A multimodal group-based immersive virtual reality intervention for improving cognition and mental health in patients with post-covid-19 condition. A quasi-experimental design study.

Introduction: Adults with Post-COVID-19 Condition (PCC) may show cognitive impairments in attention, processing speed, memory, and executive function. Multimodal programs that combine cognitive training, physical activity and emotional tasks, such as mindfulness-based interventions (MBIs), may offer a suitable alternative for improving PCC treatments. Immersive Virtual Reality (IVR) is a promising technology that can enhance traditional cognitive training, physical activity, and MBIs. The use of IVR technology may increase engagement with these interventions and potentially enhance the individual benefits of cognitive training, exercise and MBIs. The current study evaluated the impact of a multimodal IVR intervention, comparing this with a usual care intervention (control group), in order to assess changes in cognition and mental health in adults with PCC. We also aimed to assess user experience factors such as enjoyment, perceived improvement, and fatigue following each multimodal IVR session within the experimental group. Method: Thirty-one participants with PCC symptoms were assigned to either the experimental group (IVR, n = 15) or the control group (usual care intervention, n = 16) in a quasi-experimental design study. The multimodal IVR intervention consisted of MBI, cognitive training and physical exercise and was delivered in a 60-min group session with 5 participants, twice a week, for 8 weeks (16 sessions in total). Measures of global cognition, attention, processing speed, verbal episodic memory and subjective memory complaints (primary measures), and depressive and anxiety symptoms and fatigue (secondary measures) were assessed at baseline and also after 8 weeks (post-intervention). Results: Mixed between-group (group) and within-group (pre-post assessments) ANOVAs revealed significant group*time interactions in global cognition, simple attention, processing speed, memory and depressive symptoms, with large effect sizes (p < 0.05; partial η2 > 0.14). There was also a marginally significant group*time interaction for executive function (p = 0.05). Follow-up analyses comparing pre-and post-intervention outcomes for each group separately showed that the experimental group significantly improved in global cognition, processing speed, memory and depressive symptoms, while the control group showed no significant pre-post changes. Friedman tests showed a significant main effect of time (χ2(2) = 6.609, p = 0.04), with a gradual increase in enjoyment from the first, to the mid, and then to the final session. In addition, perceived improvement scores remained high throughout the intervention, and patient-reported fatigue levels did not fluctuate significantly throughout the intervention. Conclusion: To our knowledge, no previous research has combined cognitive training, physical exercise and MBI using an IVR paradigm in adults with PCC. Despite their inherent limitations, our findings mark a pioneering step toward improving cognition and mental health outcomes in PCC through the innovative use of new technology and multimodal approaches. This first study should be accompanied by more extensive, randomized clinical trials aimed at further exploring and refining these interventions.

Allostatic load, adverse childhood experiences, executive functions, and BMI status in adolescents and young adults.

OBJECTIVES: Chronic stress induces preclinical changes in the metabolic, cardiovascular, and immune systems. This phenomenon, known as allostatic load (AL), can impair executive functions (EF), which may be even more affected in individuals with excess weight due to their characteristic inflammatory state and cardiometabolic changes. Adverse childhood experiences (ACEs) contribute to AL and may influence executive functioning presumably via alterations within the hypothalamic-pituitary axis, including epigenetic modifications. We assess the relationship between AL and EF in youth with and without excess weight, and the effect ACEs on executive functioning. METHODS: One hundred eighty-two adolescents and young adults (85 with normal weight and 97 with overweight/obesity; 10-21 years) were recruited. The estimated AL index included the following: systolic and diastolic blood pressure, glycated hemoglobin, high- and low-density lipoprotein cholesterol, triglycerides, high-sensitivity C-reactive protein, fibrinogen, and cortisol. ACEs were measured using the Juvenile Victimization Questionnaire. The neuropsychological evaluation included the assessment of inhibition, working memory, and cognitive flexibility processes. RESULTS: AL was not significantly associated with executive functioning, and this relationship did not depend on body-weight status. ACEs, available for 57 of 182 participants, were significantly associated with poorer executive functioning. CONCLUSIONS: Our study shows that AL is not associated with executive functioning in adolescents and young adults. Since the current sample was young, we hypothesize that a longer exposure to AL might be required for its negative effects to surface. Nevertheless, exposure to early adversity seems to be associated with poorer executive functioning in youth.

Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19.

The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.

From the reward network to whole-brain metrics: structural connectivity in adolescents and young adults according to body mass index and genetic risk of obesity.

BACKGROUND: Obesity is a multifactorial condition. Genetic variants, such as the fat mass and obesity related gene (FTO) polymorphism, may increase the vulnerability of developing obesity by disrupting dopamine signaling within the reward network. Yet, the association of obesity, genetic risk of obesity, and structural connectivity of the reward network in adolescents and young adults remains unexplored. We investigate, in adolescents and young adults, the structural connectivity differences in the reward network and at the whole-brain level according to body mass index (BMI) and the FTO rs9939609 polymorphism. METHODS: One hundred thirty-two adolescents and young adults (age range: [10, 21] years, BMI z-score range: [-1.76, 2.69]) were included. Genetic risk of obesity was determined by the presence of the FTO A allele. Whole-brain and reward network structural connectivity were analyzed using graph metrics. Hierarchical linear regression was applied to test the association between BMI-z, genetic risk of obesity, and structural connectivity. RESULTS: Higher BMI-z was associated with higher (B = 0.76, 95% CI = [0.30, 1.21], P = 0.0015) and lower (B = -0.003, 95% CI = [-0.006, -0.00005], P = 0.048) connectivity strength for fractional anisotropy at the whole-brain level and of the reward network, respectively. The FTO polymorphism was not associated with structural connectivity nor with BMI-z. CONCLUSIONS: We provide evidence that, in healthy adolescents and young adults, higher BMI-z is associated with higher connectivity at the whole-brain level and lower connectivity of the reward network. We did not find the FTO polymorphism to correlate with structural connectivity. Future longitudinal studies with larger sample sizes are needed to assess how genetic determinants of obesity change brain structural connectivity and behavior.

Cognitive and emotional predictors of quality of life and functioning after COVID-19.

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.