How much is left in your “sleep tank”? Proof of concept for a simple model for sleep history feedback

Jillian Dorrian, Steven Hursh, Lauren Waggoner, Crystal Grant, Maja Pajcin, Charlotte Gupta, Alison Coates, David Kennaway, Gary Wittert, Leonie Heilbronn, Chris Della Vedova, Siobhan Banks

Research output: Contribution to journalArticle

2 Citations (Scopus)


Technology-supported methods for sleep recording are becoming increasingly affordable. Sleep history feedback may help with fatigue-related decision making – Should I drive? Am I fit for work? This study examines a “sleep tank” model (SleepTank ), which is analogous to the fuel tank in a car, refilled by sleep, and depleted during wake. Required inputs are sleep period time and sleep efficiency (provided by many consumer-grade actigraphs). Outputs include suggested hours remaining to “get sleep” and percentage remaining in tank (Tank%). Initial proof of concept analyses were conducted using data from a laboratory-based simulated nightshift study. Ten, healthy males (18–35y) undertook an 8h baseline sleep opportunity and daytime performance testing (BL), followed by four simulated nightshifts (2000 h–0600 h), with daytime sleep opportunities (1000 h–1600 h), then an 8 h night-time sleep opportunity to return to daytime schedule (RTDS), followed by daytime performance testing. Psychomotor Vigilance Task (PVT) and Karolinska Sleepiness Scale were performed at 1200 h on BL and RTDS, and at 1830 h, 2130 h 0000 h and 0400 h each nightshift. A 40-minute York Driving Simulation was performed at 1730 h, 2030 h and 0300 h on each nightshift. Model outputs were calculated using sleep period timing and sleep efficiency (from polysomnography) for each participant. Tank% was a significant predictor of PVT lapses (p < 0.001), and KSS (p < 0.001), such that every 5% reduction resulted in an increase of two lapses, or one point on the KSS. Tank% was also a significant predictor of %time in the Safe Zone from the driving simulator (p = 0.001), such that every 1% increase in the tank resulted in a 0.75% increase in time spent in the Safe Zone. Initial examination of the correspondence between model predictions and performance and sleepiness measures indicated relatively good predictive value. Results provide tentative evidence that this “sleep tank” model may be an informative tool to aid in individual decision-making based on sleep history.

Original languageEnglish
Pages (from-to)177-183
Number of pages7
JournalAccident Analysis and Prevention
Publication statusPublished - May 2019


  • Actigraphy
  • Fitness for work
  • Modelling
  • Shiftwork
  • Sleep

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health

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