STEP COUNT ACCURACY AND RELIABILITY FROM ACTIVITY MONITORS WORN BY MOBILITY-INTACT OLDER ADULTS
Bibliographic record
Abstract
Accurate and reliable measurement of physical activity is necessary to assess adherence to and effects of physical activity interventions within a growing population of physically inactive older adults. The STRIDES Study was designed to determine accuracy (criterion=tally counter) and reliability (test-retest) of step counting in mobility-intact older adults from six wearable activity monitors (Fitbit Charge, Fitbit One, Garmin Vivofit2, Jawbone UP2, Misfit Shine, and New-Lifestyles 1000 pedometer). Participants (n=10, 69–80 years) completed a continuous 400-meter walk (average speed 1.3m/s), a non-continuous 400-meter walk (1.1m/s) designed to mimic daily walking patterns incorporating small disturbances, and two 100-step reliability walks. For the continuous walk, five monitors had small mean errors less than +/-5% (One -0.3%,95%CI=-0.4to-0.2%; Vivofit2 -0.6%,CI=-1.4to0.2%; UP2 -3.2%,CI=-5.7to-0.6%; Shine 0.5%,CI=-6.3to7.4%; New-Lifestyles -0.7%,CI=-1.1to-0.3%) while error for the Charge was larger (-7.7%;CI=-17.1to1.6%). Step counts from the One, Vivofit2 and New-Lifestyles were strongly correlated with criterion (r≥0.95). For the non-continuous walk, the One, Vivofit2, and Shine had small errors less than +/-5% (-4.4%,CI=-5.4to-3.5%; -4.2%,CI=-8.1to-0.2%; -1.4%,CI=-5.6to2.8%), while errors for the Charge, UP2 and New-Lifestyles were larger (-8.1%,CI=-15.5to-0.6%; -6.3%,CI=-10.6to-2.1%; -6.2%,CI=-11.4to-0.9%). Only step counts from the One were strongly correlated with criterion (r=0.95). The Charge, One, Vivofit2, and New-Lifestyles were reliable with no significant differences in step counts between trials, while the Shine and UP2 were unreliable (mean differences between trials of 25.6 and 14.6%, p<0.05). In summary, we found that most activity monitors were reliable and accurate for step counting in older adults during continuous walking, while accuracy of some monitors was compromised during non-continuous walking.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".