Journal article
Designing Ruby: Protocol for a 2-Arm, Brief, Digital Randomized Controlled Trial for Internalized Weight Bias
JMIR research protocols, v 10(11), 31307
25 Nov 2021
PMID: 34842549
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background: Weight bias internalization, also known as weight self-stigma, is a serious health concern for individuals with higher body weight. Weight bias internalization is associated with the greater avoidance of health care and health-promoting activities, disordered eating, social isolation, and weight gain. Elevated weight bias internalization has been associated with low self-compassion, yet few investigations have explored self-compassion as a potential mechanism for reducing internalized weight bias.
Objective: Ruby is a 2-arm randomized controlled trial that was designed to test the efficacy of a 4-week digital self-compassion intervention to reduce internalized weight bias compared with a wait-list control.
Methods: Adults with elevated internalized weight bias and a BMI of >30 kg/m(2) (N=80) were recruited. Ruby is a standalone digital trial that will be delivered entirely via a smartphone and will involve web based data collection and text messages. The intervention content will include psychoeducation and daily mindfulness practices with a focus on self-compassion and body concerns. We will use intent-to-treat analyses to examine changes in weight bias internalization throughout time by treatment arm. The analyses will be conducted by using one-way analysis of covariance models and linear mixed models.
Results: The protocol was designed in May 2020 and approved in December 2020. Data collection is currently underway.
Conclusions: Ruby will be the first digital standalone, self-compassion-based intervention designed to reduce internalized weight bias. Owing to its standalone digital delivery, Ruby may be a highly scalable treatment for internalized weight bias that can be delivered on its own or combined with other treatments. We expect Ruby to be accessible to many, as participants can access the digital intervention at times of the day that are the most convenient in their schedule and are not burdened by in-person time commitments, which can be a barrier for participants with competing demands on their time and resources. If efficacious, Ruby will be poised to expand a burgeoning body of literature related to psychological intervention in this area.
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Details
- Title
- Designing Ruby: Protocol for a 2-Arm, Brief, Digital Randomized Controlled Trial for Internalized Weight Bias
- Creators
- Christina M. Hopkins - Duke UniversityHailey N. Miller - Duke UniversityTaylor L. Brooks - Duke UniversityLihua Mo-Hunter - Duke UniversityDori M. Steinberg - Duke UniversityGary G. Bennett - Duke UniversityChristina M. Hopkins - Duke University
- Publication Details
- JMIR research protocols, v 10(11), 31307
- Publisher
- Jmir Publications, Inc
- Number of pages
- 13
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- WELL Center
- Web of Science ID
- WOS:000738742000007
- Scopus ID
- 2-s2.0-85120431382
- Other Identifier
- 991022017429704721
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- Web of Science research areas
- Health Care Sciences & Services
- Public, Environmental & Occupational Health