Using Accelerometer/GPS Data to Validate a Neighborhood-Adapted Version of the International Physical Activity Questionnaire (IPAQ)
Bibliographic record
Abstract
Despite continued interest in neighborhood correlates of physical activity, few self-report questionnaires exist that capture neighborhood-based physical activity. Furthermore, there is little evidence about the measurement validity of self-report measures of neighborhood-based physical activity. Notably, self-reported neighborhood physical activity has not been validated against combined accelerometer and global positioning system (GPS)–assessed physical activity. Thus, the purpose of this study was to estimate the concurrent validity of a recently adapted tool for capturing self-reported neighborhood-based physical activity (i.e., the Neighborhood International Physical Activity Questionnaire; N-IPAQ). Adults (n = 75) from four Calgary (Alberta, Canada) neighborhoods wore an accelerometer and GPS monitor for 7 consecutive days after which they self-reported their physical activity from the past week using the N-IPAQ. Bland-Altman plots and Spearman correlations estimated the concurrent validity between N-IPAQ and accelerometer/GPS physical activity (estimated for the administrative boundary, 400-m and 800-m radial buffers). The mean (95% Confidence Interval [CI]) difference between the N-IPAQ and accelerometer/GPS estimated total daily minutes of physical activity differed for the 400-m (1.9 min, −26.2 to 29.9), 800-m (10.6 min, −16.0 to 37.1), and administrative boundary buffers (14.7 min, −11.5 to 41.0). The strongest Spearman correlations were found between the N-IPAQ and 800-m radial buffer accelerometer-captured vigorous-intensity physical activity (r = .41 [95% CI: .18 to .60]), and the N-IPAQ and administrative boundary accelerometer-captured vigorous-intensity physical activity (r = .43 [95% CI: .20 to .62]). Our findings suggest that the N-IPAQ provides good estimates of neighborhood-based physical activity and could be used when investigating neighborhood correlates of physical activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".