Quantitative methodologies to assess sleep, wellbeing and physical health in dairy farm workplaces

Context Farmer workloads can affect their sleep and wellbeing; however, quantitative methodologies for assessing these aspects with farmers have not been well developed or tested. Aims The aims of this exploratory study were to (1) develop and test methods to assess the impact of spring peak workloads on dairy farmer sleep, wellbeing and physical health, (2) use these methods to assess the impacts of different milking schedules on sleep, wellbeing and physical health factors. Methods The sleep patterns of nine farmers working on farms with different milking schedules were assessed using Oura™ sensors for 119 days over spring calving in 2020. Variables measured were hours in bed, hours of sleep, sleep efficiency, sleep latency, heart rate, lowest resting heart rate, and heart rate variability. Participant wellbeing of a wider group of farmers was assessed using wellbeing surveys and two short health assessments at the start and end of the milking season. Key results Results showed that tracking farmer sleep was possible by using the Oura™ sensor. The method was able to highlight differences between participants on different milking schedules during the monitoring period by using principal component analysis. Overall, participants on twice-a-day milking schedule farms got less sleep than did those on 3-in-2 milking schedule farms, and the amount of sleep declined across the study. The use of wellbeing surveys was able to identify some differences between participants. For example, potential issues among participants related to not having enough energy to connect with people outside of work in the spring calving period and feeling that there is not enough time available to complete work tasks. The methodology also showed that short, regular, SMS-based surveys could be used to collect longitudinal wellbeing data. Conclusions Quantitative methodologies can be used by researchers to assess sleep and wellbeing factors among farm teams. The approaches tested in this study were able to indicate differences among individuals and farm systems; however, they need further refinement to be scalable across more farms in future studies. Implications This study has shown the likely impact that peak workload over the calving period can have on dairy farmers.