Impacts of Hyperbaric Oxygen Therapy (HBOT) on Verbal Scores in Children With Autism: A Secondary Analysis of the HBOT Trial Using Multivariate Analysis of Variance (MANOVA).

Introduction A secondary analysis employing advanced statistical methodologies constitutes a robust means of validating initial findings in systematic empiricism. The current research will undertake a secondary analysis of the impacts of Hyperbaric Oxygen Therapy (HBOT) on verbal behaviors in children with autism using the original dataset. This approach aims to enhance the robustness of the initial results, thereby providing a deeper understanding of the data and potentially uncovering additional insights. Materials and methods From January 2018 to July 2021, all cohorts of autistic children (n = 65) were scheduled, evaluated, and treated at The Oxford Center (TOC) in Brighton and Troy, Michigan, USA. Trained research assistants retrospectively extracted pretest and posttest data from electronic medical records from the Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) and the Assessment of Basic Language and Learning Skills (ABLLS). This data collection focused on children with autism who received either non-HBOT control with Applied Behavior Analysis (ABA) treatment only or ABA + HBOT interventions. For the VB-MAPP, the experimental group (ABA + HBOT) included 23 children, while the control group (ABA only) included 12 children. For the ABLLS, the experimental group (ABA + HBOT) consisted of nine children, compared to 21 children in the control group (ABA only). Demographic information was systematically summarized. Two independent sample t-tests were recomputed from the original study. Multivariate Analysis of Variance (MANOVA) were conducted, followed by one-way Analyses of Variance (ANOVA) post hoc analyses to elucidate the findings. Results The ages in both groups ranged from 2 to 17 years (M = 5.7 years ± 3.08), with median ages of four years for the experimental group and five years for the control group. The p-values and effect sizes indicated that the two independent sample t-tests from the original study and the MANOVAs from the current research are in agreement. This concordance provided confirmatory evidence for the validity of the pretest and posttest differences in VB-MAPP and ABLLS scores for the control group (ABA only) and the experimental group (ABA + HBOT), highlighting the impact of HBOT on verbal scores in children with autism. Conclusions The results from the two independent sample t-tests from the initial study exhibited high alignment with those derived from the current study's MANOVAs. Both statistical methodologies were applied to the same VB-MAPP and ABLLS datasets. The convergence of results from these two distinct statistical analyses may reinforce the credibility of the original research findings. It supports the hypothesis that the combined ABA and HBOT intervention may offer additional benefits over ABA therapy alone, with verbal milestone behaviors in children with autism.

The Effects of Age and Treatment Intensity on Behavioral Target Mastery With Applied Behavior Analysis (ABA) Intervention Using Causal Moderation Models.

Introduction Applied behavior analysis (ABA) is a therapy that focuses on improving specific behaviors using positive and negative reinforcement through antecedents, behaviors, and consequences, particularly in individuals with autism and other developmental disorders. It uses the principles of learning theory to bring about meaningful and positive changes in behavior. In ABA treatment, intensity refers to the amount and frequency of therapy an individual receives. This includes weekly hours, session trials, and overall duration. Intensive treatment involves more hours and trials tailored to individual needs and responses. Younger individuals, particularly those with autism, often receive more intensive therapy because early intervention leads to better outcomes. Programs may recommend 25-40 hours per week for young children. As children age, therapy may become less intensive, focusing on specific skills. The study explores how age and treatment intensity affect the mastery of behavioral targets in ABA interventions. Materials and methods This study involved 100 participants (89 children, four adults, and seven instances where the individuals' ages were not recorded due to random data entry errors (MCAR)) who received ABA treatment over three months. The treatments included functional analysis, discrete trials, and mass and naturalistic training. Data on the mastery of target behaviors were collected using the Catalyst software (New York, New York). The primary outcome was the percentage of mastered behavioral targets, indicating the effectiveness of the ABA treatment. Several predictors were examined, including the participant's age and treatment intensity variables, such as the average number of trials and teaching days to achieve behavioral mastery. The interaction effects between age and these treatment intensity variables were analyzed. The study used descriptive and inferential statistics to explore these interactions, including correlational and multiple regression analyses with causal moderator modeling. Results In Model 1, a baseline multiple regression analysis showed that average teaching days significantly predict the percentage of targets mastered. However, its limited explanatory power suggests other variables also play a role. Model 2 introduced interaction effects using causal models, revealing that age moderates the relationship between treatment variables and behavioral outcomes. This model provided a more nuanced understanding but still had room for improvement. Model 3 further refined the approach, achieving higher R-values and lower standard error. It highlighted age's significant role in modifying the impact of teaching days on mastery. This model's superior performance emphasizes the importance of considering age as a moderating factor in ABA interventions, leading to more effective and personalized behavior therapy. Conclusions This study significantly enhances our understanding of the complex interactions between age and treatment intensity within ABA interventions. Practitioners and researchers can develop more tailored and effective therapeutic strategies by identifying and leveraging these interactions. This approach optimizes the treatment process and ensures that interventions are personalized to meet the unique needs of each individual. Ultimately, this leads to more successful outcomes in behavioral therapy, fostering improved adaptive behaviors and overall development.

Evaluating the Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) Scores Using Principal Components Analysis.

Introduction  The Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) is an extensive tool used to assess children with autism and other developmental disabilities who have language delays. Applied behavior analysis (ABA) professionals frequently use the VB-MAPP to create personalized intervention programs catering to each child's needs. The lack of studies examining the VB-MAPP at the pretest, posttest, and differential scores using principal components analysis (PCA) suggests an opportunity to conduct PCAs on these different VB-MAPP scores. In doing so, researchers could better understand the VB-MAPP's dimensionality and factor structure at these levels. This, in turn, could inform the development of more effective assessment strategies and intervention plans for individuals with language and social communication challenges.  Materials and methods From January 2018 to July 2021, The Oxford Center in Brighton and Troy, Michigan, treated autistic children using ABA therapy. A convenience sample of 13 children was retrospectively analyzed using VB-MAPP, which evaluates various behavioral milestones using a pretest-posttest design. Descriptive data analysis and internal consistency reliability estimates (using Cronbach's alpha) were calculated for pretest, posttest, and difference scores. A Wilcoxen signed-rank test was conducted to determine the statistical significance between the pretest and posttest. Correlation matrices were inspected for relevant relationships between VB-MAPP scales, and a PCA with orthogonal rotation was also performed on this pretest, posttest, and difference scores. Results The mean age for the children was 4.083 years ± 1.083 years, (95%CI 3.64, 4.36). Around 66.6% of the children had an autism severity level of three, 33.3% had a severity level of two, and none were at level one. Cronbach's alpha for internal consistency reliability of the pretest, posttest, and difference scores, indicating excellent reliability with values of 0.948 for the pretest and 0.937 for the posttest, respectively. The difference scores had a lower but acceptable reliability coefficient of 0.752. PCA on the pretest scores identified three factors that explain 85.584% of the total variation, indicating that these components capture most of the data's structure. The posttest PCA also identified three factors, accounting for 84.293% of the variance, suggesting a similar complexity and good model fit as the pretest. PCA revealed four factors explaining 82.317% of the variation for the difference scores. The increase in factors suggests that changes between pretest and posttest scores are complex, likely due to the ABA treatment, and require an additional component to represent the data adequately. There is a good model fit; the underlying structure is more complex than the pretest or posttest alone. Conclusions Robust coefficient alphas combined with the shift to a more detailed factor structure post-ABA treatment highlight ABA therapy's diverse and multi-faceted impact on children. The increase from three to four principal components indicates a richer and more nuanced pattern of improvements across different domains of verbal and social behavior. This detailed factor structure is a testament to the comprehensive and individualized nature of ABA treatment, reflecting the therapy's effectiveness in addressing specific needs and fostering broad developmental gains in children.

Comparative Analysis of Gender-Based Differences in Behavioral Mastery, Goals, and Characteristics in Autistic Individuals: An Applied Behavior Analysis Study.

Introduction  It is widely recognized that the prevalence and diagnosis of autism spectrum disorder (ASD) are more common in males than in females. Despite this, there is a significant gap in the body of autism research that investigates gender differences for treatment effects of applied behavior analysis (ABA) across a variety of measured variables. This research aims to comprehensively evaluate gender distinctions concerning target behavioral objectives, goals, and deficit variables. Materials and methods This study analyzed retrospective data from 100 participants, including 89 juveniles and four adults, with seven cases lacking age documentation, who underwent a three-month ABA program from March 19 to June 11, 2023. The ABA program included various methodologies such as functional analysis, discrete trial training, mass trials, and naturalistic training. Data on outcome measures, including target behavioral proficiency, age, average trials to proficiency, average teaching days to proficiency, open behavioral objectives, and target trends, were collected using the "Catalyst" software (Catalyst Software Corporation, New York, NY). Participant demographics were summarized using statistical analyses for categorical (gender and race/ethnicity) and continuous variables (percentage of mastered behavioral objectives, age, average trials, average teaching days, open objectives, percentage of failed objectives during maintenance, percentage of objectives with upward, downward, and flat trends). These statistics included mean, standard deviation, median, and range and were analyzed inferentially using nine separate two-sample independent t-tests and corresponding effect sizes using Cohen's d. Results There were no statistically significant disparities based on gender (p > 0.05) across all nine variables examined: Percentage of Targets Mastered, Age, Average Trials to Mastery, Average Teaching Days to Mastery, Open Targets, Percentage of Targets Failed in Maintenance, Percentage of Targets Trending Up, Percentage of Targets Trending Down, and Percentage of Targets Trending Flat, and wide confidence intervals were detected. Conclusions  Non-significant gender differences in response to ABA treatments regarding these nine behavioral goals, mastery, and deficit variables may be relevant. They suggest that ABA treatments could be equally beneficial for both male and female autistic individuals. These results should be interpreted cautiously. The general pattern observed, characterized by broad confidence intervals, carries a degree of statistical uncertainty, which may suggest substantial gender differences. These results might question the prevailing beliefs about the variation in treatment response based on gender. This could profoundly impact clinical practices, implying that healthcare professionals should not favor one gender over another when suggesting ABA therapies. Instead, the treatment advice should be tailored to each child's unique requirements and traits, regardless of gender. The investigators expect these results to encourage additional research in this field. Comprehending the elements that affect treatment response is vital for improving treatment results and customizing care.

Comparative Effects of Applied Behavior Analysis on Male and Female Individuals With Autism Spectrum Disorder.

Introduction Current evidence-based treatments for autism spectrum disorder (ASD) are based on applied behavior analysis (ABA). However, research on gender differences in ABA therapy response is limited. This study seeks to (1) confirm the 4:1 male-to-female ratio reported in the literature and (2) identify any possible gender differences in target behaviors over seven timepoints measured every two weeks. Materials and methods  For three months, from March 19, 2023, to June 11, 2023, a team of 3-5 behavioral technicians per individual collected daily data on general target mastery for 100 individuals with ASD treated with ABA. Data was collected at seven timepoints every two weeks. Descriptive demographics were computed. Two independent sample t-tests were performed to determine significant or nonsignificant gender differences with the seven timepoint variables.  Results Nonstatistically significant gender differences (p > .05) were found on all seven cumulative target behavior timepoints measured at two-week intervals. For targets mastered Time 1, baseline between males and females, there was no significant difference in the means for males (M = 1.0571, SD = 1.9196) and females (M = 2.0455, SD = 3.9457) (t(90) = -1.591, p = 0.115, confidence interval (CI) = -2.2223, 0.2456, d = -0.389). For targets mastered Time 2, two weeks between males and females, there was no significant difference in the means for males (M = 3.7132; SD = 4.5065) and females (M = 4.0682, SD = 5.1508) (t(88) = -0.310, p = 0.757, CI = -2.6305, 1.92056, d = -0.076). For targets mastered Time 3, four weeks between males and females, there was no significant difference in the means for males (M = 7.0956; SD = 8.7781) and females (M = 8.6136; SD = 11.2799) (t(88) = -0.656, p = 0.514, CI = -6.1173, 3.0811, d = -0.161). For targets mastered Time 4, six weeks between males and females, there was no significant difference in the means for males (M = 13.1728, SD = 16.2003) and females (M = 13.0682, SD = 16.9272) (t(88) = 0.026, p = 0.979, CI = -7.8779, 8.0871, d = 0.006). For targets mastered Time 5, eight weeks between males and females, there was no significant difference in the means for males (M = 17.2096; SD = 18.8546) and females (M = 17.4286, SD = 22.1683) (t(87) = -0.045, p = 0.965, CI = -9.9773, 9.5393, d = -0.011). For targets mastered Time 6, 10 weeks between males and females, there was no significant difference in the means for males (M = 21.0074, SD = 21.3329) and females (M = 20.6818, SD = 26.1231) (t(88) = 0.059, p = 0.953, CI = -10.6752, 11.3262, d = 0.014). For targets mastered Time 7, 12 weeks between males and females, there was no significant difference in the means for males (M = 26.1196, SD = 24.2235) and females (M = 29.6364, SD = 33.7406) (t(89) = -0.536, p = 0.593, CI = -16.5431, 9.5094, d = -0.131). Conclusions The study indicates that ABA treatments may be equally beneficial for both genders with ASD, showing no significant gender differences. However, the broad CIs in this study imply a level of statistical uncertainty, indicating potential gender differences, suggesting the results may not be uniform across genders. These findings challenge assumptions on gender-specific treatment responses, implying that ABA treatments shouldn't be recommended based on gender. Instead, individual needs should guide treatment recommendations. Future research could consider other response moderators like age, ASD severity, or coexisting mental health conditions.

A Non-parametric Analysis of Gender Differences With the Impacts of Applied Behavior Analysis on Autistic Individuals.

Introduction Considering the scarcity of research that directly investigates the differences between genders in their response to applied behavior analysis (ABA) therapy for individuals diagnosed with autism spectrum disorder (ASD), the objective of this study is twofold. First, it aims to reinforce the male-to-female ratio reported in existing scientific literature, thereby contributing to a broader understanding of gender distribution in ABA therapy for ASD. Second, it seeks to identify gender-based differences in aggregate target behaviors at various time intervals using three distinct datasets. The goal is to determine if gender influences the effectiveness of ABA therapy for ASD, which could inform future therapeutic strategies. Ultimately, this study strives to enhance our understanding of gender disparities in ABA therapy response among ASD individuals and hopes to improve therapeutic outcomes for all, regardless of gender. Materials and methods Three to five behavioral technicians per child collected daily general target mastery data for 263 individuals with autism. This data was gathered using a large N design through retrospective chart reviews within the "Catalyst" tracking software (DataFinch Technologies, Atlanta, USA). Three separate datasets were collected from June 7, 2023 to January 7, 2024. Behavior analysts employed a mixed model of discrete trial training, mass trials, and naturalistic environment treatment over seven months. General target mastery data was assembled for 259 children and four adults, with seven data instances missing. Descriptive statistics encompassed central tendency and dispersion measures, including the data distribution's mean, standard deviation, median, and range. Non-parametric inferential analysis was performed with the Mann-Whitney U test. Results Mann-Whitney U computations resulted in non-significant gender differences on all gender comparisons for the three datasets covering the seven-month timeframe. Dataset #1: Time 1-(U=727.5, p=0.846, ή2=0.0002, Time 2-(U=736, p=0.910, ή2=0.00005), Time 3-(U=687.5, p=0.569, ή2=0.001) Dataset #2: Time 1-(U=781, p=0.383, ή2=0.003), Time 2-(U=819.5, p=0.585, ή2=0.001), Time 3-(U=825, p=0.618, ή2=0.001) Dataset #3: Time 1-(U=395, p=0.198, ή2=0.007), Time 2-(U=373.5, p=0.365, ή2 =0.003), Time 3-(U=363, p=0.471, ή2=0.002), Time 4-(U=366.5, p=0.436, ή2 =0.003), Time 5-(U=371, p=0.391, ή2=0.003), Time 6-(U=394, p=0.208, ή2=0.007), Time 7-(U=373, p=0.373, ή2=0.003), Time 8-(U=371.5, p=0.387, ή2=0.003), Time 9-(U=464.5, p=0.512, ή2=0.002), Time 10-(U=356.5, p=0.546, ή2=0.002), Time 11-(U=357.5, p=0.535, ή2=0.002), Time 12-(U=350.5, p=0.346, ή2=0.004) Conclusions This study suggests no significant gender differences in response to ABA therapy among individuals with autism, indicating its potential effectiveness for both genders. However, these findings should be interpreted cautiously due to statistical uncertainties reflected in the broad confidence intervals as they hint at possible substantial gender differences. Further research, including an extension study, must confirm these results and understand potential gender nuances in ABA therapy response. This could help tailor more effective, personalized therapeutic strategies for individuals with autism.

An Internal Consistency Reliability Study of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application With Autistic Individuals.

Introduction Many psychometric studies have scrutinized the dependability of different instruments for evaluating and treating autism using applied behavior analysis (ABA). However, there has been no exploration into the psychometric attributes of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application, namely, internal consistency reliability measures. Materials and methods  Four datasets were extracted (n=100, 98, 103, and 62) from published studies at The Oxford Center, Brighton, MI, ranging from March 19, 2023, through January 8, 2024, using Catalyst Datafinch as the data collection tool. All data were gathered by Board Certified Behavior Analysts (BCBAs) and behavioral technicians and designed to replicate how practitioners collect traditional paper and pencil data. SPSS Statistics (v. 29.0) computed internal consistency reliability measures, including Cronbach's alpha, inter-item, split-half, and interclass correlation coefficients. Results  Dataset #1: Cronbach's alpha was 0.916 with seven items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.777, indicating good reliability, and for Part 2 was 0.972, indicating excellent reliability. Guttman split-half coefficient was 0.817, indicating good reliability. Inter-item correlation coefficients ranged from 0.474 to 0.970. The average measures interclass correlation (ICC) was 0.916, indicating excellent reliability. Single measures (ICC) reliability was 0.609, indicating acceptable reliability. Dataset #2: Cronbach's alpha was 0.954 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.912, indicating excellent reliability, and for Part 2 was 0.975, indicating excellent reliability. Guttman split-half coefficient was 0.917, indicating excellent reliability. Inter-item correlation coefficients ranged from 0.827 to 0.977. Average measures (ICC) was 0.954, indicating excellent reliability. Single measures (ICC) reliability was 0.875, indicating good reliability. Dataset #3: Cronbach's alpha was 0.974 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.978, indicating excellent reliability. Split-half reliability for Part 2 was 0.970, indicating excellent reliability. Guttman split-half coefficient was 0.935, indicating excellent reliability. Inter-item correlation coefficients ranged from 0.931 to 0.972. The average measures (ICC) was 0.974, indicating excellent reliability. Single measures (ICC) reliability was 0.926, indicating excellent reliability. Dataset #4: Cronbach's alpha was 0.980 with 12 items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.973, indicating excellent reliability. Split-half reliability for Part 2 was 0.996, indicating excellent reliability. Guttman split-half coefficient was 0.838, indicating good reliability. Inter-item correlation coefficients ranged from 0.692 to 0.999. The average measures (ICC) was 0.980, indicating excellent reliability. Single measures (ICC) reliability was 0.804, indicating good reliability. Conclusions These results suggest that Catalyst Datafinch demonstrates high internal consistency reliability when used with individuals with autism. This indicates that the application is reliable for collecting and analyzing behavioral data in this population. The ratings ranged from good to excellent, indicating a high consistency in the measurements.

Replicative Study of the Impacts of Applied Behavior Analysis on Target Behaviors in Individuals With Autism Using Repeated Measures.

Background  The effectiveness of interventions based on applied behavior analysis (ABA) for individuals with autism has been well documented in numerous meta-analyses, systematic reviews, and cost-benefit analyses. However, an observed 'efficacy-effectiveness gap' exists, which can be attributed to various factors. This third replication study, therefore, has significant implications for the field. By assessing the impact of ABA treatment, specifically involving discrete trial training and mass trials, within a naturalistic environment, the study provides valuable insights that can inform and improve the delivery of ABA treatments in real-world settings. Methods  The study was conducted using a repeated measures research design. Retrospective chart review data were collected from 62 individuals with autism, age (M=8.65, SD=4.53), all of whom were level two autistic and required moderate support in communication, socialization, and daily life. These individuals received ABA treatment over five months. The study measured cumulative target behaviors using a repeated measures design, which allowed for the identification of statistically significant differences across 12 time points. This robust methodology ensures the validity and reliability of the study's findings. Results  Mixed repeated measures analysis of variance (ANOVA) indicated statistical significance (sphericity assumed), F(11,495) = 55.432, p < 0.001 (time). Multiple comparisons using bootstrapped paired t-tests showed p < 0.05 on time points 1-8 and non-significance (p > 0.05) on time points 9-12. There was a significant interaction effect (sphericity assumed) with time x (age category), F(44,495) = 2.338, p < 0.001. Interaction contrasts indicated statistically significant differences over time, mainly within the one-year to four-year-old, five to eight-year-old, and most in the nine to 12-year-old age groups. There was some significance within the 13- to 16-year-old age group and no significance within the 17- to 26-year-old age group. Conclusions  Over five months, individuals with autism who underwent ABA treatments demonstrated a statistically significant enhancement in general target behaviors. This finding is crucial as it underscores the effectiveness of ABA treatments in a naturalistic environment. Moreover, the study's discovery of a significant interaction between time and age in these behaviors provides valuable insights into the impact of age on treatment outcomes. Extensive large-N studies of general ABA broad effectiveness and repeated measures designs are lacking and can lead to further research to improve quality and outcomes. These findings contribute to the body of empirical evidence and emphasize the importance of replicative efficacy studies in ensuring the reliability of research findings.

The Effects of Applied Behavior Analysis on Verbal Behavior With Autistic Individuals Using the Verbal Behavior Milestones Assessment and Placement Program (VBMAPP) and the Assessment of Basic Language and Learning Skills (ABLLS).

Introduction  Applied behavior analysis (ABA) is a fundamental practice-based intervention for treating autism spectrum disorder (ASD). Few studies have directly measured and evaluated the effects of ABA on verbal behaviors, mainly using the Verbal Behavior Milestones Assessment and Placement Program (VBMAPP) and the Assessment of Basic Language and Learning Skills (ABLLS) as outcome measures. This study aims to fill this gap by examining the relationship between ABA interventions and the enhancement of verbal skills, as measured by the VBMAPP and the ABLLS, in a convenience sample of individuals with ASD.  Materials and methods At The Oxford Centers (TOCs) in Brighton and Troy, Michigan, USA, 33 individuals with autism received treatment from January 2018 to July 2021, spanning 43 months. A pretest-posttest design was employed to retrospectively examine any impacts between ABA interventions and alterations in verbal scores among individuals with ASD. Depending on developmental age, all subjects underwent two verbal assessments with a six-month interval in-between. Twelve children were administered the VBMAPP, while 21 were given the ABLLS. Results Paired t-tests for pretest and posttest VBMAPP subscales resulted in statistically significant effects (p<0.05) for (VBMAPP - Mand), (VBMAPP - Tact), (VBMAPP - Listener Responding), (VBMAPP - Visual Perceptual Skills and Matching-to-Sample), (VBMAPP -Independent Play), (VBMAPP - Social Play), (VBMAPP - Motor Imitation), (VBMAPP - Spontaneous Vocalization), (VBMAPP - Intraverbal), (VBMAPP - Group Behavior), and (VBMAPP - Linguistic Structure). As measured by Cohen's d, effect sizes were moderate to mostly high (-0.623 to -1.688). There were non-significant results (p>0.05) for (VBMAPP - Listener Responding by Feature, Function, and Class) and (VBMAPP - Echoic). Paired t-tests for pretest and posttest ABLLS subscales resulted in statistically significant effects (p<.05) for all ABLLS scales: (ABLLS - Receptive Language), (ABLLS - Requests), (ABLLS - Labeling), (ABLLS - Intraverbals), (ABLLS - Spontaneous Vocalizations), (ABLLS - Syntax Grammar), (ABLLS - Social Interactions), and (ABLLS - Generalized Responding). As measured by Cohen's d, effect sizes were moderate to mostly high (-0.656 to -1.372). Conclusions  The administration of ABA treatments had a noteworthy influence, with statistically significant impacts on improving verbal behaviors on 11 of the 13 VBMAPP scales and all of the ABLLS scales. As measured by Cohen's d, effect sizes were moderate to high for both scales. These findings underscore the importance and effectiveness of ABA interventions in enhancing verbal skills in children with ASD. However, it's crucial to note that further confirmatory studies are required to verify the reliability of these original findings, emphasizing the ongoing need for research in this field.

Impact of Applied Behavior Analysis on Autistic Children Target Behaviors: A Replication Using Repeated Measures.

INTRODUCTION: Applied behavior analysis (ABA) is a primary evidence-based practice in treating autism spectrum disorder (ASD). Ongoing research is needed to report the results of ABA relative to attaining target behaviors. This study aims to replicate the results of previous research to determine the effectiveness of ABA of target behaviors in autistic children with a new timepoint sample of data.  Materials & methods: A repeated measures analysis tracked 98 autistic children, which included four adult participants, over three timepoints during a one-month snapshot period from 6/7/23 to 7/7/23. This study used a retrospective chart review to gather data on target behaviors to determine the effectiveness of ABA treatments across age categories. A mixed (between x within) analysis of variance (ANOVA) and subsequent post hoc and interaction contrasts were used to determine statistical significance. RESULTS: Mixed (between x within) ANOVA indicated statistical significance (sphericity assumed), F(2,160) = 32.893, and p < 0.05, across time. Using bootstrapped paired t-tests, multiple comparisons indicated p < 0.001 on all three multiple comparisons, with Bonferroni corrected α = 0.017. There was also a non-significant interaction effect (sphericity assumed) with (time) x (age category), F(8,160) = 0.333, p = 0.952, likely due to sizeable within-group variation resulting in a lowered statistical power.  Conclusions: This replication found that autistic children receiving the ABA intervention demonstrated statistically significant improvement in target behaviors over the one-month snapshot period.

Predicting Behavioral Target Mastery With Age, Intensity and Duration, Open Targets, and Maintenance Failure With Applied Behavior Analysis in Individuals With Autism.

Background Researchers have studied the effects of age, treatment intensity, and treatment duration with applied behavior analysis (ABA) outcomes in autistic individuals. This study's primary objective was to evaluate the predictive capabilities of age, intensity, and duration of treatment, open behavioral targets, and behavior maintenance failure on behavioral target mastery. Methods A retrospective cohort of 100 autistic individuals treated with ABA with functional analysis and discrete trial training, mass trials, and naturalistic training were treated and analyzed. Target behavioral mastery data was collected via a retrospective chart review contained within the "Catalyst" tracking software. ABA treatment was administered for three months between March 19, 2023, and June 11, 2023. Multiple linear regression was performed using the percentage of behavioral targets mastered as the dependent variable. The independent variables were age, average trials to behavioral mastery, average teaching days to behavioral mastery, and percentage of behavioral targets that failed in maintenance. Results The multiple linear regression model was statistically significant (R=0.443, R²=0.196, adjusted R2=0.150, F(5, 87)=4.239, p=0.002). The average teaching days to mastery (ß=0.416, p=0.019) and percentage of targets failed in maintenance (ß=0.201, p=0.047) significantly predicted the percentage of behavioral targets mastered. However, age (ß=0.079, p=0.419), average trials to mastery (ß=-0.271, p=0.114), and open targets (ß=0.184, p=0.081) did not significantly predict the percentage of behavioral targets mastered. The analysis of variance (ANOVA) resulted in non-significant (p>0.05) age group differences between the above variables. Conclusions The predictor variables average teaching days to mastery (intensity and duration) and percentage of targets failed in maintenance had a statistically significant effect on the percentage of behavioral targets mastered. The predictor variables age, average trials to behavioral mastery (intensity and duration), and open behavioral targets had a non-significant influence on the percentage of behavioral targets mastered. A non-significant difference between age groups was found in all study variables.

Examining the Effects of Discrete Trials, Mass Trials, and Naturalistic Environment Training on Autistic Individuals Using Repeated Measures.

INTRODUCTION: Behavioral interventions based on applied behavior analysis (ABA) form current evidence-based practices in treating autism spectrum disorder (ASD). Research is scarce relative to the broad effects of intensive repetitive, discrete trial training, and mass trials combined with a naturalistic environment as measured by overall general target behaviors. The primary objective of this study was to evaluate the effectiveness of a mixed behavioral model consisting of discrete trial training and mass trial interventions in the naturalistic environment, using a repeated measures design with a retrospective snapshot cohort of 93 individuals with autism. METHODS: A repeated measures analysis tracked 89 autistic children with four adult autistic individuals over seven time points during a three-month snapshot period from March 19, 2023, to June 11, 2023. This study determined the effectiveness of applied behavior analysis (ABA) interventions combining discrete trial training, mass trials, and naturalistic environment training on mastered broad target behaviors in autistic individuals using a mixed (between and within) ANOVA statistical design. RESULTS: Mixed (between and within) ANOVA indicated overall statistical significance, F (6,674)=45.447, p<0.001, partial eta squared=0.365 across time. These results indicated a large effect size. Multiple comparisons showed statistical significance (p<0.001) on all 21 multiple comparisons between timepoints. There was also a significant interaction effect with time × age category, F (24,474)=2.961, p<0.001, partial eta squared=0.130. These results also indicated a large effect size. CONCLUSIONS: Autistic individuals who received applied behavior analysis combining discrete trial training, mass trials, and naturalistic environment training intervention demonstrated statistically significant improvement in target behaviors over the three-month snapshot period, the most prominent being in the 13-16 years age category.

Treating Target Behaviors of Autistic Individuals With Applied Behavior Analysis: An Ongoing Replication Study.

Background Ongoing outcome data replication on target behaviors with autistic individuals using applied behavior analysis (ABA) confirms its effectiveness and remains an essential evidenced-based standard of care. This replication study aims to further confirm the impact of discrete trial training and mass trials on general target behaviors within a naturalistic environment. Methods Data was gathered from 92 children and four adult autistic individuals over one month from 7/7/23 to 8/8/23 using a repeated measures design. This study used a retrospective chart review with general target behaviors to determine the effectiveness of ABA treatments using discrete trial training and mass trials across time and age categories in a naturalistic environment. Results A mixed analysis of variance (ANOVA) indicated statistical significance (sphericity assumed), F(2,168) = 31.663, p < 0.001 (time). Multiple comparisons using bootstrapped paired t-tests indicated p < 0.001 on the three comparisons. There was a significant interaction effect (sphericity assumed) with time x age category, F(8,168) = 2.918, p = 0.004. Interaction contrasts indicated statistically significant differences over time within the 1-4 years, 5-8 years, and a portion of 9-12 years, and not within the 13-16 years and 17-73 years age groups. Conclusions Autistic individuals receiving ABA demonstrated statistically significant improvement in target behaviors over one month. There was a significant interaction between time and age on target behaviors, suggesting a significant association between time and age categories. The reporting of ongoing intervention outcomes provides further justification for continued treatments relative to target behavior mastery with autistic individuals.