Previous studies show that sleep is essential in preventing symptoms related to chronic levels of fatigue. In the present study, we move beyond the traditional variable-centered approach and adopt a person-centered approach by considering antecedents and outcomes of sleep profiles. Specifically, we consider job characteristics (i.e., workload, job control, and their interaction) as predictors of sleep profiles and indicators of chronic fatigue (i.e., prolonged fatigue and burnout) as outcomes. In establishing sleep profiles, we consider levels as well as the variability of the sleep dimensions across a week. Based on daily diary data from 296 Indonesian employees, the present article uses latent profile analysis to identify sleep profiles based on both weekly averages of several sleep dimensions (i.e., sleep quality, fragmentation, duration, bedtime, and wake-up time) and their intraindividual variability. Moreover, it explores the relationship between the identified profiles to prolonged fatigue and burnout 2 weeks later as outcomes, as well as to baseline workload, job control, and their interaction as predictors. We find four different profiles ("Average Sleepers," "Deep Owls," "Short Sleep Compensators," and "Restless Erratic Sleepers"). While workload, job control, and their interaction could not predict profile membership, these profiles relate differently to prolonged fatigue and burnout. As such, our study shows the importance of understanding the combination of sleep levels and variability across a week through sleep profiles, and how they differentially relate to symptoms of chronic fatigue. Our findings also highlight the need to study indicators of sleep variability alongside sleep levels. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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甜蜜的梦是这样构成的：以人为本的方法来了解睡眠在慢性疲劳中的作用。

先前的研究表明，睡眠对于预防与慢性疲劳相关的症状至关重要。在本研究中，我们超越了传统的以变量为中心的方法，通过考虑睡眠概况的前因和结果，采用以人为中心的方法。具体来说，我们将工作特征（即工作量、工作控制及其相互作用）视为睡眠状况的预测因素，并将慢性疲劳（即长期疲劳和倦怠）的指标视为结果。在建立睡眠档案时，我们考虑一周内睡眠维度的水平和变化。本文基于 296 名印度尼西亚员工的每日日记数据，根据多个睡眠维度（即睡眠质量、碎片化、持续时间、就寝时间和起床时间）的每周平均值及其睡眠情况，使用潜在档案分析来识别睡眠档案。个体内部变异性。此外，它还探讨了已确定的概况与两周后的长期疲劳和倦怠（作为结果）之间的关系，以及与基线工作量、工作控制及其作为预测因素的相互作用之间的关系。我们发现了四种不同的特征（“平均睡眠者”、“深度猫头鹰”、“短睡眠补偿器”和“不安分不稳定的睡眠者”）。虽然工作量、工作控制及其相互作用无法预测档案成员资格，但这些档案与长期疲劳和倦怠的关系不同。因此，我们的研究表明，通过睡眠概况了解一周内睡眠水平和变异性的组合以及它们与慢性疲劳症状的差异关系非常重要。我们的研究结果还强调需要研究睡眠变异性指标和睡眠水平。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。