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Record W4224706192 · doi:10.1016/j.jneb.2022.01.008

Pilot Study of a Virtual Nutrition Intervention for Adolescents and Young Adults With Autism Spectrum Disorder

2022· article· en· W4224706192 on OpenAlexfundvenueno aff
Acadia W. Buro, Heewon L. Gray, Russell S. Kirby, Jennifer Marshall, Mikaela Strange, Syed Hasan, Jamie M. Holloway

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2022
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsnot available
FundersNational Cancer InstituteCanadian Society of TransplantationUniversity of South Florida
KeywordsAutism spectrum disorderIntervention (counseling)AutismPsychologyClinical psychologyDevelopmental psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.305
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2022
Admission routes2
Has abstractyes

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