The Sleep of Shift Workers in a Remote Mining Operation

Sleep-Remote-Mining

Methodology for a Randomized Control Trial to Determine Evidence-Based Interventions

By Dr Ian C Dunican

As part of a PhD program supported by the Australian Government Research Training Program and the Sleep4Performance Industry Engagement Scholarship, we have published a methodology paper. This project was led by Gemma Maisey and co-authored by Marcus Cattani, Amanda Devine, Johnny Lo and myself (Ian Dunican).

The reference for this paper and full free access is here1: Maisey, Gemma, Cattani, Marcus, Devine, Amanda, Lo, Johnny & Dunican, Ian C. (2021). “The Sleep of Shift Workers in a Remote Mining Operation: Methodology for a Randomized Control Trial to Determine Evidence-Based Interventions.” Frontiers in Neuroscience 14: 1380.

Background

Mining companies must operate 24 h, 7 days a week, 365 days (24/7/365) a year to maximise their return on investment by utilising assets and people to achieve production levels. The ability to turn a mining operation off during periods of darkness when we should be asleep is not feasible. The engineering constraints do not allow this to occur without a major shutdown. Therefore, a continuous shiftwork pattern is required to keep the mining operation running. Shiftwork can cause loss of sleep, sleep problems and potential disorders that may cause fatigue. Whilst we know that shiftwork and working at night increases the risk of accidents, incidents, and injuries, we do not know much about interventions and their efficacy in shiftwork operations. To date, mining companies try several approaches to minimise fatigue and sleep loss including the provision of sleep science education, the use of self-reported tools, wearable technology and to a lesser extent the use of roster modelling software to design rosters and the use of survey instruments to assess potential health, safety, and sleep-related issues. In this paper, we propose a robust methodology for evaluating the efficacy of these approaches that are multi-layered within an organisation that may be classified as organisational design controls (e.g., biomathematical modelling for roster design and the provision of education) or individual responsibilities (e.g., staying fit and healthy, reducing body mass, reducing alcohol consumption, using technologies, systems and processed provided to monitor their fitness for work).

Our approach

We partnered with a major mining company that conducts remote fly in fly out (FIFO) work on a 2 week on/1 week off roster. This roster comprises of a week of days followed by a week of nights with a week off. At baseline, shift and rosters were assessed and generated a measure of alertness over time that can be drilled down into 1 min increments to identify periods of high risk and reduced alertness. This paper recommends using the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) biomathematical model to predict alertness across the roster schedule 2. Sleep4Performance has used this approach previously in elite sports and high-risk industry.

Then all participants who do a FIFO roster (n=88) received a Readiband device3 and wore it for the remainder of the study period (~42 days and nights). They also completed an online survey that assessed elements of health, safety, and sleep. Sleep4Performance provides a variation of this service for organisations with recommendations and personalised advice (You can read more about this service here). Once these steps were completed, then the assignment of participants to groups occurred. Participants wore the Readiband for the 42-day study and wore assigned to one of these groups.

  • Control group
  • Sleep education program
  • Biofeedback on sleep through a smartphone app
  • Sleep education program and biofeedback on sleep through a smartphone app

An overview of this process can be seen in the below figure

What is next?

We are currently analysing the data collected from this research and hope to communicate the findings in two primary papers that focus on:

  • Sleep behaviours, patterns, and potential problems in remote mining shift workers.
  • The benefits of evidence-based interventions to improve sleep in remote mining shift workers.

The study findings have the potential to inform business practice on how to manage risk effectively. The study protocol described in this paper may be applied to other industries, including oil and gas, aviation, rail, and healthcare, to assess similar fatigue interventions and determine the potential prevalence of sleep problems and disorders.

If you would like to discuss such a program for your organisation, please get in touch.

 

References

  1. Maisey G, Cattani M, Devine A, et al. The Sleep of Shift Workers in a Remote Mining Operation: Methodology for a Randomized Control Trial to Determine Evidence-Based Interventions. Frontiers in Neuroscience 2021;14:1380.
  2. Steven R. Hursh DPR, Michael L. Johnson, David R. Thorne, Gregory Belenky, Thomas J. Balkin, William F. Storm, James C. Miller and Douglas R. Eddy. Fatigue models for applied research in warfighting. Aviation Space Environmental Medicine 2004;75(Suppl 3 ): A44–53.
  3. Dunican IC, Murray K, Slater JA, et al. Laboratory and home comparison of wrist-activity monitors and polysomnography in middle-aged adults. Sleep and Biological Rhythms 2018;16(1):85-97. doi: 10.1007/s41105-017-0130-x

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