Labre Tarco, Verónica
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Labre Tarco, Verónica
Main Affiliation
Ambato
Email
veronicalabre@uti.edu.ec
ORCID
Scopus Author ID
58759297100

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Immersive Virtual Reality Application for the Evaluation of Depression in Older Adults

2026 , Buele, Jorge , Varela Aldas, José , Labre Tarco, Verónica

Although evidence supports the use of virtual reality for cognitive assessment in the older adult, studies on the assessment of mental disorders such as depression are scarce. Previous studies show satisfactory results in the young adult population, but further analysis is needed in the geriatric population. This research analyzed the ability of a virtual reality application to assess levels of depression in older adults, analyzing the correlation between demographic variables and performance on the application. Fourteen older adults with an age of 74.86 (5.4) participated in the study. Demographic variables, depressive symptoms were evaluated, and Spearman correlation tests were performed to analyze the relationship between age, schooling and performance in the application. The research reveals a significant association between age and task execution time, indicating that the older the age, the longer it takes to complete the task. A correlation is identified between schooling and the number of errors, highlighting that more education does not guarantee the absence of errors. Although no direct correlation was found between level of depression and application performance, the promising utility of virtual reality in this area, supported by recent studies, is underscored. The study contributes to the understanding of how virtual reality applications can be valuable in the assessment of mental health in older adults. Although limitations such as sample size are acknowledged, the results establish a foundation for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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Predicting Academic Performance in Psychology Students from Their Social Skills Using Classification Algorithms

2023 , Espinosa-Pinos C.A. , Vasco-Álvarez M.M. , Cisneros-Bedón J.L. , Labre Tarco, Verónica

The high academic performance of students is the result of several factors, one of them being the development of social skills. Social skills allow students to face different circumstances, such as academic, personal, or professional. Students who develop these skills can quickly adapt to stressful and conflictive situations, generating complete well-being that facilitates learning, reflected in their academic performance. This study is critical because it helps educators identify students at risk of poor academic performance and provide adequate academic support. The research aims to identify predictors of academic performance based on social skills through knowledge discovery in databases (KDD) to clean data using classification algorithms, specifically Random Forest. To collect the data, a sociodemographic form, and the Social Skills Scale (SSS) were applied to 93 students of General Psychology, face-to-face modality, at Indoamerica University. The research results indicate that academic performance predictors are linked to gender and social skills, such as the ability to say no and cut interactions and the expression of states or disagreement. These findings suggest structuring support programs, academic guidance, and social skills development to improve academic performance and future career success. In conclusion, the research provides a new perspective to work in student welfare departments to improve students' academic performance. © 2023 IEEE.

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