Emodiversity evaluation of remote workers through health monitoring based on intra-day emotion sampling

IntroductionIn recent years, the widespread shift from on-site to remote work has led to a decline in employees’ mental health. Consequently, this transition to remote work poses several challenges for both employees and employers. To address these challenges, there is an urgent need for techniques to detect declining mental health in employees’ daily lives. Emotion-based health assessment, which examines emotional diversity (emodiversity) experienced in daily life, is a possible solution. However, the feasibility of emodiversity remains unclear, especially from the perspectives of its applicability to remote workers and countries other than Europe and the United States. This study investigated the association between subjective mental health decline and emotional factors, such as emodiversity, as well as physical conditions, in remote workers in Japan.MethodTo explore this association, we conducted a consecutive 14-day prospective observational experiment on 18 Japanese remote workers. This experiment comprised pre-and post-questionnaire surveys, physiological sensing, daytime emotion self-reports, and subjective health reports at end-of-day. In daytime emotion self-reports, we introduced smartphone-based experience sampling (also known as ecological momentary assessment), which is suitable for collecting context-dependent self-reports precisely in a recall bias-less manner. For 17 eligible participants (mean ± SD, 39.1 ± 9.1 years), we evaluated whether and how the psycho-physical characteristics, including emodiversity, changed on subjective mental health-declined experimental days after analyzing descriptive statistics.ResultsApproximately half of the experimental days (46.3 ± 18.9%) were conducted under remote work conditions. Our analysis showed that physical and emotional indices significantly decreased on mental health-declined days. Especially on high anxiety and depressive days, we found that emodiversity indicators significantly decreased (global emodiversity on anxiety conditions, 0.409 ± 0.173 vs. 0.366 ± 0.143, p = 0.041), and positive emotional experiences were significantly suppressed (61.5 ± 7.7 vs. 55.5 ± 6.4, p < 0.001).DiscussionOur results indicated that the concept of emodiversity can be applicable even to Japanese remote workers, whose cultural background differs from that of individuals in Europe and the United States. Emodiversity showed significant associations with emotion dysregulation-related mental health deterioration, suggesting the potential of emodiversity as useful indicators in managing such mental health deterioration among remote workers

Prediction of Future Collision Risk for Truck Drivers Using the Time-Series Autonomic Nerve Function

Healthcare for truck drivers is an important issue. To prevent fatigue-related collisions among drivers, objective assessments of their physiological states are essential. A simple and quantitative evaluation method for fatigue involves the use of autonomic nerve function (ANF) indices obtained from heart rate variability analysis. However, predicting the occurrence of crashes using only physiological data is challenging. In most previous studies, the targets of driving situations have been often limited, or the prediction targets have been set as the driver’s internal state rather than the accident. In this paper, we propose a novel collision risk prediction model using ANF and several simple external information types, which can be extracted from standard in-vehicle sensors without limiting the driving scene. Our experiments using actual truck drivers’ data reveal that the proposed model can achieve collision risk prediction for the following 30 min with an accuracy of 74.9% recall and 0.79 AUC. Furthermore, we discover that simple external information obtained based on the vehicle speed significantly contributes to the prediction accuracy. As our prediction method only requires commonly equipped sensors as the sources of external information, this method is expected to be easily implemented not only for truck driving but also for general vehicle driving, where crashes are often likely