Longitudinal changes in developmental functioning among children with autism spectrum disorders receiving school-based early intensive behavioral intervention in China

Abstract

Introduction:

Early Intensive Behavioral Intervention (EIBI) is widely recognized as an effective treatment for children with autism spectrum disorder (ASD), yet longitudinal evidence from non-Western educational settings remains limited. This study examined developmental trajectories among children with ASD participating in a school-based EIBI program in China.

Methods:

Archival data from 39 children aged 2–12 enrolled in a private special education school were analyzed. Developmental functioning was assessed using the Assessment of Basic Language and Learning Skills–Revised (ABLLS-R) across three assessment points at three-month intervals.

Results:

Linear mixed-effects models showed a significant effect of time on composite scores, indicating progressive developmental improvement. Significant improvements were observed in basic learner skills, motor skills, and self-help skills. Academic skills showed moderate improvement across the intervention period. Multiple regression analysis indicated baseline composite score significantly predicted third composite scores, where age at program entry and total program hours were not significantly associated with outcomes.

Discussion:

These findings provide longitudinal evidence supporting the effectiveness of school-based EIBI in promoting developmental progress among children with ASD in a Chinese educational context.

Introduction

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social communication, repetitive behaviors, and restricted interests (American Psychiatric Association, 2022). Global prevalence of ASD has risen significantly in recent decades, making it a major public health concern (Elsabbagh et al., 2012; Maenner et al., 2023). With estimates indicating that 1 in 127 children are diagnosed with ASD worldwide in 2021, the demand for effective intervention strategies has never been more pressing (World Health Organization, 2025). However, the understanding and management of ASD vary considerably across different regions, influenced by a combination of cultural, social, and economic factors (Divan et al., 2012; Durkin et al., 2017; Tekola et al., 2016).

Behavioral based interventions are one particularly effective set of methods applied to ASD treatment. Foundations of Applied Behavior Analysis (ABA)-based interventions can be traced back to the pioneering work of Baer et al. (1968), who established the foundational principles of behavior analysis in their seminal publication. These principles emphasize the importance of observable and measurable behavior, manipulation of environmental variables, and the use of reinforcement to shape behavior (Baer et al., 1968; Cooper et al., 2020). Lovaas' work (1987) about ABA's application to ASD laid the groundwork for the development of Early Intensive Behavioral Intervention (EIBI), which has since become the gold standard for early intervention in ASD for young children (Hume et al., 2021; Reichow et al., 2012).

EIBI is rooted in behavioral principles and has emerged as one of the most effective approaches within the broader category of ABA-based interventions used with young children. EIBI promotes improvements in language, cognitive, social, and adaptive functioning using structured teaching methods such as Discrete Trial Teaching (DTT) and Natural Environment Training (NET) (Reichow et al., 2012; Smith and Iadarola, 2015). Numerous studies have documented the efficacy of EIBI, demonstrating its ability to generate meaningful developmental gains in children with ASD (Linstead et al., 2017; Reichow et al., 2012; Wong et al., 2015). These positive outcomes are particularly pronounced when interventions are initiated at an early age, capitalizing on the neuroplasticity of the developing brain (Dawson et al., 2012; Vivanti et al., 2016).

While ABA and EIBI originated in the United States, their application has spread globally. Many countries have adopted these evidence-based practices to support children with ASD (Eikeseth et al., 2012; Peters-Scheffer et al., 2010). However, implementation of ABA-based interventions outside the United States presents unique challenges, particularly in culturally diverse settings where social norms, educational practices, and family dynamics may differ significantly from those in Western countries (Divan et al., 2012; McCabe, 2013; Tekola et al., 2016). Cultural factors play a crucial role in shaping how ASD is perceived and managed (Elsabbagh et al., 2012; Tekola et al., 2016). For instance, in some cultures, there may be a stigma associated with developmental disabilities, leading to delays in diagnosis and intervention (Liao et al., 2019). Moreover, emphasis on collectivism versus individualism, as well as differing parental expectations, can influence how interventions like EIBI are received and implemented (Love et al., 2009). Recognizing these challenges, researchers and practitioners have increasingly emphasized the importance of culturally adapting ABA interventions to enhance their relevance and effectiveness in diverse cultural contexts (Bernal and Domenech Rodríguez, 2012; Zhou et al., 2020).

In China, recognition and understanding of ASD have evolved considerably since the condition was first identified in the country in 1982 (Sun et al., 2015). Over the past few decades, China has made significant strides in raising awareness of ASD, improving diagnostic practices, and expanding access to intervention services (McCabe, 2013; Zhou et al., 2020). This can be attributed to factors such as media coverage, parents advocacy, and government initiatives aimed at improving services for children with developmental disabilities (Wang et al., 2019). Despite these advances, the implementation of EIBI in China is still in its early stages, with several challenges hindering its widespread adoption (Luo et al., 2022). One major challenge is the lack of trained professionals with expertise in ABA and EIBI, which limits the availability of high-quality services for children with ASD (Sun et al., 2013). This shortage of skilled practitioners is particularly acute in rural areas, where access to specialized services is already limited (McCabe, 2013). Consequently, families in these regions often face significant barriers to obtaining effective interventions for their children (Luan et al., 2025).

Cultural factors also play a significant role in shaping implementation of EIBI in China (Li and Kimble, 2015). Traditional Chinese cultural values, such as collectivism, filial piety, and the importance of social harmony, may influence parental expectations and attitudes toward intervention for children with ASD (Fan and Ko, 2025; Ng and Ng, 2022). For example, Chinese parents may prioritize interventions that emphasize social integration and developing skills that enable their children to function within the family and community (Wang et al., 2023). Additionally, the Confucian emphasis on education and academic success may shape parental preferences for interventions that promote cognitive and academic skills (Hu et al., 2012; Karst and Van Hecke, 2012).

Another critical factor to consider is the role of the family in the implementation of EIBI. In China, families play a central role in the care and education of children with ASD, and parental involvement is often a key component of intervention programs (Tait et al., 2016). However, EIBI requires a significant time and financial commitment, which can pose challenges for families, particularly those with limited resources (Peters-Scheffer et al., 2012). The cost of EIBI, which can be prohibitively expensive, coupled with the intensive time commitment required, can be overwhelming for many families, leading to disparities in access to these services (McCabe, 2008; Wang and Michaels, 2010).

The availability of EIBI services in China is also uneven, with urban areas such as Beijing and Shanghai having greater access to trained professionals and resources than rural regions (Sun et al., 2013; Zhou et al., 2020). This urban-rural divide in service availability exacerbates existing inequalities, leaving many children in less-developed regions without access to effective early interventions (Hsu et al., 2023). Despite these challenges, there is increasing recognition of the importance of early intervention for children with ASD in China (Shen et al., 2025). Recent policy initiatives by the Chinese government have sought to improve access to services for children with ASD, including increasing funding for intervention programs and expanding training opportunities for professionals (Luo et al., 2022). These efforts reflect a broader commitment to addressing the needs of children with developmental disabilities and ensuring that they have the opportunity to reach their full potential (Zou et al., 2025). For instance, government programs have been launched to provide financial subsidies to families of children with ASD, particularly those in low-income households (He et al., 2018; Zhao et al., 2023). Additionally, partnerships between governmental and non-governmental organizations have been established to develop and disseminate best practices for ASD intervention across the country (Luan et al., 2025). Some regions have made considerable progress in adopting EIBI and other ABA-based interventions; even though others lag behind (Luo et al., 2022). The special education school (TSES) involved in the present study represents one example of a structured, school-based EIBI model currently operating within this evolving service landscape.

Although service availability and policy support for early intervention have expanded in recent years, empirical evidence evaluating longitudinal developmental trajectories of children receiving EIBI within Chinese school-based settings remains limited. A systematic review conducted in mainland China found that most reported improvements in language and communication, social functioning, motor and self-help skills, and challenging behaviors following ABA-based interventions (Liao et al., 2020). However, intervention periods were frequently short, and considerable variation existed in intervention procedures and outcome measures. A more recent scoping review in Chinese autism intervention research has largely focused on short-term outcomes, pilot trials, or descriptive accounts of service provision (Shen et al., 2025). Few longitudinal cohort studies have examined sustained developmental change across multiple functional domains using curriculum-based standardized assessments, and limited research has explored predictors of treatment response in real-world educational environments (Luo et al., 2022). Consequently, understanding of how school-based EIBI programs influence developmental progression over time within the Chinese context remains incomplete.

The aim of the present study was to evaluate longitudinal developmental outcomes among children with ASD receiving school-based EIBI in China. Using archival data from a private special education setting, we examined changes in core developmental skill domains across three assessment time points. In addition, we investigated whether baseline functioning, age at program entry, and total program hours were associated with variation in developmental outcomes.

Materials and methods

Study participants and procedure

Participants in this study were children enrolled at TSES who met the following criteria: (a) they had an independent diagnosis of autism, which could have been made by a developmental pediatrician, psychiatrist, or another qualified professional according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V). It is important to note that diagnoses were not conducted by personnel affiliated with TSES; (b) they were aged 2 to 12 years; and (c) they were found eligible to receive special education services by either their local early intervention service board or their local school district. Children were excluded from the study if baseline assessment data were unavailable or if assessment records were incomplete.

At TSES, EIBI was implemented as part of each child's regular school program rather than as a separate clinic- or home-based intervention. Each child attends four 40-minute classes per day, five days a week, for a minimum of 20 days per month. Total program hours were operationalized as the cumulative number of hours each child participated in the EIBI program during the study period. Although the program followed a standardized schedule, total program hours varied across children because of differences in individual attendance and missed program days. Each child's curriculum is designed and managed by program coordinators with at least five years of experience overseeing ABA-based programs for autistic children (Cooper et al., 2020; Reichow et al., 2012). Each program coordinator supervises four to six children, along with 10 to 15 therapists (teachers) who provide direct instruction throughout the school day (Peters-Scheffer et al., 2010; Reichow et al., 2012). Parents receive monthly training on their children's programs, and daily updates are provided to keep them informed of their child's progress (Vismara and Rogers, 2010).

Intervention goals were individualized according to each child's current skill repertoire and educational needs and were incorporated into the child's school curriculum. These goals broadly corresponded to the functional domains assessed by the ABLLS-R, including basic learner, academic, self-help, and motor skills. Basic learner targets included foundational learning behaviors such as attending, imitation, following instructions, and readiness to engage in structured learning activities. Academic targets addressed skills relevant to educational participation, whereas self-help and motor targets addressed functional independence and skills needed to participate in everyday school and daily routines. The ABLLS-R was therefore used as a curriculum-based measure to monitor progress in skill areas that were also addressed within the children's individualized educational and behavioral programs.

The data analyzed in this study were archival. The ABLLS-R assessments were administered and scored by TSES staff as part of the school's standard protocols. Because these assessments were conducted within the context of routine program monitoring, assessors were not blinded to the children's participation in the EIBI program or to the assessment time point. Informed consent for data collection and use from the children's parents or legal guardians was also obtained by TSES staff. The use of this data for research was approved by both TSES and the Institutional Review Board of Xi'an Tus-Children's Hospital (Protocol Number: 20240407001), ensuring adherence to all ethical guidelines throughout the analysis.

Measures

The ABLLS-R (Partington, 2008) was used as the primary tool for assessing children's skills and developmental progress. The ABLLS-R is a performance-based assessment designed to evaluate environment-specific behaviors in autistic children and other language delays. The ABLLS-R is divided into a series of tasks grouped into four domains: basic learner, academics, self-help, and motor. Each task is scored based on the child's performance. A score of 0 indicates that the child cannot perform the skill or does not meet the minimum criteria for a score of 1, while the highest score indicates mastery of the skill. The composite score across all domains ranges from 0 to 1,540. The maximum scores for the domains are 1,164 for language and social, 230 for academics, 88 for self-help, and 58 for motor. Higher composite scores indicate higher levels of functioning.

The ABLLS-R is not age-normed, meaning that scores from this measure are not directly interpretable in relation to typical developmental milestones for specific age groups. Instead, the instrument is designed to track a child's progress over time within specific skill domains. At TSES, the ABLLS-R is administered to each child upon school entry (baseline) prior to the initiation of EIBI, and at subsequent 3-month intervals thereafter to monitor developmental progress.

Data analysis

All analyses were completed via the following packages in RStudio (Posit team, 2024), an interface for R (R Core Team, 2024). Descriptive statistics were computed for all study variables at each time point. To evaluate longitudinal changes in the developmental outcomes, linear mixed-effects models for repeated measures were estimated with timepoint (baseline, Time 2, Time 3) entered as a fixed effect and participant assigned ID included as a random intercept to account for the repeated-measures structure of the data. Separate models were estimated for composite scores and for each ABLLS-R domain (basic learner skills, academic skills, self-help skills, and motor skills). The significance of the time effect was evaluated using Type III tests with Satterthwaite approximations for degrees of freedom. Bonferroni-adjusted pairwise comparisons were conducted to examine differences between assessment points. For all analyses, 95% confidence intervals were reported for pairwise differences. To examine predictors of developmental outcomes, multiple regression analyses were conducted with third time-point composite scores as the dependent variable. Age at program entry, total program hours, and baseline composite scores were entered simultaneously as predictors. Statistical significance was set at p < .05.

Results

A total of 66 children enrolled in the program and were assessed at baseline prior to the start of the EIBI program. Of these, 52 (78.8%) children remained enrolled and were assessed at the second time point. By the third assessment, 39 (59.1%) children were retained for further analysis, resulting in an overall dropout rate of approximately 40.9%. Among the 39 children, 34 (87.1%) were male and 5 (12.8%) were female. The average age at program entry was 55.4 months (SD = 19.7), with a range from 24 to 96 months. Attrition resulted from families withdrawing their children from the program before completing all scheduled assessments. Because baseline information for withdrawn participants was incomplete, baseline differences between withdrawn and retained participants could not be examined. Therefore, systematic attrition and potential selection bias cannot be excluded. The children's overall performance scores, as well as scores in each domain, are detailed in Table 1.

Separate linear mixed-effects models were conducted to examine changes in children's composite scores and each of the ABLLS-R domains across three time points, with participant included as a random effect to account for repeated measurements within individuals. The results showed that children's functioning, as measured by the ABLLS-R, improved with each subsequent assessment. Figure 1 illustrates the trajectories of individual children's composite scores across the three assessment points, along with the overall group mean.

Figure 1

The omnibus test indicated a significant effect of time on composite scores, F (2, 76) = 55.99, p < .001, indicating a significant improvement in children's performance over time​. Bonferroni-adjusted pairwise comparisons showed that composite scores increased from the first to the second assessment (mean difference = 137.5, 95% CI [87.9, 187.0], d = 1.54, 95% CI [1.05, 2.04]), from the first to the third assessment (mean difference = 210.4, 95% CI [160.8, 260.0], d = 2.36, 95% CI [1.81, 2.91]), and from the second to the third assessment (mean difference = 72.8, 95% CI [23.6, 122.0], d = 0.82, 95% CI [0.35, 1.28]). To examine whether changes in composite scores differed by age, an additional mixed-effects model included age at program entry and the age-by-time interaction. The age-by-time interaction was not significant, F (2, 74) = 1.90, p = .157, indicating no statistically significant evidence that changes in composite scores across the three assessments varied by age.

For basic learner skills, the linear mixed-effects model indicated a significant effect of time, F (2, 76) = 51.04, p < .001. Bonferroni-adjusted pairwise comparisons showed increases from the first to the second assessment (mean difference = 118.5, 95% CI [74.1, 163.0], d = 1.48, 95% CI [.99, 1.97]), from the first to the third assessment (mean difference = 180.5, 95% CI [135.9, 225.0], d = 2.25, 95% CI [1.71, 2.79]), and from the second to the third assessment (mean difference = 61.7, 95% CI [17.4, 106.0], d = .77, 95% CI [.31, 1.23]). These results indicated large increases from the first to the second and third assessments, as well as a moderate-to-large additional increase between the second and third assessments.

For academic skills, the linear mixed-effects model also indicated a significant effect of time, F (2, 76) = 8.76, p < .001. Bonferroni-adjusted pairwise comparisons showed a significant increase from the first to the third assessment (mean difference = 7.18, 95% CI [2.98, 11.38], d = .95, 95% CI [.48, 1.42]). The increases from the first to the second assessment (mean difference = 3.85, 95% CI [−0.36, 8.05], d = .51, 95% CI [.05,.96]) and from the second to the third assessment (mean difference = 3.34, 95% CI [−0.87, 7.54], d = .44, 95% CI [−.01,.89]) were not significant.

For self-help skills, the model indicated a moderate effect of time, F (2, 76) = 27.52, p < .001. Bonferroni-adjusted pairwise comparisons showed increases from the first to the second assessment (mean difference = 7.00, 95% CI [3.30, 10.71], d = 1.05, 95% CI [.58, 1.52]), from the first to the third assessment (mean difference = 11.10, 95% CI [7.40, 14.81], d = 1.66, 95% CI [1.16, 2.16]), and from the second to the third assessment (mean difference = 4.10, 95% CI [0.40, 7.81], d = .61, 95% CI [.16, 1.07]).

For motor skills, the model also indicated a time-related effect, F (2, 76) = 44.05, p < .001. Bonferroni-adjusted pairwise comparisons showed increases from the first to the second assessment (mean difference = 7.92, 95% CI [4.82, 11.02], d = 1.42, 95% CI [.93, 1.90]), from the first to the third assessment (mean difference = 11.64, 95% CI [8.54, 14.74], d = 2.08, 95% CI [1.55, 2.61]), and from the second to the third assessment (mean difference = 3.72, 95% CI [0.62, 6.82], d = .67, 95% CI [.21, 1.12]). Figure 2 presents the trajectories of each developmental domain across assessment timepoints, including both individual participant scores and the group mean.

Figure 2

To explore factors potentially related to treatment effects, a multiple regression analysis was conducted. Children's ages at the time of school entry, total program hours, and their baseline ABLLS-R composite scores were included as predictors of their third composite scores. The regression model was significant, F (3, 35) = 21.31, p < .001, with an adjusted R² = .62, indicating that approximately 62% of the variance in third composite scores was accounted for by the model.

Among the predictors, baseline composite scores were the strongest predictor of third composite scores (β = .83, SE = .13, t = 6.28, 95% CI [.56, 1.10]), indicating that children with higher initial scores tended to show higher composite scores after treatment. Children's age at the time of school entry showed a positive, but non- significant trend (β = 2.65, SE = 1.57, t = 1.69, 95% CI [−.53, 5.84]). In contrast, total program hours did not significantly predict third composite scores (β = .006, SE = .02, t = .31, 95% CI [−.04, .05]), indicating that the amount of time in the program alone was not a strong determinant of outcome.

Discussion

The goal of this study was to examine the longitudinal developmental trajectories of children with ASD receiving school-based EIBI in China and to identify demographic and program-related predictors of treatment outcomes. Our findings indicate that children demonstrated significant improvements in composite developmental functioning as well as domain-specific skills over the course of the intervention period. In addition, baseline functioning emerged as a significant predictor of later composite scores, whereas age at entry and total program hours showed more limited predictive effects. Together, these findings provide longitudinal evidence supporting the effectiveness of school-based EIBI within a Chinese educational context and extend the literature on the global implementation of ABA-based interventions.

Consistent with the study aim, children exhibited significant improvements in overall developmental functioning across the three assessment time points. The results indicated a substantial effect of time on ABLLS-R composite scores, suggesting that children's overall skill acquisition increased progressively during participation in the program. These findings are consistent with previous research demonstrating that intensive behavioral interventions can produce meaningful developmental gains in children with ASD (Hume et al., 2021; Reichow et al., 2012; Wong et al., 2015). In addition, improvements are often observed across multiple functional domains including communication, adaptive functioning, and social learning in early behavioral interventions (Smith and Iadarola, 2015; Vismara and Rogers, 2010). The present study contributes to the literature by demonstrating that structured EIBI programs can produce measurable developmental gains within a Chinese school-based educational context. This finding aligns with emerging research suggesting that evidence-based autism interventions can be successfully implemented across diverse cultural contexts when appropriate training, supervision, and program structure are present (Luo et al., 2022; Shen et al., 2025).

Domain-specific analyses further revealed significant improvements in basic learner, self-help, motor, and academic skills over time, although the magnitude and trajectory of improvement differed across domains. The largest gains were observed in basic learner skills. Structured behavioral teaching procedures such as discrete trial teaching and natural environment training emphasize repeated practice, prompting strategies, and reinforcement, which are designed to strengthen these foundational learning behaviors that frequently can be conceptualized as prerequisite skills, including attending, imitation, compliance with instructions, and learning readiness (Linstead et al., 2017; Smith and Iadarola, 2015). Improvements in these prerequisite skills may therefore represent a key mechanism through which EIBI facilitates broader developmental gains. Previous research has similarly reported that improvements in attention, imitation, and instructional control often precede more advanced developmental progress in children receiving behavioral interventions (Hume et al., 2021; Wong et al., 2015).

Significant improvements were also observed in self-help and motor skill domains. Gains in self-help skills are particularly meaningful because they reflect improvements in functional independence in daily living activities such as dressing, feeding, hygiene routines, and task completion. Improvements in adaptive functioning have been shown to play an important role in improving long-term outcomes for individuals with ASD and in reducing caregiver burden (Karst and Van Hecke, 2012; Vismara and Rogers, 2010). Similarly, the improvement in motor skills observed in the present study is consistent with previous research demonstrating that children with ASD frequently present with motor coordination delays and that structured interventions can indirectly support motor development through repeated practice and skill generalization (Lloyd et al., 2013). Although motor development is not always the primary target of EIBI programs, the improvements observed in the present study suggest that structured behavioral programs may promote developmental progress across multiple functional domains simultaneously.

Academic skills also showed improvement between the first and third assessment time points, although the changes between adjacent assessments were less pronounced. This pattern may reflect the developmental sequencing commonly observed in early intervention programs. Previous research has similarly reported that early behavioral interventions often produce improvements in learning readiness and adaptive functioning prior to significant academic gains (Hume et al., 2021; Smith and Iadarola, 2015). Studies examining developmental trajectories in children receiving behavioral interventions have also noted that academic progress may emerge more gradually compared to gains in behavioral regulation or adaptive functioning (Vivanti et al., 2016). The present findings therefore suggest that longer intervention durations or increased emphasis on academic targeting within behavioral programs may be necessary to produce larger improvements in academic functioning.

The regression analyses indicated that baseline functioning was the strongest predictor of later composite scores. Children who entered the program with stronger initial skill repertoires tended to demonstrate higher composite scores at the third assessment. This finding is consistent with prior research suggesting that children entering intervention with stronger baseline cognitive or adaptive functioning often demonstrate greater measurable gains during behavioral intervention programs (Eldevik et al., 2012). One possible explanation is that children with stronger baseline skills may be better able to engage with structured instructional formats and benefit more readily from reinforcement-based learning procedures. From a clinical perspective, this finding highlights the importance of early identification and referral to intervention services before developmental delays become more pronounced.

In contrast, the total number of hours spent in the program did not significantly predict developmental outcomes. This finding suggests that intervention quality, fidelity, and individualized treatment planning may be more important determinants of developmental progress than the sheer quantity of intervention hours alone. Previous research has similarly emphasized that variability in treatment implementation, therapist expertise, and family engagement can influence intervention outcomes (Hume et al., 2021; Peters-Scheffer et al., 2012). Consequently, while intervention intensity remains an important component of EIBI programs, simply increasing intervention hours without maintaining high implementation fidelity may not necessarily lead to improved outcomes.

Age at program entry demonstrated only a marginal association with later developmental outcomes and the present findings suggest that age alone may not fully determine responsiveness to intervention within structured school-based programs in China. Developmental readiness, cognitive functioning, and behavioral characteristics may interact with age to influence treatment response. For example, older children can possess more developed attentional control, behavioral regulation, and cognitive readiness, which may facilitate engagement with structured discrete trial teaching procedures (Smith and Iadarola, 2015).

Although the observed gains suggest that children demonstrated broader skill repertoires across the basic learner, academic, self-help, and motor domains, their clinical relevance should be interpreted cautiously. The ABLLS-R is a criterion-referenced skills assessment intended to identify instructional targets and monitor performance against task-specific criteria rather than a norm-referenced measure. Because items have different scoring criteria and can reflect partial or complete mastery, changes in the composite score also cannot be interpreted as a direct count of newly acquired skills. Moreover, the ABLLS-R does not provide an established threshold for clinically meaningful change. Therefore, it was not possible to classify children as having achieved clinically meaningful improvement or to calculate a clinically meaningful responder rate. The findings should instead be interpreted as evidence of improvement in assessed skill performance. Future research should incorporate item-level mastery data, standardized measures of adaptive functioning, and observations across everyday settings to determine whether improvements in ABLLS-R performance translate into meaningful changes in children's daily functioning.

Limitations

While this study provides valuable insights into the effectiveness of EIBI in a Chinese context, several limitations must be acknowledged. One key limitation is the reduced sample size due to dropout rates, which may affect the generalizability of the findings. Additionally, the focus on age as a primary predictor may have overlooked other critical factors, such as socioeconomic status, family environment, and parental involvement, all of which have been shown to influence the efficacy of interventions for children with ASD. Future studies with larger samples should aim to include these variables to provide a more comprehensive understanding of the factors shaping EIBI outcomes.

Another limitation is the study's emphasis on quantitative outcomes, with less attention to the qualitative experiences of children and their families. Future research could benefit from incorporating qualitative methods, such as interviews or case studies, to capture the nuanced ways in which cultural and familial factors influence the implementation and effectiveness of EIBI. In addition, the reliance on archival data limited the ability to control variability in intervention quality and fidelity. The assessors were also not blinded to intervention participation or assessment time point. Consequently, rater bias may have influenced the assessment of developmental change and potentially contributed to the observed improvements. Future studies should consider employing more rigorous designs, such as randomized controlled trials, and incorporating independent assessors blinded to intervention exposure and assessment time point to better assess the impact of intervention quality and therapist expertise on outcomes.

The longitudinal design allowed us to document progressive within-child gains across repeated assessments; however, the absence of a concurrent control group limits the interpretation of these changes. Specifically, we cannot determine the extent to which the observed gains were attributable to EIBI rather than natural development, repeated assessment, or participation in other educational activities. Future research should include a comparison group assessed over the same period to determine whether gains among children receiving EIBI exceed those expected from natural development.

Although the observed gains suggest that children demonstrated broader skill repertoires across the basic learner, academic, self-help, and motor domains, their clinical relevance should be interpreted cautiously. The ABLLS-R does not provide an established threshold for clinically meaningful change. Because items have different scoring criteria, changes in the composite score also cannot be interpreted as a direct count of newly acquired skills. Therefore, it was not possible to classify children as having achieved clinically meaningful improvement. The findings should instead be interpreted as evidence of improvement in assessed skill performance. Future research should incorporate item-level mastery data, standardized measures of adaptive functioning, and observations across everyday settings to determine whether improvements in ABLLS-R performance translate into meaningful changes in children's daily functioning.

Implications for practice

The findings of this study also have several important implications for the implementation of EIBI programs in China despite the limitations. First, the results underscore the potential of ABA-based interventions to produce significant developmental gains for children with ASD within a cultural context that differs markedly from the Western settings where these interventions originated. The data from the program at TSES, suggest that EIBI can be effectively adapted to meet the needs of Chinese children with ASD, particularly in the domains of basic learner, motor, academic, and self-help skills. However, cultural factors such as parental expectations, educational priorities, and family involvement may influence the outcomes of these interventions, as suggested by previous research (Hu et al., 2012).

Second, the results highlight the importance of addressing disparities in service availability, particularly in rural areas where access to trained professionals and high-quality interventions remains limited (McCabe, 2013). The lack of a significant effect of program hours on outcomes suggests that efforts to improve the quality of intervention services, rather than simply increasing their duration, may be more effective in promoting positive outcomes. Given the shortage of ABA-trained professionals in China (Sun et al., 2013), efforts to expand training opportunities and provide support for families in rural and underserved areas are crucial for ensuring equitable access to EIBI.

Conclusion

In conclusion, this study provides longitudinal evidence supporting the effectiveness of school-based EIBI programs for children with ASD within a Chinese educational context. However, there is a pressing need to address regional disparities in service availability to ensure that all children, regardless of location or socioeconomic background, have access to high-quality early intervention programs. By tackling these challenges, policymakers and practitioners can work together to improve the lives of children with ASD and their families across China.

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