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This paper is in the following e-collection/theme issue:

Falls Prevention in the Elderly (108) Artificial Intelligence (2365) Mobile Health (mhealth) (3668) mHealth for Telemedicine and Homecare (566) mHealth in a Clinical Setting (777) Nursing in a Long-Term Care Facility / Nursing Home for the Elderly (57)

Published on in Vol 7 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55681, first published .
Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis

Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis

Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis

Authors of this article:

Sónia A Alves1 Author Orcid Image ;   Steffen Temme1 Author Orcid Image ;   Seyedamirhosein Motamedi1 Author Orcid Image ;   Marie Kura1 Author Orcid Image ;   Sebastian Weber2 Author Orcid Image ;   Johannes Zeichen2 Author Orcid Image ;   Wolfgang Pommer3 Author Orcid Image ;   André Baumgart4, 5 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

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  2. Safarina L, Kosasih C, Pramukti M, Nursjamsi N. E-Counselling for Fall Prevention in Older Adults: A Scoping Review (Preprint). JMIR Aging 2025 View
  3. Qahtani A, Aslam S, Ali O, Abd EL-Rahim S, Mamoun H, Ali R. “Falls in older people receiving home healthcare services at armed forces hospital Southern Region: prevalence, associated factors, and consequent injuries”. BMC Geriatrics 2026 View

Books/Policy Documents

  1. Wolf-Ostermann K, Mena E, Seibert K. Safe at Home with Assistive Technology. View