Emily Ferguson, Ph.D., discusses self-regulation strategies for self-injurious behaviors (SIB). She outlines recent research on the frequency and distribution of different SIBs across a large sample, underscoring the importance of assessing individual behavior severity instead of an overall SIB score. The presenter considers the role of emotion regulation in SIB interventions. Ferguson considers emotion recognition, coping strategies, and prevention methods and provides free online resources for parents and clinicians.

In this webinar:

2:25 – Self-injurious behavior and autism
7:20 – Study: Frequency and distribution of unique SIBs
10:30 – Results
17:25 – Summary
19:45 – Helpful questions
22:20 – Adult self-reports
24:12 – Dialectical behavior therapy (DBT)
27:00 – Emotion regulation (ER) and challenging behaviors
30:45 – Emotion recognition and ER strategies
33:55 – Prevention
39:30 – Resources
42:40 – Q&A

Self-injurious behavior and autism

Ferguson explains that self-injurious behaviors (SIBs) are present in 40 – 50% of autistic people. They often persist in varying forms and intensities across the lifespan and significantly increase the likelihood of tissue damage or physical injury, hospitalization, and more restrictive residential placements (2:25). Common forms of repetitive behaviors include head-banging, skin-scratching, self-hitting, hair-pulling, lip or cheek-biting, nail-biting, skin-picking, and hitting against hard objects. Research shows that lower speech production, lower cognitive functioning, greater emotion dysregulation, greater sensory sensitivity, sex, and age are all linked to greater severity of SIB (6:30). The presenter notes that measurements and conceptualization of SIB vary across studies (5:25).

Frequency and distribution of unique SIBs

The speaker outlines her recent publication on the frequency and distribution of unique SIBs across a large sample. The study uses 593 survey responses to explore speech level, cognitive function, emotion dysregulation, and sensory sensitivities for each of the eleven most common non-suicidal SIBs (7:20).

Roughly 30-50% of caregivers reported mild to severe concerns for many types of SIB, highlighting the importance of looking at severity for every kind of SIB instead of an overall score. Results show that higher emotion dysregulation, or difficulty managing, responding, or calming strong emotions, was significantly associated with greater severity of nine types of SIB ranging from self-hitting to skin-picking (10:30). Higher sensory hypersensitivity was associated with greater severity of self-scratching (with and without tissue damage), repetitive hair-twirling, and skin-picking and lip-biting (with and without tissue damage). Ferguson asserts these findings indicate that these types of SIB may be a means of self-soothing or communicating distress in response to overwhelming sensory stimuli (13:30).

Self-scratching, self-hitting, and lip-biting (with tissue damage) were significantly associated with lower IQ, suggesting that the presence of intellectual disabilities (ID) might increase the risk of engaging in specific types of SIB. The study also found that youth who speak in full sentences demonstrate lower severity of self-hitting and head-banging than youth who use no words to communicate. Ferguson reiterates that self-hitting and head-banging may be a way of communicating, especially for autistic people with ID who are non-verbal (15:45). 

She summarizes research findings, reiterating the important insights we can gain by examining the severity of unique types of SIB rather than their overall occurrence. She notes that head-banging and self-hitting can result in serious physical harm, posing a greater risk to those with lower IQs and speech ability (17:25). 

The speaker suggests asking these questions to help determine causes and care strategies for SIB (19:45):

• Is the situation placing demands that exceed ER skills? (Adaptive emotion regulation (ER) skills)
• Is the behavior in response to unmet sensory needs or sensory overload? (Sensory dysregulation)
• Are the demands on the individual too high or low for their cognitive level? (Cognitive capacities)
• Does this person have a functional way to communicate? (Adequacy of communication system)

•  Exploring the Heterogeneity of Self-Injurious Behaviors in Autistic Youth: Patterns, Predictors, and Implications for Intervention (Ferguson et al., 2025)

Adult Self-Reports

The presenter outlines a recent study by Moseley and colleagues that asked autistic adults about the perceived role of non-suicidal SIBs (a more extensive list than the previous study). Participants reported hurting themselves in order to feel something, change emotional pain into something physical, relieve stress or pressure, deal with frustration, shock or hurt someone, self-punishment, and to prevent hurting themselves in other ways (22:20). 

• A ‘choice’, an ‘addiction’, a way ‘out of the lost’: exploring self-injury in autistic people without intellectual disability (Moseley et al., 2019)

Dialectical behavior therapy (DBT)

Dialectical Behavior Therapy (DBT) was developed for people with borderline personality disorder but can be applied to people across the spectrum of life. DBT is often a first-line treatment for people who engage in SIB or have suicidal thoughts or tendencies. It works to identify and distinguish repetitive behaviors, accepting the ones that serve a good purpose while changing others that increase the risk of harm to self or others (24:21). 

Emotion regulation and challenging behaviors

The speaker outlines the pathway from difficulties with ER to challenging behavior presentation and considers the role that self-regulation has in SIB interventions (27:00):

 Individual factors (hungry or tired) → precipitating cues (loud/surprising noise) → difficulty identifying and labeling emotions (overwhelmed) →  negative experience continues or increases (no coping strategy) → challenging behavior is expressed.

Emotion recognition

Emotion recognition can be difficult for everyone, and creating a personalized feeling chart can significantly help. Ferguson discusses how to make a feeling chart that captures the sounds and feelings of one’s emotions as well as what someone needs in those moments (30:45). 

Emotion regulation (ER) strategies

ER strategies include coping skills and co-regulation techniques, which the speaker suggests should be practiced daily. For caretakers, it is essential to do these things with their child or patient to model effective coping strategies. Ferguson highlights the benefits of parent skills training and provides a free training video that teaches some quick coping skills for calming down and returning to a place where you can work through problems and big emotions more effectively (31:40). 

• https://www.youtube.com/watch?v=0cq5RuE8SXk

Prevention

SIB prevention techniques focus on reducing the likelihood of distressing situations or contexts. If an SIB consistently occurs in a specific location, consider what about that place may cause distress to you or your child. It’s also helpful to note setting events, like sleep, that can affect irritability and ER throughout the day (33:55). The Research Units in Behavioral Intervention (RUBI) provides free online materials to help with SIB prevention.

• https://academic.oup.com/book/1248/chapter/140180019

Functional behavior analyses of problem behaviors are commonly used to identify antecedents, behaviors, and consequences and can help identify positive and negative communication cycles. Ferguson emphasizes that behavioral approaches must be strengths-based and aimed at reducing negative impact. Other prevention methods include mental health therapies and parent training in behavior management, as outlined in the RUBI resources and Parent-Child Interaction Therapy (ages 2 – 7) (35:55). The speaker shares a clinical guide for treating severe behaviors, toolkits, and ECHO training (39:30) before the Q&A (42:40). 

Resources provided during the talk and in text:

• Article by Dr. Ferguson mentioned in the talk: https://onlinelibrary.wiley.com/doi/full/10.1002/aur.3269
• ECHO Autism Challenging Behavior: https://echoautism.org/challenging-behavior/
• Autism Speaks Challenging Behavior Toolkit: https://www.autismspeaks.org/tool-kit/challenging-behaviors-tool-kit
• RUBI intervention online materials: https://academic.oup.com/book/1248/chapter/140180019
• Moseley article referenced during the talk: https://link.springer.com/content/pdf/10.1186/s13229-019-0267-3.pdf

Originally published December 3, 2024.

Dr. Connor Kerns discusses research on the prevalence, sources, and impact of traumatic experiences in autistic people. She underscores the prevalence of childhood adversity in autistic groups and highlights the need for more research in this area. The presenter details recent investigations on the drivers and indicators of trauma reactions in autistic children and young adults. Results show autism-specific sources and representations of trauma that can inform assessment and care development. Kerns touches on the Childhood Adversity and Social Stress Questionnaire (CASSQ) and other ongoing research before the Q&A.

You can take the knowledge quiz HERE

In this webinar: 

1:20 – Disclosures and land acknowledgments
2:00 – Trauma and autism
7:30 – Community providers and trauma care
9:10 – Childhood adversity
13:10 – Adversity and trauma
17:40 – Study 1: Sources of trauma
21:15 – Chronic stressors
25:24 – Results
33:05 – Study 2: Trauma indicators
38:50 – Results
44:50 – Conclusions and future directions
50:40 – Q&A

Trauma and autism

Kerns discusses the lack of attention given to trauma-related conditions in autism and considers whether current assessment tools accurately capture experiences of adversity, trauma, and anxiety in autistic people (2:00). She cites a recent study where one-third of participants reported histories of abuse, but only two percent had received a diagnosis for post-traumatic stress disorder (PTSD) (5:40). The speaker emphasizes understanding the sources and signs of trauma in autism as a critical avenue of ongoing research (4:30). 

For example, a recent survey of community care providers found that 54% of respondents report treating, 56% report screening, and 74% report inquiring about trauma-related symptoms in autistic individuals (7:30). Kerns asserts that these findings provide hope because although research has been slow to make this connection, experienced community providers can advise on investigation and assessment development (8:45). 

• Behavioral Symptoms of Reported Abuse in Children and Adolescents with Autism Spectrum Disorder in Inpatient Settings (Brenner et al., 2018)
• Traumatic Childhood Events and Autism Spectrum Disorder (Kerns et al., 2015)
• Increased risk of acute stress disorder and post-traumatic stress disorder in children and adolescents with autism spectrum disorder: a nation-wide cohort study in Taiwan (Li et al., 2024)
• Experience of Trauma and PTSD Symptoms in Autistic Adults: Risk of PTSD Development Following DSM-5 and Non-DSM-5 Traumatic Life Events (Rumball et al., 2020)
• Service use by youth with autism within a system-driven implementation of evidence-based practices in children’s mental health services (Stadnick et al., 2020)
• Screening and treatment of trauma-related symptoms in youth with autism spectrum disorder among community providers in the United States (Kerns et al., 2020)

Childhood adversity

Childhood adversity is a significant risk factor for mental and physical health in the general population. Kerns cites a population-based U.S. study showing that parents of children with autism report more adversity, where 28% report two or more adversities compared to 18% in the general population (9:10). Research also shows increased instances of maltreatment (emotional, physical, and sexual abuse) for autistic children and that risk of adversity exposure is inflated for those living at or around poverty level (10:15). The presenter questions why levels of PTSD are so low in the autistic population and why researchers are not paying more attention to this topic (10:50). 

• Brief Report: Examining the Association of Autism and Adverse Childhood Experiences in the National Survey of Children’s Health: The Important Role of Income and Co-occurring Mental Health Conditions (Kerns et al., 2017)
• Adverse childhood experiences in children with autism spectrum disorder (Hoover & Kaufman, 2018)
• Child maltreatment in autism spectrum disorder and intellectual disability: results from a population-based sample (McDonnell et al., 2019)
• A population-based examination of maltreatment referrals and substantiation for children with autism spectrum disorder (Fisher et al., 2019)

Adversity and trauma

The speaker outlines DSM-5 trauma parameters (e.g., exposure to death, threatened serious injury, or sexual violence…) and highlights the exclusivity of this description. She asserts the need for a broader definition of trauma as any experience or event that negatively impacts an individual for an extended period (13:10). Kerns explains that while many adversities and stressors qualify for the DSM-5 description, we must keep in mind the broad range of mental health outcomes associated with trauma (e.g., PTSD, depression, anxiety) (14:00). She highlights that both chronic low magnitude and extreme stressors can cause PTSD symptoms, meaning trauma responses can be based on a single event or long-term exposure to specific adversities (15:40). 

• Rates of post-traumatic stress disorder in trauma-exposed children and adolescents: meta-analysis (Alisic et al., 2014)
• Traumatic Events and Posttraumatic Stress in Childhood (Copeland et al., 2007)
• Childhood life events and childhood trauma in adult patients with depressive, anxiety and comorbid disorders vs. controls (Hoven et al., 2010)
• Posttraumatic Stress Without Trauma in Children (Copeland et al., 2010)

Current research

Kerns’s ongoing research focuses on strengthening our ability to recognize, characterize, prevent, and treat adverse consequences of trauma in autistic individuals. We must begin, she asserts, by better understanding how individuals with autism experience and cope with trauma. Only then can we develop tools and guidelines for accurately assessing these difficulties (16:30). 

Study 1: Sources of trauma

The presenter outlines methods for a recent study that used semi-structured interviews to discuss trauma experiences (broader definition) with 14 autistic adults and 15 caregivers (17:40). She provides participant details, highlighting that only three were diagnosed with PTSD, while 100% reported trauma (19:40). Trauma history questionnaires found the most common adversities faced were the death of a loved one, emotional abuse, physical abuse, bullying, and “other” (21:15). Qualitative interviews illustrated three other main points of trauma (23:00). Kerns describes each and provides participant quotes:

• Feeling trapped due to emotional or physical restraints in therapeutic environments or school, the loss of autonomy in decision-making, and loss of opportunity and self-determination (25:24). 

“This need of wanting to be successful and make his mark on society, it’s such a basic need, and there doesn’t seem to be any kind of support.” – Mother of a 22-year-old son

• Social exclusion resulting from bullying, social isolation, alienation, stigma, discrimination, and betrayal by individuals in their lives (26:25).

“I felt like everywhere I went, I was just treated like I was a space alien, yet nobody had a reason why; there was no explanation.” – 39-year-old woman

• Traumatic incongruities that stem from experiences in inhospitable environments, including sensory experiences, overwhelming transitions/changes, and the chronic stress of needing to predict and decipher social situations (27:40). 

“I don’t know how I pieced it together! But the leaves were falling off the trees, and it was disruptive to him! His whole life was disrupted at the age of four by leaves falling off of the trees.” – Mother of a 22-year-old son

The presenter summarizes study findings, highlighting that both traditional and more broadly defined instances of trauma impact autistic youth. She underscores chronic forms of trauma for people with autism, including social exclusion and marginalization. Some life events, like transitions, haircuts, or social interactions, can also impact health and well-being. Kerns reiterates that much more research is needed to understand whether these experiences lead to PTSD or other related health concerns (29:25). She notes study limitations and a recent publication on how people cope with the long-term effects of trauma (31:00). 

• Exploring potential sources of childhood trauma: A qualitative study with autistic adults and caregivers (Kerns et al., 2022)
• Heightened risk of posttraumatic stress disorder in adults with autism spectrum disorder: The role of cumulative trauma and memory deficits (Rumball et al., 2021)
• Autism Spectrum Disorder and Post-Traumatic Stress Disorder: An unexplored co-occurrence of conditions (Haruvi-Lamden et al., 2020)
• A Qualitative Study of Self and Caregiver Perspectives on How Autistic Individuals Cope With Trauma (Ng-Cordell et al., 2022)

Study 2: Trauma indicators

The goal of this study was to develop expert consensus on essential indicators of traumatic reactions in autistic children and subsequently inform assessment guidelines. The study sample included clinicians and clinical researchers with more than five years of experience who have helped at least one autistic child with traumatic event exposure (33:05). 72% of experts in the study regularly work with minimally verbal youth; 78% had seen more than ten autistic youth with a trauma history, and 35% had seen more than 50 (36:33). Researchers administered a Delphi survey that uses multiple iterations of questionnaires and controlled feedback to build consensus (75% or more agree) (36:55). Survey indicators included DSM-5 and more broadly defined trauma behaviors (38:12). 

Many trauma indicators endorsed by participants did fit into the general PTSD definitions. All intrusion symptoms were generally endorsed, aside from nightmares. All avoidance and arousal/reactivity indicators were endorsed. Kerns explains that negative alterations in cognition and mood were not endorsed as many experts feel these changes are too complex to address in autistic patients (38:50). Six other important indicative behaviors not listed in the DSM-5 were found (40:42): 

• Regression of adaptive skills
• Suicidality
• Self-injurious behavior
• Non-suicidal self-injury
• Excessive reassurance seeking
• Reduced communicative language

Experts also reached consenus around key diagnostic issues, including the need for caregiver reports, autism-specific tools, cross-discipline evaluations and work, the importance of developmental profiles in assessing trauma, and how social-communication differences in autism can make it challenging to assess trauma and overall health (41:15). The speaker reiterates that many (not all) DSM-5 criteria are relevant to autism, along with six other indicators identified by experts. These findings, she asserts, can help guide the development of autism-specific measures to improve assessment and diagnosis (42:30). 

• Expert consensus regarding indicators of a traumatic reaction in autistic youth: a Delphi survey (Kerns et al., 2022)

Conclusions and future directions

She reviews the growing evidence that autistic individuals experience above-average rates of childhood adversity, which is significantly associated with adverse mental and physical health across the lifetime. She reminds viewers that community providers endorse the relevance of this issue and urges researchers and practitioners to consider a broader range of potentially traumatic stressors and symptoms when working with autistic youth (44:50). 

Childhood Adversity and Social Stress Questionnaire (CASSQ)

Kerns and her team are currently developing an autism-tailored measure of adversity and traumatic reactions for children and young adults with autism (45:50). She explains how the studies outlined in this presentation laid the foundation for the Childhood Adversity and Social Stress Questionnaire (CASSQ), which is currently being piloted for validity. She notes CASSQ subscales and describes the process of direct feedback from autistic participants about the readability of the questionnaires (47:05). Kerns asserts that future avenues of research should also assess memory and how it affects cognitive ability. She notes an ongoing study on the relationship between social processing and the experience of anxiety in autism before the Q&A (50:40). 

You can find more information and sign up for the ongoing study discussed HERE.

Originally published June 4, 2024

Dr. Jennifer Frankovich reviews what we know about the underlying mechanisms, trajectories, and symptoms of Pediatric Acute Neuropsychiatric Syndrome (PANS). She discusses the role of the Basal Ganglia in PANS symptoms and cites contemporary research that highlights this connection. Frankovich touches on the disruption of the blood-brain barrier and auto-antibody regulation in PANS. 

Playback of Dr. Mondal’s presentation and Dr. Hussein’s presentation will be available at a future date.

In this webinar:

1:30 – PANS/PANDAS overview
3:10 – Underlying mechanisms
5:00 – Common symptoms
7:55 – PANS trajectories
9:35 – Basal Ganglia
13:12 – Antibodies and the blood-brain barrier

PANS/PANDAS

Frankovich outlines Pediatric Acute Neuropsychiatric Syndrome (PANS) and Pediatric Acute Neuropsychiatric Disorders Associated with Strep (PANDAS), underscoring the different triggers attributed to both conditions (1:30). Classification criteria for both PANS and PANDAS include a sudden onset of obsessive-compulsive disorder or eating restrictions along with at least two other co-occurring conditions like anxiety, sensory dysregulation, motor abnormalities, developmental regression, and deterioration of cognitive functioning (2:15). 

Underlying mechanisms

Children with PANS-related symptoms often have some form of immune predisposition. The speaker explains that PANS occurs after a person gets an infection, which, due to predisposition, causes a systemic inflammatory response. She notes that these inflammatory responses may lead to Basal Ganglia inflammation, altered neuronal signaling, microbial activation, and more (3:10). 

Common symptoms

The presenter describes classic PANS experiences, such as swelling in the knees, hips, and heel bones, back pain and inflammation, and evidence of psoriasis. She explains that children who experience their first case between the ages of five and ten will likely have arthritis by the time they are 14 (5:00). Frankovich highlights our bodies’ abilities to self-regulate inflammation, noting that in many cases, PANS symptoms are resolved on their own (7:15). 

PANS Trajectories

There are generally four different trajectories for PANS:

• Relapsing and remitting – returning to the same baseline
• Relapsing and remitting – worsening baseline across time
• Primary persistent – no return to baseline, remains in chronic episode
• Secondary persistent – multiple episodes with increasing baseline until it reaches a chronic episode. 

Frankovich asserts that the primary and secondary persistent trajectories are likely more related to autoimmune predispositions than the others. Therefore, she continues, these trajectories require the most intense treatments and assessments (7:55). 

Basal Ganglia

The Basal Ganglia (BG), a group of nuclei located beneath the cerebral cortex, has an inhibitory influence on motor and behavior systems. The speaker notes that inflammation, autoantibodies, and injury can disrupt the BG, affecting movements, mood, emotion, behavior, procedural learning, and cognition (9:35). 

Frankovich briefly presents four brain imaging studies suggesting BG inflammation in PANS. She also discusses three studies indicating that patients experience abnormal movements during REM sleep cycles. These REM movements predict Parkinson’s in adults, making this a critical area of research and care (10:25). 

Other physical signs of BG disruption include specific tongue and mouth movements. For example, a positive glabellar tap reflex is present in children with PANS, though it should disappear after infancy. Other abnormal tongue movements, like milkmaid grip, are discussed (11:55). The speaker notes that between 80 and 92% of patients in her clinic exhibit at least one sign of BG disruption (12:25).

19% of autistic youth also have a positive glabellar tap, and 27% have milkmaid grip tongue movements. The presenter, therefore, asserts that these BG signs are not unique to PANS and should be investigated carefully across groups (12:38). 

Antibodies and the blood-brain barrier

PANS autoantibodies target interneurons and have been found in healthy kids and children with PANDAS. Frankovich explains that if these antibodies are causing problems in the body, it is because they are crossing the blood-brain barrier (BBB) (13:12). She highlights that current research suggests local disruptions to the BBB are associated with PANS symptoms (13:50).

Originally published on June 14, 2024

Dr. Waizbard-Bartov discusses changes in autism symptoms across childhood. She outlines the Autism Phenome Project and study methods for her recent work. The speaker presents findings on the frequency, patterns, and predictors of symptom severity changes across childhood periods in autism. Waizbard-Bartov touches on the intersection of autism symptom severity, assigned sex, and environmental factors. She summarizes presentation findings and considers pathways of future research before the Q&A. 

In this webinar:

1:09 – What is autism
5:00 – Social communication and RRB
12:06 – Autism Phenome Project
16:15 – Study methods
20:15 – Changes in autism symptom severity across childhood
24:30 – Variations in patterns of symptom change
28:05 – Predictors of changes in severity across childhood
30:25 – Sex differences
35:40 – Adaptive function
37:44 – Parental characteristics
39:10 – Co-occurring mental health conditions

What is autism?

Waizbard-Bartov describes autism as a neurodevelopmental condition broadly defined by difficulties with social communication and restrictive, repetitive behaviors (RRB) (1:09). For an autism assessment, social communication differences are subcategorized into social-emotional behavior, atypical nonverbal social behavior, and difficulty creating and maintaining relationships (2:15). RRBs are also subcategorized into stereotyped repetitive speech or actions, excessive adherence to non-functional routines, restricted/fixated interests, and atypical sensory behaviors (4:19). The speaker draws on her time working with preschoolers to illustrate how core characteristics/symptoms of autism range in presentation and severity across individuals and time (8:00). 

Study methods

The presenter outlines the Autism Phenome Project (APP), a longitudinal study of nearly 700 autistic and non-autistic children across five time points from early childhood to early adulthood (12:06). At each time point, the ongoing study assesses blood, MRI, language development, memory and attention, co-occurring conditions, and parental perspectives (14:20). Waizbard-Bartov describes her recent work on autism symptom trajectories across early (ages 3 – 6) and middle childhood (ages 6 – 11.5) (11:25). Her team used APP data for 183 autistic children (30% female-presenting) at three times points: between ages two and three and a half (2 – 3.5 yrs), between ages four and six (4 – 6 yrs), and again between ages nine and twelve (9 – 12 yrs) (16:15). Researchers used the calibrated severity scores from the Autism Diagnostic Observation Schedule (ADOS) to track symptom severity across time and domains (17:50). 

Q1: How common is change in autism symptom severity across childhood?

Researchers divided children into three groups based on observed changes in symptom severity across the first two time points (early childhood; ages 3 – 6). Of these, 54% remained stable, 29% significantly decreased, and 17% increased dramatically in symptom severity across early childhood (20:15). Correlations between behaviors and brain development were also found, where children with increased severity had slower white matter development compared to those with decreased symptom severity (21:43). When assessments were extended to the third time point (middle childhood; around age 11.5), the same three groups were identified, where 49% remained stable, 27% experienced a consistent decrease, and 24% experienced a consistent increase in symptom severity (22:55). Waizbard-Bartov reiterates that about half of children in the study demonstrated changes in severity across early and middle childhood, suggesting that such changes may be expected in autism (24:00). 

Q2: Do patterns of change vary across periods of childhood?

To understand patterns of symptom change across time, the presenter and her team compared early childhood severity changes to those of middle childhood (third time point). Results showed increases in symptom severity are equally as likely to occur during early and middle childhood, while decreases in severity are significantly more likely to occur in early childhood only (24:30). Researchers also found that more than 60% of the sample showed different patterns of change across childhood periods (i.e., decreased in early childhood and stable or increasing during middle childhood) (25:35). Waizbard-Bartov summarizes these findings, asserting that patterns of severity change across periods of childhood in autism (27:15). 

Q3: What predicts directional changes in symptoms across childhood?

To assess predictors of symptom severity changes in autism, Waizbard-Bartov and her team assessed related variables:

Cognitive ability/IQ

Results showed that children with decreased severity in early childhood had a higher IQ at the first two time points and exhibited IQ gains over time (28:45). Comparatively, those with increased severity had lower IQ at both time points that remained stable across time (29:30). The speaker asserts that these findings suggest a strong association between cognitive abilities and symptom severity during early development. 

Sex differences

Researchers found in female-presenting participants, symptom severity is likely to decrease or remain stable. However, for male-presenting participants, increases and decreases in severity are equally likely to occur (30:25). Further, calibrated severity scores revealed that female-presenting individuals show significant severity decreases in total symptoms and, more specifically, in RRB, especially during middle school. Conversely, male-presenting individuals show stable total symptom and RRB scores across childhood (31:15). The presenter discusses sex-compared changes across ADOS items, highlighting the stark trajectory differences between sex groups (33:15).  

Adaptive function

Adaptive functioning is meaningful for everyday life, and all three groups had the same score at age one. However, by age six, those experiencing decreases in severity had significantly higher adaptive functioning than those with increasing severity (35:40). The presenter explains how those with increasing severity did not necessarily lose adaptive function skills but that their rate of progress steadily slowed over time (36:40). 

Parental characteristics

Waizbard-Bartov and her team also found that fathers and mothers of children with decreasing severity were generally older and more educated. Contrastingly, parents of children with increasing severity were younger and less educated. The speaker notes the intersectionality of education and socioeconomic status and its impact on resource accessibility and self-advocacy (37:44). 

Co-occurring mental health conditions

Results showed a significant correlation between aspects of mental health and autism symptom domains (39:10). For example, 21% of participants had significant increases in both the severity of social communication issues and anxiety as they entered elementary school. ADHD levels also rose across middle school, and by age eleven, 84% of participants met the clinical requirements for an anxiety disorder (41:25). In female-presenting participants, improvements in RRB overtime ran parallel to increases in anxiety, where 94% had clinical anxiety disorders by age eleven (44:26). Waizbard-Bartov and her team found no evidence that initial symptom severity can predict changes across childhood, meaning everyone has the same potential for change (45:05). The presenter summarizes the findings for question three, noting that severity changes are correlated with assigned sex, IQ, adaptive functioning, parental characteristics, and mental health conditions (46:45).

• Trajectories of Autism Symptom Severity Change During Early Childhood (Waizbard-Bartov et al., 2021)
• Identifying autism symptom severity trajectories across childhood (Waizbard-Bartov et al., 2022)
• Changes in the severity of autism symptom domains are related to mental health challenges during middle childhood (Waizbard-Bartov et al., 2023)

Conclusion

Waizbard-Bartov summarizes research findings, underscoring that the severity of autism symptoms can change substantially across childhood periods and that patterns of change are not linear. She highlights that a child’s characteristics and environment can predict directional changes and that children have the potential for different severity trajectories regardless of their initial levels (47:55). 

She outlines future research directions, including how severity is affected during adolescence and how interactions between symptom severity and other characteristics play out over time (49:15). The presenter highlights current research around the impact of sex on symptom severity, underscoring the potential effects of camouflaging specific to female-presenting individuals (51:10). The speaker provides thanks and acknowledgments before the Q&A (53:25). 

Originally published on May 15, 2024

Hilde Geurts, Ph.D., discusses the impact of aging on health, quality of life, and cognition in autistic adults. She discusses aging in the general population and how it relates to autistic experiences. The speaker considers differences in aging experiences across groups and how they inform autism care and research. Geurts dives into cognition and aging, highlighting potential cognitive aging profiles for autism and what evidence there is to support them. The presenter discusses avenues and caveats for future research before the Q&A.

In this webinar: 

0:00 – 4:55 – Organization introductions
4:56 – Speaker and affiliate introduction
7:20 – Outline
8:50 – Aging in the general population
13:15 – Autism and aging
15:30 – Health and quality of life across groups
24:10 – Similarities in differences across groups
30:00 – Aging and cognition
36:00 – Cognition profiles
41:45 – What’s next?
44:50 – Q&A

Aging in the general population

Geurts outlines presentation goals and highlights the overlap of mental health, cognition, and quality of life (QoL) (7:20). She briefly describes a ten-year study on autism heterogeneity from which much of the data discussed is drawn (8:30). The speaker outlines the three pillars of a successful aging process, as described by Rowe & Kahn in 1997: 

1. Minimize risk of disease and disability
2. Maintain physical and cognitive function
3. Continue engagement with life

She explains that our bodies undergo physical and cognitive changes as we age, like brain shrinkage, decreased flexibility, and increased forgetfulness. Conversely, happiness and quality of life (QoL) increase with age in the general population (9:55). Geurts lists risk factors for aging, including genetics, poverty, stress, physical and mental health, and a lack of social support. She underscores that many of these are more prevalent in autistic adults and considers whether or not this means that autistic people are at risk for aging faster with a lower QoL (11:30).

• Aging and heterogeneity regarding autism spectrum conditions: a protocol paper of an accelerated longitudinal study (Geurts et al., 2020)
• Successful Aging (Rowe & Kahn, 1997)

Autism and aging

The presenter describes patterns of co-occurring conditions in autism. She cites studies showing that those who receive a late autism diagnosis have a higher chance of mental health disorders and that people assigned female at birth have a higher chance of general health problems (13:15). Therefore, Geurts considers whether late-diagnosed female-presenting autistic adults are at higher risk for mental health difficulties (14:45). 

• The physical and mental health of middle-aged and older adults on the autism spectrum and the impact of intellectual disability (Bishop-Fitzpatrick & Rubenstein, 2019)
• Health Conditions and Functional Status in Adults with Autism: A Cross-Sectional Evaluation (Fortuna et al., 2016)
• General health of adults with autism spectrum disorders–a whole country population cross-sectional study (Rydzewska et al., 2019)
• Objective and subjective psychosocial outcomes in adults with autism spectrum disorder: A 6-year longitudinal study (Scheeren et al., 2022)
• Associations between co‐occurring conditions and age of autism diagnosis: Implications for mental health training and adult autism research (Jadav & Bal, 2022)

Health and quality of life across groups

Geurts details a recent study showing that autistic adults have more mental health conditions compared to controls, with a peak in prevalence between 40 and 54 years old (15:30). She describes research that revealed autistic women experience more menopausal problems and depressive symptoms than their non-autistic peers. Therefore, the speaker posits that the observed peak in middle-aged mental health conditions could be in part attributed to the menopausal experiences of female-presenting autistic adults (17:00). 

• Psychiatric Co-occurring Symptoms and Disorders in Young, Middle-Aged, and Older Adults with Autism Spectrum Disorder (Lever & Geurts, 2016). 
• Menstruation and menopause in autistic adults: Periods of importance? (Groenman et al., 2022) 

The speaker presents data showing a decline in social anxiety with age in autism, where participants reported caring less about what other people think of them and feeling more comfortable in their own skin (20:15). She notes the impact of camouflaging on mental health and considers the intersection of age, camouflaging, and mental health in autism (22:35). Geurts summarizes that research to date shows both mental and physical health problems are more common in autistic adults, though causes differ across groups (i.e., menopause, camouflaging). She asserts that these differences are essential for understanding autism subgroups (23:38). 

• How to measure camouflaging? A conceptual replication of the validation of the Camouflaging Autistic Traits Questionnaire in Dutch adults (van der Putten et al., 2023)

The similarities and differences between autistic profiles are crucial for identifying subgroups of autism and finetuning research and care. Geurts describes a study that revealed autistic adults feel more vulnerable and like they have less control and emotional support compared to non-autistic adults (24:10). Feelings of control and emotional support were stable at two and five-year follow-ups and predicted mental health and QoL (27:50). The speaker notes that between 14% and 17% of participants switched profiles across time, underscoring the need to study those changes and how they affect QoL and mental health (28:30). 

• Finding Similarities in Differences Between Autistic Adults: Two Replicated Subgroups (Radhoe et al., 2023)

Aging and cognition

Cognition is a predictor of Neurocognitive Disorders (NCD), like dementia, which are diagnosed five times as often in autistic adults compared to the general population (30:00). NCD diagnoses generally include:

• Cognitive challenges.
• A significant decline in daily life.
• Low performance in at least one cognitive domain (i.e., memory, flexibility, social cognition).

The speaker cites research revealing more self-reported cognitive challenges and lower performance in social and quick response (flexibility) tests for autistic adults compared to controls (32:18). She asserts that although autistic people are more likely to meet phenotypic NCD criteria, the underlying mechanisms may not align. 

• Prevalence of physical and mental health conditions in Medicare-enrolled, autistic older adults (Hand et al., 2020)
• A longitudinal study on cognitive aging in autism (Torenvliet et al., 2021)
• Age-related differences in cognition across the adult lifespan in autism spectrum disorder (Lever & Geurts, 2016) 
• Parallel age-related cognitive effects in autism: A cross-sectional replication study (van der Putten et al., 2022)
• Self-reported cognitive decline among middle and older age autistic adults (Klein et al., 2023)

For example, research shows that depressive symptoms may drive self-reported cognitive challenges in autism, meaning that mood, not memory, is the issue (34:00). Geurts notes that cognitive differences were observed across autism independent of age, meaning that there is no significant evidence that autistic individuals experience faster cognitive decline compared to the general population (35:24). 

• When mind and measurement diverge; the interplay between subjective cognitive complaints (SCCs), objective cognition, age, and depression in autistic adults (Torenvliet et al., 2024)
• The cognitive profile of middle-aged and older adults with high vs. low autistic traits (Stewart et al., 2023)
• A longitudinal study on cognitive aging in autism (Torenvliet et al., 2021)

Cognitive aging profiles

Geurts outlines three cognitive aging profiles comparing autism and controls: Faster, Parallel 1, and Parallel 2, where Parallel 1 has no cognitive difference by age and Parallel 2 does. No conclusive or replicable evidence supports the Faster cognitive aging profile for autism, though comparatively early decline has been found in individual cognitive domains (36:00). The speaker describes recent research suggesting parallel cognitive aging patterns between autism and the general population (37:00). For example, an autistic middle-aged person may have the same cognitive profile as someone who is older and not autistic. Geurts asserts that this could be why some autistic adults feel older or that they are declining faster, though this may not be the case (38:41). The presenter urges viewers to be careful in diagnosing NCD, especially in autistic adults, where NCD presentations could result from a different, less-understood cognitive profile (39:53). She reiterates that little to no evidence exists for accelerated (faster) cognitive aging in autism, though some adults with autism have old-age profiles (40:30). 

• One size does not fit all: An individualized approach to understand heterogeneous cognitive performance in autistic adults (Torenvliet et al., 2023)

What’s next?

Future research on the intersection of cognition, aging, and autism must consider how the age of diagnosis and assigned sex seem to play a role. Geurts also underscores the need for research that includes people with intellectual disabilities and early diagnosis (41:45). 

• Autism Through the Ages: A Mixed Methods Approach to Understanding How Age and Age of Diagnosis Affect Quality of Life (Atherton et al., 2020)
• Associations between co‐occurring conditions and age of autism diagnosis: Implications for mental health training and adult autism research (Jadav & Bal, 2022)
• Objective and subjective psychosocial outcomes in adults with autism spectrum disorder: A 6-year longitudinal study (Scheeren et al., 2022)
• Cognitive and language skills in adults with autism: a 40-year follow-up (Howlin et al., 2014)

The presenter asserts that current avenues of research should focus on the interplay of mental and physical health problems in autistic adults, drivers of seemingly age-related health changes, and subgroups that may be at risk for faster aging. She notes two studies currently under review for publication (42:30). Geurts provides thanks and acknowledgments before the Q&A (44:50). 

• The prevalence and incidence of early-onset dementia among adults with autism spectrum disorder (Vivanti et al., 2021)
• The influence of age and ASD on verbal fluency networks (Baxter et al., 2019)

Originally published April 9, 2024

Karen Heffler, MD, takes viewers on a comprehensive exploration of the relationship between early-life screen time exposure and autism risk. She delves into the intricate interplay of genetics, environmental factors, and development outcomes. The presentation highlights critical findings about screen time, social engagement, and autism symptoms. Heffler considers the potential consequences of screen media on young children’s development and discusses promising new intervention studies.

Printable handouts of the slides (pdf) are online HERE

A list of references from the talk (pdf) are online HERE

In this webinar: 

0:00 – Presenter introduction
3:40 – Background and positive developmental predictors
7:30 – Screen time and social experiences
9:20 – Video and television learning (Video deficit)
11:55 – Developmental outcome associated with early TV/Screen media viewing
13:25 – Brain differences in autism
16:48 – Brain plasticity, social development, and screen time
18:34 – Autism risk factors
21:35 – Study 1 – Association of early-life social and digital media experiences with the development of ASD-like symptoms
26:50 – Study strengths and limitations
28:20 – Literature review on early-life screen time and autism association studies
29:56 – Study 2 – Screen media and social intervention in autism: a 6-month pilot study
36:30 – Study strengths and limitations
37:54 – Literature review on intervention studies
39:12 – Case reports of interest
41:46 – Drivers and mechanisms of association of early-life screen time with autism
43:53 – Summary and next steps
45:45 – Q & A

Background

Autism risk is affected by both genetics and modifiable environmental factors such as verbal stimulation, parental responsiveness, and parental involvement in play (3:42). These factors have been linked to positive developmental and social outcomes, emphasizing the importance of early nurturing experiences (5:25). Conversely, Heffler explains, screen media is related to diminished parental responsiveness, hindered language development, and less toy play, all of which may contribute to adverse developmental outcomes (7:30). 

As defined in this presentation, screen time encompasses TV, video, gaming, mobile apps with viewing on tablets or smartphones, and some electronic toys. The presenter highlights that video chatting with family and friends is social and considered differently (20:55). Research indicates that children do not learn well from screens as pre-recorded videos are not socially responsive to the child’s actions. Some developmental outcomes associated with early TV/screen media viewing include language delay, attention problems, executive function difficulties, and disorganization in the white matter of the brain (11:55). Joint attention, or when a child looks back and forth between an adult’s eyes and an object of interest, does predict learning (9:20). 

Brain development and plasticity

The presenter discusses differences in autistic brains and underscores the high correlation between superior auditory and visual processing abilities and autism. She notes, however, that this does not necessarily convey good overall brain function (13:20). Some abilities have been linked to autism symptom severity, and other developmental differences predict whole brain overgrowth and/or autism development (15:16). 

Brain plasticity is how the brain responds to one’s experiences, and brain connections are formed based on those experiences and responses. Social factors like eyes, voices, and smiles naturally react to young children and promote the development of social brain pathways. Contrastingly, non-social factors, like screens and electronics, likely promote highly sensory-oriented brain connectivity due to their lack of natural social features (16:48).  

Risk factors and recommendations

Heffler outlines a study that found early-life social experiences and early-life screen time are two important risk factors for autism. The study found that infants with autism traits who were exposed to both screen time and social training from their parents are less likely to develop autism (18:34). She explains that high-screen viewing is concerning in early life because electronic media distracts the child from people and distracts the parents/caregivers from the child, both of which may directly affect brain connectivity and attention mechanisms in autism. Heffler and her colleagues recommend no screen viewing before 18-24 months of age and no more than one hour of screen time per day through age five (20:20).

Drexel Studies

Study 1: Association of early-life social and digital media experiences with the development of ASD-like symptoms 

Using parental report data from the National Children’s Study, researchers investigated the association between TV/DVD exposure and social experiences on autism symptoms. Findings suggested that higher screen time (4 or more hours per day) correlated with an increased risk of autism symptoms at 12 months and two years of age and that autistic children reported more screen time (21:35). Researchers also found that when parents play with the child less than daily at 12 months, there was an 8.9% increase in autism symptoms. Screen exposure at 12 months was also associated with a 4.2% increase (24:15).

Study 2: A literature review on early-life screentime and autism association studies

Heffler defines the parameters of the literature review and discusses critical findings: Greater daily screentime was associated with autism diagnosis (9 studies), autism symptoms (7 studies), and symptom severity (4 studies). Similarly, earlier first screen-viewing was associated with autism diagnosis and symptoms, and less parent-child interaction was also associated with autism risk and severity (28:20). 

Study 3 – Screen media and social intervention in autism: a 6-month pilot study

In collaboration with Lori Frome, Dr. Heffler developed a parent training program to reduce screen time and enhance social engagement, particularly for young children with autism and high media exposure (29:56). The program was implemented over six months and included instruction on digital media and child development, parent-child interactions, and poor screen learning. Parents received weekly, 1-hour, in-home support and were encouraged to involve children in family routines like laundry and cleaning to support social and learning development (32:05). 

Results showed a significant reduction in screen time, from 5.6 hours per day to five minutes a day, over six months. There was also a 23% reduction in core autism symptoms and a 19% increase in adaptive behavior function (did not quite reach significance) (34:20). Parental stress declined by 37% (large effect size), and positive feedback indicated awareness and improvements in social and developmental outcomes. Heffler notes that changes in child behavior began almost immediately, which made it easier for parents to cut screen time and interact with their children (36:00). 

Study 4: A literature review on intervention studies

This review considered six studies, five of which included parent training on social engagement and screen time reduction; these five studies pointed to a correlation between reducing screen time to less than one hour per day and a statistically significant decrease in autism symptoms. Similarly, more than one hour of screen time per day negatively affected therapeutic outcomes. Overall, improvement correlated with screen time reduction, parent stress was significantly improved, and in one study, EEG patterns also improved (37:54). 

The speaker discusses the strengths and limitations of each study.

Case reports and summary

The speaker outlines three case reports that further demonstrate the positive impact of minimizing screentime during early development, including one where autism symptoms entirely resolved in 4 months after eliminating screens (39:12). Heffler reiterates the potential impact of reduced screentime on autism symptom development and lists driving factors and mechanisms of this association (41:46). The speaker highlights the difference between association and causation, noting that all studies presented show associations. 

She summarizes the presentation by reiterating the main findings and suggestions:

• More screen time in the first year of life is associated with the development of autism and autism symptoms. 
• Early-life parent-child social engagement is associated with a decreased risk of autism development.
• Interventions, including screen time reduction and parental support for social engagement, are associated with a rapid decrease in autism symptoms in children with high screen viewing.

Researchers assert the need for randomized controlled trials, measures of gene-environment interactions, and community-based parent education studies in the future (44:58). Heffler also urges viewers to help raise awareness of these findings among parents of young children and across healthcare providers, government agencies, and everyone in between. She provides thanks before the Q&A (45:45). 

Giacomo Vivanti, Ph.D., dives into the complexities of establishing evidence-based autism interventions. He describes the ever-changing state of intervention literature, noting that autistic individuals still experience health inequalities. The speaker discusses four aspects of the interplay between research and practice and details studies demonstrating each assertion. Vivanti calls for universal outcome metrics based on well-being and considers how behavioral therapies can impact core autism symptoms (i.e., communication). The presenter asserts the usefulness of public health frameworks in assessing disparities between research and practice. He summarizes the presentation before the Q&A.

Take the knowledge quiz for this webinar HERE

In this webinar: 

1:15 – Individual evidence-based care
2:50 – Landscape of autism early interventions
4:55 – Health inequities for autistic individuals
6:45 – Study: Early onset dementia in autism
9:00 – Interplay of science and information dissemination
11:40 – Issue 1: The gap between research and practice
13:30 – Study: Treatment fidelity impact on skill acquisition
16:23 – Community implementation
19:00 – Community-partnered participatory research (CPPR)|
20:02 – Issue 2: Disagreements on what counts as evidence
24:15 – Study: Behavior analysts’ knowledge on naturalistic strategies
27:10 – Issue 3: Disagreements on conceptual categories
28:15 – Study: Meta-analysis of ABA treatments
29:55 – U.S. state insurance coverage
32:47 – Issue 4: Disagreement on intervention goals
37:05 – Complexity of well-being and autism symptoms
40:00 – Study: Consensus on target mechanisms
42:53 – Study: Inclusive vs autism-specific classroom learning opportunities
46:05 – Conclusions
48:20 – Q&A

Introduction

More research on autism early interventions has been published in the last three years than over the previous four decades combined (2:50). Vivanti explains that while the quality of early interventions has increased substantially, our knowledge is still rapidly evolving (4:05). Despite this, autistic individuals continue to experience health inequalities that lead to preventable adverse outcomes associated with mental and physical health, self-realization and self-determination, community participation, and educational opportunities (4:55). The presenter cites a study that found the chance of early onset dementia (Alzheimer’s) in autistic individuals is double that of the non-autistic population. He posits that this is due to the continued lack of cognitive, social, and educational support for autistic adults (6:45). Vivanti specifies four complexities of establishing evidence-based interventions:

# 1 – The gap between research and practice

The speaker recalls that his mother was blamed for his brother’s autism diagnosis in the 1990s, even though “cold parenting” had been disproven in the 1960s (9:00). Therefore, he continues, the first main issue in establishing evidence-based interventions is the gap between research and practice (11:40). The speaker explains how intervention procedures in community settings are often different from the procedures used in clinical trials (11:40). Vivanti details a study that assessed the association between outcomes of the Early Start Denver Model (ESDM) and intervention fidelity (adherence) to clinical implementation. Researchers found a lot of implementation variation not only across practitioners but also across patients of each practitioner (13:30). Children with the best outcomes were treated by therapists implementing interventions at higher fidelity (15:35). The presenter considers feasibility and philosophical commitments as reasons for such variations in community implementation (16:23). He underscores that procedures should be flexible to the individuals but rigorous as well. The speaker highlights community-partnered participatory research (CPPR) (19:00). 

# 2 – Disagreements about what counts as evidence

Vivanit explains that practitioners rely on information from different agencies and reviews that analyze the literature. However, each agency/review adheres to its own guidelines and criteria for what qualifies as evidenced-based (e.g., randomized trial vs. single subject design) (20:02). Different agencies therefore come to different conclusions about evidence-based interventions. Such confusion around facts makes clinicians and caregivers hesitant to adopt new interventions, despite documented effectiveness (23:05). The speaker outlines a survey study of 901 board-certified behavior analysts’ knowledge of naturalistic developmental behavioral interventions (NDBI). Although significant evidence supports NDBI efficacy, few participants believed these practices were effective or appropriate (24:15). 

# 3 – Disagreements on conceptual categories

Review agencies generally classify interventions according to conceptual categories like ABA, NDBI, CBT, etc. Vivanti explains that, again, arbitrary criteria for each category result in different recommendations for the evidence-based status of interventions across agencies (27:10). He outlines a meta-analysis of ABA-based interventions that found ABA therapies in various categories in several meta-analyses and reviews (28:15). Most states in the U.S., he continues, have an insurance coverage mandate for autism that specifically mentions ABA. Although providers are often willing to incorporate new evidence-based practices like the ESDM, insurance companies frequently refuse treatments if they are not identified as ABA (29:55). Therefore, many natural-based models are not provided, thus highlighting the extremely practical consequences of conceptual disagreements (31:50). 

# 4 – Disagreements on intervention goals

Vivanti asserts that “any consensus of what to do must be based on a consensus of why we are doing it.” He remembers thinking that the goals for his brother’s interventions ought to be based on safety and support in daily environments and activities (32:47). However, intervention goals are highly debated, which leads patients/users to assume interventions prioritize conformity and compliance at the expense of neurodiversity and individualized care. The speaker asserts that this lack of a universal metric for “successful outcomes” keeps service providers from being able to specifically communicate what they want to achieve (34:00). He underscores using language and concepts based on freedom from distress, community participation, and well-being across the lifetime (37:05). Vivanti and his colleagues tried to generate a consensus on legitimate aspects of effective interventions that are respectful to the diversity and uniqueness of children but also rigorously empower individuals. Researchers found that interventions informed by this model emphasize agency, learning through positive interactions, engagement in novel and familiar schemas, and an interplay of comfort and challenge (40:00). Another study found that autistic children with more social attention had better outcomes in inclusive classrooms compared to autism-specific classrooms (42:53). Such findings underscore the importance of rigorous individualized interventions.

Conclusion

The speaker emphasizes that knowledge of evidence-based interventions is advancing at an unprecedented pace. However, autistic people still experience healthcare differences. This is due to the complex interplay of research and practice. Key factors include disagreements about what counts as evidence, behavioral interventions, and intervention goals. These and other factors impacting implementation and adoption must be scientifically addressed and researched. Vivanti highlights that a public health framework can help examine these factors and contextualize challenges and opportunities associated with autism in the broader context of research and practice (46:05). The speaker provides thanks and resources before the Q&A, where he discusses recommended interventions, various treatment concepts, how behavioral education programs impact autism neural structure, and much more (48:20).