Validity of the Modified SIT-Q 7d for Estimating Sedentary Break Frequency and Duration in Home-Based Office Workers During the COVID-19 Global Pandemic: A Secondary Analysis
Bibliographic record
Abstract
Office workers who transitioned to working from home are spending an even higher percentage of their workday sitting compared with being “in-office” and this is an emerging health concern. With many office workers continuing to work from home since the onset of the COVID-19 pandemic, it is imperative to have a validated self-report questionnaire to assess sedentary behavior, break frequency, and duration, to reduce the cost and burden of using device-based assessments. This secondary analysis study aimed to validate the modified Last 7-Day Sedentary Behavior Questionnaire (SIT-Q 7d) against an activPAL4™ device in full-time home-based “office” workers (n = 148; mean age = 44.90). Participants completed the modified SIT-Q 7d and wore an activPAL4 for a full work week. The findings showed that the modified SIT-Q 7d had low (ρ = .35–.37) and weak (ρ = .27–.28) criterion validity for accurate estimates of break frequency and break duration, respectively. The 95% limits of agreement were large for break frequency (26.85–29.01) and medium for break duration (5.81–8.47), indicating that the modified SIT-Q 7d may not be appropriate for measuring occupational sedentary behavior patterns at the individual level. Further validation is still required before confidently recommending this self-report questionnaire to be used among this population to assess breaks in sedentary time.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".