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

Mobile Health (mhealth) (5099) Research Instruments, Questionnaires, and Tools (1177) New Methods (1763) Neurology and Neurosciences (379) Dementia and Cognitive Decline (873) Digital Biomarkers and Digital Phenotyping (426) Psychiatry (131)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59247, first published .
Elderly hands using a smartphone displaying a brain scan and health data.

Discriminant Power of Smartphone-Derived Keystroke Dynamics for Mild Cognitive Impairment Compared to a Neuropsychological Screening Test: Cross-Sectional Study

Discriminant Power of Smartphone-Derived Keystroke Dynamics for Mild Cognitive Impairment Compared to a Neuropsychological Screening Test: Cross-Sectional Study

Authors of this article:

Jin-Hyuck Park1 Author Orcid Image
Article Authors Cited by (9) Tweetations Metrics

Journals

  1. Park J. Detection of mild cognitive impairment using a virtual reality-based stroop task: a cross-sectional study of embodied behavioral markers. Journal of NeuroEngineering and Rehabilitation 2025;22(1) View
  2. Um Din N, Maronnat F, Oquendo B, Pariel S, Lafuente-Lafuente C, Badra F, Belmin J. Diagnostic Accuracy of Touchscreen-Based Tests for Mild Cognitive Disorders: A Systematic Review and Meta-Analysis. Diagnostics 2025;15(18):2383 View
  3. Park J. Correction: Discriminant Power of Smartphone-Derived Keystroke Dynamics for Mild Cognitive Impairment Compared to a Neuropsychological Screening Test: Cross-Sectional Study. Journal of Medical Internet Research 2025;27:e86291 View
  4. Moon J, Huh Y, Cho H, Kim C, Lee H, Jang D, Cho B. Exploring Age-Related Patterns in Smartphone Keystroke Dynamics Considering Temporal Variability: Cross-Sectional Study With AI-Based Analysis. JMIR mHealth and uHealth 2025;13:e80094 View
  5. Joo H, Kim Y, Lee S, Min J, Min K. Development and Validation of the Short-Form BrainOK: An Efficient Digital Screening Tool for Mild Cognitive Impairment. Diagnostics 2025;15(24):3223 View
  6. Shen Y, Huang M, Park J, Benge J, Rousseau J, Lester-Smith R, Thomaz E, Quin Yow W. Multimodal passive smartphone sensing in older adults: a guide for clinical scientists based upon an ongoing cohort study. Innovation in Aging 2026;10(4) View
  7. 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;18(1) View
  8. Chen W, Pei M, Shi F, Gao Y, Yang H, Tang L, Ye S, Zhang Y, Chen H, Lin J, Yang S, Shi C, Ning H, Fan D, Yang Q, Li N. A Multicenter Smartphone-Based Multimodal Database with Temporally Coupled Cognitive and Behavioral Data for Cognitive Health Research. AI in Neurology 2026;1(2):1 View

Conference Proceedings

  1. Shen Y, Park J, Huang M, Benge J, Rousseau J, Lester-Smith R, Thomaz E. 2025 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Deep Learning-Based Detection of Cognitive Impairment from Passive Smartphone Sensing with Routine-Aware Augmentation and Demographic Personalization View