Together we Move: A Multi-component Intervention to Increase Physical Activity for Ethnic Minority Older Adults

The benefits of physical activity (PA) for healthy aging are well known; however \< 16% of U.S. older adults meet the federal recommendations for moderate to vigorous PA (MVPA). Asian Americans (AA) are a fast-growing segment of the older adult U.S. population and are likely to have limited English proficiency, lower-incomes, and low PA levels. Older AA adults are under represented in clinical trials and as a result, evidence-based PA programs remain inaccessible. Similarly, while the number of PA interventions for older adults incorporating technology to promote and sustain behavior change is increasing, the applicability and efficacy of these approaches for AA is poorly understood. Thus, there is a critical need to develop and test innovative PA interventions for this at-risk group. The primary objective of this study is to evaluate if a culturally and linguistically adapted community-based walking program enhanced with a cognitive behavioral intervention delivered via WeChat - a widely used Chinese social networking application - (enhanced walking) improves physical activity compared with walking only. Secondary objectives are to identify participant experiences in engaging in this platform to improve PA, and gain insights into the scalability of the intervention for future pivotal trials. The proposed research is a randomized, controlled trial that will recruit 60 community-dwelling Chinese older adults. The primary outcomes are PA (measured by FitBit step counts over time, and proportion meeting federal recommendations for moderate physical activity (MPA)). Secondary outcomes include patient-reported outcomes, and patient-reported experience measures. We aim to evaluate the acceptability of this enhanced program, and determine the feasibility of WeChat as a platform for increasing PA. The primary outcome will be analyzed using mixed effects ANOVA, and latent growth curve modeling. Secondary outcomes will be analyzed using linear regression. Semi-structured int