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Purpose: To determine the efficacy of the "interpersonal influence intervention" conducted by health professionals to increase the commitment to adopt health-promoting behavior in nursing students compared to the usual care of a university wellness program.Patients and Methods: A quasi-experimental study was performed. The study included 114 nursing students from a university in Cali, Colombia, who were divided into a control group (n = 57) that received usual care and an experimental group (n = 57) that received the intervention designed and validated according to Nola Pender's Health Promotion Model. The main outcome was the lifestyle measured before and after the test using the Health-Promoting Lifestyle Profile II Spanish version. The effect of the intervention was carried out through the average comparison, effect size measures were calculated using Cohen's d and analysis of the effect of possible confounding variables on the intervention (ANCOVA).Results: A statistically significant difference was observed between the experimental group and the control group (p = 0.015; 95% CI − 0.42, − 0.05). The effect size of the intervention was 0.49. The interpersonal influences exhibited by health professionals can increase the commitment to adopt health-promoting behaviors (Hypothesis 1), and the greater the commitment to a specific action plan, the more likely it is that health-promoting behaviors will be maintained over time (Hypothesis 2).Conclusion: The effectiveness of the intervention interpersonal influences exerted by health professionals to increase the commitment to adopt health-promoting behavior is proven. Evidence demonstrates the practical utility of the Health Promotion Model. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to determine how personal factors influence health-promoting behavior in university students using a structural equation modeling approach guided by the Health Promotion Model. Patients and Methods: An analytical cross-sectional study was conducted. The study included 763 health science students from four universities in Cali, Colombia, who answered a questionnaire on personal factors and Health Promoting Lifestyle Profile II, Spanish version, which was validated in the study population. The direct and indirect relationships between personal factors and health-promoting behaviors were assessed using structural equation modeling. Data analysis was performed using descriptive statistics and structural equation modeling. Results: A significant relationship was noted between the biological and psychological personal factors of the measurement model (p < 0.05). Psychological personal factors (self-esteem and perceived health status) positively influence health promoting behavior in university students (Hypothesis 2). It's not possible to demonstrate that health promoting behavior is positively influenced by personal biological factors (Hypothesis 1) and by personal sociocultural factors (Hypothesis 3). Conclusion: There is a need for interventions that help improve the health-promoting lifestyle profile and are focused on enhancing the self-esteem and perceived health status of university students. [ABSTRACT FROM AUTHOR]

Recent 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 effects on several aspects of human life (e.g., the development of societal sedentary lifestyles and new addictions). Smartphone dependency is new addiction that primarily affects 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. Different classification 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 classifiers exhibited the highest prediction accuracy, 76–77%, for smartphone dependency, estimated through the stratified-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. [ABSTRACT FROM AUTHOR]

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Fourteen variables including socioeconomic factors, demographic variables, reproductive ideals and contact with family planning programmes are tested as correlates of contraceptive use among a low-income population of Cali, Colombia. Data from surveys in 1976 and 1978 show two factors to be key determinants: number of children and contact with the local contraceptive distribution post. The availability of family planning services is discussed as an important concern for policy makers. [ABSTRACT FROM PUBLISHER]