Utilization of a Smart Sock for the Remote Monitoring of Patients With Peripheral Neuropathy: Cross-sectional Study of a Real-world Registry
Bibliographic record
Abstract
BACKGROUND: Remote patient monitoring (RPM) devices are increasingly being used in caring for patients to reduce risks of complications. Temperature monitoring specifically has been shown in previous studies to provide a useful signal of inflammation that may help prevent foot ulcers. OBJECTIVE: In this cross-sectional study, we evaluated utilization data for patients who were prescribed smart socks as remote temperature monitoring devices. METHODS: This study evaluated data from a patient registry from January to July 2021. The utilization data, which were collected starting from the first full month since patients were prescribed the smart socks, were evaluated along with retention over time, the average time that the socks were worn, and the number of days that the socks were worn per month and per week. RESULTS: A total of 160 patients wore the smart sock RPM device for 22 to 25 days per month on average. The retention rate was 91.9% (147/160) at the end of the 7-month period; a total of 13 patients were lost to follow-up during this period. The average number of days that the socks were worn per week was 5.8. The percentage of patients with a utilization rate of >15 days ranged from 79.7% (106/133) to 91.9% (125/136) each month. CONCLUSIONS: This study shows a high level of utilization for a smart sock RPM device and a high compliance rate. A future prospective study on the clinical outcomes after the use of the smart socks may further solidify the idea of conducting temperature monitoring for foot ulcer prevention.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".