Consumer-Based Physical Activity Monitor as a Practical Way to Measure Walking Intensity During Inpatient Stroke Rehabilitation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Identifying practical ways to accurately measure exercise intensity and dose in clinical environments is essential to advancing stroke rehabilitation. This is especially relevant in monitoring walking activity during inpatient rehabilitation where recovery is greatest. This study evaluated the accuracy of a readily available consumer-based physical activity monitor during daily inpatient stroke rehabilitation physical therapy sessions. METHODS: Twenty-one individuals admitted to inpatient rehabilitation were monitored for a total of 471 one-hour physical therapy sessions which consisted of walking and nonwalking therapeutic activities. Participants wore a consumer-based physical activity monitor (Fitbit One) and the gold standard for assessing step count (StepWatch Activity Monitor) during physical therapy sessions. Linear mixed modeling was used to assess the relationship of the step count of the Fitbit to the StepWatch Activity Monitor. Device accuracy is reported as the percent error of the Fitbit compared with the StepWatch Activity Monitor. RESULTS: A strong relationship (slope=0.99; 95% confidence interval, 0.97-1.01) was found between the number of steps captured by the Fitbit One and the StepWatch Activity Monitor. The Fitbit One had a mean error of 10.9% (5.3) for participants with walking velocities <0.4 m/s, 6.8% (3.0) for walking velocities between 0.4 and 0.8 m/s, and 4.4% (2.8) for walking velocities >0.8 m/s. CONCLUSIONS: This study provides preliminary evidence that the Fitbit One, when positioned on the nonparetic ankle, can accurately measure walking steps early after stroke during inpatient rehabilitation physical therapy sessions. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01915368.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".