Using Momentary Measures to Understand Physical Activity Adoption and Maintenance: Protocol for a Longitudinal Study.

Background: Physical inactivity is prevalent among adults in the United States and is related to various health disparities. The search for effective policies and interventions to promote physical activity (PA) is severely hampered by the paucity of research on the mechanisms underlying PA behavior change. Objective: This paper describes a research protocol that uses mobile health technology to examine the influence of contextual and environmental factors and acute momentary precipitants on PA adoption and maintenance among Pacific Islanders in the United States. Methods: The study is guided by an overarching conceptual framework derived from models of the social and environmental determinants of health, social cognitive theories of behavior change, and prior empirical findings. Participants will be assessed using real-time, field-based, state-of-the-art methodologies consisting of MotionSense, ecological momentary assessment, and GPS tracking. MotionSense tracks behavioral and physiological data in real time and can objectively detect PA behaviors of participants. GPS tracking permits real-time mapping of an individual's space-time trajectories and relevant environmental exposures and characteristics (eg, proximity to PA facilities and neighborhood safety) using Geographic Information System data. Principal outcomes of interest are PA adoption and PA maintenance. Results: This study was funded by the National Cancer Institute of the National Institutes of Health in August 2023. Data collection started on May 23, 2025, and is expected to finish by March 2028. As of August 10, 2026, the project has recruited all 150 participants. Data analysis is ongoing, and results are expected to be published in May 2028. Conclusions: This is among the first studies to link objective and momentary indexes of PA to key environmental and psychosocial factors in PA behavior studies. The comprehensive, multi-method approach addresses 2 long-standing limitations in PA research: the reliance on self-reported outcome measures and the use of static residential locations as proxies for neighborhood exposure. In addition, this study is among the first to apply dynamic prediction models, a novel statistical approach well suited to the high-frequency, intensive longitudinal data generated by real-time mobile health assessment. The findings will provide actionable evidence to inform policies and interventions aimed at reducing PA-related health disparities among Pacific Islanders and other racial and ethnic groups that experience similar health problems.