The current study aimed to describe the molecular epidemiology of mixed respiratory viral infections during consecutive winter seasons in a tertiary care hospital. Patients with symptoms of respiratory tract infection were evaluated during the 2009-2011 and 2013-15 winter seasons. A clinical microarray technique was used for viral detection. Clinical and epidemiological data were correlated with mixed viral detection and the need for hospitalization. In 332 out of 604 (54.4%) evaluated patients (17.6% children) a respiratory virus was identified. Mixed viral infections were diagnosed in 68/332 (20.5%) patients with virus detection (66.2% mixed Influenza-RSV infections). Mixed viral infections were more commonly detected in children (OR 3.7; 95%CI 1.9-5.6, P < 0.01) and patients with comorbidities. In logistic regression analyses, mixed viral infections were associated with younger age (mean age 30.4 years vs. 41.8 years, P ≤ 0.001) and increased rates of fever (OR: 2.7; 95%CI 1.04-7.2, P < 0.05) but no adverse outcomes or increased rates of hospitalization. High rates of mixed viral infections were noted during all winter seasons (especially Influenza and RSV) and were more common in younger patients. The clinical significance of mixed respiratory viral infection needs further elucidation.

The COVID-19 pandemic has spread rapidly worldwide with critical consequences in health, as well as in social, economic, and particularly in psychological conditions of vulnerable people, especially older adults. Therefore, it is necessary the direct attention to their health care needs and related interventions. Information and Communication Technology (ICT) have direct impact on older adults’ health and quality of life leading to decreased depression and loneliness, along with empowerment of independent life. Many studies involve cognitive training programs/software based on new technological systems that provide to vulnerable people access to gamified, attractive, cognitive exercises for overall functionality everywhere and at any time. Twenty-four participants (mean age 69.3 years) were assigned to this study. The cognitive training component of LLM Care was used as an interactive software to enhance participants’ cognitive functions. The intervention lasted 12 weeks with the frequency of 2–4 times per week in sessions of at least 30 min. Participants used their personal devices (tablets/laptops) in their own residence, while technical and consulting guidance was provided by LLM Care certified trainers. They were informed about the purpose of the study, while consent forms along with psychological assessments were distributed every 2 weeks to periodically evaluate their psychosocial and mental health conditions. The assessments included the World Health Organization-Five Well-Being Index (WHO-5), the Short Anxiety Screening Test (SAST), the System Usability Scale (SUS) and the Impact Factor Event Scale (IES-R). According to the results, the participants with improved well-being tended to report decreased subjective distress caused by COVID-19, and their engagement with new technologies can potentially minimize the negative outcomes occurred by the current stressful situation, mitigating the effect of hyperarousal symptoms, while increasing their overall well-being. Well-being seems to remain relatively stable among older adults and decreases only when adversities occur, while the usability of the software was perceived as marginally acceptable by participants. The exploitation of the LLM Care contributes to the improvement of older adults’ well-being and alleviates the negative experience caused by stressful situations like COVID-19.

Background Ecologically valid evaluations of patient states or well-being by means of new technologies is a key issue in contemporary research in health and well-being of the aging population. The in-game metrics generated from the interaction of users with serious games (SG) can potentially be used to predict or characterize a user’s state of health and well-being. There is currently an increasing body of research that investigates the use of measures of interaction with games as digital biomarkers for health and well-being. Objective The aim of this paper is to predict well-being digital biomarkers from data collected during interactions with SG, using the values of standard clinical assessment tests as ground truth. Methods The data set was gathered during the interaction with patients with Parkinson disease with the webFitForAll exergame platform, an SG engine designed to promote physical activity among older adults, patients, and vulnerable populations. The collected data, referred to as in-game metrics, represent the body movements captured by a 3D sensor camera and translated into game analytics. Standard clinical tests gathered before and after the long-term interaction with exergames (preintervention test vs postintervention test) were used to provide user baselines. Results Our results showed that in-game metrics can effectively categorize participants into groups of different cognitive and physical states. Different in-game metrics have higher descriptive values for specific tests and can be used to predict the value range for these tests. Conclusions Our results provide encouraging evidence for the value of in-game metrics as digital biomarkers and can boost the analysis of improving in-game metrics to obtain more detailed results.

Aiming at limiting the risk of ageism & social exclusion of older adults in society, the Thess-AHALL looks at co-design and open science solutions for social inclusion for the ageing population.

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