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Record W4396683207

The benefits of physical literacy in overweight and obese middle school adolescents

2023· dissertation· en· W4396683207 on OpenAlexaboutno aff
Charlie Nezondet

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2023
Typedissertation
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsOverweightLiteracyPsychologyObesityMedicineGerontologyDevelopmental psychologyPedagogyInternal medicine
DOInot available

Abstract

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Promoting Physical Activity (PA) is one of the main objectives in the prevention and treatment of paediatric overweight and obesity. PA has many physical, psychological and social benefits in the fight against the emergence or progression of overweight and obesity in adolescents. However, this population has difficulty being physically active. Physical Inactivity (PI), described as "obesogenic behaviour", can therefore be explained by environmental opportunities that are unfavourable to the practice of PA and physical and psycho-motivational limitations due to excess weight. The implementation of PA support programmes for overweight and obese adolescents must therefore take into account their physical and social environments as well as their various limitations to PA. It therefore seems appropriate to introduce the concept of Physical Literacy (PL) into PA support for this population. PL takes into account the individual's environment ("socio-ecological" model) and the physical and psycho-motivational aspects of PA. The aim of this thesis is to develop an intervention based on the development of PL for overweight and obese adolescents, enabling them to increase their Moderate to Vigorous Physical Activity (MVPA) and improve their health indicators.In the first study, we summarised the roles and guidelines for PA in overweight and obese adolescents. In conclusion, this study shows that promoting PA in this population requires the implementation of a multidisciplinary adapted exercise programme that is integrated into the adolescent's environment.The second study in this thesis explored the relationship between PL, MVPA and health indicators (body composition and Cardio-Respiratory Fitness (CRF)) in 85 adolescents at secondary school. We presumed that adolescents with a high PL were also those with high MVPA and favourable health indicators. The main results show that PL is positively associated with MVPA, CRF and % Skeletal Muscle Mass (%SMM). LP is also negatively associated with % Body Fat (%BF). These results confirm our initial hypotheses and the links between PL, MVPA and health indicators. They therefore suggest that the development of PL may increase MVPA and the health of adolescents. This study also enabled us to identify the adolescents targeted by our intervention.Studies 3 and 4 focus on the results of the CAPACITES 64 intervention on 14 overweight and obese adolescents. The aim of this 15-month intervention was to develop PL in the target group of teenagers. All the parameters were assessed objectively (0, 9 and 15 months), the PL with the Canadian Assessment Physical Literacy (CAPL), the MVPA with accelerometry, the CRF with an adapted shuttle running test and the body composition with impedancemetry. This intervention increased PL by 10.6 (±9.1) points (p≤ 0.01), decreased %MG by 4.5% (±1.8) and BMI zscore by -0.4 (±0.4) (p≤ 0.01). It also increased %SMM by 2.6 (±2.7) (p≤ 0.01), CRF by 2.7 ml.min.kg-1 (±2.1) (p≤ 0.01) and MVPA by 8.5 min/day (±18.3) (p= 0.09) after 15 months. This thesis therefore showed that developing PL in overweight and obese adolescents was a promising strategy for increasing their active behaviour and improving their long-term health. Moreover, the CAPACITES 64 intervention shows that a systemic approach in the adolescent's environment is adapted to this adolescents in order to promote PA.

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)0.004
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.257
Teacher spread0.245 · 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
Published2023
Admission routes1
Has abstractyes

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