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    Area of Science:

    • Biostatistics
    • Psychological Measurement
    • Health Informatics

    Background:

    • Traditional analysis of health diary data often uses sample means and ad hoc methods for individual variation.
    • These methods may not fully capture the nuances of self-reported health outcomes.

    Purpose of the Study:

    • To apply an advanced statistical model to daily self-reported health outcomes.
    • To simultaneously analyze an individual's likelihood of reporting an outcome, daily mean intensity, and variability.
    • To improve the accuracy of measuring individual symptom variation in health diaries.

    Main Methods:

    • Utilized observational, secondary data from 782 adults.
    • Analyzed daily self-reported fatigue symptoms, differentiating between reporting and severity.
    • Employed self-reported depressed affect and participant characteristics as predictors.

    Main Results:

    • Higher likelihood of reporting fatigue correlated with higher mean severity and greater stability.
    • Higher mean severity was associated with greater stability in severity ratings.
    • Females and individuals with high depressed affect were more prone to reporting fatigue and reported greater mean severity.

    Conclusions:

    • The advanced model enables simultaneous study of reporting likelihood, mean severity, and severity variability.
    • Individual daily symptom severity variation was modeled without measurement error inherent in ad hoc methods.
    • This approach offers a more robust analysis of longitudinal health diary data.