Feasibility StudiesNCT07768670
AI-Based Personalized Exercise Prescription Through Mobile Health on Physical Activity and Health Outcomes in Older Adults: A Feasibility Study
A study testing Fitness app powered by AI and wearable device in people with Feasibility Studies and Physical Activity, run by The University of Tennessee, Knoxville at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 65 years and olderHealthy volunteers can take part
- Conditions the study is for
- Feasibility Studies, Physical Activity, Mental Health (Depression)
- What is being tested?
- Fitness app powered by AI and wearable device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 20 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Oct 2026to 31 Dec 2026
- 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
- Healthy volunteers accepted
- Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Kinesiology, Recreation, and Sport Studies (865) 974-3340 krss@utk.edu
Knoxville, Tennessee, 37996-2700
John Oginni, (865) 974-3340, joginni@vols.utk.edu
About this study
Official title: Artificial Intelligence-based Personalized Exercise Prescription Through Mobile Health on Physical Activity and Health Outcomes in Older Adults: A Feasibility Study
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.
Read the detailed description
By the late 2070s, the global population aged 65 and older is projected to reach 2.2 billion, surpassing the number of children under age 18. In 2020, about 1 in 6 people in the United States (U.S) were age 65 and over, compared to less than 1 in 20 in 1920. The number of U.S. adults 65 years and over is projected to increase to 82 million by 2050, with the older group's share of the total population projected to rise to 23%. Ageing brings about an increased vulnerability to mental health challenges, and chronic conditions such as heart disease, cancer, diabetes, and Alzheimer's. Among adults ages 65 and older, more than 90% have at least one chronic condition and 88% have at least one multiple chronic condition. Chronic conditions contribute to over 75% of healthcare expenditures for individuals aged 65 and above in the U.S, amounting to over $1.5 trillion. An active lifestyle reduces the likelihood of developing chronic diseases and also enhances the overall health outcomes of the older adult population. Despite these benefits, a majority of older adults fail to meet the recommended U.S. physical activity guidelines (150 minutes of moderate intensity PA a week).
Mobile health (mHealth) technologies-such as smartphone applications (apps), wearables, and other mobile devices-have emerged as an increasingly adopted tool for promoting health among older adults. Several studies have demonstrated the potential of mHealth technologies to increase PA, reduce sedentary time, and improve health outcomes. However, findings have shown that the sustainability of mHealth-based exercise interventions remains uncertain. Recent reviews also emphasized the need for more personalized, adaptable mHealth solutions tailored to older adults' unique health conditions and physical ability to ensure better engagement and more durable behavior change. While recent studies show that personalized mHealth-based exercise programs outperform one-size-fits-all approaches, yet their impact is constrained by insufficient personalization data, low adherence, limited scalability, and inadequate integration of behavior-change techniques (BCTs).
Emerging evidence suggests that Artificial Intelligence (AI) can enhance PA. Integrating AI with mHealth tools-such as smartphone apps and wearable devices grounded in BCTs-offers a promising pathway to deliver an automated exercise program tailored to each person's PA goals, health status, and overall well-being, reaching many people at once with the potential of helping older adults adhere longer to an active lifestyle. Such an approach aligns with the growing emphasis on precision health and scalable interventions for aging populations. Despite this promise, the feasibility, acceptability, appropriateness and preliminary impact of an AI-driven, personalized exercise program delivered through mHealth and grounded in SDT and BCTs on older adults' PA and health outcomes remain unexplored.
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
- Conditions
- Feasibility Studies, Physical Activity, Mental Health (Depression), Anxiety, Quality of Life
- Being tested
- Fitness app powered by AI and wearable device
- Keywords
- AI-based intervention, mobile health technology, wearable, fitness app, feasibility study, study acceptability and appropriateness, older adult, age 65 years, 60 years
- Medical subject headings
- Motor Activity, Psychological Well-Being, Depression, Anxiety Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Fitness app powered by AI and wearable deviceOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Fitness app powered by AI and wearable device |
What is being measured
The primary outcome is the main question the study is designed to answer.
Recruitment
Number screened/month per recruitment method
Measured: 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).
Measured: Pre-intervention
Participant Dropout Rate
Number dropouts from AI-based exercise intervention; Reason for dropout (open-ended).
Measured: Baseline (week 1) - Follow up (Week 10)
Retention
Number that completed the intervention program
Measured: Baseline (week 1) - Follow up (Week 10)
Adherence
Percentage of the exercise program completed weekly (%)
Measured: 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.
Measured: At follow up (week 10)
Secondary outcomes (5)
Fitbit Daily Step
Fitbit Flex 2 will be used to assess participants' daily steps
Measured: Baseline (week 0) and Follow up (week 10)
Physical Activity
We will use the Physical Activity Scale for the Elderly to assess PA across leisure (6-item question), household (3-item question), and occupational domains (1-item question) over the past 7 days. Higher scores means more physical activity. (Low activity: <100; Moderate: 100-250; High: >250)
Measured: Baseline (week 1) and Follow up (Week 10)
Depression
Depression will be measured using Patient Health Questionnaire (PHQ-8), a 8-item symptom severity rating scale for depression (Kroenke et al., 2009). PHQ-8 is a validated diagnostic and severity measure of symptoms of depressive disorders.
Adults with scores of 0-4 are considered to have no or minimal symptoms of depression.
Score 5-9 are considered mild symptom Score 10-14 are considered moderate symptom Score 15-24 are considered severe symptom
Measured: Baseline (week 0) and Follow up (week 10)
Anxiety
Anxiety will be assessed using the Geriatric Anxiety Scale (GAS). GAS is a 10-item rating scale on a 4-Likert Scale (Not at all [0] to All of the time [3]). Item 1 through 10 are summed to provide a total score. The higher the score, the worse the anxiety outcome.
Score 1-6 are considered minimal symptom Score 7-9 are considered mild symptom Score 10 is considered moderate Score 12-30 are considered severe symptom
Measured: Baseline (week 0) and Follow up (week 10)
Older Adult Quality of Life
Quality of life (QoL) will be assessed using the Older Adult Quality of Life Scale.
Each of the 13 items is scored Strongly agree=1, Agree=2, Neither=3, Disagree=4, Strongly disagree=5. The items are summed for a total OPQOL-Brief score, then positive items are reverse coded, so that higher scores represented higher QoL.
Minimum: 13 (very poor quality of life) Maximum: 65 (excellent quality of life)
Measured: Baseline (week 0) and Follow up (week 10)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Aug 2026
- Study start1 Oct 2026 (estimated)
- Primary completion20 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Feasibility StudiesStudies 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
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 20 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The University of Tennessee, Knoxville
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Zan Gao, PhD, Principal investigator, University Of Tennessee,knoxville
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07768670
- Sponsor's protocol ID
- STUDY00000770
- Status last verified
- Aug 2026
- First submitted
- 3 Aug 2026
- Last updated
- 20 Aug 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-based Personalized Exercise Prescription Through Mobile Health on Physical Activity and Health Outcomes in Older Adults: A Feasibility Study
- ClinicalTrials.gov ID
- NCT07768670
- Acronym / sponsor ID
- STUDY00000770
- 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
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 20 participants (estimated)
- Lead sponsor
- The University of Tennessee, KnoxvilleUniversity, hospital or other
- Study start
- 1 Oct 2026 (estimated)
- Primary completion
- 20 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 17 Aug 2026 (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 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.
Detailed description
By the late 2070s, the global population aged 65 and older is projected to reach 2.2 billion, surpassing the number of children under age 18. In 2020, about 1 in 6 people in the United States (U.S) were age 65 and over, compared to less than 1 in 20 in 1920. The number of U.S. adults 65 years and over is projected to increase to 82 million by 2050, with the older group's share of the total population projected to rise to 23%. Ageing brings about an increased vulnerability to mental health challenges, and chronic conditions such as heart disease, cancer, diabetes, and Alzheimer's. Among adults ages 65 and older, more than 90% have at least one chronic condition and 88% have at least one multiple chronic condition. Chronic conditions contribute to over 75% of healthcare expenditures for individuals aged 65 and above in the U.S, amounting to over $1.5 trillion. An active lifestyle reduces the likelihood of developing chronic diseases and also enhances the overall health outcomes of the older adult population. Despite these benefits, a majority of older adults fail to meet the recommended U.S. physical activity guidelines (150 minutes of moderate intensity PA a week).
Mobile health (mHealth) technologies-such as smartphone applications (apps), wearables, and other mobile devices-have emerged as an increasingly adopted tool for promoting health among older adults. Several studies have demonstrated the potential of mHealth technologies to increase PA, reduce sedentary time, and improve health outcomes. However, findings have shown that the sustainability of mHealth-based exercise interventions remains uncertain. Recent reviews also emphasized the need for more personalized, adaptable mHealth solutions tailored to older adults' unique health conditions and physical ability to ensure better engagement and more durable behavior change. While recent studies show that personalized mHealth-based exercise programs outperform one-size-fits-all approaches, yet their impact is constrained by insufficient personalization data, low adherence, limited scalability, and inadequate integration of behavior-change techniques (BCTs).
Emerging evidence suggests that Artificial Intelligence (AI) can enhance PA. Integrating AI with mHealth tools-such as smartphone apps and wearable devices grounded in BCTs-offers a promising pathway to deliver an automated exercise program tailored to each person's PA goals, health status, and overall well-being, reaching many people at once with the potential of helping older adults adhere longer to an active lifestyle. Such an approach aligns with the growing emphasis on precision health and scalable interventions for aging populations. Despite this promise, the feasibility, acceptability, appropriateness and preliminary impact of an AI-driven, personalized exercise program delivered through mHealth and grounded in SDT and BCTs on older adults' PA and health outcomes remain unexplored.
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
Study design
- Primary purpose
- Prevention
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Single-arm | Experimental | Other: Fitness app powered by AI and wearable deviceAll 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. |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Older adult
Key inclusion criteria (7 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
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 powered by AI and wearable deviceAll 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. | Other | Single-arm |
Primary outcome
Recruitment
Time frame: Pre-intervention
Number screened/month per recruitment method
Enrollment
Time frame: Pre-intervention
Number excluded by criterion; Number that decline participation; Reason for declining participation (open-ended); Average time from screening to enrollment (minutes/hours/days/month).
Participant Dropout Rate
Time frame: Baseline (week 1) - Follow up (Week 10)
Number dropouts from AI-based exercise intervention; Reason for dropout (open-ended).
Retention
Time frame: Baseline (week 1) - Follow up (Week 10)
Number that completed the intervention program
Adherence
Time frame: Baseline (week 1) - Follow up (Week 10)
Percentage of the exercise program completed weekly (%)
Intervention Feasibility, Acceptability, and Appropriateness
Time frame: At follow up (week 10)
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.
5 secondary outcomes
Fitbit Daily Step
Time frame: Baseline (week 0) and Follow up (week 10)
Physical Activity
Time frame: Baseline (week 1) and Follow up (Week 10)
Depression
Time frame: Baseline (week 0) and Follow up (week 10)
Anxiety
Time frame: Baseline (week 0) and Follow up (week 10)
Older Adult Quality of Life
Time frame: Baseline (week 0) and Follow up (week 10)
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 20 Aug 2026.
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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.
Zan Gao, PhD principal investigator
University Of Tennessee,knoxville
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Fitness app powered by AI and wearable device in people with Feasibility Studies and Physical Activity, run by The University of Tennessee, Knoxville at 1 site 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 7 inclusion and 6 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 20 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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 John Oginni, MS (8659747971, pael@utk.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Recruitment (time frame: Pre-intervention). The study lists 6 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 20 Aug 2026.