Performance of the international physical activity questionnaire (short form) in subgroups of the Hong Kong chinese population
Bibliographic record
Abstract
BACKGROUND: The International Physical Activity Questionnaire (IPAQ-SF) has been validated and recommended as an efficient method to assess physical activity, but its validity has not been investigated in different population subgroups. We examined variations in IPAQ validity in the Hong Kong Chinese population by six factors: sex, age, job status, educational level, body mass index (BMI), and visceral fat level (VFL). METHODS: A total of 1,270 adults (aged 42.9 ± SD 14.4 years, 46.1% male) completed the Chinese version of IPAQ (IPAQ-C) and wore an accelerometer (ActiGraph) for four days afterwards. The IPAQ-C and the ActiGraph were compared in terms of estimated Metabolic Equivalent Task minutes per week (MET-min/wk), minutes spent in activity of moderate or vigorous intensity (MVPA), and agreement in the classification of physical activity. RESULTS: The overall Spearman correlation (ρ) of between the IPAQ-C and ActiGraph was low (0.11 ± 0.03; range in subgroups 0.06-0.24) and was the highest among high VFL participants (0.24 ± 0.05). Difference between self-reported and ActiGraph-derived MET-min/wk (overall 2966 ± 140) was the smallest among participants with tertiary education (1804 ± 208). When physical activity was categorized into over or under 150 min/wk, overall agreement between self-report and accelerometer was 81.3% (± 1.1%; subgroup range: 77.2%-91.4%); agreement was the highest among those who were employed full-time in physically demanding jobs (91.4% ± 2.7%). CONCLUSIONS: Sex, age, job status, educational level, and obesity were found to influence the criterion validity of IPAQ-C, yet none of the subgroups showed good validity (ρ = 0.06 to 0.24). IPAQ-SF validity is questionable in our Chinese population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".