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

De Lima, Ana Lígia Silva, Hahn, Tim, Evers, Luc J.W., De Vries, Nienke M., Cohen, Eli, Afek, Michal, Bataille, Lauren, Daeschler, Margaret, Claes, Kasper, Boroojerdi, Babak, Terricabras, Dolors, Little, Max A., Baldus, Heribert, Bloem, Bastiaan R. and Faber, Marjan J. (2017). Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease. PLoS ONE, 12 (12),

Abstract

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.

Publication DOI: https://doi.org/10.1371/journal.pone.0189161
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright: © 2017 Silva de Lima et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Michael J Fox Foundation (https://www.michaeljfox.org/), Intel Corporation (Tel Aviv, Israel—https://www.intel.com) [grant number 10231.01], the Stichting Parkinson Fonds (https://www.parkinsonfonds.nl/), The Netherlands Organisation for Health Research and Development (https://www.zonmw.nl/en/), Philips Research (https://www.philips.com/aw/research/home.html), UCB pharma (http://www.ucb.com/), and the Movement Disorders Society (https://www.movementdisorders.org/MDS.htm). The author Ana Lígia Silva de Lima is supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES (http://www.capes.gov.br/) [grant number 0428-140].
Uncontrolled Keywords: Biochemistry, Genetics and Molecular Biology(all),Agricultural and Biological Sciences(all)
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2017-12-20
Authors: De Lima, Ana Lígia Silva
Hahn, Tim
Evers, Luc J.W.
De Vries, Nienke M.
Cohen, Eli
Afek, Michal
Bataille, Lauren
Daeschler, Margaret
Claes, Kasper
Boroojerdi, Babak
Terricabras, Dolors
Little, Max A. ( 0000-0002-1507-3822)
Baldus, Heribert
Bloem, Bastiaan R.
Faber, Marjan J.

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