SmartBoot: Real-Time Monitoring of Patient Activity via Remote Edge Computing Technologies

Area of Science:

• Biomedical Engineering
• Digital Health
• Diabetes Management

Background:

• Diabetic foot ulcers (DFUs) present a significant clinical challenge with high recurrence and amputation rates.
• Effective management of DFUs necessitates strict adherence to offloading devices, which is poorly monitored in real-world settings.
• Current monitoring methods for offloading device adherence lack real-time data and scalability.

Purpose of the Study:

• To evaluate the SmartBoot edge-computing system for its validity in quantifying cadence and step count as digital biomarkers.
• To assess the SmartBoot system's accuracy in detecting adherence to offloading devices for diabetic foot ulcer management.
• To determine user acceptance and perceived usability of the SmartBoot system among healthy individuals and DFU patients.

Main Methods:

• The SmartBoot system, integrating an inertial measurement unit (Sensoria Core) and smartwatch, was tested in 12 healthy adults wearing two types of removable offloading boots.
• System outputs for cadence and step count were validated against a gold-standard wearable device.
• Adherence detection accuracy was compared with staff-recorded logs, and user experience was assessed using the Technology Acceptance Model (TAM) in 12 healthy participants and 81 DFU patients.

Main Results:

• The SmartBoot system demonstrated high accuracy in measuring cadence and step count (bias < 5.5%) across various walking speeds.
• Adherence detection accuracy reached 96% for Össur boots and 97% for Foot Defender boots.
• Technology Acceptance Model (TAM) results indicated strong user acceptance and perceived ease of use in both healthy participants and DFU patients.

Conclusions:

• The SmartBoot system is a valid and accurate tool for quantifying mobility metrics and detecting adherence to offloading devices in DFU management.
• The system exhibits high accuracy and strong user acceptance, suggesting its potential as a scalable solution for remote monitoring.
• Further clinical validation in ongoing studies with DFU patients is recommended to confirm its real-world efficacy.