Sleep disturbances and associated factors in children with autism spectrum disorder

Introduction

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by persistent deficits in social communication and interaction, as well as restricted and repetitive behaviors, interests, or activities (1). Globally, the incidence of ASD has been rising, with the Centers for Disease Control and Prevention reporting a prevalence of 1 in 36 children aged 8 years in the United States as of 2020 (2). The increasing prevalence highlights the necessity for comprehensive research into various aspects of ASD, including comorbid conditions such as sleep disturbances.

Sleep disturbances are notably prevalent in children with ASD, with rates ranging from 50% to 80%, compared to 20% to 30% in typically developing children (3). The disturbances include insomnia, delayed sleep onset, reduced sleep duration, frequent night awakenings, and parasomnias (4). A meta-analysis by Elrod and Hood [2015] indicated that children with ASD exhibit increased bedtime resistance, sleep onset delay, shorter sleep duration, and more night awakenings than their typically developing peers (5).

The repercussions of these sleep disturbances extend beyond nighttime, adversely affecting daily functioning. Poor sleep quality in children with ASD is linked to exacerbated ASD symptoms, heightened behavioral problems, impaired cognitive functioning, and diminished quality of life for both the child and their family (6). Mazurek and Sohl [2016] reported that sleep disturbances in children with ASD are significantly correlated with increased anxiety, hyperactivity, and stereotypic behaviors (7).

Although research on sleep disturbances in individuals with ASD has grown substantially in recent years, there remains a significant gap in our understanding of how these issues manifest across different cultural and socioeconomic contexts, particularly in developing countries. A study in Thailand by Inthikoot and Chonchaiya [2021] revealed that children with ASD experienced significantly more sleep disturbances than typically developing children did, particularly in areas such as bedtime resistance and sleep anxiety (8). However, this study was limited to an urban setting, potentially overlooking the experiences of families in more diverse or rural areas.

Chonburi Province in Thailand presents a unique opportunity to address this research gap. As a large province with a diverse population in terms of socioeconomic status, parenting styles, occupations, and education levels, it offers a representative sample of both urban and rural populations. Investigating the prevalence of sleep disturbances and their associated factors in children with ASD in this region can provide valuable insights into how these issues vary across different contexts. Therefore, this study aimed to explore the prevalence of sleep disturbances and identify their associated factors among children with ASD in Chonburi Province, contributing to a broader understanding of sleep issues in individuals with ASD and informing targeted interventions and support strategies. This study hypothesizes that the prevalence of sleep disturbances in children with ASD in Chonburi Province is higher than in previous studies that focused solely on urban communities. Furthermore, we expect that factors such as caregiving practices, bedroom environment, and media use before bedtime are significantly associated with these sleep disturbances. We present this article in accordance with the STROBE reporting checklist (available at https://pm.amegroups.com/article/view/10.21037/pm-24-36/rc).

Methods

Study design and population

This prospective cross-sectional study was conducted among children with ASD in Chonburi Province, Thailand, from January to April 2024. The participants included parents of children aged 4–10 years who were diagnosed with ASD and who were receiving treatment at Chonburi Hospital. We employed consecutive sampling, where all eligible patients visiting the Developmental and Behavioral Clinic during the study period were invited to participate until the required sample size was achieved. This sampling strategy was chosen to ensure efficient recruitment while maintaining representation across different clinic days. The sample size was calculated using the formula for estimating a population proportion:

n=(Zα2)2×p(1−p)d2[1]

where:

• $n$ = required sample
• $\frac{Z\alpha }{2}$ =1.96 (standard normal variate at 5% type I error)
• $p$ = expected proportion of sleep disturbances (0.815) based on Hodge’s study (3)
• $d$ = absolute precision (0.05)

Using these parameters, the minimum required sample size was calculated as:

n=(1.96)2×0.815(1−0.815)(0.05)2=232[2]

After adjusting for an anticipated 5% non-response rate, the target sample size was increased to 245 participants. The sample size was calculated with 95% confidence level (α=0.05) and 80% power.

Eligible participants were Thai-speaking primary caregivers who co-sleep with their ASD child for at least five nights per week. This inclusion criterion was based on the need to accurately observe the child’s sleep patterns. It is important to note that in Thai culture, particularly for children with special needs, co-sleeping with caregivers is a common practice. While this criterion may underestimate cases of children who sleep alone, it reflects the cultural norm in Thailand and enhances the reliability of our sleep behavior data. To identify sleep disturbances, we used the standard criterion of a total Children’s Sleep Habits Questionnaire (CSHQ) score greater than 41, which was applied to all participants regardless of their sleeping arrangements. This approach allowed us to accurately assess sleep problems while considering the cultural context of our study population.

The exclusion criteria encompassed primary caregivers under 18 years of age and children with concurrent genetic disorders, cerebral palsy, chronic lung disease, or epilepsy. Additionally, non-Thai nationals and nonresidents of Chonburi Province were excluded.

Data collection and measurements

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Chonburi Hospital Research Ethics Committee (14/66/S/h3) and informed consent was obtained from all individual participants’ parents. Participants were recruited through announcements at the Developmental and Behavioral Clinic of Chonburi Hospital and their parents completed a general information questionnaire and the CSHQ. These questionnaires were administered in private rooms at the outpatient department of Chonburi Hospital, and the process took approximately 10 to 15 minutes.

Three primary measurement tools were employed in this study:

• General information questionnaire: this questionnaire collected data on children’s sex, age, education level, media consumption, and participation in occupational therapy or alternative treatments; family history of developmental and behavioral disorders; the primary caregiver; and monthly family income.
• CSHQ-Thai version (8): the 48-item CSHQ assesses sleep behavior problems in children aged 4–10 years. The questionnaire includes 33 scored items in 8 domains: bedtime resistance, sleep onset delay, sleep duration, sleep anxiety, night waking, sleep-disordered breathing, parasomnias, and daytime sleepiness. Scores range from 33 to 99, with scores above 41 indicating sleep disturbances. The CSHQ has an overall reliability of 0.83, with subscale reliabilities ranging from 0.39 to 0.77.
  • Originally validated for typically developing children, a recent study by Shui [2021] has shown its applicability to children with ASD, though the optimal cutoff scores may differ (9). To maintain consistency with existing literature (3,8) and allow for direct comparisons, we used the standard cutoff score of 41. This decision is supported by the high sensitivity of the CSHQ (89.9–93.0%) in identifying sleep problems, though its specificity is lower (42.6–57.7%), suggesting a tendency to overidentify sleep issues while capturing most true cases. This makes the CSHQ a valuable screening tool for our study.
  • Although the validation process of the CSHQ-Thai version did not explicitly account for the culturally accepted practice of co-sleeping, which may influence behaviors such as bedtime resistance and sleep anxiety, this questionnaire must be completed by caregivers who closely observe the child’s sleep. As a result, caregivers inevitably co-sleep with the child. Therefore, the use of this questionnaire aligns well with our study design and population.
• Patient medical information: supplementary patient medical information was collected from outpatient medical records at Chonburi Hospital. This information included age at ASD diagnosis, ASD severity level, and details of medication use (type, dosage, and duration). The ASD severity was categorized based on DSM-5 criteria as mild (requiring support), moderate (requiring substantial support), or severe (requiring very substantial support) (1).

To minimize potential sources of bias, we implemented several strategies: use of the validated CSHQ-Thai version for standardized assessment, limitation of sleep habit assessment to the past week to reduce recall bias, and collection of comprehensive data on potential confounding factors for subsequent adjustment in multivariable analysis.

Statistical analysis

Descriptive statistics, which comprised means, standard deviations, medians, and percentages, were utilized for general data presentation. Chi-square and Fisher’s exact test and independent t-tests were employed to examine relationships between groups with and without sleep disturbances. Univariate logistic regression was first performed to identify potential predictors of sleep disturbances. Variables with a P value <0.2 in the univariate analysis were included in the multivariable logistic regression model. We used a stepwise backward elimination method to select the final set of variables in the multivariable model, with a threshold of P<0.05 for retention in the final model. Prior to conducting the multivariable logistic regression, we assessed multicollinearity among the independent variables using variance inflation factors (VIFs). Variables with VIF values less than 5 were considered acceptable for inclusion in the multivariable model. The predictive accuracy of the final model was assessed using the area under the receiver operating characteristic (ROC) curve. Data analysis was conducted using Stata statistical software, release 18 (Stata Corp LLC, College Station, TX, USA). Statistical significance was set at 0.05.

Results

This study included 235 children with ASD from Chonburi Province, Thailand. Initially, 280 children were identified as potentially eligible based on clinic records. After screening, 250 children met the eligibility criteria. Of these, 240 children and their caregivers agreed to participate and provided informed consent. Five participants were excluded due to incomplete questionnaires, resulting in a final sample of 235 children with no missing data included in the analysis (Figure 1). The general characteristics of the children are detailed in Table 1. The participants had a mean age of 64.31±20.56 months, and males comprised 81.28% of the sample. A high prevalence of sleep disturbances was observed, affecting 212 children (90.21%), while 23 children (9.79%) did not exhibit sleep disturbances. The mean age was comparable between children with sleep disturbances (64.53±20.79) and those without sleep disturbances (62.26±18.58 months, P=0.61). Most children (59.15%) had normal weight-for-height ratios (91–120%), with no differences between the two study groups (P=0.25). Family income predominantly ranged between 20,000 and 50,000 Thai Baht/month (54.89%). Although caregiver education levels varied (P=0.12), there were no differences between groups. A small proportion of children (5.96%) had a family history of genetic disorders. Caregiving responsibilities differed significantly, with children with sleep disturbances being more likely to be cared for by both parents (69.81% vs. 47.83%, P=0.058) and less likely to be cared for by grandparents (26.42% vs. 47.83%, P=0.049). Children typically began schooling at 34.53±19.68 months, with no difference in school entry age (P=0.58) or duration of education between the children with and without sleep disturbances.

Figure 1 Flow diagram for participant inclusion. ASD, autism spectrum disorder.

The mean age at ASD diagnosis was 42.33±13.77 months, with no notable difference between groups (42.35±14.02 months for those with sleep disturbances vs. 42.09±11.53 months for those without, P=0.93). ASD severity was similar in both groups (P=0.95), with severe levels being the most common (58.49% in children with sleep disturbances vs. 56.52% in those without). Both groups had similar levels of participation in occupational therapy (56.60% vs. 65.22%, P=0.51) and speech therapy (56.13% vs. 65.22%, P=0.51). The use of medication, particularly risperidone, was prevalent in both groups (56.13% vs. 65.22%, P=0.38). Children with sleep disturbances were more likely to have mobile phones in their bedrooms (66.51% vs. 43.48%, P=0.04). The frequency of prebedtime media use differed (P=0.01), with children with sleep disturbances being more likely to use media sometimes (32.55% vs. 8.70%) or often (25.94% vs. 17.39%). However, the duration of daily multimedia use did not differ notably between children with and without sleep disturbances (P=0.26) (Table 1).

Table 2 presents the CSHQ results. There were significant differences across all subscales between children with and without sleep disturbances. The mean ± SD total CSHQ score was 50.27±7.53, with children with sleep disturbances scoring significantly higher (51.48±7.53) than those without sleep disturbances (39.17±1.75, P<0.001). Specifically, children with sleep disturbances had higher scores for bedtime resistance (11.84±2.18 vs. 8.69±1.79, P<0.001), sleep onset delay (1.68±0.61 vs. 1.26±0.54, P=0.01), and sleep duration (4.62±1.41 vs. 3.39±0.89, P<0.001). They also had higher scores for sleep anxiety (7.21±1.82 vs. 4.78±0.74, P<0.001), night waking (4.49±1.47 vs. 3.65±0.88, P=0.007), parasomnias (9.41±2.21 vs. 7.52±0.89, P<0.001), sleep-disordered breathing (3.77±1.12 vs. 3.17±0.39, P=0.01), and daytime sleepiness (12.69±2.57 vs. 9.43±1.19, P<0.001).

As shown in Table 3, univariate regression analysis identified several factors significantly associated with sleep disturbances. Having both parents as primary caregivers increased the odds of having sleep disturbances [odds ratio (OR) 2.52, 95% confidence interval (CI): 1.06–6.02, P=0.04], whereas having grandparents as primary caregivers decreased the odds (OR 0.39, 95% CI: 0.16–0.94, P=0.04). The presence of a mobile phone in the bedroom was associated with greater odds of having sleep disturbances (OR 2.58, 95% CI: 1.08–6.17, P=0.03). Occasional multimedia use before bedtime (2–4 days/week) increased the odds of sleep disturbances compared to rare use (OR 6.66, 95% CI: 1.49–29.83, P=0.01).

Assessment of multicollinearity showed low VIF values for all variables included in the multivariable model: having both parents as primary caregivers (VIF =1.01), presence of a mobile phone in the bedroom (VIF =1.02), and occasional multimedia use before bedtime (VIF =1.02), indicating no significant multicollinearity among the predictors. As shown in Table 4, multivariable logistic regression analysis confirmed three factors as significant independent predictors of sleep disturbances. The factors were having both parents as primary caregivers [adjusted OR (AOR) 4.47, 95% CI: 1.71–11.67, P=0.002], the presence of a mobile phone in the bedroom (AOR 3.21, 95% CI: 1.24–8.30, P=0.02), and occasional multimedia use before bedtime (AOR 5.15, 95% CI: 1.11–23.87, P=0.04). The predictive model incorporating these three factors demonstrated good discriminatory power, as indicated by an area under the ROC curve of 0.74 (95% CI: 0.64–0.83, P=0.048) (Figure 2).

Figure 2 Area under the ROC curve for variables in the multivariable analysis predicting sleep disturbances in children with autism spectrum disorder. ROC, receiver operating characteristic; CI, confidence interval.

Discussion

Sleep disturbances remain prevalent among children with ASD worldwide, as reported in previous studies (3,5,6). Our findings revealed a 90.21% prevalence of sleep disturbances among children with ASD in Chonburi Province, Thailand. This prevalence is notably higher than some studies conducted in Western countries (50–80%) (3-5,7), which could be attributed to several factors unique to the Thai context.

Our study sample comprised 81.28% males, which is consistent with the well-established gender disparity in ASD diagnosis. Maenner et al. [2023] reported that ASD is approximately four times more common among boys than among girls (2). This gender distribution in our sample aligns with these epidemiological findings, suggesting that our study population is representative of the broader ASD population in terms of gender distribution.

The cultural practice of co-sleeping, which is common in Thailand, especially for children with special needs, may contribute to the high prevalence of reported sleep disturbances (10). While co-sleeping can provide comfort and security for children, it may also lead to increased awareness of sleep disturbances by parents, potentially resulting in higher reporting of sleep disturbances. This cultural norm differs from many Western countries where independent sleeping is more commonly encouraged, which might lead to differences in sleep disturbance prevalence and reporting (11). Moreover, the diverse socioeconomic backgrounds represented in Chonburi Province, ranging from urban to rural settings, provide a comprehensive view of factors influencing sleep patterns in children with ASD. In urban areas, factors such as higher noise levels, increased light pollution, and potentially more screen time due to greater access to technology may contribute to sleep disturbances (12). Conversely, in rural areas, different challenges may arise, such as less consistent access to healthcare and interventions that could address sleep issues, or different daily routines influenced by agricultural or community practices that affect sleep schedules (13).

The CSHQ indicated significantly higher total scores among children with sleep disturbances, reflecting more severe disturbances across multiple domains. This finding supports earlier research indicating that children with ASD face multiple sleep-related issues. Specifically, our study found higher scores in bedtime resistance, sleep onset delay, and sleep duration for children with sleep disturbances. These results are consistent with Elrod and Hood’s [2015] meta-analysis, which reported increased bedtime resistance and sleep onset delay in children with ASD (5). These sleep issues often stem from the difficulties children with ASD face in adapting to changes and their adherence to routines, making the transition to bedtime a challenging process. Mazurek and Sohl [2016] found that deficits in executive function, including inhibitory control, in children with ASD affect their ability to initiate the sleep process (7). Consequently, children with ASD who have sleep disturbances are frequently assessed with higher scores in bedtime resistance and sleep onset delay. Furthermore, abnormalities in circadian rhythms and melatonin secretion in children with ASD may contribute to poorer sleep quality and duration (14). As a result, one of the common sleep disturbances observed in children with ASD is reduced sleep duration.

The higher scores in sleep anxiety and night waking in our study align with findings from Souders et al. [2017], who reported these as common sleep disturbances in children with ASD (4). Children with ASD often experience higher levels of anxiety compared to typically developing children, which can significantly impact their sleep quality (8). Furthermore, our study found that children with ASD who have sleep disturbances also scored higher on parasomnia issues. This can be attributed to functional brain abnormalities related to the sleep cycle in children with ASD, consistent with the study by Ming et al. [2009], which reported that children with ASD have a high rate of parasomnias, reaching up to 60% (15). Additionally, the consequences of poor sleep quality and insufficient sleep, as explained above, can lead to daytime sleepiness (16). This aligns with our study’s findings, which showed that children with ASD who have sleep disturbances often experience daytime sleepiness as well.

Several significant factors associated with sleep disturbances in children with ASD were identified. Notably, children with both parents as primary caregivers had greater odds of experiencing sleep disturbances (AOR 4.47, 95% CI: 1.71–11.67). This finding supports previous research suggesting that consistent caregiving practices are essential for maintaining regular sleep patterns in children with ASD (6,17,18). The involvement of both parents may introduce variability in bedtime routines and sleeping environments, contributing to sleep disturbances. Interestingly, while having grandparents as primary caregivers appeared protective in the univariate analysis, this effect became insignificant in the multivariate model. This shift might suggest that the consistency of caregiving practices, rather than the specific caregiver, is more crucial for sleep quality.

Second, the presence of mobile phones in the bedroom significantly increased the odds of having sleep disturbances (AOR 3.21, 95% CI: 1.24–8.30). This observation aligns with previous studies highlighting the adverse impacts of electronic devices on children’s sleep quality (7,19). Mobile phones emit blue light, which can disrupt the production of melatonin, a hormone crucial for regulating sleep-wake cycles (20). The higher scores in sleep onset delay and reduced sleep duration in our CSHQ results support this finding. Exposure to blue light could contribute to difficulties in falling asleep, leading to delayed sleep onset. Furthermore, this delay, combined with fixed wake-up times (often dictated by school schedules), can result in reduced overall sleep duration. Additionally, engaging with multimedia content before bedtime can be stimulating, further delaying sleep onset (21).

Finally, occasional multimedia use before bedtime (2–4 times/week) was identified as a significant predictor of sleep disturbances (AOR 5.15, 95% CI: 1.11–23.87). This finding is consistent with other studies that demonstrated a correlation between screen time and sleep disturbances (22,23). Intermittent exposure to multimedia before bedtime may disrupt sleep patterns, leading to prolonged sleep onset latency and reduced sleep duration (24). The higher scores in bedtime resistance and sleep anxiety in our CSHQ results could be related to this factor, as prebedtime media use might increase arousal and make it more difficult for children to settle down for sleep.

The high prevalence of sleep disturbances and the associated factors identified underscore the necessity for targeted interventions to improve sleep hygiene in children with ASD. The interventions might include educating parents and caregivers about the importance of consistent bedtime routines, minimizing the presence of electronic devices in bedrooms, and limiting multimedia use before bedtime. Implementing these strategies could help mitigate the adverse effects of sleep disturbances, thereby improving the overall quality of life for children with ASD and their families.

Our study has several strengths, including its focus on both urban and rural populations in Chonburi Province, which provides a more comprehensive picture of sleep disturbances in children with ASD across diverse settings. The use of the validated CSHQ-Thai version enhances the reliability of our findings in the Thai context.

This study has several limitations to consider. Its cross-sectional design prevents establishing causal relationships, necessitating longitudinal studies to elucidate the long-term impacts of sleep disturbances on children with ASD. While providing a comprehensive view across Chonburi Province, our research doesn’t identify specific urban-rural differences in sleep patterns and associated factors, an area future study should explore. We also didn’t explicitly examine economic factors influencing co-sleeping practices; future research could benefit from collecting data on household composition and bedroom ratios to clarify whether co-sleeping is driven by cultural preferences, economic necessities, or both. Our inclusion criterion of co-sleeping with caregivers, while culturally appropriate, may have underestimated sleep disturbances in children who sleep alone, suggesting future studies should include various sleeping arrangements. Additionally, the effectiveness of proposed interventions should be assessed across diverse cultural and socioeconomic contexts to develop more effective, culturally sensitive support strategies for children with ASD and their families. These efforts would address the current limitations and contribute to a more comprehensive understanding of sleep disturbances in children with ASD.

Conclusions

This study reveals a high prevalence of sleep disturbances among children with ASD in Chonburi Province, Thailand, reflecting the complex interplay of cultural, socioeconomic, and environmental factors in both urban and rural settings. Key factors associated with sleep disturbances include having both parents as primary caregivers, the presence of mobile phones in bedrooms, and occasional multimedia use before bedtime. These findings underscore the need for targeted, culturally sensitive interventions that address the unique challenges in different environments.

Acknowledgments

We gratefully thank the children and their caregivers who participated in this study and Professor Weerasak Chonchaiya for allowing us to use the CSHQ–Thai version.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://pm.amegroups.com/article/view/10.21037/pm-24-36/rc

Data Sharing Statement: Available at https://pm.amegroups.com/article/view/10.21037/pm-24-36/dss

Peer Review File: Available at https://pm.amegroups.com/article/view/10.21037/pm-24-36/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://pm.amegroups.com/article/view/10.21037/pm-24-36/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Chonburi Hospital Research Ethics Committee (14/66/S/h3) and informed consent was obtained from all individual participants’ parents.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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