AI-Based Personalized Exercise Prescription Through Mobile Health on Physical Activity and Health Outcomes in Older Adults: A Feasibility Study

NCT07768670 | Recruiting

• 1 other identifier: STUDY00000770
• Study Type: interventional
• Target: 20
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of this research is to investigate the feasibility, acceptability, appropriateness, and preliminary efficacy of a wearable and artificial intelligence-driven mobile application-based exercise prescription grounded in self-determination theory (SDT) and behavioral change techniques (BCTs) among older adults. As no data exists on the feasibility of AI-driven exercise programs grounded in SDT and multiple BCTs specifically for older adults, this will be a pioneering study to explore the feasibility of wearable and AI-driven exercise program protocols and the rates of acceptance and appropriateness of the exercise prescription intervention among older adults. In addition, this study will test the preliminary efficacy of the intervention on physical activity (PA), mental health, and quality of life. This study follows the National Institute of Health (NIH) stage model, and it represents stage 1b of the NIH stage model, which emphasizes the feasibility and actionable processes for delivering a new health intervention. Specifically, this study will: 1.Evaluate the research protocol feasibility of a wearable and AI-driven mobile application-based exercise prescription grounded in SDT and BCTs (of goal setting, self-monitoring, graded task, and demonstration) on older adults over 8 weeks 2. Evaluate the acceptability and appropriateness of a wearable, AI-driven, mobile application-based exercise prescription grounded in SDT and BCTs among older adults over 8 weeks. 3. Evaluate the preliminary impact of a wearable and AI-driven mobile application-based exercise prescription grounded in SDT and BCTs on older adult PA (steps per day and daily duration of light PA and Moderate-vigorous PA), sedentary time, sleep, mental health (depression and anxiety), and quality of life.

Conditions

Feasibility Studies; Physical Activity; Mental Health (Depression); Anxiety; Quality of Life

Keywords

AI-based intervention; mobile health technology; wearable; fitness app; feasibility study; study acceptability and appropriateness; older adult; age 65 years; 60 years

Outcome Measures

Primary Outcomes (6)

• Recruitment

  Number screened/month per recruitment method

  Pre-intervention

• Enrollment

  Number excluded by criterion; Number that decline participation; Reason for declining participation (open-ended); Average time from screening to enrollment (minutes/hours/days/month).

  Pre-intervention

• Participant Dropout Rate

  Number dropouts from AI-based exercise intervention; Reason for dropout (open-ended).

  Baseline (week 1) - Follow up (Week 10)

• Retention

  Number that completed the intervention program

  Baseline (week 1) - Follow up (Week 10)

• Adherence

  Percentage of the exercise program completed weekly (%)

  Baseline (week 1) - Follow up (Week 10)

• Intervention Feasibility, Acceptability, and Appropriateness

  Intervention Feasibility (ease of delivery). will be assessed using the Weiner et al. (2017)1 validated questionnaire for the feasibility of intervention measure (FIM). This questionnaire contains 4-item questions on a 5-point Likert scale (completely disagree \[1\]-completely agree \[5\]), with a score range of 1-5. Intervention Acceptability (assessing participant satisfaction). will be obtained after the intervention (8 weeks) on a 1-5 Likert scale of 4-item questions, including "The exercise program delivered by the app meets my approval" using the Acceptability of Intervention Measure (AIM) by Weiner et al., 20171. Appropriateness (perceived fit for rural older adults). will be assessed using the Weiner et al., 20171 intervention appropriateness measure (IAM). The IAM is on 1-5 Likert scale of 4-item questions, including "The exercise program seems fitting", "The exercise program seems suitable". The AIM and IAM are robust, with Cronbach alphas of 0.89 and 0.87, respectively.

  At follow up (week 10)

Secondary Outcomes (5)

• Fitbit Daily Step

  Baseline (week 0) and Follow up (week 10)

• Physical Activity

  Baseline (week 1) and Follow up (Week 10)

• Depression

  Baseline (week 0) and Follow up (week 10)

• Anxiety

  Baseline (week 0) and Follow up (week 10)

• Older Adult Quality of Life

  Baseline (week 0) and Follow up (week 10)

Study Arms (1)

Single-arm

EXPERIMENTAL

All participants will be provided the researcher-developed sFitRx fitness application and a Fitbit Flex 2 tracker. sFitRx will provide participants with an established daily and weekly exercise prescription program based on each participant's: (1) daily step goals, (2) previous week's PA, as collected by the Fitbit Flex 2; and (3) current physical conditioning and well-being, as collected by sFitRx. The sFitRx will provide participants with a video demonstration of warm up exercise and all prescribed exercise types, including aerobics, resistance, balance, and flexibility exercises. These exercises follow a graded task BCT. For instance, a participant with a low baseline PA might be prescribed a beginner programs with a base (entry) level of 1.1. However, the sFitRx will progressively modify the individual exercise program based on the % of exercise completed, daily goal and other metrics.

Other: Fitness app powered by AI and wearable device

Interventions

Fitness app powered by AI and wearable device OTHER

All participants will be provided the researcher-developed sFitRx mHealth application. sFitRx will provide participants with an established daily and weekly exercise prescription program based on each participant's: (1) daily step goals, (2) previous week's PA, as collected by the Fitbit Flex 2; and (3) current physical conditioning and well-being, as collected by sFitRx. The sFitRx will provide participants with a video demonstration of warm up exercise and all prescribed exercise types, including aerobics, resistance, balance, and flexibility exercises. These exercises follow a graded task BCT. The exercise program delivered by the sFitRx app is grounded in self-determination theory, most especially in satisfying the basic psychological needs that drive intrinsic motivation.

Single-arm

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Older Adult (65+)

You may qualify if:

• Be aged 65 years or older.
• Own a smartphone (Android or iOS) compatible with study applications.
• Have reliable access to the internet or Wi-Fi.
• Demonstrate basic digital literacy sufficient to operate a smartphone (e.g., ability to open applications, navigate simple interfaces, and follow on-screen instructions), with or without initial guidance from the research staff.
• Be able to safely engage in physical activity, as determined by the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) or physician approval if indicated.
• Report engaging in less than 150 minutes of moderate-to-vigorous physical activity (MVPA) per week.
• Be able to communicate in English sufficiently to complete study procedures and assessments.

You may not qualify if:

• Are younger than 65 years of age.
• Do not own a compatible smartphone (Android or iOS) or do not have reliable internet/Wi-Fi access.
• Are deemed unable to safely participate in physical activity based on the screening process (including PAR-Q+, STEADI, and research staff assessment), or do not obtain required physician clearance when indicated.
• Currently meet or exceed 150 minutes of moderate-to-vigorous physical activity (MVPA) per week.
• Are unable to communicate in English sufficiently to understand study procedures, provide informed consent, or complete study-related assessments.
• Have any medical, physical, or cognitive condition that, in the judgment of the research team, would make participation unsafe or prevent completion of study procedures.

Sponsors & Collaborators

• The University of Tennessee, Knoxville: lead

Study Sites (1)

Kinesiology, Recreation, and Sport Studies (865) 974-3340 krss@utk.edu

Knoxville, Tennessee, 37996-2700, United States

RECRUITING

MeSH Terms

Conditions

Motor Activity; Psychological Well-Being; Depression; Anxiety Disorders

Interventions

Wearable Electronic Devices

Condition Hierarchy (Ancestors)

Behavior; Personal Satisfaction; Behavioral Symptoms; Mental Disorders

Intervention Hierarchy (Ancestors)

Electrical Equipment and Supplies; Equipment and Supplies

Study Officials

• Zan Gao, PhD

  University Of Tennessee,knoxville

  PRINCIPAL INVESTIGATOR

Central Study Contacts

John Oginni, MS

CONTACT

8659747971; pael@utk.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 3, 2026
• First Posted: August 17, 2026
• Study Start (Estimated): October 1, 2026
• Primary Completion (Estimated): December 20, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: August 20, 2026

Record last verified: 2026-08

Locations

US (1)