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Salvi, D., Ymeri, G., Jimeno, D., Soto-Léon, V., Pérez Borrego, Y., Olsson, C. M. & Carrasco-Lopez, C. (2023). An IoT-based system for the study of neuropathic pain in spinal cord injury. In: Athanasios Tsanas; Andreas Triantafyllidis (Ed.), Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceeding. Paper presented at 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022 (pp. 93-103). Springer
Open this publication in new window or tab >>An IoT-based system for the study of neuropathic pain in spinal cord injury
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2023 (English)In: Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceeding / [ed] Athanasios Tsanas; Andreas Triantafyllidis, Springer, 2023, p. 93-103Conference paper, Published paper (Refereed)
Abstract [en]

Neuropathic pain is a difficult condition to treat and would require reliable biomarkers to personalise and optimise treatments. To date, pain levels are mostly measured with subjective scales, but research has shown that electroencephalography (EEG) and heart rate variability (HRV) can be linked to those levels. Internet of Things technology could allow embedding EEG and HRV in easy-to-use systems that patients can use at home in their daily life. We have developed a system for home monitoring that includes a portable EEG device, a tablet application to guide patients through imaginary motor tasks while recording EEG, a wearable HRV sensor and a mobile phone app to report pain levels. We are using this system in a clinical study involving 15 spinal cord injury patients for one month. Preliminary results show that relevant data are being collected, with inter and intra-patients variability for both HRV and pain levels, and that the mobile phone app is perceived as usable, of good quality and useful. However, because of its complexity, the system requires some effort from patients, is sometimes unreliable and the collected EEG signals are not always of the desired quality.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 488
Keywords
IoT, EEG, HRV, Neuropathic pain, Mobile health
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-58645 (URN)10.1007/978-3-031-34586-9_7 (DOI)2-s2.0-85164160734 (Scopus ID)978-3-031-34585-2 (ISBN)978-3-031-34586-9 (ISBN)
Conference
16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022
Funder
EU, Horizon Europe, 101030384
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2024-02-05Bibliographically approved
Ymeri, G., Salvi, D., Olsson, C. M., Wassenburg, M. V., Tsanas, A. & Svenningsson, P. (2023). Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol. BMJ Open, 13(12), Article ID e077766.
Open this publication in new window or tab >>Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol
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2023 (English)In: BMJ Open, E-ISSN 2044-6055, Vol. 13, no 12, article id e077766Article in journal (Refereed) Published
Abstract [en]

INTRODUCTION: The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.

AIM: To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

METHODS AND ANALYSIS: In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck's Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson's Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson's Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

ETHICS AND DISSEMINATION: The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2023
Keywords
health informatics, parkinson's disease, telemedicine
National Category
Neurology
Identifiers
urn:nbn:se:mau:diva-64860 (URN)10.1136/bmjopen-2023-077766 (DOI)001134943800008 ()38154904 (PubMedID)2-s2.0-85181165016 (Scopus ID)
Available from: 2024-01-08 Created: 2024-01-08 Last updated: 2024-03-07Bibliographically approved
Ymeri, G., Salvi, D. & Olsson, C. M. (2022). Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease: Dataexploration of the mPower study. In: Tsanas, Athanasios; Triantafyllidis, Andreas (Ed.), Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings. Paper presented at 16th EAI International Conference, Pervasive Health 2022, Thessaloniki, Greece, December 12-14, 2022. Cham: Springer
Open this publication in new window or tab >>Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease: Dataexploration of the mPower study
2022 (English)In: Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings / [ed] Tsanas, Athanasios; Triantafyllidis, Andreas, Cham: Springer, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Advancements in technology, such as smartphones and wearabledevices, can be used for collecting movement data through embeddedsensors. This paper focuses on linking Parkinson’s Disease severitywith data collected from mobile phones in the mPower study. As referencefor symptoms’ severity, we use the answers provided to part 2 ofthe standard MDS-UPDRS scale. As input variables, we use the featurescomputed within mPower from the raw data collected during 4 phonebasedactivities: walking, rest, voice and finger tapping. The features arefiltered in order to remove unreliable datapoints and associated to referencevalues. After pre-processing, 5 Machine Learning algorithms areapplied for predictive analysis. We show that, notwithstanding the noisedue to the data being collected in an uncontrolled manner, the regressedsymptom levels are moderately to strongly correlated with the actualvalues (highest Pearson’s correlation = 0.6). However, the high differencebetween the values also implies that the regressed values can not beconsidered as a substitute for a conventional clinical assessment (lowestmean absolute error = 5.4).

Place, publisher, year, edition, pages
Cham: Springer, 2022
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211
Keywords
mobile health, Parkinson’s disease, mPower data
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-58646 (URN)10.1007/978-3-031-34586-9_29 (DOI)2-s2.0-85164108273 (Scopus ID)978-3-031-34585-2 (ISBN)978-3-031-34586-9 (ISBN)
Conference
16th EAI International Conference, Pervasive Health 2022, Thessaloniki, Greece, December 12-14, 2022
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2024-02-05Bibliographically approved
Ymeri, G., Salvi, D., Olsson, C. M., Thanasis, T. & Svenningsson, P. (2022). Mobile-based multi-dimensional data collection for Parkinson’s symptoms in home environments. In: : . Paper presented at 44th International Engineering in Medicine and Biology, 11-15 July 2022, Glasgow, UK.
Open this publication in new window or tab >>Mobile-based multi-dimensional data collection for Parkinson’s symptoms in home environments
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2022 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

We extended the Mobistudy app for clinical research in order to gather data about Parkinson’s motor and non-motor symptoms. We developed 5 tests that make use of the phone’s embedded sensors and 3 questionnaires. We show through data collected by healthy individuals simulating PD symptoms that the tests are able to identify the presence of symptoms.

Keywords
mobile health, Parkinson’s disease
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mau:diva-59125 (URN)
Conference
44th International Engineering in Medicine and Biology, 11-15 July 2022, Glasgow, UK
Available from: 2023-04-05 Created: 2023-04-05 Last updated: 2024-01-08Bibliographically approved
Salvi, D., Olsson, C. M., Ymeri, G., Carrasco-Lopez, C., Tsang, K. C. .. & Shah, S. A. (2022). Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies. In: IoT 2021: Conference Proceedings. Paper presented at 11th International Conference on the Internet of Things, November 8-11, 2021. St.Gallen, Switzerland (pp. 219-222). ACM Digital Library
Open this publication in new window or tab >>Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies
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2022 (English)In: IoT 2021: Conference Proceedings, ACM Digital Library, 2022, p. 219-222Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) can work as a useful tool for clinical research. We developed a software platform that allows researchers to publish clinical studies and volunteers to participate into them using an app and connected IoT devices. The platform includes a REST API, a web interface for researchers and an app that collects data during tasks volunteers are invited to contribute. Nine tasks have been developed: Forms, Positioning, Finger tapping, Pulse-oximetry, Peak Flow measurement, Activity tracking, Data query, Queen’s College step test and Six-minute walk test. These leverage sensors embedded in the phone, connected Bluetooth devices and additional APIs like HealthKit and Google Fit. Currently, the platform is used in two clinical studies by 25 patients: an asthma management study in the United Kingdom, and a neuropathic pain management study in Spain.

Place, publisher, year, edition, pages
ACM Digital Library, 2022
Keywords
clinical research, m-Health, IoT
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-50618 (URN)10.1145/3494322.3494363 (DOI)000936000600025 ()2-s2.0-85127119368 (Scopus ID)978-1-4503-8566-4 (ISBN)
Conference
11th International Conference on the Internet of Things, November 8-11, 2021. St.Gallen, Switzerland
Funder
Knowledge Foundation, 20140035
Available from: 2022-03-14 Created: 2022-03-14 Last updated: 2024-01-08Bibliographically approved
Projects
Securing IOT Devices in a Dynamic Environment: The Case of Drones; Malmö University, Internet of Things and People (IOTAP)Parkapp
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7102-083X

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