Proof-of-concept testing of a mobile application-delivered mindfulness exercise for emotional eaters: RAIN delivered as a step-by-step image sequence
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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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".