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Multicenter Study
. 2025 Oct;41(7):e70096.
doi: 10.1002/dmrr.70096.

Assessing the Possibility of Recurrent Diabetic Foot Ulcer Prevention via Remote Patient Monitoring: A Feasibility Study

Affiliations
Multicenter Study

Assessing the Possibility of Recurrent Diabetic Foot Ulcer Prevention via Remote Patient Monitoring: A Feasibility Study

Caroline A Abbott et al. Diabetes Metab Res Rev. 2025 Oct.

Abstract

Aims: Our main aims in this study of diabetic patients at risk of foot ulcers were to evaluate: (a) adherence to the use of an at-home thermal and visual digital foot scanner, (b) the feasibility of utilising thermovisual scan data to perform remote foot assessments, thereby enabling, if indicated, remote intervention by podiatrists and (c) the validity of scanned images to identify skin lesions consistent with those found at a podiatric clinical evaluation.

Methods: In this single arm, open-label, 12-week pilot study in two countries, recruited patients with previous diabetic foot ulcer (DFU) (n = 27) were asked to stand on a digital foot scanner (OneStep), once a day at home. Plantar thermal and visual scan data were transmitted daily to a centralised monitoring service for daily review. Any abnormalities were immediately reported to the patient's podiatric healthcare provider, who determined appropriate intervention. Primary endpoints were patient adherence, device utility and data validity.

Results: All participants with an active device (n = 26) took thermal and visual scans on 1547 days during 1940 active study days, averaging 5.3 ± 1.4 scans/week, with 80 ± 19% adherence (days scan recorded/days in study*100). Visual scans correctly identified all incident DFUs (n = 7). Podiatrists agreed that scans enabled the identification of skin integrity issues earlier than standard care (in 82% cases), finding visual scan images useful in 90% of reports and thermal data in 12%. Remote visual assessments agreed well with gold-standard podiatric examinations in identifying skin integrity risks (kappa = 0.67 [95% CI, 0.53-0.82, p < 0.001]), also showing good sensitivity (80%) and specificity (100%).

Conclusions: Remote foot scanning was easy to perform and was used consistently by vulnerable patients. Scans were useful for remote podiatric foot assessments and interventions, and visual images identified DFUs/skin problems to a good level. We now aim to test this monitoring system in a larger scale randomised controlled trial for DFU prevention.

Keywords: diabetic foot; diabetic foot ulcer (DFU); digital health; remote patient monitoring.

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Conflict of interest statement

R.S. received partial funding from Bluedrop Medical, the company acting as sponsor for the study and providing study devices and financial support to the clinical sites for study operations. There are no other potential conflicts of interest relevant to this article.

Figures

FIGURE 1
FIGURE 1
(a) OneStep device, designed to look and behave like a standard home weight scale, taking the Patient 30 s to use per day at home. (b) Visual monitoring scan image from SRI software. The black dots are the patented eTLC (thermochromic liquid crystal) temperature sensors providing the thermal readings.
FIGURE 2
FIGURE 2
Report utility statements, completed by study podiatrists. The clinical usefulness of the thermovisual data from reports (n = 73) to remotely assess and issue guidance to the patient was assessed using a 5‐point Likert scale to grade podiatrists' responses to statements. Data is presented as the percentage number of reports in each Likert response category.
FIGURE 3
FIGURE 3
Device usability statements, completed by the study participants. The usability of the device was assessed in study participants (n = 23) using a 5‐point Likert scale to grade participant's responses to statements. Data are presented as the percentage number of reports in each Likert response category.
FIGURE 4
FIGURE 4
Percentage number of patients with high, medium or no adherence to using their foot scanner device for each study week. Participant data: Week 1, n = 26; Week 2, n = 25; Week 3, n = 25; Week 4, n = 26; Week 5, n = 25; Week 6, n = 24; Week 7, n = 23; Week 8, n = 22; Week 9, n = 22; Week 10, n = 20; Week 11, n = 18; Week 12, n = 18.

References

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