A latent profile analysis based on diet quality and eating behaviours in participants of the PREDISE study characterized by a higher BMI
Bibliographic record
Abstract
The aim of this study was to identify eating-related latent profiles based on diet quality and eating behaviours within a population characterized by a body mass index (BMI) of at least 25 kg/m2, and to compare metabolic variables between profiles. This analysis was conducted in a sample of 614 adults (45.6% women; 44.8 ± 12.9 years) from the cross-sectional PREDISE study. Participants completed the Three-Factor Eating Questionnaire, the Intuitive Eating Scale-2, the Regulation of Eating Behavior Scale, and three self-administered 24 h food recalls. Waist circumference, blood lipids, blood pressure, and fasting glucose were measured to identify carriers of the metabolic syndrome. A latent profile analysis was performed, and cases of metabolic syndrome were compared between profiles. A three-profile solution was found. Profile 1 (22.8%) was characterized by lower diet quality, lower self-determined motivation for eating, lower restraint, and higher intuitive eating. Profile 2 (44.5%) was characterized by higher diet quality, higher self-determined motivation for eating, higher restraint, lower disinhibition, and higher intuitive eating. Profile 3 (32.7%) was characterized by intermediate diet quality, higher non-self-determined motivation for eating, higher restraint and disinhibition, and lower intuitive eating. We found fewer cases of metabolic syndrome among participants in profile 2 than in the other profiles ( p = 0.0001). This study suggests that a profile characterized by a lower disinhibition and higher levels of restraint, intuitive eating, self-determined motivation, and diet quality is associated with a better metabolic health among individuals with a higher BMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".