scholarly journals Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study (Preprint)

2021 ◽  
Author(s):  
Daniel Di Matteo ◽  
Kathryn Fotinos ◽  
Sachinthya Lokuge ◽  
Geneva Mason ◽  
Tia Sternat ◽  
...  
Keyword(s):  
Mental Health ◽  
Anxiety Disorder ◽  
Social Anxiety ◽  
Mental States ◽  
Self Report ◽  
Sensor Data ◽  
Generalized Anxiety ◽  
Sufficient Information ◽  
Cross Sectional ◽  
Automated Screening

BACKGROUND The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals’ behaviors to infer their mental states and therefore screen for anxiety disorders and depression. OBJECTIVE The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression. METHODS An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated. RESULTS Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression. CONCLUSIONS We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries.

scholarly journals Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

10.2196/28918 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e28918
Author(s):  
Daniel Di Matteo ◽  
Kathryn Fotinos ◽  
Sachinthya Lokuge ◽  
Geneva Mason ◽  
Tia Sternat ◽  
...  
Keyword(s):  
Mental Health ◽  
Anxiety Disorder ◽  
Social Anxiety ◽  
Mental States ◽  
Self Report ◽  
Sensor Data ◽  
Generalized Anxiety ◽  
Sufficient Information ◽  
Cross Sectional ◽  
Automated Screening

Background The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals’ behaviors to infer their mental states and therefore screen for anxiety disorders and depression. Objective The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression. Methods An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated. Results Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression. Conclusions We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries.

scholarly journals Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

10.2196/22844 ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. e22844
Author(s):  
Jonah Meyerhoff ◽  
Tony Liu ◽  
Konrad P Kording ◽  
Lyle H Ungar ◽  
Susan M Kaiser ◽  
...  
Keyword(s):  
Mental Health ◽  
Cohort Study ◽  
Social Anxiety ◽  
Exercise Duration ◽  
Self Report ◽  
Sensor Data ◽  
Longitudinal Cohort Study ◽  
Generalized Anxiety ◽  
Longitudinal Cohort ◽  
Behavioral Features

Background The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort. Objective This study aims to evaluate whether changes in phone sensor–derived behavioral features were associated with subsequent changes in mental health symptoms. Methods This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms. Results A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=−0.23, P=.02; Locations: r=−0.36, P<.001), exercise duration (r=0.39; P=.03) and use of active apps (r=−0.31; P<.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for Locations (r=−0.20; P=.03) and Transitions (r=−0.21; P=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety. Conclusions Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms.

Mental health impact of COVID-19 pandemic among physicians, nurses, and other healthcare providers, in the province of Alberta (Preprint)

2021 ◽  
Author(s):  
Hany ElGindi ◽  
Reham Shalaby ◽  
April Gusnowski ◽  
Wesley Vuong ◽  
Shireen Surood ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Anxiety Disorder ◽  
Generalized Anxiety Disorder ◽  
High Stress ◽  
Well Being ◽  
Self Report ◽  
Generalized Anxiety ◽  
Cross Sectional Survey ◽  
Cross Sectional

BACKGROUND During the COVID-19 pandemic, threats to mental health, psychological safety, and well-being are evident, particularly among the first responders and the healthcare staff. OBJECTIVE This study aimed to examine the prevalence and the potential predictors of the likely stress, generalized anxiety disorder, and major depressive disorder among healthcare workers (HCW). METHODS A cross-sectional survey was used through a survey link sent to gather demographic information and responses on several self-report scales, including the Perceived Stress Scale (PSS), Generalized Anxiety Disorder 7-item (GAD-7) scale, and Patient Health Questionnaire-9 (PHQ-9) among the various HCW groupings who subscribed to the Text4Hope program. RESULTS This study revealed that the HCW expressed an estimated high prevalence of moderate/high stress rates 840 (81.2%), while the likelihood of moderate/severe anxiety and depressive symptoms were 369 (38.6%), and 317 (32.7%), respectively, during COVID-19 pandemic. Nurses and other HCW were significantly more likely to report depressive symptoms, compared to physicians, (F (2, 159.47) =15.89, 95% CI= (-5.05) -(-2.04). Younger age groups of HCW (≤30 y) were more prone to report likely stress, anxiety, and depressive symptoms, compared to HCW 41-50y and >50y (Odd’s ratio range: 1.82- 3.03). Similarly, females and those who reported a lack of social support (separated/divorced and single) among HCW, had a higher likelihood to report likely stress and depressive symptoms, respectively (OR=1.8 and 1.6). CONCLUSIONS This cross-sectional study revealed the significant impact of COVID-19 pandemic on mental health and indicated significant vulnerability among groups of HCW in Alberta. CLINICALTRIAL Ethical approval for this research was obtained through the University of Alberta Health Research Ethics Board (Pro00086163).

scholarly journals Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study (Preprint)

2020 ◽  
Author(s):  
Jonah Meyerhoff ◽  
Tony Liu ◽  
Konrad P Kording ◽  
Lyle H Ungar ◽  
Susan M Kaiser ◽  
...  
Keyword(s):  
Mental Health ◽  
Cohort Study ◽  
Social Anxiety ◽  
Exercise Duration ◽  
Self Report ◽  
Sensor Data ◽  
Longitudinal Cohort Study ◽  
Generalized Anxiety ◽  
Longitudinal Cohort ◽  
Behavioral Features

BACKGROUND The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort. OBJECTIVE This study aims to evaluate whether changes in phone sensor–derived behavioral features were associated with subsequent changes in mental health symptoms. METHODS This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms. RESULTS A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (<i>Time</i>: <i>r</i>=−0.23, <i>P</i>=.02; <i>Locations</i>: <i>r=</i>−0.36, <i>P&lt;</i>.001), exercise duration (<i>r=</i>0.39; <i>P=</i>.03) and use of active apps (<i>r=</i>−0.31; <i>P</i>&lt;.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for <i>Locations</i> (<i>r</i>=−0.20; <i>P</i>=.03) and <i>Transitions</i> (<i>r</i>=−0.21; <i>P</i>=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety. CONCLUSIONS Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms. CLINICALTRIAL

scholarly journals Mental health literacy for anxiety disorders: how perceptions of symptom severity might relate to recognition of psychological distress

2015 ◽  
Vol 14 (2) ◽  
pp. 94-106 ◽  
Author(s):  
Daniel J. Paulus ◽  
Lauren Page Wadsworth ◽  
Sarah A. Hayes-Skelton
Keyword(s):  
Mental Health ◽  
Health Literacy ◽  
Anxiety Disorder ◽  
Major Depression ◽  
Social Anxiety ◽  
Symptom Severity ◽  
Mental Health Literacy ◽  
Psychological Disorders ◽  
Generalized Anxiety ◽  
Content Type

Purpose – Improving mental health literacy is an important consideration when promoting expedient and effective treatment seeking for psychological disorders. Low recognition serves as a barrier to treatment and the purpose of this paper is to examine recognition by lay individuals of severity for three psychological disorders: social anxiety, generalized anxiety, and major depression using a dimensional approach. Design/methodology/approach – Vignettes of mild/subclinical, moderate, and severe cases of each disorder were rated for severity by a team of expert assessors and 270 participants (mean age=26.8; 76.7 percent women). Findings – Difference ratings were calculated comparing participants’ responses to scores from the assessors. A within-groups factorial ANOVA with LSD follow-up was performed to examine the effects of Diagnosis and Severity on difference ratings. Both main effects (Diagnosis, F(2, 536)=35.26, Mse=1.24; Severity, F(2, 536)=9.44, Mse=1.93) and the interaction were significant (F(4, 1,072)=13.70, Mse=1.13) all p’s < 0.001. Social anxiety cases were under-rated in the mild/subclinical and moderate cases, generalized anxiety cases were under-rated at all three severities, and major depression cases were over-rated at all three severities. Social implications – Judgments of severity may underlie the low recognition rates for social anxiety disorder and generalized anxiety disorder. Future efforts should focus on improved recognition and education regarding anxiety disorders in the population, particularly before they become severe. Originality/value – This project demonstrates the importance of considering judgments of symptom severity on a continuum, and in a range of cases, rather than just the ability to correctly label symptoms, when determining whether or not people recognize psychological disorders.

scholarly journals Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study (Preprint)

2020 ◽  
Author(s):  
Daniel Di Matteo ◽  
Wendy Wang ◽  
Kathryn Fotinos ◽  
Sachinthya Lokuge ◽  
Julia Yu ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Observational Study ◽  
Generalized Anxiety Disorder ◽  
Social Anxiety Disorder ◽  
Strong Relationship ◽  
Self Report ◽  
Generalized Anxiety ◽  
Depression And Anxiety ◽  
Severity Of Depression

BACKGROUND The ability to objectively measure the severity of depression and anxiety disorders in a passive manner could have a profound impact on the way in which these disorders are diagnosed, assessed, and treated. Existing studies have demonstrated links between both depression and anxiety and the linguistic properties of words that people use to communicate. Smartphones offer the ability to passively and continuously detect spoken words to monitor and analyze the linguistic properties of speech produced by the speaker and other sources of ambient speech in their environment. The linguistic properties of automatically detected and recognized speech may be used to build objective severity measures of depression and anxiety. OBJECTIVE The aim of this study was to determine if the linguistic properties of words passively detected from environmental audio recorded using a participant’s smartphone can be used to find correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment. METHODS An Android app was designed to collect periodic audiorecordings of participants’ environments and to detect English words using automatic speech recognition. Participants were recruited into a 2-week observational study. The app was installed on the participants’ personal smartphones to record and analyze audio. The participants also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Words detected from audiorecordings were categorized, and correlations were measured between words counts in each category and the 4 self-report measures to determine if any categories could serve as correlates of social anxiety disorder, generalized anxiety disorder, depression, or general impairment. RESULTS The participants were 112 adults who resided in Canada from a nonclinical population; 86 participants yielded sufficient data for analysis. Correlations between word counts in 67 word categories and each of the 4 self-report measures revealed a strong relationship between the usage rates of death-related words and depressive symptoms (<i>r</i>=0.41, <i>P</i>&lt;.001). There were also interesting correlations between rates of word usage in the categories of reward-related words with depression (<i>r</i>=–0.22, <i>P</i>=.04) and generalized anxiety (<i>r</i>=–0.29, <i>P</i>=.007), and vision-related words with social anxiety (<i>r</i>=0.31, <i>P</i>=.003). CONCLUSIONS In this study, words automatically recognized from environmental audio were shown to contain a number of potential associations with severity of depression and anxiety. This work suggests that sparsely sampled audio could provide relevant insight into individuals’ mental health. CLINICALTRIAL

THE EFFECTS OF THE COVID-19 PANDEMIC ON THE MENTAL HEALTH OF UNIVERSITY STUDENTS IN THE FIELD OF HEALTH AND RELATED FACTORS: A WEB-BASED CROSS-SECTIONAL STUDY HEALTH STUDENT IN THE PANDEMIC

2021 ◽  
Vol 5 (20) ◽  
pp. 723-737
Author(s):  
Ayfer BAYINDIR ÇEVİK ◽  
Elçin Sebahat KASAPOĞLU
Keyword(s):  
Mental Health ◽  
Anxiety Disorder ◽  
University Students ◽  
Generalized Anxiety Disorder ◽  
Cross Sectional Study ◽  
Generalized Anxiety ◽  
Sectional Study ◽  
Cross Sectional ◽  
Previous Diagnosis ◽  
Level Of Knowledge

Aim: The aim of the study is to evaluate the knowledge, attitudes, behaviors and anxiety of university students about the Covid-19 epidemic at the beginning of the Covid-19 pandemic. Material and Methods: The study is a cross-sectional study. The sample consists of 1243 health students. The data were collected through an online questionnaire consisting of three parts. The questionnaire included questions to assess the socio-demographic characteristics of students, their level of knowledge, awareness and behavior about the Covid-19 pandemic, and the impact of the epidemic on their psychological health. The GAD-7 scale was used to assess students' anxiety levels. Results: In this study; 79.60% of the Health Care Students (HCSs) did not attend any training on COVID-19. Their sources of information were internet/social media (97.18%) and TV (97.18%). In the COVID-19 knowledge assessment test, it was found that 65.7% of them had a high level of knowledge. Most of HCSs thought that their knowledge about COVID-19 was very good (55%). The correct answer means of the 31 questions on the COVID-19 knowledge questionnaire were 22.07±1.70. In this questionnaire, it stated that the most effective methods of protection were the use of a mask in crowded environments (99.1%). Most of the HCSs stated that outbreak was affected their mental health negatively (73.1%) and some of them named this state as "Coronaphobia" (33.2%). In this study was found that some HCSs have a generalized anxiety disorder (22.6%) and anxiety experienced due to COVID "completely affected" their lives (19.5%). The average knowledge score of the students who received applied training in the hospital at the beginning of the pandemic (22.29±1.58) and anxiety level of students (6.77±5.85) was higher than those who did not receive applied training in the hospital (5.84±5.47, p<0.05). The GAD-7 scores for those with a previous diagnosis of psychological disease were 2 times higher than those without a previous diagnosis (p<0.05)Conclusion: At the beginning of the pandemic, students who received hands-on training at the hospital had higher knowledge and awareness levels. Generalized Anxiety Disorder was observed more frequently in those with a previous psychological disorder than in other students. Keywords: Anxiety; coronavirus disease 2019 (covid-19); knowledge; health students; pandemic and mental health

scholarly journals Generalized anxiety disorder and resilience during the COVID-19 pandemic: evidence from China during the early rapid outbreak

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hao Chen ◽  
Junling Gao ◽  
Junming Dai ◽  
Yimeng Mao ◽  
Yi Wang ◽  
...  
Keyword(s):  
Mental Health ◽  
Anxiety Disorder ◽  
Generalized Anxiety Disorder ◽  
Online Survey ◽  
Disease Outbreaks ◽  
Public Mental Health ◽  
Negative Influence ◽  
Mediating Effect ◽  
Generalized Anxiety ◽  
Cross Sectional

Abstract Background Generalized Anxiety Disorder (GAD) is a common but urgent mental health problem during disease outbreaks. Resilience buffers against the negative impacts of life stressors on common internalizing psychopathology such as GAD. This study assesses the prevalence of GAD and examines the protective or compensatory effect of resilience against worry factors during the COVID-19 outbreak. Methods A cross-sectional online survey was conducted among Chinese citizens aged ≥18 years from January 31 to February 2, 2020. A total of 4827 participants across 31 provinces and autonomous regions of the mainland of China participated in this study. The Generalized Anxiety Disorder scale (GAD-7), the Connor-Davidson Resilience Scale (CD-RISC), and a self-designed worry questionnaire were used to asses anxiety disorder prevalence, resilience level, and anxiety risk factors. Multivariable logistic regression was used to identify the associations of resilience and worry factors with GAD prevalence after controlling for other covariates. Results The prevalence of anxiety disorder was 22.6% across the 31 areas, and the highest prevalence was 35.4% in Hubei province. After controlling for covariates, the results suggested a higher GAD prevalence among participants who were worried about themselves or family members being infected with COVID-19 (adjusted odds ratio, AOR 3.40, 95%CI 2.43–4.75), worried about difficulty obtaining masks (AOR 1.92, 95%CI 1.47–2.50), worried about difficulty of distinguishing true information (AOR 1.65, 95%CI 1.36–2.02), worried about the prognosis of COVID-19 (AOR 2.41, 95%CI 1.75–3.33), worried about delays in working (AOR 1.71, 95%CI 1.27–.31), or worried about decreased income (AOR 1.45, 95%CI 1.14–1.85) compared with those without such worries. Additionally, those with a higher resilience level had a lower prevalence of GAD (AOR 0.59, 95%CI 0.51–0.70). Resilience also showed a mediating effect, with a negative influence on worry factors and thereby a negative association with GAD prevalence. Conclusion It may be beneficial to promote public mental health during the COVID-19 outbreak through enhancing resilience, which may buffer against adverse psychological effects from worry factors.

scholarly journals The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study (Preprint)

2020 ◽  
Author(s):  
Daniel Di Matteo ◽  
Kathryn Fotinos ◽  
Sachinthya Lokuge ◽  
Julia Yu ◽  
Tia Sternat ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Generalized Anxiety Disorder ◽  
Social Anxiety Disorder ◽  
Functional Impairment ◽  
Self Report ◽  
Anxiety And Depression ◽  
Generalized Anxiety ◽  
Severity Of Depression ◽  
Insight Into

BACKGROUND <i>Objective</i> and <i>continuous</i> severity measures of anxiety and depression are highly valuable and would have many applications in psychiatry and psychology. A collective source of data for objective measures are the sensors in a person’s smartphone, and a particularly rich source is the microphone that can be used to sample the audio environment. This may give broad insight into activity, sleep, and social interaction, which may be associated with quality of life and severity of anxiety and depression. OBJECTIVE This study aimed to explore the properties of passively recorded environmental audio from a subject’s smartphone to find potential correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment. METHODS An Android app was designed, together with a centralized server system, to collect periodic measurements of the volume of sounds in the environment and to detect the presence or absence of English-speaking voices. Subjects were recruited into a 2-week observational study during which the app was run on their personal smartphone to collect audio data. Subjects also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the environmental audio of 84 participants with sufficient data, and correlations were measured between the 4 audio features and the 4 self-report measures. RESULTS The regularity in daily patterns of activity and inactivity inferred from the environmental audio volume was correlated with the severity of depression (<i>r</i>=−0.37; <i>P</i>&lt;.001). A measure of sleep disturbance inferred from the environmental audio volume was also correlated with the severity of depression (<i>r</i>=0.23; <i>P</i>=.03). A proxy measure of social interaction based on the detection of speaking voices in the environmental audio was correlated with depression (<i>r</i>=−0.37; <i>P</i>&lt;.001) and functional impairment (<i>r</i>=−0.29; <i>P</i>=.01). None of the 4 environmental audio-based features tested showed significant correlations with the measures of generalized anxiety or social anxiety. CONCLUSIONS In this study group, the environmental audio was shown to contain signals that were associated with the severity of depression and functional impairment. Associations with the severity of social anxiety disorder and generalized anxiety disorder were much weaker in comparison and not statistically significant at the 5% significance level. This work also confirmed previous work showing that the presence of voices is associated with depression. Furthermore, this study suggests that sparsely sampled audio volume could provide potentially relevant insight into subjects’ mental health.

scholarly journals The relations between different factors of intolerance of uncertainty and symptoms of generalized anxiety disorder: a network analysis

2021 ◽  
Author(s):  
Lei Ren ◽  
Zihan Wei ◽  
Ye Li ◽  
Long-Biao Cui ◽  
Yifei Wang ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Generalized Anxiety Disorder ◽  
Full Range ◽  
Intolerance Of Uncertainty ◽  
Self Report ◽  
Generalized Anxiety ◽  
Cross Sectional ◽  
Correlation Networks ◽  
Two Factors ◽  
Differential Relations

Abstract Background: Intolerance of uncertainty (IU) is considered as a specific risk factor in the development and maintenance of generalized anxiety disorder (GAD). Yet, researches have investigated the relations between IU and GAD (or worry) using total scores on self-report measures. This ignores that there are two different factors exist in IU, and clinical heterogeneity and differential relations among symptoms of GAD. In the present study, we explored the relations among different factors of IU and symptoms of GAD.Methods: A dimensional approach which take individual differences into consideration in different factors of IU along a full range of normal to abnormal symptom severity levels of GAD were used in this study. Unregularized partial-correlation networks were estimated using cross-sectional data from 624 university students. Factors of IU were measured by 12-item Intolerance of Uncertainty Scale and symptoms of GAD were measured by Generalized Anxiety Disorder 7-Item Questionnaire. Results: Five edges between two factors of IU and symptoms of GAD, including edges are between “prospective anxiety” and “nervousness or anxiety”, between “prospective anxiety” and “worry too much”, between “inhibitory anxiety” and “uncontrollable worry”, between “inhibitory anxiety” and “worry too much”, and between “inhibitory anxiety” and “restlessness”. The symptom “worry too much” had the highest strength centrality in the present network. In the community of IU, factor “inhibitory anxiety” has the higher bridge strength than factor “prospective anxiety”. And in the community of GAD symptoms, symptom “worry too much” has the higher bridge strength than other symptoms.Conclusions: This study reveals the underlying relationship between factors of IU and various symptoms of GAD. These findings may provide some references for related preventions and interventions, such as targeting “worry too much” may minimize the level of both IU and GAD symptoms and focusing on “inhibitory anxiety” may be more effective at reducing symptoms of GAD than focusing on “prospective anxiety”.

Sign in / Sign up

Export Citation Format

Share Document