Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40197, first published .
Utility of Smartphone-Based Digital Phenotyping Biomarkers in Assessing Treatment Response to Transcranial Magnetic Stimulation in Depression: Proof-of-Concept Study

Utility of Smartphone-Based Digital Phenotyping Biomarkers in Assessing Treatment Response to Transcranial Magnetic Stimulation in Depression: Proof-of-Concept Study

Utility of Smartphone-Based Digital Phenotyping Biomarkers in Assessing Treatment Response to Transcranial Magnetic Stimulation in Depression: Proof-of-Concept Study

Journals

  1. Kilshaw R, Boggins A, Everett O, Butner E, Leifker F, Baucom B. Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study. JMIR Research Protocols 2024;13:e53857 View
  2. 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
  3. Cappon D, Pascual-Leone A. Toward Precision Noninvasive Brain Stimulation. American Journal of Psychiatry 2024;181(9):795 View
  4. Sonig A, Deeney C, Hurley M, Storch E, Herrington J, Lázaro-Muñoz G, Zampella C, Tunc B, Parish-Morris J, Blumenthal-Barby J, Kostick-Quenet K. Ethical concerns of using computer perception technologies among pediatric patients. AI and Ethics 2025;5(4):3593 View
  5. Hackett K, Xu S, McKniff M, Paglia L, Barnett I, Giovannetti T. Mobility-Based Smartphone Digital Phenotypes for Unobtrusively Capturing Everyday Cognition, Mood, and Community Life-Space in Older Adults: Feasibility, Acceptability, and Preliminary Validity Study. JMIR Human Factors 2024;11:e59974 View