Browsing by Author "Gaviria Chavarro, Javier"
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Item Análisis del efecto de tres programas de ejercicio en la salud percibida y el afrontamiento Adaptativo de Roy, en adultos mayores. Comuna 11 Cali-Valle del cauca(Universidad Santiago de Cali, 2025) Gaviria Chavarro, Javier; Zambrano Bermeo, Rosa Nury (Director)In recent years, Latin American countries have experienced a rapid process of demographic and epidemiological change due to population aging. This phenomenon has led to an increased prevalence of chronic diseases and age?related muscle mass loss, contributing to frailty, reduced mobility, and a decline in both physical and mental health among older adults. Various studies have suggested that physical activity is an effective strategy for improving quality of life and coping with the changes associated with aging. Objective: To analyze the effect of three exercise programs (coordinative, aerobic, and multimodal) on perceived health, well-being, and Roy’s adaptive coping in older adults from Comuna 11 in Cali, Valle del Cauca. Methodology: A quantitative study was conducted using a quasi-experimental design with a longitudinal and comparative approach. The sample consisted of 450 older adults, selected by convenience sampling and distributed into three intervention groups. Various measurement instruments were applied before and after the intervention, including the SF-12 questionnaire to assess perceived health, the Roy Adaptation Coping Scale (EsCAPS) to measure the adaptation process, the WHO-5 Well-Being Index, and the Senior Fitness Test to evaluate functional physical capacity. Participants engaged in the exercise programs for 12 weeks, with one-hour sessions three times per week. Statistical models, including mean comparison and multivariate analysis, were employed to determine the effects of the interventions. Results: The findings revealed significant improvements in perceived health, well?being, and adaptive coping across all intervention groups. The multimodal program yielded the greatest benefits in physical function, whereas the aerobic program had a more pronounced impact on emotional well-being. A significant reduction in low coping levels and an increase in moderate and high coping levels were observed. Additionally, differences in the effects were identified based on participants' age and initial condition, highlighting a greater impact among those with lower initial physical fitness. Conclusion: This study provides evidence on the effectiveness of exercise programs in enhancing the overall health of older adults. The results support the implementation of exercise-based intervention strategies to promote well-being and adaptation to aging in this population.Item Objeto virtual de aprendizaje para la enseñanza- aprendizaje de métodos estadísticos no paramétricos(Universidad Santiago de Cali, 2019) Gaviria Chavarro, Javierlevel. Despite this, they do not delve into information on statistical methods related to non-parametric statistics. This situation arises due to the large number of topics that have the inferential statistics in addition to its long extension. For this reason, a virtual learning object was created for the non-parametric statistical methods of Kruskal Wallis, Mann-Whitney U and Wilcoxon. These methods are relevant for the investigations analysis performed with small samples (less than 30) which do not comply with the statistical assumptions. The main purpose of this research is teaching and learning these three statistical tests. To achieve this, the methodology of construction of virtual learning objects proposed by Borrero and Cruz was implemented to support the training of students in the biostatistics area. The objects were evaluated by experts through the LORI instrument, showing a quality level in the medium-high interval according to the final weighting. The evaluation instrument indicated that the virtual learning object is suitable for the purpose and objectives set.Item Smartphones dependency risk analysis using machine-learning predictive models(2022-12) Giraldo Jiménez, Claudia Fernanda; Gaviria Chavarro, Javier; Sarria Paja, Milton; Bermeo Varón, Leonardo Antonio; Villarejo Mayor, John Jairo; Rodacki, André Luiz FelixRecent technological advances have changed how people interact, run businesses, learn, and use their free time. The advantages and facilities provided by electronic devices have played a major role. On the other hand, extensive use of such technology also has adverse efects on several aspects of human life (e.g., the development of societal sedentary lifestyles and new addictions). Smartphone dependency is new addiction that primarily afects the young population. The consequences may negatively impact mental and physical health (e.g., lack of attention or local pain). Health professionals rely on self-reported subjective information to assess the dependency level, requiring specialists’ opinions to diagnose such a dependency. This study proposes a data-driven prediction model for smartphone dependency based on machine learning techniques using an analytical retrospective case–control approach. Diferent classifcation methods were applied, including classical and modern machine learning models. Students from a private university in Cali—Colombia (n= 1228) were tested for (i) smartphone dependency, (ii) musculoskeletal symptoms, and (iii) the Risk Factors Questionnaire. Random forest, logistic regression, and support vector machine-based classifers exhibited the highest prediction accuracy, 76–77%, for smartphone dependency, estimated through the stratifed-k-fold cross-validation technique. Results showed that self-reported information provides insight into predicting smartphone dependency correctly. Such an approach opens doors for future research aiming to include objective measures to increase accuracy and help to reduce the negative consequences of this new addiction form.