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  1. Article: Impact of Human Disasters and COVID-19 Pandemic on Mental Health: Potential of Digital Psychiatry.

    Ćosić, Krešimir / Popović, Siniša / Šarlija, Marko / Kesedžić, Ivan

    Psychiatria Danubina

    2020  Volume 32, Issue 1, Page(s) 25–31

    Abstract: Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause massive stress-related disorders. Motivated by the ... ...

    Abstract Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause massive stress-related disorders. Motivated by the ongoing global coronavirus pandemic, the article provides an overview of scientific evidence regarding adverse impact of diverse human disasters on mental health in afflicted groups and societies. Following this broader context, psychosocial impact of COVID-19 as a specific global human disaster is presented, with an emphasis on disturbing mental health aspects of the ongoing pandemic. Limited resources of mental health services in a number of countries around the world are illustrated, which will be further stretched by the forthcoming increase in demand for mental health services due to the global COVID-19 pandemic. Mental health challenges are particularly important for the Republic of Croatia in the current situation, due to disturbing stress of the 2020 Zagreb earthquake and the high pre-pandemic prevalence of chronic Homeland-War-related posttraumatic stress disorders. Comprehensive approach based on digital psychiatry is proposed to address the lack of access to psychiatric services, which includes artificial intelligence, telepsychiatry and an array of new technologies, like internet-based computer-aided mental health tools and services. These tools and means should be utilized as an important part of the whole package of measures to mitigate negative mental health effects of the global coronavirus pandemic. Our scientific and engineering experiences in the design and development of digital tools and means in mitigation of stress-related disorders and assessment of stress resilience are presented. Croatian initiative on enhancement of interdisciplinary research of psychiatrists, psychologists and computer scientists on the national and EU level is important in addressing pressing mental health concerns related to the ongoing pandemic and similar human disasters.
    MeSH term(s) Artificial Intelligence ; Betacoronavirus ; COVID-19 ; Coronavirus Infections/psychology ; Croatia ; Disasters ; Humans ; Internet ; Mental Health ; Mental Health Services ; Pandemics ; Pneumonia, Viral/psychology ; Psychiatry/trends ; SARS-CoV-2 ; Telemedicine/trends ; User-Computer Interface
    Keywords covid19
    Language English
    Publishing date 2020-04-10
    Publishing country Croatia
    Document type Journal Article
    ZDB-ID 1067580-2
    ISSN 0353-5053
    ISSN 0353-5053
    DOI 10.24869/psyd.2020.25
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers.

    Ćosić, Krešimir / Popović, Siniša / Šarlija, Marko / Kesedžić, Ivan / Jovanovic, Tanja

    Croatian medical journal

    2020  Volume 61, Issue 3, Page(s) 279–288

    Keywords covid19
    Language English
    Publishing date 2020-07-08
    Publishing country Croatia
    Document type Journal Article
    ZDB-ID 1157623-6
    ISSN 1332-8166 ; 0353-9504
    ISSN (online) 1332-8166
    ISSN 0353-9504
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  3. Article: Impact of Human Disasters and COVID-19 Pandemic on Mental Health: Potential of Digital Psychiatry

    Cosic, Kresimir / Popovic, Sinisa / Sarlija, Marko / Kesedzic, Ivan

    Psychiatr Danub

    Abstract: Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause massive stress-related disorders. Motivated by the ... ...

    Abstract Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause massive stress-related disorders. Motivated by the ongoing global coronavirus pandemic, the article provides an overview of scientific evidence regarding adverse impact of diverse human disasters on mental health in afflicted groups and societies. Following this broader context, psychosocial impact of COVID-19 as a specific global human disaster is presented, with an emphasis on disturbing mental health aspects of the ongoing pandemic. Limited resources of mental health services in a number of countries around the world are illustrated, which will be further stretched by the forthcoming increase in demand for mental health services due to the global COVID-19 pandemic. Mental health challenges are particularly important for the Republic of Croatia in the current situation, due to disturbing stress of the 2020 Zagreb earthquake and the high pre-pandemic prevalence of chronic Homeland-War-related posttraumatic stress disorders. Comprehensive approach based on digital psychiatry is proposed to address the lack of access to psychiatric services, which includes artificial intelligence, telepsychiatry and an array of new technologies, like internet-based computer-aided mental health tools and services. These tools and means should be utilized as an important part of the whole package of measures to mitigate negative mental health effects of the global coronavirus pandemic. Our scientific and engineering experiences in the design and development of digital tools and means in mitigation of stress-related disorders and assessment of stress resilience are presented. Croatian initiative on enhancement of interdisciplinary research of psychiatrists, psychologists and computer scientists on the national and EU level is important in addressing pressing mental health concerns related to the ongoing pandemic and similar human disasters.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #32303026
    Database COVID19

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  4. Article ; Online: IMPACT OF HUMAN DISASTERS AND COVID-19 PANDEMIC ON MENTAL HEALTH

    Ćosić, Krešimir / Popović, Siniša / Šarlija, Marko / Kesedžić, Ivan

    Psychiatria Danubina ; ISSN 0353-5053 (Print) ; Volume 32 ; Issue 1

    POTENTIAL OF DIGITAL PSYCHIATRY

    2020  

    Abstract: Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause massive stress related disorders. Motivated by the ... ...

    Abstract Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause massive stress related disorders. Motivated by the ongoing global coronavirus pandemic, the article provides an overview of scientific evidence regarding adverse impact of diverse human disasters on mental health in afflicted groups and societies. Following this broader context, psychosocial impact of COVID-19 as a specific global human disaster is presented, with an emphasis on disturbing mental health aspects of the ongoing pandemic. Limited resources of mental health services in a number of countries around the world are illustrated, which will be further stretched by the forthcoming increase in demand for mental health services due to the global COVID-19 pandemic. Mental health challenges are particularly important for the Republic of Croatia in the current situation, due to disturbing stress of the 2020 Zagreb earthquake and the high pre-pandemic prevalence of chronic Homeland War-related posttraumatic stress disorders. Comprehensive approach based on digital psychiatry is proposed to address the lack of access to psychiatric services, which includes artificial intelligence, telepsychiatry and an array of new technologies, like internet-based computer-aided mental health tools and services. These tools and means should be utilized as an important part of the whole package of measures to mitigate negative mental health effects of the global coronavirus pandemic. Our scientific and engineering experiences in the design and development of digital tools and means in mitigation of stress-related disorders and assessment of stress resilience are presented. Croatian initiative on enhancement of interdisciplinary research of psychiatrists, psychologists and computer scientists on the national and EU level is important in addressing pressing mental health concerns related to the ongoing pandemic and similar human disasters.
    Keywords human disasters ; COVID-19 ; mental health ; digital psychiatry ; artificial intelligence ; covid19
    Subject code 306
    Language English
    Publisher Medicinska naklada
    Publishing country hr
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients.

    Ćosić, Krešimir / Popović, Siniša / Šarlija, Marko / Kesedžić, Ivan / Gambiraža, Mate / Dropuljić, Branimir / Mijić, Igor / Henigsberg, Neven / Jovanovic, Tanja

    Frontiers in psychology

    2021  Volume 12, Page(s) 782866

    Abstract: The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put ... ...

    Abstract The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.
    Language English
    Publishing date 2021-12-28
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2563826-9
    ISSN 1664-1078
    ISSN 1664-1078
    DOI 10.3389/fpsyg.2021.782866
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Can Injuries Be Predicted by Functional Movement Screen in Adolescents? The Application of Machine Learning.

    Karuc, Josip / Mišigoj-Duraković, Marjeta / Šarlija, Marko / Marković, Goran / Hadžić, Vedran / Trošt-Bobić, Tatjana / Sorić, Maroje

    Journal of strength and conditioning research

    2021  Volume 35, Issue 4, Page(s) 910–919

    Abstract: Abstract: Karuc, J, Mišigoj-Duraković, M, Šarlija, M, Marković, G, Hadžić, V, Trošt-Bobić, T, and Sorić, M. Can injuries be predicted by functional movement screen in adolescents? The application of machine learning. J Strength Cond Res 35(4): 910-919, ... ...

    Abstract Abstract: Karuc, J, Mišigoj-Duraković, M, Šarlija, M, Marković, G, Hadžić, V, Trošt-Bobić, T, and Sorić, M. Can injuries be predicted by functional movement screen in adolescents? The application of machine learning. J Strength Cond Res 35(4): 910-919, 2021-This study used machine learning (ML) to predict injuries among adolescents by functional movement testing. This research is a part of the CRO-PALS study conducted in a representative sample of adolescents and analyses for this study are based on nonathletic (n = 364) and athletic (n = 192) subgroups of the cohort (16-17 years). Sex, age, body mass index (BMI), body fatness, moderate-to-vigorous physical activity (MVPA), training hours per week, Functional Movement Screen (FMS), and socioeconomic status were assessed at baseline. A year later, data on injury occurrence were collected. The optimal cut-point of the total FMS score for predicting injury was calculated using receiver operating characteristic curve. These predictors were included in ML analyses with calculated metrics: area under the curve (AUC), sensitivity, specificity, and odds ratio (95% confidence interval [CI]). Receiver operating characteristic curve analyses with associated criterium of total FMS score >12 showed AUC of 0.54 (95% CI: 0.48-0.59) and 0.56 (95% CI: 0.47-0.63), for the nonathletic and athletic youth, respectively. However, in the nonathletic subgroup, ML showed that the Naïve Bayes exhibited highest AUC (0.58), whereas in the athletic group, logistic regression was demonstrated as the model with the best predictive accuracy (AUC: 0.62). In both subgroups, with given predictors: sex, age, BMI, body fat percentage, MVPA, training hours per week, socioeconomic status, and total FMS score, ML can give a more accurate prediction then FMS alone. Results indicate that nonathletic boys who have lower-body fat could be more prone to suffer from injury incidence, whereas among athletic subjects, boys who spend more time training are at a higher risk of being injured. Conclusively, total FMS cut-off scores for each subgroup did not successfully discriminate those who suffered from those who did not suffer from injury, and, therefore, our research does not support FMS as an injury prediction tool.
    MeSH term(s) Adolescent ; Athletic Injuries/diagnosis ; Athletic Injuries/epidemiology ; Bayes Theorem ; Humans ; Machine Learning ; Male ; Movement ; ROC Curve
    Language English
    Publishing date 2021-02-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1156349-7
    ISSN 1533-4287 ; 1064-8011
    ISSN (online) 1533-4287
    ISSN 1064-8011
    DOI 10.1519/JSC.0000000000003982
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  7. Article ; Online: Prediction of Task Performance From Physiological Features of Stress Resilience.

    Sarlija, Marko / Popovic, Sinisa / Jagodic, Marko / Jovanovic, Tanja / Ivkovic, Vladimir / Zhang, Quan / Strangman, Gary / Cosic, Kresimir

    IEEE journal of biomedical and health informatics

    2021  Volume 25, Issue 6, Page(s) 2150–2161

    Abstract: In this paper, we investigate the potential of generic physiological features of stress resilience in predicting air traffic control (ATC) candidates' performance in a highly-stressful low-fidelity ATC simulator scenario. Stress resilience is highlighted ...

    Abstract In this paper, we investigate the potential of generic physiological features of stress resilience in predicting air traffic control (ATC) candidates' performance in a highly-stressful low-fidelity ATC simulator scenario. Stress resilience is highlighted as an important occupational factor that influences the performance and well-being of air traffic control officers (ATCO). Poor stress management, besides the lack of skills, can be a direct cause of poor performance under stress, both in the selection process of ATCOs and later in the workplace. 40 ATC candidates, within the final stages of their selection process, underwent a stimulation paradigm for elicitation and assessment of various generic task-unrelated physiological features, related to resting heart rate variability (HRV) and respiratory sinus arrhythmia (RSA), acoustic startle response (ASR) and the physiological allostatic response, which are all recognized as relevant psychophysiological markers of stress resilience. The multimodal approach included analysis of electrocardiography, electromyography, electrodermal activity and respiration. We make advances in computational methodology for assessment of physiological features of stress resilience, and investigate the predictive power of the obtained feature space in a binary classification problem: prediction of high- vs. low-performance on the developed ATC simulator. Our novel approach yields a relatively high 78.16% classification accuracy. These results are discussed in the context of prior work, while considering study limitations and proposing directions for future work.
    MeSH term(s) Electromyography ; Heart Rate ; Reflex, Startle ; Stress, Psychological ; Task Performance and Analysis
    Language English
    Publishing date 2021-06-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2020.3041315
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  8. Article: Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers

    Cosic, Kresimir / Popovic, Sinisa / Sarlija, Marko / Kesedzic, Ivan / Jovanovic, Tanja

    Croatian medical journal

    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #638409
    Database COVID19

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