Year: 2026
Type: review
Study design: Systematic Review
Abstract: Social connectedness plays a critical role in human health and well-being, yet increasing physical separation has contributed to rising social isolation and loneliness. Quantified Self (QS) technologies enable the continuous sharing of personal data and offer new opportunities to support connection at a distance. However, it remains unclear how QS technologies are designed and used to mediate social connectedness. This study systematically examines QS technologies from a design and contextual perspective through a literature review of 31 studies selected from Google Scholar, ACM, IEEE Xplore, and PubMed. The analysis is structured across six dimensions: data collection, design, context, design–context configurations, social connectedness measurement, and concerns. The findings show that current QS systems predominantly focus on strong-tie relationships in domestic settings, relying on physiological and activity data with primarily visual feedback. While low-attention and non-intrusive interaction is emerging, weaker ties, outdoor contexts, and multi-sensory modalities remain underexplored. Importantly, QS systems operate through recurring design–context configurations and support connectedness by reconstructing forms of co-presence at a distance. This review contributes a structured analytical framework and a configuration-level perspective, offering new insights into how self-tracked data mediate interpersonal connection and informing the design of socially aware, context-sensitive QS systems.
Authors: Rui Wang, Mengyan Guo, Yuan Lu, Jun Hu
Institution: Eindhoven University of Technology
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7213236829