research studies about autism

Our groundbreaking research makes meaningful and lasting improvements in the quality of life for autistic individuals and their families.

Our interdisciplinary team includes neurobiologists, computer engineers, data scientists, psychiatrists, psychologists, and physicians. We are conducting groundbreaking research to create more accurate and scalable methods of early detection, improve and disseminate therapies, test new interventions, and make new discoveries in genetic and molecular science.

Our research discoveries meaningfully impact autistic individuals throughout the lifespan and play a pivotal role in reducing global healthcare inequities and increasing access to proven therapies worldwide. We are an NIH Autism Center of Excellence and part of the multi-site NIH Autism Biomarkers Consortium for Clinical Trials, the largest autism research study in NIH history.

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Creating accurate, scalable tools for early detection & intervention monitoring

A collaborative team of neurobiologists, computer engineers, data scientists, and psychologists have created a digital app to track behaviors such as attention span, motor skills, emotional expressiveness, vocalizing, and interest in social cues.

Using computer vision analysis and machine learning, the team has published research showing that the app detects early signs of autism in toddlers and is now testing the same tool in Duke Primary Care clinics with infants as young as six months. The app allows precision in measuring changes in behavior, providing a more reliable, sensitive tool for measuring improvement in clinical trials.

Harnessing artificial intelligence to guide physicians

The Duke University Health System sees nearly 3,000 patients on the autism spectrum each year. The center’s data scientists and clinicians are applying artificial intelligence, such as machine learning and natural language processing, to Duke patients’ electronic health records to determine whether information collected during routine health care visits could alert physicians to patients who may develop neurodevelopmental disorders.

The same methods are being used to better understand variations in lifelong health trajectory for autistic individuals or developmental conditions, helping medical providers anticipate unique health needs and customize patient care.

Unveiling how genetic mutations affect synaptic pathways

A center team of molecular biologists, neurobiologists, and neuropsychopharmacologists are using animal models and state-of-the-art technologies, such as CRISPR, to help understand how genetic mutations impact synaptic pathways in the brain, affecting speech, social and communication skills.

Using novel techniques developed at Duke, the team is developing a deeper understanding of how rare gene mutations affect brain function, setting the stage for finding new treatments to improve quality of life.  

Adapting & disseminating therapies to reduce disparities in access to treatment

Proven behavioral therapy methods developed by center investigators, such as the Early Start Denver Model, can have a significant impact on outcomes for those on the autism spectrum. Yet, for many people in low-resource communities in the U.S. and worldwide, these therapies are out of reach. Center investigators are assessing whether effective caregiver coaching can be delivered via telehealth and by non-specialist providers.

Another study is extending methods that were originally developed for young children to school-age children. These studies could open the door to greater access to scientifically proven therapies for those living in socio-economically disadvantaged and rural communities worldwide.

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ARI donors support research that has practical application in the evolution of autism understanding and the lives of autistic people. Last fall, ARI awarded more than $400,000 in grants to fund research on evidence-based therapeutic interventions and underlying biological mechanisms.

We work to advance the biological understanding of autism. Whether you’re an autistic individual, parent, professional, or researcher, we’re here to connect you with information you need.

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Disordered Eating and Autism – Obesity

autismAdmin 2024-06-05T14:39:19-05:00 October 23rd, 2024 |

Caregiver Strategies for Building Infant Social Interaction

autismAdmin 2024-06-24T15:39:22-05:00 October 9th, 2024 |

Emerging Findings: Pediatric Acute Neuropsychiatric Syndrome

autismAdmin 2024-06-17T15:37:46-05:00 October 2nd, 2024 |

Wellbeing Wins: Integrating Positive Psychology into the Autism Community

autismAdmin 2024-08-16T11:41:39-05:00 September 25th, 2024 |

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The Low-Hanging Fruit: Exploring Monogenic Syndromes with Elevated Rates of Autism

autismAdmin 2024-08-07T10:37:37-05:00 July 15th, 2024 |

Gender, Sexuality, and Autism

autismAdmin 2024-08-07T10:38:10-05:00 July 12th, 2024 |

Autism and Trauma

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Motor Skills and Executive Function in Autism

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Stanford Autism Center

Autism research studies at stanford, study title/ age ,                 description, spark (simons powering autism research) study.

If you or your child has a professional diagnosis of autism, Stanford University invites you to learn more about SPARK, a new online research study sponsored by the Simons Foundation Autism Research Initiative. The mission of SPARK is clear: speed up research and advance understanding of autism by creating the nation’s largest autism study. Joining SPARK is simple – register online and provide a DNA sample via a saliva collection kit in the comfort of your own home. Together, we can help spark a better future for all individuals and families affected by autism.

Register in person at Stanford University by contacting us at  [email protected]  or online at  www.sparkforautism.org/stanford .

Targeting the neurobiology of restricted and repetitive behaviors in children with autism using N-acetylcysteine: Randomized Controlled Trial

3-12 Years  

We are recruiting children with autism spectrum disorder to participate in a research study at Stanford University. Our goal is to examineth effects of N-acetyl cysteine, an over-the-counter dietary supplement, on the brain circuits that underlie some restricted and repetitive behaviors.   

To be elligible for this trial, your child must:

• be aged between 3 and 12 years old
• exhibit restricted and repetitive behaviors
• be willing to drink N-acetyl cysteine dissolved in water
• be willing to undergo brain scanning with magnetic resonance imaging (MRI)
• be willing to undergo brain scanning with electroencephalography (EEG)

The study will take place at Stanford University over 12-to-16-week period. Our safety protocols have been updated for COVID-19 and many research activities will be completed remotely using Zoom and virtual surveys. Your child must be willing to:

• complete cognitive and behvaiorial assessments (such as IQ tetsing)
• be able to either sleep (young children) or lie still in the scanner during an MRI
• tolerate wearing an EEG cap
• drink N-acetyl cysteine dissolved in water for a total of 12-week period

For Participant inquiries contact: [email protected]

Autism Center of Excellence Sleep Study

8-17 Years  

Dear Parents,

We are excited to tell you about a new research study for children. We are looking to partner with parents who have children that are between the ages of 4 and 17 years old,  with and without  an Autism Spectrum Disorder (ASD) diagnosis.

What is involved?

• In-person cognitive and behavioral assessments
• Day-time Electroencephalogram (EEG)
• In-home, 2 night sleep monitoring session
• Collection of saliva to measure cortisol and melatonin levels
• Wearing a watch device that tracks sleep and daily activity

What will I receive if I participate?

• Research sleep report and behavioral testing summary upon request
• $50 for each in-person visit to Stanford and $100 for the 2 night in-home sleep assessment

Treatment extension study:

• If your child has ASD, sleep difficulties, and ages 8-17, they may also qualify for sleep medication trials

Interested in participating or want to learn more?  Click Here!

If you would like to reach out to our team directly with any questions, please contact our team below!

Email:  [email protected]

650-498-7215

Neuroimaging Predictors of Improvement to Pivotal Response Treatment (PRT) in Young Children With Autism

Stanford University researchers are recruiting children with autism to identify brain imaging predictors of benefits from Pivotal Response Treatment (PRT) targeting language abilities.

In order to participate in this research study, your child must:

• Be between the ages of 2 and 4 years
• Be able to complete an MRI of the brain during natural sleep
• Participate in a 16-week parent training program
• Meet inclusion based on testing.

Vasopressin Treatment Trial for Children with Autism

6 - 17 years

The purpose of this clinical trial is to investigate the effectiveness of vasopressin nasal spray for treating symptoms associated with autism. Vasopressin is a hormone that is produced naturally within the body and has been implicated in regulating social behaviors. It has been proposed that administration of the hormone may also help improve social functioning in individuals with autism.

Link to study at clinicaltrials.gov

Pregnenolone Randomized Controlled Trial

14 - 21 years

Neurosteroid Pregnenolone Treatment for Irritability in Adolescents with Autism

Medication treatments for core symptoms of autism spectrum disorder (ASD) continue to be unmet medical needs. The only medications approved by the U.S. Food and Drug Administration (FDA) for the treatment of individuals with ASD are effective in treating irritability and associated aggressive behaviors, but these medications can also cause severe long-term side effects such as diabetes and involuntary motor movements. Therefore, effective medications with more tolerable side effect profiles are highly desirable. This profile is consistent with pregnenolone (PREG). PREG belongs to a new class of hormones known as neurosteroids, which have been shown to be effective in treating various psychiatric conditions including bipolar depression and schizophrenia. As compared to currently FDA-approved medications, our preliminary data suggested that PREG may represent a potentially effective and well-tolerated agent for treating irritability in individuals with ASD. In addition, our experience suggests that PREG might be helpful in improving selected core symptoms such as social deficits and sensory abnormalities of ASD. This study provides the opportunity to further explore the usefulness of PREG in the treatment of irritability and some core symptoms of ASD. We are performing a 12-week randomized double-blind controlled pilot trial to examine the effectiveness of orally administered PREG in reducing irritability and associated behaviors in adolescents with ASD. In this study, we also aim to examine the usefulness of biomarkers (blood levels of neurosteroids, eyetracking and brain wave recording) in predicting treatment response and assessing biologic changes with PREG treatment.

Study Flyer 

Link to study in Stanford's Clinical Trials Directory

Helpful Links

• Development-Behavioral Pediatrics at Stanford Children's Health
• Divison of Child & Adolescent Psychiatry and Child Development

• Introduction
• Conclusions
• Article Information

A, Performance metrics for each of the 4 ML models—decision tree, logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost)—developed. B, The receiver operating characteristic curve for the best-performing XGBoost algorithm. C, The precision-recall curve for the best-performing XGBoost algorithm. AUR indicates area under the curve; PPV, positive predictive value.

A, The confusion matrix shows the model performance by indicating the number of participants correctly identified with or without autism spectrum disorder (ASD) in each group. Label 1 indicates ASD and 0 indicates non-ASD. The numbers in each cell correspond to the number of samples. B, Differences in Child Behavior Checklist (CBCL) t score for 1 to 5 and 6 to 18 years of age (range, 50-100, with higher scores indicating more severe behavioral and emotional problems), Full-Scale IQ (FSIQ), and Social Communication Questionnaire (SCQ; range, 0-39, with higher scores indicating more social communication challenges) among those predicted to have ASD with the model separated by correct and incorrect predictions.

a P  < .001.

b Not significant.

Data are given among individuals correctly vs incorrectly predicted to have autism spectrum disorder (ASD) and correctly vs incorrectly predicted to have no ASD. OR indicates odds ratio.

eMethods. Data Preprocessing and Model Training and Development

eTable 1. List of Predictors From the SPARK Version 8 Cohort Used for Model Development and Validation

eTable 2. Performance of ML Algorithms

eTable 3. Performance of the XGBoost Algorithm AutMedAI Stratified by Age

eTable 4. Performance of the AutMedAI Assessed Using the SPARK Version 10 Cohort

eTable 5. Performance of AutMedAI Using the SSC Cohort With 2854 Individuals With ASD

eTable 6. Differences in Quantitative Measures for Those in the ASD Group (Correct vs Wrong Prediction) and Those in the Non-ASD Group (Correct vs Wrong Prediction)

eTable 7. Frequency and Odds Ratio for Different Behavioral and Developmental Diagnoses Between Those in the ASD Group (Correct vs Wrong Prediction) and Those in the Non-ASD Group (Correct vs Wrong Prediction)

eFigure 1. Overview of the Study and the ML Model Development

eFigure 2. Influencing Predictors for Autism Detection in the Group Aged 0 to 2 Years

eFigure 3. A Beeswarm Plot for All Test Set Samples in the SPARK Version 10 Cohort Aged 2 to 4 Years

eFigure 4. A Beeswarm Plot for All Test Set Samples in the SPARK Version 10 Cohort Aged 4 to 10 Years

eFigure 5. A Beeswarm Plot for Test Set Samples of the SPARK Version 10 Cohort

eFigure 6. The Influence of the Top 20 Features for 6 Individuals in the SPARK Version 10 Cohort

eFigure 7. Decision Plot Showing the Influence of Features on Model Prediction

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Rajagopalan SS , Zhang Y , Yahia A , Tammimies K. Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information. JAMA Netw Open. 2024;7(8):e2429229. doi:10.1001/jamanetworkopen.2024.29229

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Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information

• 1 Center of Neurodevelopmental Disorders, Centre for Psychiatry Research, Department of Women’s and Children’s Health, Karolinska Institutet, Solna, Sweden
• 2 Department of Highly Specialized Pediatric Orthopedics and Medicine, Astrid Lindgren Children’s Hospital, Karolinska University Hospital, Region Stockholm, Stockholm, Sweden
• 3 Institute of Bioinformatics and Applied Biotechnology, Bengaluru, India

Question   Can a machine learning (ML) model based on minimal background and medical information accurately predict autism spectrum disorder (ASD)?

Findings   This diagnostic study of 30 660 participants using ML prediction of ASD with only 28 features found high predictive accuracy, sensitivity, and specificity. Validation on independent cohorts showed good generalizability, and developmental milestones and eating behavior emerged as important predictive factors.

Meaning   The model developed in this study shows promise in the early identification of individuals with an elevated likelihood of ASD, using minimal information, which could affect early diagnosis and intervention strategies.

Importance   Early identification of the likelihood of autism spectrum disorder (ASD) using minimal information is crucial for early diagnosis and intervention, which can affect developmental outcomes.

Objective   To develop and validate a machine learning (ML) model for predicting ASD using a minimal set of features from background and medical information and to evaluate the predictors and the utility of the ML model.

Design, Setting, and Participants   For this diagnostic study, a retrospective analysis of the Simons Foundation Powering Autism Research for Knowledge (SPARK) database, version 8 (released June 6, 2022), was conducted, including data from 30 660 participants after adjustments for missing values and class imbalances (15 330 with ASD and 15 330 without ASD). The SPARK database contains participants recruited from 31 university-affiliated research clinicals and online in 26 states in the US. All individuals with a professional ASD diagnosis and their families were eligible to participate. The model performance was validated on independent datasets from SPARK, version 10 (released July 21, 2023), and the Simons Simplex Collection (SSC), consisting of 14 790 participants, followed by phenotypic associations.

Exposures   Twenty-eight basic medical screening and background history items present before 24 months of age.

Main Outcomes and Measures   Generalizable ML prediction models were developed for detecting ASD using 4 algorithms (logistic regression, decision tree, random forest, and eXtreme Gradient Boosting [XGBoost]). Performance metrics included accuracy, area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and F1 score, offering a comprehensive assessment of the predictive accuracy of the model. Explainable AI methods were applied to determine the effect of individual features in predicting ASD as secondary outcomes, enhancing the interpretability of the best-performing model. The secondary outcome analyses were further complemented by examining differences in various phenotypic measures using nonparametric statistical methods, providing insights into the ability of the model to differentiate between different presentations of ASD.

Results   The study included 19 477 (63.5%) male and 11 183 (36.5%) female participants (mean [SD] age, 106 [62] months). The mean (SD) age was 113 (68) months for the ASD group and 100 (55) months for the non-ASD group. The XGBoost (termed AutMedAI) model demonstrated strong performance with an AUROC score of 0.895, sensitivity of 0.805, specificity of 0.829, and PPV of 0.897. Developmental milestones and eating behavior were the most important predictors. Validation on independent cohorts showed an AUROC of 0.790, indicating good generalizability.

Conclusions and Relevance   In this diagnostic study of ML prediction of ASD, robust model performance was observed to identify autistic individuals with more symptoms and lower cognitive levels. The robustness and ML model generalizability results are promising for further validation and use in clinical and population settings.

Autism spectrum disorder (ASD) is a neurodevelopmental condition with challenges in communication and social interaction and the presence of restricted and repetitive behaviors. 1 , 2 The estimated prevalence of ASD is approximately 1.0%, but higher rates have been reported, such as 2.78% in a 2023 report from the US. 3 The current median age of diagnosis is 60.48 (range, 30.90-234.57) months. 4 Early detection is crucial for targeted early intervention and improved outcomes. 5

Widely used ASD screening tools such as the Modified Checklist for Autism in Toddlers, Revised With Follow-Up (M-CHAT-R/F), 6 the Social Attention and Communication Surveillance–Revised, 7 the Parents’ Evaluation of Developmental Status, 8 and the Childhood Autism Rating Scale 9 have been instrumental in identifying individuals with elevated likelihood of ASD. 10 However, these tools face some challenges related to biases and subjectivity in their measures, which could be related to the interpretation of the questions, cultural differences, and the child’s behavior at the time of assessment. 11 , 12 Also, the ASD heterogeneity and associated co-occurring conditions pose further challenges for screening and early diagnosis. The delay in diagnosis can result in a lack of timely interventions, posing a significant family and societal burden. 13 To mitigate such challenges, new approaches using machine learning (ML) and behavior phenotypes captured using mobile applications 14 or ML models using electronic health records (EHR) 15 are emerging but still only used in research settings.

Machine learning methods can potentially develop robust prediction models for ASD using different data modalities. 11 , 13 If designed correctly, the power of prediction models comes from their generalization ability to predict unseen individual outcomes reliably. Machine learning methods offer the potential to handle large datasets and identify hidden patterns in the data, quantify behavior phenotypes, and develop automated solutions for scaling.

For ASD, there have been recent advances in using ML methods to predict the condition. For instance, in a recent Israeli study, 16 routine developmental surveillance data from 1.2 million children were used to develop an automatic prediction tool for ASD diagnosis, which was further compared with M-CHAT screening with superior results. Furthermore, Engelhard et al 15 proposed an early ASD prediction model from the EHR data collected before the first birthday and showed its promise for integration with other screening tools. Additionally, Onishchenko et al 17 developed digital biomarkers for ASD from past medical conditions termed the autism comorbid risk score.

We hypothesized that ML models incorporating easily obtainable measures from medical records and background history data can reliably detect ASD. To investigate this hypothesis, we used a large database with multilayered information collected within the Simons Foundation Powering Autism Research for Knowledge (SPARK) study 18 to develop and test an ML model for early ASD screening. Furthermore, we provided explanatory information on the classification of individuals and investigated differences in several phenotype measures between those participants who were correctly and incorrectly classified using our model.

The prediction model development and validation for this diagnostic study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis ( TRIPOD ) reporting guidelines. Ethical approval for the data collection and informed consent were obtained from the participants within the SPARK and Simons Simplex Collection (SSC) projects. The Swedish Ethical Committee approved this study and data analysis in Sweden.

We used 2 available datasets for ASD research, the SPARK 18 and the SSC 19 (eFigure 1 in Supplement 1 ). Both databases have a collection of medical and behavioral data from individuals with ASD and family members living in the US. The SPARK project is coordinated by the Simons Foundation Autism Research Initiative, which collaborates with 31 university-affiliated research clinics in 26 states in the US and has online recruitment. The project was launched in December 2015, and the recruitment of participants is ongoing. All individuals with a professional ASD diagnosis and their families can participate in the study. The clinical validation of the ASD diagnosis within SPARK has shown to be high. 20

We used the SPARK collection, version 8 (released June 6, 2022), as the primary cohort ( Table 1 ). Version 8 has the following phenotype measures: (1) the basic medical screening measures administered to all individuals with ASD, parents without ASD, and siblings, and (2) the background history responses (eTable 1 in Supplement 1 ). We furthermore used the reported data on participants’ race and ethnicity to investigate the model performance across these different groups.

We included new SPARK participants in the database for ML model testing in version 10 (released July 21, 2023) (eFigure 1 in Supplement 1 ). Based on the availability of the needed data, we included 11 936 participants (10 476 with ASD and 1460 without ASD) from the 31 384 in the version 10 release not included in the version 8 release.

In addition to the features used for the ML model, we used the following measures available in the SPARK database: the Child Behavior Checklist (CBCL) 21 for 1 to 5 and 6 to 18 years of age with the total problems t score, Full-scale Intelligence Quotient (FSIQ), and Social Communication Questionnaire (SCQ) score. 22 , 23 Furthermore, we analyzed the co-occurring diagnosis within behavioral and developmental categories.

The SSC cohort, which consists of 2643 simplex families with 10 474 individuals, 19 was also used for model testing. The phenotype measures of interest to this study were available for 2854 individuals with ASD. We mapped the predictor variables between the SSC and SPARK cohort databases (eTable 1 in Supplement 1 ). In total, 14 790 individuals were used in the validation experiments from these 2 cohorts.

We recorded participants’ race and ethnicity. Categories for ethnicity and included Hispanic or Latino and non-Hispanic or Latino; categories for race included Asian, American Indian or Alaska Native, Black or African American, Native Hawaiian or Other Pacific Islander, and White.

We developed early screening prediction models using the measures that can be obtained without elaborative behavior assessments and medical tests before 24 months of age. This inclusion criterion is applied to identify a subset of measures from the basic medical screening and background history administered within the SPARK collection. The selection was based on identifying easily obtainable, noninvasive, parent-reported information in the medical and background questionnaires. The selection of measures used a consensus-based approach prior to the development of the ML model. Twenty-eight variables were selected, of which 11 were present in the basic medical screening and 17 in the background history data (eTable 1 in Supplement 1 ). Further preprocessing of the features and data is described in the eMethods in Supplement 1 . After preprocessing and downsampling for an equal number of individuals with and without ASD, the final dataset size used for model development was 30 660 samples with an equal distribution of participants with and without ASD (eFigure 1 in Supplement 1 ). Furthermore, age, race, and ethnicity groups were used to investigate ML model performance in secondary analyses.

We used 4 algorithms—logistic regression, decision tree, random forest, and eXtreme Gradient Boosting (XGBoost)—to train models combining the selected features from the SPARK version 8 cohort. These ML models were developed using the Python scikit-learn library (Python Software Foundation). 24 The SPARK version 8 cohort was divided into 60% training, 20% validation, and 20% test sets in all experiment conditions.

The validation set facilitated the tuning hyperparameters and was later integrated with the training set for the final model training. The remaining 20% of the data, constituting the unseen test set, was used for model evaluation. Model training was conducted using 10-fold cross-validation.

To avoid overfitting, the XGBoost model was specifically trained using the validation set by using early stopping criteria. Additionally, model parameters were tuned using the bayesian optimization approach within the hyperparameter optimization library 25 for each fold. The details of all tuned model hyperparameters are provided in the eMethods in Supplement 1 .

We implemented the DeLong algorithm 26 to compute the area under the receiver operating characteristics curve (AUROC) with a 95% CI for model evaluation. To assess the effect of each predictor on the model performance, we computed the mean Shapley additive explanations (SHAP) values. 27 Furthermore, various other performance metrics were calculated, including accuracy, AUROC, sensitivity, specificity, positive predictive value (PPV), and F1 score. We report the mean values for these measures across 10-fold validation. The best-performing model was chosen for further testing using other datasets, and performance metrics on validation were calculated for the first fold only. Furthermore, we have computed calibrated precision (PPV) and F1 scores to study the model performance in scenarios of class imbalance. 28

Additional details about the model development, further experiments, including sex- and age-specific models, and evaluation are provided in eMethods in Supplement 1 .

We applied the SHAP 27 explainable approach to study the influence of predictors toward ASD classification using the python SHAP module’s TreeExplainer for the XGBoost model generated for the first fold. For categorical features, we aggregated the SHAP values of their corresponding one hot encoded features to determine the overall SHAP value. For numerical features, the SHAP values are directly obtained.

All statistical analyses were performed in R, version 4.2.2 (R Project for Statistical Computing). We examined clinical measures between individuals correctly predicted with ASD or without ASD and those incorrectly predicted in the SPARK, version 10 sample using quantitative measures: CBCL t scores, FSIQ, and SCQ scores. We assessed statistical significance using 2-sided Wilcoxon rank sum tests with the stats package in R, due to the nonnormal distribution of the data, as confirmed by the Shapiro-Wilk test. Additionally, we investigated whether other diagnoses were more prevalent in participants identified as having ASD compared with those identified as not having ASD using χ 2 tests with Bonferroni-adjusted P values for significance testing, and odds ratios with 95% CIs using the epitools package, version 0.5-10.1. We visualized the forest plot using forestploter package, version 1.1.2. Two-sided P  < .05 indicated statistical significance.

The study included 30 660 participants for the ML model development, including 15 330 participants in ASD and non-ASD groups. The study cohort included 19 477 (63.5%) male and 11 183 (36.5%) female participants (mean [SD] age, 106 [62] months) ( Table 1 ). In terms of ethnicity, 3728 (12.2%) were Hispanic or Latino and 26 932 (87.8%) were non-Hispanic or Latino. In terms of race, 652 (2.1%), American Indian or Alaska Native; 2008 (6.5%), Black or African American; 176 (0.6%), Native Hawaiian or Other Pacific Islander; and 18 296 (59.7%), White. The mean (SD) age for the ASD group was 113 (68) months; for the non-ASD group, 100 (55) months. In this sample of 30 660 SPARK participants with both medical screening and background history measures available, we trained 4 different ML models using 28 selected features (eFigure 1 and eTable 1 in Supplement 1 ). All the trained models showed good performance metrics ( Table 2 and Figure 1 A). However, the XGBoost algorithm achieved the best performance with a mean AUROC of 0.895 (σ = 0.004 [σ value indicates the SD]) in the test set across 10-fold validations. The receiver operating characteristics curve and the precision recall curves for the best-performing XGBoost algorithm are shown in Figure 1 , B and C, respectively. The XGBoost-based model was chosen for the rest of the analysis and termed AutMedAI.

Additional experiments conducted using combined medical screening and background history without the sex variable achieved an AUROC of 0.885 (σ = 0.004) (eTable 2 in Supplement 1 ). Also, the AUROC scores stratified across age groups of individuals at the time of evaluation were 0.868 for 0 to 2 years, 0.920 for 2 to 4 years, and 0.906 for 4 to 10 years (eTable 3 in Supplement 1 ). The most important predictors for each age group varied and can be found in the eFigures 2 to 4 in Supplement 1 .

Our additional model development exercises using only the medical screening items resulted in inferior model performance, with the highest AUROC being 0.783 (σ = 0.005) (eTable 2 in Supplement 1 ). However, the background history items performed similarly to the combined model, with the highest AUROC of 0.870 (σ = 0.004) (eTable 2 in Supplement 1 ).

To ensure model generalizability, we tested the model performance on 11 936 participants from SPARK version 10 (only new recruits) and 2854 participants with ASD from SSC (eFigure 1 in Supplement 1 ), in total 14 790 participants. Our main testing cohort was the SPARK version 10. When testing the model for 11 936 participants, including 10 476 in the ASD group and 1460 in the non-ASD group, we correctly identified 9417 participants with or without ASD (78.9%). Among the children with ASD, the model correctly identified 8262 (78.9%) individuals ( Figure 2 A). The AUROC score of the validation was 0.790. The results of other metrics are shown in Table 2 . The AUROC score for the SPARK version 10 cohort validation without the sex variable was 0.781 (eTable 4 in Supplement 1 ). The AUROC scores for different age groups were 0.807 (0-2 years), 0.798 (2-4 years), and 0.791 (4-10 years) (eTable 4 in Supplement 1 ). The validation scores for other evaluation metrics stratified by race and sex were robust and are shown in eTable 4 in Supplement 1 .

Next, we analyzed SHAP values for each participant within the SPARK version 10 cohort to determine the features most influential in ASD prediction (eFigure 5 in Supplement 1 ). Notably, features like problems with eating foods, age at first use of short phrases or sentences including an action word, age at first construction of longer sentences, age at achieving bowel training, and age at first smile emerge as the most significant predictors, as evidenced by their high SHAP values.

The influence of the top 20 features toward correct ASD prediction for 3 children with ASD correctly predicted by the model (eFigure 6 in Supplement 1 ) and for 3 children with ASD incorrectly predicted by the model are shown in eFigure 6 in Supplement 1 . A decision plot of all the features across individuals is shown in eFigure 7 in Supplement 1 .

Furthermore, we tested the model in 2854 individuals with ASD from the SSC cohort. Our experiment resulted in a sensitivity score of 0.680. The results of other metrics are shown in eTable 5 in Supplement 1 . Since the SSC dataset in this study contained only children with ASD, the AUROC, specificity, and F1 scores were not calculated.

We studied the association between model predictions (correctly or incorrectly as ASD or non-ASD) and clinical measures to understand the usefulness of our model. We investigated CBCL scores, FSIQ, and SCQ scores of individuals belonging to ASD and non-ASD groups predicted by the model (eTable 6 in Supplement 1 and Figure 2 B). The information about the cohort sample size, missing data, and P values are shown in eTable 6 in Supplement 1 . The correctly predicted ASD group had significantly more problems at 1 to 5 years of age as indicated by the CBCL t scores (2-sided Wilcoxon rank sum test, P  < .001), significantly lower FSIQ (2-sided Wilcoxon rank sum test, P  < .001), and more social communication difficulties as indicated by the SCQ (2-sided Wilcoxon rank sum test, P  < .001) as the incorrectly predicted (as non-ASD) ASD group. However, we did not find a significant difference between the 2 groups on CBCL t scores at 6 to 18 years of age (2-sided Wilcoxon rank sum test, P  = .73).

When we tested the prevalence of the other behavioral and developmental diagnoses among the groups, we found that the group with correctly predicted ASD had a lower rate of diagnosis of attention-deficit/hyperactivity disorder ( Figure 3 and eTable 7 in Supplement 1 ). As expected, based on the predictors, all developmental diagnoses were more common in the group with correctly labeled ASD than in the group with incorrect predictions ( Figure 3 and eTable 7 in Supplement 1 ).

Furthermore, we compared the same measures within participants without ASD, of whom 1155 were predicted correctly (as non-ASD), and 305 were predicted incorrectly (as ASD). From the quantitative clinical measures, only SCQ score was available for this group. We demonstrate that the incorrectly labeled individuals in the non-ASD group (predicted as having ASD) had a significantly higher SCQ score ( P  < .001) than those correctly labeled as not having ASD ( Figure 2 B). Similarly, incorrectly labeled individuals (as having ASD) in the non-ASD group were more likely to have other developmental diagnoses ( Figure 3 and eTable 7 in Supplement 1 ). These results indicate that the model identified ASD in individuals with more severe symptoms and more general developmental issues. The model also revealed a group of participants without ASD but with higher ASD traits measured by the SCQ score.

In this diagnostic study, we used the currently largest database for ASD research, 18 SPARK, to develop a prediction model, AutMedAI, to screen for ASD in infancy and early childhood using minimal background, developmental, and medical information. We found a robust performance of the model with an AUROC of 0.895 and correctly identifying 78.9% of newly introduced participants as either having ASD or not having ASD. Among the approximately 21% of participants who were incorrectly identified with or without ASD, significant differences were found when compared with those correctly identified, showcasing that the model can identify those individuals with more symptoms, especially in communication skills and social functioning, as measured by the screening SCQ.

Numerous tools exist for ASD screening and prediction, ranging from questionnaires to advanced ML-based digital platforms. 2 , 10 While many questionnaire-based tools are used in clinical practices, most ML-based digital platforms are currently explored in research settings. The efficacy of these tools often varies based on the age group assessed and differences in race and ethnicity, sex, and the cutoff scores used for performance metrics. For example, M-CHAT-R/F reported an AUROC of 0.907, with a sensitivity of 0.73 and specificity of 0.83, using a cutoff score of 3 for screening toddlers aged 18 to 24 months. 6 The SCQ score at the cutoff of 15 yields an AUROC of 0.80, sensitivity of 0.69, and specificity of 0.71 when administered to children aged 4 to 12 years. 29 Similarly, newly emerging digital screening tools using ML models demonstrate acceptable performance for early detection. The digitization enables using data from different modalities, such as EHR for 1 year or younger (sensitivity, 59.8%; specificity, 81.0%; PPV, 17.6%), 15 behavioral phenotypes captured via mobile devices for 17 to 36 years of age (AUROC, 0.90; sensitivity, 87.8%; specificity, 80.8%; PPV, 40.6%), 14 and surveillance data to build screening applications for 18 to 24 months of age (AUROC, 0.8; sensitivity, 45.1%; specificity, 95.0%). 16 Our proposed ML-based model using combined basic medical screening and background history information achieved good performance with an AUROC of 0.895, sensitivity of 0.805, specificity of 0.829, and PPV of 0.897 showing overall strong model performance.

While some of the existing questionnaire-based screen tools are sensitive to age, sex, and race and ethnicity, 10 the performance of our proposed model is robust in handling this diversity. For example, the difference in the AUROC, sensitivity, specificity, and PPV scores for our best-performing ML model with and without the sex variable is less than 2% for the SPARK version 10 validation cohort. Similarly, the model performance is consistent across different age groups, indicating the model’s robustness for these diverse populations. We did not see any large differences within the different race groups either.

Different modalities used in screening tools, the diversity in population, the administration protocol, and the cutoff scores used to evaluate performance make it difficult to compare tools. Depending on the input data used for screening, different tools may apply to different age groups. Our proposed model, using only basic medical and background information, can be used to screen children at a very early age. Our study found that when the model is used in cohorts of children younger than 2 years, the ML model achieves an AUROC of 0.868. The challenges associated with the heterogeneity of autism, the applicability of different screening instruments in various settings, and the quantification and scaling abilities of digital tools may warrant using a combination of such multimodal screening tools at scale for reliable and robust prediction accuracy.

Our developed model has the potential for clinical use as a noninvasive ASD screening tool. In addition to robust performance, identifying discriminating predictors, such as past medical conditions and phenotype behaviors, for ASD detection is crucial for the clinical adaptation of such screening tools. Explanatory ML models can inform clinicians about the underlying factors leading to ASD detection. Further, they can assist in targeted intervention and follow-up. 30

We acknowledge several limitations in our approach. Some of the features in our model vary widely within typically developing children, such as the timing of learning to speak and toilet training. Our model needs further validation for its generalizability across different population types in multiple sites. Also, combination with the additional tools using objective measurements such as eye-tracking or brain-based biomarkers should be considered in the future. 31

This diagnostic study represents a significant development in applying ML to ASD prediction. We found that early medical information in child care clinics can be used to screen for those with a higher probability of being diagnosed with ASD. The robustness and ML model generalizability results are promising for further validation and use in clinical and population settings.

Accepted for Publication: June 26, 2024.

Published: August 19, 2024. doi:10.1001/jamanetworkopen.2024.29229

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Rajagopalan SS et al. JAMA Network Open .

Corresponding Author: Kristiina Tammimies, PhD, Biomedicum A04 Tammimies, Karolinska Institutet, 171 77 Stockholm, Sweden ( [email protected] ).

Author Contributions: Drs Rajagopalan and Tammimies had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Rajagopalan, Tammimies.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Rajagopalan, Tammimies.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Rajagopalan, Zhang.

Obtained funding: Tammimies.

Administrative, technical, or material support: Tammimies.

Supervision: Tammimies.

Conflict of Interest Disclosures: Dr Tammimies reported receiving grant funding from the Swedish Research Council and FORTE outside the submitted work, personal fees from npj Genomic Medicine and the International Journal of Developmental Neuroscience for working as an associate editor, and personal fees from the European Research Council for reviewing outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported by grant FFL18-0104 from the Swedish Foundation for Strategic Research, grants FO2021-0073 and F02023-0186 from the Swedish Brain Foundation, by Strategic Research Area Neuroscience (StratNeuro), and by the China Scholarship Council (Ms Zhang). Computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden and the Swedish National Infrastructure for Computing at Uppsala Multidisciplinary Center for Advanced Computational Science, partially funded by grants 2022-06725 and 2018-05973 from the Swedish Research Council.

Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We thank the Simons Foundation Powering Autism Research for Knowledge study and the Simons Foundation for providing access to the data via the Simons Foundation Autism Research Initiative database.

Additional Information: The python implementation of the experiments is available on GitHub ( https://github.com/Tammimies-Lab/ASD_Prediction_ML_Rajagopalan ).

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• Review Article
• Published: 05 September 2022

A capabilities approach to understanding and supporting autistic adulthood

• Elizabeth Pellicano   ORCID: orcid.org/0000-0002-7246-8003 1 , 2 ,
• Unsa Fatima 1 ,
• Gabrielle Hall 1 ,
• Melanie Heyworth 1 , 3 ,
• Wenn Lawson 1 ,
• Rozanna Lilley   ORCID: orcid.org/0000-0001-6143-8805 1 ,
• Joanne Mahony 1 &
• Marc Stears 4  

Nature Reviews Psychology volume  1 ,  pages 624–639 ( 2022 ) Cite this article

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• Autism spectrum disorders

There is little comprehensive research into autistic adulthood, and even less into the services and supports that are most likely to foster flourishing adult autistic lives. This limited research is partly because autism is largely conceived as a condition of childhood, but this focus of research has also resulted from the orthodox scientific approach to autism, which conceptualizes autistic experience almost entirely as a series of biologically derived functional deficits. Approaching autism in this way severely limits what is known about this neurodevelopmental difference, how research is conducted and the services and supports available. In this Review, we adopt an alternative research strategy: we apply Martha Nussbaum’s capabilities approach, which focuses on ten core elements of a thriving human life, to research on autistic adulthood. In doing so, we identify areas where autistic adults thrive and where they often struggle, and highlight issues to which researchers, clinicians and policymakers should respond. The resulting picture is far more complex than conventional accounts of autism imply. It also reveals the importance of engaging autistic adults directly in the research process to make progress towards genuinely knowing autism and supporting flourishing autistic lives.

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Introduction.

Autism is a lifelong neurodevelopmental difference that influences the way a person interacts and communicates with others and experiences the world around them 1 . For decades, autism research focused predominantly on autistic children 2 , in line with the very earliest descriptions of autism 3 , 4 and the tendency for society to depict autism as a disability of childhood 5 . The result is a substantial lack of understanding about the opportunities and challenges that autistic adults face in building their futures, achieving their goals and living satisfying and fulfilling lives. These issues clearly matter, however, and in the past decade there has been an increase in publications on autistic adulthood, a new journal specifically dedicated to autism in adulthood, a notable increase in funding dedicated to adult-related issues 6 and numerous policy interventions designed to assist autistic adults to live good lives 7 .

Serious obstacles nevertheless continue to prevent researchers, clinicians, educators, policymakers and the broader public from fully grasping the nature of contemporary autistic adulthood. Overcoming these obstacles is vital not only because they constrain understanding but because they also hinder efforts to inform and transform the services and supports that might enhance autistic adults’ lives.

Paramount among these obstacles is the orthodox approach taken in conventional autism research, in which there is an overfocus on ‘deficits’ or ‘impairments’ of autistic adulthood and an overemphasis on specific attributes of individuals as opposed to the broader contexts in which autistic adults live 8 , 9 . This conventional research paradigm derives both from long-standing conventions in medicine, which prioritize a putatively objective standard of ‘bodily health’ over a subjective understanding of ‘well-being’ 10 , and from the developmental psychopathology literature, which stresses the importance of ‘patterns of maladaptation’ in shaping the life course of autistic people 11 . Consequently, individual autistic adults’ behavioural, cognitive and neural functionings are frequently compared with some typical or ‘normal’ level of ability that is held as the ideal ‘state of health’ 9 ; interventions and treatments typically aim to remediate these apparent shortcomings to align functioning with the accepted norm. This narrow focus on deficits results in a radically constrained understanding of the experiences that shape autistic lives, limiting the range of supports and services to those that seek to ‘change the individual’ rather than consider how to ‘change the world’. Conventional research efforts are also routinely conducted without meaningful input from autistic people themselves 12 , meaning that often the wrong questions are posed and findings are misinterpreted. Research of this kind can be said to be ‘lost in translation’ 13 . As such, most research on autism prioritizes researcher-defined normative life goals without discovering how much they matter to a diverse range of autistic people 14 , 15 .

In this Review, we — a team of autistic and non-autistic researchers — propose an alternative way of approaching adult autism research. First, we provide some context by briefly discussing the diagnosis and developmental trajectories of autistic adults. Next, we describe Nussbaum’s capabilities approach 16 , 17 , which outlines ten central capabilities that enable people, whether autistic or non-autistic, to lead lives that are of value to them on their own terms rather than to meet a predetermined normative standard set by others. We then examine each of the ten capabilities in the context of available autism research. This approach enables us to evaluate the opportunities and challenges facing autistic adults, the forces shaping them and the ways in which services and other interventions might enhance the quality of their lives.

Diagnosis and developmental trajectory

Adult diagnosis of autism first became available in the 1980s (ref. 18 ) and was further encouraged by changes in the Diagnostic and Statistical Manual of Mental Disorders , fifth edition (DSM-5) (refs. 1 , 19 ) several decades later. Many autistic adults initially seek their diagnosis following concerns about social relationships and mental health, sometimes precipitated by a personal crisis or by the diagnosis of their own children. For many, this search for diagnostic clarity is preceded by decades of feeling ‘different’ and of relationship or employment difficulties 20 , 21 . Challenges to adult autism diagnosis are discussed in Box  1 .

A growing number of adults self-identify as autistic without a formal diagnosis 22 . This self-identification is controversial in research and clinical communities but is often accepted in the autistic community, in part because, even in high-income countries, autistic adults often remain undiagnosed 2 , 23 , 24 and, even when formally diagnosed, are only minimally supported 2 , 7 , 23 , 24 , 25 . Those diagnosed later in life may have higher self-reported autistic traits and poorer quality of life, especially mental health, than those diagnosed in childhood 26 .

Following the normative tendencies of the conventional approach to autism research, the vast majority of studies that have examined the developmental trajectories of autistic adults diagnosed in childhood focus on areas thought to be critical for achieving ‘good’ adult outcomes. In longitudinal studies, these outcomes are often defined in terms of a set of standard ‘life achievements’, on which autistic adults typically fare badly 14 , 15 . For example, autistic adults with and without intellectual disability followed from childhood are less likely than non-autistic people to hold down a job, live independently or have friends and intimate relationships 2 , 14 , 15 . Other longitudinal studies have examined whether people remain ‘autistic’ (that is, meet instrument and/or clinical thresholds for autism) as they move from childhood into adulthood. These studies show that the diagnostic status of individuals diagnosed in childhood generally endures into adulthood 15 , 27 , with the exception of a minority of individuals who no longer display sufficient core autistic features to warrant a clinical diagnosis, which is sometimes described as an ‘optimal outcome’ 28 . Yet despite initial variability, many people show little change in researcher-defined ‘autistic symptoms’ as they move into adulthood 29 , potentially placing them at greater risk for poor psychosocial outcomes in adulthood 30 .

More detailed research on the quality of life of autistic adults also largely focuses on the achievement of standard life outcomes, irrespective of whether those outcomes are considered meaningful by autistic adults themselves 31 , 32 . Studies that have complemented standard, researcher-defined measures with more subjective, autistic person-led measures (such as quality of life) consistently demonstrate that outcomes are more positive when subjective factors are accounted for 14 , 15 . For example, an autistic person who is highly dependent on others for their care — a so-called ‘poor outcome’ according to the standard framework — might nevertheless be happy and subjectively enjoy a very good quality of life. Another autistic person who no longer meets the diagnostic criteria for autism — a so-called ‘good’ outcome — might struggle to find their way in the world and feel different and distant from others. Approaches that focus on researcher-defined measures in this way limit understanding and risk failing to grant autistic people the dignity, agency and respect they deserve.

In considering how to respond to these limitations, it is helpful to establish two clear aims. First, research into autistic adulthood must recognize that people’s life chances (opportunities each individual has to improve their quality of life) are shaped by a range of factors beyond the person, consistent with an ecological perspective 33 . That is, quality of life is influenced both by biological factors at the heart of the conventional medical model and a broader set of contextual factors as stressed by the social model of disability 34 . Second, no one, autistic or not, has high quality of life if their life goals are primarily set by others. Thus, quality of life should not be measured by a standard set of outcomes judged to be important by researchers, clinicians or policymakers. Instead, the goals of each individual’s varied human life should be at least partly set by the person themselves 35 .

Box 1 Challenges for autism diagnosis in adulthood

In most countries, adults seeking an autism assessment and diagnosis face severe challenges, and the individual is expected to initiate and navigate the process 24 . Although there are published guidelines 7 , 294 , major differences exist between guidelines and actual experience 295 . Adults seeking diagnosis report lengthy waiting times and prohibitive costs 2 , 24 , and encounter clinicians who lack a nuanced understanding of autism 75 , 174 . Further, the guidelines are far from standardized in their recommendations for the use of adult diagnostic tools and there is much variation in practice 2 , 7 , 294 .

The process of adult autism diagnosis is also challenging owing to difficulties in recovering early developmental history and the self-reported tendency of many autistic adults to use strategies (masking or camouflaging) to minimize autistic features 274 , 275 . Although autistic adults of all genders have been reported to mask 275 , it is more often reported among women 296 , which could be one reason why twice as many men present to adult diagnostic services 297 . These findings dovetail with a growing recognition of gender bias in autism diagnosis 2 , 7 .

More research concerning adult autism diagnosis is needed. For example, little is known about the diagnostic experiences of autistic adults with intellectual disability 24 , about how autism is identified in different cultural contexts or about adult autistic experiences in the Global South 298 . It is likely that autistic adults in many low and middle-income countries do not have access to formal diagnosis, post-diagnostic supports or the positive transformations in self-understanding and connections to a peer community that often accompany diagnosis 181 , 217 , 261 .

A capabilities approach to autistic lives

Martha Nussbaum’s 16 , 17 capabilities approach to quality of life, which has been widely used to analyse social disadvantage in multiple settings, satisfies both of the aims outlined above. First, according to the capabilities approach, a human ‘capability’ is not an intrinsic ability that a person has or does not have solely by virtue of who they are. Instead, ‘capability’ refers to the actual opportunity to be or do something that is facilitated or constrained by features of the person and by the broader contexts in which a person is embedded. The relevant contexts can include close family and household influences; everyday community interactions; educational institutions; economic factors, including the cost of living; services and supports, including accessibility and performance of healthcare institutions; and the broader social and political context, including social attitudes towards autism. Second, flourishing human lives are characterized by a set of these capabilities which enable a person to achieve any number of a range of outcomes, rather than by the attainment of a small number of pre-specified outcomes. These capabilities are considered foundations for a range of doings and beings; they shape what a person can do and, critically, who and how they can be in the world. Capabilities are not a narrow or specific set of achievements, nor are they possessions. Similarly, capabilities cannot be ranked or interpreted by a group of people, such as professionals, or reduced to a single score on a standardized scale. Instead, they refer to the preconditions for a broad range of ways of living.

According to Nussbaum, there are ten central capabilities that most people need if they are to be able to choose and create lives that are meaningful and fulfilling on their own terms 16 , 17 (Table  1 ). In what follows, we outline how analysing the life chances of autistic adults through this lens can enable a far richer understanding of autistic adults’ lives of all abilities (see Box  2 ) than the conventional research approach. We do so by highlighting the strengths and challenges of autistic adults in each of the ten central capabilities, and their causes, and consider the potential supports, services and changes in societal attitudes that might help to transform those challenges into strengths. Analysing these capabilities provides a way to examine the lives of autistic adults without narrow normative judgement, while also directing attention to issues that require intervention and support. Readers are advised that some of this material may be distressing and evoke difficult past associations.

Box 2 Inclusivity and the capabilities approach

The capabilities approach focuses on the real opportunities that are open to each person to live in ways that are meaningful to them. Applying such an approach to research on autistic adulthood enables identification of the ways in which autistic people can thrive on their own terms and the nature of the obstacles to this thriving. Diverging from more conventional medical frameworks, the key to this approach is the value of personal autonomy: the belief that all people, including autistic people, should enjoy the right to be at least ‘part author’ of their own lives 35 and that their quality of life should always be measured, at least in part, according to their own aspirations.

Although widely used in other settings 299 , the capabilities approach is novel in the context of autism, partly because it has previously been suggested that this sort of autonomy-inflected approach is ill-suited to a substantial proportion of the autistic community 300 . Non-speaking autistic people, those with intellectual disabilities and/or those with very high support needs have sometimes been considered unable to communicate or conceptualize their precise wishes in the ways the capabilities approach seems to require. From this perspective, the capabilities approach is applicable only to those who can make and articulate judgements about their own life purposes and not to the entire autistic population.

Some have called for a fine-grained approach to the heterogeneity within autism, suggesting that the autism spectrum should be split into those for whom an autonomy-inflected approach could be appropriately applied and those for whom the traditional medical model may be better suited 300 . Similarly, others have called for the creation of a separate ‘profound’ or ‘severe autism’ diagnostic category for those with the most severe impairments 7 , 301 .

We do not believe that we need to be this pessimistic. There is no clear scientific basis for segmenting the autism spectrum in the way that proponents of a separate ‘severe’ or ‘profound’ autism label suggest. Moreover, doing so poses grave risks, potentially excluding people deemed ‘severe’ or ‘profound’ from the concern, dignity and respect offered to others 302 , 303 . Nonetheless, it is crucial for future research into autistic quality of life to consider people of all abilities. Such research should investigate whether augmentative and alternative communication can enable those with higher support needs to make their needs and desires known 304 . Future research should also examine the effectiveness of available long-term services and supports to enable those with the greatest needs to fulfil key aspects of quality of life. This work would acknowledge the inevitable complexities of deploying the capabilities approach in these instances while recognizing that it remains possible to develop a broad and subtle framework for the evaluation of quality of life across the whole autistic community.

The first central capability is “being able to live to the end of a human life of normal length; not dying prematurely, or before one’s life is so reduced as to be not worth living” 17 . Autistic adults are currently at a substantial disadvantage in this capability. There are persistent patterns of premature mortality in the autistic population 36 , 37 . Autistic people are twice as likely to die prematurely as non-autistic people 36 , 37 , 38 , and this risk is greater for autistic women 36 , 38 (but see ref. 37 ) and those with intellectual disability 36 , 37 , 38 . The lives of autistic people are, on average, 16 years shorter than those of non-autistic people 36 . The risk of death is elevated in autistic people who experience poor physical health or chronic illness (including epilepsy) 36 , 37 , 38 , 39 . Little is known about the influence of social and economic factors, including access to healthcare, on these mortality rates, but it is widely hypothesized that an important contributor is the extent to which physicians listen to, and learn from, their autistic patients 40 .

Among the specific causes of premature mortality, there is a higher risk of suicide 41 , 42 . Suicide attempts are more frequent and more likely to result in death in autistic people than in non-autistic people 36 , 37 , 43 , 44 , 45 , possibly owing to co-occurring psychiatric conditions 36 . Research focused on understanding why autistic people are at increased risk of self-harm and suicide has identified individual risk markers common to those in the general population, including (younger) age 46 , low mood and rumination 47 . More work is needed to understand potentially unique risk markers for increased suicidality in autistic people, including broader interpersonal causes (such as thwarted belonging and perceived burdensomeness) which might mediate associations between autistic traits and suicidality 48 , and systemic issues (such as clinicians’ lack of knowledge 49 ).

More generally, autistic quality of life in older adulthood (adults aged 50 years and older 50 ) — albeit as assessed using normative measures — is seen as considerably poorer than that in non-autistic older adults 51 . Social isolation and loneliness are major issues for all older adults, leading to greater risk of dementia and other serious medical conditions 52 . Both social isolation and loneliness might disproportionately influence older autistic adults, who might be more prone to reclusiveness 53 , despite many autistic adults describing a longing for interpersonal connection 54 . For example, in a study in which autistic adults’ experiences of growing older were elicited, one autistic participant said “I think I’m a born loner, quite frankly … Maybe I’m not the kind of person to have a life. Oh, I’d love it, with a person that would understand me” 54 . There are few longitudinal and participatory studies focusing on autistic older people, including under-represented populations who might have poorer life satisfaction. Thus, little is known about how autistic adults can be supported to live a full and satisfying life into old age in diverse sociocultural contexts 55 , 56 .

Bodily health

The second central capability is “being able to have good health, including reproductive health; to be adequately nourished; to have adequate shelter” 17 . Once again, the evidence suggests that autistic adults are disadvantaged in this regard. Co-occurring physical conditions are common across the autistic lifespan 57 , 58 , 59 and are more prevalent than in the general population for almost all conditions assessed 43 , 58 , 59 , even when lifestyle factors are considered 58 . Autistic adults with intellectual disability have distinctive needs 59 and might be especially vulnerable to poor physical health 60 .

Risks for most physical health conditions are further exacerbated for autistic women 58 , 61 . Understanding the mechanisms for these differences in health outcomes is critical for reducing these inequalities. Moreover, further clarifying the temporal development of these health problems should inform how interventions are designed to prevent and treat them 62 . There are at present very few studies on autistic people’s reproductive health. Autistic women report challenging experiences with menstruation, including a cyclical amplification of sensory differences and difficulties with emotional regulation 63 , 64 , and autistic women are at greater risk for pregnancy complications 65 . Autistic women also report significant deterioration in everyday quality of life during menopause 66 . None of these concerns have yet been investigated in depth. Likewise, there are no studies specifically addressing the reproductive health experiences of autistic men, those with intellectual disability and/or those who are non-speaking; no studies have adopted a less gender-binary approach to reproductive health in autistic adults. This absence of research potentially leaves crucial areas of experience unsupported by clinicians and other policy interventions.

Autistic adults also face barriers to healthcare 67 , 68 , 69 . Despite greater healthcare utilization, medication use and higher healthcare costs than the general population 70 , autistic adults report more unmet health needs 71 , lower utilization of preventative care 71 and more frequent use of emergency departments 71 , 72 than non-autistic adults. Healthcare settings are often inaccessible to autistic adults, with significant risk of sensory and social overwhelm , miscommunication and lack of autistic-informed care 67 , 73 . Autistic people also experience reduced coordination of care compared with non-autistic people, particularly during the transition from paediatric to adult services 74 . Thus, autistic adults are often left to fend for themselves in navigating the healthcare system 75 , resulting in negative healthcare experiences and feelings of distrust 66 , 67 .

Autistic adults also report poor patient–provider communication (in both directions): autistic adults often face difficulties identifying and articulating their physical health symptoms 76 and professionals often do not appreciate the need to adapt their communication style for autistic patients and do not take their autistic patients’ concerns seriously 67 , 68 , 71 . Clinicians’ limited knowledge of 68 , 69 and lack of confidence in 75 understanding autistic adults’ specific needs further exacerbate these difficulties. Some tools have been developed to assess barriers to healthcare access experienced by autistic adults from their own perspective 71 or from their caregiver’s or healthcare provider’s perspective 77 . The person-related, provider-related and system-related barriers identified using these tools should facilitate future research that seeks to improve the care and health of autistic people 71 , 78 . However, research designed in collaboration with autistic people is needed to assess the most effective ways of improving their healthcare experiences 56 , 67 , 78 .

Many other external factors influence autistic adults’ physical health, such as access to affordable, appropriate housing. Initial studies suggest that autistic adults might be over-represented in homeless communities at rates substantially higher (12–18% 79 , 80 ) than adult population prevalence estimates (1% 81 ). The range of challenges facing autistic adults might predispose them to homelessness, and reduced social support networks might compound other risk factors, including unemployment, making it difficult for autistic adults to exit homelessness.

Other housing challenges also influence this crucial capability. Compared with other people with disabilities, autistic adults are less likely to live independently, leaving them vulnerable to the inadequacies of institutionalized housing. Formal institutional living and similar settings that purport to be community-based, but are often only nominally so 82 , have been criticized for displacing people from their families and communities and for providing poor and unresponsive services to residents 83 , 84 . Nonetheless, autistic adults continue to be over-represented in more restrictive and segregated settings 85 .

In sum, the bodily health of autistic adults is severely compromised at present in many regards, owing to failings in clinical provision and in the broader social and economic context within which they must lead their lives.

Bodily integrity

The third capability is that people should be “able to move freely from place to place; to be secure against violent assault; having opportunities for sexual satisfaction and for choice in matters of reproduction” 17 . This capability is underpinned by a person’s right to make decisions about their body.

There are good reasons to be concerned about autistic disadvantage in accessing this capability. Autistic children are at substantial risk of experiencing multiple forms and repeated occurrences of victimization and abuse 86 , and this vulnerability persists into adulthood 87 , 88 , 89 , 90 . In particular, there are elevated rates of sexual victimization in autistic compared with non-autistic adults 89 , 90 , especially in autistic women 91 , 92 , 93 and those who identify as a gender minority 92 or as a member of the LGBTQI+ community 94 . This increased vulnerability might be exacerbated by the fact that autistic people often have reduced access to good quality, effective sexual education 95 , which can impart vital protective knowledge, as well as by broader structural inequalities (for example, lack of access to healthcare 67 , 68 , 69 ).

Autistic adults also experience increased rates of physical assault 87 , 92 and domestic violence, largely perpetrated by people known to them 90 . Autistic women, particularly those who report multiple traumatic experiences, emphasize deeply distressing betrayals of trust 91 and how they often “just couldn’t see it coming” 93 . Worryingly, these already high victimization rates are likely to be an underestimate: autistic adults are less likely to report experiences of violence to the police 87 or even to confide in others 87 . Autistic adults who experience victimization therefore receive neither the requisite mental health support nor the critical social support that could reduce the likelihood of developing post-traumatic symptoms.

Concerns about physical safety also influence the ability to move freely. Many autistic adults want to be able to access work and go about their daily activities within their communities 96 , and parents often want this independence for their children too 96 . Yet both groups worry about safety. Use of public transportation can be challenging for autistic adults owing to lack of accessibility 97 and difficulties with wayfinding and traffic judgement 98 . Furthermore, despite research showing that autistic drivers are more rule-abiding than non-autistic drivers 99 and are no more likely to be at fault for a police-reported car crash 100 , few autistic people take up driving 101 , partly because of perceived difficulties in spatial awareness, motor coordination, processing speed and executive function 96 . Consequently, autistic adults can remain reliant on their parents. As one autistic adult expressed in a focus group on understanding autistic adults’ transportation needs and barriers: “If I want to go shopping in the middle of the day I can’t. I have to wait for my mom to come home from work” 96 .

Finding a balance between autonomy and safety is critical. Autistic children and adults can be more susceptible to wandering 102 , 103 , and parents sometimes advocate the use of measures such as tracking devices 104 . Yet wandering can occur for many reasons 102 and is often purposeful 104 . Researchers and activists warn of the negative impact surveillance technologies can have on people’s independence and urge investment in alternatives such as community supports and safety skills training 104 , 105 .

Bodily integrity is inextricably linked to other capabilities. Violations of bodily integrity have adverse effects on other capabilities 106 , including mental health 107 , bodily health, interpersonal relationships and sense of agency. Threats to bodily integrity are also likely to influence autistic people’s sense of sexual well-being and their freedom to achieve it. Long-held views of autistic people being uninterested in sexual experiences 108 have been firmly quashed by research showing that autistic adults desire sexual relationships to a similar extent as non-autistic adults 109 , 110 . Autistic adults in satisfying relationships are more likely to report greater sexual satisfaction, just like non-autistic adults 111 . They also identify with a wider range of sexual orientations 94 , 109 , 112 and gender identities 113 , 114 , 115 , 116 , their sexual ‘debuts’ occur at a later age 117 and they have fewer lifetime sexual experiences 112 than non-autistic adults. The lack of qualitative studies on the realities of autistic adults’ sexual lives limits understanding, despite the fact that this topic is prioritized by the autistic community 118 .

Senses, imagination and thought

The fourth capability focuses on being “able to use the senses, to imagine, think, and reason — and to do these things in … a way informed and cultivated by an adequate education … being able to use imagination and thought in connection with experiencing and producing [creative] works … Being able to have pleasurable experiences and to avoid nonbeneficial pain” 17 . The dominance of the conventional medical model has meant that autism is often associated with deficits in this regard 119 . There is often a presumption that autistic adults will struggle with higher-order cognition or have low intelligence owing to poor performance on standard intelligence tests 120 . This stereotype persists even though there is little evidence for it in the everyday experience of the autistic population 121 . There is an even greater presumption of low intelligence in autistic people who are non-speaking or do not use traditional forms of communication 122 , who are routinely under-recruited in research 123 . Similarly, researchers, clinicians and educators have long presumed that creative and imaginative skills and aspirations are limited in autistic people 124 .

However, the predominant use of standard intelligence tests can lead to an underestimation of autistic people’s intellectual ability 120 , particularly in non-speaking people 125 . Autistic people have also been shown to excel at producing novel responses on creative tasks 126 and are increasingly recognized for their creative talents 127 , with major companies investing in autistic people’s ‘out-of-the-box’ thinking 128 . These strengths have been linked to autistic people’s different way of perceiving the world, including detail-focused processing style 129 and enhanced perceptual abilities 130 , which might be underpinned by heightened sensory perception 131 .

Nevertheless, autistic people are, in general, poorly served by the educational environments that might further enhance this capability 132 . They regularly encounter sensory overwhelm within the physical school environment 133 , struggle with complex social expectations and interactions 134 , experience bullying and social isolation 135 , and are stigmatized by a presumption of low competence 136 . Moreover, limited attention is given to their specific needs, strengths and preferences 132 , 137 , including by school staff who lack confidence in supporting autistic students 138 . Being excluded from 139 or not completing 140 school can have persisting negative effects on mental health and well-being.

Increasing numbers of autistic adults are enrolling in higher education 141 , but barriers exist there too. Autistic adults rarely receive relevant supports and accommodations, partly because they are hesitant to disclose their diagnosis or find it difficult to reach out for help 141 and partly owing to the absence of formal transition planning 142 . Consequently, autistic adults are at high risk of dropping out of university 143 . There is also limited research on the destinations of autistic students who complete higher education 144 , so it is unclear how to best respond to these challenges.

The senses, imagination and thought capability also emphasizes the importance of being able to take pleasure from sensory experiences. Although research tends to focus on the challenges that autistic sensory differences — such as experiences of sensory overload — bring to people’s everyday lives 145 , sensory stimuli can also be a source of pleasure 146 , 147 . For example, one autistic adult reported enjoying “touching metal a lot … cold smooth metal is, like, just amazing” 147 . There is also evidence that autistic adults with limited spoken communication in a supported living environment find joy in the everyday, for example in the sound of the washing machine on the last spin or the feel of bubbles while dishwashing 146 , 148 .

However, these distinctive sources of pleasure are often pathologized. This is captured by the debate over certain ‘repetitive motor stereotypies’ such as hand-flapping 1 , which have been reclaimed by autistic adults as ‘stimming’ 149 . These behaviours tend to be perceived as an individual problem with no clear purpose or function that prevent the person from learning skills and interacting with others 150 . Stimming behaviours are often the target behaviour for interventions that promote ‘calm’ or ‘quiet’ hands 151 (cf. ref. 152 ). However, there is very little evidence that stimming behaviours are harmful to autistic people or their peers (the same cannot be said for self-injurious behaviours, which might also be purposeful but are nevertheless harmful to the person). In fact, it now seems likely that stimming behaviours can serve as a source of pleasure or reassurance or a form of self-regulation 149 .

The next capability is defined as “[b]eing able to have attachments to things and people outside ourselves; to love those who love and care for us, to grieve at their absence; in general, to love, to grieve, to experience longing … not having one’s emotional development blighted by fear and anxiety” 17 . The empirical literature shows that autistic adults have more difficulties recognizing others’ emotions 153 , 154 and identifying and describing their own emotions (alexithymia) than non-autistic people 155 , 156 . However, emerging work suggests a far more nuanced picture: autistic adults describe feeling emotions and empathy intensely 157 and often experience deeply satisfying emotional lives 158 .

At their most extreme, the conventionally reported difficulties with emotions were thought to preclude autistic people from the capacity to love or desire meaningful romantic and intimate relationships 159 . However, research is inconsistent with this claim 160 . Romantically involved autistic adults report high relationship satisfaction 93 , 161 . The strong bonds that autistic adults report with their partners, particularly with those who are also autistic 160 , extend to their autistic children, with whom they describe an intense connection and love 162 .

These reports speak strongly against an understanding of autism as a ‘disorder’ of affect. Rather than lack of interest, autistic adults often cite significant challenges with initiating and maintaining romantic relationships 154 , including difficulties reading and interpreting others’ emotions 161 , which can impact their capacity to remain romantically involved. The stereotyped assumptions of non-autistic people that autistic people are uninterested in interpersonal relationships might also be an obstacle 163 . These challenges can intensify feelings of loneliness and are linked to significant negative emotional experiences and poor mental health 164 . Autistic adults who desire intimate connection but whose needs are unfulfilled might be at particular risk of depression and low self-worth 164 , 165 .

This loneliness, depression and poor self-perception can take a substantial toll on mental health and well-being 164 , 166 , 167 . A substantial proportion of autistic adults experience a co-occurring psychiatric condition during their lifetime, with anxiety and mood disorders being the most common 168 , 169 . Rates of co-occurring psychiatric conditions are somewhat lower for autistic adults with intellectual disability 170 , but these rates might be underestimated owing to a lack of detailed understanding in how best to characterize and measure mental health in this context 168 . The risk of developing mood disorders increases with age 168 and autistic adults are at elevated risk of developing post-traumatic stress disorder 107 . Some mental health problems in autistic adults have been attributed to everyday discrimination and internalized stigma 171 .

The reliance on mental health assessments and diagnostic criteria that were established in non-autistic people 168 , 172 , 173 and a lack of necessary expertise among health professionals 174 might result in an overestimation or underestimation of mental ill health in the autistic population 173 . Some autistic characteristics might overshadow indicators of mental health conditions (for example, social withdrawal and sleep disturbance are common to both autism and depression), suggesting that co-occurring mental health conditions might go unrecognized 173 , 175 . Similarly, mental health diagnoses might overshadow an autism diagnosis, resulting in misdiagnosis 175 .

Mental health difficulties in autistic adults are likely to be compounded by the inadequacies of formal and informal supports. Autistic adults report a significantly higher number of unmet support needs than the general population 25 , struggle to obtain appropriate post-diagnostic support 176 and face challenges in accessing individually tailored treatment for mental health problems 25 , 176 . As one autistic adult put it: “I haven’t requested any, because people like me don’t get support” 25 . There is a clear need for mental health interventions that are adapted to autistic people’s needs and preferences 176 .

Practical reason

The next capability, practical reason, is defined as “being able to form a conception of the good and to engage in critical reflection about the planning of one’s own life” 17 . The three key elements of this capability — choosing what one wants to do, critically reflecting on that choice and making a plan to realize it — are fundamental to making full use of all the other capabilities.

It is sometimes assumed that people with cognitive disability, including some autistic people, are incapable of practical reason, failing even at the initial task of deciding what it is that they value or desire 177 . Autistic people were traditionally thought to have impaired self-awareness 178 . A substantial minority of autistic adults have co-occurring intellectual disability (29% 179 ) and some do not use speech to communicate 180 , which can make it difficult for others to gain insight into their thinking. However, research demonstrates that autistic people have a deep capacity to reflect on many aspects of the self, regardless of their intellect or communication preferences 181 , 182 .

The practical reason capability also requires people to be able to reflect critically on their choices, and to change their mind. Here, it seems that autistic people might approach decision-making differently to non-autistic people 183 , 184 . Autistic adults make more logically consistent, rational decisions 185 , are more circumspect in their decision-making, sample more information prior to making a decision 186 , are less susceptible to social influence 187 and are more deliberative in their reasoning 188 , 189 .

However, first-hand accounts suggest that such an approach to decision-making can have its disadvantages. For example, autistic people report challenges changing their decisions, especially if the change is unanticipated or requires a shift in routine 190 . Indeed, autistic people’s tendency to focus intensely on topics or objects of interests ( monotropism ) 191 can make it difficult to ‘move on’ or ‘change gears’ 192 . Interrupting activities after such states of flow and difficulties starting new activities (autistic inertia) can lead to pervasive and often debilitating effects on autistic adults 192 , including on their ability to design and execute a plan.

Many of the above skills come under the broader umbrella of executive function (higher-order processes that underpin goal-directed activity and enable individuals to respond flexibly to change and plan their actions accordingly) 193 . Problems with planning, organization and future-oriented thinking are common in autistic adults 189 , are linked to adaptive difficulties 194 , 195 , might be compounded by particular contexts (such as in parenting 196 or the workplace 197 ) and are perceived to be real obstacles to achieving desired outcomes 198 . Interventions and supports that focus on planning and decision-making are scarce, but those that do exist are associated with gains in executive function-related behaviours in real-world settings 199 .

Affiliation

The next capability is “being able to live with and toward others, to recognise and show concern for other human beings, to engage in various forms of social interaction … and having the social bases of self-respect and nonhumiliation; being able to be treated as a dignified being whose worth is equal to that of others” 17 . Simply put, that the person is respected as a social being 17 . Prima facie this might be the capability in which autistic adults might be expected to be at the greatest disadvantage. After all, the term ‘autism’ comes from the Greek autos , meaning both ‘self’ and ‘by itself’, and autistic people are often described as preferring a life of self-isolation 163 . Dominant characterizations suggest that autistic people lack the motivation 200 and/or cognitive building blocks 201 for social interaction, which prevents them from establishing and maintaining the types of reciprocal relationships that are fundamental for this capability.

Research has repeatedly shown that autistic children and adolescents have fewer reciprocal friendships 202 , 203 , are often on the periphery of social networks 202 , 203 and spend less time with their friends outside school than their non-autistic counterparts 204 . Autistic adolescents also report a growing awareness of feeling different from others despite wanting to ‘fit in’ 205 , 206 , and frequently experience social exclusion and bullying 135 , which might exacerbate their challenges in making and keeping friends. These patterns persist into adulthood 207 . It is therefore unsurprising that many interventions in adolescence and early adulthood focus on formal social skills training 208 , 209 , with the aim of equipping autistic people to manage everyday social relationships on their own terms and, thereby, secure this capability.

However, such interventions fail to appreciate that autistic sociality is shaped by the sociocultural context in which people are embedded 208 , 210 , 211 . Autistic people can and do have fulfilling connections with others, even if negotiating those relationships can be challenging 93 . They are drawn to those who accept them for who they are 154 , 159 , 161 and with whom they do not have to mask their autistic ways 212 , 213 . These friendships include (but are not restricted to) autistic-to-autistic interactions 214 , 215 . As one participant reported in a study on autistic adults’ experiences of loneliness and social relationships: “though many of us have only met each other three to four times, it feels as if we have known each other forever. Because all of a sudden you are in a community with someone where you are on the same wavelength … it is a really strong experience” 216 . Such autistic-to-autistic interactions promote self-understanding 181 , 214 , 217 , positive self-identity 217 , 218 and well-being 219 .

Isolation owing to the COVID-19 pandemic has also revealed that autistic people long for social connection in the same way as everyone else, both in terms of close, trusting relationships and fleeting, incidental interactions. As one autistic interviewee said when describing their lockdown experience: “I didn’t realise how important that incidental human contact was to me. It was so incidental that it never really registered on my radar until it was gone” 167 . Autistic people’s need for human connection and the extent to which social isolation plays a role in autistic people’s mental health distress have been underestimated by conventional accounts.

The double empathy problem 220 suggests that there is a misalignment between the minds of autistic and non-autistic people. This misalignment leads to a lack of reciprocity in cross-neurotype interactions and is the source of social communication difficulties between autistic and non-autistic people 221 , 222 . Empirical evidence suggests that non-autistic people have difficulties understanding the minds and behaviours of autistic people 221 , 222 , and that they are unwilling to interact with autistic people on the basis of initial judgements or interactions 221 , 222 , 223 . Thus, non-autistic people also interact less successfully with autistic people, compared with other non-autistic people 224 .

These cross-neurotype interaction difficulties can lead to stereotyping of and discrimination against autistic people. Although non-autistic people tend to deny feeling negatively inclined towards autistic people 225 , autistic people often report experiencing bullying, exclusion and discrimination. Attitudinal research has shown that considerable implicit biases are present, even among non-autistic people who report no explicit biases 226 , suggesting they may be unaware that they have negative attitudes towards autistic people. These implicit, negative biases are likely to be difficult to shift using short-term educational training programmes 227 . Such discrimination and stigma constitute a substantial barrier for autistic people seeking to develop social connections. Discrimination and stigma could be countered by widespread public acceptance campaigns (including those developed with autistic people 228 ), and programmes that increase the number of everyday interactions between autistic and non-autistic people 229 , 230 .

Other species

The eighth capability requires that humans are “able to live with concern for and in relation to animals, plants and the world of nature” 17 . Prominent autistic naturalists (such as Temple Grandin) and environmentalists (such as Greta Thunberg) have captured the public’s attention 231 . Yet there is remarkably little written about autistic people’s connections to nature and non-human animals.

Research with parents of autistic children has revealed that natural elements (such as sand, mud, leaves, twigs and water) can keep children engrossed for extended periods of time 232 . Some autistic children also prefer interacting with animals over inanimate objects and humans 233 , and report strong attachments to pets 234 . Studies have therefore focused on the potential therapeutic benefits of interacting with nature for children, with some purporting to show ‘reduced autistic severity’ or improvements in family functioning following interaction with trained animals 235 .

Research with autistic adults also reveals benefits of interacting with animals and nature 236 . Nature and gardening are two of the interests most reported by autistic adults, particularly women, and the pursuit of these interests is positively associated with subjective well-being 237 . In a study using photovoice methodology , images of natural scenes were frequently included among the photographs shared by autistic adults, demonstrating the importance of nature in contributing to a good autistic life 238 . Autistic adults’ autobiographies reveal the emotional depth of these connections to nature 239 , which some autistic people say offer respite from the intensity of an often inhospitable social world.

The capability of play emphasizes the right to be “able to laugh, to play, to enjoy recreational activities” 17 . This capability is one in which autistic adults might excel. Researchers and clinicians often refer to autistic people’s passions and interests as ‘highly restricted’, ‘perseverative’ or ‘circumscribed’, or as ‘obsessions’ or ‘fixations’, and as differing qualitatively (in content) and quantitatively (in intensity) from the interests of non-autistic people 240 . Yet autistic testimony attests that these passions are often a great source of joy and enjoyment 241 , which situates them within the play capability. Intense interests are common in autistic people 237 , 242 and become more diverse over time 243 . They are not limited to the sciences or computers, as popular stereotypes suggest 244 , but extend broadly to a range of areas 237 , 242 and might be more idiosyncratic in autistic adults with limited spoken language and/or intellectual disabilities 245 .

Autistic adults often view their capacity to pursue their passions as an advantage 181 , 237 , 241 , 246 that can be affirming and have positive implications for identity and self-concept 243 . Indeed, one autistic participant, who once “owned about 15,000 CDs,” celebrated the capacity “to be intense in stuff” 181 . Passions and interests have been likened to experiences of flow 237 , 247 and to monotropism 191 , which are driven by intrinsic (interest and knowledge) rather than extrinsic (prestige or achievement) motivation 237 . Finding others who share similar interests can form the basis of long-lasting friendships 93 . Nevertheless, exceptionally high intensity of engagement may, in some circumstances, negatively impact well-being 237 .

The generally positive effects of engaging in one’s interests also extend to taking part in recreational activities. Autistic adults report relatively high levels of weekly participation in exercise and hobbies 248 . However, they participate in conventional social and recreational activities to a lesser extent than the general population 249 , despite saying these are important to them 250 . Future research should consider the possible reasons for this disparity and the constraints that autistic adults face when engaging in meaningful and satisfying leisure activities. Inaccessible and inhospitable environments might be barriers for autistic adults 251 , and the effectiveness of programmes designed to support such participation appear to be limited 251 , 252 . Enhancing the play capability is important because engaging in recreational activities might buffer the relationship between perceived stress and quality of life 253 .

Control over one’s environment

The final capability emphasizes the importance of “being able to participate effectively in political choices that govern one’s life … being able to hold property and having property rights on an equal basis with others; having the right to seek employment on an equal basis with others; having the freedom from unwarranted search and seizure” 17 .

There is virtually no research on autistic adults’ engagement in mainstream political processes. Individuals with intellectual disability are less likely to vote than the general population 254 , especially if they live in supported accommodation rather than with family 255 . They often lack support and accessible information for political engagement 255 , 256 and are even explicitly told they cannot vote due to their intellectual disability 256 . More research is needed on autistic citizenship to identify precisely how these obstacles can be overcome 256 .

Extant data suggest that autistic people might be more politically disengaged than non-autistic people. This suggestion stands in contrast to high-profile autistic activists and political commentators, such as Australia’s Grace Tame and Eric Garcia from the United States, and increasing autistic involvement in self-advocacy since the 1990s. The autistic self-advocacy movement grew out of the self-advocacy efforts of people with intellectual and developmental disabilities in the United States and the United Kingdom 257 , and is perhaps epitomized most by Jim Sinclair’s 258 foundational essay (‘Don’t Mourn For Us’) which implored parents not to see their autistic child as a tragedy but, instead, to embrace their differences. Autistic and neurodiversity activists now promote individual self-advocacy, harnessing self-understanding and knowledge to ensure that individuals have greater control over their own lives. Such individual self-advocacy is complemented by collective advocacy, sometimes led by organizations run by and for autistic people (for example, Autistic Self-Advocacy Network ), where autistic people collectively campaign on a range of issues 259 , 260 and come together in dedicated autistic spaces and events 261 . Consequently, self-advocates have begun to shift conceptions of autism from a disorder that needs to be eradicated, prevented or ‘fixed’ to a distinct way of being, which demands acceptance and emphasizes human rights and a positive autistic identity and culture 261 , 262 , 263 , 264 , 265 , 266 , 267 .

There is much for autistic self-advocates to campaign about. Autistic people’s opportunities are constrained by others’ unjustified assumptions about their capacity 268 . Autistic adults are at far greater risk of prejudice, stigmatization and discrimination in many facets of their lives, such as education 141 , 269 , health 40 , 72 , care 270 , intimate relationships 271 , community 171 , justice 272 and work 273 . Moreover, to navigate a world that is not typically set up for them, autistic adults often (consciously or unconsciously) hide or mask aspects of their autistic self 274 , 275 to keep themselves safe or adjust their abilities through ‘compensation’ 276 . Such adaptation can come at serious personal cost, including poor mental and physical health 277 , 278 , negative self-perceptions 275 , 278 and autistic burnout 279 , 280 .

Work provides a particularly constrained environment. Autistic people face substantial challenges in gaining and sustaining meaningful employment, even relative to other disabled people 281 , 282 , 283 , despite possessing a range of skills that might be prized by employers 127 , 246 , 282 , 283 . Autistic adults who do obtain employment are often in positions that fail to match up with their abilities (malemployment) or for which they are overqualified (underemployment) 284 . They can also face challenges maintaining employment 285 , owing to inhospitable work environments 286 , negative experiences with (and sometimes bullying by) colleagues 281 , failure to have their needs and preferences met 287 , and experiences of discrimination, including following the disclosure of an autism diagnosis 288 . There is growing interest in paid short-term autism-specific employment programmes or internships, which are designed to reduce barriers to employment for autistic jobseekers, introduce them to workplace life and provide training in job-relevant skills 289 , 290 . These initiatives show promising effects on autistic trainees’ occupational self-efficacy 289 , 290 but deserve sustained attention to determine whether they help autistic adults to secure and maintain suitable employment in the longer term. Research is also needed on what constitutes a successful employment outcome according to autistic people themselves, and how it should be measured 291 .

Summary and future directions

Autistic people deserve to live long, healthy and creative lives of their own design. Just like all people, they need to be equipped with a set of fundamental capabilities to do so. In this Review, we have examined the lives and life chances of autistic adults through Nussbaum’s capabilities 16 , 17 lens. Doing so allows us to escape the narrowly normative focus on specific life outcomes and to consider the broader foundations for a range of possible good autistic lives. When approached in this way, the literature suggests that there are some capabilities in which autistic people have the potential to excel despite conventional stereotypes to the contrary, such as emotions, affiliation, play, connections to other species, practical reason and control over one’s own environment. At the same time, the literature suggests that in these capability areas and others (especially life, bodily health and integrity), autistic adults are often constrained by a range of social, economic and other environmental disadvantages and barriers, which prohibit them from enjoying a good life that they have the right to expect.

This Review suggests two clear directions for future research. First, it will be important for researchers to more clearly identify these externally shaped disadvantages and find ways to alleviate them. That is, once researchers are collectively equipped with a fuller understanding of what currently prevents autistic adults from enjoying a particular capability, they should be able to begin the task of removing those constraints so that further opportunities are provided. Second, it will be equally important to encourage autistic people themselves to reflect further on the capabilities to which they aspire and the obstacles which they believe obstruct them. The capabilities reviewed here are only a starting point and further amendment might be needed to capture the breadth and specificity of autistic experience (see ref. 292 ). Determining what autistic capabilities to add to this list can be resolved only through research that is genuinely participatory (see Box  3 ); that is, research that places the interests of autistic adults first and takes their own experience and expertise as seriously as any other input.

Box 3 New agendas and approaches to autism research

Despite the large literature on autism since it was first identified in the 1940s, this research generally does not have a positive, meaningful impact on the day-to-day lives of autistic people and their allies. There has been an extensive focus on underlying biological questions and relatively little research on the design of services and supports, the social contexts within which autistic people live or the policy settings that influence their quality of life. Through advocacy and other means, autistic people are increasingly making it clear that they are dissatisfied with this mix and, in line with the emphases of the capabilities approach, want the massive public investment in autism research to provide a greater direct return 305 . They want to address the imbalance in current autism research: research that has a direct impact on the daily lives of autistic people should be valued as much as research on the underlying biology and causes of autism 306 .

Crucially, autistic people also want to have greater input into research decisions 307 , 308 , 309 . Autism research has traditionally been designed and conducted by non-autistic people. Autistic people, their family members and even practitioners have rarely been involved in the decision-making processes that shape research and its application 12 , 13 , beyond being passive research participants. This limited involvement in research has begun to change in the past decade. There is a slow but growing movement towards collaborating with autistic people and their allies as part of the research process, such that autistic researchers and community members are actively involved in making decisions about research 308 , 309 . These decisions can include what kind of research is done, how it is done, how research results are interpreted and how the findings are used.

Such participatory research has a long history outside autism research 310 . In these contexts, participatory processes that draw on the ‘practical wisdom’ of non-scientists have been shown to have a dramatic effect on both the research agenda and the effectiveness of the research 311 . Participation itself can take many forms, ranging from being a consultant on a research project to sitting on a formal advisory board, being a full collaborative partner or even leading projects. The critical issue in participatory research is who makes the research decisions. In research involving community members only to a minimal extent (for example, through consultation), the researchers are typically in control. When that involvement deepens, researchers relinquish control to share decision-making power with community members.

There are some excellent examples of autism research that uses participatory approaches 40 , 181 , 312 , 313 , but it is still very much in its infancy. Although there is much enthusiasm for involving autistic people in the decisions that influence them 314 , 315 , researchers can be worried about how time-consuming participatory research can be, can find it hard to relinquish control in research decision-making and worry that community members might introduce bias into otherwise rigorous research processes. These concerns could lead to tokenism when community involvement is attempted 312 . Instead, researchers and community members need to appreciate that they each have different ‘experiential expertise’ 316 ; they must take that expertise seriously to enable valuable insights for those involved in the research and for the research itself 317 .

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Acknowledgements

The authors thank L. Crane, J. Lowe and D. Tan for their extremely helpful comments on a previous version of this manuscript. This work has been funded through an Australian Research Council Future Fellowship awarded to E.P. (FT190100077). The views expressed are the views of the authors alone and do not necessarily represent the views of their organizations or funding sources.

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Elizabeth Pellicano, Unsa Fatima, Gabrielle Hall, Melanie Heyworth, Wenn Lawson, Rozanna Lilley & Joanne Mahony

Department of Clinical, Educational and Health Psychology, University College London, London, UK

Elizabeth Pellicano

Reframing Autism, Sydney, New South Wales, Australia

Melanie Heyworth

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Contributions

This Review was a collaboration between non-autistic researchers (E.P., U.F., R.L. and M.S.) and autistic researchers (G.H., M.H., W.L. and J.M.), who all actively participated in making decisions about the Review. E.P. and M.S. identified the theoretical framework in discussion with U.F., G.H., M.H., W.L., R.L. and J.M.; E.P., U.F., G.H., M.H., W.L., R.L. and J.M. identified the search terms; U.F. and E.P. conducted the literature searches. All authors identified areas of interest from across and within the capabilities and read and reflected on the existing literature in those areas, focusing in particular on the aspects of relevant papers that were least and most compelling and the next steps for research. E.P. and M.S. wrote the original draft of the manuscript. All authors contributed to reviewing and editing the manuscript. The analytic approach was informed by the authors’ training in education (E.P., U.F. and R.L.), psychology (E.P. and W.L.), anthropology (R.L.), nursing (G.H.), history (J.M.) and political philosophy (M.S.), as well as positionalities as autistic researchers and advocates (G.H., M.H., W.L. and J.M.). These participatory processes ensured that the Review was approached through a strengths-based, rather than deficits-based, lens.

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Correspondence to Elizabeth Pellicano .

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Competing interests.

E.P. reports grants from the Australian Government Department of Education, Skills and Employment, the Australian Research Council, Australia’s National Disability and Insurance Agency’s Information, Linkages and Capacity Building Program, Australia’s National Health and Medical Research Council, Australia’s Cooperative Research Centre for Living with Autism (Autism CRC) and Simons Foundation Autism Research Initiative, and has received honoraria for invited talks from the International Society for Autism Research and Aspect Australia. G.H. reports grants from the Australian Government Department of Education, Skills and Employment, and sits on the Board of Directors for Amaze, the peak organization for autistic people and their families in Victoria, Australia, and the Disability Advisory Council for Australia’s Victorian State Government, for which she receives meeting attendance payments. M.H. is CEO of the not-for-profit organization Reframing Autism Ltd and co-chair of the Australasian Autism Research Council (unremunerated), and reports grants from the Australian Government Department of Education, Skills and Employment, and Australia’s National Disability and Insurance Agency’s Information, Linkages and Capacity Building Program. W.L. reports grants from the Australian Government Department of Education, Skills and Employment, and Autism CRC. He is a member of the Australasian Autism Research Council (unremunerated), a participant and advisor for Autism CRC and an ambassador for the I CAN Network, and receives royalties from books and occasional fees for workshop and invited addresses. R.L. reports grants from the Australian Government Department of Education, Skills and Employment, and Autism CRC. M.S. reports grants from the Paul Ramsay Foundation and from the University of Sydney, is an Associate Fellow at the Said Business School, Oxford and assists fundraising efforts with various philanthropic groups in his role as Director of the UCL Policy Lab. All other authors declare no competing interests.

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A term used by autistic people to describe a state caused by excessive sensory or social stimulation.

A state that occurs when a person’s sensory system becomes overwhelmed, possibly owing to difficulties processing and integrating perceptual information, causing significant distress.

When a person accepts negative stereotypes about autism and applies them to themselves.

A cognitive theory of autism, which suggests that the primary feature of autism is a tendency for a singular attentional focus.

An optimal state in which a person becomes fully immersed in an activity, resulting in intense concentration, creative engagement and the loss of awareness of time and self.

A qualitative research methodology in which participants take photographs to illustrate, and possibly prompt discussion of, their experiences.

A community-driven term describing a highly debilitating condition involving exhaustion, withdrawal, executive function problems and generally reduced functioning, with increased manifestation of autistic traits.

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Pellicano, E., Fatima, U., Hall, G. et al. A capabilities approach to understanding and supporting autistic adulthood. Nat Rev Psychol 1 , 624–639 (2022). https://doi.org/10.1038/s44159-022-00099-z

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Study confirms the utility of screening to identify autism in toddlers born preterm

New research published in Developmental Medicine & Child Neurology reveals that children born preterm are more likely to screen positive for autism than full-term children.

For the study, 9,725 toddlers were screened at 15-, 18-, or 24-month well child visits using a test called the Modified Checklist for Autism in Toddlers, Revised.

Screening results that were positive for autism were most common among children born extremely preterm (51.35%) and least common among those born full-term (6.95%). Subsequent evaluations after positive screening revealed the following rates of autism diagnoses: 16.05% of extremely preterm, 2.00% of very preterm, 2.89% of moderately preterm, and 1.49% of full-term births.

Utilizing the screening test at ages unadjusted for early birth was effective for identifying autism, as only a small number of preterm children (1.90%) who screened positive with the test did not receive a diagnosis of autism or other developmental delay following evaluation.

"With this research, we are hoping to help dissipate doubts that clinicians might have about the utility of screening for autism in toddlers born preterm," said corresponding author Georgina Perez Liz, MD, of the AJ Drexel Autism Institute. "Low-cost, universal public health strategies such as screening can lead to less disparity in autism detection and help children on the spectrum start specific intervention and supports earlier in life."

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In new study, AI helps spot autism early in children

AI can help predict which young kids are more likely to develop autism, a new study says.

The AI looks for patterns in medical data that can be easily obtained from children 2 or younger without extensive assessments or clinical tests, researchers said. Advertisement

The "AutMedAI" program was able to identify about 80% of children with autism, when tested using data from a group of 12,000 kids, researchers reported Monday in the journal JAMA Network Open .

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"With an accuracy of almost 80% for children under the age of 2, we hope that this will be a valuable tool for healthcare," said senior researcher Kristiina Tammimies , an associate professor at the Karolinska Institute's Department of Women's and Children's Health in Sweden.

The AI also showed good results in identifying kids who will face more extensive problems in social communication, cognitive ability and developmental delay, researchers said.

For the study, researchers trained four separate AI programs to look for autism using data on about 30,000 people with and without autism spectrum disorders. The AutMedAI program wound up being the best of the four in follow-up testing. Advertisement

"The results of the study are significant because they show that it is possible to identify individuals who are likely to have autism from relatively limited and readily available information," said lead researcher Shyam Rajagopalan , an affiliated researcher at Karolinska Institute and an assistant professor at the Institute of Bioinfomatics and Applied Technology in India.

Early diagnosis of autism is important because the earlier kids receive effective therapies and interventions for the disorder, the better their outcomes, researchers said.

"This can drastically change the conditions for early diagnosis and interventions, and ultimately improve the quality of life for many individuals and their families," Rajagopalan said in a Karolinska news release.

Researchers are now working to further hone the AI program, including the possible addition of genetic information to the parameters it considers.

"To ensure that the model is reliable enough to be implemented in clinical contexts, rigorous work and careful validation are required," Tammimies said. "I want to emphasize that our goal is for the model to become a valuable tool for health care, and it is not intended to replace a clinical assessment of autism."

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Autism Research Via Smartphone

One of the most effective means of investigating and understanding autism is eye tracking. Participants are shown photos or videos, and computer software records where their gaze rests. Autistic individuals are more likely to focus on nonsocial aspects of an image, such as objects or background patterns, while neurotypical subjects have an increased propensity to focus on people's faces.

Ralph Adolphs, the Bren Professor of Psychology, Neuroscience, and Biology and an affiliated faculty member of the Tianqiao and Chrissy Chen Institute for Neuroscience , has been researching autism for decades as part of a larger project aimed at understanding the neuroscience of human social behavior. In his Emotion and Social Cognition Lab , researchers get a finer grasp on the mechanics of the brain when processing emotion and interacting with others by studying both neurotypical individuals and those who have brain damage or brain malformations or who have neuropsychiatric conditions such as obsessive compulsive disorder (OCD) or autism spectrum disorder (ASD).

Autism is a particularly rich field for research into emotion and social cognition since it is characterized by, among other things, differences in social behavior. Adolphs has been exploring its features by bringing adults with autism into the lab to track their eye movements when they are exposed to a variety of visual stimuli.

This research has yielded many interesting findings but has been inherently limited by the expense of laboratory eye tracking technology. "Eye tracking is a sensitive measure that gives us insight into some cognitive processes that are thought to be different in autism," Adolphs explains. "But previous studies have required a desktop eye tracker, which can cost $30,000 or $40,000, and the assistance of a graduate student or postdoc to calibrate the equipment and set up the research subjects for tests. It's very time intensive and money intensive research."

Adolphs and others have asked whether smartphones, which are able to display images and video and use camera technology to record, display, or share elements of the user's face or environment, might be able to capture the same information that established eye-tracking technology already does but at considerably less expense.

In a recent proof-of-concept study, Adolphs's lab recruited participants with and without autism spectrum disorder to undergo eye-tracking experiments, first with established desktop eye-tracking technology (the Tobii Pro Spectrum eye tracker), then with smartphone eye tracking administered in the lab with the assistance of researchers who adjust the smartphones and the participants' angle of view, and finally with the same participants participating in eye-tracking experiments at home via smartphone. Impressively, similar results were found across all three modalities.

This holds enormous promise for research into autism. "If we can only get a dozen or 20 people into the lab at Caltech at a time, our sample size is obviously limited," Adolphs says. "Not only that, it is biased: Participants are people who can travel on their own, who are in the Los Angeles area, and who are high functioning. With smartphones we can scale research to much larger sample sizes and include participants from underserved communities. This will help us get a much better understanding of the features of autism."

There is a saying about autism: "If you've seen one person with autism, you've seen one person with autism." Because the characteristics of autism can vary so widely, small sample sizes limit the conclusions that can be drawn from research. Adolphs hopes that with the implementation of smartphone eye-tracking technology and the larger sample sizes it permits, "we will have the statistical power to look at a lot of questions about autism. There might be two or three or four or a dozen different types of autism that can be identified, which could greatly improve diagnosis and treatment for autistic individuals."

Smartphone eye tracking can also benefit autism research by enabling longitudinal studies—those that collect data about specific individuals over a longer period of time. "Sometimes, when a person comes into the lab for a study, they're nervous, or they've taken a medication, or they haven't slept well the night before, and all these things can give results that might be quite different on another day," Adolphs says. "It's too impractical for people to come to the lab repeatedly, but if they can perform these tests at home alone, week after week, we can establish a baseline and then document changes due to development, treatment, or aging."

"There are still a lot of practical hurdles with smartphone research," Adolphs cautions. "Getting people to reliably do these tests is not trivial. They have to remember to do it, follow instructions, hold the phone in a certain way, and upload the data to us."

A potentially bigger problem concerns privacy, especially as this technology is commercialized, as it inevitably will be (and to some extent, already is). "If you have an app that advertises itself as diagnosing or tracking autism, it will take a video of your face, send it to some machine on the internet, and then give you results," Adolphs explains. "This is identifiable data. Whoever gets the video can tell who you are." In Adolphs's study, videos of participants were cropped to show only the eyes. "The data became immediately anonymized," Adolphs says.

These difficulties notwithstanding, Adolphs's experiments with smartphone eye tracking "have the potential to scale sample size by several orders of magnitude and include participants from all over the world."

" Smartphone-based gaze estimation for in-home autism research " was published in the journal Autism Research. The authors on the paper include postdoc Na Yeon Kim and graduate student Qianying Wu, who together led the study, as well as co-authors Jasmin Turner, Lynn K. Paul, and Adolphs of Caltech; Daniel P. Kennedy of Indiana University; and Junfeng He, Kai Kohlhoff, Na Dai, and Vidhya Navalpakkam, who all work for Google Research in Mountain View, California, and who were responsible for analyzing the data. The study was funded by the Della Martin Foundation, the Simons Foundation Autism Research Initiative, and Google.

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Autistic community views on supports for infants

La Trobe University has led a world-first study to better understand how to support infants who might be Autistic.

Researchers from Australia, United States, United Kingdom and India engaged with 128 Autistic people and 110 non-Autistic members of the autism community to understand their views on providing support to infants who might be Autistic.

Dr Catherine Bent La Trobe’s Senior Research Fellow and lead researcher, said the study, published in the international journal Autism, looked at community views about the acceptability of providing support to parents of infants who might be Autistic in the first two years of their child’s life.

“Most families have to wait until after an autism diagnosis to receive support, but neurodivergent infants often show communication differences in the first years of life and parents can now be offered support early, before an autism diagnosis,” Dr Bent said.

“Recent research evidence is telling us that by supporting parents early, we can promote developmental outcomes for neurodivergent infants. But we wanted to hear from families – and communities – with lived experience to understand what they think about providing supports at such a young age and before a child is diagnosed as Autistic.”

Almost all Autistic and non-Autistic participants surveyed agreed with the idea of offering support for parents and babies but stressed that it depended on the type of support. For example, programs that help caregivers understand and respond to their infant’s feelings, needs and behaviour and improve infants’ quality of life were considered acceptable.

Dr Rachel Jellett, a parent, psychologist and researcher who consulted on the study as part of an international advisory group said working with parents to understand and respond to their baby’s cues ultimately helped to nurture a supportive and attuned parent-child relationship.

La Trobe Associate Professor Kristelle Hudry, a psychologist and senior author of the study said strong early support that focused on communication skills, the parent-child relationship, and parents’ ability to respond to their child in supportive ways was foundational to supporting child development and learning, and for wellbeing in later life.

Many Autistic and non-Autistic community members surveyed indicated that early family supports should not be about preventing autism or suppressing Autistic behaviour.

La Trobe’s Dr Patrick Dwyer, an Autistic researcher and study co-author said the team heard from participants that the goal should not be to enforce narrow neurotypical standards of behaviour and development.

Study participants indicated that programs for parents and babies should be neuro-affirming, individualised and child-led, and should also consider parents’ own possible neurodivergence.

“These findings reflect the nuanced nature of the neurodiversity movement,” Dr Dwyer said. “Autistic and non-Autistic community members often advocate values aligned with the neurodiversity movement. But they’re not saying we shouldn’t offer supports to Autistic children. They’re saying those supports should be thoughtful, respectful and guided by children’s preferences.”

The study findings also highlighted some differences in opinion between Autistic and non-Autistic community members, with non-Autistic participants placing greater emphasis on improving family quality of life, while Autistic participants clarified that parent wellbeing was important but should not be prioritised over the needs of the child.

They were also more likely than non-Autistic participants to suggest that Autistic community members be involved in the development and delivery of support programs. “This emphasises the importance of listening to Autistic people, including Autistic parents, whose insights have unfortunately been ignored too often,” Dr Dwyer said.

The research published in Autism will feature in a forthcoming Special Issue on Autistic Rights Movement and Social Model in Autism Research and Practice.

Media Contact: Elaine Cooney – [email protected], 0487 448 734

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