Attention deficit hyperactivity disorder (ADHD) is clinically diagnosed; however, quantitative analysis to statistically analyze the symptom severity of children with ADHD via the measurement of head movement is still in progress. Studies focusing on the cues that may influence the attention of children with ADHD in classroom settings, where children spend a considerable amount of time, are relatively scarce. Virtual reality allows real-life simulation of classroom environments and thus provides an opportunity to test a range of theories in a naturalistic and controlled manner. The objective of this study was to investigate the correlation between participants’ head movements and their reports of inattention and hyperactivity, and to investigate how their head movements are affected by different social cues of different sensory modalities.n = 20) and without (n = 17) ADHD were recruited for this study. All participants were assessed for diagnoses, clinical symptoms, and self-reported symptoms. A virtual reality-continuous performance test (VR-CPT) was conducted under four conditions: (1) control, (2) no-cue, (3) visual cue, and (4) visual/audio cue. A quantitativecomparison of the participants’ head movements was conducted in three dimensions  using a head-mounted display (HMD) in a VR classroom environment. Task-irrelevant head movements were analyzed separately, considering the dimension of movement needed to perform the VR-CPT.Thirty-seven children and adolescents with  groups.Children and adolescents with ADHD showed decreasing task-irrelevant movements in the presence of social stimuli toward the intended orientation. As a proof-of-concept study, this study preliminarily identifies the potential of VR as a tool to understand and investigate the classroom behavior of children with ADHD in a controlled, systematic manner. Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, observed in multiple daily environments . ADHD isEfforts have been made to incorporate objective neuropsychological tests, such as the continuous performance test (CPT), to evaluate sustained attention . In partThe US FDA has started to focus on real-world data from novel digital sources/monitoring and neurobehavioral testing, suggesting a trend toward emphasizing the role of technology in assisting and augmenting clinical care . FurtherWhile the coronavirus disease (COVID-19) pandemic has triggered research on the behavior of students in virtual environments for the general pediatric population, there have been few studies on children and adolescents with ADHD. This field of study is critical in the current situation, in which a large proportion of education is being replaced with remote online learning. It is important to understand how a teacher giving instructions on the screen influences attention performance in both individuals with and without ADHD and how they differ in an online classroom environment. VR technology has the advantage that it can be used to construct desired environments, including various stimuli that may influence attention performance. Thus, it provides the participant with a more ecologically valid environment for assessment compared to the conventional CPT conducted in a quiet, isolated room. In addition, these environments and stimuli can be easily manipulated and fully controlled by the experimenter, allowing assessment of the impact of each stimulus on patients’ behavior .Generally, children are required to utilize their attention in the presence of various environmental stimuli and interactions with teachers. This study focused on how specific features of a VR program may affect the attention performance of patients with ADHD. For example, the wide variety of contexts that can be simulated within VR allows us to compare the effects of different degrees of social cues or stimuli of different sensory modalities in a differential manner. We implemented the social component by adding a teacher avatar that could move and vocalize to mediate the attention of participants in the VR classroom setting. Previous studies have shown that gestures or gaze cues from a teacher in a VR classroom environment can evoke attention shifts, leading to similar motor responses in the student, such as gaze and head movements . AttentiTo implement stimuli with multiple sensory modalities, we created several conditions for the participants to perform the evaluation, each with a stepwise increase in the presentation of sensory modalities. Prior studies suggest that the hyperactivity domain is associated with the sensory modality of the stimulus  and that it may be differentially affected in each modality in individuals with ADHD . InvestiBased on this background, head-movement data based on a head-tracking unit on an HMD device were collected by our VR system, and its potential as an objective index of hyperactivity was examined. We also investigated how specific features in a VR classroom environment influence the attention of children and adolescents. While most previous studies on VR classrooms have focused on an individual’s regulation, control, and management of cognitive processes, we created a VR environment in which a virtual teacher alerts the participants toward the stimuli of the VR-CPT via visual (gesturing) and/or verbal instructions. In this proof-of-concept study, we hypothesized the following: First, head movement will show a significant correlation with the current standard clinical symptom measurements, and thus, would be a valid digital phenotype that may represent ADHD severity objectively. Second, differently simulated social or sensory cues from a teacher avatar will affect head movement in both ADHD and healthy control (HC) participants in the VR classroom environment. Finally, children with and without ADHD will exhibit different patterns in response to various cues simulated in our VR setting. We believe that if particular cognitive or behavioral profiles under different stimuli conditions in ADHD and HC groups can be established through our study, our VR system may serve as a useful tool for clinicians to understand the characteristics of patients with ADHD in greater depth in terms of their responses to environmental stimuli.Twenty children and adolescents with ADHD and 17 HCs participated in this study. The inclusion criteria were as follows: (1) ADHD diagnosis according to the DSM-V criteria, (2) age of 6–17 years, and (3) allowance of common comorbid disorders, such as depressive disorder, anxiety disorder, and oppositional defiant disorder. The exclusion criteria were as follows: (1) an intelligence quotient (IQ) < 70, (2) past or current serious medical or neurological diseases, and (3) comorbid autism spectrum disorder, psychotic spectrum disorder, or bipolar disorder.All participants with ADHD were taking medications, such as stimulants for ADHD symptoms, aripiprazole for irritability, and/or escitalopram for depression or anxiety. As methylphenidates can affect the performance of attentional tasks, those taking methylphenidates were required to stop medication for at least 48 h before participating in the study. Psychiatric interviews with HCs were also conducted to rule out any past or current medical and/or psychiatric disorders, and psychotropic medication usage.All participants underwent a practice session to familiarize themselves with the VR-CPT. Following the practice session, the participants were presented with the four VR conditions for the task. The order of the VR-CPT conditions was counterbalanced to control for systematic order effects. After going through all four VR conditions, the participants filled out a debriefing questionnaire regarding their VR experience. This study was approved by the Institutional Review Board of Yonsei University College of Medicine, Gangnam Severance Hospital, and informed consent was obtained from all participants and their guardians prior to participation.The diagnosis of ADHD and comorbid disorders was established based on the Mini-International Neuropsychiatric Interview for Children and Adolescents  IV . This 18M = 50, SD = 10), with higher T-scores indicating more problematic behavior.The child behavior checklist (CBCL) is an assessment tool completed by parents to measure the behavioral problems of children. The Korean CBCL (K-CBCL) is a standardized and validated instrument used to evaluate the behavior of children aged 6–18 years . The K-We developed a system that captures and quantifies the motor activity of participants while they are engaged in a CPT task. The virtual reality continuous performance test (VR-CPT) utilized was based on the two-button AX-CPT version of the CPT , which lcontrol condition, participants were instructed to complete the two-button AX-CPT, with letters appearing on a white screen in the center, without a virtual classroom environment or a virtual teacher present. This task is essentially equivalent to the traditional computerized CPT, with the exception of wearing an HMD for the baseline measurement of head movements. In the other three VR conditions, the participants were presented with an image of the upper body and face of a male teacher avatar in a virtual classroom setting and asked to complete the CPTs that appeared on the screen to the left or right of the avatar teacher. The three VR conditions differed in how the participants were directed toward the screens to the right or left of the teacher avatar. In the first VR condition (no-cue condition), participants conducted the task without any cues from the virtual teacher; this condition was implemented to determine the effects of social cues from the presence of the avatar teacher alone. Half of the trials were presented on the left screen and the remaining half on the right screen so that the user would have to turn their head once between the two screens. Whether the stimulus was first presented on the right or left screen was counterbalanced across participants according to the program’s protocol.We constructed four experimental conditions for the VR-CPT, each providing social cues of different degrees preceding the appearance of the to-be-detected stimuli in a virtual classroom environment so that the cues may direct the participants’ attention toward the stimuli beforehand . In the visual cue condition), the teacher avatar chose the left or right screen and directed the participants to look at the screen by pointing. In the third VR condition , the teacher avatar both pointed and provided a verbal (audio) cue, saying “Look at me,” when he wanted the participant to attend to him , and “Look at this,” as he pointed to each screen. The selected screens were changed eleven times under these two conditions. The number of trials appearing on each side of the screen was equal and presented in a counterbalanced order across participants, according to the program’s protocol. The other task variables were equally controlled for throughout all four conditions.In the second VR condition . The participants needed to turn from screen to screen 11 times in the visual and visual/audio social cue conditions, but required only a single head turn in the no-cue condition, whereas the control condition required no head turns. To eliminate this variability, we marked all timestamps in a log file and excluded yaw movements that occurred at the timestamp of the stimuli presented. The following calculations were performed using these values:Head telemetry was measured in degrees (°) of movement in three rotation axes – yaw , pitch , and roll , and movement was calculated by subtracting the current frame values of every 1/60 s from the previous frame values  were indexed by dividing each by the total head movements of that time frame (THMt), defined as the sum of the absolute values of movement for all three axes . The sum of these values was defined as the total magnitude of head movement in each axis during each VR session.The absolute values of movement  with 16GB memory, a monitor, keyboard, speaker, and an Oculus Rift Stereo-HMD with a tracker , and a screen resolution of 960 × 1080 pixels per eye. The VR-CPT was designed using a commercial three-dimensional development platform .t-tests and chi-square tests were used to compare the ADHD and HC groups in terms of demographics, clinical characteristics, and attention variables. Because the ADHD-RS total score and CBCL attention problems subscale did not follow normal distribution when appropriate outliers were excluded, a Spearman’s r bivariate correlation test was conducted to analyze the associations between head movement data and ADHD symptom severity. Most head movement data satisfied normality via the Shapiro-Wilk test and the Q-Q plots were close to the line, and thus linear mixed models were constructed to compare differences in task-irrelevant head movements according to the four conditions, both within each group and between the groups. Post-hoc analyses were corrected using the false discovery rate (FDR) correction method. SPSS for Windows  was used for the t-tests, chi-square tests, and the correlation analysis and the SAS version 9.4  was used for the development of linear mixed models. All analyses were considered statistically significant if two-tailed p < 0.05.For ADHD-RS and CBCL, data with values higher than 1.5 interquartile ranges above the 3rd quartile or lower than 1.5 interquartile ranges below the 1st quartile were excluded as outliers. One participant from the ADHD group had abnormally measured head movement data and was excluded in analyses regarding the data. Independent sample t-tests, as shown in t (35) = 4.56, p < 0.001] and CBCL attention problem subscale scores .The demographic and clinical characteristics of the ADHD and HC participants were compared using independent r = –0.492, p = 0.038), average task-irrelevant head movement , and the summary index of ADHD-RS in the ADHD group . Chronological age and IQ were included in the analyses as covariates to disentangle their effects on the participants’ performance. Age was specifically included as a covariant because a strong correlation was found between age and task-irrelevant movement in the ADHD group.To investigate whether social cues presented from a teacher avatar affect the head movement and task performance of participants in a differential manner during the VR-CPT, linear mixed models were constructed for each group with each VR condition as the condition factor and task-irrelevant head movement as dependent variables. One participant from the ADHD group was excluded because of abnormally measured head movement data  and group-by-condition interactions , but no significant effect of group . Post-hoc analyses with FDR correction are shown in detail in p < 0.001, except between the visual and visual/audio cue conditions in the HC group, p = 0.016), apart from showing no significant difference between the visual and visual/audio cue conditions in the ADHD group (p = 0.615). When corrected for multiple analyses, there was no significant difference observed in the post-hoc analysis of group-by-condition interactions . For group-by-condition interactions, the Bayesian factors are presented in Therefore, in terms of the overall trend, there was a decreasing trend in task-irrelevant head movement in the presence of the virtual classroom environment and again with visual and/or audio cues added . HoweverOur study examined the impact of different contexts simulated within a VR classroom on the head movements of children and adolescents with and without ADHD. Task-irrelevant head movements in the ADHD group showed a significant correlation with the current standard ADHD clinical scale. In addition, by utilizing digital technology to strategically alter various components, the participants were exposed to different sensory modalities of social cues. The head movement of children with ADHD seemed to decrease in response to a virtual avatar teacher and their social cues compared to when they were absent. While visual cues seemed to positively influence the attention of both ADHD and HC individuals, the effect of an additional audio cue is debatable. Overall, these results imply that virtual environments provide researchers with a platform on which rigorous experimental control and manipulation can be applied while simultaneously enabling the collection of objective behavioral data. Our results also demonstrate that the alteration of physical or social components in VR may be used as an effective method for optimizing students’ attention.Our results showed a significant positive correlation between task-irrelevant head movement during the VR-CPT with visual/audio social cues and ADHD-RS scores, the most commonly used standard scale to measure ADHD symptoms in clinical practice. This result supports our first hypothesis that head movement data in a virtual environment can effectively represent the degree of ADHD symptoms, reflecting real-life performance reported via ADHD-RS scales. This correlation was not observed in the HC group, possibly implying the specificity of this mode of measurement in the ADHD group. However, these results should be interpreted with our small sample size in consideration. Future replicative studies with a larger sample size would be needed to solidify our findings.Discovering and defining objective means to quantify the symptoms of ADHD is important, as it may compensate for the lack of objectivity in the current method, which relies on self-or parent-reported ADHD symptom scales. This finding adds to the small but growing body of literature utilizing automated measurement of body and head movement as one such method. However, in our study, no significant differences were observed in task-irrelevant movements between the two groups. This result is inconsistent with previously published meta-analytic results, indicating a higher level of movement in children with ADHD . The reaIn addition, the VR classroom environment itself seemed to decrease the hyperactivity of ADHD and HC participants, as reflected by the decrease in the task-irrelevant head movement of participants during the no-cue condition compared to the control condition. VR-CPT has been seen to captivate the attention of children with ADHD more effectively than traditional CPTs in many studies , as wellWe explored whether head movement varied depending on the addition of visual cues only and both visual and audio cues provided by a teacher avatar and found that head movement could be affected by these social cues in a virtual classroom environment. In this study, we found no significant group differences in task-relevant or task-irrelevant head movement between children with ADHD and HCs; however, significant differences across conditions were found, which we attributed to the different VR conditions.Regardless of ADHD diagnosis, previous studies have revealed that social cues tend to improve students’ attention performance. For example, in a classroom environment, the synchronous behavior of the teacher and students can increase rapport and result in better learning outcomes . CoherenGroup-by-condition interaction for the pairs of conditions were not statistically significant, against our third hypothesis that the two groups would respond differently to the two conditions. However, as we have stated in section 4.1, significant correlation between task-irrelevant head movement and ADHD-RS was only observed in the ADHD group, and not in the HC group, possibly indicating that task-irrelevant head movement may represent clinically significant hyperactivity/inattention in those with ADHD diagnosis only. Therefore, it may be questionable to say that this represents clinically significantly increased attention for the HC group, and the interpretation of the group-by-condition interaction should be done with care. Here, we focused on the difference in task-irrelevant head movement by condition in the ADHD group and propose some theories based on previous studies that may provide a reasonable explanation for why there was no significant decrease in the amount of task-irrelevant head movement when audio cues were added to the visual cue condition in the ADHD group.First, it is possible that the results were influenced by the poor multisensory integration ability of the ADHD participants. While synchronous auditory stimuli may act synergistically with visual stimuli by increasing connectivity between the low-level visual and auditory areas of the brain , the synOur second theory is based on the use of top-down  and bottom-up  orientation in head movement . Bottom-Lastly, while it was our intention for both visual and visual/audio cues to be perceived as social cues, it is possible that the visual cue may have acted as a simple “pointer” and hence had partial properties as a non-social cue , unlike To conclude, it is unclear within our study which theory provides the best explanation for the absence of head movement difference between the visual vs visual/audio cue conditions in the ADHD group. Revealing the actual reason behind our results and clarifying whether the two groups are different in each condition would require additional investigation, including assessment of other body movements  and/or aThis study has several limitations. First, our VR was designed to be relatively simple, and may not demonstrate ADHD-related differences to the same extent as in real-life situations. More work is needed to better understand how attention performance is influenced in these simulated situations compared to an individual’s behavior in real life.Second, the absence of distractions, which inevitably exist in a real classroom environment, is an important limitation. Deeper insight into the underlying mechanism of how non-social and social stimuli affect the attention-orienting process of ADHD may be obtainable if a more sophisticated distractor setting is created. Third, post-hoc power analysis for our study revealed limited power, implying that our sample size may not have been large enough to provide substantial experimental power. Also, the male-to-female ratio was high, which may be a limitation in generalizing our results to female patients. Fourth, even though participants in the ADHD group were asked to withhold methylphenidate 48 h prior to the study, its effect cannot be completely ruled out. Although it would have been preferable to require children to participate after a longer period of medication abstinence, this may have exposed our study participants to an unnecessary burden. Additionally, the comorbidities included in our inclusion criteria may have influenced the results. Fifth, while previous exposure to video games and VR , or selfAlthough the use of VR improves the ecological validity of attention task performance, our study setting was still based on the traditional CPT format, making it difficult to represent real-life attentional capacity . FurtherWe quantified head movements in children and adolescents with and without ADHD using automated measurement, which showed different tendencies depending on the presence or absence of social and non-social stimuli. These findings suggest the possibility of using automated head motion measurement in a VR environment as a reliable objective method to quantify the hyperactivity component of ADHD symptoms to supplement the current subjective manner of ADHD symptom measurement. This proof-of-concept study not only provides a new, relatively ecologically valid way of conducting studies aimed at understanding how various cues affect the attention of children in a classroom environment, but may also improve the understanding of nonverbal behavior in ADHD and assist with designing online educational systems for those affected. Further research may reveal a more extensive potential of VR technology in identifying and longitudinally monitoring ADHD symptoms, as well as providing a clinical aid as a digital phenotype to stratify severity.The raw data supporting the conclusions 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 of Yonsei University College of Medicine, Gangnam Severance Hospital. Written informed consent to participate in this study was provided by the participants or their legal guardian/next of kin.EK and KK designed this study. HE and YH collected the data. JYY, YJC, and HSL performed statistical analyses. JYY and YJC wrote the manuscript. EK, JYY, JK, HSL, and BS contributed to the discussion and reviewed and edited the manuscript. All authors have read and approved the final version of the manuscript.


To evaluate the relationship among family, personal, parental correlates, and behavioral disturbances in boys with attention-deficit/hyperactivity disorder (ADHD).We performed a hospital-based cross-sectional study. School-aged boys who first visited the hospital between 2000 and 2011 with ADHD were identified. Through medical records review, demographic information, family characteristics, personal characteristics, parental characteristics, and the Child Behavior Checklist (CBCL) collected during the first outpatient visit were retrieved. A T-score higher than 63 in the internalizing or externalizing subscale of the CBCL indicated severe behavioral disturbances in each domain. Multivariable logistic regression was used to evaluate the relationship between the correlates and behavioral disturbances. Eligible patients were further classified into groups without behavioral disturbance, with either only severe internalizing or only severe externalizing behaviors, or with both behaviors. Multivariable ordinal logistic regression was used to investigate the association between the correlates and the number of types of behavioral disturbances.A total of 1855 boys with ADHD were included. In the multivariable logistic regression, family factors, including being first-born, living in a family not with both parents, and family history of mental disorder, were associated with severe internalizing and externalizing behaviors. Personal factors, including prenatal complications, perinatal complications, and medical and psychiatric comorbidities, were associated with severe internalizing behaviors, but only prenatal complications and medical comorbidities were associated with severe externalizing behaviors. Parental factors were only associated with severe externalizing behaviors. A higher paternal education level had a protective effect, but younger motherhood increased the risk. In ordinal logistic regression, these factors were also associated with more types of behavioral disturbances.Multiple factors are related to behavioral disturbances in ADHD. Our study reported the association among family, personal, parental factors, severe internalizing behavior, severe externalizing behavior, and number of behavioral disturbances in boys with ADHD. However, the impacts differed as the behavior phenotypes varied. Further research is needed to better understand the heterogeneity of ADHD behavior. Attention-deficit/hyperactivity disorder (ADHD), a common psychiatric disorder among children, is associated with long-term negative consequences –4. CoexiThe pathogenic mechanism of behavioral disturbances in children with ADHD is complex. Although most contemporary research on ADHD focuses on biological factors such as genetic characteristics and structural or functional abnormalities of the brain, the role of socioenvironmental factors should not be underrated. Although several different classes of genome variants have been found to be associated with ADHD, some observational epidemiological studies have also shown that exposure to environmental toxins, dietary factors, low income, and family adversity is related to ADHD . Other sThe aim of this study was to evaluate the relationship among family, personal, parental factors, and behavioral disturbances among school-aged boys with ADHD using a medical record database from a public psychiatry hospital in Taiwan, a country in the Asia-Pacific region nurtured by Eastern culture.Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR), is performed by board-certified psychiatrists with both the child and the main caretaker to make a diagnosis and decide further treatment plans. All data collected are stored in either electronic or paper forms in the medical record database.The Tao-Yuan Psychiatric Center (TYPC) is a public psychiatric hospital located in northern Taiwan. Covered by the National Health Insurance system, TYPC provides services for patients with mental disorders with a catchment area for more than 2 million residents. The Department of Child and Adolescent Psychiatry in TYPC provides psychiatric service for patient younger than 18 years. A comprehensive assessment is performed on the first visit. First, a questionnaire about demographic, personal, parental, and family information of the child and the Child Behavior Checklist (CBCL) must be completed by the caretaker. Second, the main caretaker is interviewed by a trained interviewer to verify the collected information. Third, a semi-structured interview based on the diagnostic criteria of the We performed a hospital-based cross-sectional study using the TYPC medical records database. First, we identified outpatients who first visited the Child and Adolescent Psychiatry Department between 2000 and 2011. Second, male patients aged 6 to 11 years at the first visit were considered. Third, patients receiving either a primary or secondary diagnosis of ADHD (ICD-9-CM: 314.XX) at the first visit were included in the study. No exclusion criteria were applied. After ascertainment of our study population, the medical records of eligible patients were reviewed. Information including personal characteristics, parental characteristics, family characteristics, diagnosis at the first visit, and the CBCL were collected for analysis.Family factors, including birth order (first-born or not), sibling status (with or without siblings), family structure (with both parents or not with both parents), and family history of mental disorders , were collected and dichotomously classified. The characteristics of the family structure, such as living in a family with both parents, with single parents, with only grandparents or other relatives, or living in a foster institute, were also collected and classified into categories as living in a family with both parents and not with both parents.Demographic information regarding age and sex was collected. Additional information during prenatal and perinatal periods, including birth mode , birth term , birth body weight , prenatal complications , and perinatal complications  were also collected. Medical or psychiatric comorbidities recorded during the first visit were also collected and categorized as having comorbid diseases.Information about parental characteristics, including education level, occupational status, and age at childbirth, were also collected. Parental educational level was classified as receiving education for more than 12 years. Parental occupational status at the first visit was classified as being employed or not. Parental age at childbirth was classified as younger than 20 years of childbirth or not. Both paternal and maternal information were collected.The CBCL, which includes items on a three-point Likert scale, is a common instrument for measuring problematic behaviors in children. In this study, the CBCL was used to assess behavioral disturbances in boys with ADHD , 25. Firt-test.Descriptive statistics were computed, and the differences between school-aged boys with ADHD with and without internalizing or externalizing behaviors were compared. Categorical variables were analyzed with chi-square tests, whereas continuous variables were analyzed using Student’s The associations between correlates and behavioral disturbances among school-aged boys with ADHD were assessed using logistic regression. Both unadjusted and adjusted estimates with simultaneous control of all relevant variables were reported. Ordinal logistic regression was performed to assess the association between the correlates and numbers of types of behavioral disturbances. Both crude and adjusted estimates were reported.P value < 0.05 was considered statistically significant. Association estimates were indexed as odds ratios (ORs) and 95% confidence intervals (CIs). All statistical analyses were performed using SAS version 9.4 .A two-tailed A total of 1855 school-aged boys with ADHD were included in our study. The mean age was 8.3 years. A total of 818 boys (44.1%) had severe internalizing behaviors, 982 boys (52.9%) had severe externalizing behaviors, 552 (29.8%) had only one type of behavioral disturbance in either severe internalizing or externalizing behaviors, and 624 (33.6%) had behavioral disturbances in both classes. The characteristics of the participants are presented in Table With regard to externalizing behaviors, school-aged boys with ADHD with such behaviors were more likely to be first-born, to be living in a family not with both parents, to have a family history of mental disorders, to have prenatal complications, and to have medical comorbidities than those without such behaviors. Moreover, boys with ADHD with severe externalizing behavior problems were more likely to have parents with a lower education level and to be born to mothers younger than 20 years.Table In the analysis of severe externalizing behaviors, most results were similar to internalizing behaviors. However, some differences, particularly the effects of parental factors, existed Table . Being fTable This study evaluated the relationship among family, personal, parental factors, and behavioral disturbances among school-aged boys with ADHD using medical records from a public psychiatry hospital in Taiwan, and the results showed that family, personal, and parental factors contribute to the occurrence and heterogeneity of behavioral disturbances in school-aged boys with ADHD. Both internalizing and externalizing behaviors are associated with family factors, including being first-born, living in a family not with both parents, and family history of mental disorders. Among personal factors, prenatal complications, perinatal complications, and medical or psychiatric comorbidities are associated with severe internalizing behaviors, but only prenatal complications and medical comorbidities are associated with severe externalizing behaviors. Regarding parental factors, higher paternal education level is protective, whereas maternal age younger than 20 at childbirth increases the risk of externalizing behaviors; however, such an association is not noted with regard to severe internalizing behaviors. Assessment of the association between the correlates and the numbers of types of behavioral disturbances showed that the results were similar.In our study, several family factors were associated with severe internalizing behaviors, externalizing behaviors, and the number of types of behavioral disturbances. The negative effect of an altered family structure on child development has been reported in previous studies , 27. LivFamily history of mental disorders and being first-born are two other important family factors associated with severe behavioral disturbances in boys with ADHD. Several hypothetical mechanisms can explain how a family history of mental disorders influences child behaviors. First, the effect of genetic transmission of mental disorders may have a direct influence on child behaviors, resulting in both the incidence and coexisting behavioral disturbances in ADHD –35. SecoBeing first-born is another risk factor for the development of severe behavioral disturbances in boys with ADHD. Several studies have reported that the first-borns often face unique challenges, such as the changed interaction between parents after the birth of a new child or the issue of sibling rivalry as they grow up . In addiPrenatal and perinatal complications can interfere with the normal development of children through changes in both neurophysiological mechanisms and the dopaminergic system , 43. OurComorbid conditions are also related to behavioral disturbances in boys with ADHD. This result can be explained in several ways. First, from the perspective of children, having coexisting diseases may cause distress –49. The Two parental factors, paternal education level and maternal age at childbirth, are associated with the occurrence of severe externalizing behaviors and heterogeneity in behavioral disturbances in boys with ADHD. The higher education level of parents is associated with efficient parenting, flexibility in interactions, stress buffering, and management of children’s problems –55. TherThe association between parental age at childbirth and behavioral disturbances can be explained by common genetic and socioeconomic mechanisms . ParentsAlthough our findings support the association between parental education level and age of childbirth on behavioral disturbances in boys with ADHD, the results between parents vary. The effect of parental gender on child development has been discussed in several studies. Parental gender may play a role in social development, quality of life, and family interactions of the child , 22, 60.Our study has some limitations. First, because of the cross-sectional study design, the causal relationship should be interpreted conservatively. Second, the study population was identified from a hospital-based medical record database, and only male patients were included. The representativeness of our study was limited. In addition, the study population was recruited from a public psychiatric hospital. In Taiwan, disadvantaged families, which are also a risk factor for more severe disturbances in children, often visit public hospitals for medical treatment. This may result in referral bias in our study and interfere the inference of our results. Third, some possible unmeasured confounders, such as child temperament, parenting patterns, or family atmosphere, were not considered. Therefore, residual confounding effects still exist.Regardless of these limitations, our study has several strengths. First, our study provides a broader perspective on ADHD, focusing on the severity and diversity of behavioral disturbances. Second, our study highlights the important role of environmental factors in children with ADHD. Third, our results reflect the association between the correlates and child behaviors in Eastern society, which has been less investigated in previous studies. Fourth, our findings can be used to identify higher-risk school-aged boys with ADHD who may have more negative outcomes and need further intervention.ADHD is a highly heterogeneous condition, and the unique family, personal, and parental characteristics of children with ADHD who have behavioral disturbances should be identified. Our study reports that family structure, birth order, family history of mental disorders, prenatal complications, perinatal complications, comorbid status, paternal education level, and maternal age at childbirth are associated with severe and heterogeneous behavioral disturbances in boys with ADHD. More research is needed to elucidate the underlying mechanism for early identification of children with ADHD at risk, adequate treatment referral, and resource allocation.


Individuals with an evening chronotype prefer to sleep later at night, wake up later in the day and perform best later in the day as compared to individuals with morning chronotype. Thus, college students without ADHD symptoms with evening chronotypes show reduced cognitive performance in the morning relative to nighttime . In combination with symptoms presented in attention deficit hyperactivity disorder (ADHD), we predicted that having evening chronotype renders impairment in attention during the morning, when students require optimal performance, amplifying desynchrony.Four hundred college students were surveyed for evening chronotype and symptoms of ADHD. Of those surveyed, 43 students with evening chronotype (19 with ADHD symptoms) performed laboratory attention tasks and were queried about fatigue during morning and evening sessions.Students with ADHD symptoms demonstrated a greater decrement in sustained attentional vigilance when abstaining from stimulants and asked to perform cognitive tests at times misaligned with natural circadian rhythms in arousal compared to their non-ADHD counterparts with the same chronotype. While individuals with ADHD symptoms had slower reaction-times during sustained attention tasks in the morning session compared to those without symptoms, there was no significant group difference in working memory performance, even though both groups made more errors in the morning session compared to the evening session.These findings suggest that evening chronotype students with ADHD symptoms are at a greater disadvantage when having to perform sustained attention tasks at times that are not aligned to their circadian rhythm compared to their neuro-typical peers. The implications of this finding may be useful for the provision of disability accommodations to college age students with ADHD when they are expected to perform tasks requiring sustained attention at times misaligned with their circadian rhythms. Circadian rhythm is based on the sleep/wake cycle, which determines when it is time to sleep and when it is time to be awake. Different neurochemicals and hormones such as melatonin, cortisol, acetylcholine, and adenosine affect the sleep/wake cycle, causing it to rise and fall over the course of 24 h . As a reEvidence links evening chronotype and arousal instability with greater cognitive impairment in individuals with Attention-Deficit/Hyperactivity Disorder (ADHD), a neurological disorder marked by inattention and/or hyperactivity-impulsivity . ResearcThere is a higher prevalence of evening chronotype among individuals with ADHD, with a rate of 73–78% in both children and adults ; eveningFurthermore, daytime sleepiness—which may occur when individuals with evening chronotype must adhere to a standard 8 AM to 6 PM class or work schedule—is associated with greater academic impairment for college students with ADHD . While cIndividuals with ADHD have been shown to have impairments in working memory  and impaAdults with ADHD have also been shown to have impairments in attention, which is believed to be a hallmark of the disorder . ParticuHere we address the question of whether individuals who report evening chronotype show impaired working memory and sustained attentional performance at desynchrony with their time of peak arousal and whether having symptoms of ADHD that can lead to a diagnosis further exacerbates these impairments. There are two phases of this study; in phase I, a large pool of participants were screened for chronotype and ADHD symptoms. In phase II, eligible participants were invited to participate in cognitive testing in the laboratory during morning and evening sessions.M = 19.5, SD = 1.4). They self-identified as White/Eastern European (30%) Black or African American (9.1%), Hispanic or Latino (12.9%), Asian (39.7%), American Indian or Alaska Native (1.1%), Mixed Race (5.5%), and Other (1.7%).Undergraduate students were identified for recruitment from the New York University Subject Pool. Phase I consisted of a 15-min questionnaire administered through Qualtrics. Informed consent was obtained prior to completing the questionnaire and participants were compensated for their time with pay or extra credit toward a psychology course in which they were enrolled. Phase I respondents were excluded from analyses if (1) they did not complete the entirety of the prescreen questionnaire according to instructions; (2) they were not college-age ; (3) they reported having a sleep disorder including Restless Leg Syndrome, Narcolepsy, Sleep Apnea, and sleep paralysis; or (4) they endorsed of a primary sleep disorder, or travel disruption (if they traveled out of the EST time zone in the 2 weeks prior to completing the survey). The final analysis for phase I included 363 respondents . Respondents ranged in age from 17 to 24 years old (n = 98). Those who scored less than 4 are referred to as “without ADHD symptoms” (n = 265)  , the ADHn = 265) . The PSQn = 265) . Finallyn = 265)  was usedt-tests . Participants were further excluded from participating in phase II if they traveled out of the EST time zone in the 2 weeks prior to completing the survey. By excluding participants who indicated any of the above, we were able control for any confounding effect of these factors on arousal variability or temporary changes in chronotype. Participants who reported taking stimulant medications were instructed not to modify their typical medication schedule, but instead to schedule their test sessions on a day they would normally abstain from any prescribed stimulant medications . Written informed consent was obtained for these participants who qualified and chose to enroll (n = 43) and all procedures were approved and overseen by the New York University Institutional Review Board. Each in-person test session took approximately 45 min, totaling up to 2 h for the complete study. Participants were compensated for their time with cash or extra credit toward a psychology course in which they were enrolled. In this phase, 43 participants (28 women and 15 men) ranging in age between 18 and 33  were tested in person. Participants self-identified as White/Eastern European (30.2%) Black or African American (14%), Hispanic or Latino (11.6%) and Asian (44.2%). Of the 43 participants, the group without ADHD symptoms consisted of 16 women and 8 men ; the group with ADHD symptoms consisted of 12 women and 7 men, .Phase II of the study consisted of a subset of respondents from phase I. Respondents from phase I who met criteria for phase II were then emailed to alert them to their eligibility for a face-to-face phase II experiment at the NYU laboratory. Only respondents whose composite rMEQ score indicated an evening chronotype (score < 12) were eligible to progress to phase II of the study (We used a python-based computerized Sustained Attention to Response Task (SART) in order to assess lapses of sustained attention, processing speed, and error rates in this performance based task . We usedThese procedures were followed for each participant in phase II: Morning session started upon arrival to the lab at 8:00 AM, informed consent was obtained, followed by the “Sleep Diary” questionnaire. Following this, we administered the computerized N-back task and SART. Evening session started upon returning to the lab at 9:00 PM, where we administered a second “Sleep Diary” questionnaire asking participants if they have napped or used any substances  since Session 1; if so, they were later excluded from analyses. Participants were again administered the SSS to indicate their current level of subjective sleepiness. Participants then completed the same version of the computerized N-back task and SART as Session 1.t-test to measure differences in sustained attention and sleepiness by group  and commission errors . Omission errors occurred when no key was pressed in cases where a key should have been pressed ; commission errors occurred when a key was pressed when no key should be pressed . Reaction time for each response was collected to assess processing speed , and theroup see –5. We usroup see . ANCOVA r2 (363) = 0.243 p < 0.001, two-tailed], indicating a significant positive relationship between global sleep quality and daytime sleepiness. This remained true for individuals who met criteria for ADHD (ASRS score > 4)  and those who did not meet ADHD criteria .PSQI and ESS scores were correlated for all participants  and higher levels of daytime sleepiness, than those who did not meet criteria  (see r2 (363) = 0.393, p < 0.001, two-tailed], and daytime sleepiness . A significant linear regression further shows ADHD symptom scores as a predictor of impaired sleep  and daytime sleepiness .Individuals who met criteria for an ADHD diagnosis had indicating worse sleep [led] see . Similar2  = 6.77, p < 0.05], suggesting a higher prevalence of evening chronotype in individuals who may meet criteria for a clinical diagnosis of ADHD  = 7.26, p = 0.01]  than in the group without symptoms  t (41) = 2.70, p < 0.05  as compared to evening session (Session 2) [.01] see , indicat0.05 see .F  = 5.31, p < 0.01], such that in both the ADHD symptoms and without symptoms groups, the difference in the total amount of errors increased as cognitive load increased   = 0.01, p = 0.918], and no significant interaction between Group and N-back Level   see . Each N-ack) see . However354] see .F  = 0.958, p < 0.01]  = 1.80, p = 0.187] and no session by group interaction  that would indicate a greater degree of sleepiness at any given session for individuals with ADHD symptoms as compared to those without ADHD symptoms.As expected for their evening chronotype, participants in Phase II reported greater subjective sleepiness during the morning session conducted at 8:00 AM compared to the evening test session conducted at 9:00 PM [.01] see . HoweverF = 1.67, p = 0.203], indicating no difference in impaired vigilance by time of day between groups. Additionally, there were no differences in total error score by session  and by Group  (see t (23) = 1.91, p = 0.07, two-tailed], a trend that was not present in individuals with ADHD symptoms. Overall, there was no significant difference in objective sleepiness for individuals with ADHD symptoms compared to those without ADHD symptoms.Although participants reported greater subjective sleepiness in the morning session as compared to the evening session, there was no significant difference between the groups in SART total error score by session [840] see . HoweverOur study aimed to measure the relative impact of circadian desynchrony effects on working memory and sustained attention between college students with and without ADHD symptomatology. We expected to find a greater decrement in both working memory and sustained attention for students with ADHD symptoms compared to those without during the morning session compared to evening. Indeed, there was increased impairment for those with ADHD symptoms in cognitive functioning. In our study, participants with ADHD symptoms demonstrated a significantly greater reaction time on sustained attention tasks when assessed at times desynchronous to their circadian rhythms as compared to those without ADHD symptoms—a finding that has not been studied or observed elsewhere. Although this group difference in synchrony effect was not observed for total errors made on sustained attention task, poorer sustained attention during desynchronous times suggests that ADHD symptoms are exacerbated during desynchronous cognitive performance. Even though there were no significant differences in total errors on the sustained attention task or on the working memory task, differences in reaction time indicate decremented sustained attention. Poorer sustained attention may result in a number of increased academic challenges, such as limited endurance when completing complex cognitive tasks and overall impaired academic performance. Therefore, our results suggest that individuals with ADHD symptoms are at an increased decrement when performing tasks desynchronous with their circadian rhythms compared to their peers. Existing studies have found that individuals with ADHD demonstrate poorer sustained attention due to sleepiness compared to neuro-typical controls , but no We found that overall both groups showed improved working memory at times in synchrony with their circadian rhythms, especially as the cognitive load increases; a finding which is supported by existing literature . HoweverFinally, we found higher reporting of subjective sleepiness in the morning than in the evening for all students, as expected for students with evening chronotypes. However, we did not find any decrement in the objective measure of impaired vigilance by time of day for both groups. This finding suggests that feeling sleepy and attentional impairments are two different but related mechanisms, such that feeling sleepy does not necessarily result in impaired attentional vigilance . In addiSeveral limitations to the work described should be noted. First, because of our extensive inclusion/exclusion criteria, we were only able to recruit and include a limited number of participants in our Phase II analyses . Second, it is possible that there was a rebound effect for individuals in the ADHD symptoms group who did not take stimulant medication that day, whereas these symptoms would otherwise be controlled. Skipping a stimulant dose may reduce procedural memory performance for individuals on that day , but thaImportantly, our work points to a need for additional research assessing the potential interaction of chronotype and time of day on other domains of cognitive functioning. Another high-potential candidate for an ADHD-desynchrony effect may be response inhibition, a major feature of ADHD-related cognitive impairment . AdditioOverall, our results demonstrate the importance of observing time of day effects on performance and its effects on individuals with ADHD symptoms, as it informs potential interventions to support the overall success of college students and others suffering from the disorder.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by New York University Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.LG and MPL contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript. NA-K and PM contributed to the analysis of the results and writing of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A recent genome-wide association study (GWAS) identified 12 independent loci significantly associated with attention-deficit/hyperactivity disorder (ADHD). Polygenic risk scores (PRS), derived from the GWAS, can be used to assess genetic overlap between ADHD and other traits. Using ADHD samples from several international sites, we derived PRS for ADHD from the recent GWAS to test whether genetic variants that contribute to ADHD also influence two cognitive functions that show strong association with ADHD: attention regulation and response inhibition, captured by reaction time variability (RTV) and commission errors (CE).The discovery GWAS included 19 099 ADHD cases and 34 194 control participants. The combined target sample included 845 people with ADHD (age: 8–40 years). RTV and CE were available from reaction time and response inhibition tasks. ADHD PRS were calculated from the GWAS using a leave-one-study-out approach. Regression analyses were run to investigate whether ADHD PRS were associated with CE and RTV. Results across sites were combined via random effect meta-analyses.R2 = 0.011, β = 0.088, p = 0.02) but not for CE . No significant association was found between ADHD PRS and RTV or CE in any sample individually (p > 0.10).When combining the studies in meta-analyses, results were significant for RTV (We detected a significant association between PRS for ADHD and RTV (but not CE) in individuals with ADHD, suggesting that common genetic risk variants for ADHD influence attention regulation. A recent case-control genome-wide association study (GWAS) identified, for the first time, 12 independent loci significantly associated with attention-deficit/hyperactivity disorder (ADHD)  and commission errors (CE) , to test the associations using molecular genetic data in unrelated individuals. Specifically, in this collaborative study using ADHD samples from several international sites, we derive PRS for ADHD from the recent GWAS  and iPSYCH Danish data analyzed in the recently published GWAS of ADHD  of reaction times] was obtained from each of the tasks. Evidence for comparability between tasks was previously obtained from model fitting analyses on the fast task and Go/No-Go task, which indicated a high familial correlation (rf = 0.75) between RTVs obtained from each task, suggesting they are measuring largely the same liability , University of California Los Angeles (UCLA), Toronto and Barcelona. All participants for each site completed a comprehensive protocol of cognitive tasks, which differed for each site. Participants from IMAGE-8 performed a four-choice reaction time task (Fast task) and a version of the Go/No-Go task with fast and slow conditions, whereas IMAGE-Dutch participants performed the Stop-Signal Task (SST). At UCLA and Barcelona, participants performed the Continuous Performance Test II (CPT-II), whereas the Go/No-Go task was administered in Toronto. Descriptive statistics for each sample are shown in Sample: IMAGE-I is a European project on ADHD familiality using a common protocol of centralized training and data management. IMAGE-I includes data from different European sites and Israel, recruited from specialist clinics in Tel-Aviv, Essen, Gottingen, Brussels, Dublin, Valencia, Zurich, London, Nijmegen and Amsterdam  and IMAGE-Dutch (including participants from Nijmegen and Amsterdam). In the current study, we included only participants with an ADHD diagnosis who had both cognitive and genetic data available. The final sample consisted of 143 ADHD participants from the IMAGE-8 study and 226 ADHD participants from the IMAGE-Dutch study.Tasks: Fast-Task, Go/No-Go and SST: The Fast task is a computerized four-choice reaction time (RT) task which measures performance under a baseline (slow-unrewarded) and a fast-incentive condition  , Washington University, and Massachusetts General Hospital (MGH)], which included 540 children and adolescents aged 5–18 years, and 519 of their parents, ascertained from 370 families with ADHD-affected sibling pairs. Children and adolescents were assessed according to DSM-IV-TR criteria. Families were recruited through clinical referrals, schools and responses to advertisements . Respondents without a previous diagnosis of ADHD were screened with the parent and teacher version of the Swanson, Nolan and Pelham Rating Scale, SNAP-IV , recruited and evaluated at the Hospital Universitari Vall d'Hebron in Barcelona. The diagnosis of ADHD was evaluated by clinicians with the Structured Clinical Interview for DSM-IV Axis I and II Disorders (SCID-I and SCID-II) and the Conner's Adult ADHD Diagnostic Interview for DSM-IV (CAADID Parts I and II). Exclusion criteria were IQ < 70, schizophrenia or other psychotic disorders, ADHD symptoms due to mood, anxiety, dissociative or personality disorders, adoption, sexual or physical abuse, birth weight <1.5 kg and other neurological or systemic disorders that might explain ADHD symptoms. Cognitive and genetic data were available from 367 ADHD participants. More information about this sample can be found elsewhere  on each trial. Participants were required to make a response to the Go task stimuli as quickly and as accurately as possible by pressing one key of a handheld response box for an X and the other for an O (Go stimuli). The No-Go task involved an auditory tone which was presented, at the same time as the stimulus (letters), at random, on 25% of trials. Participants were instructed to withhold their response when they heard the tone. The Go task stimulus was presented for 1000 ms immediately following a fixation point of 500 ms. The task included four blocks, each with 24 Go trials and eight No-Go Trials. The Go:No-Go ratio was 3:1.Quality control of genetic data was previously performed and was available for analyses .To account for positive skewness of the cognitive data, we applied appropriate transformations to all cognitive measures for each variable prior to analyses. Square root transformations were used in all samples for CE. For RTV, we used a logarithm transformation for IMAGE-8 team, Dutch-IMAGE and UCLA, and square root transformation for Barcelona and Toronto. There were no extreme outliers for RTV or CE (>3.5 https://www.prsice.info) and applying standard procedures   including all single-nucleotide polymorphisms (SNPs)  was used for linkage disequilibrium (LD) clumping to keep a set of independent SNPs. Linear regression models were used to estimate associations between PRS and phenotypes in the target samples. PRS were calculated at a number of p value thresholds for SNP inclusion to provide the most predictive PRS. The p value thresholds used were 0.001, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5 and 1. We included age, sex and the first five principal components (PCs) as covariates in all analyses, to control for population stratification. The number of PCs was chosen based on the cohort's sample size  in order to avoid overfitting and to reflect the differential power to capture true population structure by principal component analysis, as reported in Demontis et al.  that we report for each study are adjusted from a baseline model including the covariates; the reported regression coefficients and standard errors (s.e.) were standardized to have mean = 0 and s.d.  1 using the PRSice command (--score std). We performed stringent permutation testing within PRSice-2 using 10 000 permutations to control for type 1 error and to prevent data overfitting across the range of p value thresholds considered . The p values are reported before correction (indicated with ‘p’), and after correction . Online Supplementary Figs S1–S9 provides plots for the PRS prediction models for RTV and CE across all sites.The GWAS summary statistics used as the discovery sample included the four target sub-groups . For this reason, PRS were calculated from the main GWAS each time excluding one of the target samples using four leave-one-out association meta-analyses, to ensure entirely independent discovery and target samples. PRS were estimated for each target sample using PRSice-2 software . Combining all samples, the sample size for the meta-analysis consisted of R2 = 0.004, p = 0.771, empirical-p = 0.993, β = 0.024 for IMAGE-8; R2 = 0.016, p = 0.124, empirical-p = 0.317, β = 0.135 for IMAGE-Dutch; R2 = 0.008, p = 0.466, empirical-p = 0.823, β = 0.032 for UCLA; R2 = 0.031, p = 0.362, empirical-p = 0.459, β = 0.112 for Toronto; R2 = 0.012, p = 0.029, empirical-p = 0.079, β = 0.122 for Barcelona). All associations showed a positive direction. PRS for ADHD were not significantly associated and showed inconsistent direction of association with CE in any of the individual samples .PRS for ADHD were not significantly associated with RTV in any of the individual datasets , with a positive direction. The best threshold for PRS association with CE was 0.001 (online Supplementary Table S3), but the association with CE did not reach significance CE . Heterogeneity tests showed low heterogeneity across studies for both measures . Forest plots for each variable are reported in Meta-analysis across all thresholds for RTV showed that the best threshold for PRS association with RTV was 0.2 (online Supplementary Table S2). At this threshold, the PRS for ADHD was significantly associated with RTV  but not response inhibition processes (CE) in a clinical ADHD sample. Whether the lack of an association with CE could reflect possible involvement of rare variants not detectable in this analysis or limited power to detect a potentially smaller association, requires further study.p value thresholds in the meta-analysis, with only slight fluctuations in results possibly due to low power. Similarly, our results on CE are consistent with a previous population-based study and a clinical study showing no association between polygenic risk for ADHD and other inhibition measures  and single-nucleotide variants (SNVs) that may underlie the association of ADHD with RTV or CE. Several studies indicate a role for CNVs and SNVs in contributing to ADHD risk . It would be informative in future larger studies to explore results separately for participants of different age groups . Third, our study included only participants of European ancestry; the generalizability of our findings to non-European populations requires further investigation. Fourth, the use of different tasks to reflect the two constructs of interest at different sites could have introduced heterogeneity in our data; however, we used random effects in the meta-analyses to account for between-study variation across sites. A further direction for future research is to widen the PRS investigation to additional cognitive impairments associated with ADHD.Although this is the largest study to date to investigate RTV and CE with a cutting-edge PRS method in a sample of individuals with clinically diagnosed ADHD, certain limitations need to be considered. First, our individual study analyses were underpowered due to the small sample sizes available in each single study. To increase statistical power, we analyzed the target studies with meta-analyses, reaching a combined sample size of Overall, polygenic risk associated with clinical ADHD diagnosis was associated with higher RTV in individuals with clinically diagnosed ADHD. Our results provide molecular genetic evidence that attention regulation and ADHD share common genetic factors. In other words, ADHD common variants not only contribute to risk of ADHD diagnosis, but are also a marker of poorer RTV performance in the context of having such a diagnosis. Further investigation, with bigger sample sizes, is needed to replicate these findings and to further determine the neurobiological mechanisms underlying this association. Furthermore, it is unknown whether the findings reported here are specific to ADHD or generalize to other disorders where increased RTV is also observed  (Brotman, Rooney, Skup, Pine, & Leibenluft,


N = 504, 49% male). Classical regression analyses indicated that both ASD traits and ADHD traits were uniquely associated with internalising problems. Dominance and Bayesian analyses confirmed that ADHD traits were a stronger, more important predictor of internalising problems. However, brief depression and anxiety measures may not provide a comprehensive index of internalising problems. Additionally, we focused on recruiting a sample that was representative of the UK population according to age and sex, but not ethnicity, a variable that may be linked to internalising disorders. Nevertheless, our findings indicate that while ASD and ADHD uniquely predict internalising problems, ADHD traits are a more important statistical predictor than ASD traits. We discuss potential mechanisms underlying this pattern of results and the implications for research and clinical practice concerning neurodevelopmental conditions.Autism Spectrum Disorder (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) are both linked to internalising problems like anxiety and depression. ASD and ADHD also often co-occur, making their individual statistical contributions to internalising disorders difficult to investigate. To address this issue, we explored the unique associations of self-reported ASD traits and ADHD traits with internalising problems using a large general population sample of adults from the United Kingdom ( In addition to core ASD symptoms, a large body of research has focused on the associations between ASD and mental health problems, particularly internalising disorders. For example, recent meta-analyses have indicated that anxiety disorders, depression and obsessive–compulsive disorder (OCD) are more prevalent in autism compared to the general population3. Notably, internalising problems have also been linked to lower quality of life, physical wellbeing, and social functioning in ASD7, making them important targets for intervention. Attention-deficit hyperactivity disorder (ADHD)—characterised by inattention and/or hyperactivity and impulsivity1—is another neurodevelopmental condition associated with internalising disorders, including anxiety, depression and OCD10. Research has further indicated that co-occurring depression and anxiety is associated with greater social difficulties in people with ADHD, which can often be difficult to differentiate from ASD symptomology12. Moreover, anxiety and depression are thought to underlie the relationship between childhood ADHD and lower quality of life in adulthood13.Autism Spectrum Disorder (ASD) is characterised by social-communication difficulties and restrictive and repetitive patterns of behaviour14) and a lower quality of life in people with these neurodevelopmental conditions15. Surprisingly, however, there is far greater emphasis on ASD over ADHD in both research on internalising problems16 and clinical practice17. To complicate matters, ASD and ADHD often co-occur, with a recent estimate that around 28% of autistic individuals have co-occurring ADHD3. The diagnostic co-occurrence and overlapping cognitive profiles between ASD and ADHD  makes it especially difficult to statistically examine their unique, relative contributions to internalising problems. There is little empirical research on this topic and almost nothing in adults, despite indications that the prevalence of anxiety and depression increases notably in adulthood in these clinical populations22. Existing studies, mainly in children and adolescents, have used small samples and/or seldom report effect sizes . They are likely to be under-powered and it is difficult to derive implications for clinical practice based on such research .It is now clear that both ASD and ADHD are associated with internalising problems, which also contribute to many other health difficulties . The sample had 504 participants , aged 18–79 years . This gave us at least 95% statistical power to detect small-to-medium unique effects in the multivariate analyses, where f2 = 0.05 and α = 0.05 . Participants provided their age (years), sex, and education level between 0  and 7  on the International Standard Classification of Education33. No additional information was collected about clinical diagnoses. Participants completed the following self-report measures in a randomised order.Recruitment through 29) assessed social and non-social ASD traits, generating a score between 28 (few autistic traits) and 112 (many autistic traits). Importantly, the AQ-Short is suitable for use in both males and females as it is potentially less biased towards the male autism phenotype compared to other ASD trait measures34. The AQ-Short had good reliability in the present study .The 28-item Short Autism-Spectrum Quotient  assessed ADHD traits. Its 18 items measure the frequency of symptoms related to inattention and hyperactivity/impulsivity, producing scores between 0 (no ADHD traits) and 72 (many ADHD traits). Comparing the ASRS to clinician ratings, Kessler and colleagues30 reported that the classification accuracy of the ASRS for ADHD was 96.2%, supporting its construct validity. In the present study, the ASRS had good reliability .The Adult ADHD Self-Report Scale (ASRS35) assessed how frequently participants experienced anxiety symptoms in the preceding 2-week period, leading to a score between 0 (no anxiety) and 21 (severe anxiety). Spitzer et al.35 showed that the GAD-7 had good convergent validity using the Beck Anxiety Inventory (r = 0.72) and the Symptom Checklist-90 (r = 0.74). The GAD-7 had good reliability in the present study .The 7-Item Generalised Anxiety Disorder Scale (GAD-736) measured the frequency of depression symptoms in the preceding 2-week period, generating a score between 0 (no depression) and 27 (severe depression). Martin et al.37 showed that the PHQ-9 was associated with the Brief Beck Depression Inventory (r = 0.73), demonstrating its convergent validity and, in the present study, this measure had good reliability .The 9-Item Depression Module of the Patient Health Questionnaire , no datapoints were identified as multivariate outliers. Correlations among variables were assessed using two-tailed Pearson’s correlations, before conducting a multiple regression analysis to investigate the associations of ASD traits and ADHD traits with internalising disorder symptoms, controlling for sex, age, and education level.The data were first analysed using JASP (Version 0.14.1)While multicollinearity was not a concern , the visual inspection of a Q-Q plot of the standardised residuals and a scatterplot of the standardised residuals against the standardised predicted values indicated that the assumptions of normality and homoscedasticity were potentially violated. Thus, 95% bias-corrected and accelerated bootstrap confidence intervals (2000 resamples) are reported to complement the ordinary least squares estimates.yhat package39 in RStudio (Version 1.3.959)40. Finally, two Bayesian regression analyses were carried out in JASP to derive Bayes Factors and thus quantify the evidence supporting ASD and ADHD traits as unique predictors of internalising problems.To determine the relative statistical importance of each predictor variable, dominance analyses were then conducted using the r = 0.30, p < 0.001. Both ASD and ADHD traits were correlated with greater anxiety  and depression . Anxiety and depression were also highly correlated . Therefore, to avoid concerns with multicollinearity in the multivariate analyses, anxiety and depression were combined into a composite measure of internalising problems [following42]. Both ASD traits, r = 0.32, and ADHD traits, r = 0.58, were correlated with greater internalising problems .Descriptive statistics and correlations are presented in Table β = 0.17, p < 0.001, and ADHD, β = 0.49, p < 0.001, were unique predictors of internalising problems. Inspection of beta coefficients suggested that trait ADHD was a stronger predictor than trait ASD. Comparing beta coefficients, however, is not sufficient to test the relative importance of predictors [see43]. Therefore, using dominance analysis, as increasingly used in autism research , we calculated General Dominance Weights (GDWs), where larger weights indicated greater variance explained in internalising problems. These GDWs were used to rank the predictors in order of statistical importance [following46]. ADHD traits (GDW = 0.27) clearly dominated ASD traits (GDW = 0.06) as the most important predictor of internalising problems (Table 47), had a reproducibility rate of 100%. This indicated that ADHD traits would always dominate ASD traits as a predictor of internalising problems at the population level.Multiple regression Table  assessed GDW = 0.7 clearly48]. We compared models containing the predictor of interest (either ASD traits or ADHD traits) with null models including all other variables, following recent autism research . The Bayes Factors (BF10), therefore, indexed ASD and ADHD traits as unique predictors of internalising problems. There was “extreme” evidence that both ASD and ADHD traits predicted internalising problems but, consistent with the foregoing analyses, ADHD traits, BF10 = 1.049 × 1029, were a much stronger predictor of internalising problems than ASD traits, BF10 = 2160.73. The overall pattern of results was identical to separate analyses for anxiety , although replication is, of course, now required in clinically diagnosed samples. Given that ADHD/ASD traits share similar correlates with clinical diagnoses [see27], we hypothesise that a similar pattern of results will be found in such research. Indeed, a recent study in clinically diagnosed autistic people found that ADHD traits, compared to autistic traits, were more strongly associated with a lower quality of life50. As quality of life and internalising problems are correlated52, we would expect that our pattern of results will, in future, be found in a sample of clinically diagnosed autistic people.To our knowledge, this is the first evidence that ADHD traits are more predictive of internalising problems than ASD traits. While its novelty might appear surprising, it is reflective of the dearth of research on this topic. Unfortunately, ASD has often been prioritised over ADHD in both research on internalising problems and clinical practice, particularly for anxiety . Crucially, such response inhibition problems have also been documented in depression57 and anxiety60. We therefore speculate that response inhibition difficulties, which appear to be a cognitive feature of ADHD, may also potentially underpin the stronger association between ADHD traits and internalising problems. The residual link between autism and internalising problems may be driven by other atypicalities often reported in these conditions, such as alexithymia [see61]. Our study was neither designed nor able to test for such mechanisms, and alexithymia has not been investigated in ADHD controlling for ASD, however it provides some direction for future research on the processes underlying differential contributions of ADHD and ASD to poor mental health.Before these potential suggestions could be translated into practice, there is a need to investigate the mechanisms underlying our results. While the specific neurocognitive processes that underpin our findings are unknown, a possible explanation for this pattern of results may be the differential executive function difficulties that characterise ASD and ADHD. A systematic review by Craig and colleagues15 for a review]. Family studies have consistently shown an increased prevalence of depression in biological relatives of individuals with ADHD and/or ASD  with similar findings for anxiety . Further, studies identifying common genetic variants  demonstrate genetic correlations between internalising disorders and both ADHD and ASD . Interestingly, a recent GWAS of eight major psychiatric conditions68 showed that, whilst ADHD and ASD show similar genetic correlations with depression , significant genetic correlations with anxiety were found with ADHD, but not ASD. That is, a pattern of results in a genetic study in line with our findings. Future research could investigate, more directly, whether genetic factors can explain why ADHD traits are more strongly associated with internalising problems than autistic traits.In considering mechanisms to explain our findings, it may also be important to think about the overlap between ADHD, ASD and internalising problems at the genetic level , it will be important to assess whether the dominance relationship observed holds after accounting for participants’ ethnicity and other socio-demographic factors. Finally, while the trait-based approach was an important step in understanding the unique contributions of ASD and ADHD to internalising problems, the study will require a conceptual replication with clinical samples. Such research would benefit from using measures of internalising symptoms that have been validated in this population, which are currently lacking15. This will be difficult to achieve as it is only with the most recent revision of the Diagnostic and Statistical Manual of Mental Disorders1 that individuals can receive concurrent ASD and ADHD diagnoses72. Consequently, recruiting a sufficiently large sample of clinically diagnosed participants, as required to undertake the analyses we have employed, will require considerable resources. Our study hopefully provides the impetus for such research.Further research is also required to address limitations of the present study. First, internalising problems were assessed using brief depression and anxiety measures, which may not provide a comprehensive index of internalising problems. Additionally, several internalising symptoms measured by these brief screeners  overlap with the symptom profiles of ASD and ADHDOverall, our findings demonstrate that ASD and ADHD uniquely predict internalising problems, such as anxiety and depression. Crucially, ADHD traits were a much more important predictor of internalising problems and our analyses indicated that this relationship is almost certain to occur in the overall population. While further research is necessary to replicate these findings and elucidate mechanisms that underpin the observed relationships, our study provides important new evidence linking neurodevelopmental conditions and mental health in adulthood.


Low birth weight (LBW) remains a global concern for childhood morbidity and mortality. This study examined the socioeconomic factors associated with LBW among Bangladeshi newborns and drew a district-level prevalence map.Data were extracted from the 2019 Multiple Indicator Cluster Survey for Bangladesh. Multivariate logistic regression was used to identify the predictors of LBW.The prevalence of LBW among Bangladeshi newborns was found to be 14.5%. Overall, the Eastern and South-Eastern regions had a higher burden of LBW. Mothers’ educational status, mode of delivery, wealth index quintile of the household and area were identified as independent predictors of newborns’ LBW. Mothers who completed primary and secondary education grades had a 1.6- and 1.3-fold higher possibility of having an LBW baby compared with those who completed higher secondary or higher educational grades . Children belonging to the poorest households and residing in urban areas had a 1.4-fold higher likelihood of being LBW (p<0.05).This study indicates that LBW is still highly prevalent in Bangladesh. Immediate public health action is required in the highly prevalent regions identified in this study. LBW may cause mortality, diarrhea and respiratory illness in childhood and chronic non-communicable diseases in adulthood.12 Several factors have been identified as determinants of LBW, such as gestational age, early marriage, less utilization of antenatal care (ANC) services, active or passive smoking and drug abuse, maternal nutritional deficiencies, maternal education and household wealth status.2A study based on the 2012–2013 Bangladesh Multiple Indicator Cluster Survey (MICS) reported that about 20% of infants were born with LBW.8 Given the high prevalence of LBW, previous national data on LBW appeared 4 y ago. A national prevalence map of LBW for Bangladesh is also lacking. Therefore, it is essential to fill this research gap and report the latest statistics that could assist in initiating immediate public health action. Further identification of the associated factors and pocket areas are crucial for the policymakers and public health activists to understand where progression has been made and where to allocate additional resources. This study, therefore, aimed to assess the prevalence and associated socioeconomic factors of LBW among newborns. This study also aimed to draw a district-level map based on the prevalence of LBW to identify the pocket areas.As a lower-middle-income country, Bangladesh has made substantial improvements in reducing maternal and child mortality; however, the burden of LBW is still very high and is a leading cause of child mortality. This higher LBW rate may hold up the country's improvement in reducing child mortality followed by achieving SDG targets. Previous statistics also indicate an inconsistency in the trend of LBW in Bangladesh.13 This cross-sectional survey was part of a six-round global MICS conducted by the Bangladesh Bureau of Statistics (BBS) with support from UNICEF. This survey aimed to capture various health indicators and household characteristics (HHs) from district-level samples. Data were collected from January to June 2019. Datasets are freely available at the MICS website (https://mics.unicef.org/surveys). However, to access/download datasets, a formal request of access is required. The author received authorization from the MICS to download and use datasets.The present study analyzed the national presentative data of the latest MICS in 2019.Survey data were collected from all 64 administrative districts of Bangladesh. MICS employed a two-stage stratified random sampling procedure to collect data at the household level. The urban-rural areas within each district were considered as the main sampling strata. A specific number of census enumeration areas (EAs) were systematically selected with probability proportional to size within each stratum. After listing HHs within selected EAs, a systematic sample of 20 HHs was drawn from each primary sampling unit (PSU). The total number of PSUs and final sample sizes for this survey were 3220 and 64 400, respectively. For this study, the women's data file was used. Complete data of 64 378 women were available in the dataset, among whom 9183 women had ≥1 live birth in the last 2 y. Data on birth weight (in grams) were available for 4762 children and were considered for analysis in this study.Data of each individual were collected from the respective mothers from selected households through one-to-one interviews. For this study, women who gave live birth in the last 2 y of the survey date and children weighed at birth were included. Data on birth weight (measured) were collected from birth cards.The supervisor of each data collection team was responsible for the daily monitoring of field data collection. During fieldwork, each team was visited multiple times by survey management team members and UNICEF team members. Field check tables were produced weekly for analysis and action with field teams.14: (1) LBW (birth weight <2500 g) and (2) normal weight (birth weight ≥2500 g).Newborns’ birth weight was the outcome variable for this study, which was categorized as a binary variable following the WHO definition2 Study variables included: (1) gender of the child ; (2) age of the mother (15–49 y); (3) educational status of the mother ; (4) ANC received ; (5) ANC assisted by ; (6) watch television every day (yes or no); (7) place of delivery (where the mother gave birth to her child); (8) mode of delivery ; (9) wealth index quintile ; (10) area (type of place of residence); and (11) division (of the eight administrative divisions of Bangladesh).Demographic and socioeconomic status (SES) of the children were considered as independent variables for this study. Independent variables were selected based on a previous study that used the 2012–2013 Bangladesh MICS dataset.2 test was performed to compare the outcome variable (i.e. LBW) across different independent variables. Multivariate binary logistic regression analysis was performed to identify the predictors of LBW and to estimate the adjusted OR (AOR) with 95% CI. Variables showing p<0.25 in bivariate analysis were entered into the adjusted regression model.15 The tests for the main effects were considered significant at a 5% level of significance. Data were analyzed using SPSS for Windows version 23  using sampling weight to ensure the results at the national level. Arc GIS v. 10.5 software was used for the graphical distribution of the prevalence of LBW across the 64 districts.Pearson's χThe data of 4762 (weighed) children, for whom birth weight had been measured after birth, were included in this analysis. Demographics and SES of the participants according to the prevalence of LBW are presented in Table Among the 64 administrative districts, Sherpur had the highest burden of LBW (25.0%), which means that one in every four children are born with LBW. This analysis also shows that none of the newborns from the Jaipurhat district had LBW. District-wise prevalence of LBW is presented in Table 8 This study found that about one in every seven newborns has the possibility of being LBW in Bangladesh. In other words, about 14.5% of newborns are at risk of the consequences of LBW in early and later life.16 and Nepal (15.4%)17; however, the prevalence was higher than African countries, for example, Burkina Faso (13.4%), Ghana (10.2%), Malawi (12.1%) and Uganda (10.0%).18This study calculated the prevalence and associated factors of LBW from a national representative cross-sectional survey. Current prevalence of LBW was lower compared with the earlier statistics in Bangladesh.2 Among the 64 administrative districts, 9 were identified as high-risk zones where the prevalence of LBW was >20%, and only 3 had a prevalence <5%. This variation indicates a gap in the existing maternal and child care service system across the country and a low level of awareness of LBW's consequences among the communities in those regions.A significant regional variation across the country was observed in the prevalence of LBW. This study found that the Chattogram division had the highest (19.0%), while the Khulna division (10.6%) had the lowest rate of LBW, whereas the previous study based on the 2012–2013 MICS reported that the Rajshahi division had the lowest and the Rangpur division had the highest rate of LBW.16 Lower educational attainment may limit a mother’s understanding of the consequences of LBW for the newborn and the significance of healthcare-seeking behavior for safe and nourished childbirth.19 A previous study also identified women's education as an independent predictor of ANC in Bangladesh.20 All these factors could adversely affect fetal growth and increase a woman's risk of delivering an LBW baby.7 From the practical viewpoint, it is not feasible to increase formal education to mothers at a later stage of life; however, health education on obstetrical care can certainly be improved. Those children who were delivered by cesarean section had less possibility of being LBW. A previous Ethiopian study reported similar statistics.21 The pathology behind this association is not clear. One possible explanation for this could be that because women with higher academic grades who belong to the rich/richest wealth strata mainly have cesarean deliveries in Bangladesh,22 they might better understand the consequences of LBW and receive sufficient care during pregnancy. Children from the poorest households had a higher likelihood of being LBW, which is also in line with the earlier study.2 Children from urban areas had higher odds of being LBW compared with children from rural areas. It is evident that urban areas had adequate availability and accessibility to healthcare facilities and civic benefits compared with rural areas. However, increased industrialization and environmental pollution in urban areas may adversely affect birth weight (i.e. LBW), which is supported by the previous finding.23Regarding the risk factors, educational status of the mother, mode of delivery, wealth index and type of place of residence (i.e. area) were identified as independent predictors of LBW among Bangladeshi newborns. The likelihood of having an LBW child is negatively associated with the educational status of the mother , which was consistent with earlier studies.This study is not without limitations. The major limitation of this study is the cross-sectional nature of the data. Therefore, a temporal relationship cannot be established. We failed to include all the potential risk factors for LBW, including genetic and environmental factors.About one in every seven newborns are delivered with LBW in Bangladesh. Educational status of the mother, mode of delivery, wealth index quintile of the household and area were identified as independent predictors of newborns’ LBW.ihab048_Supplemental_FileClick here for additional data file.


Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common psychiatric disorders of childhood with significant impairment in overall functioning and associated psychiatric comorbidities. Knowledge of determinants is vital for the development of effective preventive strategies and tailor made interventions.The study was aimed at determining risk factors for the development of ADHD among primary school children (PSC)) in state schools in Colombo district of Sri Lanka.An unmatched case control study was conducted consisting of 73 cases (with ADHD) and 264 controls (without ADHD) selected randomly among 6-10 year old PSC from Sinhala medium state schools in Colombo district. Primary Care Givers completed the validated Sinhala version of Swanson, Nolan, Pelham –IV (SNAP-IV-S) scale for the assessment of ADHD and an interviewer administered questionnaire on risk factors. Children’s diagnostic status was confirmed by a Consultant Child and Adolescent Psychiatrist following a clinical assessment. Bivariate analysis followed by multivariate regression model identified potential risk factors.Multivariate analysis revealed, male sex , lower educational level of the mother , maternal psycho pathology , prenatal exposure to passive tobacco smoke , Birth weight <2500g and /or gestation period of <37 completed weeks , neonatal complications  , minimal leisure time with family  and subjected to teasing/ bullying  as significant predictors of ADHD.Primary prevention focusing on strengthening neonatal, maternal and child health services needs highlighting together with special emphasis on the need for anti-bullying policies in state schools.No significant relationships.