Go Gig or Go Home: Enabling Social Sensing to Share Personal Data with Intimate Partner for the Health and Wellbeing of Long-Hour workers

Title of the Paper

Go Gig or Go Home: Enabling Social Sensing to Share Personal Data with Intimate Partner for the Health and Wellbeing of Long-Hour Workers

Paper Information

• Field of Study: Health and Work-Life Balance
• Keywords: Social Sensing, Technology Sensing, Health Data Sharing, Long-Hour Workers, Health Information Understanding, Behavior Change

Research Background and Problem

• Problem or Challenge: Long-hour workers, particularly flexible drivers (e.g., taxi and Uber drivers), often face issues such as fatigue, sleep deprivation, and low awareness of medical health. Additionally, relying solely on technology-sensed data (e.g., data generated by wearable devices) may not effectively drive behavior change.
• Significance: Reducing the risks associated with long working hours and improving work-life balance can enhance the physical and mental health of these workers while supporting more sustainable career development.
• Research Motivation and Related Work: Previous studies have found that technology sensing, personal health tracking, and social support can improve health awareness. However, the integration of social sensing technologies tailored for long-hour workers has not been adequately explored.

Solution

• Proposed Method or Solution: The authors propose a "social sensing" mechanism that combines technology-sensed data (e.g., Fitbit tracking data) with social sensing data (observations and feedback from intimate partners) to enhance health awareness and promote behavior change among long-hour workers.
• Innovations:
  • Combining technology-tracked data with observations from intimate partners to achieve social sensing collaboration.
  • Using explicit design interfaces to facilitate data sharing and visualization, such as the DriveProbe prototype system.
  • Highlighting the role of socialized health data sharing in improving mutual health awareness between participants and their partners.
• Implementation Steps and Key Technologies:
  1. Develop the DriveProbe system, including technology sensing (Fitbit data collection and visualization), diary data recording, and social sensing data sharing functionalities.
  2. Encourage users to record health data and fill out diaries daily, sharing them via a web platform with their partners for joint reflection.
  3. Design two participant groups—one with social sensing (data shared with partners) and the other using only technology sensing.
  4. Collect data and conduct qualitative and quantitative analyses to validate the effectiveness of social sensing in driving health behavior changes.

Research Findings

• Key Findings:
  1. Both groups of drivers showed improved health awareness and willingness to change behavior with the help of technology sensing.
  2. The group with partner-involved social sensing demonstrated significantly better performance in translating intentions into actions compared to the technology-only group.
  3. The social sensing mechanism enhanced participants' ability to engage in interactive reflection when resolving data inconsistencies (e.g., conflicts between activity data and bodily sensations).
• Advantages:
  • Integrating social sensing compensates for the limitations of technological data, such as noise and lack of context.
  • Combining observations from intimate partners with data provides specific and credible behavior adjustment suggestions.
  • Data sharing promotes mutual reflection on physical and mental states, driving behavior change.
• Experiment or Evaluation Results:
  • The driver group combining technology and social sensing outperformed the technology-only group in the frequency of behavioral change intentions and actual adjustments.
  • The Mann-Whitney U test confirmed the significant impact of social sensing on raising driver awareness.
  • After the experiment, the prioritization of economic factors and customer demands in the long-hour work environment decreased.
• Limitations and Future Directions:
  • The sample size was small and limited to professional drivers in specific contexts, making it insufficient to generalize to all long-hour worker groups.
  • Future research should aim for a gender-balanced participant group to reduce the impact of gender bias.
  • Expand the diversity of social sensing designs, such as extending to work teams and broader social networks.

This structured approach clearly demonstrates the potential of social sensing technologies in improving health awareness and driving behavior change among workers, while providing valuable directions for future design and research.