Mobile Phone App for Self-Monitoring of Eating Rhythm: Field Experiment
Bibliographic record
Abstract
BACKGROUND: Temporal aspects of eating are an integral part of healthy eating, and regular eating has been associated with good diet quality and more successful weight control. Unfortunately, irregular eating is becoming more common. Self-monitoring of behavior has been found to be an efficient behavioral change technique, but the solution should be simple enough to ensure long-lasting adherence. OBJECTIVE: This study aimed to explore the influence of self-monitoring of daily eating pattern with mobile phone app on eating rhythm, eating behavior tendencies, and the underlying motives and attitudes related to eating. METHODS: A mobile phone app, Button, was developed for effortless self-monitoring of eating rhythm. The feasibility of the app was tested in a 30-day intervention. The participants (N=74) recorded their eating occasions during the intervention by pressing a button in the app widget. RESULTS: The average interval between meals increased (96 [SD 24] min during the first 10 days vs 109.1[SD 36.4] during the last 10 days) and the number of daily eating occasions decreased (4.9 [SD 0.9] during the first 10 days vs 4.4 [SD 0.9] during the last 10 days). The tendencies for cognitive restraint, emotional eating, and uncontrolled eating increased. Eating-related attitudes and motives remained largely unchanged. CONCLUSIONS: These results indicate that a simple self-monitoring tool is able to draw a user's attention to eating and is a potential tool to aid people to change their eating rhythm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".