| Unique ID issued by UMIN | UMIN000062660 |
|---|---|
| Receipt number | R000071729 |
| Scientific Title | Development and Evaluation of the Real-World Implementation Feasibility of a Stratified Self-Care Support Model Integrating Artificial Intelligence and Nursing Expertise Based on Risk Perception Mismatch for Fall Prevention in Older Adults |
| Date of disclosure of the study information | 2026/08/23 |
| Last modified on | 2026/08/23 11:10:08 |
A Study of AI- and Nurse-Supported Self-Care for Fall Prevention in Older Adults
Fall Prevention Study for Older Adults Using AI and Nursing Expertise
Development and Evaluation of the Real-World Implementation Feasibility of a Stratified Self-Care Support Model Integrating Artificial Intelligence and Nursing Expertise Based on Risk Perception Mismatch for Fall Prevention in Older Adults
RPM-AI Nursing Fall Prevention Study
| Japan |
Risk of falls and fractures in older adults
| Nursing | Adult |
Others
NO
The aim of this study is to develop a stratified self-care support model for community-dwelling older adults that integrates AI-based daily self-care support with support from nursing experts, focusing on risk perception mismatch, RPM, between objective fall and fracture risk and individuals' subjective risk perceptions. Through a three-month intervention, the study will evaluate changes in fear of falling, self-efficacy, physical activity, self-perception, and health behaviors, as well as the acceptability, feasibility, sustainability, and efficiency of the model, to assess its potential for real-world implementation.
Safety,Efficacy
Changes in the following outcomes from baseline (V1) to 3 months after the start of the intervention (V3) will be evaluated as the primary outcomes:
Fear of falling, assessed using the Falls Efficacy Scale-International (FES-I)
Self-efficacy, assessed using a brief self-efficacy scale developed for this study
Physical activity, assessed using the International Physical Activity Questionnaire Short Form (IPAQ-SF)
Indicators of self-perception and verbalization of reasons for health-related behaviors derived from AI dialogue logs
Interventional
Single arm
Non-randomized
Open -no one is blinded
Uncontrolled
1
Prevention
| Behavior,custom | Other |
All participants will receive self-care support using a research AI system for three months. The AI system will support assessment of lifestyle, physical activity, sleep, and health conditions; explanation of assessment results; daily reflection; verbalization of reasons for successful or unsuccessful health behaviors; review of behavioral goals; and provision of general information on fall prevention and lifestyle improvement. Participants at high risk, defined as those with a history of falls or fragility fractures, will additionally receive consultations with nursing experts to establish feasible behavioral goals based on their lifestyle, values, and risk perceptions. An interim assessment will be conducted one month after the start of the intervention, with an additional nursing consultation provided to high-risk participants as needed. The final assessment will be conducted three months after the start of the intervention.
| 65 | years-old | <= |
| 84 | years-old | >= |
Male and Female
Individuals aged 65 to 84 years
Community-dwelling individuals
Individuals with a history of falls or fear of falling, or an interest in fall and fracture prevention
Individuals who are able to use a smartphone
Individuals who are able to use the research AI application or research AI system
Individuals who are able to provide informed consent to participate in the study
Individuals who have been advised by a physician to restrict physical activity
Individuals with an implanted pacemaker
Individuals certified as requiring support or long-term care under the Japanese long-term care insurance system
Individuals who are unable to provide informed consent or participate in the study due to severe cognitive impairment or other reasons
Individuals considered unsuitable for participation by the principal investigator
160
| 1st name | Miyae |
| Middle name | |
| Last name | Yamakawa |
The University of Osaka
Division of Health Sciences, Graduate School of Medicine
5650871
1-7 Yamadaoka, Suita City, Osaka
0668792543
miyatabu@sahs.med.osaka-u.ac.jp
| 1st name | Miyae |
| Middle name | |
| Last name | Yamakawa |
The University of Osaka
Division of Health Sciences, Graduate School of Medicine
5650871
1-7 Yamadaoka, Suita city, Osaka
0668792543
miyatabu@sahs.med.osaka-u.ac.jp
The University of Osaka
Ministry of Education, Culture, Sports, Science and Technology (MEXT)
Japanese Governmental office
Ethical Review Board of Osaka University Hospital
2-2 Yamadaoka, Suita, Osaka
06-6210-8296
rinri@hp-crc.med.osaka-u.ac.jp
NO
| 2026 | Year | 08 | Month | 23 | Day |
Unpublished
Preinitiation
| 2026 | Year | 08 | Month | 19 | Day |
| 2026 | Year | 09 | Month | 28 | Day |
| 2027 | Year | 01 | Month | 27 | Day |
| 2026 | Year | 08 | Month | 23 | Day |
| 2026 | Year | 08 | Month | 23 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000071729