This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (1068) Human Factors and Usability Case Studies (532) mHealth for Wellness, Behavior Change and Prevention (2342) Mental Health Issues in Adolescence (302)

Published on in Vol 7 , No 1 (2020) :January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14045, first published .
Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study

Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study

Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study

Authors of this article:

Jian Cao 1 Author Orcid Image ;   Anh Lan Truong 2 Author Orcid Image ;   Sophia Banu 2 Author Orcid Image ;   Asim A Shah 2 Author Orcid Image ;   Ashutosh Sabharwal 1 Author Orcid Image ;   Nidal Moukaddam 2 Author Orcid Image
Article Authors Cited by (20) Tweetations (4) Metrics

Journals

  1. Bai R, Xiao L, Guo Y, Zhu X, Li N, Wang Y, Chen Q, Feng L, Wang Y, Yu X, Wang C, Hu Y, Liu Z, Xie H, Wang G. Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study. JMIR mHealth and uHealth 2021;9(3):e24365 View
  2. Melbye S, Kessing L, Bardram J, Faurholt-Jepsen M. Smartphone-Based Self-Monitoring, Treatment, and Automatically Generated Data in Children, Adolescents, and Young Adults With Psychiatric Disorders: Systematic Review. JMIR Mental Health 2020;7(10):e17453 View
  3. Goltermann J, Emden D, Leehr E, Dohm K, Redlich R, Dannlowski U, Hahn T, Opel N. Smartphone-Based Self-Reports of Depressive Symptoms Using the Remote Monitoring Application in Psychiatry (ReMAP): Interformat Validation Study. JMIR Mental Health 2021;8(1):e24333 View
  4. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  5. Moukaddam N, Sano A, Salas R, Hammal Z, Sabharwal A. Turning data into better mental health: Past, present, and future. Frontiers in Digital Health 2022;4 View
  6. Baryshnikov I, Aledavood T, Rosenström T, Heikkilä R, Darst R, Riihimäki K, Saleva O, Ekelund J, Isometsä E. Relationship between daily rated depression symptom severity and the retrospective self-report on PHQ-9: A prospective ecological momentary assessment study on 80 psychiatric outpatients. Journal of Affective Disorders 2023;324:170 View
  7. Girousse E, Vuillerme N. The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review. JMIR Research Protocols 2022;11(12):e38785 View
  8. Mullick T, Radovic A, Shaaban S, Doryab A. Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study. JMIR Formative Research 2022;6(6):e35807 View
  9. Kostyrka‐Allchorne K, Stoilova M, Bourgaize J, Rahali M, Livingstone S, Sonuga‐Barke E. Review: Digital experiences and their impact on the lives of adolescents with pre‐existing anxiety, depression, eating and nonsuicidal self‐injury conditions – a systematic review. Child and Adolescent Mental Health 2023;28(1):22 View
  10. MacLeod L, Suruliraj B, Gall D, Bessenyei K, Hamm S, Romkey I, Bagnell A, Mattheisen M, Muthukumaraswamy V, Orji R, Meier S. A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. JMIR mHealth and uHealth 2021;9(10):e20638 View
  11. Karthan M, Martin R, Holl F, Swoboda W, Kestler H, Pryss R, Schobel J. Enhancing mHealth data collection applications with sensing capabilities. Frontiers in Public Health 2022;10 View
  12. Mendes J, Moura I, Van de Ven P, Viana D, Silva F, Coutinho L, Teixeira S, Rodrigues J, Teles A. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. Journal of Medical Internet Research 2022;24(2):e28735 View
  13. Agarwal A, Ali Z, Shofer F, Xiong R, Hemmons J, Spencer E, Abdel-Rahman D, Sennett B, Delgado M. Testing Digital Methods of Patient-Reported Outcomes Data Collection: Prospective Cluster Randomized Trial to Test SMS Text Messaging and Mobile Surveys. JMIR Formative Research 2022;6(3):e31894 View
  14. Ren B, Balkind E, Pastro B, Israel E, Pizzagalli D, Rahimi-Eichi H, Baker J, Webb C. Predicting states of elevated negative affect in adolescents from smartphone sensors: a novel personalized machine learning approach. Psychological Medicine 2022:1 View
  15. Aalbers G, Hendrickson A, Vanden Abeele M, Keijsers L. Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis. JMIR mHealth and uHealth 2023;11:e37469 View
  16. Lee K, Lee T, Yefimova M, Kumar S, Puga F, Azuero A, Kamal A, Bakitas M, Wright A, Demiris G, Ritchie C, Pickering C, Nicholas Dionne-Odom J. Using digital phenotyping to understand health-related outcomes: A scoping review. International Journal of Medical Informatics 2023;174:105061 View
  17. Grant S, Tonkin E, Craddock I, Blom A, Holmes M, Judge A, Masullo A, Perello Nieto M, Song H, Whitehouse M, Flach P, Gooberman-Hill R. Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons. JMIR Perioperative Medicine 2023;6:e36172 View
  18. Kim J, Wang B, Kim M, Lee J, Kim H, Roh D, Lee K, Hong S, Lim J, Kim J, Ryan N. Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study. JMIR Formative Research 2023;7:e45991 View
  19. Ahmed M, Ahmed N. A Minimal and Faster System to Identify Depression Through Smartphone: An Explainable Machine Learning-Based Approach (Preprint). JMIR Formative Research 2022 View