2021
DOI: 10.2196/22125
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A Digital Health Platform for Integrated and Proactive Patient-Centered Multimorbidity Self-management and Care (ProACT): Protocol for an Action Research Proof-of-Concept Trial

Abstract: Background Multimorbidity is defined as the presence of two or more chronic diseases and associated comorbidities. There is a need to improve best practices around the provision of well-coordinated, person-centered care for persons with multimorbidities. Present health systems across the European Union (EU) focus on supporting a single-disease framework of care; the primary challenge is to create a patient-centric, integrated care ecosystem to understand and manage multimorbidity. ProACT is a large… Show more

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Cited by 12 publications
(3 citation statements)
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“…Full details on the study protocol can be found in. 25 During the project, 60 older adults who are aged 65 years or over with multiple chronic conditions (two or more of the following: chronic obstructive pulmonary disease (COPD), heart failure (HF), heart disease (HD) and diabetes) used the ProACT platform, consisting of a suite of sensor devices and a tablet-based app to monitor and manage their conditions. All participants received a BP monitor (to measure BP and heart rate), a smartwatch (to measure activity and sleep) and a weight scale.…”
Section: Methodsmentioning
confidence: 99%
“…Full details on the study protocol can be found in. 25 During the project, 60 older adults who are aged 65 years or over with multiple chronic conditions (two or more of the following: chronic obstructive pulmonary disease (COPD), heart failure (HF), heart disease (HD) and diabetes) used the ProACT platform, consisting of a suite of sensor devices and a tablet-based app to monitor and manage their conditions. All participants received a BP monitor (to measure BP and heart rate), a smartwatch (to measure activity and sleep) and a weight scale.…”
Section: Methodsmentioning
confidence: 99%
“…The full study protocol has been published elsewhere. 32 The study received ethical approval from three ethics committees in Ireland and four in Belgium.…”
Section: Methodsmentioning
confidence: 99%
“…The training phase of the model collects data variables primarily based on publicly available health conditions datasets. These variables include data about blood pressure, pulse oximetry, the concentration of glucose in the blood, activity tracking, sleep tracking with the corresponding prediction class; in addition to patient feedback and evaluation data (Dinsmore et al, 2021 ); finally, data related to health trajectories from Healthcare Administrative Databases (HADs) such as diagnoses and medication prescriptions are also included (Veronica et al, 2022 ). The production phase of the model collects data coming from the proposed tool and its associated sensory fabrics, as described in Figures 2 , 4 to predict emergent health situations requiring interventions such as early hospital admission, diagnosis, clinical procedures, and medications, will be compared with deep learning (DL) and convolutional neural network (CNN) (Nguyen et al, 2016 ; Pham et al, 2016 ) vs. traditional machine learning algorithms, e.g., Bayesian probabilistic model, k nearest neighbors, logistic regression, support vector machines, and decision tree (Deparis et al, 2018 ; Hansen et al, 2018 ; Khalid et al, 2018 ; Noh et al, 2019 ; Ben-Assuli and Padman, 2020 ; Franz et al, 2020 ; Veronica et al, 2022 ).…”
Section: Increasing Ict Usage Trends In the Elderly Population Groupmentioning
confidence: 99%