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Screen exposure and circadian disruption in paediatric epilepsy: risks and technology-based approaches – a literature review

Martyna A. Czylok
1
,
Milena Prokopiuk
1
,
Katarzyna Meller
1
,
Grzegorz Nazar
1
,
Helena Kamieniecka
1
,
Wawrzyniec Jankowski
1
,
Marta Zawadzka
1
,
Maria Mazurkiewicz-Bełdzińska
1

  1. Department of Developmental Neurology, Medical University of Gdansk, Poland
Adv Psychiatry Neurol 2026; 35 (1): 65-78
Data publikacji online: 2026/03/04
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- PPiN-00517-Screen.pdf  [0.21 MB]
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INTRODUCTION

With the widespread presence of digital devices in daily social life in the modern, post-COVID-19 pandemic world, concerns are growing about the effects of new technologies on children’s sleep and development. Salway et al. [1] reported that total leisure screen time increased by 11% on weekdays and 8% on weekends among schoolchildren compared with pre- and post-pandemic habits. This tendency was further confirmed by another meta-analysis, which revealed that children’s total screen time had increased by 1.35 h per day, with greater diffe-rences observed in older children [2]. We live in a reality where tablets, desktops, and smartphones have replaced traditional paper-based activities. While we acknowledge the usefulness of these devices, we are also concerned about their impact on patients with epilepsy and the general population. For many researchers focused on epilepsy, an additional concern is the effect of blue light emission on seizure frequency and control. A key factor in those investigations is exposure to blue light emitted by screens, such as smartphones, tablets, and computers, factors that were not as prominent just a few years ago.

Studies have shown that blue light decreases the production of melatonin, a hormone that regulates sleep-wake cycles, leading to disrupted circadian rhythms and poorer sleep quality [3]. This impact can be even more significant in children, whose developing brains are more sensitive to such disruptions. Research suggests that increased screen time may lead not only to sleep disturbances but also to various other health problems, such as obesity [4]. According to research conducted by Lewien et al. [5] as many as 22.6% of healthy children and 20% of adolescents suffer from different sleep-related difficulties.

In patients with epilepsy, poor sleep is known to exa-cerbate the frequency and severity of seizures, creating a potential feedback loop between technology use and seizure control [6]. Children with photosensitive epilepsy are particularly vulnerable, as flashing lights and screen flickers can act as direct triggers for seizures [7].

The widespread use of devices that emit blue light has drawn attention to how this light disrupts natural circadian rhythms, especially in children. Studies consistently show that exposure to blue light not only worsens sleep quality but also reduces sleep duration and decreases melatonin levels [3, 8, 9].

The main aim of our study was to review the current knowledge concerning the influence of blue light exposure, screen time, and the general use of new technologies on sleep quality and seizure control in paediatric patients with epilepsy.

METHODS

The literature search was conducted in PubMed, Scopus, Embase, and Cochrane databases, to search for peer- reviewed articles and studies examining the effects of blue light exposure, screen time, and technology use on sleep and seizure control in children, particularly those diagnosed with epilepsy. A targeted literature search was conducted to identify studies examining the effects of blue light exposure, screen time, and technology use on sleep and seizure control in children, particularly those diagnosed with epilepsy. The search was performed without initial time restrictions; however, for consistency and relevance, studies published between 2000 and 2025 were primarily included. The refe-rence lists of identified articles were manually screened to locate further relevant studies. The searches were based on specific keywords and phrases related to the impact of digital technology on children’s sleep patterns and epilepsy.

The search terms used in PubMed and ScienceDirect (number of results given in brackets):

(screen time) AND (epilepsy)) AND (sleep)

Total DOIs in PubMed: 6

Total DOIs in Scopus: 19

Number of Duplicates (found in both): 4

Number of DOIs Only in PubMed: 2

Number of DOIs Only in Scopus: 15

((melatonin level) AND (epilepsy)) AND (children)

Total DOIs in PubMed: 31

Total DOIs in Scopus: 77

Number of Duplicates (found in both): 24

Number of DOIs Only in PubMed: 7

Number of DOIs Only in Scopus: 53

((epilepsy) AND (blue light)) AND (impact)

Total DOIs in PubMed: 4

Total DOIs in Scopus: 6

Number of Duplicates (found in both): 2

Number of DOIs Only in PubMed: 2

Number of DOIs Only in Scopus: 4

(((social media) AND (epilepsy)) AND (children)) AND (sleep)

Total DOIs in PubMed: 6

Total DOIs in Scopus: 19

Number of Duplicates (found in both): 4

Number of DOIs Only in PubMed: 2

Number of DOIs Only in Scopus: 15

((sleep monitoring) AND (epilepsy)) AND (wearable devices)

Total DOIs in PubMed: 33

Total DOIs in Scopus: 35

Number of Duplicates (found in both): 19

Number of DOIs Only in PubMed: 14

Number of DOIs Only in Scopus: 16

((epilepsy) AND (sleep)) AND (mobile apps)

Total DOIs in PubMed: 10

Total DOIs in Scopus: 10

Number of Duplicates (found in both): 3

Number of DOIs Only in PubMed: 7

Number of DOIs Only in Scopus: 7

(((screen time) AND (epilepsy)) AND (wearable devices)) AND (monitoring)

Total DOIs in PubMed: 1

Total DOIs in Scopus: 1

Number of Duplicates (found in both): 1

Number of DOIs Only in PubMed: 0

Number of DOIs Only in Scopus: 0

These search queries were conducted without time restrictions to include the most recent evidence. Additio-nally, websites and online platforms for parents of children with epilepsy were explored to identify mobile applications used for monitoring sleep and managing seizures. Two independent reviewers screened all titles/abstracts and subsequently the full texts. Disagreements were resolved through discussion or by consulting a third reviewer.

Articles were included if they met the following criteria:

  • studies on the influence of technology, blue light, or screen time on sleep patterns in children,

  • studies examining the effects of technology use, blue light exposure, and screen time on sleep in children with epilepsy,

  • peer-reviewed studies investigating melatonin levels in response to light or screen exposure in children with epilepsy.

Articles were excluded if they:

  • focused solely on adult populations,

  • did not discuss sleep disturbances or epilepsy in the children,

  • did not discuss correlations between sleep disturbances and screen time or blue light or social media or mobile (which monitor apps for sleep/screen time) or wearable devices (which monitor sleep/screen time),

  • were non-peer-reviewed or were anecdotal reports,

  • used a small study population (fewer than 10 participants).

Some limitations were identified across the studies reviewed, including reliance on self-reported screen time, small sample sizes, heterogeneous study designs, and limited objective sleep measures (e.g., polysomnography was rarely used). These factors may affect the generalizability and strength of the conclusions. In several cases, sleep assessment tools (e.g., CSHQ, actigraphy protocols) were used without formal validation in children with epilepsy, or without evidence of regulatory approval. This should be considered when interpreting the results.

A PRISMA flow diagram summarizing the study selection process is provided in Supplementary Figure I.

Supplementary Figure I

We assessed the methodological quality of the studies included using simplified risk-of-bias categories (low, moderate, high). The results are summarised in Table 1.

Table 1

Assessment of methodological quality, study design, and sample size of included studies

Ref.Study – author, yearDesignSample sizeRisk of bias (l/M/h)Notes
1Salway, 2023Mixed methodology, cross-sectional questionnaire50 schools, children aged 10-11 years (total of 1689 questionnaires) + Qualitative data from 47 children and 27 parentsModerateCombines quantitative survey and qualitative data; self-reported screen time may introduce bias
2Choi, 2023Systematic review and meta-analysis91 studies (51 reported both pre-pandemic and pandemic screen time)ModerateReview seems comprehensive, but publication bias unclear
3Figueiro, 2011Comparative study21 participants.LowRandomized light exposure, clear outcome measures
4Lin, 2020Cross-sectional study141 childrenModerateParent-reported data and potential underreporting
5Lewien, 2021Descriptive study using data from a population-based cohort study in Germany855 children aged 4-9 and 1,047 adolescents aged 10-17LowData from 2011-2015, different reporting methods for children and adolescents, no separate questions for weekdays and weekends
6Bonilla-Jaime, 2021ReviewModerateincludes preclinical and clinical studies, as well as animal experiments
7Fisher, 2022Non-systematic literature reviewHighNon-systematic, potential selection bias
8Silvani, 2022Systematic review of randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies36 studiesModerateHeterogeneity of included studies
9Zeitzer, 2020Experimental23 participantsLowWell-controlled lab conditions, small sample
10Christakis, 2009Narrative reviewOver 1300 childrenHighNarrative review, not systematic; potential selection bias
11WHO, 2019GuidelinesLowExpert consensus with evidence-based recommendations
12American Academy of PediatricsGuidelinesLowEvidence-based international guidelines, low risk of bias
13McArthur, 2022Systematic review and meta-analysisLowLarge sample, clear methodology
14Johansson, 2016Secondary analysis of a cross-sectional survey259 respondents aged 13 to 21 yearsModerateRecall bias, potential under- or over-estimation of sleep or technology use
15Goodlin, 2008Longitudinal study194 children (68 with autism, 57 with developmental delay, 69 typically developing)LowStudy validates the Children’s Sleep Habits Questionnaire; clear methodology and standardized measures
16Eggermont, 2006Cross-sectional studyInitially 3022, final sample size 2546 after exclusionsModerateSmall sample, potential confounding
17Gradisar, 2013Cross-sectional survey1,508 participantsLowLack of data on e-survey completion rates
18Hysing, 2015Cross-sectional population-based survey9846 adolescentsLowPotential overlap in questions about daytime and bedtime use; reliance on self-reported data
19Levenson, 2017Cross-sectional1763 young adults aged 19-32ModerateSelf-reported data
20Arora, 2014Cross-sectional study738 adolescentsModerateReliance on self-reported data
21Paavonen, 2006Randomized population-based survey (cross-sectional)321 parents of children aged 5-6 yearsModerateDifficulty in measuring passive TV exposure accurately
22Hilda, 2015Cross-sectional study350 children aged 6 months to 4 yearsLowLarge sample, standardized measures
23Palladino, 2020Observational study57 patientsLowObservational and retrospective design, lack of control population, use of telephone-administered questionnaire, limited comparison to only 2019-2020
24van der Lely, 2015Randomized controlled trial67 teenagersLowRandomized, objective measures, small sample
25Crowley, 2015Cross-sectional experimental study67 participantsLowParticipants were screened for good health and consistent sleep-wake patterns, and absence of medications affecting sleep or circadian rhythms
26Bazil, 2000Observational study11 patients with TLE, 6 control patientsModerateSmall sample size, potential influence of anticonvulsant treatment
27Gibbon, 2019Narrative reviewHighNarrative review, not systematic; potential selection bias
28Larson, 2012Questionnaire-based, case-control study105 households with a child with epilepsy, 79 control householdsModerateLow survey response rate, significant age difference between cohorts, confounding variables (developmental delay, autism spectrum disorders)
29Ekinci, 2016Cross-sectional, case-control cohort survey53 children and adolescents with epilepsy, 28 controlsHighLack of individual age-sex matching between groups; small sample size, cross-sectional design, reliance on questionnaires for diagnosing sleep disorders
30Tosini, 2016Literature reviewModerateMechanistic review, some studies heterogeneous
31Nowozin, 2017Observational studyModerateApplied melanopic lux measures, moderate sample
32Ishizawa, 2021Experimental11 healthy young menModerateSmall sample size
33Capovilla, 2006Clinical cohort610 epilepsy patientsLowLarge sample size and multicentre design help in mitigating potential biases
34Strzelecka, 2021Retrospective cohort study100 paediatric patientsLowObjective EEG measures, clear methodology
35AlDajani, 2024ReviewModerateSmall sample, limited blinding
36Özdemir, 2023Observational cross-sectional140 mothers with children aged 4-6 yearsModerateReliance on parental reports
37Janssen, 2020Systematic review and meta-analysis31 papers, 60,445 childrenLowComprehensive search, meta-analysis with low risk of bias
38Martin, 2021Systematic review and meta-analysis of experimental studies (RCTs and cluster RCTs)4,656 children aged 2-13 years across 11 studiesModerateInterventions heterogeneous; some risk of publication bias
39Hale, 2015Systematic review67 studiesModerateNarrative synthesis; heterogeneity of included studies
40Qiu-Ye Lan, 2014Cross-sectional2903 childrenModerateLimitation: recall bias due to parent-reported data; lack of examination
41Zhu, 2020Cross-sectional2278 children aged 3-6 yearsModerateConducted in a single city, self-reported data
42Pickard, 2024Randomized clinical trial105 familiesLowLimitation: reliance on parent-reported screen use, single city study; well-randomized, clearly defined outcome
43Rauchenzauner,Cross-sectional cohort48 patients, 59 controlsModerateClinical cohort study;
2017studymoderate sample size and
potential confounding variables
44Ronen,Cross-sectional study22115 studentsLowSmall sample size of young people
2019(163 with epilepsy)with epilepsy
45Pohl,Cross-sectional study35 children with epilepsyModerateSmall sample size
2018(controlled by matching
with healthy peers)
46Fitzpatrick,Cross-sectional study316Moderate24-hour recall diary based on
2022a single weekday; COVID-19
context may bias screen time
behaviors; parental reports
47Mohammadza-Systematic review20 epileptic self-care apps,ModerateReview and reflection on epilepsy
deh, 2021analysis of 55 apps related toapp design and mark about sleep
epilepsymonitoring in such apps
48Shuang Wang,Comprehensive literatureHighNarrative review, not systematic;
2020reviewrisk of selection bias
49Stirling,Review37 participantsModerateSeizure forecasting study; moderate
2020sample, potential confounding
50Abreu,Systematic review55 apps, with detailedModerateReview and reflection on epilepsy
2022analysis on 26 appsapp design and mark about sleep
monitoring in such apps
51Alzamanan,Systematic review22 apps; Search for apps onModerateLimitations: user ratings used
2021Google Play and App Store,for quality assessment, limited to
May-August 2018free English apps in Malaysia
52Alzamanan,Content validity study12 experts reviewingLowClear methods,
2024structured framework
53Al Mahmud,Scoping review51 studies; Peer-reviewedModerateWide range of apps reviewed,
2022articles published from 2010some subjective assessment
to 2022 discussing sleep apps
and sleep monitoring
54Ong,Review51 appsModerateLimit: reliance on in-store
2016descriptions and developer
websites, apps not downloaded
for detailed analysis
55Lancaster,Systematic review56 appsModerateBehaviour modification techniques
2024assessed, heterogeneity present
56Choi,Systematic review73 out of 2,431 potentiallyModerateSome heterogeneity
2018relevant appsin app evaluation
57Shah,Prospective large106 participantsLowObjective seizure monitoring,
2024scale multicentremultiple sites
study; observational,
prospective, multi-center
cohort study design
58Bruno,Market survey conducted73 participantsModerateMarket devices assessed,
2020at the end of 2019some selection bias
focusing on seizure
detection devices.
59Nasseri,ObservationalTotal of 70 patients,ModerateSignal quality and patient
2020EEG from 21experience, potential bias
60Brinkmann,Literature review47 patientsLowWearable data, clear methodology
2021
61Jørgensen,Retrospective studyInitially 15 adults,ModerateEar-EEG vs. scalp-EEG,
2020final sample size 13limited sample
62Bruno,Mixed-methods approach87 participantsModerateSelection bias due to online
2018using an online surveyadvertisement,
based on previous focuspotentially favouring those familiar
group datawith digital technology
63Haut, 2012Prospective electronic diary study19 participants with epilepsyModerateReliance on self-reporting, potential issues with missing data
64Schulze-Bonhage, 2011Cross-sectional500 participantsModerateProspective data collection using paper and electronic diaries; lack of time stamping in paper diaries; subjective experiences may not be specific enough for accurate prediction
65Williamson, 2024Narrative reviewHighPerspective article; not a systematic review, risk of bias high
66Li, 2022ReviewModerateReview of wearable seizure detection devices; methodology heterogeneous
67Davies, 2021Observational20 paediatric patientsModeratePilot study in paediatric population; small sample, limited generalizability
68Ahorsu, 2020Randomized control trial78 patientsLowRandomized controlled trial, clear design and outcome measures
69Tsai, 2018Observational49 childrenLowActigraphy vs. diary comparison; objective measures used
70Vandecasteele, 2017Observational22 patientsModerateAutomated seizure detection; hospital environment, moderate sample size
71Onorati, 2017Multicentre clinical assessment72 patientsLowWell-designed multicentre evaluation, clear methodology
72Vandecasteele, 2020Observational22 patientsModerateLimited sample diversity
73Hartstein, 2024Narrative reviewHighExpert consensus; not systematic, risk of bias high

RESULTS AND DISCUSSION

Exposure to technology

Exposure to technology refers to the overall contact with electronic devices and digital content in everyday life, encompassing both passive and active interactions, including background television, environmental screen exposure, and interactive device use. This conceptuali-zation is consistent with the framework described by Christakis [10] who distinguishes between direct and indirect forms of media exposure in early childhood. While international medical organizations such as the World Health Organization (WHO) and the American Acade-my of Pediatrics (APP) have issued clear screen time guidelines for children, large-scale surveys consistently show that actual usage among healthy children and adolescents far exceeds these recommendations [11, 12]. Fewer than 25% of children younger than two years old met their screen time guideline, and 36% of children aged 2-to-5 met theirs [13]. Among adolescents, 97% admit to using some kind of technology during the hour before sleep [14]. Almost half of them declare using 3 or 4 technological devices at the time, and approximately 1 in every 10 uses six devices or more. The most commonly used tools were phones, computers, music devices, and television [14]. A higher number of devices used before bedtime correlates with waking too early and excessive daytime sleepiness. Internet use, social media, texting, and violent video games are particularly associated with feelings of having had unrefreshing sleep upon waking.

In 2020, Yong-Ying et al. [4] investigated how the usage of technology and screen time alter the sleep of preschool children and toddlers with epilepsy. The identified sleep disturbances included duration of sleep, sleep- disordered breathing, delay of sleep onset, sleep anxiety, bedtime resistance of children, reducing sleep duration, night awakenings, and parasomnias. Each disruption was graded by parents applying the Children’s Sleep Habits Questionnaire (CSHQ) [15]. The findings indicated that children with screen time exceeding 1 hour experienced more severe sleep disturbances, delayed sleep onset and offset, and shorter total sleep durations [16].

Among adolescents, inadequate sleep has been linked to texting and social media use before bedtime. A 2016 study reported that one-third of adolescents were woken during the night by their phone [14], which can lead to a state of continuous arousal, resulting in lighter sleep patterns [17]. Differences between genders consist mostly of the purpose of technology usage before bed. Females usually text, talk on the phone, and use social media, whereas males spend their time on computer games [18].

The hypotheses on how technology affects the brain and body before sleep are diverse. One points to the stimulation delivered by the use of devices [19]. By indulging themselves in the online world for contact with other people and listening to music or video games, young people provide multiple stimuli which affect sleep quality and quantity [20].

Studies have shown that children aged 5 and 6 experience more frequent sleep problems with prolonged television exposure. Passive exposure to technology within the family environment also correlates with shorter sleep durations and recurrent sleep disruptions in children. Moreover, the screen’s content was also significant – watching current affairs programs led to issues with the onset of sleep and sleep hyperhidrosis, called night sweats. Watching adult movies corresponded with difficulties with falling asleep, sleep hyperhidrosis, and problems staying asleep [21]. Given that 77% of children aged 4-10 include television viewing as part of their bedtime routine [22], this may have serious consequences.

The WHO and the APP recommend that children under 2 years of age avoid screen exposure entirely, while children aged 2-5 years should be limited to no more than 1 hour per day of high-quality, supervised screen time [11, 12]. For older children and adolescents, consistent limits and screen-free routines – especially before bedtime – are advised to promote healthy sleep and development.

Despite these guidelines, studies show that a large proportion of children significantly exceed recommended limits, especially adolescents who often engage in multi-device use before sleep. Children with epilepsy may require even stricter screen-time management due to their increased sensitivity to sleep disruption and light-triggered seizures. Research indicates that children with epilepsy experience more severe consequences from excessive screen exposure, including delayed sleep onset, reduced melatonin levels, and increased risk of seizure compared to their healthy peers [23].

Other studies have emphasized the impact of blue light on the sleep cycle and circadian rhythm [24]. Among various factors, melatonin levels are critical in regulating sleepiness and fatigue. The use of technology reduces the melatonin level [9], to which children in puberty are even more sensitive [25]. Reduced melatonin levels disrupt sleep and may increase the risk of seizures [26]. This creates a vicious cycle, with sleep disturbances further lowering melatonin levels and exacerbating seizure activity [27]. Furthermore, the circadian system is more prone to disarrangement due to higher light sensitivity in comparison to adults [25].

Despite these findings, there is a limited number of observational studies exploring the effects of technology use before bedtime on sleep quality and seizure patterns in children with epilepsy. Further research is necessary to understand this relationship better, as such insights could improve the quality of life for these children. It is important to remember that technology isn’t the only factor that can contribute to sleep disturbances in children with epilepsy [28]; anticonvulsant medications and other conditions, such as ADHD, may also play a role [27, 29]. However, technology exposure, even in the form of a music player, significantly contributes to the poor sleep quality and quantity observed in young patients with epilepsy [14].

Excessive and unregulated technology use among children and adolescents is strongly associated with poor sleep hygiene, reduced sleep duration, and increased night- time awakenings. Children with epilepsy appear to be particularly vulnerable to the stimulating and disruptive effects of digital media, which may compound existing neurological and behavioural challenges. These findings support the need for individualized screen-time management and broader awareness of digital behaviours’ impact on sleep health.

Blue light and melatonin suppression

Exposure to blue light emitted by digital devices affects the circadian rhythm in children and adolescents, primarily through the action of melanopsin, a photo-pigment located in retinal ganglion cells. Melanopsin is particularly sensitive to wavelengths of approximately 460-480 nm, effectively suppressing the secretion of me-latonin, the hormone that regulates the sleep-wake cycle. Research confirms that light within this wavelength range more effectively shifts the biological clock phase than other wavelengths do, intensifying the effects of evening exposure to blue light on the circadian rhythm [30].

Altered sleep architecture related to screen use

Evening screen exposure has been shown to affect the architecture of sleep by reducing the amount of slow-wave sleep (SWS) and delaying the onset of rapid eye movement (REM) sleep – both essential for brain recovery and memory processes. Blue light emitted from digital devices can suppress melatonin and lead to altered sleep stages, especially when screens are used within 1-2 hours before bedtime [3, 31]. These disruptions are particularly relevant for children with epilepsy, in whom reduced REM and SWS may increase cortical excitability and lower seizure thresholds. Altered sleep architecture may also exacerbate daytime fatigue, affect mood regulation, and impair learning.

Further studies indicate that younger individuals, particularly those in the pre- and mid-pubertal stages (approximately 9-14 years), exhibit greater sensitivity to light-induced melatonin suppression than older adolescents (11-16 years). Researchers have reported that children in the pre- and mid-puberty stages experience significantly greater melatonin suppression even at low illumination levels (15 lux) in the evening, suggesting a critical sensitivity period for light exposure in this age group [25]. Similarly, Nowozin et al. [31] emphasized the importance of the melanopic lux metric in predicting melatonin suppression under various lighting conditions, highlighting the broad impact of blue light on the circadian system in both daily and clinical settings. Additionally, studies by Figueiro et al. [3] and Zeitzer et al. [9] showed that blue light from computer screens or ambient lighting can lead to dose-dependent melatonin suppression, with blue wavelengths showing highly potent effects.

Exposure to blue light is associated with decreased sleep quality, reduced sleep efficiency, and shortened deep sleep duration. Studies conducted on healthy young adults (18-44 years) suggest that pre-sleep exposure to blue light may reduce the proportion of deep sleep relative to total sleep time, ultimately impairing overall sleep quality [32]. A systematic review revealed that approximately 50% of studies on blue light exposure reported decreased sleep quality, one-third reported reduced total sleep duration, and half reported increased sleep latency [8].

In children with photosensitive epilepsy, exposure to blue light or other flickering visual stimuli may provoke photogenic seizures, particularly under high-intensity light or dynamic colour changes on screens. For example, Fisher et al. [7] reported that light emitted by modern LEDs used in monitors, televisions, and other digital devices can readily trigger photoparoxysmal responses (PPRs) in individuals sensitive to light.

The implementation of blue light-blocking glasses seems to be an effective method for reducing the frequency of seizures. Studies indicate that Z1 lenses, which block wavelengths between 550-700 nm, can significantly reduce the frequency of photogenic seizures; 75.9% of patients experience no PPR events while using these lenses [33, 34]. Furthermore, Capovilla et al. [33] reported that screens with a 100 Hz refresh rate are less likely to provoke seizures in epilepsy patients than are 50 Hz screens, suggesting that higher refresh rates and specia-lized light filters may provide potential protective benefits for individuals sensitive to visual stimuli.

The blue light emitted by digital devices can disrupt circadian rhythms, decrease sleep quality, and potentially trigger seizures in children with photosensitive epilepsy. Studies have demonstrated variability in light sensitivity across different age groups, highlighting the need for further research into age-related light sensitivity and strategies to minimize risk in populations susceptible to seizure triggers.

In a 2024 review, AlDajani et al. [35] noted that epilepsy is a common neurological disorder and that antiepileptic drugs (AEDs) often fail in drug-resistant cases and are unsatisfactory for photosensitive epilepsy. They suggest that light therapy has emerged as a potential non-pharmacological approach for several conditions, including depression, seasonal affective disorder, migraine, pain and epilepsy. According to this review, red light can provoke seizures in photosensitive individuals, whereas blue lenses that filter red wavelengths may significantly reduce seizure frequency. The authors highlight that the effects of green light on the latter are still unknown. They also discuss experimental techniques such as optogenetics and light-activated gene therapy as emerging therapeutic options; however, these strategies are primarily supported by animal studies and human evidence remains limited. The review concludes that while light therapy shows promise in reducing seizure frequency, more research is required to establish its efficacy and safety in clinical practice [35].

Effects of screen time on sleep in children

Screen time refers more specifically to the active duration that an individual spends using screen-based technologies (e.g., phones, tablets, computers, televisions), typically measured in hours per day. This definition aligns with the position of the APP (2016), which defines screen time as the total time actively spent engaging with digital media [12]. Numerous studies underscore the significant impact of prolonged screen time on sleep quality, sleep duration, and delayed sleep onset in children [36-38]. For example, a comprehensive review by Hale and Guan [39] reported that 90% of studies reported adverse associations between screen time and sleep outcomes, with children who engaged with screens for at least an hour before bedtime showing increased risks of sleep disruptions, including shorter sleep duration and more fragmented sleep patterns.

Furthermore, Lan et al. [40] reported that each additional hour of portable electronic device use was associated with an 11-minute reduction in average daily sleep duration for boys and a 6-minute reduction for girls. Nonportable screen use also had a notable impact, as every additional hour contributed to a 3-minute increase in social jetlag for boys. Zhu et al. [41] reported that each hour of television watching was associated with a 12.35% increased risk of sleep disorders, highlighting the wide-reaching implications of screen exposure for paediatric sleep health.

A large randomized clinical trial conducted by Pickard et al. [42] investigated the effects of a 7-week intervention aimed at reducing pre-bedtime screen exposure in toddlers. It involved 105 families and assessed the feasibility and impact on sleep and attention. Results indicated that removing screens before bed was practical and led to modest improvements in sleep quality, such as better sleep efficiency (a higher percentage of time spent in bed is actually spent sleeping) and fewer night awakenings. However, there was no significant effect on attention measures.

These results seem to be especially relevant for children with epilepsy, who demonstrate significantly greater screen usage than their healthy peers do [43]. In a study involving children aged 10-17 years, those with epilepsy recorded an average of 8.7 hours of daily screen time, compared with 7.4 hours in general population norms [44]. Pohl et al. [45] further reported that children with epilepsy not only have increased screen time but also exhibit lower physical literacy scores, reduced agility, weaker movement skills, and lower muscular endurance than healthy controls do.

The COVID-19 pandemic exacerbated the problem of screen exposure among children, with increased access to electronic devices during lockdown, Palladino et al. [23] reported that during this period emergency rooms also saw a statistically significant increase in seizure occurrence compared with before the pandemic. Furthermore, they documented a negative correlation between daily screen time and seizure latency, finding that children with greater screen exposure experienced earlier seizure onset, underscoring the risks associated with increased screen exposure for epileptic children [46].

These findings are particularly concerning, as numerous studies indicate a strong association between sleep disturbances and elevated seizure risk in children with epilepsy. A study conducted on epileptic preschool children revealed that those who had experienced seizures in the past 3 months experienced more sleep disturbances than other children. The study also revealed that daily screen exposure exceeding one hour for children younger than six years was linked to a 23.4-minute delay in sleep onset, a 20.4-minute delay in sleep offset, and more severe sleep disturbances. They concluded that screen-induced sleep delays and disturbances could exacerbate seizure frequency in children with poorly controlled epilepsy, as sleep deprivation and poor sleep quality are established precipitants of epileptic events [4].

Collectively, these findings suggest that reducing screen exposure, especially before bedtime, could be a critical strategy for improving sleep and potentially reducing the risk of seizure in paediatric epilepsy patients.

Prolonged screen time is consistently linked to delayed sleep onset, decreased total sleep time, and poorer sleep quality in children, with particularly concerning implications for those with epilepsy. The increased exposure during the COVID-19 pandemic has further exacerbated these trends. These findings underscore the importance of screen time reduction, especially in the evening hours, as a potential strategy for improving seizure control in paediatric epilepsy.

Innovative tools for epilepsy care: monitoring sleep quality and screen times via apps and devices

Managing epilepsy requires a multifaceted approach, encompassing adherence to medication, lifestyle adjustments, and the integration of technology, such as mobile health applications and wearable devices, into epilepsy self-management and clinical care for monitoring sleep, screen exposure, and seizure-related factors. Awareness of sleep quality and the impact of screen time on seizures have led to the development of online tools for sleep monitoring and seizure forecasting [27, 47]. Existing applications have also added sleep-tracking features [47].

Research suggests that monitoring sleep, screen time, and activities may aid in anticipating seizures and promoting healthier habits [4, 48]. In addition, self-management applications can be an important aid in collecting data for algorithm development and showing results to patients, who can respond to feedback. This engagement loop can benefit both patients and research, providing additional goals for these m-health solutions [49].

Presently, mobile applications for the self-management of epilepsy mainly focus on seizure tracking, seizure response, treatment management, and medication adherence [50, 51]. While numerous apps exist, their installation rates are generally low, and the majority do not comprehensively address all self-management needs, like sleep [50]. A lack of features in this regard, like sleep or screen time recording, indicates a potential area for future app development and research. To enhance app efficacy, experts recommend involving stakeholders in development, conducting usability analyses, and promoting the apps more widely [50].

To remedy the absence of sleep-monitoring features in epilepsy-focused mobile apps, the use of popular sleep management applications is suggested. Many of these apps have surged in popularity, offering a variety of features for sleep self-regulation and self-education about sleep hygiene and its importance. Common functionalities include sleep monitoring, duration tracking, alarm systems, and data logging [52]. While many apps provide insights into sleep structure, such as total sleep time and the stages of sleep, most lack scientific validation of their algorithms [53]. There is considerable variability in quality and functionality, with some of these apps effectively employing behaviour modification techniques [54]. This raises doubts about the usefulness of out-of-hospital monitoring and the quality of data for evaluation by the attending physician. However, the clinical utility of these apps is hampered by insufficient validation studies [55]. Despite these challenges, sleep management applications can facilitate sleep self-regulation, with some integrating automatic data collection through built-in sensors [56].

Wearable devices have potential when long-term, out-of-hospital recordings are considered. Moreover, their use in combination with online apps may increase the value of both tools and help in collecting more accurate data on sleep and screen time. Studies have evaluated various devices, including wristbands, armbands, scalp electrodes, and even wristwatches [57] for signal quality, comfort, and stability [58-60]. Ear-EEG has demonstrated good agreement with scalp-EEG for sleep staging in epilepsy patients [61]. Importantly, patients generally prefer comfortable wireless designs, with watch-based devices being acceptable to more than 70% of people with epilepsy; however, leg, upper-arm, chest, and head-based systems had < 50% acceptance, and ring-style wearables had over 60% approval [62].

Despite offering various potentially useful solutions and capabilities, most commercially available seizure-tracking sensors lack regulatory approval. Wearable devices, combined with smartphone apps, go beyond basic biosignal tracking by monitoring complex behaviors such as activity patterns, movement, self-assessed sleep quality, and mood indicators through analyses of movement speed, social interactions, and speech affective tone. This capability faci-litates the analysis of behavioural changes over time, potentially correlating with seizure risk, as indicated by studies exploring preictal states [63, 64].

Despite challenges such as variable app quality and insufficient validation, ongoing advancements in mobile health applications and wearable technology offer significant potential for personalized care and a better understanding of the interplay between sleep, screen time, and epilepsy. The future of epilepsy management looks promising, with advancements in technological solutions for monitoring, interventions, and continuous patient feedback from data while actively involving patients in the process.

This reciprocal engagement could enhance patient experience and research outcomes, adding an essential dimension to mobile-health solutions in epilepsy mana-gement. Future research is fundamental to increasing the performance of these apps and devices and validating their effectiveness as therapeutic tools. Most mobile health (mHealth) apps for epilepsy are designed for adolescents and school-aged children (typically aged 6 and above), as they can actively engage with app interfaces. However, younger children may still benefit from passive monitoring through wearable sensors or apps used by caregivers [51, 53]. While not yet integrated into standard epilepsy care due to lack of regulatory approval and validation, these tools are increasingly used in clinical studies and home monitoring. They offer continuous, passive data collection on sleep duration, sleep quality, and user interactions, which can be shared with healthcare providers to inform treatment decisions or identify seizure triggers [47, 50].

Should we recommend digital monitoring tools despite the need to limit screen time?

Paradoxically, while screen exposure should be limited before bedtime, sleep-monitoring apps or screen-time trackers may support reduction efforts if used strategically – especially when operated by caregivers or designed with minimal light exposure (e.g., dark mode, no notifications). Behavioural data collected through these apps may help reinforce sleep hygiene routines and detect early signs of deterioration in seizure control [65-69]. Despite the growing popularity of such tools, their clinical validity is still under investigation. Most do not meet medical device regulatory standards and lack standardized validation across paediatric populations. Further research is needed to confirm their reliability and effectiveness in epilepsy care [66, 68].

Digital health technologies, including mobile applications and wearable devices, show promise in monitoring sleep patterns and seizure activity in paediatric patients with epilepsy. However, their current clinical value remains limited due to a lack of scientific validation and regulatory approval. Continued development, patient engagement, and research into their effectiveness are necessary to transform these tools into evidence-based support strategies for epilepsy self-management.

Cognitive and academic impact of sleep disturbances

Sleep disturbances caused by excessive screen exposure may not only increase susceptibility to seizure but also have long-term effects on cognitive development and academic achievement. Children with epilepsy who experience chronic sleep disruption often show reduced attention span, impaired memory consolidation, slower processing speed, and difficulties in emotional regulation [27, 28]. These impairments can significantly affect school performance, concentration, and overall quality of life. Furthermore, long-term sleep fragmentation and insufficient REM and slow-wave sleep may disrupt neurodevelopmental processes during critical periods, leading to cumulative cognitive deficits. Children with epilepsy and comorbid sleep disorders frequently score lower on academic and executive functioning measures than their peers with better sleep quality [28].

Sleep hygiene recommendations for children with epilepsy

To mitigate the impact of screen use on sleep and seizure control, structured sleep hygiene practices are strongly recommended for paediatric epilepsy patients. Based on guidelines from the APP and recent sleep research [12, 69], the following strategies may improve sleep quality and seizure management:

  • maintain a consistent bedtime and wake-up time across the week,

  • eliminate screen use at least 60 minutes before bedtime,

  • keep all electronic devices out of the bedroom during the night,

  • enable night mode or blue-light filters on screens if necessary,

  • encourage calming pre-sleep routines such as reading or listening to quiet music,

  • avoid stimulating content and bright lighting in the evening,

  • discuss screen habits and sleep quality routinely during epilepsy follow-up visits (Tables 2 and 3).

Table 2

Features and target groups of epilepsy management apps

AppMeasurementsTarget group
App 1 [46, 50, 51]Seizures occurrence Medicines Sleep Additional symptoms self-evaluationPatients
App 2 [51]Movements of the patient during sleepCaregivers
App 3 [50]Medication intake Mood Sleep qualityPatients
App 4 [46]Medication intake Stress level Sleep managementPatients
App 5 [68]Medication intake Seizures (including video capture) Behavioural difficulties Sleep quality Current health condition as additional diseases Other significant events in real-time Recordings of healthcare appointmentsPatients/ caregivers
Table 3

Portable devices for additional sleep monitoring

Device (+ app)Measures
Device 1Steps
[68]Heart rate
Light and deep sleep duration
Calculated total sleep duration
Device 2Sleep onset
[69, 70]Sleep offset
Daytime sleep
Device 3Seizure detection
[60, 71, 72]Movement
Photoplethysmogram (PPG)
Electrodermal activity (EDA)
Temperature
Event tagging
Sleep quality
Device 4Seizure detection
[60, 61, 73]EEG
Movement
Heart rate
Sleep quality
Device 6Heart rate
[57]Basic ECG
Sleep quality
Screen time
Detect tonic-clonic seizures

CONCLUSIONS AND FUTURE DIRECTIONS

Additionally, while certain mobile apps or wearable devices may assist in tracking sleep or seizure patterns, their use should be carefully timed to avoid increasing overall screen exposure – particularly in the evening hours [55].

Despite these concerns, technological advancements offer promising solutions for monitoring and managing sleep disturbances in children with epilepsy. The widespread use of mobile devices and reliance on digital technology significantly affect children’s sleep quality and seizure management. The evidence suggests that blue light exposure and excessive screen time disrupt sleep patterns, leading to decreased melatonin levels, sleep fragmentation, and an increased likelihood of seizures. Studies confirm that screen-based activities before bedtime negatively impact sleep quality.

Furthermore, mobile devices and applications can provide real-time sleep tracking and behavioural insights, supporting self-management strategies for patients and their caregivers. However, the clinical effectiveness of these tools remains limited due to a lack of validation and regulatory approval.

Future research should prioritize investigating the long-term effects of exposure to blue light on patients with epilepsy, validating digital tools for sleep and seizure monitoring, and exploring intervention strategies such as blue-light-blocking technologies. Additionally, studies should investigate how different types of digital engagement influence the risk of seizure and how technological solutions can personalize seizure management. Understanding the neurobiological mechanisms of technology-related sleep disturbances will be crucial for developing effective, evidence-based interventions.

Reducing screen exposure before bedtime and integrating validated digital health interventions can improve sleep quality and potentially decrease seizure risk. Healthcare professionals, caregivers, and technology developers must collaborate to create accessible and effective solutions tailored to the vulnerable population of children with epilepsy.

Conflict of interest

Absent.

Financial support

Absent.

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