Cluster analysis identifies distinct psychosocial profiles of digital behavior and mental health, suggesting tailored interventions for university students.
The pervasive integration of smartphones and social media into daily life has transformed digital behavior into a central component of psychosocial functioning among young adults. In Spain, nearly half of university students report problematic smartphone use, and over 30% spend more than five hours per day on their devices during weekdays. This study aimed to identify distinct psychosocial profiles based on digital behavior and mental health indicators in a sample of 416 Spanish university students (71.9% female; M age = 21.9, SD = 4.93). A cluster analysis was conducted on standardized scores for digital addiction symptoms, nomophobia, recreational digital engagement, attachment-related anxiety, depression, loneliness, self-esteem, psychological well-being, and sleep health. Cluster 1 (n=159) exhibited high scores for addiction symptoms, nomophobia, attachment-related anxiety, depression, loneliness and poor sleep quality, and lower scores for sleep quantity; Cluster 2 (n=128) showed the lowest scores across digital addiction symptoms, nomophobia, and psychological symptoms and Cluster 3 (n=129) demonstrated lowest scores in poor sleep quality, moderate digital engagement, attachment-related anxiety, depression and loneliness but significantly better self-esteem and sleep quantity. ANOVA and Tukey’s post-hoc tests confirmed significant differences across clusters (p < .001) for all variables. These findings highlight the multidimensional nature of digital addiction and its cumulative associations with psychological and sleep-related outcomes. The identification of differentiated profiles supports the development of tailored interventions ranging from cognitive-behavioral therapy for high-risk groups to preventive psychoeducation for low-risk users. This study contributes to a nuanced understanding of digital behavior and its psychological correlates in university populations. • This paper explores psychosocial profiles of digital behavior and mental health • Three distinct psychosocial profiles of digital behavior were identified • High addiction cluster showed greater attachment-related anxiety, depression, loneliness, and poor sleep • Low addiction cluster reported better psychosocial functioning and healthier sleep patterns • These findings underscore the need for tailored interventions for university populations.
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Velasco-Furlong et al. (2026) studied this question.
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