Validity of the SC-StepMX pedometer during treadmill walking and running
Bibliographic record
Abstract
The purpose of this study was to examine the validity of the SC-StepMX pedometer for measuring step counts. A convenience sample of 40 participants wore 4 SC-StepMX pedometers, 2 Yamax DigiWalker pedometers, and 2 Actical accelerometers around their waist on a treadmill at 4 speeds based on each participant's self-paced walking speed (50%, 100%, 180%, and 250%; range: 1.4-14.1 km·h(-1)). The SC-StepMX demonstrated lower mean absolute percent error (-0.2%) compared with the Yamax DigiWalker (-20.5%) and the Actical (-26.1%). Mean measurement bias was lower for the SC-StepMX (0.1 ± 9.1; 95% confidence interval = -17.8 to 18.0 steps·min(-1)) when compared with both the Yamax DigiWalker (-15.9 ± 23.3; 95% confidence interval = -61.6 to 29.7 steps·min(-1)) and the Actical (-22.0 ± 36.3; 95% CI = -93.1 to 49.1 steps·min(-1)). This study demonstrates that the SC-StepMX pedometer is a valid tool for the measurement of step counts. The SC-StepMX accurately measures step counts at slower walking speeds when compared with 2 other commercially available activity monitors. This makes the SC-StepMX useful in measuring step counts in populations that are active at lower intensities (e.g., sedentary individuals, the elderly).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
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 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".