Smartphone-based continuous mobility monitoring of Parkinsons disease patients reveals impacts of ambulatory bout length on gait features
Bibliographic record
Abstract
Smartphone-based remote monitoring is a potential solution for providing long-term, objective assessment of gait and mobility in patients with Parkinsons disease (PD). In the Multiple Ascending Dose study of PRX002/RG7935, forty-four mild to moderate PD patients from cohorts 4 to 6 were included in a smartphone-based assessment for up to 24 weeks, while in a separate control study, thirty-five age-and gender-matched healthy individuals performed the same assessment up to 6 weeks. In total, over 30,000 hours of sensor data from subjects' daily activities were collected. A convolutional recurrent neural network was used for human activity recognition and extracted gait-related activities, followed by a mobility analysis on extracted mobility features during ambulatory bouts and turns. The analysis revealed that PD patients showed significantly lower mobility in terms of average ambulatory bout length - length of time of one continuous ambulatory segment, average per-step power, turn speed, and number of turns per ambulatory minute. In addition, bout-length stratified analysis shows the between-group difference of multiple features is associated with bout lengths. These study results support the potential use of smartphones for long-term mobility monitoring in future clinical practice, and also shed lights on previously inaccessible relationships between bout length and gait features under free-living condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".