ArticleSensors (Basel, Switzerland)2023
Preventative Sensor-Based Remote Monitoring of the Diabetic Foot in Clinical Practice.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Usability and Feasibility of an In-Home Full-Color Imaging Scale for Remote Monitoring of High-Risk Patients With a History of Diabetic Foot Ulcers: Retrospective Observational Formative Evaluation.JMIR formative research · 2026Observational
- One Shoe Doesn't Fit All: A Contextual Approach to Therapeutic Footwear for People at Risk of Diabetes-Related Foot Ulceration.Journal of foot and ankle research · 2026Review
- Foot Temperature Measurement in People With Diabetes: Validation of a Non-Contact Thermal Device.Journal of diabetes science and technology · 2025Article
- Assessing the Possibility of Recurrent Diabetic Foot Ulcer Prevention via Remote Patient Monitoring: A Feasibility Study.Diabetes/metabolism research and reviews · 2025Article
- [Research advances on the mechanism and clinical application of cold atmospheric plasma in promoting wound healing].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2025Review
- Plantar pressure measurement in diabetic foot disease: A scoping review.Journal of diabetes investigation · 2024Article
- In-shoe plantar pressure measurement technologies for the diabetic foot: A systematic review.Heliyon · 2024Article
- A Multi-Faceted Digital Health Solution for Monitoring and Managing Diabetic Foot Ulcer Risk: A Case Series.Sensors (Basel, Switzerland) · 2024Article
- Article
- Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes and its complications, particularly diabetic foot ulcers (DFUs), pose significant challenges to healthcare systems worldwide. DFUs result in severe consequences such as amputation, increased mortality rates, reduced mobility, and substantial healthcare costs. The majority of DFUs are preventable and treatable through early detection. Sensor-based remote patient monitoring (RPM) has been proposed as a possible solution to overcome limitations, and enhance the effectiveness, of existing foot care best practices. However, there are limited frameworks available on how to approach and act on data collected through sensor-based RPM in DFU prevention. This perspective article offers insights from deploying sensor-based RPM through digital DFU prevention regimens. We summarize the data domains and technical architecture that characterize existing commercially available solutions. We then highlight key elements for effective RPM integration based on these new data domains, including appropriate patient selection and the need for detailed clinical assessments to contextualize sensor data. Guidance on establishing escalation pathways for remotely monitored at-risk patients and the importance of predictive system management is provided. DFU prevention RPM should be integrated into a comprehensive disease management strategy to mitigate foot health concerns, reduce activity-associated risks, and thereby seek to be synergistic with other components of diabetes disease management. This integrated approach has the potential to enhance disease management in diabetes, positively impacting foot health and the healthspan of patients living with diabetes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.