Validation of PiezoRx Pedometer Derived Sedentary Time
Bibliographic record
Abstract
International Journal of Exercise Science 11(7): 552-560, 2018. Although pedometers are valid tools for measuring physical activity, to date they have not been used to assess sedentary time. The primary purpose of this study was to determine if the PiezoRx pedometer is a valid and reliable measure of sedentary time compared to the hip-worn Actical accelerometer. A secondary purpose was to compare sedentary time derived via the Fitbit Flex with that of the Actical. Finally, a third purpose was to compare sedentary time derived from the above devices, with that of the ActivPAL inclinometer. Thirty-five participants ages 11-69 years (Mage= 23.3; 21 Female) wore five devices for up to one week: two PiezoRx pedometers, an Actical, an ActivPAL and a Fitbit Flex. Participants recorded daily wear-time of each device using a log sheet. The average sedentary time calculated from the PiezoRx (716±137.68 min/day) was not different from the Actical (694 ±136.11 min/day, p>0.05), although it was higher than the ActivPAL (475±171.52 min/day) and Fitbit Flex (530±149.94 min/day, all p<0.001). Sedentary time from all devices were significantly correlated with each other, with the strongest relationship seen between the Actical and PiezoRx (R2=0.93, p<0.001). In comparison to the ActivPAL, error in PiezoRx- (R2=0.96), Actical- (R2=0.96) and Fitbit Flex- (R2=0.34) determined sedentary time was strongly associated with standing time (all p<0.001). Sedentary time derived using the PiezoRx pedometer may be statistically equivalent to the Actical accelerometer, but not the ActivPAL inclinometer or Fitbit Flex.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".