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Unveiling Usage Patterns and Explaining Usage of Symptom Checker Apps: Explorative Longitudinal Mixed Methods Study.

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  • 1Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.

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|December 9, 2024
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Summary
This summary is machine-generated.

Symptom checker apps are frequently used for new or unclear symptoms, especially those affecting the eyes, skin, or cardiovascular system. Users value these tools flexibly, integrating them into their personal health management strategies.

Keywords:
GLMMGeneral Linear Mixed ModelsKuckartzapplicationsappscircumstancescontent analysiseHealthexplorative longitudinal studymHealthmixed methodmobile healthparticipantspatternspredictionpredictsqualitative dataself careself managementself-diagnosisself-ratedself-triagesurveysymptoms checkerusageusers

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Area of Science:

  • Digital Health
  • Health Informatics
  • Patient Empowerment

Background:

  • Symptom checker apps (SCA) aim to help individuals classify symptoms and guide self-care or professional medical help.
  • Understanding the context and user opinions of SCA is limited.

Purpose of the Study:

  • To explore the circumstances of SCA use by medical laypeople.
  • To identify noteworthy aspects of SCA from the user's perspective.

Main Methods:

  • A 6-week longitudinal study with 48 SCA users documenting symptoms and app usage.
  • Mixed methods approach combining generalized linear mixed models and qualitative content analysis.

Main Results:

  • SCA use was predicted by new symptoms and specific symptom clusters (cardiovascular, eye, skin, general/unspecified).
  • Qualitative analysis revealed themes of usage reasons, emotional responses, behavioral reactions, and unmet needs like personalization.

Conclusions:

  • New and unfamiliar symptoms strongly predict SCA use.
  • SCA are utilized as flexible tools within a broader health literacy framework (bricolage).