Predict Fatigue. Prevent Road Collisions.

By: Tara O’Leary, BSc, MSc, RGN, COHN, CIH, PepsiCo Global Director Occupational & Industrial Hygiene, EHS Capability & Sales

Consistent with our efforts to explore new, innovative, and disruptive technology solutions that have the potential to reduce fatigue-related collisions within our fleet operations, we are investigating leveraging technology to provide the benefits of good sleep health for all our people – which is part of our ‘Pursue Positive’ ethos, aiming to help make our team members fitter and healthier. Predictive fatigue risk management technology combines connected wearables with bio-mathematical science, which is designed to enable users to provide objective visibility around which drivers are, or will become fatigued over the duration of their duty period.

The SAFTE bio-mathematical fatigue model simulates changes in cognitive effectiveness as a result of sleep deprivation, was developed by the US Army Research Laboratory and is the product of decades’ worth of research and development. It has been scientifically validated and suitable for application across a broad range of populations, and has been approved for use by both the US Federal Aviation Administration and the US Federal Railroad Administration.

By leveraging a detailed set of sleep factors made available by the Readiband sleep tracker, the SAFTE model generates an hour-by-hour fatigue prediction for each driver, at a SAFTE score of 70, a driver’s fatigue is equivalent to the physiological effects of alcohol impairment at a 0.08 BAC. At this level, a driver’s reaction time is estimated to slow by as much as 43 percent, and they are 5.2x MORE likely to suffer from a lapse in attention – associated with a micro sleep.

So how does this all come together, and how is it implemented in real-world applications?

  • First, it starts with equipping drivers with the Readiband sleep tracker – enabling an objective and scientifically valid assessment of sleep duration, sleep quality and sleep/wake timing for the driver.
  • Next, the driver’s sleep data is captured via a mobile device, either phone or tablet, PRIOR to the start of their duty-period.
  • Those sleep data are then instantly analyzed using the SAFTE bio-mathematical fatigue model, generating predictions for the day ahead.
  • These fatigue predictions are then made available across a suite of software tools, including a predictive dashboard specifically designed for frontline supervisors – enabling highly targeted interventions.
  • Finally, sleep and fatigue data can be made available to individual drivers via mobile app – enabling self-management of fatigue, and encouraging drivers to inform better sleep behaviors

The Predictive Dashboard provides a ‘Birds Eye View’ of all Readiband equipped workers, providing for the first-time objective visibility around who IS or who WILL become critically fatigue-impaired while on-duty. The Predictive Dashboard also enables setting the desired SAFTE fatigue threshold, instantly calculating the number of hours before each driver will reach that threshold.

Now you may be asking yourself, “At which SAFTE Score is intervention warranted?”

A key part of piloting the Readiband technology is leveraging the objective data to establish Fatigue Risk Profiles across a number of fleet operations– all of which serve to inform the trade-off between risk and operational continuity, allowing the selection of appropriate and informed cease-driving thresholds.

By establishing an objective Fatigue Risk Profile within a specific operation or cohort of drivers, the potential sources and extent of fatigue present can be understood. There is inherently greater fatigue-risk associated with nighttime work due to circadian factors alone, and it is expected that drivers experience lower levels of effectiveness when driving at night. This baseline data can enable the evaluation of the need for controls and mitigation, and to measure the effectiveness of those when implemented.

By setting a baseline Fatigue Risk Profile, the operational impact when imposing a Cease Driving Threshold tied to a specific SAFTE score can be evaluated – enabled by the predictive monitoring solution. Objective fatigue data can be used to balance trade-off between operational risk and operational continuity. In addition to enabling target fatigue interventions, the Readiband also provides a high-resolution picture of drivers’ sleep, including parameters around: sleep duration, sleep quality, sleep/wake timing, sleep latency, and more.

It is then we’re not only predicting fatigue, but we’re preventing road collisions.