extended-abstract

Mobistudy: mobile-based, platform-independent, multi-dimensional data collection for clinical studies

Authors:

Carl Magnus Olsson,

Carmen Carrasco-Lopez,

Kevin C.H. Tsang,

Syed Ahmar ShahAuthors Info & Claims

IoT '21: Proceedings of the 11th International Conference on the Internet of Things

Pages 219 - 222

https://doi.org/10.1145/3494322.3494363

Published: 08 March 2022 Publication History

Abstract

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.

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Cited By

Ymeri GGrech NWassenburg MOlsson CSvenningsson PSalvi D(2024)Measuring finger dexterity in Parkinson’s disease with mobile phones2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10503245(112-116)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10503245
Ymeri GMaus BWassenburg MOlsson CSvenningsson PSalvi D(2024)Usability of a Mobile Application for Patients with Parkinson’s Disease2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10782276(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/EMBC53108.2024.10782276
Caramaschi SYmeri GOlsson CTsanas AWassenburg MSvenningsson PSalvi D(2024)A Smartphone-Based Timed Up and Go Test for Parkinson’s DiseasePervasive Computing Technologies for Healthcare10.1007/978-3-031-59717-6_34(515-519)Online publication date: 4-Jun-2024
https://doi.org/10.1007/978-3-031-59717-6_34
Show More Cited By

Index Terms

Mobistudy: mobile-based, platform-independent, multi-dimensional data collection for clinical studies
1. Applied computing
  1. Life and medical sciences
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Other conferences

IoT '21: Proceedings of the 11th International Conference on the Internet of Things

November 2021

233 pages

ISBN:9781450385664

DOI:10.1145/3494322

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2022

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IoT '21

IoT '21: 11th International Conference on the Internet of Things

November 8 - 12, 2021

St.Gallen, Switzerland

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Overall Acceptance Rate 28 of 84 submissions, 33%

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Cited By

Ymeri GGrech NWassenburg MOlsson CSvenningsson PSalvi D(2024)Measuring finger dexterity in Parkinson’s disease with mobile phones2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10503245(112-116)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10503245
Ymeri GMaus BWassenburg MOlsson CSvenningsson PSalvi D(2024)Usability of a Mobile Application for Patients with Parkinson’s Disease2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10782276(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/EMBC53108.2024.10782276
Caramaschi SYmeri GOlsson CTsanas AWassenburg MSvenningsson PSalvi D(2024)A Smartphone-Based Timed Up and Go Test for Parkinson’s DiseasePervasive Computing Technologies for Healthcare10.1007/978-3-031-59717-6_34(515-519)Online publication date: 4-Jun-2024
https://doi.org/10.1007/978-3-031-59717-6_34
Van Gorp PNuijten R(2023)8-year Evaluation of GameBus: Status quo in Aiming for an Open Access Platform to Prototype and Test Digital Health AppsProceedings of the ACM on Human-Computer Interaction10.1145/35932237:EICS(1-24)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3593223
Ymeri GSalvi DOlsson CWassenburg MTsanas ASvenningsson P(2023)Quantifying Parkinson’s disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocolBMJ Open10.1136/bmjopen-2023-07776613:12(e077766)Online publication date: 28-Dec-2023
https://doi.org/10.1136/bmjopen-2023-077766
Tsang KPinnock HWilson ASalvi DShah S(2023)Home monitoring with connected mobile devices for asthma attack prediction with machine learningScientific Data10.1038/s41597-023-02241-910:1Online publication date: 8-Jun-2023
https://doi.org/10.1038/s41597-023-02241-9
Tsang KPinnock HWilson ASalvi DOlsson CShah S(2023)Compliance and Usability of an Asthma Home Monitoring SystemPervasive Computing Technologies for Healthcare10.1007/978-3-031-34586-9_9(116-126)Online publication date: 11-Jun-2023
https://doi.org/10.1007/978-3-031-34586-9_9
Salvi DYmeri GJimeno DSoto-León VPérez-Borrego YOlsson CCarrasco-Lopez C(2023)An IoT-Based System for the Study of Neuropathic Pain in Spinal Cord InjuryPervasive Computing Technologies for Healthcare10.1007/978-3-031-34586-9_7(93-103)Online publication date: 11-Jun-2023
https://doi.org/10.1007/978-3-031-34586-9_7
Tsang KPinnock HWilson ASalvi DShah S(2022)Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocolBMJ Open10.1136/bmjopen-2022-06416612:10(e064166)Online publication date: 3-Oct-2022
https://doi.org/10.1136/bmjopen-2022-064166

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