A Smartphone App to Improve Physical Activity

Summary

Participation Requirements

Description

In this study, we will optimize a set of tailored specialty app features designed to be paired with a physical activity (PA)-tracking app to boost older adults' PA. This package, termed the MovingUp suite, is distinct from generic fitness apps because it blends a set of specialized components that r...

In this study, we will optimize a set of tailored specialty app features designed to be paired with a physical activity (PA)-tracking app to boost older adults' PA. This package, termed the MovingUp suite, is distinct from generic fitness apps because it blends a set of specialized components that reflect empirically supported constructs from social cognitive and stereotype embodiment theory with evidence-based behavior change techniques (e.g., self-regulation) foundational to basic activity monitoring. Specialty features include: (a) explicit and implicit messaging to promote positive aging views; (b) sedentary activity monitoring with motivational messaging and peer suggestions; and (c) tailored messaging to increase the intensity level of everyday activities and overcome barriers. We will utilize a highly efficient, innovative methodological approach-Multiphase Optimization Strategy (MOST)-to provide an experimental context for evaluating the viability of each MovingUp specialty feature. Aim 1: Assess the feasibility and acceptability of the three MovingUp specialty features. We will first examine MovingUp's feasibility and acceptability in three groups of five older adults (aged 65-84 years). A basic PA-tracking app plus one of three specialty features will be introduced-a different feature per group-at an orientation session. Groups will then test their assigned specialty feature with the PA tracker for two weeks. This step will involve real-time user data collection, check-ins via phone, and follow-up focus groups. Feasibility and acceptability will be determined by analyzing participants' usage patterns, evaluations of MovingUp features (based on a health technology usability scale and focus group interviews), and self-reported facilitators and barriers to successful app use. Our team will review the data and integrate changes as needed, producing an upgraded prototype to be assessed in Aim 2. Aim 2: Conduct a pilot test to examine performance characteristics and PA-relevant outcomes of MovingUp's specialty features. Aim 2 includes the MOST Screening Phase: theory-guided experimentation to identify viable components within a multifaceted preliminary intervention plan. Using a factorial design as specified in MOST procedures, 100 underactive older adults (i.e., accumulating <150 minutes of moderate intensity activity per week) will be randomly assigned to one of eight conditions which reflect all possible combinations of presence vs. absence of the three respective specialty features, given usage of a PA tracker app. At the end of a four-month intervention period, for each specialty feature we will examine changes from baseline in PA-related outcomes including: objective PA (primary outcome), sedentary activity time, self-reported PA, and functional mobility. We will also examine the app components' relationships to theoretically postulated mediating constructs (self-efficacy, self-regulation, outcome expectation, social support, aging self-perception, and views of aging). In addition, we will document usage rate, sustained usage, and perceived usefulness for achieving PA goals for each suite component. Aim 3: Synthesize information from Aim 2 to design an optimized MovingUp suite to be evaluated in a future RCT. Our study team will interpret and synthesize the array of resulting data to derive an optimized MovingUp suite. A set of pre-specified criteria will be used to guide selection of components in the optimized app. Using preliminary efficacy data, the stage will be set for a fully powered RCT of MovingUp's beneficial effects in comparison to alternate technologies such as web-based or mHealth solutions. This project will help establish a methodological foundation for future attempts to enhance PA apps via the addition of theoretically based component features. Moreover, it will provide insights into the theoretical underpinnings of successful PA interventions for older adults, leading to information that transcends any single technology-based solution.

Tracking Information