Pilot Study of a Virtual Nutrition Intervention for Adolescents and Young Adults With Autism Spectrum Disorder
Bibliographic record
Abstract
OBJECTIVE: Examine the impact of a virtual nutrition education program, Bringing Adolescent Learners with Autism Nutrition and Culinary Education (BALANCE), on dietary intake and psychosocial determinants of healthy eating in adolescents and young adults (AYA) with autism spectrum disorder (ASD). METHODS: A sample of AYA with ASD aged 12-21 years (n = 27; 6 groups of 2-7 adolescents) participated in BALANCE, a Social Cognitive Theory-based intervention, for eight 30-45-minute lessons. Outcomes were compared using a pre-post design and included dietary intake (assessed using a food frequency questionnaire) and psychosocial determinants of healthy eating (assessed by a validated survey). Wilcoxon signed-rank tests compared preintervention and postintervention medians with an alpha level of 0.05. RESULTS: Mean added sugar intake (P = 0.026) decreased, and behavioral strategies (P = 0.010), self-efficacy (P < 0.001), and outcome expectations (P = 0.009) improved. There was no difference in fruit or vegetable intake or other psychosocial determinants. CONCLUSIONS AND IMPLICATIONS: The BALANCE intervention may improve psychosocial determinants and dietary behaviors in AYA with ASD. Future virtual programs may incorporate more assistance and support to be accessible for AYA with ASD of varying severity levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".