Predictive Analytics for Burnout Prevention Through Dynamic Goal Adjustment in Gig Work
Keywords:
gig work, burnout prevention, dynamic goal adjustment, predictive analytics, algorithmic management, worker well-being, system architectureAbstract
The rapid expansion of platform-mediated gig work has introduced novel forms of algorithmic management that intensify worker precarity and accelerate burnout trajectories. Unlike traditional employment, gig workers face non-negotiable, dynamically assigned goals and constant surveillance through automated systems, creating unique psycho-physiological demands. This paper proposes a system-level framework for predictive analytics to prevent burnout through dynamic goal adjustment, integrating real-time behavioral telemetry, physiological markers, and contextual labor market signals. We examine the architectural requirements for such interventions, emphasizing multi-layered data fusion, federated learning, and edge-based inference that respects worker privacy. The discussion addresses structural trade-offs between platform efficiency and worker well-being, including the tension between personalized goal plasticity and system-wide fairness. We scrutinize the governance infrastructure necessary to prevent predictive tools from morphing into coercive optimization engines, highlighting the need for worker-centric explainability, contestability, and data sovereignty. By comparing intervention models from clinical behavioral health, critical algorithm studies, and industrial human factors engineering, we identify key design principles for sustainable deployment. We further analyze regulatory and policy implications around algorithmic accountability in labor platforms, proposing boundary conditions for ethical dynamic adjustment. The paper closes with a roadmap for cross-disciplinary research that treats burnout not as an individual pathology but as a systemic property of the socio-technical gig infrastructure, foregrounding resilience, autonomy, and collective bargaining as co-design objectives.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
