Smartphone Addiction Linked to Attention Problems, Hyperactivity, and Sleep Quality in University Students

The utilization of digital devices among young adults, particularly university students, is pervasive for both social and educational purposes. Consequently, smartphones, televisions, computers, and tablets have become indispensable components of their lives (Chen and Peng 2008). On a global scale, the number of individuals with smartphone data plans currently exceeds 6 billion, and projections estimate this figure to surpass 7.5 billion by 2026 (Statista 2021). The prevalence of mobile phone ownership among students is remarkably high, as evidenced by a study where 99.7% of respondents reported owning one or more mobile phones (Chaputula and Mutula 2018). Research indicates that the occurrence of mobile phone dependence resulting from Internet addiction (IA) is widespread among adolescents (Mak et al. 2014).

Smartphones have the same functions as computers, but they are much smaller and wireless. This makes them more convenient for young people, who have more access to smartphones than other devices (Brodersen, Hammami, and Katapally 2022). However, this convenience also comes with some risks. Many studies have looked at how screen time affects teenagers. For example, one study found that college students spend a lot of time on electronic devices, at least 3 h a day, on one or more devices (Montagni, Guichard, and Kurth 2016).

Smartphone addiction is a problem that arises from its widespread use and many functions (Kwon, Kim, et al., 2013). It has a lot in common with IA (H. Kim 2013). However, it also has some unique features, such as mobility, real‐time Internet access, and direct communication with smartphones (Kwon, Lee, et al., 2013). The longer and more often people use smartphones, the worse their addiction gets (Lee et al. 2014). There is no official way to diagnose smartphone addiction. However, it has been described as the overuse of smartphones that disrupts users' daily lives (Demirci, Akgönül, and Akpinar 2015).

Attention‐deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder typically diagnosed in childhood and can persist into adulthood. It is characterized by symptoms of hyperactivity, impulsivity, and inattentiveness, which can impact social, academic, and occupational performance and development, either individually or in combination (Batstra and Frances 2012). The prevalence of ADHD during childhood is estimated to be around 5%–7% (Thomas et al. 2015), while in adulthood, it ranges from 2% to 3% (Fayyad et al. 2017). Research indicates that young individuals with ADHD may display unhealthy behaviors such as excessive screen time, limited engagement in physical activities, and poor dietary habits (Must et al. 2015). Furthermore, there is a notable association between ADHD symptoms among university students and IA (Dalbudak and Evren 2014; D. Kim et al. 2013).

According to a review paper, there is evidence suggesting a connection between smartphone usage and negative effects on mental health (Thomée 2018). Moreover, a meta‐analytic review specifically emphasizes the relationship between smartphone use and stress and anxiety (D. Kim et al. 2013). Another systematic review confirms the correlations between problematic smartphone use and the severity of depression (Elhai et al. 2017). In the United States and other countries, watching TV and using computers are common sedentary activities. People who spend more than 4 h engaging in screen time, which includes TV watching and computer use, have an increased likelihood of developing depression (de Wit et al. 2011).

The impact of economic and technological progress on young students is evident in the increased amount of time they spend in front of screens. Numerous health problems, including cardiovascular disease, mental health issues, disrupted sleep quality, and decreased academic performance, have consistently been associated with excessive screen exposure among young individuals (Wu et al. 2015).

The complete impact of screen time on young adults remains uncertain. A meta‐analysis of 45 empirical studies exploring the correlation between screen time and ADHD‐related behaviors revealed conflicting results, highlighting a limited amount of research in this field (Nikkelen et al. 2014). A study conducted in India reported a prevalence of smartphone addiction ranging from 39% to 44% among teenagers and young adults, emphasizing the significance of this issue (Davey and Davey 2014). Another study observed that 51.0% of the 506 participants displayed symptoms of smartphone addiction (Alageel et al. 2021). Excessive use of electronic devices has the potential to pose various challenges for students in their daily lives.

In technologically advanced societies, poor sleep quality has emerged as a significant public health concern (Cheung and Wong 2011). The increasing use of technology, including smartphones, has contributed to the growing prevalence of sleep difficulties (Maurya et al. 2022). Spending 3 or more hours per day engaged with screens has been associated with a notably higher risk of experiencing frequent sleep problems in early adulthood (Hysing et al. 2015). To promote overall health and development, the US National Sleep Foundation recommends that adolescents (aged 14–17 years) aim for 8–10 h of sleep while young adults (aged 18–25 years) should target approximately 7–9 h per night (NS Foundation n.d.). Insufficient or disrupted sleep has become a prevalent issue among youth and adolescents worldwide (Gradisar, Gardner, and Dohnt 2011). Several studies have shown that students who excessively use mobile phones, especially at night or past midnight instead of resting, face disruptions in their sleep and wake cycles. This disruption can impact melatonin levels and increase the likelihood of mental health problems, including depression, stress, anxiety, and social dysfunction (Thomée, Härenstam, and Hagberg 2011; Yogesh, Abha, and Priyanka 2014).

The primary objective of this study is to explore the connection between smartphone addiction, ADHD, and sleep problems among university students. Furthermore, it aims to provide policy recommendations that can support teenagers in improving their mental well‐being and academic performance by promoting limited screen time and adopting healthy sleep habits. In essence, this study seeks to investigate the association between smartphone screen time, sleep problems, and ADHD among young adults, with the intention of shedding light on these relationships.

The increasing prevalence of smartphone addiction among university students has become a significant concern, prompting a deeper investigation into its broader impacts on health and well‐being. This study hypothesizes that excessive smartphone use negatively affects sleep quality and exacerbates symptoms of ADHD among university students. The motivation for this study stems from the observed rise in smartphone dependency, particularly within the academic context, where students rely heavily on their devices for both educational and social purposes. Given the critical role of adequate sleep and attentive functioning in academic success and overall health, understanding the extent to which smartphone addiction influences these factors is essential. By exploring these correlations, the study aims to contribute valuable insights into the potential risks associated with smartphone overuse.

The total number of participants was 443. The mean age of the participants was 20.97 ± 3.29 and 72.7% (n = 322) were female. The most exposed screen for the majority of participants was the smartphone (94.8%, n = 420) and many used it for social media surfing (49.9%, n = 221). The sociodemographic data of the participants are summarized in Table 1.

The participants were asked how many times a day they check their phones, how many hours they use on average, which application they use the most, and whether they consider themselves to be smartphone addicts. Smartphone active screen time learned (via app). Smartphone on‐screen time reported via the app was found to be 25.1% for 4–6 h, 10.2% for 6–8 h, and 6.3% for more than 8 h. The data are summarized in Table 2.

The effects of sociodemographic characteristics on sleep quality were examined. We found that smoking, using a smartphone as a screen preference, and long‐term screen exposure significantly impair sleep quality. The effects of all variables on sleep quality are presented in Table 3. When the effect of smartphone usage patterns on sleep quality was examined, it was found that the average number of checks per day and the most frequently used application did not significantly affect sleep quality. It was determined that the average daily smartphone usage time, addiction self‐assessment and active screen time obtained via application significantly affect sleep quality. The effect of smartphone usage patterns on sleep quality is presented in Table 4.

It was obtained from the literature that the cut‐off point of SAS‐SV was 31 for men and 33 for women. While the frequency of smartphone addiction was 50.4% (n = 61) in males, this rate was 47.2% (n = 152) in females. When the effect of smartphone addiction on sleep quality was examined, it was found that the sleep quality of addicted participants was statistically significantly worse in both men and women. The findings are presented in Table 5. While the ASRS‐v1.1 score of the smartphone‐addicted group was 17.40 ± 4.00, it was 14.21 ± 3.43 in the non‐addicted group (p < 0.001).

The correlation between age, average sleep time, and scale scores of the SAS‐SV, PSQI, and ASRS‐v1.1 was examined. A statistically significant negative correlation was found between age and SAS‐SV and ASRS‐v1.1 scores (respectively, r = −0.152, p = 0.001; r = −0.96, p = 0.04). A statistically significant positive correlation was found between PSQI scores and SAS‐SV and ASRS‐v1.1 scores (respectively, r = 0.286, p < 0.001; r = 0.361, p < 0.001). A statistically significant positive correlation was found between the SAS‐SV score and the ASRS‐v1.1 score (r = 0.528, p < 0.001). A statistically significant negative correlation was found between ASRS‐v1.1 scores and average sleep duration (r = −0.17, p < 0.001).

The effects of age, gender, ASRS‐v1.1 scores, PSQI scores, alcohol use, and smoking on SAS‐SV scores were analyzed in a linear regression model. A statistically significant negative effect of age on SAS‐SV was found. ASRS‐v1.1 and PSQI scores showed a significant positive effect on SAS‐SV. The findings are presented in Table 6.

Before starting the discussion, we would like to briefly summarize the study findings. In line with our study hypotheses, we found that smartphone addiction increased among young people. ADHD scores were greater and sleep quality was significantly worse among addicts, supporting our hypotheses that these variables would rise in tandem with increased addiction.

According to a study conducted by Kwon, Kim, et al. (2013), using cut‐off values of ≥ 31 for males and ≥ 33 for females to assess smartphone addiction, it was found that 16.9% of the 1519 participants exhibited smartphone addiction (Haug et al. 2015). However, other studies report even lower rates of smartphone addiction (S. M. Kim et al. 2014; S.‐G. Kim et al. 2019). The overall prevalence of smartphone addiction in a sample examined from these studies was 7.5%, with a higher rate observed in women than in men. In our study, the prevalence of smartphone addiction was determined as 50.4% (n = 61) in men and 47.2% (n = 152) in women. The fact that this rate was high in our study may depend on the time duration the study was conducted. The increase in smartphone use after COVID‐19 may have contributed to this rate. These findings are consistent with the results reported by Alageel et al. (2021). The higher prevalence of smartphone addiction among men may be attributed to their different patterns of smartphone usage, such as using smartphones more for work‐related activities, internet searches, and entertainment. On the other hand, women tend to spend more time on social network services or instant messaging, leading to increased smartphone usage (Bianchi and Phillips 2005; S.‐G. Kim et al. 2019). In addition, the addiction to smartphones among women has also been influenced by the rise of online shopping activities.

The inconsistency in the current literature regarding the prevalence of smartphone addiction may stem from variations in study methodologies, sample characteristics, and geographical factors. To enhance the validity and comparability of findings, future research should employ a standardized assessment tool and consider regional variations (S.‐G. Kim et al. 2019). Within the scope of this study, a linear regression model was utilized to investigate the impact of age, ADHD, and sleep quality on smartphone addiction. The analysis demonstrated statistically significant associations between these factors and smartphone addiction.

In this study, a significant majority of participants, comprising 79.9%, reported being exposed to screens for more than 2 h per day. Among them, 42.9% reported screen exposure exceeding 4 h. Furthermore, 17.2% of participants self‐identified as being addicted to their smartphones. Notably, 49.9% of participants reported using their smartphones primarily for browsing social media platforms. An overwhelming majority of 94.8% stated that they used smartphones as their primary screen. The portability and easy accessibility of smartphones contribute to their potential for addictive behaviors.

Individuals with ADHD are known to have a higher vulnerability to addiction compared to those without ADHD. Research has indicated that individuals with smartphone addiction are more likely to develop symptoms of ADHD (S.‐G. Kim et al. 2019). A study conducted among university students also found a positive correlation between increasing levels of screen time exposure and a higher risk of self‐perceived attention problems and hyperactivity levels (Montagni, Guichard, and Kurth 2016). Similarly, in the present study, it was observed that smartphone addiction is associated with self‐perceived attention deficit and hyperactivity. Even after controlling for other variables, the presence of ADHD showed a significant association with smartphone addiction. Individuals with ADHD often exhibit impulsivity, difficulties in behavior control, and heightened sensitivity to rewards. The sense of control, immediate response, and the opportunity for self‐expression associated with smartphone use can provide strong motivation and rewards for adolescents with ADHD. Due to the instant response and rewards offered by smartphones, as well as their portability, adolescents with ADHD may find smartphone use more appealing than those without the condition. Deficits in self‐control and inhibition can contribute to increased difficulties with excessive smartphone use and the exacerbation of ADHD symptoms (S.‐G. Kim et al. 2019). Furthermore, Billieux et al. suggested that impulsive traits such as impatience, low perseverance, and the duration of cell phone possession are robust predictors of addiction (Billieux, Van der Linden, and Rochat 2008). The widespread adoption of smartphones in recent years has led to an increased susceptibility to smartphone addiction, which can be viewed as the convergence of preexisting issues related to mobile phones and IA (Haripriya, Samuel, and Megha 2019). Research has demonstrated similarities between smartphone addiction and IA (Ben‐Yehuda, Greenberg, and Weinstein 2016). Children with ADHD often exhibit deficits in attention, strategic flexibility, planning, working memory, and behavior regulation. The lack of behavioral control in individuals with ADHD can contribute to difficulties in self‐regulating internet use (Sergeant 2000). In addition, psychological tests, experimental studies, and neuroimaging research have indicated a potential biological basis for the higher association between IA and ADHD, specifically involving reward deficiency and dopaminergic systems (Yoo et al. 2004).

The use of smartphones has been associated with a range of positive effects, such as access to updated information and improved academic performance. However, it also carries negative consequences, including substance abuse and addiction, which can adversely affect individuals' social and personal lives (Argiansya et al. 2021; Karki et al. 2021). One significant negative impact is the disruption of sleep, leading to various problems among adolescents and young adults (Maurya et al. 2022). Several studies have explored the relationship between mobile phone use and sleep quality. For instance, a survey conducted among college students in China found that 9.8% reported poor sleep quality associated with excessive mobile phone use (Tao et al. 2017). In this study, it was observed that both male and female participants who were addicted to smartphones experienced significantly poorer sleep quality. The problematic use of smartphones has been attributed to time displacement, where excessive smartphone usage delays bedtime as individuals engage with media content, leading to arousal and hindering sleep initiation. In addition, the biological impact of smartphones emitting blue light, which suppresses melatonin production, further contributes to difficulties in falling asleep and experiencing restful sleep (Argiansya et al. 2021; Hysing et al. 2015; Kuss and Lopez‐Fernandez 2016). Another study conducted in the United States found a correlation between screen time on mass media platforms, such as reading news online and using social media, and an increased likelihood of having a shorter sleep duration (Twenge, Krizan, and Hisler 2017). Similarly, adolescents who engaged in excessive screen time behaviors were more prone to insufficient sleep compared to those who did not engage in such behaviors (Baiden, Tadeo, and Peters 2019).

Previous studies conducted with university students worldwide have consistently reported a high prevalence of poor sleep quality, which is in line with our own findings. The prevalence rates of poor sleep quality were documented as 59.4% in Lithuania (Preišegolavičiūtė, Leskauskas, and Adomaitienė 2010), 52.7% in Lebanon (Assaad et al. 2014), 60% in the United States (Lund et al. 2010), 63% in India (Haripriya, Samuel, and Megha 2019), and 61.5% in Brazil (Rique et al. 2014). In our study, 81.8% of men and 85.7% of women exhibited poor sleep quality. A study conducted in Turkey in 2018 reported a lower prevalence rate of 52.4% for poor sleep quality. The recent surge in smartphone usage, especially during the COVID‐19 pandemic, may have contributed to the elevated rates of poor sleep quality observed in our study. Research has shown an increase in internet use and smartphone addiction during the pandemic (Hosen et al. 2021; Saadeh et al. 2021; Serra et al. 2021).

This study found a high prevalence of smartphone addiction among university students, which was associated with poor sleep quality and higher ADHD symptoms. However, the cross‐sectional design of the study makes it difficult to establish causality, and self‐reported data may be subject to subjective bias. The sample of university students limits the generalizability of the findings to the wider population. Although the findings are consistent with similar studies, methodological differences, and regional factors may cause variations in the results. Future research should examine these relationships more comprehensively using longitudinal designs in larger and more diverse populations.

Future studies should use standardized assessment tools to enhance comparability across studies. Longitudinal studies are needed to determine the causal relationships between smartphone addiction, ADHD, and sleep quality. Research should include more diverse populations beyond university students to improve generalizability.

The study revealed that impaired sleep quality was linked to factors such as using a smartphone as the primary screen and prolonged screen exposure. In addition, smartphone addiction, with around 50.4% of males and 47.2% of females classified as addicts, was strongly associated with poorer sleep quality in both genders.

The study found that poorer sleep quality was positively associated with both higher smartphone addiction and increased ADHD symptom scores. In addition, a strong positive correlation was identified between greater smartphone addiction and more severe ADHD symptoms. Furthermore, individuals with higher levels of ADHD symptoms tended to have shorter sleep durations.

In conclusion, our study contributes to the existing literature by examining the associations between smartphone overuse, ADHD symptoms, and sleep quality in university students. Furthermore, our findings shed light on the prevalent sleep quality issues experienced by university students, emphasizing the need for interventions and increased awareness of this important health concern.

Ibrahim Zeyrek: conceptualization, investigation, writing–original draft, methodology, writing–review and editing, project administration. Muhammed Fatih Tabara: investigation, writing–original draft, methodology, validation, formal analysis, writing–review and editing, supervision. Mahmut Çakan: data curation, supervision.

The present study was approved by the Bingol University Ethics Committee (23.12.2022‐33117789/044/89758), and the principles reported in the Declaration of Helsinki were strictly followed in all study procedures.

Written informed consent was obtained from all subjects that they wanted to voluntarily participate in the study.

The authors declare no conflicts of interest.