| 1. |
- Gonzalez, Manuel Cruz, et al.
(författare)
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International feasibility trial on the use of an interactive mobile health platform for cardiac rehabilitation : protocol of the Diversity 1 study
- 2019
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Ingår i: BMJ Health & Care Informatics. - : BMJ Publishing Group Ltd. - 2632-1009. ; 26:1
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The implementation of home-based cardiac rehabilitation has demonstrated potential to increase patient participation, but the content and the delivering of the programmes varies across countries. The objective of this study is to investigate whether an Australian-validated mobile health (mHealth) platform for cardiac rehabilitation will be accepted and adopted irrespectively from the existing organisational and contextual factors in five different European countries.Methods and analysis: This international multicentre feasibility study will use surveys, preliminary observations and analysis to evaluate the use and the user's perceptions (satisfaction) of a validated mHealth platform in different contextual settings.Ethics and dissemination: This study protocol has been approved by the Australian research organisation CSIRO and the respective ethical committees of the European sites. The dissemination of this trial will serve as a ground for the further implementation of an international large randomised controlled trial which will contribute to an effective global introduction of mHealth into daily clinical practice.
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| 2. |
- Gonzalez, Manuel, et al.
(författare)
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Use of eHealth in the management of pulmonary arterial hypertension : review of the literature
- 2020
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Ingår i: BMJ Health & Care Informatics. - : BMJ Publishing Group Ltd. - 2632-1009. ; 27:3
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Forskningsöversikt (refereegranskat)abstract
- BACKGROUND: Pulmonary arterial hypertension (PAH) is a severe chronic condition associated with poor quality of life and high risks of mortality and hospitalisation. The utilisation of novel diagnostic technologies has improved survival rates although the effectiveness of Electronic Health (eHealth) interventions in patients with a chronic cardiopulmonary disease remains controversial. As the effectiveness of eHealth can be established by specific evaluation for different chronic health conditions, the aim of this study was to explore and summarise the utilisation of eHealth in PAH.METHOD: We searched PubMed, CINAHL and Embase for all studies reporting clinical trials on eHealth solutions for the management of PAH. No limitations in terms of study design or date of publication were imposed.RESULTS: 18 studies (6 peer-reviewed journal papers and 12 conference papers) were identified. Seven studies addressed the accuracy, safety or reliability of eHealth technologies such as intra-arterial haemodynamic monitoring of the pulmonary artery pressure, self-administered 6-Minute walk test App, computerised step-pulse oximeter and ambulatory impedance cardiography. Two studies evaluated eHealth as part of the medical management and showed a reduction in hospitalisation rate.CONCLUSIONS: The evidence of eHealth supporting the management of people with PAH is limited and only embraced through a few studies of small sample size and short-term duration. Given the proposed clinical benefits in heart failure, we postulate that the evaluation of eHealth for the clinical management of PAH is highly warranted.
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| 3. |
- Tran, Son N., et al.
(författare)
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On multi-resident activity recognition in ambient smart-homes
- 2020
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Ingår i: Artificial Intelligence Review. - : Springer. - 0269-2821 .- 1573-7462. ; 53:6, s. 3929-3945
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Tidskriftsartikel (refereegranskat)abstract
- Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue. Several approaches have been proposed for multi-resident activity recognition, however, there still lacks a comprehensive benchmark for future research and practical selection of models. In this paper, we study different methods for multi-resident activity recognition and evaluate them on the same sets of data. In particular, we explore the effectiveness and efficiency of temporal learning algorithms using sequential data and non-temporal learning algorithms using temporally-manipulated features. In the experiments we compare and analyse the results of the studied methods using datasets from three smart homes.
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