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  • 1.
    Brondin, Anna
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Nordström, Marcus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Open source step counter algorithm for wearable devices2020In: Companion Proceedings of the 10th International Conference on the Internet of Things (IoT 2020), New York, United States: ACM Digital Library, 2020, article id 6Conference paper (Refereed)
    Abstract [en]

    Commercial wearable devices and fitness trackers are commonly sold as black boxes of which little is known about their accuracy. This poses serious issues especially in health-related contexts such as clinical research, where transparency about accuracy and reliability are paramount.

    We present a validated algorithm for computing step counting that is optimised for use in constrained computing environments. Released as open source, the algorithm is based on the windowed peak detection approach, which has previously shown high accuracy on smartphones. The algorithm is optimised to run on a programmable smartwatch (Pine Time) and tested on 10 subjects in 8 scenarios, with varying varying positions of the wearable and walking paces.

    Our approach achieves a 89% average accuracy, with the highest average accuracy when walking outdoor (98%) and the lowest in a slow-walk scenario (77%). This result can be compared with the built-in step counter of the smartwatch (Bosch BMA421), which yielded a 94% average accuracy for the same use cases. Our work thus shows that an open-source approach for extracting physical activity data from wearable devices is possible and achieves an accuracy comparable to the one produced by proprietary embedded algorithms.

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  • 2.
    Mahdi, Adam
    et al.
    University of Oxford, Oxford, UK.
    Błaszczyk, Piotr
    University of Science and Technology, Krakow, Poland.
    Dłotko, Paweł
    Polish Academy of Sciences, Warsaw, Poland; Swansea University, Swansea, UK.
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3).
    Chan, Tak-Shing
    Swansea University, Swansea, UK.
    Harvey, John
    Swansea University, Swansea, UK.
    Gurnari, Davide
    University of Padova, Padova, Italy.
    Wu, Yue
    University of Oxford, Oxford, UK; The Alan Turing Institute, London, UK.
    Farhat, Ahmad
    American University of Sharjah, Sharjah, United Arab Emirates.
    Hellmer, Niklas
    Swansea University, Swansea, UK.
    Zarebski, Alexander
    University of Oxford, Oxford, UK.
    Hogan, Bernie
    University of Oxford, Oxford, UK.
    Tarassenko, Lionel
    University of Oxford, Oxford, UK.
    OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19.2021In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 9237Article in journal (Refereed)
    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.

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  • 3.
    Maus, Benjamin
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Privacy Personas for IoT-Based Health Research: A Privacy Calculus Approach2021In: Frontiers in Digital Health, E-ISSN 2673-253X, Vol. 3, p. 1-12, article id 675754Article in journal (Refereed)
    Abstract [en]

    The reliance on data donation from citizens as a driver for research, known as citizen science, has accelerated during the Sars-Cov-2 pandemic. An important enabler of this is Internet of Things (IoT) devices, such as mobile phones and wearable devices, that allow continuous data collection and convenient sharing. However, potentially sensitive health data raises privacy and security concerns for citizens, which research institutions and industries must consider. In e-commerce or social network studies of citizen science, a privacy calculus related to user perceptions is commonly developed, capturing the information disclosure intent of the participants. In this study, we develop a privacy calculus model adapted for IoT-based health research using citizen science for user engagement and data collection. Based on an online survey with 85 participants, we make use of the privacy calculus to analyse the respondents' perceptions. The emerging privacy personas are clustered and compared with previous research, resulting in three distinct personas which can be used by designers and technologists who are responsible for developing suitable forms of data collection. These are the 1) Citizen Science Optimist, the 2) Selective Data Donor, and the 3) Health Data Controller. Together with our privacy calculus for citizen science based digital health research, the three privacy personas are the main contributions of this study.

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  • 4.
    Maus, Benjamin
    et al.
    Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3).
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Enhancing citizens trust in technologies for data donation in clinical research: validation of a design prototype2020In: Companion Proceedings of the 10th International Conference on the Internet of Things (IoT 2020), ACM Digital Library, 2020Conference paper (Refereed)
    Abstract [en]

    Mobile phones, wearable trackers and Internet of Things devices continuously produce data about our health and lifestyle that can be used for medical research. However, how data is accessed, by whom and for what purpose is not always understood. This lack of transparency undermines citizens trust in the use of such technologies for research purposes. This paper proposes a set of 6 use cases and related mock-up interfaces for citizen science, mobile-based health research: “Curated information about the institution”, “Sequential consent of shared data”, “Updates from the institution”, “Privacy notifications”, “Overview of donated data” and “Personal impact in medical research”. Interviews and Kano analysis of the interfaces with 6 prospective users show that all except “Privacy notifications” are perceived as important and beneficial for increasing users’ trust. The defined use cases can guide the development of future data collection platforms.

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  • 5.
    Rouyard, Thomas
    et al.
    University of Oxford, UK.
    Leal, José
    University of Oxford, UK.
    Salvi, Dario
    Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3). University of Oxford, UK.
    Baskerville, Richard
    University of Oxford, UK.
    Velardo, Carmelo
    University of Oxford, UK.
    Gray, Alastair
    University of Oxford, UK.
    An Intuitive Risk Communication Tool to Enhance Patient-Provider Partnership in Diabetes Consultation.2022In: Journal of Diabetes Science and Technology, E-ISSN 1932-2968, Vol. 16, no 4, p. 988-994, article id 1932296821995800Article in journal (Refereed)
    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.

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  • 6.
    Salvi, Dario
    et al.
    Malmö University, Internet of Things and People (IOTAP). Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England..
    Lee, Jameson
    Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England..
    Velardo, Carmelo
    Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England..
    Goburdhun, Rishi Arvin
    Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England..
    Tarassenko, Lionel
    Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford, England..
    Mobistudy: an open mobile-health platform for clinical research2019In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), IEEE, 2019, p. 918-921Conference paper (Refereed)
    Abstract [en]

    Collecting data from smartphones can provide useful information for health research, but developing mobile health (mHealth) apps requires extensive resources and implies often overlooked regulatory and privacy-related requirements. Mobile health "aggregators" like HealthKit or Google Fit and specialised platforms may allow reducing costs in mHealth research. Current platforms, however, do not efficiently exploit mHealth aggregators and provide little attention to regulations. We propose an open-source platform that can be used by health researchers without the need to develop custom apps. The platform has a strong focus on regulatory compliance, patient consent and transparency, and allows collecting data through electronic surveys and querying aggregators. We tested the feasibility of our platform in a pilot study with 18 healthy volunteers. The results show that the participants' app is usable and well-accepted and is able to frequently collect data about physical activity from both phones and connected wearables. Some limitations were identified regarding data loss because of insufficient connectivity and the impossibility of extracting data from wearables that are not compatible with aggregators.

  • 7.
    Salvi, Dario
    et al.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Ymeri, Gent
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Carrasco-Lopez, Carmen
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Tsang, Kevin C.H.
    University of Edinburgh, United Kingdom.
    Shah, Seyed Ahmar
    University of Edinburgh, United Kingdom.
    Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies2022In: IoT 2021: Conference Proceedings, ACM Digital Library, 2022, p. 219-222Conference 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.

  • 8.
    Salvi, Dario
    et al.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Ymeri, Gent
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Jimeno, Daniel
    Escuela Tecnica Superior de Ingenieria y sistemas de Telecomunicacion, Universidad Politecnica de Madrid.
    Soto-Léon, Vanesa
    National Hospital for Paraplegics, Toledo.
    Pérez Borrego, Yolanda
    National Hospital for Paraplegics, Toledo.
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Carrasco-Lopez, Carmen
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    An IoT-based system for the study of neuropathic pain in spinal cord injury2023In: 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 (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.

    The full text will be freely available from 2024-06-11 11:20
  • 9.
    Tsang, Kevin C H
    et al.
    Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
    Pinnock, Hilary
    Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK.
    Wilson, Andrew M
    Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Norwich Medical School, University of East Anglia, Norwich, UK; Norwich University Hospital Foundation Trust, Colney Lane, Norwich, UK.
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Shah, Syed Ahmar
    Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
    Home monitoring with connected mobile devices for asthma attack prediction with machine learning2023In: Scientific Data, E-ISSN 2052-4463, Vol. 10, no 1, article id 370Article in journal (Refereed)
    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.

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  • 10.
    Tsang, Kevin CH
    et al.
    Usher Institute, University of Edinburgh.
    Pinnock, Hilary
    Usher Institute, University of Edinburgh.
    Wilson, Andrew M
    Norwich Medical School, University of East Anglia.
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Syed Ahmar, Shah
    Usher Institute, University of Edinburgh.
    Compliance and Usability of an Asthma Home Monitoring System2023In: 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 (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.

    The full text will be freely available from 2024-06-11 08:26
  • 11.
    Tsang, Kevin Cheuk Him
    et al.
    Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
    Pinnock, Hilary
    Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK.
    Wilson, Andrew M
    Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Norwich Medical School, University of East Anglia, Norwich, UK; Norwich University Hospital Foundation Trust, Colney Lane, Norwich, UK.
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Shah, Syed Ahmar
    Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK; Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
    Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol2022In: BMJ Open, E-ISSN 2044-6055, Vol. 12, no 10, article id e064166Article in journal (Refereed)
    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.

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  • 12.
    Ymeri, Gent
    et al.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease: Dataexploration of the mPower study2022In: 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 (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).

    The full text will be freely available from 2024-07-11 08:28
  • 13.
    Ymeri, Gent
    et al.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Thanasis, Tsanas
    Usher Institute, The University of Edinburgh, UK.
    Svenningsson, Per
    Department of Clinical Neuroscience, Karolinska Institute.
    Mobile-based multi-dimensional data collection for Parkinson’s symptoms in home environments2022Conference paper (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.

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  • 14.
    Ymeri, Gent
    et al.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Wassenburg, Myrthe Vivianne
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Center for Neurology, Academic Specialist Center Torsplan, Region Stockholm, Sweden.
    Tsanas, Athanasios
    Usher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK; Alan Turing Institute, London, UK.
    Svenningsson, Per
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Center for Neurology, Academic Specialist Center Torsplan, Region Stockholm, Sweden.
    Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol2023In: BMJ Open, E-ISSN 2044-6055, Vol. 13, no 12, article id e077766Article in journal (Refereed)
    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.

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