Skip to main content
Clinical Trials/NCT07207993
NCT07207993Not yet recruitingNot Applicable

Evaluating Health Outcomes of AI-Based Fitness Wearables and App Programs in Older Adults Living Alone With Cognitive Decline

The University of Tennessee, Knoxville1 site in 1 country64 target enrollmentStarted: April 1, 2026Last updated:
Conditions
Interventions

Trial Snapshot

Phase
Not Applicable
Status
Not yet recruiting
Sponsor
Enrollment
64
Locations
1
Primary Endpoint
Fitbit MVPA

Study Overview

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.

Study Design

Study Type
Interventional
Allocation
Randomized
Intervention Model
Factorial
Primary Purpose
Prevention
Masking
Double (Participant, Investigator)

Masking Description

The design is blinded, with all investigators except the biostatistician unaware of group and intervention assignments.

Eligibility Criteria

Ages
65 Years to — (Older Adult)
Sex
All
Accepts Healthy Volunteers
No

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

Arms & Interventions

Access to all applications

Experimental

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

Intervention: Fitness app for self-efficacy (Other)

Access to all applications

Experimental

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

Intervention: Social network via app for social support (Other)

Access to all applications

Experimental

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

Intervention: Health education app targeting outcome expectations (Other)

Access to application 1 & 2

Experimental

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

Intervention: Fitness app for self-efficacy (Other)

Access to application 1 & 2

Experimental

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

Intervention: Social network via app for social support (Other)

Access to application 1 & 3

Experimental

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.

Intervention: Fitness app for self-efficacy (Other)

Access to application 1 & 3

Experimental

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.

Intervention: Health education app targeting outcome expectations (Other)

Access to application 1 only

Experimental

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

Intervention: Fitness app for self-efficacy (Other)

Access to application 2 & 3

Experimental

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

Intervention: Social network via app for social support (Other)

Access to application 2 & 3

Experimental

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

Intervention: Health education app targeting outcome expectations (Other)

Access to application 2 only

Experimental

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.

Intervention: Social network via app for social support (Other)

Access to application 3 only

Experimental

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

Intervention: Health education app targeting outcome expectations (Other)

No access to any application

No Intervention

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

Outcomes

Primary Outcomes

Fitbit MVPA

Time Frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).

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).

Physical Activity

Time Frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).

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)

Mechanism of behavior change (MoBC) variables

Time Frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).

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.

Secondary Outcomes

  • Quality of life (QoL)(Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).)
  • Psychosocial wellbeing(Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).)
  • Cognition(Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).)

Investigators

Sponsor
The University of Tennessee, Knoxville
Sponsor Class
Other
Responsible Party
Principal Investigator
Principal Investigator

Zan Gao

Professor and Department Head

The University of Tennessee, Knoxville

Study Sites (1)

Loading locations...

Similar Trials