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Disorder, Attention Deficit-Hyperactivity

Disorder, Attention Deficit-Hyperactivity: A persistent pattern of inattention and/or hyperactivity-impulsivity that can interfere with development, functioning, and quality of life.
Symptoms may include difficulty sustaining attention, restlessness, impulsive actions, and problems with organization.
Onset is typically in childhood and can continue into adulthood.
Accurate diagnosis and evidence-based management are crucial for improving outcomes for individuals affected by this neurodevelopmental disoder.

Most cited protocols related to «Disorder, Attention Deficit-Hyperactivity»

1
Autism Research Protocol Database
Cited 163 times
Initial contributions were sought from members of the ADHD-200 Consortium
conducting autism research (Kennedy Krieger Institute, NYU Langone Medical
Center, Oregon Health & Science University, University of Pittsburgh).
Invitations to participate were extended based on personal communications,
recent publications and conference presentations. All investigators willing and
able to openly share previously collected awake R-fMRI data from individuals
with ASD and age- and sex-group matched TC were included. Institutional Review
Board (IRB) approval to participate, or explicit waiver to provide fully
anonymized data, was required prior to data contribution.
All contributions were based on studies approved by local IRBs, and data
were fully anonymized (removing all 18 HIPAA protected health information
identifiers, and face information from structural images). All data distributed
were visually inspected prior to release.
Di Martino A., Yan C.G., Li Q., Denio E., Castellanos F.X., Alaerts K., Anderson J.S., Assaf M., Bookheimer S.Y., Dapretto M., Deen B., Delmonte S., Dinstein I., Ertl-Wagner B., Fair D.A., Gallagher L., Kennedy D.P., Keown C.L., Keysers C., Lainhart J.E., Lord C., Luna B., Menon V., Minshew N., Monk C.S., Mueller S., Müller R.A., Nebel M.B., Nigg J.T., O’Hearn K., Pelphrey K.A., Peltier S.J., Rudie J.D., Sunaert S., Thioux M., Tyszka J.M., Uddin L.Q., Verhoeven J.S., Wenderoth N., Wiggins J.L., Mostofsky S.H, & Milham M.P. (2013). The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism. Molecular psychiatry, 19(6), 659-667.
Publication 2013
Autistic Disorder Conferences Disorder, Attention Deficit-Hyperactivity Ethics Committees, Research Face fMRI
2
ADOS Severity Metrics Calibration
Cited 160 times
For comparability, the same sample used to standardize the overall ADOS total (see Gotham et al. 2009 (link)) was also employed to calibrate separate severity metrics for the Social Affect (SA) and Restricted, Repetitive Behavior (RRB) domains. Briefly, this included data from 1,415 individuals ranging in age from 2 to 16 years. With repeated assessments for 25 % of the sample, data from 2,195 ADOSes with contemporaneous best estimate clinical diagnoses were available for analysis. Of these assessments, 1,786 cases were given an autism spectrum disorder diagnosis (ASD; 1,187 Autistic Disorder, 599 Other-ASD) and 409 had a Non-ASD diagnosis. Non-ASD diagnoses included language disorders (27 %), nonspecific intellectual disability (20 %), Down syndrome (14 %), oppositional defiant disorder or ADD/ADHD (13 %), mood or anxiety disorders (8 %), Fetal Alcohol Spectrum Disorders (7 %), other genetic or physical disabilities, such as Fragile X or mild cerebral palsy (6 %) and early developmental delays (5 %).
Individuals were consecutive referrals to specialty clinics in Ann Arbor, Michigan and Chicago, Illinois, and participants in research studies conducted through the University of North Carolina—Chapel Hill, University of Chicago, and University of Michigan. All participants provided informed consent and all procedures related to this project were approved by institutional review boards at the University of Chicago or University of Michigan. Sample characteristics are provided in Table 1.
Hus V., Gotham K, & Lord C. (2014). Standardizing ADOS Domain Scores: Separating Severity of Social Affect and Restricted and Repetitive Behaviors. Journal of autism and developmental disorders, 44(10), 2400-2412.
Publication 2012
Adenosine Anxiety Disorders Autistic Disorder Cerebral Palsy Childbirth Diagnosis Disabled Persons Disorder, Attention Deficit-Hyperactivity Down Syndrome Ethics Committees, Research Fetal Alcohol Syndrome Intellectual Disability Language Disorders Mood Oppositional Defiant Disorder Physical Examination
3
Autism Research Protocol Database
Cited 154 times
Initial contributions were sought from members of the ADHD-200 Consortium
conducting autism research (Kennedy Krieger Institute, NYU Langone Medical
Center, Oregon Health & Science University, University of Pittsburgh).
Invitations to participate were extended based on personal communications,
recent publications and conference presentations. All investigators willing and
able to openly share previously collected awake R-fMRI data from individuals
with ASD and age- and sex-group matched TC were included. Institutional Review
Board (IRB) approval to participate, or explicit waiver to provide fully
anonymized data, was required prior to data contribution.
All contributions were based on studies approved by local IRBs, and data
were fully anonymized (removing all 18 HIPAA protected health information
identifiers, and face information from structural images). All data distributed
were visually inspected prior to release.
Di Martino A., Yan C.G., Li Q., Denio E., Castellanos F.X., Alaerts K., Anderson J.S., Assaf M., Bookheimer S.Y., Dapretto M., Deen B., Delmonte S., Dinstein I., Ertl-Wagner B., Fair D.A., Gallagher L., Kennedy D.P., Keown C.L., Keysers C., Lainhart J.E., Lord C., Luna B., Menon V., Minshew N., Monk C.S., Mueller S., Müller R.A., Nebel M.B., Nigg J.T., O’Hearn K., Pelphrey K.A., Peltier S.J., Rudie J.D., Sunaert S., Thioux M., Tyszka J.M., Uddin L.Q., Verhoeven J.S., Wenderoth N., Wiggins J.L., Mostofsky S.H, & Milham M.P. (2013). The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism. Molecular psychiatry, 19(6), 659-667.
Publication 2013
Autistic Disorder Conferences Disorder, Attention Deficit-Hyperactivity Ethics Committees, Research Face fMRI
4
Characterizing ADHD Symptom Distributions
Cited 97 times
A factor analysis was performed with SPSS, using the principal component method and varimax rotation. As expected, a 2-factor solution was obtained, Inattention representing the ADHD-INN domain and Hyperactivity/ Impulsivity representing the ADHD-HY/IMP domain. These two factors explained 77.78% of the variance, with the rotated factors each accounting for about the same percentage of variance in this sample (see Table 2). The Inattention factor accounted for 41.52% of the variance and the Hyperactivity/Impulsivity factor accounted for 36.26% of the variance.
The mean rating across all of the 847 children based on the 18 ADHD items (i. e., the ADHD-Combined summary score) was .54, with sd= .67 and skewness = 1.474. The mean, sd and skewness for the ADHD-INN subscale were .73, .86, and 1.12, respectively; for ADHD-HY/IMP, they were .34, .61, and 2.36 respectively. The population distribution of ADHD-Combined (ADHD-C) summary scores is presented in Figure 1.
As expected from a general (non-clinical) population, most scores were equal to or below 1.0, which represents a normal level of behavior (i.e., “Just a Little” or less) that would not meet the DSM-IV criteria for symptom presence. This extreme rightward skewness also held at the item level: 75% to 96% of the sample had scores of 1 or less (“Just a Little” or “Not at All”) on the ADHD items of the SNAP-IV. In this sample 79.9% of all students had scores equal to or below 1.0. Different procedures can be used to develop statistical norms based on absolute or relative evaluation of ADHD symptoms with respect to age and sex. In the example above, the average rating was calculated across all students, but averages based on age and gender subgroups have also been used (see Swanson, 1992 ; DuPaul, 1998 ; Conners, 2008 ). By the former absolute method, a higher percentage of young and male cases are expected to be identified as extreme (as in clinical practice), while by the latter relative method, an equal percentage of cases in the age and gender subgroups are expected to be identified as extreme.
As an exercise to demonstrate some consequences of using statistical cutoffs from a non-normal (skewed) distribution, we applied theoretical cutoffs based on the assumption that the distribution was normal. Using a standard theoretical cutoff equal to the mean + 1.65 sd, about 5% of the sample (about 42 cases) would be expected to be identified as extreme. However, as expected for a skewed distribution, this theoretical cutoff identified a significantly higher number of cases than expected. Based on the ADHD-C score, 71 individuals had scores above the theoretical 5% cutoff (8.4%). A comparison of the expected and observed proportions was statistically significant (Z = −4.53, p< .01).
As an additional exercise, we also used the theoretical cutoffs to estimate prevalence of ADHD based on exceeding the cutoff on domains of the SNAP-IV as an operational definition of symptom presence. In this sample, of n = 847, the difference between the observed percentage (9.2%) identified by the theoretical cutoff on the ADHD-INN subscale (78 cases rather than 42) was also statistically significant compared to the expected value of 5% (Z = −5.86, p < .01), as was the percentage (7.6%, or 64 cases) identified by the theoretical cutoff on the ADHD-HY/IMP subscale (Z = −3.47, p< .01).
A Venn diagram (see Figure 2) shows the overlap of extreme cases identified by the multiple cutoff values for the three subtypes: ADHD-C, ADHD-I, and ADHD-HI. This diagram makes it clear that few cases met the criteria for ADHD-C, ADHD-INN, and ADHD-HY/IMP simultaneously (n = 31, or 3.7%), or the criteria for ADHD-I only (n=28, 3.3%) or ADHD-HI only (n=13, 2.4%). Some cases met the criteria for ADHD-C but not ADHD-I (n = 20, 2.4%) or not ADHD-HI (n = 19, 2.2%). One case (0.1%) met the cutoff criteria for ADHD-C but not for ADHD-I or ADHD-HI. A total of 112 cases (n=28+19+31+20+13+1=112) or 13.2% met the cutoff criteria for these subtypes of ADHD.
Swanson J.M., Schuck S., Porter M.M., Carlson C., Hartman C.A., Sergeant J.A., Clevenger W., Wasdell M., McCleary R., Lakes K, & Wigal T. (2012). Categorical and Dimensional Definitions and Evaluations of Symptoms of ADHD: History of the SNAP and the SWAN Rating Scales. The International journal of educational and psychological assessment, 10(1), 51-70.
Publication 2012
ARID1A protein, human Child Disorder, Attention Deficit-Hyperactivity Males Student Symptom Evaluation
5
Genetic Correlations of ADHD Phenotypes
Cited 86 times
The genetic correlations of ADHD with other phenotypes were evaluated using LD Score regression42 (link). For a given pair of traits, LD score regession estimates the expected population correlation between the best possible linear SNP-based predictor for each trait, restricting to common SNPs. Such correlation of genetic risk may reflect a combination of colocalization, pleiotropy, shared biological mechanisms, and causal relationships between traits. Correlations were tested for 211 phenotypes with publically available GWAS summary statistics using LD Hub41 (link) (Supplementary Information; see URLs). Additonally, we analysed on our local computer cluster, the genetic correlation of ADHD with eight phenotypes: human intelligence103 (link), four phenotypes related to education and cognition analyzed in samples from the UK_Biobank49 (link) (college/university degree, verbal–numerical reasoning, memory and reaction time), insomnia60 (link), anorexia nervosa44 (link), and major depressive disorder43 (link). The genetic correlation with major depressive disorder was tested using GWAS results from an updated analysis of 130,664 cases with major depressive disorder and 330,470 controls from the Psychiatric Genomics Consortium. As in the previous LD score regression analyses, this estimation was based on summary statistics from the European GWAS meta-analysis, and significant correlations reported are for traits analysed using individuals with European ancestry.
Demontis D., Walters R.K., Martin J., Mattheisen M., Als T.D., Agerbo E., Baldursson G., Belliveau R., Bybjerg-Grauholm J., Bækvad-Hansen M., Cerrato F., Chambert K., Churchhouse C., Dumont A., Eriksson N., Gandal M., Goldstein J.I., Grasby K.L., Grove J., Gudmundsson O.O., Hansen C.S., Hauberg M.E., Hollegaard M.V., Howrigan D.P., Huang H., Maller J.B., Martin A.R., Martin N.G., Moran J., Pallesen J., Palmer D.S., Pedersen C.B., Pedersen M.G., Poterba T., Poulsen J.B., Ripke S., Robinson E.B., Satterstrom F.K., Stefansson H., Stevens C., Turley P., Walters G.B., Won H., Wright M.J., Andreassen O.A., Asherson P., Burton C.L., Boomsma D.I., Cormand B., Dalsgaard S., Franke B., Gelernter J., Geschwind D., Hakonarson H., Haavik J., Kranzler H.R., Kuntsi J., Langley K., Lesch K.P., Middeldorp C., Reif A., Rohde L.A., Roussos P., Schachar R., Sklar P., Sonuga-Barke E.J., Sullivan P.F., Thapar A., Tung J.Y., Waldman I.D., Medland S.E., Stefansson K., Nordentoft M., Hougaard D.M., Werge T., Mors O., Mortensen P.B., Daly M.J., Faraone S.V., Børglum A.D, & Neale B.M. (2018). Discovery of the first genome-wide significant risk loci for attention-deficit/hyperactivity disorder. Nature genetics, 51(1), 63-75.
Publication 2018
Anorexia Biopharmaceuticals Cognition Disorder, Attention Deficit-Hyperactivity Europeans Genome-Wide Association Study Hereditary Diseases Homo sapiens Major Depressive Disorder Memory Phenotype Reproduction Single Nucleotide Polymorphism

Most recents protocols related to «Disorder, Attention Deficit-Hyperactivity»

1
Comorbidity of Substance Use Disorders and Psychiatric Diagnoses
The SUD diagnoses and psychiatric diagnoses according to ICD-10 criteria, were made by a medical specialist or clinical psychologist using standardized clinical interviews and tools.
For the purpose of the current study, information on the dependence-level SUD diagnosis and any co-occurring psychiatric diagnosis was obtained from the medical record. The following binary SUD diagnosis (1 = presence, 0 = absence) were included in analyses: Alcohol use disorder (F10); Opioid use disorder (F11); Cannabis use disorder (F12); Sedatives use disorder (F13); Stimulant use disorder (F15). The psychiatric diagnoses were grouped into the following binary variables (1 = presence, 0 = absence): Mood disorders (F30-F39); Anxiety disorders (F40-F49); Personality disorders (F60-F69); ADHD (F90-F90.0), and other psychiatric diagnoses.
Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.
Full text: Click here
Publication 2023
Alcohol Use Disorder Anxiety Disorders Cannabis Diagnosis Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Mood Disorders Opioid Use Disorder Personality Disorders Psychologist Sedatives
2
Factors Influencing Substance Abuse Relapse
Statistical analysis included descriptive statistics for the prevalence of co-occurring psychiatric diagnoses in the total sample, and according to types of SUD diagnoses. We compared the characteristics of patients with - and without COD using proportion tests and independent samples t-tests. The prevalence of types of CODs was examined for the following psychiatric disorders: anxiety (F40-F49); mood (F30-39), ADHD (F90-90.9); personality disorder (F60-69); multiple CODs. Gender differences in the prevalence of each types of CODs were examined using bivariate logistic regression analysis. Bivariate logistic regression analyses were also undertaken to investigate factors associated with relapse. Repeated-measures generalized logistic mixed modeling (GLMM) with a diagonal covariance matrix was used to assess the multivariate association of demographic (age, gender, education), psychological (motivation, mental distress) and types of SUD diagnoses with relapse at 3 month follow-up. The analysis accounted for the prospective nested nature of the data structure (i.e., the same patients nested over time). Since mental distress was measured at two time points (baseline and follow-up), this variable was entered as a time-varying covariate accounting for variation in mental distress across the study period. Variables indicating the center where the patients were treated (unit 1–5) and the length of stay (number of days) were included in the multivariate models to control for any treatment- related differences in relapse rates. We did not incorporate the treatment center variable as a random effect in the analysis due to the small number of patients at each treatment center, which made it complicated to account for the variance of treatment center as a random effect due to the substantial risk of Type II error. The variance inflation factors were < 2 for all independent variables, indicating that multicollinearity was not a concern [35 ]. We ran the GLMM analyses separately for patients with and without COD. SPSS 28 was used for statistical analyses.
Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.
Full text: Click here
Publication 2023
Anxiety Cods Diagnosis Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Gender Mental Disorders Mood Motivation Patients Personality Disorders Relapse Respiratory Diaphragm
3
Comorbid Psychiatric Disorders in Patients
The proportion of patients with COD was not significantly different for male (45.4%) and female (52.0%) patients (p = 0.139). The prevalence rates for the types of co-occurring psychiatric diagnoses are shown in Table 2. Among patients with COD, anxiety (22.9%) and mood disorders (17.3%) were the two most common psychiatric disorders. About one in five had more than one COD (21.4%), with a higher prevalence rate of multiple CODs among females (30.3%) than males (18.0%). Having anxiety disorders were significantly more prevalent among females (30.3%) than males (19.8%).

Prevalence of co-occurring psychiatric disorders

Total(N = 611)Females(N = 175)Males(N = 434)Females versus malesref
N%N%N%ORp-value
Without COD32252.78448.023754.6
With COD28947.39152.019745.41.300.139
Psychiatric diagnoses1
- Anxiety disorders F40-4914022.95330.38619.81.760.005
- Mood disorders F30-3910617.33620.67016.11.350.191
- ADHD F90.0-F90.97912.92112.05813.40.880.650
- Personality disorders F60-697011.52715.4439.91.660.053
- Multiple CODs13121.453*30.37818.01.98< 0.001

1 Other psychiatric diagnoses (n = 17) included Schizophrenia, F20-F29 (n = 8); Behavioral syndromes associated with physiological disorders and physical factors (n = 16); Mental retardation, F70-F79 (n = 6); Pervasive and specific developmental disorders, F80-89 (n = 16); Behavioral disorders, F91-F98 (n = 8)

Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.
Full text: Click here
Publication 2023
Anxiety Disorders Behavior Disorders Cods Developmental Disabilities Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Females Intellectual Disability Males Mental Disorders Mood Mood Disorders Patients Personality Disorders Physical Examination physiology Schizophrenia Syndrome Woman
4
Impact of COVID-19 on Children's Well-being
The survey included questions about the impact of the COVID-19 outbreak on their wellbeing as well as the requirements for dealing with the pandemic. Each multiple-choice question allowed participants to choose only one item. This measure of emotional and behavioral change was reported by parents who were asked about their children’s emotional and behavioral changes (e.g., emotional reactions to stress, emotional self-regulation system’s stability and emotional and behavioral problems related to ASD or DD) during the COVID-19 pandemic lockdown. Specifically, a scale of 1 = “improved,” 2 = “no change,” and 3 = “worse” was used. Demographic variables, family socioeconomic variables, and family treatment history variables were used as control variables in this study. The demographic variables included the age of the children, their gender, and the number of children in the household, and having comorbidities or not. The age was the age at the time of the survey. The comorbidities referred to neurodevelopment disorders, including intellectual disabilities (ID) and attention deficit and hyperactivity disorder (ADHD) in this study. Information on family sociodemographic and medical history was gathered. The income of families was divided into three categories: below average, average, and above average. According to the data distribution, the below average group had an annual income of less than $12,327 (RMB80,000), the average group had an annual income between $12,327 (RMB80,001) and $23,112 (RMB150,000), and the above average group had an annual income greater than $23,112 (RMB150,000).
Zhao Y., Luo Y., Zhang R, & Zheng X. (2023). The impact of COVID-19 pandemic on emotional and behavioral problems of children with autism spectrum disorder and developmental delay aged 1–6 years in China. Frontiers in Psychiatry, 14, 1134396.
Full text: Click here
Publication 2023
AN 12 Attention Deficit Disorder BAD protein, human Child COVID 19 Disorder, Attention Deficit-Hyperactivity Emotional Regulation Emotional Stress Emotions Gender Households Intellectual Disability Neurodevelopmental Disorders Pandemics Parent Problem Behavior
5
IDDEAS Prototype for ADHD Diagnosis
The IDDEAS prototype allows for exploration of the ability of IDDEAS guidelines to provide decision support for diagnosis and treatment of attention deficit and hyperactivity disorder (ADHD) (see Figure 2). ADHD is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, ultimately causing impaired functioning for the individual (9 (link)). The IDDEAS prototype at this stage uses ADHD as the first clinical model paradigm. Preparation of IDDEAS includes the validation of the clinical materials and the user-interface. The IDDEAS guidelines were previously validated by the IDDEAS clinical research team using the DSM-5 and ICD-10 criteria. Focus groups were used to pre-test content prior to the IDDEAS prototype evaluation.
Each IDDEAS prototype evaluation session included having a clinician participant complete a concurrent, cognitive walk through/think-aloud procedure, as they critically appraised hypothetical patient case scenarios developed from real cases within CAMHS. A total of 20 patient case scenarios were collaboratively designed and validated by the IDDEAS team (BL, NS, RK). Out of the 20 possible cases, each participant was randomly assigned four to assess, two of which were to be assessed while using the IDDEAS prototype (ADHD modeled guidelines) and two without. Use of IDDEAS was similarly randomly assigned. Throughout the assessment of the four cases, participants were asked to follow a think-aloud procedure and provide a concurrent walk through of the clinical procedure they would follow if the patients were real. They were also asked to provide additional patient information they perceived to be potentially necessary to complete their clinical assessment. Finally, participants were asked to provide their overall perceptions of the IDDEAS prototype and its usability, functionality, and potential utility.
Clausen C., Leventhal B., Nytrø Ø., Koposov R., Røst T.B., Westbye O.S., Koochakpour K., Frodl T., Stien L, & Skokauskas N. (2023). Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development. Frontiers in Psychiatry, 14, 1033724.
Full text: Click here
Publication 2023
Attention Deficit Disorder Cognition Diagnosis Disorder, Attention Deficit-Hyperactivity Neurodevelopmental Disorders Patients

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More about "Disorder, Attention Deficit-Hyperactivity"

Attention Deficit Disorder (ADD), Attention Deficit Hyperactivity Disorder (ADHD), and Hyperkinetic Disorder are all terms used to describe a persistent pattern of inattention, hyperactivity, and impulsivity that can interfere with an individual's development, functioning, and quality of life.
This neurodevelopmental disorder, which typically onsets in childhood and can continue into adulthood, is characterized by difficulty sustaining attention, restlessness, impulsive actions, and problems with organization.
Accurate diagnosis and evidence-based management are crucial for improving outcomes for those affected by ADHD.
Statistical software packages like SAS version 9.4, SPSS version 22.0, SPSS version 25, SPSS version 21, and Stata 13 can be used to analyze data and study the prevalence, risk factors, and treatment efficacy for ADHD.
MATLAB, a widely used computational software, can also be leveraged to model and simulate ADHD-related neural processes and cognitive functions.
Researchers and clinicians may utilize SPSS version 20 and SPSS version 26 to conduct surveys, assess symptom severity, and evaluate the impact of ADHD on an individual's daily life and overall well-being.
By incorporating these statistical tools and techniques, along with a deep understanding of the disorder's characteristics and manifestations, we can advance our knowledge and develop more effective interventions to support individuals with ADHD.