A Systematic Review on Visualizations for Self-Generated Health Data for Daily Activities

Area of Science:

• Digital Health
• Human-Computer Interaction
• Health Informatics

Background:

• Advancements in sensing technology enable widespread personal health tracking.
• Growing interest in personal healthcare data empowers individuals to manage their wellness.
• Mobile devices facilitate easy access to self-generated health data, often utilizing data visualization.

Purpose of the Study:

• To systematically review the literature on data visualization in personal healthcare management.
• To understand the role and effectiveness of visualizations for non-clinical health data.
• To develop a framework for categorizing visualization efficacy based on user changes.

Main Methods:

• Conducted a systematic literature review.
• Analyzed thirteen selected papers focusing on personal health data and visualization in non-clinical settings.
• Evaluated data types, visualization characteristics, and their effectiveness for healthcare management.

Main Results:

• Identified various types of personal health data and visualization characteristics used in everyday life tracking.
• Assessed the effectiveness of visualizations for managing health outside clinical environments.
• Proposed a six-level categorization for visualization efficacy, considering user cognitive and physical changes.

Conclusions:

• Effective data visualization is crucial for personal healthcare management and self-awareness.
• The proposed framework provides a foundation for developing and evaluating visualizations for mobile and wearable health applications.
• Recommendations for future evaluation studies are identified to advance the field of personal health data.