Relationship Between Sensory Processing and Sleep in Children With Autism Spectrum Disorder
Article information
Abstract
Objective
Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder, often associated with co-occurring sleep disturbances. Children with ASD frequently experience sleep disruptions, which can worsen core symptoms and challenging behaviors. Sensory processing difficulties, commonly observed in children with ASD, may contribute to these sleep problems. Therefore, this study aims to investigate the impact of sensory processing patterns on sleep disturbances among children with ASD aged 3 to 11 years.
Methods
A quantitative cross-sectional study design was employed with 74 children diagnosed with ASD. Child Sensory Profile-2 was used to assess sensory processing, and sleep disturbances were evaluated with the Child Sleep Habit Questionnaire. Partial Spearman’s rank correlation and multiple regression analyses were used to explore the relationships between sensory processing and sleep disturbances.
Results
Partial Spearman’s rank correlation revealed a significant positive correlation between all four quadrants of the Child Sensory Profile-2 (seeking, avoiding, sensitivity, and registration), as well as sensory sections (auditory, touch, movement, body position, and oral) and sleep disturbances (p≤0.05) while controlling for severity of autism. Multiple regression analysis revealed that sensory avoidance behavior significantly predicted overall sleep disturbance scores in children with ASD (β=0.414, p=0.003).
Conclusion
The study found that sensory avoiding demonstrates a significant association with sleep problems in children with ASD. These findings highlight the importance of incorporating sensory processing assessments in clinical evaluations of ASD children with sleep problems and suggest that sensory-based interventions may help improve their sleep quality.
INTRODUCTION
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder, which involves persistent difficulties in social interaction and communication, along with restricted and repetitive patterns of behavior [1]. Beyond the core diagnostic criteria, individuals diagnosed with ASD face various co-occurring difficulties, with sleep disturbances emerging as one of the most prominent. Research indicates that 40% to 73% of children with ASD experience sleep problems compared to the general population [2-4]. Most individuals with ASD experience disruptions in sleep patterns, including difficulties with sleep onset and maintenance, night awakenings, and irregular sleep-wake cycles [4,5]. Additionally, 80% of the parents whose children had been diagnosed with autism reported that their children’s sleeping difficulties caused them to have disturbed sleep [3].
Sleep disturbance in children with ASD extends beyond the night hours and influences daytime functioning and increases core ASD symptoms, including communication difficulties, restricted interests, and repetitive behaviors [6]. Sleep disruptions may contribute to increased levels of challenging behaviors, including irritability, hyperactivity, and aggression, in children diagnosed with ASD [7]. Sleep is considered a crucial factor for optimal development, and is also essential for cognitive function, emotional regulation, and physical restoration, especially during childhood [8,9]. Therefore, the disruption of sleep in children with ASD can also affect mood, cognitive function, and social interactions.
Sleep disturbances in ASD are multifaceted; however, sensory processing difficulties may emerge as a prominent factor, as children with ASD often struggle to tolerate the typical sensory experiences associated with sleep. Sensory processing (SP) refers to the complex neurological processes through which the nervous system receives, organizes, and interprets sensory information from the environment, including touch, sound, sight, taste, and smell [10]. Children with autism frequently present with atypical SP challenges, characterized by hyper-reactivity, hypo-reactivity, and sensory-seeking behaviors across different sensory modalities [11]. Tomchek and Dunn [12] reported that 95% of children with ASD experience challenges in SP, which underscores the widespread prevalence of SP dysfunction among these populations. Preliminary evidence suggests that increased sensitivities or aversions to sensory stimuli may contribute to sleep problems in autistic children. For example, Appleyard et al. [13] reported that SP challenges are related to sleep problems in typical toddlers. Additionally, Tzischinsky et al. [14] found that children with autism aged 3–7 years had sleep disturbances due to their hypersensitivity to touch.
The role of SP abnormalities in contributing to sleep disturbances among children with ASD is an evolving area of interest. However, an extensive understanding of the complex link between specific SP patterns and distinct sleep parameters is lacking. Understanding how SP patterns influence sleep is essential for developing tailored interventions aimed at improving sleep quality and overall well-being of children with ASD. Additionally, cultural factors, such as family expectations, societal norms, and community dynamics, can significantly influence how children with autism sleep [15]. Given the significant influence of cultural factors on the sleep of children with ASD, there is a need for research that examines the relationship between SP difficulties and sleep within specific cultural contexts.
Therefore, this study aims to examine the relationship between SP and sleep disturbances in children aged 3 to 11 years diagnosed with ASD. Specifically, the study focuses on the impact of Dunn’s sensory processing patterns, such as sensory seeking, avoiding, sensitivity, and registration, in contributing to sleep disturbances. The research hypothesizes that SP challenges are significantly associated with increased sleep-related difficulties.
METHODS
The study obtained ethical approval from the Institutional Ethics Committee of SRM Medical College and Research Centre, with the ethical clearance No: 8493/ IEC/2022. The study employed a quantitative cross-sectional research design to investigate the group of 74 children with ASD aged 3 to 11 years. The participants were selected using a convenience sampling method, adhering to specific selection criteria. The inclusion criteria for participant recruitment included: 1) a diagnosis of ASD and age ranging from 3–11 years, and 2) a score above 70 on the Indian Scale for Assessment of Autism. The exclusion criteria were: 1) children with other specific diagnoses to maintain the focus on ASD and 2) children who are receiving medication for sleep disturbances.
Instruments used
Indian Scale for Assessment of Autism
The Indian Scale for Assessment of Autism (ISAA) is a diagnostic tool specifically designed to evaluate and determine the severity of ASD, within the Indian context. It consists of 40 items and uses a 5-point scale for scoring, where 1 indicates “never” and 5 signifies “always.” The assessment conducted using the ISAA includes various domains, including social relationship and reciprocity, emotional responsiveness, speech-language and communication, behavior patterns, sensory aspects, and cognitive component. The ISAA determines the levels of autism symptoms based on the total score: a score of 70 to 106 indicates mild autism, 107 to 153 indicates moderate autism, and a score above 153 indicates severe autism [16].
The ISAA shows excellent internal consistency reliability, with an alpha coefficient of 0.93. Additionally, both inter-rater reliability and test-retest reliability are reported to be good. The content and discriminant validity of the ISAA have also been well established [16,17].
Child Sensory Profile-2
The Child Sensory Profile-2 (CSP-2) is a standardized questionnaire designed for parents to provide insights into how a child responds to different sensory stimuli in the context of home, school, and community. The items of CSP-2 are organized into quadrants (seeking, avoiding, sensitivity, and registration), a sensory section (auditory, visual, touch, movement, body position, and oral), and a behavioral section (conduct, social-emotional, and attentional). Scoring for CSP-2 uses a 5-point scale, with scores ranging from 5 (almost always) to 1 (almost never). The results of CSP-2 are interpreted using five classification categories: much less than others, less than others, just like the majority of others, more than others, and much more than others [18].
The CSP-2 shows strong reliability and validity according to research findings. Its internal consistency, measured by coefficient alpha, ranges from 0.60 to 0.90, indicating good reliability. The test-retest reliability, measured by intraclass correlation coefficient, ranges from 0.87 to 0.97, indicating excellent consistency over time. The convergent validity of the CSP-2 is established by comparisons with Vineland-II, Behaviour Assessment System for Children-2nd edition, and Social Skills Improvement System Rating Scale, as well as the original Sensory Profile. Furthermore, the CSP-2 demonstrates discriminant validity by effectively identifying sensory issues in both typical children and clinical groups [19].
Child Sleep Habit Questionnaire
The Child Sleep Habit Questionnaire (CSHQ) was developed to evaluate sleep in children. It is completed by parents or caregivers and addresses various aspects of a child’s sleep behavior. The sections of the CSHQ include bedtime resistance, sleep onset delay, sleep duration, sleep anxiety, night wakings, parasomnias, sleep-disordered breathing, and daytime sleepiness. The CSHQ employs a 3-point rating scale for scoring, which indicates the frequency or severity of each sleep behavior [20].
The CSHQ showed good internal consistency in both the normal (α=0.68) and atypical samples (α=0.78). Regarding its diagnostic performance, the CSHQ shows a sensitivity of 0.80 and a specificity of 0.72. The test-retest reliability of the CSHQ is considered acceptable, with values ranging from 0.62 to 0.79 [20].
Procedure
The parents of the participants were informed about the study’s objectives and provided their consent before enrolling their children in the study. The screening process involved the parents completing the ISAA for the children identified for potential participation. Children who scored above 70 on the ISAA were included in the study. Following this, the parents of these children were asked to complete the CSP-2 and the CSHQ assessments.
Statistical analysis
Data analysis was conducted using SPSS Statistics version 27 (IBM Corp.). Partial Spearman’s rank correlation was performed to assess the relationships between CSP-2 and CSHQ while controlling for autism severity as measured by the ISAA. Multiple regression analysis was conducted to determine the predictive value of SP quadrants (seeking, avoiding, sensitivity, and registration) on the total sleep disturbance score, as measured by the CSHQ. Multiple regression was used because it allows for the examination of how each sensory quadrant contributes to sleep disturbances while controlling for the influence of the other quadrants, providing a clearer understanding of the unique and combined effects of each SP pattern. Additionally, a simple linear regression was conducted to examine the relationship between the sensory sections of the CSP-2 and the total sleep disturbance scores from the CSHQ. This analysis aimed to explore the individual contribution of each sensory modality to sleep disturbances.
RESULTS
The study involved a total of 74 children diagnosed with ASD. An overview of the sample’s demographic characteristics can be found in Table 1. The participant’s mean age was 5.48 years (standard deviation=2.16), with ages ranging from 3 to 11 years. In terms of gender distribution, 67.6% of the participants were boys, and 32.4% were girls. The majority of the participants had no prenatal complications (81.1%), were delivered through cesarean section (68.9%), and were neither pre-term (90.5%) nor had low birth weight (90.5%). The ISAA scores were used to determine the severity of ASD among the participants, with the majority of children (54.1%) classified as having “mild” ASD, 43.2% classified as “moderate,” and 2.7% classified as “severe” ASD.
Demographic characteristics of the children diagnosed with autism spectrum disorder
To evaluate the connection between SP and sleep in children with ASD, partial Spearman’s rank correlation analyses were performed between quadrants, sensory section, and behavioral section of CSP-2 and different domains of sleep disturbances as measured by CSHQ while controlling for the autism severity (mild, moderate, and severe) as shown in Table 2. The findings suggest that children with different SP patterns show significant associations with multiple sleep disturbances, underscoring the interplay between SP challenges and sleep problems in ASD.
Correlation between sensory processing and sleep (partial Spearman rank correlation)
Dunn's sensory quadrants of CSP-2
The sensory quadrants outlined in Dunn’s model—seeking, avoiding, sensitivity, and registration—showed different levels of association with sleep disturbances. Among Dunn’s sensory quadrants, the “avoiding” quadrant showed the strongest associations with sleep disturbances. There were significant positive correlations found between “avoiding” and sleep onset delay (r=0.283, p=0.015), sleep anxiety (r=0.234, p=0.047), night wakings (r=0.476, p≤0.001), parasomnias (r=0.367, p≤0.001), sleep-disordered breathing (r=0.311, p=0.008), daytime sleepiness (r=0.469, p≤0.001), and the overall CSHQ score (r=0.476, p≤0.001). The “seeking” quadrant showed mild positive correlations with night wakings (r=0.285, p=0.015), daytime sleepiness (r=0.270, p=0.021), and the total CSHQ score (r=0.268, p=0.022). While these correlations were significant for certain domains, the overall impact of “seeking” on sleep disturbances appeared to be mild. The “sensitivity” and “registration” quadrants were positively correlated with sleep anxiety, night wakings, parasomnias, daytime sleepiness, and the total CSHQ score.
To further explore the relationship between SP patterns and overall sleep disturbances, a multiple regression analysis was performed. The model included seeking, avoiding, sensitivity, and registration as predictors, with the CSHQ total score as the dependent variable (Table 3). The overall model was significant (F=8.203, p≤0.001), explaining 32.2% of the variance in sleep disturbances (R2=0.322). This indicates that challenges in SP are important predictors of sleep problems in children with ASD. Among the predictors, avoiding showed the strongest relationship with sleep disturbances, as indicated by the highest beta value (β=0.414, p=0.003), suggesting that children who tend to avoid sensory input are more likely to experience sleep disturbances. While seeking, sensitivity, and registration did not show comparable beta values (β=-0.193, p=0.229; β=0.091, p=0.537; and β=0.277, p=0.103, respectively), avoiding remained the most significant predictor of sleep disturbances in this model.
Multiple regression analysis of CSHQ and quadrant section of CSP-2
Sensory section of CSP-2
Auditory processing difficulties are associated with sleep anxiety (r=0.393, p=0.001) and daytime sleepiness (r=0.315, p=0.007). Children with oral processing difficulties showed a positive correlation with sleep anxiety (r=0.249, p=0.033), night wakings (r=0.305, p=0.009), and the CSHQ total score (r=0.257, p=0.028). Additionally, touch and movement processing difficulties showed a stronger correlation with sleep anxiety, night wakings, parasomnias, daytime sleepiness, and the CSHQ total score. However, no significant correlations were observed between visual processing difficulties and the sleep outcomes measured by the CSHQ.
A simple linear regression analysis was performed using the CSHQ total scores as the dependent variable and the sensory section of CSP-2 (which includes auditory, visual, touch, movement, body position, and oral) as independent variables separately, as shown in Table 4. The regression models revealed statistically significant relationships between several SP domains and the CSHQ total score: movement processing, body position processing, and touch processing showed the strongest correlations with sleep quality, explaining 14.4%, 14.2%, and 13.2% of the variance in sleep, respectively. Auditory processing and oral processing contributed to 10.6% and 8.0% of the variance in sleep disturbances, respectively. Visual processing had the weakest association with sleep, explaining only 6.5% of the variance. Overall, these results suggest that the sensory section of CSP-2 is significantly linked to variations in sleep quality among children with ASD.
Simple linear regression analysis of CSHQ and sensory section of CSP-2
DISCUSSION
This study investigated the relationship between SP and sleep disturbances among children with ASD aged 3 to 11 years. It adds to the growing body of evidence that challenges with SP can have a significant effect on various aspects of daily functioning, including sleep in children with ASD. The study found that SP patterns, assessed using the CSP-2, are closely associated with multiple domains of sleep disturbances measured by the CSHQ. These results align with previous research suggesting that atypical SP may play an important role in the sleep difficulties often experienced by children with ASD [14,21,22].
This study revealed that the “avoiding” sensory quadrant of CSP-2 showed the strongest link to sleep disturbances, such as sleep onset delay, sleep anxiety, night wakings, parasomnias, daytime sleepiness, and the total CSHQ score. The significant connection between sensory avoidance and sleep-related problems indicates that children who actively avoid sensory input may experience increased arousal, which disrupts their ability to initiate and maintain sleep. The multiple regression analysis of the CSHQ and the quadrants of CSP-2 highlighted sensory-avoiding behavior as a significant predictor of sleep problems in children with ASD. This is in line with earlier studies that have found that sensory over-responsivity contributes to both anxiety and sleep-related difficulties in children with ASD [21,23].
Children with sensory sensitivity often experience significant sleep disturbances. These children may be more aware of and responsive to environmental stimuli, like sounds or textures, which can interfere with their sleep and contribute to anxiety at bedtime [14]. Additionally, the registration quadrant of Dunn’s model revealed a strong link to sleep problems, likely due to challenges identifying and responding to environmental cues that help regulate the sleep/wake cycle, resulting in sleep disruptions [22].
The study found that difficulties in oral and movement processing are linked to sleep disturbance scores, which aligns with the conclusions of Kosaka et al. [24]. They found that a lower threshold to oral sensory stimuli might contribute to difficulties with falling asleep in children with ASD, possibly due to discomfort during oral hygiene routines (like tooth brushing) or hypersensitivity to oral stimuli at bedtime [24]. Similarly, difficulties in vestibular processing can affect sleep, as children may experience discomfort from postural changes or instability when shifting positions at bedtime. This discomfort can result in increased restlessness and difficulties in both falling asleep and staying asleep [24]. Additionally, touch processing difficulties were strongly correlated with various sleep disturbances, such as sleep anxiety, night wakings, parasomnias, and daytime sleepiness. This supports the earlier findings that identified tactile processing difficulties as significant predictors of poor sleep quality in children with ASD [14,22,25]. In contrast, visual and auditory processing did not show significant correlations with total CSHQ scores, which is consistent with Tzischinsky et al. [14] and Wang et al. [22]. However, this finding differs from Tyagi et al. [26], who reported a link between decreased visual processing scores and higher CSHQ scores. These differences may be due to variations in sample characteristics, cultural contexts, or measurement methods used in the studies.
Several physiological and neurobiological mechanisms may explain the relationship between SP and sleep in children with ASD. A significant aspect is the altered levels of gamma-aminobutyric acid (GABA), a neurotransmitter, in children with ASD, which is associated with changes in SP. These deficits might lead to difficulties in behavior regulation, making it challenging for the child to stay calm and achieve a sound sleep [27].
This study highlights the importance of including SP assessments in the clinical evaluations of sleep problems in individuals with ASD. Due to the strong links between SP, especially sensory avoidance, and sleep disturbances, clinicians need to consider sensory-based interventions, like sensory integration therapy or sensory diets, to improve sleep quality in individuals with ASD [28].
The small sample size in this study may limit the generalizability of the results. Future studies should attempt to incorporate larger and more diverse populations to validate these findings across different groups. Moreover, longitudinal research is required to investigate the directionality of these associations across time. Research should also investigate the impact of specific sensory-based interventions on sleep quality to develop more effective, personalized treatment strategies for individuals with ASD. Another limitation is the dependence on parent-reported questionnaires, like the CSP-2 and CSHQ, which can be affected by response bias, recall bias, and parents’ subjective interpretations of their children’s behavior. To overcome these limitations, future studies should use objective assessment tools to evaluate SP and sleep disturbances. For instance, actigraphy or polysomnography could offer more precise and quantifiable data on sleep patterns and quality.
This study emphasizes the important role that SP difficulties in contributing to sleep disturbances among individuals with ASD. The results indicate that specific SP quadrants from CSP-2–avoiding, sensitivity, and registration—are significantly associated with different aspects of sleep disturbances. Among these, “sensory avoiding” emerges as the significant predictor of overall sleep disturbances, underscoring the vital influence on sleep-related difficulties in individuals with ASD. Additionally, the sensory sections of CSP-2, which include auditory, oral, touch, and movement processing, have been associated with sleep problems in children with ASD. The findings from this study can guide the tailored sensory-based interventions, such as sensory integration therapy, aimed at improving sleep quality and overall well-being for children with ASD by addressing their specific SP challenges.
Notes
The authors have no potential conflicts of interest to disclose.
Availability of Data and Material
The dataset generated or analyzed during the study is available from the corresponding author upon reasonable request.
Author Contributions
Conceptualization: Ganapathy Sankar Umaiorubagam, Deepak Vignesh Raj S. Data curation: Deepak Vignesh Raj S. Methodology: Deepak Vignesh Raj S, Ganapathy Sankar Umaiorubagam. Supervision: Ganapathy Sankar Umaiorubagam. Writing—original draft: Deepak Vignesh Raj S. Writing—review & editing: Ganapathy Sankar Umaiorubagam, Deepak Vignesh Raj S.
Funding Statement
None
Acknowledgements
None