Validating MOSPA questionnaire for measuring physical activity in Pakistani women
Bibliographic record
Abstract
BACKGROUND: Precise measurements of activity at a population level are important for monitoring trends and evaluating health promotion strategies. Few studies have assessed the measurement of physical activity in developing countries. The aim of this study was to validate the MOSPA (Monica Optional Study of Physical Activity) questionnaire which was developed for the WHO-Monitoring trends and determinants of cardiovasculr disease (MONICA) study sites. METHODS: The MOSPA questionnaire assesses energy expendtiture (EE) related to physical activity (employment, household work, transportation, and leisure time) over a one year period. This questionnaire has been described in the manuscript as the long term (LT) questionnaire. An adapted short term (ST) 5 day questionnaire was developed to assess convergent validity. Questionnaire data were compared with physical activity EE estimates from a Caltrac accelerometer and with body composition measures (height, weight and bioelectrical impedance) in 50 women from the Aga Khan University (AKU) hospital antenatal clinics, Pakistan. Other forms of EE i.e. resting EE and thermic effect of food were not assessd in this study. RESULTS: Subjects were aged 26 +/- 3.8 years and were 16.1 +/- 6.7 weeks pregnant. Their average weight was 58.8 +/- 10.7 Kg. The average EE/day assessed by the Caltrac accelerometer, was 224 kcal and by MOSPA LT questionnaire it was 404 kcal. The questionnaires and Caltrac data were reasonably well correlated: r = 0.51 and r = 0.60 (P < 0.01) for LT and ST questionnaires respectively. Energy expenditure from questionnaire data was not correlated with body composition measures. CONCLUSION: The MOSPA questionnaire is useful in assessing physical activity levels in a sedentary population over a one year period.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".