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

mHealth for Rehabilitation (269) Innovations and Technology for Physical Activity Education (756) Fitness Trackers and Smart Pedometers/Accelerometers (571) Usability of Apps and User Perceptions of mHealth (993) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (505) Web-based and Mobile Health Interventions (3357) Mobile Health (mhealth) (2982) Wearable Devices and Sensors (903) mHealth for Wellness, Behavior Change and Prevention (2922)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50729, first published .
Implementation of Remote Activity Sensing to Support a Rehabilitation Aftercare Program: Observational Mixed Methods Study With Patients and Health Care Professionals

Implementation of Remote Activity Sensing to Support a Rehabilitation Aftercare Program: Observational Mixed Methods Study With Patients and Health Care Professionals

Implementation of Remote Activity Sensing to Support a Rehabilitation Aftercare Program: Observational Mixed Methods Study With Patients and Health Care Professionals

Authors of this article:

Ziyuan Lu1 Author Orcid Image ;   Tabea Signer2 Author Orcid Image ;   Ramona Sylvester2 Author Orcid Image ;   Roman Gonzenbach2 Author Orcid Image ;   Viktor von Wyl1, 3 Author Orcid Image ;   Christina Haag1, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Daniore P, Nittas V, Haag C, Bernard J, Gonzenbach R, von Wyl V. From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal. npj Digital Medicine 2024;7(1) View
  2. Létourneau S, Jin H, Peters E, Grewal R, Ross D, Symonette C. Augmented Reality-Based Finger Joint Range of Motion Measurement: Assessment of Reliability and Concurrent Validity. The Journal of Hand Surgery 2024 View