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Record W4319017567 · doi:10.3389/fped.2023.920629

Exploratory examination of the association between physical-mental multimorbidity and physical activity in children

2023· article· en· W4319017567 on OpenAlexafffund
Chloe Bedard, Brian W. Timmons, Mark A. Ferro

Bibliographic record

VenueFrontiers in Pediatrics · 2023
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcMaster UniversityUniversity of Waterloo
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsMedicineAssociation (psychology)Physical activityPhysical examinationPsychiatryGerontologyClinical psychologyPhysical therapyInternal medicinePsychology

Abstract

fetched live from OpenAlex

Children with physical illnesses often experience co-occurring mental illness (known as multimorbidity; MM) and it is currently unknown if MM is associated with physical activity (PA) and if the association differs between internalizing and externalizing disorders. Therefore, the aim of this study was to examine the association between MM and PA. Baseline data from the Multimorbidity in Children and Youth Across the Life Course (MY LIFE) cohort was used. MY LIFE is an ongoing prospective study that follows children ages 2 to 16 years with a chronic physical illness and measures PA using accelerometry and mental illness using the Mini International Neuropsychiatric Interview for Children and Adolescents. 140 children (53.2%) provided valid accelerometer data. Children with internalizing disorders recorded less light (B = -5.87), moderate (B = -1.82), and vigorous PA (B = -1.93) and fewer days meeting PA guidelines [Exp(B) = 0.73] and those with externalizing disorders recorded more light (B = 4.85), moderate (B = 1.78), and vigorous PA (B = 2.41) and more days meeting PA guidelines [Exp(B) = 1.06]. However, only the association between internalizing disorder and days meeting PA guidelines was statistically significant. This study provides preliminary evidence that children with MM may accumulate less PA depending on the type of mental illness they experience.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.265
Teacher spread0.248 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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