Implementation of foot thermometry plus mHealth to prevent diabetic foot ulcers: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Diabetic foot neuropathy (DFN) is one of the most important complications of diabetes mellitus; its early diagnosis and intervention can prevent foot ulcers and the need for amputation. Thermometry, measuring the temperature of the feet, is a promising emerging modality for diabetic foot ulcer prevention. However, patient compliance with at-home monitoring is concerning. Delivering messages to remind patients to perform thermometry and foot care might be helpful to guarantee regular foot monitoring. This trial was designed to compare the incidence of diabetic foot ulcers (DFUs) between participants who receive thermometry alone and those who receive thermometry as well as mHealth (SMS and voice messaging) over a year-long study period. METHODS/DESIGN: This is an evaluator-blinded, randomized, 12-month trial. Individuals with a diagnosis of type 2 diabetes mellitus, aged between 18-80 years, having a present dorsalis pedis pulse in both feet, are in risk group 2 or 3 using the diabetic foot risk classification system (as specified by the International Working Group on the Diabetic Foot), have an operating cell phone or a caregiver with an operating cell phone, and have the ability to provide informed consent will be eligible to participate in the study. Recruitment will be performed in diabetes outpatient clinics at two Ministry of Health tertiary hospitals in Lima, Peru. INTERVENTIONS: participants in both groups will receive education about foot care at the beginning of the study and they will be provided with a thermometry device (TempStat™). TempStat™ is a tool that captures a thermal image of the feet, which, depending on the temperature of the feet, shows different colors. In this study, if a participant notes a single yellow image or variance between one foot and the contralateral foot, they will be prompted to notify a nurse to evaluate their activity within the previous 2 weeks and make appropriate recommendations. In addition to thermometry, participants in the intervention arm will receive an mHealth component in the form of SMS and voice messages as reminders to use the thermometry device, and instructions to promote foot care. OUTCOMES: the primary outcome is foot ulceration, evaluated by a trained nurse, occurring at any point during the study. DISCUSSION: This study has two principal contributions towards the prevention of DFU. First, the introduction of messages to promote self-management of diabetes foot care as well as using reminders as a strategy to improve adherence to daily home-based measurements. Secondly, the implementation of a thermometry-based strategy complemented by SMS and voice messages in an LMIC setting, with wider implications for scalability. TRIAL REGISTRATION: This study is registered in ClinicalTrials.gov: Identifier NCT02373592 .
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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 | 0.017 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".