Behavioral teacher training is an effective intervention for children with attention-deficit/hyperactivity disorder (ADHD). Intervention effectiveness may be enhanced by including intervention components that carry the strongest evidence for their effectiveness. A previous article of this group showed that both antecedent-  and consequent-based  techniques were highly effective in reducing daily teacher-rated, individually selected problem behaviors in a specific situation of the child. Effects were observed up to three months post intervention. Here, we tested whether effects were also present in teacher-rated and masked DSM-based assessments that comprise the full range of ADHD and oppositional defiant disorder (ODD) symptoms, as well as on teacher-rated impairment. Teachers of 90 children with (subthreshold) ADHD (6–12 years) were randomly assigned to one of three conditions: a short (two sessions), individualized intervention consisting of either a) antecedent-based techniques or b) consequent-based techniques; or c) waitlist. Multilevel analyses showed that both sets of techniques were effective in reducing teacher-rated ADHD symptoms and impairment immediately after the intervention and up to three months later, as compared to waitlist. Masked observations of ADHD behavior were in line with teacher ratings, with effects being most pronounced for inattention. No effects on teacher-rated or masked ODD behavior were found. This study showed that antecedent- and consequent-based techniques were effective in improving classroom ADHD symptoms and impairment. Long-term changes in teacher-rated ADHD are promising. These results extend previous findings and show the potential of short individually tailored interventions in classroom settings as treatment of ADHD symptoms.The online version contains supplementary material available at 10.1007/s10802-021-00892-z. Behavioral teacher training is an effective intervention to reduce children’s attention-deficit/hyperactivity disorder (ADHD) symptoms and related behavioral problems in the classroom  and consequent-based techniques  , and remained stable up to three months later.In a previous article of our group  broader assessments of ADHD and ODD behaviors, and 2) impairment. Regarding the first question, we were interested whether effects were also obtained if outcomes comprised the full range of DSM-based teacher-rated ADHD and ODD symptoms assessed on a rating scale, i.e., whether effects could also be observed when teachers were asked to report behaviors averaged over the past week and during all situations, rather than during a specific situation during each day. Using traditional DSM-based questionnaires also provides possibilities to compare results with the findings of other behavioral interventions for ADHD. Further, we were interested whether effects were observed by raters who were not involved in treatment delivery and thus less susceptible to social desirability and/or investment bias  teacher-rated and masked observations of ADHD and ODD behaviors according to DSM-criteria, and (2) teacher-rated functional impairment. Data were collected in our randomized controlled microtrial that tested two short and individualized behavioral teacher interventions focusing on either antecedent- or consequent-based techniques. Based on our previous findings regarding our EMA outcome, we hypothesized that both sets of techniques would be effective compared to a waitlist control condition in reducing ADHD and ODD symptoms as rated by teachers, both immediately after the intervention as well as at three months follow-up. We expected smaller effect sizes compared to our EMA outcomes . Longer term effects on teacher-rated ADHD and ODD were investigated three months after baseline (T3), in the intervention conditions only. The total study duration was three months (T0-T3) and allowed no holidays between randomization and T2. In case the summer holiday started prior to T3, T3 took place three weeks prior to the end of the school year (but at least four weeks after T2). Because there are no guidelines for reporting on microtrials, we used the CONSORT guidelines for reporting on randomized controlled trials  or a waitlist control condition. A random list of numbers 1–90 was created to allocate participants to these conditions. Randomization occurred at school level to prevent contamination from teachers receiving different interventions. There was a maximum of two included students per participating teacher. Outcome measures were assessed at three time points: at baseline prior to randomization (T0), during the week immediately after the intervention or the waiting period (T1), and three weeks after the intervention or waiting period (T2). Figure The study sample comprised 90 regular primary school aged children (grades 1 to 6), from rural and urban areas in The Netherlands, showing ADHD symptoms, and their teachers who were in (self-reported) need of effective management techniques for their student(s). Inclusion criteria were: (a) high levels of ADHD symptoms (> 90th percentile) as rated by teachers on the Inattention and/or Hyperactivity-Impulsivity scale of the Disruptive Behavior Disorders Rating Scale (DBDRS)  , the therapists were instructed to explain that the current intervention focused on the trained techniques and therefore the intervention plan consisted of these techniques only. The teacher was advised to implement and/or optimize the trained techniques first and at least until the last week of assessments, to monitor its effectiveness and to decide whether the use of other techniques was needed at a later time. More information on the interventions and examples of intervention plans for every intervention are available in our previous publication . Both therapists reached optimal quality scores for each condition after the first sessions.Interventions were carried out by two psychologists with postgraduate training in behavioral therapy and ADHD (AS and RH) . TherapTreatment fidelity was assessed by scoring contamination and by scoring the percentage of addressed session items in each session. The procedure of Abikoff et al.  was usedfar below average to + 3 = far above average). Scores were reverse scored for consistency with other measures used in this study. Scores may range between -27 and 27 for both scales, with higher scores indicating more ADHD symptoms. The internal consistency for the SWAN in this sample was good (α = 0.85) and convergent validity has been established (Strengths and Difficulties Questionnaire (SDQ) Hyperactivity scale; r = 0.54) /M = 45.7 (girls), SD = 8.08 for the Inattention scale and M = 43.9 (boys)/M = 45.6 (girls), SD = 8.63 for the Hyperactivity/Impulsivity scale   and Hyperactivity-Impulsivity (nine items) of the DSM-based Strengths and Weaknesses of ADHD-symptoms and Normal Behavior (SWAN) rating scale  to 3 (‘very much’). Scores may range between 0 and 24, with higher scores indicating more ODD symptoms. The internal consistency for the teacher-rated ODD-scale of the Dutch version of the DBDRS is high , and convergent validity is strong   to 10 (‘excessive impairment’), in line with the Dutch system for academic grading. A score above 5 indicated functional impairment on that particular area of functioning. Outcome was the average score on the four items (ranging from 0 to 10).An overall measure of functional impairment of the child at school was assessed using an adjusted version of the teacher-rated Impairment Rating Scale .Classroom observations were conducted in a randomly selected subset of the sample, given the time required for coding . For twenty randomly selected children from every condition (67%), classroom observations were coded. The total subsample did not differ from the full sample on baseline characteristics nor in their response to the two interventions studied here as assessed in terms of the proximal outcome (results available from the first author). Observations were conducted when children attended morning lessons in their own classroom led by their primary teacher, and were on similar time and day at both time points  for approximately 90 minutes per child. The first 60 minutes that contained actual lessons were used for coding   for this sample. Convergent validity of the GUCCI was adequate (r = -0.04—0.29), although relatively low correlations between rating scale scores and observational scores indicate that both instruments measure different aspects of ADHD and ODD behavior , who were individually trained by the first author in at least two sessions of two hours. Observers coded a maximum of two scales, in order to increase accuracy and inter-observer reliability . Observers were masked to treatment condition of the child as well as to whether an observation was conducted at pre- (T0) or post-intervention (T2). During the training they were introduced to the behavioral categories of the scale(s) and the coding system. Observers practiced coding until inter-observer agreement with the trainer reached ≥ 0.80 . Given f et al. ).This study was carried out between April 2017 and April 2019. Teachers were recruited through school principals, school collaboration networks, and an outpatient mental health clinic. Teachers showing interest in participation in the study received an information letter explaining the research aims and responsibilities of all parties involved. Teachers who agreed in participating enlisted one to two children showing profound and impairing ADHD symptoms in the classroom, and informed parents about the study . Written consent was obtained from teachers, parents, and children older than 11 years. After receiving consent, teachers administered the ADHD scales of the DBDRS and TTI to screen for eligibility. If inclusion criteria were met, baseline assessments (T0) took place through teacher rating scales and classroom observations, all conducted in the same week. For the classroom observations, observers were introduced as interns. Teachers explained to children that the interns had to observe how children are working during lessons in different classes for study purposes. To prevent target children being aware of being subject of the observations, cameras were positioned in a corner at the front of the classroom, targeted at the whole classroom (but zoomed in at a particular child). Randomization occurred after baseline assessments were completed. Teachers of children in the waitlist condition were allowed to receive care as usual during the study period, and were offered the possibility to use a self-directed behavioral teacher program targeting ADHD symptoms immediately after T2 , and chi-squared or Fisher’s exact tests were used to compare groups on the demographic variables assessed at baseline.n = 40) and compared this to the waitlist condition. Effect sizes (Cohen’s d) were calculated by dividing the difference in mean scores between two conditions averaged over T1 and T2 by the pooled SD  were conducted in Stata (version 16). Missing data was random (≤ 5%) for all outcomes, and was taken into account in multilevel analysis  at T3 as an indication of the feasibility of the interventions.To examine intervention fidelity , with the exception of hyperactivity-impulsivity symptoms on the TTI and DBDRS on which lower ratings were obtained for children in the antecedent condition than for children in the consequent condition (TTI) and waitlist condition (TTI and DBDRS). Parents reported that 23 children  were allocated to the antecedent condition, 30 children (from 26 teachers of 18 schools) to the consequent condition, and 30 children (from 26 teachers of 17 schools) to the waitlist condition. Table Characteristics of the subset of the sample for which the classroom observations were coded is described in Supplementary Information n = 1 for the antecedent and waitlist condition), and two other teachers (n = 1 for the consequent and waitlist condition) discontinued after T2 due to personal problems.Intervention effects on all short-term outcomes are depicted in Table Results showed that for the teacher-rated inattention scale . Approximately similar effects were found for hyperactivity-impulsivity symptoms .Analyses of longer-term changes as assessed with teacher ratings revealed that inattention symptoms remained low (even decreased) from T2 to T3 in both intervention conditions , while the decrease in attention problems within the antecedent- and consequent conditions was non-significant . For motor hyperactivity and verbal hyperactivity, no significant reductions were observed when comparing the intervention conditions to the waitlist condition. There were no significant differences between the antecedent and consequent condition in the effectivity of the two interventions on observed attention problems and motor hyperactivity. For verbal hyperactivity, however, results showed that verbal hyperactivity increased over time in the antecedent condition as compared to the consequent condition with a medium to large effect. Post-hoc analyses within each condition revealed that there was a significant increase in verbal hyperactivity in the antecedent condition from T0 to T2 , while verbal hyperactivity remained stable from T0 to T2 in the consequent condition .Masked assessments of ADHD behavior using classroom observations revealed that there was a decrease in inattention in children in both the antecedent- and consequent condition as compared to children in the waitlist condition from T0 to T2 with medium to large short-term effects, see Table Analyses of short-term effects showed that there were no significant reductions in teacher-rated ODD symptoms (DBDRS) in the intervention conditions compared to the waitlist condition, and when comparing both intervention conditions to each other, see Table B = -0.46, SE = 0.67, p = 0.492; for consequent: B = -0.96, SE = 0.67, p = 0.153).Analyses of longer-term effects (T2 to T3) of teacher-rated ODD symptoms showed that there were no significant changes in ODD symptoms in any of the intervention conditions . There was also a small to medium sized decrease (trend significant) in motor hyperactivity obtained between the “active” intervention condition compared to the waitlist group . No significant differences in verbal hyperactivity and oppositional behavior were observed between the “active” intervention and waitlist condition .Results showed a medium sized decrease in attention problems from T0 to T2 in the “active” intervention group as compared to the waitlist group (t(3.00) = -1.00, p = 0.391. The average percentage of addressed session items was high in the antecedent and consequent condition according to both therapists’ self-report (98.9% and 99.4% respectively) and recorded sessions (98.0% and 97.8% respectively). Most teachers would recommend the training to colleagues , with no differences between the two conditions .Contamination occurred once in one session of the consequent condition and did not occur in any of the sessions of the antecedent condition. Contamination scores did not differ between the two interventions: Using a microtrial design, this study was aimed to gain insight into whether previously found effects of antecedent- and consequent-based techniques in teacher training for children with ADHD on EMA outcomes Supplementary file2 (DOCX 16 KB)Supplementary file3 (DOCX 15 KB)Supplementary file4 (DOCX 78 KB)Below is the link to the electronic supplementary material.


N = 199) at a public university in the northeastern United States completed self-report measures of ADHD characteristics and daily executive functioning, as well as divergent thinking  and intelligence quotient (IQ) tests. The results of a series of multiple regression models showed that (1) executive functioning difficulties negatively, and non-verbal IQ and figural divergent thinking positively, predicted engineering grade point average , (2) GPA and verbal IQ positively predicted figural divergent thinking scores, and (3) verbal IQ positively predicted verbal divergent thinking scores. A series of multiple regression models testing the assertion that controlling for IQ would strengthen the relationship between divergent thinking and ADHD characteristics or executive functioning were not supported but did show associations between select components of characteristics and divergent thinking. Taken together, these results support previous conclusions that students with ADHD characteristics and executive functioning difficulties may struggle academically yet exhibit select enhanced divergent thinking abilities.Characteristics of attention-deficit hyperactivity disorder (ADHD) and executive functioning difficulties have been found to correspond with poorer academic outcomes on the one hand and enhanced divergent thinking on the other hand. The current study was conducted to better understand the relationship between ADHD characteristics, executive functioning difficulties, divergent thinking, and academic outcomes by conceptually replicating and expanding on a previous study. Undergraduate engineering students ( Recent meta-analytic evidence suggests that 506.27 million adults worldwide meet the diagnostic criteria for attention-deficit hyperactivity disorder ADHD; . AlthougAttention-deficit hyperactivity disorder is characterized by a persistent pattern of inattention and/or hyperactivity–impulsivity that impacts one’s daily life . A cliniAlthough a diagnostic interview is necessary for a clinical diagnosis of ADHD, self-report screening measures developed for use in the community and workplace e.g.,  are freqAttention-deficit hyperactivity disorder has been found to be strongly associated with difficulties in executive functioning, a group of higher-order cognitive functions that manage cognitive processing, such as inhibition, shifting, and working memory . Adults Indeed, individuals with ADHD score higher on self-report measures of executive functioning difficulties than others and executive functioning difficulties are positively associated with the severity of ADHD characteristics . For exaStudents with ADHD have poorer academic outcomes in general than students without ADHD . CollegeResearch shows that ADHD characteristics and executive functioning difficulties are also associated with poorer academic outcomes in the general  student population . For exaDivergent thinking refers to the generation of many original and diverse responses to a stimulus , 1967. AHow divergent thinking relates to ADHD characteristics and executive functioning difficulties in adults remains uncertain. Whereas some studies found evidence for enhanced divergent thinking ability in college students diagnosed with ADHD compared to students not diagnosed with ADHD , 2016, oThe current literature suggests that ADHD characteristics and executive functioning difficulties correspond with poorer academic outcomes on the one hand  and enhaHowever, the Understanding this relationship may be particularly important for engineering because the structure of post-secondary engineering education differs from that of other fields in ways that may make it more demanding for those with executive functioning difficulties . These dThus, to better understand the relationship between ADHD characteristics and executive functioning difficulties, divergent thinking, and academic performance, the current study presents a conceptual replication of Hypothesis 1: Executive functioning difficulties will negatively predict engineering GPA.Hypothesis 2: Executive functioning difficulties will positively predict divergent thinking, whereas engineering GPA will negatively predict divergent thinking.In addition, we expand on this research by examining the role of IQ in the relationship between modalities of divergent thinking  and the components of ADHD assessed by one measure focused explicitly on executive functioning difficulties associated with ADHD (B-DEFS) and one measure that aligns with DSM and WHO diagnostic criteria (ASRS). If these scales are measuring the same underlying phenomenon, results for both scales should be broadly similar. Therefore, we expect that:Hypothesis 3: The relationship between ADHD characteristics on the B-DEFS and divergent thinking will be stronger when controlling for IQ.Hypothesis 4: The relationship between ADHD characteristics on the ASRS and divergent thinking will be stronger when controlling for IQ.This study was approved by the institutional review board at the participating university (protocol #H17-196).M = 19.89, SD = 1.86), and their grade level classification was distributed as follows: 32 sophomores, 49 juniors, and 118 seniors.Undergraduate engineering students at a public university in the northeastern United States were recruited to participate in the study with flyers and emails sent to their student account. Participants were compensated with a $35 gift card (prorated at $8.75 per session) after completing or withdrawing from the study. Of 220 students who participated, 20 withdrew from the study and one was discovered to not be eligible for participation , resulting in a final sample of 199 participants (56.3% men and 43.7% women). Participants’ age ranged from 18 to 33 years .The TTCT  assessesThe Kaufman Brief Intelligence Test, Second Edition (KBIT-2)  assessesParticipants completed the study in a private office across four 30-to-60-min sessions on different days. In the first session, participants completed informed consent, followed by the TTCT-Figural test. In the second session, participants used a tablet to complete the B-DEFS and ASRS  on Qualtrics, with all scale items presented in a random order. In the third session, participants completed an engineering design activity (for use in a different study) followed by the KBIT-2, administered by a trained researcher. In the fourth session, participants completed the TTCT-Verbal test. Following completion of the study, cumulative engineering GPA was provided by the university’s Office of the Registrar.Data were analyzed using IBM SPSS Statistics, version 27. Data for one student who did not complete the IQ test were excluded from analyses involving IQ scores. There were no other missing data. Descriptive statistics  were obtained for all variables included in the analyses. Bivariate correlations were analyzed using Pearson correlations (α = 0.05) with pair-wise deletion excluding data for univariate outliers (>±3.5 SD from the mean). Hypotheses were tested using a series of multiple regression models (α = 0.05), with data for outliers  excluded from the corresponding models.p > 0.01) even after removing data for univariate outliers. Therefore, bivariate correlations among the variables were also examined using complete data with Spearman’s rank correlations. There were no appreciable differences in the values between the contrasting correlations, and conclusions based on the results remained the same.Descriptive statistics for all variables are shown in Several multiple regression models were tested to conceptually replicate those of A series of hierarchical regression models were used to examine how components of the B-DEFS  or ASRS  predict the components of figural and verbal divergent thinking, controlling for IQ. Given that, theoretically, controlling for IQ should increase the strength of the relationship between scores on the B-DEFS (and ASRS) scales and components of divergent thinking, scale variables were entered in step one and verbal IQ and non-verbal IQ were entered in step two in all models. Assumptions were met for all models, after removing outlying cases from several of the models as indicated below.Results and coefficients for all models predicting TTCT-Verbal scores are shown in Results and coefficients for all models predicting TTCT-Figural scores are shown in Results and coefficients for all models predicting TTCT-Verbal scores are shown in Results and coefficients for all models predicting TTCT-Figural scores are shown in The purpose of this study was 2-fold. First, we sought to better understand how ADHD characteristics and executive functioning difficulties, divergent thinking, and engineering GPA relate to one another. The results of our conceptual replication of Our hypotheses that controlling for IQ would strengthen the relationship between divergent thinking and executive functioning difficulties (Hypothesis 3) or ADHD characteristics measured by the ASRS (Hypothesis 4) were not supported. Not surprisingly, given the strength of the correlations between fluency, originality, and flexibility on the TTCT-Verbal, results for models predicting these scores using the same predictors were nearly identical. Although components of the B-DEFS alone did not significantly predict scores on any component of the TTCT-Verbal, models were significant after adding IQ, with verbal IQ emerging as the only significant positive predictor in all three cases. Although the ASRS dimensions  did not significantly predict any TTCT-Verbal scores alone, models were significant after adding IQ, with both verbal IQ and the hyperactive/impulsive dimension of the ASRS positively predicting all three scores. Taken together, these results demonstrate two things: First, the relationship between components of divergent thinking on the TTCT-Verbal and inattentive characteristics related to ADHD is broadly similar using either the B-DEFS or the ASRS. Thus, the B-DEFS and the inattentive dimension of the ASRS may indeed assess similar behaviors, ostensibly stemming from executive functioning difficulties related to attention. Second, hyperactive/impulsive (though not inattentive) characteristics of ADHD predict TTCT scores, even after accounting for IQ. This aligns with Results for models predicting component scores on the TTCT-Figural were also broadly similar whether ADHD characteristics were measured using the B-DEFS or the ASRS in most cases. ADHD characteristics did not predict fluency, titles, or resistance to closure, either alone or after including IQ, and elaboration was significantly predicted by verbal IQ only. However, results for the models predicting originality scores on the TTCT-Figural differed when including the B-DEFS dimensions or the ASRS dimensions as predictors. Although ASRS scores did not predict originality either alone or after controlling for IQ, B-DEFS scores did significantly predict originality scores both alone and after controlling for IQ. Although the model explained only a small amount of variance in originality scores (4%), difficulty with time management positively predicted originality and difficulty with motivation negatively predicted originality. This is consistent with N = 50). Third, verbal and figural divergent thinking may relate to different dimensions of ADHD characteristics and executive functioning difficulties in distinct ways. Our results align with those of Our results, along with existing evidence, point to several key issues in understanding how engineering GPA, ADHD characteristics or executive functioning difficulties, and divergent thinking relate to one another. First, engineering GPA can be predicted by executive functioning difficulties, IQ, and divergent thinking. Controlling for other variables, behaviors reflecting strong executive functioning, non-verbal IQ, and figural divergent thinking were positively associated with GPA. Second, the relationship between engineering GPA and figural divergent thinking may be stronger than previously thought. Although TTCT-Figural scores were not a significant predictor of engineering GPA in Although the current study fills important gaps in our understanding of how ADHD characteristics and executive functioning difficulties, divergent thinking, and academic performance relate to one another, there are several limitations that can be addressed in future research. First, we are unable to pinpoint the causes of discrepancies in the results of our conceptual replication and those of Second, we did not include a clinical diagnostic assessment for ADHD, which limits the generalizability of our results to the general population. There is evidence that, because the primary characteristics of ADHD are distributed in the population, ADHD is best conceptualized as the extreme end of a dimension  influence how ADHD characteristics and executive functioning difficulties predict divergent thinking.The current study was conducted to better understand the relationship between ADHD characteristics and executive functioning difficulties, divergent thinking, and academic performance. Results align with previous research showing that executive functioning difficulties are associated with poorer engineering GPA. However, results contradict previous research by finding that figural divergent thinking ability is associated with better engineering GPA. Additionally, hyperactive/impulsive ADHD characteristics were associated with greater verbal divergent thinking, whereas select executive functioning difficulties (primarily reflecting inattentive characteristics) were associated with greater figural divergent thinking originality. Although the relevance of these results for students in other academic fields requires further study, engineering students who struggle academically due to executive functioning difficulties associated with ADHD may benefit from focusing on their strengths related to figural divergent thinking.The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional Review Board at the University of Connecticut. The patients/participants provided their written informed consent to participate in this study.CT and AZ contributed to the conception and design of the study. AZ managed the data collection. CT performed the statistical analysis and wrote the first draft of the manuscript. Both authors contributed to manuscript revision and read and approved the submitted version.


ADHD symptoms can adversely impact functioning in a range of domains relevantfor maternal well-being and fetal development; however, there has beenalmost no research examining their impact during pregnancy. We aimed toaddress this gap.n = 1,204) from a longitudinal birth cohortstudy spanning eight countries to address this gap.We used data , friends , andsignificant others ;higher stress  anddepressive symptoms , and increased likelihood of an unwanted pregnancy . Significant associations with tobaccouse  and prematurebirth  did not survivecorrection for multiple comparisons and there were no significantassociations with alcohol use, low birth weight, or unplanned pregnancy.ADHD symptoms in the third trimester of pregnancy were associated with lowersocial support from family (Results suggest that women with ADHD symptoms could benefit from earlier,more regular screening for mental health difficulties and greater mentalhealth support during pregnancy. ADHD is characterized by the core symptoms of inattention  and/or hyperactivity/impulsivity , occurring with a frequency andseverity that is clinically impairing . ThesesWhile traditionally perceived as primarily affecting children, especially young boys,ADHD symptoms are now also recognized to affect both adult men and women, with anattenuated gender gap in those with a diagnosis in adulthood relative to childhood.Estimates suggest that approximately 3% of adult women meet diagnostic criteria forADHD, with a male : female ratio of only 1:1 to 2:1 . HoweverADHD symptoms can be impairing across the entire life cycle, but may have additionalsignificance in the perinatal period, as their sequelae could impact fetal development.ADHD symptoms have, for example, been associated with an increased risk of sexualrisk-taking leading to a greater prevalence of unplanned pregnancies  systematic review involved samples fromhigh income countries. Currently, we know almost nothing about the impact of ADHDsymptoms in lower resource settings. We do, however, know from epidemiological studiesthat adulthood ADHD is an issue in low- and middle-income countries, where it isstrongly associated with outcomes such as anxiety, depression, behavioral disorders,substance use, and impairments in domains such as cognition and social interaction.It is, An improved understanding of the impacts of ADHD symptoms in pregnancy offers a rangepotential of clinical implications. For example, evaluating the relative risks andbenefits of pharmacological treatments for ADHD symptoms during pregnancy requiresevidence regarding the possible range and severity of the impacts of ADHD symptoms inpregnancy .For womWe thus used data from an eight-country birth cohort study to examine the associationsbetween ADHD symptoms in the third trimester of pregnancy and a range of maternal andbirth outcomes based on previous evidence of ADHD sequelae likely to be especiallyrelevant in the perinatal period. Specifically, we examined the relations between ADHDsymptoms during pregnancy and social support, substance use, stress, depressivesymptoms, unplanned, and unwanted pregnancy as reported in the third trimester ofpregnancy and with infant low birth weight and prematurity.Participants were from the Evidence for Better Lives Study—Foundational Research(EBLS-FR) study . ParticiM = 14, SD = 5.3). Data at the thirdtrimester were collected by trained fieldworkers using computer-assistedpersonal interviews (CAPI). Participants were interviewed in the antenatalclinics where they attended their healthcare appointment in a private space. Atthis stage participants reported on their health, wellbeing, adversityexposures, feelings about their pregnancy, reproductive history, attitudestoward future parenting, and social support. Following the birth of the child,participants were re-contacted to provide follow-up information. At this stageparticipants provided information about the birth of the child, early childhealth, and their current wellbeing. Further information on the study can befound in the study protocol paper into nine other languages: Urdu (Pakistan), Afrikaans and IsiXhosa (SouthAfrica), Romanian (Romania), Filipino , Sinhala andTamil (Sri Lanka), Vietnamese (Vietnam), and Twi (Ghana). The English versionswere used in Jamaica. These languages were selected based on reflecting thelargest linguistic groups in each site. The translation process was guided bybest practice guidelines offered by the World Health Organization and theTranslation Review Adjudication Pretest Documentation (TRAPD) method which isconsidered state of the art: z-proso Supplemental Material.ADHD symptoms were measured using five items in the questionnaire administeredduring the third trimester of pregnancy. Three items were adapted from the AdultADHD Self-report Scale ASRS;  for adulr = .42–.61). Cronbach’s alphas forthe family, friends, and significant other subscale scores were: .87, .92, and.85 respectivelySocial support was measured using the self-reported Multidimensional Scale ofPerceived Social Support , 1990, aSubstance use was measured using the self-reported Alcohol, Smoking, andSubstance Use Involvement Screening Test ASSIST; ,adminisThe PHQ-9 measures depressive symptoms experienced in the previous fortnight. Itsnine items are based on DSM-IV-TR criteria for depression and include thefollowing symptoms: anhedonia, dysphoria, sleep disturbances, fatigue, changesin eating, low self-esteem, concentration difficulties, hypo- or hyper-activebehaviors, and thoughts of suicide or self-harm . ResponsPerceived Stress Scale  prenatal questionnaire  prenatal questionnaire  was collected at the follow-up wave, drawingon the same sources. Prematurity was defined as being born before 37 weeks ofgestation.Multi-level linear and logistic regression models were fit with linear orlogistic link functions for continuous and binary outcomes respectively, withseparate models estimated for each outcome. We estimated only unadjusted models to obtain estimates of the raw associationsbetween ADHD symptoms and maternal and birth outcomes. Models were estimatedusing maximum likelihood estimation in the lme4 package in R statisticalsoftware. Random intercepts and random slopes were both included to account forthe fact that our sampling design involved eight clusters (countries). Giventhat there were very few missing data for most models, missingness was dealtwith using listwise deletion. https://osf.io/jydm8/?view_only=5e9cf62150c24f68bcc8f5f2eb4624db.Analyses suggested that ADHD symptoms are significantly related to lower socialsupport from family , friends, and significant others; greater tobacco use; and higher levels ofdepressive symptoms  andstress  during pregnancy.ADHD symptoms were also associated with a greater likelihood of the pregnancy beingunwanted  and with prematurebirth . However, aftercorrecting for multiple comparisons, only support from significant others, stress,and depression remained statistically significant.ADHD symptoms were not significantly related to alcohol use during pregnancy nor tothe pregnancy being unplanned, nor to low birth weight. Some caution is due,however, in the interpretation of the significant effects on tobacco use, socialsupport from significant other, unplanned pregnancy, and low birth weights becauseit was necessary to trim the random effects structure to random intercepts only toachieve convergence. Specifically, for these, a random slopes model could not be fitand a random intercepts model was fit instead. The standard errors for these effectsmay, therefore, be slightly smaller than they should be.Despite a long-dominant conceptualization of ADHD as a problem primarily affectingyoung boys, there is gradually increasing recognition that ADHD symptoms affectadult women . Indeed,The associations with stress and depressive symptoms are consistent with previousresearch suggesting that ADHD symptoms often co-occur with internalizing problemssuch as anxiety and depression , includiThe associations identified with stress and depression suggest that women inantenatal care with known ADHD symptoms could benefit from earlier and more frequentscreening for internalizing symptoms in order to provide timely intervention andminimize their impact on mother and child. However, ADHD symptoms are more likely tobe missed in females than in males, so many women may not have had their symptomsrecognized previously . In factThough the evidence was less compelling given that the effect did not surviveBonferroni correction for multiple comparisons, ADHD symptoms were also associatedwith increased tobacco use during pregnancy. This is consistent with previousevidence that ADHD symptoms are related to greater cigarette use and dependence.Taken tr = .64 based on atetrachoric correlation and r = .40 based on a point biserialcorrelation).Our study also identified a potential association between ADHD symptoms and unwantedpregnancy but not unplanned pregnancy. The association with unwanted pregnancy isconsistent with previous evidence that ADHD symptoms are associated with a greaterrisk of unintended pregnancies . PreviouWe also identified an association between ADHD symptoms during pregnancy and reducedfamily, friend, and significant other social support. This is consistent withprevious evidence that adults with ADHD symptoms experience difficulties in socialand romantic relationships. One recent study found that both inattention andimpulsivity (but not hyperactivity) symptoms predicted relationship impairmentduring this period . UnfortuThe current study focused on the associations between ADHD symptoms and maternalfunctioning in pregnancy, as well as neonatal outcomes; however, it is likelythat the impact of ADHD symptoms during this time is much broader, and mayinclude impacts on additional health behaviors, planning for the arrival of thebaby, and the establishment of adaptive parenting behaviors and family climate.Future research will be beneficial to characterize the full range of effectsthat ADHD symptoms may have in the perinatal period and how these may impactlong-term child development and family well-being. An improved understanding ofwhether and how to screen for ADHD symptoms in the antenatal period could alsohave a range of additional benefits. First, identification of maternal ADHDsymptoms during pregnancy could facilitate early interventions for parentingbehaviors. ADHD symptoms are known to adversely impact parenting and is onemechanism thought to contribute to the intergenerational transmission ofsymptoms to offspring . Second,The current study uses a large longitudinal sample from a set of eight culturallydiverse countries. However, the majority of the analyses were cross-sectional innature and we did not have any information on the long-term development of thechild nor pre-pregnancy ADHD symptom levels. We were also unable to conduct fulldiagnostic assessments for ADHD and therefore relied on a screeningquestionnaire to estimate symptom levels. This also meant that were only able toanalyze the impact of ADHD symptoms at a relatively broad level and could notidentify the effects of specific types of symptoms . This will be important to address in futureresearch as there is some preliminary evidence that these domains aredifferentially related to impairment among pregnant women .We alsoADHD symptoms during pregnancy were associated with higher levels of antenatal stressand depression, lower social support, greater tobacco use, and an increasedlikelihood that a pregnancy is unwanted and that a birth is premature (though thetobacco use and premature birth associations did not survive correction for multiplecomparisons). These preliminary findings underline the importance of furtherresearch to improve the assessment of ADHD symptoms in the perinatal period andbetter understand their impacts on maternal and child outcomes. This information iscritical for informing optimal antenatal care for women with ADHD symptoms.Click here for additional data file.Supplemental material, sj-docx-1-jad-10.1177_10870547221105064 for AssociationsBetween ADHD Symptoms and Maternal and Birth Outcomes: An Exploratory Analysisin a Multi-Country Cohort of Expectant Mothers by Aja Louise Murray, Diana Taut,Adriana Baban, Chad Lance Hemady, Susan Walker, Joseph Osafo, Siham Sikander,Mark Tomlinson, Stefani Du Toit, Marguerite Marlow, Catherine L. Ward, AsviniFernando, Bernadette Madrid, Vo Van Thang, Hoang Dinh Tuyen, Michael Dunne,Claire Hughes, Pasco Fearon, Sara Valdebenito and Manuel Eisner in Journal ofAttention Disorders


Previous research indicates that training individuals to recruit cognitive control before exposure to negative pictures can facilitate the propensity to use reappraisal and reappraisal success. Individuals with attention deficit hyperactivity disorder (ADHD) experience difficulties in cognitive control and emotion regulation, so they may especially benefit from such training. Individuals reporting high ADHD symptoms and controls were randomly assigned to one of two training conditions. In the high emotion control (H-EC) training condition, negative pictures were typically preceded by a stimulus that recruits cognitive control. In contrast, in the low emotion control (L-EC) training condition, negative pictures were typically preceded by a stimulus that does not recruit cognitive control. Participants were then asked to recall an adverse personal event and to reappraise the event. As predicted, instructed reappraisal was more effective in reducing negative mood in the H-EC training compared to the L-EC training. Furthermore, compared to controls, individuals with reported ADHD symptoms showed a greater propensity to use reappraisal after writing the event and a more considerable reduction in event significance and negativity following the instructed reappraisal assignment. We argue that employing cognitive control over emotional information has a causal role in reappraisal use and success among individuals with ADHD symptoms. The inattentive subtype is characterized by attention deficits, while difficulties in controlling behavior characterize the hyperactive-impulsive subtype. The combined subtype includes both attention deficits and hyperactive-impulsive symptoms. These three core symptoms of ADHD  may lead to feelings of frustration, poor planning and poor decision-making2, dysfunctional interpersonal relationships5, and impaired academic performance6. These, in turn, may impair the ability of these individuals to adapt and cope within the environment7.Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder divided into three subtypes: inattentive, hyperactive-impulsive, and combined type  or Dialectical Behavioral Therapy (DBT) has been found to reduce inattention and impulsivity symptoms in adults26, as well as in children and adolescents21.Reappraisal is associated with improved emotional health27. Cognitive control comprises the set of processes that enable individuals to behave according to their goals and ignore irrelevant information. Similarly, reappraisal involves the suppression of irrelevant information to perform goal-oriented behavior22. Indeed, several studies found a positive link between cognitive control and reappraisal28. For example, McRae et al.26 found that reappraisal is positively associated with specific cognitive control components (working memory capacity and set-shifting costs) for both emotional and neutral material. Similarly, greater use of reappraisal was found to be associated with less emotional interference by aversive images following a flanker task that recruits inhibitory control29.Recent notions argue that reappraisal may depend on cognitive control35, while others fail to find such an effect37. For example, Cohen and Mor24 showed that a training procedure in which participants recruit cognitive control before the presentation of negative pictures enhances reappraisal propensity and the ability of instructed reappraisal to reduce negative mood. In their study, healthy individuals completed a task in which a flanker stimulus was shown before a negative or a neutral picture. Incongruent flanker stimuli consist of a conflict between the target and distractors and are therefore associated with recruitment of cognitive control. Reaction time (RT) to a discrimination task (deciding whether a square is blue or green) at the end of the trial was used to assess the effect of incongruent flankers on emotional interference39. The authors paired the appearance of incongruent flanker stimuli (that recruit cognitive control) with negative pictures. Therefore, in the training group, participants recruited cognitive control before encountering the emotional information in most (80%) of the trials. In the control group, however, most of the negative pictures were preceded by a congruent flanker stimulus, which does not recruit cognitive control. Following this task, the two groups were asked to recall a personal negative event and reappraise it. The results revealed less emotional interference by negative pictures on reaction times of the discrimination target when these pictures appeared following the recruitment of cognitive control . Compared to the control group, the training group reported higher use of reappraisal after recalling the event and greater success in the instructed reappraisal assignment, as indicated by a larger reduction in negative mood. These results are in line with findings showing that transcranial direct current stimulation (tDCS) on brain regions associated with cognitive control decreases arousal ratings in a reappraisal task42.The findings mentioned above show a correlational link between the tendency to use reappraisal and cognitive control. Studies have only recently begun testing whether there is a causal link between cognitive control and the propensity to use reappraisal and between cognitive control and the ability to implement reappraisal when facing a negative event. One way of assessing this causal link is by training individuals with a cognitive control task and measuring whether such training increases the propensity to use reappraisal or the effectiveness of an instructed reappraisal assignment. The results of these studies are inconsistent, with some findings showing that cognitive control training can enhance the use and effectiveness of cognitive reappraisal45, what may explain their emotion regulation difficulties47. This idea is in line with findings showing that these individuals have difficulties ignoring irrelevant information48, both neutrally and emotionally valenced49. Inability to ignore irrelevant emotional information is indeed associated with reduced ability to use reappraisal29 and with elevated levels of depression and rumination50. Prior studies on ADHD assessed either deficits in cognitive control or deficits in emotion regulation, and therefore it is yet unknown whether cognitive control plays a role in enabling adaptive emotion regulation use among this population.The role of cognitive control in emotion regulation may be especially relevant in the context of ADHD. Individuals with ADHD symptoms show deficits in cognitive control39. This trial sequence, in which a discrimination target is used to assess the interaction between cognitive control and emotional processing is common and was shown to be effective in revealing an interaction between cognitive control and emotion in previous studies51. Following the training task, participants were asked to recall a negative personal event and reappraise it24. Based on prior literature showing difficulties both in emotion regulation48 and cognitive control52 among individuals with ADHD symptoms, we expected the high emotion control (H-EC) training would be highly beneficial for these individuals. Specifically, we had several predictions:The current study is the first to examine whether a training procedure in which cognitive control is employed over negatively valenced stimuli can enhance reappraisal ability among individuals reporting high levels of ADHD symptoms. Individuals who reported having ADHD symptoms and controls performed a training task in which cognitive control recruitment  was paired with negative pictures  or with neutral pictures . Reaction time (RT) to a discrimination task (deciding whether a square is blue or green) that appeared at the end of the trial was used to assess the influence of cognitive control on emotional interferenceRegarding the flanker task, we expected to replicate the common congruity effect found in this task. Specifically, we expected to find slower RTs for incongruent vs. congruent flanker stimuli. We did not expect this effect to differ between the two training conditions nor between the ADHD symptoms and control groups.24 showing less emotional interference  following incongruent vs. congruent flankers. This effect was predicted to be larger in the H-EC condition vs. the L-EC condition. We did not have a specific prediction regarding group difference (controls vs. ADHD symptoms) in this effect.Considering the emotional interference effect measured using the discrimination task, we expected to replicate the effects observed in Cohen and Mor24, participants in the H-EC condition would show a greater propensity to use reappraisal, compared to the L-EC condition. This effect was expected to be larger among individuals reporting high levels of ADHD symptoms compared to the control group.Considering reappraisal propensity, we predicted that similar to what was found in Cohen and Mor24. Specifically, compared to the L-EC group, the H-EC group was expected to report a lower negative mood following reappraising the event vs. before reappraising it. This effect was expected to be larger among individuals reporting high levels of ADHD symptoms compared to the control group.Considering reappraisal success, we predicted greater success in implementing the instructed reappraisal assignment in the H-EC vs. the L-EC conditionAn examination of the effects of the instructed reappraisal assignment on the reported negativity and personal significance of the event was conducted on an exploratory basis with no specific predictions.This study was approved by the institutional review board of the Faculty of Education, University of Haifa (No. 008/19). All methods were carried out in accordance with standard human research ethics guidelines (Declaration of Helsinki) and regulations. Written informed consent was obtained from the participants.24 study. All participants had normal or corrected-to-normal vision and were native Hebrew speakers. Participants in the control group reported no history of psychiatric disorders or ADHD, while the group with reported ADHD symptoms included only individuals who reported having ADHD symptoms. We did not conduct a formal ADHD assessment or assessment of comorbid disorders by clinicians. A request for a formal diagnosis was sent to all participants in the ADHD symptoms group who agreed to be contacted by the researchers via email. This request was sent approximately 1.5 years following the end of data collection and 39% of the participants provided official documentation specifying a diagnosis of ADHD. Moreover, in a questionnaire administered to this group at the end of the study, 52% of the participants reported taking medications for ADHD regularly. None of the participants in the control group reported taking a medication for ADHD. Participants in the ADHD symptoms group also filled out a questionnaire assessing ADHD symptoms which indicated that above 80% of the participants reported experiencing a high level of at least five symptoms of inattention, hyperactivity, or impulsivity. Excluding participants who reported lower levels of ADHD symptoms (more than 2SD below the mean score in the ADHD Symptoms Checklist) did not change the pattern of the observed effects.A total of 193 individuals participated in the study (91 controls and 102 with ADHD symptoms). Data of control participants were taken from Cohen and Mor’sParticipants in the reported ADHD symptoms group were asked to withhold taking medication for their disorder on the day of the experiment. The experiment session was conducted mostly during morning hours meaning that participants were asked to avoid taking their morning pill. Data from 17 participants were removed due to a high error rate in the training task (more than 20% errors in the flanker or the discrimination tasks), and data of two participants were removed because they did not follow the writing task instructions. Additional nine participants were removed as they rated the event negativity/significance/their mood following writing it as 0. Three participants from the ADHD symptoms group were removed as they mentioned taking medicine before the experiment in a questionnaire administered at the end of the study. Therefore, the analyses included 163 participants  and low emotion control (L-EC). The training task was programmed using E-Prime . After the training task (see below), participants completed an instructed reappraisal assignment that included recalling and writing a negative personal event and reappraising it. Then, participants completed self-report measures of state reappraisal and negative mood. They were also asked about the negativity of the event and its significance. Finally, participants completed the Emotion Regulation Questionnaire24. Each trial  appeared until response, but for no longer than 2000 ms. An inter-trial interval (ITI) of 1000 ms ended each trial. Participants were asked to respond to the flanker stimulus by indicating the direction of the middle arrow. Response to the discrimination target included indicating whether the square is blue or green. While the proportions of incongruent and congruent trials, as well as the proportions of negative and neutral pictures, were equal across the H-EC and L-EC conditions, the proportion of the pairing between the flanker stimulus  and the picture valence  varied between these conditions. Specifically, in the H-EC condition, negative pictures were usually (80%) preceded by incongruent stimuli and seldom by congruent stimuli, while in the L-EC condition, the opposite was true . Therefore, participants in the H-EC condition recruited cognitive control before most of the negative stimuli. The task consisted of 320 randomly presented trials. Picture stimuli included 12 negative and 12 neutral pictures selected from the International Affective Pictures System . Sixteen practice trials were given before the actual task.Participants in the two training conditions  were asked to respond as rapidly as possible to two tasks on each trial using the computer keyboard24. Participants were given four minutes to recall an event that made them feel bad about themselves and write about it. Then they were required to indicate the extent to which the event was negative (“How negative is the event for you?) and significant (“How significance is the event for you?”) for them. Afterward, they completed state measures of reappraisal and mood (see below). For all measures, participants answered by using a mouse cursor to indicate a location on a visual analog scale that ranged from highly agree (0) to highly disagree (100). Subsequently, participants performed an instructed reappraisal task in which they were asked to reinterpret the event to make it seem less negative. They were given four minutes to write how they would reappraise the event. Following the instructed reappraisal assignment, participants again answered the questions about negativity and significance and completed the state measures.The instructed reappraisal assignment consisted of two stages: recalling a negative personal event and reappraising the event13. The assessment of negative mood included four items . For more details, see Cohen and Mor24. Participants answered by clicking with a mouse cursor on a visual analog scale (VAS) ranging from 0  to 100 (very much).State reappraisal was assessed using five statements modified from the ERQ1). The questionnaire included nine items assessing inattention difficulties and nine items assessing hyperactivity/impulsivity. Participants answered on a scale ranging from 1 (never) to 7 (often). The questionnaire is available at https://osf.io/zpurj/.As noted above, this study did not conduct a formal ADHD assessment by clinicians. It relied on reported ADHD symptoms. As participants in the control group reported having no psychiatric disorders including ADHD, the level of ADHD symptoms was assessed only among individuals recruited for the ADHD symptoms group. This assessment was conducted using the DSM-5 ADHD Symptom Checklist. This questionnaire includes 18 statements modified from the Diagnostic and Statistical Manual of Mental Disorders , I change what I'm thinking about”) and 4 statements measuring suppression . The scale ranges from 1—strongly disagree to 7—strongly agree.Trials with incorrect responses on the flanker and discrimination tasks were eliminated from the analysis . Furthermore, we excluded trials with extreme RTs (2.5 SD above the participant’s mean RT), resulting in the exclusion of 2.04% trials in the flanker task and 1.68% trials in the discrimination tasks for the ADHD group, and 2.42% trials in the flanker task and 2.50% trials in the discrimination tasks for the control group.F = 1347, p < 0.001, partial η2 = 0.89, reflecting faster RTs on congruent trials  than on incongruent ones . The interaction between congruity and training condition was not significant, F = 0.71, p = 0.40, partial η2 = 0.004, indicating that the training conditions did not differ in congruity. Moreover, no interaction was found between congruity and group, F = 1.37, p = 0.24, partial η2 = 0.009, or between congruity, condition and group, F = 0.61 p = 0.44, partial η2 = 0.004.RTs in the flanker task were subjected to a two-way mixed analysis of variance (ANOVA), with congruity (congruent vs. incongruent) as a within-subjects factor and condition  and group  as between-subject factors. A typical main effect of congruity was found, F = 5.27, p = 0.02, partial η2 = 0.03. As predicted, the interaction between congruity, valence, and condition was significant, F = 5.03, p = 0.03, partial η2 = 0.03. All other interactions with experiment or group were not significant. Post hoc analysis revealed that the interaction between congruity and valence was not significant in the L-EC condition F = 1.88, p = 0.17, partial η2 = 0.02, in which a main effect for valence was observed, F = 6.42, p = 0.02, partial η2 = 0.07, with overall slower responses following negative compared to neutral pictures. In contrast, the congruity–valence interaction was marginally significant in the H-EC condition, F = 3.28, p = 0.07, partial η2 = 0.04. In this training condition, the response to discrimination targets was delayed following negative  compared to neutral  pictures when a congruent stimulus preceded the picture. This effect was smaller when an incongruent stimulus preceded the picture .RTs to the discrimination targets were subjected to a three-way mixed ANOVA. Flanker congruity (congruent vs. incongruent) and picture valence  served as within-subjects factors, and condition (H-EC vs. L-EC) and group (ADHD symptoms vs. control) served as between-subjects factors. A main effect of congruity was found indicating slower RTs for discrimination targets that appeared after incongruent vs. congruent stimuli, F = 1.94, p = 0.17, partial η2 = . 01, indicating that individuals in the H-EC training condition did not report using reappraisal more than individuals in the L-EC condition. However, the effect of group was significant, F = 10.87, p < 0.001, partial η2 = 0.07, indicating that individuals with ADHD symptoms reported greater use of reappraisal than controls. The interaction between condition and group was not significant, F = 2.40, p = 0.12, partial η2 = 0.02.To examine the hypothesis that participants in the H-EC condition would demonstrate a higher propensity to use reappraisal than those in the L-EC condition, reappraisal scores were subjected to a two-way ANOVA. Training condition (H-EC vs. L-EC) and group (ADHD symptoms vs. control) were used as between-subject factors, and state reappraisal after writing up the event served as the dependent variable. Contrary to our prediction, the effect of condition was not significant, F = 30.49, p < 0.001, partial η2 = 0.16, reflecting reduction in negative mood following the instructed reappraisal assignment. The interaction between time and condition was also significant, F = 5.70, p = 0.02, partial η2 = 0.04, indicating a larger reduction in negative mood following the instructed reappraisal assignment in the H-EC training condition than in the L-EC condition. No interaction was found between time and group, F = 0.22, p = 0.64, partial η2 = 0.001, or between time, condition and group, F = 0.08, p = 0.81 partial η2 < 0.001 , mood scores were subjected to a three-way mixed ANOVA. Time  served as a within-subjects factor, while condition (H-EC vs. L-EC) and group (ADHD symptoms vs. control) served as between-subjects factors. A main effect of time was found, r = − 40.13, p = 0.10). To further test whether the interaction between training group  and the propensity to use reappraisal predicted reappraisal success, a hierarchical regression model was conducted. Negative mood difference scores served as the dependent variable. Training condition  and reappraisal propensity scores were entered first as independent variables into the model, followed by the interaction between these two variables. Results showed that, in the first model, only training condition significantly predicted the difference in negative mood . This model accounted for 4.6% of the variance in negative mood change, F = 3.73, p = 0.03. The interaction between training condition and spontaneous reappraisal, entered in the second step, did not significantly predict change in negative mood . The second model accounted for 4.6% of the variance, F = 2.47, p = 0.06, and did not add significantly to the first model, Fchange = 0.00, p = 0.998. Together, these findings indicate no link between reappraisal propensity and reappraisal success.A correlation analysis was conducted to assess whether the level of reported spontaneous reappraisal following the event writing was correlated with reappraisal success (change in negative mood from after writing the event to after reappraising it). When looking at the entire sample, this correlation was not significant  = 147.99, p < 0.001, partial η2 = 0.49, reflecting a reduction in the reported negativity of the event following the instructed reappraisal assignment. There was no interaction between time and condition, F = 0.61, p = 0.44, partial η2 = 0.004, but the interaction between time and group was significant, F = 9.27, p = 0.003, partial η2 = 0.06, indicating a larger reduction in reported negativity following the instructed reappraisal assignment among individuals with reported ADHD symptoms compared to controls. There was no interaction between time, condition and group, F = 0.15, p = 0.70, partial η2 = 0.001.Negativity scores were subjected to a three-way mixed ANOVA. Time  served as a within-subjects factor and condition (H-EC vs. L-EC), and group (ADHD symptoms vs. control) served as between-subjects factors. A main effect of time was found, F = 46.50, p < 0.001, partial η2 = 0.23, reflecting a reduction in reported significance following the instructed reappraisal assignment. The interaction between time and condition was not significant, F = 2.94, p = 0.09, partial η2 = 0.02. However, the interaction between time and group was significant, F = 6.11, p = 0.02, partial η2 = 0.04, indicating a larger reduction in significance following the instructed reappraisal assignment among individuals with reported ADHD symptoms than among controls. There was no interaction between time, condition and group, F = 0.50, p = 0.48, partial η2 = 0.003  served as a within-subjects factor, and condition (H-EC vs. L-EC) and group (ADHD symptoms vs. control) served as between-subjects factors. A main effect of time was found, The current study examined whether training individuals reporting high levels of ADHD symptoms to employ cognitive control over negative information improves their emotion regulation ability in terms of their propensity to use reappraisal and the success of instructed reappraisal. To that end, we compared individuals with reported ADHD symptoms to controls. The results revealed no difference between the ADHD symptoms and control groups in cognitive control (congruity effect) or emotion control . Furthermore, training effect on reappraisal success was similar among both groups, implying that individuals with reported ADHD symptoms can benefit from such training similarly to controls. Nevertheless, regardless of the training condition, individuals reporting ADHD symptoms showed a higher propensity to use reappraisal than controls following the event writing and demonstrated a more considerable reduction in event significance and negativity following the instructed reappraisal assignment.56 and findings showing that these individuals tend to use positive reappraisal in stressful situations57, especially when they are instructed to do so19. Therefore, these results imply that while individuals with ADHD symptoms may show lower levels of trait reappraisal58, they do use reappraisal when recalling an adverse event, as well as able to use reappraisal effectively to reduce negative mood, even more than typical individuals. The ability of individuals with ADHD symptoms to reappraise is critical as reappraisal has been shown to moderate the relationship between inattention and perceived stress7. Furthermore, the efficacy of instructed reappraisal among this population is especially promising as it suggests that they can be taught to implement reappraisal, what may lower their tendency to use maladaptive coping strategies56.The current study's findings revealed that when recalling a negative event, individuals reporting ADHD symptoms show a higher propensity to use reappraisal than controls. Furthermore, the reappraisal assignment was more successful in reducing the reported negativity and significance of the event among these individuals. Specifically, while the negativity and significance of the event were reduced in both samples following the instructed reappraisal assignment, this effect was more prominent among individuals reporting ADHD symptoms. These results support prior findings showing that individuals with ADHD symptoms demonstrate reappraisal success similar to that of controls60, our results did not reveal any difference in cognitive control ability (congruity effect) between individuals reporting ADHD symptoms and controls. Although numerous prior studies indicated cognitive control difficulties among these individuals61, some failed to find a difference between ADHD and typical individuals in cognitive control tasks62.Contrary to previous findings showing that individuals with ADHD exhibit difficulties in cognitive control tasks that require inhibitory control48. Prior research indicates that adults with ADHD show increased emotional responsivity toward negative stimuli17, and exhibit greater cognitive effort when employing emotion regulation than controls17. These findings are supported by brain imaging studies that indicate emotional hyperresponsivity in adults with ADHD compared to controls56. These studies, however, did not assess the direct influence of cognitive control on emotion, which seems to be intact in these individuals, according to our findings.Our findings also do not support prior findings showing increased emotional interference or emotion control difficulties  among individuals with ADHD symptoms24 showing that H-EC training is associated with a higher propensity to use reappraisal, we did replicate the findings showing a greater success in instructed reappraisal in the H-EC compared to the L-EC condition. This effect was not dependent on the group  and therefore implies that individuals with reported ADHD symptoms could benefit from the training similarly to controls. These results are encouraging as it seems that individuals with ADHD symptoms can use reappraisal effectively when instructed to do so. Furthermore, our results show that similarly to typical individuals, those with reported ADHD symptoms show enhanced reappraisal success following a training procedure in which cognitive control is paired with negatively-valenced stimuli (high emotion control training). Therefore, although individuals with ADHD symptoms were found to be characterized by both cognitive control and emotion regulation difficulties, they seem to benefit from both emotion control training and instructed reappraisal assignment.Regarding training effect on reappraisal propensity and success, while we did not replicate the findings of Cohen and Mor24 that was not aimed at examining ADHD-related effects and therefore we did not administer the ADHD Checklist questionnaire. However, the control group included only participants reporting no current or past ADHD diagnosis. We also asked participants in this group about medication use and none of them reported taking medication for ADHD. However, future studies should conduct a formal diagnosis to assess ADHD in both groups. A second limitation of the current study is that no pre-training assessment of spontaneous reappraisal and reappraisal success was administered. The current study aimed at examining the influence of emotion control training on a relatively ecological measure of reappraisal that consisted of the writing of a recent personal event. Participants may find it difficult to find two upsetting events that happened recently, and these events may differ in their emotional impact, with the first one probably being more negative than the second one. Therefore, the reappraisal assignment was administered only following the training (for similar designs see41). To assess training-related changes in reappraisal propensity and effectiveness future studies may examine both pre- and post-training reappraisal success by using tasks that do not involve recalling a personal event. These tasks can include, for example, reappraising emotional images or unpleasant movie clips. It is noteworthy that trait reappraisal levels, measured using the ERQ, did not differ between the ADHD symptoms and control groups in our study , indicating no baseline difference in the tendency to use reappraisal. Trait suppression level, however, was higher for the ADHD symptoms vs. the control group, similar to what was found in other studies . A third limitation is that an unequal number of women and men participated in the two groups . This difference is reasonable considering that ADHD is more prevalent among men than among women. To ensure that the effects were not related to gender, we did control for gender in our analyses and found no main effect for gender or interaction between gender and any of the other dependent variables.The current study has several limitations. First, participants were recruited based on their self-report of having or not having ADHD. Although we did not perform a formal diagnosis of ADHD and other comorbid disorders by clinicians, we were able to get official documentation from almost 40% of the participants in the ADHD symptoms group regarding their ADHD diagnosis. Furthermore, more than half of the participants in this group indicated taking medications for ADHD, and above 80% reported high levels of inattention, hyperactivity, and impulsive symptoms in the DSM-5 ADHD Checklist questionnaire. The data of the control group was taken from a previous studyAll in all, this study suggests that individuals with reported ADHD symptoms could benefit from a training procedure that pairs cognitive control with negative information. Specifically, emotion control training was found to enhance the effectiveness of instructed reappraisal among this population, similarly to what was found among controls. Moreover, individuals with reported ADHD symptoms showed a more considerable benefit for the instructed reappraisal assignment than controls, indicating their ability to use reappraisal effectively when instructed to do so. We hope that the current research will lead to further testing and ultimately to the development of effective interventions for individuals with ADHD symptoms.


Agathobacter, Phascolarctobacterium, Prevotella_2, Acidaminococcus, Roseburia, and Ruminococcus gnavus group was increased in the ADHD group compared with the healthy group. Linear discriminant effect size (LEfSe) analysis was used to highlight specific bacteria phylotypes that were differentially altered between the ADHD and control groups. A regression analysis was performed to investigate the association between microbiota and emotional–behavioral symptoms in children with ADHD. A significant association was noted between withdrawal and depression symptoms and Agathobacter (p = 0.044), and between rule-breaking behavior and the Ruminococcus gnavus group (p = 0.046) after adjusting for sex, age, and the ADHD core symptoms score. This study advances the knowledge of how gut microbiota composition may contribute to emotional–behavioral symptoms in children with ADHD. The detailed mechanisms underlying the role of the gut microbiota in ADHD pathophysiology still require further investigation.Previous studies have explored the role of the microbiome in attention-deficit/hyperactivity disorder (ADHD). However, whether the microbiome is correlated with emotional–behavioral disturbances, the most common comorbid symptom of ADHD, remains unclear. We established a cross-sectional study in which 6- to 18-year-old children with ADHD who were receiving no medication and a healthy control group of children without ADHD were recruited to analyze their microbiome composition. Microbiota of fecal samples were collected and analyzed using a 16s rRNA gene sequencing approach. In comparison with the healthy control group, the gut microbiota in children with ADHD exhibited significantly lower beta diversity. The abundance of the phylum Proteobacteria and the genera  Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder among children and adolescents, with an estimated prevalence rate of approximately 3% to 10% globally ,3. In adADHD is a multiple pathogenetic disorder influenced by several genes and environmental factors ,7. In adOnly a few studies have investigated the gut microbial profiles of patients with ADHD, with the results being inconsistent. Some research has compared patients with ADHD and healthy controls but without demonstrating significant differences in alpha and beta diversity between the two cohorts ,17,18. HStudies have examined the correlation between the gut microbiota and clinical symptoms among patients with ADHD ,17,19,20All participants were recruited at Chiayi Chang Gung Memorial Hospital. The participants were aged 6-to-18 years old, and participants and their parents agreed to provide written informed consent. Patients with ADHD were diagnosed according to the DSM-5 criteria by a senior psychiatrist . All parTo guarantee the reliability of the data, quality control was performed at each step of study process . Total genomic DNA from samples was extracted using the column-based method . DNA concentration was determined and adjusted to 5 ng/μL for the sequencing process.For the 16S rRNA gene sequencing, the V3-V4 region was amplified by a specific primer set  according to the 16S Metagenomic Sequencing Library Preparation procedure . In brief, 5 ng of gDNA was used for the PCR reaction, which was conducted using KAPA HiFi HotStart ReadyMix  under the following PCR conditions: 95 °C for 3 min; 25 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s; 72 °C for 5 min; and hold at 4 °C. The PCR products were monitored on 1.5% agarose gel. Samples with a bright main strip at 550 bp were chosen and purified by using AMPure XP beads for the following library preparation.The sequencing library was prepared according to the 16S Metagenomic Sequencing Library Preparation procedure (Illumina). In brief, a secondary PCR was performed using the 16S rRNA V3-V4 region PCR amplicon and Nextera DNA Indexes Kit with dual indices and Illumina sequencing adapters (Illumina). The indexed PCR product quality was assessed using the Qubit dsDNA High Sensitivity Quantification Assay  and Qubit Fluorometric 2.0. An equal amount of the indexed PCR product was mixed to generate the sequencing library. Lastly, the library was sequenced on an Illumina MiSeq platform, and paired 300-bp reads were generated.Amplicon sequencing was performed by using 300-bp paired-end raw reads, and the entire paired-end reads were assembled using FLASH v1.2.11 . DemultiThe severity of ADHD symptoms, including inattention, hyperactivity, and impulsivity, was measured and rated by parents with the SNAP-IV parent form ,36. The t test was performed on the UniFrac distance matrix. To further increase the group distinction, supervised partial-least-squares discriminant analysis (PLS-DA) was used to evaluate and visualize variance between the groups on the basis of gut microbiota composition (OTUs). PLS-DA, which is based on a least-squares regression model, indicated structural differences in the gut bacterial community between the groups. These differences were assessed by calculating UniFrac distances, a phylogenetic-based distance metric for measuring the difference between two collections of sequences.Microbiome 16S sequencing data were analyzed using a combination of the software programs mothur and R v3.2.5 . To compThe composition analysis was calculated using QIIME V1.9.1. We used a linear discriminant analysis effect size (LEfSe) for statistical analysis and visualizing the results . LEfSe tt tests for normally distributed continuous variables, and Mann–Whitney U tests for skewed continuous variables. Pearson’s correlation analysis was performed to assess the taxonomic association between gut microbiota and clinical symptoms. Multiple linear regression was performed to analyze the relationship between the relative abundance of bacteria and the CBCL rating score after controlling for sex, age, and SNAP-IVscore. Clinical data were analyzed using the statistical software package SPSS, version 20 . Data were presented as mean ± SD, median (interquartile range), or number (percentage), as appropriate. Basic characteristics of the children with ADHD and the healthy controls were compared using chi-square tests for categorical variables, independent Student’s p = 0.042). The mean age of the ADHD cohort (mean = 8.39 ± 1.75) was younger than that of the healthy control group (mean = 9.73 ± 2.23). No significant differences were apparent between the groups in terms of BMI, or a history of preterm birth or allergic rhinitis. We compared the core ADHD symptoms through SNAP-IV ratings assigned by parents. Scores for inattention, hyperactivity/impulsivity, ADHD total, opposition/defiant, and CBCL were higher in children with ADHD compared with the healthy controls. The characteristics of the children with ADHD and those of the healthy controls are summarized in A total 371 participants were recruited, and 270 participants were excluded due to taking probiotics, having gastrointestinal disease, developmental or psychiatric disorders. There were 79 children with ADHD enrolled and 25 children ADHD who received medication among them were excluded. Finally, a total of 54 children with ADHD  without drug treatment and 22 healthy controls  were enrolled . The prop = 0.00; weighted UniFrac, p = 0.00; Microbial diversity was assessed either within a community  or between the collection of samples (beta diversity). Alpha diversity indices, including the Simpson index, the Shannon diversity index, the ACE metric (Abundance-based Coverage Estimator), and Chao 1, were calculated using Kruskal-Wallis LSD post-hoc tests. No significant difference was evident between the children with and without ADHD in alpha diversity. Subsequently, we characterized beta diversity using UniFrac and PLS-DA and determined that the gut microbiota communities of the children with ADHD and the healthy controls were significantly different  and Acidaminococcus (p = 0.035). Somatic complaints were associated with Rikenellaceae (p = 0.016) and Alistipes (p = 0.016). Thought problems were associated with Acidaminococcus (p = 0.021). In addition to rule-breaking behavior, aggressive behavior and externalizing behavior were associated with the Ruminococcus gnavus group . p = 0.044), and between rule-breaking behavior and the Ruminococcus gnavus group (p = 0.046). Furthermore, we examined the effect of each bacteria taxon on the CBCL symptoms among children with ADHD. We chose the bacteria which is more abundant among ADHD cohort than that of healthy control, including Agathobacter, Acidaminococcus and Ruminococcus gnavus group, and examined the association between the selected bacteria taxon and their correlated CBCL score by using a regression analysis and adjusting for sex, age, and SNAP-IV scores . A signiTo the best of our knowledge, this is the first study to investigate the association between the selected bacteria and emotional–behavioral symptoms in children with ADHD. In the present study, we determined that Agathobacter, Acidaminococcus, Rikenellaceae, and Alistipes were associated with internalizing symptoms and the Ruminococcus gnavus group was associated with externalizing symptoms in children with ADHD. After controlling for confounding factors, including basic characteristics and SNAP-IV scores, the results indicated that Agathobacter was associated with withdrawal and depression symptoms and the Ruminococcus gnavus group was associated with rule-breaking behavior. Previous research has reported that several bacteria were associated with clinical ADHD symptoms ,19,20. OIn our study, we found that the Ruminococcus gnavus group was associated with externalizing behavior (per the CBCL) in children with ADHD. Ruminococcus gnavus, an anaerobic, gram-positive microbiome, is common in the human gut . RuminocThis study has several limitations that should be noted. First, this study was conducted using a cross-sectional method and was unable to determine longitudinal changes and factors associated with the gut microbiota. Second, although we observed that several bacterial genera were correlated with ADHD clinical symptoms, no specific bacteria species were identified. Further gene mapping of bacteria is necessary to explore the mechanisms underpinning the association between the gut microbiota and ADHD. Third, we did not investigate diet in this study. Some specific diets  may influence the microbiota. Previous studies have reported that the Oriental diet is abundant in isoflavone, which may interact with microbiota and produce bioactive compounds contributing benefits including inducing nitric oxide production and anti-inflammatory effect on human health ,65. MoreIn conclusion, we determined that gut microbiome variability differs between children with ADHD and a healthy population. Two specific genera, Ruminococcu and Agathobacter, were determined to be associated with the emotional–behavioral symptoms of children with ADHD. Further study to explore the gut microbiome on ADHD should considered the effect of comorbid emotional–behavioral symptoms. The specific mechanisms responsible for the association between the gut microbiota and ADHD still require further investigation.