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Autism spectrum disorders; Early intervention; Efficacy

Introduction

Autism spectrum disorders (ASDs) are neurodevelopmental disorders defined by social and communication deficits and repetitive and stereotypical patterns of behavior and interests [1]. The median age of diagnosis in the United States is 4 years [2]. However, ASD can also be detected as early as 14 months of age [3]. Although the stability of diagnosis is high by 18 months of age [4, 5], many children with evidence of risk for ASD are not recognized or diagnosed until this age [6]. Awareness of her ASD risk at a young age increases the demand for early intervention (EI) services [6]. To support clinicians in her EI decisions, we review her EI literature for the past 15 years here.

Neurodevelopmental Context of Early Intervention

A recent meta-analysis study and systematic review found that EI positively impacts the development of children with ASD (e.g. Beaudoin, Sébire, & Couture [7]; Eldevik et al. [8]; Hampton & Kaiser [9]; Lane, Lieberman-Betz, & Gast [10]; Reichow [11]; Virués- Ortega [12]); however, some children are affected more than others [13]. EI is designed to exploit experience-dependent neuroplasticity. This is a fundamental property of the brain that forms and organizes neural connections, and learning takes place in response to a child's environmental experiences. Self-generated experiences, rather than observations or passive experiences, most influence the early learning process. However, infants later diagnosed with ASD have unusual patterns of attention and engagement, and altered sensory and motor function (e.g., Baranek [14]; Flanagan, Landa, Bhat, and Bauman [15]) themselves. These deficits in the developmental process were detected as early as 3 months of age and are prodromal hallmarks of ASD and may extend into the 2nd and her 3rd year of life.

During the prodromal period and when symptoms of ASD begin to appear, infants and young children engage in continuous, dynamic dyadic (social partner-child) and triadic (social partner-child) and triadic (social partner-child) and object-child) interactions. Such altered experiences can interfere with cortical specialization for faces, language, and related processes, and the functional integration of these circuits. Indeed, future longitudinal neuroimaging studies have identified an association between cortical surface enlargement and visual attention irregularities in 6-month-old children later diagnosed with ASD and having older siblings with ASD has been [16]. Thus, developmental processes involving selected neural circuits are altered, disrupting the refinement of these circuits. Over time, the formation of distributed networks of brain regions and the interactions between these regions are disrupted, affecting cognitive and sensorimotor function when the ASD symptom complex occurs. Brain abnormalities in children with ASD change during the first two years. This is likely the result of a dynamic interaction between neurobiological influences and the cascading effects of atypical developmental processes [17], which are cumulative impact, will continue shifting phenotypic traits [18]. This highlights the importance of early access to interventions, the need for interventions to address multiple aspects of development, and the importance of ongoing interventions to address developmental delays and atypicalities that develop over time emphasizes sexuality [17].

The neurosciences offer a compelling rationale for providing strategically enhanced experiences for children with disrupted development early in life. Yet ASD cannot be diagnosed in infants, and there is no clear predictor of ASD in infants. Given that ~20% and 30% of younger siblings of children with ASD, respectively, will receive a diagnosis of ASD or meet criteria for other developmental disruptions by 36 months of age, a public health approach to detecting ASD risk and monitoring at-risk children is needed (e.g. pre-term infants). Given the decline in skills and social engagement seen in most children with ASD in the second year of life, infants or toddlers showing concerning signs of social and communication delays or qualitatively atypical developmental patterns, despite being sub-threshold for an ASD diagnosis, should have access to EI or developmental enrichment programs (including parent coaching to immerse children in development-enhancing experiences to accelerate learning and generalization of skills [18]). However, access to such services for undiagnosed infants and toddlers is variable, due to the wide discrepancy in eligibility criteria for accessing EI services [19]. Among the children least enrolled in EI services are those with mild severity and those without a diagnosis [20]. A diagnosis of ASD often qualifies you for public EI services. Intervention is required because they are not developing at the expected pace or in the expected multimodal and integrated manner in the social, play, and, for the most part, language and cognitive domains. Accidental learning during interaction with others is hampered by ASD-related deficits, particularly those affecting attention and social initiation. In fact, an association between attentiontracking deficits and random vocabulary learning has been reported in young children with ASD [20]. By maximizing the benefits of experience-dependent neuroplasticity, EI aims to accelerate children's learning rates, facilitate generalization of new development and skills, and reduce the developmental impact of ASD increase.

Because there is no medical cure for core ASD disorders, intervention approaches for young children with ASD are behavioral and educational [21]. There are two main evidence-based approaches to EI. Naturalistic developmental behavioral interventions, along with the principles of operant conditioning, commonly referred to as Applied Behavior Analysis (ABA) or Early Intensive Behavioral Intervention (EIBI), involve individualized experimental instructional formats [22].

Naturalistic developmental behavioral interventions (NDBI)

The NDBI approach typically fosters a continuous flow of social interaction patterns between the child and the intervenor. Intervention providers intentionally and incidentally respond to the child's interests, communicative obligations, and play. The child has natural consequences (rewards/reinforcement) and clear, developmental cues (precedents) to evoke specific behaviors, along with systematic rapid hierarchies to facilitate child engagement and skill development) is provided. Enhancing children's communication, social cues and play by giving them carefully timed and sculpted models to make their verbal, social and play behaviors more coherent, complex and differentiated. These goals permeate the entire interaction rather than being carefully and explicitly taught. A development sequence is generally followed when determining the level of complexity of a skill to target. NDBI is in line with his Department of Early Childhood (DEC) recommendations for EI practice and emphasizes the importance of incorporating EI into routines and contextual settings.

Applied behavior analysis (ABA) and early intensive behavioral interventions (EIBI)

In contrast to the NDBIs, delivery of ABA principles in an operant conditioning paradigm employs a more explicit, decontextualized, and highly structured approach. This method is commonly referred to as EIBI. Specific, discrete skills are taught in a prescribed order. EIBI curricula are comprehensive, targeting social, communication, cognitive, pre-academic (e.g. matching; color, letter, and number recognition), and self-management skills [23]. Adult-selected materials and tasks are presented in sets of structured discrete trials, often adultinitiated, characterized by antecedent–behavior–consequence chains. Well-defined prompt hierarchies and reinforcement schedules are used. Unlike in the NDBIs, reinforces usually are not related to the social–communication–play context and the child’s behavior (e.g. giving access to a toy car if the child says ‘car’), but, rather, are selected based on individualized motivators for the child (e.g. favorite toy or food). EIBI is usually conducted in a 1:1 instructional, non-distracting context. Upon acquisition of a skill, generalization training begins, systematically reinforcing target behaviours and teaching children to distinguish between different cues.

Early intensive behavioral intervention (EIBI)/applied behaviour analysis

Two reports focus on evidence-based evaluations of ASA early interventions and rank EIBI as an established intervention approach. This classification is consistent with most systematic reviews and meta-analyses investigating a single comprehensive ABA. Based on the original Lovaas model, four studies of 1:1 (adult:children) implementation of the EIBI using a comprehensive curriculum tested efficacy based on the methodological criteria of the Journal of Clinical Child and Adolescent Psychology Meets Smith and Iadarola criteria for All four of these studies used a quasi-experimental design. Interventions were administered to her 6.5 to 28 hours per week in a school setting with children aged 2 to her 7 years by trained intervention professionals. In all four studies, the EIBI group showed greater improvements in IQ and adaptive behavior than the comparison group. The only such study that assessed ASD symptoms and behavioral problems failed to find an effect of her EIBI on these aspects of child functioning. Significant improvements in IQ and adaptive behavior are associated with higher intervention intensity (36 hours or more per week). Little is known about the effectiveness of her EIBI on language and social functioning in young children with ASD.

Some studies show that not all children benefit equally from her EIBI. Approximately 19-30% of children receiving EIBI (vs. 8.7% of controls) show an unexpected IQ improvement due to random fluctuations in IQ performance. These children were more likely to achieve age-expected IQ and/or adaptive functioning during the study and met the Sallows and Graupner criteria for rapid learning. Sallows and Graupner warned that even fast learners can exhibit unequal developmental rates across developmental domains such as: B. Significant improvement in the cognitive domain over the social domain. Based on a meta-analysis, approximately 20% of children undergoing EIBI (versus 5% of controls) show robust improvement in adaptive behavior. It is expected that her 10-20% of children with ASD will respond poorly to EIBI. Community-provided EIBI has limited efficacy in reducing the severity of ASD symptoms.

Conclusion

EIBI, usually delivered in a one-on-one instructional format, is an effective intervention approach for many children. The holistic skills targeted by EIBI can contribute to cognitive enhancement. Studies are needed to examine the effects of combining EIBI/ABA with developmental/NDBI approaches to maximize development in children with ASD.

1. https://psychiatry.org/psychiatrists/practice/dsm
2. Christensen DL, Bilder DA, Zahorodny W, Pettygrove S, Durkin MS, et al.(2016) Prevalence and characteristics of autism spectrum disorder among 4-year-old children in the Autism and developmental disabilities monitoring network. J Dev Behav Pediatr 37: 1-8.

Indexed at, Google Scholar, Crossref

3. Landa RJ, Gross AL, Stuart EA, Faherty A (2013) Developmental trajectories in children with and without autism spectrum disorders: The first 3 years. Child Dev 84: 429-442.

Indexed at, Google Scholar, Crossref

4. Landa RJ, Holman KC, Garrett-Mayer E (2007) Social and communication development in toddlers with early and later diagnosis of autism spectrum disorders. Arch Gen Psychiatry 64: 835-864.

Indexed at, Google Scholar, Crossref

5. Ozonoff S, Young GS, Landa RJ, Brian J, Bryson S, et al. (2015) Diagnostic stability in young children at risk for autism spectrum disorder: A baby sibling’s research consortium study. J Child Psychol Psychiatry 56: 988-998.

Indexed at, Google Scholar, Crossref

6. https://www.asha.org/practice-portal/clinical-topics/autism/
7. Beaudoin AJ, Sébire G, Couture M (2014) Parent training interventions for toddlers with autism spectrum disorder. Autism Res 2014: 839890.

Indexed at, Google Scholar, Crossref

8. Eldevik S, Hastings RP, Hughes JC, Jahr E, Eikeseth S, et al. (2010) Using participant data to extend the evidence base for intensive behavioral intervention for children with autism. Am J Intellect Dev Disabil 115: 381-405.

Indexed at, Google Scholar, Crossref

9. Hampton LH, Kaiser AP (2016) Intervention effects on spoken-language outcomes for children with autism: A systematic review and meta-analysis. J Intellect Disabil Res 60: 444-463.

Indexed at, Google Scholar, Crossref

10. Lane JD, Lieberman-Betz R, Gast D (2016) An analysis of naturalistic interventions for increasing spontaneous expressive language in children with autism spectrum disorder. J Spec Educ 50: 49-61.

Google Scholar, Crossref

11. Reichow B (2012) Overview of meta-analyses on early intensive behavioral interventions for children with autism spectrum disorders. J Autism Dev Disord 42: 512-520.

Indexed at, Google Scholar, Crossref

12. Virues-Ortega J (2010) Applied behavior analytic intervention for autism in early childhood: Meta-analysis, meta-regression and dose–response meta-analysis of multiple outcomes. Clin Psychol Rev 30: 387-399.

Indexed at, Google Scholar, Crossref

13. Sallows GO, Graupner TD (2005) Intensive behavioral treatment for children with autism: Four-year outcome and predictors. Am J Ment Defic 110: 417-438.

Indexed at, Google Scholar, Crossref

14. Baranek GT (1999) Autism during infancy: A retrospective video analysis of sensory-motor and social behaviors at 9-12 months of age. J Autism Dev Disord 29: 213-224.

Indexed at, Google Scholar, Crossref

15. Flanagan JE, Landa RJ, Bhat A, Bauman M (2012) Head lag in infants at risk for autism: A preliminary study. Am J Occup Ther 66: 577-585.

Indexed at, Google Scholar, Crossref

16. Baygi F, Jensen OC, Farshad AA, Shidfar F, Mohammadi-Nasrabadi F (2018) A qualitative study on physical health threatening factors of Iranian seafarers working on ocean going tankers. Int Marit Health 69: 192-200.

Indexed at , Google Scholar, Crossref

17. Lin X, Chajakis ED, Floudas CA (2003) Scheduling of tanker lightering via a novel continuous-time optimization framework. Ind Eng Chem Res 42: 4441-4451.

Google Scholar, Crossref

18. Carotenuto A, Molino I, Fasanaro AM, Amenta F (2012) Psychological stress in seafarers: a review. Int Marit Health 63:188-94.

Indexed at, Google Scholar 

19. Doyle N, MacLachlan M, Fraser A, Stilz R, Lismont K, et al. (2016) Resilience and well-being amongst seafarers: cross-sectional study of crew across 51 ships. Int Arch Occup Environ Health 89: 199-209.

Indexed at, Google Scholar, Crossref

20. Sandal GM, Leon GR, Palinkas L (2006) Human challenges in polar and space environments. Environ Sci Biotechnol 5: 281-96.

Indexed at, Google Scholar, Crossref

21. Oldenburg M, Jensen HJ, Latza U, Baur X (2009) Seafaring stressors aboard merchant and passenger ships. Int J Public Health 54: 96-105.

Indexed at, Google Scholar, Crossref

22. Carter T (2005) Working at sea and psychosocial health problems: Report of an International Maritime Health Association Workshop. Travel Med Infect Dis 3: 61-65.

Indexed at, Google Scholar, Crossref

23. Akerstedt T (2003) Shift work and disturbed sleep/wakefulness. Occupational Med 53: 89-94.

Indexed at, Google Scholar, Crossref