Effects of a Digital Therapeutic Adjunct to Eating Disorder Treatment on Health Care Service Utilization and Clinical Outcomes: Retrospective Observational Study Using Electronic Health Records
Bibliographic record
Abstract
BACKGROUND: The need for scalable solutions facilitating access to eating disorder (ED) treatment services that are efficient, effective, and inclusive is a major public health priority. Remote access to synchronous and asynchronous support delivered via health apps has shown promise, but results are so far mixed, and there are limited data on whether apps can enhance health care utilization. OBJECTIVE: This study aims to examine the effects of app-augmented treatment on clinical outcomes and health care utilization for patients receiving treatment for an ED in outpatient and intensive outpatient levels of care. METHODS: Recovery Record was implemented in outpatient and intensive outpatient services in a California-based health maintenance organization. We examined outcomes for eligible patients with ED by comparing clinical and service utilization medical record data over a 6-month period after implementation with analogous data for the control group in the year prior. We used a logistic regression model and inverse-weighted estimates of the probability of treatment to adjust for treatment selection bias. RESULTS: App-augmented treatment was associated with a significant decrease in emergency department visits (P<.001) and a significant increase in outpatient treatment utilization (P<.001). There was a significantly larger weight gain for patients in low-weight categories (ie, underweight, those with anorexia, or those with severe anorexia) with app-augmented treatment (treatment effect: 0.74, 0.25, and 0.35, respectively; P=.02), with a greater percentage of patients moving into a higher BMI class (P=.01). CONCLUSIONS: Integrating remote patient engagement apps into ED treatment plans can have beneficial effects on both clinical outcomes and service utilization. More research should be undertaken on long-term efficacy and cost-effectiveness to further explore the impact of digital health interventions in ED care.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".