Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions
Year: 2015
Type: conference-paper
Abstract: We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) - OVERALL, Center for Complex Data to Knowledge in Drug Abuse and HIV Behavioral Science, CSR: Large: Collaborative Research: Enabling Privacy-Utility Trade-Offs in Pervasive Computing Systems, Novel Methods for Intensive Longitudinal Data in SMART Studies of Drug Abuse and HIV, Predicting Smoking Abstinence via Mobile Monitoring of Stress and Social Context, Data-Based Methods for Just-In-Time Adaptive interventions in Alcohol Use
OpenAlex ID: W2024937971