A Checklist for Evaluating the Methodological Quality of Validation Studies on Self-Report Instruments for Physical Activity and Sedentary Behavior
Bibliographic record
Abstract
CONTEXT: The quality of methodological papers assessing physical activity instruments depends upon the rigor of a study's design. OBJECTIVES: We present a checklist to assess key criteria for instrument validation studies. PROCESS: A Medline/PubMed search was performed to identify guidelines for evaluating the methodological quality of instrument validation studies. Based upon the literature, a pilot version of a checklist was developed consisting of 21 items with 3 subscales: 1) quality of the reported data (9 items: assess whether the reported information is sufficient to make an unbiased assessment of the findings); 2) external validity of the results (3 items: assess the extent to which the findings are generalizable); 3) internal validity of the study (9 items: assess the rigor of the study design). The checklist was tested for interrater reliability and feasibility with 6 raters. FINDINGS: Raters viewed the checklist as helpful for reviewing studies. They suggested minor wording changes for 8 items to clarify intent. One item was divided into 2 items for a total of 22 items. DISCUSSION: Checklists may be useful to assess the quality of studies designed to validate physical activity instruments. Future research should test checklist internal consistency, test-retest reliability, and criterion validity.
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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 | 0.458 | 0.583 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.041 | 0.020 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".