Test-retest reliability of a modified International Physical Activity Questionnaire (IPAQ) to capture neighbourhood physical activity
Bibliographic record
Abstract
Introduction: Few self-report tools capture neighbourhood physical activity.The aim of our study was to modify a widely-used self-report tool (International Physical Activity Questionnaire -IPAQ) to capture neighbourhood physical activity and estimate the test-retest reliability of these modifications.Material and Methods: Seventy-five adults completed the modified IPAQ twice, 7-days apart, capturing neighbourhood days•week -1 and usual minutes•day -1 of bicycling and walking for transport and leisure, moderate physical activity, and vigorous physical activity.Test-retest reliability was assessed with Intraclass Correlations (ICC), percent of overall agreement and Kappa statistics (κ).Results: Consistency in participation in neighbourhood PA ranged from k = 0.21 for moderate physical activity to k = 0.55 for vigorous physical activity, while proportion of overall agreement ranged from 64.0% for moderate physical activity to 81.3% for bicycling for transportation.ICC for reported neighbourhood PA between the two occasions ranged from ICC = 0.33 for moderate physical activity to ICC = 0.69 for bicycling for transportation for days•week -1 , ICC = 0.17 for bicycling for transportation to ICC = 0.48 for walking for leisure for minutes•day -1 , and ICC = 0.31 for vigorous physical activity to ICC = 0.52 for walking for leisure for minutes•week -1 .Conclusions: With the exception of minutes spent bicycling for transportation, our findings suggest that IPAQ items can be modified to provide
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 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".