Combating Attrition in Digital Self-Improvement Programs using Avatar Customization
Bibliographic record
Abstract
Digital self-improvement programs (e.g., interventions, training programs, self-help apps) are widely accessible, but can not employ the same degree of external regulation as programs delivered in controlled environments. As a result, they suffer from high attrition -- even the best programs won't work if people don't use them. We propose that volitional engagement -- facilitated through avatar customization -- can help combat attrition. We asked 250 participants to engage daily for 3 weeks in a one-minute breathing exercise for anxiety reduction, using either a generic avatar or one that they customized. Customizing an avatar resulted in significantly less attrition and more sustained engagement as measured through login counts. The problem of attrition affects self-improvement programs across a range of do-mains; we provide a subtle, versatile, and broadly-applicable solution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".