The relationship between smartphone addiction and sleep quality in patients with epilepsy in the digital age

Abstract

This study aimed to investigate the relationship between smartphone addiction, seizure frequency, and sleep quality in patients with epilepsy. A total of 78 consecutive patients who visited our epilepsy outpatient clinic between November 2022 and April 2023 and agreed to participate were enrolled, and their demographic and clinical characteristics were recorded. The Pittsburgh sleep quality index (PSQI) and the smartphone addiction scale – short version (SAS-SV) were administered. Seventy-eight patients with epilepsy (62.8% female, mean age 30.9 ± 8.3 years) were analyzed. Poor sleep quality (PSQI ≥ 5) was observed in 55.1% of participants. Smartphone addiction scores (SAS-SV) were significantly higher among patients with poor sleep quality (P < .001) and those with only primary school education (P = .028). A moderate positive correlation was found between SAS-SV and PSQI scores (R = 0.452, P < .001). In multivariable regression analyses adjusted for demographic and clinical factors, smartphone addiction remained an independent predictor of poor sleep quality (β = 0.083, 95% confidence interval: 0.042–0.123, P < .001). Moreover, ordinal logistic regression revealed that higher SAS-SV scores were independently associated with greater seizure frequency (odds ratio = 1.06, 95% confidence interval: 1.02–1.11, P = .005), while PSQI scores were not. Our findings demonstrate that smartphone addiction is independently associated with both poor sleep quality and greater seizure frequency in patients with epilepsy. These results highlight the importance of assessing digital media use in epilepsy management. Screening for problematic smartphone use and providing interventions such as digital hygiene education and sleep counseling may contribute to improved seizure control and overall quality of life.

1. Introduction

Epilepsy, affecting millions worldwide, is a chronic neurological disorder characterized by abnormal electrical discharges in the brain and associated with neurobiological, cognitive, psychological, and social complications.^[1] It is the most common neurological disorder in childhood and adolescence and the second most common in adulthood.^[2]

Epilepsy and sleep have a complex and bidirectional relationship. Epilepsy can alter the organization and microstructure of sleep,^[3,4] while sleep disorders, such as reduced rapid eye movement sleep and impaired overall sleep quality, are highly prevalent among epilepsy patients. A recent meta-analysis reported that nearly half of individuals with epilepsy experience sleep disturbances, with a pooled prevalence of around 47%.^[5] Patients with epilepsy often show reduced sleep efficiency, prolonged sleep latency, altered sleep-stage distribution, frequent nocturnal awakenings, and excessive daytime sleepiness, which negatively impact prognosis, treatment adherence, and seizure control.^[6–8] Quality sleep, evidently, plays a crucial role in seizure regulation.^[9] Recent evidence further suggests that variations in sleep quality may influence seizure risk. Nights with lower sleep efficiency or deviations in sleep duration have been associated with higher seizure probability in subsequent days.^[10,11] However, the specific thresholds of sleep quality required for seizure protection remain unclear.

Poor sleep quality has also emerged as a public health concern in technologically advanced societies. Numerous studies have revealed that addiction to smartphones, the internet, and social media – such as excessive nighttime screen use, late-night browsing, and constant notifications – adversely affects sleep quality.^[12–14] With rapid technological advancements, smartphone usage has increased significantly and continues to rise.^[15] Although no universally accepted definition of smartphone addiction exists, the literature often defines it as a behavioral addiction characterized by smartphone use that disrupts daily functioning.^[16] According to the Turkish Statistical Institute’s 2024 data, smartphone usage among Turkish adolescents was 76.1%.^[17] Although smartphones have many advantages, their excessive use may lead to addiction.^[18,19]

In particular, several studies have identified significant associations between problematic smartphone use, such as extended screen time, bedtime use, and nighttime use, and sleep disturbances in adolescents and university students.^[20,21] Excessive smartphone use also leads to poor sleep habits and increased daytime fatigue.^[22] However, despite the growing body of research linking smartphone addiction to poor sleep in adolescents and the general population, there is a clear paucity of studies investigating this relationship specifically in patients with epilepsy. To our knowledge, no prior research has directly examined how smartphone addiction affects sleep quality and seizure control in this population, highlighting the novelty and importance of our study.

If not thoroughly evaluated, the interaction between epilepsy and sleep may produce a vicious cycle of increasing seizure frequency and augmenting sleep disturbances. For effective disease management, both conditions must be carefully assessed. In light of the growing prevalence of smartphone use, smartphone addiction has become one of the contributing factors of poor sleep quality. Therefore, our study aimed to evaluate the effects of this addiction on seizure frequency and sleep quality in patients with epilepsy. Based on the existing literature, we hypothesized that: higher levels of smartphone addiction would be associated with poorer sleep quality; poorer sleep quality would be related to increased seizure frequency; and smartphone addiction would indirectly affect seizure control through its impact on sleep quality. Understanding this relationship is not only academically valuable but also clinically significant, as it may inform strategies to improve seizure control, treatment adherence, and overall quality of life in individuals with epilepsy.

2. Materials and methods

This study had 78 participants who were consecutive patients aged 18 years and older and diagnosed with epilepsy according to the International League Against Epilepsy criteria,^[23] followed-up for at least 6 months, and who visited the epilepsy outpatient clinic of the neurology department at our hospital between November 2022 and April 2023. The participants were informed about the study objectives, and their written informed consent for participation was collected. We included only those patients who used smartphones and consented to participate. We excluded those who were under the age of 18, did not use smartphones, had major depression, psychotic disorders, dementia, psychogenic non-epileptic seizures, were taking medications that could affect their sleep quality, worked night shifts, or had limited cooperation that prevented accurate questionnaire completion.

The participants’ sociodemographic and clinical data were collected via face-to-face interviews. Their sleep quality was assessed using the Pittsburgh sleep quality index (PSQI), and their smartphone addiction was measured using the smartphone addiction scale – short version (SAS-SV).

2.1. Assessment tools

2.1.1. Patient information form

We developed this form to collect the participants’ sociodemographic and clinical data, including age, sex, education level, seizure type and frequency, and current antiepileptic drug (AED) use. Based on seizure frequency, they were classified into 4 groups, as previously defined in the literature: once every few years, 1 to 11 times per year, 1 to 3 times per month, and 1 to 6 times per week.^[24]

2.1.2. Smartphone addiction scale – short version

Developed by Kwon et al to assess smartphone addiction in adolescents, the SAS-SV scale comprises 6-point Likert-type items and provides scores ranging from 10 to 60.^[18] The Turkish validity and reliability study of the scale was conducted by Noyan et al in 2015.^[25] For analysis purposes, the median SAS-SV score was calculated, and the participants were categorized into 2 groups: low smartphone use (below the median of 23.5) and high smartphone use (23.5 and above).

2.1.3. Pittsburgh sleep quality index

The PSQI is a reliable and valid instrument (α = 0.77) that evaluates sleep quality over the past month.^[26] It features 19 items covering multiple dimensions, such as subjective sleep quality, duration, disturbances, and use of sleep medications. Each component is scored between 0 and 3, with the total score ranging from 0 to 21. Higher scores indicate poorer sleep quality. A total score of ≥5 is considered clinically significant for poor sleep quality. The Turkish version was adapted by Agargün et al.^[27]

3. Statistical analysis

The data were analyzed using SPSS version 25.0. The normality of distribution was assessed using histogram plots and the Kolmogorov–Smirnov test. The descriptive statistics were presented as mean, standard deviation, median, and minimum–maximum values. For non-normally distributed variables, the Mann–Whitney U test was used to compare 2 groups, and the Kruskal–Wallis test was used for comparisons involving more than 2 groups. To examine the associations between continuous variables, Spearman correlation analysis was used. To further evaluate the relationship between smartphone addiction, sleep quality, and seizure frequency, an ordinal logistic regression analysis (proportional odds model) was conducted. Seizure frequency (categorical: rare → frequent) was defined as the dependent variable, while smartphone addiction scores (SAS-SV), sleep quality (PSQI), age, sex, educational level, and AED regimen (monotherapy vs polytherapy) were included as independent variables. The results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). A P-value of <.05 was considered statistically significant.

4. Results

In total, 78 patients with epilepsy, of which 62.8% (n = 49) were female and 37.2% (n = 29) male, were analyzed. They had a mean age of 30.87 ± 8.33 years, ranging from 18 to 49.

Regarding educational background, 6.4% completed primary school, 12.8% middle school, 32% high school, and 48.7% were university graduates. Regarding marital status, 64.1% (n = 50) were single, while 35.9% (n = 28) were married.

Seizure type was unknown in 33.3%, while 30.8% presented focal onset seizures and 35.9% generalized onset seizures. For 61.5% of the patients (n = 48), seizure frequency was once every few years, while 23.1% had 1 to 11 seizures per year (n = 18), 7.7% had 1 to 3 seizures per month (n = 6), and 7.7% had 1 to 6 seizures per week (n = 6).

Regarding AED use, 71.8% (n = 56) of the participants were on monotherapy, 26.9% (n = 21) on polytherapy, and 1 patient (1.3%) on no medication (Table 1).

Table 1.

Sociodemographic and clinical characteristics of the patients.

		n	%
Gender	Female	49	62.82
	Male	29	37.18
Educational level	Primary school	5	6.41
	Middle school	10	12.82
	High school	25	32.05
	University	38	48.72
Marital status	Single	50	64.10
	Married	28	35.90
Seizure type	Focal onset	24	30.77
	Generalized onset	28	35.90
	Unknown	26	33.33
Seizure frequency	Once every few years	48	61.54
	1–11 times per year	18	23.08
	1–3 times per month	6	7.69
	1–6 times per week	6	7.69
Antiepileptic drug use	None	1	1.28
	Monotherapy	56	71.79
	Polytherapy	21	26.92

The mean SAS-SV score was 25.42 ± 11.72, with a median of 23.5, and the mean PSQI score was 5.04 ± 2.22. Poor sleep quality (PSQI ≥ 5) was identified in 55.1% of the participants (n = 43) (Table 2).

Table 2.

Descriptive statistics for participants’ age, smartphone addiction, and sleep quality scores.

Variable	Mean ± SD	Median (Min–Max)
Age	30.87 ± 8.33	30 (18–49)
SAS (smartphone addiction scale)	25.42 ± 11.72	23.5 (10–58)
PSQI (Pittsburgh sleep quality index)	5.04 ± 2.22	5 (1–11)

Max = maximum, Min = minimum, PSQI = Pittsburgh sleep quality index, SAS = smartphone addiction scale, SD = standard deviation.

As hypothesized, smartphone addiction was significantly associated with sleep quality. Patients with poor sleep quality (PSQI ≥ 5) had higher SAS-SV scores compared with those with good sleep quality (P < .001). Spearman correlation analysis confirmed a moderate positive association between SAS-SV and PSQI scores (R = 0.452, P < .001), indicating that higher smartphone addiction was linked to worse sleep quality (Table 3). In multivariable linear regression adjusted for age, sex, education, and AED regimen, smartphone addiction remained an independent predictor of poor sleep quality (β = 0.083, 95% CI: 0.042–0.123, P < .001), explaining 21% of the variance in PSQI scores. The positive correlation between SAS-SV and PSQI is illustrated in Figure 1, and the group differences in SAS-SV scores by sleep quality are shown in Figure 2.

Table 3.

Distribution of smartphone addiction scores across groups.

		SAS		P-value
		Mean ± SD	Median (Min–Max)
Gender	Female	26.35 ± 12.03	25 (10–51)	.387*
	Male	23.86 ± 11.22	20 (10–58)
Educational level	Primary school	40.00 ± 18.12	45 (10–58)	.028
	Middle school	30.30 ± 10.33	27.5 (19–49)
	High school	21.92 ± 10.05	17 (10–46)
	University	24.53 ± 10.71	23.5 (10–51)
Marital status	Single	24.18 ± 11.64	22 (10–51)	.127*
	Married	27.64 ± 11.75	25.5 (11–58)
Seizure type	Focal onset	23.87 ± 12.10	21 (10–58)	.132
	Generalized onset	24.07 ± 12.40	20 (10–51)
	Unknown	28.31 ± 10.46	25 (13–49)
Seizure frequency	Once every few years	23.67 ± 9.90	22 (10–51)	.269
	1–11 times per year	29.22 ± 14.13	23.5 (10–58)
	1–3 times per month	31.00 ± 13.40	27.5 (14–49)
	1–6 times per week	22.50 ± 14.38	19 (10–48)
AED usage	None	19.00 ±.	19 (19–19)	.831
	Monotherapy	24.93 ± 11.03	24.5 (10–51)
	Polytherapy	27.05 ± 13.76	22 (10–58)
Sleep quality	PSQI < 5	19,29 ± 7,95	17 (10–48)	<.001 *
	PSQI ≥ 5	30.42 ± 11.99	27 (11–58)

Bold values indicate statistical significance (P < .05).

AED = antiepileptic drugs, Max = maximum, Min = minimum, PSQI = Pittsburgh sleep quality index, SAS = smartphone addiction scale, SD = standard deviation.

^*Mann–Whitney U Test/Kruskal–Wallis test.

Figure 1.

Positive correlation between SAS-SV and PSQI scores (R = 0.452; P < .001) (Spearman correlation). PSQI = Pittsburgh sleep quality index, SAS-SV = smartphone addiction scale – short version.

Figure 2.

Distribution of SAS-SV scores by sleep quality groups. Patients with poor sleep quality had significantly higher SAS-SV scores (P < .001) (Mann–Whitney U test). SAS-SV = smartphone addiction scale – short version.

When we looked at seizure frequency, both SAS-SV and PSQI scores tended to increase as seizures became more frequent, but these differences were not statistically significant (PSQI: P = .217; SAS-SV: P = .269). The post hoc power was only 42.5%, suggesting that the small number of patients in the high-frequency groups limited our ability to detect differences. However, in the multivariable ordinal logistic regression model, higher SAS-SV scores were significantly associated with greater seizure frequency, independent of PSQI (OR = 1.06, 95% CI: 1.02–1.11, P = .005). In contrast, PSQI scores were not associated with seizure frequency (OR = 0.97, 95% CI: 0.78–1.21, P = .79). These findings suggest that smartphone addiction may be directly linked to seizure burden, although the cross-sectional design precludes causal inference.

Additional analyses showed that participants with only primary school education had significantly higher SAS-SV scores (P = .028). Other subgroup comparisons by sex, marital status, seizure type, seizure frequency, or AED regimen were not significant, although SAS-SV scores were highest among patients with 1 to 3 seizures per month. Interestingly, patients with good sleep quality were more likely to have focal onset seizures (P = .001). These results were exploratory and should be interpreted with caution.

5. Discussion

Our study is one of the few to investigate the relationship between smartphone addiction, sleep quality, and seizure characteristics in patients with epilepsy. Our findings demonstrated that smartphone addiction not only had detrimental effects on sleep quality but was also associated with seizure frequency. In addition, smartphone addiction scores were found to differ according to educational level.

The significant and moderate positive correlation between the SAS-SV and PSQI scores (R = 0.452; P < .001) suggests that excessive digital device use may negatively influence sleep patterns. Increased smartphone use, numerous studies have demonstrated, is associated with shorter sleep duration and delayed sleep onset.^[28,29] In a study conducted by Zhang et al in China among 427 university students found smartphone addiction to be positively correlated with longer sleep latency, shorter sleep duration, and poorer sleep quality.^[30] More recently, Kiliç et al^[31] reported that individuals with higher levels of smartphone addiction and greater smartphone use exhibited poorer sleep quality. In line with this evidence, our findings support that excessive smartphone use may deteriorate sleep quality.

Importantly, our multivariable ordinal logistic regression analysis further revealed that higher SAS-SV scores were independently associated with greater seizure frequency (OR = 1.06, 95% CI: 1.02–1.11, P = .005). This independent association remained significant after adjusting for age, sex, educational level, sleep quality, and AED regimen, suggesting that smartphone addiction may contribute to seizure burden beyond its effects on sleep.

Patients with epilepsy experience sleep disorders more frequently than others. According to survey-based studies conducted in specialized epilepsy clinics, more than one-third of adults with drug-resistant epilepsy experience sleep disturbances, twice the rate found in control groups.^[32–34] The relationship between sleep and epilepsy is bidirectional. Sleep deprivation and poor sleep quality can increase seizure frequency, while epileptic activity alters sleep architecture, causing changes such as increased sleep latency, reduced sleep efficiency, frequent awakenings, and disruptions in sleep-stage transitions.^[35] In our study, the average PSQI score was around 5, with 55.1% of patients reporting poor sleep quality, reinforcing the high prevalence of sleep issues in this population. Previous research has also identified poor sleep quality as a significant risk factor for seizures.^[36] A multicentre study conducted in Turkey involving 1358 epilepsy patients found that 46.5% had poor sleep quality, 12.7% had insomnia, and 9.6% experienced excessive daytime sleepiness.^[36]

The emergence of smartphone addiction as a behavioral concern has coincided with the increasing prevalence of digital technology use. The potential impact of smartphone use on seizure control in epilepsy remains an area of active investigation. Smartphones and other wireless digital devices expose users to electromagnetic fields that exert neurophysiological effects on the brain. Electromagnetic fields exposure, experimental studies in animal models suggest, may lead to oxidative stress and structural damage in epileptic brain tissue.^[37] Human studies have also reported electroencephalography changes in response to smartphone exposure. In a study involving epilepsy patients, real-time smartphone use was found to significantly increase the epileptiform activity on electroencephalography.^[38] Some experimental and clinical studies have reported that electromagnetic waves may increase epileptic discharges or lower the seizure threshold, whereas certain computational modeling studies have suggested suppressive effects. However, the systematic review by Asadi-Pooya et al highlighted that several studies specifically proposed that prolonged smart device use may increase seizure frequency.^[39] However, it has been emphasized that prolonged and intensive use of such devices may have adverse effects on sleep patterns. These mechanisms may help explain why excessive smartphone use can disrupt sleep patterns and indirectly lower seizure thresholds. Furthermore, smartphone use before bedtime, particularly due to the emission of short-wavelength blue light from screens (380–495 nm), may suppress melatonin secretion, a key hormone regulating the sleep–wake cycle. This suppression can reduce sleep quality, contribute to sleep disorders, and potentially increase epileptic activity.^[37,40,41] Electromagnetic exposure has been suggested to indirectly lower seizure thresholds and disrupt neuronal integrity. In our study, the deterioration of sleep quality and the increase in seizure frequency observed among patients with higher levels of smartphone addiction can be regarded as clinical evidence supporting these proposed biological mechanisms. Notably, patients with more frequent seizures also exhibited higher SAS-SV scores, which represents one of the novel contributions of our study. This finding should be interpreted with caution due to the small subgroup size; however, it carries clinical significance, as even modest increases in smartphone addiction may meaningfully influence seizure risk in a multifactorial condition such as epilepsy. This trend provides important clues for future research, particularly for prospective studies with larger cohorts. In clinical practice, close monitoring of digital device use, assessment of sleep continuity, and timely interventions may be especially beneficial in patients with poorly controlled epilepsy. Although our study did not include neuroimaging data, previous literature has reported structural brain alterations associated with smartphone addiction.^[42] These findings suggest that the behavioral effects of smartphone overuse may have underlying neurobiological mechanisms.

In addition to our primary findings, several exploratory results emerged, providing further context regarding patient characteristics and clinical variability. Patients with only primary school education exhibited significantly higher SAS-SV scores, suggesting that lower educational attainment may predispose individuals to less controlled smartphone use. This aligns with the findings of Wenz and Keusch,^[43] who reported that individuals with higher education levels engage in more purposeful and diverse smartphone use, underscoring the potential role of education in promoting healthier digital behaviors. Moreover, patients with focal onset seizures demonstrated significantly better sleep quality compared with those with generalized seizures (P = .001), which may reflect the more localized nature of focal epileptic activity. Bazil^[44] similarly reported that generalized seizures were associated with reduced rapid eye movement sleep and broader disruption of sleep architecture. Finally, no significant difference in sleep quality was observed between patients on monotherapy and those on polytherapy, consistent with findings by Arvin et al.^[45] This suggests that sleep quality is not solely determined by the number of AEDs prescribed but is more likely influenced by seizure control, drug class, and individual susceptibility.

Our study generally yielded results consistent with the literature but also offered some novel contributions. Notably, the direct and significant correlation between smartphone addiction and sleep quality (R = 0.452) in patients with epilepsy represents a striking finding. Furthermore, the observation that smartphone addiction was independently associated with seizure frequency in multivariable analyses constitutes one of the original contributions of our study. Since the number of studies simultaneously evaluating smartphone addiction, sleep, and epilepsy in the literature is highly limited, these findings carry substantial importance for both clinical practice and public health strategies.

Our study has several limitations that should be acknowledged. First, its cross-sectional design precludes any conclusions about causality. Second, both the SAS-SV and the PSQI are self-reported measures, which may be subject to recall or response bias. Third, the relatively small sample size may have reduced statistical power, particularly for detecting subgroup differences. In fact, the post hoc power for seizure frequency comparisons was only 42.5%, increasing the risk of type II error and limiting our ability to detect potentially meaningful associations. Moreover, objective measures of sleep such as actigraphy or polysomnography were not employed. Finally, although patients with a confirmed diagnosis of depression or anxiety were excluded, no standardized assessments of mood symptoms were applied. We acknowledge that the modest sample size and the cross-sectional design limit causal inference and the generalizability of our findings. These results should therefore be interpreted as preliminary, providing a rationale for future prospective studies with larger and more homogeneous samples.

In conclusion, our study demonstrates that smartphone addiction in patients with epilepsy is associated with both poor sleep quality and greater seizure frequency. These findings underscore the importance of routinely evaluating digital device usage habits in epilepsy management and, when appropriate, implementing interventions such as digital hygiene and sleep education. While the cross-sectional nature of our study precludes causal inference, our results provide preliminary evidence that warrants further investigation. Future prospective studies with larger samples are essential to validate these associations and to elucidate the underlying mechanisms. Integrating digital health strategies into clinical care may ultimately enhance seizure control, treatment adherence, and quality of life in this population.

Author contributions

Conceptualization: Tuba Akinci, Melis Gökçe Çil, Yilmaz Çetinkaya.

Data curation: Tuba Akinci, Melis Gökçe Çil, Sefa Özaydin, Yilmaz Çetinkaya.

Formal analysis: Tuba Akinci, Melis Gökçe Çil.

Investigation: Sefa Özaydin.

Methodology: Tuba Akinci, Melis Gökçe Çil.

Project administration: Yilmaz Çetinkaya.

Supervision: Tuba Akinci.

Validation: Tuba Akinci.

Visualization: Tuba Akinci.

Writing – original draft: Tuba Akinci.

Writing – review & editing: Tuba Akinci, Yilmaz Çetinkaya.