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

Evaluation and Research Methodology for mHealth (256) Secondary Use of Clinical Data for Research and Surveillance (493) Interventions and Support for Informal Caregivers of People with Mental Illness (137) Supporting Home Care and Family Caregivers (277) Ecological Momentary Assessment (EMA) (438) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (688) Mobile Health (mhealth) (3665) Registered Report (846)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73772, first published .
Completion and Compliance Rates for an Intensive mHealth Study Design to Promote Self-Awareness and Self-Care Among Care Partners of Individuals With Traumatic Brain Injury: Secondary Analysis of a Randomized Controlled Trial

Completion and Compliance Rates for an Intensive mHealth Study Design to Promote Self-Awareness and Self-Care Among Care Partners of Individuals With Traumatic Brain Injury: Secondary Analysis of a Randomized Controlled Trial

Completion and Compliance Rates for an Intensive mHealth Study Design to Promote Self-Awareness and Self-Care Among Care Partners of Individuals With Traumatic Brain Injury: Secondary Analysis of a Randomized Controlled Trial

Authors of this article:

Noelle E Carlozzi1, 2, 3 Author Orcid Image ;   Jonathan Troost4 Author Orcid Image ;   Wendy L Lombard1 Author Orcid Image ;   Jennifer A Miner1 Author Orcid Image ;   Christopher M Graves1 Author Orcid Image ;   Sung Won Choi5 Author Orcid Image ;   Zhenke Wu6, 7 Author Orcid Image ;   Srijan Sen8 Author Orcid Image ;   Angelle M Sander9, 10 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Johnson L, Hall K. Equity, Function, and Data: A Review of Social and Functional Representation in AI Datasets for Traumatic Brain Injury. Informatics 2026;13(2):33 View
  2. Wu T, Han T, Zhang X, Dai Y, Meng X. A Telemedicine App for Nonrigid Facial Rehabilitation Training Enhanced by Efficient Fully Convolutional Neural Network With Residual Network (EffiFCNN-ResNet) to Improve Accessibility for Patients With Nasopharyngeal Carcinoma Cancer: Randomized Controlled Trial. JMIR mHealth and uHealth 2026;14:e72560 View