Objective: The use of financial incentives to promote physical activity (PA) has grown in popularity due in part to technological advances that make it easier to track and reward PA. The purpose of this study was to update the evidence on the effects of incentives on PA in adults.Data sources: Medline, PubMed, Embase, PsychINFO, CCTR, CINAHL and COCH.Eligibility criteria: Randomised controlled trials (RCT) published between 2012 and May 2018 examining the impact of incentives on PA.Design: A simple count of studies with positive and null effects (‘vote counting’) was conducted. Random-effects meta-analyses were also undertaken for studies reporting steps per day for intervention and post-intervention periods.Results: 23 studies involving 6074 participants were included (64.42% female, mean age = 41.20 years). 20 out of 22 studies reported positive intervention effects and four out of 18 reported post-intervention (after incentives withdrawn) benefits. Among the 12 of 23 studies included in the meta-analysis, incentives were associated with increased mean daily step counts during the intervention period (pooled mean difference (MD), 607.1; 95% CI: 422.1 to 792.1). Among the nine of 12 studies with post-intervention daily step count data incentives were associated with increased mean daily step counts (pooled MD, 513.8; 95% CI:312.7 to 714.9).Conclusion: Demonstrating rising interest in financial incentives, 23 RCTs were identified. Modest incentives ($1.40 US/day) increased PA for interventions of short and long durationsandafter incentives were removed, though post-intervention ‘vote counting’ and pooled results did not align. Nonetheless, and contrary to what has been previously reported, these findings suggest a short-term incentive ‘dose’ may promote sustained PA.

Increasing global urbanization limits interaction between people and natural environments, which may negatively impact population health and wellbeing. Urban residents who live near parks report better mental health. Physical activity (PA) reduces depression and improves quality of life. Despite PA’s protective effects on mental health, the added benefit of urban park use for PA is unclear. Thus, we examined whether park-based PA mediated associations between park proximity and mental distress among 3652 New York City residents (61.4% 45 + years, 58.9% female, 56.3% non-white) who completed the 2010–2011 Physical Activity and Transit (PAT) random-digit-dial survey. Measures included number of poor mental health days in the previous month (outcome), self-reported time to walk to the nearest park from home (exposure), and frequency of park use for sports, exercise or PA (mediator). We used multiple regression with bootstrap-generated 95% bias-corrected confidence intervals (BC CIs) to test for mediation by park-based PA and moderation by gender, dog ownership, PA with others, and perceived park crime. Park proximity was indirectly associated with fewer days of poor mental health via park-based PA, but only among those not concerned about park crime (index of moderated mediation = 0.04; SE = 0.02; 95% BC CI = 0.01, 0.10). Investment in park safety and park-based PA promotion in urban neighborhoods may help to maximize the mental health benefits of nearby parks.

Despite the popularity of commercially available wearable activity monitors (WAMs), there is a paucity of consistent methodology for analyzing large amounts of accelerometer data from these devices. This multimethod study aimed to inform appropriate Fitbit wear thresholds for physical activity (PA) outcomes assessment in a sample of 616 low-income, majority Latina patients with obesity enrolled in a behavioral weight-loss intervention. Secondly, this study aimed to understand intervention participants’ barriers to Fitbit use. We applied a heart rate (HR) criterion (≥10 h/day) and a step count (SC) criterion (≥1000 steps/day) to 100 days of continuous activity monitor data. We examined the prevalence of valid wear and PA outcomes between analytic subgroups of participants who met the HR criterion, SC criterion, or both. We undertook qualitative analysis of research staff notes and participant interviews to explore barriers to valid Fitbit data collection. Overall, one in three participants did not meet the SC criterion for valid wear in Weeks 1 and 13; however, we found the SC criterion to be more inclusive of participants who did not use a smartphone than the HR criterion. Older age, higher body mass index (BMI), barriers to smartphone use, device storage issues, and negative emotional responses to WAM-based self-monitoring may predict higher proportions of invalid WAM data in weight-loss intervention research.

Objective: Self-efficacy for leisure-time or health-promoting physical activity (SEPA) is a psychosocial determinant of physical activity. The socioecological model can provide a robust perspective of SEPA. The objective of this study was to synthesize the evidence on multilevel correlates of SEPA among individuals with chronic musculoskeletal pain. The second aim examined the extent to which socioecological disparities are associated with SEPA among individuals with chronic musculoskeletal pain. Methods: An integrative review was conducted. Included studies needed to investigate the relationship between SEPA and socioecological factors at the interpersonal, institutional, community, and/or macrosystem level among adults with chronic musculoskeletal pain (≥3 months). Searches in PubMed, EMBASE, PsycINFO, and CINAHL were performed (December 30, 2020, and October 12, 2022), yielding 4047 records after duplicates were removed. Two independent reviewers completed screening, full-text reviews, and data extraction. After title and abstract screening and full-text reviews, 17 studies were included. The constant comparison method included: data reduction, data display, data comparison, and conclusion drawing/verification. Quality of evidence was assessed using the Joanna Briggs Institute appraisal tools. Results: Five themes emerged with respect to relationships between SEPA and socioecological factors: social relations, social comparisons, patient–provider relationship, organizational resources, and accessibility to physical activity. Relationships between interpersonal factors and SEPA were most prominently studied. One study examined and addressed potential disparities in SEPA at the macrosystem level. Conclusion: A spectrum of relationships from supporting to straining SEPA were found at the interpersonal level. Relationships between institutional, community, and macrosystem factors and SEPA were comparably sparse. Gaps in the literature were identified regarding how health disparities present across the socioecological model with respect to SEPA. Impact: Clinicians can use this review to evaluate how SEPA can be supported or threatened by factors across the socioecological model. This may be a preliminary step towards examining and addressing health disparities in SEPA.