The Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis

Advancing Digital Medicine with Wearables in the Wild

“…In these cases, treatment would benefit from remotely and continuously characterizing these changes [35][36][37][38][39]. For example, markers of balance and mobility impairment often differ between clinic and free living environments and can change rapidly [18,[40][41][42]. Similarly, eating disorders [43] and substance use disorders [44] are often associated with context-dependent triggers that continuously change in severity and immediate treatment needs.…”

Digital Phenotypes of Instability and Fatigue Derived from Daily Standing Transitions in Persons with Multiple Sclerosis

“…To identify daily activity transitions, we employed our activity classification pipeline, which leverages a deep learning model trained on over 100,000 four-second observations of acceleration from a variety of patient populations (as previously described [23], [24]), to identify periods of sitting and standing. Each sist or stsi transition was identified using an established technique; the cranial-caudal acceleration from thigh recording was filtered and the signal was inspected for a transition from 1g towards 0 g (stsi) or from 0 g to 1g (sist) within an 18-second window of data centered on transitions between the classified activities [23].…”

“…To identify daily activity transitions, we employed our activity classification pipeline, which leverages a deep learning model trained on over 100,000 four-second observations of acceleration from a variety of patient populations (as previously described [23], [24]), to identify periods of sitting and standing. Each sist or stsi transition was identified using an established technique; the cranial-caudal acceleration from thigh recording was filtered and the signal was inspected for a transition from 1g towards 0 g (stsi) or from 0 g to 1g (sist) within an 18-second window of data centered on transitions between the classified activities [23]. After identifying transitions, the following features were calculated from the thigh and chest accelerations: 5 th , 50 th , and 95 th percentile of cranial-caudal (CC) and horizontal plane (F5, F50, F95), jerk of CC and horizontal plane [23], range of CC and horizontal plane, 5 th , 50 th , and 95 th percentile frequency of CC and horizontal plane, total power in CC and horizontal plane, approximate entropy (ApEn) of CC and horizontal plane, and spectral edge frequency (SEF) [29].…”

“…Each sist or stsi transition was identified using an established technique; the cranial-caudal acceleration from thigh recording was filtered and the signal was inspected for a transition from 1g towards 0 g (stsi) or from 0 g to 1g (sist) within an 18-second window of data centered on transitions between the classified activities [23]. After identifying transitions, the following features were calculated from the thigh and chest accelerations: 5 th , 50 th , and 95 th percentile of cranial-caudal (CC) and horizontal plane (F5, F50, F95), jerk of CC and horizontal plane [23], range of CC and horizontal plane, 5 th , 50 th , and 95 th percentile frequency of CC and horizontal plane, total power in CC and horizontal plane, approximate entropy (ApEn) of CC and horizontal plane, and spectral edge frequency (SEF) [29]. We also computed transition time and postural sway features of Jerk, Range, and 50 th percentile frequency of chest acceleration from the standing bout immediately preceding or following the transition [24], [30].…”

“…These transitions have been used extensively to assess patient stability, dating back to the introduction of the "get-up and go" test in 1986 [26]. More modernly, wearable monitoring of such tasks has been applied to augmentation of PBMs and home-monitoring of M symptoms [22], [23], but our understanding of these metrics remains limited [24], [27].…”