Ensuring children are safe during the night can be a major concern for autism families. Up to 80% of children with autism experience sleep disturbances, and caregivers express concern about elopement, self-injury, and other risks that may be incurred by a child who has difficulty sleeping through the night. Safeguarding against these risks can be challenging and lead to many sleepless nights for the parents as well as the child. This project will use data gathered through remote interviews and daily sleep diaries to adapt a newly formed questionnaire that can be validated and used as a quantitative measure of caregivers’ safety-related concerns. There will be a special focus on the concerns of underrepresented groups such as families of color, those from socioeconomically disadvantaged communities, and families supporting children with additional disabilities. Additionally, objectively measured sleep data from a larger existing study will be used to assess how caregiver safety concerns relate to the actual sleep patterns of both the child and caregiver. The ability to document and measure safety-related concerns in diverse communities and identify sleep patterns linked to these concerns will lead to better understanding and more effective targeting of the specific needs of families.
Incorporating Caregiver Concerns into Measures of Sleep Disturbance in Autistic Individuals
Many autistic people have worse daily living skills (DLS) than would be expected based on their intellectual ability (IQ). Better daily living skills have been linked to more positive outcomes in those with autism. Previous research looking at the gap between DLS and IQ focused on individuals at a single point in time, providing a snapshot of their current abilities rather than assessing these abilities across the lifespan. This study will better describe the discrepancy in DLS and IQ by engaging an existing longitudinal cohort of autistic individuals that has been followed from 2-33 years of age, focusing on specific DLS rather than lumping them all together. These findings will allow for more focused intervention targets in adults with profound autism.
Instead of grouping together people with autism based on traditional severity scores, what if groupings were done based on functional outcome? Would this help better understand the broad spectrum of autism and why some people with autism are so different than others? Researchers at the University of Minnesota led by Kyle Sterrett, together with UCLA and UNC utilized a study that followed children with autism in the early 1990’s into their adulthood, in the 2020’s. They created and asked these families a set of questions (included in the manuscript below) to help identify levels of functioning in people with autism. This was done to help them and their families get the right support at right time. They found that these questions could differentiate people with Profound Autism based on things like level of independence and safety concerns. Dr. Sterrett talks with us on this week’s podcast to explain what they did and why it is so important.
What is the probability of having a future child with autism if you already have one or more? Families want to know. It helps preparation, planning, will hopefully improve early screening and supports. The Baby Siblings Research Consortium analyzed a bigger group of siblings compared to their 2011 numbers and found the recurrence pretty stable – 1 in 5 siblings will have an autism diagnosis compared to 1 in 36 in the general population. However, this number depends on a lot of things: Sex of infant, sex of sibling with an existing ASD diagnosis, number of autistic children in the family, race and socioeconomic status. Listen this week to hear all the numbers.
In honor of the last week of Autism Awareness/Acceptance Month, we review in this podcast episode two new scientific findings that call for more awareness and action, and less acceptance of the status quo. First: sex differences in autism are not well understood, and as it turns out, the influences on a diagnosis are different. Males have a higher rate of heritability compared to females. Second, those with rare genetic disorders have very few options for treatment, but a new study promises hope for more personalized approaches. The researchers use Timothy Syndrome as an example of how cells can start to function properly through a targeted approach which focuses on a small part of a gene. This is potentially life saving for individuals with this disorder.
Thank you to Dennis Wall from Stanford University for explaining what Machine Learning is, how it’s related to Artificial Intelligence (today’s four buzz words) and how these new technologies are helping families get a diagnosis. In this week’s podcast episode, he talks about the overall goals of these techniques, highlighting Cognoa’s CanvasDx to provide remote diagnoses to potentially reduce the waiting lists for families.
Did you miss the ASF 2024 Day of Learning and can’t wait for the videos to be posted? This is a 17 minute brief summary of what was discussed, but unfortunately, with no visuals. Don’t just listen to the podcast, watch the videos when they are posted. Also included in this podcast is a shoutout to the Profound Autism Summit which brought together hundreds of advocates around those who need 24/7 care for their lives. The link to their advocacy page is here: https://www.votervoice.net/ProfoundAutism/campaigns/112917/respond
This podcast episode provides updates on studies that help with prediction of an autism diagnosis – which is important for preparing for the future and for intervening early. First, a study that uses environmental factors to create an equation for the probability of a diagnosis following a combination of of non-genetic factors only which does a fairly good, but not perfect, job at predicting a diagnosis. Second, a study that looks at the accuracy of a machine that predicts autism from eye gaze as early as 9 months of age and with only a 2 minute test. This one wasn’t as accurate as the one that takes longer and tests older kids, but it’s a first step. No ONE thing does a perfect job at predicting a diagnosis – it’s going to be a combination of things, tested over time and multiple times that will be most helpful at predicting a diagnosis. Both studies are open access!
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904522/pdf/fpsyt-15-1291356.pdf
Large gaps exist in healthcare for Black autistic children, yet the lived experiences of these families are rarely investigated or considered when designing research studies. This student will collect data from families, including information about their diagnostic experience and the factors that matter most to them. The results will help researchers and healthcare providers develop culturally competent interventions for Black families across the world.
Adults with profound autism have unique healthcare needs that are often overlooked by providers. This student will expand an existing project to add a cohort of middle and older-aged autistic adults in a residential facility to measure overall health, co-occurring conditions, healthcare quality & satisfaction, and quality of life. Determining how co-morbid health conditions change as autistic adults age will enable services to be delivered that better meet people’s needs.
Sleep problems are highly prevalent in individuals with profound autism and exacerbate emotional disturbances, cognitive deficits, and challenging behaviors. Existing studies of sleep in autism have mostly excluded children with profound autism. This omission has been blamed on the added burden, expense, and difficulty of studying sleep in children with profound autism in a lab setting. This grant will expand a sleep study currently in progress to add a cohort of children with profound autism. The goal of the study is to validate the use of a minimally invasive headband device that measures sleep quality at home and provides data on specific brainwave patterns during different phases of sleep in people with autism vs. people without autism
Females are less likely to receive an autism diagnosis than males and several studies are examining the biological, psychological, and developmental reasons for this disparity. One theory is that language abilities and patterns in females are superior to males, possibly reflecting better social ability, which may contribute to lower diagnostic rates. This study will look at a measure of language called prosody, or the rhythm, tone and pattern used during spoken language. Studies around prosody in autistic females are lacking, mostly because there are fewer girls with an autism diagnosis who can participate in research on prosody. This fellow will examine prosody in males and females with and without autism, and compare prosody to assessments of social function and interest. These results will inform caregivers, educators, and clinicians when considering a possible autism diagnosis for girls.