Preventative Sensor-Based Remote Monitoring of the Diabetic Foot in Clinical Practice

Minty, Evan; Bray, Emily; Bachus, Courtney B.; Everett, Breanne; Smith, Karen M.; Matijevich, Emily; Hajizadeh, Maryam; Armstrong, David G.; Liden, Brock

doi:10.3390/s23156712

Cited by 9 publications

(7 citation statements)

References 81 publications

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“…Participants with lower HbA1c levels demonstrated a significantly higher proportion of ulcer healing, reaffirming the importance of tight glycemic control in preventing and managing DFUs [ 22 ]. Peripheral neuropathy, although not statistically significant in this study, remains a crucial factor influencing DFU outcomes [ 23 ]. Additionally, ulcer size emerged as a vital predictor of healing, with larger ulcers being associated with higher rates of unhealed outcomes.…”

Section: Discussionmentioning

confidence: 92%

A Cohort Study on the Outcome of Diabetic Foot Ulcers

Sanjeeviraj,

Subburaj,

Aluri

et al. 2023

Cureus

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Background: Diabetic foot ulcers (DFUs) represent a significant and challenging complication of diabetes mellitus, often leading to serious morbidity and a substantial burden on healthcare systems. The study was conducted with the objectives of evaluating the outcomes of DFUs. Materials and methods: A cohort study was conducted to evaluate the outcomes of DFUs from May 2019 to May 2020 at a tertiary care hospital located in Chennai. The study included patients aged 18 to 90 years who were diagnosed with DFUs. Individuals with diabetic foot lesions (skin lesions such as fissures, abscess, cellulites) other than ulcers or those without diabetes were excluded. The data was collected from a total of 100 diabetic patients using systematic random sampling technique. Results: The mean (SD) age of the study participants was 54.68 (6.72) years with males constituting 56% of the study population. Among 100 participants, 65% experienced healing while 35% did not. Logistic regression analysis showed that glycated haemoglobin (HbA1c) levels, age, and diabetes duration had significant effect on patient outcome. Logistic regression analysis showed that HbA1c levels, age, and diabetes duration had significant effect on patient outcome. Out of 12 patients with major amputation, seven (58.3%) survived, while out of 19 patients with minor amputations, 18 (94.7%) showed remarkably higher survival rate. Meanwhile, 100% survival rate was observed in patients with no amputation. Conclusion: The study's comprehensive assessment of risk factors and their associations with healing outcomes provides essential knowledge for clinical practice. The study findings collectively support the optimization of interventions and strategies to prevent and manage DFUs, ultimately improving patient care and enhancing their quality of life. The study highlights the significance of glycemic control and limb preservation in DFU management.

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Section: Discussionmentioning

confidence: 92%

A Cohort Study on the Outcome of Diabetic Foot Ulcers

Sanjeeviraj,

Subburaj,

Aluri

et al. 2023

Cureus

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“…The digital health solution included adjunct RPM, provided through the in-house RPM service at Orpyx Medical Technologies. While the Orpyx Sensory Insoles were used for patient monitoring in this study, there is great flexibility in selecting a sensor-based technology for integration with RPM, with the goal of balancing effectiveness and practicality of the digital health solution for a specific use case [25].…”

Section: Methodsmentioning

confidence: 99%

“…While these individual strategies have shown the potential for reducing DFU risk, a multi-faceted digital health solution (i.e., fusion of multimodal sensing, direct patient biofeedback, and RPM) may better align with the multifactorial causal pathway of DFU formation. However, such holistic strategies for reducing DFU risk are underexplored, and the compounding benefits are unknown [24,25].…”

Section: Treating Ulcer Risk Factorsmentioning

confidence: 99%

“…First, expanding to multimodal sensing (rather than monitoring/actioning on a single physiologic signal) supports the monitoring of warning indicators at several stages throughout the DFU pathogenesis pathway [25]. Despite pressure overload and callus formation playing a central role in DFU pathogenesis [2], many digital health solutions designed for DFU risk reduction focus only on plantar temperature monitoring [19,22,23].…”

Section: Multi-faceted Digital Health Solutions Enable and Reinforce ...mentioning

confidence: 99%

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A Multi-Faceted Digital Health Solution for Monitoring and Managing Diabetic Foot Ulcer Risk: A Case Series

Matijevich,

Minty,

Bray

et al. 2024

Sensors

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Introduction: Diabetic foot ulcers (DFU) are a devastating complication of diabetes. There are numerous challenges with preventing diabetic foot complications and barriers to achieving the care processes suggested in established foot care guidelines. Multi-faceted digital health solutions, which combine multimodal sensing, patient-facing biofeedback, and remote patient monitoring (RPM), show promise in improving our ability to understand, prevent, and manage DFUs. Methods: Patients with a history of diabetic plantar foot ulcers were enrolled in a prospective cohort study and equipped with custom sensory insoles to track plantar pressure, plantar temperature, step count, and adherence data. Sensory insole data enabled patient-facing biofeedback to cue active plantar offloading in response to sustained high plantar pressures, and RPM assessments in response to data trends of concern in plantar pressure, plantar temperature, or sensory insole adherence. Three non-consecutive case participants that ultimately presented with pre-ulcerative lesions (a callus and/or erythematous area on the plantar surface of the foot) during the study were selected for this case series. Results: Across three illustrative patients, continuous plantar pressure monitoring demonstrated promise for empowering both the patient and provider with information for data-driven management of pressure offloading treatments. Conclusion: Multi-faceted digital health solutions can naturally enable and reinforce the integrative foot care guidelines. Multi-modal sensing across multiple physiologic domains supports the monitoring of foot health at various stages along the DFU pathogenesis pathway. Furthermore, digital health solutions equipped with remote patient monitoring unlock new opportunities for personalizing treatments, providing periodic self-care reinforcement, and encouraging patient engagement—key tools for improving patient adherence to their diabetic foot care plan.

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“…The electroencephalogram (EEG)-driven secure and reliable cognitive authentication system provides a solution to fix the security and privacy problems for an IoT-based healthcare system [5]. The effectiveness of existing diabetic foot ulcer (DFU) techniques can be enhanced by using a sensor-based remote patient monitoring (RPM) healthcare system [6]. The early detection of human health issues is very important for the better provision of cures.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza,

Ali,

Ehsan

et al. 2023

Sensors

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The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

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Preventative Sensor-Based Remote Monitoring of the Diabetic Foot in Clinical Practice

Cited by 9 publications

References 81 publications

A Cohort Study on the Outcome of Diabetic Foot Ulcers

A Cohort Study on the Outcome of Diabetic Foot Ulcers

A Multi-Faceted Digital Health Solution for Monitoring and Managing Diabetic Foot Ulcer Risk: A Case Series

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

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