Validation of the Kenz Lifecorder EX and ActiGraph GT1M accelerometers for walking and running in adults
Bibliographic record
Abstract
Accelerometer-based activity monitors are commonly used by researchers and clinicians to assess physical activity. Recently, the Kenz Lifecorder EX (KL) and ActiGraph GT1M (AG) accelerometers have been made commercially available, but there is limited research on the validity of these devices. Therefore, we sought to validate step count, activity energy expenditure (EE), and total EE output from the KL and AG during treadmill walking and running. Ten male and 10 female participants performed 10 min treadmill walking and running trials, at speeds of 54, 80, 107, 134, 161, and 188 m.min-1. Step counts were hand tallied by 2 observers, and indirect calorimetry was used to validate the accelerometers' estimates of EE. AG total EE was calculated using the Freedson equation. Analysis of variance (ANOVA) and Pearson's correlations were used to analyze the data. At the slowest walking speed, the AG and KL counted 64% +/- 15% and 92% +/- 6% of the observed steps, respectively. At all other treadmill speeds, both activity monitors undercounted, compared with observed steps, by < or =3%. The KL underestimated activity EE at faster running speeds (p < 0.01), overestimated total EE at some walking speeds, and underestimated total EE at some running speeds (p < 0.01). The Freedson equation inaccurately measured total EE at most walking and running speeds. The KL and the AG are moderately priced accelerometers that provide researchers and clinicians with accurate estimates of step counts and activity EE at most walking and running speeds.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".