Key Laboratory of Deep Coal Resource Mining, School of Mines, Ministry of Education of China, China University of Mining and Technology, Xuzhou 221116, China.
State Key Laboratory of Coal Resources and Mine Safety, China University of Mining and Technology, Xuzhou 221116, China.
Int J Environ Res Public Health. 2018 Nov 14;15(11):2555. doi: 10.3390/ijerph15112555.
Most of the research on mental fatigue evaluation has mainly concentrated on some indexes that require sophisticated and large instruments that make the detection of mental fatigue cumbersome, time-consuming, and difficult to apply on a large scale. A quick and sensitive mental fatigue detection index is necessary so that mentally fatigued workers can be alerted in time and take corresponding countermeasures. However, to date, no studies have compared the sensitivity of common objective evaluation indexes. To solve these problems, this study recruited 56 human subjects. These subjects were evaluated using six fatigue indexes: the Stanford sleepiness scale, digital span, digital decoding, short-term memory, critical flicker fusion frequency (CFF), and speed perception deviation. The results of the fatigue tests before and after mental fatigue were compared, and a one-way analysis of variance (ANOVA) was performed on the speed perception deviation. The results indicated the significance of this index. Considering individual differences, the relative fatigue index (RFI) was proposed to compare the sensitivity of the indexes. The results showed that when the self-rated fatigue grade changed from non-fatigue to mild fatigue, the ranges of RFI values for digital span, digital decoding, short-term memory, and CFF were 0.175⁻0.258, 0.194⁻0.316, 0.068⁻0.139, and 0.055⁻0.075, respectively. Correspondingly, when the self-rated fatigue grade changed to severe fatigue, the ranges of RFI values for the above indexes were 0.415⁻0.577, 0.482⁻0.669, 0.329⁻0.396, and 0.114⁻0.218, respectively. These results suggest that the sensitivity of the digital decoding, digital span, short-term memory, and CFF decreased sequentially when the self-evaluated fatigue grade changed from no fatigue to mild or severe fatigue. The RFI individuality of the speed perception deviation is highly variable and is not suitable as an evaluation index. In mental fatigue testing, digital decoding testing can provide faster, more convenient, and more accurate results.
大多数关于精神疲劳评估的研究主要集中在一些需要复杂和大型仪器的指标上，这使得精神疲劳的检测变得繁琐、耗时且难以大规模应用。因此，有必要找到一个快速而敏感的精神疲劳检测指标，以便及时提醒精神疲劳的工人并采取相应的对策。然而，迄今为止，尚无研究比较常用客观评估指标的敏感性。为了解决这些问题，本研究招募了 56 名受试者。这些受试者使用六个疲劳指标进行评估：斯坦福嗜睡量表、数字跨度、数字解码、短期记忆、临界闪烁融合频率（CFF）和速度感知偏差。比较了精神疲劳前后的疲劳测试结果，并对速度感知偏差进行了单向方差分析（ANOVA）。结果表明该指标具有显著意义。考虑到个体差异，提出了相对疲劳指数（RFI）来比较各指标的敏感性。结果表明，当自我评估的疲劳等级从非疲劳变为轻度疲劳时，数字跨度、数字解码、短期记忆和 CFF 的 RFI 值范围分别为 0.175-0.258、0.194-0.316、0.068-0.139 和 0.055-0.075。相应地，当自我评估的疲劳等级变为严重疲劳时，上述指标的 RFI 值范围分别为 0.415-0.577、0.482-0.669、0.329-0.396 和 0.114-0.218。这些结果表明，当自我评估的疲劳等级从无疲劳变为轻度或重度疲劳时，数字解码、数字跨度、短期记忆和 CFF 的敏感性依次降低。速度感知偏差的 RFI 个体差异很大，不适合作为评估指标。在精神疲劳测试中，数字解码测试可以提供更快、更方便和更准确的结果。
