Development and validation of the Global Adolescent and Child Physical Activity Questionnaire (GAC-PAQ) in 14 countries: study protocol
Bibliographic record
Abstract
INTRODUCTION: Global surveillance of physical activity (PA) of children and adolescents with questionnaires is limited by the use of instruments developed in high-income countries (HICs) lacking sociocultural adaptation, especially in low- and middle-income countries (LMICs); under-representation of some PA domains; and omission of active play, an important source of PA. Addressing these limitations would help improve international comparisons, and facilitate the cross-fertilisation of ideas to promote PA. We aim to develop and assess the reliability and validity of the app-based Global Adolescent and Child Physical Activity Questionnaire (GAC-PAQ) among 8-17 years old in 14 LMICs and HICs representing all continents; and generate the 'first available data' on active play in most participating countries. METHODS AND ANALYSIS: Our study involves eight stages: (1) systematic review of psychometric properties of existing PA questionnaires for children and adolescents; (2) development of the GAC-PAQ (first version); (3) content validity assessment with global experts; (4) cognitive interviews with children/adolescents and parents in all 14 countries; (5) development of a revised GAC-PAQ; (6) development and adaptation of the questionnaire app (application); (7) pilot-test of the app-based GAC-PAQ; and, (8) main study with a stratified, sex-balanced and urban/rural-balanced sample of 500 children/adolescents and one of their parents/guardians per country. Participants will complete the GAC-PAQ twice to assess 1-week test-retest reliability and wear an ActiGraph wGT3X-BT accelerometer for 9 days to test concurrent validity. To assess convergent validity, subsamples (50 adolescents/country) will simultaneously complete the PA module from existing international surveys. ETHICS AND DISSEMINATION: Approvals from research ethics boards and relevant organisations will be obtained in all participating countries. We anticipate that the GAC-PAQ will facilitate global surveillance of PA in children/adolescents. Our project includes a robust knowledge translation strategy sensitive to social determinants of health to inform inclusive surveillance and PA interventions globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".