Can Motor Outcomes Extracted from Wearables Inform on Non-Motor Clinical Outcomes? The Case of Sensor-Derived Turning Information

This study investigated the relationship between sensor-derived real-world turning behavior and non-motor clinical outcomes in community-dwelling older adults. Two hundred participants wore smartphones with a tri-axial accelerometer and gyroscope on their lower back during daily activities for one week. Thirteen turn digital mobility outcomes (DMOs) were derived and subsequently expanded into 52 metrics by categorizing turns as small (50–100°), medium (100–150°), and large (150–200°). Clinical assessments included fear of falling, self-perceived health, depression (CES-D), living situation, cognitive function (MMSE), and happiness. Results revealed significant associations between turning behavior and multiple non-motor outcomes. Participants without fear of falling performed more turns per day (799±421 vs. 580±357, p=0.004) with shorter duration, greater angle, and higher angular velocity compared to those with fear. Similarly, individuals reporting good health demonstrated more numerous (774±418 vs. 499±343, p=0.019) and more dynamic turns than those with poor health. Depression correlated predominantly with reduced variability in turning parameters (r=-0.281 to - 0.221, p<0.002), while cognitive function showed positive associations with turn angle (r=0.260, p<0.001) and velocity metrics, and negative correlations with turn duration. Happiness was positively associated with turn velocity (r=0.154-0.195, p<0.05) and variability measures (r=0.212, p=0.003). These findings suggest that sensor-derived turning behavior may serve as a digital biomarker reflecting psychological, mental, and emotional well-being in older adults, potentially offering new avenues for monitoring and intervention in geriatric care.