Congenital heart defects (CHDs) are the most common cause of birth defect-related infant morbidity and mortality, affecting 1% of 40,000 births per year in the United States. On the other side, the etiology of attention-deficit/hyperactivity disorder (ADHD) is multifactorial. Multiple studies have found that cardiac surgery patients have higher morbidity of having this disorder. Many studies have investigated the prevalence of ADHD in different subtypes of CHD, but few have focused on the severity of ADHD symptoms. Thus, we conducted this systematic review and meta-analysis to investigate the severity of ADHD symptoms in CHD patients.P-values <.05 are considered as significant, and we performed all statistical analyses using RevMan software Version 5.4.1.We searched PubMed, Embase, Scopus, and Web of Science were searched from inception to March 6, 2023 without any restrictions. We included observational studies published in English language that evaluated burden of symptom of ADHD in CHD patients. Moreover, the standardized mean difference (SMD) for continuous outcomes with 95% confidence interval (CI) was pooled. P < .00001, I2 = 81%) and . Regarding ADHD Inattention Subscale (informant), the pooled data showed that a significant increase of this score in the CHD group , and ADHD Combined Score (informant) showed a significant increase of this score in the CHD group .Eight studies were included in our review with a total number of 120,158 patients. CHD was associated with a statistically significant increase in both ADHD index T score and ADHD Hyperactivity-Impulsivity Subscale (informant) with (SMD = 0.65, 95% CI , Our study revealed a strong association between CHD and not only ADHD, but also the severity of ADHD, making early diagnosis of ADHD in children with CHD a mandatory step in the clinical evaluation practice to improve these children on both clinical and psychological aspects. Congenital heart defects (CHDs) are the most common cause of birth defect-related infant morbidity and mortality, affecting 1% of 40,000 births per year in the United States. CHDs related death often occurs in babies <28 days old ). CHDs cause about 4.2% of all neonatal deaths. Between 1999 to 2006, the United States recorded about 41,494 deaths related to CHDs, which means that CHDs can either be the cause of the death or contribute to death somehow.,3 This changes our focus on CHD from mortality to morbidity, where although babies born with CHD now have a longer life expectancy and better quality of life, they are more likely to have psychological distress, neurocognitive deficits, and social challenge. Studies reported that approximately 30% had mood or anxiety disorders and 20% showed symptoms comparable with posttraumatic stress disorder.Recent improvements in medical treatments, surgical procedures, imaging, and pediatric intensive care helped more than 90% of congenital heart disease (CHD) children reach adulthood and decreased the total mortality rate by 31%in the previous 20 years.Children and adolescents with CHD are at an increased risk of neurodevelopmental abnormalities, increasing in frequency and severity as the disease complexity increases. There is an increase in the prevalence of neurodevelopmental disabilities, deficits in behavior, language, and speech disorders, attention-deficit/hyperactivity disorder (ADHD), and increased use of services among young patients with CHD.,6Even though the etiology of ADHD is multifactorial, multiple studies found that cardiac surgery patients have higher morbidity of having this disorder, particularly after aortic arch repairs and corrections of great artery transposition, thus explains why CHDS children have a 30% greater chance of inattention and hyperactivity disorder than healthy persons. Approximately half of the surgically treated children require remedial school services by reaching adolescence, ADHD symptoms in cardiac patients are frequently misdiagnosed and under-treated. Other risk factors include lower birth weight, longer duration of deep hypothermic circulatory arrest, and repeated surgeries as ADHD symptoms are worsened by repeated exposure to surgery, anesthesia, and inflammatory responses.,9Several studies have tried to understand the relationship between ADHD and CHDs, where some studies suggest that complex CHD children have abnormal cerebral blood flow, which was already altered during pregnancy and peripartum periods, leading to a delay in brain maturation. Additional postponed correction of cardiac defects increases the risk for ADHD development due to hypoxemia and can further damage the highly oxygen-sensitive regions of the prefrontal cortex.To date, many studies investigated the prevalence of ADHD in different subtypes of CHD but little focused on the severity of ADHD symptoms. Also, the studies are either based on small groups of children or are limited to a single heart lesion. We conducted this systematic review and meta-analysis to investigate the severity of ADHD symptoms in CHD patients. and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The study protocol was registered on open science framework (Registration DOI: 10.17605/OSF.IO/ZH2YX).We conducted this systematic review and meta-analysis in accordance with the Meta-Analysis of Observational Studies in EpidemiologyPubMed, Embase, Scopus, and Web of Science were searched from inception to March 6, 2023 without any restrictions. We generated our search strategy using Mesh database, and the search strategy is formed of combinations of the following search terms  AND . We included observational studies published in English language that evaluated burden of symptom of ADHD in CHD patients. No restrictions regarding date of publication. We excluded studies with insufficient data for extraction, reviews, book chapters, thesis, editorial, letters, conference papers, and non-English studies, literature reviews, and meta-analysis. Two independent authors screened the articles in 3 stages; title, abstract, and full text on an excel sheet. Another independent author resolved any disagreements. to assess the risk of bias within the included observational studies. This scale is evaluating 3 essential domains:We used the Newcastle-Ottawa scale(a) Selection of the study subjects.(b) Comparability of groups on baseline characteristics and other important confounders.(c) exposure or outcome and follow up.Each article was evaluated by 2 independent authors and any disagreements were resolved by another author. The number of included studies were <10, so we could not assess publication bias using Egger test for funnel plot asymmetry.summary and baseline characteristics including: design, number of participants, gender, age, birth weight, gestation, family social status, and IQ. (2) Study outcomes: The authors extracted the following outcomes: ADHD index T score, ADHD Inattention Subscale, ADHD Hyperactivity-Impulsivity Subscale, and ADHD Combined Score.Two independent authors extracted the data in the form of the following domains: (1) P-values <.05 are considered as significant. The I-square and P-value were used to determine heterogeneity. If the P-value was <.05 or the I-square was <50%, the analysis was judged heterogeneous. If heterogeneity was identified, a random-effect model was used, and a leave one out test was used to establish which study was producing the heterogeneity. We performed all statistical analyses using RevMan software Version 5.4.1.The standardized mean difference (SMD) for continuous outcomes with 95% confidence interval (CI) was pooled. Our database search on PubMed, Scopus, Web of Science, and Embase yielded 2455 studies. We removed 151 duplicate studies. We screened 2304 studies in 3 stages: title, abstract, and full text screening. After full text screening, we excluded 2280, and after full text screening, we excluded 16. Finally, we included 8 studies in our review; only 7 studies were eligible for the meta-analysis . We included children and young adults with mean age ranged from 4 to 26 years old, average IQ, and normal birth weight. The detailed summary of included studies and baseline characteristics reported in Table All the included studies reported adequately the domain of selection of study subjects. All the included studies matched their participants according to baseline in comparability domain except Gonzalez 2020 and Holland 2017. Only 3 studies  adequately reported the third domain. The detailed quality assessment reported in Table ADHD index T score determine whether the patient have ADHD and rating the severity of the disease. Patients’ scores <60 are disease free but scores higher than 60 indicate ADHD, considering that the higher the score, the higher the disease severity. It consists of questions related to ADHD frequency of symptoms; the questions are based on the definition of ADHD provided by the Diagnostic and Statistical Manual of Mental Disorders .P < .00001) but with high heterogeneity . Therefore, a subgroup analysis was performed according to the type of reporting to self-reported and informant. The pooled data of self-reported was homogenous and showed a significant increase in score in CHD group . The data of informant showed a significant increase of the score in CHD group too  but with high heterogeneity ; after removing DeMaso 2014 and Lau-Jensen 2021, The pooled data showed that significant increase of this score in CHD group . Pooled data were homogeneous  . The pooled data were homogeneous   Fig. .P < .001). Pooled data were homogeneous  . Pooled data were homogeneous   have been used in patients meeting the criteria of both ADHD inattentive type and hyperactive/impulsive type. This score reported in 4 studies. The pooled data showed that significant increase of this score in CHD group  which stresses the importance of early detection of ADHD in children with CHD in the clinical practice and periodic reevaluation every couple of years or following any new cardiac event or surgery.,16 regarding the factors affect the severity of ADHD symptoms in patients with CHDs, our findings showed that the symptoms worsen with age and complications of heart surgeries, placement of ventricular assist devices, and transplant. The age at the time of cardiac surgery makes a difference too, the lower the age at operation, the better the outcomes.,18Moreover, most of the available data reported the prevalence of ADHD in different subtypes of CHD but failed to focus on the severity of ADHD. All factors that can affect the brain development will play a role in the development of neuropsychological deficits  CHDs may result in reduced cerebral blood flow and oxygen to the brain, which can affect how the brain develops prenatally and in early childhood compare adolescents with d-transposition of the great arteries (d-TGA) with healthy adolescents, the results showed 19% of adolescents with repaired d-TGA met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for ADHD with a significantly higher mean of T score in the d-TGA group than in the referent group (P = .01), More recently, DeMaso et al examine psychiatric disorders in adolescents with single ventricle CHD and whether the patient-related risk factors contribute to that dysfunction. They reported a stunning 55% met the DSM-IV lifetime criteria for anxiety disorder and 53% met the criteria for ADHD among adolescents with single-ventricle CHD and their results end up with Adolescents with single ventricle CHD have a high risk of psychiatric morbidity specifically with anxiety disorder and ADHD (P < .001 each) and early identification of psychiatric symptoms is critical for better management. Regarding patient-related risk factors, they find that open-cardiac surgeries or age at the first cardiac surgery, are important for better psychosocial outcomes.According to the American Psychiatric Association, ADHD symptoms come with 3 presentations: ADHD—combined, ADHD—inattention, and ADHD—hyperactivity. Many different types of scales are available to determine the type and assess the severity of ADHD. Our studies showed a significant increase in ADHD index T score, ADHD Hyperactivity-Impulsivity Subscale, ADHD Inattention Subscale (informant), and ADHD Combined Score (informant) in CHD patients more than the controls. DeMaso et al assess psychiatric disorders in adolescents with repaired tetralogy of Fallot (TOF) and end up with Adolescents with TOF either without or with an origin of a genetic defect had a higher lifetime prevalence of ADHD than referents . They also examined associations between psychiatric status and physical illness severity; the median total severity scores on the BPRS-C  were low for all group but were significantly higher in adolescents with TOF and a genetic diagnosis. With respect to ADHD symptoms, adolescents with TOF without or with a genetic diagnosis scored higher than referents on CADS ADHD Index (P < .0001 for both). Lau-Jensen et al investigate the distribution in ADHD symptoms in young adults with simple CHD. They reported 15% of all T-scores were above 65 In the CHD group to 4% of the T-scores in the control group and end up with patients with a simple CHDs have a higher symptom burden across all ADHD scores  and all symptom sub-scores  therefore they emphasize the importance of the routine screening for ADHD symptoms to facilitate adequate help as these symptoms are easily overlooked. Czobor et al investigate the differences in ADHD symptoms between children who underwent cardiac surgery at different ages and controls. The results indicted better outcomes with the lower age at the first cardiac surgery and significantly higher severity of ADHD symptoms in children who underwent cardiac surgery at or above the age of 3 compared to non-operated children or children who operated at a younger age.In 2017, Holland et al investigate if children with CHD have an increased prevalence of ADHD symptoms compared with the healthy controls and their results like previous studies in increasing the ADHD frequency among children with CHDs who had significantly increased inattention scores (P = .009) and total ADHD scores (P = .008) compared with controls. Yamada et al aim to determine if school-aged children underwent early heart surgery for CHD are more likely than healthy controls to have screening scores on the Swanson, Nolan, and Pelham IV (SNAP-IV) questionnaire suggestive of ADHD, and the results supported the significant potential increased risk of ADHD in children undergoing early surgery of CHD. Our study should be interpreted considering some limitations. First, our data is derived from observational data and retrospective studies. Second, the baseline characteristics between the groups were not equivalent with small number of CHD patients and larger number of healthy individuals; however, we observed no heterogeneity in most of our analyses and if heterogeneity was observed it was solved. Lastly, the outcome scores reported are based on informant data which make them vulnerable to subjective changes from practitioner and the other and from center and the other.Holst et al while another found that there is a tendency for certain conditions to run in families. Families with a history of ADHD or CHD may be at an increased risk of having children with these conditions; moreover, a meta-analysis in 2018 found that the health of the mother during pregnancy can impact the risk of both ADHD and CHD with maternal health conditions and genetic factors may contribute to both conditions.Moreover, several factors have been connected to the development of ADHD in patients with CHD. First is the genetic predisposition. A study conducted in 2010 found that multiple genes have been associated with an increased risk for ADHD, and some of these genes may also play a role in heart development,Our study revealed a strong association between CHD and not only ADHD, but also the severity of ADHD making early diagnosis of ADHD in children with CHD a mandatory step in the clinical evaluation practice to improve these children on both clinical and psychological aspects.Conceptualization: Mohammed Tarek Hasan.Data curation: Mohammed Tarek Hasan.Formal analysis: Mohammed Tarek Hasan, Mahmoud Shaban Abdelgalil, Ali Elkhadragy.Investigation: Mohammed Tarek Hasan, Mahmoud Shaban Abdelgalil, Merihan A. Elbadawy, Amr Mahmoud Elrosasy, Ali Elkhadragy, Mahmoud El Garhy.Methodology: Mohammed Tarek Hasan, Mahmoud Shaban Abdelgalil, Merihan A. Elbadawy, Amr Mahmoud Elrosasy, Mahmoud El Garhy.Project administration: Mohammed Tarek HasanSoftware: Mohammed Tarek Hasan.Supervision: Ahmed K. Awad.Validation: Mahmoud El Garhy.Visualization: Mohammed Tarek Hasan.Writing – original draft: Mohammed Tarek Hasan, Mahmoud Shaban Abdelgalil, Merihan A. Elbadawy.Writing – review & editing: Mohammed Tarek Hasan.


We often find it challenging to manage hyperactivity, low frustration tolerance and angry outbursts in patients with ASD and comorbid ADHD. Fewer drugs are approved for these disorders and these patients are more likely to develop adverse effects.The aim of this case is to show how the combination of lisdexamfetamine together with guanfacine has very positive effects on anger outbursts and boundary heteroaggressiveness in patients with ASD and ADHD.Case report and literature reviewThis is a 14-year-old minor admitted to the psychiatric unit after physical aggression against his family due to anger after removal of video games, requiring police intervention. He has been diagnosed since he was 11 years old with ADHD and Autism Spectrum Disorder. He was being treated with methylphenidate 54 mg and aripiprazole 10 mg. Since the beginning of the admission, the following pharmacological adjustment has been made: Methylphenidate is substituted by lisdexamfetamine up to 50 mg per day. Guanfacine has been started up to 4 mg per day and the dose of aripiprazole has been maintained. The patient had no adverse effects with adequate tolerance without sedation, hyporexia or hypotension. With this adjustment, improvement was found in the levels of restlessness and hyperactivity. The patient expressed a subjective improvement in the levels of restlessness and with a notable improvement in attention in the hospital classroom. An improvement in emotional regulation was also observed, with more tolerance to the imposition of limits, without an explosion of anger in the face of any rule during admissionThe management of hyperactivity and episodes of low frustration tolerance in patients with ASD and ADHD is complex. Many studies point out the time-limited use of some antipsychotics such as risperidone or aripiprazole. This work aims to show guanfacine in combination with lisdexamfetamine as an excellent combination for the management of agitation and rage explosion in these patients. In addition, the profile of adverse effects at metabolic level is much better than that of atypical antipsychotics.Extended-Release Guanfacine for Hyperactivity in Children With Autism Spectrum Disorder. Lawrence Scahill et al. Am J Psychiatry. 2015 Dec.None Declared


We conducted a confirmatory factor analysis to inspect the two-factor structure of the SNAP-IV. We also calculated ordinal coefficient alpha to estimate internal consistency. Fit statistics for the two-factor model approached acceptable levels. The model fit improved slightly after removing an item related to spoken language. The subscales had acceptable internal consistencies. Findings partially support the use of the SNAP-IV in this group of children. However, there are limitations to its performance in this population likely related to the presence of neurodevelopmental disorders.This study investigated the psychometric properties of the Swanson, Nolan, and Pelham ADHD Rating Scale (SNAP-IV) in a sample of South African children with neurodevelopmental disorders ( Behavioral rating scales are often used in clinical settings to evaluate symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) in children and adolescents. Data derived from ADHD rating scales may play an important supportive role in clinical assessment. For example, clinicians may rely on rating scales to quantify the severity of impairments, to monitor changes in behavior over time or monitor impact of interventions , including Autism Spectrum Disorder (ASD), Intellectual Disability (ID), and Specific Learning Disorders (SLD). Clinically significant ADHD symptoms may be present in approximately 28–78% of children with ASD, 23% of children with ID, and 40% of children with SLDs  found that the scale did not adequately distinguish between inattention and hyperactivity-impulsivity, the two constructs thought to underpin ADHD, in this group  aged 6–17 years  NDDs: ADHD, ASD, SLD, ID or Communication Disorder  to qualify for a diagnosis of ADHD. Pelham and colleagues  recommenThe CBCL/6–18 is a self-report questionnaire designed to assess specific problematic behaviors in school-age children, as reported by caregivers  verbally administered the SNAP-IV to all parents as well as the CBCL/6–18 subscales to a subset of parents, in their preferred language. Administration of the CBCL/6–18 began in May 2019, nine months after data collection for the umbrella study commenced. Although we did not record the language of administration for specific tools, anecdotal reports indicate that the majority of participants elected to complete the questionnaires in English.The University of Cape Town’s Human Research Ethics Committee approved this research (367/2019) as part of the NeuroDEV South Africa study (810/2016). The NeuroDEV study also received ethics approval from the Harvard T. H. Chan School of Public Health Institutional Review Board (17-1260) and the Western Cape Department of Health, South Africa.2) is a test of exact fit between the model and the data. The Tucker-Lewis Index (TLI) conveys information about the “goodness of fit” between the model and the data. A higher value indicates better fit, and should ideally exceed 0.95 . The Root Mean Square Error of Approximation (RMSEA) is a measure of approximate fit, where 0 indicates a perfect fit and 0.05 indicates close fit. The Standardised Root Mean Square Residual (SRMR) represents the average difference between the observed and predicted correlations matrices . Like the RMSEA, SRMR values close to 0 suggest good fit. Modification Indices (MIs) approximate the amount by which chi-square value will decrease  if an unspecified parameter were to be estimated. Parameters with MIs greater than 3.84 indicate a statistically significant decrease in the chi-square statistic, or improved model fit. Each MI is associated with a standardized expected parameter change (SEPC), the magnitude and direction of which approximates how much the parameter is expected to change if it were to be estimated. Standardised coefficients  are estimates of direct effects between latent and observed variables. The squared factor loadings indicate the proportion of variance in each observed variable that is explained by the latent factor.We performed all statistical analyses in RStudio (Version 1.3.1093) for R . Table 1 presents the sociodemographic characteristics of the sample, as well as information related to DSM-5 diagnoses, level of spoken language, and highest level of education. Most children came from low- and middle-income families that spoke one of the three main languages spoken in the Western Cape of South Africa. The predominance of male participants (> 70%) is typical of study samples comprising children with NDDs , ID (n = 11), ASD and ID (n = 3), CD (n = 2), or SLD (n = 1). Figure S1 in the supplement is a Venn diagram displaying overlap between clinical DSM-5 diagnoses. One hundred and twenty-one children (60%) had delayed  speech. Sixty-six (33%) children were not enrolled in a formal schooling system.Seventy-five child participants (37%) had more than one NDD diagnosis. The most frequent diagnoses were ID and ASD. ASD and ID were comorbid in 49 participants (24% of the sample). In this sample, CD was primarily comorbid with ID, with only five children having CD a primary diagnosis. Twenty-two children (11%) were diagnosed with ADHD, which in all but two participants was comorbid with at least one other NDD, including ASD  and percentages of “very much” responses ranging from 11 to 55% . Response patterns did not differ substantially by primary diagnosis . Eleven items had small proportions of N/A responses, while Items 15  and 16 (“Blurts out answers”) had 51 (25%) and 61 (30%) N/A responses respectively.Table ρ, see Table M = 0.35, SD = 0.13) and between items on the Hyperactivity-Impulsivity subscale  respectively were only slightly larger than cross-subscale correlations , tentatively suggesting a weak distinction between the two constructs.Average polychoric correlation coefficients (p < 0.001) between the two latent factors. In general, the factors explained relatively high proportions of variance within their respective items. An exception was Items 15 , of which Hyperactivity-Impulsivity explained only 16% variance.We conducted a CFA specifying a model (Model 1) with two latent factors, Inattention and Hyperactivity-Impulsivity, each with nine indicators (SNAP-IV Items 1–9 and Items 10–18 respectively), as well as an additional path estimating the covariance between the two latent factors. Table r > absolute value of 0.1, see Figure S5, a residual correlation matrix for Model 1, and Table S1, both in the supplement). For example, Items 15  and 16  had a large positive residual correlation, suggesting that the specified model is not fully accounting for the covariance between these two items. MIs suggested possible “cross-loadings” of Items 3  and 8  with the Hyperactivity-Impulsivity factor. We compared Model 1 to a nested one-factor model (Model 1a) to determine if variance in the SNAP-IV indicators is better explained by a single latent factor, “ADHD”. However, the Model 1a’s fit was poorer than that of the original two-factor model, χ2  = 341.38, p < 0.001, TLI = 0.859, RMSEA = 0.087.An inspection of the modification indices and a residual correlation matrix revealed several correlated residuals (n = 164). However, Item 15’s factor loading remained relatively low (see Table N = 201) which slightly improved the model fit  may be a poor item in this sample. We considered that this item’s poor performance may have been due to its irrelevance for the non-verbal children in this sample. Hence, we ran a second model (Model 2) with a subset of participants who had acquired at least phrase speech n = 16. HoweverM = 1.81, SD = 0.65), new Hyperactivity-Impulsivity , and new Combined ADHD  subscales respectively. The distributions of subscale scores were approximately normal . Ordinal alpha coefficients were good for Inattention , Hyperactivity-Impulsivity , and Combined ADHD  respectively.Given evidence to support two correlated unidimensional factors, we calculated subscale means as well as internal consistency reliability statistics for the Inattention (n = 171), and Table S3 summarizes CFA findings for the CBCL/6–18 subscales. Inattention (SNAP-IV) and Attention Problems (CBCL/6–18) scores were moderately correlated . Likewise, there was a moderate, positive correlation between scores on the SNAP-IV Combined ADHD subscale and the CBCL/6–18 ADHD DSM-oriented subscale . Combined ADHD (SNAP-IV) scores were also significantly correlated with scores on the CBCL/6–18 Aggression Problems subscale , and the CBCL/6–18 Withdrawn/Depressed scores . The difference between the latter two correlation coefficients was significant , demonstrating good convergent and discriminant validity.Table S2 in the supplement presents a summary of CBCL/6–18 scores for the subset of the sample that completed the CBCL  had a small factor loading. In this sample, excessive talking was likely an indicator of a child’s verbal abilities, rather than a measure of hyperactive/impulsive behavior. From a statistical perspective, the large number of N/A responses for Item 15, likely due to the substantial proportion of the sample with limited expressive language, may also have contributed to the item’s weak factor loading. We were not able to use multiple imputation techniques to counteract the effects of missing data points, as the data were not missing at random. The large negative residual correlation between Items 15 and 16 may further account for the item’s poor performance in the model. The model did not sufficiently account for the observed variance shared between these two items. It is possible that similar response patterns for these items may have contributed to the unexpectedly large amount of shared variance .Items 3 (“Does not listen when spoken to directly”) and 8 (“Distracted by extraneous stimuli”) were also consistently problematic across all models. The cross-loadings suggest that these behaviours were not good measures of inattention in this sample. It is possible that parents of children with ASD endorsed these items due to impairments in social communication rather than difficulties with inattention and impulsivity/hyperactivity specifically. For example, the wording of the item “easily distracted by extraneous stimuli” may be interpreted as an impairment of social reciprocity, a core symptom of ASD  or inattentiveness, a core symptom of ADHD . Misinterpretation of item wordings may result in over-endorsement of items or “false positives”, perhaps explaining the higher-than-expected proportion of children in the sample who met the cutoffs for clinically significant ADHD-related behaviour. This finding highlights the importance of using clinical interview techniques alongside behavioural screening tools to disentangle symptoms that may present similarly, albeit with different underlying causes. Thorough questioning around the context of these ADHD-related behaviours will shed light on whether a symptom is indeed suggestive of ADHD or better explained by another NDD .Moderate, positive relationships between the SNAP-IV and CBCL/6–18 ADHD-related subscales supported the concurrent validity of the SNAP. It is worth noting that the SNAP-IV and CBCL/6–18 are similar in terms of item content and response format. Hence it is possible that the correlation coefficients may have been inflated by common method variance. The SNAP-IV also demonstrated convergent and discriminant validity when correlating ADHD-related behaviors with internalizing and externalizing behaviors respectively. Combined ADHD (SNAP-IV) was more strongly associated with aggressive  behaviour than with withdrawn/depressed  behavior, as measured by the CBCL/6–18. It is likely, though, that sample-related factors affected responses to the CBCL/6–18. For example, items such as “Teases a lot”, “Threatens people” (Aggressive Behavior), “Refuses to talk” and “Secretive” (Withdrawn/Depressed) were not applicable to children who were non-verbal. Perhaps the pre-school version of the CBCL, the CBCL/1.5-5 may have been a more appropriate measure of internalizing and externalizing behaviors, given the average developmental level of the children in this sample.Most children in the current sample with a confirmed ADHD diagnosis did not meet the SNAP-IV cutoff for clinically significant ADHD-related behaviours. The DSM-5 requires that symptoms of ADHD be consistent with an individual’s developmental level. The SNAP-IV was designed for use with typically developing  school-aged children without serious comorbid conditions (Swanson et al., The framework for the current study’s analyses was based on a previous study by Yerys and colleagues . AlthougThis study had three major limitations. Most of the children in this sample were chronologically young, were diagnosed with ASD or ID, and had some degree of cognitive or language delay. Although the sample was representative of children attending neurodevelopmental clinics in sub-Saharan Africa (Bakare & Munir, Clinicians working in low-resource settings in the Western Cape often administer the SNAP-IV to evaluate ADHD symptoms in children with NDDs. Data derived from the SNAP-IV have important implications for referral and the provision of interventions for comorbid ADHD. However, little is known about its validity in the NDD population, especially in children with moderate to severe ID and language delay as part of their phenotype. The primary aim of this study was to evaluate the validity of the SNAP-IV in a sample of children with NDDs using CFA techniques. The results of the CFA partially supported the validity of the SNAP-IV in this sample. However, there were limitations to its performance related to the clinical characteristics of the sample. The presence of developmental delay and specific cognitive deficits associated with one or more NDDs likely influenced ratings of target ADHD behaviors. Importantly, some SNAP-IV items may be unsuitable measures of hyperactivity-impulsivity in children without functional speech. Other items, especially those measuring inattention, may be less relevant to children not yet enrolled in a formal schooling system. Additional studies are needed to determine whether existing DSM-based ADHD rating scales are able to capture inattentive and hyperactive/impulsive behaviors in the NDD population with sufficient precision. Refinement and rewording some SNAP-IV items may be warranted to improve measurement accuracy.


Internet gaming disorder (IGD) refers to a pattern of persistent gambling behaviour or recurring gambling, on the Internet, with impaired control, increased priority, and continuation or escalation of gambling despite the occurrence of negative consequences. Currently, online gambling - more frequent among men - represents a very important, constantly growing economic activity. Since the COVID-19 pandemic and the need to conduct classes online, there has been an increase in the rate of IGD in children and youth (7-25 years old). Comorbidities, namely attention deficit hyperactivity disorder (ADHD), have been associated with a higher prevalence of IGD. They share core traits, such as impulsiveness, seeking immediate reward, deficient motivation, and hostility, which may translate into neuroimaging similarities.Non-systematic review of the literature about neuroimaging of IGD comorbid with ADHD.A search was conducted on PubMed and other databases, using the MeSH terms “Internet Addiction Disorder”, “Attention Deficit Disorder with Hyperactivity” and {“MRI” OR “fMRI” OR “functional connectivity” OR “neuroimaging OR neural alteration OR neuronal alteration OR neural change"}.IGD and ADHD have shared and disorder specific patterns of structural and functional abnormalities, particularly in reward function. For instance, IGD has been associated with lower putamen grey matter volume (GMV), while ADHD patients have lower GMV in the orbitofrontal cortex. Disorder-specific fMRI activation has been observed in the precuneus in IGD; in ADHD, there is special activation in the fusiform gyrus. Finally, shared structural and functional alterations between IGD and ADHD seem to converge in the prefrontal-striatum circuit, especially the anterior cingulate cortex.ADHD has been suggested as the most significant predictor of IGD in cross-sectional and prospective studies, however there is no study that clarifies their relationship. It is unclear whether IGD causes ADHD symptoms or whether a problem with gambling is a prodromal sign of the development of full ADHD. Studies revealing common neurobiological foundations between these disorders are pivotal to understand their basic mechanisms, while alerting to the necessity to screen for both pathologies when one is present. They may also point to an overlapping target (the reward circuit) for behavioural and pharmacological treatment.None Declared


Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity that affects a large number of young people in the world. The current treatments for children living with ADHD combine different approaches, such as pharmacological, behavioral, cognitive, and psychological treatment. However, the computer science research community has been working on developing non-pharmacological treatments based on novel technologies for dealing with ADHD. For instance, social robots are physically embodied agents with some autonomy and social interaction capabilities. Nowadays, these social robots are used in therapy sessions as a mediator between therapists and children living with ADHD. Another novel technology for dealing with ADHD is serious video games based on a brain–computer interface (BCI). These BCI video games can offer cognitive and neurofeedback training to children living with ADHD. This paper presents a systematic review of the current state of the art of these two technologies. As a result of this review, we identified the maturation level of systems based on these technologies and how they have been evaluated. Additionally, we have highlighted ethical and technological challenges that must be faced to improve these recently introduced technologies in healthcare. Attention deficit hyperactivity disorder (ADHD) is a psychiatric neurodevelopmental disorder characterized by inappropriate levels of inattention, impulsivity, and hyperactivity . ADHD isIn recent years, non-pharmacological treatments, such as behavioral interventions and cognitive training ,8,9, havIn contrast, BCI video games are serious games characterized by implementing a brain–computer interface (BCI) to interact with the video game rather than a keyboard or joystick. BCI video games are also characterized by including the neurofeedback technique. Thus, BCI video games can help maintain the ADHD children’s motivation and attention to the task and guide them to achieve a specified goal  by maintaining a specific “mental state.” Studies have shown that children living with ADHD had improved behavioral and cognitive variables after frequency training  ,22,23. Q1: How have social robots and BCI video games been evaluated?Q2: Are there still open challenges in social robots and BCI video games to improve their impact and benefit on children living with ADHD?Studies reported in the literature discuss several recent large-scale projects  . Papers The following inclusion criteria were considered: (1) papers that have been published in international journals or international congresses; (2) papers written in English; and (3) papers that described the conceptualization, development, testing, or evaluation of robots for use with people living with ADHD. Additionally, the following exclusion criteria were considered: (1) papers dealing exclusively with ADHD, (2) papers on robots for use with people without identified health conditions, and (3) review papers.The screening process was divided into two phases see . The firThe initial search for social robots in ADHD care was conducted with different combinations of word keys see , produciMoreover, we identified that the duration of these studies was very variable. For example, some studies were based on a few sessions  and involved fewer than ten participants in the trials e.g., ,37), whe, whe37])According to the results reported in the literature, we consider that systems based on social robots for dealing with ADHD are promising technology. However, the use of social robots for diagnosing or treating children living with ADHD is still in its first phases of development. Also, based on the number of participants involved and the duration of the trials reported in the literature, we have considered that the results reported in these studies can be regarded as preliminary results. Therefore, more trials with more significant populations are needed to validate such results.This second study used the same strategy implemented in the previous study of social robots. Therefore, papers were sought from four major online databases: PubMed to provide a medical perspective, Engineering Village and IEEE Xplore to provide an engineering perspective, and Scopus to provide a cross-disciplinary perspective. The search was limited to papers published from January 2010 to 15 June 2022. The keywords used for searching papers were “ADHD” AND “serious games” AND “BCI”, “ADHD” AND “brain-computer interface,” “ADHD” AND “neurofeedback” AND “BCI”, “ADHD” AND “videogame” AND “BCI”. These keywords were adapted according to the user interface offered by each online database. Publications were included if they were published in international journals or international congresses, were written in English, and described BCI video games’ conceptualization, development, testing, or evaluation for people living with ADHD. On the other hand, publications on ADHD exclusively and BCI video games for use with people without identified health conditions, as well as review papers, were excluded.The screening process was divided into two phases see . The firAfter the screening and eligibility criteria were applied, 19 papers from the initial 103 candidates remained for review. The selected papers were utilized to investigate the features of systems based on BCI video games for dealing with ADHD. Afterward, the characteristics of each system, such as the advantages and disadvantages, experimental environment, and applications, were discussed. A total of 14 of the 19 projects described in Three types of trials were identified in the effectiveness studies: (i) trials that just involved an experimental group formed by children living with ADHD ,51, (ii)This systematic review of social robots and BCI video games was conducted from an interdisciplinary approach. Therefore, the results reported in this paper focused on highlighting relevant information in engineering and healthcare areas. For instance, Concerning the robot-based applications, we identified that the most common robots used in these applications are humanoid robots such as Nao, Silbot, Robotis Bioloid, Pepper, Sanbot Elf, and Ifbot. We also highlighted input signals and environments used in such applications . For this last characteristic, we identified that the real environment has been used most in these applications. Regarding BCI-based video game applications, we identified that the environments more used (listed in order of preference) are 3D, 2D, virtual reality, and mixed reality environments.Although most papers in the literature were published in engineering journals or proceedings, we could identify relevant information about these two technologies for the healthcare area. This systematic review lets us answer our two research questions. Most systems reported in the literature are still in the developing and testing phases, and just two applications named Focus Pocus  and EndeOn the other hand, BCI video game systems have been evaluated in their functionality and usability by including healthy participants ,45,47,48Finally, we identified a set of challenges that developers must face for developing flexible systems that can be reconfigured and customized according to the behavior and deficit of each individual. These challenges are described in detail in the following section.This section describes the challenges identified during the systematic literature review for developing computational systems based on social robots or BCI video games to treat ADHD. These challenges are related to the system’s target, the design of a general system for dealing with all types of ADHD, the problem of customizing training exercises, and some ethical issues, such as making sure not to compromise the children’s social–emotional development.Diagnosing ADHD. The symptoms presented by a child living with ADHD can differ from those of another child living with ADHD. This is because there are three different subtypes of ADHD : predomiCustomizing cognitive training exercises. Customizing cognitive training exercises according to the characteristics of each person is another hard challenge for engineers and researchers involved in designing general systems based on robots and BCI video games for dealing with ADHD ,24,32. TCapturing and maintaining attention. Engineers and researchers agree that one complex challenge when designing a cognitive training system is creating one capable of capturing and maintaining children’s attention in each cognitive training session ,57. The Long-term or longitudinal studies. Most systems reported in the literature indicate that they can be useful tools for diagnosing or treating ADHD, according to their objective (e.g., ,25,27,51Certification. The little evidence reported in the literature suggests that systems based on social robots or BCI video games with neurofeedback have great potential to improve the attention of children living with ADHD (e.g., ,22,25,45Avoiding physical harm. Some characteristics of robots can represent limitations and challenges for designing a safe human–robot interaction. Therefore, researchers and engineers should carefully choose the type of robot to use in a cognitive training system and define how these robots can interact with people involved in a cognitive training session to avoid potential physical harm. A good example of this issue can be observed in the system proposed by Cervantes et al. , called Making sure not to compromise social–emotional development. Few researchers are worried about how social robots can compromise children’s social–emotional development . ChildreThe systematic review conducted in this paper allows us to know the current state of systems based on social robots and BCI video games for dealing with ADHD. We found 36 systems; 17 are based on social robots, 18 are based on BCI video games, and 1 is based on serious video games without including a BCI. According to the functionality of these systems, we have classified them into three types: systems for diagnosis, systems for cognitive training, and systems for studying the human brain. Of all the systems identified in the literature, we observed that 2 systems focused on supporting the diagnosis of ADHD in people, 32 systems focused on supporting cognitive or behavioral rehabilitation therapies, and 2 systems focused on supporting the study of brain areas of people living with ADHD. The results reported in the literature have shown the potential of these systems for supporting non-pharmacological treatments of people living with ADHD or detecting ADHD in people. However, the research community focused on these technologies agrees that more studies must be conducted in order to identify both the advantages and disadvantages of using these systems in real clinical environments. For instance, some studies have reported positive effects on children living with ADHD when they interact with these technologies. Some of these positive effects are a quick acceptance of technology, better engagement during therapy sessions, positive experiences using these novel technological systems, and relevant improvement in the cognitive functions of people who have been trained through these systems. Nevertheless, researchers have also found that a small group of children has had bad experiences or a kind of frustration when using these new technologies. These negative findings may indicate that not all people living with ADHD can be candidates for using these technologies. As a result of this review, we identified a set of critical challenges that developers must face when developing flexible systems that can be reconfigured and customized according to the characteristics of each individual, in order to offer the best experience to the patient. Finally, most systems reported in the literature are still developing and testing processes. We hope this study can help guide the development of future systems based on social robots or BCI video games for children living with ADHD.


With the widespread use of social media, people share their real-time thoughts and feelings via interactions on these platforms, including those revolving around mental health problems. This can provide a new opportunity for researchers to collect health-related data to study and analyze mental disorders. However, as one of the most common mental disorders, there are few studies regarding the manifestations of attention-deficit/hyperactivity disorder (ADHD) on social media.This study aims to examine and identify the different behavioral patterns and interactions of users with ADHD on Twitter through the text content and metadata of their posted tweets.First, we built 2 data sets: an ADHD user data set containing 3135 users who explicitly reported having ADHD on Twitter and a control data set made up of 3223 randomly selected Twitter users without ADHD. All historical tweets of users in both data sets were collected. We applied mixed methods in this study. We performed Top2Vec topic modeling to extract topics frequently mentioned by users with ADHD and those without ADHD and used thematic analysis to further compare the differences in contents that were discussed by the 2 groups under these topics. We used a distillBERT sentiment analysis model to calculate the sentiment scores for the emotion categories and compared the sentiment intensity and frequency. Finally, we extracted users’ posting time, tweet categories, and the number of followers and followings from the metadata of tweets and compared the statistical distribution of these features between ADHD and non-ADHD groups.P<.001). Users with ADHD were more sensitive to emotions and felt more intense feelings of nervousness, sadness, confusion, anger, and amusement . As for the posting characteristics, compared with controls, users with ADHD were more active in posting tweets (P=.04), especially at night between midnight and 6 AM (P<.001); posting more tweets with original content (P<.001); and following fewer people on Twitter (P<.001).In contrast to the control group of the non-ADHD data set, users with ADHD tweeted about the inability to concentrate and manage time, sleep disturbance, and drug abuse. Users with ADHD felt confusion and annoyance more frequently, while they felt less excitement, caring, and curiosity (all This study revealed how users with ADHD behave and interact differently on Twitter compared with those without ADHD. On the basis of these differences, researchers, psychiatrists, and clinicians can use Twitter as a potentially powerful platform to monitor and study people with ADHD, provide additional health care support to them, improve the diagnostic criteria of ADHD, and design complementary tools for automatic ADHD detection. Mental health has become a major issue that demands continuous attention, as it causes individuals to have other difficulties . The CenThe diagnosis of ADHD is complex because of the blurred boundary between the affected and unaffected status. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition DSM-5) provides guidelines for diagnostic standards and identifies 3 main patterns that people with ADHD present: inattention, hyperactivity, and impulsivity  provides. AlthougSocial media platforms such as Twitter are gaining more popularity today. As of the fourth quarter of 2020, Twitter’s monetizable daily active users reached 192 million , and as Some previous studies have already investigated the representation of mental illness on Twitter, including depression ,14, postThis study aimed to uncover the patterns of behaviors and interactions of users with ADHD on Twitter. Specifically, we investigated the difference between Twitter users with ADHD and Twitter users without ADHD based on the following three aspects: (1) the pattern of talking about different topics; (2) the pattern of expressing emotions; and (3) the interactions of time, tweet type, followers, and followings.To create the ADHD data set, we used the Twitter application programming interface to search for self-identified users who tweeted in English. A user was identified as a patient with ADHD based on the following statements in their original tweets (not retweets and quoted tweets): “I have/am ADHD” and “I was diagnosed with ADHD.” Approximately 5000 users were identified and subsequently filtered through regular expressions to remove users who suspected they have ADHD or were in the process of being diagnosed. Finally, the 3 researchers independently annotated and verified the authenticity of all self-proclaimed users by manually reviewing the context of the tweets that contained the self-reporting phrase, and those with mutual agreement were retained (Cohen κ=1.0). From the remaining 3135 users with ADHD, we collected all historical tweets and returned 8.3 million  tweets from April 28, 2009, to March 24, 2022, consisting of original tweets, replies, retweets, and quoted tweets.A control group of 3223 (assumed) neurotypical (non-ADHD) users was formed for comparison. We randomly selected 5000 Twitter users who tweeted in real-time streaming without ADHD-related terms. After excluding commercial users’ and organizations’ accounts and verifying that these users were not in the ADHD data set, 3223 users remained. We cannot be certain that all these users are not affected by ADHD even if they did not report it in their tweets. It is likely that 3% to 9% of these users without ADHD have ADHD, given the rate of ADHD in the population . This coOur final data contain 2 data sets: the ADHD data set  and the non-ADHD data set . Tweets in both data sets contained text content, posted time, and language and were classified into 1 of 4 categories: original tweets, retweeted tweets, replied tweets, and quoted tweets. Original tweets are content entirely generated by users without referencing other tweets [For text-based analysis, only the original tweets were used . We preprocessed the text in 3 stages: data cleaning, stop words removal, and tokenization and lemmatization.Data cleaning: first, we converted all text to lower case for easier handling. Digits, URLs, and punctuation were removed. Special characters that frequently appear on social media, such as hashtags (#), emojis, and reply signs (@reply), were also removed. This resulted in some tweets being empty or having ≤3 words, which were discarded.Stop word removal: in this step, we customized a list of stop words comprising pronouns, conjunctions, and abbreviated words based on the domain knowledge of ADHD and Twitter. As these words carry little valuable information, eliminating them helps expose substantial clues in the remaining texts.Tokenization and lemmatization: to reduce the repeated character sequence and better adapt the language used on Twitter, we applied the Natural Language Toolkit’s (NLTK) TweetTokenizer  to splitWe aimed to detect and analyze the topics that users with ADHD and those without ADHD frequently interact with, so topic modeling was applied. Latent Dirichlet Allocation and Latent Semantic Analysis are common methods for topic modeling, but they have some limitations: (1) the number of topics is fixed, and it is difficult to find an optimal number; (2) the models cannot capture correlations between topics; and (3) some uninformative words were clustered as topics. To overcome these limitations, we trained a Top2Vec model . Owing tWe created a list of ADHD-related keywords , such asTo compare how different the sentiments expressed on Twitter by users with ADHD and those without ADHD are, we calculated sentiment scores using a pretrained DistillBERT model  from HugThe DistillBERT sentiment model generates a separate score for each emotion category, and the sum of the scores of all emotion categories equals 1. The rated text was later classified as the emotion category with the highest score. We classified the emotion category for each tweet from every user with an emotion score. Similar to other studies ,35, we uWe extracted several behavioral features related to Twitter interactions to determine the differences between the 2 data sets. The profiles of Twitter users include valuable metadata for us to explore users’ behavioral patterns such as the number of followers, followings, self-defined location, and many others. We are also interested in the preferred activity time of ADHD users and how they interact with Twitter by posting their original content or retweeting others’ opinions. All extracted features are listed in When extracting the features of the posting hour, we first inferred the geolocation for all users, because Twitter only provides a Unix Timestamp. We predict users’ countries and states (if they are from the United States) from their self-defined locations or directly from the coordinates provided by tweets, exclude unpredictable users, and convert the timestamp of a tweet to their exact local time.U test was used. Conventionally, the threshold for differentiating significant results from nonsignificant results is α=.05.Multiple statistical tests were performed to determine the significant differences in results from previous analyses. For sentiment classification and most behavioral features, the results of ADHD and non-ADHD data sets are independent; they have an approximately normal distribution, and their variances are homogeneous, so ANOVA was applied. Some behavioral features failed to satisfy the aforementioned conditions, in which case the nonparametric Mann-Whitney Conducting ADHD-related research using Twitter data raises important ethical and legal implications. In this study, we used the official Twitter application programming interface to collect only publicly available tweets that everyone could access without previous approval. According to Twitter’s privacy policy , when usThe Top2Vec model automatically detected 1579 ADHD user clusters and 1197 non-ADHD user clusters. Clusters represent topics that users might discuss, and there may be associations and similarities between these topics. We manually checked the 10 clusters with the most users, resulting in 4 topics as follows that substantially differentiated between ADHD-Twitter and non-ADHD–Twitter data sets: concentration ability, time management, sleep issues, and drug abuse. Concentration ability defines whether people can focus on tasks for a prolonged time. We searched keywords such as “focus” and “tasks” to find what users discussed about this ability in both data sets. The ADHD users frequently tweeted that ADHD makes them unable to concentrate on their tasks, they find starting tasks difficult, they are easily distracted, and they procrastinate to finish. Example tweets are as follow:I actually cannot focus on a single thing right now pls help.I cannot focus on work.I don’t know about you but I need at least 6 hours to do a task that will take 20 minutes.However, some ADHD users mentioned “hyperfixation” and “hyperfocus,” indicating they can also find themselves on the other extreme side of the focus spectrum:I yell, hyperfocusing on a videogame for 8 hours straight....how passionate and excited I could be about my favorite things...that I was just ADH hyperfixation baby!Users without ADHD referred to this topic more broadly and positively. Unlike users with ADHD, who mentioned their inability to focus, users without ADHD often described the activities they paid attention to and how they focused on various activities:I love teruyama...focusing my content on YT will be the best thing to do....Its wild as hell how my two games I’ve been focusing on...The live music was amazing, I was following the flow of guitar and paying no attention to anyone else. Must listen live at...Some tweet clusters indicated that many users with ADHD struggled with time management. ...Prioritization and time management are still my biggest weaknesses and I’m trying my best to fix it....It’s fxxking hard to make something a priority.Users with ADHD often felt unmotivated to perform tasks outside of their interests. Moreover, they felt overwhelmed by tasks that disabled them from prioritizing tasks, leading to procrastination and poor time management:...I get overwhelmed with information I can’t figure out how to organize and it just swims around in my head for days and days until everything clicks into place....I am so unmotivated when it comes to cleaning up and do not enjoy it. It is hard because I am not an organized person.Regarding time management, users without ADHD mentioned work-oriented or political tasks and objects. On the other hand, ADHD users’ perspective on time management appeared more negative using words such as dysfunction, disability, and weakness often in relation to productivity and completing mundane tasks such as cleaning or organizing. The whole word list correlated to this topic is shown in Sleeping issues are some of the most impactful problems today. People with insomnia struggle to sleep at night or constantly feel sleepy during the daytime, negatively affecting their quality of life and exacerbating mental health problems ,39. As s...I can’t sleep because I have restless hands wtf I want to cry n scream....I have insomnia due to my ADHD, so I take 3 mg of melatonin supplements almost every night...The lack of sleep caused users with ADHD to feel exhausted, frustrated, anxious, and unable to focus:...I’m so tired, I haven’t really slept in a week, and I think my brain is immune to melatonin....Insomnia I can cope with once in a while. A full week of it and I am genuinely starting to think death would be preferable.On the other hand, users without ADHD seemed to have healthier, regulated sleep patterns. The topic of sleep appears in tweets about their routine, the use of alarms and snooze buttons, how they feel, and what they do when they wake up. The whole word list correlated to this topic is shown in As shown in ...I’m a drug addict you’re my favorite poison....When is the right time to tell them that I’m a drug addict.Adderall is the primary treatment medicine for patients with ADHD, which gives the feeling of euphoria and confidence to help people with ADHD get focused and concentrated, and some patients even use it to lose weight owing to its side effects of appetite suppression . Therefo...Idk what I am going to do if my doctor can’t prescribe me more of my meds. The fact that I NEED this medication just to be able to function as a somewhat normal human being, but they treat me like a freaking drug addict because dumbasses took advantage.For some users with ADHD, when they no longer feel satisfied with the effects Adderall has on them, they turn to other drugs, such as nicotine, weed, and methamphetamine (meth), to obtain the desired effect:...Postmating a vape for lunch is the most adderall thing I’ve ever done....Adderall isn’t working anymore just give me actual meth.In the non-ADHD data set, some users enjoyed taking drugs, mainly weed and nicotine. However, other users without ADHD discussed the harmful effects of drug use and smoking. We selected several common drugs, such as meth, weed, nicotine, heroin, and cocaine, and counted their occurrences in each group of tweets, as shown in P<.001. The average frequencies of the 5 emotion categories are shown in In this study, frequency refers to how often people experience emotions. For each user, we calculated the frequency of each emotion based on the number of tweets categorized by emotion as a percentage of the total number of tweets. Of the 27 emotion categories based on the model, the 5 most common emotions that users with ADHD expressed were confusion , curiosity , excitement , caring , and annoyance . Compared with users without ADHD, users with ADHD had a higher average emotion frequency for confusion and annoyance and a lower average frequency for curiosity, excitement, and caring. All 5 emotions were expressed significantly differently by users with ADHD and users without ADHD with P<.001). For each emotion category, a higher average score from the sentiment model represents more intense feelings of that emotion. The 5 emotions with the highest average scores in the ADHD and non-ADHD data sets were nervousness , sadness , confusion , anger , and amusement . All 5 emotion categories were expressed significantly differently (P<.001). The number of tweets posted per day throughout the week is compared in P=.04). Overall, users with ADHD were more active and posted more tweets daily throughout the week.We calculated the number of tweets posted per hour as the percentage of all tweets for each user in the ADHD and non-ADHD data sets. P<.001) and retweeted tweet ratio (P<.001) by applying the Mann-Whitney U test.Tweets were classified into 4 types: original, retweeted, replied, and quoted tweets. We compared the ratios of original tweets and the ratios of retweeted tweets in the ADHD and non-ADHD data sets and examined the average values and distributions of these 2 ratios. The results are shown in P<.001). The average number of followings for users with ADHD was 804, whereas the average number of followings for users without ADHD was 1323. The difference in the number of followers was not statistically significant (P=.06). There was a significant difference in the number of followings between the 2 data sets (In this study, we examined the web-based behaviors and interactions of self-reported users with ADHD with a control group of users without ADHD on Twitter. The results suggested that users with ADHD described themselves differently under the topics of concentration ability, time management, sleep issues, and drug abuse compared with users without ADHD. Furthermore, the frequency and intensity of various emotions expressed differed notably. We also found a considerable difference in tweet activities between users with ADHD and users without ADHD in terms of the number of tweets posted, the type of tweets posted, and the number of followings. Given the statistic that 93.4% of Twitter users are aged ≥18 years , these fThe findings of this study corroborate previous findings regarding the symptoms of adult ADHD. Regarding concentration ability and time management, users with ADHD often mentioned difficulty sustaining attention, getting distracted easily, and failing to prioritize tasks and activities, which the DSM-5 lists as typical symptoms of inattention , was fouThe sentiment analysis results showed that users with ADHD expressed emotions differently than users without ADHD. Users with ADHD felt negative emotions, such as confusion and annoyance, more frequently than users without ADHD and maintained more intense feelings overall for all emotions. This could be due to emotional dysregulation, one of the common symptoms reported by adult patients with ADHD . One comAnother finding was that users with ADHD felt excitement less often than those without ADHD, which indicates a boredom proneness among users with ADHD. Boredom proneness has been considered highly correlated with impaired attention -55. AdulThis study found that users with ADHD tweeted more at night between midnight and 6 AM, whereas they tweeted less during the daytime compared with users without ADHD. This finding may correspond to the sleep issues, restlessness, and insomnia uncovered in the topic modeling analysis of users with ADHD. According to the posting pattern by day throughout the week, the daily posting activity trends of users with ADHD and users without ADHD are vastly similar, but users with ADHD kept more active and posted more tweets each day. Users with ADHD posted more original tweets than users without ADHD. These 2 patterns may be related to the hyperactivity and impulsivity symptoms of ADHD. Patients with ADHD usually talk excessively , leadingThere are several limitations to our study. First, we selected users with ADHD using specific keywords related to their self-reported patient experiences. Without proof of clinical assessment, we cannot determine whether users were professionally diagnosed with ADHD and thus rely on self-reporting. In the same manner, a potential uncontrolled factor in which users who do not explicitly self-report having ADHD may have resulted in the non-ADHD data set including users with ADHD. Second, although the results provided evidence that users with ADHD manifested different patterns compared with users without ADHD, such as affected by sleep disturbance and drug abuse, we cannot ignore that they may be caused by comorbid disorders that co-occur with ADHD, such as depression and anxiety disorders. Third, ADHD is complex, and its diagnosis is subjective and lacks standardization . The intThis study provides a preliminary examination of the behavioral and interaction patterns of users with ADHD on Twitter. Twitter can be a prospective and advantageous tool to help improve diagnostic criteria and detect users with ADHD based on their historical content on social media. It is necessary to further compare the commonalities and differences between users with ADHD on social media and medically diagnosed patients with ADHD to integrate social media and clinical assessments to screen and diagnose ADHD. Considering that the symptoms of ADHD may vary with gender, age, and other characteristics, a future study is needed to extract the demographics of users with ADHD on Twitter to observe the differences and changes in different subgroups. In addition, combining image and emoji analysis in tweets with text analysis may provide multidimensional and further insights into the behavioral and interaction patterns of users with ADHD. Given the impact that social media can have on people, it is also interesting to explore the positive and negative effects of social media on users with ADHD offline, and the relationship between social media and symptoms of ADHD.In summary, with the popularity of social media, an increasing number of people are sharing their thoughts, opinions, and feelings instantaneously on platforms such as Twitter, allowing for research on the reflection of mental health. By analyzing the text content, sentiment, and tweet interactions of users who disclosed their ADHD, our study found how users with ADHD express themselves on social media. The findings of this study corroborate the existing research on ADHD and provide new insights for further research. Our findings can help gain a deeper understanding of the thoughts, feelings, and behavior behind ADHD symptoms, and suggest that Twitter could serve as a powerful platform for improving ADHD diagnostic criteria, automating detection, and monitoring patients with ADHD.


Prospective memory (PM) is essential in everyday life because it concerns the ability to remember to perform an intended action in the future. Individuals diagnosed with attention deficit hyperactivity disorder (ADHD) often show poor performance in PM. Because age can be confounding, we decided to test PM in ADHD patients (children and adults) and healthy controls (children and adults). We examined 22 children  and 35 adults  with ADHD, in addition to 92 children  and 95 adults  as healthy controls. Each participant originally wore an actigraph around the non-dominant wrist and was requested to push the event-marker at get-up time. To assess the efficiency of PM performance, we calculated the time elapsing between the end of sleep in the morning and the pushing of the event-marker button. The results showed lower PM performance in ADHD participants, regardless of age. However, the differences between ADHD and control groups were more evident in the children group. Our data seem to confirm that PM efficiency is compromised in individuals diagnosed with ADHD regardless of age, and agree with the idea of considering the PM deficit as a neuropsychological marker of ADHD. Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of motor hyperactivity, impulsivity, and inattention. The prevalence of ADHD in childhood is high, i.e., around 5% , with a It has been proposed that children with ADHD have difficulty maintaining an appropriate problem-solving set to attain future goals, reflecting an executive function deficit ,6. This A debated issue in prospective memory studies is related to the ecological validity of the experimental data ,14. In tAnother important aspect is linked to the development of prospective memory competencies. In children, PM assumes great importance in everyday life, for example, in planning school learning, social, or free time activities. Therefore, it is surprising that, to date, PM skills in children with typical development and with ADHD have been investigated in so few ecologically valid studies . In thisA recent study , which aFor this reason, we chose to reanalyze previously collected data aiming to better explore the feature of activity-based PM performance in a naturalistic setting in a population of ADHD patients (children and adults). We aimed to check whether ADHD and age can moderate performance in an activity-based PM task. We chose to investigate PM through a naturalistic activity-based task, originally introduced by our research group ,20 and lIn general, cognitive performances after waking up in the morning can be affected by the phenomenon of sleep inertia (SI). SI refers to a complex psychophysiological phenomenon observed after morning awakening that can be described as the gradual recovery of waking-like status after a night of sleep . SI can 1To verify any difference between ADHD patients and healthy controls;2To investigate if and how activity-based PM performance changes with age;3To investigate a possible relationship between activity-based PM performance and motor sleep inertia.Therefore, the aims of the work are:This is an innovative application of actigraphy in basic and clinical research. To the best of our knowledge, there are no studies examining the activity-based PM performance, in an ecological setting through actigraphy, in children and adult ADHD patients, nor that correlate it with motor sleep inertia. However, we may expect a lower activity-based PM performance in clinical samples compared to healthy controls, a poorer PM performance in children compared to adults as well as an association between lower motor sleep inertia (in terms of higher motor activity at the morning awakening) and better PM performance .Participants belonged to the following groups: (1) childhood attention deficit hyperactivity disorder (C-ADHD): 22 patients, 4 females and 18 males, mean age (SD) = 8.77 (1.77), range 5–12; (2) adult ADHD (A-ADHD): 35 patients, 14 females and 21 males, mean age (SD) = 37.29 (12.23), range 19–59; (3) children healthy controls (C-HC): 92 participants, 57 females and 35 males, mean age (SD) = 10.13 (0.42), range 9–11; (4) adult healthy controls (A-HC): 95 participants, 57 females and 38 males, mean age (SD) = 27.93 (14.35), range 31–50. C-ADHD and C-HC came from the study by Tonetti et al. , A-ADHD Adult participants gave written informed consent prior to inclusion in the original studies; if underage, written informed consent was provided by parents. Original studies were approved by the Ethics Committee in charge. As regards the children sample, the study was approved by the Bioethics Committee of the University of Bologna . With reference to the comparison between adults with ADHD and adult healthy controls, the study was approved by the Ethics Committee of the Health Board of Alto Adige  and the Bioethics Committee of the University of Bologna .All participants used the same model of actigraph, the Actiwatch AW-64 . This device is equipped with an accelerometer, which is sensitive to movement by means of a piezoelectric accelerometer presenting a sensitivity of ≥0.05 g. Filters were set to 3–11 Hz, with a sampling frequency equal to 32 Hz.Version 5.32 of the Actiwatch Activity and Sleep Analysis software  was used to extract the raw motor activity counts, minute-by-minute, considering the minutes close to wake-up time . To avoid the possible confounding effect of weekends, for each participant, only work/school days were considered in the analyses.All participants were required to remember to push the event-marker button at get-up time. With reference to the activity-based PM performance, in the current study, each actigraphic recording was visually inspected nightly to verify whether participants remembered to press the event-marker button at wake-up, and we considered only the awakenings in which participants carried out the required task within the first 60 min after sleep end. In those cases, we took note of the time elapsed between sleep end and the pushing of the event-marker button.To compare the frequency distribution of the time elapsed between wake-up time and the pressing of the event-marker button (Response Time) between ADHD patients and healthy controls, separately for children and adults, we performed an independent-samples Mann–Whitney U test. Moreover, to simultaneously compare the four groups, we performed an independent-samples Kruskal–Wallis test; in the case of a significant effect, pairwise comparisons of group were carried out.To better detect possible qualitative differences between the groups, we combined the participants who pressed the event-marker button within the first two minutes after wake-up (compliant with the task performances) versus all the other cases (late performances). Then, we analyzed the distribution of compliant and late performances in ADHD and healthy control groups by means of the chi-square test.To analyze the possible relationship between motor sleep inertia and activity-based PM performance, we used Functional Linear Modeling (FLM) . This stp < 0.05 for all analyses performed.The significant level was set at C-HC = 448, NC-ADHD = 124) = 32,470.5, z = 2.90, p = 0.004)  = 10663, z = 2.16, p = 0.03)  are significantly denser (skewed to) near the wake-up time in comparison to C-ADHD (mean rank = 324.36) (U (N= 0.004) A. The sa = 0.03) B.23 = 31.81; p < 0.001)  . Perform< 0.001) , it appe21 = 6.16; p = 0.01) .When we grouped participants according to the compliant performances (event-marker button pressed within 2 min of wake-up) and late performances (event-marker button pressed over 2 min after wake-up), we found a significantly different distribution between the C-ADHD (compliant performances 29%) and C-HC group (compliant performances 41.3%) (χ = 0.01) a, but no = 0.01) b  to examine potential differences in activity-based PM between ADHD and HC; (2) to investigate whether age can modulate the PM performance; (3) to explore the potential association between PM performance and motor sleep inertia. In general terms, we expected a poorer PM performance in ADHD than HC, a lower PM performance in children compared to adults, and lastly an association between higher degree of motor sleep inertia and lower PM performance.As regards the first aim of the research, the results show that a significant performance difference in activity-based PM was present in the comparison between ADHD groups and control groups regardless of age, with ADHD patients performing worse than the control group. However, while further analyses confirmed the differences in the children’s groups, in the adult sample, the differences observed between ADHD and control groups no longer reached statistical significance. It is as if growth attenuated the performance gap in activity-based PM that characterizes individuals diagnosed with ADHD.Our second aim was precisely to try to understand if and to what extent growth can modulate performance in activity-based PM tasks. As regards this aim, we observed an increase in activity-based PM performance in the adult groups compared to the children’s groups in both control and ADHD patients . It woulThe third aim was to analyze a possible relationship between activity-based PM performance and motor sleep inertia. Basically, we expected that low motor sleep inertia, in terms of higher motor activity after morning awakening, was related to a better PM performance, in terms of a reduced time interval between T0 and T1. A significant result emerged only when analyzing PM performance of the C-HC group. In particular, participants who performed the activity-based prospective task in the first two minutes after awakening (excellent performances) showed high motor activation within the first ten minutes post awakening. It is possible to hypothesize that in healthy children, greater motor activation would result in a faster dissipation of sleep inertia and consequently a faster recovery of the prospective intention planned the night before. It is interesting that this result was not replicated in the C-ADHD group. A potential explanation of this pattern of results is that motor activity in the C-ADHD group is aspecific/dysfunctional and intrinsically related to the disorder. For this reason, we did not observe an association between motor activity and cognitive performance in this group; on the contrary, this association was found in the C-HC group, in line with previous literature summarized in the systematic review by Zeng and colleagues . OverallA general reflection that concerns all four experimental groups considered is that the percentages of excellent performances in general are rather low, despite the fact that the activity-based task is considered the easiest among the prospective tasks. This result could be interpreted by considering the condition in which the prospective task is required. The intention was planned before going to sleep, and the execution had to be carried out immediately upon awakening. Upon awakening, a return to typical cortical wakefulness activation is held back by the Sleep Inertia phenomenon, which involves reactivation of different cortical regions at different times ,31. The Finally, one might wonder if the processes activating the compliant and late performances are the same. In other words, how long can the intention of the activity-based prospective remain active? In the psychology of memory, there is a phenomenon known as the Zeigarnik effect , named fOur study involved a task that had to be performed after spontaneous awakening, and the execution was monitored by actigraphic recording. However, although the requirement was very precise and underlined as being extremely important, the task was performed, in a rather high percentage of cases, after a considerable delay, so an activity task performed after two minutes and one performed later after wake-up time cannot be considered in the same way. The intention was fulfilled in both cases, but the underlying cognitive mechanism could be different. The task performed late demonstrates that the intentional component of the prospective memory probably continues to have a privileged status of activation .This cognitive condition assumes an important adaptive value, suggesting the hypothesis of different mechanisms underlying the correct execution of the tasks planned, especially in PM where there is no external cue and self-initiated processing is required (time-based and activity-based PM). In the case of a compliant performance (within two minutes), we can say that the participant remembered to execute the planned intention and therefore the prospective task was successful. When the action (pressing the actigraph) was performed later after waking up, the activity cue association link was probably no longer present, but the suspended intention facilitates its recovery, even if it is delayed. Therefore, the performance could be interpreted as a Zeigarnik effect associated with the intrinsic motivation that facilitated a self-generated prospective pop-up.Among the limitations of the current study, the assessment of just one type of PM, namely activity-based PM, must be mentioned. Furthermore, due to the retrospective nature of this work, the potential use by participants of external aids, in order to remember to push the event-marker button at the get-up time, was not checked. Moreover, for the same reason, the age and gender variables are not perfectly balanced between the samples of ADHD patients and healthy controls. Finally, the threshold for discriminating between compliant and late performance is partially arbitrary.