An interventional study of Fitness app powered by AI and wearable device in Feasibility Studies, Physical Activity and Mental Health (Depression), sponsored by The University of Tennessee, Knoxville. Recruiting at 1 site in United States. Open to participants aged 65 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-08-20.
Sponsored by The University of Tennessee, Knoxville · Not applicable, Interventional, and Prevention
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.
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
8,577 studies on the registry are indexed under Depression; 1,720 are open to participants now.
This study's planned enrollment of 20 is below the median of 81 across 7,063 interventional studies indexed under Depression.
Browse Depression studies →The University of Tennessee, Knoxville is the lead sponsor of 95 studies on the registry; 6 are open to participants now.
Compared within Depression, this study's primary condition as ClinicalTrials.gg classifies it; the registry's first condition term is Motor Activity. The counts are of registry records indexed under each term, refreshed daily. How we classify →
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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
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.
Recruitment
Number screened/month per recruitment method
Time frame: 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).
Time frame: Pre-intervention
Participant Dropout Rate
Number dropouts from AI-based exercise intervention; Reason for dropout (open-ended).
Time frame: Baseline (week 1) - Follow up (Week 10)
Retention
Number that completed the intervention program
Time frame: Baseline (week 1) - Follow up (Week 10)
Adherence
Percentage of the exercise program completed weekly (%)
Time frame: 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.
Time frame: At follow up (week 10)
Fitbit Daily Step
Fitbit Flex 2 will be used to assess participants' daily steps
Time frame: 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)
Time frame: 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
Time frame: 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
Time frame: 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)
Time frame: Baseline (week 0) and Follow up (week 10)
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The University of Tennessee, Knoxville