Self-Monitoring Diabetes-Related Foot Ulcers with the MyFootCare App: A Mixed Methods Study

Guidelines on offloading foot ulcers in persons with diabetes (IWGDF 2023 update)

AIMS

Offloading mechanical tissue stress is arguably the most important of multiple interventions needed to heal diabetes-related foot ulcers. This is the 2023 International Working Group on the Diabetic Foot (IWGDF) evidence-based guideline on offloading interventions to promote healing of foot ulcers in persons with diabetes. It serves as an update of the 2019 IWGDF guideline.

MATERIALS AND METHODS

We followed the GRADE approach by devising clinical questions and important outcomes in the PICO (Patient-Intervention-Control-Outcome) format, undertaking a systematic review and meta-analyses, developing summary of judgement tables and writing recommendations and rationales for each question. Each recommendation is based on the evidence found in the systematic review, expert opinion where evidence was not available, and a careful weighing of GRADE summary of judgement items including desirable and undesirable effects, certainty of evidence, patient values, resources required, cost effectiveness, equity, feasibility, and acceptability.

RESULTS

For healing a neuropathic plantar forefoot or midfoot ulcer in a person with diabetes, use a non-removable knee-high offloading device as the first-choice offloading intervention. If contraindications or patient intolerance to non-removable offloading exist, consider using a removable knee-high or ankle-high offloading device as the second-choice offloading intervention. If no offloading devices are available, consider using appropriately fitting footwear combined with felted foam as the third-choice offloading intervention. If such a non-surgical offloading treatment fails to heal a plantar forefoot ulcer, consider an Achilles tendon lengthening, metatarsal head resection, joint arthroplasty, or metatarsal osteotomy. For healing a neuropathic plantar or apex lesser digit ulcer secondary to flexibile toe deformity, use digital flexor tendon tenotomy. For healing rearfoot, non-plantar or ulcers complicated with infection or ischaemia, further recommendations have been outlined. All recommendations have been summarised in an offloading clinical pathway to help facilitate the implementation of this guideline into clinical practice.

CONCLUSION

These offloading guideline recommendations should help healthcare professionals provide the best care and outcomes for persons with diabetes-related foot ulcers and reduce the person's risk of infection, hospitalisation and amputation.

Empowering Foot Care Literacy Among People Living With Diabetes and Their Carers With an mHealth App: Protocol for a Feasibility Study

BACKGROUND

Diabetic foot ulcers (DFUs) cause significant morbidity affecting 19% to 34% of people living with diabetes mellitus. DFUs not only impair quality of life but may also result in limb loss and mortality. Patient education has been advocated to raise awareness of proper foot self-care and the necessity of seeking assistance when a foot wound occurs. Modern technologies, including mobile health (mHealth) interventions such as health apps, bring the potential for more cost-effective and scalable interventions.

OBJECTIVE

This study aims to examine the feasibility and usability of a newly developed mHealth app called Well Feet, which is a diabetes and foot care education app for individuals at risk of developing DFU.

METHODS

Well Feet was developed using an evidence-based and expert panel cocreation approach to deliver educational content available in 3 languages (ie, English, Chinese, and Malay) via animation videos and a range of additional features, including adaptive learning. A nonrandomized, single-arm feasibility study using a mixed methods approach with a series of validated questionnaires and focus group discussions will be conducted. In total, 40 patients and carers will be recruited from a tertiary hospital diabetes clinic to receive a 1-month mHealth intervention. The primary outcomes are the usability of the app and a qualitative perspective on user experience. Secondary outcomes include changes in foot care knowledge, self-management behaviors, and quality of life.

RESULTS

Patient recruitment began in July 2023, and the intervention and data collection will be completed by the end of September 2023. This study has been approved by National Healthcare Group Domain Specific Review Board (2022/00614) on February 10, 2023. The expected results will be published in spring 2024.

CONCLUSIONS

Through this feasibility study, the Well Feet DFU education app will undergo a comprehensive quantitative and qualitative evaluation of its usability and acceptance for future improvement in its design. With local contextualization, cultural adaptation, and its multilingual functionality, the app addresses a critical aspect of DFU health education and self-management in a multiethnic population. Findings from this study will refine and enhance the features of the app based on user feedback and shape the procedural framework for a subsequent randomized controlled trial to assess the effectiveness of Well Feet.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05564728; https://clinicaltrials.gov/study/NCT05564728.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52036.

DFU-Helper: An Innovative Framework for Longitudinal Diabetic Foot Ulcer Diseases Evaluation Using Deep Learning

Diabetes affects roughly 537 million people, and is predicted to reach 783 million by 2045. Diabetes Foot Ulcer (DFU) is a major complication associated with diabetes and can lead to lower limb amputation. The rapid evolution of diabetic foot ulcers (DFUs) necessitates immediate intervention to prevent the severe consequences of amputation and related complications. Continuous and meticulous patient monitoring for individuals with diabetic foot ulcers (DFU) is crucial and is currently carried out by medical practitioners on a daily basis. This research article introduces DFU-Helper, a novel framework that employs a Siamese Neural Network (SNN) for accurate and objective assessment of the progression of diabetic foot ulcers (DFUs) over time. DFU-Helper provides healthcare professionals with a comprehensive visual and numerical representation in terms of the similarity distance of the disease, considering five distinct disease conditions: none, infection, ischemia, both (presence of ischemia and infection), and healthy. The SNN achieves the best Macro F1-score of 0.6455 on the test dataset when applying pseudo-labeling with a pseudo-threshold set to 0.9. The SNN is used in the process of creating anchors for each class using feature vectors. When a patient initially consults a healthcare professional, an image is transmitted to the model, which computes the distances from each class anchor point. It generates a comprehensive table with corresponding figures and a visually intuitive radar chart. In subsequent visits, another image is captured and fed into the model alongside the initial image. DFU-Helper then plots both images and presents the distances from the class anchor points. Our proposed system represents a significant advancement in the application of deep learning for the longitudinal assessment of DFU. To the best of our knowledge, no existing tool harnesses deep learning for DFU follow-up in a comparable manner.