The Role of Measurement Error in Estimating Levels of Physical Activity
Bibliographic record
Abstract
Epidemiologic studies have demonstrated that physical inactivity is an important determinant of numerous chronic diseases. However, self-reported estimates of physical activity contain measurement errors responsible for attenuating relative risk estimates. A validation study conducted in 2002-2003 at the Alberta Cancer Board (Canada) included a physical activity questionnaire, four 7-day physical activity logs, and four sets of accelerometer data from 154 study subjects (51% women) aged 35-65 years. The authors used a measurement error model to evaluate validity of the different types of physical activity assessment, and the attenuation factors, after taking into account error correlations between self-reported measurements. The validity coefficients, which express the correlation between measured and true exposure, were higher for accelerometers (0.81, 95% confidence interval (CI): 0.76, 0.85) compared with the physical activity log (0.57, 95% CI: 0.47, 0.66) and questionnaire measurements (0.26, 95% CI: 0.12, 0.40). The estimate of the attenuation factor for questionnaires was 0.13 (95% CI: 0.05, 0.23). Accuracy of physical activity questionnaire measurements was higher for men than for women, for younger individuals, and for those with a lower body mass index. Because the degree of attenuation in relative risk estimates is substantial, after the role of error correlations was considered, validation studies quantifying the impact of measurement errors on physical activity estimates are essential to evaluate the impact of physical inactivity on health.
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 | 0.265 | 0.583 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".