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

Digital Biomarkers and Digital Phenotyping (198) Wearable Devices and Sensors (903) Special Issue on Computing and Mental Health (2018) (33)

Published on in Vol 6, No 12 (2018): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9472, first published .
Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study

Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study

Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study

Authors of this article:

Sofian Berrouiguet1, 2, 3, 4 Author Orcid Image ;   David Ramírez5, 6 Author Orcid Image ;   María Luisa Barrigón7, 8 Author Orcid Image ;   Pablo Moreno-Muñoz5, 6 Author Orcid Image ;   Rodrigo Carmona Camacho7 Author Orcid Image ;   Enrique Baca-García7, 8, 9 Author Orcid Image ;   Antonio Artés-Rodríguez5, 6, 9 Author Orcid Image
Article Authors Cited by (34) Tweetations (2) Metrics

Journals

  1. Sultana M, Al-Jefri M, Lee J. Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study. JMIR mHealth and uHealth 2020;8(9):e17818 View
  2. Berrouiguet S, Barrigón M, Castroman J, Courtet P, Artés-Rodríguez A, Baca-García E. Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol. BMC Psychiatry 2019;19(1) View
  3. Lopez-Morinigo J, Ruiz-Ruano V, Martínez A, Estévez M, Mata-Iturralde L, Muñoz-Lorenzo L, Sánchez-Alonso S, Artés-Rodríguez A, David A, Baca-García E. Study protocol of a randomised clinical trial testing whether metacognitive training can improve insight and clinical outcomes in schizophrenia. BMC Psychiatry 2020;20(1) View
  4. Rodríguez-Blanco L, Carballo J, de León S, Baca-García E. User profiles of electronic ecological momentary assessment in outpatient child and adolescent mental health services. Spanish Journal of Psychiatry and Mental Health 2023;16(1):5 View
  5. Molina R, Porras-Segovia A, Ruiz M, Baca-García E. eHealth tools for assessing psychomotor activity in schizophrenia: a systematic review. Brazilian Journal of Psychiatry 2021;43(1):102 View
  6. Cao J, Truong A, Banu S, Shah A, Sabharwal A, Moukaddam N. Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study. JMIR Mental Health 2020;7(1):e14045 View
  7. Porras-Segovia A, Molina-Madueño R, Berrouiguet S, López-Castroman J, Barrigón M, Pérez-Rodríguez M, Marco J, Díaz-Oliván I, de León S, Courtet P, Artés-Rodríguez A, Baca-García E. Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study. Journal of Affective Disorders 2020;274:733 View
  8. Morgiève M, Genty C, Azé J, Dubois J, Leboyer M, Vaiva G, Berrouiguet S, Courtet P. A digital companion for ecological momentary assessment and prevention of suicide: A case series on the use of the emma app (Preprint). JMIR mHealth and uHealth 2019 View
  9. Barrigon M, Courtet P, Oquendo M, Baca-García E. Precision Medicine and Suicide: an Opportunity for Digital Health. Current Psychiatry Reports 2019;21(12) View
  10. Piau A, Wild K, Mattek N, Kaye J. Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review. Journal of Medical Internet Research 2019;21(8):e12785 View
  11. Norbury A, Liu S, Campaña-Montes J, Romero-Medrano L, Barrigón M, Smith E, Aroca F, Artés-Rodríguez A, Baca-García E, Berrouiguet S, Billot R, Carballo-Belloso J, Courtet P, Gomez D, Lopez-Castroman J, Perez-Rodriguez M, Díaz-Olivan I, Espinosa-Salido P, Guerra-Carrasco N, Albarracin L, Sanchez-Alonso M, Escobedo-Aedo P, Alvarez-Gutierrez A, Abascal-Peiro S, Izaguirre-Gamir L, Aznar-Carbone J, Herrera-Sanchez J, Llaguno-Sanmartín M, Vasquez-Guida M, Amodeo-Escribano S, Barrigón M, Bello-Sousa R, Cegla-Schvartzman F, Bonal-Giménez C, Carmona R, Caro-Cañizares I, Martin-Calvo M, Mata-Iturralde L, Migoya-Borja M, Miguelez-Fernandez C, Muñoz-Lorenzo L, Ovejero S, Peñuelas-Calvo I, Pérez-Colmenero S, Porras-Segovia A, Rico-Romano A, Rodriguez-Jover A, Sánchez-Alonso S, Sevilla-Vicente J, Vigil-López C, Alcón-Durán A, Di Stasio E, García-Vega J, Martín-Calvo A, Ortega A, Rodríguez-Blanco L, Segura-Valverde M, Bañón-González S, Codesal-Julián R, Frade-Ciudad A, Hernando-Merino E, Vallejo-Oñate S, Álvarez-García R, Coll-Font J, García-Jiménez M, González-Vietez G, Antonio P, Santos F, Sedano-Capdevila A, Serrano-Marugán L. Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients. Molecular Psychiatry 2021;26(8):3920 View
  12. Moreno-Muñoz P, Ramírez D, Artés-Rodríguez A. Change-point detection in hierarchical circadian models. Pattern Recognition 2021;113:107820 View
  13. Sükei E, Norbury A, Perez-Rodriguez M, Olmos P, Artés A. Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach. JMIR mHealth and uHealth 2021;9(3):e24465 View
  14. Jagesar R, Vorstman J, Kas M. Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study. Journal of Medical Internet Research 2021;23(4):e20996 View
  15. Lopez-Morinigo J, Barrigón M, Porras-Segovia A, Ruiz-Ruano V, Escribano Martínez A, Escobedo-Aedo P, Sánchez Alonso S, Mata Iturralde L, Muñoz Lorenzo L, Artés-Rodríguez A, David A, Baca-García E. Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study. Journal of Medical Internet Research 2021;23(7):e26548 View
  16. Tonti S, Marzolini B, Bulgheroni M. Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study. JMIR Biomedical Engineering 2021;6(2):e15417 View
  17. Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Mental Health 2021;8(6):e24668 View
  18. 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
  19. Romero-Medrano L, Artés-Rodríguez A. Multi-Source Change-Point Detection over Local Observation Models. Pattern Recognition 2023;134:109116 View
  20. Ryu J, Sükei E, Norbury A, H Liu S, Campaña-Montes J, Baca-Garcia E, Artés A, Perez-Rodriguez M. Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study. JMIR Mental Health 2021;8(9):e30833 View
  21. Bonilla-Escribano P, Ramirez D, Sedano-Capdevila A, Campana-Montes J, Baca-Garcia E, Courtet P, Artes-Rodriguez A. Assessment of e-Social Activity in Psychiatric Patients. IEEE Journal of Biomedical and Health Informatics 2019;23(6):2247 View
  22. Sükei E, Romero-Medrano L, de Leon-Martinez S, Herrera López J, Campaña-Montes J, Olmos P, Baca-Garcia E, Artés A. Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study. JMIR Formative Research 2023;7:e47167 View
  23. 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
  24. Sükei E, de Leon-Martinez S, Olmos P, Artés A. Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation. Internet Interventions 2023;33:100657 View
  25. Barrigon M, Romero-Medrano L, Moreno-Muñoz P, Porras-Segovia A, Lopez-Castroman J, Courtet P, Artés-Rodríguez A, Baca-Garcia E. One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study. Journal of Medical Internet Research 2023;25:e43719 View
  26. Ikäheimonen A, Triana A, Luong N, Ziaei A, Rantaharju J, Darst R, Aledavood T. Niimpy: A toolbox for behavioral data analysis. SoftwareX 2023;23:101472 View
  27. Lane E, Joshi D, Guimond S, Moore R, Ahmed A, Ajilore O, Torous J. Exploring current smartphone-based cognitive assessments in schizophrenia and bipolar disorder. Schizophrenia Research: Cognition 2024;37:100309 View
  28. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View

Books/Policy Documents

  1. Carretero P, Campana-Montes J, Artes-Rodriguez A. Behavioral Neurobiology of Suicide and Self Harm. View