Improvements in Adolescents’ Disordered Eating Behaviors in a Collaborative Care Digital Mental Health Intervention: Retrospective Observational Study
Bibliographic record
Abstract
Background Young people today are exhibiting increasing rates of disordered eating behaviors, as well as eating disorders (EDs), alongside other mental and behavioral problems such as anxiety and depression. However, limited access to mental health care means that EDs, disordered eating behaviors, and comorbid mental health problems are often underdiagnosed and undertreated. Digital mental health interventions (DMHIs) offer accessible and scalable alternatives to traditional treatment modalities, but their effectiveness has not been well established among adolescents with EDs and disordered eating behaviors. Objective This study uses data from a collaborative care pediatric DMHI to determine whether participation in a DMHI is associated with a reduction in adolescents’ disordered eating behaviors. Methods Adolescent members in care with Bend Health Inc completed the SCOFF questionnaire at baseline (before the start of care) and approximately every month during care to assess disordered eating behaviors. They also completed assessments of mental health symptoms at baseline. Member characteristics, mental health symptoms, and disordered eating behaviors of adolescents with elevated SCOFF scores at baseline (before the start of care) were compared to those of adolescents with nonelevated SCOFF scores at baseline. Members participated in web-based coaching or therapy sessions throughout the duration of mental health care. Results Compared to adolescents with nonelevated SCOFF scores (n=520), adolescents with elevated SCOFF scores (n=169) were predominantly female and exhibited higher rates of elevated anxiety and depressive symptoms. SCOFF scores decreased over time in care with the DMHI for 61.4% (n=70) of adolescents with elevated SCOFF scores, and each additional month of participation was associated with greater improvements in disordered eating behaviors (F1,233=72.82; P<.001). Conclusions Our findings offer promising preliminary evidence that participation in mental health care with a collaborative care DMHI may be beneficial in the reduction of disordered eating symptoms in adolescents, including those who are experiencing comorbid anxiety and depressive symptoms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".