An Immersive Serious Game for the Behavioral Assessment of Psychological Needs

[EN] Motivation is an essential component in mental health and well-being. In this area, researchers have identified four psychological needs that drive human behavior: attachment, self-esteem, orientation and control, and maximization of pleasure and minimization of distress. Various self-reported scales and interviews tools have been developed to assess these dimensions. Despite the validity of these, they are showing limitations in terms of abstractation and decontextualization and biases, such as social desirability bias, that can affect responses veracity. Conversely, virtual serious games (VSGs), that are games with specific purposes, can potentially provide more ecologically valid and objective assessments than traditional approaches. Starting from these premises, the aim of this study was to investigate the feasibility of a VSG to assess the four personality needs. Sixty subjects participated in five VSG sessions. Results showed that the VSG was able to recognize attachment, self-esteem, and orientation and control needs with a high accuracy, and to a lesser extent maximization of pleasure and minimization of distress need. In conclusion, this study showed the feasibility to use a VSG to enhance the assessment of psychological behavioral-based need, overcoming biases presented by traditional assessment.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness funded project "Advanced Therapeutically Tools for Mental Health" (DPI2016-77396-R) and by the European Union through the Operational Program of the European Regional Development Fund (ERDF) on the Valencian Community 2010-2020 (IDIFEDER/2018/029)Chicchi-Giglioli, IA.; Carrasco-Ribelles, LA.; Parra Vargas, E.; Marín-Morales, J.; Alcañiz Raya, ML. (2021). An Immersive Serious Game for the Behavioral Assessment of Psychological Needs. Applied Sciences. 11(4):1-17. https://doi.org/10.3390/app11041971S11711

Applying machine learning to a virtual serious game for neuropsychological assessment.

[Otros] Neuropsychological assessment has been traditionally made through paper-and-pencil batteries which usually are time-consuming, decontextualized, and nonecological. These abilities play a critical role in education since they are very related to learning capacity, academic achievement, social functioning, as well as the inhibition of maladaptive behaviors. Meanwhile, serious games are being used in education and psychology to achieve assessments without these limitations, including neuropsychological assessments. While traditional tests can be analyzed with classical statistics, a large number of variables can be extracted from serious games, the analysis of which can be more complex. Machine learning can handle this large amount of information and find patterns that allow us to recognize behaviors. This study aimed to investigate whether machine learning could be used to improve predictive validity in applying a serious game for neuropsychological assessment. Results were based on 60 subjects, including 42 cognitive activities. The validation process showed best results on attention, memory, planning, and cognitive flexibility, achieving accuracies higher or equal to 0.8 and Cohen¿s Kappas higher than 0.55, which implies that the Virtual Serious Game could be a valid tool to perform a neuropsychological evaluation along with traditional tests.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness funded project Advanced Therapeutically Tools for Mental Health (DPI2016-77396-R) and by the European Union through the Operational Program of the European Regional Development Fund (ERDF) on the Valencian Community 2010-2020 (IDIFEDER/2018/029).Marín-Morales, J.; Carrasco-Ribelles, LA.; Alcañiz Raya, ML.; Chicchi-Giglioli, IA. (2021). Applying machine learning to a virtual serious game for neuropsychological assessment. IEEE. 951-954. https://doi.org/10.1109/EDUCON46332.2021.9454138S95195

Evaluación ecológica mediante Realidad Virtual de las necesidades psicológicas básicas

Pese a que las técnicas de evaluación psicológica comúnmente utilizadas en lápiz y papel son una estrategia elegida por su adecuada validez, se presentan algunas limitaciones importantes que pueden superarse por los avances recientes en Realidad Virtual (RV), al permitir la evaluación de constructos psicológicos en entornos inmersivos, como una forma de evaluación ecológica. Es así que el propósito de la presente investigación fue determinar la eficacia de una herramienta de realidad virtual en la evaluación de cuatro necesidades psicológicas básicas: apego, autoestima, autoeficacia, maximización del placer/minimización del dolor. La muestra la conformaron 61 participantes, quienes fueron expuestos a entornos virtuales centrados en la evaluación conductual de cada uno de estos constructos. Los resultados mostraron una adecuada precisión de los entornos de RV en cuanto al reconocimiento de las necesidades evaluadas. En conclusión, los hallazgos permitieron contar con mayor evidencia en cuanto al uso de la RV como una alternativa válida para la medición de los constructos, se reconocen limitaciones importantes referentes al número limitado de participantes y a la ausencia de población clínica.

Neurocognitive Profile of the Post-COVID Condition in Adults in Catalonia-A Mixed Method Prospective Cohort and Nested Case-Control Study : Study Protocol

Altres ajuts: This study is also supported in part by grants from the CIBER-Consorcio Centro de Investigación Biomédica en Red-(CB 2021), Ministerio de Ciencia e Innovación and Unión Europea, NextGenerationEU.The diagnosis of the post-COVID condition is usually achieved by excluding other diseases; however, cognitive changes are often found in the post-COVID disorder. Therefore, monitoring and treating the recovery from the post-COVID condition is necessary to establish biomarkers to guide the diagnosis of symptoms, including cognitive impairment. Our study employs a prospected cohort and nested case-control design with mixed methods, including statistical analyses, interviews, and focus groups. Our main aim is to identify biomarkers (functional and structural neural changes, inflammatory and immune status, vascular and vestibular signs and symptoms) easily applied in primary care to detect cognitive changes in post-COVID cases. The results will open up a new line of research to inform diagnostic and therapeutic decisions with special considerations for cognitive impairment in the post-COVID condition

How priming with body odors affects decision speeds in consumer behavior

Abstract To date, odor research has primarily focused on the behavioral effects of common odors on consumer perception and choices. We report a study that examines, for the first time, the effects of human body odor cues on consumer purchase behaviors. The influence of human chemosignals produced in three conditions, namely happiness, fear, a relaxed condition (rest), and a control condition (no odor), were examined on willingness to pay (WTP) judgments across various products. We focused on the speed with which participants reached such decisions. The central finding revealed that participants exposed to human odors reached decisions significantly faster than the no odor control group. The main driving force is that human body odors activate the presence of others during decision-making. This, in turn, affects response speed. The broader implications of this finding for consumer behavior are discussed

Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis

[EN] The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations,advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults¿ rather than children¿s faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity = 91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition.Spanish Ministry of Economy, Industry, and Competitiveness-funded project "Immersive Virtual Environment for the Evaluation and Training of Children with Autism Spectrum Disorder: T Room, Grant/Award Number: IDI20170912Alcañiz Raya, ML.; Chicchi-Giglioli, IA.; Carrasco-Ribelles, LA.; Marín-Morales, J.; Minissi, ME.; Teruel-Garcia, G.; Sirera, M.... (2022). Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis. Autism Research. 15(1):131-145. https://doi.org/10.1002/aur.2636S13114515

Determinants of Anti-S Immune Response at 9 Months after COVID-19 Vaccination in a Multicentric European Cohort of Healthcare Workers-ORCHESTRA Project

Background: The persistence of antibody levels after COVID-19 vaccination has public health relevance. We analyzed the determinants of quantitative serology at 9 months after vaccination in a multicenter cohort. Methods: We analyzed data on anti-SARS-CoV-2 spike antibody levels at 9 months from the first dose of vaccinated HCW from eight centers in Italy, Germany, Spain, Romania and Slovakia. Serological levels were log-transformed to account for the skewness of the distribution and normalized by dividing them by center-specific standard errors. We fitted center-specific multivariate regression models to estimate the cohort-specific relative risks (RR) of an increase of one standard deviation of log antibody level and the corresponding 95% confidence interval (CI), and combined them in random-effects meta-analyses. Finally, we conducted a trend analysis of 1 to 7 months' serology within one cohort. Results: We included 20,216 HCW with up to two vaccine doses and showed that high antibody levels were associated with female sex (p = 0.01), age (RR = 0.87, 95% CI = 0.86-0.88 per 10-year increase), 10-day increase in time since last vaccine (RR = 0.97, 95% CI 0.97-0.98), previous infection (3.03, 95% CI = 2.92-3.13), two vaccine doses (RR = 1.22, 95% CI = 1.09-1.36), use of Spikevax (OR = 1.51, 95% CI = 1.39-1.64), Vaxzevria (OR = 0.57, 95% CI = 0.44-0.73) or heterologous vaccination (OR = 1.33, 95% CI = 1.12-1.57), compared to Comirnaty. The trend in the Bologna cohort, based on 3979 measurements, showed a decrease in mean standardized antibody level from 8.17 to 7.06 (1-7 months, p for trend 0.005). Conclusions: Our findings corroborate current knowledge on the determinants of COVID-19 vaccine-induced immunity and declining trend with time