| 1. |
- Rouyard, Thomas, et al.
(författare)
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An Intuitive Risk Communication Tool to Enhance Patient-Provider Partnership in Diabetes Consultation.
- 2022
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Ingår i: Journal of Diabetes Science and Technology. - : Sage Publications. - 1932-2968. ; 16:4, s. 988-994
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 2. |
- Salvi, Dario, et al.
(författare)
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Mobistudy : Mobile-based, platform-independent, multi-dimensional data collection for clinical studies
- 2022
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Ingår i: IoT 2021. - New York, NY, USA : ACM Digital Library. - 9781450385664 ; , s. 219-222
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Konferensbidrag (refereegranskat)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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| 3. |
- Tsang, Kevin Cheuk Him, et al.
(författare)
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Predicting asthma attacks using connected mobile devices and machine learning : the AAMOS-00 observational study protocol
- 2022
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Ingår i: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 12:10
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 4. |
- Ymeri, Gent, et al.
(författare)
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Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease : Dataexploration of the mPower study
- 2022
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Ingår i: Pervasive Computing Technologies for Healthcare. - Cham : Springer. - 9783031345852 - 9783031345869
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Konferensbidrag (refereegranskat)abstract
- 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).
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| 5. |
- Ymeri, Gent, 1996-, et al.
(författare)
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Mobile-based multi-dimensional data collection for Parkinson’s symptoms in home environments
- 2022
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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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