Changes in Eating Behaviors and Confidence Towards Cooking After an 8-Week Online Cooking and Nutrition Tutorials in Adults Living with an Overweight Condition or Obesity
Bibliographic record
Abstract
This study tested the impact of online cooking and nutrition tutorials on eating behaviours and psychosocial determinants of cooking skills among adults living with an overweight condition or obesity. Healthy adults 18–65 y living with an overweight condition or obesity (25 < body mass index [BMI] < 40 kg/m2) living in the greater Montreal (Quebec) Canada were randomized (1:1:1) to one of three groups: Control [Ctrl; weekly delivery of food with paper-based recipes], Nutrition [weekly delivery of food, access to online cooking and nutrition tutorials] or Behaviour [weekly delivery of food, access to online cooking and eating behaviour tutorials] over 8 weeks. Cooking and education videos were <4 min long total and were viewed through private YouTube links. At baseline and 8 weeks, anthropometrics and sociodemographic were surveyed, as were appetitive traits (i.e., Food Responsiveness, Hunger, Emotional Overeating, Enjoyment of Food, Satiety Responsiveness, Food Fussiness, Emotional Undereating and Slowness in Eating) using the Adult Eating Behavior Questionnaire [AEBQ]. Cooking barriers and confidence towards cooking, confidence in consuming fruits and vegetables and self-efficacy were also assessed. Mixed model ANOVAs were used to test for differences among groups over time. At baseline, forty-eight participants with a mean age of 34.0 ± 12.9 y and BMI of 30.2 ± 4.5 kg/m2 were enrolled. Significant differences were seen in the Slowness in Eating subscale between Behaviour (1.64 ± 0.98) and Ctrl (2.46 ± 0.88) (P < 0.02). At 8 weeks, Emotional Overeating scores significantly decreased in both Nutrition and Behaviour compared to Ctrl (P = 0.03). Other AEBQ subscales did not vary by time or group. From baseline to 8 weeks, all groups significantly increased scores for confidence towards cooking (P < 0.001), confidence in consuming of fruits and vegetables (P < 0.001) and general self-efficacy (P < 0.001). The weekly provision of food baskets with online cooking and nutrition tutorials elicited changes in eating behaviours in adults living with an overweight condition and obesity. Future studies including a long-term follow-up and larger sample size are needed to confirm these positive findings. R. Howard Webster Foundation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".