‘It’s not a diet, it’s a lifestyle’: a longitudinal, data-prompted interview study of weight loss maintenance
Bibliographic record
Abstract
Objective: To advance understanding of the individual and environmental factors underpinning weight loss maintenance. Design: Semi-structured, data-prompted interviews were conducted with twelve overweight adult participants (three men, nine women) who had lost over 5% of their body weight in the year before baseline. Participants gathered daily data through wireless scales, activity monitors (Fitbit™), ecological momentary assessment and experience sampling (taking photographs, writing notes). They were interviewed at 3- and 6-months post baseline. Interview stimuli included personal data of weight and activity graphs, correlations of psychological factors, and self-generated notes and photographs. Interview data were analysed using the Framework Method, applying pre-specified maintenance-relevant theoretical themes. Results: The theoretical Framework provided a good fit for the narratives, with five main themes underpinning successful weight loss maintenance: sustained motivation, effective self-regulation, plentiful resources, habit formation and a supportive environment. Additionally, participants reported an identity shift from being a dieter to accepting a new healthy lifestyle. Goal prioritising and allowing for occasional controlled lapses enhanced weight loss maintenance. Conclusions: This study successfully used the novel method of data-prompted interviews to explore weight loss maintenance experiences with new explanations emerging from the data. Future research should further develop behaviour change maintenance theory and data-prompted interview method.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".