Construction health recommendations adapt to trust and worker needs
Personalized and Trust-Aware Health Recommendation Policies for a Construction Workplace
Information Retrieval
Summary
Construction workers face health risks like fatigue and heat stress that can harm their safety and productivity. The authors created a model that looks at how workers’ health and trust in health advice change over time, and how trust affects whether workers follow recommendations. Their approach uses smart strategies to decide when and how often to give health advice personalized to each worker. This helps balance keeping workers healthy, productive, and willing to trust advice at the same time.
What this means in practice
- •For construction health managers: Design personalized health alert systems that consider worker trust and health to improve safety intervention timing.
- •For occupational safety software developers: Implement adaptive recommendation policies that dynamically adjust message frequency based on user trust and health data.
- •For industrial wearable device makers: Create health monitoring devices that integrate trust-aware feedback loops to increase worker compliance with health alerts.$Commercial implications: Enables development of smart wearable systems for workplaces that improve personalized health recommendations and compliance.
Authors
Atefeh Mollabagher, Yogesh Gautam, Houtan Jebelli, Parinaz Naghizadeh
Abstract
Construction workers face workplace risks such as fatigue, heat stress, and other physically demanding conditions that can negatively affect their health and safety. Although monitoring these risks is important, timely and personalized health interventions are also needed to help prevent negative impacts on workers' well-being and productivity. To this end, in this paper, we propose a model to capture the interactions between a trust-aware health recommender system and workers who differ in health and trust sensitivity. Specifically, in our proposed dynamic model, worker health evolves over time, worker trust is affected by both health and recommendation dynamics, and trust in turn affects compliance with future recommendations. Given this model, we characterize the recommender policy, including a health-based recommendation triggering threshold and the recommendation frequency. We do so using both model-based short-horizon control and model-free reinforcement learning. We then investigate how recommendation frequencies are adjusted for different workers to balance their health, productivity, and trust. Our findings provide insight into the design of personalized health recommendation policies in construction workplaces and beyond.