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Smartphone Typing Dynamics for Assessing Hand Function in Psoriatic Arthritis: A Proof-of-Concept Study.

Eleni Vasileiou, Georgios Apostolidis, Vasileios Charisis

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Digital biomarkers from smartphone typing can detect Psoriatic Arthritis (PsA) hand impairment. This novel method offers objective, accessible early screening for PsA symptoms, improving patient quality of life.

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    Area of Science:

    • Rheumatology
    • Digital Health
    • Biomarkers

    Background:

    • Psoriatic Arthritis (PsA) is a chronic inflammatory disease impacting quality of life.
    • Current PsA diagnosis and monitoring rely on subjective clinical assessments.
    • Hand function impairment, particularly fine motor skills, is a significant PsA manifestation.

    Purpose of the Study:

    • To develop and validate digital biomarkers (dBMs) for assessing hand function impairment in PsA patients.
    • To investigate the potential of analyzing typing patterns on smartphones for PsA detection.
    • To explore the use of dBMs for early screening of PsA symptoms.

    Main Methods:

    • A smartphone-based typing test with a custom keyboard was used to collect keystroke parameters and typing metadata.
    • Digital biomarkers were derived from typing dynamics, including key hold time and inter-key flight time.
    • Logistic regression was applied to typing-based dBMs for classification analysis.

    Main Results:

    • The study included 16 PsA patients (11 low disease activity, 5 high disease activity) and 9 healthy controls.
    • Typing-based dBMs achieved 85% classification performance in distinguishing healthy controls from PsA patients with low disease activity (AUC: 0.85).
    • Statistically significant measures and promising discriminative capabilities were observed, highlighting the potential for early PsA symptom screening.

    Conclusions:

    • Digital biomarkers derived from natural typing interactions show potential for objective and accessible PsA assessment.
    • Smartphone-based typing analysis can aid in the early detection of PsA-related hand function impairment.
    • Integrating dBMs into clinical practice may enhance PsA diagnosis and monitoring.