Older Adults With Cognitive DeclineNCT07207993
Evaluating Health Outcomes of AI-Based Fitness Wearables and App Programs in Older Adults Living Alone With Cognitive Decline
A study testing Fitness app for self-efficacy, Social network via app for social support and Health education app targeting outcome expectations in people with Older Adults With Cognitive Decline and Older Adults, run by The University of Tennessee, Knoxville at 2 sites in United States.
Trial 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). Read more about this study
- Who is it for?
- Ages 65 years and older
- Conditions the study is for
- Older Adults With Cognitive Decline, Older Adults, AI-Based Fitness
- What is being tested?
- Fitness app for self-efficacy, Social network via app for social support and Health education app targeting outcome expectations
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 64 participants
- Where
- 2 sitesUnited States
- Study dates
- 8 Jun 2026to 8 Jun 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 years and older
- All sexes
- No healthy volunteers
- Older adult
Who may be able to join
- 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.
Who cannot join
- Foreign residents or visitors
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United States2 sites, 2 recruiting
- Not yet recruiting
University of Tennessee
Knoxville, Tennessee, 37920
Kinesiology, Recreation, and Sport Studies, (865) 974-3340, krss@utk.edu
Zan Gao, PhD
- Recruiting
The University of Tennessee, Knoxville. Health, Recreation, and Physical Education Building
Knoxville, Tennessee, 37996
About this study
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.
Read the 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
- Being tested
- Fitness app for self-efficacy, Social network via app for social support, Health education app targeting outcome expectations
- Keywords
- Older adults, Cognitive decline, AI-Based Fitness, Physical Activity, Physical Inactivity, Living alone with cognitive decline, LACD, U.S Older adults
- Medical subject headings
- Cognitive Dysfunction, Motor Activity, Sedentary Behavior
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Fitness app for self-efficacyOther
AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.
Social network via app for social supportOther
Participants will be provided access to a social network via app. This targets social support.
Health education app targeting outcome expectationsOther
Participants are provided with an app-based health education. This targets outcome expectations.
| Study group | Role | What they receive |
|---|---|---|
| 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). Receives: Other: Fitness app for self-efficacy; Other: Social network via app for social support; Other: Health education app targeting outcome expectations |
| 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. Receives: Other: Fitness app for self-efficacy; Other: Social network via app for social support |
| 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. Receives: Other: Fitness app for self-efficacy; Other: Health education app targeting outcome expectations |
| 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. Receives: Other: Fitness app for self-efficacy |
| 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. Receives: Other: Social network via app for social support; Other: Health education app targeting outcome expectations |
| 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. Receives: Other: Social network via app for social support |
| 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. Receives: Other: Health education app targeting outcome expectations |
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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)
Measured: 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.
Measured: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Secondary outcomes (3)
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)
Measured: 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
Measured: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Cognition
To assess older adults' cognition (thinking and memory), we will use the 5-minute Montreal Cognitive Assessment (MoCA).
26 - 30: Normal; Indicates health cognitive functioning for most adults.
18 - 25: Mild Cognitive Impairment (MCI); Shows noticeable memory or thinking issues that do not severely disrupts daily life.
10 - 17: Moderate Cognitive Impairment; Suggests a heightened progression towards early-to-mid stage dementia.
Less than 10: Severe Cognitive Impairment.
Measured: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Oct 2025
- Study start8 Jun 2026
- Primary completion8 Jun 2028 (estimated)
- Study completion8 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Older Adults With Cognitive DeclineStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Factorial
- Masking
- Double blind
- Who is masked
- Participant, Investigator
- Enrolment
- 64 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The University of Tennessee, Knoxville
- Sponsor type
- University, hospital or other
- Collaborators
- Arizona State University, Oregon Research Institute, National Institute on Aging (NIA)
- Responsible party
- Principal investigator, Zan Gao, The University of Tennessee, Knoxville
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07207993
- Sponsor's protocol ID
- STUDY00000016
- Status last verified
- Jun 2026
- First submitted
- 19 Sep 2025
- Last updated
- 15 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 15 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating Health Outcomes of AI-Based Fitness Wearables and App Programs in Older Adults Living Alone With Cognitive Decline
- ClinicalTrials.gov ID
- NCT07207993
- Acronym / sponsor ID
- STUDY00000016
- Conditions
- Older Adults With Cognitive Decline, Older Adults, AI-Based Fitness, Wearables, Cognitive Decline, Physical Activity
- Keywords
- Older adults, Cognitive decline, AI-Based Fitness, Physical Activity, Physical Inactivity, Living alone with cognitive decline, LACD, U.S Older adults
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 64 participants (estimated)
- Lead sponsor
- The University of Tennessee, KnoxvilleUniversity, hospital or other
- Collaborators
- Arizona State University, Oregon Research Institute, National Institute on Aging (NIA)
- Study start
- 8 Jun 2026 (actual)
- Primary completion
- 8 Jun 2028 (estimated)
- Study completion
- 8 Jun 2028 (estimated)
- First posted
- 6 Oct 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Factorial
- Masking
- Double blind (participant, investigator)
- Number of arms
- 8
| Arm | Type | Receives |
|---|---|---|
| Access to all applications | Experimental | Other: Fitness app for self-efficacy, Other: Social network via app for social support, Other: Health education app targeting outcome expectationsCondition 1: Participant are provided with the prescription application (application1), social application (application 2), and health tips application (application 3). |
| Access to application 1 & 2 | Experimental | Other: Fitness app for self-efficacy, Other: Social network via app for social supportCondition 2: Participant are provided with the prescription application, social application, but they aren't provided with the health tips application. |
| Access to application 1 & 3 | Experimental | Other: Fitness app for self-efficacy, Other: Health education app targeting outcome expectationsCondition 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. |
| Access to application 1 only | Experimental | Other: Fitness app for self-efficacyCondition 4: Participant are provided with the prescription application, but aren't provided with the social application, and the health tips application. |
| Access to application 2 & 3 | Experimental | Other: Social network via app for social support, Other: Health education app targeting outcome expectationsCondition 5: Participant are not provided with the prescription application, but they are provided with the social application, and the health tips application. |
| Access to application 2 only | Experimental | Other: Social network via app for social supportCondition 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. |
| Access to application 3 only | Experimental | Other: Health education app targeting outcome expectationsCondition 7: Participant are not provided with the prescription application, or the social application, but they are provided with the health tips application. |
| 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. |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
Key inclusion criteria (8 listed)
- 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
Key exclusion criteria (1 listed)
- Foreign residents or visitors
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Fitness app for self-efficacyAI-driven personalized exercise prescription via a fitness app. This targets self-efficacy. | Other | Access to all applications, Access to application 1 & 2, Access to application 1 & 3, Access to application 1 only |
| Social network via app for social supportParticipants will be provided access to a social network via app. This targets social support. | Other | Access to all applications, Access to application 1 & 2, Access to application 2 & 3, Access to application 2 only |
| Health education app targeting outcome expectationsParticipants are provided with an app-based health education. This targets outcome expectations. | Other | Access to all applications, Access to application 1 & 3, Access to application 2 & 3, Access to application 3 only |
Primary outcome
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.
3 secondary outcomes
Quality of life (QoL)
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Psychosocial wellbeing
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Cognition
Time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jun 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Fitness app for self-efficacy, Social network via app for social support and Health education app targeting outcome expectations in people with Older Adults With Cognitive Decline and Older Adults, run by The University of Tennessee, Knoxville at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 15 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Zan Gao, PhD (865-974-7971, zan@utk.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Fitbit MVPA (time frame: Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jun 2026.