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

NCT ID: NCT07207993

Last Updated: 2025-10-06

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 64 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-11-01
• Study Completion Date: 2027-11-01

Brief Summary

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.

Detailed 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.

Conditions

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

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: FACTORIAL
• Primary Study Purpose: PREVENTION
• Blinding Strategy: DOUBLE

Study Groups

Access to application 2 & 3

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

Group Type: EXPERIMENTAL

Social network via app for social support

Intervention Type: OTHER

Participants will be provided access to a social network via app. This targets social support.

Health education app targeting outcome expectations

Intervention Type: OTHER

Participants are provided with an app-based health education. This targets outcome expectations.

Access to application 2 only

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.

Group Type: EXPERIMENTAL

Social network via app for social support

Intervention Type: OTHER

Participants will be provided access to a social network via app. This targets social support.

Access to application 3 only

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

Group Type: EXPERIMENTAL

Health education app targeting outcome expectations

Intervention Type: OTHER

Participants are provided with an app-based health education. This targets outcome expectations.

No access to any application

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

Group Type: NO_INTERVENTION

No interventions assigned to this group

Access to all applications

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

Group Type: EXPERIMENTAL

Fitness app for self-efficacy

Intervention Type: OTHER

AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.

Social network via app for social support

Intervention Type: OTHER

Participants will be provided access to a social network via app. This targets social support.

Health education app targeting outcome expectations

Intervention Type: OTHER

Participants are provided with an app-based health education. This targets outcome expectations.

Access to application 1 & 2

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

Group Type: EXPERIMENTAL

Fitness app for self-efficacy

Intervention Type: OTHER

AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.

Social network via app for social support

Intervention Type: OTHER

Participants will be provided access to a social network via app. This targets social support.

Access to application 1 & 3

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.

Group Type: EXPERIMENTAL

Fitness app for self-efficacy

Intervention Type: OTHER

AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.

Health education app targeting outcome expectations

Intervention Type: OTHER

Participants are provided with an app-based health education. This targets outcome expectations.

Access to application 1 only

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

Group Type: EXPERIMENTAL

Fitness app for self-efficacy

Intervention Type: OTHER

AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.

Interventions

Fitness app for self-efficacy

AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.

Intervention Type: OTHER

Social network via app for social support

Participants will be provided access to a social network via app. This targets social support.

Intervention Type: OTHER

Health education app targeting outcome expectations

Participants are provided with an app-based health education. This targets outcome expectations.

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Arizona State University

OTHER

Sponsor Role: collaborator

Oregon Research Institute

OTHER

Sponsor Role: collaborator

National Institute on Aging (NIA)

NIH

Sponsor Role: collaborator

The University of Tennessee, Knoxville

OTHER

Sponsor Role: lead

Responsible Party

Zan Gao

Professor and Department Head

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

University of Tennessee

Knoxville, Tennessee, United States

Countries

United States

Central Contacts

Zan Gao, PhD

Role: CONTACT

zan@utk.edu

865-974-7971

Facility Contacts

Kinesiology, Recreation, and Sport Studies

Role: primary

krss@utk.edu

(865) 974-3340

Other Identifiers

UTK IRB-25-09047-XP

Identifier Type: -

Identifier Source: org_study_id