Associations Between Appetitive Traits and Body Image in Adolescents: A Latent Profile Analysis
Bibliographic record
Abstract
Objectives: Appetitive traits are predispositions toward foods that interact with environmental factors to influence eating behaviors, dietary patterns, and weight trajectories. Few studies have used latent profile analysis (LPA) to examine food approach (e.g., hunger, food responsiveness, emotional overeating, and enjoyment of food) and food avoidance (e.g., satiety responsiveness, emotional undereating, food fussiness, and slowness in eating) appetitive traits in adolescents. Further, to our knowledge, no research has looked at whether and how appetitive traits relate to body image. The main purpose was therefore to investigate these associations in a sample of adolescents using LPA. The purpose of this research was to examine whether and how adolescents’ appetitive traits relate to various dimensions of body image such as body esteem (i.e., positive appreciation and evaluation of one's body), body appreciation (i.e., acceptance of, favorable opinions toward, and respect of one’s body), and body dissatisfaction (i.e., perceived discrepancy between one’s current and ideal body). Methods: Participants were 280 French-Canadian adolescents aged between 14 and 17 years. They completed a cross-sectional online survey assessing appetitive traits, body esteem, body appreciation, and body dissatisfaction. Results: LPA were conducted to identify homogenous subgroups of participants based on their appetitive traits scores. LPA revealed three cluster profile groups: food seekers (higher food approach traits score), moderate eaters (close to mean scores for food approach and avoidance traits, except for lower emotional overeating and higher enjoyment of food), and food avoiders (higher food avoidance traits score). Overall, food seekers and food avoiders reported more negative body image (i.e., higher body dissatisfaction as well as lower body esteem and body appreciation) than did moderate eaters. In addition, food seekers reported higher body dissatisfaction than did food avoiders. Conclusions: This study has important implications for the fields of nutrition and eating psychology given that appetitive traits and body image both relate to adolescents’ eating behaviors and food intake. Funding Sources: Canada Research Chair in the Psychological and Social Determinants of Eating Behaviors Social Sciences and Humanities Research Council of Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".