Foot thermometry with mHeath-based supplementation to prevent diabetic foot ulcers: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND Thermometry monitoring has proven to reduce the occurrence of diabetic foot ulcers (DFU). mHealth may contribute to enhance adherence to this effective intervention. OBJECTIVE Our objective was to compare incidence of DFU in the thermometry plus mHealth reminders intervention arm vs. thermometry-only control arm. METHODS We conducted a randomized trial enrolling adults with type 2 diabetes mellitus with foot-at-risk (risk groups 2 or 3) without foot ulcers and allocating them to control (instruction to use a liquid crystal-based foot thermometer daily) or to intervention (same instruction supplemented with text and voice messages with reminders to use the device and messages to promote foot care) and followed for 18 months. The primary outcome was time to occurrence of DFU. A process evaluation was also conducted. RESULTS A total of 172 patients (63% women, mean age 61 years) were enrolled, 86 to each study group. More patients enrolled in the intervention arm had a history of DFU (66% vs. 48%). Follow-up for the primary endpoint was complete for 158 of 172 participants (92%). DFU cumulative incidence was 24% (19 of 79) in the intervention arm and 11% (9 of 79) in the control arm. After adjusting for history of foot ulceration and study site, the HR for DFU was 1.44 (95% CI 0.65, 3.22). Adherence to ≥80% of daily temperature measurements was 87% (103 of 118) among the study participants that returned the logbook, without difference between intervention and control arm. CONCLUSIONS This trial contributes to the evidence about the value of mHealth to prevent diabetes foot ulcers. CLINICALTRIAL Clinical trials, NCT02373592. Registered 27 February 2015, https://clinicaltrials.gov/ct2/show/NCT02373592
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".