Monitoring Physical Activity with Wearable Technologies

Wearable Electrochemical Sensors in Parkinson's Disease

Parkinson’s disease (PD) is a neurodegenerative disorder associated with widespread aggregation of α-synuclein and dopaminergic neuronal loss in the substantia nigra pars compacta. As a result, striatal dopaminergic denervation leads to functional changes in the cortico-basal-ganglia-thalamo-cortical loop, which in turn cause most of the parkinsonian signs and symptoms. Despite tremendous advances in the field in the last two decades, the overall management (i.e., diagnosis and follow-up) of patients with PD remains largely based on clinical procedures. Accordingly, a relevant advance in the field would require the development of innovative biomarkers for PD. Recently, the development of miniaturized electrochemical sensors has opened new opportunities in the clinical management of PD thanks to wearable devices able to detect specific biological molecules from various body fluids. We here first summarize the main wearable electrochemical technologies currently available and their possible use as medical devices. Then, we critically discuss the possible strengths and weaknesses of wearable electrochemical devices in the management of chronic diseases including PD. Finally, we speculate about possible future applications of wearable electrochemical sensors in PD, such as the attractive opportunity for personalized closed-loop therapeutic approaches.

Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes.

Accuracy and precision of wrist-worn actigraphy for measuring steps taken during over-ground and treadmill walking in adults with Parkinson's disease

INTRODUCTION

Step counts represent a straight-forward method of measuring physical activity in adults with Parkinson's disease (PD). The present study examined the absolute and relative accuracy and precision of a wrist-worn research-grade accelerometer (i.e., ActiGraph GT3X+) for measuring step counts during over-ground and treadmill walking in adults with PD and controls without PD.

METHODS

Participants (PD: n = 29; controls: n = 31) wore two ActiGraph GT3X + accelerometers, one on each wrist, and completed an over-ground walking bout followed by a treadmill walking bout at the same speed. Step counts were measured manually using a hand-held tally counter. Accuracy and precision were based on absolute and relative metrics.

RESULTS

The ActiGraph GT3X + underestimated step counts in both participants with PD (4.7-11% error) and controls without PD (8.8-17% error), with a greater discrepancy in controls. The ActiGraph GT3X + provided more accurate and precise estimates of step counts when placed on the more affected wrist and non-dominant wrist for participants with PD and controls, respectively, and was more accurate and precise during over-ground walking compared with treadmill walking for both groups.

CONCLUSIONS

Our results suggest that placement of the device (i.e., dominant vs. non-dominant), type of activity (i.e., over-ground vs. treadmill walking), and presence of clinical conditions may impact the accuracy and precision of data when using the research-grade ActiGraph GT3X + accelerometer for measuring step counts.

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.