eHealth Archives - Healthentia

Enhancing HIV Patient Care through Healthentia

System — Tue, 19 Mar 2024 14:48:57 +0000

CATEGORY: eHealth, Health monitoring, telemonitoring, COPD Remote patient monitoring

SOURCE: Int. J. Environ. Res. Public Health 2020, 17(21), 7893; https://doi.org/10.3390/ijerph17217893

Acceptance and Potential Impact of the eWALL Platform for Health Monitoring and Promotion in Persons with a Chronic Disease or Age-Related Impairment

Francesco Infarinato, Stephanie Jansen-Kosterink, Paola Romano, Lex van Velsen, Harm op den Akker, Federica Rizza, Marco Ottaviani, Sofoklis Kyriazakos, Beatrix Wais-Zechmann, Markus Garschall, Stefano Bonassi, and Hermie J. Hermens

Abstract

Pervasive health technologies can increase the effectiveness of personal health monitoring and training, but more user studies are necessary to understand the interest for these technologies, and how they should be designed and implemented. In the present study, we evaluated eWALL, a user-centered pervasive health technology consisting of a platform that monitors users' physical and cognitive behavior, providing feedback and motivation via an easy-to-use, touch-based user interface. The eWALL was placed for one month in the home of 48 subjects with a chronic condition (chronic obstructive pulmonary disease-COPD or mild cognitive impairment-MCI) or with an age-related impairment. User acceptance, platform use, and potential clinical effects were evaluated using surveys, data logs, and clinical scales. Although some features of the platform need to be improved before reaching technical maturity and making a difference in patients' lives, the real-life evaluation of eWALL has shown how some features may influence patients' intention to use this promising technology. Furthermore, this study made it clear how the free use of different health apps is modulated by the real needs of the patient and by their usefulness in the context of the patient's clinical status.

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AI state-of-play around clinical research

System — Fri, 10 Nov 2023 12:28:01 +0000

CATEGORY: eHealth, Health monitoring, telemonitoring, COPD Remote patient monitoring

SOURCE: Int. J. Environ. Res. Public Health 2020, 17(21), 7893; https://doi.org/10.3390/ijerph17217893

Acceptance and Potential Impact of the eWALL Platform for Health Monitoring and Promotion in Persons with a Chronic Disease or Age-Related Impairment

Abstract

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The Role of Big Data and Artificial Intelligence in Clinical Research and Digital Therapeutics

System — Mon, 28 Aug 2023 13:06:29 +0000

CATEGORY: Artificial Intelligence, Big data, Clinical research, Real-World Data, Digital therapeutics SOURCE: Personalized Medicine Meets Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-031-32614-1_6

The Role of Big Data and Artificial Intelligence in Clinical Research and Digital Therapeutics

Kyriazakos S., Pnevmatikakis A., op den Akker H., Kostopoulou K. (2023), In: Cesario, A., D’Oria, M., Auffray, C., Scambia, G. (eds) Abstract Healthcare is among the pioneering industries in the velocity of generation of big data, from the time that Electronic Health Records, Internet-of-Things, and electronic medical devices have been introduced. Researchers around the world are curating large volumes of data and applying Artificial Intelligent (AI) algorithms that give meaning to medical conditions, as well as prediction of clinical outcomes. The clinical research domain, which has been a laggard in digital transformation, is running today at high speed, utilizing electronic solutions that enable capturing of Real-World Data (RWD) in decentralized virtual clinical studies that aim to shorten the life cycle of drug development and the associated costs. At the same time, the domain of Digital Therapeutics (DTx) has been recently established, with various electronic solutions that process RWD to provide digital interventions that improve health-related endpoints. In this chapter, we will review how clinical research and DTx domains have been accelerated by the existence of Big data and AI algorithms and we will describe smart services that are expected to further boost the healthcare industry. Keywords: Artificial Intelligence, Big data, Clinical research, Real-World Data, Digital therapeutics

More Publications

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Acceptance and Potential Impact of the eWALL Platform for Health Monitoring and Promotion in Persons with a Chronic Disease or Age-Related Impairment

System — Wed, 29 Mar 2023 13:22:48 +0000

CATEGORY: eHealth, Health monitoring, telemonitoring, COPD Remote patient monitoring

SOURCE: Int. J. Environ. Res. Public Health 2020, 17(21), 7893; https://doi.org/10.3390/ijerph17217893

Acceptance and Potential Impact of the eWALL Platform for Health Monitoring and Promotion in Persons with a Chronic Disease or Age-Related Impairment

Abstract

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eWALL: An Open-Source Cloud-Based eHealth Platform for Creating Home Caring Environments for Older Adults Living with Chronic Diseases or Frailty

System — Wed, 29 Mar 2023 13:13:33 +0000

CATEGORY: eHealth, eCare, Personal Health Systems, COPD, MCI, Frailty Assistant, Remote patient monitoring
SOURCE: Wireless Pers Commun 97, 1835-1875 (2017). https://doi.org/10.1007/s11277-017-4656-7

eWALL: An Open-Source Cloud-Based eHealth Platform for Creating Home Caring Environments for Older Adults Living with Chronic Diseases or Frailty

Sofoklis Kyriazakos, Ramjee Prasad, Albena Mihovska, Aristodemos Pnevmatikakis, Harm op den Akker, Hermie Hermens, Paolo Barone, Alessandro Mamelli, Samuele de Domenico, Matthias Pocs, Andrej Grguric, Miran Mosmondor, Dina Simunic, Antun Kerner, Nikola Zaric, Milica Pejanović-Djurišić, Vladimir Poulkov, Krasimir Tochev, Beatrix Zechmann, Markus Garschall, Angeliki Angeletou, Stefano Bonassi, Francesco Infarinato, Octavian Fratou, Alexandru Vulpe, Carmen Voicu, Liljana Gavrilovska & Vladimir Atanasovski

Abstract

Independent living of older adults is one of the main challenges linked to the ageing population. Especially those living with diseases like COPD, MCI or frailty, need more support in everyday life and this is by itself a big societal challenge with impact in multiple sectors. In this paper we present eWALL, an innovative open-source eHealth platform that aims to address these challenges by means of an advanced cloud-based infrastructure. eWALL is designed in an innovative manner and achieved technical breakthroughs in eHealth platforms, while prioritizing user and market needs that are often abandoned and are the major reason for technically sound solutions that fail. We consider this as an opportunity and we aim to change the eHealth systems' experience for older adults and break the barriers for the penetration of ICT solutions.

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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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Use of Real-World Data in clinical research

System — Mon, 16 Jan 2023 14:34:06 +0000

Definition & importance

Real-World Data (RWD) is any data relating to a patient’s health status, collected during the routine delivery of care, as opposed to data collected within the controlled setup of clinical trials. Hence RWD does not differ so much in its type but in the process and population involved in its collection. The different types and sources of RWD can be:

Clinical data from electronic health records (EHRs) and case report forms (EHRs). This data establishes who the subject is, providing demographics, family history, comorbidities, procedure and treatment history, and outcomes. Such data types are also common in clinical trials.
Patient-generated data from patient-reported outcome (PRO) questionnaires, or measurements from wearables. This is data collected in everyday setting, providing insights directly from the patient, beyond clinic visits, procedures, and hospitalization. While patient-generated data is not unusual in clinical trials, it is collected in a centralized manner at the regular visits of the trial volunteers to the healthcare facilities. In the real-world context, the collection is done continuously at home.
Public and government data including cost and utilization data. Such data provides information on the healthcare system and the different stakeholders therein.

Such information can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. While clinical trials continue to be the main tool for studying the safety and efficacy of a new medicine, their controlled environment, and well-defined cohorts constitute experimental conditions that do not represent real-world settings. RWD is a much better tool for understanding how patients react to a medicine once approved and made available in the market, i.e., in routine medical care. The lack of highly controlled settings usually results in lower levels of confidence, but the outcomes represent a wider population of subjects. Such outcomes are better suited for understanding and taking decisions in everyday medical care, in broader settings than the controlled ones in clinical trials.

RWD: Collecting in a clinical vs. everyday setting

There can be a huge quality difference between RWD collected in a clinical versus in everyday setting. In a clinical setting, the process is carried out sporadically by professionals, with subjects following strict guidelines (like time and method of collection, or diet prior to collection). In the everyday setting, the process is continuous and carried out by the subjects themselves. Whether the data is reported by the subjects or is measured by devices the subjects operate, the continuous nature and the self-supervision can lead to low quality due to device failure (usually uncharged devices, wearables not worn when they should have been, or mobile applications left unused for too long and automatically closed down) and lack of adherence (forgetting to answer instances of repeating questionnaires, amplified decline of interest in the process). Also, clinical data can be much more specialized to the medical conditions at hand, compared to most behavioral data collected in an everyday setting. But no matter these shortcomings when dealing with data collected in an everyday setting, it is now well-established that behavior is part of the intervention. The high specialization and quality of the sporadic clinical data is complemented by the continuous nature of the behavioral, everyday data, in much the same way a low-resolution film complements the understanding offered by the occasional high-resolution photo.

Patient-generated, everyday RWD types

The behavioral, everyday RWD are categorized in terms of collection method and content. The following collection options are used:

Patient-reported via questionnaires: This collection model is closer to the established clinical trial approach, but this time the questionnaires are digital, pushed to subjects via some companion mobile app. They mostly have to do with self-assessment of different aspects.
Patient-reported via widgets: Similar to questionnaires, only this time rich graphical interfaces are employed. The widgets allow manual entry, or take advantage of integration with 3^rd party devices meant for occasional use like scales or blood pressure monitors to automatically collect measurements.
Automatically reported by wearables: Continuous measurements from wearable devices is one of the most prominent sources of RWD. Ubiquitous activity trackers or more specialized devices like sleep monitors are integrated either at device level (when a Software Development Kit is available, e.g., via Apple Health Kit) or at device cloud level (when an Application Programming Interface is available).

Using any of the above methods, the following everyday RWD types are collected:

Physiological: Data about physical activity, continuous monitoring of vitals, sleep
Psychological: Emotions
Social: Interactions (phone calls, social media)
Environmental: Living and working environment

Learning on RWD

At a raw level, RWD can lead to decisions about individuals and cohorts via analytics visualizations. But a full understanding of the context of subjects is gained via processing, using machine learning techniques. Supervised algorithms facilitate learning biomarkers, while unsupervised ones lead to phenotypes. RWD facilitates learning digital composite biomarkers. Biomarkers are quantities characterizing some disease or outcome. Digital refers to their attributes being ubiquitously available, not only as clinical data. Composite refers to the combination of multiple attributes in an attempt to predict some outcome. ML algorithms are used to learn outcome predictors as non-linear combinations of the attributes into the digital composite biomarkers. Phenotypes characterize the way the internal conditions of subjects manifest themselves for external observation. The different RWD attributes measured constitute the observation, and clusters of the observations correspond to different phenotypes. The clusters are learned from RWD using unsupervised ML algorithms. The clusters are then modeled for efficient representation of the phenotypes.

RWD in Healthentia

Our product Healthentia is used to collect all types of patient-generated, everyday RWD types. Our subjects employ the Healthentia mobile app to answer questionnaires and to enter data via the widgets, either manually or using devices integrated via their Software Development Kits. Data collection also employs the Healthentia big data platform and ingests more subjects’ data using the Application Programming Interfaces of other device providers. The collected RWD is analyzed using the BI analytics available at the Healthentia portal for healthcare professionals. It is also processed using the smart services of Healthentia, namely:

The Learning Services for training models
The Inference Services for inferring with the help of the trained models
The Clinical Pathway for utilizing the raw RWD and the inference results in monitoring the state of subjects, and
The Virtual Coach for utilizing all the above in personalized advice given to the subjects.

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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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Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data

System — Thu, 18 Nov 2021 13:42:03 +0000

Topics: Decision support systems and personalized interventions for workability sustainability; Unobtrusive and pervasive health monitoring at the workplace

SOURCE: SciTePress, 2021; BOOK DOI Link, Chapter DOI Link

Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data

Jasmijn Franke ^1,2 ; Christiane Grünloh ^1,2; Dennis Hofs ² ; Boris Van Schooten ² ; Andreea Bondrea ² ; Miriam Cabrita ^1,2,3

¹Biomedical Signals and Systems Group, University of Twente, Enschede, The Netherlands

²eHealth Group, Roessingh Research and Development, Enschede, The Netherlands

³Innovation Sprint Sprl, Brussels, Belgium

Abstract

Office workers often lead sedentary lifestyles, a lifestyle responsible for higher risks of cardiovascular disease, stroke, diabetes and premature mortality. Improvements towards a more active lifestyle reduce cardiovascular risks and thus changing the sedentary lifestyle might prevent chronic illness. The Recurring Sedentary Period Detection (RSPD) algorithm described in this paper was designed to identify recurring sedentary periods using data from an activity tracker, summarise the sedentary periods and pinpoint notification times at which the user should be motivated to get some movement. The outcome of the RSPD algorithm was validated using data from a 10-week period of one typical office worker. Our results show that the RSPD algorithm could correctly identify the recurring sedentary periods, compute fitting daily summaries and pinpoint the notification times correctly. With minor differences, the RSPD algorithm was successfully implemented in the healthyMe smartphone application, one of the supporting services of the SMARTWORK project. Within the healthyMe application, an embodied virtual agent is used to communicate the daily summaries and motivate the user to move more at the identified notification times. Pilots planned as part of the SMARTWORK project will evaluate whether the RSPD algorithm helps to motivate office workers to break up sedentary periods.

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The Results of an Iterative Evaluation Process of an Mhealth Application for Rewarding Healthy Behaviour Among Older Adults

System — Sat, 02 Oct 2021 14:26:07 +0000

Topics: eHealth, Mhealth, e-Health applications

SOURCE: Springer Link 2020, Aug.; BOOK DOI Link, Chapter DOI Link

The Results of an Iterative Evaluation Process of an Mhealth Application for Rewarding Healthy Behaviour Among Older Adults

Stephanie Jansen-Kosterink ; Roos Bulthuis ; Silke ter Stal ; Lex van Velsen ; Aristodemos Pnevmatikakis ; Sofoklis Kyriazakos ; Andrew Pomazanskyi ; Harm op den Akker

Abstract

It is a challenge to find effective ways for supporting older adults to increase their levels of physical activity and develop habitual physical activity behaviours. Within the GOAL project, an mHealth intervention to motivate older adults to be active was developed, by blending the iterative design and the evaluation activities. The aim of this paper is to present the results of the iterative evaluation process of this mHealth intervention. Evaluation end-points were usability, user experience and potential effect. In total, four cycles of evaluation and redesign of GOAL were conducted in order to create value-adding technology, and demonstrate its impact. Each cycle contained test-weeks, weeks for data analysis, and time for technical modification. In total, 28 participants (students and older adults) interacted with GOAL for a total of 476 days and provided their feedback. During the process, various usability issues were solved to improve GOAL. The users rated the usability of GOAL as acceptable. Older adults were positive about the idea to encourage a healthy lifestyle by using GOAL. During the final evaluation cycle, GOAL encouraged older adults to be more active and motivated them to reach their daily goal.

Keywords: Older adults; eHealth; mHealth; Games; Rewards; Iterative design; Usability testing; Evaluation

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