Propensity score matching confirmed the non-significant differences between the ADHD and non-ADHD group at baseline. At the 10-year follow-up, n = 571 participants fulfilled complete inclusion criteria, among them 268 subjects were classified as ADHD. The two groups did not significantly differ in lipid fractions, neither cross-sectionally nor with regard to long-term changes. There was also no significant difference between methylphenidate subgroups. In this sample of children and adolescents we could not reveal any significant associations between serum lipid fractions and the diagnosis of ADHD, neither cross-sectionally nor longitudinally; even when methylphenidate use was considered. Thus, further studies using larger sample sizes are required to investigate putative long-term changes in serum lipid fractions related to ADHD.Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting approximately 5% of children worldwide. The causal mechanisms of ADHD remain unclear as the aetiology of this disorder seems to be multifactorial. One research field addresses the impact on lipid metabolism and particularly serum lipid fractions on the development of ADHD symptoms. This post hoc analysis aimed to investigate long-term changes in serum levels of lipoproteins in children and adolescents with ADHD and controls. Data of German children and adolescents from the nationwide and representative “Kinder- und Jugendgesundheitssurvey (KiGGS)” study were analysed at baseline and at a ten-year follow-up. At the two time points, participants in the control group were compared with those in the ADHD group, both before and after propensity score matching. Differences in total cholesterol, low-density lipoproteins (LDL), high-density lipoproteins (HDL) and triglycerides were assessed between matched children with and without ADHD. In addition, subgroups with versus without methylphenidate use were compared at both time points. At baseline before matching, there were no significant differences for lipid parameters between participants in the ADHD group (The online version contains supplementary material available at 10.1007/s00702-022-02583-5. Attention-deficit/hyperactivity disorder (ADHD) with a worldwide estimated prevalence of 5% , we previously reported weak but statistically significant associations between ADHD symptoms and lower serum LDL cholesterol as well as higher triglyceride concentrations , ADHD must be diagnosed before the age of 7 years , Berlin, Germany , in three inquiry periods, namely baseline (2003–2006), Wave 1 2009–2013) and Wave 2 2014–2017) and financed by the German Federal Ministry of Health . KiGGS Wave 2 (No. 2275-2014) was checked for data protection by the Federal Commissioner for Data Protection and Freedom of Information in Germany and Hannover Medical School’s ethics committee with respect to the ethics  and the participants themselves (11 years and older) reported whether they had been diagnosed with ADHD by a physician or clinical psychologist by filling in the questionnaire at baseline. Secondly, the screening instrument Strengths and Difficulties Questionnaire (SDQ) was used for detection of children with ADHD symptoms independent of the diagnosis ADHD  the ADHD group, including study participants who had been given the diagnosis of ADHD  before entering baseline investigation and/or had (at baseline assessment) a SDQ-H/I score of ≥ 7 on the hyperactivity-inattention subscale and (2) a control group with no clinical diagnosis of ADHD nor a SDQ-H/I score above the cut off.Data about medication usage were also gathered using the questionnaire filled in by the parents or children during the computer assisted medical interview. First, medication usage during the last seven days was inquired into, in general (“Did your child intake any medication during the last seven days”). Afterwards it was further specified, e.g. “Who prescribed the drug”, “Why has it been prescribed”, “What is the name of the drug” etc. Afterwards the medication names were standardized using the ATC classification, for indication of the drug ICD 10 codes were used Knopf . MethylpThe measurement of lipids was conducted centrally in Berlin at the laboratories of the German “Robert-Koch-Institute” and the German Heart Centre, according to standardized protocols , living conditions, protective and risk factors and health care utilization (Robert Koch-Institut (RKI)). The computer-assisted personal interview collected detailed data on medication usage classifying them with the specific ATC  codes. Anthropometric data, including body height and weight, waist and hip circumferences, were measured using infantometers or stadiometers  and electronic scales . This information was also used to calculate the body-mass-index (BMI) as weight in kilograms divided by height in metres squared  are given as means and standard deviations. For a comparison between participants in the ADHD group and controls, chi-square tests were used for categorical variables and Student’s n = 603 participants (5.5%) had been diagnosed with ADHD by a clinical psychologist or physician and n = 888 (8.1%) were suspected of having ADHD according to the SDQ-H/I score. Since some of the diagnosed participants showed also an elevated SDQ-H/I score, there was an overlap of n = 272 participants. Thus, both groups together (diagnosed and suspected ADHD cases) amounted to n = 1219, corresponding to 11.1% of all children. The remaining participants  were considered as control group. As described in the left panel of Table 2  = 264.44, p < 0.001, Cohen’s ω = 0.156) confirming previous findings  = − 9.528, p < 0.001, Cohen’s d = 0.291), they were younger  = − 6.997, p < 0.001, Cohen’s d = 0.213) and had a lower BMI  = − 2.676, p < 0.001, Cohen’s d = 0.082).In total, 10,960 children of the KiGGS study aged between 7 and 17 were enrolled at baseline. Among them, n = 1219). The distribution within the control group (n = 9741) is more skewed. The panel below illustrates the distribution after propensity score matching, demonstrating that the procedure resulted in a more normal distribution after matching, closer to the one observed for the ADHD group. In total, 1190 participants were included in the ADHD group and an equal number in the controls, having the closest propensity to ADHD risks. Consequently, participants of both groups presented similar characteristics in age (t (2378) = − 0.137, p = 0.891, Cohens d = 0.006), sex ratio  = 0.002, p = 0.963, Cohens ω = 0.002), SES (t (2378) = − 0.108, p = 0.914, Cohens d = 0.004) and BMI (t (2378) = − 0.253, p = 0.800, Cohens d = 0.010). Details are shown in the right-hand panel of Table For propensity score matching, the nearest neighbour matching technique was used. The distribution before and after propensity score matching is depicted in Fig. Valid information about the intake of medication for ADHD during the study period was available only for a small group and thus was not included in this analysis as a confounder. However, since methylphenidate is the first line drug in ADHD treatment and was suspected of causing hypocholesterolemic and hypotriglyceridemic effects  = − 1.578, p = 0.115, Cohens d = 0.051; HDL t = 0.766, p = 0.444, Cohens d = 0.025; triglycerides t = 1.437, p = 0.151, Cohens d = 0.046, Table In the KiGGS baseline cohort, there were no significant differences between the ADHD group and controls for all four lipid parameters tested  = − 0.844, 0,101 = −ß) = 0.999, 95%-CI 0.911–1.094, p = 0.979), LDL (Exp(ß) = 0.967, 95%-CI 0.872–1.071, p = 0.525), HDL (Exp(ß) = 1.095, 95%-CI 0.899–1.331, p = 0.366) and triglycerides (Exp(ß) = 1.038, 95%-CI 0.948–1.133, p = 0.412).In the multivariate logistic models, the associations between lipid parameters and ADHD were tested with the confounders each in separate models Table . Only agt(2157) = 0.267, p = 0.789, Cohen’s d = 0.014, LDL t = − 0.521, p = 0.602, Cohen’s d = 0.012; HDL t(2157) = 0.588, p = 0.557, Cohen’s d = 0.006 and triglycerides t(2157) = 0.989, p = 0.323, Cohen’s d = 0.023. The corresponding adjusted, multivariate models confirmed these results for total cholesterol (Exp(ß) = 1.016, 95%-CI 0.900–1.149, p = 0.794), LDL (Exp(ß) = 0.966, 95%-CI 0.844–1.106, p = 0.617), HDL (Exp(ß) = 1.053, 95%-CI 0.817–1.358, p = 0.689) and triglycerides (Exp(ß) = 1.073, 95%-CI 0.954–1.207, p = 0.243).For the matched sample including 2380 participants, the same analysis process was performed again with ADHD as dependent variable. As individuals were matched for their age, sex, SES and BMI, these factors were no longer associated with ADHD in the matched sample. As shown in the right-hand panel of Table n = 571 fulfilled the criteria for inclusion in our study cohort, representing roughly a quarter of the baseline study cohort. Among them, n = 258 subjects belonged to the ADHD group and n = 313 to the control group according to their group membership at baseline. The matching between the ADHD group and control group remained effective, as the level of confounding variables remained similar between groups: age (t (569) = 0.146, p = 0.884, Cohen’s d = 0.012), SES (t (569) = 0.638, p = 0.524, Cohen’s d = 0.054), BMI (t (567) = 2.533, p = 0.012, Cohen’s d = 0.170) and heart rate (t(569) = 1.607, p = 0.109, Cohen’s d = 0.135). In univariate analysis, there were no significant differences between the ADHD and control groups with respect to lipid parameters (for details see Table ß) = 0.972, 95%-CI 0.789–1.182, p = 0.775), LDL (Exp(ß) = 0.998, 95%-CI 0.784–1.270, p = 0.990), HDL (Exp(ß) = 0.928, 95%-CI 0.480–1.789, p = 0.823) and triglycerides (Exp(ß) = 1.047, 95%-CI 0.863–1.268, p = 0.636). Details are shown in Table For a follow-up 10 years later, 6,044 participants from the baseline were enlisted again for laboratory measurements and physical examinations Kurth . A totalThe lower part of Table Overweight and obesity could have an impact on lipid parameters  showed that nearly 50% of obese children had some kind of dyslipidaemia Below is the link to the electronic supplementary material.


Given the complex nature of seizure disorders and their treatments, ADHD may be underdiagnosed in this population. We hypothesized that a higher percentage of patients presenting to a seizure clinic would endorse ADHD symptoms compared to rates reported in the general population and that formal screening for ADHD symptoms would identify patients with previously undiagnosed comorbid ADHD.In this study, we surveyed 312 adults in a seizure clinic using the Adult ADHD Self-Report Scale (ASRS-v1.1).We found that 90 patients (28.8%) screened positive with the ASRS-v1.1, but only nine of these patients were able to complete neuropsychological testing,. Out of these patients, only one was diagnosed with possible ADHD.Through this process, we identified many challenges to making a new ADHD diagnosis in this population, including attention deficits due to other medical or psychiatric diagnoses, a positive urine drug screen, lack of collateral report/information about developmental history, and barriers to neuropsychological evaluation. ADHD is a neurodevelopmental condition that presents with varying degrees of inattentiveness, hyperactivity, and impulsivity . The preEpilepsy is a condition typically characterized by recurrent, unprovoked seizures . Among cIn adults with epilepsy, several studies point to a prevalence of up to 20% with comorbid ADHD . The preGiven the higher risk of reduced quality of life, emotional distress, and functional impairments in adults with epilepsy and comorbid ADHD, it is important to efficiently identify and treat these patients appropriately. Effective treatment of ADHD may also improve management of seizure disorders by increasing adherence to medications and/or minimizing precipitating stressors . Thus, tThis was a cohort study of adult patients at a seizure clinic in the Harris Health System in Harris County, Texas. The Harris Health System is the county’s safety net health care entity with a relatively low socioeconomic patient population  of 45.9%Part A of the ASRS-v1.1 Screener was used to screen adult patients with epilepsy (PWE) for ADHD symptoms . This paPatients who screened positive for ADHD symptoms were asked to perform a urine drug screen (UDS). Patients with a positive UDS were excluded from the study given the possibility of drug-induced attention deficits. Patients who were English or Spanish speakers and with a negative UDS were subsequently referred for neuropsychological evaluation for ADHD.We found that 28.8% of patients in our seizure clinic screened positive for ADHD symptoms  versus hyperactive symptoms in question items 5 to 6 . This is higher than the 4.4% prevalence reported in the general population, which is consistent with our first hypothesis . HoweverOf the 312 patients screened, 21 (6.7%) had a documented history of ADHD in their medical record, substantially lower than the 18% prevalence of ADHD in adults with epilepsy that has been previously reported in a study with similar sample size . The ASRThe percent of patients that screened positive (28.8%) for ADHD symptoms in our adult seizure clinic is in line with prior studies using the ASRS-v1.1 in adult clinic patients with epilepsy in Iran (35.0%), and adults with self-reported epilepsy who were sent a postal survey in the United States (18.4%) . Some ofPatients with focal seizures also were more likely to screen negative for ADHD symptoms  while those with an unknown etiology were more likely to screen positive for ADHD symptoms . One possibility is that focal seizures are more likely to be acquired later in life secondary to a focal injury, which may preclude the neurodevelopmental profile expected from patients with ADHD. In addition, patients with unknown etiologies due to negative MRI and EEG may be more likely to have psychogenic non-epileptic seizures, which we have previously found to have a higher likelihood of screening positive with the ASRS-v1.1 than patients with epilepsy in an Epilepsy Monitoring Unit (EMU) setting .Importantly, of the nine patients who screened positive for ADHD symptoms and completed neuropsychological testing, 8 (88.9%) did not meet key diagnostic criteria for ADHD. The primary reason for the low diagnostic yield was the absence of attentional symptoms and functional impairments during childhood. The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V) classifies ADHD as a neurodevelopmental disorder, which must have “several inattentive or hyperactive/impulsive symptoms” present before age 12 and associated functional impairment , which wIt is also important to note that several participants who underwent neuropsychological evaluation had a history of acquired brain injury and two had a possible intellectual disability. The presence of these comorbid factors may have contributed to the endorsement of attentional symptoms on the ASRS, rather than an underlying attentional disorder. These findings also highlight the non-specificity of attentional symptoms in adults, which further complicates ADHD diagnosis in this population. In future efforts to screen for ADHD in adult populations, the ASRSv1.1 can be coupled with brief follow-up questions regarding the nature of symptoms, symptom onset, and functional impairment to improve the value offered by referral for formal neuropsychological evaluation.Another important challenge in studying the relationship between ADHD and epilepsy in adults is that epilepsy itself, medications used to treat epilepsy, and comorbidities of epilepsy such as depression or anxiety can present with deficits in attention that do not meet diagnostic criteria for ADHD. Several biological and pharmacological factors are known to cause cognitive impairment in epilepsy, which can present as deficits in learning, memory, and attention . ExperimFurthermore, there were many non-medical barriers to diagnosing ADHD in this study, including the completion of a urine drug screen, the patient’s ability to complete neuropsychological testing, scheduling challenges, and out-of-pocket costs. These barriers are especially prominent for the predominantly lower socioeconomic patient population in our county clinic. Moreover, ADHD and its comorbidities are associated with an increased risk of substance abuse in adults. Specifically, the transition from substance use to substance dependence occurs at a faster rate in patients with ADHD than patients without ADHD, likely due to patients with ADHD using illicit substances to self-medicate . TherefoWith the medical and socio-economic barriers to diagnosis, one option is to integrate psychiatric evaluation into the seizure clinic, which may expedite the diagnosis and treatment of ADHD. As a precedent, one study evaluated the diagnosis and treatment of depression in adult patients in this same seizure clinic. Patients were screened using the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E). For positive screens, neurologists in the clinic conducted a semi-structured psychiatric interview following DSM-IV criteria. If patients received a diagnosis of depression, the same physicians would prescribe treatment accordingly . By usinIn summary, while 28.8% of adults in our seizure clinic screened positive for ADHD symptoms using the ASRS-v1.1 screener, most patients did not complete neuropsychological testing due to a multitude of medical, social, and logistical barriers, and only one out of the nine patients that underwent neuropsychological testing received a possible diagnosis of ADHD. Addressing these challenges may lead to a more efficient diagnosis of ADHD in this high-risk population.


Cannabis is frequently used by people who self-medicate for the signs of mental health conditions. Attention-deficit/hyperactivity disorder (ADHD), a neurodevelopmental illness, has been linked to increased cannabis use. However, compared to other mental disorders, cannabis use by people with ADHD has received much less research. The main goal of this systematic review was to understand the nature of the relationships between cannabis use and ADHD symptoms. We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to conduct the systematic review. We included papers published within the previous ten years from online searches on PubMed, PubMed Central (PMC), Google Scholar, and ScienceDirect until January 1st, 2023. The inclusion-exclusion criteria led to the initial selection of 136 studies. We selected twenty research articles after screening and assessing them using quality assessment techniques. These articles included two non-randomized control trials, one cross-sectional study, one meta-analysis, and sixteen observational cohorts. It can be advantageous for people with ADHD and their medical professionals to understand better how ADHD patients use cannabis and its potential risks and advantages on cannabis use disorder, ADHD symptoms, and executive dysfunction. This article further emphasizes the necessity of thorough research to comprehend cannabis use in ADHD patients. Attention-deficit hyperactivity disorder (ADHD) is a prevalent psychiatric disorder among children, with some cases persisting into adulthood. According to the Diagnostic and statistical manual of mental disorders (5th ed.) [DSM-V], six or more symptoms of inattention (such as making careless mistakes) and/or hyperactivity (such as restlessness) in children up to age 16 indicate ADHD. Adolescents 17 years and older and adults, however, must exhibit five or more symptoms . ChildreAbout 90% of ADHD patients also have co-occurring mental problems as frequent comorbidity. According to reports, 15% of the general population and up to 50% of adults with ADHD use drugs or alcohol regularly . MoreoveCannabis is one of the most widely used illicit narcotics globally, with significant usage rates in youth and early adulthood . IntroduCompared to children without ADHD, individuals with ADHD had nearly three times the likelihood of ever using marijuana and 1.5 times the likelihood of developing a cannabis dependence . MedicalStringent laws restricted cannabis usage in the past. Nevertheless, the transition is accelerating since various countries and nations have loosened or are considering loosening rules against its use and manufacture, making those with acute conditions like ADHD more susceptible to suffering the consequences. Given the reasons mentioned above, we conducted a thorough systematic review to determine the nature of the connection between cannabis and ADHD.Methods We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 criteria to plan and present the results of this systematic review.Search StrategyWe performed in-depth searches in ScienceDirect, PubMed (MEDLINE), PubMed Central, and Google Scholar. To find all possibly pertinent papers discussing the connection between cannabis and ADHD, we employed the right keywords and Medical Subject Heading (MeSH) terminology. Attention deficit hyperactivity disorder, ADHD, attention deficit disorder with hyperactivity, and cannabis, marijuana, cannabidiol, and THC, were among the keywords utilized. We combined the keywords and MeSH phrases using the Boolean approach to create a constant search over multiple databases. After removing duplicates, we carefully examined our data to ensure all potentially relevant items were present. We next reviewed the titles and abstracts of the obtained records and further evaluated their relevance. After that, two researchers independently assessed full-text papers against the inclusion and exclusion criteria, with a third researcher acting as a tiebreaker.Inclusion and Exclusion CriteriaWe limited our search to records published online between January 2012 and January 2023 that were released in English and were freely accessible as complete texts. Meta-analyses, systematic reviews, literature reviews, randomized and non-randomized clinical trials, and observational prospective and retrospective cohorts were the only research we considered. To be included, studies needed to have participants who were adolescents or young adults (age 25) or later adults (>25), compare individuals with ADHD or sub-threshold ADHD who used cannabis or synthetic cannabinoids to those who did not and measure any neurodevelopmental outcome . Table Study Quality AppraisalWe evaluated every study's potential for bias. We assessed clinical studies using the updated Cochrane risk of bias 2 (RoB 2) algorithm. The Newcastle-Ottawa Scale (NOS) was used to evaluate cohort studies. We evaluated systematic reviews and meta-analyses using the Assessment of Multiple Systematic Reviews 2 (AMSTAR 2) tool. We examined each randomized controlled trial for potential biases based on five risks of bias using the most recent Cochrane RoB 2 methodology. We evaluated the quality of the cross-sectional study using the AXIS tool. Each bias risk was rated low, high, or moderate. Additionally, we specified whether there was a low, high, or moderate risk of bias overall. Table ResultsWe identified 136 records using the various search strategies across the databases. Of 136 records, 59 originated from PubMed (MEDLINE), 29 from Google Scholar, 36 from ScienceDirect, and 12 from PubMed Central. We employed no additional sources. We removed 31 duplicates manually before screening articles. Following a thorough review of the remaining 105 records for relevance based on their titles and abstracts, we eliminated 61 due to their lack of relevance to the study's subject, goals, and inclusion and exclusion criteria. Hence, 44 articles were sought for retrieval, and after checking for free full texts, we further removed eight reports. We assessed 26 reports for quality and removed six articles due to low quality. Therefore, we included 20 articles in this review. These included sixteen observational cohorts, one meta-analysis, two randomized controlled trials, and one cross-sectional study. Figure DiscussionADHD in Adolescents and Young AdultsAccording to several studies, most people with ADHD also have additional mental illnesses, with learning impairments, oppositional behavior, drug abuse, and anxiety being prevalent comorbidities . Figure Cannabis Effects on the Human BrainRegular cannabis use during adolescence is associated with diminished verbal and working memory and sustained attention . TeenageEffects of Cannabis Intake on ADHD PatientsStructural changes in the brain: Numerous studies defined neurodevelopmental outcomes to include neuroimaging, cognitive testing, and questionnaire-based research to capture the developmental or functional effects of cannabis use on young people with ADHD. Rasmussen et al. found that cannabis use was associated with the diagnosis of ADHD in the right hippocampus and cerebellar vermis, with higher activation of these regions in cannabis-using controls during appropriate response inhibition . SimilarSubstance use problems: In 90% of cases, ADHD coexists with another mental illness. 15% of the general population and up to 50% of adults with ADHD use alcohol or drugs, according to reports . BetweenBehavioral changes: Hyperactive and impulsive symptoms have been linked to problematic cannabis use in men ,11. HoweAdditionally, ADHD does not impact cannabis withdrawal in patients with cannabis dependence seeking therapy . Sleep dTo overcome the harmful effects of ADHD: Adults with ADHD symptoms frequently cite low mood and negative affect as reasons for using cannabis to treat their symptoms . This trPerceived low risk: Despite the lack of clinical guidelines or thorough research to support these claims, online conversations indicate that marijuana is believed to be beneficial for ADHD. Cannabis veterans also believe that cannabis is less dangerous than other drugs or substances . This tyLimitationsSeveral of these studies , 13], a, a13], aThis systematic review sheds new light on the cannabis use patterns associated with ADHD symptoms, the perceived effects of cannabis on specific core symptoms of ADHD, and the potential moderating effects of cannabis use on executive functioning deficits related to ADHD. The significant findings, in particular, indicate a relationship between the frequency of cannabis use and the severity of ADHD symptoms, as well as a slightly increased risk of CUD. In conclusion, research on the effects of concurrent cannabis use in adolescents and young adults with ADHD is currently lacking. There is a need to study the detailed effects of ADHD medications and smoking cannabis on ADHD symptoms. Preliminary studies reveal abnormalities in perfusion, altered brain architecture, and altered density of dopamine transporters. The evidence to date is inconclusive regarding whether cannabis use has an addictive effect or interactions, whether beneficial or detrimental. The evidence base at this time is relatively small, so more study is needed. Understanding the neurodevelopmental impact of cannabis use is essential for both clinical and societal reasons, especially in populations like children with ADHD, who may be especially vulnerable to them, given the legalization's increased accessibility and use of cannabis and the potency of cannabis formulations.


The diagnosis of ADHD is based on real-life attentional-executive deficits, but they are harder to detect in adults than in children and objective quantitative measures reflecting these everyday problems are lacking. We developed an online version of EPELI 3D videogame for naturalistic and scalable assessment of goal-directed action and prospective memory in adult ADHD. In EPELI, participants perform instructed everyday chores in a virtual apartment from memory. Our pre-registered hypothesis predicted weaker EPELI performances in adult ADHD compared to controls. The sample comprised 112 adults with ADHD and 255 neurotypical controls comparable in age , gender distribution  and educational level. Using web-browser, the participants performed EPELI and other cognitive tasks, including Conner’s Continuous Performance Test (CPT). They also filled out questionnaires probing everyday executive performance and kept a 5-day diary of everyday prospective memory errors. Self-reported strategy use in the EPELI game was also examined. The ADHD participants’ self-ratings indicated clearly more everyday executive problems than in the controls. Differences in the EPELI game were mostly seen in the ADHD participants’ higher rates of task-irrelevant actions. Gender differences and a group × gender interaction was found in the number of correctly performed tasks, indicating poorer performance particularly in ADHD males. Discriminant validity of EPELI was similar to CPT. Strategy use strongly predicted EPELI performance in both groups. The results demonstrate the feasibility of EPELI for online assessment and highlight the role of impulsivity as a distinctive everyday life problem in adult ADHD. Even though ADHD symptoms typically manifest in real-world situations with multiple simultaneous attentional executive demands , objective assessment methods typically focus on measuring a single rather narrow cognitive domain at a time (e.g. inhibition). This could be one reason why objective methods are generally considered to have a limited role in clinical characterization of ADHD3. It has been proposed that more ecologically valid paradigms simulating real-world situations where the symptoms are manifested are needed to quantify the challenges that individuals with ADHD may encounter in their everyday lives7.Attention deficit hyperactivity disorder (ADHD) is diagnosed on the basis of a variety of attentional executive deficits hampering everyday life that span the three broad domains of inattention, hyperactivity, and impulsivity8. Only about 15% of the ADHD cases detected at childhood still meet the criteria for a diagnosis at adulthood, but about 65% of the cases show signs of residual impairment10. Overall, the prevalence of adult ADHD is approximately 2.6%11. According to some sources, only about half of the neuropsychological studies report clear cognitive deficits in ADHD adults14. Besides heterogeneity in the symptoms and mismatch between real-life problems and isolated tasks, detection of adult ADHD is complicated by the high prevalence of various comorbid disorders such as mood disorders15, which also appear to be different between females and males17. Moreover, there is evidence that the core ADHD symptoms in males are more noticeable and easier to detect than in females18. Finally, the role of possible compensatory strategies to manage situations with high executive demands may also increase from childhood to adulthood20. All aforementioned factors make adult ADHD a challenging target for reliable objective measurements.Some of the symptoms, especially hyperactivity, are diminished during the course of development, making detection of ADHD more difficult in adults than in children21. As almost any goal-directed behavior relates to ‘future actions’, the umbrella of PM has been noted to explain more than 50–70% of the memory failures in everyday life23. There are some studies examining the role of specific aspects of PM problems observed in ADHD, especially monitoring and doing things in time, and reacting to external memory cues28. As PM is needed basically in all sorts of everyday goal-directed behaviors, there are also various attentional executive functions such as multitasking or monitoring that support PM performance and can be quantified in a PM task.One cognitive construct that covers various aspects of everyday life attentional executive functions is prospective memory (PM)29. Inspired by their Multiple Errands Test and its virtual reality (VR) variant30, we have recently developed the EPELI (Executive Performance of Everyday Living) task employing game technology and operationalized several metrics to capture challenges that individuals with ADHD have in their daily life7. With the original head-mounted display (HMD) version of EPELI developed for studying children, we demonstrated that ADHD in school-age children is associated with a lower number of correctly performed tasks, poorer task efficacy  and navigation efficacy , higher activity level (indexed by Controller motion), and higher rates of all actions7.Shallice and Burgess developed a framework for PM studies where the participants perform several everyday memory tasks in real surroundings https://osf.io/3y5je/), we used a Web browser -based version of the new adult EPELI31 to study the sensitivity of this task in detecting cognitive deficits in ADHD adults in a large-enough sample that permits exploring the role of important background factors. In addition to EPELI, the participants conducted a Continuous Performance Test (CPT) version, a paradigm that can be considered as the gold-standard neuropsychological task in the ADHD assessment32. As this is the first EPELI study in an adult ADHD population, we decided to use the measures validated in our studies on children7 as the main dependent variables.In this preregistered study  analysis of response latencies (RLs) in EPELI, employing similar methods that have been previously used in CPT studies33. As we did not know beforehand how many female participants with ADHD would sign up for the study, we did not pre-register separate analyses related to possible gender differences. However, as an unexpectedly large proportion of females participated in the study, we included a post-hoc analysis examining possible sex effects. This was motivated by the fact that gender differences are known to play a major role in ADHD17 but these differences are still not well understood in the adult population.Based on the few earlier adult ADHD studies indicating problems in complex PM tasks, our first pre-registered hypothesis was that at the group level, the ADHD group shows poorer performance on EPELI than the controls. The second hypothesis was that even at the individual level, EPELI is useful in differentiating between ADHD and neurotypical individuals. Regarding CPT, we included mean reaction time and its standard deviation as dependent variables34 (CCSM). In the control group, the following exclusion criteria regarding CCSM were used: any degree of suicidality (i.e. any other score than 0 in item 11), and sum scores of 3 or more in the domains of depression, mania, or anxiety . In the ADHD group we initially used the same exclusion criteria as in the control group. However, after prescreening 855 ADHD participants, we relaxed the CCSM criteria for the ADHD participants to obtain a sufficient sample size in the ADHD group. The observed amount of comorbid symptoms was not surprising considering the commonness of mood and anxiety symptoms in ADHD adults15, with roughly 77% of individuals having one or more comorbid disorders35. The updated criteria were: suicidality score less than 2 (i.e. at most slight symptoms), and depression and anxiety scores less than 4 (i.e. at most moderate symptoms). Other criteria remained the same for both groups.The final sample in this study contained 112 adults with ADHD and 255 neurotypical controls. The inclusion criteria for the control group were normal or corrected-to-normal vision; no color blindness; no neurodevelopmental disorders; no neurological illness that affects the participant’s current life; never diagnosed with severe depression, bipolar disorder, psychosis, or schizophrenia across the lifespan; and no self-reported substance-abuse problem. Further exclusion criteria were set according to the DSM-5 Cross-Cutting Symptom Measure36 indicated symptoms consistent with ADHD, based on the instructions of the questionnaire (i.e. at least four responses of the participant were in the risk area as defined in the form).The ADHD group was defined as those responding positively to a question probing whether they have been diagnosed by a licensed medical professional as having ADHD or ADD. Additionally, in the ADHD group we only included participants whose responses in the Adult ADHD Self-Report Scale (ASRS-v1.1) Symptom Checklist (part A)n in the control group was 261 and in the ADHD group 113. Additionally, one participant in the ADHD group and six participants in the control group were removed due to missing background data that was necessary to determine their eligibility in the study, resulting in final n = 112 in the ADHD group and final n = 255 in the control group. There was some missing data in this final dataset: Eight participants in the ADHD group and 11 in the control group were missing EPELI data due to technical problems and 34 participants in the ADHD group and 45 participants in the control group dropped out after the first testing session. Outlier exclusions based on task performance are described in the section “Outliers” in the Supplementary materials, methods, and results.Before further exclusions, the sample size was 293 in the control group and 121 in the ADHD group. Next, we removed all participants who reported that they had cheated or were intoxicated in any of the sessions. This resulted in deleting 8 participants (6.6%) in the ADHD group and 32 participants (11%) in the control group. After this deletion, The participants were recruited on the crowdsourcing site Prolific. We targeted participants of age 18–50, currently living in the UK, whose first language was English. The data was gathered between August and December, 2021 .Altogether 112 participants with ADHD and 255 controls took part in the five main testing sessions. All tasks and questionnaires were counterbalanced between the participants, except for EPELI which was always administered in the first session and the diary questions which were administered in every session. All sessions were performed on separate weekdays and the whole experiment was required to be completed within 14 days. Duration of each session was about 40 min. The participants received £16.67 compensation for taking part in the study if they completed all sessions. If they did not complete all sessions, no compensation was given. Only those participants who completed the first session were allowed to partake in the other sessions; this was done to avoid missingness in the EPELI data.37. SOILE was used to gather all other data except EPELI, which is an Unity game developed by Peili Vision LTD (www.peilivision.fi). The participants were redirected from the SOILE testing platform to the EPELI game server by clicking on a link, and the game was run in a separate browser window. After conducting EPELI, the participants went back to SOILE and were asked to fill out the Virtual Reality Sickness Questionnaire (VRSQ)38 and a shortened version of the Presence Questionnaire39, screening possible side-effects and sense of presence, respectively.The participants started the experiment by logging in to the in-house SOILE online testing platform40. Field of view in the game is controlled by a mouse. The participant navigates in the apartment by teleporting, which takes place by directing a crosshair at the center of the screen on white spherical hotspots on the ground and clicking the left mouse button. A clock with running time (reset at the beginning of each block) appears in the lower right corner when the participant clicks the right mouse button. Objects can be grabbed and laid on surfaces by clicking on them.The browser-based EPELI game begins with a guided hearing threshold and volume adjustment, after which the participant conducts a practice session supervised by a virtual character, ‘Vincent’The adult version of the EPELI task consists of 10 blocks. In the beginning of each block, Vincent first orally introduces the theme of the block and then gives a list of 7–8 everyday tasks to be conducted in the virtual apartment. Besides standard tasks (seven per block), five time-based tasks and five event-based tasks were included. The ongoing event-based task was to take the teddy bear to the sofa if it appears to the visual field. This instruction was given only once before the game execution started.41, the main dependent variables were: (1) Total score (correctly performed subtasks), (2) Task efficacy , (3) Navigation efficacy , (4) Controller motion (here the controller is computer mouse), (5) Actions , (6) Time-based subtask score, (7) Number of clock checks (for the blocks with a time-based subtask), and (8) Event-based subtask score.Following the earlier child version of EPELI45, as well as our expectations concerning PM-related strategies, a coding scheme was devised . Each open-ended response for each of the 10 EPELI task blocks was coded by two independent raters. The raters coded each strategy report on three variables: the first reported strategy type , the total number of strategy types reported, and the total number of specific strategy details given. Here we focused on the first variable, the primary strategy type, that was dichotomised as strategy use vs. non-use in the analyses.After each block, the participants responded to the following open question: “Please describe in as much detail as possible how you solved the previous set of tasks”. This was included to identify strategy employment. Based on our earlier work on coding open-ended memory strategy reports47 (ICAR16) tapping participants’ general cognitive ability; (3) CPT48; (4) instruction recall task (IR) that tapped episodic memory processes similar to those required in EPELI but without action execution that characterizes EPELI performance7; and an episodic memory task called Word List Learning (WLL)45. After each task, the participants also rated the verisimilitude of the task, i.e. its likeness to real life (“How much did the task resemble your everyday life?”), answered on a seven-point scale. In addition, they rated task difficulty and their motivation, both answered on a five-point scale. During the whole measurement period with five sessions in different days, the participants also kept an everyday PM diary that included six questions 23.In addition to EPELI, a battery of other computerized tasks was administered . These included, (1) two conventional PM tasks, the Cruiser and Matching tasks; (2) the International Cognitive Ability Resource with 16 items49. A logistic regression classifier was trained to investigate discriminability between participants with ADHD and control participants based on EPELI and CPT data. For IIV analysis, RLs in EPELI were defined as the time interval between two consecutive mouse clicks . Ex-Gaussian modelling was used50 to separate extremely slow responses presumed to be associated with attentional lapses. The ex-Gaussian model assumes that a positively skewed RL distribution can be represented as a mixture of an exponential component  and gaussian distribution . Strategy use across the two groups and their associations with task performance were examined with Bayesian Linear Mixed Effects Models (LME) using R version 4.0.0 with the “BayesFactor” package51. This analysis was only performed for the dependent variables Total score, Navigation efficacy, and Task efficacy. The action-related variables were ignored because actions other than those resulting in correct performance were not expected to be directly related to the memory strategies. Moreover, time- and event-based variables were ignored, as these tasks did not provide blockwise data which is required for the LME analysis.In addition to frequentist p-values, Bayes factors (BF) were calculated for each analysis when applicable . Receiver Operating Characteristic (ROC) curve analysis was done in Python with scikit-learn package v1.0.2The study was approved by the Ethics Board of the Departments of Psychology and Logopedics at the Åbo Akademi University, Finland. Informed consent was obtained from each participant and all methods were performed in accordance with the relevant guidelines and regulations .Characteristics of the participants can be found from Table Participants with ADHD reported more problems in attentional executive functions than neurotypical controls in all questionnaires, except MPMI Table . Evidenc10 = 10.50). Frequentist t-tests showed worse performance in participants with ADHD also in Task Efficacy (p = 0.015) and Navigation Efficacy (p = 0.047), although Bayesian evidence for these group differences was only anecdotal. As a post hoc analysis, we also examined how performance in these variables that showed group difference evolved over time; there were no Group × Block interactions , clicked more on doors and drawers, grabbed more objects, and their click distance was higher (i.e. they interacted with objects further away) Table .10 = 1.56), but not in Navigation efficacy . In Bayesian terms, these effects are consistent with the analysis where the covariate was not included. However, ANCOVA including average playtime as a covariate did not show evidence for the effect of Group on Actions . As to the post hoc variables, the effects of Group on Total click distance , Empty clicks , and Number of grabbed objects  remained. However, there was no longer an effect of Group in Clicks on doors and drawers .Due to the group difference in everyday playtime  and Correct time-based tasks , and with several variables related to interaction with the game environment. All correlations between age and EPELI variables are reported in Appendix We also examined whether age was associated with EPELI performance. There was strong evidence that age correlated negatively with Total score  as a predictor, resulted in 0.61 Area Under the Curve (AUC) score that was higher than for randomly labeled permutations (t(58) = 10.17, p < 0.0001). Similar classifiers trained separately for each gender yielded AUC score of 0.64 for male data, and AUC score of 0.58 for female data. The gender difference in the classifier performance was significant (t(58) = 7.47, p < 0.0001). A classifier trained using variables related to subcategories of Total Actions  provided higher performance (t(58) = 14.63, p < 0.0001) with an AUC score of 0.65. Analysis of feature importance showed that the same classification accuracy can be achieved with reducing the features to two variables: relevant clicks, and number of grabbed objects. Training on the data separated by gender resulted in AUC = 0.67 for males, and AUC = 0.65 for females. There was no difference in performance between classifiers with male and female data here.IIV analysis included 162 089 observations, with mean RL in the clinical group 1.94 s (± 1.02 s), and in the control group 2.06 s (± 1.1 s). The ex-Gaussian analysis revealed that mu was shorter for ADHD participants than for controls across all clicks and for irrelevant clicks. There were no group differences in sigma or tau across all clicks or for relevant and irrelevant clicks analyzed separately .10 = 0.28). To examine associations between strategy use on performance across the groups, we specified LME models with the outcome variable of interest as dependent variable. Group (ADHD/controls), Strategy use (yes/no), and their mutual interaction served as fixed effects, and participant and block as random effects. There was very strong evidence for a main effect of Strategy on Total score (Mdiff = − 0.55 95% HDI [− 0.62 – − 0.49] BF10 > 100), Navigation efficacy (Mdiff =  −  0.01 95% HDI [− 0.01 – − 0.00] BF10 > 100), and Task efficacy (Mdiff = − 0.05 95% HDI [− 0.06 – − 0.04] BF10 > 100), suggesting that those participants using strategies performed better on these three performance measures, as compared to those not using strategies. However, the BF indicated evidence against an interaction effect between Group and Strategy on Total score (Mdiff = 0.01 95% HDI [− 0.05 – 0.07] BF10 = 1/16.67), Navigation efficacy (Mdiff = 0.00 95% HDI [0.00 – 0.00] BF10 = 1/16.67), and Task efficacy (Mdiff = − 0.01 95% HDI [− 0.01 – 0.00] BF10 = 1/14.29), indicating that there was no difference in how strategy use was associated with performance across the two groups.In the ADHD and the control group, spontaneous strategy usage was deployed in 39.1% and 44.8%, respectively, across all the blocks. Strategy use did not differ between the groups  = 15.35, p < 0.0001). When the classifier with CPT data was conducted separately for male and female data, AUCs were 0.65 and 0.71, respectively. The difference between these two classifiers is statistically significant (t (58) = − 15.39, p < 0.0001).To shed further light on how EPELI and CPT are related to everyday ADHD symptoms, we examined the associations between the self-rated symptoms , the EPELI variables, and CPT. The intercorrelations were calculated for the pooled data, and for the ADHD group only. The correlations are shown in Appendix 10 = 3.48 – 11.61). In the same vein, ASRS-A score was positively correlated with CPT Commissions  and Mean SD . Regarding the Diary score and EPELI interrelationships, there was evidence for a positive correlation with Empty clicks , and anecdotal evidence for Total actions, Click distance, Number of grabbed objects, and Total time . The Diary score showed strong evidence for a correlation with CPT Mean SD , and anecdotal evidence for a correlation with CPT omissions .The pooled data indicated that ASRS-A score was positively associated with EPELI Total actions, Click distance, Empty clicks and Number of grabbed objects . The Diary score correlated with Controller motion, Total actions, Total time and Clicks on doors and drawers .Within the considerably smaller ADHD group, some of the correlations listed above were observed, but only with anecdotal evidence. ASRS-A was associated with CPT Commissions  included in this study. The only EPELI main measure showing clear evidence for a group difference was the number of actions conducted during the game play, which was higher for participants in the ADHD group. However, after controlling for everyday video gaming frequency where group differences were observed, the difference remained significant only in specific subtypes of actions (i.e. empty clicks and how many objects the participant grabbed in the game). In this analysis, two other measures besides actions, namely Task efficacy , and Navigation efficacy , also showed modest group differences. Actions and other related EPELI variables, as well as several CPT variables, were also correlated with self-reported ADHD symptoms across the whole sample. To a less extent, this was true also for correlations with the Diary summative score for everyday prospective memory failures.We conducted a large-scale online study examining goal-directed behavior in adults with ADHD using a new EPELI game that simulates everyday life PM conditions with high attentional executive demands. We carefully screened 14 443 participants with a Prolific crowdsourcing platform and identified 337 eligible candidates among 2374 adults with ADHD of whom 112 took part in the study. Based on earlier PM studies in adults with ADHD53. Also considering the strong evidence that the symptoms17 and cognitive performance54 differ between female and male adults with ADHD, post hoc analyses with gender as a factor were conducted. As expected, this analysis revealed more prominent group differences in men than in women in the EPELI Total score. Main effects of gender were also observed in several other variables, for example, Task Efficacy, Navigation Efficacy, Actions, and Controller motion.The gender distribution among ADHD participants who registered for the study was not representative of the general population of people with ADHD where the ratio is about 3:1 of men to womenTaken together, this study indicates that the present, new online version of EPELI is feasible to use with adults, and that especially certain aspects of actions taken in the game can differentiate adults with ADHD from neurotypical controls. At the same time, the group differences and correlations with subjectively reported symptoms in this particular prescreened sample were more limited than in earlier EPELI studies in children. We discuss these results in more detail below.7, we observed that ADHD participants were overall more active in interacting with the virtual environment than controls. In previous studies, we have interpreted higher numbers of actions in ADHD participants to reflect impulsive behavior associated with irrelevant objects, as in the child version much of these extraneous actions relate to interacting with attractive objects . Impulsive behavior is a characteristic for ADHD, not only in children but also in adults55. Regarding types of irrelevant actions, we found that ADHD participants in the present study especially picked up irrelevant objects more frequently but showed also irrelevant actions in opening doors and drawers and in button presses to areas where there were no objects . In a previous child study with VR goggles, we recorded the gameplay videos and further labeled the actions based on the object salience. However, the group differences appeared not to be explained by stimulus-driven capture of attention6. The present findings align with this, as there were group differences also related to exploratory actions that do not specifically relate to object interaction. Participants with ADHD also interacted with the objects from longer distances. As ADHD participants played video games more often in their free time and this factor was explaining group differences in Total actions, these results should be interpreted with caution. It should be noted that the association between playtime and its link to EPELI performance is not entirely straightforward to interpret, as there is evidence that gaming may influence cognitive functions56, and there is previous evidence that ADHD symptoms are associated with gaming57. Nevertheless, the playtime variable explained part of the group differences and only the subcategories of actions continued to show group differences after accounting for participants’ playtime.Consistent with the two studies conducted in children55, the opportunities for implementing impulsivity measures reflecting unusual interaction with life-like situations have not been examined previously in adult ADHD. Hence, despite the present rather modest group differences, new paradigms like EPELI provide ways to operationalize new ecologically valid measures for quantifying behavior in complex situations. Examining spontaneous behavior in an open-ended environment with high cognitive demands may thereby open a new avenue to detect deviant features in typical behavior in adults with ADHD that has been understudied so far5.Together our findings related to atypical interaction with the environment indicates less efficient behavior that did not result in poorer task performance. Hence, it is not likely to reflect cognitive capacity as such. In EPELI, both the targets of actions and frequency of actions relate to volitional behaviors that participants initiate to interact with an open-ended environment. While the operationalization of impulsivity has been under active discussion during the past decades7. As Task efficacy is a ratio between total score and all actions and as there was no group effect in total score, this difference mainly comes from differences in task-irrelevant behavior. This could be the situation also regarding the Navigation efficacy, another relative measure of EPELI. Such deviant way of interaction when moving around in the apartment is further evidenced by the group differences in the average click distance. Since this is the first study in adults with ADHD where a naturalistic PM task was used, a direct comparison to previous findings cannot be established. However, considering the prior studies in ADHD adults indicating that even at group level systematic differences between ADHD participants and neurotypical controls are rarely observed12, it is not surprising that the group differences were small also in this sample. Findings in this sample reveal some potential mediating factors that should be accounted for when analyzing real-world goal-directed behavior in ADHD adults. Such factors include at least the use of strategies that ADHD adults may have acquired to compensate for their challenges20 and the effect of gender and age. More research on these factors that modulate group differences is needed. Due to the complex effects of age, gender, gaming background and other factors that are likely to be intertwined, larger samples are needed to tease apart these effects. A larger dataset or some other type of strategy manipulation , would also be needed to obtain enough observations per task block to probe the efficacy of specific strategy types in ADHD. Meanwhile, a separate study on strategy employment by the much larger control group of ours46 identified Grouping as a particularly effective specific strategy for EPELI.In our child studies, Task efficacy has been the most sensitive measure in differentiating ADHD participants from controls8. Consistent with these findings, Controller motion was negatively correlated with age in the present sample. It could be that such hyperactivity effects can be only observed in special population and perhaps with particularly sensitive methods such as sensor-based motion tracking of excessive activity levels in daily life over several days58.With regard to measuring hyperactivity, our child studies used the movement of a hand controller instead of mouse movement. It is possible that the hand controller measuring movement of the whole hand when it is moving freely could be a more sensitive indicator of hyperactivity than mouse movement that is more restricted, rigid and probably also overlearned. Alternatively, it could be that hyperactivity is simply more difficult to detect in adulthood. Hyperactivity is typically the symptom domain where the spontaneous remediation across the development is largest7, which was expected based on the limited group differences in EPELI performance. This was the case also for CPT and the performance in EPELI and CPT were correlated, suggesting that the two tests may measure partially the same construct.Regarding the classifiers, EPELI’s performance was clearly lower in this adult sample than in previous EPELI studies in children59. Consistent with our findings in children, our present results did provide some evidence that ADHD adults may, however, be faster in their interaction with the environment than control participants. The differences between EPELI and CPT in RLs and also other measures could be partially related to the continuously changing nature of the EPELI tasks. This may help individuals with ADHD to attend better to EPELI than to CPT60. Due to their different properties  these two tasks may have complementary features.To further compare EPELI and CPT, we also included RL measures including IIV. However, unlike in the CPT where standard deviation of RLs was higher in the ADHD group as compared with the control group, we did not find evidence of such a difference suggesting fluctuation of sustained attention in EPELI. In children, we have preliminary evidence of higher IIV in the ADHD group than in the control group33 and hence we can conclude that adult ADHD is simply a challenging target for quantitative behavioral assessment61. Despite the need for further examining the metrics that effectively differentiate adults with ADHD from neurotypical adults, this study provides important evidence demonstrating that online platforms for automatized neuropsychological assessment have high potential and currently remain vastly underinvestigated as a means to support clinical assessments. Unlike the child version that is currently already taken into clinical use in Finland, the present results do not yet provide clear evidence that the online version of EPELI could identify consistent behavioral problems in ADHD adults at the level that would be sufficient for diagnostic purposes. However, as the reliability and validity (verisimilitude) of the test is reasonable31, the broad neuropsychological characterization that EPELI provides could possibly be useful for other clinical purposes, similar to many other tests of executive functions that are not able to detect the presence vs. absence of ADHD in individual participants. Especially in crisis situations like COVID-19 where regular clinical assessments are difficult to perform, or when there are other practical or resource related issues hampering conventional examination, home-based assessment could be considered. Possibly the clinical applicability of EPELI could be further improved by including other metrics such as eye tracking, which we have also used in a child study6. This could provide more information of the group differences and particularly those related to inattention for which we did not here find as clear evidence as for impulsivity.In general, considering the previous literature these classification rates were not unexpectedly low17. In keeping with these clinical findings and our previous study in children, clear gender differences in EPELI were observed. However, the gender differences explained group differences only in the Total score where female participants performed on average better than the male participants. Also in children, we have observed gender differences favoring female participants in the Total score41. The other main EPELI measures for which a main effect of gender was observed in this study were Task efficacy, Navigation efficacy, Total actions, and Controller motion. Out of these measures, gender differences in children were observed in all other measures, except Controller motion. However, unlike in the present study, we have not yet been able to analyze interactions between gender and group in children, as the sample sizes have been too small for this purpose. Hence, this is the first study reporting how gender may influence the detection of group differences when measuring goal-directed behavior and prospective memory in an open-ended task simulating real-world situations.Gender differences in the symptoms have been a key topic in recent ADHD research, as their existence could considerably influence the accuracy of detecting ADHD in women as well as increase the risk for misdiagnosis11. We can only speculate whether this was due to some very practical reason , indicates women being generally more active in participating to this type of studies , or if women with ADHD are somehow more interested to learn from their condition by experiencing how the related problems can be assessed. Whatever the reason, the non-representative gender distribution should be carefully considered when interpreting our results. In case the group of ADHD females that signed up for the study was somehow not representative of the general population, it could also influence the group differences. However, based on the present findings there is no clear evidence of that (e.g. the self-reported symptoms were prominent in this ADHD population).Why gender differences were observed also in other measures than Total score goes beyond this study. To further interpret such effects, it would be useful to go deeper into the background measures and first see if some mediating factors may cause these effects . For such purposes, the ADHD sample reported here is rather small. With regard to the gender differences within the neurotypical sample, multivariate analyses such as structural equation model would be possible to conduct in a separate study, and the background data collected here at the prescreening stage provides opportunities to further examine the gender differences in ADHD with respect to the symptoms and self-rated attentional-executive problems. One open question regarding the gender differences is also why women with ADHD appeared to be clearly more active in signing up for this study than men, especially considering the gender-specific prevalence ratesThere are some limitations that should be considered when interpreting the results. First, although the sample size is reasonable, it is far from being representative of the population, as less than 10% of the ADHD adults who took part to the first prescreening ended up in this sample . The present participants represent the minority of ADHD population who have minimal other symptoms. In this narrow population, a clear majority of adults with ADHD appeared to be females. Rare use of medication could also hint that the present population represents those with mild symptoms. It is possible that online data collection could involve some other limitations too, such as uncontrolled measurement situation, or even cheating. However, the participants had no incentive not to report if they cheated and if they did, they were omitted from the sample.This study showed that online cognitive testing with a 3D videogame is a feasible way to assess variety of attentional executive functions in adults with ADHD. However, in the present sample with minimal rates of other symptoms , the group differences were quite limited both in EPELI as well as in other tests included in the test battery. In comparison to the previous child studies where the eligibility was not tested at the level of single symptom items, as it rarely happens in clinical studies, another clear difference was that the gender distribution was not representative of the ADHD population, with several times more females than would be expected. These three factors together – studying the minority of individuals with ADHD who report minimal number of other problems, targeting adults whose symptoms are generally milder than those of children, and sampling mostly females whose symptoms are more difficult to detect than in males—make a difficult combination for any test. At the same time, the present observations in this population give important information, as objective detection of ADHD in adults is generally an unmet challenge. What is probably most challenging is to be able to differentiate ADHD symptoms from concomitant symptoms of mood or anxiety disorders, and other psychiatric symptoms.6. Also more powerful data analysis methods, such as machine learning, employed with the rich data could be helpful58, especially in attempts to differentiate multiple covarying background factors. Considering the clinical applications, one potential use of EPELI could be in monitoring of the treatment effects . However, more research is still needed to demonstrate test–retest reliability of EPELI and to account for the possible learning effects reported in our recent study62. Such learning effects could be circumvented, for instance, by creating parallel versions that are equally demanding. Altogether, this initial online video gaming study opens interesting avenues for further research and development of clinical assessment methods. Even considering these circumstances, some group differences were found. In EPELI, the group differences were mostly observed as unusually high number of task-irrelevant actions, mostly related to unnecessary interaction with the objects, opening doors or drawers to check what is found, and interacting over higher distances to manage the tasks. Such objective description of spontaneous behavior in open-ended lifelike situation brings a valuable new contribution to the field. While the cognitive capacity in our sample was apparently high, there were clear qualitative differences in how the individuals interacted with the virtual environment. More work is still to be done to fully understand the complex factors influencing the detection of ADHD adults with 3D games such as EPELI, as clear gender differences and a moderating effect of playtime were observed. There are various untested opportunities in developing the sensitivity and specificity of EPELI in detecting ADHD in adults. There are other possible factors that could be measured with similar tasks, for example, the role of the available reward or distractibilitySupplementary Information 1.Supplementary Information 2.Supplementary Information 3.


IntroductionKingdom of Saudi Arabia (KSA) is 9.2%.Attention-deficit/hyperactivity disorder (ADHD) is the most common behavioral disorder in children and is described as a disease involving loss of self-control. The core symptoms of ADHD are inattentiveness, impulsivity, and motor unrest. Furthermore, poor concentration, distraction, hyperactivity, and poor academic achievement at school or at home are other symptoms. ADHD, like other prevalent medical disorders such as asthma and schizophrenia, may be impacted by several genes and has multiple contributing causes that are not all related to each other. The management of ADHD contains multimodal treatments, starting with psycho-education for parents and the child or adolescent patient in an age-appropriate manner called cognitive behavioral therapy. The worldwide prevalence rate of ADHD among children is 7.2% and, in some countries, can be higher and reach 15.5%; studies show the average prevalence of ADHD in the MethodA descriptive cross-sectional study was conducted from December 2022 to January 2023 among children who live in Makkah between the ages of 4 and 14 years old via an online survey that contained the ADHD Rating Scale-IV for parents/teachers, and caregivers based on Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV criteria. The scale was translated into Arabic and contains 18 questions about ADHD symptoms.ResultThe overall prevalence of combined ADHD among children in Makkah is 52.5% (n=203 out of 387); most of them were males (30.8%) and 21.7% were females (P=0.09), and most of the combined ADHD prevalence was between the ages of 11 and 14 (20.4%), then the age of 4-7 (16.8%), and 15.3% of them were between 8 and 10 years old. Additionally, the results show a higher prevalence of combined ADHD in Makkah city (33.1%) (n=121) in comparison to rural areas 19.4% (n=82) (P=0.132).ConclusionThe aim of the study was to measure the prevalence rate of ADHD in the Makkah region. The study showed a high prevalence rate of ADHD (52.5%) among children in Makkah; the study was carried out online using an ADHD scale, and more accurate results could be found by using face-to-face interviews and including both parents and teachers. Attention-deficit/hyperactivity disorder (ADHD), the most common behavioral disorder in children , is descThe core symptoms of ADHD are inattentiveness, impulsivity, and motor unrest . FurtherADHD, like other prevalent medical disorders such as asthma and schizophrenia, may be impacted by several genes and has multiple unrelated contributing causes . Some stThe management of ADHD comprises multimodal treatments starting with psycho-education to parents as well as the child or adolescent patient in an age-appropriate manner through cognitive behavioral therapy, especially if they have mild symptoms or are under the age of six years ,3. ThereThe worldwide prevalence rate of ADHD among children is 7.2%, with it reaching 15.5% in some countries . The aveStudy setting and populationA descriptive cross-sectional study was conducted from December 2022 to January 2023 among children between the ages of 4 and 14 years who live in Makkah through an online survey.Sampling methodAccording to the general authority for statistics in Saudi Arabia, the number of children in the Makkah region is approximately 1,300,000. Hence, considering the 9.2% prevalence rate of ADHD in the KSA  and keepData collectionData were collected using a self-administered online questionnaire on Google Forms, completed by parents who have children between the ages of 4 and 14 years. The form contained two sections. The first section collected demographic data, including the child’s age  and gender, where the child lives , and current academic level . The second section contained the ADHD rating scale-IV for parents/teachers and caregivers based on the DSM-IV criteria. The scale was translated into Arabic and contained 18 questions about ADHD-HI and ADHD-AI symptoms. The odd numbers of items in the scale represented the IA type, and the even numbers represented the HI type. The answers to each question were provided on a four-point scale from 0 to 3 according to ADHD symptoms: 0, never/rarely; 1, sometimes; 2, often; and 3, very often. Both AI and HI were required to be present to diagnose the child with ADHD. The ADHD rating scale-IV has been validated and used in a previous study in Vietnam .Ethical considerationsAll participants provided informed consent before contributing to this study and were aware that their personal information would be kept anonymous and protected. Ethical approval for this study was provided by the Biomedical Research Ethics Committee of the Faculty of Medicine, Umm Al-Qura University .Statistical software, IBM SPSS version 27 , was used to analyze the data. The statistics were presented using numbers, percentages, means, and standard deviations. The connection between variables was evaluated using the chi-square test, with a level of significance of p-value < 0.05 and a confidence interval of 95%. A total of 400 participants completed the questionnaire; 13 were excluded for refusing consent at the beginning of the questionnaire, and the final number of participants was 387.Out of the 387 participants, 244 (63%) were boys and 143 (37%) were girls. Most of them were aged 11-14 years , and the minority of them were aged 8-10 years . Furthermore, 244 (63%) of the participants were from Makkah city, and 143 (37%) of them lived in rural areas. Moreover, the majority were students at primary school , followed by secondary school , and pre-school  , with a rate of 30.8% for boys and 21.7% for girls (P=0.09). The prevalence rate for those aged 11-14 years was 20.4%, followed by those aged 4-7 years (16.8%) and those aged 8-10 years , with more males affected than females. Some studies have estimated that every classroom has at least one child suffering from this neurodevelopmental disorder ,2,8. Bas.2%, withThe increase in the prevalence rate of ADHD can affect social life and academic achievement at school for children, and we must increase awareness among families about this disease to diagnose it early and deal with it. The study has many limitations that might influence the interpretation of the findings. First, a larger study population would provide more significant results. Second, we used a self-administered online questionnaire translated from English into Arabic, which might have caused misunderstandings of the questions. Third, our study was used for screening purposes only, and this research was carried out as a college course requirement. Lastly, the questionnaire was distributed only to parents; instead, it should be completed by both parents and teachers.The aim of the study was to measure the prevalence rate of ADHD in the Makkah region. The study showed a high prevalence rate of ADHD (52.5%) among children in Makkah, with boys suffering more than girls. The study was carried out online using an ADHD scale, and more accurate results could be found by using face-to-face interviews and including both parents and teachers. We also recommend expanding the study across the Kingdom and increasing the sample size to get more accurate results on ADHD prevalence.


An accurate and early diagnosis of attention deficit hyperactivity disorder can improve health outcomes and prevent unnecessary medical expenses. This study developed a diagnostic support model using a machine learning approach to effectively screen individuals for attention deficit hyperactivity disorder. Three models were developed: a logistic regression model, a classification and regression tree (CART), and a neural network. The models were assessed by using a receiver operating characteristic analysis. In total, 74 participants were enrolled into the disorder group, while 21 participants were enrolled in the control group. The sensitivity and specificity of each model, indicating the rate of true positive and true negative results, respectively, were assessed. The CART model demonstrated a superior performance compared to the other two models, with region values of receiver operating characteristic analyses in the following order: CART (0.848) > logistic regression model (0.826) > neural network (0.67). The sensitivity and specificity of the CART model were 78.8% and 50%, respectively. This model can be applied to other neuroscience research fields, including the diagnoses of autism spectrum disorder, Tourette syndrome, and dementia. This will enhance the effect and practical value of our research. Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder among children and teenagers. Its symptoms in youths include inattentiveness, poor impulse and emotional control, and hyperactivity. Youths with ADHD tend to be disruptive in classroom environments and have learning and socialization difficulties and mood disorders . ChildhoThe global prevalence of ADHD among school-aged individuals has seen an increase ,4. The DRecent studies have investigated the use of machine learning technologies in an ADHD diagnosis. For example, association rule mining was applied to analyze ADHD-related comorbidity by using data from the National Health Insurance Research Database in Taiwan . To evalThe participant selection process relied on evaluations performed by a team of experienced child psychologists at a medical center. The participants underwent the test of variables of attention (TOVA) ,16, whicInattention was evaluated by dividing the number of times an omission error was made by the total number of target objects (with the subtraction of the number of anticipatory responses that were made). Impulsivity was evaluated by dividing the number of commission errors that were made by the total number of distractors (with the subtraction of the number of anticipatory responses that were made). The test included six stages . Each stSeveral studies have investigated the application of machine learning in medicine ,18,19,20X is the set of the diagnosis index, Y is the set of all cases, ijx is the [j (jY)]th index of the test of case i (iX), and iy is a binary variable that indicates whether case i involves ADHD (1 indicating YES and 0 indicating NO). The regression model function can thus be expressed as iy = F.Logistic regression model. In this model, CART ,22,23,24iX is the neuron input, which is the index that predicts ADHD; iW is the weight of iX; b is the bias that can be used as a limited value to enhance the model; S is the summation, in which each input is multiplied by the weight and then totaled; φ is the activation function, which is usually a nonlinear function, includes various types, and is the output required to map the value of S; and Y is the output, which indicates whether an individual has ADHD.Neural network ,26. The To achieve an adequate performance and improve the accuracy of an ADHD prediction and diagnosis, diagnostic support models rely on valid indices. We developed the following three diagnostic support models:The ADHD score is the output (dependent) variable, and the other five variables are the input (independent) variables. The three models were trained and tested in SAS Enterprise Miner (EM) 14.2, Cary, North Carolina, USA , and theWe utilized an ROC analysis to evaluate and compare the effectiveness of the diagnostic support models. The discriminative ability of the models was assessed using a 2 × 2 confusion matrix, as represented in Sensitivity and specificity make up the effectiveness index of the ROC analysis and represent the ratio of true positive (TP) and true negative (TN), respectively.We enrolled participants with ADHD who had been diagnosed according to the DSM-5 criteria. Subsequently, we conducted an analysis using data from a sample of 95 children, aged 6 to 12 years. Among them, there were 74 children in the ADHD group and 21 in the non-ADHD (control) group, with an average age of 9.18, as shown in The results of the ROC analysis are shown in The CART was more effective than the logistic regression model and neural network, with the response time being a critical decision variable.Based on our analysis, the CART model emerged as the most effective among the three diagnostic aid models. We observed that participants with ADHD exhibited longer response times compared to those without ADHD, possibly due to factors such as impatience and inattention. Children with ADHD also displayed a higher likelihood of repeatedly and incorrectly responding to target objects. Additionally, children with ADHD had slower response times compared to those without ADHD .While the three models performed similarly, the CART model demonstrated a higher sensitivity than the other two models. Sensitivity, which is more crucial in distinguishing between ADHD and non-ADHD cases, was consistently higher than specificity across all three models . This diIt is important to discuss the implications of the disparity in the number of ADHD and non-ADHD cases. Firstly, the substantial difference in sample sizes between the two groups may impact the statistical power and generalizability of the study findings. The larger number of ADHD participants introduces the potential for bias and limitations when drawing conclusions about the characteristics and outcomes of both groups. Addressing this imbalance and acknowledging its effect on the study’s validity is crucial. Secondly, the uneven distribution of cases and controls may introduce confounding factors and limitations when comparing the two groups. Factors like comorbid conditions, treatment history, and demographic variables could vary between the groups, influencing the interpretation of results. Recognizing and addressing these potential confounders is essential for a comprehensive analysis and accurate interpretation of the findings.Furthermore, the disproportionate ratio raises concerns about the representativeness of the study sample and its generalizability to the broader population. Understanding the potential biases introduced by the sample composition is crucial for interpreting the study’s implications and considering its applicability in real-world scenarios.By discussing the implications of the imbalance between cases and controls, we acknowledge potential limitations, address biases, and enhance the interpretation and generalizability of the study findings within the existing literature on ADHD.This study aimed to propose and evaluate the validity of a machine learning model for the clinical screening of ADHD in children. Three models, including a logistic regression model, a CART, and a neural network, were developed. The CART model exhibited a superior performance in predicting ADHD status compared to the other two models. The study’s primary goal was to enhance the efficiency of ADHD screening, reducing unnecessary medical expenses associated with repeat diagnoses. Additionally, the use of the CART model minimizes the risk of a misdiagnosis, improves the objective assessment of drug efficacy, and holds potential for application in other neuroscience fields.Children with ADHD often experience comorbidities that complicate an accurate diagnosis and increase the likelihood of errors or incorrect assessments. This may explain the lower specificity observed in the ROC analysis results. Future research should consider incorporating the influence of comorbidities. Furthermore, due to the limited sample size, the study did not investigate the impact of gender. Future studies with sample sizes larger than 100 are warranted to explore the potential influence of gender. Additionally, it would be valuable to investigate the relationship between ADHD and substance use, as well as the effects of psychopharmacological therapy.Regarding substance use, future research can delve into the potential link between ADHD and substance use, examining how substance use affects ADHD symptoms and vice versa. Understanding the prevalence of substance use among individuals with ADHD, the types of substances commonly used, and the impact of substance use on symptom severity, treatment outcomes, and overall functioning would be beneficial.In terms of psychopharmacological therapy, it is essential to investigate the effects of different medications on ADHD symptoms. This includes examining their efficacy, potential side effects, optimal dosages, and long-term outcomes. Research efforts could also focus on identifying biomarkers or predictors of the treatment response to guide personalized medication selection and improve treatment effectiveness.By studying these variables, researchers can gain valuable insights into the complex interplay between ADHD, substance use, and psychopharmacological therapy. This knowledge can inform diagnostic practices, treatment approaches, and interventions aimed at better supporting individuals with ADHD and improving their overall well-being.


Here, we investigate associations between attainment of early-life language and motor development milestones and genetic liability to autism, attention deficit hyperactivity disorder (ADHD), and schizophrenia.Timing of developmental milestones, such as age at first walking, is associated with later diagnoses of neurodevelopmental disorders. However, its relationship to N = 25699) of children in the Norwegian Mother, Father and Child Cohort Study (MoBa). We calculate polygenic scores (PGS) for autism, ADHD, and schizophrenia and predict maternal reports of children's age at first walking, first words, and first sentences, motor delays (18 months), and language delays and a generalised measure of concerns about development (3 years). We use linear and probit regression models in a multi-group framework to test for sex differences.We use data from a genotyped sub-set  in both males and females. Additionally, autism PGS were associated with later walking  in females only. No robust associations were observed for schizophrenia PGS or between any neurodevelopmental PGS and measures of language developmental milestone attainment.We found that ADHD PGS were associated with earlier walking age (Genetic liabilities for neurodevelopmental disorders show some specific associations with the age at which children first walk unsupported. Associations are small but robust and, in the case of autism PGS, differentiated by sex. These findings suggest that early-life motor developmental milestone attainment is associated with genetic liability to ADHD and autism in the general population. Children's timely attainment of developmental milestones is an important indicator of the extent to which early neurodevelopment is progressing typically  associated with language and motor difficulties. There is substantial evidence that delays in motor development Harris,  and langpolygenic score, it is possible to explore the manifestations of genetic risk for neurodevelopmental disorders in non-clinical populations .N = 25·699) with a high genotype call-rate (>95%) and 7 141 482 SNPs at a minor allele frequency >1% were identified for use in our analysis.In MoBa, blood samples were obtained from children  at birth , then in the next score if p < 1 × 10−6, and so on through a further eight thresholds  culminating in one (p < 1) with all SNPs included. Although the specific thresholds are selected somewhat arbitrarily, the approach in general aims to maximise the polygenic signal by including increasing numbers of SNPs with increasingly weak associations, with the assumption that prediction will continue to increase until additional SNPs contribute only statistical noise to the score. All PGS were regressed on genotype batch and 10 principal components to account for possible structural artefacts in the data. Next, in order to have a single polygenic predictor per neurodevelopmental trait in the analyses, we extracted the first principal component from analyses of scores at all 10 thresholds for a given phenotype – an approach recently shown by Coombes, Ploner, Bergen, and Biernacka ]. Values considered to be implausible  were excluded.Mothers reported on the age (in months) at which their child first walked unsupported at two waves of data collection: when children were 18 months old and 3 years old. We constructed a single age-at-first-walking variable for analysis by taking available data from either the 18-month or 3-year-old version if only one was available, and by calculating a mean (rounded to the nearest month) if both were available  and sentences  later than females, progressing from words to sentences at a slower rate  and were more likely to be reported as having language delays . Additionally, mothers of boys were also more likely to report that others had expressed concern about their child's development [χ2 = 75.966 (1), p < 0.001, see online Supplementary eTable 1 for full details of cross-sex comparisons on main study variables). The correlations between PGS were ADHD-autism: 0.21  0.20–0.22); ADHD-SCZ: 0.10 (0.09–0.11); and autism-SCZ: 0.01 (0.00–0.03).Descriptive statistics for the main study variables among genotyped individuals are shown, for males and females separately, in Results from our analyses of selection and selective attrition are presented in online Supplementary eTables 2–4. We found some evidence of selection bias associated with having genotype data available, but limited evidence of ongoing selective attrition associated with neurodevelopmental PGS.The results of the main analyses are presented in β = −0.033, Bonferroni-adjusted p < 0.001) an association that was present in both males and females. Additionally, we observed robust evidence for an association of autism PGS with age at first walking in females only: β = 0.039, Bonferroni-adjusted p = 0.006). We observed no robust evidence of any association of these PGS on the measure of motor delays at 18 months, nor of schizophrenia PGS being associated with either measure of motor development. Similarly, we found no robust evidence of associations between any neurodevelopmental PGS and outcomes in the language or general development domains. Full model-fitting results for these analyses are presented in online Supplementary eTables 5, 6 and 7, with parameter estimates and raw/adjusted p values from best-fitting models in online Supplementary eTable 8.In the motor development analyses . Finally, to account for the possibility that an unknown number of individuals from a particular city of Norway within our analytic sample may have had parents who were included in the ADHD GWAS used as the basis of the PGS and that this potential sample overlap could have biased results, we re-ran the analyses with all individuals from this region  excluded. This reduced the analytic sample size to N = 22 337 and, again, results from this sensitivity analyses were essentially unchanged from the main analyses (see online Supplementary eTable 11).To aid interpretation of the results from the main analyses we ran a series of We investigated the extent to which timely attainment of early-life developmental milestones was associated with genetic liability to neurodevelopmental disorders. The most robust evidence was observed for an association between genetic liability to ADHD and earlier walking in both boys and girls, and genetic liability to autism and later walking in girls only. We found no robust evidence for associations between genetic risk for neurodevelopmental disorders and language milestone attainment in early life.ADHD PGS were associated with earlier walking in our sample, in both boys and girls. Our sensitivity analyses showed that these results were not driven exclusively by the presence of individuals who would go on to receive diagnoses of ADHD and related disorders. This suggests that liability for ADHD manifests broadly, in ways that may be neither directly related to ADHD symptomatology nor even necessarily functionally impairing at all, from very early in life. It should be noted that our finding regarding ADHD genetic liability and earlier walking is partly consistent with results from a study in the Danish National Birth Cohort  correlation between ADHD and autism PGS. The finding appears to be consistent with the notion that there is a genetic basis to commonly observed associations between delays in motor development and autism (Harris, post hoc sensitivity analyses that this pattern was not entirely driven by autism cases within the sample. Moreover, the reason for the sex differentiated nature of the association is unclear. Diagnostic bias in autism may mean that female manifestations are less recognised clinically (Loomes, Hull, & Mandy, de novo risk variants for autism are associated both with female sex and later age of walking in autism case samples (Havdahl et al., We also observed associations between autism PGS and age at first walking. In contrast to those observed for ADHD PGS, these were sex-specific – appearing only in girls – and in the opposite direction, with higher PGS associated with later walking. This reversal of the direction of the effects is noteworthy given the positive ( Harris, . HoweverWe did not find any evidence for associations between neurodevelopmental PGS and measures of the timeliness with which early language milestones were attained. This absence-of-evidence should not be taken as evidence that such effects do not exist, and it should be noted that, for practical reasons detailed further in the limitations section below, none of our analyses using measures of language milestone attainment were as well powered as those using the age-at-first walking outcome. Nonetheless, it is possible that manifestations of genetic liability for neurodevelopmental disorders either emerge later in the domain of language development than for motor development, or are more observable as specific difficulties rather than delayed attainment (as we have shown in this sample elsewhere: see Askeland et al., There are some limitations to this work. First, age at first walking and talking were based on maternal recall. Age of walking has been demonstrated to have high reliability when relying on parental recall (Langendonk et al., Overall, our findings suggest that common genetic variants associated with neurodevelopmental disorders are also linked to the attainment of early motor developmental milestones. We find no robust evidence for similar relationships with language developmental milestones. The associations did not appear to be driven by clinical cases within the sample, suggesting that polygenic liability for neurodevelopmental disorders manifests – at least in respect of the attainment of early motor developmental milestones – from very early in life in the general population.