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.

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 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 Mastery and self-esteem mediate the association between visual acuity and mental health: a population-based longitudinal cohort study

2020 ◽  
Author(s):  
Ivar M Maaswinkel ◽  
Hilde PA van der Aa ◽  
Ger HMB van Rens ◽  
Aartjan TF Beekman ◽  
Jos WR Twisk ◽  
...  
Keyword(s):  
Mental Health ◽  
Visual Acuity ◽  
Cohort Study ◽  
Visual Impairment ◽  
Depression Scale ◽  
Self Esteem ◽  
Longitudinal Cohort Study ◽  
Longitudinal Cohort ◽  
The Relationship ◽  
Sense Of Mastery

Abstract Background: With deteriorating eyesight, people often become dependent on others for many aspects of their daily lives. As a result, they feel less ‘in control’ and experience lower self-esteem. Lower sense of mastery and self-esteem are known to predict depression, but their roles in people with visual impairment have only marginally been investigated. Therefore, this study aimed to determine the influence of mastery and self-esteem on the relationship between visual acuity and mental health.Methods: A longitudinal cohort study was performed using data from the Longitudinal Aging Study Amsterdam (LASA), collected between 2001 and 2012. A community-based population of 2599 older adults (mean age 72 years) were included, who were randomly selected from population registers. Outcomes of interest were the Pearlin Mastery Scale, Rosenberg Self-Esteem Scale, Center for Epidemiologic Studies – Depression scale and the Hospital Anxiety Depression Scale – Anxiety subscale. Linear mixed models were used to establish the association between visual acuity and mental health over time.Results: Mean age was 72 years, 56% was female and 1.2% qualified as having low vision. Visual impairment was associated with a lower sense of mastery (β = -0.477, p < 0.001), lower self-esteem (β = -0.166, p = 0.008) and more depression (β = 0.235, p < 0.001). No significant association between visual acuity and anxiety was found. The relationship between visual acuity and depression was mediated by self-esteem (25%) and sense of mastery (79%).Conclusions: Vision loss was associated with depression. This association was mediated by self-esteem and sense of mastery. This provides us with new possibilities to identify, support and treat those at risk for developing depression by aiming to increase their self-esteem and sense of mastery.

scholarly journals Mastery and self-esteem mediate the association between visual acuity and mental health: a population-based longitudinal cohort study

2020 ◽  
Author(s):  
Hilde PA van der Aa ◽  
Ivar M Maaswinkel ◽  
Ger HMB van Rens ◽  
Aartjan TF Beekman ◽  
Jos WR Twisk ◽  
...  
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Visual Acuity ◽  
Cohort Study ◽  
Visual Impairment ◽  
Self Esteem ◽  
Longitudinal Cohort Study ◽  
Longitudinal Cohort ◽  
The Relationship ◽  
Sense Of Mastery

Abstract Background With deteriorating eyesight, people often become dependent on others for many aspects of their daily lives. As a result, they feel less ‘in control’ and experience lower self-esteem. Lower sense of mastery and self-esteem are known to predict depression, but their roles in people with visual impairment remain unknown. Therefore, this study aimed to determine the influence of mastery and self-esteem on the relationship between visual acuity and mental health. Methods A longitudinal cohort study was performed using data from the Longitudinal Aging Study Amsterdam (LASA). Data on vision was available from the fifth cycle (2001), with a mean follow-up of 5.9 years. A community-based population was studied, containing older adults from eleven municipalities in three culturally distinct geographical regions in the Netherlands. A total of 2599 older adults (aged 55 to 85 years at baseline) were included, who were randomly selected from population registers in 1992. The first (2001) and last (2012) included measurements contained 1961 and 1522 participants, respectively. Primary study outcomes were logMAR visual acuity, sense of mastery, self-esteem, depression and anxiety. Instead of standard questionnaire scores, latent trait scores (θ) were obtained through -) Item Response Theory (IRT-) analysis. Results Mean age was 72 years, with 56% females and 2% qualifying as low vision. Visual impairment was associated with a lower sense of mastery (β = -0.477, p < 0.001), lower self-esteem (β = -0.166, p = 0.008) and more depression (β = 0.235, p < 0.001). No significant association between visual acuity and anxiety was found. The relationship between visual acuity and depression was mediated partially by self-esteem (25%) and fully by sense of mastery (76%). Conclusions Vision loss was associated with depression. This association was mediated by self-esteem and sense of mastery. This provides us with new possibilities to identify, support and treat those at risk for developing depression by aiming to increase their self-esteem and sense of mastery.

scholarly journals A longitudinal cohort study investigating inadequate preparation and death and dying in nursing students: Implications for the aftermath of the COVID-19 pandemic.

2020 ◽  
Author(s):  
John Galvin ◽  
Gareth Richards ◽  
Andrew P Smith
Keyword(s):  
Mental Health ◽  
Cohort Study ◽  
Nursing Students ◽  
Mental Health Problems ◽  
Death And Dying ◽  
Health Impact ◽  
Longitudinal Cohort Study ◽  
Longitudinal Cohort ◽  
Increased Risk ◽  
Time Point

Aims and objectivesTo investigate how changes in the levels of preparedness and experiences of death and dying influence nursing students’ mental health. BackgroundThe COVID-19 pandemic is likely to cause significant trauma in the nursing population. The lack of preparation, in combination with a substantial loss of life, may have implications for the longer-term mental health of the nursing workforce. Nursing students have, in many cases, been an important part of the emergency response.DesignA longitudinal cohort study was conducted with data collected at two time points. There was a seven-month time period between data collection.MethodsParticipants completed paper-based questionnaires measuring demographics, academic stressors, clinical stressors, and mental health. 358 nursing students at time point one and 347 at time point two (97% retention) completed the survey.ResultsInadequate preparation (OR: 1.783) and the inadequate preparation x death and dying interaction term (OR: 4.115) significantly increased risk of mental health problems over time. Increased death and dying alone did not increase mental health risk. ConclusionsThe results of this study suggest that it is not the increase in death and dying per se that causes mental health difficulties, but that it is instead the experience of high levels of death and dying in combination with inadequate preparation. The data are considered within the context of the COVID-19 pandemic, with both inadequate preparation and the scale of death and dying being two significant stressors during the emergency period. Relevance to Clinical PracticeThis paper is an initial indication of the potential longer-term mental health impact of COVID-19 for nursing students.

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