Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

Patients With Diabetes Using a New Glucose Meter With Blood Sugar Mentor and Dynamic Color Range Indicator Features Show Improved Interpretation and Willingness to Act on Blood Glucose Results (ASCEND Study)

Background We examined whether dynamic color range indicator (DCRI) and blood sugar mentor (BSM) features in a new blood glucose meter could improve interpretation of results and encourage patient action. Methods One hundred and thirty three people with type 2 (T2D) ( n = 73) or type 1 diabetes (T1D) ( n = 60) evaluated information first without and then with DCRI or BSM guidance using interactive exercises. Results Subjects improved their ability to categorize results into low, in range, or high glycemic ranges by 29% (T2D) and 22% (T1D) (each P < .001). There was significantly greater willingness to act on high and low results shown with DCRI or BSM screens. Subjects also expressed a high degree of satisfaction with these features. Conclusions Use of DCRI and BSM in this meter may help patients improve their diabetes management decisions.