Amanda L. Richdale

Heterogeneity in mental health presentations typically is treated as a methodological obstacle rather than a source of clinical insight. Using data from 799 autistic adults spanning autistic traits, transdiagnostic processes, and mental health outcomes, we applied Growing Self-Organising Maps (GSOM), an unsupervised machine learning approach that preserves topological relationships between individuals, capturing graded transitions rather than imposing rigid categories. Six clinically meaningful profiles emerged, revealing that mental health outcomes are shaped not by autistic traits alone but by configurations of transdiagnostic processes: emotion dysregulation, intolerance of uncertainty, alexithymia, sleep disturbance, camouflaging, and resilience. Women and gender-diverse participants were over-represented in higher-burden profiles. Critically, the identified treatment targets are not bound to any single therapeutic orientation and are not autism-specific, positioning this framework as a model for precision mental health intervention across heterogeneous clinical populations where one-size-fits-all approaches have proven insufficient.

Objectives: Insomnia is associated with mental ill health, worry and rumination—the two latter contribute to pre‐sleep cognitive arousal. These variables also associate with psychological inflexibility, including cognitive fusion (CF), a literal attachment to unhelpful thoughts, patterns and rules. Good sleepers generally have low CF, good sleep hygiene and a belief in sleep control, but individuals high in CF may have unhelpful beliefs about sleep hygiene and sleep control. Methods: We investigated associations between CF, sleep hygiene and sleep control, hypothesizing that each would contribute significant variance to insomnia and mediate the relationship between CF and insomnia. Results: Participants were 411 adults ( M _age = 45.9 years, SD = 15.6) completing an online survey including demographics, and questionnaires about insomnia (ISI), CF (CFQ), sleep hygiene (SHS) and sleep control (BRISC). Hierarchical regression accounted for 34% of variance in insomnia ( p < .001), showing that after controlling for demographic variables, CF, sleep hygiene and sleep control each accounted for significant variance in insomnia, with sleep control the strongest predictor ( β = −.304). Parallel mediation modelling accounting for 22.2% of variance in insomnia ( p < .001) showed both sleep hygiene and sleep control partially mediated the CF and insomnia relationship. Conclusions: Our study shows for the first time that CF, sleep hygiene and sleep control beliefs are inter‐related in their impact on insomnia. As CF is a core component of psychological inflexibility, ACT plus behaviour therapy (ACT‐I) may be a more suitable insomnia intervention than CBT plus behaviour therapy (CBT‐I) for some people.

Many autistic people experience poor mental health but often face barriers to accessing appropriate care. This study examined whether autistic adults encounter more or different barriers to mental healthcare compared to non-autistic adults. Participants (non-autistic n = 173; diagnosed autistic n = 173; self-identified autistic n = 158) completed an online survey assessing 29 barriers adapted from the Barriers to Healthcare Checklist, rated by presence and severity. Barriers were categorised as person, provider or system related. Analyses of variance compared groups on the mean number and severity of barriers, and chi-square tests compared group incidence of each barrier. Overall, systemic barriers (e.g. cost) and difficulties with system navigation (e.g. finding services) were the most commonly reported barriers and rated as the most severe. Autistic participants experienced a significantly higher number and severity of barriers than non-autistic participants. Provider-related barriers (practitioners’ knowledge, attitudes and skills) were particularly severe for autistic groups. In contrast, person-level barriers (e.g. sensory sensitivities), though often reported in general healthcare, were less impactful in mental healthcare. Findings suggest that efforts to improve mental healthcare access for autistic people should prioritise addressing the most frequent and severe barriers: system and navigation challenges, and practitioner knowledge and skills in autism. Lay Autistic people often experience poor mental health but face many challenges when trying to access mental healthcare. These challenges can include things like high costs, difficulty finding the right support or healthcare professionals not understanding autism. We looked at whether autistic adults face more or different barriers to mental healthcare compared to non-autistic adults. We surveyed over 500 adults, including those with a formal autism diagnosis, those who self-identified as autistic, and non-autistic adults. The survey asked about 29 possible barriers to getting mental healthcare. These included things related to the person (such as anxiety, sensory sensitivities or difficulty finding services), the healthcare provider (like psychologists’ and psychiatrists’ lack of autism knowledge, or poor communication) and the healthcare system (such as long waiting lists or high cost). We asked whether participants had experienced each barrier and how much each one affected their ability to get help. We found that nearly everyone experienced some barriers, especially challenges with the healthcare system and knowing how to find help, but autistic participants faced more barriers and were more strongly affected by many of them than non-autistic participants. Compared to non-autistic people, autistic people had a lot of problems with providers which prevented them getting the care they needed. We concluded that, to improve access to mental health support for autistic adults, we need to focus on the most common and harmful barriers: making the system easier to use, reducing costs and improving practitioners’ understanding of autism and working with autistic people. By addressing these areas, we can help ensure that autistic people receive the mental healthcare they need.

To assist planning for future crises, we aimed to compare mental health impacts of the COVID-19 pandemic on autistic and non-autistic adults with pre-existing mental ill-health by conducting an online survey with 532 adults. Participants self-reported mental health conditions and either an autism diagnosis (n = 181), self-identified autism (n = 167), or were non-autistic (n = 184). Self-ratings of mental health, need for mental healthcare and access to mental healthcare during COVID were compared across autism groups and other demographics. Open question responses were analysed using Reflexive Thematic Analysis. Most participants reported poor mental health, high needs for mental healthcare and great difficulty obtaining appropriate care during the pandemic. Younger participants reported poorer mental health; younger people and those with non-autism neurodiversity reported greater need for mental healthcare; neurodivergent participants and those with lower education levels reported greater difficulty obtaining care. Autism-status per se did not differentiate outcomes; instead, having pre-existing mental ill-health or non-autism neurodiversity seemed more salient. Thematic analysis identified four themes/sub-themes: “Greater mental health needs for most”; “Exceptions to the rule: improvements in mental health for some (mainly autistic people)”; “Navigating access to a scarce resource”; and “Telehealth: an enabler for some, a barrier for others”. Implications for future practice include prioritising those with pre-existing mental ill-health or neurodiversity, assisting clients with system navigation, and improving tele-mental-healthcare delivery, or alternatives, especially for neurodivergent clients.

Purpose: Sleep problems and behavioural challenges are examined rarely in very young autistic children. We investigated sleep in 173 autistic children, close to their autism diagnosis ( M _age = 2.49 years at diagnosis), focussing on sleep’s relationship with daytime behaviour and vice versa, and examining if there were specific sleep problem-behaviour relationships in this very young cohort. Methods: Caregivers of 173 autistic children ( M _age = 2.58 years at data collection) provided information on their children’s sleep (CSHQ; written descriptions) and behaviour (BASC-3, VABS-3). Demographic, ADOS-2 and developmental (MSEL) information were also available. Using parents’ written descriptions and normative sleep data, children were categorized as severe (SSP) or typical (TSP) problems sleepers, or good sleepers (NSP) (Roussis et al., 2021). Kruskal-Wallis, correlation and regression analyses examined sleep and behavioural relationships among these three sleep groups. Results: Most children (71.9%) had two or more sleep problems. The TSP and NSP groups did not differ on behaviour, showing significantly less hyperactivity, aggression, attention, and atypicality than the SSP group. Night waking/parasomnias, daytime alertness, and sleep initiation/duration for both sleep problem groups strongly correlated with increased hyperactivity, attention, anxiety, depression, and aggression. Sleep explained 38.4% of variance and 61.8% variance in behaviour, and behaviour explained 22.4% of variance and 32.1% of variance in sleep, for the TSP and SSP groups respectively. Conclusion: Reciprocal relationships between sleep and behaviour in autism emphasise the importance of addressing sleep problems in young autistic children, at the time of diagnosis, as they can negatively impact behaviour and well-being.

Despite the substantial evidence base for poor sleep in autism, sleep problems are frequently under‐identified and inefficiently managed. This is, in part, due to the fact that available tools do not comprehensively capture the specific sleep phenotypes in autism. The Sleep Disturbance Scale for Children (SDSC) is one of the highest ranked pediatric sleep assessment tools, yet it is scarcely used in autism research. Consequently, the utility of the SDSC for characterizing sleep disturbances in autism remains unknown. This study explored the factor structure of the SDSC in a sample of 513 autistic youth, aged 3–18 years (mean: 10.5 years; SD: 3.7; 18.7% female) drawn from the Healthy Brain Network secondary dataset. Fit of the previously derived SDSC factor structure was appraised using the confirmatory application of the exploratory structural equation modeling. Results indicated a five‐factor solution comprising disorders of initiating and maintaining sleep, disorders of arousal, disorders of excessive somnolence (DoES), sleep breathing disorders, and sleep–wake transition disorders (SWTD) provided an excellent fit for the data. The derived factor solution was stable across sex and age. Notably, all sleep subscales were significantly positively associated with internalizing and externalizing behaviors, while age was significantly associated with DoES ( r = 0.15) and SWTD ( r = − 0.18). This study provides the first validation of the SDSC in a large sample of well‐characterized autistic youth showing distinct patterns of associations with key clinical correlates. Findings are consistent with independent reviews, suggesting the SDSC may provide a more effective way to screen for sleep disturbances in autistic individuals.

Background: Retention and academic success are associated with accessing supports for students with disabilities and/or mental health conditions (MHCs); however, many students do not disclose to their institution. While disclosure and supports use have been investigated for Autistic students, less is known about other neurodivergent students, those with multiple minority identities, and whether this differs from the experiences of non-neurodivergent students with MHCs (NND-MHCs). Methods: Using the bioecological framework, we investigated disclosure experiences and supports use for neurodivergent and NND-MHC students via an anonymous online survey capturing diagnoses, disclosure experiences, supports usage, and new supports wanted from 131 neurodivergent and 42 NND-MHC Australian and New Zealand higher education (HE) students. Kruskal–Wallis, Chi-square, and Fisher’s Exact tests were used to compare neuro groups. Results: Neurodivergent participants were grouped into Autistic, Attention Deficity/Hyperactivity Disorder (ADHD), Autistic-and-ADHD (AuDHD), and ND-other. More AuDHD students disclosed to their institution than NND-MHC (83%–19%, p < 0.001). Similar proportions of neurodivergent and NND-MHC students found disclosure helpful and intended to disclose in the future. Minority-identity scores differed significantly, but did not affect disclosure. More AuDHD students (74%) accessed supports than the other groups (38%–58%), but this was not significant ( p = 0.074). There were significant group differences in use of note-takers, extended time for examinations/assignments, computer use in examinations, group project alternatives, and modified orientation. NND-MHC students reported lower overall supports use than the neurodivergent groups, p = 0.028. Support helpfulness ratings were similar for neurodivergent (95%) and NND-MHC (96%) students. Neurodivergent groups (77%–87%) wanted new neurodivergent-specific supports in both structured and unstructured formats, with neurodivergent mentors/facilitators preferred by the Autistic, ADHD, and AuDHD groups, 50%–71%. Conclusion: Apart from the AuDHD group, less than half of eligible students disclose to their institution or use supports, but when they do, they find them helpful. A more nuanced understanding of neurodivergent students’ support needs is required to improve their HE experience and completion rates.

Sleep problems among individuals with autism spectrum disorder (ASD) are a persistent issue that spans from early childhood to adulthood. The present study aimed to objectively investigate sleep continuity and alignment using ambulatory circadian monitoring (ACM) in a group of autistic individuals, with and without intellectual disabilities. We studied 214 participants. Sleep continuity and alignment were assessed using a minimum of 3 days of ACM. Participants were divided into four groups: (1) age < 10 years (n = 40, 87.5% males, M = 6.78 ± 1.40 years), (2) age 10–17 years (n = 53, 90.6% males, M = 12.62 ± 2.04 years), (3) age 18–27 years (n = 59, 74.6% males, M = 23.50 ± 2.60 years), and (4) age 28–65 years (n = 62, 74.2% males, M = 39.04 ± 9.49 years). All groups had significantly impaired sleep outcomes, except for TST. Adults had longer SOL and WASO duration, than children and adolescents. However, those differences were attenuated if participants ‘percentages of sleep parameters within normal range’ were compared. When evaluating circadian misalignment, sleep M5 is delayed in children and adolescents (2:56 am and 3:00 am, respectively), and strongly advanced in the older adults (group 4). Sleep problems that manifest in autism during childhood can endure throughout adulthood. Furthermore, there is a necessity to investigate how living conditions, such as enforced schedules in residential facilities, can influence the timing of the sleep midpoint.

Background: Neurodivergent students experience less success than non-neurodivergent students and underutilise available supports, as do students with mental-health conditions (MHCs). While autistic students’ disclosure and supports experiences have been examined, less is known about students with other/multiple neurodivergent conditions and non-neurodivergent students with MHCs. Method: We captured diagnoses, disclosure experiences, and supports used and wanted from 131 neurodivergent and 41 non-neurodivergent Australasian students via a mixed-methods survey. We compared groups quantitatively, and qualitatively using framework analysis. Results: More neurodivergent students disclosed to their institutions (47–19%), accessed supports (55–38%), and used more supports than non-neurodivergent students with current MHCs. Only neurodivergent students endorsed themes of ‘ shame ’, ‘ identity and transparency ’, ‘ diagnosed during higher education ’, and ‘ why discontinued ’, while more non-neurodivergent students queried ‘ is disclosure worth i t’, having ‘ found ways to compensate ’. Neurodivergent students contributed five times as many comments, and the majority wanted supports led by neurodivergent mentors/facilitators. Conclusion: Neurodivergent students reported more experiences of stigma and discrimination, including from peers, which may impact whether and where they seek support. Disclosure-related processes and costs impacted differently, yet both groups wanted more kindness and flexibility from educators. Registration processes need simplification, and new supports must be co-designed to improve student outcomes.