> Disorders > Mental or Behavioral Dysfunction > Attention Deficit Disorder

Attention Deficit Disorder

Attention Deficit Disorder (ADD): A persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development.
Characterized by difficulty sustaining attention, hyperactive behavior, and impulsive actions.
Affects cognitive, academic, and social aspects of life.
Often diagnosed in childhood and can persist into adulthood.
Requires comprehensive evaluation and tailored treatment approaches for optimal management.

Most cited protocols related to «Attention Deficit Disorder»

Genome-wide Overlap Across Psychiatric Disorders

Genome-wide significant loci for BD were assessed for overlap with genome-wide significant loci for other psychiatric disorders, using the largest available GWAS results for major depression^{61 (link)}, schizophrenia^{60 (link)}, attention deficit/hyperactivity disorder¹⁰¹, post-traumatic stress disorder¹⁰², lifetime anxiety disorder¹⁰³, Tourette’s Syndrome¹⁰⁴, anorexia nervosa¹⁰⁵, alcohol use disorder or problematic alcohol use^{68 (link)}, autism spectrum disorder¹⁰⁶, mood disorders^{91 (link)} and the cross-disorder GWAS of the Psychiatric Genomics Consortium^{66 (link)}. The boundaries of the genome-wide significant loci were calculated in the original publications. Overlap of loci was calculated using bedtools v2.29.2¹⁰⁷.

Mullins N., Forstner A.J., O’Connell K.S., Coombes B., Coleman J.R., Qiao Z., Als T.D., Bigdeli T.B., Børte S., Bryois J., Charney A.W., Drange O.K., Gandal M.J., Hagenaars S.P., Ikeda M., Kamitaki N., Kim M., Krebs K., Panagiotaropoulou G., Schilder B.M., Sloofman L.G., Steinberg S., Trubetskoy V., Winsvold B.S., Won H.H., Abramova L., Adorjan K., Agerbo E., Al Eissa M., Albani D., Alliey-Rodriguez N., Anjorin A., Antilla V., Antoniou A., Awasthi S., Baek J.H., Bækvad-Hansen M., Bass N., Bauer M., Beins E.C., Bergen S.E., Birner A., Pedersen C.B., Bøen E., Boks M.P., Bosch R., Brum M., Brumpton B.M., Brunkhorst-Kanaan N., Budde M., Bybjerg-Grauholm J., Byerley W., Cairns M., Casas M., Cervantes P., Clarke T.K., Cruceanu C., Cuellar-Barboza A., Cunningham J., Curtis D., Czerski P.M., Dale A.M., Dalkner N., David F.S., Degenhardt F., Djurovic S., Dobbyn A.L., Douzenis A., Elvsåshagen T., Escott-Price V., Ferrier I.N., Fiorentino A., Foroud T.M., Forty L., Frank J., Frei O., Freimer N.B., Frisén L., Gade K., Garnham J., Gelernter J., Pedersen M.G., Gizer I.R., Gordon S.D., Gordon-Smith K., Greenwood T.A., Grove J., Guzman-Parra J., Ha K., Haraldsson M., Hautzinger M., Heilbronner U., Hellgren D., Herms S., Hoffmann P., Holmans P.A., Huckins L., Jamain S., Johnson J.S., Kalman J.L., Kamatani Y., Kennedy J.L., Kittel-Schneider S., Knowles J.A., Kogevinas M., Koromina M., Kranz T.M., Kranzler H.R., Kubo M., Kupka R., Kushner S.A., Lavebratt C., Lawrence J., Leber M., Lee H.J., Lee P.H., Levy S.E., Lewis C., Liao C., Lucae S., Lundberg M., MacIntyre D.J., Magnusson S.H., Maier W., Maihofer A., Malaspina D., Maratou E., Martinsson L., Mattheisen M., McCarroll S.A., McGregor N.W., McGuffin P., McKay J.D., Medeiros H., Medland S.E., Millischer V., Montgomery G.W., Moran J.L., Morris D.W., Mühleisen T.W., O’Brien N., O’Donovan C., Olde Loohuis L.M., Oruc L., Papiol S., Pardiñas A.F., Perry A., Pfennig A., Porichi E., Potash J.B., Quested D., Raj T., Rapaport M.H., DePaulo J.R., Regeer E.J., Rice J.P., Rivas F., Rivera M., Roth J., Roussos P., Ruderfer D.M., Sánchez-Mora C., Schulte E.C., Senner F., Sharp S., Shilling P.D., Sigurdsson E., Sirignano L., Slaney C., Smeland O.B., Smith D.J., Sobell J.L., Hansen C.S., Artigas M.S., Spijker A.T., Stein D.J., Strauss J.S., Świątkowska B., Terao C., Thorgeirsson T.E., Toma C., Tooney P., Tsermpini E.E., Vawter M.P., Vedder H., Walters J.T., Witt S.H., Xi S., Xu W., Yang J.M., Young A.H., Young H., Zandi P.P., Zhou H., Zillich L., Adolfsson R., Agartz I., Alda M., Alfredsson L., Babadjanova G., Backlund L., Baune B.T., Bellivier F., Bengesser S., Berrettini W.H., Blackwood D.H., Boehnke M., Børglum A.D., Breen G., Carr V.J., Catts S., Corvin A., Craddock N., Dannlowski U., Dikeos D., Esko T., Etain B., Ferentinos P., Frye M., Fullerton J.M., Gawlik M., Gershon E.S., Goes F.S., Green M.J., Grigoroiu-Serbanescu M., Hauser J., Henskens F., Hillert J., Hong K.S., Hougaard D.M., Hultman C.M., Hveem K., Iwata N., Jablensky A.V., Jones I., Jones L.A., Kahn R.S., Kelsoe J.R., Kirov G., Landén M., Leboyer M., Lewis C.M., Li Q.S., Lissowska J., Lochner C., Loughland C., Martin N.G., Mathews C.A., Mayoral F., McElroy S.L., McIntosh A.M., McMahon F.J., Melle I., Michie P., Milani L., Mitchell P.B., Morken G., Mors O., Mortensen P.B., Mowry B., Müller-Myhsok B., Myers R.M., Neale B.M., Nievergelt C.M., Nordentoft M., Nöthen M.M., O’Donovan M.C., Oedegaard K.J., Olsson T., Owen M.J., Paciga S.A., Pantelis C., Pato C., Pato M.T., Patrinos G.P., Perlis R.H., Posthuma D., Ramos-Quiroga J.A., Reif A., Reininghaus E.Z., Ribasés M., Rietschel M., Ripke S., Rouleau G.A., Saito T., Schall U., Schalling M., Schofield P.R., Schulze T.G., Scott L.J., Scott R.J., Serretti A., Weickert C.S., Smoller J.W., Stefansson H., Stefansson K., Stordal E., Streit F., Sullivan P.F., Turecki G., Vaaler A.E., Vieta E., Vincent J.B., Waldman I.D., Weickert T.W., Werge T., Wray N.R., Zwart J.A., Biernacka J.M., Nurnberger J.I., Cichon S., Edenberg H.J., Stahl E.A., McQuillin A., Di Florio A., Ophoff R.A, & Andreassen O.A. (2021). Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature genetics, 53(6), 817-829.

Publication 2021

Alcohol Use Disorder Anorexia Anxiety Disorders Attention Deficit Disorder Ethanol Genome Genome-Wide Association Study Mental Disorders Mood Pervasive Development Disorders

Gender Differences in Anxiety Disorders

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2011

Adult Anxiety Disorders Attention Deficit Disorder Diagnosis Eating Disorders Gender Households Intermittent Explosive Disorder Mood Disorders Substance Use Disorders Woman

Continuous Performance Test for Attention

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2009

Attention Attention Deficit Disorder Diagnosis Fingers Genetic Predisposition to Disease Psychotic Disorders Wakefulness

College Student Cognitive Evaluation

Twenty-three right-handed, healthy college students participated in the study (13 females; average age: M = 20.91, SD = 1.73). All participants gave informed consent and were paid for their participation. All participants were informed that their participation was completely voluntary and that they may withdraw from the study at any time. All participants were over 18 years of age. All participants had normal or corrected to normal vision, were right-handed, had no history of attention deficit or learning disabilities. This study was approved by the local ethical committee of Southwest University and the Institutional Human Participants Review Board of the Southwest University Imaging Center for human brain research. The experimental procedure was in accordance with the ethical principles of the 1964 Declaration of Helsinki.

Chen S., Deng Z., Xu Y., Long Q., Yang J, & Yuan J. (2017). Individual Differences in Spontaneous Expressive Suppression Predict Amygdala Responses to Fearful Stimuli: The Role of Suppression Priming. Frontiers in Psychology, 8, 1.

Full text: Click here

Publication 2017

Attention Deficit Disorder Brain Females Homo sapiens Learning Disabilities Student

Comparing Polygenic Risk Scores Across Traits

To compare performance across PRS approaches, we used publicly available GWAS summary statistics to calculate PRSs for a variety of traits for subjects in the Mayo Bipolar Biobank dataset, including: SCZ (Consortium et al., 2014 (link)), BD (Stahl et al., 2019 (link)), major depressive disorder (MDD) (Wray et al., 2018 (link)), attention deficit and hyperactivity disorder (ADHD) (Demontis et al., 2019 (link)), anxiety disorders (Otowa et al., 2016 (link)), post-traumatic stress disorder (PTSD) (Duncan et al., 2018 (link)), obsessive compulsive disorder (OCD)(International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS), 2018 (link)), anorexia nervosa (AN) (Bulik et al., 2017 (link)), insomnia (Lane et al., 2019 (link)), and educational attainment (EA) (J. J. Lee et al., 2018 (link)). We used PRSice2 (Choi & O’Reilly, 2019 (link)) to compute the PRSs using the same settings described for the simulations. Some smaller p-value thresholds were not applicable for GWAS without genome-wide significant variants. We used the various PRS approaches to test for association of each PRS with BD case-control status (N cases = 968; N controls = 777) to compare the performances of the methods in a well-studied phenotype. We additionally repeated these analyses using the history of psychosis during mania in BD cases (N with manic psychosis = 336; N without psychosis = 309) as the phenotype. We recently demonstrated that psychosis during mania is associated with polygenic risk of schizophrenia (Markota et al., 2018 (link)). No large GWAS exists for this phenotype, thus, PRS approaches can be quite useful here to elucidate potential differences in genetic background between bipolar cases with and without psychosis, and the genetic overlap of this phenotype with other psychiatric traits in addition to SCZ. We used logistic regression to test for association of each PRS with BD or psychosis status after controlling for the first four principal components of the genotype data to adjust for population stratification. P-values for the Opt-Perm method were calculated using up to 100,000 permutations. We estimated the percent of variation of the binary phenotypes explained by each PRS using Nagelkerke’s R². For the Opt-Perm approach, we followed the standard approach of reporting the Nagelkerke’s R² estimate for the best p-value threshold, which is a biased overestimate of the true R².

Coombes B.J., Ploner A., Bergen S.E, & Biernacka J.M. (2020). A principal component approach to improve association testing with polygenic risk scores. Genetic epidemiology, 44(7), 676-686.

Publication 2020

Anorexia Nervosa Anxiety Disorders Attention Deficit Disorder Disorder, Attention Deficit-Hyperactivity Genetic Association Studies Genetic Background Genome Genome-Wide Association Study Genotype Major Depressive Disorder Mania Obsessive-Compulsive Disorder Phenotype Post-Traumatic Stress Disorder Progressive Encephalomyelitis with Rigidity Psychotic Disorders Schizophrenia Sleeplessness

Most recents protocols related to «Attention Deficit Disorder»

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

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

Chinese L2 Learners' Cognitive Profiles

The participants were 38 undergraduate students studying Chinese at a university in Beijing (18 male, 20 female; mean age = 21.53, SD = 2.05). All the participants were native Korean speakers and had studied Chinese for a mean of 6.81 years (SD = 3.65). They were intermediate and advanced Chinese L2 learners, with an average score of 25.47 (SD = 3.07, ranging from 19 to 30) in the fixed-ratio cloze test (full score: 30, Feng et al., 2020 (link)). The participants had no medical history of learning disabilities, attention deficit, hearing or visual impairment. They were recruited through experimental advertisements, gave informed consent to participate in the experiment, and were paid after the experiment.

Wang Y., Hu J., An Z., Li C, & Zhao Y. (2023). The influence of metacognition monitoring on L2 Chinese audiovisual reading comprehension. Frontiers in Psychology, 14, 1133003.

Full text: Click here

Publication 2023

Attention Deficit Disorder Chinese Disabled Persons Females Koreans Low Vision Males Student

Matching WMHP Users to Nonusers

WMHP users were coded as such in this study based upon their use of at least 1 WMHP service. WMHP providers submitted diagnostic impressions that were then mapped to ICD-10 diagnosis codes by Lyra Health clinicians. A detailed mapping from diagnostic impression to ICD-10 diagnosis codes is included in the Appendix Table A1.
To apply the Aon cost efficiency measurement process, WMHP users were matched with a comparison group of nonusers, composed of eligible members at the same employers with closely matched geography, demographics, and medical and mental health comorbidities, for the same time periods. A derivation of coarsened exact matching³² was used to match cohorts. As originally described by Iacus et al, coarsened exact matching first involves dividing members into meaningful categories selected for each matching factor of interest.
Specifically, members were first divided into age groups, gender categories, geographic area categories, and according to the existence or not of several medical conditions, as noted below. Then, all members of the treatment and comparison groups who fell into the same categories were retained for the analysis; the rest were excluded. The Iacus et al³² method also suggests using case weights to account for the proportion of treatment and comparison group members who are in each factor category, then using their original data values (not the indicators of which factor categories they fell into) in the subsequent statistical analyses.
The Iacus et al method was simplified for this analysis, by avoiding the use of case weights. Age is the only continuous measure in this data set, so individuals were matched on tightly constructed age groups, which still produced a highly balanced set of WMHP users and comparison group members for analysis. More specifically, individuals were matched by gender first; then they were matched to others within ±3 years of their ages.
Individuals were then matched on presence or absence of 22 diagnosed medical conditions (Tables 1 and 2), and combinations of selected conditions. The chronic condition indicators considered for each member were based on primary (first listed) medical diagnostic codes, using the Chronic Condition Indicator and Clinical Classifications Software developed by the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project.³³ When members had more than 1 mental health condition, a hierarchy was applied.
Specifically, when 2 or more disorders were present, members were coded according to the one that is typically thought to be more impactful in nature, resulting in greater levels of disability and incurring the most cost. Based upon the existing literature,^{2 (link),4 (link),5 (link)} mood disorders were anticipated to incur more costs than anxiety and adjustment disorders, which were in turn anticipated to incur more costs than attention-deficit disorders.
Next, WMHP users were matched to nonusers in the same geographic areas, when possible. First, an attempt was made to match members residing in the same metropolitan statistical area (MSA), based on a list of over 200 such areas across the United States. When a within-MSA match could not be found, members were matched at the state level. When that was not possible, members were not matched geographically, but were matched on the other factors mentioned above, searching across the country for the best demographic and condition level matches.
When no matches could be found (typically when members had a rare combination of disease and location values), individuals were removed from further analysis. For WMHP users who could have matched to more than 1 comparison group member, only 1 matching comparison group member was randomly selected for inclusion into the analyses. See Figure 1 for other inclusion and exclusion criteria and associated sample size reductions.

Penev T., Zhao S., Lee J.L., Chen C.E., Metcalfe L, & Ozminkowski R.J. (2023). The Impact of a Workforce Mental Health Program on Employer Medical Plan Spend: An Application of Cost Efficiency Measurement for Mental Health Care. Population Health Management, 26(1), 60-71.

Full text: Click here

Publication 2023

Adjustment Disorders Age Groups Anxiety Disorders Attention Deficit Disorder Commissure of Fornix Diagnosis Disabled Persons Disease, Chronic Gender Mental Disorders Mental Health Mood Disorders Rare Diseases

SNAP-IV Rating Scale for ADHD Diagnosis

The Swanson, Nolan, and Pelham-IV rating scale (SNAP-IV rating scale) rating scale has good reliability and validity (40 (link), 41 (link)). This scale is compiled according to the DSM-IV diagnostic criteria for ADHD, with a total of 18 items that are summarized in two factors: attention deficit (items 1–9) and hyperactivity/impulsivity (items 10–18) were scored on a scale of 4 for symptom severity (none at all 0; A little bit is one point; Not too little is 2 points; and Very many are 3 points), selected by parents according to their children's general impression. The scores are on average.

Wang Y.C., Liu J., Wu Y.C., Wei Y., Xie H.J., Zhang T, & Zhang Z. (2023). A randomized, sham-controlled trial of high-definition transcranial direct current stimulation on the right orbital frontal cortex in children and adolescents with attention-deficit hyperactivity disorder. Frontiers in Psychiatry, 14, 987093.

Full text: Click here

Publication 2023

Attention Deficit Disorder Diagnosis Disorder, Attention Deficit-Hyperactivity Parent

Top products related to «Attention Deficit Disorder»

Pvt 192 by Ambulatory Monitoring

Sourced in United States

The PVT-192 is a portable device designed for psychomotor vigilance testing. It measures and records reaction time, lapses, and other performance metrics related to sustained attention and vigilance. The device is intended for use in research and clinical settings.

Beadxpress system by Illumina

Sourced in United States

The BeadXpress System is a high-throughput genotyping platform developed by Illumina. It utilizes bead-based technology to perform multiplexed analysis of genetic variations across large sample sets. The system is designed to deliver accurate and reliable genotyping results, but a detailed description of its core function is not available while maintaining an unbiased and factual approach.

Spss version 25 by IBM

Sourced in United States, United Kingdom, Japan, Austria, Germany, Denmark, Czechia, Belgium, Sweden, New Zealand, Spain

SPSS version 25 is a statistical software package developed by IBM. It is designed to analyze and manage data, providing users with a wide range of statistical analysis tools and techniques. The software is widely used in various fields, including academia, research, and business, for data processing, analysis, and reporting purposes.

Sas version 9 by SAS Institute

Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India

SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.

Spss 20 by IBM

Sourced in United States, United Kingdom, Germany, Japan, Denmark, China, Belgium, Poland, Austria, Australia

SPSS 20.0 is a statistical software package developed by IBM for data analysis, data management, and data visualization. It provides a wide range of statistical techniques, including descriptive statistics, bivariate statistics, prediction for numerical outcomes, and prediction for identifying groups. SPSS 20.0 is designed to help users analyze and understand data quickly and efficiently.

Spss for windows ver 16 by IBM

Sourced in United States

SPSS for Windows ver. 16.0 is a software package for statistical analysis. It provides tools for data management, analysis, and presentation. The software offers a wide range of statistical techniques, including regression, correlation, and multivariate analysis.

Concerta by Johnson & Johnson

Sourced in Germany, United States

Concerta is a laboratory equipment product manufactured by Johnson & Johnson. It is designed to assist in the analysis and testing of various samples and substances within a controlled laboratory environment. The core function of Concerta is to provide consistent and reliable results for research and development purposes.

Genomestudio software v2011 by Illumina

Sourced in United States

GenomeStudio software v2011.1 is a data analysis software developed by Illumina for the processing and analysis of data generated from Illumina's microarray and sequencing platforms. The software provides a comprehensive suite of analytical tools for visualizing, normalizing, and interpreting genetic data. It is designed to enable researchers to effectively manage and analyze large-scale genomic data.

Prism 5 by GraphPad

Sourced in United States, Germany, United Kingdom, Israel, Canada, Austria, Belgium, Poland, Lao People's Democratic Republic, Japan, China, France, Brazil, New Zealand, Switzerland, Sweden, Australia

GraphPad Prism 5 is a data analysis and graphing software. It provides tools for data organization, statistical analysis, and visual representation of results.

What is Attention Deficit Disorder (ADD) and how does it affect individuals?

Attention Deficit Disorder (ADD) is a persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with a person's functioning or development. It is characterized by difficulty sustaining attention, hyperactive behavior, and impulsive actions. ADD can impact cognitive, academic, and social aspects of an individual's life. It is often diagnosed in childhood but can persist into adulthood, requiring comprehensive evaluation and tailored treatment approaches for optimal management.

What are the different types or variations of Attention Deficit Disorder?

There are three main types of Attention Deficit Disorder: 1) Predominantly Inattentive Type, which is characterized by difficulty focusing and paying attention; 2) Predominantly Hyperactive-Impulsive Type, which involves excessive movement, restlessness, and impulsive behaviors; and 3) Combined Type, which includes symptoms of both inattention and hyperactivity-impulsivity. The specific type of ADD can vary from person to person and may change over time.

How can PubCompare.ai assist in Attention Deficit Disorder research and treatment?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help identify the most effective protocols related to Attention Deficit Disorder for your specific research goals. PubCompare.ai's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy. This can streamline your research process and unlock new insights in the field of ADD.

What are some common challenges or usage considerations when working with Attention Deficit Disorder?

Some common challenges when dealing with Attention Deficit Disorder include difficulty maintaining focus and concentration, managing impulsive behaviors, and organizing tasks and activities. Individuals with ADD may also struggle with time management, following instructions, and completing work or assignments on time. It's important to tadrget these specific challenges through personalized treatment plans and accommodations to support optimal functioning.

How can PubCompare.ai enhance Attention Deficit Disorder research and protocol comparison?

PubCompare.ai's AI-powered platform can revolutionize Attention Deficit Disorder research by enabling more efficient screening of protocol literature and identifying critical insights. The platform can help researchers pinpoint the most effective protocols for their specific research goals, highlighting key differences in protocol effectiveness. This can enhance reproducibility and accuracy, streamlining the research process and unlocking new discoveries in the field of ADD.

More about "Attention Deficit Disorder"

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition characterized by persistent inattention, hyperactivity, and impulsivity.
Formerly known as Attention Deficit Disorder (ADD), this condition can significantly impact an individual's cognitive, academic, and social functioning.
ADHD is often diagnosed in childhood but can persist into adulthood, requiring comprehensive evaluation and tailored treatment approaches for optimal management.
Research on ADHD has benefited from advancements in various tools and software.
For instance, the PVT-192 and BeadXpress System have been utilized in ADHD studies, while SPSS (versions 16.0, 20.0, and 25), SAS (version 9.4), and GraphPad Prism 5 have been employed for data analysis.
Additionally, the GenomeStudio software v2011.1 has been used in genetic studies related to ADHD.
Effective management of ADHD often involves a combination of medication, such as Concerta, and behavioral interventions.
Researchers and clinicians strive to enhance the understanding and treatment of this condition, with the goal of improving the quality of life for individuals affected by ADHD.
PubCompare.ai, an AI-driven platform, can revolutionize ADHD research by optimizing research protocols, enhancing reproducibility, and facilitating the identification of the best protocols from literature, pre-prints, and patents.