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Characterizing Patterns of Nurses’ Daily Sleep Health: a Latent Profile Analysis



Nursing is a demanding occupation characterized by dramatic sleep disruptions. Yet most studies on nurses’ sleep treat sleep disturbances as a homogenous construct and do not use daily measures to address recall biases. Using person-centered analyses, we examined heterogeneity in nurses' daily sleep patterns in relation to psychological and physical health.


Nurses (N = 392; 92% female, mean age = 39.54 years) completed 14 daily sleep diaries to assess sleep duration, efficiency, quality, and nightmare severity, as well as measures of psychological functioning and a blood draw to assess inflammatory markers interleukin-6 (IL-6) and C-reactive protein (CRP). Using recommended fit indices and a 3-step approach, latent profile analysis was used to identify the best-fitting class solution.


The best-fitting solution suggested three classes: (1) “Poor Overall Sleep” (11.2%), (2) “Nightmares Only” (8.4%), (3) “Good Overall Sleep” (80.4%). Compared to nurses in the Good Overall Sleep class, nurses in the Poor Overall Sleep or Nightmares Only classes were more likely to be shift workers and had greater stress, PTSD symptoms, depression, anxiety, and insomnia severity. In multivariate models, every one-unit increase in insomnia severity and IL-6 was associated with a 33% and a 21% increase in the odds of being in the Poor Overall Sleep compared to the Good Overall Sleep class, respectively.


Nurses with more severe and diverse sleep disturbances experience worse health and may be in greatest need of sleep-related and other clinical interventions.

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  1. Zverev YP, Misiri HE. Perceived effects of rotating shift work on nurses’ sleep quality and duration. Malawi Med J J Med Assoc Malawi. 2009;21:19–21.

    Google Scholar 

  2. Adriaenssens J, de Gucht V, Maes S. The impact of traumatic events on emergency room nurses: findings from a questionnaire survey. Int J Nurs Stud. 2012;49:1411–22.

    PubMed  Google Scholar 

  3. Chin W, Guo YL, Hung Y-J, Yang C-Y, Shiao JS-C. Short sleep duration is dose-dependently related to job strain and burnout in nurses: a cross sectional survey. Int J Nurs Stud. 2015;52:297–306.

  4. Lu L, Lok K-I, Zhang Q, Zhang L, Xiang Y, Ungvari GS, et al. Sleep disturbance and its association with quality of life among psychiatric nurses in China. PeerJ. 2021;9:e10659.

  5. Gangwisch JE, Rexrode K, Forman JP, Mukamal K, Malaspina D, Feskanich D. Daytime sleepiness and risk of coronary heart disease and stroke: results from the Nurses’ Health Study II. Sleep Med. 2014;15:782–8.

    PubMed  PubMed Central  Google Scholar 

  6. Dorrian J, Tolley C, Lamond N, van den Heuvel C, Pincombe J, Rogers AE, et al. Sleep and errors in a group of Australian hospital nurses at work and during the commute. Appl Ergon. 2008;39:605–13.

    PubMed  Google Scholar 

  7. Gold DR, Rogacz S, Bock N, Tosteson TD, Baum TM, Speizer FE, et al. Rotating shift work, sleep, and accidents related to sleepiness in hospital nurses. Am J Public Health. 1992;82:1011–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Meijman TF, Mulder G. Psychological aspects of workload. In: Drenth PJD, Thierry H,  de Wolff CJ, editors. Handbook of work and organizational: Work psychology. Psychology Press/Erlbaum (UK) Taylor & Francis; 1998.p. 5–33.

  9. Hobfoll SE. Conservation of resources. A new attempt at conceptualizing stress. Am Psychol. 1989;44:513–24.

  10. Buysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014;37:9–17.

    PubMed  PubMed Central  Google Scholar 

  11. Nadorff MR, Nadorff DK, Germain A. Nightmares: under-reported, undetected, and therefore untreated. J Clin Sleep Med JCSM Off Publ Am Acad Sleep Med. 2015;11:747–50.

    PubMed  PubMed Central  Google Scholar 

  12. Ohayon MM, Morselli PL, Guilleminault C. Prevalence of nightmares and their relationship to psychopathology and daytime functioning in insomnia subjects. Sleep. 1997;20:340–8.

    CAS  PubMed  Google Scholar 

  13. Mealer M, Burnham EL, Goode CJ, Rothbaum B, Moss M. The prevalence and impact of post traumatic stress disorder and burnout syndrome in nurses. Depress Anxiety. 2009;26:1118–26.

    PubMed  PubMed Central  Google Scholar 

  14. Nylund-Gibson K, Choi AY. Ten frequently asked questions about latent class analysis. Transl Issues Psychol Sci. US: Educational Publishing Foundation; 2018;4:440–61.

  15. Drake DA, Steege LMB. Interpretation of hospital nurse fatigue using latent profile analysis. Adv Nurs Sci. 2016;39:E1-16.

    Google Scholar 

  16. Han K, Kim Y, Lee H, Lim S. Novice nurses’ sleep disturbance trajectories within the first 2 years of work and actual turnover: a prospective longitudinal study. Int J Nurs Stud. 2020;112:103575–103575.

    PubMed  Google Scholar 

  17. Jamali J, Roustaei N, Ayatollahi SMT, Sadeghi E. Factors affecting minor psychiatric disorder in southern Iranian nurses: a latent class regression analysis. Nurs Midwifery Stud. 2015;4:e28017.

  18. Wang J, Zhang X, Yang B, Li J, Li Y, Chen Q, et al. Suicidal ideation among nurses: unique and cumulative effects of different subtypes of sleep problems. J Affect Disord. 2020;276:600–7.

    PubMed  Google Scholar 

  19. Full KM, Moran K, Carlson J, Godbole S, Natarajan L, Hipp A, et al. Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women. PLOS ONE. Public Library of Science; 2019;14:e0218595.

  20. Slavish DC, Taylor DJ, Lichstein KL. Intraindividual variability in sleep and comorbid medical and mental health conditions. Sleep. 2019;42.

  21. Carney CE, Buysse DJ, Ancoli-Israel S, Edinger JD, Krystal AD, Lichstein KL, et al. The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep. 2012;35:287–302.

    PubMed  PubMed Central  Google Scholar 

  22. Dietch JR, Sethi K, Slavish DC, Taylor DJ. Validity of two retrospective questionnaire versions of the Consensus Sleep Diary: the whole week and split week Self-Assessment of Sleep Surveys. Sleep Med. 2019;63:127–36.

    PubMed  Google Scholar 

  23. Aili K, Åström-Paulsson S, Stoetzer U, Svartengren M, Hillert L. Reliability of actigraphy and subjective sleep measurements in adults: the design of sleep assessments. J Clin Sleep Med JCSM Off Publ Am Acad Sleep Med. 2017;13:39–47.

    PubMed  PubMed Central  Google Scholar 

  24. Wohlgemuth WK, Edinger JD, Fins AI, Sullivan RJ. How many nights are enough? The short-term stability of sleep parameters in elderly insomniacs and normal sleepers. Psychophysiology. 1999;36:233–44.

    CAS  PubMed  Google Scholar 

  25. Yu J, Mahendran R, Abdullah FNM, Kua E-H, Feng L. Self-reported sleep problems among the elderly: a latent class analysis. Psychiatry Res. 2017;258:415–20.

    PubMed  Google Scholar 

  26. Doong S-H, Dhruva A, Dunn LB, West C, Paul SM, Cooper BA, et al. Associations between cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression in patients prior to breast cancer surgery. Biol Res Nurs SAGE Publications. 2015;17:237–47.

    CAS  Google Scholar 

  27. Illi J, Miaskowski C, Cooper B, Levine JD, Dunn L, West C, et al. Association between pro- and anti-inflammatory cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression. Cytokine. 2012;58:437–47.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Ji Y-B, Bo C-L, Xue X-J, Weng E-M, Gao G-C, Dai B-B, et al. Association of Inflammatory cytokines with the symptom cluster of pain, fatigue, depression, and sleep disturbance in Chinese patients with cancer. J Pain Symptom Manage. 2017;54:843–52.

    PubMed  Google Scholar 

  29. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–81.

    PubMed  Google Scholar 

  30. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208.

  31. Dietch JR, Taylor DJ. Evaluation of the Consensus Sleep Diary in a community sample: comparison with single-channel EEG, actigraphy, and retrospective questionnaire. J Clin Sleep Med JCSM Off Publ Am Acad Sleep Med. 2021;

  32. Williams JM, Taylor DJ, Slavish DC, Gardner CE, Zimmerman MR, Patel K, et al. Validity of actigraphy in young adults with insomnia. Behav Sleep Med. 2020;1–16.

  33. Lichstein KL, Stone KC, Donaldson J, Nau SD, Soeffing JP, Murray D, et al. Actigraphy validation with insomnia. Sleep. 2006;29:232–9.

    PubMed  Google Scholar 

  34. Weathers FW, Litz BT, Keane TM, Palmieri PA, Marx BP, Schnurr PP. The PTSD Checklist for DSM-5 (PCL-5). 2013. Scale available from the National Center for PTSD at Date accessed: 16 July 2021.

  35. Bovin MJ, Marx BP, Weathers FW, Gallagher MW, Rodriguez P, Schnurr PP, et al. Psychometric properties of the PTSD checklist for diagnostic and statistical manual of mental disorders–fifth edition (PCL-5) in veterans. Psychol Assess. 2016;28:1379.

    PubMed  Google Scholar 

  36. Blevins CA, Weathers FW, Davis MT, Witte TK, Domino JL. The posttraumatic stress disorder checklist for DSM-5 (PCL-5): development and initial psychometric evaluation. J Trauma Stress. 2015;28:489–98.

    PubMed  Google Scholar 

  37. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med American Medical Association. 2006;166:1092–7.

    Google Scholar 

  39. Morin CM. Insomnia: Psychological assessment and management. New York: Guilford Press; 1993.

    Google Scholar 

  40. Bastien CH, Vallieres A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2:297–307.

    PubMed  Google Scholar 

  41. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24:385–96.

    CAS  PubMed  Google Scholar 

  42. Asparouhov T, Muthén B. Auxiliary variables in mixture modeling: three-step approaches using Mplus. Struct Equ Model Multidiscip J Routledge. 2014;21:329–41.

    Google Scholar 

  43. Vermunt JK. Latent class modeling with covariates: two improved three-step approaches. Polit Anal. Oxford University Press, Society for Political Methodology; 2010;18:450–69.

  44. Bolck A, Croon M, Hagenaars J. Estimating latent structure models with categorical variables: one-step versus three-step estimators. Polit Anal. Oxford University Press, Society for Political Methodology; 2004;12:3–27.

  45. Rosenberg JM, Beymer PN, Anderson DJ, Lissa C j van, Schmidt JA. tidyLPA: an R package to easily carry out latent profile analysiS (LPA) using open-source or commercial software. J Open Source Softw. 2019;3:978.

  46. DiStefano C, Kamphaus RW. Investigating subtypes of child development: a comparison of cluster analysis and latent class cluster analysis in typology creation. Educ Psychol Meas. SAGE Publications Inc; 2006;66:778–94.

  47. Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model Multidiscip J Routledge. 2007;14:535–69.

    Google Scholar 

  48. Raftery AE. Bayesian model selection in social research. Sociol Methodol. American Sociological Association, Wiley, Sage Publications, Inc.; 1995;25:111–63.

  49. Hipp JR, Bauer DJ. Local solutions in the estimation of growth mixture models. Psychol Methods. 2006;11:36–53.

    PubMed  Google Scholar 

  50. DeMartini KS, Fucito LM. Variations in sleep characteristics and sleep-related impairment in at-risk college drinkers: a latent profile analysis. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2014;33:1164–73.

    Google Scholar 

  51. Irwin MR. Why sleep is important for health: a psychoneuroimmunology perspective. Annu Rev Psychol. 2015;66:143–72.

    PubMed  Google Scholar 

  52. Loprinzi PD. Health behavior combinations and their association with inflammation. Am J Health Promot AJHP. 2016;30:331–4.

    PubMed  Google Scholar 

  53. Okun ML, Reynolds CF, Buysse DJ, Monk TH, Mazumdar S, Begley A, et al. Sleep variability, health-related practices and inflammatory markers in a community dwelling sample of older adults. Psychosom Med. 2011;73:142–50.

    PubMed  Google Scholar 

  54. Michopoulos V, Powers A, Gillespie CF, Ressler KJ, Jovanovic T. Inflammation in fear- and anxiety-based disorders: PTSD, GAD, and beyond. Neuropsychopharmacology. Nature Publishing Group; 2017;42:254–70.

  55. Bjurström MF, Olmstead R, Irwin MR. Reciprocal relationship between sleep macrostructure and evening and morning cellular inflammation in rheumatoid arthritis. Psychosom Med. 2017;79:24–33.

    PubMed  PubMed Central  Google Scholar 

  56. Weinberger JF, Raison CL, Rye DB, Montague AR, Woolwine BJ, Felger JC, et al. Inhibition of tumor necrosis factor improves sleep continuity in patients with treatment resistant depression and high inflammation. Brain Behav Immun. 2015;47:193–200.

    CAS  PubMed  Google Scholar 

  57. Carnethon MR, De Chavez PJ, Zee PC, Kim K-YA, Liu K, Goldberger JJ, et al. Disparities in sleep characteristics by race/ethnicity in a population-based sample: Chicago Area Sleep Study. Sleep Med. 2016;18:50–5.

  58. Fuller-Rowell TE, Curtis DS, El-Sheikh M, Chae DH, Boylan JM, Ryff CD. Racial disparities in sleep: the role of neighborhood disadvantage. Sleep Med. 2016;27–28:1–8.

    PubMed  PubMed Central  Google Scholar 

  59. Fuller-Rowell TE, Nichols OI, Robinson AT, Boylan JM, Chae DH, El-Sheikh M. Racial disparities in sleep health between Black and White young adults: the role of neighborhood safety in childhood. Sleep Med. 2021;81:341–9.

    PubMed  Google Scholar 

  60. Hicken MT, Lee H, Ailshire J, Burgard SA, Williams DR. “Every shut eye, ain’t sleep”: the role of racism-related vigilance in racial/ethnic disparities in sleep difficulty. Race Soc Probl. 2013;5:100–12.

    PubMed  PubMed Central  Google Scholar 

  61. Bjorvatn B, Magerøy N, Moen BE, Pallesen S, Waage S. Parasomnias are more frequent in shift workers than in day workers. Chronobiol Int. 2015;32:1352–8.

    PubMed  Google Scholar 

  62. Härmä M, Tenkanen L, Sjöblom T, Alikoski T, Heinsalmi P. Combined effects of shift work and life-style on the prevalence of insomnia, sleep deprivation and daytime sleepiness. Scand J Work Environ Health. 1998;24:300–7.

  63. Leskin GA, Woodward SH, Young HE, Sheikh JI. Effects of comorbid diagnoses on sleep disturbance in PTSD. J Psychiatr Res. 2002;36:449–52.

    PubMed  Google Scholar 

  64. Park D, Kim S, Shin C, Suh S. Prevalence of and factors associated with nightmares in the elderly in a population based cohort study. Sleep Med. 2021;78:15–23.

    PubMed  Google Scholar 

  65. Graham EK, Weston SJ, Turiano NA, Aschwanden D, Booth T, Harrison F, James BD, Lewis NA, Makkar SR, Mueller S, Wisniewski KM. Is healthy neuroticism associated with health behaviors? A coordinated integrative data analysis. Collabra: Psychology. 2020;6:1–18.

  66. Stieger M, Robinson SA, Bisson AN, Lachman ME. The relationship of personality and behavior change in a physical activity intervention: the role of conscientiousness and healthy neuroticism. Personal Individ Differ. 2020;166:110224.

  67. Baek J, Han K, Choi-Kwon S. Sleep diary- and actigraphy-derived sleep parameters of 8-hour fast-rotating shift work nurses: a prospective descriptive study. Int J Nurs Stud. 2020;112:103719.

  68. Fernandez-Mendoza J, Vgontzas AN, Liao D, Shaffer ML, Vela-Bueno A, Basta M, et al. Insomnia with objective short sleep duration and incident hypertension: the Penn State Cohort. Hypertension. 2012;60:929–35.

    CAS  PubMed  Google Scholar 

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We would like to thank all nurse participants and research assistants who contributed to this project.


This research supported by grant National Institutes of Allergy and Infectious Diseases R01AI128359‐01 (PIs: Taylor & Kelly).

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Correspondence to Danica C. Slavish.

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The author Slavish has received research grants from Canopy Growth Corporation that are outside the scope of the current work. The authors declare that they have no conflict of interest.

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Slavish, D.C., Contractor, A.A., Dietch, J.R. et al. Characterizing Patterns of Nurses’ Daily Sleep Health: a Latent Profile Analysis. Int.J. Behav. Med. (2022).

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  • Latent profile analysis
  • Nurses
  • Sleep diary
  • Longitudinal
  • Nightmares