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

RCTs - Protocols/Proposals (eHealth) (539) Mobile Health (mhealth) (2942) Obesity (75) mHealth for Patient Education (231) mHealth for Wellness, Behavior Change and Prevention (2872) Mindfulness and Meditation (205) Innovations and Technology for Healthy Eating Education (414) Validity, Reliability, and Bias (93)

Published on in Vol 10, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31307, first published .
Designing Ruby: Protocol for a 2-Arm, Brief, Digital Randomized Controlled Trial for Internalized Weight Bias

Designing Ruby: Protocol for a 2-Arm, Brief, Digital Randomized Controlled Trial for Internalized Weight Bias

Designing Ruby: Protocol for a 2-Arm, Brief, Digital Randomized Controlled Trial for Internalized Weight Bias

Authors of this article:

Christina M Hopkins1 Author Orcid Image ;   Hailey N Miller1, 2 Author Orcid Image ;   Taylor L Brooks1 Author Orcid Image ;   Lihua Mo-Hunter1 Author Orcid Image ;   Dori M Steinberg2, 3 Author Orcid Image ;   Gary G Bennett1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Miller H, Voils C, Cronin K, Jeanes E, Hawley J, Porter L, Adler R, Sharp W, Pabich S, Gavin K, Lewis M, Johnson H, Yancy Jr W, Gray K, Shaw R. A Method to Deliver Automated and Tailored Intervention Content: 24-month Clinical Trial. JMIR Formative Research 2022;6(9):e38262 View
  2. Morrow E, Zidaru T, Ross F, Mason C, Patel K, Ream M, Stockley R. Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Frontiers in Psychology 2023;13 View
  3. Adams V. Validation of the Modified Weight Bias Internalization Scale (WBIS-M) among First-Generation Asian Immigrants. Health & Social Work 2024;49(1):17 View