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 ± 39 (mean ± standard deviation) minutes of walking/day. The severity of motor fluctuations did not influence the mean time spent walking (B = 2.4 ± 1.9, p = 0.20), but higher age (B = −1.3 ± 0.3, p = < 0.001) and greater severity of motor symptoms (B = −0.6 ± 0.2, p < 0.001) was associated with less time spent walking (F(3,216) = 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

Jornada de portes obertes al fons Ferrater Mora

Acte complementari a l' acte commemoratiu del centenari del naixement de Josep Ferrater Mora celebrat a Giron

Distribution of data-contributors’ characteristics and influence on compliance for the NL and NAM cohorts.

<p>Distribution of data-contributors’ characteristics and influence on compliance for the NL and NAM cohorts.</p

Distribution of compliance among all enrolled participants in the NL (n = 304-black) and NAM (n = 649-white) study cohorts.

<p>Distribution of compliance among all enrolled participants in the NL (n = 304-black) and NAM (n = 649-white) study cohorts.</p

Number of participants actively collecting sensor data at the NL (gray) and NAM (black) cohorts during and after the follow-up period (total initial n = 805).

<p>Number of participants actively collecting sensor data at the NL (gray) and NAM (black) cohorts during and after the follow-up period (total initial n = 805).</p

SUS scoring of the Fox Wearable Companion platform (smartwatch with smartphone app) as rated by participants.

<p>SUS scoring of the Fox Wearable Companion platform (smartwatch with smartphone app) as rated by participants.</p

(a) Fox Wearable Companion app main screen; (b) Fox Wearable Companion app activity graph; (c) Fox Wearable Companion app movement during sleep graph; (d) Fox Wearable Companion app symptom self-reports.

<p>“Reprinted from [Intel and Michael J Fox Foundation] under a CC BY license, with permission from [INTEL^®], original copyright [2017].</p

Demographic and disease related characteristics of participants.

<p>Demographic and disease related characteristics of participants.</p

Attrition in compliance per day for NL (n = 291, black) and NAM participants (n = 514, gray) during the follow up period.

<p>Attrition in compliance per day for NL (n = 291, black) and NAM participants (n = 514, gray) during the follow up period.</p