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Children with Autism Spectrum Disorder Exhibit Elevated Physical Activity and Reduced Sedentary Behavior

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2023-11-13 14:42
MDPI
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1. Introduction

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by social and behavioral deficits, impairment in verbal and nonverbal communication, and restricted, repetitive behaviors and interests [1]. According to the World Health Organization, the global prevalence of ASD in 2022 was 1 per 36 people [2]. The etiology of ASD remains elusive. A combination of multiple etiologies has been suggested, including genetic, epigenetic, immunologic, environmental [3,4], and metabolic abnormalities [5]. Mitochondrial dysfunction, neuroinflammation, and glutamate excitotoxicity are the most strongly implicated etiologies that correlate with the severity of ASD symptoms [6].

A growing body of evidence indicates that children with ASD experience motor function deficits not often seen in their typically developing (TD) counterparts [7]. Motor impairments, including deficits in gross and fine motor skills [8,9], may limit the participation of ASD children in sports and other physical activities (PAs) [10,11,12]. Low levels of physical activity, late motor skills, and fitness, especially in children and adolescents with ASD, may exacerbate social and emotional deficits and related comorbidities [13]. In addition, an association between the severity of motor and social impairments has been reported in children with ASD [14,15,16,17], suggesting that social impairments may also limit ASD children’s participation in PA.

PA, especially moderate and vigorous activities (MVPAs), is important for promoting health and overall quality of life among children, including children with ASD [18,19,20]. Recently, a growing interest in understanding the relationship between social and cognitive features of ASD and physical health outcomes has been widely noted [21]. Previous studies revealed that behavioral functioning in ASD children improved after performing some PA [22,23]. PA has been shown to effectively lower the frequency of stereotypical behavior episodes in children with ASD. It is believed that PA can cause significant changes in brain structure, function, and cognition in ASD children [24]. Long-term physical exercise modifies the structure of motor areas such as the cerebellum and motor cortex, as well as parts of the hippocampus, which is critical for learning, memory, and navigation [25].

Some studies reported that autistic people typically exhibit a reduction in physical activity due to social and behavioral abnormalities. Limited opportunity for exercise impacts the behavior of autistic children, leading to chronic illnesses, such as obesity, which is common in autistic patients [10,26,27]. Furthermore, it has been reported that age and gender may influence the outcome of PA reduction [10,28,29]. Specifically, ASD children’s physiological, cognitive, psychosocial, and behavioral functioning have been found to benefit from moderate to vigorous physical activity (MVPA) [30].

Since the effects of PA on cognition have important implications for improving performance in ASD children, accurate assessment of PA levels is important for studying PA patterns in ASD children. Previous studies have used accelerometer-based activity monitors and self-reported questionnaires to evaluate the level of PA. However, accelerometers have been widely accepted as a more reliable tool for the assessment of a range of PA types in a free-living environment [24,31,32].

One of the most widely used devices in PA research is the ActiGraph monitor (GT3X+), which captures children’s total body movements and free-play activities, including sitting, standing, walking, walking up the stairs, running, and cycling. It can accurately measure the orientation and immature motor movements of a child, making it suitable for the assessment of PA and sedentary activity in persons with disabilities [28,33]. For MVPA, moderate (3–5.99 METs) is defined as 2690–6166 counts/min and vigorous (6 METs) as 6167 counts/min. The total number of minutes of light-intensity physical activity is defined as the total number of minutes between 200 and 2690 counts/min [34].

To provide PA intervention programs aimed at enhancing health-related physical fitness on a daily, systematic, and individualized basis, it is essential to evaluate PA in people with ASD. The current study aimed to determine whether PA has a differential effect on children with ASD compared with their TD age-matched children. Our findings provide novel insights that should inform the development of effective interventional strategies.

2. Materials and Methods

2.1. Participants

Twenty-one children with autism aged 3–13 years (mean age 6.43 ± 2.29 years) were recruited from the Autism Research and Treatment Center, Faculty of Medicine, King Saud University, Riyadh, Saudi Arabia. A diagnosis of autism was confirmed in all children using the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders. The exclusion criteria were comorbid ASD-related medical diseases or other neurological problems. The exclusion criteria were established by parent interviews and a review of the children’s medical records. The Childhood Autism Rating Scale (CARS), Social Responsiveness Scale (SRS), and sensory profile as measures of severity of the studied participants are presented in . It can be easily noticed that all are moderate–severe cases. For CARS, scores between 30 and 36.5 indicate mild to moderate autism, and scores from 37 to 60 indicate severe autism [34]. For SRS, 60–75 is considered mild to moderate, and a T-score >75 indicates severe impairment [35].

The control group was comprised of 30 age-matched TD children (mean age 7.2 ± 3.14 years) who attended the pediatric clinic of King Khalid University Hospital, Riyadh, Saudi Arabia, for routine follow-up. They had no clinical signs or symptoms indicative of neuropsychiatric disorders. Children with any neurological, endocrine, cardiovascular, pulmonary, liver, or kidney disease were excluded from the study.

Informed consent for the study was obtained from parents or legal guardians of the investigated subjects as approved by the ethical guidelines of medicine of King Saud University number 13/3945//IRB.

2.2. Physical Activity Measurement

Physical activity was measured using the ActiGraph GT3X+ accelerometer (ActiGraph, Pensacola, FL, USA). Parents or caregivers were instructed on how to operate the device and attach it to the child’s waist. Participants were instructed to wear the monitor during all activities except for swimming, showering/bathing, and during their sleep. Parents or guardians were provided with a log to record times when the accelerometer was not worn. Previous studies showed that at least 6 days of recordings were needed for accurate evaluation of sedentary behavior [36,37]. Participants kept the accelerometer for at least 7 consecutive days. To be included in the study, participants had to have accelerometer recordings for a minimum of 6 days, including at least 1 weekend day, and at least 10 h of recordings each day. Participants who did not meet these criteria were excluded from the study. Activity levels were stratified based on accelerometer counts per minute, according to the protocol described by Freedson [38]. Time spent in sedentary behavior, light-intensity physical activity, and moderate–vigorous physical activity (MVPA) was quantified using cut-point thresholds established specifically for preschool children.

2.3. Anthropometric Measurements

Participants’ height was measured in centimeters to the nearest tenth of a millimeter. Weight was measured in kilograms to the nearest gram, using an electronic scale (SECA S-214, Basel, Switzerland stadiometer). Measurements were taken twice for each participant, and mean values were recorded. BMI reference values have been set by the World Health Organization (WHO), and their formula was used to assess the quantity of fat in controls and children with autism [39]. The metabolic equivalent of tasks (METs) was estimated using the ActiGraph regression equation developed by Freedson et al. [38], which utilizes counts and age to estimate METs. In the current study, we selected ActiGraph cut points in preschool-age children to estimate metabolic equivalent. To estimate fat percent, the skin folds at the triceps and subscapular area were measured by Lange Skinfold Caliper. Waist and hip circumferences were measured using a measuring tape, and the data were used to calculate the hip-to-waist ratio. Muscle strength was estimated by measuring hand grip strength using Takei Hand Grip Dynamometer. All measurements were taken twice, and averages were recorded.

2.4. Statistical Analysis

Data analysis was performed using Statistical Package for Social Studies (SPSS) version 22 (IBM Corp., New York, NY, USA) unless otherwise indicated. Continuous variables were expressed as mean ± standard deviation. Significance of observed differences was evaluated using the t-test with normally distributed variables and the Wilcoxon Mann–Whitney test with non-normally distributed variables. Differences associated with a p-value < 0.05 were considered significant. Statistical power was estimated using G*Power version 3.1.9.4 [18]. Power was calculated for the study’s sample sizes (21 autism and 30 control) for each of the predictor variables. Correlations were computed using Spearman correlations (r), and a p-value was calculated to indicate the significance of the correlation, with a p-value < 0.05 indicating significance. When comparing correlation coefficients between groups, multiple computations were performed to determine whether observed differences between group correlation coefficients met statistical significance. First, Fisher z transformation was applied to the pair of correlation coefficients to be compared, r1 and r2, converting them to the Fisher z scores z1 and z2, respectively. This was performed using the equation shown below (1). A Z-test was then calculated by dividing the difference between z1 and z2 by the standard error of that difference, as shown below (2). A p-value was calculated, with values < 0.05 indicating statistical significance. Fisher z transformation and the Z-test were performed using the syntax written by Weaver and Wuensch (Weaver 2013) for SPSS.

zi = 1/2 ln ((1 + r)/(1 − r))
z = (z1 − z2)/√ (1/(n1 − 3) + 1/(n2 − 3))
where z1 and z2 are Fisher z scores corresponding to correlation coefficients r1 and r2, and n1 and n2 are the sample sizes corresponding to r1 and r2, respectively.

3. Results

Demographic and anthropometric data are presented in . Twenty-one children with ASD and 30 age-matched TD children were included in this study. We found that METs were higher in ASD children compared with TD children (p = 0.001). All other demographic and anthropometric characteristics did not significantly differ between the two groups of children.

We observed a modest but statistically significant decrease in total sedentary bouts in ASD children. We did not observe any differences in total activity counts or total time spent (p = 0.957) in all types of PA between the two groups.

Our results revealed that there was a highly significant difference in total time spent in sedentary activity (p < 0.001) and in the total sedentary activity counts (p < 0.001) in the control group compared with the ASD group. The results also indicated that there were no significant differences between groups for the total counts and time spent in LPA and MPA. However, ASD children spent more time than TD engaging in VPA (p = 0.017).

The total counts on different axes showed that PA varied substantially depending on the axis. PA assessed by axis 2 (horizontal or forward and backward motion) showed a highly significant difference in ASD compared to TD (p = 0.001), whereas axis 1 (vertical or upward and downward motion) and axis 3 (lateral or left and right motion) showed no significant difference. Total vector magnitude counts (VM counts) were also significantly higher in the ASD group compared with TD (p = 0.024). Furthermore, counts per minute (CPMs) were significantly higher in ASD over all axes compared with TD (p = 0.001), as well as for the vector magnitude CPM (p ≤ 0.001) (Figure 1 and .

Figure 1. The step counts per day showed higher correlation with vigorous-intensity physical activity (r = 0.759; p = 0.001 and MVPA, r = 0.668; p = 0.001) than with light- (r = 0.552; p = 0.009) or moderate-intensity activity (r = 0.549; p = 0.01) among ASD participants. In the control group, the step counts per day were more highly correlated with moderate-intensity physical activity and MVPA (r = 0.633; p = 0.001, r = 0.664; p = 0.001, respectively) than with vigorous-intensity activity (r = 0.497; p = 0.005).

When the correlation between step counts and total energy expenditure in METs was calculated, the correlation was slightly higher in ASD than in the control group (r = 0.768, p = 0.001; 0.773, p = 0.001). * Significant correlations with p < 0.05.

4. Discussion

Research focusing on the association between ASD symptoms and PA has substantially increased over the last few years. Previous research demonstrated that PA has important implications for the improvement of some social, cognitive, and behavioral features [18,19,20,21]. To the best of our knowledge, the current study is the first to assess sedentary behavior and physical activity in Saudi ASD children.

The precise assessment of physical activity levels is crucial for understanding the association between active lifestyle and health, especially when evaluating the effectiveness of intervention programs [13,22]. By assessing the level of PA for autistic children, it is possible to develop sports programs that support the health of this group. In the current study, the overall time spent in PA and the total activity level did not differ significantly between ASD and controls. This result agrees with previous studies. Sandt and Frey reported no differences between ASD children and controls in any physical activity setting [40].

4.1. Sedentary Activity in ASD Participants

In this study, ASD children spent less time in sedentary activity (SA) than TD (p = 0.001). This finding is in line with some previous studies comparing children with different disabilities, including ASD, with TD children in Europe and North America. For example, it has been reported that young children with ASD are more active and spend significantly less time in sedentary behavior compared with the control group [26,41], suggesting that the PA differential between ASD and TD may be age-related [11,14,28]. In contrast, other studies reported that ASD children were less physically active and had an increased sedentary lifestyle compared with TD, as sedentary activity ASD children may experience impairments in movement, communication, social skills, and behavior [10,11].

Our data indicated that the light and moderate activity counts were not significantly different between the two groups, but vigorous activity was significantly higher in ASD compared with TD. This finding contrasts with previous studies [11,42,43]. Researchers demonstrated that ASD children spent less time in PA compared with TD children. This finding could be attributed to their characteristic stereotypical and self-stimulating behaviors. They concentrate on negative habits, which prevent them from engaging in physical activity. It was noticed that the time spent in MVPA did not differ significantly between the studied groups (p = 0.132). It only accounts for a small amount of time in the day, and the majority of the time was spent in LPA and sedentary behavior.

shows a significantly stronger correlation between daily step counts and all physical activity intensities (time spent in light, moderate, and vigorous activity) in the ASD group than in the control group, despite the fact that there was no statistically significant difference between the two groups in terms of step counts. This result shows that the group with higher step counts spent a lot more time being physically active.

4.2. Vigorous PA in ASD Patients

It would be intriguing to investigate the association between increased vigorous PA in ASD patients compared with TD children and unbalanced excitatory/inhibitory neurotransmission, oxidative stress, and neuroinflammation as ASD etiological processes related to hyperactivity [44,45]. Individuals with autism have substantially greater glutamate, the primary excitatory neurotransmitter, and much lower gamma amino butyric acid (GABA), the primary inhibitory neurotransmitter [6,46]. Under normal physiological conditions, released glutamate is metabolized or taken up by neighboring astrocyte cells through glutamate transporters. When these pathways are disturbed, as in ASD, glutamate builds up and overexcites the N-methyl-D-aspartate (NMDA) receptors. These receptors, when triggered by excessive glutamate, function as a Ca2+ (calcium ion) channel. Because Mg2+ (magnesium) blocks the channel, these channels only operate when the cell membrane is depolarized. In ASD, the membrane is chronically depolarized, Mg2+ exits the channel, and Ca2+ influx is unrestricted for longer periods of time, leading to cell death via free radicals [47] or through mitochondrial overload, which results in free radical formation. Inflammatory mediators, increased oxidative stress, and decreased levels of brain-derived neurotrophic factor (BDNF) and other growth factors have more or less similar effects on glutamate microcircuits in ASD patients, regardless of whether the origin is centrally or peripherally derived. When mitochondria become damaged and electron leakage increases, ROS generation increases [48]. Theoretically, the higher PA in ASD patients could be due to the idea that the brain can use elevated glutamate as an energy source to dispose of the neurotransmitter’s excess levels. This explanation can find support by considering the fact that NMDA receptor antagonists reduce channel permeability and inhibit Ca2+ influx, providing neuroprotection, amending glutamate excitotoxicity, and perhaps ameliorating hyperactivity symptoms [44,49].

4.3. Vigorous PA and Sleep Disruptions in ASD Patients

Sleep disruptions are one of the most common comorbidities reported in ASD children, occurring in up to 80% of ASD children compared with 20–40% of typically developing children [50,51,52], and include insomnia, circadian rhythm disturbances, difficulty falling asleep, restless sleep, and frequent waking [53]. Most sleep comparison studies using objective (actigraphy) or subjective (questionnaires) assessments have found that children with ASD had lower sleep metrics than their typically developing counterparts [54]. In an attempt to find a link between the recorded increased aggressive PA and sleep problems in autistic individuals, it was interesting to consider the work of Wang et al. [54], who reported that some children with much higher PA have impaired sleep latency, bedtime resistance, and awakening latency. It was documented that minimal or excessive PA had a negative impact on sleep quality and quantity. Although sedentary living has frequently been suggested to explain this sleep disruption, it has been less frequently proven that an excess of PA may be deleterious. This can support the harmful effect of the increase in aggressive PA in our ASD participants compared with controls [55]. The suggested link between increased aggressive PA, glutamate excitotoxicity as a neurochemical characteristic of an autistic brain, and sleep disruption as a comorbidity in ASD patients could find support in the work of Bell et al. [56], in which they proved the relationship between glutamate excitotoxicity and sleep deficits in EcoHIV-infected mice.

4.4. Limitations

Our well-defined sample of children with a thorough diagnosis of ASD is one of our work’s strengths. However, our study has a number of limitations. First, it is descriptive; it does not investigate the impact of PA on selected comorbidities of ASD, such as anxiety, stress, and sleeping difficulties. Second is the small sample size, and our population was limited to ASD, which restricts the generalizability of the current findings. However, given the scarcity of studies in this field, our poor understanding of this group, the possibility for objective measurement practices, and potential correlations between PA and quality of life, the findings must be reported, replicated, and expanded.

5. Conclusions

The major finding of this study was that the ASD children were more physically active and less sedentary than TD. The outcomes of the current study should be interpreted with caution due to the small sample size. Therefore, larger studies with larger samples are needed to explore the potential role of physical activity engagement in the improvement of cognition and brain function in ASD children.

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Reproduced from:https://www.mdpi.com/2076-3425/13/11/1575
According to previous research, individuals with autism spectrum disorder (ASD) have lower levels of physical activity than their typically developed (TD) counterparts. There have been conflicting reports about physical activity (PA) levels in people with ASD. Given the conflicting evidence, further investigation is required. We believe that evaluating PA in individuals with ASD is critical in order to offer PA intervention plans aiming at increasing their health-related physical fitness on a daily, systematic, and individualized basis. In the current study, an ActiGraph monitor (GT3X+) was used to accurately measure PA and sedentary activity in 21 children with autism aged 6.43 ± 2.29 years and 30 TD children aged 7.2 ± 3.14 years. Our data indicated that while the light and moderate activity counts were not significantly different between the two groups, the vigorous activity was significantly higher in ASD compared to TD. This finding was attributed to ASD characteristic stereotypy and self-stimulating behaviors. The significantly higher vigorous PA is discussed in relation to altered neurochemistry, oxidative stress, and neuroinflammation as etiological mechanisms in ASD. This research provides a better understanding of the status of PA participation in individuals with ASD.
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Relationship between Physical Activity and Sedentary Behavior, Spinal Curvatures, Endurance and Balance of the Trunk Muscles-Extended Physical Health Analysis in Young Adults
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Background: Physical inactivity and sedentary behavior are associated with poor well-being in young people with adverse effects extending into adulthood. To date, there are many studies investigating the relationship between physical activity (PA) and posture, but there are no data on the relationship between the type and intensity of PA and sedentary behavior, their association with thoracic and lumbar spine angles, and with endurance and balance of the trunk muscles, especially in healthy young adults aged 18–25 years. Moreover, there are no data on the relationship between PA and sedentary behavior and musculoskeletal and cardiopulmonary health, as well as quality of life (QoL) and sleep that would provide a more comprehensive picture of physical health status. Aim: Therefore, the aim of this cross-sectional study was to investigate the extent to which PA and sedentary behavior are associated with each other and with changes in spinal curvatures, endurance and balance of trunk muscles in an extended analysis of physical health status in young adults aged 18–25 years by additionally including measures of body composition, cardiorespiratory capacity, and QoL and sleep. Methods: A total of 82 students (58% female, 42% male) aged 18–25 years completed all required tests. Primary outcome measures included the following: PA and sedentary behavior calculated from the long form of International PA Questionnaire (IPAQ-LF), spinal curvatures measured by a Spinal Mouse® device, endurance and balance of the trunk muscles measured using trunk endurance tests and their ratio. Results: Overall, 50% of students were classified as minimally active and 50% as health-enhancing PA (HEPA) active. The angles of thoracic kyphosis and lumbar lordosis showed no correlation with PA or time spent sitting. However, students with the lowest PA had significantly higher scores on the trunk extensor endurance test and trunk extensor/flexor endurance test ratio, indicating imbalanced trunk muscles. Moreover, these students spent the most thei
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Aflatoxin B1 Exposure Aggravates Neurobehavioral Deficits and Immune Dysfunctions of Th1, Th9, Th17, Th22, and T Regulatory Cell-Related Transcription Factor Signaling in the BTBR T+Itpr3tf/J Mouse Model of Autism
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Autism spectrum disorder (ASD) is a neurodevelopmental disease characterized by impaired communication, reciprocal social interactions, restricted sociability deficits, and stereotyped behavioral patterns. Environmental factors and genetic susceptibility have been implicated in an increased risk of ASD. Aflatoxin B1 (AFB1) is a typical contaminant of food and feed that causes severe immune dysfunction in humans and animals. Nevertheless, the impact of ASD on behavioral and immunological responses has not been thoroughly examined. To investigate this phenomenon, we subjected BTBR T+Itpr3tf/J (BTBR) mice to AFB1 and evaluated their marble-burying and self-grooming behaviors and their sociability. The exposure to AFB1 resulted in a notable escalation in marble-burying and self-grooming activities while concurrently leading to a decline in social contacts. In addition, we investigated the potential molecular mechanisms that underlie the impact of AFB1 on the production of Th1 (IFN-γ, STAT1, and T-bet), Th9 (IL-9 and IRF4), Th17 (IL-17A, IL-21, RORγT, and STAT3), Th22 (IL-22, AhR, and TNF-α), and T regulatory (Treg) (IL-10, TGF-β1, and FoxP3) cells in the spleen. This was achieved using RT-PCR and Western blot analyses to assess mRNA and protein expression in brain tissue. The exposure to AFB1 resulted in a significant upregulation of various immune-related factors, including IFN-γ, STAT1, T-bet, IL-9, IRF4, IL-17A, IL-21, RORγ, STAT3, IL-22, AhR, and TNF-α in BTBR mice. Conversely, the production of IL-10, TGF-β1, and FoxP3 by CD4+ T cells was observed to be downregulated. Exposure to AFB1 demonstrated a notable rise in Th1/Th9/Th22/Th17 levels and a decrease in mRNA and protein expression of Treg. The results above underscore the significance of AFB1 exposure in intensifying neurobehavioral and immunological abnormalities in BTBR mice, hence indicating the necessity for a more comprehensive investigation into the contribution of AFB1 to the development of ASD.
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Physical Activity, Body Composition, Serum Myokines and the Risk of Death in Hemodialysis Patients
Clinical
Background and Objectives: The aim of this study was to assess the relationship between habitual physical activity, body composition, serum myokine concentration, and all-cause mortality in chronic hemodialysis patients. Materials and Methods: A prospective cohort study with a 7-year follow-up was conducted in a group of 38 patients (24 men, 14 women, mean age 65.6 ± 13.9 years, dialysis vintage 1.17 ± 1.25 years). Baseline serum concentrations of myokines—follistatin and myostatin—were assessed along with a measurement of physical activity with multidimensional accelerometery, body composition, and the force of forearm muscle contraction. Survival analysis was performed using the Kaplan–Meier method for tertiles of follistatin, serum myostatin, body composition, and physical activity expressed in metabolic equivalents (MET). Results: The mean physical activity among patients was 81 min/24 h (median 38.5 min), and the mean weekly 3MET activity was 493 min (median 218 min). The probability of survival of patients was significantly lower in the subgroup with 3MET/24 h less than 26 min/24 h and 3METt less than 148 min per week compared to the other subgroup (p = 0.006 and p = 0.006, respectively). During the 70-month follow-up, the subgroup with the lowest baseline follistatin concentration showed a significantly lower risk of death (p = 0.02). Baseline myostatin levels were not significant risk factors for mortality, nor were BMI or lean and fat tissue index categories. Conclusions: Physical activity and low plasma follistatin, but not body composition indexes or plasma myostatin, could serve as predictors of all-cause mortality in hemodialysis patients.
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