Published on in Vol 3, No 1 (2020): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16131, first published .
Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study

Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study

Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study

Journals

  1. Wilmink G, Dupey K, Alkire S, Grote J, Zobel G, Fillit H, Movva S. Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study. JMIR Aging 2020;3(2):e19554 View
  2. Fuchs D, Tiebel J, Friedrich P. Device-supported training and assessment for fall prevention of community-dwelling elderly: a pre-post mixed methods study. Procedia Computer Science 2020;176:2322 View
  3. Onyeaka H, Romero P, Healy B, Celano C. Age Differences in the Use of Health Information Technology Among Adults in the United States: An Analysis of the Health Information National Trends Survey. Journal of Aging and Health 2021;33(1-2):147 View
  4. Stamm O, Heimann-Steinert A. Accuracy of Monocular Two-Dimensional Pose Estimation Compared With a Reference Standard for Kinematic Multiview Analysis: Validation Study. JMIR mHealth and uHealth 2020;8(12):e19608 View
  5. Singh D, Goh J, Shaharudin M, Shahar S. A Mobile App (FallSA) to Identify Fall Risk Among Malaysian Community-Dwelling Older Persons: Development and Validation Study. JMIR mHealth and uHealth 2021;9(10):e23663 View
  6. Rasche P, Schäfer K, Theis S, Seinsch T, Bröhl C, Wille M, Knobe M, Vollmar H, Mertens A. Aachener Sturzpass App. Nervenheilkunde 2022;41(07/08):492 View
  7. Hsieh K, Chen L, Sosnoff J, Lipsitz L. Mobile Technology for Falls Prevention in Older Adults. The Journals of Gerontology: Series A 2023;78(5):861 View
  8. O'Connor S, Gasteiger N, Stanmore E, Wong D, Lee J. Artificial intelligence for falls management in older adult care: A scoping review of nurses' role. Journal of Nursing Management 2022;30(8):3787 View
  9. Pedrero-Sánchez J, De-Rosario-Martínez H, Medina-Ripoll E, Garrido-Jaén D, Serra-Añó P, Mollà-Casanova S, López-Pascual J. The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment. Sensors 2023;23(14):6567 View
  10. Jahangiri S, Abdollahi M, Patil R, Rashedi E, Azadeh-Fard N. An inpatient fall risk assessment tool: Application of machine learning models on intrinsic and extrinsic risk factors. Machine Learning with Applications 2024;15:100519 View
  11. Wu W, Zhou Q, Gao Q, Li H, Zhang J, Wu J, Shen J, Li J, Shi H. Construction of an instrument to enable the assessment of the risk of falls in older outpatients: A quantitative methodological study. Journal of Advanced Nursing 2024;80(9):3825 View
  12. Sihag G, Delcroix V, Grislin-Le Strugeon E, Siebert X, Piechowiak S, Puisieux F. Combining real data and expert knowledge to build a Bayesian Network — Application to assess multiple risk factors for fall among elderly people. Expert Systems with Applications 2024;252:124106 View
  13. 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

Books/Policy Documents

  1. Pommer W. Geriatrische Nephrologie. View
  2. Zacher J. Digitale Technik für ambulante Pflege und Therapie II. View
  3. Chen T, Lee Y. Smart and Healthy Walking. View
  4. Friedrich P, Fuchs D, Wolf B. Digitale Innovationen in der Pflege. View
  5. Kemmler W. Artificial Intelligence in Sports, Movement, and Health. View