Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors (Preprint)

BACKGROUND Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. OBJECTIVE This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. METHODS In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants’ social anxiety symptom severity. RESULTS The results suggested that these passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (<i>r</i>=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. CONCLUSIONS These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

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Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

Journal of Medical Internet Research ◽

10.2196/16875 ◽

2020 ◽

Vol 22 (5) ◽

pp. e16875 ◽

Cited By ~ 5

Author(s):

Nicholas C Jacobson ◽

Berta Summers ◽

Sabine Wilhelm

Keyword(s):

Social Anxiety ◽

Negative Affect ◽

Positive Affect ◽

Symptom Severity ◽

Anxiety Symptom ◽

Self Report ◽

Sensor Data ◽

Digital Phenotyping ◽

Passive Sensor ◽

Digital Biomarkers

Background Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. Objective This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. Methods In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants’ social anxiety symptom severity. Results The results suggested that these passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. Conclusions These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

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Emotional and interpersonal mechanisms in community SSRI treatment of social anxiety disorder

Journal of Psychiatry and Neuroscience ◽

10.1503/jpn.190164 ◽

2020 ◽

Vol 46 (1) ◽

pp. E56-E64

Author(s):

Lance M. Rappaport ◽

Michael D. Hunter ◽

Jennifer J. Russell ◽

Gilbert Pinard ◽

Pierre Bleau ◽

...

Keyword(s):

Anxiety Disorder ◽

Social Anxiety ◽

Negative Affect ◽

Positive Affect ◽

Social Anxiety Disorder ◽

Symptom Severity ◽

Anxiety Symptom ◽

Control Group ◽

Subsequent Reduction ◽

Interpersonal Behaviour

Background: Affective and interpersonal behavioural patterns characteristic of social anxiety disorder show improvement during treatment with serotonin agonists (e.g., selective serotonin reuptake inhibitors), commonly used in the treatment of social anxiety disorder. The present study sought to establish whether, during community psychopharmacological treatment of social anxiety disorder, changes in positive or negative affect and agreeable or quarrelsome behaviour mediate improvement in social anxiety symptom severity or follow from it. Methods: Adults diagnosed with social anxiety disorder (n = 48) recorded their interpersonal behaviour and affect naturalistically in an event-contingent recording procedure for 1-week periods before and during the first 4 months of treatment with paroxetine. Participants and treating psychiatrists assessed the severity of social anxiety symptoms monthly. A multivariate latent change score framework examined temporally lagged associations of change in affect and interpersonal behaviour with change in social anxiety symptom severity. Results: Elevated agreeable behaviour and positive affect predicted greater subsequent reduction in social anxiety symptom severity over the following month of treatment. Elevated negative affect, but not quarrelsome behaviour, predicted less subsequent reduction in symptom severity. Limitations: Limitations included limited assessment of extreme behaviour (e.g., violence) that may have precluded examining the efficacy of paroxetine because of the lack of a placebo control group. Conclusion: The present study suggests that interpersonal behaviour and affect may be putative mechanisms of action for serotonergic treatment of social anxiety disorder. Prosocial behaviour and positive affect increase during serotonergic treatment of social anxiety disorder. Specifically, modulating agreeable behaviour, positive affect and negative affect in individuals’ daily lives may partially explain and refine clinical intervention.

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Sleep Quality and Self-Control: The Mediating Roles of Positive and Negative Affects

Frontiers in Psychology ◽

10.3389/fpsyg.2020.607548 ◽

2020 ◽

Vol 11 ◽

Author(s):

Jinru Liu ◽

Lin Zhu ◽

Conghui Liu

Keyword(s):

Negative Affect ◽

Sleep Quality ◽

Positive Affect ◽

Self Control ◽

Self Report ◽

Poor Sleep ◽

Poor Sleep Quality ◽

Negative Affects ◽

The Relationship ◽

Positive And Negative Affects

This study examined the mediating roles of both positive and negative affects in the relationship between sleep quality and self-control. A sample of 1,507 Chinese adults (37% men; mean age = 32.5 years) completed self-report questionnaires measuring sleep quality, positive and negative emotions, and self-control. Poor sleep quality was positively correlated with negative affect and negatively correlated with positive affect and self-control. Positive affect was positively correlated with self-control, while negative affect was negatively correlated with self-control. Both positive and negative affects significantly mediated the relationship between sleep quality and self-control. Improving individuals’ sleep qualities may lead to more positive emotions and less negative emotion, and these mood changes may increase resources for self-control. Regulating positive and negative affects may reduce the negative effects of poor sleep quality on self-control.

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Edwards Inventory of Emotions: Assessing Emotions in Athletes and Nonathletes

Perceptual and Motor Skills ◽

10.2466/pms.1995.80.2.444 ◽

1995 ◽

Vol 80 (2) ◽

pp. 444-446 ◽

Cited By ~ 1

Author(s):

Steven W. Edwards

Keyword(s):

Negative Affect ◽

Positive Affect ◽

Rating Scale ◽

Self Report ◽

Specific Factor ◽

General Factor ◽

Independent College ◽

College Age ◽

Point Rating Scale ◽

Affect Factor

A self-report inventory was created on which respondents indicated the frequency of occurrence of 40 basic emotions using a 5-point rating scale. The inventory was administered to two matched, independent college-age samples ( ns = 562 and 414) and the factorial validity was tested. Factor 1 was a general factor reflecting over-all Positive Affect. Factor 2 was a more specific factor reflecting Profound Negative Affect. Factor 3 was also a specific factor reflecting Moderate Negative Affect. Subsequent analyses gave significantly greater scores for athletes over nonathletes and men over women on the Profound Negative Affect subscale. Women had significantly higher Positive Affect scores. It was concluded that the questionnaire had sufficient technical merit for use in research.

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

10.2196/preprints.28918 ◽

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.

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Temporal relationships between happiness and psychiatric disorders and their symptom severity in a large cohort study: the Netherlands Study of Depression and Anxiety (NESDA)

BMC Psychiatry ◽

10.1186/s12888-021-03346-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Philip Spinhoven ◽

Bernet M. Elzinga ◽

Brenda W. J. H. Penninx ◽

Erik J. Giltay

Keyword(s):

Anxiety Disorder ◽

Social Anxiety ◽

Psychiatric Disorders ◽

Symptom Severity ◽

Anxiety Symptom ◽

Subjective Happiness ◽

Depression And Anxiety ◽

Depression Severity ◽

Interindividual Differences ◽

Temporal Relationships

Abstract Background Notwithstanding the firmly established cross-sectional association of happiness with psychiatric disorders and their symptom severity, little is known about their temporal relationships. The goal of the present study was to investigate whether happiness is predictive of subsequent psychiatric disorders and symptom severity (and vice versa). Moreover, it was examined whether changes in happiness co-occur with changes in psychiatric disorder status and symptom severity. Methods In the Netherlands Study of Depression and Anxiety (NESDA), happiness (SRH: Self-Rated Happiness scale), depressive and social anxiety disorder (CIDI: Composite Interview Diagnostic Instrument) and depressive and anxiety symptom severity (IDS: Inventory of Depressive Symptomatology; BAI: Beck Anxiety Inventory; and FQ: Fear Questionnaire) were measured in 1816 adults over a three-year period. Moreover, we focused on occurrence and remittance of 6-month recency Major Depressive Disorder (MDD) and Social Anxiety Disorders (SAD) as the two disorders most intertwined with subjective happiness. Results Interindividual differences in happiness were quite stable (ICC of .64). Higher levels of happiness predicted recovery from depression (OR = 1.41; 95% CI = 1.10–1.80), but not social anxiety disorder (OR = 1.31; 95%CI = .94–1.81), as well as non-occurrence of depression (OR = 2.41; 95%CI = 1.98–2.94) and SAD (OR = 2.93; 95%CI = 2.29–3.77) in participants without MDD, respectively SAD at baseline. Higher levels of happiness also predicted a reduction of IDS depression (sr = − 0.08; 95%CI = -0.10 - -0.04), and BAI (sr = − 0.09; 95%CI = -0.12 - -0.05) and FQ (sr = − 0.06; 95%CI = -0.09 - -0.04) anxiety symptom scores. Conversely, presence of affective disorders, as well as higher depression and anxiety symptom severity at baseline predicted a subsequent reduction of self-reported happiness (with marginal to small sr values varying between −.04 (presence of SAD) to −.17 (depression severity on the IDS)). Moreover, changes in happiness were associated with changes in psychiatric disorders and their symptom severity, in particular with depression severity on the IDS (sr = − 0.46; 95%CI = −.50 - -.42). Conclusions Results support the view of rather stable interindividual differences in subjective happiness, although level of happiness is inversely associated with changes in psychiatric disorders and their symptom severity, in particular depressive disorder and depression severity.

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

Journal of Medical Internet Research ◽

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.

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

Journal of Medical Internet Research ◽

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.

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Positive Emotions Trigger Upward Spirals Toward Emotional Well-Being

Psychological Science ◽

10.1111/1467-9280.00431 ◽

2002 ◽

Vol 13 (2) ◽

pp. 172-175 ◽

Cited By ~ 1061

Author(s):

Barbara L. Fredrickson ◽

Thomas Joiner

Keyword(s):

Health Promotion ◽

Clinical Practice ◽

Negative Affect ◽

Positive Affect ◽

Positive Emotions ◽

Well Being ◽

Self Report ◽

Regression Analyses ◽

Broaden And Build Theory ◽

And Coping

The broaden-and-build theory of positive emotions predicts that positive emotions broaden the scopes of attention and cognition, and, by consequence, initiate upward spirals toward increasing emotional well-being. The present study assessed this prediction by testing whether positive affect and broad-minded coping reciprocally and prospectively predict one another. One hundred thirty-eight college students completed self-report measures of affect and coping at two assessment periods 5 weeks apart. As hypothesized, regression analyses showed that initial positive affect, but not negative affect, predicted improved broad-minded coping, and initial broad-minded coping predicted increased positive affect, but not reductions in negative affect. Further mediational analyses showed that positive affect and broad-minded coping serially enhanced one another. These findings provide prospective evidence to support the prediction that positive emotions initiate upward spirals toward enhanced emotional well-being. Implications for clinical practice and health promotion are discussed.

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