A Review of Tools and Methods for Detection, Analysis, and Prediction of Allostatic Load Due to Workplace Stress

Abstract

Chronic stress risks an individual's overall well-being. Chronic stress is associated with allostatic load, the body's wear-and-tear due to prolonged heightened physiological and psychological states. Increased allostatic load among workers increases their risk of injuries and the likelihood of diseases and illnesses. An allostatic load model could explain the basis of a stress response. Stress research in affective computing uses wearable devices, data processing algorithms, and machine learning methods to create models that could benefit from an allostatic load model of stress. We emphasize the need for the allostatic load model in affective computing to create disease and illness prediction models. Predictive models could enhance safeguards in the workplace by helping to create proactive mitigation strategies against chronic stress. First, we briefly introduce allostasis' physiological and psychological basis. Next, we reviewed stress studies within affective computing that may benefit from an allostatic load model of stress. We focused our review on studies conducted in dynamic settings, such as the workplace, and those incorporating typical stress study elements in affective computing. We conclude our review by identifying gaps between affective computing and neuroscientific stress studies and provide recommendations for adopting the allostatic load model of stress.