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Record W3160884974 · doi:10.1016/j.orcp.2021.05.003

Mental health and socioeconomic status impact adherence to youth activity and dietary programs: a meta-analysis

2021· review· en· W3160884974 on OpenAlexafffund
Mark Lemstra, Marla Rogers

Bibliographic record

VenueObesity Research & Clinical Practice · 2021
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Saskatchewan
FundersPublic Health Agency of Canada
KeywordsSocioeconomic statusMeta-analysisMental healthEnvironmental healthGerontologyPsychologyMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Substantial efforts have gone into reducing the physiological and psychological harm of obesity in youth, but few studies have reviewed the factors contributing to adherence to pediatric weight management programs. The attrition rates to programs offering multiple components to address BMI improvement and healthy lifestyle change among youth are quite high. The purpose of this study is to review the literature for factors contributing to adherence to these programs among children and youth with obesity and determine pooled effect of these factors. METHODS: A systematic literature search and meta-analysis was conducted through the PubMed database on pediatric weight management interventions offering at least physical activity and dietary support for obese youth aged 10-17 years, where variables contributing to adherence were reported. Only those studies achieving a threshold of methodological rigour were included. RESULTS: Altogether, seven studies were included in the analysis. There was a pooled RR of lower socioeconomic status on non-adherence of 1.34 [95% confidence intervals 1.19-1.52] and poorer mental health on non-adherence of 1.12 [95% confidence intervals 1.08-1.17]. CONCLUSION: It is important to address barriers related to lower socioeconomic status in pediatric weight management programs to increase adherence. Further, addressing supports for those with poorer mental health can reduce the risk of non-adherence in multi-disciplinary programs targeting youth with obesity.

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)0.015
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.775
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0110.004
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.006
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 head0.520
GPT teacher head0.602
Teacher spread0.082 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations13
Published2021
Admission routes2
Has abstractyes

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