AI-Based Fitness, Cognitive Decline, Older Adults, Older Adults With Cognitive Decline, Physical Activity, Physical Inactivity, Wearables
Conditions
Keywords
Older adults, Cognitive decline, AI-Based Fitness, Physical Activity, Physical Inactivity, Living alone with cognitive decline, LACD, U.S Older adults
Brief 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). 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.
Interventions
OTHERFitness app for self-efficacy
AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.
OTHERSocial network via app for social support
Participants will be provided access to a social network via app. This targets social support.
OTHERHealth education app targeting outcome expectations
Participants are provided with an app-based health education. This targets outcome expectations.
Sponsors
The University of Tennessee, Knoxville
Arizona State University
Oregon Research Institute
National Institute on Aging (NIA)
Study design
Allocation
RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
PREVENTION
Masking
DOUBLE (Subject, Investigator)
Masking description
The design is blinded, with all investigators except the biostatistician unaware of group and intervention assignments.
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* Foreign residents or visitors
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Fitbit MVPA | 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 | 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 | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Quality of life (QoL) | Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point). | 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) |
| Psychosocial wellbeing | Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point). | 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 |
| Cognition | Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point). | 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. |
Countries
United States
Contacts
CONTACTZan Gao, PhD
Outcome results
None listed