Differences in Demographic, Behavioral, and Biological Variables Between Those With Valid and Invalid Accelerometry Data: Implications for Generalizability
Bibliographic record
Abstract
Background: The exclusion of participants with invalid accelerometry data (IAD) may lead to biased results and/or lack of generalizability in large population studies. The purpose of this study was to investigate whether demographic, behavioral, and biological differences occur between those with IAD and valid accelerometry data (VAD) among adults using a representative sample of the civilian noninstitutionalized U.S. population. Methods: Ambulatory participants from NHANES (2003−2004) who were 20−85 years of age were included in the current study and wore an ActiGraph 7164 accelerometer for 7 days. A “valid person” was defined as those with 4 or more days of at least 10+ hrs of monitoring per day. Among adults (20−85 yrs), 3088 participants provided VAD and 987 provided IAD. Demographic, behavioral, and biological information were obtained from the household interview or from data obtained in a mobile examination center. Results: Differences were observed in age, BMI, ethnicity, education, smoking status, marital status, use of street drugs, current health status, HDL-cholesterol, C-reactive protein, self-reported vigorous physical activity, and plasma glucose levels between those with VAD and IAD. Conclusions: Investigators should take into consideration the potential cut-off bias in interpreting results based on data that excludes IAD participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".