Background The traditional evaluation of gait in the laboratory during structured testing has provided important insights, but is limited by its “snapshot” character and observation in an unnatural environment. Wearables enable monitoring of gait in real-world environments over a week. Initial findings show that in-lab and real-world measures differ. As a step towards better understanding these gaps, we directly compared in-lab usual-walking (UW) and dual-task walking (DTW) to daily-living measures of gait. Methods In-lab gait features (e.g., gait speed, step regularity, and stride regularity) derived from UW and DTW were compared to the same gait features during daily-living in 150 elderly fallers (age: 76.5 ± 6.3 years, 37.6% men). In both settings, features were extracted from a lower-back accelerometer. In the real-world setting, subjects were asked to wear the device for 1 week and pre-processing detected 30-s daily-living walking bouts. A histogram of all walking bouts was determined for each walking feature for each subject and then each subject’s typical (percentile 50, median), worst (percentile 10) and the best (percentile 90) values over the week were determined for each feature. Statistics of reliability were assessed using Intra-Class correlations and Bland-Altman plots. Results As expected, in-lab gait speed, step regularity, and stride regularity were worse during DTW, compared to UW. In-lab gait speed, step regularity, and stride regularity during UW were significantly higher (i.e., better) than the typical daily-living values ( p < 0.001) and different ( p < 0.001) from the worst and best values. DTW values tended to be similar to typical daily-living values ( p = 0.205, p = 0.053, p = 0.013 respectively). ICC assessment and Bland-Altman plots indicated that in-lab values do not reliably reflect the daily-walking values. Conclusions Gait values measured during relatively long (30-s) daily-living walking bouts are more similar to the corresponding values obtained in the lab during dual-task walking, as compared to usual walking. Still, gait performance during most daily-living walking bouts is worse than that measured during usual and dual-tasking in the lab. The values measured in the lab do not reliably reflect daily-living measures. That is, an older adult’s typical daily-living gait cannot be estimated by simply measuring walking in a structured, laboratory setting.

Recent work suggests that wearables can augment conventional measures of Parkinson's disease (PD). We evaluated the relationship between conventional measures of disease and motor severity (e.g., MDS-UPDRS part III), laboratory-based measures of gait and balance, and daily-living physical activity measures in patients with PD. Methods: Data from 125 patients (age: 71.7 ± 6.5 years, Hoehn and Yahr: 1-3, 60.5% men) were analyzed. The MDS-UPDRS-part III was used as the gold standard of motor symptom severity. Gait and balance were quantified in the laboratory. Daily-living gait and physical activity metrics were extracted from an accelerometer worn on the lower back for 7 days. Results: In multivariate analyses, daily-living physical activity and gait metrics, laboratory-based balance, demographics and subject characteristics together explained 46% of the variance in MDS-UPDRS-part III scores. Daily-living measures accounted for 62% of the explained variance, laboratory measures 30%, and demographics and subject characteristics 7% of the explained variance. Conversely, demographics and subject characteristics, laboratory-based measures of gait symmetry, and motor symptom severity together explained less than 30% of the variance in total daily-living physical activity. MDS-UPDRS-part III scores accounted for 13% of the explained variance, i.e., < 4% of all the variance in total daily-living activity. Conclusions: Our findings suggest that conventional measures of motor symptom severity do not strongly reflect daily-living activity and that daily-living measures apparently provide important information that is not captured in a conventional one-time, laboratory assessment of gait, balance or the MDS-UPDRS. To provide a more complete evaluation, wearable devices should be considered.

Objective. Circadian and sleep dysfunction have long been symptomatic hallmarks of a variety of devastating neurodegenerative conditions. The gold standard for sleep monitoring is overnight sleep in a polysomnography (PSG) laboratory. However, this method has several limitations such as availability, cost and being labour-intensive. In recent years there has been a heightened interest in home-based sleep monitoring via wearable sensors. Our objective was to demonstrate the use of printed electrode technology as a novel platform for sleep monitoring. Approach. Printed electrode arrays offer exciting opportunities in the realm of wearable electrophysiology. In particular, soft electrodes can conform neatly to the wearer’s skin, allowing user convenience and stable recordings. As such, soft skin-adhesive non-gel-based electrodes offer a unique opportunity to combine electroencephalography (EEG), electromyography (EMG), electrooculography (EOG) and facial EMG capabilities to capture neural and motor functions in comfortable non-laboratory settings. In this investigation temporary-tattoo dry electrode system for sleep staging analysis was designed, implemented and tested. Main results. EMG, EOG and EEG were successfully recorded using a wireless system. Stable recordings were achieved both at a hospital environment and a home setting. Sleep monitoring during a 6 h session shows clear differentiation of sleep stages. Significance. The new system has great potential in monitoring sleep disorders in the home environment. Specifically, it may allow the identification of disorders associated with neurological disorders such as rapid eye movement (REM) sleep behavior disorder.

BackgroundSleep disturbances and nocturnal hypokinesia are common in Parkinson's disease (PD). Recent work using wearable technologies showed fewer nocturnal movements in PD when compared with controls. However, it is unclear how these manifest across the disease spectrum.ObjectivesWe assessed the prevalence of sleep disturbances and nocturnal hypokinesia in early and advanced PD and their relation to nonmotor symptoms and dopaminergic medication.MethodsA total of 305 patients with PD with diverse disease severity (Hoehn and Yahr [H&Y] stage 1 = 47, H&Y stage 2 = 181, H&Y stage 3 = 77) and 205 healthy controls continuously wore a tri‐axial accelerometer on the lower back for at least 2 days. Lying, turning, and upright ‐time at night were extracted from the acceleration signals. Percent upright time and nighttime walking were classified as sleep interruptions. The number, velocity, time, side, and degree of rotations in bed were used to evaluate nocturnal movements.ResultsNocturnal lying time was similar among all groups (healthy controls, 7.5 ± 1.2 hours; H&Y stage 1, 7.3 ± 0.9 hours; H&Y stage 2, 7.2 ± 1.3 hours; H&Y stage 3, 7.4 ± 1.6 hours; P = 0.501). However, patients with advanced PD had more upright periods, whereas the number and velocity of their turns were reduced (P ≤ 0.021). Recently diagnosed patients (<1 year from diagnosis) were similar to controls in the number of nocturnal turns (P = 0.148), but showed longer turning time (P = 0.001) and reduced turn magnitude (P = 0.002). Reduced nocturnal movements were associated with increased PD motor severity and worse dysautonomia and cognition and with dopaminergic medication.ConclusionsUsing wearable sensors for continuous monitoring of movement at night may offer an unbiased measure of disease severity that could enhance optimal nighttime dopaminergic treatment and utilization of turning strategies. © 2020 International Parkinson and Movement Disorder Society

People with multiple sclerosis (pwMS) often suffer from gait impairments. These changes in gait have been wellstudied in laboratory and clinical settings. A thorough investigation of gait alterations during community ambulation and their contributing factors, however, is lacking. The aim of the present study was to evaluate community ambulation and physical activity in pwMS and healthy controls and to compare in-lab gait to community ambulation. To this end, 104 subjects were studied:44 pwMS and 60 healthy controls (whose age was similar to the controls). The subjects wore a tri-axial, lower-back accelerometer during usual-walking and dual-task walking in the lab and during community ambulation (1 week) to evaluate the amount, type, and quality of activity. The results showed that during community ambulation, pwMS took fewer steps and walked more slowly, with greater asymmetry, and larger stride-to-stride variability, compared to the healthy controls (p<0.001). Gait speed during most of community ambulation was significantly lower than the in-lab usual-walking value and similar to the in-lab dual-tasking value. Significant group (pwMS /controls) by walking condition (in-lab/community ambulation) interactions were observed (e.g., gait speed).Greater disability was associated with fewer steps and reduced gait speed during community ambulation. In contrast, physical fatigue was correlated with sedentary activity but was not related to any of the measures of community ambulation gait quality including gait speed. This disparity suggests that more than one mechanism contributes to community ambulation and physical activity in pwMS. Together, these findings demonstrate that during community ambulation, pwMS have marked gait alterations in multiple gait features, reminiscent of dual-task walking measured in the laboratory. Disease-related factors associated with these changes might be targets of rehabilitation.