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1.
Health Informatics J ; 28(2): 14604582221102373, 2022.
Article in English | MEDLINE | ID: mdl-35726817

ABSTRACT

More evidence is needed on technology implementation for remote monitoring and self-management across the various settings relevant to chronic conditions. This paper describes the findings of a survey designed to explore the relevance of socio-demographic factors to attitudes towards connected health technologies in a community of patients. Stroke survivors living in the UK were invited to answer questions about themselves and about their attitudes to a prototype remote monitoring and self-management app developed around their preferences. Eighty (80) responses were received and analysed, with limitations and results presented in full. Socio-demographic factors were not found to be associated with variations in participants' willingness to use the system and attitudes to data sharing. Individuals' levels of interest in relevant technology was suggested as a more important determinant of attitudes. These observations run against the grain of most relevant literature to date, and tend to underline the importance of prioritising patient-centred participatory research in efforts to advance connected health technologies.


Subject(s)
Attitude , Stroke , Demography , Humans , Stroke/therapy , Surveys and Questionnaires , Survivors
2.
Stud Health Technol Inform ; 264: 644-648, 2019 Aug 21.
Article in English | MEDLINE | ID: mdl-31438003

ABSTRACT

This paper describes work to assess the feasibility of using a decision support tool to help patients with chronic conditions, specifically stroke, manage their condition in collaboration with their carers and the health care professionals who are looking after them. The system contains several novel elements: the integration of data from commercial wellness sensors, electronic health records and clinical guidelines; the use of computational argumentation to track the source of data and to resolve conflicts and make recommendations; and argumentation-based dialogue to support interaction with patients. The proposed approach is implemented as an application that can run on smart devices (e.g. tablets). The users have personalised dashboards where they can visualise their health data and interact with a conversational chatbot that provides further explanations about their overall well-being.


Subject(s)
Decision Support Systems, Clinical , Electronic Health Records , Expert Systems , Humans , Software , User-Computer Interface
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