A Data-Driven Methodological Framework for Representative Recruitment in Psychiatric Research: Insights from the DOCUMENT Study

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M. Gooddy, Laila Rida, Bryony Goulding Mew, Ana Rita Moura, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9292013/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Slow participant recruitment is one of the predominant determinants of failure or delay across clinical research. Even when recruitment targets are met, study populations may be unrepresentative due to sampling biases introduced by recruitment pathways. However, the effectiveness and demographic consequences of recruitment strategies are frequently underreported, undermining the generalisability of clinical findings and contributing to research waste. This study provides a data-driven, quantitative evaluation of multimodal recruitment strategies in psychiatric research, leveraging insights from the DOCUMENT study to synthesise a methodological framework for effective and representative participant recruitment. Methods Between June 2022 and December 2024, the study utilised a multimodal strategy to recruit participants with major depressive disorder (MDD), schizophrenia (SZ), and healthy volunteers (HV) for a two-phase study to investigate cognitive deficits across groups. Recruitment strategies included NHS clinical services, electronic health records, research registries, primary care sites, online and social media advertising, printed material, institutional resources, and word of mouth. For each avenue, yield, proportion of diagnostic group, recruitment rate, eligibility fraction, labour and financial cost, and demographic skew were quantified. Results Across avenues, 194 participants were recruited (66 HV, 77 MDD and 51 SZ), with high retention (85-97%). Recruitment efficacy varied substantially by diagnostic group, with online advertising and research registries successfully recruiting MDD and HV participants but failing to recruit eligible people with SZ. Instead, SZ participants were primarily enrolled from labour-intensive clinical avenues (94%). Online recruitment showed higher accrual, but lower eligibility fraction compared to clinical pathways, revealing systematic sampling differences. Despite avenue-specific sampling biases, the multimodal approach yielded close demographic alignment to the 2021 UK Census for London in the study population. Data-driven adaptations, such as protocol amendments to eligibility criteria and online self-report triaging, improved study feasibility. Conclusions No single recruitment avenue was identified as sufficient for both efficient and representative psychiatric recruitment. Instead, multimodal strategies were necessary to dilute avenue sampling biases. Synthesising 30 months of data, we introduce a 10-point framework for enhancing recruitment effectiveness, feasibility, and representativeness. While grounded in UK-based psychiatric research, these principles apply to broader clinical research contexts to reduce research waste. Participant Recruitment Depression Psychosis Schizophrenia Psychiatry Psychiatric Research Major Depressive Disorder Clinical Study Digital Health Clinical Research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Participant recruitment for clinical research is notoriously difficult, with recruitment issues accounting for ~ 54% of all study extensions, serving as the largest reason for protocol amendments and study discontinuation of randomised clinical trials in particular (Briel et al., 2021 ; Lai & Afseth, 2019 ). Estimates of studies funded in the UK by bodies such as the Health Technology Assessment (HTA) and Medical Research Council (MRC) indicate that only around half (31–56%) of studies reach their full recruitment target and only 55–79% achieve > 80% (McDonald et al., 2006 ; Walters et al., 2017 ). This has the serious, wider knock-on effect of potentially underpowered trials with increased false error rates (Liu et al., 2018 ). Recruitment issues can have serious implications, both for study quality and financial feasibility. For example, a failure to meet recruitment targets may induce unintended consequences that undermine the quality of a trial’s data. Attempts to remedy recruitment shortfalls by quickly onboarding additional recruitment sites, which often contribute small numbers of patients, risk increasing the variability in both study baseline and outcome measures across sites (Fogel, 2018 ; Pallmann et al., 2018 ). This sampling-induced heterogeneity may increase endpoint variability, complicating the interpretation of trial findings, and ultimately increasing the risk of study failure. Recruitment challenges can have substantial financial implications: economic modelling of proxy placebo-controlled surgical trials indicates additional costs of up to 50% for trials requiring extension through additional time or sites, and up to 260% above budget for incomplete trials (Schilling et al., 2023 ). For example, recruitment issues relating to study discontinuation were costing one medical and academic research site in Oregon an estimated $ 1 million US dollars a year (Kitterman et al., 2011 ). Recruitment is a pervasive challenge across clinical fields, from oncology and obstetrics to neurology and psychiatry (Gross et al., 2002 ; Rikken et al., 2025 ). However, despite the difference in clinical focus, the same core reasons underlie recruitment failure, including excessively stringent and unrealistic recruitment criteria, overly optimistic recruitment rate projections, insufficient or mishandled budgets, participant burden disproportionate to remuneration, inadequate manpower, trial fatigue, and ineffective recruitment strategy (Briel et al., 2021 ; Liu et al., 2018 ). However, with informed insights, thoughtful strategy, and realistic study design planning based on reflections of previous research experience, many of these issues can be mitigated before a study even begins. Nevertheless, psychiatric research studies face additional unique challenges, such as the reliance on syndromic diagnoses rather than diagnostic biomarkers and less structured clinical pathways for patients, which make it a challenge to identify potential participants and exacerbate recruitment complexity. For example, diagnostic instability – such as from non-affective to affective psychosis – can complicate the recruitment of a stable study population (Oduola et al., 2021 ). The unique logistical and sampling requirements of recruiting and retaining eligible psychiatric study populations are exemplified by studies aiming to recruit people with major depressive disorder (MDD) or psychosis. Depression studies often face issues recruiting due to factors such as public scepticism and stigma, lack of patients’ self-identification or insight into the disorder, and instability of symptom severity and clinical stages, such as requiring participants to be in a depressive episode or pre-onset (Cuijpers et al., 2010 ; Krusche et al., 2014 ). Stringent inclusion criteria may further exacerbate recruitment issues and even instil an unintentional sampling bias into the study. For example, a study trialling the self-enrolment of depressed participants found that around 25% of patients had never been to their GP about their depression – a population that would be missed if a formal diagnosis was required for inclusion (Brown et al. ( 2019 ). Studies recruiting psychosis patients – particularly schizophrenia – are subject to even greater difficulties, in part due to the pronounced complex clinical, functional and cognitive burden of the disorder. Barriers to recruitment include a time-consuming and labour-intensive process of finding patients from clinical records, patients’ inability to participate due to illness severity, particularly in cases of high study burden (Zahren et al., 2021 ), and questions over patients’ capacity, and gatekeeping by mental health and care professionals not wishing to add to patient burden (Deckler et al., 2022 ; Rønne et al., 2025 ). Even when recruitment targets are met, it is difficult to determine whether the resultant clinical study sample is representative of the underlying clinical population, due to the risk of recruitment sampling bias. Assessing the scope of sampling bias in the study population, by direct comparison to the real-world clinical population, becomes complicated due to study-specific inclusion and exclusion criteria (e.g. comorbid psychiatric conditions, medication use etc.), which intrinsically may limit representativeness. The identification and evaluation of these sampling biases can be obscured in the absence of comprehensive and transparent reporting of study recruitment methodology. Despite the push for transparent recruitment practices, such as The Consolidated Standards of Reporting Trials (CONSORT) requiring RCTs to report eligibility criteria and participant attrition, current estimates indicate that adequate compliance with these standards only occurs ~ 63% of the time, and in multi-site studies this drops further to ~ 25% (Walters et al., 2017 ). Representativeness of the study sample to the population of the specific diagnostic group in the real world is fundamental for the extrapolation and generalisation of the research findings (Tiego et al., 2023 ; Zhuo et al., 2019 ). The inherent complexity in reaching and successfully recruiting psychiatric patients for clinical studies, means that there is a significant need to report which recruitment avenues were the most and least effective, to inform future clinical studies to be realistic in scope, timelines, resource assignment, and reduce the chances of research waste (Kasenda et al., 2014 ). Here, we systematically evaluate the learnings garnered from the multimodal recruitment strategy employed by the DOCUMENT study – a comprehensive investigation of cognitive deficits in major depressive Disorder (MDD), schizophrenia (SZ) and healthy volunteers (HV). We aim to provide data-driven insights into recruitment and methodological considerations needed to successfully recruit an age-matched and ethnically representative participant sample, We collate learnings from this study into a framework designed to inform recruitment strategies for future studies. Methods Study design and setting The DOCUMENT study (‘ Measuring cognitive deficit using cognitive tasks ’) was a single-site, two-stage, mixed-methods prospective clinical research study conducted at King’s College London. The study was designed in collaboration with, and funded by, Boehringer Ingelheim and co-sponsored by King’s College London and the South London and Maudsley (SLaM) NHS Foundation Trust. Recruitment and data collection ran over a 30-month period from June 2022 to December 2024. Ethics approval was received from the London-Camberwell St Giles Research Ethics Committee (REC reference: 21/LO/1234; IRAS ID: 304617). Full scientific results will be published once data analysis is complete. There were two main parts to the study: Part 1 aimed to recruit 50 healthy volunteers (HV), 50 people with schizophrenia (SZ), and 75 with major depressive disorder (MDD) to undertake remote (at-home) tablet-based cognitive and (optional) speech assessments over 3 days; A subset of 25 participants in each group continued into Part 2, completing in-person clinical and cognitive tests, followed by a 14-day remote longitudinal assessment of cognitive and sleep measures. Inclusion in the Part 2 subset was offered to all participants who completed Part 1, until the Part 2 recruitment targets were fulfilled. Study Participants and Eligibility Eligible participants were required to be adults aged 18–55 years old, fluent in English, with no intellectual disability or neurodevelopmental disorder, no concurrent participation in another study, no current serious or unstable clinically important systemic illnesses, and the capacity to provide informed consent. For Part 2, cannabis use in the prior 12 hours was not permitted and was confirmed by a urine screening; for Part 1, participants were asked to abstain from all recreational drug use. HV were required to have no current or historic psychiatric diagnoses, no significant physical or neurological conditions, no psychotropic medication use, no diagnosed sleep disorders aside from insomnia, and no first-degree relatives with a psychotic or bipolar disorder. MDD participants were required to have a diagnosis of depression and be experiencing a current depressive episode, verified by the study clinician using the MINI psychiatric interview (Sheehan et al., 1998 ). They could not have any history of psychotic depression, primary psychotic disorders or bipolar disorders. Antidepressant medication or psychological treatments had to be stable and started > 4 weeks before enrolment. Comorbid psychiatric or neurological disorders were excluded, except for generalised anxiety disorder (GAD). SZ participants were required to have a DSM-5 diagnosis of schizophrenia, or an unspecified schizophrenia spectrum or psychosis disorder and fit a research diagnosis of schizophrenia, which was confirmed by a study clinician and by the MINI psychiatric interview (MINI) (Sheehan et al., 1998 ). For Part 2 eligibility, participants had to have Positive and Negative Syndrome Scale (PANSS) and Brief Negative Symptom Scale (BNSS) scores consistent with schizophrenia (Kay et al., 1987 ; Kirkpatrick et al., 2010 ). Comorbid psychiatric or neurological disorders, aside from anxiety disorders, were excluded. SZ participants had to be free from severe symptom exacerbation requiring inpatient hospitalisation and have a diagnosis and illness duration of ≤ 10 years. Antipsychotic medication use had to be stable for the 4 weeks before enrolment in the study, with a second antipsychotic only permitted when prescribed for sleep or anxiety. Clozapine use was allowed following an amendment made to the study protocol in May 2023 (see below). Recruitment avenues and timeline Participants were recruited through a multimodal recruitment strategy, incorporating clinical, institutional, online, and community outreach pathways. Recruitment avenues included NHS Clinical Services – Community Mental Health Teams (CMHTs) and early intervention services, electronic health records (SLaM Clinical Record Interactive Search (CRIS) and Consent for Contact (C4C)), the NIHR BioResource (namely the Genetic Links to Anxiety and Depression (GLAD) study (Davies et al., 2019 )), an online recruitment platform (Call for Participants (CFP)), community flyers, and word of mouth. From January 2023, the study also started using social media avenues (Meta (Facebook and Instagram) and Reddit), the NHS SLaM Take Part in Research website (slam.nhs.uk/take-part-in-research), internal King’s College London (KCL) research circulars, and previous clinically aligned research studies (PsiDer (Rucker et al., 2021 ) and COGENT), General Practitioner (GP) Participant Identification Centres (PICs) were also used from October 2023 to send NHS text messages to potential MDD and SZ participants, inviting them to take part in the study. Initially, participants contacted the research team directly with their interest in participating. intermediate step was added in May 2023 in which prospective participants were directed to a secure Qualtrics web-hosted declaration of interest and basic self-report screening form, which allowed the research team to prioritise time on screening the participants who would have otherwise only declared exclusionary criteria during a pre-screening call (Fig. 1 ). Pre-screening and eligibility confirmation Individuals interested in participating in the DOCUMENT study underwent two calls with the study team. The first was a ~ 15-minute pre-screening call to collect demographic information and assess basic inclusion and exclusion criteria, such as self-reported psychiatric and medical history. Individuals who were considered likely to be eligible were then scheduled for a clinical screening call (~ 1 hour). The clinician screening involved the administration of the MINI psychiatric interview (Sheehan et al., 1998 ), alongside a review of the pre-screening answers to confirm eligibility specific to each diagnostic group. If deemed eligible by a study clinician after the screening call, participants were then invited to enrol on the study and take part in Part 1. The study participants’ progression through pre-screening, clinical screening, Part 1 and Part 2 is summarised in Fig. 2 . Adaptive protocol amendments Two key amendments to the study protocol were implemented following HRA/NHS REC approval in January 2023 and May 2023. Both amendments were data-driven clinical decisions to improve recruitment feasibility, reduce participant burden, and ensure the representativeness of the target diagnostic populations. The January 2023 amendment increased the financial compensation for SZ participants from £40 to £80 in Part 1, and from £220 to £280 in Part 2, to better reflect the substantially greater clinical and time burden placed on this population, including longer screening calls and additional clinical visits. This amendment also included adding the collection of detailed ethnicity information and the highest level of education, to allow for better evaluations of sample representativeness. Accordingly, this data was not available for all enrolled participants. The May 2023 amendment focused on broadening the eligibility criteria for SZ participants to improve recruitment feasibility and better reflect a more general population and a couple of real-world conditions. It extended the duration of diagnosis for SZ from ≤ 5 to ≤ 10 years, permitted Clozapine use, as well as antipsychotic polypharmacy for sleep or anxiety management. In addition, the requirement for a formal clinical diagnosis of SZ was relaxed to permit unspecified psychotic disorders and schizophrenia spectrum disorders if a research diagnosis of SZ was confirmed by a study clinician using the MINI (Sheehan et al., 1998 ). For all groups, the required abstinence from cannabis before the Part 2 clinical visits was reduced from 24 to 12 hours; Participants were still encouraged to abstain during the periods of remote assessment. The study duration was initially planned as 6–12 months (aligned to REC and sponsor timelines). However, the recruitment window was extended multiple times due to a lower-than-anticipated participant accrual rate, particularly for the SZ group prior to the amendment changes. Study procedures and compensation Following enrolment into the study, participants were sent a Samsung Tablet to complete cognitive batteries via the Cognitron™ app, over two days, and optional speech sampling assessments via the Speech Vitals™ app (Linus Health), over three days (Study Part 1). Each cognitive battery took approximately 45–60 minutes, and the speech sampling took 10–15. Before being able to start any tasks, the participants gave informed consent via the Cognitron™ app. For successful completion, HV and MDD participants were compensated £40 and SZ participants £80. (Fig. 3 ). A target of seventy-five participants (twenty-five from each group) was set for Part 2 completion. As such, eighty-one participants were invited to complete Part 2 of the study, starting Part 2 within two weeks of completing Part 1. This involved, first, a clinical assessment visit to obtain written informed consent, collect lifestyle factors and symptom scales by a study clinician. All diagnostic groups completed the self-report 16-item Quick Inventory of Depressive Symptomatology (QIDS-16-SR)(Rush et al., 2003 ). Disorder-specific clinician-administered assessments were also undertaken: The MDD group alone underwent the clinician-administered Hamilton Depression Rating Scale (HAM-D)(Hamilton, 1960 ), while the SZ group underwent the administration of the Positive and Negative Syndrome Scale (PANSS)(Kay et al., 1987 ), Extrapyramidal Symptom Rating Scale-Abbreviated (ESRS-A)(Chouinard & Margolese, 2005 ) and the Brief Negative Symptom Scale (BNSS)(Kirkpatrick et al., 2010 ). Then, a cognitive assessment visit to undergo both online (Cognitron™) and gold-standard paper-based neuropsychological cognitive batteries, optional speech sampling (Speech Vitals™), and a virtual reality (VR) based functional task. For HV and MDD participants, this typically took place on the same day, and for SZ, these visits were separate days due to longer clinical visits; With the clinical and cognitive clinic visit days within seven days of each other, typically within the same working week. At both visits, a breath alcohol concentration test and a 10-panel urine drug test were administered to rule out exclusionary criteria. Then, at-home, over the next 14 days, the Part 2 subset of participants completed a shorter remote cognitive battery on 10 days of their choice, as well as continuous sleep measurements from a wrist-worn actigraphy watch, as well as lifestyle and sleep self-report questionnaires on the days they opted to complete the cognitive assessments (Johns, 1991 ; Rida et al., 2024 ). For Part 2 completion, the HV and MDD participants received a bank transfer of £220, and the SZ compensation was £280. Full details of the study protocol, including cognitive and clinical assessments, are in Supplement 1 . The proportion of participants completing Part 2 of the DOCUMENT study following completing Part 1 was projected to be 50% for HV and SZ (both 25/50) and 33.33% for MDD (25/75). The observed Part 1 to Part 2 progression rates were 37.88% for HV, 50% SZ, and 34.67% for MDD. To ensure the Part 2 recruitment targets were met, an additional 16 HV participants were recruited during Part 1. Data handling and preparation The recruitment data were collated from across various study sources, fully anonymised and then analysed in a Jupyter Notebook (.ipynb) in Visual Studio Code (1.89.1) using Python (v3.11.5), and some descriptive statistics were carried out in Jamovi (The jamovi project, 2025 ). Meta-specific recruitment metrics were obtained from Meta’s Ads Manager and then pooled with the study data. Recruitment estimated costs were pooled across study invoices, emails, and records. Results Participant demographics and group recruitment rates Over 30 months, 194 participants were recruited and enrolled in the DOCUMENT study. (Note that whilst Fig. 2 states 195 participants were sent the tablets, one participant never started data collection and was removed from the enrolled metrics). The demographics and eligibility fractions of the enrolled participants are outlined in Table 1 . Ethnicity data were collected for 133 participants (68.56%) and education level for 131 (67.53%) following the January 2023 amendment. The three diagnostic groups were closely matched for mean age: 31.90–33.40, with the greatest distribution in the HV group (± 9.7) and the smallest in the SZ group (± 6.6). Both the HV and MDD groups had more participants with female biological sex ( F = 72.7% HV, 61% MDD), whereas the SZ was more male-dominated ( M = 70.6%). In the Part 2 subpopulation, this percentage biologically female became 69% in both HV and MDD groups, with an increase in the SZ group to 34%. The median UK education level in both the HV and MDD groups was an undergraduate degree (6; IQR = 1), in the SZ group, the median was A-Level secondary education (3, IQR = 3). This difference in education was more pronounced in the Part 2 subsample, with increased median education levels of 6.5 for HV ( Bachelor’s to Master’s ) and 7 for MDD ( Master’s ), and no subsample change with 3 for SZ ( A-Level ). Table 1 DOCUMENT Study enrolled participant demographics and contribution proportions of each recruitment avenue to the study and diagnostic group sample. SD – Standard deviation, IQR – Interquartile range, GLAD – Genetic Links in Anxiety and Depression study, SLaM – South London and Maudsley NHS Trust, NIHR – National Institute for Health and Care Research. HV MDD SZ Total N = 66 N = 77 N = 51 N = 194 Age (Years) Age Range 18–54 18–55 19–50 18–55 Mean Age ( ± SD) 32.1 (9.7) 33.4 (8.4) 31.9 (6.6) 32.6 (8.4) Sex (Proportion of sample %) Male 18 (27.3%) 30 (39%) 36 (70.6%) 84 (43.3%) Female 48 (72.7%) 47 (61%) 15 (29.4%) 110 (56.7%) Median UK Education Level (IQR) 6 (1) 6 (1) 3 (3) 6 (4) Mean Years Since Diagnosis (± SD) - 9.08 (7.7) 4.38 (4.03) - Recruitment Avenue, N = (% of diagnostic group sample) Call For Participants (CFP) 12 (18.18%) 1 (1.30%) - 13 (6.7%) Clinical Services - - 37 (72.55%) 37 (19.1%) Electronic Health Records - - 11 (21.57%) 11 (5.7%) GP Services 1 (1.51%) 5 (6.49%) 1 (1.96%) 7 (3.6%) KCL Internal Studies 2 (3.03%) 3 (3.90%) - 5 (2.6%) NIHR BioResource (e.g. GLAD) 5 (7.58%) 41 (53.25%) - 46 (23.7%) Meta Advertising 43 (65.15%) 25 (32.47%) - 68 (35.1%) SLAM NHS Take Part in Research - 2 (2.60%) 2 (3.92%) 4 (2.1%) Word Of Mouth 3 (4.55%) - - 3 (1.6%) Pre-Screened 116 401 81 647 (49 Unknown) Eligible to enrol 69 78 53 200 Eligibility Fraction 59.48% 19.45% 65.43% 30.91% Study Section Attrition (N = Started, % Completion rate) Part 1 : At-Home (Remote) 66 (100%) 77 (97.4%) 51 (98%) 194 (98.5%) Part 2 : (Clinic and Remote) 26 (96.2%) 26 (100%) 28 (85.7%) 80 (94.0%) Out of the 194 enrolled participants to start Part 1 data collection, 191 completed this stage (Fig. 2 ), resulting in high retention rates of 100% for the HV group, 97.40% for MDD, and 98.04% for SZ. A subset of 81 participants started Part 2 data collection, undergoing at least one study visit, with 76 (25 HV, 26 MDD, 25 SZ) completing both the clinical and cognitive study visits alongside the longitudinal 14-day assessment. The Part 2 retention rate was also high, with 96.15% for the HV group, 100% for MDD, and 86.21% for SZ. Full intergroup and intragroup demographic comparisons are outlined in Supplement 2 , with no within-group differences observed between the Part 1 and Part 2 participant samples, and between groups the pattern of demographic differences remained consistent. The rate of recruitment varied between groups and between parts of the study. The first participant was enrolled in August 2022 and the last in October 2024. HV recruitment ran for 16.1 months until June 2024, with a pause between October 2023 and April 2024 to allow for targeted counterbalancing to match the demographics of the patient groups. MDD recruitment ran continuously over 21.5 months until June 2024, with recruitment for Part 2 halted in December 2023, and July 2024 for Part 1. SZ recruitment ran over 26.4 months until October 2024 for both Part 1 & 2. The Part 1 ( remote study ) recruitment rate per month across all groups was 7.3 participants, with HV at the highest rate of 4.1, then MDD at 3.5, followed by SZ having the lowest rate of 2, see Fig. 4 . The Part 2 ( remote and in-person longitudinal study ) recruitment rate was much slower across groups at 2.9 participants per month. In Part 2, MDD recruitment (1.7) was marginally higher than the HV group (1.6), with SZ still being the lowest (1). The data-driven decision taken to amend the inclusion criteria for the SZ participants is reflected in the recruitment rates. Before the January 2023 amendment, the rate of SZ participants enrolled was 1.4 per month ( N = 6 recruited). Between January and May 2023, the recruitment declined to 1.2 as recruitment sources were exhausted. After May 2023, the SZ rate of recruitment increased to 2.3, attributed to the cumulative criteria changes, particularly the relaxation from needing a formal clinical SZ diagnosis to allowing a research diagnosis instead. Demographic composition and census alignment The ethnic makeup of the subset of the study sample for which there was ethnicity data (N = 133; 68.6%), closely mirrored the London ethnicity distribution outlined in the 2021 UK Census (Office for National Statistics, 2022 ); Table 2 . Table 2 Ethnicity comparison of study sample to the 2021 UK Census data for (i) London, and (ii) nationally. The demographic breakdown is based on a sample of N = 133 (68.6%) for which ethnicity data were available. Frequencies of Ethnicity 2021 UK Census Ethnicity Counts % of Total London % National % White 67 50.37% 53.8% 81.7% Asian 24 18.05% 20.7% 9.3% Mixed 12 9.02% 5.7% 2.9% Black 26 19.55% 13.5% 4.0% Hispanic 2 1.5% -- (‘ Other ’: 4.4%) -- (‘ Other ’: 1.6%) Arab 2 1.5% 1.9% 0.7% A post-hoc sensitivity analysis was carried out to assess whether the composition of the retrospectively collected ( N = 28) and post- May 2023 amendment ( N = 105) data significantly differed. A Pearson’s χ 2 test of independence showed no significant difference between the samples: χ 2 (5, N = 133) = 10.21, p = .07. Cramér’s V = .277 also indicated a small-to-moderate but non-significant shift in ethnicity distribution. Within the diagnostic groups, notable differences in ethnicity composition were apparent (Table 3 ) . The HV group in both Part 1 and 2 were predominantly white and asian, whereas in the MDD group, the sample was largely white. The SZ sample, in contrast, included higher proportions of black participants than the other groups. Across all groups, participants with mixed, hispanic, and arab ethnicities were less represented in the study population, compared with the other ethnic groups. Table 3 Ethnicity representation across participant groups (HV, MDD, SZ) for the study sample and sub-sample. The proportion of missing data was calculated for each diagnostic group and study phase. The ethnicity counts and proportions were calculated from where participant data were available. * = with available data Part 2 Sub-sample Ethnicity HV MDD SZ HV MDD SZ Counts * = 44 48 41 19 14 23 White 20 (45.5%) 36 (75.0%) 11 (26.8%) 8 (42.1%) 11 (78.6%) 5 (21.7%) Black 2 (4.5%) 2 (4.2%) 22 (53.7%) 2 (10.5%) 1 (7.1%) 14 (60.9%) Asian 19 (43.2%) 3 (6.3%) 2 (4.9%) 9 (47.4%) 1 (7.1%) 0 (0%) Mixed 1 (2.3%) 6 (12.5%) 5 (12.2%) 0 (0%) 1 (7.1%) 3 (13.0%) Hispanic 1 (2.3%) 1 (2.1%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) Arab 1 (2.3%) 0 (0%) 1 (2.4%) 0 (0%) 0 (0%) 1 (4.3%) Missing data 22 (33.3%) 29 (37.7%) 10 (19.6%) 7 (26.9%) 12 (33.3%) 6 (20.7%) Recruitment Avenue Comparison Recruitment avenues differed in their contribution to diagnostic groups, eligibility fractions, and demographic composition. Each avenue is summarised below for total yield, eligibility fraction, efficiency, and demographic characteristics, with full details in Tables 1 and 4 , and visualised in Fig. 5 . Figure 5 A comprehensive recruitment avenue comparison of diagnostic group yield, age, sex, ethnicity, education, and study Part1-Part2 completion. The majority of the HV participants were recruited through Meta advertising (65.15%), CFP (18.18%) and the NIHR BioResource (7.58%). For MDD participants, the dominant sources were the NIHR BioResource (53.25%), Meta advertising (32.47%) and GP services (6.49%). The SZ recruitment came predominantly from clinical services (72.55%), EHRs (21.57%), and the SLaM Take Part in Research site (3.92%). Full granular recruitment avenue comparisons are provided in Supplement 3 , with the most pertinent avenue-specific findings provided below: Meta Advertising The largest contributor to the overall study sample was Meta advertising (35.1%), yielding 43 HV and 25 MDD participants, which had the second-highest recruitment rate across the avenues (3.3 participants per month) and a moderate eligibility fraction (47.7%). However, this avenue failed to yield any SZ participants, despite targeted efforts at a cost of £538.25. The participants from this avenue were predominantly female (70.6%) and younger adults (30.3 ± 8.2). Clinical Pathways (CMHTs and EHRs) Clinical pathways, namely CMHTs and EHRs, yielded the highest proportion of SZ participants across all recruitment avenues (94.1%) with a high eligibility fraction (73.3–77.1%). This route carried a high degree of unquantified labour costs – such as time spent searching through patient records and liaising closely with the clinical care teams. This avenue additionally proved unsuccessful for MDD recruitment (via the Clinical Record Interactive Search) due to co-morbidity exclusions. Participants through these avenues were predominantly male (70.3–81.8%), with both clinical services (31.6 ± 6.2) and EHRs (31.6 ± 7.4) having similar mean ages, and greater ethnic diversity than the online avenues. NIHR BioResource The NIHR BioResource (i.e. GLAD) recruited the highest proportion of MDD participants. It had the highest recruitment rate across avenues (3.6 participants per month), but the lowest eligibility fraction (14.5%), necessitating the adoption of intermediate pre-screening for basic exclusionary criteria through Qualtrics. This avenue also produced the strongest demographic skew, with the majority of patients from this avenue being female (78.3%, white (87.5%) and marginally older than some of the other avenues (35.5 ± 8.7). Whilst no direct financial cost was incurred by the study directly, the labour cost for this resource was provided by the NIHR. GP-PIC Sites GP-PIC across 15 sites yielded a small proportion of the study population (3.6%), with 5 MDD, 1 SZ and 1 HV, at a rate of 1.5 per recruitment attempt – discounting the untargeted enrolled HV participant. This route had a low eligibility fraction (22.6%), despite a total spend of £2,700 through the NIHR Research Delivery Network (RDN). The resultant sample was older (37.4 ± 10.1), white (66.7%), and predominantly male (85.7%); However, this is not reflective of the actual potential due to targeted sex counterbalancing. Call For Participants (CFP) CFP yielded a small proportion of the study sample (6.7%) at a negligible total cost (£40), with a relatively high eligibility fraction (68.4%; 12 HV, 1 MDD), but a low recruitment rate of 1.04 participants per month. Participants through this avenue were generally younger adults (30.5 ± 9.6) and female (62%). However, 69% of the CFP participants had missing education or ethnicity data, due to the timing of this avenue falling predominantly before the amendment that collected these additional demographics. Internal University Resources KCL Internal university resources (circular emails and previous studies), yielded 2.6% of the study sample (2 HC, 3 MDD). The recruitment rate was moderate at 1.16 participants per month, with an eligibility fraction of 38.46%. Participants via this avenue were exclusively male, and predominantly white (60%) or asian (40%). SLaM Take Part in Research SLaM Take Part in Research recruited 2.1% of the study population (2 MDD, 2 SZ) at no direct cost. There was a low recruitment rate (0.25 participants per month) but a high eligibility fraction of 80%. Participants via this avenue were on average older (40.8 ± 10.3) than the other avenues, and an even male-to-female split. Word of Mouth Word of mouth yielded 3 HV participants (1.6% of the study population), with the highest eligibility fraction (100%), but a low recruitment rate (0.17 participants per month). Notably, this avenue wasn’t actively pursued, and the participants recruited were aged 34.3 ± 11.6, and majority female (66.7%). Other approaches Community-focused flyering and poster efforts were also attempted with no success. Reddit advertisements were also tried at a sunk cost of £94.68 with no yield, due to respondents fulfilling exclusionary comorbid or physical disorder criteria and being filtered out at the Qualtrics triaging stage. Due to multiple avenues feeding concurrently into the triaging step, the exact number of Reddit advertisement respondents could not be accurately determined. Table 4 Recruitment Avenue recruitment rate and cost-per-enrolled participant, with total spend and eligibility fraction. HV MDD SZ Total N = 194 N = 66 N = 77 N = 51 Rate (Cost per eligible participant) Rate (Total spent) Eligibility Fraction Call for Participants (CFP) 0.96 (£3.08) 0.08 (£4.00) - 1.04 (£40) 68.42% Clinical Services * - - 1.41* 1.41 77.08% Electronic Health Records * - - 0.49* 0.49 73.33% GP Services - 2 (£427.82) 1 (£133.09) 1.5 (£2,700) 22.58% Institution (KCL) Internal Studies 1.96 0.66 - 1.16 38.46% NIHR BioResource (e.g. GLAD) * 0.50* 3.18* - 3.56 14.51% Meta Advertising 3.66 (£4.10) 1.59 (£25.47) - (£538.25) 3.29 (£1,351.29) 47.68% SLAM NHS Take Part in Research - 0.18 0.13 0.25 80% Word of Mouth 0.17 - - 0.11 100% Reddit Advertising - - - - (£94.68) - Group Rates and Cost (Rate per month, Cost per eligible participant) Part 1 : At-Home (Remote) 4.1 3.5 2 7.3 Part 2 : (Clinic and Remote) 1.6 1.7 1 2.9 Cost per enrolled £3.35 £42.19 £14.04* £21.58 Total Group Spend £221.18 £3,248.56 £716.23 £4,185.97 * = Significant unquantified labour costs involved Sex-disparity of Recruitment Costs Across the recruitment avenues, there was also a higher average cost per enrolled (CPE) male (£41.87) than female (£5.22) participants. The cost associated with recruiting participants with either male or female assigned sex at birth was calculated by the total spend on that recruitment avenue divided by the number of enrolled participants assigned to the targeted sex at birth; No participants with assigned sex at birth as ‘Other’ were enrolled in the study. Full comparisons of the sex disparity in recruitment costs across each diagnostic group are outlined in Supplement 4 . Benefits of Intermediate Triage The intermediate Qualtrics basic pre-screening step saved the research team approximately 84.25 hours of manual screening calls of 337 individuals who would have been considered ineligible, saving an estimated £1,802.64 in labour costs. Across recruitment avenues, Qualtrics received 1,079 total responses, of which 893 (82.76%) were unique respondents. Notably, 65 individuals (7.27%) submitted multiple completed responses, highlighting the necessity for robust duplicate response checking. The step also offered key insights into the diagnostic profiles of ineligible participants. These were: 29 individuals with anxiety, without a MDD or Psychosis diagnosis (8.61%), 95 with post-traumatic stress disorder (28.19%), 81 with an eating disorder (24.04%), 63 with an autism spectrum diagnosis (63%), 92 with attention deficit hyperactivity disorder (27.3%), 50 with dyslexia (14.84%), and 64 with another psychiatric comorbid disorder (18.99%). Across all recruitment avenues, 647 individuals were pre-screened by the research team, of whom 319 (49.3%) were then invited to be screened by a study clinician (Fig. 2 ). Eligibility rates differed substantially by group, with higher eligibility for SZ (65.3%) and HV (59.48%) than MDD (19.45%). The main exclusions for HV individuals were undisclosed psychiatric or otherwise exclusionary medical history and ineligible lifestyle factors (e.g. nightshifts). For MDD individuals, the main exclusionary reasons were failure to meet the criteria for a current ongoing episode, exclusionary comorbid conditions, and excessive episode duration. The main SZ screening exclusions were exclusionary substance use and failure to reach the criteria for a research SZ diagnosis. Detailed exclusionary characteristics are reported in Supplement 5 . Clinical and cognitive characteristics No significant associations between recruitment avenue and participants’ MDD or SZ medication type or class were identified. Full comparisons of time since first diagnosis and medication use by diagnostic group and recruitment avenue are in Supplement 6 . Due to the focus of the study on cognitive performance, exploratory analyses examined whether changes in recruitment strategy over the study duration introduced unintended sampling bias in task accuracy, whilst controlling for demographic and clinical covariates; The full analysis is presented in Supplement 6 . No significant within-group differences in cognitive performance were identified. When pooled across groups, a slight decline in total population cognitive performance was observed over time, but this was likely reflective of the later focus on recruitment of people with SZ rather than recruitment avenue effects. Recruitment Efficiency The effectiveness of each recruitment avenue for recruiting HV, MDD and SZ participants is displayed in a value quadrant diagram in Fig. 6 , showing the time and financial efficacy trade-off for each avenue (each axes), the proportion of participants recruited by said avenues for the diagnostic group (bubble size), and the eligibility quotient of participants from each avenue (bubble colour gradient). To better reflect the labour costs associated with the Clinical Service and EHR avenues, an estimated hourly rate of £21.45 has been calculated at a rate of 2 hours per enrolled participant. For the NIHR BioResource, this inherent labour cost has been estimated at 15 minutes per enrolled participant. Discussion Here, we took a data-driven, adaptive, and multimodal approach for to ensure representative recruitment of individuals with MDD and SZ, as well as healthy volunteers. Whilst timeline extensions and protocol amendments were notably necessary, particularly for the SZ group, the study populations were age-matched, achieved demographic representativeness aligned with the London 2021 UK Census (Office for National Statistics, 2022 ), and achieved high retention across both study phases (85.7–100%). These findings serve as a transparent account of how recruitment challenges can be identified early, monitored and mitigated to obtain sample representativeness and reduce research waste. Recruitment efficiency across avenues differed substantially across the DOCUMENT study, and demonstrated multidimensional trade-offs between speed, financial and labour efficiency, and eligibility fractions. Meta advertising showed high recruitment rates and low per-participant costs, but a low eligibility fraction. In contrast, traditional clinical and electronic health record (EHR) pathways were much slower and had a substantial labour cost but also showed a higher eligibility fraction. Divergence in the effectiveness of recruitment avenues also differed by diagnostic group. Online approaches were highly effective for both HV and MDD participants, consistent with prior findings of this avenue as successful in recruiting affective disorder populations (Haas et al., 2025 ; Lee et al., 2023 ). In contrast, SZ recruitment was almost entirely reliant on traditional clinical pathways (94%), potentially reflecting the clinical severity, functional impairment, and digital access barriers experienced by this population (Deckler et al., 2022 ; Iflaifel et al., 2024 ; Rønne et al., 2025 ). Despite targeted attempts at online recruitment for this population, no eligible participants were enrolled through this avenue. With no singularly efficacious avenue across all diagnostic groups, these results therefore support the utilisation of hybrid recruitment models to combine online recruitment for low-cost, high-yield recruitment of less clinically severe conditions, with the high-labour cost, slower traditional clinical pathways for severe and complex clinical populations. The overall DOCUMENT study population is notably well-aligned to the London demographics from the 2021 UK census. The skew in the DOCUMENT sample towards a population with an assigned sex at birth of female in the MDD group (61%) and male in the SZ group (71%) is consistent with London and UK-wide epidemiological patterns, with females being 1.39 times more likely to have a depressive disorder (Arias de la Torre et al., 2021), and males being 1.04 to 2.3 times more likely to have a diagnosis of SZ (Kirkbride et al., 2006 ; Oduola et al., 2021 ). The multimodal recruitment strategy adopted here may have helped mitigate the sampling biases and demographic skews inherent in over-reliance on single pathways. Thus, the NIHR BioResource, from which over half of the MDD participants were recruited, is disproportionally white and female, contrasting with the higher incidence of common mental disorders – including depression – in non-white populations (Williams et al., 2015 ), but in line with the underrepresentation of minorities in registry-based recruitment (Iflaifel et al., 2024 ). Similarly, Meta advertising, whilst recruiting a more ethnically diverse population, also maintained a female skew, in line with known online recruitment biases (Lee et al., 2023 ). In contrast, clinical avenues for SZ recruitment yielded a predominantly black clinical population reflective of the increased incidence of schizophrenia-spectrum and first-episode psychosis (FEP) diagnoses in Black and Minority Ethnic (BME) groups, particularly in London (Kirkbride et al., 2006 ; Oduola et al., 2021 ). Nevertheless, whilst aligned with higher incidence rates, the proportion of BME participants in this sample is slightly higher than general population estimates (Coid et al., 2008 ; Oduola et al., 2021 ), in line with the overrepresentation of BME participants in psychosis and FEP research noted by Michaels et al. ( 2024 ). Collectively, these findings emphasise the critical importance of adopting hybrid and multimodal recruitment strategies, alongside continuous monitoring, to actively counterbalance sampling biases. Monitoring of the eligibility fraction, exclusionary reasons, and recruitment rates of different avenues and diagnostic groups allowed for the informed relaxation of SZ eligibility criteria over the course of DOCUMENT study, which almost doubled the rate of recruitment within this group. Furthermore, awareness of the main exclusionary reasons across groups at the pre-screening stage informed the adoption of the intermediate triaging of respondents via Qualtrics, saving an estimated 84.25 hours of time that was able to be deployed elsewhere in the study. These findings illustrate how taking an adaptive and data-driven approach to recruitment can enhance feasibility and representativeness and ensure that resources are most effectively deployed. Our results further highlight that realistic research planning for clinical research should consider both direct financial costs and the potential for substantial labour demands. Consideration and awareness of these trade-offs between labour, cost, speed and representativeness, as illustrated in Fig. 6 , are essential for realistic and feasible clinical study planning. Furthermore, aiming for representativeness can come with an uncosted premium. We found that in the pursuit of aiming to recruit a more balanced sex split across groups resulted was an 8-fold increase in the cost per enrolled participant in males (£41.87) compared to females (£5.22), and in MDD participants, this was an almost 24-fold increase (£100.35 male to £4.19 female). Sex-based disparities in recruitment cost were also observed across both Call for Participants and Meta advertising. Even with successful initial recruitment, high participant attrition rates across clinical studies can still cause a study to fail (Briel et al., 2021 ). Notably, the high retention rate across both parts of the DOCUMENT study is also contrary to the high attrition rates frequently reported in interventional trials in psychiatry (Jacobsen et al., 2022 ). This may be reflect a combination of factors, such as maintaining personalised and frequent contact with the participants and offering flexible scheduling for study visits, consistent with prior literature (Cunningham-Erves et al., 2023 ). Notably, over the course of the DOCUMENT study, a dedicated member of the research team regularly engaged with clinical teams and caregivers to build trust and communication with the research team to facilitate SZ recruitment. This approach may partly reflect the higher proportion of SZ clinical service recruitment compared to EHR screening alone, as well as the high retention rate observed, even in this complex clinical population. Synthesising these collective findings from 30 months of recruitment on the DOCUMENT study, we propose a 10-point framework (Table 5 ) to apply for representative and efficient clinical research, grounded in empirical observations. Whilst recruitment avenue specific yields and demographics may differ by locality, clinical population and healthcare system, the framework principles can still be applied across clinical research contexts. Clinical Recruitment Framework Table 5 A data-driven framework for representative and adaptive recruitment in psychiatric research studies, based upon insights from the DOCUMENT Study. Recruitment Framework Implications for recruitment i. Diagnosis-specific recruitment strategy Recruitment avenues should match the engagement by the target population, rather than a one-size-fits-all approach. ii. Multimodal recruitment for representative populations Multimodal recruitment can help to sidestep inherent single-source recruitment biases and increases the diversity and representativeness of study populations. iii. Real-time demographic counterbalancing and monitoring Counter recruitment and sample bias by continuous monitoring of demographic drift and dynamically adjust recruitment strategy in response. iv. Data driven adaptability of the study protocol Monitor study data to identify barriers to study recruitment and retention that may be adapted without compromising the integrity of the research. v. Implementing digital pre-screening infrastructure Intermediate digital self-report triaging systems (such as Qualtrics), between recruitment avenue and the research team, asking likely exclusionary criteria pre-screening questions can reduce ineligible pre-screening rates. vi. Clinical pathways focus for severe psychiatric disorders Clinically severe psychiatric disorders, such as SZ, require more clinically focused pathways for recruitment, and recruitment and retention is substantially improved by building trust and rapport with the patients, caregivers and service providers. Digital-first approaches are likely insufficient for these means. vii. Transparent reporting of recruitment Clear reporting of recruitment sources, eligibility rates, exclusions, time or financial efficiency, and demographics by avenue increase reproducibility, as well as the generalisability of research findings. viii. Strategic recruitment avenue allocation Use recruitment avenues intentionally, serving an operational purpose depending on the study needs, such as filling a representativeness gap in a study population. E.g. Digital-based methods are often fast and broad but only suited for certain populations (here: HV and MDD). ix. Realistic disorder-based recruitment forecasting Recruitment timelines and study resource allocation should reflect the diagnosis-specific feasibility, e.g. severe psychiatric disorders (such as SZ) may have much slower and unpredictable rates than HV and MDD. x. Design for realistic populations, not perfect samples Study designs for realistic clinical profiles – such as high comorbidity of conditions – improves recruitment feasibility and representativeness. Overly strict criteria may exclude much of the real-world clinical population. Whilst the current recruitment insights and framework are derived from a UK and London-centric context, many of the barriers to recruitment for clinical research are widely considered global experiences and not unique to psychiatric research. Whilst clinical, institutional, regulatory and epidemiological factors may vary across countries, the tension between recruitment speed, financial and labour costs, eligibility, and representativeness remains pervasive. Therefore, the underlying framework emphasising multimodal recruitment pathways, demographic monitoring, and data-driven protocol amendment is likely transferable across broader clinical research contexts. Several limitations of the present work should be acknowledged. The DOCUMENT study was an observational clinical study conducted in a locality that is uniquely situated for psychosis and psychiatric recruitment (Kirkbride et al., 2006 ), the exact efficacy of recruitment avenues in different settings may be tied to local resources and clinical incidence rates that are different from the present study. Furthermore, whilst the barriers to recruitment largely overlap, it should also be acknowledged that randomised control trials may encounter additional constraints that come with interventional studies (Newington & Metcalfe, 2014 ), such as more rigid inclusion criteria. Likewise, this study is not an exhaustive list of possible recruitment avenues, as strategies such as Google advertisements, transport advertisements and radio could have been utilised (Krusche et al., 2014 ; Wise et al., 2016 ). In addition, other studies have shown success with online approaches to SZ recruitment that were not replicated by the present study (Domingues et al., 2011 ). However, despite these limitations, it is expected that the framework put forth in this study for adaptive and representative clinical research recruitment should still be widely applicable and beneficial. Successful recruitment is foundational to the representativeness, validity, and translational impact of clinical research. With transparent and granular reporting of recruitment challenges, adaptations, and their outcomes, the DOCUMENT study aims to provide a comprehensive evaluation of real-world psychiatric recruitment. The data-driven framework proposed herein supports the shift from a one-size-fits-all approach to recruitment and towards more efficient and hybrid research practices that aim to reduce research waste from failed recruitment and have relevance and applicability across clinical medicine. Abbreviations • ADHD Attention Deficit Hyperactivity Disorder • BME Black and Minority Ethnicities • BNSS Brief negative Symptom Scale • C4C Consent For Contact • CFP Call For Participants • CMHT Community Mental Health Teams • CONSORT The Consolidated Standards of Reporting Trials • CRIS Clinical Record Interactive Search • DOCUMENT Measuring cognitive deficits using cognitive tasks study • DSM-5 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition • ESRS-A The Extrapyramidal Symptom Rating Scale • FEP First Episode Psychosis • GAD Generalised anxiety disorder • GLAD Genetic Links to Anxiety and Depression • GP General Practitioner • HAM-D Hamilton Depression Rating Scale • HV Healthy Volunteers • HTA UK Health Technology Assessment • KCL King’s College London • MDD Major Depressive Disorder • MINI Mini-International Neuropsychiatric Interview • MRC Medical Research Council • NHS National Health Service (UK) • NIHR National Institute for Health and Care Research • PANSS Positive and Negative Syndrome Scale • PIC Participant Identification Centres • PTSD Post-Traumatic Stress Disorder • QIDS-16-SR The 16-item Quick Inventory of Depressive Symptomatology- Self Report • REC Research Ethics Committee • SLaM South London and Maudsley NHS Foundation Trust • SZ Schizophrenia Declarations Ethics approval and consent to participate Ethical approval was granted by the London – Camberwell St Giles Research Ethics Committee (REC reference: 21/LO/1234; IRAS ID: 304617). Clinical trial number: not applicable. All participants were given a copy of the study information sheet and provided consent prior to enrolment in the study. Participants were made aware of their right to withdraw at any time from the study without loss of reimbursement or to retract their data up until the point of analysis. For Part 1 (remote assessments), consent was obtained electronically via the Cognitron™ app (www.e.cognitron.co.uk). For Part 2 (in-person and remote assessments), written consent was obtained when they attended the site. The study was conducted in accordance with the Declaration of Helsinki, Good Clinical Practice (GCP) guidelines, and relevant UK legislation, including but not limited to UK-GDPR, Data Protection Act 2018, policy framework for health and social care research and the Mental Capacity Act 2005. Consent for Publication Informed consent for all data collection, analysis and publications related to the DOCUMENT study was received from all enrolled participants. Availability of data and materials Due to the presence of clinical and sensitive demographic data, the datasets generated and analysed in the present study are not publicly available. However, an anonymised version of the datasets, along with the Jupyter Notebook (Python) analysis code, may be made available upon reasonable request to the corresponding author, subject to institutional, ethical and GDPR governance requirements. Competing interests AH is the creator and owner of the Cognitron™ cognitive assessment platform, which is a product of H2 Cognitive Designs Ltd. (Company Number: 11171786) and is also the owner of Future Cognition Ltd. (Company Number: 09664003). ET is a full-time employee of Boehringer Ingelheim. Prior to joining Boehringer, she received unrestricted educational grants from Janssen (J&J Innovation), Biogen, and Boehringer Ingelheim (via the Psychiatry Consortium of the Medicines Discovery Catapult), and has provided consultancy to ONO Pharma and Boehringer Ingelheim. ET, GB, JRN, MVH, SDS, VRJ and KVA are employees of Boehringer Ingelheim Pharma GmbH & Co. KG or Boehringer Ingelheim International GmbH. SDS is also a member of the Medical Faculty of Ulm University, Ulm, Germany, but declares no conflict of interest. This study was funded by Boehringer Ingelheim as part of an ongoing collaboration with King’s College London. All other authors declare no competing interests. Funding This study was funded by Boehringer Ingelheim as part of an ongoing collaboration with King’s College London. Authors’ contributions BG was involved with the data collection, analysis, and writing of the manuscript. LR, BG, ARM, TS, BE, LS, IR, CD, DM, LP, and CG were all involved with the data collection, conceptualisation, and feedback on the manuscript. The research study was conceptualised and overseen by SW, AH, SSS, KA, ET, MH, SS, MW, JN, VRJ and GB. All authors read and approved the final manuscript. Acknowledgements We acknowledge the support of the NIHR King’s Clinical Research Facility and the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, including their Feasibility and Acceptability Support Team for Researchers (FAST-R). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. We would also like to thank all our participants for their time and dedication to taking part in the study. 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A nationwide review on obstetrical and gynaecological multicentre RCTs (2003–2023). BMJ Open. 2025;15(1):e087766. https://doi.org/10.1136/bmjopen-2024-087766 . Rønne ST, Arnfred SM, Gæde PH, Cleal B, Jørgensen R. Recruiting underrepresented populations for surveys: the case of people with schizophrenia and coexisting diabetes. Nord J Psychiatry. 2025;79(5):333–8. https://doi.org/10.1080/08039488.2025.2502932 . Rucker J, Jafari H, Mantingh T, Bird C, Modlin NL, Knight G, Reinholdt F, Day C, Carter B, Young A. Psilocybin-assisted therapy for the treatment of resistant major depressive disorder (PsiDeR): protocol for a randomised, placebo-controlled feasibility trial. BMJ Open. 2021;11(12):e056091. https://doi.org/10.1136/bmjopen-2021-056091 . Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry. 2003;54(5):573–83. https://doi.org/10.1016/s0006-3223(02)01866-8 . Schilling C, Tew M, Bunzli S, Shadbolt C, Lohmander LS, Balogh ZJ, Paolucci F, Choong PF, Dowsey MM, Clarke P. An Economic Model for Estimating Trial Costs with an Application to Placebo Surgery Trials. Appl Health Econ Health Policy. 2023;21(2):263–73. https://doi.org/10.1007/s40258-022-00775-4 . Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC. The Mini-International Neuropsychiatric Interview (M.I.N.I): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59(Suppl 20):22–33. The jamovi project. (2025). Jamovi . In (Version 2.6) https://www.jamovi.org Tiego J, Martin EA, DeYoung CG, Hagan K, Cooper SE, Pasion R, Satchell L, Shackman AJ, Bellgrove MA, Fornito A, Abend R, Goulter N, Eaton NR, Kaczkurkin AN, Nusslock R. Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology. Nat Mental Health. 2023;1(5):304–15. https://doi.org/10.1038/s44220-023-00057-5 . & the Hi, T. O. P. N. F. W. G. Walters SJ, Henriques-Cadby BDA, Bortolami I, Flight O, Hind L, Jacques D, Knox RM, Nadin C, Rothwell B, Surtees J, M., Julious SA. Recruitment and retention of participants in randomised controlled trials: a review of trials funded and published by the United Kingdom Health Technology Assessment Programme. BMJ Open. 2017;7(3):e015276. https://doi.org/10.1136/bmjopen-2016-015276 . Williams ED, Tillin T, Richards M, Tuson C, Chaturvedi N, Hughes AD, Stewart R. Depressive symptoms are doubled in older British South Asian and Black Caribbean people compared with Europeans: associations with excess co-morbidity and socioeconomic disadvantage. Psychol Med. 2015;45(9):1861–71. https://doi.org/10.1017/s0033291714002967 . Wise T, Arnone D, Marwood L, Zahn R, Lythe KE, Young AH. Recruiting for research studies using online public advertisements: examples from research in affective disorders. Neuropsychiatr Dis Treat. 2016;12:279–85. https://doi.org/10.2147/ndt.S90941 . Zahren C, Harvey S, Weekes L, Bradshaw C, Butala R, Andrews J, O’Callaghan S. Clinical trials site recruitment optimisation: Guidance from Clinical Trials: Impact and Quality. Clin Trails. 2021;18(5):594–605. https://doi.org/10.1177/17407745211015924 . Zhuo C, Li G, Lin X, Jiang D, Xu Y, Tian H, Wang W, Song X. The rise and fall of MRI studies in major depressive disorder. Translational Psychiatry. 2019;9(1):335. https://doi.org/10.1038/s41398-019-0680-6 . Additional Declarations Competing interest reported. AH is the creator and owner of the Cognitron™ cognitive assessment platform, which is a product of H2 Cognitive Designs Ltd. (Company Number: 11171786) and is also the owner of Future Cognition Ltd. (Company Number: 09664003). ET is a full-time employee of Boehringer Ingelheim. Prior to joining Boehringer, she received unrestricted educational grants from Janssen (J&J Innovation), Biogen, and Boehringer Ingelheim (via the Psychiatry Consortium of the Medicines Discovery Catapult), and has provided consultancy to ONO Pharma and Boehringer Ingelheim. ET, GB, JRN, MVH, SDS, VRJ and KVA are employees of Boehringer Ingelheim Pharma GmbH & Co. KG or Boehringer Ingelheim International GmbH. SDS is also a member of the Medical Faculty of Ulm University, Ulm, Germany, but declares no conflict of interest. This study was funded by Boehringer Ingelheim as part of an ongoing collaboration with King’s College London. All other authors declare no competing interests. Supplementary Files SUPPLEMENTARYRecruitmentPaperDOCUMENTStudy230326.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 09 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 08 Apr, 2026 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-9292013","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625501874,"identity":"afefcdc5-4e29-47af-a367-0c8344f1a119","order_by":0,"name":"Benjamin J. M. Gooddy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACAwiWgPB42BjkJBiYG8AcCWK1GEswMBLWggBALYkzCGkxZ+89UPAzxyKPgX/xwQdvyg6nz2w/2MDwo4YhcWYDdi2WPecSDHu3SRQzSDxLNpxz7nDubJ7EBsaeYwyJs3E57EaOgQHvNonEBokzZtK8bYdz50kAHcbbwJA4D5eW+28MDP8iaUmXA2ph/ItPyw0eA2OwLfw9YC0J0kAtzCBbcDnMsifHwFgWqKVNgg3kl3TDmT2JDYdljkkY4/K+OfsZM8O32+oS+/kPg0LMWl7i+OGDD9/U2MjOOIDDGgYGNnDcsEkkgKhmsNAB3LECBswPwBQ/2NA6fCpHwSgYBaNghAIA1fJZyyS83BsAAAAASUVORK5CYII=","orcid":"","institution":"King's College London","correspondingAuthor":true,"prefix":"","firstName":"Benjamin","middleName":"J. M.","lastName":"Gooddy","suffix":""},{"id":625501876,"identity":"580bb0cf-2838-4bbe-96da-8867b1ee83ee","order_by":1,"name":"Laila Rida","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Laila","middleName":"","lastName":"Rida","suffix":""},{"id":625501877,"identity":"e954bcde-f0b2-4bf1-b7b0-8cde1749ddda","order_by":2,"name":"Bryony Goulding Mew","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Bryony","middleName":"Goulding","lastName":"Mew","suffix":""},{"id":625501878,"identity":"6eba59b2-b830-43e7-87e7-d80f0dff638a","order_by":3,"name":"Ana Rita Moura","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Rita","lastName":"Moura","suffix":""},{"id":625501880,"identity":"7e8dd289-e889-4400-b7dc-18c728aceb5d","order_by":4,"name":"Timea Szentgyorgyi","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Timea","middleName":"","lastName":"Szentgyorgyi","suffix":""},{"id":625501881,"identity":"d8d03d4f-4ea3-49f2-8c6f-6ed9983ba89a","order_by":5,"name":"Brandi Eiff","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Brandi","middleName":"","lastName":"Eiff","suffix":""},{"id":625501882,"identity":"bb29b77d-8fe2-4d8e-89e0-70a55264d02c","order_by":6,"name":"Luisa Schalk","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Luisa","middleName":"","lastName":"Schalk","suffix":""},{"id":625501883,"identity":"615599cf-93a4-4ae0-9d8e-dceca9c02e63","order_by":7,"name":"Iman Rafiq","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Iman","middleName":"","lastName":"Rafiq","suffix":""},{"id":625501884,"identity":"c1a5bcd6-bf4e-490a-9417-e5ddc16a6862","order_by":8,"name":"Cathy Davies","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Cathy","middleName":"","lastName":"Davies","suffix":""},{"id":625501885,"identity":"c9a4c412-8d58-4762-84e1-0f605fffc08b","order_by":9,"name":"Daniel Martins","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Martins","suffix":""},{"id":625501886,"identity":"95c74836-1586-4c1f-8b87-134a292dbcba","order_by":10,"name":"Lilla A Porffy","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Lilla","middleName":"A","lastName":"Porffy","suffix":""},{"id":625501887,"identity":"89f94257-11f7-46f6-9524-2f9ff5d1d22b","order_by":11,"name":"Christabel Gibson","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Christabel","middleName":"","lastName":"Gibson","suffix":""},{"id":625501888,"identity":"8238ff9c-c7d2-45f5-8329-7210bdaa7262","order_by":12,"name":"Caroline Wooldridge","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Wooldridge","suffix":""},{"id":625501889,"identity":"ad8f628c-f1c7-4450-9680-800f63b5d281","order_by":13,"name":"Giorgio Bergamini","email":"","orcid":"","institution":"Boehringer Ingelheim Pharma GmbH \u0026 Co. KG","correspondingAuthor":false,"prefix":"","firstName":"Giorgio","middleName":"","lastName":"Bergamini","suffix":""},{"id":625501890,"identity":"2e98fa2b-a01d-4445-85a9-a0a34a0cdf4c","order_by":14,"name":"Janet R Nicholson","email":"","orcid":"","institution":"Boehringer Ingelheim Pharma GmbH \u0026 Co. KG","correspondingAuthor":false,"prefix":"","firstName":"Janet","middleName":"R","lastName":"Nicholson","suffix":""},{"id":625501891,"identity":"7822713a-c8ea-47fd-8df6-4aa0546691b6","order_by":15,"name":"Markus Waser","email":"","orcid":"","institution":"Boehringer Ingelheim Pharma GmbH \u0026 Co. KG","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Waser","suffix":""},{"id":625501892,"identity":"860867b9-8ea2-4b3e-bd42-b8e8cabe4d1d","order_by":16,"name":"Elizabeth Tunbridge","email":"","orcid":"","institution":"Boehringer Ingelheim Pharma GmbH \u0026 Co. KG","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Tunbridge","suffix":""},{"id":625501893,"identity":"0004410c-9a10-4e3f-bdb5-a1e6e8e47fee","order_by":17,"name":"Sigurd D Süssmuth","email":"","orcid":"","institution":"Boehringer Ingelheim Pharma GmbH \u0026 Co. KG","correspondingAuthor":false,"prefix":"","firstName":"Sigurd","middleName":"D","lastName":"Süssmuth","suffix":""},{"id":625501894,"identity":"ca4773ca-b2f4-4602-8a3a-76bb994c0100","order_by":18,"name":"Valdemar Robert Janulczyk","email":"","orcid":"","institution":"Boehringer Ingelheim Pharma GmbH \u0026 Co. KG","correspondingAuthor":false,"prefix":"","firstName":"Valdemar","middleName":"Robert","lastName":"Janulczyk","suffix":""},{"id":625501895,"identity":"8d3abf61-1532-4630-b252-61499f9c2b37","order_by":19,"name":"Karla V Allebrandt","email":"","orcid":"","institution":"Boehringer Ingelheim Pharma GmbH \u0026 Co. KG","correspondingAuthor":false,"prefix":"","firstName":"Karla","middleName":"V","lastName":"Allebrandt","suffix":""},{"id":625501896,"identity":"821de760-83c5-475e-b1e7-90d12fa46cc8","order_by":20,"name":"Adam Hampshire","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Hampshire","suffix":""},{"id":625501897,"identity":"30bdcc55-b510-4c58-9173-2921d5d4c74c","order_by":21,"name":"Sukhwinder S. Shergill","email":"","orcid":"","institution":"Kent and Medway Medical School","correspondingAuthor":false,"prefix":"","firstName":"Sukhwinder","middleName":"S.","lastName":"Shergill","suffix":""},{"id":625501898,"identity":"ee941f3a-f9e0-4378-8f25-9d388c1ae296","order_by":22,"name":"Steve Williams","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Steve","middleName":"","lastName":"Williams","suffix":""}],"badges":[],"createdAt":"2026-04-01 12:39:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9292013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9292013/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107377927,"identity":"a9ed8e80-cad2-41ff-bcc8-a01faf441160","added_by":"auto","created_at":"2026-04-21 01:32:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":297440,"visible":true,"origin":"","legend":"\u003cp\u003eDOCUMENT Study recruitment paths. Prior to May 2023 the initial contact was made by the research team directly. From May 2023, an intermediate Qualtrics based response step was introduced to further triage for eligible individuals.\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9292013/v1/ecc7fe55875500163254435a.jpeg"},{"id":107487693,"identity":"e6e2f4d3-e484-40eb-b6ee-9a291fb19542","added_by":"auto","created_at":"2026-04-22 02:42:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":470641,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant and group study flow and attrition across both Parts 1 \u0026amp; 2 of the DOCUMENT Study. Inclusion in Part 2 of the study was offered to all participants who completed Part 1 on a ‘first-come, first served’ basis until the recruitment targets for the Part 2 subset were fulfilled.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9292013/v1/af731fdaf4afecb9d240ce94.jpeg"},{"id":107485744,"identity":"54db78c2-d4be-487d-9c8b-e6219117e427","added_by":"auto","created_at":"2026-04-22 02:35:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":154893,"visible":true,"origin":"","legend":"\u003cp\u003eOutline of the DOCUMENT Study protocol for Part 1 and Part 2. HV – Healthy volunteers, MDD – Major Depressive Disorder participants, SZ – Schizophrenia participants. ‘Cog’ here refers to ‘Cognitive’, MCCB - MATRICS Consensus Cognitive Battery, WASI-II - Wechsler Abbreviated Scale of Intelligence (second edition), \u0026nbsp;UPSA-B - University of California San Diego Performance-Based Skills Assessment (Brief).\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9292013/v1/a6ab397d229fe69ff7dbcc01.jpeg"},{"id":107488398,"identity":"0e8476a8-ffd7-415a-9d46-3f26f9a1c9d0","added_by":"auto","created_at":"2026-04-22 02:44:32","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":779874,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative rates of participants recruitment per group using locally estimated scatterplot smoothed (LOESS) plots for Part 1 (first line), Part 2 (second line), and by recruitment avenue (third line). The rates of recruitment have been adjusted for any pauses in recruitment at each stage per group (represented by the shaded grey areas). January and May 2023 protocol amendments are represented by vertical dashed red lines.\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9292013/v1/0ba97080dcd8ba40e5688aa1.jpeg"},{"id":107377929,"identity":"3d0fffa8-d520-4af9-a158-60cb710d972a","added_by":"auto","created_at":"2026-04-21 01:32:36","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":373435,"visible":true,"origin":"","legend":"\u003cp\u003eA comprehensive recruitment avenue comparison of diagnostic group yield, age, sex, ethnicity, education, and study Part1-Part2 completion.\u003c/p\u003e","description":"","filename":"Figure5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9292013/v1/a4a65c0839fcfc64a7567c02.jpeg"},{"id":107377932,"identity":"506e49ed-e776-4116-acac-c03a510eabdf","added_by":"auto","created_at":"2026-04-21 01:32:36","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":432342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA value quadrant of recruitment avenues, time-based and financial efficiency, eligibility rates, and proportional contributions, based on the research insights from the case-control DOCUMENT study. Recruitment avenues with no direct financial cost are plotted above the dashed red line for visual distinction.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9292013/v1/5964b4c281320a7e9e1e27fc.jpeg"},{"id":107489654,"identity":"46ac7898-ca79-42a8-99e9-225645edba28","added_by":"auto","created_at":"2026-04-22 02:48:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3379883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9292013/v1/9f4030cf-c065-481b-a648-782a4cc26ebd.pdf"},{"id":107377930,"identity":"0bf06cef-91bb-443a-862e-822a27985fdb","added_by":"auto","created_at":"2026-04-21 01:32:36","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1109682,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYRecruitmentPaperDOCUMENTStudy230326.docx","url":"https://assets-eu.researchsquare.com/files/rs-9292013/v1/3a4c7a8acd7b24cea449ca94.docx"}],"financialInterests":"Competing interest reported. AH is the creator and owner of the Cognitron™ cognitive assessment platform, which is a product of H2 Cognitive Designs Ltd. (Company Number: 11171786) and is also the owner of Future Cognition Ltd. (Company Number: 09664003).\n\nET is a full-time employee of Boehringer Ingelheim. Prior to joining Boehringer, she received unrestricted educational grants from Janssen (J\u0026J Innovation), Biogen, and Boehringer Ingelheim (via the Psychiatry Consortium of the Medicines Discovery Catapult), and has provided consultancy to ONO Pharma and Boehringer Ingelheim. \n\nET, GB, JRN, MVH, SDS, VRJ and KVA are employees of Boehringer Ingelheim Pharma GmbH \u0026 Co. KG or Boehringer Ingelheim International GmbH. SDS is also a member of the Medical Faculty of Ulm University, Ulm, Germany, but declares no conflict of interest.\n\nThis study was funded by Boehringer Ingelheim as part of an ongoing collaboration with King’s College London. All other authors declare no competing interests.","formattedTitle":"A Data-Driven Methodological Framework for Representative Recruitment in Psychiatric Research: Insights from the DOCUMENT Study","fulltext":[{"header":"Background","content":"\u003cp\u003eParticipant recruitment for clinical research is notoriously difficult, with recruitment issues accounting for ~\u0026thinsp;54% of all study extensions, serving as the largest reason for protocol amendments and study discontinuation of randomised clinical trials in particular (Briel et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lai \u0026amp; Afseth, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Estimates of studies funded in the UK by bodies such as the Health Technology Assessment (HTA) and Medical Research Council (MRC) indicate that only around half (31\u0026ndash;56%) of studies reach their full recruitment target and only 55\u0026ndash;79% achieve\u0026thinsp;\u0026gt;\u0026thinsp;80% (McDonald et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Walters et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This has the serious, wider knock-on effect of potentially underpowered trials with increased false error rates (Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecruitment issues can have serious implications, both for study quality and financial feasibility. For example, a failure to meet recruitment targets may induce unintended consequences that undermine the quality of a trial\u0026rsquo;s data. Attempts to remedy recruitment shortfalls by quickly onboarding additional recruitment sites, which often contribute small numbers of patients, risk increasing the variability in both study baseline and outcome measures across sites (Fogel, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pallmann et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This sampling-induced heterogeneity may increase endpoint variability, complicating the interpretation of trial findings, and ultimately increasing the risk of study failure.\u003c/p\u003e \u003cp\u003eRecruitment challenges can have substantial financial implications: economic modelling of proxy placebo-controlled surgical trials indicates additional costs of up to 50% for trials requiring extension through additional time or sites, and up to 260% above budget for incomplete trials (Schilling et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, recruitment issues relating to study discontinuation were costing one medical and academic research site in Oregon an estimated \u003cspan\u003e$\u003c/span\u003e1\u0026nbsp;million US dollars a year (Kitterman et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecruitment is a pervasive challenge across clinical fields, from oncology and obstetrics to neurology and psychiatry (Gross et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Rikken et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, despite the difference in clinical focus, the same core reasons underlie recruitment failure, including excessively stringent and unrealistic recruitment criteria, overly optimistic recruitment rate projections, insufficient or mishandled budgets, participant burden disproportionate to remuneration, inadequate manpower, trial fatigue, and ineffective recruitment strategy (Briel et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, with informed insights, thoughtful strategy, and realistic study design planning based on reflections of previous research experience, many of these issues can be mitigated before a study even begins. Nevertheless, psychiatric research studies face additional unique challenges, such as the reliance on syndromic diagnoses rather than diagnostic biomarkers and less structured clinical pathways for patients, which make it a challenge to identify potential participants and exacerbate recruitment complexity. For example, diagnostic instability \u0026ndash; such as from non-affective to affective psychosis \u0026ndash; can complicate the recruitment of a stable study population (Oduola et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe unique logistical and sampling requirements of recruiting and retaining eligible psychiatric study populations are exemplified by studies aiming to recruit people with major depressive disorder (MDD) or psychosis. Depression studies often face issues recruiting due to factors such as public scepticism and stigma, lack of patients\u0026rsquo; self-identification or insight into the disorder, and instability of symptom severity and clinical stages, such as requiring participants to be in a depressive episode or pre-onset (Cuijpers et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Krusche et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Stringent inclusion criteria may further exacerbate recruitment issues and even instil an unintentional sampling bias into the study. For example, a study trialling the self-enrolment of depressed participants found that around 25% of patients had never been to their GP about their depression \u0026ndash; a population that would be missed if a formal diagnosis was required for inclusion (Brown et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies recruiting psychosis patients \u0026ndash; particularly schizophrenia \u0026ndash; are subject to even greater difficulties, in part due to the pronounced complex clinical, functional and cognitive burden of the disorder. Barriers to recruitment include a time-consuming and labour-intensive process of finding patients from clinical records, patients\u0026rsquo; inability to participate due to illness severity, particularly in cases of high study burden (Zahren et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and questions over patients\u0026rsquo; capacity, and gatekeeping by mental health and care professionals not wishing to add to patient burden (Deckler et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; R\u0026oslash;nne et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven when recruitment targets are met, it is difficult to determine whether the resultant clinical study sample is representative of the underlying clinical population, due to the risk of recruitment sampling bias. Assessing the scope of sampling bias in the study population, by direct comparison to the real-world clinical population, becomes complicated due to study-specific inclusion and exclusion criteria (e.g. comorbid psychiatric conditions, medication use etc.), which intrinsically may limit representativeness. The identification and evaluation of these sampling biases can be obscured in the absence of comprehensive and transparent reporting of study recruitment methodology.\u003c/p\u003e \u003cp\u003eDespite the push for transparent recruitment practices, such as The Consolidated Standards of Reporting Trials (CONSORT) requiring RCTs to report eligibility criteria and participant attrition, current estimates indicate that adequate compliance with these standards only occurs\u0026thinsp;~\u0026thinsp;63% of the time, and in multi-site studies this drops further to ~\u0026thinsp;25% (Walters et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Representativeness of the study sample to the population of the specific diagnostic group in the real world is fundamental for the extrapolation and generalisation of the research findings (Tiego et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhuo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe inherent complexity in reaching and successfully recruiting psychiatric patients for clinical studies, means that there is a significant need to report which recruitment avenues were the most and least effective, to inform future clinical studies to be realistic in scope, timelines, resource assignment, and reduce the chances of research waste (Kasenda et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Here, we systematically evaluate the learnings garnered from the multimodal recruitment strategy employed by the DOCUMENT study \u0026ndash; a comprehensive investigation of cognitive deficits in major depressive Disorder (MDD), schizophrenia (SZ) and healthy volunteers (HV). We aim to provide data-driven insights into recruitment and methodological considerations needed to successfully recruit an age-matched and ethnically representative participant sample, We collate learnings from this study into a framework designed to inform recruitment strategies for future studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy design and setting\u003c/h3\u003e\n\u003cp\u003eThe DOCUMENT study (\u0026lsquo;\u003cem\u003eMeasuring cognitive deficit using cognitive tasks\u003c/em\u003e\u0026rsquo;) was a single-site, two-stage, mixed-methods prospective clinical research study conducted at King\u0026rsquo;s College London. The study was designed in collaboration with, and funded by, Boehringer Ingelheim and co-sponsored by King\u0026rsquo;s College London and the South London and Maudsley (SLaM) NHS Foundation Trust. Recruitment and data collection ran over a 30-month period from June 2022 to December 2024. Ethics approval was received from the London-Camberwell St Giles Research Ethics Committee (REC reference: 21/LO/1234; IRAS ID: 304617). Full scientific results will be published once data analysis is complete.\u003c/p\u003e \u003cp\u003eThere were two main parts to the study: Part 1 aimed to recruit 50 healthy volunteers (HV), 50 people with schizophrenia (SZ), and 75 with major depressive disorder (MDD) to undertake remote (at-home) tablet-based cognitive and (optional) speech assessments over 3 days; A subset of 25 participants in each group continued into Part 2, completing in-person clinical and cognitive tests, followed by a 14-day remote longitudinal assessment of cognitive and sleep measures. Inclusion in the Part 2 subset was offered to all participants who completed Part 1, until the Part 2 recruitment targets were fulfilled.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants and Eligibility\u003c/h2\u003e \u003cp\u003eEligible participants were required to be adults aged 18\u0026ndash;55 years old, fluent in English, with no intellectual disability or neurodevelopmental disorder, no concurrent participation in another study, no current serious or unstable clinically important systemic illnesses, and the capacity to provide informed consent. For Part 2, cannabis use in the prior 12 hours was not permitted and was confirmed by a urine screening; for Part 1, participants were asked to abstain from all recreational drug use.\u003c/p\u003e \u003cp\u003eHV were required to have no current or historic psychiatric diagnoses, no significant physical or neurological conditions, no psychotropic medication use, no diagnosed sleep disorders aside from insomnia, and no first-degree relatives with a psychotic or bipolar disorder.\u003c/p\u003e \u003cp\u003eMDD participants were required to have a diagnosis of depression and be experiencing a current depressive episode, verified by the study clinician using the MINI psychiatric interview (Sheehan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). They could not have any history of psychotic depression, primary psychotic disorders or bipolar disorders. Antidepressant medication or psychological treatments had to be stable and started\u0026thinsp;\u0026gt;\u0026thinsp;4 weeks before enrolment. Comorbid psychiatric or neurological disorders were excluded, except for generalised anxiety disorder (GAD).\u003c/p\u003e \u003cp\u003eSZ participants were required to have a DSM-5 diagnosis of schizophrenia, or an unspecified schizophrenia spectrum or psychosis disorder and fit a research diagnosis of schizophrenia, which was confirmed by a study clinician and by the MINI psychiatric interview (MINI) (Sheehan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). For Part 2 eligibility, participants had to have Positive and Negative Syndrome Scale (PANSS) and Brief Negative Symptom Scale (BNSS) scores consistent with schizophrenia (Kay et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Kirkpatrick et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Comorbid psychiatric or neurological disorders, aside from anxiety disorders, were excluded. SZ participants had to be free from severe symptom exacerbation requiring inpatient hospitalisation and have a diagnosis and illness duration of \u0026le;\u0026thinsp;10 years. Antipsychotic medication use had to be stable for the 4 weeks before enrolment in the study, with a second antipsychotic only permitted when prescribed for sleep or anxiety. Clozapine use was allowed following an amendment made to the study protocol in May 2023 (see below).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRecruitment avenues and timeline\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited through a multimodal recruitment strategy, incorporating clinical, institutional, online, and community outreach pathways. Recruitment avenues included NHS Clinical Services \u0026ndash; Community Mental Health Teams (CMHTs) and early intervention services, electronic health records (SLaM Clinical Record Interactive Search (CRIS) and Consent for Contact (C4C)), the NIHR BioResource (namely the Genetic Links to Anxiety and Depression (GLAD) study (Davies et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)), an online recruitment platform (Call for Participants (CFP)), community flyers, and word of mouth. From January 2023, the study also started using social media avenues (Meta (Facebook and Instagram) and Reddit), the NHS SLaM Take Part in Research website (slam.nhs.uk/take-part-in-research), internal King\u0026rsquo;s College London (KCL) research circulars, and previous clinically aligned research studies (PsiDer (Rucker et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and COGENT), General Practitioner (GP) Participant Identification Centres (PICs) were also used from October 2023 to send NHS text messages to potential MDD and SZ participants, inviting them to take part in the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInitially, participants contacted the research team directly with their interest in participating. intermediate step was added in May 2023 in which prospective participants were directed to a secure Qualtrics web-hosted declaration of interest and basic self-report screening form, which allowed the research team to prioritise time on screening the participants who would have otherwise only declared exclusionary criteria during a pre-screening call (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePre-screening and eligibility confirmation\u003c/h3\u003e\n\u003cp\u003eIndividuals interested in participating in the DOCUMENT study underwent two calls with the study team. The first was a\u0026thinsp;~\u0026thinsp;15-minute pre-screening call to collect demographic information and assess basic inclusion and exclusion criteria, such as self-reported psychiatric and medical history. Individuals who were considered likely to be eligible were then scheduled for a clinical screening call (~\u0026thinsp;1 hour). The clinician screening involved the administration of the MINI psychiatric interview (Sheehan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), alongside a review of the pre-screening answers to confirm eligibility specific to each diagnostic group. If deemed eligible by a study clinician after the screening call, participants were then invited to enrol on the study and take part in Part 1. The study participants\u0026rsquo; progression through pre-screening, clinical screening, Part 1 and Part 2 is summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAdaptive protocol amendments\u003c/h3\u003e\n\u003cp\u003eTwo key amendments to the study protocol were implemented following HRA/NHS REC approval in January 2023 and May 2023. Both amendments were data-driven clinical decisions to improve recruitment feasibility, reduce participant burden, and ensure the representativeness of the target diagnostic populations.\u003c/p\u003e \u003cp\u003eThe January 2023 amendment increased the financial compensation for SZ participants from \u0026pound;40 to \u0026pound;80 in Part 1, and from \u0026pound;220 to \u0026pound;280 in Part 2, to better reflect the substantially greater clinical and time burden placed on this population, including longer screening calls and additional clinical visits. This amendment also included adding the collection of detailed ethnicity information and the highest level of education, to allow for better evaluations of sample representativeness. Accordingly, this data was not available for all enrolled participants.\u003c/p\u003e \u003cp\u003eThe May 2023 amendment focused on broadening the eligibility criteria for SZ participants to improve recruitment feasibility and better reflect a more general population and a couple of real-world conditions. It extended the duration of diagnosis for SZ from \u0026le;\u0026thinsp;5 to \u0026le;\u0026thinsp;10 years, permitted Clozapine use, as well as antipsychotic polypharmacy for sleep or anxiety management. In addition, the requirement for a formal clinical diagnosis of SZ was relaxed to permit unspecified psychotic disorders and schizophrenia spectrum disorders if a research diagnosis of SZ was confirmed by a study clinician using the MINI (Sheehan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). For all groups, the required abstinence from cannabis before the Part 2 clinical visits was reduced from 24 to 12 hours; Participants were still encouraged to abstain during the periods of remote assessment.\u003c/p\u003e \u003cp\u003eThe study duration was initially planned as 6\u0026ndash;12 months (aligned to REC and sponsor timelines). However, the recruitment window was extended multiple times due to a lower-than-anticipated participant accrual rate, particularly for the SZ group prior to the amendment changes.\u003c/p\u003e\n\u003ch3\u003eStudy procedures and compensation\u003c/h3\u003e\n\u003cp\u003eFollowing enrolment into the study, participants were sent a Samsung Tablet to complete cognitive batteries via the Cognitron\u0026trade; app, over two days, and optional speech sampling assessments via the Speech Vitals\u0026trade; app (Linus Health), over three days (Study Part 1). Each cognitive battery took approximately 45\u0026ndash;60 minutes, and the speech sampling took 10\u0026ndash;15. Before being able to start any tasks, the participants gave informed consent via the Cognitron\u0026trade; app. For successful completion, HV and MDD participants were compensated \u0026pound;40 and SZ participants \u0026pound;80. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA target of seventy-five participants (twenty-five from each group) was set for Part 2 completion. As such, eighty-one participants were invited to complete Part 2 of the study, starting Part 2 within two weeks of completing Part 1. This involved, first, a clinical assessment visit to obtain written informed consent, collect lifestyle factors and symptom scales by a study clinician. All diagnostic groups completed the self-report 16-item Quick Inventory of Depressive Symptomatology (QIDS-16-SR)(Rush et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Disorder-specific clinician-administered assessments were also undertaken: The MDD group alone underwent the clinician-administered Hamilton Depression Rating Scale (HAM-D)(Hamilton, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1960\u003c/span\u003e), while the SZ group underwent the administration of the Positive and Negative Syndrome Scale (PANSS)(Kay et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), Extrapyramidal Symptom Rating Scale-Abbreviated (ESRS-A)(Chouinard \u0026amp; Margolese, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and the Brief Negative Symptom Scale (BNSS)(Kirkpatrick et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Then, a cognitive assessment visit to undergo both online (Cognitron\u0026trade;) and gold-standard paper-based neuropsychological cognitive batteries, optional speech sampling (Speech Vitals\u0026trade;), and a virtual reality (VR) based functional task. For HV and MDD participants, this typically took place on the same day, and for SZ, these visits were separate days due to longer clinical visits; With the clinical and cognitive clinic visit days within seven days of each other, typically within the same working week. At both visits, a breath alcohol concentration test and a 10-panel urine drug test were administered to rule out exclusionary criteria.\u003c/p\u003e \u003cp\u003eThen, at-home, over the next 14 days, the Part 2 subset of participants completed a shorter remote cognitive battery on 10 days of their choice, as well as continuous sleep measurements from a wrist-worn actigraphy watch, as well as lifestyle and sleep self-report questionnaires on the days they opted to complete the cognitive assessments (Johns, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Rida et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For Part 2 completion, the HV and MDD participants received a bank transfer of \u0026pound;220, and the SZ compensation was \u0026pound;280. Full details of the study protocol, including cognitive and clinical assessments, are in \u003cb\u003eSupplement 1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe proportion of participants completing Part 2 of the DOCUMENT study following completing Part 1 was projected to be 50% for HV and SZ (both 25/50) and 33.33% for MDD (25/75). The observed Part 1 to Part 2 progression rates were 37.88% for HV, 50% SZ, and 34.67% for MDD. To ensure the Part 2 recruitment targets were met, an additional 16 HV participants were recruited during Part 1.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData handling and preparation\u003c/h2\u003e \u003cp\u003eThe recruitment data were collated from across various study sources, fully anonymised and then analysed in a Jupyter Notebook (.ipynb) in Visual Studio Code (1.89.1) using Python (v3.11.5), and some descriptive statistics were carried out in Jamovi (The jamovi project, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Meta-specific recruitment metrics were obtained from Meta\u0026rsquo;s Ads Manager and then pooled with the study data. Recruitment estimated costs were pooled across study invoices, emails, and records.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant demographics and group recruitment rates\u003c/h2\u003e \u003cp\u003e Over 30 months, 194 participants were recruited and enrolled in the DOCUMENT study. (Note that whilst Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e states 195 participants were sent the tablets, one participant never started data collection and was removed from the enrolled metrics).\u003c/p\u003e \u003cp\u003eThe demographics and eligibility fractions of the enrolled participants are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Ethnicity data were collected for 133 participants (68.56%) and education level for 131 (67.53%) following the January 2023 amendment. The three diagnostic groups were closely matched for mean age: 31.90\u0026ndash;33.40, with the greatest distribution in the HV group (\u0026plusmn;\u0026thinsp;9.7) and the smallest in the SZ group (\u0026plusmn;\u0026thinsp;6.6). Both the HV and MDD groups had more participants with female biological sex (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;72.7% HV, 61% MDD), whereas the SZ was more male-dominated (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70.6%). In the Part 2 subpopulation, this percentage biologically female became 69% in both HV and MDD groups, with an increase in the SZ group to 34%.\u003c/p\u003e \u003cp\u003eThe median UK education level in both the HV and MDD groups was an undergraduate degree (6; IQR\u0026thinsp;=\u0026thinsp;1), in the SZ group, the median was A-Level secondary education (3, IQR\u0026thinsp;=\u0026thinsp;3). This difference in education was more pronounced in the Part 2 subsample, with increased median education levels of 6.5 for HV (\u003cem\u003eBachelor\u0026rsquo;s to Master\u0026rsquo;s\u003c/em\u003e) and 7 for MDD (\u003cem\u003eMaster\u0026rsquo;s\u003c/em\u003e), and no subsample change with 3 for SZ (\u003cem\u003eA-Level\u003c/em\u003e).\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\u003eDOCUMENT Study enrolled participant demographics and contribution proportions of each recruitment avenue to the study and diagnostic group sample. SD \u0026ndash; Standard deviation, IQR \u0026ndash; Interquartile range, GLAD \u0026ndash; Genetic Links in Anxiety and Depression study, SLaM \u0026ndash; South London and Maudsley NHS Trust, NIHR \u0026ndash; National Institute for Health and Care Research.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;77\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;51\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;194\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e \u003cem\u003e(Years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge Range\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMean Age (\u003c/em\u003e\u0026plusmn;\u0026thinsp;\u003cem\u003eSD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.1 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.4 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.9 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.6 (8.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e \u003cem\u003e(Proportion of sample %)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian UK Education Level\u003c/b\u003e \u003cem\u003e(IQR)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Years Since Diagnosis\u003c/b\u003e (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.08 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.38 (4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecruitment Avenue, N =\u003c/b\u003e \u003cem\u003e(% of diagnostic group sample)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCall For Participants (CFP)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (18.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClinical Services\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (72.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eElectronic Health Records\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (21.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGP Services\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (6.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKCL Internal Studies\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNIHR BioResource (e.g. GLAD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (7.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (53.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMeta Advertising\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (65.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (32.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSLAM NHS Take Part in Research\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWord Of Mouth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePre-Screened\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e647 (49 Unknown)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEligible to enrol\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEligibility Fraction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStudy Section Attrition\u003c/b\u003e \u003cem\u003e(N\u0026thinsp;=\u0026thinsp;Started, % Completion rate)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePart 1\u003c/b\u003e: \u003cem\u003eAt-Home (Remote)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (97.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e194 (98.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePart 2\u003c/b\u003e: \u003cem\u003e(Clinic and Remote)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (94.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOut of the 194 enrolled participants to start Part 1 data collection, 191 completed this stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), resulting in high retention rates of 100% for the HV group, 97.40% for MDD, and 98.04% for SZ.\u003c/p\u003e \u003cp\u003eA subset of 81 participants started Part 2 data collection, undergoing at least one study visit, with 76 (25 HV, 26 MDD, 25 SZ) completing both the clinical and cognitive study visits alongside the longitudinal 14-day assessment. The Part 2 retention rate was also high, with 96.15% for the HV group, 100% for MDD, and 86.21% for SZ. Full intergroup and intragroup demographic comparisons are outlined in \u003cb\u003eSupplement 2\u003c/b\u003e, with no within-group differences observed between the Part 1 and Part 2 participant samples, and between groups the pattern of demographic differences remained consistent.\u003c/p\u003e \u003cp\u003eThe rate of recruitment varied between groups and between parts of the study. The first participant was enrolled in August 2022 and the last in October 2024. HV recruitment ran for 16.1 months until June 2024, with a pause between October 2023 and April 2024 to allow for targeted counterbalancing to match the demographics of the patient groups. MDD recruitment ran continuously over 21.5 months until June 2024, with recruitment for Part 2 halted in December 2023, and July 2024 for Part 1. SZ recruitment ran over 26.4 months until October 2024 for both Part 1 \u0026amp; 2. The Part 1 (\u003cem\u003eremote study\u003c/em\u003e) recruitment rate per month across all groups was 7.3 participants, with HV at the highest rate of 4.1, then MDD at 3.5, followed by SZ having the lowest rate of 2, see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Part 2 (\u003cem\u003eremote and in-person longitudinal study\u003c/em\u003e) recruitment rate was much slower across groups at 2.9 participants per month. In Part 2, MDD recruitment (1.7) was marginally higher than the HV group (1.6), with SZ still being the lowest (1).\u003c/p\u003e \u003cp\u003eThe data-driven decision taken to amend the inclusion criteria for the SZ participants is reflected in the recruitment rates. Before the January 2023 amendment, the rate of SZ participants enrolled was 1.4 per month (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6 recruited). Between January and May 2023, the recruitment declined to 1.2 as recruitment sources were exhausted. After May 2023, the SZ rate of recruitment increased to 2.3, attributed to the cumulative criteria changes, particularly the relaxation from needing a formal clinical SZ diagnosis to allowing a research diagnosis instead.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDemographic composition and census alignment\u003c/h2\u003e \u003cp\u003eThe ethnic makeup of the subset of the study sample for which there was ethnicity data (N\u0026thinsp;=\u0026thinsp;133; 68.6%), closely mirrored the London ethnicity distribution outlined in the 2021 UK Census (Office for National Statistics, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eEthnicity comparison of study sample to the 2021 UK Census data for (i) London, and (ii) nationally. The demographic breakdown is based on a sample of N\u0026thinsp;=\u0026thinsp;133 (68.6%) for which ethnicity data were available.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequencies of Ethnicity\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021 UK Census\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCounts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e% of Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLondon %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNational %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003cp\u003e(\u0026lsquo;\u003cem\u003eOther\u003c/em\u003e\u0026rsquo;: 4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003cp\u003e(\u0026lsquo;\u003cem\u003eOther\u003c/em\u003e\u0026rsquo;: 1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA post-hoc sensitivity analysis was carried out to assess whether the composition of the retrospectively collected (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28) and post- May 2023 amendment (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;105) data significantly differed. A Pearson\u0026rsquo;s \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e test of independence showed no significant difference between the samples: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(5, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;133)\u0026thinsp;=\u0026thinsp;10.21, \u003cem\u003ep\u003c/em\u003e = .07. Cram\u0026eacute;r\u0026rsquo;s \u003cem\u003eV\u003c/em\u003e = .277 also indicated a small-to-moderate but non-significant shift in ethnicity distribution.\u003c/p\u003e \u003cp\u003eWithin the diagnostic groups, notable differences in ethnicity composition were apparent (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The HV group in both Part 1 and 2 were predominantly white and asian, whereas in the MDD group, the sample was largely white. The SZ sample, in contrast, included higher proportions of black participants than the other groups. Across all groups, participants with mixed, hispanic, and arab ethnicities were less represented in the study population, compared with the other ethnic groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEthnicity representation across participant groups (HV, MDD, SZ) for the study sample and sub-sample. The proportion of missing data was calculated for each diagnostic group and study phase. The ethnicity counts and proportions were calculated from where participant data were available.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e* = with available data\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePart 2 Sub-sample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eMDD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounts\u003cb\u003e*\u003c/b\u003e =\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMixed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHispanic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArab\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMissing data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRecruitment Avenue Comparison\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRecruitment avenues differed in their contribution to diagnostic groups, eligibility fractions, and demographic composition. Each avenue is summarised below for total yield, eligibility fraction, efficiency, and demographic characteristics, with full details in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and visualised in \u003cb\u003eFig.\u0026nbsp;5\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 5\u003c/strong\u003e \u003cp\u003e \u003cem\u003eA comprehensive recruitment avenue comparison of diagnostic group yield, age, sex, ethnicity, education, and study Part1-Part2 completion.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe majority of the HV participants were recruited through Meta advertising (65.15%), CFP (18.18%) and the NIHR BioResource (7.58%). For MDD participants, the dominant sources were the NIHR BioResource (53.25%), Meta advertising (32.47%) and GP services (6.49%). The SZ recruitment came predominantly from clinical services (72.55%), EHRs (21.57%), and the SLaM Take Part in Research site (3.92%).\u003c/p\u003e \u003cp\u003eFull granular recruitment avenue comparisons are provided in \u003cb\u003eSupplement 3\u003c/b\u003e, with the most pertinent avenue-specific findings provided below:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMeta Advertising\u003c/h2\u003e \u003cp\u003e The largest contributor to the overall study sample was Meta advertising (35.1%), yielding 43 HV and 25 MDD participants, which had the second-highest recruitment rate across the avenues (3.3 participants per month) and a moderate eligibility fraction (47.7%). However, this avenue failed to yield any SZ participants, despite targeted efforts at a cost of \u0026pound;538.25. The participants from this avenue were predominantly female (70.6%) and younger adults (30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical Pathways (CMHTs and EHRs)\u003c/h2\u003e \u003cp\u003eClinical pathways, namely CMHTs and EHRs, yielded the highest proportion of SZ participants across all recruitment avenues (94.1%) with a high eligibility fraction (73.3\u0026ndash;77.1%). This route carried a high degree of unquantified labour costs \u0026ndash; such as time spent searching through patient records and liaising closely with the clinical care teams. This avenue additionally proved unsuccessful for MDD recruitment (via the Clinical Record Interactive Search) due to co-morbidity exclusions. Participants through these avenues were predominantly male (70.3\u0026ndash;81.8%), with both clinical services (31.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2) and EHRs (31.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4) having similar mean ages, and greater ethnic diversity than the online avenues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNIHR BioResource\u003c/h2\u003e \u003cp\u003eThe NIHR BioResource (i.e. GLAD) recruited the highest proportion of MDD participants. It had the highest recruitment rate across avenues (3.6 participants per month), but the lowest eligibility fraction (14.5%), necessitating the adoption of intermediate pre-screening for basic exclusionary criteria through Qualtrics. This avenue also produced the strongest demographic skew, with the majority of patients from this avenue being female (78.3%, white (87.5%) and marginally older than some of the other avenues (35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7). Whilst no direct financial cost was incurred by the study directly, the labour cost for this resource was provided by the NIHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGP-PIC Sites\u003c/h2\u003e \u003cp\u003eGP-PIC across 15 sites yielded a small proportion of the study population (3.6%), with 5 MDD, 1 SZ and 1 HV, at a rate of 1.5 per recruitment attempt \u0026ndash; discounting the untargeted enrolled HV participant. This route had a low eligibility fraction (22.6%), despite a total spend of \u0026pound;2,700 through the NIHR Research Delivery Network (RDN). The resultant sample was older (37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1), white (66.7%), and predominantly male (85.7%); However, this is not reflective of the actual potential due to targeted sex counterbalancing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCall For Participants (CFP)\u003c/h2\u003e \u003cp\u003e CFP yielded a small proportion of the study sample (6.7%) at a negligible total cost (\u0026pound;40), with a relatively high eligibility fraction (68.4%; 12 HV, 1 MDD), but a low recruitment rate of 1.04 participants per month. Participants through this avenue were generally younger adults (30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6) and female (62%). However, 69% of the CFP participants had missing education or ethnicity data, due to the timing of this avenue falling predominantly before the amendment that collected these additional demographics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInternal University Resources\u003c/h2\u003e \u003cp\u003eKCL Internal university resources (circular emails and previous studies), yielded 2.6% of the study sample (2 HC, 3 MDD). The recruitment rate was moderate at 1.16 participants per month, with an eligibility fraction of 38.46%. Participants via this avenue were exclusively male, and predominantly white (60%) or asian (40%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSLaM Take Part in Research\u003c/h2\u003e \u003cp\u003eSLaM Take Part in Research recruited 2.1% of the study population (2 MDD, 2 SZ) at no direct cost. There was a low recruitment rate (0.25 participants per month) but a high eligibility fraction of 80%. Participants via this avenue were on average older (40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3) than the other avenues, and an even male-to-female split.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eWord of Mouth\u003c/h2\u003e \u003cp\u003eWord of mouth yielded 3 HV participants (1.6% of the study population), with the highest eligibility fraction (100%), but a low recruitment rate (0.17 participants per month). Notably, this avenue wasn\u0026rsquo;t actively pursued, and the participants recruited were aged 34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6, and majority female (66.7%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eOther approaches\u003c/h2\u003e \u003cp\u003eCommunity-focused flyering and poster efforts were also attempted with no success. Reddit advertisements were also tried at a sunk cost of \u0026pound;94.68 with no yield, due to respondents fulfilling exclusionary comorbid or physical disorder criteria and being filtered out at the Qualtrics triaging stage. Due to multiple avenues feeding concurrently into the triaging step, the exact number of Reddit advertisement respondents could not be accurately determined.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRecruitment Avenue recruitment rate and cost-per-enrolled participant, with total spend and eligibility fraction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMDD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c8\" namest=\"c7\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;194\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;77\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;51\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRate (Cost per eligible participant)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eRate (Total spent)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eEligibility Fraction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCall for Participants (CFP)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.96 (\u0026pound;3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.08 (\u0026pound;4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04 (\u0026pound;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClinical Services *\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eElectronic Health Records *\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGP Services\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2 (\u0026pound;427.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (\u0026pound;133.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5 (\u0026pound;2,700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInstitution (KCL) Internal Studies\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNIHR BioResource (e.g. GLAD) *\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.50*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.18*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.51%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMeta Advertising\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.66 (\u0026pound;4.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.59 (\u0026pound;25.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e- (\u0026pound;538.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.29 (\u0026pound;1,351.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSLAM NHS Take Part in Research\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWord of Mouth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReddit Advertising\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e- (\u0026pound;94.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGroup Rates and Cost\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(Rate per month, Cost per eligible participant)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePart 1\u003c/b\u003e: \u003cem\u003eAt-Home (Remote)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePart 2\u003c/b\u003e: \u003cem\u003e(Clinic and Remote)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCost per enrolled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026pound;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026pound;42.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026pound;14.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026pound;21.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal Group Spend\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026pound;221.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026pound;3,248.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026pound;716.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026pound;4,185.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e* = Significant unquantified labour costs involved\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSex-disparity of Recruitment Costs\u003c/h2\u003e \u003cp\u003eAcross the recruitment avenues, there was also a higher average cost per enrolled (CPE) male (\u0026pound;41.87) than female (\u0026pound;5.22) participants. The cost associated with recruiting participants with either male or female assigned sex at birth was calculated by the total spend on that recruitment avenue divided by the number of enrolled participants assigned to the targeted sex at birth; No participants with assigned sex at birth as \u0026lsquo;Other\u0026rsquo; were enrolled in the study. Full comparisons of the sex disparity in recruitment costs across each diagnostic group are outlined \u003cb\u003ein Supplement 4\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eBenefits of Intermediate Triage\u003c/h2\u003e \u003cp\u003eThe intermediate Qualtrics basic pre-screening step saved the research team approximately 84.25 hours of manual screening calls of 337 individuals who would have been considered ineligible, saving an estimated \u0026pound;1,802.64 in labour costs. Across recruitment avenues, Qualtrics received 1,079 total responses, of which 893 (82.76%) were unique respondents. Notably, 65 individuals (7.27%) submitted multiple completed responses, highlighting the necessity for robust duplicate response checking.\u003c/p\u003e \u003cp\u003eThe step also offered key insights into the diagnostic profiles of ineligible participants. These were: 29 individuals with anxiety, without a MDD or Psychosis diagnosis (8.61%), 95 with post-traumatic stress disorder (28.19%), 81 with an eating disorder (24.04%), 63 with an autism spectrum diagnosis (63%), 92 with attention deficit hyperactivity disorder (27.3%), 50 with dyslexia (14.84%), and 64 with another psychiatric comorbid disorder (18.99%).\u003c/p\u003e \u003cp\u003eAcross all recruitment avenues, 647 individuals were pre-screened by the research team, of whom 319 (49.3%) were then invited to be screened by a study clinician (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Eligibility rates differed substantially by group, with higher eligibility for SZ (65.3%) and HV (59.48%) than MDD (19.45%). The main exclusions for HV individuals were undisclosed psychiatric or otherwise exclusionary medical history and ineligible lifestyle factors (e.g. nightshifts). For MDD individuals, the main exclusionary reasons were failure to meet the criteria for a current ongoing episode, exclusionary comorbid conditions, and excessive episode duration. The main SZ screening exclusions were exclusionary substance use and failure to reach the criteria for a research SZ diagnosis. Detailed exclusionary characteristics are reported in \u003cb\u003eSupplement 5\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eClinical and cognitive characteristics\u003c/h2\u003e \u003cp\u003eNo significant associations between recruitment avenue and participants\u0026rsquo; MDD or SZ medication type or class were identified. Full comparisons of time since first diagnosis and medication use by diagnostic group and recruitment avenue are in \u003cb\u003eSupplement 6\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eDue to the focus of the study on cognitive performance, exploratory analyses examined whether changes in recruitment strategy over the study duration introduced unintended sampling bias in task accuracy, whilst controlling for demographic and clinical covariates; The full analysis is presented in \u003cb\u003eSupplement 6\u003c/b\u003e. No significant within-group differences in cognitive performance were identified. When pooled across groups, a slight decline in total population cognitive performance was observed over time, but this was likely reflective of the later focus on recruitment of people with SZ rather than recruitment avenue effects.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eRecruitment Efficiency\u003c/h2\u003e \u003cp\u003eThe effectiveness of each recruitment avenue for recruiting HV, MDD and SZ participants is displayed in a value quadrant diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, showing the time and financial efficacy trade-off for each avenue (each axes), the proportion of participants recruited by said avenues for the diagnostic group (bubble size), and the eligibility quotient of participants from each avenue (bubble colour gradient). To better reflect the labour costs associated with the Clinical Service and EHR avenues, an estimated hourly rate of \u0026pound;21.45 has been calculated at a rate of 2 hours per enrolled participant. For the NIHR BioResource, this inherent labour cost has been estimated at 15 minutes per enrolled participant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we took a data-driven, adaptive, and multimodal approach for to ensure representative recruitment of individuals with MDD and SZ, as well as healthy volunteers. Whilst timeline extensions and protocol amendments were notably necessary, particularly for the SZ group, the study populations were age-matched, achieved demographic representativeness aligned with the London 2021 UK Census (Office for National Statistics, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and achieved high retention across both study phases (85.7\u0026ndash;100%). These findings serve as a transparent account of how recruitment challenges can be identified early, monitored and mitigated to obtain sample representativeness and reduce research waste.\u003c/p\u003e \u003cp\u003eRecruitment efficiency across avenues differed substantially across the DOCUMENT study, and demonstrated multidimensional trade-offs between speed, financial and labour efficiency, and eligibility fractions. Meta advertising showed high recruitment rates and low per-participant costs, but a low eligibility fraction. In contrast, traditional clinical and electronic health record (EHR) pathways were much slower and had a substantial labour cost but also showed a higher eligibility fraction. Divergence in the effectiveness of recruitment avenues also differed by diagnostic group. Online approaches were highly effective for both HV and MDD participants, consistent with prior findings of this avenue as successful in recruiting affective disorder populations (Haas et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, SZ recruitment was almost entirely reliant on traditional clinical pathways (94%), potentially reflecting the clinical severity, functional impairment, and digital access barriers experienced by this population (Deckler et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Iflaifel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; R\u0026oslash;nne et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite targeted attempts at online recruitment for this population, no eligible participants were enrolled through this avenue. With no singularly efficacious avenue across all diagnostic groups, these results therefore support the utilisation of hybrid recruitment models to combine online recruitment for low-cost, high-yield recruitment of less clinically severe conditions, with the high-labour cost, slower traditional clinical pathways for severe and complex clinical populations.\u003c/p\u003e \u003cp\u003eThe overall DOCUMENT study population is notably well-aligned to the London demographics from the 2021 UK census. The skew in the DOCUMENT sample towards a population with an assigned sex at birth of female in the MDD group (61%) and male in the SZ group (71%) is consistent with London and UK-wide epidemiological patterns, with females being 1.39 times more likely to have a depressive disorder (Arias de la Torre et al., 2021), and males being 1.04 to 2.3 times more likely to have a diagnosis of SZ (Kirkbride et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Oduola et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The multimodal recruitment strategy adopted here may have helped mitigate the sampling biases and demographic skews inherent in over-reliance on single pathways. Thus, the NIHR BioResource, from which over half of the MDD participants were recruited, is disproportionally white and female, contrasting with the higher incidence of common mental disorders \u0026ndash; including depression \u0026ndash; in non-white populations (Williams et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), but in line with the underrepresentation of minorities in registry-based recruitment (Iflaifel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, Meta advertising, whilst recruiting a more ethnically diverse population, also maintained a female skew, in line with known online recruitment biases (Lee et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, clinical avenues for SZ recruitment yielded a predominantly black clinical population reflective of the increased incidence of schizophrenia-spectrum and first-episode psychosis (FEP) diagnoses in Black and Minority Ethnic (BME) groups, particularly in London (Kirkbride et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Oduola et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, whilst aligned with higher incidence rates, the proportion of BME participants in this sample is slightly higher than general population estimates (Coid et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Oduola et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), in line with the overrepresentation of BME participants in psychosis and FEP research noted by Michaels et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Collectively, these findings emphasise the critical importance of adopting hybrid and multimodal recruitment strategies, alongside continuous monitoring, to actively counterbalance sampling biases.\u003c/p\u003e \u003cp\u003eMonitoring of the eligibility fraction, exclusionary reasons, and recruitment rates of different avenues and diagnostic groups allowed for the informed relaxation of SZ eligibility criteria over the course of DOCUMENT study, which almost doubled the rate of recruitment within this group. Furthermore, awareness of the main exclusionary reasons across groups at the pre-screening stage informed the adoption of the intermediate triaging of respondents via Qualtrics, saving an estimated 84.25 hours of time that was able to be deployed elsewhere in the study. These findings illustrate how taking an adaptive and data-driven approach to recruitment can enhance feasibility and representativeness and ensure that resources are most effectively deployed.\u003c/p\u003e \u003cp\u003eOur results further highlight that realistic research planning for clinical research should consider both direct financial costs and the potential for substantial labour demands. Consideration and awareness of these trade-offs between labour, cost, speed and representativeness, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, are essential for realistic and feasible clinical study planning. Furthermore, aiming for representativeness can come with an uncosted premium. We found that in the pursuit of aiming to recruit a more balanced sex split across groups resulted was an 8-fold increase in the cost per enrolled participant in males (\u0026pound;41.87) compared to females (\u0026pound;5.22), and in MDD participants, this was an almost 24-fold increase (\u0026pound;100.35 male to \u0026pound;4.19 female). Sex-based disparities in recruitment cost were also observed across both Call for Participants and Meta advertising.\u003c/p\u003e \u003cp\u003eEven with successful initial recruitment, high participant attrition rates across clinical studies can still cause a study to fail (Briel et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, the high retention rate across both parts of the DOCUMENT study is also contrary to the high attrition rates frequently reported in interventional trials in psychiatry (Jacobsen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This may be reflect a combination of factors, such as maintaining personalised and frequent contact with the participants and offering flexible scheduling for study visits, consistent with prior literature (Cunningham-Erves et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, over the course of the DOCUMENT study, a dedicated member of the research team regularly engaged with clinical teams and caregivers to build trust and communication with the research team to facilitate SZ recruitment. This approach may partly reflect the higher proportion of SZ clinical service recruitment compared to EHR screening alone, as well as the high retention rate observed, even in this complex clinical population.\u003c/p\u003e \u003cp\u003eSynthesising these collective findings from 30 months of recruitment on the DOCUMENT study, we propose a 10-point framework (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) to apply for representative and efficient clinical research, grounded in empirical observations. Whilst recruitment avenue specific yields and demographics may differ by locality, clinical population and healthcare system, the framework principles can still be applied across clinical research contexts.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eClinical Recruitment Framework\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA data-driven framework for representative and adaptive recruitment in psychiatric research studies, based upon insights from the DOCUMENT Study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecruitment Framework\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImplications for recruitment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ei. Diagnosis-specific recruitment strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRecruitment avenues should match the engagement by the target population, rather than a one-size-fits-all approach.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eii. Multimodal recruitment for representative populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMultimodal recruitment can help to sidestep inherent single-source recruitment biases and increases the diversity and representativeness of study populations.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiii. Real-time demographic counterbalancing and monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCounter recruitment and sample bias by continuous monitoring of demographic drift and dynamically adjust recruitment strategy in response.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiv. Data driven adaptability of the study protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMonitor study data to identify barriers to study recruitment and retention that may be adapted without compromising the integrity of the research.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ev. Implementing digital pre-screening infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIntermediate digital self-report triaging systems (such as Qualtrics), between recruitment avenue and the research team, asking likely exclusionary criteria pre-screening questions can reduce ineligible pre-screening rates.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evi. Clinical pathways focus for severe psychiatric disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClinically severe psychiatric disorders, such as SZ, require more clinically focused pathways for recruitment, and recruitment and retention is substantially improved by building trust and rapport with the patients, caregivers and service providers. Digital-first approaches are likely insufficient for these means.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evii. Transparent reporting of recruitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClear reporting of recruitment sources, eligibility rates, exclusions, time or financial efficiency, and demographics by avenue increase reproducibility, as well as the generalisability of research findings.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eviii. Strategic recruitment avenue allocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUse recruitment avenues intentionally, serving an operational purpose depending on the study needs, such as filling a representativeness gap in a study population. E.g. Digital-based methods are often fast and broad but only suited for certain populations (here: HV and MDD).\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eix. Realistic disorder-based recruitment forecasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRecruitment timelines and study resource allocation should reflect the diagnosis-specific feasibility, e.g. severe psychiatric disorders (such as SZ) may have much slower and unpredictable rates than HV and MDD.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ex. Design for realistic populations, not perfect samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStudy designs for realistic clinical profiles \u0026ndash; such as high comorbidity of conditions \u0026ndash; improves recruitment feasibility and representativeness. Overly strict criteria may exclude much of the real-world clinical population.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhilst the current recruitment insights and framework are derived from a UK and London-centric context, many of the barriers to recruitment for clinical research are widely considered global experiences and not unique to psychiatric research. Whilst clinical, institutional, regulatory and epidemiological factors may vary across countries, the tension between recruitment speed, financial and labour costs, eligibility, and representativeness remains pervasive. Therefore, the underlying framework emphasising multimodal recruitment pathways, demographic monitoring, and data-driven protocol amendment is likely transferable across broader clinical research contexts.\u003c/p\u003e \u003cp\u003eSeveral limitations of the present work should be acknowledged. The DOCUMENT study was an observational clinical study conducted in a locality that is uniquely situated for psychosis and psychiatric recruitment (Kirkbride et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the exact efficacy of recruitment avenues in different settings may be tied to local resources and clinical incidence rates that are different from the present study. Furthermore, whilst the barriers to recruitment largely overlap, it should also be acknowledged that randomised control trials may encounter additional constraints that come with interventional studies (Newington \u0026amp; Metcalfe, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), such as more rigid inclusion criteria. Likewise, this study is not an exhaustive list of possible recruitment avenues, as strategies such as Google advertisements, transport advertisements and radio could have been utilised (Krusche et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wise et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, other studies have shown success with online approaches to SZ recruitment that were not replicated by the present study (Domingues et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, despite these limitations, it is expected that the framework put forth in this study for adaptive and representative clinical research recruitment should still be widely applicable and beneficial.\u003c/p\u003e \u003cp\u003eSuccessful recruitment is foundational to the representativeness, validity, and translational impact of clinical research. With transparent and granular reporting of recruitment challenges, adaptations, and their outcomes, the DOCUMENT study aims to provide a comprehensive evaluation of real-world psychiatric recruitment. The data-driven framework proposed herein supports the shift from a one-size-fits-all approach to recruitment and towards more efficient and hybrid research practices that aim to reduce research waste from failed recruitment and have relevance and applicability across clinical medicine.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; ADHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttention Deficit Hyperactivity Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; BME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlack and Minority Ethnicities\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; BNSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrief negative Symptom Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; C4C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsent For Contact\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; CFP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCall For Participants\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; CMHT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity Mental Health Teams\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; CONSORT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Consolidated Standards of Reporting Trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; CRIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Record Interactive Search\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; DOCUMENT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMeasuring cognitive deficits using cognitive tasks study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; DSM-5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; ESRS-A\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Extrapyramidal Symptom Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; FEP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFirst Episode Psychosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; GAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralised anxiety disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; GLAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenetic Links to Anxiety and Depression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; GP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Practitioner\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; HAM-D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHamilton Depression Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; HV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthy Volunteers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; HTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUK Health Technology Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; KCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKing\u0026rsquo;s College London\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; MDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor Depressive Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; MINI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini-International Neuropsychiatric Interview\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; MRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Research Council\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; NHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health Service (UK)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; NIHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institute for Health and Care Research\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PANSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive and Negative Syndrome Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParticipant Identification Centres\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PTSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePost-Traumatic Stress Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; QIDS-16-SR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe 16-item Quick Inventory of Depressive Symptomatology- Self Report\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; REC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResearch Ethics Committee\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SLaM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSouth London and Maudsley NHS Foundation Trust\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SZ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the London \u0026ndash; Camberwell St Giles Research Ethics Committee (REC reference: 21/LO/1234; IRAS ID: 304617). Clinical trial number: not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants were given a copy of the study information sheet and provided consent prior to enrolment in the study. Participants were made aware of their right to withdraw at any time from the study without loss of reimbursement or to retract their data up until the point of analysis. For Part 1 (remote assessments), consent was obtained electronically via the Cognitron\u0026trade; app (www.e.cognitron.co.uk). For Part 2 (in-person and remote assessments), written consent was obtained when they attended the site. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, Good Clinical Practice (GCP) guidelines, and relevant UK legislation, including but not limited to UK-GDPR, Data Protection Act 2018, policy framework for health and social care research and the Mental Capacity Act 2005.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for all data collection, analysis and publications related to the DOCUMENT study was received from all enrolled participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the presence of clinical and sensitive demographic data, the datasets generated and analysed in the present study are not publicly available. However, an anonymised version of the datasets, along with the Jupyter Notebook (Python) analysis code, may be made available upon reasonable request to the corresponding author, subject to institutional, ethical and GDPR governance requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAH is the creator and owner of the Cognitron\u0026trade; cognitive assessment platform, which is a product of H2 Cognitive Designs Ltd. (Company Number: 11171786) and is also the owner of Future Cognition Ltd. (Company Number: 09664003).\u003c/p\u003e\n\u003cp\u003eET is a full-time employee of Boehringer Ingelheim. Prior to joining Boehringer, she received unrestricted educational grants from Janssen (J\u0026amp;J Innovation), Biogen, and Boehringer Ingelheim (via the Psychiatry Consortium of the Medicines Discovery Catapult), and has provided consultancy to ONO Pharma and Boehringer Ingelheim. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eET, GB, JRN, MVH, SDS, VRJ and KVA are employees of Boehringer Ingelheim Pharma GmbH \u0026amp; Co. KG or Boehringer Ingelheim International GmbH. SDS is also a member of the Medical Faculty of Ulm University, Ulm, Germany, but declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003eThis study was funded by Boehringer Ingelheim as part of an ongoing collaboration with King\u0026rsquo;s College London. All other authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Boehringer Ingelheim as part of an ongoing collaboration with King\u0026rsquo;s College London.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBG was involved with the data collection, analysis, and writing of the manuscript. LR, BG, ARM, TS, BE, LS, IR, CD, DM, LP, and CG were all involved with the data collection, conceptualisation, and feedback on the manuscript. The research study was conceptualised and overseen by SW, AH, SSS, KA, ET, MH, SS, MW, JN, VRJ and GB. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the support of the NIHR King\u0026rsquo;s Clinical Research Facility and the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King\u0026rsquo;s College London, including their Feasibility and Acceptability Support Team for Researchers (FAST-R). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. We would also like to thank all our participants for their time and dedication to taking part in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor correspondence concerning this research, please contact the corresponding author at: [email protected]\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ede la Arias J, Vilagut G, Ronaldson A, Dregan A, Ricci-Cabello I, Hatch SL, Serrano-Blanco A, Valderas JM, Hotopf M, Alonso J. Prevalence and age patterns of depression in the United Kingdom. A population-based study. 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Translational Psychiatry. 2019;9(1):335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-019-0680-6\u003c/span\u003e\u003cspan address=\"10.1038/s41398-019-0680-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Participant Recruitment, Depression, Psychosis, Schizophrenia, Psychiatry, Psychiatric Research, Major Depressive Disorder, Clinical Study, Digital Health, Clinical Research","lastPublishedDoi":"10.21203/rs.3.rs-9292013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9292013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSlow participant recruitment is one of the predominant determinants of failure or delay across clinical research. Even when recruitment targets are met, study populations may be unrepresentative due to sampling biases introduced by recruitment pathways. However, the effectiveness and demographic consequences of recruitment strategies are frequently underreported, undermining the generalisability of clinical findings and contributing to research waste.\u003c/p\u003e\n\u003cp\u003eThis study provides a data-driven, quantitative evaluation of multimodal recruitment strategies in psychiatric research, leveraging insights from the DOCUMENT study to synthesise a methodological framework for effective and representative participant recruitment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween June 2022 and December 2024, the study utilised a multimodal strategy to recruit participants with major depressive disorder (MDD), schizophrenia (SZ), and healthy volunteers (HV) for a two-phase study to investigate cognitive deficits across groups.\u003c/p\u003e\n\u003cp\u003eRecruitment strategies included NHS clinical services, electronic health records, research registries, primary care sites, online and social media advertising, printed material, institutional resources, and word of mouth. For each avenue, yield, proportion of diagnostic group, recruitment rate, eligibility fraction, labour and financial cost, and demographic skew were quantified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross avenues, 194 participants were recruited (66 HV, 77 MDD and 51 SZ), with high retention (85-97%). \u0026nbsp;Recruitment efficacy varied substantially by diagnostic group, with online advertising and research registries successfully recruiting MDD and HV participants but failing to recruit eligible people with SZ. Instead, SZ participants were primarily enrolled from labour-intensive clinical avenues (94%). Online recruitment showed higher accrual, but lower eligibility fraction compared to clinical pathways, revealing systematic sampling differences. Despite avenue-specific sampling biases, the multimodal approach yielded close demographic alignment to the 2021 UK Census for London in the study population. Data-driven adaptations, such as protocol amendments to eligibility criteria and online self-report triaging, improved study feasibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo single recruitment avenue was identified as sufficient for both efficient and representative psychiatric recruitment. Instead, multimodal strategies were necessary to dilute avenue sampling biases. Synthesising 30 months of data, we introduce a 10-point framework for enhancing recruitment effectiveness, feasibility, and representativeness. While grounded in UK-based psychiatric research, these principles apply to broader clinical research contexts to reduce research waste.\u003c/p\u003e","manuscriptTitle":"A Data-Driven Methodological Framework for Representative Recruitment in Psychiatric Research: Insights from the DOCUMENT Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 01:32:31","doi":"10.21203/rs.3.rs-9292013/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"79837958988471031929059498840084399490","date":"2026-05-11T16:42:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T21:53:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234518441192168808883212894942631454361","date":"2026-04-15T13:09:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207904397758589610888114884335597715983","date":"2026-04-14T12:24:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T11:09:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T11:04:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-09T09:24:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T11:19:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-04-08T10:34:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35e7af49-6f6b-447c-9d03-29517ec8e9aa","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"79837958988471031929059498840084399490","date":"2026-05-11T16:42:46+00:00","index":67,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T01:32:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 01:32:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9292013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9292013","identity":"rs-9292013","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}