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Automatic depression prediction using Internet traffic characteristics on smartphones
16 references·6 citing·49 second hopsampled
Automatic depression prediction using Internet traffic characteristics on smartphones (2020)Automatic depression prediction using Int…Predicting depressive symptoms using smartphone data (2019)Predicting depressive symptom…Depression screening using mobile phone usage metadata: a machine learning approach (2019)Depression screening using mo…Detecting Bipolar Depression From Geographic Location Data (2016)Detecting Bipolar Depression …Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data (2016)Behavior vs. introspection: r…Trajectories of depression (2015)Trajectories of depressionNext-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. (2015)Next-generation psychiatric a…Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study (2015)Mobile Phone Sensor Correlate…StudentLife (2014)StudentLifeIs Facebook creating “iDisorders”? The link between clinical symptoms of psychiatric disorders and technology use, attitudes and anxiety (2013)Is Facebook creating “iDisord…Towards smart phone based monitoring of bipolar disorder (2012)Towards smart phone based mon…Ambulatory Assessment (2012)Ambulatory AssessmentRetail therapy: A strategic effort to improve mood (2011)Retail therapy: A strategic e…The Relationship between Excessive Internet Use and Depression: A Questionnaire-Based Study of 1,319 Young People and Adults (2010)The Relationship between Exce…Measuring serendipity (2009)Measuring serendipityThe PHQ-9 (2001)The PHQ-9The Relationship Between Depression and Internet Addiction (1998)The Relationship Between Depr…Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches (2024)Machine Learning for Multimod…Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study (2024)Investigating Rhythmicity in …Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment (2024)Behind the Screen: A Narrativ…Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study (2023)Prediction of Diagnosis and T…A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach (2023)A Fast and Minimal System to …Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study (2021)Evaluation of Changes in Depr…Lifetime and 12-Month Prevalence of DSM-III-R Psychiatric Disorders in the United States (1994)Lifetime Prevalence and Age-of-Onset Distributions of DSM-IV Disorders in the National Comorbidity Survey Replication (2005)The WPA- Lancet Psychiatry Commission on the Future of Psychiatry (2017)Facebook language predicts depression in medical records (2018)Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018)Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) (2000)Internet Paradox Revisited (2002)The social context of well–being (2004)The Extended iSelf: The Impact of iPhone Separation on Cognition, Emotion, and Physiology (2015)Adolescent social media use and mental health from adolescent and parent perspectives (2017)A comprehensive meta-analysis on Problematic Facebook Use (2018)The PHQ-9: A New Depression Diagnostic and Severity Measure (2002)The validity of the Hospital Anxiety and Depression Scale (2002)The relationship between mobile phone location sensor data and depressive symptom severity (2016)Measuring objective and subjective well-being: dimensions and data sources (2020)An Inventory for Measuring Depression (1961)Caught in the Net: How to Recognize the Signs of Internet Addiction--and a Winning Strategy for Recovery (1998)Issues for DSM-V: Internet Addiction (2008)Problematic Internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction (2011)Internet and Gaming Addiction: A Systematic Literature Review of Neuroimaging Studies (2012)Association of Facebook Use With Compromised Well-Being: A Longitudinal Study (2016)Social psychological aspects of computer-mediated communication. 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References

  • 2001The PHQ-944,795 cited
  • 1998The Relationship Between Depression and Internet Addiction1,195 cited
  • 2014StudentLife1,117 cited
  • 2015Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study781 cited
  • 2012Ambulatory Assessment754 cited
  • 2015Trajectories of depression475 cited
  • 2013Is Facebook creating “iDisorders”? The link between clinical symptoms of psychiatric disorders and technology use, attitudes and anxiety472 cited
  • 2010The Relationship between Excessive Internet Use and Depression: A Questionnaire-Based Study of 1,319 Young People and Adults435 cited
  • 2015Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.429 cited
  • 2011Retail therapy: A strategic effort to improve mood261 cited
  • 2016Detecting Bipolar Depression From Geographic Location Data180 cited
  • 2009Measuring serendipity170 cited
  • 2016Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data114 cited
  • 2019Predicting depressive symptoms using smartphone data86 cited
  • 2019Depression screening using mobile phone usage metadata: a machine learning approach

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74 cited
  • 2012Towards smart phone based monitoring of bipolar disorder53 cited
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    • 2024Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches153 cited
    • 2021Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study82 cited
    • 2023Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study25 cited
    • 2023A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach19 cited
    • 2024Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment18 cited
    • 2024Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study2 cited