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

Non-randomized Protocols and Methods (ehealth) (564) Fitness Trackers and Smart Pedometers/Accelerometers (558) mHealth for Wellness, Behavior Change and Prevention (2850) Falls Prevention in the Elderly (68) Wearable Devices and Sensors (876) Physical Activity for Older People (174) Accident and Injury Prevention (42)

Published on in Vol 10, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24455, first published .
The Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II to Assess the Use of the Apple Watch in Older Emergency Department Patients With Falls: Protocol for a Mixed Methods Study

The Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II to Assess the Use of the Apple Watch in Older Emergency Department Patients With Falls: Protocol for a Mixed Methods Study

The Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II to Assess the Use of the Apple Watch in Older Emergency Department Patients With Falls: Protocol for a Mixed Methods Study

Authors of this article:

Daniel H Strauss1 Author Orcid Image ;   Natalie M Davoodi2 Author Orcid Image ;   Margaret Healy3 Author Orcid Image ;   Christopher L Metts4 Author Orcid Image ;   Roland C Merchant5 Author Orcid Image ;   Swechya Banskota1 Author Orcid Image ;   Elizabeth M Goldberg6 Author Orcid Image
Article Authors Cited by (16) Tweetations (7) Metrics

Journals

  1. Dandapani H, Davoodi N, Joerg L, Li M, Strauss D, Fan K, Massachi T, Goldberg E. Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment. Frontiers in Digital Health 2022;4 View
  2. Qiu S, Zhao H, Jiang N, Wang Z, Liu L, An Y, Zhao H, Miao X, Liu R, Fortino G. Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges. Information Fusion 2022;80:241 View
  3. Nishimura K, Cordeiro J, Ahmed A, Yokobori S, Gajavelli S. Advances in Traumatic Brain Injury Biomarkers. Cureus 2022 View
  4. Ranney M, Stettenbauer E, Delgado M, Yao K, Orchowski L. Uses of mHealth in Injury Prevention and Control: a Critical Review. Current Epidemiology Reports 2022;9(4):273 View
  5. Hunold K, Goldberg E, Caterino J, Hwang U, Platts‐Mills T, Shah M, Rosen T. Inclusion of older adults in emergency department clinical research: Strategies to achieve a critical goal. Academic Emergency Medicine 2022;29(3):376 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. Zhang T, Meng D, Lyu D, Fang B. The Efficacy of Wearable Cueing Devices on Gait and Motor Function in Parkinson Disease: A Systematic Review and Meta-analysis of Randomized Controlled Trials. Archives of Physical Medicine and Rehabilitation 2023 View
  8. Shankar K, Li A. Older Adult Falls in Emergency Medicine, 2023 Update. Clinics in Geriatric Medicine 2023;39(4):503 View
  9. Mazzeo M, Hernan G, Veerubhotla A. Usability and ease of use of long-term remote monitoring of physical activity for individuals with acquired brain injury in community: a qualitative analysis. Frontiers in Neuroscience 2023;17 View
  10. Marmor S, Karaca-Mandic P, Adams M. Use of Physical Therapy and Subsequent Falls Among Patients With Dizziness in the US. JAMA Otolaryngology–Head & Neck Surgery 2023;149(12):1083 View
  11. Chen C, Ding S, Wang J. Digital health for aging populations. Nature Medicine 2023;29(7):1623 View
  12. Strehlow M, Alvarez A, Blomkalns A, Caretta‐Wyer H, Gharahbaghian L, Imler D, Khan A, Lee M, Lobo V, Newberry J, Ribeira R, Sebok‐Syer S, Shen S, Gisondi M. Precision emergency medicine. Academic Emergency Medicine 2024 View
  13. Murphy S, Vitali R. Assessment of IMU Configurations for Human Activity Recognition Using Ensemble Learning. IEEE Access 2024;12:111433 View
  14. Köhler C, Bartschke A, Fürstenau D, Schaaf T, Salgado-Baez E. The Value of Smartwatches in the Health Care Sector for Monitoring, Nudging, and Predicting: Viewpoint on 25 Years of Research. Journal of Medical Internet Research 2024;26:e58936 View
  15. Sridhar A, Cheung J, Lampert R, Silva J, Gopinathannair R, Sotomonte J, Tarakji K, Fellman M, Chrispin J, Varma N, Kabra R, Mehta N, Al-Khatib S, Mayfield J, Navara R, Rajagopalan B, Passman R, Fleureau Y, Shah M, Turakhia M, Lakkireddy D. State of the art of mobile health technologies use in clinical arrhythmia care. Communications Medicine 2024;4(1) View

Books/Policy Documents

  1. Wang Z. Activity Recognition and Prediction for Smart IoT Environments. View