Published on in Vol 5, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36872, first published .
App-Based Evaluation of Older People’s Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study

App-Based Evaluation of Older People’s Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study

App-Based Evaluation of Older People’s Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study

Journals

  1. Alves S, Temme S, Motamedi S, Kura M, Weber S, Zeichen J, Pommer W, Baumgart A. 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. JMIR Aging 2024;7:e55681 View
  2. Raeder K, Strutz N, Kuntz S, Strube-Lahmann S, Schwesig R, Müller-Werdan U, Lahmann N. Using 3D skeleton tracking for gait analysis in older adults compared to a kinect-based mobility analysis system. BMC Geriatrics 2025;25(1) View
  3. Kang J, Zanotto A, Snyder M, Sosnoff J, Rice L. Clinicians’ perspectives on the usability and usefulness of an mHealth fall risk assessment for individuals who use wheelchairs: a pilot study. Disability and Rehabilitation: Assistive Technology 2025:1 View
  4. Luis U, Rodrigo M, Cristhian S, Mauricio S. Beyond timing: A critical review of the iTUG test and its implementation challenges for fall risk assessment in community-dwelling older adults. Health Policy and Technology 2026;15(3):101166 View

Books/Policy Documents

  1. Puppe F, Krug M, Kempf S. Digitale Innovationen in der Pflege. View
  2. Kemmler W. Artificial Intelligence in Sports, Movement, and Health. View
  3. Hallensleben J, Becker K, Kükemück S, Hoffmann F. Künstliche Intelligenz im Einsatz für die erfolgreiche Patientenreise. View