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Mobile Technology for Falls Prevention in Older Adults
Katherine L Hsieh1, Lingjun Chen2, Jacob J Sosnoff2
1Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Summary
Mobile technology offers personalized fall risk assessment and prevention for older adults. This approach, based on the P4 model, shows promise in measuring fall risk factors and integrating into prevention programs.
Area of Science:
- Gerontology
- Biomedical Engineering
- Digital Health
Background:
- Falls are a leading cause of accidental death in older adults, stemming from complex risk factors.
- A person-centered, P4 model (personalization, prediction, prevention, participation) is increasingly recognized for effective fall prevention.
- Mobile technology presents a suitable infrastructure for implementing P4-based fall prevention strategies.
Approach:
- This narrative review systematically analyzed research published since 2017 on mobile technology for personalized fall risk assessment and prevention in older adults.
- Searches were conducted in PubMed and Web of Science using keywords related to older adults, mobile technology, and falls prevention.
- Twenty-three articles were included, focusing on aspects of the P4 model: prediction, personalization, prevention, and participation.
Key Points:
- Mobile technology demonstrated comparable accuracy to gold-standard methods in measuring key fall risk factors like static and dynamic balance.
- Seven mobile applications were developed and tested for personalization and/or participation in fall risk assessment.
- Four of these applications were successfully integrated into existing falls prevention programs.
Conclusions:
- Mobile health technology provides an innovative platform for tailored fall risk screening, prediction, and user engagement.
- Future research should focus on integrating multiple objective fall risk measures within community-based settings.
- Determining the scalability and effectiveness of mobile technology for personalized fall prevention interventions requires further investigation.
