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Record W2469800819 · doi:10.3390/ijerph13070664

Physical Activity Programs with Post-Intervention Follow-Up in Children: A Comprehensive Review According to Categories of Intervention

2016· review· en· W2469800819 on OpenAlexaffabout
Sally Nguyen, Anna‐Luisa Häcker, Mélanie Henderson, Tracie A. Barnett, Marie-Eve Mathieu, Linda S. Pagani, Jean‐Luc Bigras

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2016
Typereview
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de Montréal
Fundersnot available
KeywordsIntervention (counseling)PsychologyMedicineMedical educationPsychiatry

Abstract

fetched live from OpenAlex

Only 9% of Canadian children meet the National Guidelines of 60 min of daily moderate-to-vigorous intensity physical activity. The aim of this review is to assess the mid- and long-term effectiveness of physical activity interventions and their impact on cardiovascular risk factors in children. We assessed the success of interventions within three different categories: those using a behavioural and social approach, an informational approach or an environmental approach. The average number of children included in these studies was 860 (range of 30-5106); the age range was from 2 to 18 years; and the mean intervention duration was 1607 min (range of 12-8160 min). The length of follow-up post-intervention averaged 13 months (ranging from 0.25 to 96 months). A positive impact on physical activity was found in 74% and on any measured outcomes in 90% of the studies reviewed. However, the benefits of physical activity interventions decreased with longer follow-up. Regardless of the approaches, physical activity interventions improved cardiovascular risk factors. However, the challenge of any program is to maintain beneficial effects once the intervention is completed. These findings will inform the development of future intervention programs in order to optimize sustained cardiovascular benefits.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.441
Teacher spread0.351 · 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 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

Citations34
Published2016
Admission routes2
Has abstractyes

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