Predictors Of Anxiety Disorders Research Articles

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.

Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently. This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety. Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate. Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods. Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI. PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560.

Anxiety and depression from age 3 to 8 years in children with and without ADHD symptoms

Childhood anxiety and depressive symptoms may be influenced by symptoms of attention deficit/hyperactivity disorder (ADHD). We investigated whether parent- and teacher-reported anxiety, depressive and ADHD symptoms at age 3 years predicted anxiety disorders and/or depression in children with and without ADHD at age 8 years. This study is part of the longitudinal, population-based Norwegian Mother, Father and Child Cohort Study. Parents of 3-year-olds were interviewed, and preschool teachers rated symptoms of anxiety disorders, depression and ADHD. At age 8 years (n = 783), Child Symptom Inventory-4 was used to identify children who fulfilled the diagnostic criteria for anxiety disorders and/or depression (hereinafter: Anx/Dep), and ADHD. Univariable and multivariable logistic regression analyses were used. In the univariable analyses, parent-reported anxiety, depressive and ADHD symptoms, and teacher-reported anxiety symptoms at age 3 years all significantly predicted subsequent Anx/Dep. In the multivariable analyses, including co-occurring symptoms at age 3 years and ADHD at 8 years, parent-reported anxiety and depressive symptoms remained significant predictors of subsequent Anx/Dep. At age 3 years, regardless of ADHD symptoms being present, asking parents about anxiety and depressive symptoms, and teachers about anxiety symptoms, may be important to identify children at risk for school-age anxiety disorders and/or depression.

The Warm Temperament (Mizaj) as a Predictor of Anxiety Disorder among Participants in the Kerman Coronary Artery Disease Risk Factors Study (KERCADRS)

Anxiety disorders are the most common mental disorders in communities. This cross-sectional study assessed the relationship between temperament and other demographic characteristics of participants with anxiety. It also determined the predictors of anxiety disorders among this cohort. This cross-sectional study was conducted in Kerman, Iran from 11 Mars, 2018 to 22 May, 2019. The participants were 1532 people who were selected through convenience sampling from those who attended the second round of KERCARDS in 2017. Physical activity, anxiety, depression, and temperament were assessed through related questionnaires. The collected data were described and analyzed by mean (Standard Deviation (SD)) and logistic regression models in SPSS v.23. The mean (SD) age of the participants was 30.02 (6.83) years (age range: 11 to 40). More than two-thirds of them were married (n=1084, 70.7%) and held a diploma and university degrees (n=1112, 72.6%). The anxiety symptoms were prevalent among more than one-third of the participants (n=574, 37.5%, 95%CI: 35.2, 40.1). According to multivariate logistic regression model, female sex (OR:1.80, 95%CI:1.41, 2.30; P value:0.001), illiteracy, lower education (OR:1.40, 95%CI:1.09, 1.81; P value:0.009), a history of depression (OR:14.51, 95%CI:9.65, 21.80; P value:0.001), and having warm Mizaj (OR:1.31, 95%CI:1, 1.71; P value:0.04) were determined as predictors of anxiety. In the present study, female sex, lower educational status, the experience of depression, and warm temperament increased the risk of anxiety and were determined as predictors of anxiety disorder. Diagnosis of temperament seems to be necessary for recognizing anxiety symptoms and also finding an effective treatment.

Elimination Disorders and their relationship to anxiety disorders in mentally disabled children with autism

The current research aims to identify the patterns of Elimination Disorders and their relationship to anxiety disorders in a sample of mentally handicapped children with autism spectrum disorder, numbering (70) of them (43 males, 27 females), ranging in age from (5-9 years) with an average age of (5.89). And a standard deviation of (1.22), from Israr Association and Ayyad Najd Center in Arar, Northern Borders Region. The study relied on the descriptive correlative approach. The results of the research have revealed the prevalence of some patterns of Elimination Disorders and anxiety disorders in children in the research sample, and there is a correlation between patterns of Elimination Disorders and anxiety disorders resulting from them in children with autism spectrum disorder who are mentally handicapped, as well as the contribution of Elimination Disorders(Enuresis- Encopresis- dysury- dysuria) in predicting anxiety disorders (specific phobia, panic disorder, agoraphobia, generalized anxiety disorder) in mentally disabled children with autism spectrum disorder.

Prediction of anxiety disorders using a feature ensemble based bayesian neural network

Anxiety disorders are common among youth, posing risks to physical and mental health development. Early screening can help identify such disorders and pave the way for preventative treatment. To this end, the Youth Online Diagnostic Assessment (YODA) tool was developed and deployed to predict youth disorders using online screening questionnaires filled by parents. YODA facilitated collection of several novel unique datasets of self-reported anxiety disorder symptoms. Since the data is self-reported and often noisy, feature selection needs to be performed on the raw data to improve accuracy. However, a single set of selected features may not be informative enough. Consequently, in this work we propose and evaluate a novel feature ensemble based Bayesian Neural Network (FE-BNN) that exploits an ensemble of features for improving the accuracy of disorder predictions. We evaluate the performance of FE-BNN on three disorder-specific datasets collected by YODA. Our method achieved the AUC of 0.8683, 0.8769, 0.9091 for the predictions of Separation Anxiety Disorder, Generalized Anxiety Disorder and Social Anxiety Disorder, respectively. These results provide initial evidence that our method outperforms the original diagnostic scoring function of YODA and several other baseline methods for three anxiety disorders, which can practically help prioritizing diagnostic interviews. Our promising results call for investigation of interpretable methods maintaining high predictive accuracy.

ANXIETY DISORDERS IN MALAYSIA FROM 2005 TO 2015: A SCOPING REVIEW OF THEIR PREVALENCE RATES, ASSOCIATED FACTORS, AND PREDICTORS

Introduction: The current study aims to investigate the prevalence rates, associated factors, and predictors of anxiety disorders in Malaysia through a scoping review. Methods: PsycINFO and MEDLINE databases and a total of 28 local journals were used to search for published papers in this particular area. In this case, 37 out of 105 articles managed to meet the inclusion criteria and were subjected to review. A total of 17,673 respondents, which comprised university students, the general community, and numerous types of clinical patients, were included in the review. Results: The results showed that the prevalence rate of anxiety disorders was in the range between 1% and 67.6% for the mentioned populations. Moreover, various types of associated factors were found to be related to anxiety such as being divorced, widowed, or single; having a lower than tertiary level of education; being unemployed or retired; and having a low socioeconomic status. Meanwhile, there were also several predictors of anxiety such as having to deal with negative life events; domestic violence; family history of mental illness; severe psychological problems; and chronic physical illness. Conclusion: Generally, the prevalence of anxiety disorders is noticeable, and anxiety disorders may contribute to a decrease in the quality of life, an increase in social burden, reduced productivity, and increased utilization of health services. Therefore, awareness among community and health service providers regarding the prevalence rate of anxiety is believed to help in developing access to evidence-based psychological and pharmacological interventions.

Взаимосвязь гормонального профиля и тревожных расстройств у женщин — внутренне перемещенных лиц с угрожающим прерыванием беременности

Актуальность. Беременность — очень важный, критический этап в жизни каждой женщины. Хронический стресс может влиять на течение беременности, приводя к увеличению числа осложнений. Перемещенные лица обычно имеют более высокие показатели депрессивных и посттравматических симптомов, чем население регионов, в которые они переселяются. В антенатальном периоде повышена склонность к психоэмоциональным расстройствам, около 54 % женщин испытывают тревогу в течение всей беременности. Угроза прерывания беременности (УПБ) — самое распространенное осложнение беременности, возникающее в 15–20 % случаев продолжающейся беременности и связанное с осложненным течением беременности и родов. Гормональный гомеостаз играет важную роль в поддержании и развитии беременности. Цель работы: изучить особенности взаимосвязи гормонального профиля и тревожных расстройств у женщин — внутренне перемещенных лиц (ВПЛ) с угрожающим прерыванием беременности, проживающих в Луганской области, для усовершенствования лечебно-профилактических мероприятий и профилактики акушерских и перинатальных осложнений у таких женщин. Материалы и методы. Обследовали 24 беременных со статусом ВПЛ, находившихся на стационарном лечении по поводу УПБ в учреждениях здравоохранения Луганской области (I группа). Контрольную группу (II группа) составили 25 беременных с неосложненным анамнезом с физиологическим течением беременности аналогичного гестационного срока. Определяли концентрацию эстрадиола, прогестерона, пролактина, кортизола. Для оценки уровня тревожности применялся тест Спилбергера. Результаты. У пациенток группы I выявлен статистически значимый относительный прогестероновый дефицит, достоверное превышение уровней стресс-ассоциированных гормонов пролактина и кортизола. Показатели реактивной и личностной тревожности достоверно выше в группе I. Установлена положительная корреляционная связь между показателем реактивной тревожности и концентрацией кортизола. Предикторами развития тревожных расстройств, по данным множественного регрессионного анализа, выступают уровень личностной тревожности и концентрации кортизола и прогестерона. Выводы. Антенатальное изучение показателей тревожности, уровня стресс-ассоциированных гормонов, прогестерона у беременных — ВПЛ с симптомами УПБ позволит индивидуализировать подход к ведению и своевременно разработать в случае необходимости корректирующие и реабилитационные мероприятия, которые будут способствовать благополучному завершению беременности и влиять на здоровье матери и ее потомства.

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study.

This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) This study presented decision tree visualization that could help psychiatric consultants and primary physicians easily interpret the path of predicting high-risk groups based on major predictors derived from final machine learning models with the best performance. This study analyzed 1558 elderly (695 males and 863 females) who were 60 years or older and completed the Zung’s Self-Rating Anxiety Scale (SAS). We used support vector machine (SVM), random forest, LightGBM, and Adaboost for the base model, a single predictive model, while using XGBoost algorithm for the meta model. The analysis results confirmed that the predictive performance of the “SVM + Random forest + LightGBM + AdaBoost + XGBoost model (stacking ensemble: accuracy 87.4%, precision 85.1%, recall 87.4%, and F1-score 85.5%)” was the best. Also, the results of this study showed that the elderly who often (or mostly) felt subjective loneliness, had a Self Esteem Scale score of 26 or less, and had a subjective communication with their family of 4 or less (on a 10-point scale) were the group with the highest risk anxiety disorder. The results of this study imply that it is necessary to establish a community-based mental health policy that can identify elderly groups with high anxiety risks based on multiple risk factors and manage them constantly.

Familial aggregation of anxiety disorder subtypes and anxious temperament in the NIMH Family Study of Affective Spectrum Disorders

BackgroundEvidence from family and twin studies suggests that mood and anxiety disorders, and related temperamental factors may share common etiologic factors. We examine the familial aggregation and coaggregation of anxiety disorder subtypes and anxiety-related temperamental traits, and their association with mood disorders. MethodsA total of 477 probands and 549 first-degree adult relatives from a large community based family study of affective spectrum disorders completed semi-structured diagnostic interviews and self-reported assessments of temperamental traits including: negative affectivity on the ‘Positive and Negative Affect Schedule’ (PANAS), neuroticism anxiety on the ‘Zuckerman-Kuhlman Personality Questionnaire’ (ZKPQ), and anxiety sensitivity on the ‘Anxiety Sensitivity Index’ (ASI). ResultsThe anxiety-related temperamental traits of negative affectivity, neuroticism anxiety and anxiety sensitivity had significant familial specificity, even after controlling for comorbid mood and anxiety disorders in probands and relatives. Yet, these traits in probands did not predict anxiety disorders in relatives. Although some anxiety subtypes were familial, there were no longer familial links between anxiety disorder subtypes (generalized anxiety disorder, social anxiety or panic disorder) after controlling for mood disorder subtypes in probands and relatives. LimitationsCross-sectional interviews were used to estimate disorders, and self-report measures were used for temperamental traits. ConclusionsThese results confirm previous research regarding familial overlap between anxiety subtypes and mood disorders, however their shared liability cannot be fully explained by anxiety-related temperamental traits. These findings suggest that anxiety-related temperamental traits may indicate a vulnerability for mood and anxiety disorders or a potential consequence of these conditions.

Psychosocial and biological risk factors of anxiety disorders in adolescents: a TRAILS report

Anxiety disorders are a common problem in adolescent mental health. Previous studies have investigated only a limited number of risk factors for the development of anxiety disorders concurrently. By investigating multiple factors simultaneously, a more complete understanding of the etiology of anxiety disorders can be reached. Therefore, we assessed preadolescent socio-demographic, familial, psychosocial, and biological factors and their association with the onset of anxiety disorders in adolescence. This study was conducted among 1584 Dutch participants of the TRacking Adolescents’ Individual Lives Survey (TRAILS). Potential risk factors were assessed at baseline (age 10–12), and included socio-demographic (sex, socioeconomic status), familial (parental anxiety and depression), psychosocial (childhood adversity, temperament), and biological (body mass index, heart rate, blood pressure, cortisol) variables. Anxiety disorders were assessed at about age 19 years through the Composite International Diagnostic Interview (CIDI). Univariate and multivariate logistic regression analyses were performed with onset of anxiety disorder as a dependent variable and the above-mentioned putative risk factors as predictors. Of the total sample, 25.7% had a lifetime diagnosis of anxiety disorder at age 19 years. Anxiety disorders were twice as prevalent in girls as in boys. Multivariate logistic regression analysis showed that being female (OR = 2.38, p < .01), parental depression and anxiety (OR = 1.34, p = .04), temperamental frustration (OR = 1.31, p = .02) and low effortful control (OR = 0.76, p = .01) independently predicted anxiety disorders. We found no associations between biological factors and anxiety disorder. After exclusion of adolescents with an onset of anxiety disorder before age 12 years, being female was the only significant predictor of anxiety disorder. Being female was the strongest predictor for the onset of anxiety disorder. Psychological and parental psychopathology factors increased the risk of diagnosis of anxiety, but to a lesser extent. Biological factors (heart rate, blood pressure, cortisol, and BMI), at least as measured in the present study, are unlikely to be useful tools for anxiety prevention and intervention strategies.

Prevalence, comorbidity and predictors of anxiety disorders among children and adolescents.

Childhood anxiety may lead to serious health consequences in later life. The present study provides the prevalence, comorbidity, and predictors of anxiety disorders among children and adolescents. This was a cross-sectional national project that was implemented on 28,698 children and adolescents in Iran. Participants entered the study by multistage cluster sampling with an equal number of each gender and three age groups (6-9, 10-14, and 15-18 years) within each cluster. The tools used in this research were the demographic questionnaire and K-SADS-PL. To analyze the data logistic regression and chi-square tests were used in SPSS (ver. 16). The prevalence of anxiety disorder in children and adolescents was 13.2 in boys and 15.1 in girls. Furthermore, gender, age, place of residence and history of psychiatric hospitalization of parents could predict anxiety disorders. Anxiety disorders had comorbidity with behavioral disorders, neurodevelopmental disorders, mood disorders, psychotic disorders, substance abuse disorders, and elimination disorders. According to our findings in this study, anxiety disorders affect the performance, health and life of children and adolescents, identifying the childhood anxiety, as well as finding diseases that are associated with anxiety disorders, can help in the prevention of the disorder.

Factors associated with anxiety among the Lebanese population: the role of alexithymia, self-esteem, alcohol use disorders, emotional intelligence and stress and burnout

Objective: To assess factors associated with anxiety among a sample of the Lebanese population.Methods: A cross-sectional, conducted between November 2017 and March 2018, enrolled 789 participants. Anxiety was measured using the Hamilton Anxiety Scale. A cluster analysis was then performed with the identified factor scores to identify the different profiles of the participants.Results: A cluster analysis based on the three factors derived three mutually exclusive clusters, which form 29.62%, 34.54%, and 35.84% of all participants, respectively. The first cluster represented people in distress (low emotional intelligence, high depersonalisation, alcohol use disorder, burnout, stress, alexithymia and low self-esteem); the second one represented people with wellbeing (High emotional intelligence, low depersonalisation, low alcohol use disorder, low burnout, low stress, low alexithymia and high self-esteem), whereas cluster 3 represented people in between. Higher age (Beta = 0.065) was significantly associated with higher anxiety, whereas being in cluster 2 (people with wellbeing) (Beta = −12.37) and cluster 3 (people in between) (Beta = −5.426) were significantly associated with lower anxiety compared to being in cluster 1 (people in distress).Conclusions: The findings of this study are overall consistent with those of epidemiologic community-based surveys and may help inform structural models of classification and prediction of anxiety disorders (ADs).

Predicting anxiety disorders and suicide tendency using machine learning: a review

Anxiety disorders constitute the largest group and the most common type of mental disorders. At the same time, machine learning techniques can be used for analysing a patient's history and diagnose problems imitating the human reasoning or in making logical decisions. This work reviews the main concepts and applications of machine learning techniques in predicting anxiety disorder types. Seventeen (17) studies were considered, that applied machine learning techniques for predicting anxiety disorders and five (5) additional studies were examined for predicting suicide tendencies. The accuracy of the results varies according to the type of anxiety disorder and the type of methods utilised for predicting the disorder.

Predicting anxiety disorders and suicide tendency using machine learning: a review

Anxiety disorders constitute the largest group and the most common type of mental disorders. At the same time, machine learning techniques can be used for analysing a patient's history and diagnose problems imitating the human reasoning or in making logical decisions. This work reviews the main concepts and applications of machine learning techniques in predicting anxiety disorder types. Seventeen (17) studies were considered, that applied machine learning techniques for predicting anxiety disorders and five (5) additional studies were examined for predicting suicide tendencies. The accuracy of the results varies according to the type of anxiety disorder and the type of methods utilised for predicting the disorder.

Prevalence and Predictors of Anxiety Disorders in Adolescent and Adult Males with Autism Spectrum Disorder and Fragile X Syndrome.

Anxiety disorders affect ~ 15-20% of youths without neurodevelopmental disorders, with persons having autism spectrum disorder (ASD) and fragile X syndrome (FXS) at elevated risk for anxiety disorders. Few studies have compared rates and predictors of anxiety disorders in adolescents with FXS or ASD. This study directly compares rates, predictors, and medication of anxiety disorders between age-matched, male adolescents with FXS (n = 31) or ASD (n = 20). Results indicate that 51.6% of FXS and 50.0% of ASD adolescents met criteria for an anxiety disorder. Cognitive scores and ASD severity did not predict anxiety. Of those with anxiety, ~ 40% of the FXS and 20% of the ASD participants were prescribed medications for anxiety.

Network tools identify early signs of anxiety

Novel network analyses identify constellations of childhood symptoms that predict anxiety disorders and depression during adolescence and young adulthood.

Prevalence and stability of mental disorders among young adults: findings from a longitudinal study

BackgroundMental disorders often have onset early in life, contribute substantially to the global disease burden, and may interfere with young people’s ability to complete age-relevant tasks in important developmental periods. However, knowledge about prevalence and course of mental disorders in young adulthood is sparse. The aim of the current study was to estimate prevalence and stability of mental disorders from the twenties to the thirties/forties.MethodsDSM-IV mental disorders were assessed with the Composite International Diagnostic Interview in two waves (1999–2004 and 2010–2011) in 1623 young adult Norwegian twins (63.2% women, aged 19–29 years in wave 1).ResultsIn wave 1, the 12-month prevalence of any mental disorder among people in the twenties was 19.8% (men) and 32.4% (women), anxiety disorders: 9.6% (men) and 26.7% (women), anxiety disorders excluding specific phobias: 2.5% (men) and 6.9% (women), major depressive disorder (MDD): 4.4% (men) and 7.2% (women), and alcohol use disorder (AUD): 8.7% (men) and 4.4% (women). The prevalence of any mental disorder decreased from the twenties to the thirties/forties. This was due to a decrease in AUD and specific phobias. Anxiety disorders in the twenties predicted anxiety disorders and MDD ten years later, even when controlling for the association between these disorders in the twenties. MDD in the twenties predicted MDD ten years later. At both ages, two-week and 12-month prevalence estimates differed markedly for MDD - indicating an episodic course.ConclusionsCommon mental disorders are highly prevalent among young adults in the twenties, and somewhat less prevalent in the thirties/forties. Those who suffer from one mental disorder in the twenties are at considerably increased risk for suffering from a disorder ten years later as well. This may have significant implications for young people’s ability to attain education, establish a family, and participate in occupational life.

Prevalence and predictors of anxiety disorders amongst low-income pregnant women in urban South Africa: a cross-sectional study.

Anxiety is highly prevalent in many populations; however, the burden of anxiety disorders amongst pregnant women in low-resource settings is not well documented. We investigated the prevalence and predictors of antenatal anxiety disorders amongst low-income women living with psychosocial adversity. Pregnant women were recruited from an urban, primary level clinic in Cape Town, South Africa. The Mini-International Neuropsychiatric Interview diagnostic interview assessed prevalence of anxiety disorders. Four self-report questionnaires measured psychosocial characteristics. Logistic regression models explored demographic and socioeconomic characteristics, psychosocial risk factors and psychiatric comorbidity as predictors for anxiety disorders. Amongst 376 participants, the prevalence of any anxiety disorder was 23%. Although 11% of all women had post-traumatic stress disorder, 18% of the total sample was diagnosed with other anxiety disorders. Multivariable analysis revealed several predictors for anxiety including a history of mental health problems (adjusted odds ratio [AOR] 4.11; 95% confidence interval (CI) 2.03-8.32), Major depressive episode (MDE) diagnosis (AOR 3.83; CI 1.99-7.31), multigravidity (AOR 2.87; CI 1.17-7.07), food insecurity (AOR 2.57; CI 1.48-4.46), unplanned and unwanted pregnancy (AOR 2.14; CI 1.11-4.15), pregnancy loss (AOR 2.10; CI 1.19-3.75) and experience of threatening life events (AOR 1.30; CI 1.04-1.57). Increased perceived social support appeared to reduce the risk for antenatal anxiety (AOR 0.95; CI 0.91-0.99). A range of antenatal anxiety disorders are prevalent amongst pregnant women living in low-resource settings. Women who experience psychosocial adversity may be exposed to multiple risk factors, which render them vulnerable to developing antenatal anxiety disorders.

The role of Maternal Attachment Styles in Predicting the Parent-Child Relationship and Anxiety Disorders in Children

The present study was conducted to assess the role of maternal attachment styles in predicting anxiety disorders in children. The present correlational study was conducted on 300 mothers and primary school students in Tehran selected through multistage random cluster sampling. The data collection tools used included the Adult Attachment Scale and Spence Children’s Anxiety Scale. The data obtained were then analyzed using the multivariate regression, the multivariate analysis of variance and the Pearson Correlation test. The results obtained showed that attachment styles predict anxiety disorders. There was a negative relationship between the secure attachment style and children’s anxiety and a positive relationship between the insecure attachment style and anxiety disorders. Problems in separation, the lack of tolerance for turmoil and the tendency to believe that events are out of one’s own control are mechanisms that explain the relationship between attachment styles and anxiety. Anxiety disorders associated with the parent-child interactions can be reduced through identifying the attachment style with the parents as a factor associated with children’s anxiety.Keywords: attachment styles; anxiety disorders;

The role of Maternal Attachment Styles in Predicting the Parent-Child Relationship and Anxiety Disorders in Children

The present study was conducted to assess the role of maternal attachment styles in predicting anxiety disorders in children. The present correlational study was conducted on 300 mothers and primary school students in Tehran selected through multistage random cluster sampling. The data collection tools used included the Adult Attachment Scale and Spence Children’s Anxiety Scale. The data obtained were then analyzed using the multivariate regression, the multivariate analysis of variance and the Pearson Correlation test. The results obtained showed that attachment styles predict anxiety disorders. There was a negative relationship between the secure attachment style and children’s anxiety and a positive relationship between the insecure attachment style and anxiety disorders. Problems in separation, the lack of tolerance for turmoil and the tendency to believe that events are out of one’s own control are mechanisms that explain the relationship between attachment styles and anxiety. Anxiety disorders associated with the parent-child interactions can be reduced through identifying the attachment style with the parents as a factor associated with children’s anxiety.Keywords: attachment styles; anxiety disorders