Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market.

Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology.

Predictive Biomathematical Modeling Compared to Objective Sleep During COVID-19 Humanitarian Flights.

BACKGROUND: Biomathematical modeling software like the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model and Fatigue Avoidance Scheduling Tool (FAST) help carriers predict fatigue risk for planned rosters. The ability of a biomathematical model to accurately estimate fatigue risk during unprecedented operations, such as COVID-19 humanitarian ultra-long-range flights, is unknown. Azul Cargo Express organized and conducted five separate humanitarian missions to China between May and July 2020. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within SAFTE-FAST was used to predict in-flight sleep duration and pilot effectiveness. Here we compare AutoSleep predictions against pilots' sleep diary and a sleep-tracking actigraphy device (Zulu watch, Institutes for Behavior Resources) from Azul's COVID-19 humanitarian missions.METHODS: Pilots wore Zulu watches throughout the mission period and reported sleep duration for their in-flight rest periods using a sleep diary. Agreement between AutoSleep, diary, and Zulu watch measures was compared using intraclass correlation coefficients (ICC). Goodness-of-fit between predicted effectiveness distribution between scenarios was evaluated using the R² statistic.RESULTS: A total of 20 (N = 20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. ICC and R² values were >0.90, indicating excellent agreement between sleep measures and predicted effectiveness distribution, respectively.DISCUSSION: Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances. Pilots may overestimate the amount of sleep that they receive during extreme flight-duty periods, which could constitute a fatigue risk.Devine JK, Garcia CR, Simoes AS, Guelere MR, de Godoy B, Silva DS, Pacheco PC, Choynowski J, Hursh SR. Predictive biomathematical modeling compared to objective sleep during COVID-19 humanitarian flights. Aerosp Med Hum Perform. 2022; 93(1):4-12.

Pilot Sleep Behavior across Time during Ultra-Long-Range Flights.

Fatigue risk to the pilot has been a deterrent for conducting direct flights longer than 12 h under normal conditions, but such flights were a necessity during the COVID-19 pandemic. Twenty (N = 20) pilots flying across five humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep score (wake, interrupted, light, or deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration, and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep score during in-flight sleep, which has not been reported previously in the literature.

Addressing fatigue in medical residents with biomathematical fatigue modeling.

Fatigue in resident physicians has been identified as a factor that contributes to burnout and a decline in overall wellbeing. Fatigue risk exists because of poor sleep habits and demanding work schedules that have only increased due to the COVID-19 pandemic. At this time, it is important not to lose sight of how fatigue can impact residents and how fatigue risk can be mitigated. While fatigue mitigation is currently addressed by duty hour restrictions and education about fatigue, Fatigue Risk Management Systems (FRMSs) offer a more comprehensive strategy for addressing these issues. An important component of FRMS in other shiftwork industries, such as aviation and trucking, is the use of biomathematical models to prospectively identify fatigue risk in work schedules. Such an approach incorporates decades of knowledge of sleep and circadian rhythm research into shift schedules, taking into account not just duty hour restrictions but the temporal placement of work schedules. Recent research has shown that biomathematical models of fatigue can be adapted to a resident physician population and can help address fatigue risk. Such models do not require subject matter experts and can be applied in graduate medical education program shift scheduling. It is important for graduate medical education program providers to consider these alternative methods of fatigue mitigation. These tools can help reduce fatigue risk and may improve wellness as they allow for a more precise fatigue management strategy without reducing overall work hours.