Game-Based Assessment of Peripheral Neuropathy Combining Sensor-Equipped Insoles, Video Games, and AI: Proof-of-Concept Study.

BACKGROUND: Detecting peripheral neuropathy (PNP) is crucial in preventing complications such as foot ulceration. Clinical examinations for PNP are infrequently provided to patients at high risk due to restrictions on facilities, care providers, or time. A gamified health assessment approach combining wearable sensors holds the potential to address these challenges and provide individuals with instantaneous feedback on their health status. OBJECTIVE: We aimed to develop and evaluate an application that assesses PNP through video games controlled by pressure sensor-equipped insoles. METHODS: In the proof-of-concept exploratory cohort study, a complete game-based framework that allowed the study participant to play 4 video games solely by modulating plantar pressure values was established in an outpatient clinic setting. Foot plantar pressures were measured by the sensor-equipped insole and transferred via Bluetooth to an Android tablet for game control in real time. Game results and sensor data were delivered to the study server for visualization and analysis. Each session lasted about 15 minutes. In total, 299 patients with diabetes mellitus and 30 with metabolic syndrome were tested using the game application. Patients' game performance was initially assessed by hypothesis-driven key capabilities that consisted of reaction time, sensation, skillfulness, balance, endurance, and muscle strength. Subsequently, specific game features were extracted from gaming data sets and compared with nerve conduction study findings, neuropathy symptoms, or disability scores. Multiple machine learning algorithms were applied to 70% (n=122) of acquired data to train predictive models for PNP, while the remaining data were held out for final model evaluation. RESULTS: Overall, clinically evident PNP was present in 247 of 329 (75.1%) participants, with 88 (26.7%) individuals showing asymmetric nerve deficits. In a subcohort (n=37) undergoing nerve conduction study as the gold standard, sensory and motor nerve conduction velocities and nerve amplitudes in lower extremities significantly correlated with 79 game features (|R|>0.4, highest R value +0.65; P<.001; adjusted R2=0.36). Within another subcohort (n=173) with normal cognition and matched covariates (age, sex, BMI, etc), hypothesis-driven key capabilities and specific game features were significantly correlated with the presence of PNP. Predictive models using selected game features achieved 76.1% (left) and 81.7% (right foot) accuracy for PNP detection. Multiclass models yielded an area under the receiver operating characteristic curve of 0.76 (left foot) and 0.72 (right foot) for assessing nerve damage patterns (small, large, or mixed nerve fiber damage). CONCLUSIONS: The game-based application presents a promising avenue for PNP screening and classification. Evaluation in expanded cohorts may iteratively optimize artificial intelligence model efficacy. The integration of engaging motivational elements and automated data interpretation will support acceptance as a telemedical application.

Telemedical Monitoring of Plantar Temperature in Diabetic Patients at Risk of Foot Ulcers.

BACKGROUND: The worldwide prevalence of diabetic foot ulcers (DFUs) among persons with diabetes is estimated at 6.3%, with an annual incidence of 9.1 to 26.1 million persons. The early detection of asymmetrical plantar temperature elevation, followed by reduction of weight-bearing on the affected foot, may be an effective mode of prevention. METHODS: Patients with diabetes and peripheral neuropathy (DFU risk groups 2/3) were monitored for plantar abnormalities with a telemedical system consisting of sole inserts with temperature sensors and photographic documentation. An open, prospective, randomized controlled trial was performed to determine whether this system prevented DFUs. The intervention and control groups were also trained in ulcer prevention and observed in follow-up at 6-month intervals for 24 months. RESULTS: 283 patients were recruited. In 85 137 observation days, DFUs arose in five patients in the control group (n = 143) and in no patient in the intervention group (n = 140). The primary outcome measure was the hazard ratio, which was calculated to be 0.015 (95% confidence interval [0; 19,717]; p = 0.25) after adjustment for age, sex, severity of neuropathy, and risk class. There were 239 alarms and 75 instructions to reduce weight-bearing on the foot. The subjects carried out the telemedical application on about 70% of the days of observation. Quality of life improved in both groups. CONCLUSION: The tele-health system used in this trial is practical and enables the early detection of morbidity. Likely explanations for the unexpectedly low ulceration rate in this trial (and, in turn, for the lack of statistical significance) include the availability of a training program and regular follow-up examinations to patients in both arms of the trial, along with lower mobility levels due to the COVID pandemic.

Study protocol for a randomized controlled trial to test for preventive effects of diabetic foot ulceration by telemedicine that includes sensor-equipped insoles combined with photo documentation.

BACKGROUND: Early detection of diabetic foot ulcerations (DFUs) can avoid or delay any progression into more severe stages, which may require limb amputation or lead to infectious sequelae and death. However, frequent clinical screening would be too intrusive and costly, and self-examination may be hampered by concomitant diseases and social disabilities. In addition, it requires professional knowledge and experience using specialized devices. Researchers reported that skin temperature monitoring could reduce the risk of DFUs in high-risk patients. The main research objects in this field are effective and convenient means of temperature measurement, accurate and reasonable early warning mechanisms, and timely and appropriate interventions. This trial aims to investigate the effectiveness of daily home-based foot temperature measurements in the prevention of DFUs with the aid of intelligent sensor-equipped insoles combined with photo documentation. METHODS/DESIGN: In this open-label, prospective, randomized, 24-month trial, 300 patients with diabetes mellitus (type 1 or 2) and severe diabetic peripheral neuropathy (vibration sensation ≤ 4/8), aged 18-85 years, will be recruited and assigned to control and intervention groups in a ratio of 1:1. Main inclusion criteria to be eligible for study participation encompass in particular risk group 2 or 3 for the development of DFUs using the diabetic foot risk classification system (as specified by the International Working Group on the Diabetic Feet [IWGDF]) and the ability to use a mobile phone. INTERVENTIONS: Participants in both groups will receive education about regular foot care at the beginning of the study (visit 0). In the intervention group, every patient will receive a pair of slippers with the inserted sensor-equipped insole as well as a smartphone with the corresponding smartphone application (Smart Prevent Diabetic Feet Application). The insole is a tool that records the temperature variabilities of the plantar foot. Patients will measure their foot temperature twice a day at home with a time interval > 4 h during the entire course of the study (24 months). The measured data will be initially analyzed and visualized, and further transferred to a remote server that allows the physician to perform specific interpretations. In case of temperature differences > 1.5 °C between left and right corresponding sites lasting > 32 h (assigned alarm level 4), the physician will start an intervention phase, which requires the patient to reduce daily activities and relax his feet for five days. At the same time, photo documentation is encouraged to be performed by the patient. Possibly, additional visits to a private doctor or clinical examinations will be arranged for the patient during this intervention period. OUTCOMES: The primary outcome is foot ulceration, evaluated by a physician, and occurring at any point during the study. DISCUSSION: This study addresses principal aspects in the prevention of DFUs. First, the sensor-equipped insole will be evaluated for daily performance in home-based measurements of foot temperatures. Second, a telemedicine structure is tested that evaluates sensor data automatically and proposes suitable intervention measures under the supervision of a physician. Third, predictive models for DFUs will be built using the collected sensor data allowing for interpretations, which in the future may support medical care providers. TRIAL REGISTRATION: German Clinical Trials Register (DRKS), DRKS00013798 . Registered on 18 January 2018.