Effect of ActiGraph GT3X+ Position and Algorithm Choice on Step Count Accuracy in Older Adults
Bibliographic record
Abstract
Accelerometer step count accuracy may be affected by gait speed, device positioning, and analysis algorithm selection. We assessed ActiGraph GT3X+ step count accuracy related to device placement and analysis algorithm in older adults with walking aids (n = 13) and without walking aids (n = 22). Participants (81.5 ± 5.0 years of age) completed a timed 100-m walk wearing five GT3X+ monitors (hips, ankles, lumbar spine). Individuals with walking aids had slower gait speeds (0.8 ± 0.20 m/s versus 1.2 ± 0.20 m/s without walking aids, p < .001). Intraclass correlation coefficient values for observed versus monitored steps were highest when ankle placement and the low frequency extension (LFE) algorithm were used (left ankle ICC = .989, right ankle ICC = .998). Using the GT3X+ ankle placement and analyzing data with the LFE algorithm resulted in the most accurate step counts in older adults.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".