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Purpose Research has shown that recent post-treatment breast cancer survivors face significant challenges around physical activity as they transition to recovery. This review examined randomized controlled trials targeting physical activity behavior change in breast cancer survivors <5 years post-treatment and describes 1) characteristics of interventions for breast cancer survivors as well as 2) effect size estimates for these studies. Methods A systematic search was conducted following PRISMA guidelines with Medline, PubMed, PsycINFO, CINAHL, and Scopus databases. Data were abstracted for primary intervention strategies and other details (e.g., setting, duration, theory use). A subgroup analysis was conducted to assess intensity of exercise supervision/monitoring and intervention effectiveness. Results The search produced 14 unique behavior intervention trials from the US and abroad published 2005-2013. The mean sample size was 153 participants per study. All interventions included moderate-intensity activities plus various behavioral change strategies. Most interventions were partially or entirely home-based. The overall standardized mean difference was 0.47 (0.23, 0.67) with p < 0.001. Conclusion Most interventions were effective in producing short-term behavior changes in physical activity, but varied greatly relative to intervention strategies and intensity of supervision/monitoring. Highly structured interventions tended to produce larger behavior change effects overall, but many larger effect sizes came from interventions supported by phone counseling or email. We observed that ‘more’ may not be better in terms of direct supervision/monitoring in physical activity behavior interventions. This may be important in exploring less resource-intensive options for effective behavior change strategies for recent post-treatment survivors.

Introduction Weight loss has been associated with higher physical activity (PA) levels and frequent dietary self-monitoring. Less is known about how PA self-monitoring affects adherence to PA goals, PA levels and weight change. Methods The SMART Trial is a clinical weight loss trial in which 210 overweight adults were randomized equally to one of three arms: 1) paper record (PR); 2) personal digital assistant with self-monitoring software (PDA); and 3) PDA with daily tailored feedback message (PDA+FB). PA self-monitoring and adherence to PA goals were based on entries in weekly submitted diaries. PA levels were measured via self-report by the past 6 month Modifiable Activity Questionnaire at baseline and 6 months. Results Data are presented on 189 participants with complete 6-month PA data [84% female, 77% White, mean age: 47.3 ± 8.8 years, mean BMI: 34.1 ± 4.5 kg/m2]. Median PA level was 7.96 MET-hr-wk−1 at baseline and 13.4 MET-hr-wk−1 at 6 months, with significant PA increases in all three arms. PDA+FB arm had a higher mean number of weekly self-monitoring entries than the PR arm (3.4 vs. 2.4; p=0.003) and were more likely to maintain high (i.e., 100%) adherence to PA goals over time than the PDA (p=0.02) or PR arms (p=0.0003). Both PA self-monitoring and adherence to PA goals were related to higher PA levels at 6 months. A higher mean rate of PA self-monitoring was associated with a greater percentage of weight decrease (rho=−0.49; p<0.0001) at 6 months. Conclusions PA self-monitoring and adherence to PA goals were more likely in participants in the PDA+FB arm and in turn predicted higher PA levels and weight loss.

Purpose To examine the temporal and bidirectional relationship between accelerometer-derived physical activity estimates and actigraphy-assessed sleep characteristics among older women. Methods A sub-group of participants [N=143, mean age= 73y] enrolled in the Healthy Women Study wore an ActiGraph accelerometer on their waist and an Actiwatch sleep monitor on their wrist concurrently for 7-consecutive days. Multi-level models examined whether ActiGraph-assessed daily activity counts (ct·min·d-1) and moderate- to vigorous- intensity physical activity (MVPA; min·d-1) predicted Actiwatch-assessed sleep onset latency, total sleep time, sleep efficiency, and sleep fragmentation. Similar models were used to determine if nighttime sleep characteristics predicted physical activity the following day. Results In unadjusted models, greater daily activity counts (B=-.05, p=.005) and more minutes of MVPA (B=-.03, p=.01) were temporally associated with less total sleep time across the week. Greater sleep efficiency was associated with greater daily activity counts (B=.37, p=.01) and more minutes of MVPA (B=.64, p=.009) the following day. Less sleep fragmentation was also associated with greater daily activity counts and more MVPA the following day. Findings were similar after adjustment for age, education, BMI, depressive symptoms, arthritis, and accelerometer wear time. Conclusions Few studies have used objective measures to examine the temporal relationship between physical activity and sleep. Notably, these findings suggest that nightly variations in sleep efficiency influence physical activity the following day. Thus, improving overall sleep quality in addition to reducing nightly fluctuations in sleep may be important for encouraging a physically active lifestyle in older women.