1 Introduction

There is a broad consensus that the internet has come into many aspects of daily life by taking on an informing, educating, and entertaining role for all age groups, and especially for adolescents (Tsitsika et al., 2014). As access can be provided through various devices regardless of time, place, and conditions, the internet has become an important part of life for adolescents. Although the internet has become inevitable in daily life, the term, “problematic internet use (PIU)” is used to refer to patterns of problematic behaviour associated with internet use (Restrepo et al., 2020). When these increasing rates of PIU are considered, there have been increasing concerns related to the potential negative effects of PIU on mental health and it has been reported that there could be negative effects on the sleep quality of young people (Demirtaş et al., 2021; Wang et al., 2011; Yang et al., 2018). There are studies in literature showing that PIU could be associated with psychological issues such as anxiety, sleep disorders, depression, and attention deficit hyperactivity disorder (ADHD) (Yang et al., 2018; Restrepo et al., 2020, Çelikkol Sadiç et al., 2023) .

Approximately 5.3% of children and adolescents worldwide are thought to suffer with ADHD, a prevalent neurodevelopmental disease (Polanczyk et al., 2007). There may be accompanying additional mental health disorders in a considerable proportion of children and adolescents diagnosed with ADHD. Sleep problems, including sleep latency and duration, have been identified in 25–50% of children and adolescents with ADHD (Hvolby, 2015). Furthermore, studies have shown that PIU is elevated in children and adolescents with ADHD compared to control participants, that PIU could impact the sleep cycle, and that PIU can result in issues including poor sleep quality and insomnia (Lam, 2014; Werling et al., 2022).

As stated above, PIU, sleep and ADHD interact with each other. In a 2014 study, it was suggested that PIU could lead to atrophy of the prefrontal cortex and cognitive dysfunction (Brand et al., 2014). Individuals with ADHD may experience difficulties in cognitive skills, which can negatively impact academic achievement (Arnold et al., 2020). Additionally, some studies indicate a significant association between ADHD and PIU, as well as sleep disturbances (Hvolby, 2015; Lam, 2014; Werling et al., 2022).To the best of our knowledge, however, no research has been published in the literature that compares the PIU and sleep quality of adolescents diagnosed with ADHD with a control group based on the assessment of a structured interview conducted with a child psychiatrist. The purpose of this research was to compare sleep quality and PIU in a group of adolescents with ADHD-combined type or ADHD-inattentive type and a healthy control group, and to examine the relationships of these with ADHD severity.

The hypotheses of the study were that (i) the ADHD groups would have greater rates of poor sleep quality and PIU than the control group, (ii) in comparison to ADHD patients without PIU, individuals with PIU would have increased rates of poor sleep quality and ADHD severity, and (iii) there would be a significant correlation between ADHD symptom severity and poor sleep quality and PIU.

2 Methods

This single-centre, cross-sectional study included patients who presented at the Child and Adolescent Psychiatry outpatient clinic of Afyonkarahisar Health Sciences University Faculty of Medicine Hospital between August 2022 and March 2023. A total of 53 adolescents aged 12–18 years and who met the exclusion and inclusion criteria were included in the study. Based on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria, all of these patients were diagnosed with ADHD after receiving the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL) from Ç.Ç.S., a child psychiatrist. The adolescents in this ADHD group had clinically normal intellect, an ADHD diagnosis, and were not on psychiatric medication. The presence of concomitant psychopathologies, the use of any clinical mental retardation, psychotropic drugs, or the existence of any long-term medical or neurological condition that needs to be treated (such as diabetes, epilepsy, etc.) were the exclusion criteria for the study.

The control group was formed of 53 age- and gender-matched adolescents who presented at the outpatient clinic with various complaints (routine checkup or counseling for issues such as puberty, school, friendship problems). They were evaluated with K-SADS-PL and had no past or present psychiatric disorders or chronic medical disorders (such as diabetes mellitus, hypertension, rheumatic and immunological diseases, epilepsy, and genetic disorders).

Following the diagnostic assessment of those who satisfied the inclusion requirements, the research was explained to all of the adolescents and their family members. Tests and scales were given once the adolescents' and their parents' written and verbal informed agreement was received. The Pittsburgh Sleep Quality Index (PSQI), Internet Addiction Test (IAT), and Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version were used to evaluate each study participant. Turgay DSM-IV Based Disruptive Behavior Disorders Child and Adolescent Rating and Screening Scale (T-DSM-IV-S) was filled out by parents.

64 adolescents with ADHD were included in the trial out of the 93 adolescents who presented between August 2022 and March 2023. 9 adolescents who were using psychotropic medications, 3 adolescents who declined to participate in the study, and 17 adolescents who did not fit the criteria for ADHD diagnosis were all removed from the research. Due to comorbidities, 11 of the adolescents with ADHD were eliminated following the examinations. 53 adolescents were consequently added to the ADHD research group.

3 Ethics approval

Every study process was carried out in compliance with the Helsinki Declaration. The Ethics Committee of the Afyonkarahisar Health Sciences University Faculty of Medicine gave its approval to the project (date: May 13, 2022 and ethics committee number: 2022/302).

4 Measures

Sociodemographic form

The researchers created this form, which included questions on the gender, age, educational background, family structure, and history of mental health diseases of the parents and adolescents.

Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL)

Kauffman et al. developed this semi-structured interview method to investigate psychopathology in children and adolescents (Kaufman et al., 1997). The results showed that intra-observer agreement ranged from 93 to 100% for both screening scores and diagnosis. For a variety of diagnoses, the test–retest reliability kappa coefficient ranges from 0.63 to 1.00. Gökler et al. have conducted validity and reliability evaluations in Turkish for this interview form. (Gökler et al., 2004).

The Turgay DSM-IV based disruptive behavior disorders child and adolescent rating and screening scale (T-DSM-IV-S)

Measures of the adolescants’ impulsivity, hyperactivity, and attention were conducted with the parents using the T-DSM-IV-S. Based on DSM-IV diagnostic criteria, Turgay (Turgay, 1995) created this scale to screen for disruptive behavior disorders. Studies on the scale's validity and reliability have been conducted in Turkey (Ercan et al., 2001). The 4-point Likert scale (0 = rarely, 1 = sometimes, 2 = often, and 3 = very often) is used in three components of the scale to assess attention deficit, hyperactivity/impulsivity, and comorbid characteristics linked to ADHD. Six out of the nine items in the inattention category receive at least two points, while the hyperactivity/impulsivity category receives at least two points for six out of the nine items. Elevated scores suggest increased severity of psychopathology (Ercan et al., 2001).

Pittsburgh sleep quality index (PSQI)

The PSQI is a measure used to assess the quality of sleep (Buyssen et al. 1989; American Psychiatric Association, 1994). Subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction are the 7 components of the 24-item test that assesses the quality of sleep during the past month (Buysse et al., 1989). A maximum score of 21 points is possible, with each component receiving a score ranging from 0 to 3. Indicative of poor sleep quality is a total score greater than five. The Cronbach's alpha coefficient of the PSQI scale is 0.80, according to validity and reliability tests carried out in Turkey (Agargun, 1996). The Cronbach's alpha value for the PSQI in the present study is 0.81.

Internet addiction test (IAT)

To assess the degree of internet addiction, Young et al. devised the 20-item IAT. Normal internet use is indicated by an IAT total score of 20 to 49 points, while potentially problematic internet use is indicated by a cut-off score of greater than 50 points (Young, 1998). IAT is a six-point likert-type scale in which participants are asked to indicate how often they encounter particular events. A four-factor model was created for the scale: mood, relationship, responsibilities and duration. The validity and reliability of the Turkish version of the test was evaluated (Bayraktar, 2001; Kaya et al., 2016). The study conducted by Bayraktar (2001) yielded an internal consistency coefficient of 0.91 for Cronbach Alpha (Bayraktar, 2001). The Cronbach's alpha value for the IAT in the present study is 0.94.

5 Statistical analysis

The SPSS 26.0 (Statistical Package for the Social Sciences) program was used to statistically analyze the study's data. Using both analytical (Shapiro–Wilk test, skewness and kurtosis values) and visual (histograms and probability plots) techniques, the conformance of the variables to the normal distribution was examined. The values that met these parameters were deemed to conform to a normal distribution. As the data did not show normal distribution between the ADHD-IDT, ADHD-CT and control groups, these parameters were compared between the groups using the Kruskal–Wallis test. The Mann–Whitney U test was used to compare pairwise, and the Bonferroni correction was applied to assess the results. The control and case groups' sociodemographic factors were assessed using percentages and numbers. The Spearman correlation test was applied to non-parametric data to determine correlations between variables.

The ADHD patients were separated into two groups: those with and those without PIU. The Student's t-test or the Mann–Whitney U test, depending on the data distribution, were then used to compare the groups. In order to prevent Type-2 errors that can result from comparing multiple variables, multivariate analyses were designed. Taking gender and age as covariates, the variables in the ADHD groups with and without PIU were compared using MANCOVA. One-way analysis of covariance (ANCOVA) was conducted on the outcome variables separately when the MANCOVA test indicated a significant difference between the PIU and non-PIU groups. With a 95% confidence interval, p < 0.05 was considered the level of statistical significance.

6 Results

Evaluation was made of a total of 106 cases, comprising 19 ADHD-combined type (ADHD-C), 34 ADHD-inattentive type (ADHD-I), and 53 healthy control subjects. No statistically significant difference was found between the groups in terms of mean age (p = 0.359), gender distribution (p = 0.192), maternal education level (p = 0.254), and paternal education level (p = 0.15) (Tables 1 and 2).

Table 1 Evaluation of the ADHD-CT, ADHD-IDT, and Control Groups according to Gender and the Maternal and Paternal Level of Education
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Table 2 Evaluation of the Groups according to Age, IAT, and PSQI Points
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6.1 Evaluation of the IAT points of the groups

In the evaluation of the groups according to the IAT, the total points, and subscale points of mood, duration, responsibilities, and relationships were determined to be statistically significantly higher in the ADHD groups than in the control group (p < 0.001). The IAT total points, and subscale points of mood and relationships were determined to be significantly higher in the ADHD-C group than in the ADHD-I group. No significant difference was determined between the ADHD-C and ADHD-I groups in respect of the IAT duration and responsibilities points (Table 2).

6.2 Evaluation of the PSQI points of the groups

In the evaluations of the groups according to the PSQI, the PSQI Subjective Sleep Quality, Sleep Latency, Sleep Duration, Habitual Sleep Efficiency, Daytime Dysfunction, and PSQI total points were determined to be statistically significantly higher in the ADHD groups than in the control group (p < 0.001). No significant difference was determined between the ADHD-C and ADHD-I groups in respect of the PSQI Subjective Sleep Quality, Sleep Latency, Sleep Duration, Habitual Sleep Efficiency, Daytime Dysfunction, and PSQI total points. The PSQI Sleep Disturbances points were seen to be statistically significantly higher in the ADHD-I group than in the control group (Table 2).

6.3 Comparisons of the T-DSM-IV-S and PSQI points of the ADHD cases with and without PIU

The T-DSM-IV-S inattention points were found to be significantly higher in the group with PIU (19.21 ± 0.83) compared to the group without PIU (16.60 ± 0.41) (t = -3.025, p = 0.004). No significant difference was determined between the ADHD patients with and without PIU in respect of the T-DSM-IV-S- hyperactivity-impulsivity points and the PSQI total points (with PIU; 12.21 ± 1.60 and 8.21 ± 0.70, respectively; without PIU: 9.23 ± 1.16 and 7.86 ± 0.60, respectively) (t = -1.538, p = 0.13 and t = -0.379, p = 0.706). The distribution of the inattention, hyperactivity-impulsivity T-DSM-IV-S points and the PSQI total points of the cases diagnosed with ADHD with and without PIU is shown in Fig. 1.

Fig. 1
figure 1

Distribution of the inattention, hyperactivity-impulsivity T-DSM-IV-S points and the PSQI total points of the cases diagnosed with ADHD with and without PIU

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6.4 Comparisons of the T-DSM-IV-S and PSQI points using MANCOVA

A MANCOVA test was performed to control confounding variables, such as age and gender, and prevent type II errors brought on by the multiple testing effect. The MANCOVA test findings for the entire sample indicated a significant difference between the groups (Pillai’s trace V = 0.161, F (3.47) = 2.998, p = 0.04, np2 = 0.161). The same variables were used as covariants in the ANCOVA analysis to identify which variables caused the differences between the groups, and the outcomes remained the same. The T-DSM-IV-S- inattention points (F (1.49) = 9.41, p = 0.004, np2 = 0.156) were significantly higher in the PIU group than in the non-PIU group. There was no significant difference between the groups in terms of T-DSM-IV-S- hyperactivity-impulsivity points and the PSQI total points. The comparisons of the T-DSM-IV-S and PSQI points of the ADHD cases with and without PIU are shown in Table 3.

Table 3 Comparisons of the T-DSM-IV-S and PSQI points of the ADHD cases with and without PIU
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6.5 Correlations between the T-DSM-IV-S, PSQI and IAT points in the ADHD groups

A statistically significant positive correlation was determined in the ADHD group between the IAT total points and the PSQI Daytime Dysfunction points (p = 0.041), between the IAT mood points and the T-DSM-IV-S inattention (p = 0.012) and hyperactivity-impulsivity points (p = 0.004), between IAT duration and PSQI Daytime Dysfunction points (p = 0.006), and between IAT responsibilities and PSQI Sleep Disturbances points (p = 0.015). No significant correlations were determined between the other variables (Table 4).

Table 4 Correlations between the T-DSM-IV-S, IAT, and PSQI points in the ADHD group
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7 Discussion

As far as we are aware, this is the first study in literature to have examined the relationship between poor sleep quality and PIU in adolescents diagnosed with ADHD. The results of this study demonstrated significantly higher rates of poor sleep quality and PIU in the adolescents diagnosed with ADHD comparred to the control group, and this significant increase was also valid for both the ADHD-I and ADHD-C subgroups. These results support the findings of previous studies that have established an association between PIU and ADHD, and between sleep problems and ADHD (Lam, 2014; Werling et al., 2022).

The current study results showed a significantly higher rate of PIU in both the ADHD-I and the ADHD-C groups compared to the control group. In addition, the T-DSM-IV-S inattention points were found to be significantly higher in the children diagnosed with ADHD with PIU (independently of age and gender) than in the ADHD group without PIU. In another study which measured PIU with IAT points, there was reported to be a significant relationship between the points and PIU in patients with ADHD-I (Chan & Rabinowitz, 2006). A recent systematic examination also reported a moderate level correlation between PIU and ADHD symptoms (Wang et al., 2017). In another study of male adolescents aged 13–15 years, there was seen to be more PIU in the children diagnosed with ADHD compared to those with no ADHD diagnosis (Weinstein et al., 2015). Consistent with the current study findings, Yoo et al. found signifcant relationships between the PIU severity and the level of ADHD symptoms in an examination of 535 primary school students. It was also suggested that the presence of ADHD symptoms could be a significant risk factor for PIU (Yoo et al., 2004).

The IAT mood subscale points and the T-DSM-IV-S subscales were demonstrated to have a significant positive relationship in the current study. According to these results, as the T-DSM-IV-S subscale points increased in adolescents diagnosed with ADHD, so the IAT mood subscale points increased, meaning that negative moods and behaviours of the adolescent increased with PIU (eg., feeling angry and irritable when there is no internet, finding life boring without the internet, etc.). As far as we are aware, there is no previous study in the literature that has compared IAT subscale points and PSQI subscale points in a control group and in adolescents with a ADHD-I or ADHD-C diagnosis assessed by a child psychiatrist using the K-SADS.

In another study of adolescents, ADHD was found to be one of the most frequently seen psychiatric disorders in adolescents with PIU(Bozkurt et al., 2013). There are many studies in the literature about the underlying mechanism of this significant relationship between ADHD and PIU. As children and adolescents diagnosed with ADHD are more sensitive to instant gratification it is thought that they may be predisposed to PIU (Barth & Renner, 2015). When examined from a neurobiological perspective, dopamine-mediated reward mechanisms have been reported to be impaired in ADHD as in internet gaming disorders (Rubia, 2018; Weinstein & Lejoyeux, 2020). Excessive computer game playing is thought to persist because of the stimulating effects on reward and sensitivity, similar to the long-term changes in the reward system of the brain that are thought to sustain substance abuse. It has been suggested that computer and video games stimulate children and adolescents with ADHD who suffer from low dopamine in the brain reward sytem and reward deficiency syndrome. The lack of reward can make an individual prone to addictive, impulsive, and compulsive behaviours and it has been reported that the individual can then resort to computer and video games to increase dopamine expression (Blum et al., 2008; Weinstein & Lejoyeux, 2010; Weinstein & Weizman, 2012). These findings suggest that because of the probable low dopamine amount in the brain reward system in children diagnosed with ADHD, PIU can develop as a means to develop dopamine expression. In the light of this information, it can be thought that the high level of PIU observed in ADHD patients, as in the current and other studies, could reflect dopamine deficiency.

Previous studies in literature have reported that young people with ADHD experience more sleep disorders than their normally developing peers (Cohen-Zion & Ancoli-Israel, 2004; Lunsford-Avery et al., 2016). In the current study, poor sleep quality was found at a significantly higher rate in the ADHD-I and ADHD-C groups compared to the control group. A previous study showed that children diagnosed with ADHD had higher rates of PIU, spent more time on the internet, and went to sleep later than children not diagnosed with ADHD (Weinstein et al., 2015). The current study results showed no significant relationship between the T-DSM-IV-S subscale points and the PSQI points in the adolescents diagnosed with ADHD. However, there are studies in literature that have suggested that insufficient sleep can exacerbate ADHD symptoms which are associated with behavioural and functional disorders (Hvolby, 2015; Papadopoulos et al., 2019). The association of sleep with ADHD is known to be multifactorial and complex. It has been reported that just as sleep problems are a specific characteristic of ADHD itself, sleep problems can also lead to the development of ADHD-like symptoms (Cortese et al., 2006; Owens, 2008). Not only is there a greater probability of seeing sleep disorders in ADHD children and adolescents, but neurocognitive performance can also significantly affect sleep disorders in adolescence (Carskadon, 2011; Herman, 2015). When it is considered that sleep could be important in respect of brain development, the emergence of symptoms, and prognosis, the above-stated information implies that it could be important to develop effective strategies to improve the sleep problems of children with ADHD.

Although no significant relationship was found between the IAT total points and PSQI total points in the ADHD groups in the current study, a significant positive correlation was determined between some IAT subscales and PSQI subscales. This suggests that PIU and poor sleep quality could be related to each other and interact. In a previous study that investigated the relationship between sleep problems and night-time social media use in adolescents diagnosed with ADHD, a relationship was found between night-time social media use and daytime sleepiness, shorter sleep duration, and increased general sleep problems (Becker & Lienesch, 2018). The theoretical reasons that social media use disrupts sleep include that social media use directly replaces sleep time, social media use causes mental, emotional, and/or physical stimulation, and the bright light from the device on which social media is used delays the circadian rhythym (Cain & Gradisar, 2010). In addition, recent experimental evidence has shown that adolescents who stopped using their smartphones in the early hours of the evening slept for longer (Bartel et al., 2019). According to earlier research, excessive screen time combined with PIU might suppress melatonin, reduce sleep duration, and result in poor sleep quality (Duffy & Czeisler, 2009). The fact that the current study found a significant relationship and potential interaction between poor sleep quality and PIU in the group of adolescents with ADHD but without other psychiatric diagnoses suggests that more research is necessary to fully understand this relationship's interaction.

Sleep problems can negatively affect processes that may be important for academic success such as cognitive function, concentration and memory. Some studies have indicated that poor sleep quality is associated with decreased academic performance (Maheshwari & Shaukat, 2019; Rathakrishnanet al., 2021). It is stated that PIU negatively affects both sleep quality and academic performance of individuals (Güçlü et al., 2024; Lam, 2014). In addition to this, it is also known that children and adolescents with ADHD experience difficulties in academic achievement such as low grades and grade repetition (Loe & Feldman, 2007). Therefore, providing children and young people diagnosed with ADHD, education departments, and their parents with practical and effective interventions and education can help children and young people regulate their internet use and sleep problems and help them cope with negative situations.

This study has some limitations, primarily the cross-sectional design. The sample studied is small and does not allow generalizing the statements given in the conclusions. Consequently, further studies with larger numbers of samples is required. In future studies, more intervention studies are expected with the aim of reducing PIU and poor sleep quality among adolescents diagnosed with ADHD. Another limitation was the use of self-reported scales for the evaluation of PIU and sleep quality. Additionally, objective intelligence tests may have been used to assess the adolescents' mental capacity. The study's strong points were that a single child psychiatrist examined both the patient and control groups, both groups received administration of the K-SADS-PL, and included only adolescents diagnosed with ADHD who did not have any psychiatric comorbidities and were not taking psychiatric medications in the patient group.

8 Conclusion

The study findings demonstrated that in comparison to the healthy control group, adolescents with ADHD diagnoses, in both the ADHD-I and ADHD-C subgroups, had higher rates of poor sleep quality and PIU. The study also presented additional evidence that the severity of the inattentive subtype of ADHD could play a role in the development of PIU in children diagnosed with ADHD. A significant relationship was shown between ADHD severity and IAT mood subscale points and between some IAT and PSQI subscales in the ADHD group. The relationship between ADHD, PIU, and sleep quality can be taken into consideration by educational authorities in the organization and structuring of educational policies. Additionally, education departments, parents, and adolescents diagnosed with ADHD should be made aware of PIU and poor sleep quality, with necessary preventive measures taken to address these issues. It is important to support and provide necessary guidance regarding the problem of adolescents who are at risk for PIU and poor sleep quality.These results demonstrate that in adolescents with ADHD diagnoses, there is a complicated, multivariate interaction between PIU and poor sleep quality.