Validity of the Fitbit wearable activity monitor to estimate step counts in free-living conditions in ambulatory children and youth living with disability
Bibliographic record
Abstract
PURPOSE: To assess the validity of the Fitbit ChargeHR versus a research-grade accelerometer (ActiGraph GT3X) for estimating daily step counts in free-living conditions in ambulatory children and youth living with physical and non-physical disabilities. MATERIALS AND METHODS: = 2 with mobility aid) wore the GT3X ActiGraph accelerometer (hip) and the Fitbit ChargeHR (wrist) for seven days. Inter-device agreement in steps/day was assessed by intraclass correlation coefficients (ICCs) and Bland-Altman plots. A receiver operating curve (ROC) was used to determine a Fitbit step-count cut-point that corresponds to meeting physical activity guidelines (defined as ≥60 min of moderate-to-vigorous physical activity per day). RESULTS: < 0.001) between daily step counts measured by the two devices. Bland-Altman analyses revealed a mean difference ("bias") between the devices with the Fitbit recording, on average, 1,388 more steps/day than the accelerometer (Limits of Agreement (LoA) 1,741 to -4,518 steps per day). The ROC revealed a Fitbit cut-point of 12,272 steps/day corresponding to meeting guidelines. CONCLUSIONS: Fitbit ChargeHR devices tend to overestimate daily step counts, but may still provide useful estimates of step counts and patterns in children and youth living with disability.
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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 | 0.022 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".