Efficacy and acceptability of self‐monitoring via a smartphone application versus traditional paper records in an intensive outpatient eating disorder treatment setting
Bibliographic record
Abstract
OBJECTIVE: Although self-monitoring is an important part of eating disorder treatment, non-adherence is commonly observed among patients asked to maintain paper food records. This study aims to compare the efficacy and acceptability of electronic self-monitoring via Recovery Record to self-monitoring via traditional paper records, in an intensive outpatient (IOP) eating disorder treatment for adults. METHOD: Ninety patients were recruited from an IOP eating disorder clinic and randomly assigned to the experimental or control condition. Those in the control condition received the standard treatment delivered by the IOP programme, including the use of paper records for self-monitoring. Those in the experimental condition received the same treatment but used Recovery Record for self-monitoring. RESULTS: The results did not demonstrate statistically significant group differences over time on eating disorder symptomatology, and there were no statistically significant group differences on acceptability or adherence. CONCLUSIONS: Our pilot efficacy data do not support superiority of the app over paper records in an IOP setting, so proceeding to a larger efficacy trial is not warranted. Future studies should aim to determine whether the app is efficacious as an adjunct to less intensive treatment or to further explore adherence and acceptability outcomes in studies with larger sample sizes. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02484794.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".