Mental Health Profiles Based on Self-Regulation and Technology Use in the Digital Era in a Spanish-Speaking Sample: Latent Profile Analysis.

Four distinct latent profiles were identified based on self-regulation, nomophobia, and problematic internet and social media use.

• The optimal solution revealed 4 distinct latent profiles with entropy=0.85, indicating good classification quality.
• Class 1 showed high self-regulation and low problematic technology use.
• Class 2 presented moderate levels across all indicators.
• Classes 3 and 4 showed mixed patterns, with Class 3 characterized by higher ICT use and lower self-regulation, and Class 4 by younger individuals with low self-regulation and moderately high ICT use.
• Model fit was assessed using Bayesian information criterion, entropy, and bootstrapped likelihood ratio test.

Class 1, characterized by high self-regulation and low problematic technology use, displayed the lowest psychological distress scores.

• Class 1 had high self-regulation and low problematic technology use simultaneously.
• This class showed the lowest psychological distress scores among all four profiles.
• Psychological distress differed significantly across profiles (ANOVA, P<.001).

Class 2, despite having moderate levels across all indicators, showed the highest level of psychological distress.

• Class 2 presented moderate levels across self-regulation, nomophobia, and problematic technology use.
• Yet Class 2 had the highest psychological distress scores of all four profiles.
• Class 3, which had higher ICT use and lower self-regulation than Class 2, exhibited lower distress than Class 2, suggesting distress is not simply a function of technology use level.

Class 4, composed of younger individuals with low self-regulation and moderately high ICT use, showed higher distress than Class 3.

• Class 4 was characterized by younger individuals with low self-regulation and moderately high ICT use.
• Class 4 showed higher psychological distress than Class 3.
• Class 3 had higher ICT use and lower self-regulation but showed lower distress than Class 4, indicating age and self-regulation interact in shaping distress.

Age and gender predicted latent class membership.

• A multinomial logistic regression tested the predictive value of age and gender for class membership.
• Older males were more likely to belong to Class 1 (high self-regulation, low problematic technology use, lowest distress).
• Younger females were more likely to be classified into Classes 3 and 4.
• Participants ranged in age from 12 to 57 years (mean 21.03, SD 8.41 years), with 56.7% female (257/453).

Self-regulation consistently differentiated profiles associated with lower psychological distress scores across all four latent classes.

• Self-regulation was measured using the Abbreviated Self-Regulation Questionnaire.
• Higher self-regulation was associated with lower distress in Class 1.
• Lower self-regulation appeared in the classes with more problematic technology use patterns (Classes 3 and 4).
• The authors concluded that self-regulation is relevant for understanding how individuals manage ICT use.

The study sample consisted of 453 Colombian participants aged 12 to 57 years recruited through convenience sampling aimed at ensuring heterogeneity.

• Total N=453 participants; mean age 21.03 years (SD 8.41); range 12–57 years.
• 56.7% female (257/453).
• Participants completed validated measures of self-regulation (Abbreviated Self-Regulation Questionnaire), nomophobia (Nomophobia Questionnaire), internet and social media use (MULTICAGE-TIC), and psychological distress (General Health Questionnaire-12).
• Recruitment used a convenience sampling strategy aimed at ensuring heterogeneity of the sample in terms of age and gender.