Changing from the inside out? Examining relationships between overweight identification, dieting behaviours, and body measurements over time
Bibliographic record
Abstract
OBJECTIVE: To investigate whether changes in overweight identification were associated with dieting behaviours and body measurements over time. DESIGN: Longitudinal study with assessments at three time points: before and twice during (i.e., baseline, 6 months, 12 months) a 1-year self-directed weight loss attempt. METHOD: Eighty individuals with overweight or obesity (classified by BMI ≥ 25) reported their personal (i.e., I see myself as overweight), social (i.e., I identify/feel strong ties with other overweight people), and affective (i.e., I am pleased to be overweight) overweight identification; dieting behaviours (e.g., eat less, exercise, eat more fruit and vegetables); and had their body measurements taken (i.e., weight, height, body fat, waist circumference). RESULTS: Linear mixed modelling was used to examine between-person differences and within-person changes in overweight identification on dieting behaviours and body measurements over time. Between-person differences mattered for measurements: Higher personal overweight identification was associated with higher BMI, body fat, and waist circumference over time. Higher social overweight identification was associated with higher BMI over time. Within-person changes mattered for behaviours over time: At 12 months, decreases in social overweight identification were associated with increases in a subset of 'Eat Less, Move More' dieting behaviours, but not a subset of 'Healthy' dieting behaviours. At 12 months, decreases in affective overweight identification were also associated with increases in 'Eat Less, Move More' dieting behaviours. CONCLUSION: Addressing different aspects of overweight identification and how they change over time, may harness an important psychological pathway to support behavioural change and health irrespective of weight loss. Statement of contribution What is already known on this subject? Psychological factors, such as self-concept clarity and weight stigma, are associated with dieting behaviours and body measurements. Qualitative data suggest that identity change may be tied to dieting behaviours and weight loss. What does this study add? New insights into the nature of another psychological factor, overweight identification, among individuals with overweight and obesity attempting to lose weight. The first quantitative evidence that different aspects of overweight identification, and changes in these aspects of overweight identification over time, influence body measurements and dieting behaviours.
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How this classification was reachedexpand
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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".