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Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study
13 references·2 citing·47 second hop
Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study (2020)Effects of mood and aging on keystroke dy…Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study (2019)Mood Prediction of Patients W…Current limitations of neuropsychological tests and assessment procedures (2019)Current limitations of neurop…Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study (2018)Predicting Mood Disturbance S…Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics (2014)Revealing Real-Time Emotional…Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis (2013)Wearable Monitoring for Mood …Ambulatory Assessment (2012)Ambulatory AssessmentThe Patient Health Questionnaire-9 for detection of major depressive disorder in primary care: consequences of current thresholds in a crosssectional study (2010)The Patient Health Questionna…Defining successful treatment outcome in depression using the PHQ-9: A comparison of methods (2010)Defining successful treatment…The PHQ-8 as a measure of current depression in the general population (2008)The PHQ-8 as a measure of cur…The Ecological Validity of Neuropsychological Tests: A Review of the Literature on Everyday Cognitive Skills (2003)The Ecological Validity of Ne…A Three-Component Model for Reengineering Systems for the Treatment of Depression in Primary Care (2002)A Three-Component Model for R…The PHQ-9 (2001)The PHQ-9Validation and Utility of a Self-report Version of PRIME-MD The PHQ Primary Care Study (1999)Validation and Utility of a S…Evaluating the Acceptability and Feasibility of Collecting Passive Smartphone Data to Estimate Psychological Functioning in U.S. Service Members and Veterans: A Pilot Study (2024)Evaluating the Acceptability …A model of healthy aging based on smartphone interactions reveals advanced behavioral age in neurological disease (2022)A model of healthy aging base…Diagnostic and Statistical Manual of Mental Disorders (2013)Diagnostic and Statistical Manual of Mental Disorders (2013)Does Response on the PHQ-9 Depression Questionnaire Predict Subsequent Suicide Attempt or Suicide Death? 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(2007)Mental Health Surveillance Among Children — United States, 2013–2019 (2022)Ecological Momentary Assessment (2007)Estimating the true global burden of mental illness (2016)Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook (2019)Touchscreen typing pattern analysis for remote detection of the depressive tendency (2019)Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones (2020)Social and economic burden of mood disorders (2003)Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010)Beyond Self-Report: Tools to Compare Estimated and Real-World Smartphone Use (2015)Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone (2019)Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses (2020)Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression (2023)Ecological Momentary Assessment (Ema) in Behavioral Medicine (1994)Handbook of research methods for studying daily life (2012)ESM 2.0: State of the Art and Future Potential of Experience Sampling Methods in Organizational Research (2015)At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study (2018)Teenagers, screens and social media: a narrative review of reviews and key studies (2020)The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology (2006)Insomnia and daytime cognitive performance: A meta-analysis (2011)Neuropsychological Assessment (2011)
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References

  • 2001The PHQ-944,795 cited
  • 1999Validation and Utility of a Self-report Version of PRIME-MD The PHQ Primary Care Study9,795 cited
  • 2008The PHQ-8 as a measure of current depression in the general population5,113 cited
  • 2003The Ecological Validity of Neuropsychological Tests: A Review of the Literature on Everyday Cognitive Skills1,055 cited
  • 2012Ambulatory Assessment754 cited
  • 2010Defining successful treatment outcome in depression using the PHQ-9: A comparison of methods247 cited
  • 2018Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study233 cited
  • 2014Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics229 cited
  • 2010The Patient Health Questionnaire-9 for detection of major depressive disorder in primary care: consequences of current thresholds in a crosssectional study218 cited
  • 2019Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study183 cited
  • 2013Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis165 cited
  • 2019Current limitations of neuropsychological tests and assessment procedures143 cited
  • 2002A Three-Component Model for Reengineering Systems for the Treatment of Depression in Primary Care141 cited

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  • 2022A model of healthy aging based on smartphone interactions reveals advanced behavioral age in neurological disease12 cited
  • 2024Evaluating the Acceptability and Feasibility of Collecting Passive Smartphone Data to Estimate Psychological Functioning in U.S. Service Members and Veterans: A Pilot Study4 cited