‘I have been all in, I have been all out and I have been everything in‐between’: A 2‐year longitudinal qualitative study of weight loss maintenance
Bibliographic record
Abstract
BACKGROUND: Qualitative studies investigating weight management experiences are usually cross-sectional or of short duration, which limits understanding of the long-term challenges. METHODS: ) and ongoing support for weight loss maintenance (WLM), to 2 years. Semi-structured interviews were conducted at baseline and programme end, as well as at key intervals during the intervention. Questions examined five theoretical themes: motivation, self-regulation, habits, psychological resources and social/environmental influences. Data were coded and analysed in nvivo (https://qsrinternational.com/nvivo) using the framework method. RESULTS: In total, 64 interviews were completed (median, n = 6 per participant). Mean (SD) weight loss was 15.7 (9.6) kg (14.6% body weight) at 6 months and 9.6 (9.9) kg (8.8% body weight) at 2 years. The prespecified theoretical model offered a useful framework to capture the variability of experiences. Negative aspects of obesity were strong motivations for weight loss and maintenance. Perceiving new routines as sustainable and developing a 'maintenance mindset' was characteristic of 'Maintainers', whereas meeting emotional needs at the expense of WLM goals during periods of stress and negative mood states was reported more often by 'Regainers'. Optimistic beliefs about maintaining weight losses appeared to interfere with barrier identification and coping planning for most participants. CONCLUSIONS: People tended to be very optimistic about WLM without acknowledging barriers and this may undermine longer-term outcomes. The potential for regain remained over time, mainly as a result of emotion-triggered eating to alleviate stress and negative feelings. More active self-regulation during these circumstances may improve WLM, and these situations represent important targets for intervention.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".