Proof-of-concept testing of a mobile application-delivered mindfulness exercise for emotional eaters: RAIN delivered as a step-by-step image sequence
Why this work is in the frame
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Bibliographic record
Abstract
Background: Over fifty percent of individuals with overweight and obesity are emotional eaters. Emotional eating can be theorized as a conditioned response to eat for reasons that are not associated with physiological hunger. We conducted this proof-of-concept study to gather evidence that a mobile app that delivers a common non-meditative mindfulness exercise called RAIN, in a step-by-step image sequence can improve emotional eating and other outcomes over a 3-week period. Methods: Forty-nine Canadian adults who self reported as emotional eaters (mean age =30.7 years) were recruited through social media and participated in a workshop in which RAIN and its use on the app were introduced. Participants were asked to use the app every time that they experienced a non-homeostatic craving to eat for three weeks. Emotional eating, reactivity to food cravings, perceived loss of control around food, distress tolerance, and eating-specific mindfulness were assessed pre- and post-intervention. Results: Improvements on all outcomes were found (r-range, -0.58 to -0.28). The feasibility of the mobile application was demonstrated by a low attrition rate (8%), high user satisfaction, and strong app engagement metrics. Conclusions: The data provide proof-of-concept evidence that a mobile app that delivers a mindfulness exercise in a step-by-step image sequence has potential to be effective and thus identifies a new approach that may reduce emotional eating in an accessible and affordable manner.
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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) | 0.003 | 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 it