Counter-Epidemiological Projections of e-Coaching

TL;DR

This paper explores the use of e-coaching during pandemics, employing the Emergency Management Cycle framework and machine learning-based stress monitoring to enhance disaster management and mental health support.

Contribution

It introduces a stress monitoring assistant integrated into e-coaching systems, applying EMC principles and demonstrating its potential across disaster management phases.

Findings

Stress monitoring can effectively support e-coaching during pandemics

Machine learning-based stress detection shows promising results

E-coaching strategies can mitigate pandemic-related anxiety and stress

Abstract

This paper considers e-coaching at times of pandemic. It utilizes the Emergency Management Cycle (EMC), a core doctrine for managing disasters. The EMC dimensions provide a useful taxonomical view for the development and application of e-coaching systems, emphasizing technological and societal issues. Typical pandemic symptoms such as anxiety, panic, avoidance, and stress, if properly detected, can be mitigated using the e-coaching tactic and strategy. In this work, we focus on a stress monitoring assistant developed upon machine learning techniques. We provide the results of an experimental study of a prototype of such an assistant. Our study leads to the conclusion that stress monitoring shall become a valuable component of e-coaching at all EMC phases.