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Record W4296248590 · doi:10.1186/s12889-022-14131-7

Body image and health-related behaviors among fitspirit participants

2022· article· en· W4296248590 on OpenAlexafffund
Manon Bordeleau, Jo‐Anne Gilbert, Natalie Alméras, Johana Monthuy‐Blanc, Joël Gagnon, Marie-Eve Mathieu, Vicky Drapeau

Bibliographic record

VenueBMC Public Health · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversité du Québec à Trois-RivièresInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalUniversité LavalNatural Sciences and Engineering Research Council of CanadaCentre Hospitalier Universitaire Sainte-JustineInstitut universitaire de cardiologie et de pneumologie de Québec
FundersInstitut de Cardiologie de MontréalUniversité de MontréalPublic Health AgencyPublic Health Agency of CanadaUniversity of Ottawa
KeywordsOverweightBody mass indexBiostatisticsMedicineObesityLinear regressionDemographyCross-sectional studyIntervention (counseling)Public healthGerontologyStatisticsPsychiatryInternal medicineMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Body image variables such as body size perception (BSP) and body size dissatisfaction (BSD) can influence health-related behaviors. However, few studies have investigated these body image variables in adolescent girls participating in a physical activity intervention. Therefore, the study objectives were to examine the 1) associations between health-related behaviors (physical activity, screen-time, eating habits and sleep duration) and BSP and BSD among girls participating in FitSpirit, a physical activity intervention for girls; and 2) influence of weight control practices on the association between health-related behaviors and BSP and BSD. METHODS: This cross-sectional study assessed a sample of 545 adolescent girls (mean age: 15.0±1.5 years) from 240 schools. Body mass index, health-related behaviors, perceived actual body size and desired body size variables were self-reported and collected via an online questionnaire at the end of the FitSpirit intervention. A negative BSP score [perceived actual body size - calculated BMI z-score] indicates an underestimation of body size. A positive BSD score [perceived actual body size - desired body size] indicates a desire to reduce body size. A multiple linear regression analysis examined the effects of age, zBMI and health behaviors on BSP and BSD. A second multiple linear regression analysis examined the independent associations between BSP and BSD by weight control practice. The linear relationships between BSP and BSD were evaluated with Pearson's correlations. RESULTS: Underestimation and dissatisfaction of body size are more prevalent in participants living with overweight/obesity. Screen-time and sleep duration were independently associated with BSP score (Beta=0.02; P<0.05 and Beta=-0.07; P<0.05, respectively), whereas only screen-time was associated with the BSD score (Beta=0.07; P<0.001). Physical activity was independently associated with the BSP score only in participants trying to control (maintain) their weight (Beta=-0.18; P<0.05). CONCLUSIONS: Body size overestimation and dissatisfaction are associated with health-related behaviors, specifically with more screen-time and less optimal sleeping habits. Physical activity level does not appear to be associated with body image in girls engaged in a physical activity intervention and who want to lose or gain weight. Health promotion interventions could include screen-time and sleep components as they may influence body image.

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)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.097
GPT teacher head0.395
Teacher spread0.298 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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