Taxonomy-based content analysis of sedentary behavior questionnaires: A systematic review
Bibliographic record
Abstract
BACKGROUND: Health effects of sedentary behaviors (SB) may vary depending on their characteristics such as type, purpose, duration, and intensity of the behavior. While a growing number of questionnaires assess sedentary behaviors, it is unclear which characteristics of SB are measured. The aim of this review was to examine the content of self-report SB questionnaires. METHODS: Three databases were searched for sedentary behavior questionnaires published before January 1st, 2016. Based on the inclusion criteria, 82 articles out of 1369 were retrieved for a total of 60 questionnaires. For each questionnaire, the sedentary behavior characteristics identified were reported and analyzed. RESULTS: Most of the questionnaires assessed the time (n = 60), posture (n = 54), purpose (n = 46) and the types (n = 45) of SB performed. Fewer questionnaires assessed the environment (n = 20) social context (n = 11), status (n = 2), and associated behaviors (n = 2) related to sedentary behaviors. All the questionnaires except two assessed time spent in SB with 17 assessing frequency and 6 assessing breaks in SB. The most frequent characteristics identified in the questionnaires were the categories of sitting (90%), a day (95%), watching television (65%) and using a computer (55%). Many characteristics of SB were not measured. CONCLUSIONS: By knowing the breadth of SB included in questionnaires, this review provides support to shape the design of new questionnaires designed to reduce the gaps in measuring sedentary behaviors.
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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 | 0.038 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.040 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".