Ignite Creation Date:
2025-12-25 @ 1:23 AM
Ignite Modification Date:
2025-12-25 @ 11:32 PM
Study NCT ID:
NCT07207993
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-10-06
First Post:
2025-09-19
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Evaluating Health Outcomes of AI-Based Fitness Wearables & App Programs in Elderly With Cognitive Decline
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D060825', 'term': 'Cognitive Dysfunction'}, {'id': 'D009043', 'term': 'Motor Activity'}, {'id': 'D057185', 'term': 'Sedentary Behavior'}], 'ancestors': [{'id': 'D003072', 'term': 'Cognition Disorders'}, {'id': 'D019965', 'term': 'Neurocognitive Disorders'}, {'id': 'D001523', 'term': 'Mental Disorders'}, {'id': 'D001519', 'term': 'Behavior'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['PARTICIPANT', 'INVESTIGATOR'], 'maskingDescription': 'The design is blinded, with all investigators except the biostatistician unaware of group and intervention assignments.'}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'FACTORIAL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 64}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-11-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2027-11-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-27', 'studyFirstSubmitDate': '2025-09-19', 'studyFirstSubmitQcDate': '2025-09-27', 'lastUpdatePostDateStruct': {'date': '2025-10-06', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-10-06', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2027-11-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Fitbit MVPA', 'timeFrame': 'Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).', 'description': "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)."}, {'measure': 'Physical Activity', 'timeFrame': 'Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).', 'description': '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)'}, {'measure': 'Mechanism of behavior change (MoBC) variables', 'timeFrame': 'Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).', 'description': "Psychometrically validated questionnaires will be used to assess beliefs: self-efficacy, social support, and outcome expectations.\n\nSelf-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.\n\nOutcome expectation; low score indicates the belief that behavior won't help, high score indicates the belief that the behavior will lead to positive outcome."}], 'secondaryOutcomes': [{'measure': 'Quality of life (QoL)', 'timeFrame': 'Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).', 'description': "We will select the brief Older People's Quality of Life questionnaire to assess quality of life (OPQOL-BRIEF).\n\nMinimum: 13 (very poor quality of life) Maximum: 65 (excellent quality of life)"}, {'measure': 'Psychosocial wellbeing', 'timeFrame': 'Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).', 'description': 'We will assess wellbeing using the World Health Organization Well-Being Index (WHO-5) questionnaire.\n\n0 -12 - Low well-being; possible depression (screen positive) 13 - 19 - Moderate well-being 20 - 25 - High well-being'}, {'measure': 'Cognition', 'timeFrame': 'Baseline (i.e., pre-intervention), 3 months (mid-point), and 6 months (end-point).', 'description': "We will adopt a web-based self-administered instrument, NeuroCognitive Performance Test, to assess older adults' cognition.\n\nLow score: TBD High score: TBD"}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Older adults', 'Cognitive decline', 'AI-Based Fitness', 'Physical Activity', 'Physical Inactivity', 'Living alone with cognitive decline', 'LACD', 'U.S Older adults'], 'conditions': ['Older Adults With Cognitive Decline', 'Older Adults', 'AI-Based Fitness', 'Wearables', 'Cognitive Decline', 'Physical Activity', 'Physical Inactivity']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['OLDER_ADULT'], 'minimumAge': '65 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Participant must be at least 65 years of age older\n* Participant must be living alone in the U.S. for the next 6 months\n* 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\\];\n* Participant must own an Android/Apple smartphone\n* Participant must have access to internet or Wi-Fi access\n* Participant must be capable of engaging in some PA as determined by the PA Readiness Questionnaire or physician approval\n* Participant must currently participate in weekly moderate-to-vigorous PA (MVPA) or less than 150 minutes\n* Participant must have basic English communication skills.\n\nExclusion Criteria:\n\n* Foreign residents or visitors'}, 'identificationModule': {'nctId': 'NCT07207993', 'briefTitle': 'Evaluating Health Outcomes of AI-Based Fitness Wearables & App Programs in Elderly With Cognitive Decline', 'organization': {'class': 'OTHER', 'fullName': 'The University of Tennessee, Knoxville'}, 'officialTitle': 'Evaluating Health Outcomes of AI-Based Fitness Wearables and App Programs in Older Adults Living Alone With Cognitive Decline', 'orgStudyIdInfo': {'id': 'UTK IRB-25-09047-XP'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Access to all applications', 'description': 'Condition 1: Participant are provided with the prescription application (application1), social application (application 2), and health tips application (application 3).', 'interventionNames': ['Other: Fitness app for self-efficacy', 'Other: Social network via app for social support', 'Other: Health education app targeting outcome expectations']}, {'type': 'EXPERIMENTAL', 'label': 'Access to application 1 & 2', 'description': "Condition 2: Participant are provided with the prescription application, social application, but they aren't provided with the health tips application.", 'interventionNames': ['Other: Fitness app for self-efficacy', 'Other: Social network via app for social support']}, {'type': 'EXPERIMENTAL', 'label': 'Access to application 1 & 3', 'description': "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.", 'interventionNames': ['Other: Fitness app for self-efficacy', 'Other: Health education app targeting outcome expectations']}, {'type': 'EXPERIMENTAL', 'label': 'Access to application 1 only', 'description': "Condition 4: Participant are provided with the prescription application, but aren't provided with the social application, and the health tips application.", 'interventionNames': ['Other: Fitness app for self-efficacy']}, {'type': 'EXPERIMENTAL', 'label': 'Access to application 2 & 3', 'description': 'Condition 5: Participant are not provided with the prescription application, but they are provided with the social application, and the health tips application.', 'interventionNames': ['Other: Social network via app for social support', 'Other: Health education app targeting outcome expectations']}, {'type': 'EXPERIMENTAL', 'label': 'Access to application 2 only', 'description': "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.", 'interventionNames': ['Other: Social network via app for social support']}, {'type': 'EXPERIMENTAL', 'label': 'Access to application 3 only', 'description': 'Condition 7: Participant are not provided with the prescription application, or the social application, but they are provided with the health tips application.', 'interventionNames': ['Other: Health education app targeting outcome expectations']}, {'type': 'NO_INTERVENTION', 'label': 'No access to any application', 'description': 'Condition 8: Participant are not provided with the prescription application, or the social application, or with the health tips application.'}], 'interventions': [{'name': 'Fitness app for self-efficacy', 'type': 'OTHER', 'description': 'AI-driven personalized exercise prescription via a fitness app. This targets self-efficacy.', 'armGroupLabels': ['Access to all applications', 'Access to application 1 & 2', 'Access to application 1 & 3', 'Access to application 1 only']}, {'name': 'Social network via app for social support', 'type': 'OTHER', 'description': 'Participants will be provided access to a social network via app. This targets social support.', 'armGroupLabels': ['Access to all applications', 'Access to application 1 & 2', 'Access to application 2 & 3', 'Access to application 2 only']}, {'name': 'Health education app targeting outcome expectations', 'type': 'OTHER', 'description': 'Participants are provided with an app-based health education. This targets outcome expectations.', 'armGroupLabels': ['Access to all applications', 'Access to application 1 & 3', 'Access to application 2 & 3', 'Access to application 3 only']}]}, 'contactsLocationsModule': {'locations': [{'zip': '37920', 'city': 'Knoxville', 'state': 'Tennessee', 'country': 'United States', 'contacts': [{'name': 'Kinesiology, Recreation, and Sport Studies', 'role': 'CONTACT', 'email': 'krss@utk.edu', 'phone': '(865) 974-3340'}, {'name': 'Zan Gao, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Danielle Ostendorf, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Xiaopeng Zhao, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Jeffrey Labban, PhD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'University of Tennessee', 'geoPoint': {'lat': 35.96064, 'lon': -83.92074}}], 'centralContacts': [{'name': 'Zan Gao, PhD', 'role': 'CONTACT', 'email': 'zan@utk.edu', 'phone': '865-974-7971'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The University of Tennessee, Knoxville', 'class': 'OTHER'}, 'collaborators': [{'name': 'Arizona State University', 'class': 'OTHER'}, {'name': 'Oregon Research Institute', 'class': 'OTHER'}, {'name': 'National Institute on Aging (NIA)', 'class': 'NIH'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor and Department Head', 'investigatorFullName': 'Zan Gao', 'investigatorAffiliation': 'The University of Tennessee, Knoxville'}}}}