health monitoring Archives - Healthentia

Assessing the Efficacy of a Virtual Assistant in the Remote Cardiac Rehabilitation of Heart Failure and Ischemic Heart Disease Patients: Case-Control Study of Romanian Adult Patients

System — Tue, 28 Feb 2023 10:32:06 +0000

CATEGORY: eHealth, Health monitoring, e-Health applications, Cardiovascular diseases, Virtual Assistant, Remote patient monitoring
SOURCE: Int. J. Environ. Res. Public Health 2023, FEB. 22, 20(5), 3937; https://doi.org/10.3390/ijerph20053937;

Assessing the Efficacy of a Virtual Assistant in the Remote Cardiac Rehabilitation of Heart Failure and Ischemic Heart Disease Patients: Case-Control Study of Romanian Adult Patients

Andreea Lăcraru, Ștefan-Sebastian Busnatu, Maria-Alexandra Pană, Gabriel Olteanu, Liviu Șerbănoiu, Kai Gand, Hannes Schlieter, Sofoklis Kyriazakos, Octavian Ceban, Cătălina Liliana Andrei & Crina-Julieta Sinescu

Abstract

Cardiovascular diseases (CVDs) are the leading cause of mortality in Europe, with potentially more than 60 million deaths per year, with an age-standardized rate of morbidity-mortality higher in men than women, exceeding deaths from cancer. Heart attacks and strokes account for more than four out of every five CVD fatalities globally. After a patient overcomes an acute cardiovascular event, they are referred for rehabilitation to help them to restore most of their normal cardiac functions. One effective way to provide this activity regimen is via virtual models or telerehabilitation, where the patient can avail themselves of the rehabilitation services from the comfort of their homes at designated timings. Under the funding of the European Union’s Horizon 2020 Research and Innovation program, grant no 769807, a virtual rehabilitation assistant has been designed for elderly patients (vCare), with the overall objective of supporting recovery and an active life at home, enhancing patients’ quality of life, lowering disease-specific risk factors, and ensuring better adherence to a home rehabilitation program. In the vCare project, the Carol Davila University of Bucharest (UMFCD) was in charge of the heart failure (HF) and ischemic heart disease (IHD) groups of patients. By creating a digital environment at patients’ homes, the vCare system’s effectiveness, use, and feasibility was evaluated. A total of 30 heart failure patients and 20 ischemic heart disease patients were included in the study. Despite the COVID-19 restrictions and a few technical difficulties, HF and IHD patients who performed cardiac rehabilitation using the vCare system had similar results compared to the ambulatory group, and better results compared to the control group.

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Opportunities, ethical challenges, and value implications of pervasive sensing technology for supporting older adults in the work environment

System — Mon, 09 May 2022 12:46:50 +0000

CATEGORY: eHealth, Health monitoring, e-Health applications

SOURCE: AUSTRALASIAN JOURNAL OF INFORMATION SYSTEMS , 2022, May; DOI Link, Research Article DOI Link

Opportunities, ethical challenges, and value implications of pervasive sensing technology for supporting older adults in the work environment

Christiane Grünloh, Miriam Cabrita, Carina Dantas & Sofia Ortet

Abstract

Responding to the challenges of demographic change, a growing number of eHealth solutions are appearing on the market, aiming to enable age-friendly living and working environments. Pervasive sensing and monitoring of workers' health-, behavioural-, emotional- and cognitive status to support their health and workability enable the creation of adaptive work environments and the provision of personalised interventions. However, this technology also introduces new challenges that go beyond user acceptance and privacy concerns. Based on a conceptual investigation and lessons learnt within the SmartWork project (H2020-826343), this paper outlines opportunities and ethical challenges of pervasive sensing technology in the work environment that aims to support active and healthy ageing for office workers in a holistic way, including their values and preferences. Only by identifying those challenges, implicated values and value tensions is it possible to convert them into design opportunities and find innovative ways to address identified tensions. The article outlines steps taken within the project and closes with a reflection on the limits of technological responses to societal problems and the need for regulations and changes on a societal level.

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Risk Assessment for Personalized Health Insurance Based on Real-World Data

System — Thu, 04 Mar 2021 14:50:29 +0000

Topics: eHealth, Health monitoring, e-Health applications

SOURCE: MDPI Open Access Journals, 2021, Feb. ; BOOK DOI Link, Chapter DOI Link

Risk Assessment for Personalized Health Insurance Based on Real-World Data

Aristodemos Pnevmatikakis ^1* ; Efstathios Kanavos ¹; George Matikas¹ ; Konstantina Kostopoulou¹ ; Alfredo Cesario^1,2 ; Sofoklis Kyriazakos^1,3

¹Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium

²Scientific Directorate, Fondazione Policlinico A. Gemelli IRCCS, 00168 Rome, Italy

³Business Development and Technology Department, School of Business and Social Sciences, Aarhus University, Birk Centerpark 15, 7400 Herning, Denmark

^*Author to whom correspondence should be addressed

Abstract

The way one leads their life is considered an important factor in health. In this paper we propose a system to provide risk assessment based on behavior for the health insurance sector. To do so we built a platform to collect real-world data that enumerate different aspects of behavior, and a simulator to augment actual data with synthetic. Using the data, we built classifiers to predict variations in important quantities for the lifestyle of a person. We offer a risk assessment service to the health insurance professionals by manipulating the classifier predictions in the long-term. We also address virtual coaching by using explainable Artificial Intelligence (AI) techniques on the classifier itself to gain insights on the advice to be offered to insurance customers.

Keywords: machine learning; classification; explainable AI; risk assessment

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