Digitally enabled, self-referral as an effective approach for young autistic people to access support

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We sought to explore the feasibility and effectiveness of meeting this support need through an end-to-end digital self-referral and digital mental health service. Methods Together with Health and Social care teams and young autistic people we developed a self-referral pathway that allowed young autistic people (aged 16–25) to access the digital self-management support system, Brain in Hand (BiH), without the need for diagnosis or referral by an external agency. Participants were reached using digital media channels which linked to a BiH landing page. Reach, progress and engagement through the pathway was monitored and participants were surveyed on their eligibility and suitability for BiH. Results A total of 243 BiH licences were issued within 9 weeks of the start of the digital media campaign which reached nearly half a million people with close to 20,000 clicking through to the BiH landing page. Most of the young people being issued with the digital support tool demonstrated high levels of need, 69% experienced clinically significant depression, 83% anxiety, 99% moderate or high executive function challenges, and 60% lacked current support. Conclusions This pilot demonstrates that young people understand their needs and directing them to a support service through a digital media campaign presents an efficient and effective approach in reaching young autistic people in need. This suggests that digital media channels and self-referral could offer a practical solution to broaden access to a range of digital mental health platforms without placing additional resource burden on health and care teams. Anxiety Autistic Depression Digital Mental Health Neurodiversity Figures Figure 1 Background Health and social care services are under the dual pressure of high levels of vacancies and staff sickness (Buchan et al., 2017) and increased demand for support (Russell et al., 2022 ), especially in the area of mental health. This is particularly pertinent for autistic people, where there are long waiting lists for diagnostic assessment, and no guarantee of obtaining appropriate support for those who do obtain a diagnosis (Brede et al., 2022 ). In practice, support is often restricted to those with very severe needs, despite evidence that autistic people experience anxiety and are a high risk of self-harm (Blanchard et al., 2021 ; Lai et al., 2019 ). Given that early intervention among autistic adolescents can build resilience and help prevent mental health difficulties (Shochet et al., 2022 ), opportunities are being missed to support people effectively, and early enough. Innovation is needed in service access and service delivery to meet the demand and to improve prevention efforts. Delivery of support services most frequently involves an initial referral by a professional, followed by an assessment, before any decision is made about whether support can be provided. This approach introduces barriers to receiving appropriate support (Howes, Burns & Surtees, 2021 ) and is not effective at engaging vulnerable populations. Self-referral, where an individual decides themselves that they need support, can mitigate many of these barriers and is commonly used by charities and in the private sector. Self-referral, for example, is a key aspect of the Improving Access to Psychological Therapies (IAPT) programme within NHS England (Brown et al., 2010), more recently renamed NHS Talking Therapies. As of 2020, there were over 200 IAPT services across England (Wakefield et al., 2021 ) which have significantly increased access to psychological therapies. Given the high numbers of autistic people, 1.8% of young people (Roman-Urrestarazu et al., 2021 ), and suggestions that as many as 72% of autistic people in England are undiagnosed (O’Nions et al., 2023a), any service providing support for those self-referring needs to be scalable. High levels of smartphone ownership and the increasing role they play in the lives of young people offers a valuable opportunity for digital mental health. The National Health Service has highlighted mental health apps as cost-effective and scalable solutions to barriers (Chandrashekar, 2018 ). Existing digital mental health services, including websites and mobile apps, can offer greater and more rapid accessibility and anonymity (Bond et al., 2023 ) and show promise for marginalized and under-reached populations (Magid et al., 2023; Schueller et al., 2019 ). Brain in Hand (BiH) is such a service. It combines digital tools and practical human support to enable people to live more independently, with a particular focus on supporting the impact of executive functioning difficulties experienced by many autistic people. Research has shown that BiH is able to improve quality of life and reduce anxiety among autistic people (Tromans et al, 2023 ) and is a good example of a digital mental health solution that can be offered at scale to people aged 16 and up who are diagnosed autistic or who believe they may be autistic. A scalable product needs to be linked to scalable access. Autistic people and their supporters often look for support through internet searches and social media (BiH, unpublished data) and young people specifically use search engines to find information on mental health (Pretorius, Chambers & Coyle, 2019 ). Searches containing the word ‘Autistic’ take place over 1 million times every month in the UK (BiH, unpublished data). Furthermore, digital media channels and social media are increasingly being used to reach research participants (Darko, Kleib & Olson, 2022 ) and in public health campaigns (de Vere Hunt & Linos, 2022 ), and evidence shows that social media is effective at targeting hard to reach groups (Jones & Salazar, 2016 ) and young people aged 16–24 (Darko et al., 2022 ). All the above suggests that digital media channels could be an effective approach to reach young autistic people with a service that they could self-refer to. This study aimed to explore whether self-referral by autistic people to the digital mental health service, BiH, through a digital media campaign is a feasible and effective way to reach those in need, and potentially an innovative way to improve access to a range of scalable mental health interventions. Methods Research Design and data collection The study used a prospective cohort design to test the feasibility and effectiveness of using digital media channels and online tools to reach and engage young autistic people as potential users of BiH. The research design, including eligibility and end-to-end self-referral pathway, was co-created with our partnering health and social care teams and young autistic people to ensure suitability. The BiH service was offered directly to people who self-referred, without the need for a direct health or social care referral. Surrey County Council, Cheshire West and Chester Council, Devon Partnership NHS Trust, Joined Up Care Derbyshire, and the Royal Borough of Greenwich were partners in the project. The self-referral process used several stages to reach and engage potential BiH users with the BiH service. Those who saw the digital campaign and wanted to learn more accessed the landing page. Those who remained interested after reading more about the study and BiH were then asked to complete a range of questions to assess eligibility (criteria was 16–25 years old, autistic (clinically diagnosed, on the waiting list, or think they might be autistic), and living in one of the five project partners as identified by their postcode). Those not eligible were screened out. Those who were eligible were then asked to complete suitability questions (mental health needs, executive functioning challenges, and autism traits), demographic information (postcode, ethnicity, gender identity and sexual orientation) and informed consent as part of the sign-up process. Once they had signed up, participants were sent an activation email to issue the software and get them started with BiH. They could login to access the tool and schedule a session with a BiH coach to support the participant to personalise the tool to meet their needs. Participants and recruitment It is unethical to target digital media campaigns to under 18’s so autistic people aged 18–25 and the supporters of young (16–17 years old) autistic people were targeted via two digital marketing channels: (1) anyone searching on Google for specific search terms (I.e. autistic, autism support, support for autistic teens) within target geographical locations was presented with an advert about the study. (2) People within a specific geographical location who matched the target audience profile (age, interests, location, etc) were shown social media (Facebook and Instagram) adverts about the study. The campaign was initiated on 19th January 2023 in Devon and closed on 7th March 2023 in Greenwich with a total of 261 people signed up. The first user signed up on the first day of the campaign (19th January) and people continued to sign up during the campaign period with the last sign up on the 5th March (2 days before the campaign closed). People could either sign themselves up directly or be signed up by their supporter. The social media campaigns for Facebook and Instagram were managed via Facebook Ads Manager and search ads were managed in Google Ads. These provided analytic data so the campaigns could be adjusted based on performance and stopped once we had reached the desired number of sign-ups. The adverts directed people to an online landing page, which explained the service offer, eligibility criteria and research project. The landing page included a simple application and consent process. People that met the eligibility criteria were able to sign up for the BiH service which was offered free of charge for one year. For 16- and 17-year-olds, their supporters were informed that the user had signed up to BiH but they did not need to provide additional consent. Reach, progress and engagement Google analytics was used to track visits to the BiH landing page and the users’ journey through the self-referral process. Reach of the campaigns was assessed by the number of times an advert was presented in search results (impressions) and how many people clicked the link to take them to the landing page. Progress was noted at stages of checking eligibility, meeting eligibility, sign up and being issued with the software. The engagement of those that were issued with software was assessed in terms of utilisation of the digital tool and the human support component as of 19th May 2023 (17 weeks after the first sign-up and issue of BiH licence). Autism traits Participants who had not received a clinical diagnosis were asked the first 5 questions of Ritvo Autism Asperger Diagnostic Scale 14 (RAADS 14) screening tool to assess their autistic traits. The recommended cut-off score of 4 or above for the 5 questions was used to identify those who are ‘likely autistic’ (Eriksson, Andersen & Bejerot, 2013 ; Ritvo et al., 2008 ). Executive functioning A set of 8 questions (see Table 1 ) were developed with a group of autistic people from the domains used in the Executive Skills Questionnaire-Revised (ESQ-R) (Dawson & Guare, 2010) and the Behaviour Rating Inventory of Executive Function (BRIEF) (Gioia et al., 2002 ). The questions cover the domains of self-monitoring, planning and organisation, cognitive flexibility, emotion regulation and generativity, and task initiation. They were designed to provide a snapshot of the level of executive functioning difficulties being experienced by those signing up. High executive functioning challenges reflects a score of 25 to 32 (out of a possible 32) and moderate challenges a score of 16 to 24. Table 1 Executive functioning questions The following 4-point scale (1 = Not at all, 2 = a lot, 3 = a little bit, 4 = completely) was used to answer the question: ‘how well do these statements reflect you’: Questions I don’t notice I’m tired/stressed until it’s too late I find it hard to prioritise everything I have to do I find it difficult when things change unexpectedly I get stuck trying to solve a problem and can’t think of different approaches I have trouble getting started on projects even if they’re important to me I tend to react without thinking first I forget spoken instructions if they’re not written down I find it difficult to estimate how long it will take to do things Anxiety and depression The Generalized Anxiety Disorder 2-item (GAD-2) (Plummer et al., 2016 ) and the Patient Health Questionnaire 2-item (PHQ-2) (Kroenke et al., 2003 ) were used to highlight potential mental health needs among our users. A person was identified as experiencing clinically significant anxiety if they had a GAD-2 score ≥ 3 and or clinically significant depression if PHQ-2 score ≥ 3. Ethics REC approval was received from Lab Research Ethics Panel (LabREP) ( CSP-BiH-020923). Informed consent to participate in the evaluation was obtained online from all participants at sign up, immediately following consent for service use. Results Reach, progress and engagement Reach and progress through the self-referral journey is illustrated as a Funnel Diagram in Fig. 1. The digital media campaign that lasted 6.5 weeks resulted in over 460,700 total impressions and 18,522 visits to the landing page. After processing the information on the landing page related to eligibility, service offer and participation in the study, 685 people checked their eligibility for the study, the majority of whom (n = 562, 82%) met the criteria. Just under half (n = 261, 46%) of eligible people signed up for the study and almost all of these (n = 243, 93%) went on to be issued with the software by 20th March 2023 (the first licence was issued on the 20th January) and formed the final sample. Sixty-two percent of participants who were issued the software (150 young people) went on to use the service (as of 19th May 2023). The total cost of the campaign was £10,198 giving a cost per person recruited of £41.97. The cost per person would be expected to reduce if the recruitment window had been extended beyond the 6 weeks. INSERT Fig. 1: Funnel chart depicting numbers at each stage of the self-referral journey Participant characteristics The 243 participants in this study were those who had chosen to sign-up for the service and had been issued with the software. The most common reason why 18 of those who signed up were not issued with software was an incorrect phone number or email address. Participants demographic characteristics are summarised in Table 2 . The cohort was skewed towards the younger end of the 16–25 age range with 53% aged 16–18. Many (62%) registered female at birth, with 47% of the cohort identifying as a woman or girl. The group included 7% who identified as non-binary and 2.5% who identified as gender fluid. Of those who chose to answer, 46% identified as straight or heterosexual. The majority (93%) of the participants identified as white. Postcodes were provided by all participants and used to calculate the Index of Multiple Deprivation (Office for National Statistics, 2018 ) for each individual. Approximately 7% of the participants resided in areas in the top 20% of deprivation in England. Just over half of participants signed up themselves (55%). Table 2 Participant demographics characteristics and needs (n = 243) Sample size Percentage Age 16–17 89 36.6% 18–21 94 38.7% 22–26 60 24.7% Diagnosis Clinically diagnosed as autistic 137 56.4% On the waiting list for autism assessment 52 21.4% Think they might be autistic 54 22.2% Referred by Referred by self 134 55.1% Referred by supporter 109 44.9% Assigned sex at birth Female 150 62.0% Male 88 36.3% Prefer not to say 4 1.7% Did not respond 1 - Sexual orientation Heterosexual / straight 105 43.4% Don't know 36 14.9% Bisexual 33 13.6% Gay / Lesbian 24 9.9% Pansexual 17 7.0% Asexual 10 4.1% Prefer to self-describe 5 2.1% Prefer not to say 12 5.0% Did not respond 1 - Gender identity Woman/girl 113 46.7% Man/boy 84 34.7% Non-binary 16 6.6% Gender fluid 6 2.5% Trans man/boy 4 1.7% Trans woman/girl 2 0.8% Gender queer 1 0.4% Prefer to self-describe 2 0.8% Prefer not to say 8 3.3% Don't know 6 2.5% Did not respond 1 - Ethnicity White: English/Welsh/Scottish /Northern Irish /British 212 87.6%* White: Other white background 10 4.1% Mixed Ethnicity: White and Black Caribbean 5 2.1% Mixed Ethnicity: White and Asian 3 1.2% White: Irish 2 0.8% Arab 1 0.4% Black: African 1 0.4% Black: Caribbean 1 0.4% Other 3 1.2% Prefer not to say 3 1.2% Don't know 1 0.4% Did not respond 1 - RAADS Categories (asked of the 106 who did not have a clinical diagnosis) Likely Autistic 96 95.0% Not Likely Autistic 5 5.0% Did not respond 5 - Executive Functioning Categories High Executive Functioning Challenges 141 58.0% Medium Executive function Challenges 99 40.8% Low Executive Functioning Challenges 3 1.2% Clinically significant anxiety symptoms (GAD2 score > or = 3) Clinically significant 201 82.7% Not clinically significant 42 17.3% Clinically significant depression symptoms (PHQ2 score > or = 3) Clinically significant 167 68.7% Not clinically significant 76 31.3% Current Support Currently receiving support 98 40.3% No current Support 145 59.7% Past Support Received services in past 182 74.9% No past services 61 25.1% *Total percentage is 99.8% due to rounding errors and small numbers in disaggregated sub-group Autistic traits Just over half of the participants (56%) had a clinical diagnosis of autism, with the remainder equally split between those on the waiting list (22%) and those who think they might be autistic (22%) (see Table 2 ). The first 5 items of the RAADS-14 indicated high levels of autism traits among those who had not been formally diagnosed. Using a cut-off score of 4 or above identified that 95% of this group were likely autistic. Level of need Levels of anxiety and depression were high, 83% scored in the clinically significant range of the GAD-2 and 69% scored in the clinically significant range of the PHQ-2 (see Table 2 ). Most participants (99%) indicated they were experiencing moderate (41%) or high (58%) levels of executive functioning challenges. Three quarters of participants had received support services in the past, but only 40% were receiving some form of support service at the time of the project. Discussion The overall objective of this study was to investigate the potential of self-referral through digital media campaigns to reduce health inequalities among young autistic people. We found that a diverse group of young autistic people could be reached using digital media campaigns with close to half a million people viewing the adverts and nearly 20,000 visiting the landing page over 6.5 weeks. Furthermore, our final group of 243 eligible participants who had been issued with BiH software demonstrated high levels of need in terms of anxiety, depression and executive function and were suitable for support by the BiH service. They were also highly motivated with 150 (62%) already actively using the support tool within 2 months of the licence being issued. This compares favourably with retention rates for many digital mental health apps which typically fall below 10% by 30 days (Baumel et al., 2019 ). The overall findings of this study represent a powerful example of how self-referral and digital media campaigns can efficiently and effectively provide access to a digital tool. Any concerns that a self-referral approach engaged those with low need are completely negated when seen alongside levels of clinically significant anxiety and depression in more than two-thirds of the participants and moderate and high levels of executive functioning challenges in nearly all (99%). As a population, autistic people experience poorer health outcomes compared to their neurotypical peers – reduced life expectancy, elevated risk of suicide and high prevalence of mental health difficulties (Cassidy et al., 2022 ; O’Nions et al., 2023b). These disparities can be further exacerbated by other factors such as living in deprived areas, belonging to an ethnic minority, or being LGBTQ+. Often those with elevated risk of health disparities who fall into these categories are described as ‘under-reached’ and are less likely to engage with services when offered (Office for National Statistics, 2018 ). The government’s strategy for tackling healthcare inequalities is set out in the Core20PLUS5 framework and identifies children and young people (CYP) most at risk of health inequity as being the most deprived 20% of the population, those with certain protected characteristics (including gender, race/ethnicity, autism) and those with specific needs, including mental health needs (NHS England, 2022 ). The digital media campaigns were successful at reaching the LGBTQ + community. Participants were richly diverse compared to the general population (Office for National Statistics, 2023 ). For example, Trans representation at 2.4% was much higher than the 0.3% in the general population with all other gender identities accounting for 9.5% of our participants compared with only 0.6% in the general population. The challenge came in reaching minority ethnic groups with most of our participants identifying as white. Underpinning the Core20PLUS5 framework is evidence that CYP from ethnic minority backgrounds are less likely to access services around early intervention to prevent mental health problems escalating, and recent research has shown that most minoritised ethnic groups are offered and receive psychological interventions in Early Intervention in Psychosis (EIP) less often than White British people (Schlief et al., 2023 ). There is also evidence of ethnic disparities in the diagnosis of autism, with BAME children being diagnosed with autism later and at a lower rate when compared to white children (Roman-Urrestarazu et al., 2021 ), possibly resulting in lower awareness in these communities (Kandeh et al, 2018). Data and digital poverty are also more prevalent among ethnic minorities (Allmann, 2022 ). While there were obvious challenges in reaching ethnic minority groups with this approach, it is not clear whether this was a result of access to digital media and tools or related to awareness and acceptance of neurodiversity and its mental health impacts, or a combination of both. Although the approach piloted here is of obvious interest to those implementing a public health approach to early intervention and prevention in the autistic population, the findings are also relevant more widely to other underserved populations whose needs could be met with digital solutions. We have shown that there is the potential to deliver services differently. In the short time window of fewer than 9 weeks, digital media campaigns resulted in 243 autistic people being issued the licence for BiH at a recruitment cost of less than £50 per person. The recruitment window closed as we reached our licence limit for the study, but an extension of the recruitment window could have made this even more cost-effective. Strengths and limitations This study used only two digital media channels to reach participants, Meta (Facebook and Instagram) and google search, and future work could benefit from examining other popular digital media channels. Although the age range for participation was 16–25 years, 53% were aged between 16 to 18 years which may limit the generalisability of the findings for older age groups. In addition, few people came from minority ethnic groups and more investment should be put into understanding how better to reach these communities. Conclusion The current structure and capacity of the health and social care system is unable to manage the growing levels of need for support in the autistic population. Innovation is urgently needed not only in the interventions that are offered to support greater independence but also in the way that support is offered. This study shows that a direct to person approach is both a feasible and effective way of reaching and engaging a high need population. When coupled with an effective digital mental health solution, the potential for impact at scale, including within underreached communities, is considerable. Declarations Author contribution declaration : Authors ND and LM initially conceptualised the research study. ZS led on the study design and data collection with support from ND, JL and LM. JL led on the development of the eight executive function questions. ZS, ND and HG performed the data analysis, data interpretation, and generation of figures and tables. HG prepared the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements: The research was commissioned and funded by SBRI Healthcare. SBRI Healthcare is an Accelerated Access Collaborative (AAC) initiative, in partnership with the Health Innovation Networks (HINs). The authors would like to thank our partnering sites who co-created this innovation with us: Surrey County Council, Cheshire West and Chester Council, Devon Partnership NHS Trust, Joined Up Care Derbyshire and Royal Borough of Greenwich and to South West Academic Health Science Network who helped to design this research. The views expressed in the publication are those of the author(s) and not necessarily those of SBRI Healthcare or its stakeholders. The authors have declared that they have no competing or potential conflicts of interest. Funding declaration: This work was commissioned and funded by SBRI Healthcare (Reference number: SBRIH19P3027). SBRI Healthcare is an Accelerated Access Collaborative (AAC) initiative, in partnership with the Health Innovation Networks (HINs). The views expressed in the publication are those of the author(s) and not necessarily those of SBRI Healthcare or its stakeholders. Ethics declarations: Ethical approval and consent to participate: REC approval was received from Lab Research Ethics Panel (LabREP) (CSP-BiH-020923). Informed consent to participate in the evaluation was obtained online from all participants at sign up, immediately following consent for service use. Consent for publication: not applicable. Competing interests: The authors declare no competing interests. References Allmann K. UK digital poverty evidence review 2022. London: Digital Poverty Alliance; 2022. Blanchard A, Chihuri S, DiGuiseppi CG, Li G. Risk of self-harm in children and adults with autism spectrum disorder: a systematic review and meta-analysis. JAMA network open. 2021;4(10):e2130272-. Baumel A, Muench F, Edan S, Kane JM. Objective user engagement with mental health apps: systematic search and panel-based usage analysis. 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Shochet IM, Saggers BR, Carrington SB, Orr JA, Wurfl AM, Kelly RL, Duncan BM. A school-based approach to building resilience and mental health among adolescents on the autism spectrum: a longitudinal mixed methods study. School Mental Health. 2022;14(3):753–75. Tromans S, Henley W, Summers I, Bilkey D, Datson J, Doherty N, Morpeth L, Benbow S, Jelbert R, Roy A, Watkins L. The psychological and social impact of the digital self-support system ‘Brain in Hand’ on autistic people: prospective cohort study in England and Wales. BJPsych Open. 2023;9(3):e96. Wakefield S, Kellett S, Simmonds-Buckley M, Stockton D, Bradbury A, Delgadillo J. Improving Access to Psychological Therapies (IAPT) in the United Kingdom: A systematic review and meta‐analysis of 10‐years of practice‐based evidence. British Journal of Clinical Psychology. 2021;60(1):1–37. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Apr, 2024 Editor assigned by journal 08 Apr, 2024 Submission checks completed at journal 06 Apr, 2024 First submitted to journal 08 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4048189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288708545,"identity":"8a037ab9-948d-4346-942d-1eed32fab88f","order_by":0,"name":"Helen Guyatt","email":"data:image/png;base64,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","orcid":"","institution":"Brain in Hand","correspondingAuthor":true,"prefix":"","firstName":"Helen","middleName":"","lastName":"Guyatt","suffix":""},{"id":288708549,"identity":"530510ae-6f4a-4096-9729-d78ea18afab0","order_by":1,"name":"Nicola Doherty","email":"","orcid":"","institution":"Brain in Hand","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Doherty","suffix":""},{"id":288708550,"identity":"a7dadc28-86ff-4b2c-960c-0e0693e87ff8","order_by":2,"name":"Jenny Limond","email":"","orcid":"","institution":"University of Exeter","correspondingAuthor":false,"prefix":"","firstName":"Jenny","middleName":"","lastName":"Limond","suffix":""},{"id":288708551,"identity":"9a37189c-d64f-4b88-b868-17e85411a156","order_by":3,"name":"Zoe Swaine","email":"","orcid":"","institution":"Brain in Hand","correspondingAuthor":false,"prefix":"","firstName":"Zoe","middleName":"","lastName":"Swaine","suffix":""},{"id":288708552,"identity":"770b4d85-5e4c-4ec6-843f-2f6f14843cf2","order_by":4,"name":"Louise Morpeth","email":"","orcid":"","institution":"Brain in Hand","correspondingAuthor":false,"prefix":"","firstName":"Louise","middleName":"","lastName":"Morpeth","suffix":""}],"badges":[],"createdAt":"2024-03-08 18:31:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4048189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4048189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54454114,"identity":"e95d96e3-b159-49de-8210-5ca3a36c719f","added_by":"auto","created_at":"2024-04-10 18:44:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":221990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunnel chart depicting numbers at each stage of the self-referral journey\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1Funnelchartdepictingnumbersateachstageoftheselfreferraljourney.png","url":"https://assets-eu.researchsquare.com/files/rs-4048189/v1/f3ccf51fd888dacd6d50407b.png"},{"id":54455153,"identity":"4e3b98e0-6040-4949-bf06-4ecebb1124f3","added_by":"auto","created_at":"2024-04-10 18:52:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":594485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4048189/v1/60621516-9987-4cd0-b5d0-0c4eec22d126.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digitally enabled, self-referral as an effective approach for young autistic people to access support","fulltext":[{"header":"Background","content":"\u003cp\u003eHealth and social care services are under the dual pressure of high levels of vacancies and staff sickness (Buchan et al., 2017) and increased demand for support (Russell et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), especially in the area of mental health. This is particularly pertinent for autistic people, where there are long waiting lists for diagnostic assessment, and no guarantee of obtaining appropriate support for those who do obtain a diagnosis (Brede et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In practice, support is often restricted to those with very severe needs, despite evidence that autistic people experience anxiety and are a high risk of self-harm (Blanchard et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lai et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given that early intervention among autistic adolescents can build resilience and help prevent mental health difficulties (Shochet et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), opportunities are being missed to support people effectively, and early enough. Innovation is needed in service access and service delivery to meet the demand and to improve prevention efforts.\u003c/p\u003e \u003cp\u003eDelivery of support services most frequently involves an initial referral by a professional, followed by an assessment, before any decision is made about whether support can be provided. This approach introduces barriers to receiving appropriate support (Howes, Burns \u0026amp; Surtees, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and is not effective at engaging vulnerable populations. Self-referral, where an individual decides themselves that they need support, can mitigate many of these barriers and is commonly used by charities and in the private sector. Self-referral, for example, is a key aspect of the Improving Access to Psychological Therapies (IAPT) programme within NHS England (Brown et al., 2010), more recently renamed NHS Talking Therapies. As of 2020, there were over 200 IAPT services across England (Wakefield et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) which have significantly increased access to psychological therapies.\u003c/p\u003e \u003cp\u003eGiven the high numbers of autistic people, 1.8% of young people (Roman-Urrestarazu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and suggestions that as many as 72% of autistic people in England are undiagnosed (O\u0026rsquo;Nions et al., 2023a), any service providing support for those self-referring needs to be scalable. High levels of smartphone ownership and the increasing role they play in the lives of young people offers a valuable opportunity for digital mental health. The National Health Service has highlighted mental health apps as cost-effective and scalable solutions to barriers (Chandrashekar, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Existing digital mental health services, including websites and mobile apps, can offer greater and more rapid accessibility and anonymity (Bond et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and show promise for marginalized and under-reached populations (Magid et al., 2023; Schueller et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Brain in Hand (BiH) is such a service. It combines digital tools and practical human support to enable people to live more independently, with a particular focus on supporting the impact of executive functioning difficulties experienced by many autistic people. Research has shown that BiH is able to improve quality of life and reduce anxiety among autistic people (Tromans et al, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and is a good example of a digital mental health solution that can be offered at scale to people aged 16 and up who are diagnosed autistic or who believe they may be autistic.\u003c/p\u003e \u003cp\u003eA scalable product needs to be linked to scalable access. Autistic people and their supporters often look for support through internet searches and social media (BiH, unpublished data) and young people specifically use search engines to find information on mental health (Pretorius, Chambers \u0026amp; Coyle, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Searches containing the word \u0026lsquo;Autistic\u0026rsquo; take place over 1\u0026nbsp;million times every month in the UK (BiH, unpublished data). Furthermore, digital media channels and social media are increasingly being used to reach research participants (Darko, Kleib \u0026amp; Olson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and in public health campaigns (de Vere Hunt \u0026amp; Linos, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and evidence shows that social media is effective at targeting hard to reach groups (Jones \u0026amp; Salazar, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and young people aged 16\u0026ndash;24 (Darko et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). All the above suggests that digital media channels could be an effective approach to reach young autistic people with a service that they could self-refer to.\u003c/p\u003e \u003cp\u003eThis study aimed to explore whether self-referral by autistic people to the digital mental health service, BiH, through a digital media campaign is a feasible and effective way to reach those in need, and potentially an innovative way to improve access to a range of scalable mental health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design and data collection\u003c/h2\u003e \u003cp\u003eThe study used a prospective cohort design to test the feasibility and effectiveness of using digital media channels and online tools to reach and engage young autistic people as potential users of BiH. The research design, including eligibility and end-to-end self-referral pathway, was co-created with our partnering health and social care teams and young autistic people to ensure suitability. The BiH service was offered directly to people who self-referred, without the need for a direct health or social care referral. Surrey County Council, Cheshire West and Chester Council, Devon Partnership NHS Trust, Joined Up Care Derbyshire, and the Royal Borough of Greenwich were partners in the project.\u003c/p\u003e \u003cp\u003eThe self-referral process used several stages to reach and engage potential BiH users with the BiH service. Those who saw the digital campaign and wanted to learn more accessed the landing page. Those who remained interested after reading more about the study and BiH were then asked to complete a range of questions to assess eligibility (criteria was 16\u0026ndash;25 years old, autistic (clinically diagnosed, on the waiting list, or think they might be autistic), and living in one of the five project partners as identified by their postcode). Those not eligible were screened out. Those who were eligible were then asked to complete suitability questions (mental health needs, executive functioning challenges, and autism traits), demographic information (postcode, ethnicity, gender identity and sexual orientation) and informed consent as part of the sign-up process. Once they had signed up, participants were sent an activation email to issue the software and get them started with BiH. They could login to access the tool and schedule a session with a BiH coach to support the participant to personalise the tool to meet their needs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and recruitment\u003c/h2\u003e \u003cp\u003eIt is unethical to target digital media campaigns to under 18\u0026rsquo;s so autistic people aged 18\u0026ndash;25 and the supporters of young (16\u0026ndash;17 years old) autistic people were targeted via two digital marketing channels: (1) anyone searching on Google for specific search terms (I.e. autistic, autism support, support for autistic teens) within target geographical locations was presented with an advert about the study. (2) People within a specific geographical location who matched the target audience profile (age, interests, location, etc) were shown social media (Facebook and Instagram) adverts about the study. The campaign was initiated on 19th January 2023 in Devon and closed on 7th March 2023 in Greenwich with a total of 261 people signed up. The first user signed up on the first day of the campaign (19th January) and people continued to sign up during the campaign period with the last sign up on the 5th March (2 days before the campaign closed). People could either sign themselves up directly or be signed up by their supporter. The social media campaigns for Facebook and Instagram were managed via Facebook Ads Manager and search ads were managed in Google Ads. These provided analytic data so the campaigns could be adjusted based on performance and stopped once we had reached the desired number of sign-ups.\u003c/p\u003e \u003cp\u003eThe adverts directed people to an online landing page, which explained the service offer, eligibility criteria and research project. The landing page included a simple application and consent process. People that met the eligibility criteria were able to sign up for the BiH service which was offered free of charge for one year. For 16- and 17-year-olds, their supporters were informed that the user had signed up to BiH but they did not need to provide additional consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eReach, progress and engagement\u003c/h2\u003e \u003cp\u003eGoogle analytics was used to track visits to the BiH landing page and the users\u0026rsquo; journey through the self-referral process. Reach of the campaigns was assessed by the number of times an advert was presented in search results (impressions) and how many people clicked the link to take them to the landing page. Progress was noted at stages of checking eligibility, meeting eligibility, sign up and being issued with the software. The engagement of those that were issued with software was assessed in terms of utilisation of the digital tool and the human support component as of 19th May 2023 (17 weeks after the first sign-up and issue of BiH licence).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAutism traits\u003c/h2\u003e \u003cp\u003eParticipants who had not received a clinical diagnosis were asked the first 5 questions of Ritvo Autism Asperger Diagnostic Scale 14 (RAADS 14) screening tool to assess their autistic traits. The recommended cut-off score of 4 or above for the 5 questions was used to identify those who are \u0026lsquo;likely autistic\u0026rsquo; (Eriksson, Andersen \u0026amp; Bejerot, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ritvo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eExecutive functioning\u003c/h2\u003e \u003cp\u003eA set of 8 questions (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were developed with a group of autistic people from the domains used in the Executive Skills Questionnaire-Revised (ESQ-R) (Dawson \u0026amp; Guare, 2010) and the Behaviour Rating Inventory of Executive Function (BRIEF) (Gioia et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The questions cover the domains of self-monitoring, planning and organisation, cognitive flexibility, emotion regulation and generativity, and task initiation. They were designed to provide a snapshot of the level of executive functioning difficulties being experienced by those signing up. High executive functioning challenges reflects a score of 25 to 32 (out of a possible 32) and moderate challenges a score of 16 to 24.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eExecutive functioning questions\u003c/b\u003e The following 4-point scale (1\u0026thinsp;=\u0026thinsp;Not at all, 2\u0026thinsp;=\u0026thinsp;a lot, 3\u0026thinsp;=\u0026thinsp;a little bit, 4\u0026thinsp;=\u0026thinsp;completely) was used to answer the question: \u0026lsquo;how well do these statements reflect you\u0026rsquo;:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuestions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don\u0026rsquo;t notice I\u0026rsquo;m tired/stressed until it\u0026rsquo;s too late\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI find it hard to prioritise everything I have to do\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI find it difficult when things change unexpectedly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI get stuck trying to solve a problem and can\u0026rsquo;t think of different approaches\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI have trouble getting started on projects even if they\u0026rsquo;re important to me\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI tend to react without thinking first\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI forget spoken instructions if they\u0026rsquo;re not written down\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI find it difficult to estimate how long it will take to do things\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnxiety and depression\u003c/h2\u003e \u003cp\u003eThe Generalized Anxiety Disorder 2-item (GAD-2) (Plummer et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and the Patient Health Questionnaire 2-item (PHQ-2) (Kroenke et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) were used to highlight potential mental health needs among our users. A person was identified as experiencing clinically significant anxiety if they had a GAD-2 score\u0026thinsp;\u0026ge;\u0026thinsp;3 and or clinically significant depression if PHQ-2 score\u0026thinsp;\u0026ge;\u0026thinsp;3.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eREC approval was received from Lab Research Ethics Panel (LabREP) (\u003cb\u003eCSP-BiH-020923).\u003c/b\u003e Informed consent to participate in the evaluation was obtained online from all participants at sign up, immediately following consent for service use.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReach, progress and engagement\u003c/h2\u003e \u003cp\u003eReach and progress through the self-referral journey is illustrated as a Funnel Diagram in Fig.\u0026nbsp;1. The digital media campaign that lasted 6.5 weeks resulted in over 460,700 total impressions and 18,522 visits to the landing page. After processing the information on the landing page related to eligibility, service offer and participation in the study, 685 people checked their eligibility for the study, the majority of whom (n\u0026thinsp;=\u0026thinsp;562, 82%) met the criteria. Just under half (n\u0026thinsp;=\u0026thinsp;261, 46%) of eligible people signed up for the study and almost all of these (n\u0026thinsp;=\u0026thinsp;243, 93%) went on to be issued with the software by 20th March 2023 (the first licence was issued on the 20th January) and formed the final sample. Sixty-two percent of participants who were issued the software (150 young people) went on to use the service (as of 19th May 2023). The total cost of the campaign was \u0026pound;10,198 giving a cost per person recruited of \u0026pound;41.97. The cost per person would be expected to reduce if the recruitment window had been extended beyond the 6 weeks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eINSERT Fig.\u0026nbsp;1: Funnel chart depicting numbers at each stage of the self-referral journey\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eThe 243 participants in this study were those who had chosen to sign-up for the service and had been issued with the software. The most common reason why 18 of those who signed up were not issued with software was an incorrect phone number or email address. Participants demographic characteristics are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The cohort was skewed towards the younger end of the 16\u0026ndash;25 age range with 53% aged 16\u0026ndash;18. Many (62%) registered female at birth, with 47% of the cohort identifying as a woman or girl. The group included 7% who identified as non-binary and 2.5% who identified as gender fluid. Of those who chose to answer, 46% identified as straight or heterosexual. The majority (93%) of the participants identified as white. Postcodes were provided by all participants and used to calculate the Index of Multiple Deprivation (Office for National Statistics, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for each individual. Approximately 7% of the participants resided in areas in the top 20% of deprivation in England. Just over half of participants signed up themselves (55%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant demographics characteristics and needs (n\u0026thinsp;=\u0026thinsp;243)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eDiagnosis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinically diagnosed as autistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn the waiting list for autism assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThink they might be autistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eReferred by\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReferred by self\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReferred by supporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eAssigned sex at birth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDid not respond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cem\u003eSexual orientation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeterosexual / straight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBisexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGay / Lesbian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePansexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrefer to self-describe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDid not respond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e\u003cem\u003eGender identity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWoman/girl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan/boy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-binary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender fluid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrans man/boy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrans woman/girl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender queer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrefer to self-describe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDid not respond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cem\u003eEthnicity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite: English/Welsh/Scottish /Northern Irish /British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.6%*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite: Other white background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed Ethnicity: White and Black Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed Ethnicity: White and Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite: Irish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack: African\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack: Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDid not respond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eRAADS Categories (asked of the 106 who did not have a clinical diagnosis)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLikely Autistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Likely Autistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDid not respond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eExecutive Functioning Categories\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Executive Functioning Challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium Executive function Challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Executive Functioning Challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eClinically significant anxiety symptoms (GAD2 score\u0026thinsp;\u0026gt;\u0026thinsp;or =\u0026thinsp;3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinically significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot clinically significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eClinically significant depression symptoms (PHQ2 score\u0026thinsp;\u0026gt;\u0026thinsp;or =\u0026thinsp;3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinically significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot clinically significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eCurrent Support\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrently receiving support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo current Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePast Support\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReceived services in past\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo past services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e*Total percentage is 99.8% due to rounding errors and small numbers in disaggregated sub-group\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eAutistic traits\u003c/h2\u003e \u003cp\u003eJust over half of the participants (56%) had a clinical diagnosis of autism, with the remainder equally split between those on the waiting list (22%) and those who think they might be autistic (22%) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The first 5 items of the RAADS-14 indicated high levels of autism traits among those who had not been formally diagnosed. Using a cut-off score of 4 or above identified that 95% of this group were likely autistic.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLevel of need\u003c/h2\u003e \u003cp\u003eLevels of anxiety and depression were high, 83% scored in the clinically significant range of the GAD-2 and 69% scored in the clinically significant range of the PHQ-2 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most participants (99%) indicated they were experiencing moderate (41%) or high (58%) levels of executive functioning challenges. Three quarters of participants had received support services in the past, but only 40% were receiving some form of support service at the time of the project.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe overall objective of this study was to investigate the potential of self-referral through digital media campaigns to reduce health inequalities among young autistic people. We found that a diverse group of young autistic people could be reached using digital media campaigns with close to half a million people viewing the adverts and nearly 20,000 visiting the landing page over 6.5 weeks. Furthermore, our final group of 243 eligible participants who had been issued with BiH software demonstrated high levels of need in terms of anxiety, depression and executive function and were suitable for support by the BiH service. They were also highly motivated with 150 (62%) already actively using the support tool within 2 months of the licence being issued. This compares favourably with retention rates for many digital mental health apps which typically fall below 10% by 30 days (Baumel et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The overall findings of this study represent a powerful example of how self-referral and digital media campaigns can efficiently and effectively provide access to a digital tool. Any concerns that a self-referral approach engaged those with low need are completely negated when seen alongside levels of clinically significant anxiety and depression in more than two-thirds of the participants and moderate and high levels of executive functioning challenges in nearly all (99%).\u003c/p\u003e \u003cp\u003eAs a population, autistic people experience poorer health outcomes compared to their neurotypical peers \u0026ndash; reduced life expectancy, elevated risk of suicide and high prevalence of mental health difficulties (Cassidy et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; O\u0026rsquo;Nions et al., 2023b). These disparities can be further exacerbated by other factors such as living in deprived areas, belonging to an ethnic minority, or being LGBTQ+. Often those with elevated risk of health disparities who fall into these categories are described as \u0026lsquo;under-reached\u0026rsquo; and are less likely to engage with services when offered (Office for National Statistics, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The government\u0026rsquo;s strategy for tackling healthcare inequalities is set out in the Core20PLUS5 framework and identifies children and young people (CYP) most at risk of health inequity as being the most deprived 20% of the population, those with certain protected characteristics (including gender, race/ethnicity, autism) and those with specific needs, including mental health needs (NHS England, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The digital media campaigns were successful at reaching the LGBTQ\u0026thinsp;+\u0026thinsp;community. Participants were richly diverse compared to the general population (Office for National Statistics, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, Trans representation at 2.4% was much higher than the 0.3% in the general population with all other gender identities accounting for 9.5% of our participants compared with only 0.6% in the general population.\u003c/p\u003e \u003cp\u003eThe challenge came in reaching minority ethnic groups with most of our participants identifying as white. Underpinning the Core20PLUS5 framework is evidence that CYP from ethnic minority backgrounds are less likely to access services around early intervention to prevent mental health problems escalating, and recent research has shown that most minoritised ethnic groups are offered and receive psychological interventions in Early Intervention in Psychosis (EIP) less often than White British people (Schlief et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There is also evidence of ethnic disparities in the diagnosis of autism, with BAME children being diagnosed with autism later and at a lower rate when compared to white children (Roman-Urrestarazu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), possibly resulting in lower awareness in these communities (Kandeh et al, 2018). Data and digital poverty are also more prevalent among ethnic minorities (Allmann, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While there were obvious challenges in reaching ethnic minority groups with this approach, it is not clear whether this was a result of access to digital media and tools or related to awareness and acceptance of neurodiversity and its mental health impacts, or a combination of both.\u003c/p\u003e \u003cp\u003eAlthough the approach piloted here is of obvious interest to those implementing a public health approach to early intervention and prevention in the autistic population, the findings are also relevant more widely to other underserved populations whose needs could be met with digital solutions. We have shown that there is the potential to deliver services differently. In the short time window of fewer than 9 weeks, digital media campaigns resulted in 243 autistic people being issued the licence for BiH at a recruitment cost of less than \u0026pound;50 per person. The recruitment window closed as we reached our licence limit for the study, but an extension of the recruitment window could have made this even more cost-effective.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study used only two digital media channels to reach participants, Meta (Facebook and Instagram) and google search, and future work could benefit from examining other popular digital media channels. Although the age range for participation was 16\u0026ndash;25 years, 53% were aged between 16 to 18 years which may limit the generalisability of the findings for older age groups. In addition, few people came from minority ethnic groups and more investment should be put into understanding how better to reach these communities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current structure and capacity of the health and social care system is unable to manage the growing levels of need for support in the autistic population. Innovation is urgently needed not only in the interventions that are offered to support greater independence but also in the way that support is offered. This study shows that a direct to person approach is both a feasible and effective way of reaching and engaging a high need population. When coupled with an effective digital mental health solution, the potential for impact at scale, including within underreached communities, is considerable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution declaration\u003c/strong\u003e: Authors ND and LM initially conceptualised the research study. ZS led on the study design and data collection with support from ND, JL and LM. JL led on the development of the eight executive function questions. ZS, ND and HG performed the data analysis, data interpretation, and generation of figures and tables. HG prepared the manuscript. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe research was commissioned and funded by SBRI Healthcare. SBRI Healthcare is an Accelerated Access Collaborative (AAC) initiative, in partnership with the Health Innovation Networks (HINs). \u0026nbsp;The authors would like to thank our partnering sites who co-created this innovation with us: Surrey County Council, Cheshire West and Chester Council, Devon Partnership NHS Trust, Joined Up Care Derbyshire and Royal Borough of Greenwich and to South West Academic Health Science Network who helped to design this research. \u0026nbsp;The views expressed in the publication are those of the author(s) and not necessarily those of SBRI Healthcare or its stakeholders. The authors have declared that they have no competing or potential conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration:\u0026nbsp;\u003c/strong\u003eThis work was commissioned and funded by SBRI Healthcare (Reference number: SBRIH19P3027). SBRI Healthcare is an Accelerated Access Collaborative (AAC) initiative, in partnership with the Health Innovation Networks (HINs). The views expressed in the publication are those of the author(s) and not necessarily those of SBRI Healthcare or its stakeholders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eEthical approval and consent to participate:\u0026nbsp;\u003c/em\u003eREC approval was received from Lab Research Ethics Panel (LabREP) (CSP-BiH-020923). Informed consent to participate in the evaluation was obtained online from all participants at sign up, immediately following consent for service use. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u0026nbsp;\u003c/em\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllmann K. UK digital poverty evidence review 2022. London: Digital Poverty Alliance; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanchard A, Chihuri S, DiGuiseppi CG, Li G. Risk of self-harm in children and adults with autism spectrum disorder: a systematic review and meta-analysis. JAMA network open. 2021;4(10):e2130272-.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaumel A, Muench F, Edan S, Kane JM. 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A review of HIV prevention studies that use social networking sites: implications for recruitment, health promotion campaigns, and efficacy trials. AIDS and Behavior. 2016;20:2772\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandeh MS, Kandeh MK, Martin N, Krupa J. Autism in black, Asian and minority ethnic communities: a report on the first Autism Voice UK Symposium. Advances in Autism. 2020;6(2):165\u0026thinsp;\u0026ndash;\u0026thinsp;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Medical care. 2003 Nov 1:1284\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai MC, Kassee C, Besney R, Bonato S, Hull L, Mandy W, Szatmari P, Ameis SH. Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. The Lancet Psychiatry. 2019;6(10):819\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagid K, Sagui-Henson SJ, Sweet CC, Smith BJ, Chamberlain CE, Levens SM. The Impact of Digital Mental Health Services on Loneliness and Mental Health: Results from a Prospective, Observational Study. International Journal of Behavioral Medicine. 2023 Jul 24:1\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngland NHS. Core20PLUS5\u0026ndash;an approach to reducing health inequalities for children and young people: 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOffice for National Statistics. Population denominators by ethnic group, regions and countries: England and Wales, 2011 to 2018\u0026mdash;Office for National Statistics: 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOffice for National Statistics. Gender identity, England and Wales: Census 2021. The gender identity of usual residents aged 16 years and over in England and Wales, Census 2021 data: 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Nions E, Petersen I, Buckman JE, Charlton R, Cooper C, Corbett A, Happ\u0026eacute; F, Manthorpe J, Richards M, Saunders R, Zanker C. Autism in England: assessing underdiagnosis in a population-based cohort study of prospectively collected primary care data. The Lancet Regional Health\u0026ndash;Europe. 2023;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Nions E, Lewer D, Petersen I, Brown J, Buckman JE, Charlton R, Cooper C, El Baou C, Happ\u0026eacute; F, Manthorpe J, McKechnie DG. Estimating life expectancy and years of life lost for autistic people in the UK: a matched cohort study. The Lancet Regional Health\u0026ndash;Europe. 2024;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlummer F, Manea L, Trepel D, McMillan D. 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Current Treatment Options in Psychiatry. 2019;6:243\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShochet IM, Saggers BR, Carrington SB, Orr JA, Wurfl AM, Kelly RL, Duncan BM. A school-based approach to building resilience and mental health among adolescents on the autism spectrum: a longitudinal mixed methods study. School Mental Health. 2022;14(3):753\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTromans S, Henley W, Summers I, Bilkey D, Datson J, Doherty N, Morpeth L, Benbow S, Jelbert R, Roy A, Watkins L. The psychological and social impact of the digital self-support system \u0026lsquo;Brain in Hand\u0026rsquo; on autistic people: prospective cohort study in England and Wales. BJPsych Open. 2023;9(3):e96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWakefield S, Kellett S, Simmonds-Buckley M, Stockton D, Bradbury A, Delgadillo J. 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British Journal of Clinical Psychology. 2021;60(1):1\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, Autistic, Depression, Digital, Mental Health, Neurodiversity","lastPublishedDoi":"10.21203/rs.3.rs-4048189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4048189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLimited resources in health and social care and long waiting lists for autism assessment are resulting in high numbers of autistic people not being adequately supported. We sought to explore the feasibility and effectiveness of meeting this support need through an end-to-end digital self-referral and digital mental health service.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTogether with Health and Social care teams and young autistic people we developed a self-referral pathway that allowed young autistic people (aged 16\u0026ndash;25) to access the digital self-management support system, Brain in Hand (BiH), without the need for diagnosis or referral by an external agency. Participants were reached using digital media channels which linked to a BiH landing page. Reach, progress and engagement through the pathway was monitored and participants were surveyed on their eligibility and suitability for BiH.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 243 BiH licences were issued within 9 weeks of the start of the digital media campaign which reached nearly half a million people with close to 20,000 clicking through to the BiH landing page. Most of the young people being issued with the digital support tool demonstrated high levels of need, 69% experienced clinically significant depression, 83% anxiety, 99% moderate or high executive function challenges, and 60% lacked current support.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis pilot demonstrates that young people understand their needs and directing them to a support service through a digital media campaign presents an efficient and effective approach in reaching young autistic people in need. This suggests that digital media channels and self-referral could offer a practical solution to broaden access to a range of digital mental health platforms without placing additional resource burden on health and care teams.\u003c/p\u003e","manuscriptTitle":"Digitally enabled, self-referral as an effective approach for young autistic people to access support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 18:44:11","doi":"10.21203/rs.3.rs-4048189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-11T14:43:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-08T08:27:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-06T21:59:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2024-03-08T18:16:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd503a30-023c-466f-ae63-35ccf659399f","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T12:23:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 18:44:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4048189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4048189","identity":"rs-4048189","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}