Validity and reproducibility of a physical activity questionnaire for older adults: questionnaire versus accelerometer for assessing physical activity in older adults
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) is important in older adults for the maintenance of functional ability. Assessing PA may be difficult. Few PA questionnaires have been compared to activity monitors. We examined reproducibility and validity of the self-administered Longitudinal Ageing Study Amsterdam Physical Activity Questionnaire (LAPAQ) against a triaxial accelerometer (ACTR) (Sensewear(®) Pro) in older adults. METHODS: Participants wore the ACTR continuously for two weeks. After 2 (T [time] = 1) and 4 (T = 2) weeks, participants completed the LAPAQ. Since the LAPAQ asks about 2 weeks' worth of physical activity, the ACTR and LAPAQ coincided at T1. T2 was used to assess the reproducibility of the LAPAQ results only. We calculated Pearson's correlation coefficients (PCC) to examine reproducibility and validity. For visualization, we used scatterplots and Bland-Altman plots. With a receiver operating characteristics (ROC) curve we assessed how well the LAPAQ identifies older adults whose activity level is below official recommendations. RESULTS: A total of 89 persons were included. Of the participants, 48% were men; median age was 73, and median body mass index was 25. The 2-week mean total duration of activity was 2788 (ACTR, T = 1), 2439 (LAPAQ T = 1), and 1994 (LAPAQ T = 2) minutes. As a reference, 2 full weeks contained 20,160 minutes. Reproducibility of the LAPAQ was moderate (PCC 0.68, 95% CI 0.55-0.80). The median difference between LAPAQ at T = 1 and the ACTR (LAPAQ minus ACTR) was -510 minutes and the PCC was 0.25 (95% CI 0.07-0.44). The area under the ROC curve was 0.73 (95% CI 0.59-0.86). CONCLUSION: LAPAQ underestimates PA and seems unsuitable for exact measurement in older adults. However, it may be used to determine if a person's PA level is below the recommended level.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".