The Evaluation of Emotional Intelligence by the Analysis of Heart Rate Variability

A Pilot Feasibility Evaluation of a Heart Rate Variability Biofeedback App to Improve Self-Care in COVID-19 Healthcare Workers

COVID-19 exacerbated burnout and mental health concerns among the healthcare workforce. Due to high work stress, demanding schedules made attuned eating behaviors a particularly challenging aspect of self-care for healthcare workers. This study aimed to examine the feasibility and acceptability of a heart rate variability biofeedback (HRVB) mobile app for improving well-being among healthcare workers reporting elevated disordered eating during COVID-19. We conducted a mixed methods pre-mid-post single-arm pilot feasibility trial (ClinicalTrials.gov NCT04921228). Deductive content analysis of participants' commentary generated qualitative themes. Linear mixed models were used to examine changes in pre- mid- to post-assessment scores on well-being outcomes. We consented 28 healthcare workers (25/89% female; 23/82% Non-Hispanic White; 22/79% nurses) to use and evaluate an HRVB mobile app. Of these, 25/89% fully enrolled by attending the app and device training; 23/82% were engaged in all elements of the protocol. Thirteen (52%) completed at least 10 min of HRVB on two-thirds or more study days. Most participants (18/75%) reported being likely or extremely likely to continue HRVB. Common barriers to engagement were busy schedules, fatigue, and technology difficulties. However, participants felt that HRVB helped them relax and connect better to their body's signals and experiences. Results suggested preliminary evidence of efficacy for improving interoceptive sensibility, mindful self-care, body appreciation, intuitive eating, stress, resilience, and disordered eating. HRVB has potential as a low-cost adjunct tool for enhancing well-being in healthcare workers through positively connecting to the body, especially during times of increased stress when attuned eating behavior becomes difficult to uphold.

Human Digital Twin in Industry 5.0: A Holistic Approach to Worker Safety and Well-Being through Advanced AI and Emotional Analytics

This research introduces a conceptual framework designed to enhance worker safety and well-being in industrial environments, such as oil and gas construction plants, by leveraging Human Digital Twin (HDT) cutting-edge technologies and advanced artificial intelligence (AI) techniques. At its core, this study is in the developmental phase, aiming to create an integrated system that could enable real-time monitoring and analysis of the physical, mental, and emotional states of workers. It provides valuable insights into the impact of Digital Twins (DT) technology and its role in Industry 5.0. With the development of a chatbot trained as an empathic evaluator that analyses emotions expressed in written conversations using natural language processing (NLP); video logs capable of extracting emotions through facial expressions and speech analysis; and personality tests, this research intends to obtain a deeper understanding of workers' psychological characteristics and stress levels. This innovative approach might enable the identification of stress, anxiety, or other emotional factors that may affect worker safety. Whilst this study does not encompass a case study or an application in a real-world setting, it lays the groundwork for the future implementation of these technologies. The insights derived from this research are intended to inform the development of practical applications aimed at creating safer work environments.