Determining critical monitoring periods for accurate wearable step counts in patients with degenerative spine disorders
Bibliographic record
Abstract
Longitudinal physical activity monitoring is a novel and promising objective outcome measure for patients with degenerative spine disorder (DSD) that currently lacks established standards for data collection and interpretation. Here, we monitored 100 patients with DSD with the Apple Watch to establish the optimal duration and pattern of step count monitoring needed to estimate their weekly physical activity before their elective surgery. Participants were predominantly female (65.3%), had an average age of 61.5 years, and showed consistent step counts between preoperative days, as well as across weekends and weekdays. Intraclass correlations (ICC) analysis showed that a step count average over 2 days achieved an ICC of 0.92 when compared to a 7-day average before surgery, while 4 days were required for a similar agreement of 0.93 with a 14-day average. Sequential linear regression demonstrated that incorporating additional preoperative days improved the model's ability to predict 7- and 14-days step count averages. We conclude that, while daily preoperative step counts remain relatively stable, longer activity monitoring is necessary to account for the variance in step count over an increasing time frame, and the full extent of data fluctuation may only become apparent with long-term trend analysis.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".