Third chapter of the English version of the book 'Energieke Arbeid' published by the Centre of Applied Labour Market Research and Innovation (Dutch abbreviation: KCA) to celebrate the 10th anniversary of applied labour market research at Hanze University of Applied Sciences. This chapter discusses the second line of research of KCA: The Labour Market in the EnergyPort Groningen Region.
Annual rhythms in humans have been described for a limited number of behavioral and physiological parameters. The aim of this study was to investigate time-of-year variations in late arrivals, sick leaves, dismissals from class (attendance), and grades (performance). Data were collected in Dutch high school students across 4 academic years (indicators of attendance in about 1700 students; grades in about 200 students). Absenteeism showed a seasonal variation, with a peak in winter, which was more strongly associated with photoperiod (number of hours of daylight) compared with other factors assessed (e.g., weather conditions). Grades also varied with time of year, albeit differently across the 4 years. The observed time-of-year variation in the number of sick leaves was in accordance with the literature on the seasonality of infectious diseases (e.g., influenza usually breaks out in winter). The winter peak in late arrivals was unexpected and requires more research. Our findings could be relevant for a seasonal adaptation of school schedules and working environments (e.g., later school and work hours in winter, especially at higher latitudes where seasonal differences in photoperiod are more pronounced).
Occupational stress can cause health problems, productivity loss or absenteeism. Resilience interventions that help employees positively adapt to adversity can help prevent the negative consequences of occupational stress. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee's context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. This paper presents the conceptual framework and methods behind the WearMe project, which aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented as the basis for the WearMe project. The operationalization of the concepts and the daily measurement cycle are described, including the use of wearable sensor technology (e.g., sleep tracking and heart rate variability measurements) and Ecological Momentary Assessment (mobile app). Analyses target the development of within-subject (n=1) and between-subjects models and include repeated measures correlation, multilevel modelling, time series analysis and Bayesian network statistics. Future work will focus on further developing these models and eventually explore the effectiveness of the envisioned personalized resilience system.
