Cross-sectional associations between ADHD symptom levels and the incidence of ADHD diagnoses were assessed with fractional response models. Geographical variation in ADHD diagnosis rates is much larger than what can be explained by geographical variation in ADHD symptoms levels. Treatment in the Norwegian child and adolescent mental health services is free, universally available upon referral, and practically without competition from the private sector. Factors beyond health care access and unequal symptom levels seem responsible for the geographical variation in ADHD diagnosis.Rates of ADHD diagnosis vary across regions in many countries. However, no prior study has investigated how much within-country geographic variation in ADHD diagnoses is explained by variation in ADHD symptom levels. We examine whether ADHD symptom levels explain variation in ADHD diagnoses among children and adolescents using nationwide survey and register data in Norway. Geographical variation in incidence of ADHD diagnosis was measured using Norwegian registry data from the child and adolescent mental health services for 2011–2016. Geographical variation in ADHD symptom levels in clinics’ catchment areas was measured using data from the Norwegian mother, father and child cohort study for 2011–2016 (The online version contains supplementary material available at 10.1007/s00787-022-01996-7. Diagnosis rates of attention-deficit/hyperactivity disorder (ADHD) vary across many countries . InternaAlthough ADHD symptoms are fundamental in diagnosing ADHD, no prior research investigates the extent to which geographical variation in ADHD symptoms explain geographical variation in ADHD diagnoses. ADHD diagnosis is a precondition for ADHD treatment, especially pharmacological treatment. Geographical variation in ADHD diagnoses that does not correlate with variation in symptoms would thus raise concerns about over- and undertreatment of ADHD –10.We study the extent to which geographic variation in ADHD symptoms explains geographic variation in ADHD diagnosis in Norway, where 5% of children and adolescents are diagnosed with ADHD . StudyinThe aim of this study is to examine whether ADHD symptom levels explain variation in ADHD diagnoses among children and adolescents. We explore three research questions: (1) Does between-clinics variation in the incidence rate of ADHD diagnosis exceed chance variation? (2) Does between-clinics variation in symptom levels of ADHD exceed chance variation? (3) Does between-clinics variation in the incidence rate of ADHD diagnosis, conditional on symptoms levels of ADHD, exceed chance variation?We measured ADHD symptoms levels for the general population using mother-reported data from MoBa for 2011–2016. We used two measures of ADHD symptoms: (1) The proportion of the population in a clinics’ catchment area with ADHD symptoms equals to or above the 95th percentile, in line with a country prevalence of 5% among children and adolescents [n = 39,850). 323 individuals in MoBa were dropped as they did not have data on municipality.Data were reported when the child was 8 years old, corresponding to the average age of diagnosis in Norway . The ParMoBa is a population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health. Participants were recruited from all over Norway from 1999 to 2008. Women consented to participation in 41% of the pregnancies. The cohort includes 114,500 children, 95,200 mothers and 75,200 fathers . MoBa wan = 19,342) divided by the number of all individuals in that population using population data from Statistics Norway [n = 1,189,496).We used municipality-level data on all new patients registered with ADHD diagnosis in NPR  between 2011 and 2016. We calculated the cumulative incidence proportion of ADHD among individuals aged 0–18 years, defined as the number of new ADHD diagnoses  while the map data follow the municipality classification prior to 2018 (n = 428). For the map data, we adjusted for five municipality mergers providing six additional municipalities (n = 428) given same value as the municipality they were merged to. NPR is a health registry with information on all individuals who have received or are awaiting treatment in specialist healthcare services since 2008 [Clinics are our unit of analysis because decision-making on diagnosis and potential treatment cultures manifest at clinic level. Clinics’ catchment area was inferred in collaboration with NPR using data on patient contacts at clinics by patients’ residence municipality in 2009. CAMHS are organized with clinics serving one or more municipalities (and/or city districts in Norway’s four largest cities) which comprise the clinics’ catchment area. The clinic a municipality is served by was defined as the clinic with the highest number of patient contacts from that municipality. For example, if a clinic in northern Norway is registered with 25 contacts from patients residing in a municipality in western Norway, and 800 contacts from patients residing in a municipality in northern Norway, the latter was defined as the main municipality the clinic serves. There were no major changes in municipality codes during the period of this study. Cities are represented by one clinic as we only have municipality-level data, reducing number of clinics from 73 to 63. The clinics catchment area list was quality-assessed by examining clinics’ own descriptions of catchment areas. When combining data from MoBa and NPR, six municipalities were not merged as these were not represented in MoBa in 2011–2016, giving a total of 416 municipalities. We use geographical data on latitude and longitude collected by Fiva et al.  to map ance 2008 , Sweden nce 2008 , Greece nce 2008 , Irelandnce 2008 .p > χ2 =  < 0.0001; full model in Supplement). Data on symptoms and diagnosis of ADHD on the individual- and municipality-levels were aggregated to the clinic-level to examine between-clinics variation and associations between ADHD symptoms levels and ADHD diagnosis.Confirmatory factor analysis (CFA) was applied to measure the latent ADHD symptoms construct from the symptoms score items in RS-DBD . GoodnesWe examined the extent to which ADHD diagnosis and ADHD symptoms varied at clinic level by comparing observed proportions to expected values under Variance-components models were used to partition the variance in ADHD symptoms and ADHD diagnosis with municipality-level data nested within clinics. We examined variation in ADHD diagnosis and symptoms levels using the coefficient of variation (CV), a variability measure for the extent of variation relative to the mean calculated as the variable’s standard deviation (SD) divided by its mean value. Bootstrapping was used to derive the expected distribution of CV under We used fractional response regression models (FRM)  to test We examined the extent of unexplained variation in incidence of ADHD diagnosis by CV for the residual and for the squared correlation coefficient between the observed and the predicted values. To formally test whether the unexplained variation in ADHD diagnosis conditional on ADHD symptoms was larger than expected by chance, we compared the observed CV to the distribution of expected CVs under The cumulative incidence of ADHD diagnosis was 0.016  in 2011–2016. The proportion of children scoring over the 95th percentile on ADHD symptoms was 0.05 . For children scoring over the 90th percentile, the proportion was 0.1 . Two clinics had no MoBa-respondents scoring ≥ 95%, while one clinic had no participants scoring over ≥ 90%. There was nearly a tenfold difference in the incidence of ADHD diagnosis proportion from the clinic with the lowest to the highest level. .In Fig. , the uppp = 0.23), thus there was not support for more than chance variation as the observed CV was well within the null distribution (lower graph Panel B). In Panel C, there are more observed proportions of children scoring ≥ 90th percentile for ADHD symptoms compared to Panel B. The CV was 15% (p = 0.025), and there is evidence for more than chance variation.The lower graph of Panel A presents the null distribution for CV with the excess variation in ADHD diagnosis compared to the mean of the null distribution measured in percentage difference. Similarly, the upper graph in Panel B presents proportions of children scoring ≥ 95th percentile for ADHD symptoms. Here, few observed proportions were outside 95% CI. The CV was 18% (p = 0.002) when the proportion of children and adolescents with ADHD symptoms ≥ 95% increase with one percentage point. We did not find support for an association between ADHD symptoms ≥ 90% and ADHD diagnosis  .From fractional response regression models (FRM) at the clinic level with the incidence of ADHD diagnosis as the outcome, the average marginal effect (AME) shows that the proportion of ADHD diagnosis increases 0.26 percentage points Below is the link to the electronic supplementary material.


First-year college students with ADHD reported significantly higher rates of sexual risk behavior than their peers without ADHD, with no significant differences found based on medication status. Students with ADHD who were taking medication for ADHD reported significant decreases in risky sexual behavior over time. Among college students with ADHD, anxiety was related to increased current risky sexual behavior in the medicated group, while depression was predictive of decreased future risky sexual behavior in the unmedicated group. Alcohol and cannabis use were significantly associated with increased mean levels of risky sexual behavior across all three groups, and cannabis use was associated with decreased future risky sexual behavior within the comparison group. Executive functioning deficits and male gender were predictive of risky sexual behavior within the comparison group. The results demonstrate that college students with ADHD, regardless of medication status, are at an increased likelihood of engaging in risky sexual behavior.The present study sought to identify differences in the rates and predictors of risky sexual behavior among college students with and without attention-deficit hyperactivity disorder (ADHD). Current ADHD diagnosis, medication status among those with ADHD, executive functioning, substance use, comorbid anxiety, comorbid depression, and gender were identified as potential predictors of increased risky sexual behavior. Multiple group latent growth curve modeling was used to estimate trajectories of risky sexual behavior across four years of college among college students with ADHD ( Attention-deficit/hyperactivity disorder (ADHD), a neurodevelopmental disorder characterized by deficits in sustained attention, hyperactivity, and impulsivity, is estimated to affect 2–4% of the adult population  that increases the likelihood of negative health outcomes, including sexually transmitted infections (STIs) such as human immunodeficiency virus (HIV), as well as unwanted pregnancies” Cooper, . Follow-Although studies have explored sexual risk behavior among the general college student population, a dearth of studies has investigated sexual risk behavior in college students with ADHD relative to their neurotypical peers, even though ADHD is commonly characterized by deficits in self-regulation. Indeed, preliminary studies have found that childhood ADHD predicts risky sexual behavior in adulthood  are important to consider in order to adequately understand these risk behaviors and inform appropriate and effective interventions for college students with ADHD. Numerous studies have found that substance use  commonly occurs before, or in combination with, sexual activity among college students   across 4 years of college attendance? Among medication status, executive functioning, substance use, psychiatric comorbidity, and demographic characteristics, which of these factors is associated with risky sexual behaviors over time? Given the limited longitudinal data concerning sexual risk behavior in college students with ADHD, trajectory means and associations with covariates were modeled in the absence of directional hypotheses. However, hypotheses concerning overall group mean  differences and associations with covariates were reasonable given the previous literature. Specifically, it was hypothesized that across the 4 years of the study: (1) College students with ADHD, regardless of ADHD medication status, would report significantly higher rates of sexual risk behavior than college students without the disorder; (2) college students with ADHD who were prescribed medication would report significantly lower rates of sexual risk behavior than college students with ADHD not taking medication; (3) higher rates of executive functioning deficits, substance use, and symptoms of anxiety and depression would be associated with higher rates of sexual risk behavior for all groups; and (4) males would report significantly higher rates of sexual risk behavior than females, regardless of ADHD diagnosis or medication status.Participants included first-year college students who were initially screened to determine their eligibility for the Trajectories Related to ADHD in College (TRAC) project and were then followed for the duration of a 4-year multi-site longitudinal investigation. The original sample consisted of 572 first-year students; however, after screening, 456 met the study’s eligibility requirements. Included in this total were 228 students with ADHD and 228 non-ADHD comparison group students. Of the 456 participants who met the study's eligibility requirements, 35 withdrew from the study before completing the year 1 assessment process. An additional 10 participants with ADHD did not provide their initial medication status. These 10 participants were similar to the analytic sample in terms of racial and ethnic identity; however, whereas the analytic sample was approximately balanced by gender, the excluded participants were primarily male. This withdrawal of participants resulted in a final sample of 421, including 204 students with ADHD and 217 non-ADHD comparison participants, who were assessed annually across 4 years. To consider the potential impact of medications, the ADHD group was subdivided based on their medication status in assessment year 1. Medication status was defined as individuals taking stimulant or non-stimulant medication to treat symptoms of ADHD; henceforth, “medication” will indicate use of medication, stimulant or non-stimulant, for the treatment of ADHD. This division resulted in 105 ADHD participants taking medication and 99 not taking medication. Medication status was determined at Year 1 to inform interventions targeting risk factors in Year 1 that may impact trajectories throughout college. As shown in Table Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria . ADHD status, as well as non-ADHD comparison group status, was determined based on a multi-method assessment procedure that included expert panel review. At the first stage of this assessment process, all participants initially completed a self-reported ADHD Rating Scale , 1 (sometimes present), 2 (often present), or 3 (very often present). The total number of items scored 2 or 3 yields symptom frequency counts for both IN and HI. In contrast to the original ADHD RS-IV, the ADHD RS-SRV simultaneously assesses ADHD symptoms both during childhood and during the past 6 months, while also considering medication status. Coefficient alphas based on the current sample were very good (0.74) to excellent (0.94) for the childhood and past 6 months reports of both IN and HI symptoms, regardless of medication status.The ADHD RS-SRV is a modified version of the ADHD RS-IV  based on the current sample.The Semi-Structured Interview for Adult ADHD was developed specifically for this study to assess (1) functional impairment for each of the 18 symptoms and (2) symptom frequency counts as a function of medication status. For symptoms endorsed as present “most of the time,” additional questioning examines that symptom’s impact on daily functioning. The interview also addressed the duration, age of onset, and other DSM-5 criteria for ADHD. Coefficient alphas based on the current sample were good for the IN (0.90) and HI (0.85) portions of the interview.The SCID-I . Higher scores indicate greater degrees of executive dysfunction. In addition to providing nine specific EF subscales, the BRIEF-A generates three general composite scores—Behavior Regulation Index (BRI), Metacognition Index (MCI), and General Executive Composite (GEC). The scores from all nine subscales form the GEC, which was used as the dependent variable in the present study. The BRIEF-A has demonstrated reliability, validity, and clinical utility as an ecologically sensitive measure of EF in healthy respondents, as well as individuals with a range of psychiatric and neurological conditions  were assessed using the BRIEF-A  was operationalized using the ASSIST  across this early period of emerging adulthood and to identify risk and protective factors that inform clinical assessment and treatment. Three primary sites were involved, including one university in the Southeast and two universities in the Northeast United States. In addition, six colleges and universities near the primary sites served exclusively as recruitment sites. To achieve recruitment goals, two cohorts of first-year students were recruited successively across the first 2 years of the project and were assessed annually until graduation. A total of 219 participants were recruited in Cohort 1 and another 237 participants were recruited in Cohort 2. All participants underwent an annual four-stage assessment process, for which they earned up to $100 as an incentive for completing all required procedures. Recruitment and data collection occurred continuously throughout the fall and spring semesters at times that were convenient to each participant. Participants were recruited from multiple sources, including summer orientation sessions, the Disability, Access, and Inclusion office, student counseling centers, fliers, and presentations to large first-year classes.First-year college students who were between 18 and 25 years of age and entering college for the first time were recruited, provided informed consent, and underwent subsequent annual assessments for 4 years. The data collected by the self-report and parent report of the ADHD RS served as the basis of decisions regarding which participants were excluded from the study and which moved to the next phase of assessment. Participants whose ADHD RS score indicated possible inclusion underwent additional evaluation by the Semi-Structured Interview for Adult ADHD and the Structured Clinical Interview for DSM-5 (SCID-5), which informed decisions pertaining to which cases were brought to the expert panel for review and final determination of ADHD or comparison group classification, as well as psychiatric comorbidity status. To be eligible for the study, participants either met a full DSM-5 diagnosis for ADHD by demonstrating five or more symptoms of inattention or hyperactivity, or they met criteria for the comparison group by demonstrating three or fewer symptoms of inattention or hyperactivity characterized by ADHD, both during childhood and in the past 6 months. The expert panel was comprised of four doctoral-level, licensed psychologists with expertise and experience in ADHD. Group assignment required unanimous agreement by the panel.All data were collected by predoctoral- and doctoral-level staff from clinical psychology and school psychology backgrounds. All staff received extensive training before the start of the project, and their adherence to the various assessment protocols was monitored on an ongoing basis to maintain consistency across sites. Study procedures were reviewed on an annual basis and were approved by the Institutional Review Boards at each site.All statistical modeling was conducted using Mplus software, version 8  to calculate bias-corrected confidence intervals when making determinations of statistical significance: 95% CI (CI95) for growth parameters and 90% CI (CI90) for covariates. Therefore, statements regarding statistical significance refer to instances in which the confidence intervals did not include 0. Alternatively, between-groups comparisons of growth factors were made using the Wald Chi-square test of parameter equalities  = 93.23, p = 0.023; RMSEA = 0.051 ; CFI = 0.970; SRMR = 0.059. At the group level, model fit for the non-medicated ADHD group was mediocre (χ2(23) = 37.81, p = 0.02; RMSEA = 0.078), while fit was good for both the medicated ADHD group ( χ2(22) = 22.60, p = 0.42; RMSEA = 0.017) and the non-ADHD group (χ2(23) = 32.82, p = 0.08; RMSEA = 0.044). Slope estimates for both the comparison and non-medicated ADHD groups were small and nonsignificant; therefore, these growth terms were fixed to 0 and did not diminish model fit (LRT p = 0.574). See Table Fit statistics for the omnibus model suggested adequate-to-good fit:χ2(1) > 14.0, p < 0.001).The results of the present study support the hypothesis that college students with ADHD would report significantly higher rates of sexual risk behavior than college students without ADHD see Fig. . Specifip > 0.17), there was a significant decrease in risky sexual behavior over time observed in the group with ADHD taking medication . This decline, however, did not result in sufficient change for year 4 rates to deviate significantly from the rates reported by the unmedicated group with ADHD. Slope estimates for the comparison group and the ADHD group not taking medication were trivial, such that fixing them to zero did not significantly alter model fit (χ2 (df = 2) = 1.11, p = 0.574).The hypothesis that college students with ADHD who were prescribed ADHD medication would report significantly lower rates of sexual risk behavior than college students with ADHD not taking medication was not supported. Although differences in estimated mean rates of risky sexual behavior between the ADHD groups failed to achieve significance for any year . In contrast, both alcohol and cannabis use were significantly associated with increased mean levels of sexual risk behavior at intercept (Year 1) for all three groups (see Table 90). Notably, cannabis use was negatively associated with change in sexual risk-taking over time for the comparison group  and approached significance for medicated ADHD participants . Anxiety and depression symptoms were not consistently associated with either mean levels of sexual risk behavior or change over time. Anxiety symptoms were only significantly associated with the intercept mean for the ADHD group taking medication , and depression symptoms were only significantly associated with the slope mean for the ADHD group not taking medication . Anxiety was associated with increased mean sexual risk behavior in the medicated ADHD group, while depression was associated with decreased mean sexual risk behavior in the unmedicated group.The findings revealed that executive functioning was negatively associated with sexual risk behavior  only in the comparison group .Regarding potential gender differences, binary gender was not found to be consistently related to either mean rates of sexual risk behavior or change in rates of risky sexual behavior over time. Only in the comparison group did male students report greater increases in rates of sexual risk behavior over time compared to their female counterparts . The relationship between cannabis and risky sexual behavior, however, was somewhat more complex. Across all three groups, cannabis use was associated with heightened mean risky sexual behavior at intercept; however, for the comparison group, cannabis use was predictive of decreased further risky sexual behavior , running contrary to the expectations of the current study. This pattern was not seen in either ADHD group. Previous research has demonstrated that including alcohol and cannabis-related topics in interventions directed at reducing risky sexual behavior in young adults was more effective than those that did not include these topics  across all participant groups , whereas depression symptoms were predictive of lower rates of future risky sexual behavior for the non-medicated ADHD group . Multiple mechanisms have been proposed to explain the influence of anxiety and depression on sexual risk behavior, including impaired decision making, poor coping skills, and substance use , and served as a significant protective factor, with increased executive functioning ability associated with decreased sexual risk. The ADHD groups, contrary to our hypotheses, did not show a significant association between executive functioning ability and risky sexual behavior. These findings were unexpected given previous research that supported the effectiveness of prescription stimulants  in improving executive functioning , but there was no significant difference across binary gender in the other groups. These results are comparable to those found recently in similar groups of college students  without differentiating outcomes between forms of medication. Adherence to medication was not tracked throughout the 4 years; participants were categorized by medication status in Year 1. This approach is limited because participants may have begun or ceased medication for ADHD over the course of the 4 years. Future research should aim to identify the independent contributions of stimulant and non-stimulant medications to the mitigation of risky sexual behavior, as well as identify potentially effective doses of these medications.Additionally, the sample was comprised of students who volunteered to participate in the study; therefore, participants may differ from the larger population of college students on several variables, such as ADHD symptomatology and sexual risk behavior, and may therefore limit the generalizability of the findings. Only binary male and female responses were collected, limiting the generalizability of results based on sex. Future studies should recruit a more diverse sample of college students within and outside of the USA to address the present restrictions in the generalizability of results. Future research should also aim to compare ADHD with other mental health conditions to further clarify whether heightened risky sexual behavior is specific to ADHD, whether ADHD is contributing to sexual risk-taking behavior in isolation, or whether the comorbidity of other mental illnesses contributes to risk-taking behaviors in college students.decline in risky sexual behavior across the 4 years of the study. Furthermore, alcohol use was related to increased risky sexual behavior among all groups of college students; however, higher rates of cannabis use were associated with reduced risky sexual behavior in college students with ADHD who were medicated and the comparison group.As more students with ADHD attend college , additional research in an experimental context may help to clarify this issue (e.g., Christman et al., Additionally, the results of the present study demonstrate that medicated individuals with ADHD exhibiting greater symptoms of anxiety are more likely to engage in risky sexual behavior, while unmedicated individuals with greater symptoms of depression are less likely to engage in risky sexual behavior. These findings suggest that prescription medication may have possible protective effects; however, these relationships require additional exploration as they relate to sexual behavior. It is recommended that future research promote a greater understanding of risky behavior in college students with ADHD and, importantly, design and evaluate interventions that will improve outcomes in this population. The present finding suggests that, among college students without ADHD, cannabis use may not be an important target of interventions designed to reduce risky sexual behavior. Among individuals with ADHD, however, cannabis use was predictive of increased risky sexual behavior at baseline without a significant decrease over time. Interventions to reduce risky sexual behavior specifically among college students with ADHD may therefore benefit from targeting cannabis use. Lastly, the present findings address important gaps in the literature and indicate that college students with ADHD, regardless of medication status, engaged in greater risky sexual behavior than their peers without ADHD.


Previous longitudinal studies found significant associations between screen time and increase in attention-deficit/hyperactivity disorder (ADHD) symptoms, but the mechanisms mediating this association remain understudied. Thus, we used data from a 5-year population-based longitudinal cohort of nearly 4000 Canadian high school students, modeled using multivariate multilevel mediation, to investigate the association of screen time  with ADHD symptoms via different potential behavioral and neuropsychological mediators . We studied direct and indirect between-person, concurrent within-person, and lagged-within-person effects of screens on ADHD symptoms. Results showed that increases in screen time in a given year were associated with an exacerbation of ADHD symptoms within that same year (within-person association), over and above potential common vulnerability (between-person association). Impulsivity proved to be the most robust mediator in the association of screen time with ADHD symptoms at both between and within-person levels. Only social media use displayed a significant lagged-within-person association with ADHD symptoms mediated by impulsivity, indicating an enduring influence on behavior, which was further shown to be mediated by chained changes in response inhibition on a Go/No-Go task. These findings provide clinical implications of screen time and should be an important focus in the management and prevention of ADHD symptoms among adolescents. ADHD during adolescence has been associated not only with neuropsychological functions, but also with significant academic, psychosocial, emotional, and cognitive impairment. For instance, it has been linked to poor social relationships, low self-esteem, deviant behavior, and substance use and abuse2.Attention-deficit/hyperactivity disorder (ADHD) is a behavioral disorder characterized by symptoms of inattention and/or impulsivity and hyperactivity, which can affect many aspects of behavior and performance among youth, both at school and at home4. Furthermore, the U.S. national prevalence of ADHD was shown to increase by 12.6% over the past decade (from 8.47 to 9.54% from 2009–2011 to 2015–2017)4. It has been estimated that U.S. healthcare expenditures for ADHD total $23 billion5 and that the annual costs including healthcare, education, and reduced family productivity associated with youth ADHD in the U.S. have been estimated to range from $38 to $72 billion6.In 2016, an estimated 6.1 million North-American children 2–17 years of age (9.4%) had received an ADHD diagnosis, of which the highest rates of ADHD were found among adolescents aged 12–17 years7. One of these environmental risk factors might be the rise of continuous expansion of the digital media landscape, which can be considered one of the most dramatic cultural changes that has occurred in the past decade. The ubiquity of internet access, social media platforms, online video gaming, and of the wide variety of mobile digital devices result in near-constant exposure to virtual information, which may interfere with adolescent neurodevelopment processes9 and make them increasingly digitally dependent showing behavior disorders14.Considering that ADHD risk is linked to both genetic and non-genetic environmental risk factors, identifying potential environmental factors that might explain epidemiologic shifts in ADHD severity or prevalence might help to inform more comprehensive treatment and public health strategies to promote youth mental health15. Moreover, the recent outbreak of the COVID-19 pandemic has resulted in a substantial increase in screen time among adolescents18. Finally, it is noteworthy that many digital platforms are designed to tap natural human attentional, impulsive, and reward processes, which in turn leads to habit formation and repeated use of the platform23.It has been suggested that North-American youth spend an average of 6–9 h interacting with digital devices, and it must be pointed out that many young people often use more than one screen medium simultaneously, increasing their total exposure to digital devices25. In particular, deficits in working memory are typical among individuals with ADHD26, and a meta-analysis found significantly more and more severe verbal and visual-spatial working memory impairments in adolescents with ADHD compared to non-ADHD adolescents27. Previous works also suggested that those diagnosed with ADHD have been shown to differ from those without a diagnosis on a variety of tests of inhibitory control, such as, go/no-go tasks31 and continuous performance tasks33.Research on the impact of digital screen time on adolescent health is still in its infancy and the neurodevelopmental consequences of exposure to such digital platforms are not yet fully understood. This is particularly relevant to ADHD symptoms, as impulsivity, reward processing, and attention/concentration are key features that drive ADHD behavioral impairments35. More specifically for social media, heavy Facebook users had worse short-term memory than light users36 and problematic use of social networking sites has been associated with attention problems37. Moreover, previous studies have shown that total television and video game exposure were related to future attention problems, controlling for earlier attention39. Longitudinal studies also showed significant, though modest, associations between modern digital media use  and symptoms of ADHD40, as well as long-term effects of media multitasking on attention problems for adolescents aged 11 and 13 years old41.Findings from emerging research suggest that excessive usage of digital media in adolescents may affect brain functioning and cognitive development, including impaired attention and impaired memory processing, as well as impairment in impulse regulation and reward processing42, and even fewer have attempted to address these hypotheses by investigating potential pathways through the effect of digital media use on neuropsychological development in adolescence. As previously suggested42, mediating variables could shine a light on the underlying mechanism between screen time and its consequences, thereby providing further evidence of a causal relationship43. Furthermore, from a methodological perspective, ignoring mediating variables could lead to an underestimation of effect sizes in empirical research, and subsequent meta-analyses44. Hence, to obtain a true understanding of the association of digital media use and ADHD symptoms through direct and indirect effects, there is a need for studies that measure adolescents’ responses to different digital media over time, while taking into account mediating factors. The empirical findings and insights derived from such studies may prove pivotal in designing remedial and preventive interventions to assist in the management of ADHD-related behaviors among adolescents. To address this gap in the literature, potential mediating factors of interest could be impulsivity and neuropsychological functions, which are not only identified as core behavioral and cognitive features of ADHD45, but also seem to be affected by screen time, as reviewed above. Demonstrating chained mediation of cognitive processes in the link between screentime and mental health would contribute to a growing literature on potential causal effects of screentime on child brain health.Although the above-presented findings of previous works suggest an important relationship between digital media use and ADHD symptom severity, little research directly tested mediation hypothesesIn this study, we used multivariate multilevel linear models (MLMs) to analyze longitudinal data to test causal hypotheses on the role of screen time  in the growth of ADHD symptoms across adolescence, by: (i) first modeling, and controlling for potential common vulnerability to high levels of digital media use, impulsivity, neuropsychological functioning, and ADHD symptoms (between-person effects); (ii) modeling how changes in impulsivity and neuropsychological functioning co-vary directly with an increase in digital media use in the same year and the following year ; (iii) modeling how impulsivity and neuropsychological functions mediate indirect associations of digital media and ADHD symptoms at between-, within- and lagged-within person levels; (iv) and also compared the four types of screen time as predictors of ADHD symptoms and neuropsychological functioning to explore if certain types of digital media are particularly linked to cognitive and mental health outcomes.We designed a multilevel analytic strategy in order to be able to differentiate between-person variation from within-person variation. The former reflects the time-invariant degree to which individuals differ in their average scores from others in the sample, in other words, the set point of an individual compared with their peers. The latter effect represents the time-varying degree to which individuals deviate from their own average scores when tested in successive years through repeated measures, representing the displacement from the set point at a given time point. Therefore, our longitudinal study allowed disaggregating inter-individual and intra-individual differences in the association of screen time and ADHD symptoms.45.This study also applies the same strategy to investigating neurocognitive and behavioral mediators of this relationship to confirm its validity, by including objectively measured (task-based) response inhibition and working memory performance, as well as self-reported impulsivity, annually for five consecutive years. We will uniquely investigate temporal precedence of neurocognitive mediators in this relationship, in accordance with current etiologic theories of ADHD placing response inhibition, working memory and trait impulsivity at the core of this complex brain disorderThe sample included 3779 adolescents  who consented and completed the first survey of this 5-year longitudinal study.The descriptive statistics of each variable and the frequency distribution for the different types of screen time over 5 years are available in Tables As shown by the frequency distribution for the different types of screen time, we reported an increase in missing data over the course of the study .Over the 5-year period, there were reported substantial increases in minimum time spent using social media  and slight increases in time spent viewing television  and using the computer , while there were reported decreases in video gaming .Scores related to ADHD symptoms increased across the 5-year study . Impulsivity reported stable scores across the five survey waves . Concerning neuropsychological function variables, both response inhibition errors  and working memory errors  decreased from year-to-year.Participants reported a mean[SD] socio-economic status score of 2.94[1.08].All results of MLM analysis indicating the direct associations of screen time and ADHD symptoms among adolescents . With regards to the association of social media to ADHD symptoms mediated by neuropsychological functions, ADHD symptoms were predicted by social media use only through response inhibition at between-person level.We found a statistically significant impulsivity-mediated association of television viewing on ADHD symptoms at both between-person and concurrent within-person levels, while it showed a statistically significant association with ADHD symptoms mediated by both response inhibition and working memory only at between-person level.Video gaming revealed a statistically significant impulsivity-mediated association with ADHD at between- and concurrent within-person levels, while response inhibition and working memory revealed a statistically significant mediation role only at between-person level.Considering that impulsivity proved to be a robust mediator of the longitudinal relationship between screen time and ADHD symptoms, MLM models investigated potential chained mediation processes whereby neuropsychological functions were considered as potential intermediate mediators between screen time and impulsivity, which in turn predicted ADHD symptoms 1 year later.As first step, two different mediation MLMs were performed where impulsivity was predicted by screen time through each of the neuropsychological functions  with ADHD symptoms, this study distinguished between different time-varying factors: between-person effect, concurrent within-person effect, and lagged-within-person effect. Different potential mediators of the association of screen time and ADHD  were evaluated, and several important conclusions can be drawn.First, our findings indicated that social media use, television viewing and video gaming were shown to be directly associated with ADHD symptoms during adolescence at both between- and concurrent within-person levels. In other words, the results demonstrated significant common vulnerability between higher levels of these types of screen time and general vulnerability to ADHD symptoms. Over and above this potential common vulnerability, increases in screen time on a given year predicted concurrent increases in ADHD symptoms relative to a person’s mean level of ADHD symptoms throughout adolescence, further suggesting a direct short-term negative impact of screen time on ADHD symptoms.The study did not reveal a significant mediating role of both neuropsychological functions in the longitudinal association of screen time and ADHD symptoms (indirect lagged within-person effect), but did demonstrate significant concurrent and lagged relationships of social media with ADHD symptoms through impulsivity at within-person level, implying that any further increases in a given year of social media use were associated with more severe ADHD symptoms that same year and from 1 year to the next through higher self-reported impulsivity. This means that social media heavy users showed the strongest effect on ADHD symptoms through impulsive behavior compared with their peers, showing also a mediated effect on their ADHD symptoms on any given year comparing with their average set point, and a small-mediated effect on their ADHD symptoms in the following year.45. Accordingly, we further investigated the association of screen time and ADHD symptoms, testing the mediational role of neurophysiological functions in the relationship between screen time and this important proximal mediator of risk in a MLM with multiple chained mediators. Results showed that the common vulnerability to greater social media use, television viewing, and video gaming and ADHD behaviors was mediated by neurophysiological functions and impulsivity at between-person levels. However, only social media was shown to be longitudinally linked to increased risk for ADHD symptoms through a mediation path that involved disrupted neuropsychological functioning (inhibitory control) and impulsive temperament.Trait impulsivity, which moderately correlates with individual neuropsychological measures, like response inhibition and working memory, is considered a multifaceted construct and a key proximal indicator of risk for externalising mental health problems40, and stress the importance of monitoring screen time during adolescence in order to protect those at risk for heavy screen time and to reduce the likelihood of ADHD symptoms being exacerbated through disrupted neurocognitive functioning and behavioural regulation.Altogether, our findings are in line with previous longitudinal studies showing significant associations between digital media use and ADHD symptom severity46. Indeed, according to the American Psychiatric Association47, adolescents who live with ADHD often have difficulties in finishing cognitive tasks requiring extended periods of sustained attention and show a preference for immediate as opposed to delayed rewards, because of their impulsive attitude. This symptom profile is, in fact, characterized by decreased activation of brain regions implicated in cognitive control and inhibitory control systems48. Children with higher polygenic risk score for ADHD present alterations in white matter tracts related to visual functions, resulting in impaired executive control of visual functions, and making subjects with ADHD symptoms more sensitive to external visual stimuli and more easily distracted by digital media49. Based on our findings, we posit that social media, television and video games exposure, which showed between-effects on ADHD symptoms mediated by impulsivity and neuropsychological functions, might appeal to adolescents with higher levels of ADHD symptoms.According to our results, it appears that there is strong evidence for a common vulnerability to heavy screen time use and adolescents’ neurocognitive risk for ADHD symptoms. It might be argued that adolescents vulnerable to ADHD or those with higher levels of ADHD symptoms are particularly drawn to digital media because they offer activities presenting them with brief, superficial and stimulating parcels of content, without much need to exert cognitive control and allowing for constant attentional switchingOver and above this common vulnerability and focusing on the longitudinal risk for ADHD highlighted by this study, our findings support an empirical model showing neuropsychological consequences of digital media use on cognition and temperament during adolescence, where social media proved to be the most robust longitudinal predictor for ADHD symptoms and this relationship was explained by the effects of social media on response inhibition and impulsivity through a chained pathway. Interestingly, in this chained pathway of effects, temporal precedence was demonstrated between social media and impulsivity and between impulsivity and ADHD symptoms, but neuropsychological outcomes of social media only proved to be concurrent. As shown in Fig. 50. Compared to video game use, social media users’ responses are very limited, defined by a simple gesture in the form of short phrases, likes and/or emoji and are not goal oriented. Similarly, compared to content viewed on television, social media also rarely contains a sustained narrative, involving plot or character development, or a series of facts linked to an argument or a thesis. Finally, social media involves viewing content shared by peers, which, more strongly promotes social norms in young people, for better or worse51.The mechanisms by which social media have a unique relationship to impaired response inhibition, are not well understood, but might be related to the nature of the brief and unnarrated content that youth are exposed to on social media. Indeed, social media involves rapidly scrolling through brief, diverse segments of visual information with sound bites, which requires limited cognitive control . Interacting with this content can be characterized by instant rewards and constant stimulation that could raise the threshold for adolescent sustained attention on other less stimulating situations and tasks, exacerbating inattention and distractibility54, and high exposure to screen time appears to contribute to some of these symptoms and exacerbate them in those who are already vulnerable39. A major strength of our study is the use of state-of-the-art multilevel mediation modeling to investigate the underlying mechanism of this association, confirming the role of impulsivity and executive functions in linking screen time to ADHD symptoms in adolescence. This model provides an overall picture of the onset and development of ADHD symptoms by highlighting specific longitudinal direct and indirect between- and within-person associations, considering concurrent and lagged time-varying effects. Another strength of the study is the use of a large sample size of adolescents.While this study did not focus on the interaction between risk factors and consequences of digital media on mental health, it is noteworthy that youth with ADHD often experience difficulties with behavior, self-regulation, and task focus making them vulnerable to social isolation and other disabilities55, no clinical diagnostic confirmation was provided and participants may have under-reported their ADHD symptoms due to social desirability or lack of insight into impairment.However, the findings need to be interpreted in light of some limitations. It remains unclear what specific types of social media, digital media content, and what kind of video games or computer-related activities youth participated in. Additionally, even though ADHD symptoms were evaluated with the five items of the Hyperactivity/Inattention scale of the validated and reliable Strength and Difficulties QuestionnaireIn conclusion, in the present study, social media was found to be the type of screen time most robustly associated with symptoms of ADHD in adolescence, while impulsivity seemed to be the strongest mediator between screen time and ADHD symptoms at between- and concurrent within-person levels, as well as at lagged-within-person level. Because of the implications of these findings for interventions, clinicians may want to examine the degree to which adolescents spend time in front of various digital screens and target social media use during therapy as a main potential means of reducing cognitive, behavioral and clinical manifestations of these patterns of difficulties. Furthermore, since the association of all studied types of screen time and ADHD symptoms were mediated by impulsivity, interventions targeting impulsive temperament and behavior and the role of screen time in both managing and exacerbating impulsivity could help young people and their families to better manage ADHD symptoms to promote well-being. Finally, further longitudinal studies are needed to understand the impact of specific contents, and presentation formats on ADHD-related behaviors among adolescents to potentially inform how social media platforms could provide services to families without these potential unintended cognitive and behavioral consequences. Such research will require close collaborations with social media companies to be able to better investigate how the algorithmic nature of content presentation contributes to the potential impact on child brain and mental health.56). From September 2012, 3826 adolescents  from 7th grade were recruited with no exclusion criteria from 31 representative schools in the Greater Montreal Area  with respect to size, socioeconomic indicators, and district. This cohort represents a unique research opportunity in the history of digital media, when teen access to smartphones and social media platforms exploded and adolescent-targeted products and platforms became more and more available57. In total, 3779 participants  consented and completed the survey in the 7th grade and were invited to complete repeated assessments every year for five consecutive years until the end of high school (11th grade). Data were acquired through a confidential annual web-based survey, which was downloaded to school computer laboratories to ensure that time-sensitive data  were reliably collected and that students were closely supervised when completing the battery. The scores were saved on computers until a secure connection was available and then automatically transferred to a central anonymized database through a unique identifying code.This study used data from the Co-Venture cohort, which has an embedded cluster randomized trial for a subset of high-risk youth .This research complies with all relevant ethical guidelines and regulations. Ethical approval was obtained from the Research Ethics Board of the Sainte-Justine Hospital Research Center in Montreal . Students and their parents or legal guardians provided informed consent before taking part in the study. This study has been registered as sub-study from Co-Venture trial with the number NCT01655615 at Screen time was assessed using a self-report question requiring participants to state how much time per day they spend using social media , watching shows or movies on television or on the computer, playing video games , and using the computer to engage in another kind of activities. The amount of screen time was established according to four categories: 0 to 30 min, 30 min to 1 h and 30 min, 1 h and 30 min to 2 h and 30 min, 2 h and 30 min to 3 h and 30 min, and 3 h and 30 min or more.55, measuring the key symptoms of ADHD: inattention (two items), hyperactivity (two items) and impulsiveness (one item). Participants indicated whether each statement was not true, somewhat true, or certainly true, yielding a final score from zero to two points.ADHD symptoms were assessed using the five items of the Hyperactivity/Inattention scale of the Strength and Difficulties Questionnaire58. Participants indicated whether they agreed with each statement by selecting one of four response options , yielding scores from one to four points.Participant impulsivity was assessed using five items related to the impulsivity personality dimension of the Substance Use Risk Profile Scale, which consists of twenty-three items that allow identifying four personality dimensions 59. During the task, participants learn to react to “good’’ numbers and to avoid reacting to “wrong’’ numbers through trial and error. Correct and incorrect responses were respectively accompanied by reward cues and punishments. Participants received performance feedback following each response. The dependent measure on this task is the total number of errors of commission (responding to no-go numbers).A modified version of the Go/No-Go Passive Avoidance Learning Paradigm (PALP) was used to measure response inhibition in order to study inhibitory control and cognitive control. The Go/No-Go PALP is a discrimination task that demands participants to inhibit a rewarded response in order to win points or prevent loss of points60 and the spatial working memory task of the Cambridge Neuropsychological Test Automated Battery61. This task presented participants with a number of colored boxes, which required them to remember which boxes they already searched. Participants were required to find a phone ‘token’ hidden amongst the boxes through trial and error. The location and colour of the boxes were changed from trial to trial and with the number of boxes progressively increasing (max. 8) to prevent reliance on stereotyped search strategies. The measure of spatial working memory deficit corresponds to the number of times that participants reselected the items that had already rung.Working memory was assessed using the “Find the phone task” which is similar to the Self-Order Pointing Task62 and sex .We assessed baseline socio-economic status with the Family Affluence Scale for adolescentsWe applied Bayesian MLMs to investigate direct and indirect associations of screen time  and ADHD symptoms . The MLMs apply linear regression analysis to describe outcome variables as a function of predictor variables at two levels of data organization, the between-person and the within-person (change over time) levels. The between-person effects (the association between the predictor and the outcome variables averaged over five time points), the concurrent within-person effects , and the lagged-within-person effects  were assessed within the same MLM at each analytical step. To assess time-varying associations, significant within-person associations would provide support for potential causal short-term concurrent associations between variables, while significant lagged-within-person associations would reflect a lasting effect of screen time on adolescent behavior.Figure We first tested the direct association of screen time and ADHD symptoms by entering the four types of screen time in the same MLM Fig. a. Second19. In the context of such a mediation, we then tested 4-variable chained mediation MLMs, where the multiple sequential mediators were either response inhibition and impulsivity, working memory and impulsivity . In all statistical models, we controlled for covariates  and time. The time parameter of our 5-year study was coded from one to five, considering the five survey waves as time points. To model the indirect effects of screen time on ADHD symptoms, the “model constraint” function in Mplus allowed multiplying the direct associations between predictors, mediators and outcomes.Supplementary Information.


Animal models of attention-deficit/hyperactivity disorder (ADHD) have been used to study and understand the behavioral, neural, and physiological mechanisms underlying ADHD. These models allow researchers to conduct controlled experiments and manipulate specific brain regions or neurotransmitter systems to investigate the underlying causes of ADHD and test potential drug targets or treatments. However, it is essential to note that while these models can provide valuable insights, they do not ideally mimic the complex and heterogeneous nature of ADHD and should be interpreted cautiously. Additionally, since ADHD is a multifactorial disorder, environmental and epigenetic factors should be considered simultaneously. In this review, the animal models of ADHD reported thus far are classified into genetic, pharmacological, and environmental models, and the limitations of the representative models are discussed. Furthermore, we provide insights into a more reliable alternative model for the comprehensive study of ADHD. In the early stages of life, the brain and body are still adjusting to the environment . This imAlthough little is known about the causes of ADHD, it is considered a multifactor model because of the interaction between the genetic causes and environmental factors . AccordiSeveral animal models and human studies have attempted to elucidate the pathophysiological mechanisms underlying ADHD. Recently, genome-wide association studies (GWAS) and single-nucleotide polymorphism (SNP) studies have been reported . HoweverSpontaneously hypertensive rats (SHRs) have been used as animal models for ADHD, since they were first developed in 1963 . SHRs arInitially, most studies were performed using single-gene knockout mice. Various genetic models that exhibit one or more symptoms of ADHD and alter the expression of the genes related to the neurotransmitter system, such as dopamine, serotonin, and norepinephrine, have been developed. Despite the limitations of genetic models, single-gene knock-in/out or transgenic mice have been considered the best methods to date, because they allow the processing of molecular targets or drugs based on mechanisms related to specific genes and the exploration of pathological mechanisms. In particular, the scientific basis can be reported more reliably because it was based on mechanisms related not only to the catecholamine system , but also to the ADHD-like symptoms with unknown causes, such as the SHR. The characteristics of the representative models are presented in Dopamine is involved in the control of attention, motivation, and alterations. Therefore, the dopamine system has been implicated in the development of ADHD. Dopamine-transporter knockout (DAT-KO) mice are among the most widely studied ADHD models. Since dopamine cannot be cleared from the synapse due to the lack of dopamine transporters, DAT-KO mice showed hyperactivity. Moreover, DAT-KO mice exhibited hyperactivity by moving greater distances in the open-field test, which could be rescued by treatment with amphetamine, an ADHD drug. Therefore, the DAT-KO mouse model could be used as an ADHD model, showing hyperlocomotion, attention deficits, and impulsivity . PsychosRecently, a new single-gene-targeting deletion model has been reported. For example, 14-3-3γ deficient heterozygote mice display hyperactive and depressive-like behavior . HoweverPharmacological models that facilitate experiments beyond the variables and limitations of genetic models have also been widely used in ADHD research.Dopamine is one of the most widely studied neurotransmitters associated with ADHD. Initially, experimental destruction of DA-containing neurons with 6-hydroxydopamine (6-OHDA) in adult rats was established as a model of Parkinson’s disease. Researchers observed that lesions in the dopaminergic system of neonatal rats lead to age-limited spontaneous motor hyperactivity, relieving these symptoms in adults . TherefoA prenatal nicotine exposure mouse model has been reported showing hyperactivity and responses to methylphenidate treatment . AdditioRecently, the N-methyl-D-aspartate receptor agonist (MK-801), an agonist of the N-methyl-D-aspartate (NMDA) receptor, was shown to effectively induce various symptoms of different neuropsychiatric disorders in a dose-dependent manner. A sensitivity has been observed against spatial memory impairment and impulsive behaviors at low concentrations of MK-801 . HoweverIn non-human primates, α2A-adrenoreceptors in the prefrontal cortex (PFC) are associated with prefrontal cognitive dysfunction in ADHD. Monkeys administered with yohimbine, an α2A-adrenoreceptors antagonist, in the PFC showed impaired no-go performance in the go/no-go task, indicating impulsive behavior . This stMaternal heavy metal exposure is frequently associated with an increased risk of ADHD . Lead isPrenatal cadmium exposure induces behavioral hyperactivity in rat offspring. Therefore, 50 ppm cadmium in the drinking water of pregnant rats was used. Since most heavy metal pollution occurs through water, soil, and air, this review classified it as an environmental factor model. In addition, this effect is enhanced by ethanol co-exposure with cadmium during prenatal development .Polychlorinated biphenyls (PCBs) are a group of synthetic chemicals widely used in various industrial and commercial products. PCBs persist in the environment and contaminate food and water. The effects of PCBs on neurodevelopment and the dopamine system have been reported in several cohort studies. As a result, prenatal exposure to PCBs has been reported to result in decreased concentration or focus attention, reduced performance accuracy, and delayed reaction times . The PCBPhthalate is a chemical that makes the plastic used in toys and medical devices flexible and is used as an air freshener. Phthalates have been reported to induce hyperactivity in animal experiments and primarily affect the dopamine system . In addiHypoxia is related to monoamine alterations that further affect neonatal brain lesions. Hypoxia also leads to changes in the release patterns of various neurotransmitters. These factors can lead to the progression of ADHD-like symptoms in animals. ADHD-like behaviors have been reported in nitrogen-induced hypoxic rat models after birth. The treatment period varied depending on age; however, most patients were aged >20 days. These animals exhibited learning and memory deficits and hyperactivity . MorpholDirect X-ray irradiation of rat pups leads to behavioral deficits. Evidence suggests that a reduction in hippocampal neurons leads to ADHD symptoms . The expThe adult offspring of mice treated with restraint stress during pregnancy were hyperactive. Maternally stressed adult mice also show reduced DAT expression and increased DA turnover in the striatum. In addition, treatment with a DA antagonist reduced wheel-running activity. Maternal stress can induce hyperactivity in offspring . An assoDrinking during pregnancy is known to cause fetal alcohol syndrome, characterized by mental retardation, abnormal facial features, and a small head size. ADHD and fetal alcohol syndrome differ; however, the behavioral symptoms of fetal alcohol syndrome show patterns similar to those of ADHD. Prenatal alcohol exposure has an overall effect on brain structure, especially in the cerebellum. It has been reported that exposure to alcohol during brain growth acceleration leads to the loss of the cerebellum and Purkinje cells. Extensive studies have been conducted on the effects of alcohol on the brain. In animal studies, cell suicide neurodegeneration increased in alcohol-administered mice, and functional brain magnetic resonance imaging showed difficulty in working memory, a function of the frontal lobe of the abdomen. Single binge consumption has been reported to have neurotoxic effects . MaternaADHD is believed to be caused by genetic, neurological, and environmental factors. Some leading theories suggest that the underlying mechanism of ADHD involves an imbalance in neurotransmitters, such as dopamine and norepinephrine, which regulate attention and behavior. Various drugs have been developed to modulate brain neurotransmitter levels. The most commonly used drugs for ADHD are stimulants, such as methylphenidate and amphetamine. These medications increase the levels of dopamine and norepinephrine in the brain, which can help improve attention and decrease impulsiveness and hyperactivity. In addition to stimulants, non-stimulant medications, such as atomoxetine, are also used to treat ADHD. These medications increase the norepinephrine levels in the brain, which can help improve attention and focus. However, it did not work in approximately 1/3 of the patients. Additionally, although these drugs may be effective treatments for ADHD, the pathology of ADHD remains unknown. Animal models are being developed and used as alternatives to identify the underlying causes of ADHD and effectively treat more patients.The single-gene knockout homozygote type has been used as an ADHD animal model, but this is a very extreme case in which both copies have been blown away. These homozygous types are favored in animal studies because their clinical symptoms are behaviorally evident. For example, if you look closely at the data in the study that proposed GIT1 knockout as an ADHD model, the knockout type showed a record close to zero in most behavioral experiments. Therefore, it was equal to the control group, but it was impossible to perform such behavioral experiments . This fiA combination of environmental factors and genetic variants is known to influence the development and expression of ADHD . This haThe last point that we should not overlook in neurodevelopmental disorders, such as ADHD and ASD, is that these diseases have a large difference in prevalence and symptoms depending on sex. Accordingly, it is necessary to study the molecular and physiological differences that exist prior to pathology. In addition, in clinical practice, drug treatment, and counseling according to sex or symptoms can be appropriately performed. Unfortunately, most previous studies have focused on male mice or rats, as most studies were performed before sex differences in medicine came into the spotlight. Moreover, most researchers have used only male mice in their experiments to achieve ease of study and prevent fluctuations in outcomes that may result from the female menstrual cycle. According to a recent report, only male mice show repetitive behavioral and glial changes in their offspring after exposure to low lead concentrations during pregnancy. The expression patterns of glia-related molecules vary according to gender differences . TherefoIn this review, animal models of ADHD are classified into genetic, drug, and environmental models. However, the classification of drugs and their environments may be ambiguous. These substances are occasionally administered due to direct exposure to environmental factors. In this study, we focused on the impact of environmental factors on ADHD. In addition to what has been reported thus far, new substances and their effects have been reported. It is estimated that genetic and environmental factors have combined effects, or that more than one environmental factor interacts to cause ADHD. Harmful substances might affect various organs of the body and cause various diseases. Conversely, various substances may interact with one another and cause ADHD. Some hazardous substances may not induce disease, but only cause diseases in people with vulnerable genes. Due to the difficulty of such a complex process, it is necessary to take advantage of multi-factor-induced models or study the interactions between factors rather than only single-factor models in both males and females.


Comorbidities are common in children with epilepsy, with nearly half of the patients having at least one comorbidity. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder characterized by hyperactivity and inattentiveness level disproportional to the child’s developmental stage. The burden of ADHD in children with epilepsy is high and can adversely affect the patients’ clinical outcomes, psychosocial aspects, and quality of life. Several hypotheses were proposed to explain the high burden of ADHD in childhood epilepsy; the well-established bidirectional connection and shared genetic/non-genetic factors between epilepsy and comorbid ADHD largely rule out the possibility of a chance in this association. Stimulants are effective in children with comorbid ADHD, and the current body of evidence supports their safety within the approved dose. Nonetheless, safety data should be further studied in randomized, double-blinded, placebo-controlled trials. Comorbid ADHD is still under-recognized in clinical practice. Early identification and management of comorbid ADHD are crucial to optimize the prognosis and reduce the risk of adverse long-term neurodevelopmental outcomes. The identification of the shared genetic background of epilepsy and ADHD can open the gate for tailoring treatment options for these patients through precision medicine. Epilepsy is a common neurological disorder affecting 70 million people worldwide . StatistWith the advent of neuroscience, the definition of epilepsy evolved dramatically due to our better understanding of the disease’s underlying mechanisms and natural history . AccordiThe recent advance in antiseizure medications (ASMs) has expanded largely over the past five decades, including at least 24 different ASMs , leadingelectroencephalogram (EEG)  is a dual dopamine and norepinephrine reuptake inhibitor (DNRI). However, human PET studies demonstrated its preferential inhibition of dopamine transporters (DAT) and NET and weaker inhibition of serotonin transporters ,245. SevVTX phenyl]piperazine) is approved as an antidepressive medication with multiple mechanisms of action: serotonin reuptake inhibitor, 5-HT7, 5-HT3 and 5-HT1D receptor antagonist, 5-HT1B receptor partial agonist, and 5-HT1A receptor agonist, leading to modulation of histamine, norepinephrine, acetylcholine, glutamate, and γ-aminobutyric acid (GABA) ,250. An threo-dihydroxyphenylserine), is chemically analogous to levodopa. Droxidopa is a CNS-penetrant norepinephrine prodrug that is metabolized by DOPA decarboxylase to increase extracellular concentrations of norepinephrine in the brain [2A-adrenoceptor agonists. Droxidopa is approved for adults with neurogenic orthostatic hypotension associated with primary autonomic failure , dopamine β-hydroxylase deficiency or nondiabetic autonomic neuropathy) and is administered orally [Droxidopa , a Chinese traditional medicine is used to treat fever, hepatitis, hypertension, inflammation, and jaundice . BaicaliPrevious reports showed that CWE, particularly complex types, suffer from impaired sustained attention, working memory, processing speed, and neuroanatomical derangements . ImagingPrecision medicine is an emerging field that aims to tailor the treatment of disorders with genetic backgrounds according to the detected genetic mutation and functional alteration. The concept of precision medicine stems from the fact that genetic mutations affect the eligibility and response to treatment, as well as the safety outcomes . In the The association between CWE and comorbid ADHD is still under-recognized in clinical practice, even though the well-established bidirectional connection and shared genetic/non-genetic factors between epilepsy and comorbid ADHD largely rule out the possibility of a chance in this association. Stimulants are effective in children with comorbid ADHD, and the current body of evidence supports their safety in CWE within the approved dose. Early identification and proper management of comorbid ADHD are crucial to optimize the prognosis and reduce the risk of adverse long-term neurodevelopmental outcomes.


Mage = 20.47) read vignettes depicting trolling incidents and rated the acceptability of the perpetrators’ actions and whether they had ever written similar comments. Participants then completed measures of psychopathy, sadism, and toxic anonymous motivations. We find that toxic anonymous motivations partially mediate the relationship between psychopathy and sadism, and online trolling. Whereas trolling is often understood through its underlying personality traits, toxic motivations to seek anonymity may be a more proximal predictor of who is likely to troll online.Online trolling is often linked to sadism and psychopathy. Yet, little research has assessed why people high in these traits seek online environments to achieve their nefarious goals. We employ a functionalist approach to examine whether people high in sadism and psychopathy are motivated to seek the affordances of online environments  to reveal their malevolent self-aspects by engaging in trolling behavior. A sample of 515 university undergraduates ( Online trolling is a behavior that deliberately attempts to deceive, aggress, or disrupt others on the Internet . The behAnonymous situations can often lead people to behave differently than when they are identifiable . As a reTraits such as psychopathy  and sadism (a desire to harm other people for pleasure) are most associated with trolling . HoweverSome researchers have suggested that anonymity facilitates trolling . HoweverAccording to the Uses and Gratifications approach, people seek environments where they can pursue their goals . Some peUsing a functionalist approach, we tested whether psychopathy and sadism are associated with toxic anonymous motivations that increase trolling perpetration and beliefs that trolling is acceptable and appropriate. To assess trolling acceptance and perpetration, participants read a series of vignettes where one person trolled another online. Participants rated how acceptable and appropriate the perpetrator’s actions were and whether they had ever written a comment similar to the perpetrator in each scenario. To assess individual differences, we had participants complete measures of psychopathy, sadism, Machiavellianism, narcissism, global trolling, and motivations to seek anonymity.Assuming the above causal model, we predicted that toxic anonymous motivations would explain the previously observed associations between psychopathy, sadism, and trolling. Of course, statistical tests are conditional on the tested model’s validity and cannot identify a true causal model without recognizing the possibility of alternative explanations . Therefohttps://osf.io/jc386/?view_only=2b9038ab5e4d470297ff208035ac028e. The predicted associations were preregistered,An institutional ethics board approved this study. We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study. All data, analysis code, and research materials are available on OSF and can be accessed at: Mage = 20.47, SD = 4.33). Of these, 79% of participants’ primary language was English, 16% was Chinese, and 5% other.We recruited participants via the student pool of an Australian university in exchange for course credit. The initial sample included 565 participants. We excluded 50 people due to incomplete responses. Therefore, the final sample included 515 participants  to 5 (Strongly Agree). Finally, to assess trait sadism, we used the 10-item Short Sadistic Impulse Scale (SSIS), measured on a 2-point scale  to assess motivations to behave toxically or reveal malevolent self-aspects in anonymous online environments  responded to another person’s comment or actions. The scenarios occurred in various online contexts, including social media or playing an online game. The perpetrator responded by trolling the individual or responding with a benign message. The research team formulated each trolling scenario, following the definition that trolling is a deliberate attempt to deceive, aggress, or disrupt others on the Internet . Twenty-An example of the wording of a scenario that is deceptive in nature is:Dylan was playing a multiplayer online game with strangers on Friday night. Over the chat Dylan told a member of his team to head right, however, the team member instead decided to go left and was promptly killed, meaning they lost the match. At the conclusion of the game Dylan wrote, “Wow, you are actually my hero, great decision bud, keep up the good work.”Very Unacceptable/Inappropriate, 5 = Very Acceptable/Appropriate), and if they had ever written a comment like the perpetrator’s while online, again using a 5-point scale . Participants could skip the question or answer “not applicable” if they did not play multiplayer online games or use social media. Finally, participants completed a questionnaire package that included the OAQ, SD3, Itroll, and SSIS.For each scenario, participants rated how acceptable and appropriate they viewed the perpetrator’s comments on a 5-point scale , indicating that the variables can be combined. We created the variable “trolling perpetration” by averaging the scores of whether participants had written a similar comment to the perpetrator in the six trolling vignettes. For trolling perpetration, 16.6% of responses were answered as not applicable. To accommodate these missing data, trolling perpetration was only calculated for participants who responded to at least half of the items on trolling perpetration.We created the variable “trolling acceptance” by averaging scores of how acceptable and appropriate participants viewed the six trolling vignettes. Trolling acceptability and appropriateness were highly correlated  and trolling perpetration . The indirect effect of sadism via toxic motivations was significant for both trolling acceptance  and trolling perpetration . The direct effect was significant between sadism and both trolling acceptance  and trolling perpetration .The results indicate that the effects of sadism and psychopathy on trolling acceptance and perpetration were partially mediated by toxic motivations see , 2. SpecSE = 0.03, p < 0.001, 95% CI ) and trolling perpetration . The indirect effect of psychopathy via toxic motivations was significant for both trolling acceptance  and trolling perpetration . The direct effect was significant between psychopathy and both trolling acceptance  and trolling perpetration .The total effect of psychopathy was significant for both trolling acceptance  can be found in the article/The studies involving human participants were reviewed and approved by the Health and Behavioral Sciences Low and Negligible Research Committee. The patients/participants provided their written informed consent to participate in this study.LN: conceptualization, methodology, formal analysis, investigation, writing—original draft, writing—reviewing and editing, visualization. ST: formal analysis, writing—review and editing, supervision. EV: conceptualization, methodology, writing—reviewing and editing, supervision. All authors contributed to the article and approved the submitted version.


Attention deficit hyperactivity disorder (ADHD) is a prevalent mental health condition, occurring in 3% to 5% of school-aged children. Although stimulant medications are a recommended treatment for this disorder, physicians' views of these medications have not been systematically evaluated.This study examined physician-prescriber perceptions of using medications to treat ADHD symptoms in children or adolescents.A survey was developed with 4 physician expert in treating ADHD in children. The survey was pilot-tested with a sample of 10 practicing physicians. A sample of 1,000 physicians, with a history of prescribing stimulant medications to children or adolescents, was randomly selected and mailed a 30-item survey. Items were rated on a 7-point response scale .A total of 365 physicians responded to the survey, for a 37% response rate. More than 92% of respondents strongly agreed or agreed that ADHD symptoms cause problems in pediatric patients and stimulants are effective in treating ADHD. The stimulant drug side effects of decreased appetite or weight loss, sleep disruption, and exacerbation of anxiety were a concern (strongly agree or agree response) for 32%, 50%, and 22% of physicians, respectively. Diversion of ADHD medication was a concern for 19% of respondents. Physicians reported that controlled medications for children or adolescents with ADHD are a burden for themselves (32% strongly agreed or agreed), for their staff (37% strongly agreed or agreed), and for parents (40% strongly agreed or agreed). Approximately 38% of physicians responded that they would prefer prescribing a nonstimulant medication with a U.S. Food and Drug Administration indication for treating children or adolescents instead of a stimulant medication, and 58% would prefer prescribing noncontrolled medication that does not have evidence of abuse potential versus one that is controlled and has evidence of abuse potential.Although physicians overwhelmingly perceive stimulant medications as being effective for treating ADHD symptoms in children or adolescents, many would prefer a nonstimulant medication. While many physicians consider the side effects of the stimulants easily managed, others are concerned about prescribing stimulants because of their side effects, risk of diversion, and administrative burden. The majority of physicians would prefer to prescribe a noncontrolled medication without abuse potential instead of a controlled medication to treat children or adolescents with ADHD.


The relationship between attention-deficit/hyperactivity disorder (ADHD) symptoms and type 2 diabetes mellitus (T2D) and its cardiovascular outcomes have not been sufficiently studied.2,986 adults with T2D from the Joslin Diabetes Center at Upstate Medical University were assessed for ADHD-like symptoms, executive dysfunction, and emotional control using the Adult Self-Report Scale V1.1 (ASRS) expanded version. Surveys were sent electronically, and clinical data were obtained from the electronic medical record. Pearson chi-square test was used for categorical variables association. When ASRS scores were the dependent variable, negative binomial regression correcting for demographic variables that were associated with the ASRS scores was used.155 (49.2%) of respondents met DSM-5 criteria for ADHD using the ASRS scores; Only ten (3.6%) of respondents had an ICD10 diagnosis of ADHD in their medical record; Forty-three (13.7%) had either a diagnosis of ADHD in the medical history or were taking medications used by people with ADHD. Higher levels of ADHD-like symptoms were found in patients with T2D compared with population norms. There was a modest association of the ASRS executive dysfunction subscale with overall cardiovascular comorbidities (p = 0.03). However, the p-value did not survive the multiple testing correction. Both ADHD-like symptoms and symptoms associated with emotional control, however, were not associated with specific cardiovascular diseases, hypertension, or with HbA1c, LDL-cholesterol, triglycerides, ALT, creatinine, or eGFR.Our results suggest that adults with T2D attending a tertiary care diabetes clinic are at risk for having ADHD-like symptoms, highlighting the importance of screening for ADHD symptoms in this specialty setting and referring undiagnosed adult patients for further assessment and treatment of ADHD. Larger studies are needed to clarify the relationship between ADHD-like symptoms, executive dysfunction, and emotional control with diabetic control and comorbidities. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects between 2% and 7% of the population globally Several large population studies have examined the association between ADHD and T2D. A longitudinal study using the Taiwan National Health Insurance Research Database enrolled over 35,000 patients with ADHD and over 70,000 age- and sex-matched controls. Adolescents and young adults with ADHD were about three times more likely to develop T2D Some studies have shown that adults with T2D and ADHD have significantly higher levels of fasting blood glucose (FBG), postprandial blood glucose (PBG), hemoglobin A1C (HbA1c), and required insulin dose/kg than those without ADHD In the current study, we assessed ADHD-like symptoms among 312 adults with T2D and examined if their ADHD-like symptom counts or subscales of executive dysfunction and emotional control were associated with cardiovascular comorbidities, elevated HgbA1c (worse glycemic control), LDL-cholesterol and triglycerides (risk factors for cardiovascular disease), alanine transaminase  or reduced kidney function .Individuals with T2D (ICD-10-CM codes E11.XX), ages ≥ 18 years, receiving diabetes care at the Joslin Diabetes Center at Upstate Medical University in Syracuse NY from November 2019 to November 2021 with email addresses in the electronic medical record  were identified. They were contacted electronically and asked to complete surveys in RedCap. Consent for the completion of the surveys were obtained electronically. Consent to have additional data (described below) extracted from Epic were also obtained electronically. All data were deidentified. This work was approved by the Institutional Review Board for the Protection of Human Subjects of SUNY Upstate Medical University.Participants were asked to complete the Adult Self-Report Scale, V1.1 (ASRS) expanded version, which assesses all 18 DSM-5 symptoms of ADHD (nine symptoms for inattention and nine for hyperactivity/impulsivity) and nine symptoms of executive dysfunction and four symptoms of emotional control. Each symptom is rated on a five-point scale indicating the frequency of the symptom over the past month. The scale’s categories are never, rarely, sometimes, often, and very often. Following Adler et al. 2 defined obesity), smoking status, and laboratory results for HbA1c, LDL-cholesterol, triglycerides, ALT, creatinine and estimated glomerular filtration rate (eGFR). From the problem list and medical history fields in Epic, the presence of several diagnoses was obtained including ADHD (ICD-10-CM codes F90.X), hypertension (I10-I15) and cardiovascular diseases . Medications used to treat ADHD  and insulin use were obtained from medication lists in Epic.From the electronic medical record Epic, the following data were extracted: age, sex, race/ethnicity, insurance type , body mass index , of which 278 (89.1%) also consented to the extraction of data from the electronic medical record and 271 (88.3%) completed the survey fully; 155 (49.2%) of respondents met symptom diagnostic criteria for ADHD on the ASRS (“ASRS positive”); Ten (3.6%) of respondents had an ICD10 diagnosis of ADHD in their medical record; Forty-three (13.7%) had either a diagnosis of ADHD or were taking medications used by people with ADHD. Demographic features of study population in ASRS positive and ASRS negative groups are summarized in 2 = 5.81, p = 0.016). For the ASRS positive compared to the ASRS negative cohort, the mean (SD) ages were 59.6 (13.6) years vs 65.7 (12.2) years respectively .As shown in The numbers of ADHD-like symptoms, executive dysfunction symptoms, and emotional control symptoms were also not significantly associated with the individual’s most recent laboratory values of HbA1c, LDL-cholesterol, triglycerides, ALT, creatinine, or eGFR .In exploratory analyses, we found no significant association between the EHR diagnoses of ADHD, total ASRS symptoms or total emotional control symptoms with any of the individual CVDs in The number of ASRS reported symptoms of ADHD declined significantly with age , In this first study of the presence of symptoms of ADHD and cardiovascular diseases among adults with T2D, we found evidence for high levels of ADHD-like symptoms compared with population norms. We also find a modest association of ADHD executive dysfunction subscale with the overall cardiovascular comorbidity status, although the p-value did not survive multiple correction. The ADHD-like symptoms, as well as the symptoms of emotional dysregulation, however, were not associated with specific cardiovascular diseases or with laboratory values of HbA1c, LDL-cholesterol, triglycerides, ALT, creatinine, or eGFR.The prevalence of an ADHD diagnosis in people with diabetes has been reported to be variable, ranging from about 2% to almost 12% in different studies Undiagnosed ADHD in people with diabetes has been shown to have a negative impact on diabetes treatment and metabolic control Among ADHD characteristics, executive functioning skills have been well documented to be associated with diabetes control and treatment adherence In contrast with our findings, several previous studies have shown that the co-occurrence of ADHD in people with diabetes is associated with a greater rate of diabetes complications including, chronic renal failure meta-analysis of over 2 million people, significantly increases the risk of CVD mortality and is linked to an increased risk of coronary heart disease, stroke, and heart failure Cardiovascular comorbidities and complications are common in adults with T2D and are a leading cause of morbidity and mortality There are several limitations to this study. Our survey response rate was 10.4% (n = 312) and it is possible that T2D adults with ADHD preferentially responded. In-person interview using the ASRS screening questionnaire may help to decrease information bias. A comparison group  would have been useful to control for nonspecific effects. Future studies with larger sample sizes may help to further clarify the question regarding how ADHD symptoms, executive dysfunction and emotional control may affect metabolic control and CVD comorbidities. Although EHR data are widely used for research, diagnostic codes are not uniformly applied across providers. However, a strength of our study includes investigating the association of numerous different cardiovascular conditions with ADHD-like symptoms in adults with T2D.In conclusion, our results suggest that adults with T2D attending a tertiary care diabetes center are at high risk for having symptoms of ADHD. Given that the symptoms of ADHD can interfere with self-care and diabetes management, clinicians treating adults with T2D should consider screening for ADHD.The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965381. This report reflects only the author’s view, and the European Union is not responsible for any use that may be made of the information it contains.www.ADHDEvidence.org and www.ADHDinAdults.com.In the past year, Dr. Faraone received income, potential income, travel expenses continuing education support and/or research support from Aardvark, Aardwolf, Tris, Otsuka, Ironshore, KemPharm/Corium, Akili, Supernus, Atentiv, Noven, Axsome and Genomind. With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. He also receives royalties from books published by Guilford Press: Straight Talk about Your Child’s Mental Health, Oxford University Press: Schizophrenia: The Facts and Elsevier: ADHD: Non-Pharmacologic Interventions. He is Program Director of Dr Weinstock participates in multicenter clinical trials, through her institution, sponsored by Eli Lilly, Novo Nordisk, Medtronic, Insulet, Kowa and Boehringer Ingelheim. Tandem has donated insulin pumps and DexCom continuous glucose monitors for clinical studies. She also receives royalties for her contributions to Up-to-Date.All other authors declare no conflict of interest.Ali Zare Dehnavi: . Yanli Zhang-James: Supervision. Dan Draytsel: Data curation. Ben Carguello: Data curation. Stephen V. Faraone: Conceptualization. Ruth S. Weinstock: Conceptualization, Data curation, Supervision.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.