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

Depression and Mood Disorders; Suicide Prevention (1392) Human Factors and Usability Case Studies (809) mHealth for Wellness, Behavior Change and Prevention (2922) Mental Health Issues in Adolescence (457)

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 Cao1 Author Orcid Image ;   Anh Lan Truong2 Author Orcid Image ;   Sophia Banu2 Author Orcid Image ;   Asim A Shah2 Author Orcid Image ;   Ashutosh Sabharwal1 Author Orcid Image ;   Nidal Moukaddam2 Author Orcid Image
Article Authors Cited by (39) 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 2023;53(11):5146 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 Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach. JMIR Formative Research 2023;7:e28848 View
  20. Marin-Dragu S, Forbes A, Sheikh S, Iyer R, Pereira dos Santos D, Alda M, Hajek T, Uher R, Wozney L, Paulovich F, Campbell L, Yakovenko I, Stewart S, Corkum P, Bagnell A, Orji R, Meier S. Associations of active and passive smartphone use with measures of youth mental health during the COVID-19 pandemic. Psychiatry Research 2023;326:115298 View
  21. Kadirvelu B, Bellido Bel T, Wu X, Burmester V, Ananth S, Cabral C C Branco B, Girela-Serrano B, Gledhill J, Di Simplicio M, Nicholls D, Faisal A. Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study. JMIR Formative Research 2023;7:e44877 View
  22. Piccin J, Viduani A, Buchweitz C, Pereira R, Zimerman A, Amando G, Cosenza V, Ferreira L, McMahon N, Melo R, Richter D, Reckziegel F, Rohrsetzer F, Souza L, Tonon A, Costa-Valle M, Zajkowska Z, Araújo R, Hauser T, van Heerden A, Hidalgo M, Kohrt B, Mondelli V, Swartz J, Fisher H, Kieling C. Prospective Follow-Up of Adolescents With and at Risk for Depression: Protocol and Methods of the Identifying Depression Early in Adolescence Risk Stratified Cohort Longitudinal Assessments. JAACAP Open 2024;2(2):145 View
  23. Leaning I, Ikani N, Savage H, Leow A, Beckmann C, Ruhé H, Marquand A. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neuroscience & Biobehavioral Reviews 2024;158:105541 View
  24. Walsh A, Naughton G, Sharpe T, Zajkowska Z, Malys M, van Heerden A, Mondelli V. A collaborative realist review of remote measurement technologies for depression in young people. Nature Human Behaviour 2024;8(3):480 View
  25. Ahmed M, Hasan T, Islam S, Ahmed N. Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study. JMIR Research Protocols 2024;13:e51540 View
  26. Schalkamp A, Harrison N, Peall K, Sandor C. Digital outcome measures from smartwatch data relate to non-motor features of Parkinson’s disease. npj Parkinson's Disease 2024;10(1) View
  27. Lee J, Kim M, Hwang S, Lee K, Park J, Shin T, Lim H, Urtnasan E, Chung M, Lee J. Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol. BMJ Open 2024;14(6):e073290 View
  28. Ng M, Frederick J, Fisher A, Allen N, Pettit J, McMakin D. Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study. JMIR Research Protocols 2024;13:e43931 View
  29. Langlais M, Marich A. Should We Track Our Children?: An Exploratory Examination of Life360 and Interpersonal and Relational Well-Being. The Family Journal 2024;32(4):605 View
  30. Beames J, Han J, Shvetcov A, Zheng W, Slade A, Dabash O, Rosenberg J, O'Dea B, Kasturi S, Hoon L, Whitton A, Christensen H, Newby J. Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12–25 years): A scoping review. Heliyon 2024;10(15):e35472 View
  31. Lamichhane B, Moukaddam N, Sabharwal A. Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions. Scientific Reports 2024;14(1) View
  32. Bosma C, Wojcik C, Haigh E. Evaluating Individual Differences in Emotion Regulation in Response to Sadness Using Digital Phenotyping. Journal of Technology in Behavioral Science 2024 View
  33. Yang X, Long S, Lu F, Ma Z. Knowledge, attitude, and practice toward family-based treatment among parents of children with leukemia. Frontiers in Public Health 2024;12 View
  34. Sidani L, Nadar S, Tfaili J, El Rayes S, Sharara F, Elhage J, Fakhoury M. Digital Psychiatry: Opportunities, Challenges, and Future Directions. Journal of Psychiatric Practice 2024;30(6):400 View

Books/Policy Documents

  1. Reindl-Spanner P, Prommegger B, Ikonomi T, Gensichen J, Krcmar H. Human-Computer Interaction. View
  2. Perna G, Spiti A, Torti T, Daccò S, Caldirola D. Recent Advances and Challenges in the Treatment of Major Depressive Disorder. View