Open access peer-reviewed chapter

Potential of Physical Activity-Based Intervention on Sleep in Children with and without Autism Spectrum Disorder

Written By

Thai Duy Nguyen

Submitted: 04 October 2021 Reviewed: 07 January 2022 Published: 18 May 2022

DOI: 10.5772/intechopen.102534

From the Edited Volume

Exercise Physiology

Edited by Ricardo Ferraz, Henrique Neiva, Daniel A. Marinho, José E. Teixeira, Pedro Forte and Luís Branquinho

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Abstract

Sleep problems are widespread, and sleep disorders are frequent in children with autism spectrum disorders (ASD). Physical activities (PA) are considered a practical, non-pharmacological approach for improving sleep. This study aims to explore the impact of PA on sleep in children with or without ASD. Seventy-five children were recruited, including 57 children with ASD and 18 typically developing (TD) children as control. Participants wore an accelerometer monitor (Sense Wear® Pro Armband 3, Body media) for 6 consecutive days and nights to assess sleep and PA. The results indicated ASD children had limited participation in PA compared with TD children (Total time for PA: 156 ± 79 vs. 216 ± 59 minutes on weekdays; 145 ± 93 vs. 178 ± 108 minutes on weekend). The children usually had more opportunities to participate in PA on weekdays and they tended to resist recommended bedtime (Sleep duration: 7.0 ± 0.8 vs. 9.6 ± 1.2 hours with ASD children; 7.1 ± 0.7 vs. 9.5 ± 1 hours with TD children). It also reported PA with moderate to vigorous intensity was better to improve sleep in children both with and without ASD. Finally, this study recommended promoting PA will help to improve sleep quality and reduce sedentary behaviors for children with ASD in particular and children in general.

Keywords

  • autism spectrum disorders
  • sleep disorder
  • physical activity
  • sleep quality

1. Introduction

Autism spectrum disorder (ASD) is known as a developmental disorder characterized by social communication difficulties and repetitive behavior. This is a complex syndrome related to genetic and environmental factors [1]. Great concerns about the high prevalence of poor sleep and the impact of sleep disturbance on ASD children are widely reported worldwide [2]. It is estimated that sleep disorders affect up to 80% of children with ASD compared with 10–25% of typically developing (TD) children [3, 4]. Children with ASD often face difficulties with sleep, and this has a strong relationship with daytime behavior problems; the most frequently reported issues include difficulty falling asleep, restless sleep, and frequent waking [5, 6]. Disturbed sleep could also exacerbate the core symptoms of ASD. Sleep education, environmental changes, behavioral interventions, and exogenous melatonin medication are frequently used for promoting and improving sleep quality [7, 8]. Improving the quality of sleep plays a critical role for children because sleep helps to optimize cognition, memory, behavioral adjustment, and learning [9].

Compared with TD children, the rate of sleep problems is higher in children with ASD. Significant impairments in social interaction and restricted behavior, combined with increased rates of motor problems, are frequently observed in individuals with ASD [10]. It may make these children less motivated and less likely to participate in physical activities (PA), leading to the risk of increased sedentary behavior (SB). It also may contribute to harmful health outcomes like overweight or obesity [11]. Recent reports showed difficulties in motor skills and motor capacities for ASD children compared with TD children, and limitations in PA may reduce opportunities for social interaction and learning in children with ASD [12, 13].

Physical activity is defined as any form of movement that leads to energy expenditure and is not performed in competition, including all daily activities, leisure activities, and exercises [14]. PA is indispensable for children’s health. However, most children in the world do not participate in at least 60 minutes per day of moderate to vigorous physical activity (MVPA) as was recommended [15, 16]. Studies showed children diagnosed with ASD had PA levels lower than typically developed peers [17]. There are many individuals, social, and community barriers that make PA participation more difficult and may contribute to increased screen time by children with ASD [18]. Evidence has also been presented of PA decreasing negative behaviors and promoting positive behaviors. It improved social contacts and friendships and increased motor skills [11]. Thus, participation in PA is particularly essential for children with developmental disabilities, who could potentially benefit from increased PA and reduced SB; it has a positive impact on their development, quality of life, health, and future [9].

A reciprocal relationship between sleep and PA has been documented in children with and without ASD. Increasing exercise has been reported as helping produce better sleep quality, reduced weight, pain prevention, and improved mood in insomnia patients [19]. Adjusting factors of PA such as level, intensity, and duration of exercise has a positive effect on sleep quality [20]. Association between sleep patterns and PA levels suggests that being more physically active tends to support healthier sleep in children without disabilities [21]. Other studies also revealed a significant improvement in sleep efficiency, sleep onset latency, sleep duration, and wake after sleep onset with increased PA. It highlighted the role of PA in improving sleep quality among children with ASD [22]. Accordingly, regular moderate-intensity PA is recommended to treat and prevent sleep disorders without using medications [23]. In contrast, sleep disorders can lead to reduced cognitive performance and PA, while increasing the risk of injury during exercises. Getting insufficient sleep has been identified as a risk factor associated with public health problems such as obesity, depression, and limited PA [19]. Also, poor sleep was associated with higher rates of repetitive behavior and had a negative effect on challenging behaviors [24].

The mechanism of how PA affects sleep is not yet fully understood. Therefore, new studies must be carried out to understand the benefit of PA in the promotion of sleep and understand better the physiological responses to sleep loss. Within the scope of this study, we wanted to explore the relationship between PA intensity and sleep quality, its specific impact on improving sleep parameters. Thereby, it is possible to establish an optimal PA plan as a non-drug intervention to improve sleep quality as well as the quality of life in children with ASD.

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2. Method

This study was approved by the local Ethics Committee of the Hospital (N°A00-865 40). It was conducted according to the Declaration of Helsinki and registered on the Clinicaltrials.gov registry (N°CT: 02830022).

2.1 Subjects

Each subject and their parents received both written and oral information, and those that agreed to participate signed a consent form. Seventy-five children were recruited to participate in the study, including 57 children with ASD and 18 typically developing children as a control group. All of them attended regular schools. Diagnosis of ASD was performed by experienced physicians and psychologists, according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition criteria [25]. The subjects were also assessed with the Autism Diagnostic Observation Schedule (ADOS) [26]. Intelligence Quotient (IQ) was estimated using the Wechsler Intelligence Scale for Children, 4th edition [27]. IQ criterion for children is IQ > 70, excluding children with intellectual disabilities (IQ < 70). Following the ethical guidelines, IQ scores and ADOS results were not provided to researchers. However, score certification of IQ > 70 for all ASD children in this study was confirmed by a clinical psychologist experienced in diagnosing children with ASD and autism. Children with psychiatric disorders, comorbid medical conditions, contraindications for exercise, and those taking medication were excluded from the study [9].

2.2 Actigraphy

Participants wore the accelerometer monitor (Sensewear® Pro Armband 3, Body media) for 6 days and nights (5 weekdays and 1 weekend day). Participants and their parents completed daily diaries to distinguish periods in which participants did not wear the accelerometer (shower or bath, swimming, or other water activities). Time not wearing the device was excluded from the analysis. The monitoring device used in this study is a bi-axial accelerometer, worn on the right arm triceps. It can estimate energy expenditure based on algorithms from measured parameters such as acceleration, heat flux, galvanic skin response, skin temperature, near-body temperature, and demographic characteristics like sex, age, height, and weight [9].

This device can measure sleep parameters such as sleep time, sleep latency, wake up time, wake after sleep onset (WASO), and sleep efficiency. It was also used to calculate the parameters related to PA, such as the time spent for PA with different intensities and energy expenditure for PA. In the experiment period, children slept in their own bedroom, and their parents often described a consistent bedtime routine.

2.3 Child sleep diary

Children and their parents recorded information related to sleep each night for 6 consecutive days. It included bedtime (the time when the children went to bed each evening), wake up time (the time when the children woke up each morning), and sleep time (parents’ estimation of duration of the children’s sleep time each night).

Parents of the children also completed other questionnaires on sleep, physical activity, and parental assessments.

  • The children’s sleep habits questionnaire (CSHQ): This is the most used questionnaire to evaluate the sleep of ASD children, translated into French. CSHQ included 45 items with scores rated from 1 to 3, and was divided into eight subscales (bedtime resistance, sleep onset delay, sleep duration, sleep anxiety, night wakings, parasomnias, sleep-disordered breathing, and daytime sleepiness). CSHQ’s total score was calculated and compared with the threshold value of 41. CSHQ’s total score higher than 41 indicated sleep disorders or low sleep quality [28].

  • The physical activity questionnaire for children (PAQ-C): PAQ-C provided general estimates of the level of PA and sedentary activities over the previous 7 days in children. It contained nine items with scores rated from 1 to 5. The final PAQ-C summary score was calculated to reflect PA levels, respectively Light (score = 1), Moderate (score = 2–4), and Vigorous (score = 5) [29].

  • The questionnaire assessed parental awareness of children’s sports practice. These questionnaire results helped to assess barriers to PA (time, economics, and emotion) and other values like the importance of sports practice, children’s athletic level, parental support, and parents’ sports practice.

2.4 Statistics

The data collected by actigraphy, sleep diary, and questionnaires were processed by specialized software (Sensewear Software) and Excel software in different ways. We calculated the average values of weekdays (WD), weekends (WE), and all days (AD) during the experiment for each child. Data used for statistical analysis were the mean values ± standard deviation. The differences of data between questionnaires and actigraphy method; WD and WE measured by actimetry in children with and without ASD were compared by R software (t-test, Pearson’s chi-squared, one-way test with significance considered as p < 0.05). The relationship between factors related to sleep and PA was assessed by correlation and linear regression with significance considered as p < 0.05 (R software). Finally, principal component analysis (PCA) and agglomerative hierarchical cluster analysis (AHCA) were applied to classify individuals according to the component group (R software). In this study, we had n = 75 observations (57 children with ASD, 18 control children) and p = 24 predictors (anthropometric characteristics and data from monitoring devices). We used the elastic net method and appropriate criteria to select variables [12]. Finally, only 17 pertinent variables were used for PCA and AHCA.

The factors related to sleep used for analysis were total time on the bed (h), sleep duration (h), sleep quality index (%), bedtime resistance (min), sleep latency (min), wake-up time resistance (min), awakening latency (min), and wake after sleep onset (min). The factors related to PA used for analysis were sleep energy expenditure (kcal), total PA energy expenditure in 24 h (kcal), MVPA energy expenditure in 24 h (kcal), sedentary PA energy expenditure in 24 h (kcal), total time for PA (min), time for sedentary PA (min), time for moderate PA (min), time for MVPA (min), time for vigorous PA (min), time for strong vigorous PA (min), and daily step number.

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3. Results

No difference between children with or without ASD was found with regard to demographic characteristics. However, we observed ASD children had a low PAQ-C score and a higher CSHQ score than control children as reported by their parents. Also, sleep duration as collected by questionnaires was more than by monitoring device by around 2.6 hours (p < 0.001) in ASD children and 2.4 hours (p < 0.001) in control children (Table 1).

No difference was found on PA factors between weekdays and weekends in ASD children, but there were differences in control children. They were more active in PA participation on weekdays than weekends (Table 1). On weekdays, ASD children had less energy expenditure for MVPA and time for PA (moderate, moderate to vigorous, vigorous, and strong vigorous) than control children. Meanwhile, they had more time for sedentary PA than control children on both weekdays and weekends. No difference in sleep factors was found between the two groups (Table 1).

Principal component analysis (PCA) and agglomerative hierarchical cluster analysis (AHCA) were performed with 17 pertinent variables. PCA results indicated five main component groups which helped explain 86.1% of the variance, with an eigenvalue ≥1. Two clusters were determined with blue space representing ASD children and yellow space representing control children (Figure 1). Two children with ASD (17, 23) and one control child (70) were classified outside of these clusters. In this graphic representation, a child on the same side as a given variable obtained a high score for this variable. A low value for this variable was attributed to a child on the opposite side. The distribution of children in both groups was not focused on their specific clusters. ASD children had discrete distribution (blue cluster), while control children were more concentrated (yellow cluster).

Figure 1.

Principal component analysis biplot of PA and sleep. ASD children are represented from 1 to 57, control children are represented from 58 to 75. (a1) Total time on bed (h); (a2) sleep duration; (a3) sleep quality index (%); (a4) bedtime resistance (min); (a5) sleep latency (min); (a7) awakening latency (min); (a8) wake after sleep onset (min); (a9) sleep energy expenditure (kcal); (a10) total PA energy expenditure (kcal); (a11) MVPA energy expenditure (kcal); (a12) sedentary PA energy expenditure (kcal); (a13) total time for physical activity; (a14) time for sedentary PA (min); (a15) time for moderate PA (min); (a16) time for MVPA (min); (a17) time for vigorous PA (min); and (a19) daily steps number.

Child #17 was characterized by a total of daily steps twice the group average (25,620 vs. 12,354 steps/24 h) and had the highest time for vigorous PA (117.87 vs. 17.87 min/24 h), strong vigorous PA (16.17 vs. 2.07 min/24 h) compared with the group average. Child #23 was characterized by a total PA energy expenditure two times higher than the group average (3826 vs. 1581 kcal/24 h) and a sleep latency multiplied by 2.5 compared to the other children (35.4 vs. 13.6 min/24 h). Child #70 was characterized by a total PA energy expenditure twice the group average (3470 vs. 1642 kcal/24 h) and the highest sleep energy expenditure of all children in the group (500 vs. 280 kcal/24 h).

The results of an individual’s classification by AHCA in Figure 2 showed the ranking of each child and clusters in which they were classified. Based on the dendrogram graph, all children with similar characteristics in both the ASD group (green color) and the control group (red color) were classified into four different clusters. The characteristics of children in clusters were shown by comparing the result of the mean values of variables (Table 2).

  • Cluster 1 (saffron): A total of 22 ASD children. This cluster is characterized by children with limited participation in PA and bad sleep. They had the lowest PA (a11, a13, a15, a16, a17, and a19) except for sedentary PA (a12 and a14) and low sleep quality (a3) compared with other clusters.

  • Cluster 2 (pink): A total of 24 members, including 18 ASD children and six control children. This cluster was characterized by children with moderate participation in PA and good sleep. They had high sleep quality (a3), high sleep duration (a2), and were moderate for PA compared with other clusters.

  • Cluster 3 (gray): A total of 26 members, including 15 ASD children and 11 control children. This cluster was characterized by children with strong vigorous participation in PA and bad sleep. They had the highest PA (a13, a15, a16, a17, and a19) excepted for sedentary PA (a12 and a14) and the lowest sleep quality (a3) compared with other clusters.

  • Cluster 4 (green): Total of three members, including two ASD children and only one control child. This cluster was characterized by children with moderate participation in PA and good sleep. They had the highest sleep quality (a3) and were vigorous for PA compared with other clusters.

Figure 2.

(a) Dendrogram of individual’s classification by AHCA. (b) Factor map of individual’s classification by AHCA. *Cluster 1 (saffron), cluster 2 (pink), cluster 3 (gray), and cluster 4 (green).

ASD (n = 57)Control (n = 18)
DemographicAge (years)10.9 ± 2.910.1 ± 2.2
Weight (kg)39.2 ± 15.635.7 ± 13
Height (cm)146.5 ± 15.9145.9 ± 15.6
BMI (kg/m2)17.6 ± 3.917.4 ± 3.5
Gender (male, %)87.7%100%
SurveyPAQ-C (questionnaire)2.5 ± 0.8**3.2 ± 0.6
CSHQ (questionnaire)47.9 ± 6.8**44.4 ± 4.5
Sleep duration (questionnaire)9.6 ± 1.29.5 ± 1
Sleep duration (actigraphy)7.0 ± 0.8*7.1 ± 0.7*
WeekdaysWeekend
ASDControlASDControl
Sleep variablesTotal time on bed in 24 h (h)9.7 ± 0.89.9 ± 0.89.8 ± 1.29.4 ± 2.7
Sleep duration in 24 h (h)7 ± 0.97.1 ± 0.87.1 ± 1.26.8 ± 2
Sleep quality index in 24 h (%)74.7 ± 6.974.4 ± 7.475.3 ± 11.270.9 ± 19.4
Bedtime resistance in 24 h (min)12.9 ± 8.210.5 ± 4.49.9 ± 9.311 ± 6.6
Sleep latency in 24 h (min)13.6 ± 9.915.9 ± 6.614 ± 18.113.1 ± 11.1
Wake-up time resistance in 24 h (min)1.5 ± 3.20.2 ± 0.31.3 ± 50.8 ± 3
Awakening latency in 24 h (min)15.5 ± 8.419.3 ± 6.716.6 ± 20.418.2 ± 13.2
Wake after sleep onset in 24 h (min)115.3 ± 41.4126.7 ± 49.3120 ± 61.4115.6 ± 73.7
Sleep energy expenditure in 24 h (kcal)291 ± 91.7282.3 ± 83.7290.4 ± 105.2259.8 ± 88.2
Physical activity variablesTotal PA energy expenditure in 24 h (kcal)1584 ± 5481650 ± 5951565 ± 5961511 ± 699
Sedentary PA energy expenditure in 24 h (kcal)1154 ± 4311013.5 ± 3701153 ± 4381004 ± 487
MVPA energy expenditure in 24 h (kcal)415 ± 231$594 ± 272391 ± 276445 ± 309#
Total time for PA in 24 h (min)156 ± 79$216 ± 59145 ± 93178 ± 108
Time for sedentary PA in 24 h (min)1284 ± 79$1223 ± 611293 ± 911182 ± 311
Time for moderate PA in 24 h (min)136 ± 66167 ± 47125 ± 75150 ± 80
Time for MVPA in 24 h (min)154 ± 78$209 ± 59142 ± 88170 ± 98
Time for vigorous PA in 24 h (min)18 ± 20$$42 ± 1917 ± 2319 ± 25#
Time for strong vigorous PA in 24 h (min)1.8 ± 3$$6.3 ± 5.93.3 ± 9.77.9 ± 21.6
Daily step number in 24 h12,400 ± 4691$15,739 ± 328712,080 ± 618012,065 ± 5336#

Table 1.

Demographic data, and characteristics of sleep and PA.

p < 0.001 significantly different between sleep duration by questionnaire and actigraphy.


p < 0.05 significantly different between ASD group and control group by questionnaire.


p < 0.05 significantly different between weekdays and weekends in the control group.


p < 0.001 significantly different between ASD group and control group on weekdays.


p < 0.05 significantly different between ASD group and control group on weekdays.


p < 0.05 significantly different between ASD group and control group on weekend.


Values are mean ± SD. BMI (body mass index).

Cluster 1Cluster 2Cluster 3Cluster 4
Sleep variableTotal time on bed in 24 h (h)a19.889.659.967.92
Sleep duration in 24 h (h)a27.217.156.945.98
Sleep quality index in 24 h (%)a374.8176.5772.5578.28
Bedtime resistance in 24 h (min)a411.1514.3010.0715.55
Sleep latency in 24 h (min)a514.879.9216.4421.90
Awakening latency in 24 h (min)a717.0813.3019.1814.85
Wake after sleep onset in 24 h (min)a8116.29110.30134.8064.19
Physical activity variableSleep energy expenditure in 24 h (kcal)a9337.99246.42266.88447.91
Total PA energy expenditure in 24 h (kcal)a101594.621327.801653.543262.96
MVPA energy expenditure in 24 h (kcal)a11247.81364.77637.311013.32
Sedentary PA energy expenditure in 24 h (kcal)a121343.80945.53976.692163.82
Total time for physical activity in 24 h (min)a1379.44162.02247.64182.54
Time for sedentary PA in 24 h (min)a141360.601277.521192.131257.52
Time for moderate PA in 24 h (min)a1573.31140.69201.00148.26
Time for MVPA (min)a1679.04159.19241.40178.99
Time for vigorous PA in 24 h (min)a175.7318.5040.3930.73
Daily steps number in 24 ha198154.5113,128.6916,886.7715,080.22

Table 2.

Characteristics of ASD children in classification clusters.

Values are mean of cluster.

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4. Discussion

Most current studies still have not explained how PA affects sleep exactly and vice versa. PA and sleep influence each other through multiple complex interactions, both physiological and psychological [19]. PA is considered beneficial for improving sleep quality, but the effectiveness of PA-based interventions remains a question [30]. Conversely, sleep problems could increase symptoms of anxiety and depression, and thereby indirectly affect PA performance.

According to the survey results, we found a difference between children in the two groups. PAQ-C score indicated ASD children had PA levels lower than control children. CSHQ score presented higher sleep problems in ASD children than control children. Besides these, parent-reported estimated sleep time was higher than actigraphy-measured sleep time by 37.1% in ASD children and 33.8% in control children (Table 1). This demonstrated the children did not go to bed according to the schedule that their parents set. It was consistent with data collected from the monitoring device about total time in bed being more than sleep duration. Moreover, studies have reported on factors affecting sleep in a modern society like sleep environment, lifestyle habits, high-tech devices, physical activities, and learning activities [18, 31, 32]. Also, the downside of social development, children were free in their own rooms with little control from their parents. Therefore, they tended to resist recommended bedtime and fell asleep later. A typical example was when children went to bed, and even were lying in bed, but did not sleep, and instead, read stories or used smart entertainment devices.

Comparing results between children in the two groups, we observed ASD children had less participation in PA than control children. They had a low energy consumption for MVPA, daily step number, and total time for PA compared with control children on weekdays (Table 1). Also, time for MVPA, vigorous PA, and strong vigorous PA was equal to 74.7, 42.8, and 28.6% of control children on weekdays. Meanwhile, the time for sedentary PA was higher than in the control group both weekdays and weekends (Table 1). It proved ASD children often faced difficulties related to PA, especially MVPA, and tended to increase sedentary behaviors. Recent studies on the relevance of obesity and sedentary PA to sleep in adolescents and children with ASD showed similar results [33, 34]. No any significant differences in sleep between ASD children and control children, the reason was PA does not always affect sleep directly, as sleep also depends on control factors (such as age, health status, and mode and intensity of exercise intervention) or psychological factors [35, 36]. Our results also indicated control children had more active participation in PA (MVPA, vigorous PA, and daily steps) on weekdays compared to the weekend (Table 1). These differences may come from children usually going to school and participating in school activities on weekdays, while ASD children tended to be less sedentary and have less PA participation than TD children [37]. On the weekend, children were free to stay at home with their families, so they tended to have less PA participation.

PCA and AHCA analysis showed characteristics of PA and sleep in all children. We identified two relatively distinct clusters on the factors related to PA and sleep by PCA. One cluster included ASD children who had a positive correlation with sedentary PA, and the cluster with control children had a positive correlation with PA from moderate to vigorous, except for sedentary PA (Figure 1). Then, we presented the classification of individuals more clearly by AHCA, with four clusters determined to have a higher rate of ASD children than control children (Figure 2, Table 2). All of cluster 1 was ASD children; its characteristics were the lowest level of PA participation, highest sedentary behaviors, and bad sleep quality. This was consistent with the characteristics of children with ASD [6, 38]. Cluster 2 had 75% ASD children; its characteristics were a moderate level of PA participation, but they had better sleep in both duration and quality. Cluster 3 contained 57.7% ASD children; its characteristics were the highest level of PA participation, lowest sedentary behaviors, and worst sleep. Meanwhile, cluster 4 had 66.67% ASD children; its characteristics were a moderate level of PA participation and the best sleep quality. These classification results showed a difference between sedentary PA in ASD children and active PA participation in control children. Also, they indicated that PA with moderate to vigorous intensity was related to good sleep while limited participation in PA or strong vigorous PA was related to poor sleep. The positive effect of physical activity on sleep quality has also been discussed in studies of children with ASD [7, 39].

The findings in our study suggested the role of PA in improving sleep quality. Better sleep was the result of increased sleep duration and decreased sleep latency and wake after sleep onset. We should spend more daily energy consumption on MVPA, vigorous PA. It helps to increase PA and reduce SB. MVPA also was reported to enhance sleep quality by decreasing sleep latency and wake after sleep onset [40, 41]. Thus, we recommended increasing PA with moderate to vigorous intensity and sleep duration for improved sleep quality, especially with ASD children. This suggestion is in line with results reported in a comprehensive review of studies about the effects of PA on sleep quality with different PA intensities [23].

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5. Conclusion

This study indicated ASD children tend to have low participation in PA and increased sedentary behaviors compared to control children. These children had more active PA participation on weekdays than the weekend, and they tended to resist bedtime by parents’ request. Also, we provided evidence that PA with moderate to vigorous intensity can improve sleep quality, especially for children with ASD. It could be used as a non-pharmacological method to treat sleep disorders for ASD children. However, the nature and the magnitude of this impact are still controversial. Future studies need to clarify the mechanism of PA intensity effects on sleep quality. This way, they can give PA protocols based on reliable evidence to promote PA and prevent sedentary behaviors in children with ASD. This study also contributed to palliative treatment for children with ASD.

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6. Limitations

The main limitation of our study was a disparity in the number of children between the ASD group and the control group. The sample sizes of the two groups were inconsistent. This affected the criteria on sleep quality used to distinguish children with and without ASD. These limitations need to be addressed in future studies.

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Written By

Thai Duy Nguyen

Submitted: 04 October 2021 Reviewed: 07 January 2022 Published: 18 May 2022