This investigation aimed to analyze the predictive differential value of personal (self-regulation, self-efficacy, procrastination) and contextual characteristics (parents’ socio-educational level), regarding academic achievement, among Colombian adolescents. A total of 430 students (from 11 to 18 years old) from both genders filled out validated self-reports and informed their academic achievement. We performed an ex-post-facto design, simple regression analyses, structural equations predictions analyses (SEM), and variance analyses (ANOVAs). The results showed that self-regulation is the most potent personal variable predictive of procrastination and achievement, positively associated with self-efficacy; additionally, the parents’ educational level was also a predictor, although to a lesser level. The female group and the elderly group negatively predicted academic achievement, behaving as modulatory variables of the above results.

Among high school students, academic procrastination is frequent, affecting their school performance, health, and well-being. We validate the Spanish version of the Procrastination Assessment Scale- Students, which evaluates the frequency and reasons to procrastinate. Four hundred fifty-one high school students from Bogotá (Colombia) participated. Content validity discriminant analyses and statistics are performed according to the Rasch model. We found good item fit to the Rasch model, good reliability of the measures and persons, without DIF by gender; we also found significant negative associations regarding self-regulation and performance of the participants.

Background: The widespread use of digital technologies has raised growing concerns about their impact on mental health. While self-regulation has been proposed as a protective factor, little is known about how distinct psychological profiles based on self-regulatory and technology use patterns relate to psychological distress. Person-centered approaches, such as latent profile analysis, may offer deeper insights, particularly in underrepresented populations. Objective: This study aimed to identify latent psychological profiles based on self-regulation, nomophobia (fear of being without a phone), and problematic use of the internet and social media (defined by behavioral symptoms), to examine their associations with general psychological distress and the presence of emotional symptoms in a Colombian sample. Additionally, the predictive roles of age and gender in class membership were explored. Methods: Participants were recruited through a convenience sampling strategy aimed at ensuring heterogeneity of the sample in terms of age and gender. A total of 453 participants aged 12 to 57 years (mean 21.03, SD 8.41 years; 257/453, 56.7% female) completed validated measures of self-regulation (Abbreviated Self-Regulation Questionnaire), nomophobia (Nomophobia Questionnaire), internet and social media use (MULTICAGE-TIC, a multidomain screening questionnaire based on the CAGE framework), and psychological distress (General Health Questionnaire-12). Latent profile analysis was conducted using standardized scores of continuous variables. Model fit was assessed using the Bayesian information criterion, entropy, and bootstrapped likelihood ratio test. Differences in psychological distress scores across latent classes were examined through variance analysis (ANOVA) and regression models. A multinomial logistic regression tested the predictive value of age and gender for class membership. Results: The optimal solution revealed 4 distinct latent profiles (entropy=0.85). Class 1 showed high self-regulation and low problematic technology use, displaying the lowest psychological distress scores. Class 2 presented moderate levels across all indicators but the highest level of psychological distress. Classes 3 and 4 showed mixed patterns. Class 3 (higher information and communication technology [ICT] use and lower self-regulation) exhibited lower distress than class 2, whereas class 4 (younger individuals with low self-regulation and moderately high ICT use) showed higher distress than class 3. Psychological distress differed significantly across profiles (ANOVA, P <.001). Age and gender predicted class membership. Older males were more likely to belong to class 1, and younger females were more likely to be classified into classes 3 and 4. Conclusions: Latent profile analysis identified distinct configurations of digital behavior, self-regulation, and psychological distress. Self-regulation consistently differentiated profiles with lower distress scores, suggesting its relevance for understanding how individuals manage ICT use. These findings support the value of person-centered approaches to characterize heterogeneous patterns of technology-related behaviors. The study provides evidence from a Spanish-speaking sample, offering a novel perspective on psychological distress and problematic technology use in contexts that remain underrepresented in the literature.

BACKGROUND The widespread use of digital technologies—especially the internet and social media—has raised growing concerns about their impact on mental health. While self-regulation has been proposed as a protective factor, little is known about how distinct psychological profiles based on self-regulatory and technology use patterns relate to well-being. Person-centered approaches such as Latent Profile Analysis (LPA) may offer deeper insights, particularly in underrepresented populations. OBJECTIVE This study aimed to identify latent psychological profiles based on self-regulation, nomophobia, and problematic use of the internet and social media and to examine their association with mental health outcomes in a Colombian sample. Additionally, the predictive roles of age and gender on class membership were explored. Methods METHODS 453 participants aged 12 to 57 years (M = 21.03, SD = 8.41; 57% female) completed validated measures of self-regulation, nomophobia, internet use, social media use, and psychological health (GHQ-12). Latent Profile Analysis was conducted using standardized scores of continuous variables. Model fit was assessed using BIC, entropy, and BLRT. Differences in psychological health across latent classes were examined through ANOVA and regression models. A multinomial logistic regression tested the predictive value of age and gender on class membership. RESULTS The optimal solution revealed four distinct latent profiles (entropy = 0.85): Class 1 (adaptive): high self-regulation, low nomophobia, and low ICT use; presented the best psychological health. Class 4 (vulnerable): low self-regulation, high nomophobia, and high ICT use; reported the poorest health outcomes. Classes 2 and 3 displayed intermediate profiles, with Class 3 showing slightly better health than Class 4. Differences in psychological health across classes were statistically significant (ANOVA, p < .001). Age and gender were significant predictors of class membership: younger females were more likely to belong to Class 1, whereas older males were more likely to be classified into Classes 3 and 4. CONCLUSIONS LPA enabled the identification of distinct psychological profiles that vary in mental health outcomes and digital vulnerability. Self-regulation emerged as a central protective factor, suggesting the importance of tailored digital interventions to enhance regulatory capacities. These findings reinforce the value of person-centered approaches and highlight the need for scalable strategies to mitigate the mental health risks associated with problematic ICT use in Spanish-speaking populations.