Validating and extending the Three Process Model of alertness in airline operations

Sleepiness and fatigue are important risk factors in the transport sector and bio-mathematical sleepiness, sleep and fatigue modeling is increasingly becoming a valuable tool for assessing safety of work schedules and rosters in Fatigue Risk Management Systems (FRMS). The present study sought to validate the inner workings of one such model, Three Process Model (TPM), on aircrews and extend the model with functions to model jetlag and to directly assess the risk of any sleepiness level in any shift schedule or roster with and without knowledge of sleep timings. We collected sleep and sleepiness data from 136 aircrews in a real life situation by means of an application running on a handheld touch screen computer device (iPhone, iPod or iPad) and used the TPM to predict sleepiness with varying level of complexity of model equations and data. The results based on multilevel linear and non-linear mixed effects models showed that the TPM predictions correlated with observed ratings of sleepiness, but explorative analyses suggest tha t the default model can be improved and reduced to include only two-processes (S+C), with adjusted phases of the circadian process based on a single question of circadian type. We also extended the model with a function to model jetlag acclimatization and with estimates of individual differences including reference limits accounting for 50%, 75% and 90% of the population as well as functions for predicting the probability of any level of sleepiness for ecological assessment of absolute and relative risk of sleepiness in shift systems for safety applications.

嗜睡与疲劳是交通运输领域的重要风险因素，而生物数学嗜睡、睡眠与疲劳建模在疲劳风险管理系统（Fatigue Risk Management Systems, FRMS）中，正日益成为评估工作排班与轮班表安全性的极具价值的工具。本研究旨在针对空勤人员验证此类模型之一——三过程模型（Three Process Model, TPM）的内在运行机制，并为该模型拓展两项功能：一是模拟时差反应，二是在已知或未知睡眠时段的前提下，直接评估任意轮班排班或轮班表中任意嗜睡程度对应的风险。本研究通过运行于手持触屏电子设备（iPhone、iPod或iPad）的应用程序，在真实场景中收集了136名空勤人员的睡眠与嗜睡数据，并基于复杂度各异的模型方程与数据集，使用TPM进行嗜睡程度预测。基于多层线性与非线性混合效应模型的分析结果显示，TPM的预测结果与实测嗜睡评分具有相关性，但探索性分析表明，默认模型可得到优化并简化为仅包含双过程（S+C）的形式，且可基于单一昼夜类型问卷调整昼夜节律过程的相位。本研究还为模型拓展了时差适应模拟功能，以及包含覆盖人群50%、75%与90%的参考限值的个体差异估算模块，同时开发了可预测任意嗜睡程度概率的功能，用于安全应用场景下轮班系统中嗜睡绝对风险与相对风险的生态学评估。