Smartphone Addiction Risk, Anxiety Symptom Severity, and Depression Psychopathology

2020 ◽  
Vol 19 (0) ◽  
pp. 57
Keyword(s):  
Symptom Severity ◽  
Anxiety Symptom ◽  
Smartphone Addiction ◽  
Addiction Risk

Randomized controlled trial of digital cognitive behavior therapy for prenatal insomnia symptoms: Effects on postpartum insomnia and mental health

SLEEP ◽  
2021 ◽  
Author(s):  
Jennifer N Felder ◽  
Elissa S Epel ◽  
John Neuhaus ◽  
Andrew D Krystal ◽  
Aric A Prather
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Behavior Therapy ◽  
Cognitive Behavior Therapy ◽  
Symptom Severity ◽  
Standard Care ◽  
Anxiety Symptom ◽  
Controlled Trial ◽  
Cognitive Behavior ◽  
Insomnia Symptom

Abstract Study objectives To evaluate the effects of digital cognitive behavior therapy for insomnia (dCBT-I) delivered during pregnancy on subjective sleep outcomes, depressive symptoms, and anxiety symptoms through six months postpartum. Methods People up to 28 weeks gestation (N=208) with insomnia were randomized to six weekly sessions of dCBT-I or standard care. We report follow-up data at three and six months postpartum. The primary outcome was insomnia symptom severity. Secondary sleep outcomes included global sleep quality and insomnia caseness. Mental health outcomes included depressive and anxiety symptom severity. We evaluated between-condition differences in change from baseline for each postpartum timepoint and categorical outcomes. Results dCBT-I participants did not experience significantly greater improvements in insomnia symptom severity relative to standard care participants, but they did experience higher rates of insomnia remission and lower rates of insomnia caseness at six months postpartum. dCBT-I participants experienced greater improvements in depressive symptom severity from baseline to both postpartum timepoints, and in anxiety symptom severity from baseline to three months postpartum. The proportion of participants with probable major depression at three months postpartum was significantly higher among standard care (18%) than dCBT-I (4%, p=.006) participants; this between-condition difference was pronounced among the subset (n=143) with minimal depressive symptoms at baseline (18% vs 0%). Conclusion dCBT-I use during pregnancy leads to enduring benefits for postpartum insomnia remission. Findings provide strong preliminary evidence that dCBT-I use during pregnancy may prevent postpartum depression and anxiety, which is notable when considering the high frequency and importance of these problems.

Generalized Anxiety Symptom Severity Inventory

2021 ◽  
Author(s):  
Imelu G. Mordeno ◽  
Ma. Jenina N. Nalipay ◽  
Jelli Grace C. Luzano ◽  
Debi S. Galela ◽  
Michelle Anne L. Ferolino
Keyword(s):  
Symptom Severity ◽  
Anxiety Symptom ◽  
Generalized Anxiety

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

10.2196/16875 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16875 ◽  
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.

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

2019 ◽  
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 (<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.

Factors associated with smartphone addiction risk in preschool children

2019 ◽  
Author(s):  
Jeong Hye Park
Keyword(s):  
Preschool Children ◽  
Age Groups ◽  
Nationwide Survey ◽  
Cross Sectional Study ◽  
Smartphone Addiction ◽  
Older Children ◽  
Cross Sectional ◽  
Factors Associated ◽  
Addiction Risk ◽  
Smartphone Use

Abstract Background This study aimed to identify the factors associated with smartphone addiction risk among preschool children. Thus far, little is known about these factors in younger children compared to older children or adolescents, although the age at first use of a smartphone is decreasing. Methods A cross-sectional study design was used. Data were obtained from the nationwide survey on smartphone overdependence conducted in 2017 in South Korea by the Ministry of Science and ICT and the National Information Society Agency. Data from 1,378 preschool children were analysed using binominal logistic regression analysis. This study complied with the Strengthening the Reporting of Observational Studies in Epidemiology. Results Of the samples, 17.1% showed a risk of smartphone addiction. The odds of high risk for smartphone addiction increased with the duration of smartphone use during the week and the frequency of smartphone use over the weekend. Using smartphones to watch TV shows and videos significantly increased the odds of smartphone addiction risk, whereas using smartphones for education and games did not. Conclusions The findings of the present study showed that, similar to children in other age groups, preschool children were also exposed to the risk of smartphone addiction. To reduce smartphone addiction risk in these children, parents should be aware of their risk of smartphone addiction and consider allowing their children less than 30 minutes of smartphone use during the week and more opportunities for physical activities especially over the weekend. Our findings also suggested that parent-centred education needs to be provided to correct parents’ perceptions. Information on smartphone addiction should be provided to parents along with an explanation that children’s smartphone use for educational purposes is beneficial; however, its use for the parents’ convenience or other purposes was not.

Automated facial video-based recognition of depression and anxiety symptom severity: cross-corpus validation

2020 ◽  
Vol 31 (4) ◽  
Author(s):  
A. Pampouchidou ◽  
M. Pediaditis ◽  
E. Kazantzaki ◽  
S. Sfakianakis ◽  
I. A. Apostolaki ◽  
...  
Keyword(s):  
Symptom Severity ◽  
Anxiety Symptom ◽  
Depression And Anxiety

Proneness to Boredom Mediates Relationships Between Problematic Smartphone Use With Depression and Anxiety Severity

2017 ◽  
Vol 36 (6) ◽  
pp. 707-720 ◽  
Author(s):  
Jon D. Elhai ◽  
Juanita K. Vasquez ◽  
Samuel D. Lustgarten ◽  
Jason C. Levine ◽  
Brian J. Hall
Keyword(s):  
College Students ◽  
Symptom Severity ◽  
Structural Equation ◽  
Anxiety Symptom ◽  
Equation Modeling ◽  
Depression And Anxiety ◽  
Cross Sectional ◽  
Problematic Smartphone Use ◽  
Smartphone Use ◽  
Boredom Proneness

Research demonstrates that depression and anxiety symptom severity are related to problematic smartphone use (PSU). However, less is known about variables mediating these relationships. This study aimed to test whether proneness to boredom increased PSU. We also tested whether boredom proneness mediates relations between both depression and anxiety symptom severity with PSU. Using a cross-sectional design, we surveyed 298 American college students about their frequency of smartphone use, levels of PSU, depression, anxiety, and boredom proneness. Using structural equation modeling, we modeled depression and anxiety symptom severity predicting boredom proneness, in turn predicting levels of PSU and smartphone use frequency (SUF). Results demonstrate that boredom proneness predicted PSU, but not SUF. Boredom proneness mediated relations between both depression and anxiety symptom severity with PSU levels (but not usage frequency). We discuss the phenomenon in terms of depressed or anxious college students having difficulty attending to their schoolwork, subsequently experiencing boredom, and engaging in PSU to relieve their boredom.

The antidepressant duloxetine reduces anxiety symptom severity

2002 ◽  
Vol 12 ◽  
pp. 213 ◽  
Author(s):  
D.J. Goldstein ◽  
M. Detke ◽  
Y. Lu ◽  
M.A. Demitrack
Keyword(s):  
Symptom Severity ◽  
Anxiety Symptom
Sign in / Sign up

Export Citation Format

Share Document