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

Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1043) Tools, Programs and Algorithms (414) mHealth for Data Collection and Research (1270) Advanced Data Analytics in eHealth (240) Dementia and Cognitive Decline (747) Mobile Health in Psychiatry (557) Psychotic Disorders (208) Digital Biomarkers and Digital Phenotyping (329)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64007, first published .
Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

Authors of this article:

Imogen E Leaning1, 2 Author Orcid Image ;   Andrea Costanzo3 Author Orcid Image ;   Raj Jagesar3 Author Orcid Image ;   Lianne M Reus4, 5, 6 Author Orcid Image ;   Pieter Jelle Visser4, 7, 8 Author Orcid Image ;   Martien J H Kas3 Author Orcid Image ;   Christian F Beckmann1, 2 Author Orcid Image ;   Henricus G Ruhé1, 9 Author Orcid Image ;   Andre F Marquand1, 2, 10 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Leaning I, Costanzo A, Jagesar R, Reus L, Visser P, Kas M, Beckmann C, Ruhé H, Marquand A. Correction: Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study. Journal of Medical Internet Research 2025;27:e87810 View
  2. Zhang T, Cao Z, Li W, Lv Z. Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review. Pediatric Investigation 2026 View
  3. Long Y, Chen J, Ye X, Huang Y. Two‐Part Hidden Semi‐Markov Mixed Effects Models for Semi‐Continuous Longitudinal Data. Statistics in Medicine 2026;45(6-7) View
  4. Kim S, Kim M, Kim J, Jang J, Choi W, Lee H, Song J, Hoe H. AI-enabled digital phenotyping for Alzheimer’s disease: a review of multimodal sensor integration and symptom trajectories. Alzheimer's Research & Therapy 2026 View