Optimizing and Testing an Individualized and Adaptive Physical Activity Digital Health Intervention: Protocol of Control Optimization Trial embedded within a Randomized Controlled Trial (Preprint)
Authors: Kim Meelim , Mansour-Assi Shadia , El Mistiri Mohamed , Park Junghwan , Banerjee Sarasij , Khan Owais , De La Torre Steven , Higgins Michael , Godino Job , Patrick Kevin , Nebeker Camille , Jane Sonia , Klasnja Predrag , E. Rivera Daniel , Hekler Eric 
Published: December 2024

BACKGROUND

Strong evidence indicates physical activity (PA) reduces risk of various cancers, yet only a third of adults in the US meet guidelines for PA. While effective PA interventions exist, interventions often work only for some individuals or only for a limited time. Thus, there is a need for digital health interventions (DHIs) that account for dynamic, idiosyncratic PA determinants to support each person’s PA. We hypothesize that supporting individuals with their personal PA goals requires a personalized intervention that both supports each person in forming daily habits of walking more coupled with the development of personalized knowledge, skills, and practices in engaging in exercise routines. We operationalized these adaptive features via a digital health intervention, called YourMove, that uses a control systems approach to support personalized habit formation and via a self-experimentation approach to develop personalized knowledge, skills, and practices.

OBJECTIVE

The primary aim is to evaluate differences in minutes of moderate to vigorous physical activity (MVPA) per week at 12-month, comparing our personalized intervention, called YourMove, with an active control that is similar, but without personalization of the intervention components and mimics best-in-class digital health worksite wellness programs.

METHODS

The YourMove Study is a 12-month randomized controlled trial (RCT) that includes 386 inactive adults aged 25-80 years. All participants receive, 1) a Fitbit Versa smartwatch and corresponding smartphone application, 2) weekly PA goal suggestions and feedback, behavioral change strategies, and reminders via text messaging, and 3) up to $50 in incentives for reaching daily step goals. Participants randomized to the active control group, modeled after worksite wellness programs, receive all the elements described in addition to a static daily step goal and static point rewards. Participants randomized to the intervention group receive, 1) a “habit formation” element with daily personalized step goals and personalized point rewards generated by “Control Optimization Trial” (COT) approach, and 2) a “knowledge, skills, and practices development” element featuring a self-guided self-experimentation tool that helps individuals find strategies to improve MVPA. The primary outcome is objectively assessed weekly minutes of MVPA, assessed via Actigraph.

RESULTS

Recruitment began in October 2022 and concluded in August 2024. Data collection will conclude in August 2025 with results expected by the early 2026.

CONCLUSIONS

We hypothesize that the intervention group will show greater improvement in MVPA than the active control group at 12 months. If the hypothesis is supported, it will provide compelling evidence to suggest that personalized and perpetually adaptive support can enhance PA more effectively than intervention elements commonly used in digital health worksite wellness programs. If successful, results will provide justification to explore both the COT approach and self-experimentation approach for other complex, idiosyncratic, and dynamic behaviors such as weight management, smoking, or substance abuse.

CLINICALTRIAL

ClinicalTrials.gov NCT05598996