The use of technology in the treatment of youth with eating disorders: A scoping review
Bibliographic record
Abstract
Abstract Background Adolescence and young adulthood is a high-risk period for the development of eating disorders. In recent years, there has been an increase in use of technology-based interventions (TBIs) for the treatment of eating disorders. The objective of this study was to determine the types of technology used for eating disorder treatment in youth and their effectiveness. Methods A scoping review was conducted according to PRISMA-ScR guidelines. Four databases were searched. Eligible articles included: (1) a TBI (2) participants with a mean age between 10- and 25-years and meeting DSM-IV or DSM-5 criteria for any eating disorder and (3) qualitative or quantitative designs. Quantitative and qualitative studies were assessed for quality. Results The search identified 1621 articles. After screening of titles and abstracts, 130 articles were read in full and assessed for eligibility by two raters. Forty-nine (29 quantitative and 20 qualitative, observational, or mixed methods studies) met inclusion criteria. Quality ratings indicated that 78% of quantitative studies had a low risk of bias and 22% had a moderate risk. Technologies reviewed in our study included videoconference therapy, mobile applications, and online self-help. We considered interventions used both within sessions with clinicians as well as those used in between sessions by patients alone. Fifteen of 18 (83%) quantitative studies found that TBIs reduce eating disorder symptomatology, with nine of those reporting medium-to-large effect sizes. Qualitative data was of high quality and suggested that virtual interventions are acceptable in this population. Conclusions Although identified studies are of high quality, they are limited in number. More research is needed, particularly regarding videoconferencing and mobile applications. Nonetheless, TBIs show promise for the treatment of eating disorders in youth. Trial registration: Not applicable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".