0396 Sleep Behavior at Base and Layover in Australian Regular Public Transport and Charter Flights Using Consumer Sleep Technologies (CSTs)

Abstract Introduction Fatigue risk management is an important part of maintaining safety in aviation. Alliance Airlines provides contracted aviation services and short-term charter services in the Australasian region. The airline recently began a trial of a sleep hygiene mobile application that uses data from consumer sleep technologies (CSTs) to provide predictions of alertness to the user and remotely extract data from devices. This trial is on-going. The object of this analysis is to compare crew member sleep during layover nights to sleep at homebase. Methods Flight crew participants wore either a Fitbit, Garmin, or Oura brand CST and provided data through the SleepTank mobile app using study-provided emails. Sleep data was extracted via the mobile app; work schedules were provided by the airline’s flight operations team. Summary statistics (M±STD) were computed for average bedtime and waketime, time in bed (TIB) and total sleep time (TST) in minutes, and sleep efficiency (SE). Schedule data were used to assign sleep events to base or layover conditions. Two-sample t tests with unequal variances compared sleep variables between base and layover conditions. Effect size was estimated using Hedges’ g. Results To date, N=12 pilots based in Queensland provided 2+ weeks’ worth of wearable data which coincided with work periods during the 2025 calendar year. Each participant provided an average of 33 days of base sleep data and 5 days of layover sleep data. Sleep events were earlier and shorter during layover compared to base: (Bedtime:21:27±2:02hr vs. 22:28±2:19hr, t=2.24, g=0.83, p< 0.001; Waketime: 5:39±2:12hr vs. 6:57±2:19hr, t=4.38, g=0.56, p< 0.001; TIB: 428±78min vs. 495±124min, t=5.61, g=0.57, p< 0.001; TST: 373±67min vs. 430±104min, t=5.48, g=0.57, p< 0.001). SE did not differ (92±4% vs. 91±6%; t=0.45, g=0.07, p=0.33). Differences in sleep timing could not be explained by time zone changes. Conclusion Charter pilots flying daytime regional operations exhibited shorter/earlier sleep behavior during layover compared to base. Sleep behavior may have been affected by time limitations during layovers. This on-going study will collect data from cabin and ground crew and crew from other bases. Future analyses will examine the impact of feedback about alertness on crew member sleep behavior. Support (if any) No external funding.

Authors

Institutions

Publication Details

Journal
SLEEP
Published
2026-05-01
DOI
https://doi.org/10.1093/sleep/zsag091.0396
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

0396 Sleep Behavior at Base and Layover in Australian Regular Public Transport and Charter Flights Using Consumer Sleep Technologies (CSTs)

Steven Hursh, Kayler Marshall, Melissa Clarke, Jaime Devine et al.
SLEEP
Sleep and Work-Related Fatigue
article

0396 Sleep Behavior at Base and Layover in Australian Regular Public Transport and Charter Flights Using Consumer Sleep Technologies (CSTs)

Steven Hursh, Kayler Marshall, Melissa Clarke, Jaime Devine, Jane Thomson
article en

Abstract

Abstract Introduction Fatigue risk management is an important part of maintaining safety in aviation. Alliance Airlines provides contracted aviation services and short-term charter services in the Australasian region. The airline recently began a trial of a sleep hygiene mobile application that uses data from consumer sleep technologies (CSTs) to provide predictions of alertness to the user and remotely extract data from devices. This trial is on-going. The object of this analysis is to compare crew member sleep during layover nights to sleep at homebase. Methods Flight crew participants wore either a Fitbit, Garmin, or Oura brand CST and provided data through the SleepTank mobile app using study-provided emails. Sleep data was extracted via the mobile app; work schedules were provided by the airline’s flight operations team. Summary statistics (M±STD) were computed for average bedtime and waketime, time in bed (TIB) and total sleep time (TST) in minutes, and sleep efficiency (SE). Schedule data were used to assign sleep events to base or layover conditions. Two-sample t tests with unequal variances compared sleep variables between base and layover conditions. Effect size was estimated using Hedges’ g. Results To date, N=12 pilots based in Queensland provided 2+ weeks’ worth of wearable data which coincided with work periods during the 2025 calendar year. Each participant provided an average of 33 days of base sleep data and 5 days of layover sleep data. Sleep events were earlier and shorter during layover compared to base: (Bedtime:21:27±2:02hr vs. 22:28±2:19hr, t=2.24, g=0.83, p< 0.001; Waketime: 5:39±2:12hr vs. 6:57±2:19hr, t=4.38, g=0.56, p< 0.001; TIB: 428±78min vs. 495±124min, t=5.61, g=0.57, p< 0.001; TST: 373±67min vs. 430±104min, t=5.48, g=0.57, p< 0.001). SE did not differ (92±4% vs. 91±6%; t=0.45, g=0.07, p=0.33). Differences in sleep timing could not be explained by time zone changes. Conclusion Charter pilots flying daytime regional operations exhibited shorter/earlier sleep behavior during layover compared to base. Sleep behavior may have been affected by time limitations during layovers. This on-going study will collect data from cabin and ground crew and crew from other bases. Future analyses will examine the impact of feedback about alertness on crew member sleep behavior. Support (if any) No external funding.

SLEEPVol. 49(Supplement_1)
Institutes for Behavior Resources (US), Airline Training Program Flight School (US)
Openalex Percentile: Top 5%
Sleep and Work-Related Fatigue
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.