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Behavior Disorders

Behavoral Disorders encompass a range of conditions characterized by persistent, disruptive behaviors that deviate from social norms.
This broad category includes disorders like ADHD, conduct disorder, oppositional defiant disorder, and more.
Individuals with Behavioral Disorders may exhibit challenges with impulse control, emotional regulation, and social interaction.
Understanding the latest research on effective interventions and management strategies is crucial for healthcare providers and researchers in this field.
PubCompare.ai offers a powerful platform to streamline your Behavior Disorders research, providing AI-driven comparisons of protocols, products, and best practices from the literature.

Most cited protocols related to «Behavior Disorders»

1
Promoting Health Among Mexican-American Youth
The sample consisted of 598 seventh-grade adolescents and their Mexican American parents from 5 junior high schools that served primarily low-income populations (80% of students were eligible for free lunches) in a large southwestern metropolitan area with a substantial proportion of Mexican American and European American families and a relatively smaller proportion of families from other ethnic/racial groups. Family incomes ranged from $1,000 per year to $150,000 per year, with a mean of $36,310 per year. The original study aimed to recruit Mexican-origin families into a program designed to prevent high school dropout and mental and behavioral health disorders in youth. Sixty-two percent of the 955 eligible families enrolled and completed the first wave of assessments. In addition, the project required that both parents and youth be able to participate in the assessments and the intervention sessions in the same language; 6% of the families were ineligible because of this requirement. The current investigation uses data from the assessments that occurred prior to exposure to the intervention.
Of the 598 adolescents, 303 (50.6%) were female, 295 (49.2%) were male, 112 (18.7%) were born in Mexico, and 447 (74.7%) were born in the United States. Adolescents ranged in age from 11 to 14 years, with a mean age of 12.3 years. Three hundred and nineteen adolescents (53.4%) were interviewed in Spanish and 278 in English (46.6%). Of the parents, 573 mothers and 331 fathers participated in the interviews. Among the mothers, 347 (60.6%) were born in Mexico, 222 (38.7%) were born in the United States (4 mothers did not report their birthplace), 314 (54.8%) were interviewed in Spanish and 259 (45.2%) were interviewed in English. Among the fathers, 227 (68.6%) were born in Mexico, 104 (31.4%) were born in the United States, 200 (60.4%) were interviewed in Spanish and 131 (39.6%) were interviewed in English.
In-home interviews were conducted by trained interviewers using laptop computers. Interviewers were trained to conduct the parent and child surveys in separate rooms and/or out of hearing of other family members. Interviewers read each survey question and possible responses aloud in either Spanish or English to reduce problems associated with variations in literacy. All measures were translated and back-translated to ensure equivalence of all content (Behling & Law, 2000 ). Family members received $30 for participating, for a total of $60 for one-parent and $90 for two-parent families.
Knight G.P., Gonzales N.A., Saenz D.S., Bonds D.D., Germán M., Deardorff J., Roosa M.W, & Updegraff K.A. (2010). The Mexican American Cultural Values scales for Adolescents and Adults. The Journal of early adolescence, 30(3), 444-481.
Publication 2009
Adolescent Behavior Disorders Child Childbirth Europeans Family Member Fathers Hispanic or Latino Interviewers Low-Income Population Males Mexican Americans Mothers Parent Racial Groups Student Student Dropouts Woman Youth
2
Diagnosing Mental Disorders in Adolescents

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Publication 2010
Abuse, Alcohol Adolescent Agoraphobia Alcoholic Intoxication, Chronic Anorexia Nervosa Anxiety Disorders Behavior Disorders Bulimia Nervosa Conduct Disorder Diagnosis Disorder, Attention Deficit-Hyperactivity Disorder, Binge-Eating Drug Abuse Drug Dependence Dysthymic Disorder Eating Disorders Emotions Interviewers Major Depressive Disorder Mental Disorders Mood Disorders Oppositional Defiant Disorder Panic Disorder Parent Phobia, Social Phobia, Specific Physical Examination Post-Traumatic Stress Disorder Problem Behavior Separation Anxiety Disorder Substance Use Disorders
3
Collaborative Health Behaviors Impact
Information on individual-level health behaviors came from the 2015 Behavioral Risk Factor Surveillance Survey (BRFSS) SMART dataset. The 2015 BRFSS SMART dataset is a sub-sample of the 2015 state BRFSS surveys based on geographies defined as metropolitan statistical areas, micropolitan statistical areas, and metropolitan divisions (collectively called MMSAs) made publicly available to researchers. The 2015 BRFSS Smart dataset included 132 MMSAs where at least 500 BRFSS surveys were collected [18 ].
Individual health behaviors selected for this study were those identified by the Centers for Disease Control and Prevention (CDC) as “Winnable Battles”. Winnable Battles are health outcomes for which the CDC believes that public health can make significant progress in a relatively short time frame (i.e., within one to 4 years), have a large-scale public impact, and have evidenced-based interventions readily available for ease of implementation. From the “Winnable Battles” list, we included the following modifiable behaviors/conditions into our analysis: smoking, wearing a seatbelt, binge drinking, eating vegetables daily, eating fruit daily, general exercise in a month, vigorous exercise (300 min) in a week and being overweight or obese (based on self-reported height and weight). Additionally, while not designated by the CDC as a “Winnable Battle”, flu vaccinations are also an important measure of community health where LHDs and nonprofit hospitals may collaborate and was thus included in our analysis. While we did not have access to specific collaborative action strategies, many of the included measures are commonly identified in community health needs assessments (CHNAs) as being health needs in the community (healthy eating, physical activity, smoking, etc.) [19 , 20 ].
In addition to studying the impact of LHD-hospital collaboration on specific individual health behaviors, we also conducted analyses using two index measures of the health behaviors. We created one index for risky behaviors and another for healthy lifestyle behaviors (healthy eating and exercise).
The risky behaviors index included wearing a seatbelt, not smoking, not binge drinking and getting a flu shot. For each individual respondent, we assigned a score for this index based on the number of specific behaviors that the individual reported undertaking (or in the case of smoking and binge drinking reported not undertaking). Thus, if an individual did not report undertaking any of these behaviors, we assigned a score of zero. If an individual reported all of the behaviors, we assigned a score of four. To create the healthy lifestyle index, we combined the specific behaviors of eating vegetable(s) daily, eating fruit daily and vigorously exercising. We found a high correlation between general exercise and vigorous exercise and felt the latter was more representative of a healthy lifestyle, so we included that variable only. For this index, we also assigned a score to each individual respondent based on the number of behaviors that reportedly were undertaken. Thus, the healthy lifestyle index ranged from zero (if an individual did not report any of the variables) to three (if an individual reported eating fruit, vegetables and vigorously exercising). Index variables were analyzed as continuous outcomes.
Cramer G.R., Young G.J., Singh S., McGuire J, & Kim D. (2021). Evidence that collaborative action between local health departments and nonprofit hospitals helps foster healthy behaviors in communities: a multilevel study. BMC Health Services Research, 21, 1.
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Publication 2021
Behavior Disorders Feeding Behaviors Feelings Fruit Needs Assessment Obesity Reading Frames Trivalent Influenza Vaccine Vaccination Vegetables
4
Autism Assessment Profiles and Diagnoses
The sample included data from 393 different individual participants. Some participants had repeated assessments, yielding a total of 437 cases. Each case was defined by an ADOS and best estimate clinical diagnosis; 29 participants provided data for multiple cases (M=2.52, SD=1.06, range=2–6) based on evaluations conducted at different points in time. The majority of participants (n=319) were research participants and clinic referrals for assessment of possible autism to the University of Chicago Developmental Disorders Clinic (UCDDC), the University of Michigan Autism and Communication Disorders Center (UMACC), or the New York Presbyterian Center for Autism and the Developing Brain (CADB) at Weill-Cornell. Seventy-four participants were evaluated as part of the Simons Simplex Collection (SSC; Fischbach & Lord 2011), a multi-site genetic study. Approximately 80% of the sample was male and 83% Caucasian. Inclusion/Exclusion criteria varied by research study. However, individuals with significant hearing, vision or motor problems that interfered with standardized testing or who were exhibiting active psychosis or uncontrolled seizures at the time of assessment were excluded from each study. Participants in the SSC were also required to meet Collaborative Programs for Excellence in Autism criteria for ASD (see Hus et al., 2013 (link) for details) and were excluded if the individual had a diagnosis of Fragile X syndrome, tuberous sclerosis, Down syndrome or significant early medical history. Additionally, SSC participants could not have any first, second or third degree relatives with ASD or a sibling with substantial language or psychological problems related to ASD. Ages ranged from 9.92 to 62.25 years at the time of assessment (mean=21.56, standard deviation=8.62 years).
Of the 437 cases, 177 had clinical diagnoses of autism (40% of entire sample), 170 Other-ASD (i.e., PDD-NOS or Asperger’s; 39%), and 90 Non-ASD diagnoses (21%). The Non-ASD sample was comprised of both clinical referrals and individuals recruited to research studies as controls. In addition to having first ruled-out an ASD diagnosis, 84% of non-ASD participants received a primary diagnosis of a non-ASD DSM-IV-TR disorder; 30% had a primary diagnosis of mood and/or anxiety disorders, 26% had non-specific intellectual disability, 14% had externalizing behavioral disorders (e.g., ADHD/ODD), 5% had Down syndrome or Fragile X, 4% had language disorders, 1% had Fetal Alcohol syndrome, 1% had Cerebral Palsy and 3% of cases had unspecified difficulties. The remaining 16% of Non-ASD sample did not meet criteria for a DSM-IV-TR diagnosis at the time of assessment; 64% of these individuals (n=9) had had a previous diagnosis of ASD and 36% (n=5) had had a previous Non-ASD diagnosis. There was no significant difference in ADOS totals between the 9 individuals with previous ASD diagnoses and the remaining non-ASD group (data available from authors upon request). Table 1 provides a more detailed sample description.
Hus V, & Lord C. (2014). The Autism Diagnostic Observation Schedule, Module 4: Revised Algorithm and Standardized Severity Scores. Journal of autism and developmental disorders, 44(8), 1996-2012.
Publication 2014
Adenosine Anxiety Disorders Autistic Disorder Behavior Disorders Brain Caucasoid Races Cerebral Palsy Communicative Disorders Developmental Disabilities Diagnosis Disorder, Attention Deficit-Hyperactivity Down Syndrome Fetal Alcohol Syndrome Fragile X Syndrome Intellectual Disability Language Disorders Males Mood Multiple Birth Offspring Psychotic Disorders Seizures Tuberous Sclerosis
5
Comprehensive Assessment of Disease Burden

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Publication 2012
Acquired Immunodeficiency Syndrome Acute Disease African Trypanosomiasis Appendicitis Behavior Disorders Care, Prenatal Cerebrovascular Accident Child Cholera Chronic Kidney Diseases Congenital Abnormality Dementia Dengue Fever Disease, Chronic Drug Abuser Epilepsy Households Injuries Intellectual Disability Leprosy Liver Cirrhosis Malignant Neoplasms Measles Mental Health Multiple Sclerosis Myocardial Infarction Outpatients Pancreatitis Patient Discharge Patients Pertussis Pneumoconiosis Population Group Projective Techniques Respiratory Diaphragm Schistosomiasis sequels Skin Diseases Syphilis Tuberculosis Vision Woman Yellow Fever

Most recents protocols related to «Behavior Disorders»

1
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.
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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
2
Nationwide Mental Health Trends During Pandemic
We conducted a nationwide register-based retrospective surveillance study from 2019 to 2021. Data were gathered from three open-access registers. The number of primary health care visits to physicians due to mental health problems was collected from the Care Register for Primary Health Care, which is maintained by the Finnish Institute of Health and Welfare. The register has excellent coverage, as over 90% of the Finnish primary care centers report data to it [17 ]. Visits with mental health related F category (mental and behavioral disorders) diagnostic codes (International Classification of Diseases 10th version) were included (Additional file 1: Table S1). Based on these diagnoses, we calculated the yearly incidence per 1000 adolescents and young adults aged 15–24 years in primary care due to mental health problems. The age group is pre-stratified by the Finnish Institute of Health and Welfare (registry owner) for the open-access data. As we defined the inclusion based on the diagnostic code (F-class) we do not have missing information on visit rates. Visits without F-class diagnoses were thus all excluded. One visit may have more than one diagnose, but all of the diagnoses should be relevant to the visit and describe the visit.
In Finland mental health problems are treated in primary care. The patient first meets either a physician or nurse who is specialized to mental health. Prescriptions and medication decisions are made by physicians and similarly sick leave is prescribed physicians only. Severe or treatment persistent cases are referred from primary care to specialized psychiatric healthcare (secondary or tertiary level units with outpatient clinics). Some larger primary care centers have own specialized psychiatrics hired to reduce the need to referrals to specialized healthcare, but these practices have large variations between cities.
In addition, we collected all psychotropic medications prescribed by a physician from the Register of Reimbursable Medicine Costs, which is maintained by the Social Insurance Institution of Finland. Finland has a universal tax-funded health care system, where all medication purchases are reported to the register regardless of the setting of the prescription (primary care, specialized care, hospitals, and private clinics) [18 , 19 (link)]. The register does not, however, contain information on dosage, indication, or for how long the medication was prescribed. Therefore, we calculated the prevalence of medication users per 1000, and labeled persons as users if they purchased medication from the pharmacy. One person might have purchased several different classes of medication. We have included the medications based on the Anatomical Therapeutic Chemical (ATC) Classification system (Additional file 1: Table S2). We analyzed the main classes and the most used medications more specifically. As the register uses default age stratification (which is defined by the register holder), we included medication information for all adolescents aged 13 to 24 years.
Finally, we gathered the population size for each age group from the Population Information System at the end of the year in question and used it as the denominator in the incidence and prevalence calculations [20 ]. Incidence and prevalence were calculated per 1000 persons per age-group with 95% confidence intervals (CI). Incidence comparisons between the pandemic years (2020 and 2021) and the reference year (2019) were made by incidence rate ratios (IRR), and prevalence comparisons were made by prevalence rate ratios (PRR) with CI.
As we used open-access data, no research permissions or ethical committee evaluations were required. All the data generated in this process have been provided as an appendix (Additional file 2: Table S2).
Kuitunen I., Uimonen M.M., Ponkilainen V.T, & Mattila V.M. (2023). Primary care visits due to mental health problems and use of psychotropic medication during the COVID-19 pandemic in Finnish adolescents and young adults. Child and Adolescent Psychiatry and Mental Health, 17, 35.
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Publication 2023
Adolescent Age Groups Behavior Disorders Diagnosis Mental Health Nurses Pandemics Patients Pharmaceutical Preparations Physicians Primary Health Care Psychotropic Drugs Therapeutics Young Adult
3
Racial Disparities in Pain and Behavioral Health
Our primary variable of interest was race/ethnicity, which we classified using the US census categories non-Latino White (referred to as White), non-Latino Black (referred to as Black), Hispanic/Latino (referred to as Latino), and non-Latino Asian (referred to as Asian). To address confounding, regression models included the following covariates: age (18–24, 25–34, 35–44, 45-54, 55–64, 65+), sex (males, females), indicator for chronic pain, and indicators for behavioral health disorder diagnoses (anxiety disorder, bipolar disorder, major depression disorder, schizophrenia disorder, alcohol use disorder, cocaine use disorder, cannabis use disorder, and tobacco use disorder). We used ICD-10 codes, as in prior research [50 (link)], to identify chronic pain in the electronic health record.
Flores M.W., Sharp A., Lu F, & Cook B.L. (2023). Examining Racial/Ethnic Differences in Patterns of Opioid Prescribing: Results from an Urban Safety-Net Healthcare System. Journal of Racial and Ethnic Health Disparities.
Publication 2023
Alcohol Use Disorder Anxiety Disorders Asian Persons Behavior Disorders Bipolar Disorder Cannabis Chronic Pain Cocaine Diagnosis Ethnicity Females Hispanics Latinos Major Depressive Disorder Males Schizophrenia Tobacco Use Disorder
4
Parental Report of Child Behavioral Health
Parents reported if their child had ever received a diagnosis from a physician for any of the following behavioral health disorders: depression, anxiety, attention deficit hyperactivity disorder. For analyses, responses were dichotomized into “none” and “1 or more.”
Schmied E.A., Madanat H., Chuang E., Moody J., Ibarra L., Cervantes G., Strong D., Boutelle K, & Ayala G.X. (2023). Factors predicting parent engagement in a family-based childhood obesity prevention and control program. BMC Public Health, 23, 457.
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Publication 2023
Anxiety Disorders Behavior Disorders Child Diagnosis Disorder, Attention Deficit-Hyperactivity Parent Physicians
5
Assessing Health Anxiety in Primary Care
By the purpose of the study, a questionnaire form was prepared using the literature [1 (link), 6 , 12 (link), 15 –24 ]. The questionnaire form consisted of two parts. The first part was including questions about the sociodemographic characteristics of individuals (age, gender, marital status, education level, family income, and childbearing status) and some factors that may be associated with health anxiety (presence of chronic disease with a physician diagnosis, presence of health insurance, mental and behavioral disorder symptoms, general health status, number of admissions to any health institution in the last p year, and hospitalization in the past 1 year). The second part was composed of The Short Health Anxiety Inventory (SHAI).
The SHAI was developed in 2002 by Salkovskis et al. [25 (link)]. The Turkish validity and reliability study of the inventory was conducted in 2013 by Aydemir et al. [5 (link)]. The inventory consists of 4-point Likert-scale (never=0 and always=3) 18 questions. The score that can be obtained from the inventory varies between 0 and 54 and it is accepted that the level of health anxiety increases as the score increases. In the Turkish validity and reliability study, the Crobach’s alpha coefficient showing the internal consistency of the scale was given as 0.918 [5 (link)].
During the study, individuals who admitted to primary healthcare institutions were informed about the subject and purpose of the study. Verbal consent was obtained from those who agreed to participate in the study. The previously prepared questionnaire forms were filled out by the researchers through the face-to-face interview method. This process took approximately 20–25 min.
In the study, the family income of individuals was evaluated as “high, medium, and low” according to their perceptions. General health status was evaluated as “good, medium, and bad” by questioning how people define their health status. Those who declared that they had been diagnosed with chronic disease by a physician before were accepted as “they have a physician-diagnosed chronic disease.” Likewise, the person’s state of having a mental and behavioral disorder (psychiatric illness) and experiencing symptoms of mental and behavioral disorders was also evaluated according to the person’s response to the relevant questions.
Gedik S.A., Atay E., Pala S.C., Zencirci S.A., Ocal E.E., Demirtas Z., Yenilmez C., Onsuz M.F, & Metintas S. (2023). Evaluation of health anxiety in adults admitting to primary healthcare institutions. Northern Clinics of Istanbul, 10(1), 87-94.
Publication 2023
Anxiety Behavioral Symptoms Behavior Disorders Disease, Chronic Face Family Member Gender Health Insurance Hospitalization Mental Disorders Physicians Primary Health Care Vaginal Diaphragm

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1
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More about "Behavior Disorders"

Behavioral disorders, also known as conduct disorders or externalizing disorders, encompass a wide range of persistent, disruptive behaviors that deviate from social norms.
This broad category includes conditions like Attention Deficit Hyperactivity Disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD).
Individuals with these disorders may exhibit challenges with impulse control, emotional regulation, and social interaction.
Understanding the latest research on effective interventions and management strategies is crucial for healthcare providers, researchers, and clinicians working in this field.
Leveraging tools like SAS 9.4, MATLAB, and Stata 14 can help streamline data analysis and optimize research workflows.
Emerging technologies, such as Media Recorder 4.0 and VFC-008, may also play a role in studying behavioral disorders, particularly in animal models like Male Sprague-Dawley rats.
The VideoFreeze software, for example, can be used to analyze freezing behavior, a common measure of fear and anxiety in rodent studies.
Additionally, pharmacological interventions like 2,2,2-tribromoethanol have been explored for their potential to modify behavioral patterns in animal models.
By synthesizing insights from the latest research, healthcare professionals can develop more effective strategies for the management and treatment of behavioral disorders.
PubCompare.ai offers a powerful platform to support this critical work, providing AI-driven comparisons of protocols, products, and best practices from the literature.
This tool can help enhance reproducibility and streamline the research process, enabling researchers and clinicians to take their behavioral disorders studies to new heights.