Browsing by Subject "Autism Spectrum Disorder (ASD)"

Now showing 1 - 10 of 77
  • Thumbnail Image
    Item
    A global perspective: Quantitative changes in training participants’ knowledge of autism across selected settings within the United States and Singapore
    (2023-11) Neal, Tiffany; Nazarloo, Shawn; Deodhar, Aditi; Somasundaram, Manasi; Gandhi, Siddhi; Swiezy, Naomi
    The present study aimed to assess and compare the effectiveness of the HANDS in Autism™ Model training curriculum, framework and process specific to changes in autism knowledge via the Autism Knowledge Survey-Revised (AKS-R; HANDS in Autism®, 2005). Additional exploration using the AKS-R, sought to explore differences in the global, Singapore training cohorts specific to their participation in either single-week or multi-week training formats. Results from this preliminary exploration demonstrated statistically significant improvement in autism knowledge across both countries. These findings provide initial evidence as to both the effectiveness and transportability of the HANDS in Autism® Model across participants and countries. While findings are specific to improved autism knowledge, the emerging potential of the Hands in Autism® Model as a comprehensive treatment model will be further discussed.
  • Thumbnail Image
    Item
    Analyzing Autism Spectrum Disorder Behaviors Through Evidence-Based Educational Models in School Support Settings
    (2022-05) Boligorla, Srinivasulu; Neal, Tiffany; Deodhar, Aditi; Swiezy, Naomi
    Autism Spectrum Disorder (ASD) often presents with challenging behaviors that require structured, evidence-based educational strategies. This practicum focused on implementing and evaluating the HANDS in Autism® model across three collaborative school sites (Warsaw, Lakeview, and Gateway) to monitor and improve the use of evidence-based practices (EBPs) for managing problem behaviors among students with ASD. Data were collected using REDCap, cleaned and analyzed in R and Excel, and visualized to compare the proportion of students exhibiting problem behaviors across schools and visits. Results suggested variation in behavioral trends across school sites, with Warsaw showing higher proportions of students demonstrating problem behaviors during observed visits. The findings support the value of systematic monitoring and data-driven implementation of EBPs in improving behavioral outcomes and reducing the use of exclusionary discipline in autism support classrooms.
  • Thumbnail Image
    Item
    Analyzing Behavioral Patterns in Acute Inpatient Psychiatric Settings for Individuals with Autism Spectrum Disorder
    (2023) Bodempudi, Sai Tejaswi; Neal, Tiffany; Deodhar, Aditi; Swiezy, Naomi
    This project focused on analyzing behavioral patterns in patients at the Indiana NeuroDiagnostic Institute (NDI) over a three-year period (2021–2023). Using data from Cerner and REDCap, the study examined the frequency and types of physical and verbal aggression among 100 patients. The analysis identified “Other/Unspecified” as the most commonly reported category for both physical and verbal aggression, suggesting the need for improved classification methods. “Hitting,” “kicking,” “verbal threats,” and “screaming” were also frequent behaviors. Year-to-year variation in certain behaviors, such as an increase in “punching” in 2023, points to changing trends in patient aggression. Recommendations include refining behavior categorization, improving data extraction from Cerner, and developing more targeted intervention strategies to support patient care and staff safety. The project also emphasized the value of ethical research practices, collaborative teamwork, and data accuracy through recurring tasks such as scoring, entry, and validation.
  • Thumbnail Image
    Item
    Analyzing client denial trends in the NDI dataset: Patterns and predictive insights
    (2024-05) Samala, Vishwasree; Neal, Tiffany; Deodhar, A; Devarapalli, Baby Amulya; Swiezy, Naomi
    This project analyzed denial patterns among clients in the HANDS in Autism® NDI Exploratory dataset. Using REDCap and Cerner data, a structured coding scheme was implemented for consistent data entry and scoring. Python was used to quantitatively analyze denial reasons across 2021–2023. The most frequent denial factors included unmet family/parent criteria and issues unrelated to autism. Statistical testing, including Chi-Square and Fisher’s exact tests, revealed no significant relationship between gender and denial reasons. The project also produced a user guide for REDCap data entry and proposed future directions, including expanding the dataset and improving data completeness through enhanced data collection practices.
  • Thumbnail Image
    Item
    Analyzing gender distribution of HANDS ECHO participants
    (2024-05) Pancholi, Kushal; Neal, Tiffany; Gottipati, Mounika; Swiezy, Naomi
    The research focuses on the correlation between years of experience and confidence levels, examining knowledge, support, and perceived effectiveness in caring for individuals with ASD. Data collection involved surveys and interviews, with subsequent data entry into Excel and REDCap. Key findings indicate a significant positive correlation between healthcare providers' total years of experience and their confidence in ASD care. The study also highlights the gender distribution among participants, with a notable representation of females and participants from Indiana. These insights aim to inform the development of targeted training programs, enhancing provider readiness and improving care outcomes for individuals with ASD.
  • Thumbnail Image
    Item
    Analyzing Participant Feedback on various training components to enhance future HANDS Intensive trainings (2006-2025)
    (2025-05-09) Maddipatla, Vignitha; Neal, Tiffany; Gottipati, Mounika; Swiezy, Naomi
    This project analyzed nearly two decades of participant feedback from HANDS in Autism® Intensive Trainings conducted between 2006 and 2025. The goal was to identify satisfaction trends and improvement opportunities in training logistics, content, communication, and participant engagement. Using REDCap datasets, the data was cleaned, standardized, and analyzed using Python, Power BI, and Excel. Results revealed consistently high satisfaction scores (averaging 4.8/5), with increased engagement over the course of each training week. Top-rated components included speaker knowledge and small group activities, while lecture engagement showed room for improvement. The project demonstrated the value of health informatics in translating large-scale feedback into actionable insights and highlighted the importance of data-driven strategies to enhance the delivery of autism-focused professional training programs.
  • Thumbnail Image
    Item
    Analyzing participants demographics, distribution and engagement for the HANDS Developmental Disabilities and/or Autism ECHO tele-mentoring program
    (2024-08) Alluri, Dimple Sushma; Neal, Tiffany; Gottipati, Mounika; Swiezy, Naomi
    This research aimed to analyze participants' demographics, distribution, and engagement for the HANDS Developmental Disabilities and/or Autism ECHO tele-mentoring program. Data was collected through surveys to understand the representation of different participant types, race composition, gender distribution, and geographic distribution. The study also examined the preferred modes of consultation and professional involvement in diagnosing and supporting individuals with developmental disabilities and autism. Key findings indicated that family/caregivers were the most represented participant type, with a predominant race of White or Caucasians and a higher female participation rate. Indiana showed the highest geographic interest. Preferred consultation methods included phone and online consultations, with email consultations being favored by the majority. Professional involvement highlighted the need for increased engagement in early diagnosis and intervention. Workshop preferences leaned towards webinars, with less interest in all-day conferences. Recommendations were made to increase outreach to community providers, expand email consultation topics, diversify workshop offerings, strengthen professional awareness, and leverage geographic interest to develop localized programs.
  • Thumbnail Image
    Item
    Analyzing self-injurious behaviors (SIB) in individuals with autism spectrum disorder: Trends, interventions, and treatment outcomes
    (2024-08) Viswanath, Adarsh; Neal, Tiffany; Devarapalli, Baby Amulya; Swiezy, Naomi
    This project explored self-injurious behaviors (SIB) in individuals with Autism Spectrum Disorder (ASD) using the NDI Exploratory dataset comprising progress notes for 110 patients. Data was managed via REDCap, analyzed using Python, and visualized through Power BI. The study examined how SIB trends varied over five weeks and their association with gender and age. Findings revealed a significant reduction in behaviors such as hitting oneself, hitting the body against objects, and cutting. Males exhibited higher SIB frequencies overall, with early adolescence, particularly around ages 12 to 16, showing peak incidences. The consistent improvement in weekly recovery scores indicates that tailored interventions are effective. Recommendations include age- and gender-specific strategies, continuous treatment monitoring, and increased caregiver support to enhance outcomes and reduce long-term SIB risk.
  • Thumbnail Image
    Item
    Analyzing Team Engagement and Participation Patterns Through Dashboard-Driven Behavioral Profiling in Autism Support Settings
    (2025-05-09) Aluru, Sai Srilekha; Neal, Tiffany; Devarapalli, Baby Amulya; Swiezy, Naomi
    This project involved developing interactive data visualizations and analyzing team participation metrics in autism support classrooms as part of an internship at HANDS in Autism® Interdisciplinary Training and Resource Center. The work focused on REDCap data entry, survey tracking, and Power BI dashboard development for the Team Participation and Observation Profile (TPOP). These dashboards enabled site-specific filtering and scoring analysis across key behavioral dimensions such as engagement, participation, and cultural competence. Findings revealed strengths in staff engagement and roles clarity, with areas of improvement noted in cultural responsiveness and inclusive practices. This project enhanced technical fluency in Power BI, strengthened skills in structured data collection and analysis, and supported data-informed planning for school teams. The intern’s contributions directly advanced the organization’s goal of using behavioral insights to improve team performance and educational outcomes for students with ASD.
  • Thumbnail Image
    Item
    APPlications of amyloid-β precursor protein metabolites in macrocephaly and autism spectrum disorder
    (Frontiers Media, 2023-09-20) Sokol, Deborah K.; Lahiri, Debomoy K.; Neurology, School of Medicine
    Metabolites of the Amyloid-β precursor protein (APP) proteolysis may underlie brain overgrowth in Autism Spectrum Disorder (ASD). We have found elevated APP metabolites (total APP, secreted (s) APPα, and α-secretase adamalysins in the plasma and brain tissue of children with ASD). In this review, we highlight several lines of evidence supporting APP metabolites’ potential contribution to macrocephaly in ASD. First, APP appears early in corticogenesis, placing APP in a prime position to accelerate growth in neurons and glia. APP metabolites are upregulated in neuroinflammation, another potential contributor to excessive brain growth in ASD. APP metabolites appear to directly affect translational signaling pathways, which have been linked to single gene forms of syndromic ASD (Fragile X Syndrome, PTEN, Tuberous Sclerosis Complex). Finally, APP metabolites, and microRNA, which regulates APP expression, may contribute to ASD brain overgrowth, particularly increased white matter, through ERK receptor activation on the PI3K/Akt/mTOR/Rho GTPase pathway, favoring myelination.