Editorial

4 pages, 210 KiB

Open AccessEditorial

A Deep Dive into the Nexus between Digital Health and Life Sciences Amidst the COVID-19 Pandemic: An Editorial Expedition

by Daniele Giansanti

Life 2023, 13(5), 1154; https://doi.org/10.3390/life13051154 - 10 May 2023

Viewed by 747

Abstract

I am proposing this editorial to briefly trace the evidences that emerged from the Special Issue (SI)—The Digital Health in the Pandemic Era— [...] Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

4 pages, 187 KiB

Open AccessEditorial

The Digital Health: From the Experience of the COVID-19 Pandemic Onwards

by Daniele Giansanti

Life 2022, 12(1), 78; https://doi.org/10.3390/life12010078 - 06 Jan 2022

Cited by 4 | Viewed by 1607

Abstract

Digital health has a long history of development and is particularly resonant in the last two years, due to the pandemic [...] Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

Research

Jump to: Editorial, Review, Other

34 pages, 2577 KiB

Open AccessArticle

Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

by Fouad H. Awad, Murtadha M. Hamad and Laith Alzubaidi

Life 2023, 13(3), 691; https://doi.org/10.3390/life13030691 - 03 Mar 2023

Cited by 11 | Viewed by 2847

Abstract

Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations [...] Read more.

Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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19 pages, 2333 KiB

Open AccessArticle

Understanding COVID: Collaborative Government Campaign for Citizen Digital Health Literacy in the COVID-19 Pandemic

by Mónica López-Ventoso, Marta Pisano González, Cristina Fernández García, Isabel Diez Valcarce, Inés Rey Hidalgo, María Jesús Rodríguez Nachón, Ana María Menéndez García, Michelle Perello, Beatrice Avagnina, Oscar Zanutto and Alberto Lana

Life 2023, 13(2), 589; https://doi.org/10.3390/life13020589 - 20 Feb 2023

Cited by 1 | Viewed by 1656

Abstract

The strategy “Understanding COVID” was a Public Health campaign designed in 2020 and launched in 2021 in Asturias-Spain to provide reliable and comprehensive information oriented to vulnerable populations. The campaign involved groups considered socially vulnerable and/or highly exposed to COVID-19 infection: shopkeepers and [...] Read more.

The strategy “Understanding COVID” was a Public Health campaign designed in 2020 and launched in 2021 in Asturias-Spain to provide reliable and comprehensive information oriented to vulnerable populations. The campaign involved groups considered socially vulnerable and/or highly exposed to COVID-19 infection: shopkeepers and hoteliers, worship and religious event participants, school children and their families, and scattered rural populations exposed to the digital divide. The purpose of this article was to describe the design of the “Understanding COVID” strategy and the evaluation of the implementation process. The strategy included the design and use of several educational resources and communication strategies, including some hundred online training sessions based on the published studies and adapted to the language and dissemination approaches, that reached 1056 people of different ages and target groups, an accessible website, an informative video channel, posters and other pedagogical actions in education centers. It required a great coordination effort involving different public and third-sector entities to provide the intended pandemic protection and prevention information at that difficult time. A communication strategy was implemented to achieve different goals: reaching a diverse population and adapting the published studies to different ages and groups, focusing on making it comprehensible and accessible for them. In conclusion, given there is a common and sufficiently important goal, it is possible to achieve effective collaboration between different governmental bodies to develop a coordinated strategy to reach the most vulnerable populations while taking into consideration their different interests and needs. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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19 pages, 2636 KiB

Open AccessArticle

Assessing the Suitability of a Virtual ‘Pink Warrior’ for Older Breast Cancer Survivors during COVID-19: A Pilot Study

by Maria C. Swartz, Michael C. Robertson, Ursela Christopherson, Stephanie J. Wells, Zakkoyya H. Lewis, Jinbing Bai, Michael D. Swartz, H. Colleen Silva, Eloisa Martinez and Elizabeth J. Lyons

Life 2023, 13(2), 574; https://doi.org/10.3390/life13020574 - 18 Feb 2023

Cited by 2 | Viewed by 1834

Abstract

The COVID-19 pandemic impacted the conduct of in-person physical activity (PA) interventions among older survivors of BC, who need such interventions to stay active and prevent functional decline. We tested the feasibility of virtually delivering an exergame-based PA intervention to older BC survivors. [...] Read more.

The COVID-19 pandemic impacted the conduct of in-person physical activity (PA) interventions among older survivors of BC, who need such interventions to stay active and prevent functional decline. We tested the feasibility of virtually delivering an exergame-based PA intervention to older BC survivors. We enrolled 20 female BC survivors ≥55 years and randomly assigned them to two groups. The intervention group (Pink Warrior 2) received 12 weekly virtual exergame sessions with behavioral coaching, survivorship navigation support, and a Fitbit for self-monitoring. The control group received 12 weekly phone-based survivorship discussion sessions and wore a Mi Band 3. Feasibility was evaluated by rates of recruitment (≥0.92 participants/center/month), retention (≥80%), and group attendance (≥10 sessions), percentage of completed virtual assessments, and number of technology-related issues and adverse events. Intervention acceptability was measured by participants’ ratings on a scale of 1 (strongly disagree) to 5 (strongly agree). The recruitment rate was 1.93. The retention and attendance rates were 90% and 88% (≥10 sessions), respectively. Ninety-six percent completed virtual assessments without an adverse event. Acceptability was high (≥4). The intervention met benchmarks for feasibility. Additional research is needed to further understand the impact of virtually delivered PA interventions on older BC survivors. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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22 pages, 1694 KiB

Open AccessArticle

Remote Psychotherapy during the COVID-19 Pandemic: A Mixed-Methods Study on the Changes Experienced by Austrian Psychotherapists

by Michael Stadler, Andrea Jesser, Elke Humer, Barbara Haid, Peter Stippl, Wolfgang Schimböck, Elisabeth Maaß, Helmut Schwanzar, Daniela Leithner, Christoph Pieh and Thomas Probst

Life 2023, 13(2), 360; https://doi.org/10.3390/life13020360 - 29 Jan 2023

Cited by 2 | Viewed by 1913

Abstract

The outbreak of the COVID-19 pandemic and associated measures to contain the SARS-CoV-2 coronavirus required a change in treatment format from face-to-face to remote psychotherapy. This study investigated the changes experienced by Austrian therapists when switching to psychotherapy at a distance. A total [...] Read more.

The outbreak of the COVID-19 pandemic and associated measures to contain the SARS-CoV-2 coronavirus required a change in treatment format from face-to-face to remote psychotherapy. This study investigated the changes experienced by Austrian therapists when switching to psychotherapy at a distance. A total of 217 therapists participated in an online survey on changes experienced when switching settings. The survey was open from 26 June until 3 September 2020. Several open questions were evaluated using qualitative content analysis. The results show that the setting at a distance was appreciated by the therapists as a possibility to continue therapy even during an exceptional situation. Moreover, remote therapy offered the respondents more flexibility in terms of space and time. Nevertheless, the therapists also reported challenges of remote therapy, such as limited sensory perceptions, technical problems and signs of fatigue. They also described differences in terms of the therapeutic interventions used. There was a great deal of ambivalence in the data regarding the intensity of sessions and the establishment and/or maintenance of a psychotherapeutic relationship. Overall, the study shows that remote psychotherapy seems to have been well accepted by Austrian psychotherapists in many settings and can offer benefits. Clinical studies are also necessary to investigate in which contexts and for which patient groups the remote setting is suitable and where it is potentially contraindicated. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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16 pages, 7498 KiB

Open AccessArticle

Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images

by Ali Alqahtani, Mirza Mumtaz Zahoor, Rimsha Nasrullah, Aqil Fareed, Ahmad Afzaal Cheema, Abdullah Shahrose, Muhammad Irfan, Abdulmajeed Alqhatani, Abdulaziz A. Alsulami, Maryam Zaffar and Saifur Rahman

Life 2022, 12(11), 1709; https://doi.org/10.3390/life12111709 - 26 Oct 2022

Cited by 8 | Viewed by 2022

Abstract

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid [...] Read more.

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images. We developed a novel computationally light and optimized deep Convolutional Neural Networks (CNNs) based framework for chest X-ray analysis. We proposed a new COV-Net to learn COVID-specific patterns from chest X-rays and employed several machine learning classifiers to enhance the discrimination power of the presented framework. Systematic exploitation of max-pooling operations facilitates the proposed COV-Net in learning the boundaries of infected patterns in chest X-rays and helps for multi-class classification of two diverse infection types along with normal images. The proposed framework has been evaluated on a publicly available benchmark dataset containing X-ray images of coronavirus-infected, pneumonia-infected, and normal patients. The empirical performance of the proposed method with developed COV-Net and support vector machine is compared with the state-of-the-art deep models which show that the proposed deep hybrid learning-based method achieves 96.69% recall, 96.72% precision, 96.73% accuracy, and 96.71% F-score. For multi-class classification and binary classification of COVID-19 and pneumonia, the proposed model achieved 99.21% recall, 99.22% precision, 99.21% F-score, and 99.23% accuracy. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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17 pages, 1722 KiB

Open AccessArticle

IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults

by Saeed Ali Alsareii, Ahmad Shaf, Tariq Ali, Maryam Zafar, Abdulrahman Manaa Alamri, Mansour Yousef AlAsmari, Muhammad Irfan and Muhammad Awais

Life 2022, 12(9), 1414; https://doi.org/10.3390/life12091414 - 10 Sep 2022

Cited by 4 | Viewed by 1779

Abstract

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will [...] Read more.

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient’s chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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18 pages, 2064 KiB

Open AccessArticle

The Impact of Digital Contact Tracing Apps Overuse on Prevention of COVID-19: A Normative Activation Model Perspective

by Junwei Cao, Dong Liu, Guihua Zhang and Meng Shang

Life 2022, 12(9), 1371; https://doi.org/10.3390/life12091371 - 02 Sep 2022

Cited by 3 | Viewed by 1879

Abstract

During the COVID-19 pandemic, many countries have used digital contact tracing apps (DCTAs) to implement contact tracing. Although the use of DCTAs has contributed to the prevention and control of COVID-19, there are doubts in academia about their actual effectiveness. In this study, [...] Read more.

During the COVID-19 pandemic, many countries have used digital contact tracing apps (DCTAs) to implement contact tracing. Although the use of DCTAs has contributed to the prevention and control of COVID-19, there are doubts in academia about their actual effectiveness. In this study, the role of DCTAs in the prevention of COVID-19 was analyzed in terms of both the responsibility and inconvenience to life in a large-scale DCTA overuse environment, based on the normative activation model. The findings suggest that the overuse of a DCTA activates people’s personal norms by triggering awareness of the consequences and ascription of responsibility, leading people to consistently cooperate with the government to prevent COVID-19. However, the inconvenience of living with DCTA overuse weakens the effect of the awareness of consequences and ascription of responsibility and the role of the ascription of responsibility in influencing personal norms. These effects may bear on people’s willingness to consistently cooperate with the government to prevent COVID-19. The results of this study confirm the effectiveness of DCTA in counteracting pandemics from a social responsibility perspective in a large-scale environment where DCTA is used, enriching the literature on DCTA research in the COVID-19 pandemic. The results of this study can also help governments develop and improve policies to prevent COVID-19, as well as improve the DCTAs’ operating patterns. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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13 pages, 2766 KiB

Open AccessArticle

A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic

by Alqahtani Saeed, Maryam Zaffar, Mohammed Ali Abbas, Khurrum Shehzad Quraishi, Abdullah Shahrose, Muhammad Irfan, Mohammed Ayed Huneif, Alqahtani Abdulwahab, Sharifa Khalid Alduraibi, Fahad Alshehri, Alaa Khalid Alduraibi and Ziyad Almushayti

Life 2022, 12(9), 1367; https://doi.org/10.3390/life12091367 - 01 Sep 2022

Cited by 2 | Viewed by 1613

Abstract

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays [...] Read more.

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19’s impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students’ health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student’s remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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15 pages, 853 KiB

Open AccessArticle

Did Usage of Mental Health Apps Change during COVID-19? A Comparative Study Based on an Objective Recording of Usage Data and Demographics

by Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John McAlaney and Raian Ali

Life 2022, 12(8), 1266; https://doi.org/10.3390/life12081266 - 19 Aug 2022

Cited by 6 | Viewed by 2388

Abstract

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone [...] Read more.

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users’ sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. The mental health apps were classified into two categories: guidance-based and tracking-based apps. The results include the increased number of users of mental health apps during the COVID-19 period as compared to pre-COVID-19. Adults (aged 24 and above), compared to emerging adults (aged 15–24 years), were found to have a higher usage of overall mental health apps and guidance-based mental health apps. Furthermore, during the COVID-19 pandemic, males were found to be more likely to launch overall mental health apps and guidance-based mental health apps compared to females. The findings from this paper suggest that despite the increased usage of mental health apps amongst males and adults, user engagement with mental health apps remained minimal. This suggests the need for these apps to work towards improved user engagement and retention. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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18 pages, 4067 KiB

Open AccessArticle

Physical Activity Monitoring and Classification Using Machine Learning Techniques

by Saeed Ali Alsareii, Muhammad Awais, Abdulrahman Manaa Alamri, Mansour Yousef AlAsmari, Muhammad Irfan, Nauman Aslam and Mohsin Raza

Life 2022, 12(8), 1103; https://doi.org/10.3390/life12081103 - 22 Jul 2022

Cited by 6 | Viewed by 2195

Abstract

Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities. Tracking physical activities using miniature wearable sensors and state-of-the-art machine learning techniques can encourage healthy living and [...] Read more.

Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities. Tracking physical activities using miniature wearable sensors and state-of-the-art machine learning techniques can encourage healthy living and control obesity. This work focuses on introducing novel techniques to identify and log physical activities using machine learning techniques and wearable sensors. Physical activities performed in daily life are often unstructured and unplanned, and one activity or set of activities (sitting, standing) might be more frequent than others (walking, stairs up, stairs down). None of the existing activities classification systems have explored the impact of such class imbalance on the performance of machine learning classifiers. Therefore, the main aim of the study is to investigate the impact of class imbalance on the performance of machine learning classifiers and also to observe which classifier or set of classifiers is more sensitive to class imbalance than others. The study utilizes motion sensors’ data of 30 participants, recorded while performing a variety of daily life activities. Different training splits are used to introduce class imbalance which reveals the performance of the selected state-of-the-art algorithms with various degrees of imbalance. The findings suggest that the class imbalance plays a significant role in the performance of the system, and the underrepresentation of physical activity during the training stage significantly impacts the performance of machine learning classifiers. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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12 pages, 2300 KiB

Open AccessArticle

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis

by Yassir Edrees Almalki, Muhammad Umair Ali, Waqas Ahmed, Karam Dad Kallu, Amad Zafar, Sharifa Khalid Alduraibi, Muhammad Irfan, Mohammad Abd Alkhalik Basha, Hassan A. Alshamrani and Alaa Khalid Alduraibi

Life 2022, 12(7), 1084; https://doi.org/10.3390/life12071084 - 20 Jul 2022

Cited by 7 | Viewed by 1732

Abstract

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their [...] Read more.

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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12 pages, 282 KiB

Open AccessArticle

What Went Wrong with the IMMUNI Contact-Tracing App in Italy? A Cross-Sectional Survey on the Attitudes and Experiences among Healthcare University Students

by Claudia Isonne, Maria Roberta De Blasiis, Federica Turatto, Elena Mazzalai, Carolina Marzuillo, Corrado De Vito, Paolo Villari and Valentina Baccolini

Life 2022, 12(6), 871; https://doi.org/10.3390/life12060871 - 10 Jun 2022

Cited by 6 | Viewed by 1600

Abstract

The adoption of digital contact-tracing apps to limit the spread of SARS-CoV-2 has been sup-optimal, but studies that clearly identify factors associated with the app uptake are still limited. In April 2021, we administered a questionnaire to healthcare university students to investigate their [...] Read more.

The adoption of digital contact-tracing apps to limit the spread of SARS-CoV-2 has been sup-optimal, but studies that clearly identify factors associated with the app uptake are still limited. In April 2021, we administered a questionnaire to healthcare university students to investigate their attitudes towards and experiences of the IMMUNI app. A multivariable logistic regression model was built to identify app download predictors. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated. We surveyed 247 students. Most respondents (65.6%) had not downloaded IMMUNI, reporting as the main reason the perceived app uselessness (32.7%). In the multivariable analysis, being advised to use the app (aOR: 3.21, 95%CI: 1.80–5.73), greater fear of infecting others (aOR: 1.50, 95%CI: 1.01–2.23), and greater trust in the institutional response to the emergency (aOR: 1.33, 95%CI: 1.00–1.76) were positively associated with the outcome, whereas greater belief in the “lab-leak theory” of COVID-19 was a negative predictor (aOR: 0.75, 95%CI: 0.60–0.93). Major technical issues were reported by app users. Targeted strategies aimed at improving awareness of digital health applications should be devised. Furthermore, institutions should invest in the development of these technologies, to minimize technical issues and make them accessible to the entire population. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

Review

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22 pages, 2988 KiB

Open AccessReview

Healthcare Professionals’ Experience of Performing Digital Care Visits—A Scoping Review

by Ieva Lampickienė and Nadia Davoody

Life 2022, 12(6), 913; https://doi.org/10.3390/life12060913 - 17 Jun 2022

Cited by 5 | Viewed by 1891

Abstract

The use of digital care visits has been increasing during the COVID-19 pandemic. Learning more about healthcare professionals’ technology experiences provides valuable insight and a basis for improving digital visits. This study aimed to explore the existing literature on healthcare professionals’ experience performing [...] Read more.

The use of digital care visits has been increasing during the COVID-19 pandemic. Learning more about healthcare professionals’ technology experiences provides valuable insight and a basis for improving digital visits. This study aimed to explore the existing literature on healthcare professionals’ experience performing digital care visits. A scoping review was performed following Arksey & O’Malley’s proposed framework using the Preferred Reporting Items for Systematic reviews and Meta-Analyses. The collected data were analyzed using thematic content analysis. Five main themes were identified in the literature: positive experiences/benefits, facilitators, negative experiences/challenges, barriers, and suggestions for improvement. Healthcare professionals mostly reported having an overall positive experience with digital visits and discovered benefits for themselves and the patients. However, opinions were mixed or negative regarding the complexity of decision making, workload and workflow, suitability of this type of care, and other challenges. The suggestions for improvement included training and education, improvements within the system and tools, along with support for professionals. Despite overall positive experiences and benefits for both professionals and patients, clinicians reported challenges such as physical barriers, technical issues, suitability concerns, and others. Digital care visits could not fully replace face-to-face visits. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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Other

Jump to: Editorial, Research, Review

13 pages, 302 KiB

Open AccessOpinion

The Chatbots Are Invading Us: A Map Point on the Evolution, Applications, Opportunities, and Emerging Problems in the Health Domain

by Daniele Giansanti

Life 2023, 13(5), 1130; https://doi.org/10.3390/life13051130 - 05 May 2023

Cited by 9 | Viewed by 2780

Abstract

The inclusion of chatbots is potentially disruptive in society, introducing opportunities, but also important implications that need to be addressed on different domains. The aim of this study is to examine chatbots in-depth, by mapping out their technological evolution, current usage, and potential [...] Read more.

The inclusion of chatbots is potentially disruptive in society, introducing opportunities, but also important implications that need to be addressed on different domains. The aim of this study is to examine chatbots in-depth, by mapping out their technological evolution, current usage, and potential applications, opportunities, and emerging problems within the health domain. The study examined three points of view. The first point of view traces the technological evolution of chatbots. The second point of view reports the fields of application of the chatbots, giving space to the expectations of use and the expected benefits from a cross-domain point of view, also affecting the health domain. The third and main point of view is that of the analysis of the state of use of chatbots in the health domain based on the scientific literature represented by systematic reviews. The overview identified the topics of greatest interest with the opportunities. The analysis revealed the need for initiatives that simultaneously evaluate multiple domains all together in a synergistic way. Concerted efforts to achieve this are recommended. It is also believed to monitor both the process of osmosis between other sectors and the health domain, as well as the chatbots that can create psychological and behavioural problems with an impact on the health domain. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

3 pages, 199 KiB

Open AccessReply

Reply to Giansanti, D. Why Has Digital Contact Tracing Worked Differently in Different Countries? Comment on “Cao et al. The Impact of Digital Contact Tracing Apps Overuse on Prevention of COVID-19: A Normative Activation Model Perspective. Life 2022, 12, 1371”

by Junwei Cao, Dong Liu, Guihua Zhang and Meng Shang

Life 2022, 12(10), 1593; https://doi.org/10.3390/life12101593 - 13 Oct 2022

Cited by 1 | Viewed by 890

Abstract

Thank you for your comments [...] Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

2 pages, 180 KiB

Open AccessComment

Why Has Digital Contact Tracing Worked Differently in Different Countries? Comment on Cao et al. The Impact of Digital Contact Tracing Apps Overuse on Prevention of COVID-19: A Normative Activation Model Perspective. Life 2022, 12, 1371

by Daniele Giansanti

Life 2022, 12(10), 1592; https://doi.org/10.3390/life12101592 - 13 Oct 2022

Cited by 2 | Viewed by 885

Abstract

I am writing you regarding your interesting article “The Impact of Digital Contact Tracing Apps Overuse on Prevention of COVID-19: A Normative Activation Model Perspective” [...] Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

16 pages, 644 KiB

Open AccessSystematic Review

The Impact of eHealth Interventions on the Improvement of Self-Care in Chronic Patients: An Overview of Systematic Reviews

by Erika Renzi, Valentina Baccolini, Giuseppe Migliara, Corrado De Vito, Giulia Gasperini, Angelo Cianciulli, Carolina Marzuillo, Paolo Villari and Azzurra Massimi

Life 2022, 12(8), 1253; https://doi.org/10.3390/life12081253 - 17 Aug 2022

Cited by 9 | Viewed by 3422

Abstract

Promoting self-care is one of the most promising strategies for managing chronic conditions. This overview aimed to investigate the effectiveness of eHealth interventions at improving self-care in patients with type-2 diabetes mellitus, cardiovascular disease, and chronic obstructive pulmonary disease when compared to standard [...] Read more.

Promoting self-care is one of the most promising strategies for managing chronic conditions. This overview aimed to investigate the effectiveness of eHealth interventions at improving self-care in patients with type-2 diabetes mellitus, cardiovascular disease, and chronic obstructive pulmonary disease when compared to standard care. We carried out a review of systematic reviews on PubMed, Scopus, Cochrane, PsychInfo, and CINAHL. AMSTAR-2 was used for quality appraisal. Eight systematic reviews (six with meta-analysis) were included, involving a total of 41,579 participants. eHealth interventions were categorized into three subgroups: (i) reminders via messaging apps, emails, and apps; (ii) telemonitoring and online operator support; (iii) internet and web-based educational programs. Six systematic reviews showed an improvement in self-care measurements through eHealth interventions, which also led to a better quality of life and clinical outcomes (HbA1C, blood pressure, hospitalization, cholesterol, body weight). This overview provided some implications for practice and research: eHealth is effective in increasing self-care in chronic patients; however, it is required to designate the type of eHealth intervention based on the needed outcome (e.g., implementing telemonitoring to increase self-monitoring of blood pressure). In addition, there is a need to standardize self-care measures through increased use of validated assessment tools. Full article

(This article belongs to the Special Issue The Digital Health in the Pandemic Era)

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