Evaluating Health Outcomes of AI-Based Fitness Wearables & App Programs in Elderly With Cognitive Decline

The University of Tennessee, Knoxville

Status: Not yet enrolling

Conditions: Older Adults With Cognitive Decline; Physical Inactivity; Physical Activity; AI-Based Fitness; Cognitive Decline; Wearables; Older Adults

Treatments: Other: Fitness app for self-efficacy; Other: Health education app targeting outcome expectations; Other: Social network via app for social support

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT07207993

UTK IRB-25-09047-XP

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 64 estimated patients

Sex: All

Ages: 65+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Participant must be at least 65 years of age older
• Participant must be living alone in the U.S. for the next 6 months
• Participant must have report mild cognitive decline [We will use a short self-report AD8 measure of cognitive concerns. Those scoring positive on the AD8 (≥2) will qualify as mild cognitive decline];
• Participant must own an Android/Apple smartphone
• Participant must have access to internet or Wi-Fi access
• Participant must be capable of engaging in some PA as determined by the PA Readiness Questionnaire or physician approval
• Participant must currently participate in weekly moderate-to-vigorous PA (MVPA) or less than 150 minutes
• Participant must have basic English communication skills.

Exclusion criteria

• Foreign residents or visitors

Trial design

Primary purpose: Prevention

Allocation: Randomized

Interventional model: Factorial Assignment

Masking: Double Blind

---

64 participants in 8 patient groups

Access to all applications

Experimental group

Description:

Condition 1: Participant are provided with the prescription application (application1), social application (application 2), and health tips application (application 3).

Treatment:

Other: Social network via app for social support

Other: Health education app targeting outcome expectations

Other: Fitness app for self-efficacy

Access to application 1 & 2

Experimental group

Description:

Condition 2: Participant are provided with the prescription application, social application, but they aren't provided with the health tips application.

Treatment:

Other: Social network via app for social support

Other: Fitness app for self-efficacy

Access to application 1 & 3

Experimental group

Description:

Condition 3: Participant are provided with the prescription application, and they aren't provided with the social application, but they are provided with the health tips application.

Treatment:

Other: Health education app targeting outcome expectations

Other: Fitness app for self-efficacy

Access to application 1 only

Experimental group

Description:

Condition 4: Participant are provided with the prescription application, but aren't provided with the social application, and the health tips application.

Treatment:

Other: Fitness app for self-efficacy

Access to application 2 & 3

Experimental group

Description:

Condition 5: Participant are not provided with the prescription application, but they are provided with the social application, and the health tips application.

Treatment:

Other: Social network via app for social support

Other: Health education app targeting outcome expectations

Access to application 2 only

Experimental group

Description:

Condition 6: Participant are not provided with the prescription application, but they are provided with the social application, and they aren't provided with the health tips application.

Treatment:

Other: Social network via app for social support

Access to application 3 only

Experimental group

Description:

Condition 7: Participant are not provided with the prescription application, or the social application, but they are provided with the health tips application.

Treatment:

Other: Health education app targeting outcome expectations

No access to any application

No Intervention group

Description:

Condition 8: Participant are not provided with the prescription application, or the social application, or with the health tips application.