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Exploring the Relationship of Smartphone Addiction on Attention Deficit, Hyperactivity Symptoms, and Sleep Quality Among University Students: A Cross-Sectional Study

Psychology

Exploring the Relationship of Smartphone Addiction on Attention Deficit, Hyperactivity Symptoms, and Sleep Quality Among University Students: A Cross-Sectional Study

I. Zeyrek, M. F. Tabara, et al.

Rising smartphone use among 443 Bingöl University students was linked to notable rates of smartphone addiction, poorer sleep quality, and self-perceived attention‑deficit symptoms; social media was the main use and age, attention deficit scores, and sleep quality predicted addiction. This research was conducted by Ibrahim Zeyrek, Muhammed Fatih Tabara, and Mahmut Çakan.

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~3 min • Beginner • English
Introduction
The study investigates whether excessive smartphone use is associated with poorer sleep quality and higher attention-deficit/hyperactivity disorder (ADHD) symptoms among university students. With smartphone ownership and use nearly ubiquitous in young adults, concerns have grown about potential consequences, including addictive use patterns, mental health symptoms, and sleep disturbances. Prior reports show high daily screen exposure among students and suggest links between problematic smartphone use, stress, anxiety, depression, and ADHD-related behaviors. Given the importance of adequate sleep and attention for academic success and well-being, this study aims to quantify associations between smartphone addiction, self-perceived ADHD symptoms, and sleep quality, hypothesizing that greater smartphone addiction is related to worse sleep quality and higher ADHD symptoms.
Literature Review
Prior research highlights near-universal smartphone ownership among students and increased screen exposure among youth. Smartphone addiction, sharing features with internet addiction, has unique elements such as mobility and real-time access. Although no formal diagnostic criteria exist, problematic overuse is linked to functional impairment. ADHD affects ~5–7% of children and 2–3% of adults and has been associated with unhealthy behaviors and higher risk of internet-related problems, including among university students. Reviews indicate associations between smartphone use and mental health outcomes (stress, anxiety, depression), while high screen time (>4 h) correlates with depressive symptoms. Evidence on screen time and ADHD-related behaviors is mixed, with meta-analyses showing inconsistent results and a limited evidence base. Reported smartphone addiction prevalence varies widely (e.g., 7.5% overall in some pooled analyses; 39–51% in other cohorts), potentially reflecting methodological and regional differences. Excessive screen time is associated with poor sleep quality and shorter sleep duration; mechanisms include time displacement and blue light–induced melatonin suppression. High rates of poor sleep quality among university students have been reported globally, with increases potentially exacerbated post–COVID-19 due to rising smartphone and internet use.
Methodology
Design: Cross-sectional survey. Setting and population: Students at Bingöl University during the 2022–2023 academic year. Sampling: Stratified random sampling; all consenting students were invited. Inclusion/exclusion: No specific criteria beyond consent; complete and error-free responses were analyzed. Final sample: 443 participants. Procedure: Online data collection via a link; participation was voluntary. Measures: (1) Sociodemographic form (13 items) including smoking, alcohol use, exercise, screen preferences, and self-reported usage patterns; smartphone screen time was retrieved via device digital health/balance apps. (2) Smartphone Addiction Scale–Short Version (SAS-SV): 10 items, 6-point Likert (1–6), total 10–60; higher scores indicate greater addiction risk; validated Turkish version; no inherent diagnostic cutoff. (3) Adult ADHD Self-Report Scale (ASRS-v1.1) 6-item screener: 4 inattention and 2 hyperactivity items; scored 0–4 per item (never to very often), summed to a global score (Kessler method). (4) Pittsburgh Sleep Quality Index (PSQI): 19 self-rated items forming seven components; global score 0–21; higher scores denote poorer sleep; PSQI > 5 defined as poor sleep; Turkish adaptation used. Statistical analysis: SPSS v22.0. Normality via Shapiro–Wilk. Between-group comparisons: Student’s t-test for continuous normal variables; chi-square for categorical. Correlations: Pearson’s r among age, average sleep time, SAS-SV, PSQI, and ASRS scores. Linear regression: SAS-SV score as dependent variable; predictors included gender, age, alcohol, smoking, ASRS, and PSQI. Alpha set at 0.05.
Key Findings
Sample characteristics: n=443; mean age 20.97 ± 3.29; 72.7% female (n=322). Primary screen: smartphone 94.8% (n=420). Main purpose: social media surfing 49.9% (n=221). Screen exposure time: 2–4 h 37.0%, 4–8 h 34.1%, >8 h 8.8%. Phone checks/day: >40 checks in 26.4%. Smartphone active on-screen time (via app): 4–6 h 25.1%; 6–8 h 10.2%; >8 h 6.3%. Sleep quality (PSQI): Poor sleep (PSQI ≥6) was prevalent (male 81.8%, female 85.7%). Sociodemographic factors and sleep quality: • Smoking associated with poorer sleep (poor sleep 93.5% vs 81.8% in nonsmokers; p=0.003). • Screen preference smartphone associated with poorer sleep (85.7% poor vs 65.2% for other screens; p=0.015). • Longer overall screen exposure associated with poorer sleep (p=0.003). • Intended use: those using screens primarily “to study” had better sleep (29.5% good vs lower good-sleep rates in other uses; overall p=0.026). Smartphone usage patterns and sleep quality: • Average hours of phone use/day associated with poorer sleep (p=0.003); highest poor-sleep rates at ≥5 h/day (≥90%). • Self-assessed smartphone addiction status associated with poorer sleep (p=0.003). • App-measured on-screen time associated with poorer sleep (p=0.032). • Number of phone checks and most-used app not significantly associated with sleep quality (p=0.192; p=0.853). Smartphone addiction prevalence (using literature cutoffs SAS-SV ≥31 men; ≥33 women): • Men: 50.4% (n=61). • Women: 47.2% (n=152). Sleep quality by addiction status: • Addicted participants had significantly worse sleep in both women (93.4% poor vs 78.8% in non-addicted; p<0.001) and men (93.4% poor vs 70.0%; p=0.002). ADHD symptoms by addiction status: • ASRS mean ± SD: addicted 17.40 ± 4.00 vs non-addicted 14.21 ± 3.43 (p<0.001). Correlations: • Age negatively correlated with SAS-SV (r = −0.152, p = 0.001) and ASRS (r = −0.96, p = 0.04). • PSQI positively correlated with SAS-SV (r = 0.286, p < 0.001) and ASRS (r = 0.361, p < 0.001). • SAS-SV positively correlated with ASRS (r = 0.528, p < 0.001). • ASRS negatively correlated with average sleep duration (r = −0.17, p < 0.001). Linear regression predicting SAS-SV (smartphone addiction) scores: • Age: B = −0.370, p = 0.008 (older age associated with lower SAS-SV). • ASRS-v1.1: B = 1.336, p < 0.001 (higher ADHD symptoms associated with higher SAS-SV). • PSQI: B = 0.332, p = 0.035 (poorer sleep associated with higher SAS-SV). • Gender, alcohol use, and smoking were not significant predictors.
Discussion
Findings support the hypothesis that greater smartphone addiction is associated with poorer sleep quality and higher self-perceived ADHD symptoms among university students. High addiction prevalence and poor sleep rates align with reports of increasing post–COVID-19 device use. The robust associations between SAS-SV and ASRS, and between PSQI and both SAS-SV and ASRS, suggest interrelated pathways: time displacement and arousal from device use, blue light–induced melatonin suppression affecting sleep, and ADHD-related impulsivity/reward sensitivity predisposing to problematic phone use. Age showed a small negative association with addiction scores, indicating younger students may be at higher risk. Regression results indicate that independent of demographic and substance-use factors, ADHD symptoms and poorer sleep quality contribute to higher levels of smartphone addiction. These results are consistent with prior literature linking problematic smartphone/internet use to psychological symptoms and sleep disruption. The high prevalence versus some earlier studies may reflect methodological and temporal factors, including increased usage during the pandemic.
Conclusion
This study demonstrates that smartphone addiction is common in university students and is linked to poorer sleep quality and higher ADHD symptom burden. Poor sleep quality was associated with using smartphones as the primary screen and with longer screen exposure. Strong positive associations were observed between smartphone addiction and both poorer sleep and higher ADHD symptoms, and ADHD symptoms were inversely related to sleep duration. The study contributes to understanding interrelations among smartphone overuse, attentional symptoms, and sleep problems in young adults, highlighting the need for awareness and interventions to promote healthier digital habits and sleep hygiene. Future research should employ standardized measures, include diverse populations beyond university settings, and use longitudinal designs to clarify causal pathways and assess interventions.
Limitations
• Cross-sectional design precludes causal inference. • Self-reported questionnaires (including device use behaviors apart from app-derived metrics) may introduce recall and social desirability bias. • University student sample limits generalizability to broader populations and other age groups. • Variability in methodologies and regional factors across studies may affect prevalence comparisons. • SAS-SV lacks an inherent diagnostic cutoff; reliance on literature cutoffs may affect estimated prevalence.
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