AI-Based Personalized Exercise Prescription Through Mobile Health on Physical Activity and Health Outcomes in Older Adults: A Feasibility Study
Purpose
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
Eligibility
- Eligible Ages
- Over 65 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- 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.
Exclusion Criteria
- 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.
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- N/A
- Intervention Model
- Single Group Assignment
- Primary Purpose
- Prevention
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
Experimental Single-arm |
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. |
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Recruiting Locations
More Details
- Status
- Recruiting
- Sponsor
- The University of Tennessee, Knoxville
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
