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Record W4392602319 · doi:10.21203/rs.3.rs-4013928/v1

Recent Trends and Disparities in 24-hour Movement Behaviors among US Youth with Mental, Behavioral and Neurodevelopmental Conditions

2024· preprint· en· W4392602319 on OpenAlexaff
Meijun Hou, Fabian Herold, Boris Cheval, Neville Owen, Megan Teychenne, Markus Gerber, Sebastian Ludyga, Tine Van Damme, Md Mahbub Hossain, Albert Yeung, David A. Raichlen, Mats Hallgren, Dominika M. Pindus, Silvio Maltagliati, André O. Werneck, Arthur F. Kramer, Ashleigh E. Smith, Audrey M. Collins, Kirk I. Erickson, Seán Healy, Justin A. Haegele, Martin E. Block, Eun‐Young Lee, Antônio García‐Hermoso, Emmanuel Stamatakis, Teresa Liu‐Ambrose, Ryan S. Falck, Liye Zou

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of British ColumbiaQueen's University
FundersMaternal and Child Health BureauNational Center for Mental HealthNational Health and Medical Research CouncilMedical Research CouncilDementia AustraliaHealth Resources and Services AdministrationU.S. Department of Health and Human ServicesScience, Technology and Innovation Commission of Shenzhen MunicipalityFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPsychologyMovement (music)Mental healthDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Meeting 24-hour movement behaviors (24-HMB: physical activity [PA], screen time [ST], and sleep [SL]) recommendations may be associated with positive health outcomes among youth with specific mental, behavioral, and neurodevelopmental (MBD) conditions. However, temporal trends and disparities in meeting 24-HMB guidelines in these higher-risk groups have not been investigated, hampering the development of evidence-based clinical and public health interventions. Methods Serial, cross-sectional analyses of nationally representative data (including U.S. youth aged 6–17 years with MBD conditions) were conducted. The time-trends survey data was conducted between 2016 and 2021. Meeting single-behavior and integrated 24-HMB guidelines were operationalized as follows: PA ≥ 60 min/d moderate-to-vigorous-intensity PA, no more than 2 h/d of ST, and a sleep duration of 9 to 11 h/d for those aged 6 to 13 years (children) and 8 to 10 h/d for those aged 14 to 17 years (adolescents). Results Data on 52,634 individuals (mean age, 12.0 years [SD,3.5]; 28,829 [58.0%] boys) were analyzed. From 2016 to 2021, the estimated trend of meeting SL guideline alone increased (3.7% [95%CI,3.2–4.9%], P for trend < 0.001), whereas meeting ST guideline alone decreased (-2.0% [-2.4% to -1.5%], P for trend < 0.001). The estimated trend in meeting integrated (PA + ST + SL) guidelines declined (-0.8% [95%CI, -1.0% to -0.5%], P for trend < 0.001), whereas meeting none of 24-HMB guidelines increased (2.2% [1.8–2.6%], P for trend < 0.001). White participants, children, and boys reported higher estimated prevalence of meeting full integrated (PA + ST + SL) guidelines. Discussion The temporal trends observed in this study highlight the importance of consistently monitoring physical behavior among MBD youth and identifying variations by sociodemographic groups in meeting 24-HMB guidelines for health promotion within these vulnerable groups.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.094
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.386
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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