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Record W4403109008 · doi:10.1007/s40279-024-02104-2

Improving National and International Surveillance of Movement Behaviours in Childhood and Adolescence: An International Modified Delphi Study

2024· article· en· W4403109008 on OpenAlexaff
John J. Reilly, Rachel Andrew, Chalchisa Abdeta, Liane B. Azevedo, Nicolás Aguilar-Farías, Sharon Barak, Farid Bardid, Bruno Bizzozero‐Peroni, Javier Brazo‐Sayavera, Jonathan Y. Cagas, Mohamed Souhaiel Chelly, Lars Breum Christiansen, Visnja Djordjic, Catherine E. Draper, Asmaa El Hamdouchi, Elie-Jacques Fares, Aleš Gába, Kylie D. Hesketh, Mohammad Sorowar Hossain, Yajun Huang, Alejandra Jáuregui, Sanjay Juvekar, Nicholas Kuzik, Richard Larouche, Eun‐Young Lee, Sharon Levi, Yang Liu, Marie Löf, Tom Loney, José Francisco López‐Gil, Evelin Mäestu, Taru Manyanga, Clarice Martins, María Mendoza-Muñoz, Shawnda A. Morrison, Nyaradzai Munambah, Tawonga Mwase‐Vuma, Rowena Naidoo, Reginald T-A. Ocansey, Anthony D. Okely, Aoko Oluwayomi, Susan Paudel, Bee Koon Poh, Evelyn Helena Corgosinho Ribeiro, Diego Augusto Santos Silva, Mohd Razif Shahril, Melody Smith, Amanda E. Staiano, Martyn Standage, Narayan Subedi, Chiaki Tanaka, Hong Tang, David Thivel, Mark S. Tremblay, Edin Užičanin, Dimitris Vlachopoulos, E. Kipling Webster, Dyah Anantalia Widyastari, Paweł Zembura, Salomé Aubert

Bibliographic record

VenueSports Medicine · 2024
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsHealth CanadaUniversity of Northern British ColumbiaUniversity of LethbridgeQueen's UniversityChildren's Hospital of Eastern Ontario
FundersScottish Funding Council
KeywordsLikert scaleDelphi methodScale (ratio)Medical educationMedicineDelphiApplied psychologyPsychologyGeographyDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The actions required to achieve higher-quality and harmonised global surveillance of child and adolescent movement behaviours (physical activity, sedentary behaviour including screen time, sleep) are unclear. OBJECTIVE: To identify how to improve surveillance of movement behaviours, from the perspective of experts. METHODS: This Delphi Study involved 62 experts from the SUNRISE International Study of Movement Behaviours in the Early Years and Active Healthy Kids Global Alliance (AHKGA). Two survey rounds were used, with items categorised under: (1) funding, (2) capacity building, (3) methods, and (4) other issues (e.g., policymaker awareness of relevant WHO Guidelines and Strategies). Expert participants ranked 40 items on a five-point Likert scale from 'extremely' to 'not at all' important. Consensus was defined as > 70% rating of 'extremely' or 'very' important. RESULTS: We received 62 responses to round 1 of the survey and 59 to round 2. There was consensus for most items. The two highest rated round 2 items in each category were the following; for funding (1) it was greater funding for surveillance and public funding of surveillance; for capacity building (2) it was increased human capacity for surveillance (e.g. knowledge, skills) and regional or global partnerships to support national surveillance; for methods (3) it was standard protocols for surveillance measures and improved measurement method for screen time; and for other issues (4) it was greater awareness of physical activity guidelines and strategies from WHO and greater awareness of the importance of surveillance for NCD prevention. We generally found no significant differences in priorities between low-middle-income (n = 29) and high-income countries (n = 30) or between SUNRISE (n = 20), AHKGA (n = 26) or both (n = 13) initiatives. There was a lack of agreement on using private funding for surveillance or surveillance research. CONCLUSIONS: This study provides a prioritised and international consensus list of actions required to improve surveillance of movement behaviours in children and adolescents globally.

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 score0.125
Threshold uncertainty score0.385

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 head0.314
Teacher spread0.297 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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