Development of a transdiagnostic digital interactive application for eating disorders: psychometric properties, satisfaction, and perceptions on implementation in clinical practice
Bibliographic record
Abstract
BACKGROUND: Given limited availability of informed treatments for people affected by eating disorders (EDs), there has been increasing interest in developing self-administered, technology-based ED interventions. However, many available interventions are limited to a specific ED diagnosis or assume that participants are ready to change. We developed a digital self-help application (called ASTrA) that was explicitly designed to be transdiagnostic and to help increase motivation for change. The aim of the present study was to describe the development and examine the psychometric properties, user satisfaction and rated potentials for practical use of our application. METHODS: The content of our application was based on concepts derived from self-determination theory, the transtheoretical model of change, and cognitive theory. The application was developed by a multidisciplinary team of clinicians, researchers, staff members and individuals with lived ED experience, each being involved in all steps of the application's development. We tested validity, reliability, satisfaction and perceived feasibility for clinical implementation in an independent sample of 15 patients with an ED and 13 clinicians specialized in ED treatment. Psychometric properties were evaluated using descriptive statistics, correlations, content validity indices and intraclass coefficients. Differences in satisfaction ratings and perceived potential for clinical implementation of the application between clinicians and patients were examined using Mann-Whitney U tests. RESULTS: The digital application showed excellent validity (mean i-CVI: .93, range: .86-.96) and internal reliability (all Cronbach alpha's > .88). Patients and clinicians both considered the application acceptable, appropriate, and feasible for use in clinical practice. CONCLUSIONS: Findings suggest that our transdiagnostic interactive application has excellent psychometric properties. Furthermore, patients and clinicians alike were positive about the possible use of the application in clinical practice. The next step will be to investigate the application's effectiveness as an intervention to promote autonomous motivation and to facilitate remission in people on the waitlist for specialized ED treatment.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".