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Depression Screening from Text Message Reply Latency.

M L Tlachac, Elke A Rundensteiner

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Texting reply latencies can help screen for depression without compromising privacy. This method uses metadata, not content, offering a promising approach for early detection of depressive symptoms.

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    Area of Science:

    • Digital phenotyping
    • Computational psychiatry
    • Mental health informatics

    Background:

    • Depression is a prevalent and debilitating condition, often undiagnosed.
    • Passive screening methods are needed, but smartphone and social media data raise privacy issues.
    • Slower information processing speed is linked to depression.

    Purpose of the Study:

    • To investigate the utility of text message reply latencies for depression screening.
    • To develop a privacy-preserving method for depression detection using metadata.

    Main Methods:

    • Extracted nine features related to reply latency from crowd-sourced text message metadata.
    • Utilized machine learning models, including XGBoost, on principal components of latency features.
    • Focused on metadata (reply times) rather than message content to ensure privacy.

    Main Results:

    • An XGBoost model achieved an F1 score of 0.67, AUC of 0.72, and Accuracy of 0.69.
    • The model, built on a single principal component of latency features, demonstrated predictive capability.
    • Reply latency features showed significant promise for depression screening.

    Conclusions:

    • Text message reply latency is a viable modality for passive depression screening.
    • This metadata-driven approach mitigates privacy concerns associated with content analysis.
    • Further research into digital phenotyping for mental health assessment is warranted.