Quality control and data reduction procedures for accelerometry-derived measures of physical activity.
Bibliographic record
Abstract
BACKGROUND: This article describes four key quality control and data reduction issues that researchers should consider when using accelerometry to measure physical activity: monitor reliability, spurious data, monitor wear time, and number of valid days required for analysis. DATA SOURCE AND METHODS: Exploratory analyses were conducted on an unweighted subsample (n=987) of the accelerometry data from the Canadian Health Measures Survey. Participants were asked to wear an accelerometer for 7 consecutive days. Calibration, reliability, biological plausibility and compliance issues were explored using descriptive statistics. RESULTS: Ongoing calibration is an effective method for identifying malfunctioning accelerometers. The percentage of files deemed viable for analysis depends on participant compliance, the allowable interruption period chosen and the minimum wear-time-per-day criterion. A 60-minute allowable interruption period and 10-hours-per-day wear time criteria resulted in 95% of the subsample having at least 1 valid day, and 84% having at least 4 valid days. INTERPRETATION: Before the derivation of physical activity outcomes, accelerometry data should undergo standardized quality control and data reduction procedures to prevent mis-representation of the results. Incomplete accelerometry data should be handled carefully, and strategies to improve compliance in the field are warranted.
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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 | 0.299 | 0.505 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".