Effect of using a wearable device on clinical decision-making and motor symptoms in patients with Parkinson's disease starting transdermal rotigotine patch: A pilot study.

BACKGROUND: Feedback from wearable biosensors may help assess motor function in Parkinson's disease (PD) patients and titrate medication. Kinesia 360 continuously monitors motor symptoms via wrist and ankle sensors. METHODS: PD0049 was a 12-week pilot study to investigate whether using Kinesia 360 at home could improve motor symptom management in PD patients starting transdermal dopamine agonist rotigotine. Adults with PD and insufficiently controlled motor symptoms (prescribed rotigotine) were randomized 1:1 to Control Group (CG) or Experimental Group (EG) before starting rotigotine. Motor symptoms were assessed in all patients at baseline and Week 12 (W12) using Unified PD Rating Scale (UPDRS) III and Kinesia ONE, which measures standardized motor tasks via a sensor on the index finger. Between baseline and W12, EG used Kinesia 360 at home; clinicians used the data to supplement standard care in adjusting rotigotine dosage. RESULTS: At W12, least squares mean improvements in UPDRS II (-2.1 vs 0.5, p = 0.004) and UPDRS III (-5.3 vs -1.0, p = 0.134) were clinically meaningfully greater, and mean rotigotine dosage higher (4.8 vs 3.9 mg/24 h) in EG (n = 19) vs CG (n = 20). Mean rotigotine dosage increase (+2.8 vs + 1.9 mg/24 h) and mean number of dosage changes (2.8 vs 1.8) during the study were higher in EG vs CG. Tolerability and retention rates were similar. CONCLUSION: Continuous, objective, motor symptom monitoring using a wearable biosensor as an adjunct to standard care may enhance clinical decision-making, and may improve outcomes in PD patients starting rotigotine.

Clinical feasibility of a wearable, conformable sensor patch to monitor motor symptoms in Parkinson's disease.

INTRODUCTION: Clinical assessment of motor symptoms in Parkinson's disease (PD) is subjective and may not reflect patient real-world experience. This two-part pilot study evaluated the accuracy of the NIMBLE wearable biosensor patch (containing an accelerometer and electromyography sensor) to record body movements in clinic and home environments versus clinical measurement of motor symptoms. METHODS: Patients (Hoehn & Yahr 2-3) had motor symptom fluctuations and were on a stable levodopa dose. Part 1 investigated different sensor body locations (six patients). In Part 2, 21 patients wore four sensors (chest, and most affected side of shin, forearm and back-of-hand) during a 2-day clinic- and 1-day home-based evaluation. Patients underwent Unified Parkinson's Disease Rating Scale assessments on days 1-2, and performed pre-defined motor activities at home on day 3. An algorithm estimated motor-symptom severity (predicted scores) using patch data (in-clinic); this was compared with in-clinic motor symptom assessments (observed scores). RESULTS: The overall correlation coefficient between in-clinic observed and sensor algorithm-predicted scores was 0.471 (p = 0.031). Predicted and observed scores were identical 45% of the time, with a predicted score within a ±1 range 91% of the time. Exact accuracy for each activity varied, ranging from 32% (pronation/supination) to 67% (rest-tremor-amplitude). Patients rated the patch easy-to-use and as providing valuable data for managing PD symptoms. Overall patch-adhesion success was 97.2%. The patch was safe and generally well tolerated. CONCLUSIONS: This study showed a correlation between sensor algorithm-predicted and clinician-observed motor-symptom scores. Algorithm refinement using patient populations with greater symptom-severity range may potentially improve the correlation.

Impact of motor fluctuations on real-life gait in Parkinson's patients.

BACKGROUND: People with PD (PWP) have an increased risk of becoming inactive. Wearable sensors can provide insights into daily physical activity and walking patterns. RESEARCH QUESTIONS: (1) Is the severity of motor fluctuations associated with sensor-derived average daily walking quantity? (2) Is the severity of motor fluctuations associated with the amount of change in sensor-derived walking quantity after levodopa intake? METHODS: 304 Dutch PWP from the Parkinson@Home study were included. At baseline, all participants received a clinical examination. During the follow-up period (median: 97 days; 25-Interquartile range-IQR: 91 days, 75-IQR: 188 days), participants used the Fox Wearable Companion app and streamed smartwatch accelerometer data to a cloud platform. The first research question was assessed by linear regression on the sensor-derived mean time spent walking/day with the severity of fluctuations (MDS-UPDRS item 4.4) as independent variable, controlled for age and MDS-UPDRS part-III score. The second research question was assessed by linear regression on the sensor-derived mean post-levodopa walking quantity, with the sensor-derived mean pre-levodopa walking quantity and severity of fluctuations as independent variables, controlled for mean time spent walking per day, age and MDS-UPDRS part-III score. RESULTS: PWP spent most time walking between 8am and 1pm, summing up to 72 ±â€¯39 (mean ±â€¯standard deviation) minutes of walking/day. The severity of motor fluctuations did not influence the mean time spent walking (B = 2.4 ±â€¯1.9, p = 0.20), but higher age (B = -1.3 ±â€¯0.3, p = < 0.001) and greater severity of motor symptoms (B = -0.6 ±â€¯0.2, p < 0.001) was associated with less time spent walking (F(3216) = 14.6, p < .001, R2 = .17). The severity of fluctuations was not associated with the amount of change in time spent walking in relation to levodopa intake in any part of the day. SIGNIFICANCE: Analysis of sensor-derived gait quantity suggests that the severity of motor fluctuations is not associated with changes in real-life walking patterns in mildly to moderate affected PWP.

Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease.

Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort.