The overarching goal of our research is to develop personalized and accessible healthy aging lifestyle interventions aimed at promoting physical activity (PA) and improving health among community-dwelling older adults living alone with cognitive decline (LACD). To achieve this goal, the purpose of this project is to determine whether wearable and app-based mHealth intervention component(s) will contribute to increased PA and improved health outcomes in older adults LACD. Our specific aims are to: identify and evaluate mHealth intervention components that practically and significantly contribute to enhanced mechanistic outcomes (e.g., self-efficacy, outcome expectations) and increased PA (primary outcome) in older adults LACD over a 6-month period; determine the optimal combinations of intervention components for future efficacy testing; elucidate the mechanism of behavioral change (MoBC) and potential outcomes of these intervention components, namely, the mediating effects of MoBC variables (e.g., self-efficacy, outcome expectations) on the relationship between intervention components and change in PA. The first two aims are primary and fully-powered. The third aim is exploratory. The aims will support a refined, data-driven intervention design for a subsequent larger trial.
Mobile health (mHealth) is a promising approach to improving health behaviors, defined as "health services and information delivered or enhanced through the Internet and related technologies." It includes disease prevention and management tools, remote interventions, personalized health monitoring, and mobile healthcare data access. With widespread technology adoption, researchers increasingly use wearable devices and apps to enhance health outcomes by promoting PA and reducing sedentary behavior. Wearable devices and fitness apps are now widely integrated into PA intervention programs, helping individuals adopt more active lifestyles. These tools track steps, activity duration, and progress, providing real-time feedback, goal-setting, and social integration to enhance motivation and behavior regulation. Notably, 21% of U.S. adults regularly use smartwatches or fitness trackers, making them feasible for PA interventions in older adults. RCTs have shown their positive effects on PA, QoL, and psychosocial well-being in older adults though some studies reported modest improvements. Recent advancements in data science and AI-driven mHealth interventions enable scalable, personalized exercise prescriptions. Personalized approaches, particularly those enhancing self-efficacy, yield better outcomes than generalized interventions. However, few studies have leveraged fitness wearables and apps for older adult LACD. This trial addresses this major weakness by implementing an AI-driven mHealth intervention for tailored precision health programs in older adult LACD.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
DOUBLE
Enrollment
64
AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.
Participants will be provided access to a social network via app. This targets social support.
Participants are provided with an app-based health education. This targets outcome expectations.
University of Tennessee
Knoxville, Tennessee, United States
Fitbit MVPA
Fitbit Inspire 3 Tracker will be used to assess participants' MVPA (active time which includes both fairly active time and very active time). Fairly active; duration associated with light intensity activities, i.e., walking, light cycling, housework (\~3-6 METs). Very active; duration associated with high intensity activities, i.e., running, aerobic workouts (\>6 METs).
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Physical Activity
We will use the Physical Activity Scale for the Elderly to assess PA. Higher scores means more physical activity. (Low activity: \<100; Moderate: 100-250; High: \>250)
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Mechanism of behavior change (MoBC) variables
Psychometrically validated questionnaires will be used to assess beliefs: self-efficacy, social support, and outcome expectations. Self-efficacy; low score indicates low confidence in ability to perform behavior, high score indicates strong confidence. Social support; low score indicates poor support from family or friends, high score indicates great support. Outcome expectation; low score indicates the belief that behavior won't help, high score indicates the belief that the behavior will lead to positive outcome.
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Quality of life (QoL)
We will select the brief Older People's Quality of Life questionnaire to assess quality of life (OPQOL-BRIEF). Minimum: 13 (very poor quality of life) Maximum: 65 (excellent quality of life)
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Psychosocial wellbeing
We will assess wellbeing using the World Health Organization Well-Being Index (WHO-5) questionnaire. 0 -12 - Low well-being; possible depression (screen positive) 13 - 19 - Moderate well-being 20 - 25 - High well-being
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Cognition
We will adopt a web-based self-administered instrument, NeuroCognitive Performance Test, to assess older adults' cognition. Low score: TBD High score: TBD
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
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