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Abstract Objective A long‐term decline in health‐related quality of life (HRQoL) has been reported after coronavirus disease 2019 (COVID‐19). Studies with people with persistent symptoms showed inconsistent outcomes. Cognition and emotion are important determinants in HRQoL, but few studies have examined their prognostic significance for HRQoL and functionality in post‐COVID patients with persisting symptoms. We aimed to describe QoL, HRQoL, and functioning in individuals post‐COVID with varying COVID‐19 severities and to investigate the predictive value of cognitive and emotional variables for QoL, HRQoL, and functioning. Methods In total, 492 participants (398 post‐COVID and 124 healthy controls) underwent a neurobehavioral examination that included assessments of cognition, mood, QoL/HRQoL (WHOQOL‐BREF, EQ‐5D), and functioning (WHODAS‐II). Analysis of covariance and linear regression models were used to study intergroup differences and the relationship between cognitive and emotional variables and QoL and functioning. Results The Physical and Psychological dimensions of WHOQoL, EQ‐5D, and WHODAS Cognition, Mobility, Life Activities, and Participation dimensions were significantly lower in post‐COVID groups compared with a control group. Regression models explaining 23.9%–53.9% of variance were obtained for the WHOQoL‐BREF dimensions and EQ‐5D, with depressive symptoms, post‐COVID symptoms, employment status, income, and mental speed processing as main predictors. For the WHODAS, models explaining 17%–60.2% of the variance were obtained. Fatigue, depressive symptoms, mental speed processing, and post‐COVID symptoms were the main predictors. Interpretation QoL/HRQoL and functioning after COVID‐19 in individuals with persistent symptoms were lower than in non‐affected persons. Depressive symptoms, fatigue, and slower mental processing speed were predictors of lower QoL/HRQoL and functioning.

En el tan estudiado contexto de la pandemia de COVID-19, las TIC fueron de especial interés respecto de su adopción en poblaciones vulnerables, entre ellas, las personas mayores. Sin embargo, las circunstancias de apropiación tecnológicas fueron complejas con usuarios que sin acceso o habilidades digitales fueron forzados a migrar a lo online para desarrollar sus actividades cotidianas. Es por ello que el presente artículo desarrolla una revisión sistemática de alcance en revistas indexadas de habla inglesa y española entre los años 2020 y 2022, para identificar la literatura que aborda el concepto de adopción forzada en términos de volumen, naturaleza y características e identificar los posibles vacíos teóricos. Utilizando el modelo propuesto por Arksey y O´Malley (2005), se identificaron 117 artículos en cinco bases de datos, de los cuales solo siete cumplieron con los criterios de inclusión y fueron seleccionados para su análisis. Los resultados muestran que la utilización del concepto de adopción forzada de tecnología aplicado en personas mayores sigue siendo escasa y que se presenta la oportunidad de abordarlo desde la resistencia producida por este tipo de adopción y la necesidad de desarrollar políticas públicas que naturalicen el acceso y uso de TIC en contextos de crisis y catástrofes para no dejar aislados a un grupo vulnerable de la población.

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems.

One of the most prevalent symptoms of post-COVID condition is cognitive impairment, which results in a significant degree of disability and low quality of life. In studies with large sample sizes, attention, memory, and executive function were reported as long-term cognitive symptoms. This study aims to describe cognitive dysfunction in large post-COVID condition individuals, compare objective neuropsychological performance in those post-COVID condition individuals with and without cognitive complaints, and identify short cognitive exams that can differentiate individuals with post-COVID symptoms from controls. To address these aims, the Nautilus project was started in June 2021. During the first year, we collected 428 participants’ data, including 319 post-COVID and 109 healthy controls (18–65 years old) from those who underwent a comprehensive neuropsychological battery for cognitive assessment. Scores on tests assessing global cognition, learning and long-term memory, processing speed, language and executive functions were significantly worse in the post-COVID condition group than in healthy controls. Montreal Cognitive Assessment, digit symbol test, and phonetic verbal fluency were significant in the binomial logistic regression model and could effectively distinguish patients from controls with good overall sensitivity and accuracy. Neuropsychological test results did not differ between those with and without cognitive complaints. Our research suggests that patients with post-COVID conditions experience significant cognitive impairment and that routine tests like the Montreal Cognitive Assessment, digit symbol, and phonetic verbal fluency test might identify cognitive impairment. Thus, the administration of these tests would be helpful for all patients with post-COVID-19 symptoms, regardless of whether cognitive complaints are present or absent.Study registrationwww.ClinicalTrials.gov, identifiers NCT05307549 and NCT05307575.

Background. Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL. Methods. In a sample of 481 elderly people at risk of falls recruited in this multicenter randomised controlled trial, the effectiveness of a motor treatment (pure motor or mixed with EFt) of 24 one-hour sessions delivered through an i-Walker with a non-motor treatment (pure EFt or control condition) was evaluated. Similarly, a 24 one-hour session cognitive treatment (pure EFt or mixed with motor training), delivered through a touch-screen computer was compared with a non-cognitive treatment (pure motor or control condition). Results. Motor treatment, particularly when mixed with EFt, reduced significantly fear of falling (F(1,478) = 6.786, p = 0.009) although to a limited extent (ES −0.25) restricted to the period after intervention. Conclusions. This study suggests the effectiveness of motor treatment empowered by EFt in reducing fear of falling.