Cognitive Testing of the STAR-Q: Insights in Activity and Sedentary Time Reporting
Bibliographic record
Abstract
PURPOSE: The qualitative attributes and quantitative measurement properties of physical activity questionnaires are equally important considerations in questionnaire appraisal, yet fundamental aspects such as question comprehension are not often described in the literature. Here we describe the use of cognitive interviewing to evaluate the Sedentary Time and Activity Reporting Questionnaire (STAR-Q), a self-administered questionnaire designed to assess overall activity energy expenditure and sedentary behavior. METHODS: Several rounds of one-on-one interviews were conducted by an interviewer trained in qualitative research methods. Interviewees included a convenience sample of volunteers and participants in the Tomorrow Project, a large cohort study in Alberta, Canada. Following each round of interviews the STAR-Q was revised and cognitively tested until saturation was achieved. RESULTS: Six rounds of cognitive interviewing in 22 adults (5 males, 17 females) age 23-74 years, led to revisions involving 1) use of recall aids; 2) ambiguous terms; and 3) specific tasks, such as averaging across multiple routines, reporting time asleep and self-care, and reporting by activity domain. CONCLUSIONS: Cognitive interviewing is a critical step in questionnaire development. Knowledge gained in this study led to revisions that improved respondent acceptability and comprehension of the STAR-Q and will complement ongoing validity testing.
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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 | 0.134 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".