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Robot-Mediated Nudges for Workplace Health: Not a One-Size-Fits-All Modeling Problem
Rhian C Preston1, Kenna Dinsdale1, Madison R Shippy1
1CoRIS Institute, Oregon State University, P.O. Box 1212, Corvallis 97331, OR, USA.
International Journal of Social Robotics
|September 6, 2024
Summary
Socially assistive robots (SARs) can help office and remote workers reduce prolonged sedentary behavior. Personalizing robot prompts based on user archetypes improves break-taking success, addressing a key workplace health challenge.
Area of Science:
- Human-Computer Interaction
- Robotics
- Occupational Health
Background:
- Prolonged sedentary behavior among office and remote workers causes significant cardiovascular and musculoskeletal health issues.
- Current interventions like health coaches or ignored notifications are insufficient for promoting regular breaks.
- Socially assistive robots (SARs) offer a scalable solution combining a physical presence with notification system benefits.
Purpose of the Study:
- To investigate the impact of a SAR intervention on sedentary workers' break-taking habits.
- To develop and refine a robot behavior model for promoting healthy workplace practices.
- To explore personalization strategies for SAR-based break reminders.
Main Methods:
- Implementation of a SAR system to encourage breaks among study participants.
- Long-term deployment studies (two months with N=7, followed by one week with N=14) to assess intervention effectiveness.
- Analysis of user data to inform a robot behavior model and identify user archetypes.
Main Results:
- The SAR intervention demonstrated a reduction in uninterrupted sitting time.
- Participant responsiveness to break prompts was categorized into three distinct archetypes.
- Personalizing robot prompts based on these archetypes significantly improved system success.
Conclusions:
- SARs can effectively reduce sedentary behavior in workplace settings.
- Personalization of robot-assisted interventions is crucial for maximizing user engagement and effectiveness.
- Even limited data can support the development of tailored robotic systems for health promotion.
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