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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences".

Deadline for manuscript submissions: 10 June 2024 | Viewed by 17313

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Special Issue Editors

Dr. Giacinto Barresi

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Rehab Technologies Lab, Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genoa, Italy
Interests: neuroergonomics; biomedical robotics; human–robot interaction; human augmentation; rehabilitation technology; assistive technology; prosthetics; extended reality; digital health; gamification
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Dr. Sabrina Cipolletta

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Department of General Psychology, University of Padua, 35131 Padua, Italy
Interests: constructivism; health psychology; interpersonal relationships; personal construct theory; qualitative methods
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Dr. Andreas Triantafyllidis

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Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Interests: mobile health; medical informatics; pervasive computing; artificial intelligence; computerised decision support
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Prof. Dr. Kenji Suzuki

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Biomedical Artificial Intelligence Research Unit (BMAI), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
Interests: machine learning; deep learning; artificial intelligence; medical image analysis; medical imaging; computer-aided diagnosis; signal and image processing; computer vision
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Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is radically enhancing the quality, safety, and cost-effectiveness of biomedical solutions. Ubiquitous, interconnected, data-intensive intelligent systems for digital health can enable predictive models and personalized interventions to improve health research (from genetics to pharmacology to epidemiology) and healthcare services. Furthermore, the harmonized interdisciplinary framework of One Digital Health (ODH) can improve this approach, holistically processing and managing the real complexity of local and global connections across people and animals in socioeconomic settings within the same ecosystem. Such a strategy can improve individual and systemic sustainability and resilience, especially during emergencies such as the recent COVID-19 pandemic. This Special Issue will present contributions on intelligent systems that (currently or potentially) enhance ODH across domains such as:

Machine learning and deep learning in biomedical applications;
Digital health, digital therapeutics, digital biomarkers;
Big data, omics models, digital twins, precision medicine;
Psychology of health, aging, disability, inclusion;
Wearable technologies, Internet of Things, ambient intelligence;
Robotics, extended reality, telemedicine, telerehabilitation;
Human, animal, environmental monitoring for public health;
Smart systems in agriculture, farming, the food industry, and the drugs industry;
Medical devices usability and ergonomics, health technology assessment;
Ethical and legal issues in healthcare systems.

You may choose our Joint Special Issue in Healthcare.

Dr. Giacinto Barresi
Dr. Sabrina Cipolletta
Dr. Andreas Triantafyllidis
Prof. Dr. Kenji Suzuki
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
big data
digital health
ecosystem
healthcare
machine learning
one digital health

Published Papers (8 papers)

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Research

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21 pages, 6499 KiB

Open AccessArticle

DigiHEALTH: Suite of Digital Solutions for Long-Term Healthy and Active Aging

by Cristina Martin, Isabel Amaya, Jordi Torres, Garazi Artola, Meritxell García, Teresa García-Navarro, Verónica De Ramos, Camilo Cortés, Jon Kerexeta, Maia Aguirre, Ariane Méndez, Luis Unzueta, Arantza Del Pozo, Nekane Larburu and Iván Macía

Int. J. Environ. Res. Public Health 2023, 20(13), 6200; https://doi.org/10.3390/ijerph20136200 - 22 Jun 2023

Cited by 2 | Viewed by 1499

Abstract

The population in the world is aging dramatically, and therefore, the economic and social effort required to maintain the quality of life is being increased. Assistive technologies are progressively expanding and present great opportunities; however, given the sensitivity of health issues and the vulnerability of older adults, some considerations need to be considered. This paper presents DigiHEALTH, a suite of digital solutions for long-term healthy and active aging. It is the result of a fruitful trajectory of research in healthy aging where we have understood stakeholders’ needs, defined the main suite properties (that would allow scalability and interoperability with health services), and codesigned a set of digital solutions by applying a continuous reflexive cycle. At the current stage of development, the digital suite presents eight digital solutions to carry out the following: (a) minimize digital barriers for older adults (authentication system based on face recognition and digital voice assistant), (b) facilitate active and healthy living (well-being assessment module, recommendation system, and personalized nutritional system), and (c) mitigate specific impairments (heart failure decompensation, mobility assessment and correction, and orofacial gesture trainer). The suite is available online and it includes specific details in terms of technology readiness level and specific conditions for usage and acquisition. This live website will be continually updated and enriched with more digital solutions and further experiences of collaboration. Full article

(This article belongs to the Special Issue Intelligent Systems for One Digital Health)

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33 pages, 4120 KiB

Open AccessArticle

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

by Momna Javaid, Muhammad Shahzad Sarfraz, Muhammad Umar Aftab, Qamar uz Zaman, Hafiz Tayyab Rauf and Khalid A. Alnowibet

Int. J. Environ. Res. Public Health 2023, 20(4), 3740; https://doi.org/10.3390/ijerph20043740 - 20 Feb 2023

Cited by 2 | Viewed by 2619

Abstract

The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers. Full article

(This article belongs to the Special Issue Intelligent Systems for One Digital Health)

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18 pages, 1556 KiB

Open AccessArticle

Breaking Down the Screen: Italian Psychologists’ and Psychotherapists’ Experiences of the Therapeutic Relationship in Online Interventions during the COVID-19 Pandemic

by Silvia Caterina Maria Tomaino, Gian Mauro Manzoni, Giada Brotto and Sabrina Cipolletta

Int. J. Environ. Res. Public Health 2023, 20(2), 1037; https://doi.org/10.3390/ijerph20021037 - 06 Jan 2023

Cited by 2 | Viewed by 1505

Abstract

(1) Background: The COVID-19 pandemic posed new challenges to clinical practice and delineated future directions for online interventions in psychological care. The present study aimed to explore Italian psychologists’ and psychotherapists’ experiences of online interventions during the pandemic, focusing on the strategies they used to develop and maintain therapeutic relationships with their patients. (2) Methods: Between February and July 2021, 368 Italian psychologists and/or psychotherapists completed an online survey. A mixed-methods analysis was conducted, using Jamovi to analyze quantitative data and ATLAS.ti 9 to analyze qualitative data. (3) Results: Of the participants, 62% had never delivered online interventions before the pandemic; though 95.4% were delivering online interventions at the time of the survey, many reported facing technical disruptions (77.1%) and having little confidence in the online setting (45.3%). Feeling present in online sessions—facilitated by emotional attunement, active listening, and conversational spontaneity—was reported as “very important” by 93.6%. (4) Conclusions: Overall, the COVID-19 pandemic allowed a great leap forward in the use of online interventions by Italian psychologists and psychotherapists. This period of upheaval generated not only a positive change in their attitudes toward and intention to use online interventions but also revealed associated technical and relational issues that must be properly addressed. Full article

(This article belongs to the Special Issue Intelligent Systems for One Digital Health)

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42 pages, 4837 KiB

Open AccessArticle

Co-Design in Electrical Medical Beds with Caregivers

by Davide Bacchin, Gabriella Francesca Amalia Pernice, Leonardo Pierobon, Elena Zanella, Marcello Sardena, Marino Malvestio and Luciano Gamberini

Int. J. Environ. Res. Public Health 2022, 19(23), 16353; https://doi.org/10.3390/ijerph192316353 - 06 Dec 2022

Cited by 1 | Viewed by 3904

Abstract

Among the plethora of instruments present in healthcare environments, the hospital bed is undoubtedly one of the most important for patients and caregivers. However, their design usually follows a top-down approach without considering end-users opinions and desires. Exploiting Human-centered design (HCD) permits these users to have a substantial role in the final product outcome. This study aims to empower caregivers to express their opinion about the hospital bed using a qualitative approach. For a holistic vision, we conducted six focus groups and six semi-structured interviews with nurses, nursing students, social-health operators and physiotherapists belonging to many healthcare situations. We then used thematic analysis to extract the themes that participants faced during the procedures, providing a comprehensive guide to designing the future electrical medical bed. These work results could also help overcome many issues that caregivers face during their everyday working life. Moreover, we identified the User Experience features that could represent the essential elements to consider. Full article

(This article belongs to the Special Issue Intelligent Systems for One Digital Health)

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10 pages, 2837 KiB

Open AccessArticle

Development of a Cyclic Voltammetry-Based Method for the Detection of Antigens and Antibodies as a Novel Strategy for Syphilis Diagnosis

by Gabriel M. C. Barros, Dionísio D. A. Carvalho, Agnaldo S. Cruz, Ellen K. L. Morais, Ana Isabela L. Sales-Moioli, Talita K. B. Pinto, Melise C. D. Almeida, Ignacio Sanchez-Gendriz, Felipe Fernandes, Ingridy M. P. Barbalho, João P. Q. Santos, Jorge M. O. Henriques, César A. D. Teixeira, Paulo Gil, Lúcio Gama, Angélica E. Miranda, Karilany D. Coutinho, Leonardo J. Galvão-Lima and Ricardo A. M. Valentim

Int. J. Environ. Res. Public Health 2022, 19(23), 16206; https://doi.org/10.3390/ijerph192316206 - 03 Dec 2022

Cited by 4 | Viewed by 1910

Abstract

The improvement of laboratory diagnosis is a critical step for the reduction of syphilis cases around the world. In this paper, we present the development of an impedance-based method for detecting T. pallidum antigens and antibodies as an auxiliary tool for syphilis laboratory diagnosis. We evaluate the voltammetric signal obtained after incubation in carbon or gold nanoparticle-modified carbon electrodes in the presence or absence of Poly-L-Lysine. Our results indicate that the signal obtained from the electrodes was sufficient to distinguish between infected and non-infected samples immediately (T0′) or 15 min (T15′) after incubation, indicating its potential use as a point-of-care method as a screening strategy. Full article

(This article belongs to the Special Issue Intelligent Systems for One Digital Health)

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15 pages, 502 KiB

Open AccessArticle

Effect of Digital-Based Self-Learned Educational Intervention about COVID-19 Using Protection Motivation Theory on Non-Health Students’ Knowledge and Self-Protective Behaviors at Saudi Electronic University

by Samiha Hamdi Sayed, Mohammed Al-Mohaithef and Wafaa Taha Elgzar

Int. J. Environ. Res. Public Health 2022, 19(22), 14626; https://doi.org/10.3390/ijerph192214626 - 08 Nov 2022

Cited by 2 | Viewed by 1400

Abstract

Background: The COVID-19 pandemic has disastrous impacts that impose the cultivation of knowledge and motivation of self-protection to foster disease containment. Aim: Evaluate the effect of digital self-learned educational intervention about COVID-19 using the protection motivation theory (PMT) on non-health students’ knowledge and self-protective behaviors at Saudi Electronic University (SEU). Methods: A quasi-experimental study was accomplished at three randomly chosen branches of SEU (Riyadh, Dammam, Jeddah) using a multistage sampling technique to conveniently select 219 students. An electronic self-administered questionnaire was used, which included three scales for assessing the students’ knowledge, self-protective behaviors, and the constructs of the PMT. The educational intervention was designed using four stages: need assessment, planning, implementation, and evaluation. A peer-reviewed digital educational content was developed after assessing the participants’ educational needs using the pretest. Then, distributed through their university emails. A weekly synchronous Zoom cloud meeting and daily key health messages were shared with them. Finally, the post-test was conducted after two months. Results: The mean participants’ age (SD) among the experimental group was 28.94 (6.719), and the control group was 27.80 (7.256), with a high female percentage (63.4%, 73.8%) and a previous history of direct contact with verified COVID-19 patients (78.6%, 69.2%), respectively. A significant positive mean change (p = 0.000) was detected in the total COVID-19 knowledge of the experimental group post-intervention, either when it was adjusted for the covariates effect of the control group (F₁ = 630.547) or the pretest (F₁ = 8.585) with a large effect size (η² = 0.745, η² = 0.268, respectively). The same was proved by the ANCOVA test for the total self-protective behaviors either when it adjusted for the covariates effect of the control group (F₁ = 66.671, p = 0.000) or the pretest (F₁ = 5.873, p = 0.020) with a large effect size (η² = 0.236, η² = 0.164, respectively). The ANCOVA test proved that post-intervention, all the PMT constructs (perceived threats, reward appraisal, efficacy appraisal, response cost, and protection intention) and the total PMT score were significantly improved (p = 0.000) among the experimental group either when adjusted for the covariates effect of the control group (F₁ = 83.835) or the pretest (F₁ = 11.658) with a large effect size (η² = 0.280, η² = 0.561, respectively). Conclusions: The digital PMT-based self-learned educational intervention effectively boosts non-health university students’ COVID-19 knowledge, protection motivation, and self-protective behaviors. Thus, PMT is highly praised as a basis for COVID-19-related educational intervention and, on similar occasions, future outbreaks. Full article

(This article belongs to the Special Issue Intelligent Systems for One Digital Health)

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18 pages, 1455 KiB

Open AccessArticle

Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing

by Huan Liu, Jinliang Xu, Xiaodong Zhang, Chao Gao and Rishuang Sun

Int. J. Environ. Res. Public Health 2022, 19(12), 7063; https://doi.org/10.3390/ijerph19127063 - 09 Jun 2022

Cited by 4 | Viewed by 1521

Abstract

The aim of this study was to quantify the effect of radius over horizontal curve sections on driving workload (DW). Twenty-five participants participated in the driving simulation experiments and completed five driving scenes. The NASA-TLX scale was used to measure the mental demand, physical demand, and temporal demand in various scenes, which were applied to assess subjective workload (SW). Objective workload (OW) assessment methods were divided into three types, in which the eye tracker was used to measure the blink frequency and pupil diameter, and the electrocardiograph (ECG) was used to measure the heart rate and the heart rate variability. Additionally, the simulator was used to measure the lateral position and the steering wheel angle. The results indicate that radius is negatively correlated with DW and SW, and the SW in a radius of 300 m is approximately twice that in a radius of 550 m. Compared with the ECG, the explanatory power of the OW can be increased to 0.974 by combining eye-movement, ECG, and driving performance. Moreover, the main source of the DW is the maneuver stage, which accounts for more than 50%. When the radius is over 550 m, the DW shows few differences in the maneuver stage. These findings may provide new avenues of research to harness the role of DWs in optimizing traffic safety. Full article

(This article belongs to the Special Issue Intelligent Systems for One Digital Health)

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16 pages, 1751 KiB

Open AccessPerspective

Aligning Federated Learning with Existing Trust Structures in Health Care Systems

by Imrana Yari Abdullahi, René Raab, Arne Küderle and Björn Eskofier

Int. J. Environ. Res. Public Health 2023, 20(7), 5378; https://doi.org/10.3390/ijerph20075378 - 03 Apr 2023

Cited by 1 | Viewed by 1592

Abstract

Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and adoption in medical workflow due to non-alignment to current health care processes and stakeholders’ needs. The distributed nature of the data makes it more challenging to train and deploy machine learning models (using traditional methods) at the edge, for instance, for disease prediction. Federated learning (FL) has been proposed as a possible solution to these limitations. However, the P2P PHS architecture challenges current FL solutions because they use centralized engines (or random entities that could pose privacy concerns) for model update aggregation. Consequently, we propose a novel conceptual FL framework, CareNetFL, that is suitable for P2P PHS multi-tier and hybrid architecture and leverages existing trust structures in health care systems to ensure scalability, trust, and security. Entrusted parties (practitioners’ nodes) are used in CareNetFL to aggregate local model updates in the network hierarchy for their patients instead of random entities that could actively become malicious. Involving practitioners in their patients’ FL model training increases trust and eases access to medical data. The proposed concepts mitigate communication latency and improve FL performance through patient–practitioner clustering, reducing skewed and imbalanced data distributions and system heterogeneity challenges of FL at the edge. The framework also ensures end-to-end security and accountability through leveraging identity-based systems and privacy-preserving techniques that only guarantee security during training. Full article

(This article belongs to the Special Issue Intelligent Systems for One Digital Health)

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