Digital Addiction and Mental Health in University Students

According to the research of Lilian Velasco-Furlong, Gabriel Andrade, and Luis M. Romero-Rodríguez, digital addiction and mental health in university students should not be understood as separate or uniform issues. Their study identified three psychosocial profiles in which problematic digital behavior appeared alongside different combinations of anxiety, depression, loneliness, self-esteem, psychological well-being, and sleep health.

This article explains how these profiles were identified, what distinguishes students at greater risk, and how universities could use the findings to develop more personalized prevention and mental-health interventions.

The main findings indicate that students with more digital addiction symptoms and nomophobia also tended to report greater emotional distress, poorer sleep quality, lower self-esteem, and reduced well-being. However, the results describe associations rather than proving that digital technology directly caused these outcomes.

What is digital addiction?

In this study, digital addiction is an umbrella term for problematic or addiction-like patterns of Internet and social-network use. It does not represent a formal psychiatric diagnosis.

The researchers specifically measured two dimensions:

• Addiction symptoms, including compulsive engagement, difficulty controlling use, and possible disruption of daily activities.
• Nomophobia, meaning anxiety or distress associated with being unable to access a mobile phone or Internet connection.

The authors emphasize that broad digital addiction, smartphone addiction, and social-media addiction are not recognized as general disorders in the DSM-5-TR or ICD-11. Gaming disorder is a separate exception. Therefore, the findings should be interpreted as evidence of problematic, non-clinical digital behavior, not as a clinical diagnosis.

Why is it important?

Smartphones and social platforms are deeply integrated into university life. Students use them for communication, entertainment, information, social validation, and academic work. This makes it difficult to distinguish ordinary high-frequency use from patterns that may interfere with psychological or physical functioning.

The study notes earlier Spanish evidence suggesting that nearly half of university students had reported problematic smartphone use, while more than 30% spent over five hours per weekday on their phones. These figures provide the context for examining problematic smartphone use among Spanish students.

Potentially harmful digital patterns may coexist with:

• Attachment-related anxiety
• Depressive symptoms
• Loneliness and perceived isolation
• Low self-esteem
• Poor sleep quality
• Shorter sleep duration
• Reduced psychological well-being

Importantly, these relationships may be bidirectional. Emotional distress may encourage compensatory or compulsive digital use, while problematic use may intensify distress through social comparison, fear of missing out, disrupted routines, notification overload, and reduced offline interaction.

How was the study conducted?

The cross-sectional study included 416 Spanish university students between the ages of 16 and 54. Their average age was 21.9 years, and 71.9% of the sample was female.

Data were collected through an online questionnaire between November 2023 and May 2024. Participants were recruited from several Spanish universities and completed validated measures covering:

• Digital addiction symptoms
• Nomophobia
• Recreational digital engagement
• Attachment-related anxiety
• Depression
• Loneliness
• Self-esteem
• Sleep quality and duration
• Psychological well-being

The researchers standardized the variables and used the Hartigan-Wong K-means cluster analysis method with ten random initializations. This procedure identified naturally occurring groups of students with similar psychosocial characteristics.

According to the methods section, psychological well-being was not used to create the clusters. It was subsequently compared across them as an external indicator, helping the researchers assess whether the profiles differed on an outcome that had not determined group membership.

Three digital behavior and mental-health profiles

High digital distress profile

Cluster 1 included 159 students and represented the group with the greatest combined psychosocial risk.

Students in this profile reported:

• The highest digital addiction symptoms
• Elevated nomophobia
• Greater recreational digital engagement
• High attachment-related anxiety
• More depressive symptoms
• Greater loneliness
• Lower self-esteem
• Poorer sleep quality
• Relatively short sleep duration
• Lower psychological well-being than the most resilient profile

Mean scores included 22.32 for addiction symptoms, 12.77 for nomophobia, 15.56 for depression, and 20.09 for loneliness. Average self-reported sleep duration was 4.96 hours, compared with 8.14 hours in Cluster 3.

The main findings indicate that high-risk digital behavior rarely appeared in isolation. Instead, it formed part of a broader profile involving emotional vulnerability, relational insecurity, and impaired sleep.

The standardized-centroid chart on page 6 visually shows Cluster 1 scoring above the other profiles on addiction, attachment anxiety, depression, loneliness, and poor sleep quality, while scoring substantially below them on self-esteem.

Low digital-risk profile

Cluster 2 contained 128 students and had the lowest average scores for:

• Addiction symptoms
• Nomophobia
• Recreational digital engagement
• Attachment-related anxiety
• Depression
• Loneliness

This profile suggests that lower problematic engagement can coexist with relatively better emotional functioning. Nevertheless, it should not be described as uniformly healthy.

The table reports an average sleep duration of 4.45 hours for Cluster 2. This was slightly lower than Cluster 1, although the difference between those two groups was not statistically significant. Cluster 2 also did not achieve the high self-esteem, well-being, and sleep-duration scores observed in Cluster 3.

This distinction is important: low digital addiction risk does not automatically guarantee optimal sleep or psychological flourishing.

Resilient moderate-use profile

Cluster 3 included 129 students. Participants showed moderate digital engagement and intermediate addiction indicators but substantially better positive-health outcomes.

This profile was characterized by:

• The highest self-esteem
• The longest sleep duration
• The highest psychological well-being
• Lower depression than the high-risk profile
• Lower loneliness than the high-risk profile
• Better sleep quality than Cluster 1

Cluster 3 reported an average self-esteem score of 33.37 and approximately 8.14 hours of sleep. Its mean well-being score was 31.24, compared with 26.24 in Cluster 1.

This article explains an important nuance: moderate technology use was not necessarily associated with poor functioning. The students with the strongest self-esteem and well-being were not those with the lowest use across every digital measure.

The PCA diagram on page 7 reinforces this pattern. Addiction, nomophobia, attachment anxiety, depression, loneliness, and poor sleep quality point toward the high-distress profile, whereas self-esteem and sleep duration are positioned in the opposite direction.

What were the strongest differences?

Statistical comparisons found significant differences among the clusters across the principal behavioral, psychological, and sleep variables.

Compared with Cluster 2, Cluster 1 had average scores that were:

• 5.22 points higher for addiction symptoms
• 3.95 points higher for nomophobia
• 7.66 points higher for attachment-related anxiety
• 7.65 points higher for depression
• 6.27 points higher for loneliness

Cluster 1 also scored 8.18 points lower in self-esteem than Cluster 3 and slept an average of 3.18 fewer hours.

These findings support a cumulative-risk interpretation. The most problematic profile combined digital dependency, emotional distress, social disconnection, low self-worth, and sleep difficulties rather than presenting only one isolated problem.

How might digital behavior affect mental health?

The study was not designed to test specific causal mechanisms. However, the authors discuss several processes supported by previous research.

Social comparison and online validation

Social platforms provide rapid feedback through likes, comments, messages, and other forms of approval. Positive feedback may temporarily strengthen self-esteem, but repeated comparison with idealized portrayals of other people can undermine self-worth.

Students with attachment-related anxiety may be especially sensitive to rejection, delayed responses, or signs of exclusion. Digital environments can then become a source of reassurance while simultaneously increasing dependence on external validation.

Fear of missing out

Fear of missing out, or FoMO, can encourage repeated checking and persistent connectivity. Students may worry that they are missing social events, conversations, opportunities, or important information.

This anxiety can create a reinforcement loop: checking the phone briefly reduces uncertainty, which makes future checking more likely.

Loneliness and reduced offline connection

Online communication can provide meaningful support, but it does not always produce strong offline relationships. When online interaction replaces rather than complements face-to-face contact, students may experience greater perceived isolation.

Sleep disruption

Nighttime device use may delay sleep through cognitive stimulation, notifications, bedtime procrastination, and exposure to illuminated screens. Poor sleep may then worsen attention, mood regulation, stress tolerance, and academic functioning.

The relationship may become cyclical: distress encourages more digital engagement, digital engagement interferes with sleep, and sleep loss increases emotional vulnerability.

How is it applied?

The researchers recommend profile-based interventions rather than applying the same strategy to every student.

Support for the high-distress profile

Students resembling Cluster 1 may require intensive, multidimensional support. Possible approaches include:

• Cognitive behavioral therapy to address FoMO, reassurance seeking, and maladaptive beliefs
• Self-regulation and impulse-control training
• Mindfulness-based techniques for compulsive urges
• Sleep-hygiene education
• Limits on nighttime notifications and device access
• Treatment for anxiety or depressive symptoms when clinically indicated
• Acceptance and commitment approaches to strengthen psychological flexibility

Prevention for low-risk students

Students resembling Cluster 2 may benefit from preventive education designed to preserve balanced habits. Programs could focus on digital literacy, intentional screen use, sleep routines, notification management, and early recognition of problematic behavior.

Reinforcement for resilient moderate users

Students resembling Cluster 3 may benefit from strategies that maintain existing protective factors, including strong self-esteem, adaptive coping, sufficient sleep, and meaningful offline relationships.

Universities could combine these approaches with counseling, peer-support programs, screening services, sleep-health campaigns, and educational initiatives on healthy digital habits.

These are proposed applications of the results. The study did not experimentally test whether the interventions are effective.

Limitations of the research

The findings must be interpreted within several limitations.

First, the cross-sectional design cannot determine whether problematic digital use precedes mental-health difficulties or develops in response to them.

Second, all variables were based on self-report. Students may have inaccurately estimated screen time, sleep duration, or psychological symptoms.

Third, the sample was predominantly female and restricted to Spanish university students. The identified profiles may differ in other countries, age groups, occupational settings, or more gender-balanced samples.

Fourth, recreational digital engagement was measured globally. The study did not separately examine messaging, social-media browsing, gaming, streaming, news consumption, or other activities.

Finally, the operational definition of digital addiction was limited to addiction symptoms and nomophobia measured through selected AR-SNIS subscales. The findings should not be generalized to every form of technology use.

What are the key conclusions?

According to the research of Velasco-Furlong and colleagues, digital addiction and mental health in university students are best understood through interconnected psychosocial profiles.

The main findings indicate that the profile with the greatest digital dependency also displayed the highest emotional distress and the broadest sleep-related difficulties.

This article explains why screen time alone is an incomplete measure of risk. A more useful assessment should consider control over use, connectivity anxiety, motives for going online, self-esteem, loneliness, emotional symptoms, and sleep health.

Balanced digital engagement may coexist with strong well-being. The central issue is therefore not simply whether students use technology, but how, why, and under what psychosocial conditions they use it.

Frequently asked questions

Is digital addiction an official mental-health diagnosis?

No. In this study, it is a non-clinical umbrella term for problematic or addiction-like digital behavior. The researchers measured addiction symptoms and nomophobia rather than diagnosing a psychiatric disorder.

Does smartphone use cause depression?

The study found an association between problematic digital behavior and depressive symptoms, but its cross-sectional design cannot establish causation. The relationship may also be bidirectional.

What is nomophobia?

Nomophobia is anxiety or distress associated with being unable to use a mobile phone or access digital connectivity.

Which students showed the greatest risk?

The highest-risk profile combined elevated addiction symptoms and nomophobia with attachment anxiety, depression, loneliness, low self-esteem, poor sleep quality, and lower well-being.

Is all frequent digital use harmful?

No. Cluster 3 showed that moderate digital engagement can coexist with high self-esteem, longer sleep duration, and better psychological well-being. Frequency should be evaluated alongside control, motivation, distress, and functional consequences.

What can universities do?

Universities can provide tiered support ranging from preventive digital-literacy education to counseling, cognitive behavioral interventions, sleep-health programs, and treatment for students experiencing substantial psychological distress.