JMIR Formative Research

View query in Help articles search

Field

e.g. mhealth

Operator

Field

Criteria

e.g. John Smith or 0000-0001-6479-5330

Exact Match for Criteria

Yes No

Search Results (1 to 4 of 4 Results)

Download search results: CSV END BibTex RIS

Sort by:

mindLAMPVis as a Co-Designed Clinician-Facing Data Visualization Portal to Integrate Clinical Observations From Digital Phenotyping in Schizophrenia: User-Centered Design Process and Pilot Implementation

Karthik Sama, Jaya Sreevalsan-Nair, Soumya Choudhary, Srilakshmi Nagendra, Preethi V Reddy, Asher Cohen, Urvakhsh Meherwan Mehta, John Torous

JMIR Form Res 2025;9:e70073

Download Citation: END BibTex RIS

Download PDF Download XML

Analyzing Trends in Suicidal Thoughts Among Patients With Psychosis in India: Exploratory Secondary Analysis of Smartphone Ecological Momentary Assessment Data

Ameya P Bondre, Aashish Ranjan, Ritu Shrivastava, Deepak Tugnawat, Nirmal Kumar Chaturvedi, Anant Bhan, Snehil Gupta, Abhijit R Rozatkar, Srilakshmi Nagendra, Siddharth Dutt, Soumya Choudhary, Preethi V Reddy, Urvakhsh Meherwan Mehta, John A Naslund, John Torous

JMIR Form Res 2025;9:e67745

Download Citation: END BibTex RIS

Download PDF Download XML

A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study

Choudhary et al [26] found that machine learning models that generated an MHSS for depression had high accuracy metrics (≥89%) and were able to distinguish between users with depression and those without. Coupled with the findings of this study, MHSS can distinguish between comorbid depression and anxiety, thereby improving clinical decision making. One of the limitations of the study was that the GAD-7 questionnaire was collected at only 1 time point during the study.

Soumya Choudhary, Nikita Thomas, Sultan Alshamrani, Girish Srinivasan, Janine Ellenberger, Usman Nawaz, Roy Cohen