Preliminary Case Series of the Worth Warrior Mobile App for Young People With Low Self-Esteem and Mild Eating Disorders: Pre– and Post–Follow-Up Study
Bibliographic record
Abstract
Background: Eating difficulties are increasingly prevalent among young people, yet service capacity remains limited. Digital interventions may provide accessible, scalable support, particularly for those with mild, subthreshold, or early-stage symptoms who do not meet criteria for specialist care. Low self-esteem is widely recognized as a key psychological risk factor in the onset and persistence of eating disorders, and negative self-evaluation, particularly around body image and social acceptance, can heighten vulnerability to the maladaptive thoughts and behaviors seen in conditions such as anorexia nervosa, bulimia nervosa, and binge-eating disorder. Clarifying this relationship is essential for developing effective prevention and early intervention strategies. Objective: This pilot case series reports on 5 individuals aged 19-25 (mean 22, SD 2.19) years with mild eating disorders who used the Worth Warrior app, a mobile intervention incorporating principles of enhanced cognitive behavioral therapy strategies targeting low self-esteem, body image concerns, and disordered eating behaviors. Methods: An uncontrolled 3-phase design (baseline, post-app familiarization, and follow-up) was used. Participants completed standardized self-report tools, including the Eating Disorder Examination Questionnaire and Rosenberg Self-Esteem Scale. Feedback on usability, acceptability, and safety was also collected via online questionnaires. Results: Outcome measures at follow-up showed improvements in eating disorder symptomatology in 3/5 cases, and in self-esteem in 4/5 cases; those with milder symptomatology indicated the most benefit. Reductions in eating concerns, weight concerns, and related behaviors were observed in most, though not all, cases. Participants valued interactive enhanced cognitive behavioral therapy features and journaling functions, while noting areas for improvement such as reminders and incentives for use and preventions for maladaptive use of the free-text facilities. Conclusions: Findings suggest the Worth Warrior app may be suited as an acceptable and effective standalone tool for individuals with mild eating disorder symptoms, and used as an adjunct to traditional treatment alongside clinician supervision for those with more severe presentations to promote the greatest patient safety. These exploratory case study findings suggest that the app has the potential to support improvements in self-esteem and mild eating disorder symptomatology; however, as a preliminary case series, these results are not generalizable but provide a foundation for larger, controlled studies of digital early intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".