) physical activity behavior change intervention (i.e., intervention group), whereas the remaining half comprised the usual care group (33). BEAT Cancer included 12 supervised exercise sessions with exercise specialists during the first 6 wk that were tapered to entirely unsupervised exercise off-site (e.g., home based) supported by update counseling sessions with exercise specialists every 2 wk. BEAT Cancer participants also completed six discussion group sessions that included topics such as exercise barriers (e.g., time management, stress management, etc.), exercise benefits, goal setting with self-monitoring, behavioral modification strategies, safety, relapse prevention, and exercise role models (33). Achieving ≥150 weekly minutes of moderate- to vigorous-intensity physical activity was the goal of the intervention with exercise progression previously described (33).


Demographic and medical factors (e.g., age, race, income, marital status, comorbidities scale [12], cancer stage, cancer treatment, and months since diagnosis) were self-reported at baseline. Remaining measures were collected at baseline, immediately postintervention (month 3; M3), and 3 months postintervention (month 6; M6). Perceived sleep quality was measured with the PSQI and scored according to published protocol (5). A higher score indicates poorer sleep quality, and the PSQI yields seven ordinal subscales (0, 1, 2, or 3; subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction) that are summed for a continuous global PSQI score (5). Global PSQI score was analyzed as a continuous variable and also dichotomized as ≤5 (“good” sleepers) versus >5 (“poor” sleepers) (5). If ≤2 items were missing from the sleep disturbances subscale, imputation was performed by calculating the mean of the provided responses. Only four surveys required imputation (one at baseline, two at month 3, and one at month 6). No other imputations were required for the remaining PSQI components or global score.

A wrist-worn accelerometer was worn for seven nights (MTI/ActiGraph accelerometer; models GT1M and GT3X; MTI/ActiGraph Corp, Pensacola, FL) (6,38). The participant recorded the time in and out of bed on a record sheet. Three valid nights were required; ActiLife software was used along with default algorithm (i.e., Sadeh et al. [35]). Accelerometer validity has been previously reported (6,38). The objective, accelerometer outcomes of sleep latency, and efficiency are described in this report.

Statistical analyses

Intervention effects on continuous sleep outcomes were analyzed using adjusted linear mixed models incorporating a first-order autoregressive covariance matrix (i.e., PROC MIXED). Ordinal (PSQI subscales) and dichotomous (“poor” vs “good” sleepers) variables were analyzed using generalized linear mixed models (i.e., PROC GLIMMIX). All of these models used restricted maximum likelihood as the estimation technique. Statistical analyses were performed using SAS, version 9.4 (SAS Institute, Inc., Cary, NC). All statistical tests were two tailed, with a P value <0.05 denoting statistical significance. Because there were no substantial differences between the unadjusted and the adjusted analyses, we only report our primary analysis (i.e., adjusted). Covariates included baseline value of the outcome, study site (stratification variable for randomization), hormonal therapy (baseline difference between study groups [28]; none vs on hormonal therapy for ≤1 yr vs on hormonal therapy for >1 yr), breast cancer stage (based on literature review [3]), and baseline factors associated with sleep quality at month 6 (income, race [Caucasian vs other], marital group [married/significant other vs other], and number of comorbidities; self-report scale by Groll et al. [12]). PSQI global and subscale factors were normally distributed, as determined by an examination of normal probability plots, box plots, and the Kolmogorov–Smirnov test, and no outliers were noted. The accelerometer outcomes were analyzed with and without five possible outliers (from participants whose data remained in the analyses) without change in the results; hence, we report the analyses with all available data. Accelerometer latency was analyzed with and without log10 transformation because of concerns about a skewed distribution. No difference in results was observed; hence, we report the analyses without log transformation. Of the 222 participants randomized, 214 were available for the PSQI analyses and 209 for the accelerometer outcome analyses. This is due to incomplete assessments at both month 3 and month 6 or the exclusion of accelerometer data from five participants because of a high likelihood of invalid data (e.g., activity intensity too vigorous to indicate sleep; erratic, erroneous, or uncertain time in and out of bed).


Of the 222 participants randomized, 110 were assigned to BEAT Cancer and 112 to usual care (28). Retention was 97% at M3 and 96% at M6 (similar for both study groups) (28). Study groups differed at baseline with regard to percent on hormonal therapy for ≤1 yr (i.e., 17% for BEAT Cancer vs 30% for usual care; P = 0.02) but were otherwise balanced as previously published in table format (28). Hence, baseline demographic and medical characteristics for both groups combined are summarized as follows: mean ± SD age = 54.4 ± 8.5 yr, mean ± SD education = 15.5 ± 2.6 yr, 98% non-Hispanic, 84% White, 11% African American, 11% ductal carcinoma in situ, 42% stage I, 35% stage II, 12% stage III, mean ± SD months since diagnosis = 54 ± 54.5, 58% history of chemotherapy, 68% history of radiation therapy, and 49% currently on hormonal therapy (28).

After adjusting for covariates, BEAT Cancer significantly improved PSQI global sleep quality when compared with usual care at M3 (model study group effect P < 0.001, mean between-group difference = −1.4, 95% confidence interval [CI] = −2.1 to −0.7, P < 0.001) and remained statistically significant at M6 (mean between-group difference = −1.0, 95% CI = −1.7 to −0.2, P = 0.01) (Table 1). A statistically significant between-group difference favoring the BEAT Cancer intervention was noted at M3 for the following PSQI subscales: sleep quality (model study group effect P = 0.02, mean between-group difference = −0.3, 95% CI = −0.4 to −0.1, P = 0.002), sleep disturbances (model study group effect P = 0.014, mean between-group difference = −0.2, 95% CI = −0.3 to −0.03, P = 0.016), and daytime dysfunction (model study group effect P = 0.019, mean between-group difference = −0.2, 95% CI = −0.4 to −0.02, P = 0.027) (Table 1). Although the M3 between-group comparison was significant for PSQI efficiency subscale (Table 1), the model study group effect P value was not (P = 0.13). Although the percent of participants classified as good sleepers increased, the odds of being a good sleeper for BEAT Cancer versus usual care was not statistically significant (Table 2). No significant between-group difference was noted for accelerometer latency or efficiency (Table 1).

Effects of a physical activity behavior change intervention (BEAT Cancer) on self-report sleep outcomes postintervention (month 3) and 3 months after intervention completion (month 6) in breast cancer survivors.
Odds of being a good sleeper (PSQI score of ≤5) postintervention (month 3) and 3 months after BEAT Cancer intervention completion (month 6) in breast cancer survivors.a


When compared with usual care, the BEAT Cancer physical activity behavior change intervention significantly improved perceived global sleep quality at 3 and 6 months. This improvement was primarily due to improvements in the perceived sleep quality, sleep disturbances, and daytime dysfunction subscales. Although a significant between-group difference was noted for the efficiency subscale, the overall group effect P value was not significant, suggesting minimal contribution to the intervention effects on global sleep quality. No significant intervention effects were noted for accelerometer efficiency or latency.

Our findings of improvements in self-reported sleep quality but not accelerometer efficiency or latency are consistent with prior smaller randomized trials (27,30,31). Several explanations for intervention effects on self-report and not accelerometer measured sleep outcomes exist. Given the lack of statistically significant associations between PSQI and ActiGraph sleep measures in other studies (11), the statistically significant baseline correlations between PSQI subscale and accelerometer outcome in our data set for efficiency (r = −0.32, P < 0.0001) and latency (r = 0.19, P = 0.005) suggest that lack of relationship between the two measures may have contributed to but does not fully explain the inconsistency in our study reported here. Importantly, the PSQI global improvement was due to subscales other than efficiency and latency, suggesting that the accelerometer does not measure the aspects of sleep quality responsible for perceived sleep improvements. Also, participants may have inconsistently provided accurate information regarding time in and out of bed to sleep, information key to delineating the monitor interval for analysis. For example, some participants had difficulty differentiating getting in the bed to read or watch television rather than getting in the bed to sleep. Also, the possibility of social desirability bias exists because it was impossible to blind participants to their study group allocation and placebo effects may have occurred given the lack of attention equivalent control group. Polysomnography would provide more objective measures of latency, efficiency, and sleep architecture possibly responsible for perceived improvements with no prior exercise training study including such a measure. We also acknowledge that inclusion of more current sleep measurement options such as bed sensors or home sleep recorders could also overcome some of the accelerometer limitations (36). Although integrating these procedures into future studies will increase study cost and participant burden, doing so should be considered to better elucidate physical activity effects on sleep quality.

Our beneficial intervention effect on self-reported sleep disturbances is consistent with the meta-analysis by Mishra et al. (20), which reported a standardized mean difference of −0.46 (95% CI = −0.72 to −0.20) for exercise effects on sleep disturbances (all cancer types combined; only significant at the 12-wk follow-up time point). Nevertheless, comparison with the meta-analysis is limited by the heterogeneity of the exercise interventions (e.g., aerobic, yoga, etc.) and cancer types (e.g., mixed, colon, etc.) included. Moreover, the meta-analytic results are not consistent with the lack of effect on sleep outcomes in a more recent meta-analysis examining exercise effects on sleep in breast cancer survivors specifically due, in part, to few studies all enrolling small sample sizes (39). Of note, the data reported here are the first sleep outcomes data from a large randomized controlled physical activity (walking or traditional supervised aerobic exercise) and cancer trial specifically focused on post–primary treatment breast cancer survivors. Hence, our report describing a small to medium intervention effect size of 0.35 and significant between-group difference with regard to PSQI global score for an aerobic physical activity intervention versus usual care group is noteworthy.

Our statistically significant intervention effects on PSQI global score, sleep quality subscale, and daytime dysfunction subscale at month 3 exceeded the threshold for clinically important benefits (1) with the month 6 intervention effect on PSQI global approaching a clinically important benefit. Interventions that improve sleep quality are of value in the clinical care of cancer survivors. Diminished sleep quality is associated with fatigue, depression, and poorer quality of life in breast cancer survivors (15,16). Similarly, being able to sleep is among the top 5 patient-reported outcomes of importance relevant to breast cancer survivors with 96% indicating being able to sleep was important or very important (13). Reducing sleep disturbances may also be important for improved health. Although the association between sleep quality and breast cancer mortality has not been well studied, poor sleep quality has been associated with all-cause mortality (not cancer specific) (7,14). Furthermore, sleep disturbances have been associated with detrimental metabolic states linked to breast cancer biomarkers and medical comorbidities (e.g., impaired glucose tolerance and insulin resistance) (4). Similarly, abnormal sleep has been associated with an increase in cardiovascular risk (2), an outcome of particular importance given that cardiovascular disease competes with breast cancer as the leading cause of death in breast cancer survivors (25,26). Clearly, the ability of our physical activity behavior change intervention to improve global sleep quality during the intervention with persistent improvements months after completing the intervention has noteworthy potential for improving the health and well-being of breast cancer survivors.

Sleep quality was a secondary health outcome in this randomized trial (33), and as such, a “floor effect” with regard to sleep quality might be expected because study inclusion was not limited to only breast cancer survivors who were “poor sleepers.” Hence, it is noteworthy that most of our participants answered >5 on the PSQI scale (i.e., “poor sleepers”), reinforcing the importance of this symptom in the breast cancer survivor experience. Although our between-group difference was significant for the continuous PSQI global score, our lack of statistical significance related to odds of being a “good sleeper” suggests that physical activity can improve perceived sleep quality, yet moving participants out of the “poor sleeper” category may require combining physical activity with other strategies (e.g., good sleep hygiene counseling, screening/treatment for undiagnosed obstructive sleep apnea, etc.). Randomization evenly distributed baseline anxiety and depressive symptoms between intervention and control groups (i.e., mean Hospital Anxiety and Depression Scale scores were 4.8 vs 4.7 for depression, P = 0.80, and 7.0 vs 7.0 for anxiety, P = 0.90), yet we did not measure personality disorders that might influence sleep outcomes. Also, several factors associated with sleep in cross-sectional studies (i.e., fatigue, depression, and anxiety) improved with the intervention (29). Longitudinal, multilevel path analyses were beyond the scope of this report, yet future studies should consider including such an approach to elucidate the relationships among these variables over time.

Our study strengths include its randomized controlled design, multicenter enrollment, and high retention rates. Circadian rhythm was not measured in our study and should be considered in future trials aiming to improve our understanding of how physical activity improves sleep outcomes. Also, generalizing our study results to underrepresented ethnic and racial groups is limited. As with any physical activity intervention that includes group and individual interactions providing behavioral support for improving adherence (9), discriminating between the effects of physical activity independent of the behavioral and staff support is not possible. Yet continued improvements months after intervention completion (and without the ongoing support) add credence to the presumed role of physical activity in intervention outcomes on sleep quality.

Although trials have reported the effects of yoga on sleep outcomes (23), this study is the first large randomized controlled physical activity (walking or traditional supervised aerobic exercise) trial in post–primary treatment breast cancer survivors reporting sleep outcomes. Our rigorous study design, high retention rate, and use of both actigraphy and PSQI suggest future directions in the field. Poor sleep is a frequent problem of importance for post–primary treatment breast cancer survivors and warrants attention in future randomized trials. The field could be advanced by considering polysomnography or ambulatory measures other than accelerometry (when feasible and appropriate), testing physical activity interventions in combination with other strategies for possible synergistic effects, integrating measures of circadian rhythm, and exploring exercise effects on sleep in relation to breast cancer mortality. In so doing, the exercise oncology scientific field can optimize the use of physical activity in improving the health and well-being of cancer survivors through improved sleep.

The authors acknowledge the contributions of Robert Mocharnuk, M.D.; Karen Hoelzer, M.D.; Sara Mansfield, M.S.; Amanda Fogleman, M.Eng.; Ruth Sosnoff, Ph.D.; the Southern Illinois University School of Medicine Center for Clinical Research; the University of Illinois at Urbana Champaign kinesiology graduate students and assistants; and the University of Alabama at Birmingham Nutrition Obesity Research Center Physical Activity core (P30DK056336). This project was supported by the National Cancer Institute R01CA136859. Kerry S. Courneya is supported by the Canada Research Chairs Program.

Laura Rogers and all contributing authors declare they have no personal or professional relationships that may represent a potential conflict of interest. All procedures, including the informed consent process, were conducted in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. The results of the present study do not constitute endorsement by the American College of Sports Medicine. Study results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.


1. Philadelphia Panel evidence-based clinical practice guidelines on selected rehabilitation interventions: overview and methodology. Phys Ther. 2001;81(10):1629–40.
2. Badran M, Yassin BA, Fox N, Laher I, Ayas N. Epidemiology of sleep disturbances and cardiovascular consequences. Can J Cardiol. 2015;31(7):873–9.
3. Banthia R, Malcarne VL, Ko CM, Varni JW, Sadler GR. Fatigued breast cancer survivors: the role of sleep quality, depressed mood, stage and age. Psychol Health. 2009;24(8):965–80.
4. Briançon-Marjollet A, Weiszenstein M, Henri M, Thomas A, Godin-Ribuot D, Polak J. The impact of sleep disorders on glucose metabolism: endocrine and molecular mechanisms. Diabetol Metab Syndr. 2015;7:25.
5. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.
6. Cellini N, Buman MP, McDevitt EA, Ricker AA, Mednick SC. Direct comparison of two actigraphy devices with polysomnographically recorded naps in healthy young adults. Chronobiol Int. 2013;30(5):691–8.
7. Chen HC, Su TP, Chou P. A nine-year follow-up study of sleep patterns and mortality in community-dwelling older adults in Taiwan. Sleep. 2013;36(8):1187–98.
8. Cleeland CS, Zhao F, Chang VT, et al. The symptom burden of cancer: evidence for a core set of cancer-related and treatment-related symptoms from the Eastern Cooperative Oncology Group Symptom Outcomes and Practice Patterns study. Cancer. 2013;119(24):4333–40.
9. Courneya KS. Efficacy, effectiveness, and behavior change trials in exercise research. Int J Behav Nutr Phys Act. 2010;7:81.
10. Courneya KS, Segal RJ, Mackey JR, et al. Effects of exercise dose and type on sleep quality in breast cancer patients receiving chemotherapy: a multicenter randomized trial. Breast Cancer Res Treat. 2014;144(2):361–9.
11. Grandner MA, Kripke DF, Yoon IY, Youngstedt SD. Criterion validity of the Pittsburgh Sleep Quality Index: investigation in a non-clinical sample. Sleep Biol Rhythms. 2006;4(2):129–39.
12. Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol. 2005;58(6):595–602.
13. Hollen PJ, Msaouel P, Gralla RJ. Determining issues of importance for the evaluation of quality of life and patient-reported outcomes in breast cancer: results of a survey of 1072 patients. Breast Cancer Res Treat. 2015;151(3):679–86.
14. Hublin C, Partinen M, Koskenvuo M, Kaprio J. Heritability and mortality risk of insomnia-related symptoms: a genetic epidemiologic study in a population-based twin cohort. Sleep. 2011;34(7):957–64.
15. Jones SM, LaCroix AZ, Li W, et al. Depression and quality of life before and after breast cancer diagnosis in older women from the Women’s Health Initiative. J Cancer Surviv. 2015;9(4):620–9.
16. Kim SH, Son BH, Hwang SY, et al. Fatigue and depression in disease-free breast cancer survivors: prevalence, correlates, and association with quality of life. J Pain Symptom Manage. 2008;35(6):644–55.
17. Krystal AD, Edinger JD. Measuring sleep quality. Sleep Med. 2008;9(1 Suppl):S10–7.
18. Lehrer S, Green S, Ramanathan L, Rosenzweig KE. Insufficient sleep associated with increased breast cancer mortality. Sleep Med. 2013;14(5):469.
19. Lehrer S, Green S, Ramanathan L, Rosenzweig KE. Obesity and deranged sleep are independently associated with increased cancer mortality in 50 US states and the District of Columbia. Sleep Breath. 2013;17(3):1117–8.
20. Mishra SI, Scherer RW, Geigle PM, et al. Exercise interventions on health-related quality of life for cancer survivors. Cochrane Database Syst Rev. 2012;(8):CD007566.
21. Mishra SI, Scherer RW, Snyder C, Geigle PM, Berlanstein DR, Topaloglu O. Exercise interventions on health-related quality of life for people with cancer during active treatment. Cochrane Database Syst Rev. 2012;(8):CD008465.
22. Mock V, Dow KH, Meares CJ, et al. Effects of exercise on fatigue, physical functioning, and emotional distress during radiation therapy for breast cancer. Oncol Nurs Forum. 1997;24(6):991–1000.
23. Mustian KM, Sprod LK, Janelsins M, et al. Multicenter, randomized controlled trial of yoga for sleep quality among cancer survivors. J Clin Oncol. 2013;31(26):3233–41.
24. NCCN. Survivorship version 2.2014. National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology (NCCN Guidelines). 2014.
25. Nichols HB, Trentham-Dietz A, Egan KM, et al. Body mass index before and after breast cancer diagnosis: associations with all-cause, breast cancer, and cardiovascular disease mortality. Cancer Epidemiol Biomarkers Prev. 2009;18(5):1403–9.
26. Patnaik JL, Byers T, DiGuiseppi C, Dabelea D, Denberg TD. Cardiovascular disease competes with breast cancer as the leading cause of death for older females diagnosed with breast cancer: a retrospective cohort study. Breast Cancer Res. 2011;13(3):R64.
27. Payne JK, Held J, Thorpe J, Shaw H. Effect of exercise on biomarkers, fatigue, sleep disturbances, and depressive symptoms in older women with breast cancer receiving hormonal therapy. Oncol Nurs Forum. 2008;35(4):635–42.
28. Rogers LQ, Courneya KS, Anton PM, et al. Effects of the BEAT Cancer physical activity behavior change intervention on physical activity, aerobic fitness, and quality of life in breast cancer survivors: a multicenter randomized controlled trial. Breast Cancer Res Treat. 2015;149(1):109–19.
29. Rogers LQ, Courneya KS, Anton PM, et al. Effects of a multicomponent physical activity behavior change intervention on fatigue, anxiety, and depressive symptomatology in breast cancer survivors: randomized trial. Psychooncology. 2016. Aug 17. doi: 10.1002/pon.4254. [Epub ahead of print].
30. Rogers LQ, Fogleman A, Trammell R, et al. Inflammation and psychosocial factors mediate exercise effects on sleep quality in breast cancer survivors: pilot randomized controlled trial. Psychooncology. 2015;24(3):302–10.
31. Rogers LQ, Fogleman A, Trammell R, et al. Effects of a physical activity behavior change intervention on inflammation and related health outcomes in breast cancer survivors: pilot randomized trial. Integr Cancer Ther. 2013;12(4):323–35.
32. Rogers LQ, Hopkins-Price P, Vicari S, et al. A randomized trial to increase physical activity in breast cancer survivors. Med Sci Sports Exerc. 2009;41(4):935–46.
33. Rogers LQ, McAuley E, Anton PM, et al. Better Exercise Adherence after Treatment for Cancer (BEAT Cancer) study: rationale, design, and methods. Contemp Clin Trials. 2012;33(1):124–37.
34. Roveda E, Vitale JA, Bruno E, et al. Protective effect of aerobic physical activity on sleep behavior in breast cancer survivors. Integr Cancer Ther. 2017;16(1):21–31.
35. Sadeh A, Sharkey KM, Carskadon MA. Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep. 1994;17(3):201–7.
36. Van de Water AT, Holmes A, Hurley DA. Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography—a systematic review. J Sleep Res. 2011;20(1 Pt 2):183–200.
37. Wang YJ, Boehmke M, Wu YW, Dickerson SS, Fisher N. Effects of a 6-week walking program on Taiwanese women newly diagnosed with early-stage breast cancer. Cancer Nurs. 2011;34(2):E1–13.
38. Weiss AR, Johnson NL, Berger NA, Redline S. Validity of activity-based devices to estimate sleep. J Clin Sleep Med. 2010;6(4):336–42.
39. Zhu G, Zhang X, Wang Y, Xiong H, Zhao Y, Sun F. Effects of exercise intervention in breast cancer survivors: a meta-analysis of 33 randomized controlled trails. Onco Targets Ther. 2016;9:2153–68.


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