Fatigued but not sleepy? An empirical investigation of the differentiation between fatigue and sleepiness in sleep disorder patients in a cross-sectional study

被引:3
作者
Suh, Sooyeon [1 ,2 ,5 ]
Lok, Renske [2 ]
Weed, Lara [3 ]
Cho, Ayeong [1 ]
Mignot, Emmanuel [2 ]
Leary, Eileen B. [2 ]
Zeitzer, Jamie M. [2 ,4 ]
机构
[1] Sungshin Womens Univ, Dept Psychol, Seoul, South Korea
[2] Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA USA
[3] Stanford Univ, Dept Biomech Engn, Stanford, CA USA
[4] VA Palo Alto Hlth Care Syst, Mental Illness Res Educ & Clin Ctr, Palo Alto, CA USA
[5] Bomun ro 34da gil,Sungshin Bldg 911, Seoul, South Korea
基金
美国国家卫生研究院;
关键词
Depression; Fatigue; Machine learning; Random forest; Sleepiness; REST-ACTIVITY RHYTHM; DAYTIME SLEEPINESS; MULTIPLE-SCLEROSIS; APNEA; DEPRESSION; QUESTIONNAIRE; INSOMNIA; EPIDEMIOLOGY; PREVALENCE; VALIDATION;
D O I
10.1016/j.jpsychores.2024.111606
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Objective: Sleepiness and fatigue are common complaints among individuals with sleep disorders. The two concepts are often used interchangeably, causing difficulty with differential diagnosis and treatment decisions. The current study investigated sleep disorder patients to determine which factors best differentiated sleepiness from fatigue. Methods: The study used a subset of participants from a multi-site study (n = 606), using a cross-sectional study design. We selected 60 variables associated with either sleepiness or fatigue, including demographic, mental health, and lifestyle factors, medical history, sleep questionnaires, rest-activity rhythms (actigraphy), polysomnographic (PSG) variables, and sleep diaries. Fatigue was measured with the Fatigue Severity Scale and sleepiness was measured with the Epworth Sleepiness Scale. A Random Forest machine learning approach was utilized for analysis. Results: Participants' average age was 47.5 years (SD 14.0), 54.6% female, and the most common sleep disorder diagnosis was obstructive sleep apnea (67.4%). Sleepiness and fatigue were moderately correlated (r = 0.334). The model for fatigue (explained variance 49.5%) indicated depression was the strongest predictor (relative explained variance 42.7%), followed by insomnia severity (12.3%). The model for sleepiness (explained variance 17.9%), indicated insomnia symptoms was the strongest predictor (relative explained variance 17.6%). A post hoc receiver operating characteristic analysis indicated depression could be used to discriminate fatigue (AUC = 0.856) but not sleepiness (AUC = 0.643). Conclusions: The moderate correlation between fatigue and sleepiness supports previous literature that the two concepts are overlapping yet distinct. Importantly, depression played a more prominent role in characterizing fatigue than sleepiness, suggesting depression could be used to differentiate the two concepts.
引用
收藏
页数:9
相关论文
共 40 条
  • [1] HORNE AND OSTBERG MORNINGNESS EVENINGNESS QUESTIONNAIRE - A REDUCED SCALE
    ADAN, A
    ALMIRALL, H
    [J]. PERSONALITY AND INDIVIDUAL DIFFERENCES, 1991, 12 (03) : 241 - 253
  • [2] Levis Brooke, 2019, BMJ, V365, pl1476, DOI [10.1136/bmj.l1781, 10.1136/bmj.l1476]
  • [3] [Anonymous], 2010, R: A Language and Environment for Statistical Computing
  • [4] Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies
    Baglioni, Chiara
    Battagliese, Gemma
    Feige, Bernd
    Spiegelhalder, Kai
    Nissen, Christoph
    Voderholzer, Ulrich
    Lombardo, Caterina
    Riemann, Dieter
    [J]. JOURNAL OF AFFECTIVE DISORDERS, 2011, 135 (1-3) : 10 - 19
  • [5] Fatigue in obstructive sleep apnea: Driven by depressive symptoms instead of apnea severity.?
    Bardwell, WA
    Moore, P
    Ancoli-Israel, S
    Dimsdale, JE
    [J]. AMERICAN JOURNAL OF PSYCHIATRY, 2003, 160 (02) : 350 - 355
  • [6] Validation of the Insomnia Severity Index as an outcome measure for insomnia research
    Bastien, Celyne H.
    Vallieres, Annie
    Morin, Charles M.
    [J]. SLEEP MEDICINE, 2001, 2 (04) : 297 - 307
  • [7] AASM Scoring Manual Updates for 2017 (Version 2.4)
    Berry, Richard B.
    Brooks, Rita
    Gamaldo, Charlene
    Harding, Susan M.
    Lloyd, Robin M.
    Quan, Stuart F.
    Troester, Matthew T.
    Vaughn, Bradley V.
    [J]. JOURNAL OF CLINICAL SLEEP MEDICINE, 2017, 13 (05): : 665 - 666
  • [8] The Prevalence of Depression among Untreated Obstructive Sleep Apnea Patients Using a Standardized Psychiatric Interview
    Bjornsdottir, Erla
    Benediktsdottir, Bryndis
    Pack, Allan I.
    Arnardottir, Erna Sif
    Kuna, Samuel T.
    Gislason, Thorarinn
    Keenan, Brendan T.
    Maislin, Greg
    Sigurdsson, Jon Fridrik
    [J]. JOURNAL OF CLINICAL SLEEP MEDICINE, 2016, 12 (01): : 105 - 112
  • [9] 'nparACT' package for R: A free software tool for the non-parametric analysis of actigraphy data
    Blume, Christine
    Santhi, Nayantara
    Schabus, Manuel
    [J]. METHODSX, 2016, 3 : 430 - 435
  • [10] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32