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Salvi, Dario
Publications (10 of 14) Show all publications
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
Tsang, K. C., Pinnock, H., Wilson, A. M., Salvi, D., Olsson, C. M. & Syed Ahmar, S. (2023). Compliance and Usability of an Asthma Home Monitoring System. In: Athanasios Tsanas; Andreas Triantafyllidis (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, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022 (pp. 116-126). Springer
Open this publication in new window or tab >>Compliance and Usability of an Asthma Home Monitoring System
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2023 (English)In: Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings / [ed] Athanasios Tsanas; Andreas Triantafyllidis, Springer, 2023, p. 116-126Conference paper, Published paper (Refereed)
Abstract [en]

Asthma monitoring is an important aspect of patient self-management. However, due to its repetitive nature, patients can find long-term monitoring tedious. Mobile health can provide an avenue to monitor asthma without needing high levels of active engagement, and instead rely on passive monitoring. In our recent AAMOS-00 study, we collected mobile health data over six months from 22 asthma patients using passive and active monitoring technology, including smartwatch, peak flow measurements, and daily asthma diaries.

Compliance to smartwatch monitoring was found to lie between the compliance to complete daily asthma diaries and measuring daily peak flow. However, some study participants faced technical issues with the devices which could have affected the relative compliance of the monitoring tasks.

Moreover, as evidenced by standard usability questionnaires, we found that the AAMOS-00 study’s data collection system was similar in quality to other studies and published apps.

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
Asthma, Mobile Health, mHealth, Home Monitoring, Compliance, Passive Monitoring
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-58644 (URN)10.1007/978-3-031-34586-9_9 (DOI)2-s2.0-85164103209 (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
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2024-02-05Bibliographically approved
Tsang, K. C., Pinnock, H., Wilson, A. M., Salvi, D. & Shah, S. A. (2023). Home monitoring with connected mobile devices for asthma attack prediction with machine learning. Scientific Data, 10(1), Article ID 370.
Open this publication in new window or tab >>Home monitoring with connected mobile devices for asthma attack prediction with machine learning
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2023 (English)In: Scientific Data, E-ISSN 2052-4463, Vol. 10, no 1, article id 370Article in journal (Refereed) Published
Abstract [en]

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices (smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK's COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.

Place, publisher, year, edition, pages
Nature Publishing Group, 2023
National Category
Respiratory Medicine and Allergy
Identifiers
urn:nbn:se:mau:diva-61395 (URN)10.1038/s41597-023-02241-9 (DOI)001003519300002 ()37291158 (PubMedID)2-s2.0-85161336943 (Scopus ID)
Available from: 2023-06-27 Created: 2023-06-27 Last updated: 2023-08-16Bibliographically 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)38154904 (PubMedID)2-s2.0-85181165016 (Scopus ID)
Available from: 2024-01-08 Created: 2024-01-08 Last updated: 2024-02-05Bibliographically approved
Rouyard, T., Leal, J., Salvi, D., Baskerville, R., Velardo, C. & Gray, A. (2022). An Intuitive Risk Communication Tool to Enhance Patient-Provider Partnership in Diabetes Consultation.. Journal of Diabetes Science and Technology, 16(4), 988-994, Article ID 1932296821995800.
Open this publication in new window or tab >>An Intuitive Risk Communication Tool to Enhance Patient-Provider Partnership in Diabetes Consultation.
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2022 (English)In: Journal of Diabetes Science and Technology, E-ISSN 1932-2968, Vol. 16, no 4, p. 988-994, article id 1932296821995800Article in journal (Refereed) Published
Abstract [en]

INTRODUCTION: This technology report introduces an innovative risk communication tool developed to support providers in communicating diabetes-related risks more intuitively to people with type 2 diabetes mellitus (T2DM).

METHODS: The development process involved three main steps: (1) selecting the content and format of the risk message; (2) developing a digital interface; and (3) assessing the usability and usefulness of the tool with clinicians through validated questionnaires.

RESULTS: The tool calculates personalized risk information based on a validated simulation model (United Kingdom Prospective Diabetes Study Outcomes Model 2) and delivers it using more intuitive risk formats, such as "effective heart age" to convey cardiovascular risks. Clinicians reported high scores for the usability and usefulness of the tool, making its adoption in routine care promising.

CONCLUSIONS: Despite increased use of risk calculators in clinical care, this is the first time that such a tool has been developed in the diabetes area. Further studies are needed to confirm the benefits of using this tool on behavioral and health outcomes in T2DM populations.

Place, publisher, year, edition, pages
Sage Publications, 2022
Keywords
primary care, risk communication, risk perceptions, shared decision making, type 2 diabetes mellitus
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:mau:diva-41194 (URN)10.1177/1932296821995800 (DOI)000904153500025 ()33655766 (PubMedID)2-s2.0-85102135986 (Scopus ID)
Available from: 2021-03-10 Created: 2021-03-10 Last updated: 2024-02-05Bibliographically 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
Tsang, K. C., Pinnock, H., Wilson, A. M., Salvi, D. & Shah, S. A. (2022). Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol. BMJ Open, 12(10), Article ID e064166.
Open this publication in new window or tab >>Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol
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2022 (English)In: BMJ Open, E-ISSN 2044-6055, Vol. 12, no 10, article id e064166Article in journal (Refereed) Published
Abstract [en]

INTRODUCTION: Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices.

METHODS AND ANALYSIS: A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire.

ETHICS AND DISSEMINATION: Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.

Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2022
Keywords
Asthma, Health informatics, Information technology, World Wide Web technology
National Category
Medical and Health Sciences Respiratory Medicine and Allergy
Identifiers
urn:nbn:se:mau:diva-55425 (URN)10.1136/bmjopen-2022-064166 (DOI)000866249200013 ()36192103 (PubMedID)2-s2.0-85139121573 (Scopus ID)
Available from: 2022-10-18 Created: 2022-10-18 Last updated: 2024-02-05Bibliographically approved
Mahdi, A., Błaszczyk, P., Dłotko, P., Salvi, D., Chan, T.-S., Harvey, J., . . . Tarassenko, L. (2021). OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19.. Scientific Reports, 11(1), Article ID 9237.
Open this publication in new window or tab >>OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19.
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2021 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 9237Article in journal (Refereed) Published
Abstract [en]

Oxford COVID-19 Database (OxCOVID19 Database) is a comprehensive source of information related to the COVID-19 pandemic. This relational database contains time-series data on epidemiology, government responses, mobility, weather and more across time and space for all countries at the national level, and for more than 50 countries at the regional level. It is curated from a variety of (wherever available) official sources. Its purpose is to facilitate the analysis of the spread of SARS-CoV-2 virus and to assess the effects of non-pharmaceutical interventions to reduce the impact of the pandemic. Our database is a freely available, daily updated tool that provides unified and granular information across geographical regions. Design type Data integration objective Measurement(s) Coronavirus infectious disease, viral epidemiology Technology type(s) Digital curation Factor types(s) Sample characteristic(s) Homo sapiens.

Place, publisher, year, edition, pages
Nature Publishing Group, 2021
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:mau:diva-42151 (URN)10.1038/s41598-021-88481-4 (DOI)000656201900002 ()33927237 (PubMedID)2-s2.0-85105222945 (Scopus ID)
Available from: 2021-05-10 Created: 2021-05-10 Last updated: 2024-02-05Bibliographically approved
Projects
Context-Aware and Autonomous Behavior: Making sense of IoTmHealth in pandemic situations: Smartphone based portable and wearable sensors for COVID-19 diagnostic; Malmö UniversityParkappPain App: Predicting neuropathic pain episodes in spinal cord injury patients through portable EEG and machine learning; Malmö University
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