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Background Measuring free-living gait using wearable devices may offer higher granularity and temporal resolution than the current clinical assessments for patients with Parkinson disease (PD). However, increasing the number of devices worn on the body adds to the patient burden and impacts the compliance. Objective This study aimed to investigate the impact of reducing the number of wearable devices on the ability to assess gait impairments in patients with PD. Methods A total of 35 volunteers with PD and 60 healthy volunteers performed a gait task during 2 clinic visits. Participants with PD were assessed in the On and Off medication state using the Movement Disorder Society version of the Unified Parkinson Disease Rating Scale (MDS-UPDRS). Gait features derived from a single lumbar-mounted accelerometer were compared with those derived using 3 and 6 wearable devices for both participants with PD and healthy participants. Results A comparable performance was observed for predicting the MDS-UPDRS gait score using longitudinal mixed-effects model fit with gait features derived from a single (root mean square error [RMSE]=0.64; R2=0.53), 3 (RMSE=0.64; R2=0.54), and 6 devices (RMSE=0.54; R2=0.65). In addition, MDS-UPDRS gait scores predicted using all 3 models differed significantly between On and Off motor states (single device, P=.004; 3 devices, P=.004; 6 devices, P=.045). Conclusions We observed a marginal benefit in using multiple devices for assessing gait impairments in patients with PD when compared with gait features derived using a single lumbar-mounted accelerometer. The wearability burden associated with the use of multiple devices may offset gains in accuracy for monitoring gait under free-living conditions.

Technological advances in multimodal wearable and connected devices have enabled the measurement of human movement and physiology in naturalistic settings. The ability to collect continuous activity monitoring data with digital devices in real-world environments has opened unprecedented opportunity to establish clinical digital phenotypes across diseases. Many traditional assessments of physical function utilized in clinical trials are limited because they are episodic, therefore, cannot capture the day-to-day temporal fluctuations and longitudinal changes in activity that individuals experience. In order to understand the sensitivity of gait speed as a potential endpoint for clinical trials, we investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18–40 years) and older (n = 32, 65–85 years) adults. We observed good agreement between gait speed estimated using a lumbar-mounted accelerometer and gold standard system during the performance of traditional gait assessment task in-lab, and saw discrepancies between in-lab and at-home gait speed. We found that gait speed estimated in-lab, with or without digital devices, failed to differentiate between the age groups, whereas gait speed derived during at-home monitoring was able to distinguish the age groups. Furthermore, we found that only three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.

People with Parkinson’s (PWP) disease are under constant tension with respect to their dopamine replacement therapy (DRT) regimen. Waiting too long between doses results in more prominent symptoms, loss of motor function, and greater risk of falling per step. Shortened pill cycles can lead to accelerated habituation and faster development of disabling dyskinesias. The Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is the gold standard for monitoring Parkinson’s disease progression but requires a neurologist to administer and therefore is not an ideal instrument to continuously evaluate short-term disease fluctuations. We investigated the feasibility of using speech to detect changes in medication states, based on expectations of subtle changes in voice and content related to dopaminergic levels. We calculated acoustic and prosodic features for three speech tasks (picture description, reverse counting, and diadochokinetic rate) for 25 PWP, each evaluated “ON” and “OFF” DRT. Additionally, we generated semantic features for the picture description task. Classification of ON/OFF medication states using features generated from picture description, reverse counting and diadochokinetic rate tasks resulted in cross-validated accuracy rates of 0.89, 0.84, and 0.60, respectively. The most discriminating task was picture description which provided evidence that participants are more likely to use action words in ON than in OFF state. We also found that speech tempo was modified by DRT. Our results suggest that automatic speech assessment can capture changes associated with the DRT cycle. Given the ease of acquiring speech data, this method shows promise to remotely monitor DRT effects.

Introduction Objective monitoring of movement in Parkinson’s Disease (PD) is important to assess response to treatment. Currently, PD treatment decisions are made based on expert assessment and conduct of clinical rating scales and patient diaries. Each of these is insensitive, episodic and subjective with inherent rater bias. Wearable inertial sensor technology using accelerometers and gyroscopes, which can quantify gait metrics, have been studied for their ability to monitor PD signs. We sought to evaluate the performance of wearable inertial sensor technology in quantifying movement related to PD signs using the Mobility Lab system ( http://www.apdm.com ). Methods We compared endpoints of the Mobility Lab 2-min walk test in 41 healthy volunteers (HV) and in 25 PD patients. HV had two test sessions at greater than a one hour interval. Each PD patient also performed the task twice: once in ON state ( ∼ 1 h after administering the patient’s scheduled levodopa dose) and again in the OFF state (immediately prior to administering the patient’s next scheduled levodopa dose). PD patients were randomized with half performing on state evaluations prior to off state evaluations, and the other half reversing this order. Each subject walked 10 meter linear laps with a 180 degree turn at the apex for two minutes while wearing sensors on each limb, and the sternum and lumbar regions. Results We found the highest reliability of the following endpoints in HV: cadence (Pearson’s r = 0.9860), stride length (r = 0.963), and arm range of motion (r = 0.962). In PD patients, Mobility Lab endpoints including turn duration ( p = 0.003), gait speed (p = 0.009), stride length (p = 0.006) and arm range of motion (p = 0.02) significantly differed before and after levodopa intake, and correlated with the Unified Parkinson Disease Rating Scale Motor Score (e.g. turn duration r = 0.4). Conclusion The Mobility Lab System 2-min walk test generated endpoints with good reliability and detected changes in PD patients before and after levodopa. Wearable inertial sensors have the potential to enhance our ability to detect signals of efficacy in therapeutic development.

Introduction Reliably detecting ON/OFF states is important for monitoring PD treatment and progression. Currently, subjective patient diaries capture this information. We investigate if detection of motor signs of ON/OFF states can be achieved by using kinematic measurements from wearable sensor technology combined with a machine learning (ML) pipeline. Methods Twenty-five PD subjects (19 males, 69 ± 7 years) taking levodopa performed 10-m Instrumented Stand and Walk (ISAW) tests in their ON and OFF states while wearing Ambulatory Parkinson Disease Monitoring (APDM) sensors on their sternum, wrists, lumbar and lower extremities. A neurologist scored each ISAW according to the MDS-UPDRS-III. We analyzed 98 kinematic features for significance to neurologist total motor score and ON/OFF using both statistical (repeated-measures ANOVA, step-wise mixed-model regression, likelihood-ratio test, ridge regression) and ML methods. Results Twenty-two features significantly differed between patient reported ON/OFF states, with the most significant being trunk transverse range-of-motion (RofM), arm RofM, mid-swing elevation, stride length, turn velocity, steps in turn and toe out angles. Estimates from regression model showed average difference of 14 points between OFF/ON states in total UPDRS score and 9 points when adjusted for 5 significant features for individual baseline (mean trunk transverse RofM, right arm RofM, and toe out angle having highest effect; coeff. −8.67, −5.25, −3.36 respectively). Several approaches were employed for predicting ON/OFF states based on these features: direct binary classification (acc = 0.56), regression to total UPDRS score (acc = 0.76), regression to PIGD sub-score (acc = 0.64), and classification of ON–OFF/OFF–ON transitions using feature differences (Naive Bayes: acc = 0.74, AUC = 0.78; Random Forest: acc = 0.76, AUC = 0.90). Conclusion Wearable inertial sensors hold promise for detecting ON/OFF states in PD patients using an augmented ML approach. This could be particularly useful for monitoring response to therapy in an outpatient setting.