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

Digital Biomarkers and Digital Phenotyping (138) mHealth in a Clinical Setting (492) mHealth for Wellness, Behavior Change and Prevention (2268) mHealth for Data Collection and Research (675) Ontologies, Classifications, and Coding (164) eHealth Infrastructures (222) Crowdsourcing, Big Data, Quantified Self-based Research Data (8) Big Data (169) Data Science (110) Mobile Health (mhealth) (1918)

Published on in Vol 10 , No 1 (2022) :January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30557, first published .
Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study

Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study

Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study

Authors of this article:

Aditya Vaidyam 1 Author Orcid Image ;   John Halamka 2 Author Orcid Image ;   John Torous 1 Author Orcid Image
Article Authors Cited by (9) Tweetations (28) Metrics

Journals

  1. Hussein R, Griffin A, Pichon A, Oldenburg J. A guiding framework for creating a comprehensive strategy for mHealth data sharing, privacy, and governance in low- and middle-income countries (LMICs). Journal of the American Medical Informatics Association 2022 View
  2. Richey A, Kovacs I, Browne S. Use of an Ingestible, Sensor-Based Digital Adherence System to Strengthen the Therapeutic Relationship in Serious Mental Illness. JMIR Mental Health 2022;9(12):e39047 View
  3. Currey D, Torous J. Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model. Journal of Medical Internet Research 2023;25:e39258 View
  4. Currey D, Torous J. Digital phenotyping correlations in larger mental health samples: analysis and replication. BJPsych Open 2022;8(4) View
  5. Niemeijer K, Mestdagh M, Verdonck S, Meers K, Kuppens P. Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study. JMIR Formative Research 2023;7:e43296 View
  6. Currey D, Torous J. Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study. JMIR Research Protocols 2022;11(11):e37954 View
  7. Moscarelli M, Min J, Kopelowicz A, Torous J, Chavez O, Gómez-de-Regil L, Salvador-Carulla L, Ochoa S, Gamez M, Vila-Badia R, Romero-Lopez-Alberca C, Ahmed A. The scale for the assessment of the passively received experiences (PRE) in schizophrenia and digital mental health. Schizophrenia Research 2023;251:91 View
  8. Senaratne H, Oviatt S, Ellis K, Melvin G. A Critical Review of Multimodal-multisensor Analytics for Anxiety Assessment. ACM Transactions on Computing for Healthcare 2022;3(4):1 View
  9. Schmidt S, D'Alfonso S. Clinician perspectives on how digital phenotyping can inform client treatment. Acta Psychologica 2023;235:103886 View