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

Ana Lígia Silva De Lima; Tim Hahn; Luc J.W. Evers; Nienke M. De Vries; Eli Cohen; Michal Afek; Lauren Bataille; Margaret Daeschler; Kasper Claes; Babak Boroojerdi; Dolors Terricabras; Max A. Little; Heribert Baldus; Bastiaan R. Bloem; Marjan J. Faber

doi:10.1371/journal.pone.0189161

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

Ana Lígia Silva De Lima^*, Tim Hahn, Luc J.W. Evers, Nienke M. De Vries, Eli Cohen, Michal Afek, Lauren Bataille, Margaret Daeschler, Kasper Claes, Babak Boroojerdi, Dolors Terricabras, Max A. Little, Heribert Baldus, Bastiaan R. Bloem, Marjan J. Faber

^*Corresponding author for this work

Mathematics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	e0189161
Number of pages	15
Journal	PLoS ONE
Volume	12
Issue number	12
Early online date	20 Dec 2017
DOIs	https://doi.org/10.1371/journal.pone.0189161
Publication status	Published - 20 Dec 2017

Bibliographical note

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].

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journal.pone.0189161
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.
Final published version, 2.14 MBLicence: CC BY 3.0

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De Lima, A. L. S., Hahn, T., Evers, L. J. W., De Vries, N. M., Cohen, E., Afek, M., Bataille, L., Daeschler, M., Claes, K., Boroojerdi, B., Terricabras, D., Little, M. A., Baldus, H., Bloem, B. R., & Faber, M. J. (2017). Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease. PLoS ONE, 12(12), [e0189161]. https://doi.org/10.1371/journal.pone.0189161

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abstract = "Wearable devices can capture objective day-to-day data about Parkinson{\textquoteright}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.",

author = "{De Lima}, {Ana L{\'i}gia Silva} and Tim Hahn and Evers, {Luc J.W.} and {De Vries}, {Nienke M.} and Eli Cohen and Michal Afek and Lauren Bataille and Margaret Daeschler and Kasper Claes and Babak Boroojerdi and Dolors Terricabras and Little, {Max A.} and Heribert Baldus and Bloem, {Bastiaan R.} and Faber, {Marjan J.}",

note = "Copyright: {\textcopyright} 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 L{\'i}gia Silva de Lima is supported by Coordena{\c c}{\~a}o de Aperfei{\c c}oamento de Pessoal de N{\'i}vel Superior—CAPES (http://www.capes.gov.br/) [grant number 0428-140]. ",

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AU - Cohen, Eli

AU - Afek, Michal

AU - Bataille, Lauren

AU - Daeschler, Margaret

AU - Claes, Kasper

AU - Boroojerdi, Babak

AU - Terricabras, Dolors

AU - Little, Max A.

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AU - Bloem, Bastiaan R.

AU - Faber, Marjan J.

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

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