Skip to main content

Latent profiles of fatigue in inflammatory bowel disease



Fatigue is prevalent in people with inflammatory bowel disease (IBD) and has been associated with IBD activity, sleep quality, depression, and anxiety. This study aimed to identify fatigue profiles or clusters through latent profile analysis.


An online questionnaire was administered through three tertiary IBD centres, social media and through Crohn’s Colitis Australia. Fatigue was assessed via the Functional assessment of chronic illness measurement system fatigue subscale (FACIT-F), a validated assessment of fatigue and its severity. Validated measures of anxiety, depression, IBD activity and sleep quality were also included. Latent profile analysis was performed including fatigue, sleep quality, active IBD, and depression and anxiety. The relationships between profiles and IBD and demographic data were investigated.


In a cohort of 535 respondents, 77% were female, the median age was 41 years (range 32–52 years), and the majority had Crohn’s disease (62%). Severe fatigue was seen in 62%. Latent profile analysis identified four distinct profiles differing by fatigue score - low fatigue, at-risk profile, active IBD, and a poor mental health profile. Female gender, obesity and opioid usage were associated with higher risk of being in the active IBD and poor mental health profile. Age over 40 was associated with lower risk of being in the poor mental health profile.


Latent profile analysis identifies four classes of fatigue in an IBD cohort with associations with specific risk factors for fatigue along with specific IBD and demographic attributes. This has implications for the classification of fatigue in IBD and treatment algorithms.

Peer Review reports


Inflammatory bowel disease (IBD) is a chronic relapsing remitting immune disorder that can affect any area of the gastrointestinal tract with extra-intestinal manifestations that includes joint and skin disease. Fatigue is a common symptom in people with IBD with a systemic review and meta-analysis reporting a prevalence of 48% [1]. The pathophysiology of fatigue in IBD is poorly understood [2, 3]. Frequently reported associations with fatigue in IBD include disease activity, sleep disturbance, anxiety and, depression [4,5,6,7,8].

In people with fatigue, symptom clusters have been proposed [9]. For example, in patients with advanced cancer, fatigue symptom clusters have been observed including ‘sleep, drowsiness and fatigue’ [10] and ‘sleep, depression, and fatigue’ [11]. Proposed treatment algorithms for fatigue in IBD contain flow charts that consider separate causes of fatigue in isolation [12]. Other have previously sought to identify classes of fatigue trajectories in IBD populations considering fatigue, IBD activity and psychological well-being [13]. More generally, symptom clusters in IBD have been explored in a single study considering gastrointestinal and psychological symptoms [14] producing a model similar to that reported in populations with irritable bowel syndrome [15, 16]. Others have identified that there are differences in healthcare utilisation between such symptom-defined clusters [17].

This study aimed to identify fatigue profiles or clusters in people in IBD considering known associations with fatigue using latent profile analysis incorporating fatigue, IBD activity, depression, anxiety and sleep quality. It was hypothesised that similar fatigue clusters with sleep, depression and fatigue will be seen and that there may exist a fatigue cluster that is independent of IBD activity. The authors then aimed to determine associations between demographic and IBD data and latent profile membership.


An online questionnaire was made available to people with IBD via tertiary hospital patient email lists, private gastroenterology practice email lists and social media. Individuals with a self-reported diagnosis of IBD over 18 years of age were invited to participate. Demographic data such as age and sex were recorded, along with IBD related data including disease duration and previous surgery. Ethics approval for this study was obtained from the Southern Adelaide Human Research Ethics Committee (203.20) and informed consent was obtained from all participants.

Fatigue was measured using the FACIT-F scale which is a subscale of the Functional assessment of chronic illness measurement system (FACIT). The FACIT-F subscale has been validated as a measure of fatigue in an IBD population [18]. The FACIT-F scale includes 13 questions with responses recorded on a 5-point Likert scale, with a score ranging from 0 to 52, with a lower score indicating worse fatigue. A score less than 32 indicates severe fatigue [19].

Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). The PSQI is a validated tool which assesses perceived sleep quality [20]. The index consists of subscales on sleep duration, sleep disturbance, sleep latency, daytime dysfunction, sleep efficiency, overall sleep quality and medications for sleep. The score ranges from 0 to 21, with a PSQI > 5 considered to represent poor sleep quality.

IBD disease activity was assessed using the Harvey Bradshaw Index (HBI) in the case of Crohn’s disease with HBI > 5 considered active disease [21]. The patient-reported version of the HBI was used in the survey, although a decision was made to maintain the general well-being and abdominal pain score similar to the physician HBI rather than using a ten-point Likert scale [22]. The Simple Clinical Colitis Activity Index (SCCAI) was used for ulcerative colitis, with an SCCAI > 5 considered active disease [23]. The patient reported form of the SCCAI was used [24] in the survey, which has been previously validated and shown to be closely concordant with physician reported SCCAI [25].

Anxiety was assessed using the generalised anxiety disorder 7-item scale (GAD-7) [26] with a score over 5 considered mild anxiety, 10–14 moderate and greater than 15 severe anxiety. The Patient Health Questionnaire 9 (PHQ-9) was used to assess depression with a score over 5 indicating mild depression, over 10 moderate depression, and over 20 severe depression [27].

Statistical analysis

Statistical analysis was performed using Stata SE 16 (StataCorp, College Station, TX, USA). Inadequate completion of score or index led to that result not being included. For normally distributed variables mean and standard deviation (SD) were reported with comparisons made using the student t-test. For non-normally distributed variables median, and interquartile range (IQR) were reported, with comparisons made using the Mann- Whitney U test. For categorical data Pearsons χ2 test was used or Fisher’s exact test when appropriate. If any incomplete data were present the participant was excluded.

Latent profile analysis was used to determine if respondents could be divided into groups or profiles based on responses to the questionnaire used to determine fatigue scores (FACIT-F), depression (PHQ9), anxiety (GAD7), IBD activity (SCCAI > 5 or HBI > 5), and sleep quality (PSQI). Stata latent profile analysis was used to determine the latent profile models [28]. To identify profiles of fatigue a one class model was first estimated with further classes added until the model with best fit was identified. Class size from 1 to 8 was considered. Model fit was assessed on model interpretability in addition to model performance criteria such as the Bayesian information criteria and the Akaike information criterion, and the minimum class size [29]. Entropy was calculated following determination of class size. Covariates were included based on model performance and interpretability. Posterior class membership probabilities were calculated for each survey response. Each survey response was assigned to a profile based on the posterior class membership probabilities. Multinomial regression was undertaken to assess for predictors of class membership.


There were 670 responses to the online questionnaire, following exclusions for any incomplete data there were 535 responses (79.8%) included in the analysis (see Table 1). Median age was 41 years (32–52), with most being female (77%), the majority had Crohn’s disease (61%). The mean disease duration was 10 years (5–19), 32% had undergone surgery for IBD and around half were on biologics (53%) (see Table 1).

Table 1 Cohort demographics and inflammatory bowel disease (IBD) data, Severe fatigue defined by FACIT-F < 32, clinically significant anxiety defined by GAD-7 > 10, clinically significant depression defined by PHQ-9 > 15

Latent profile analysis was undertaken including fatigue scores (FACIT-F score inverted), depression scores (PHQ9), anxiety scores (GAD7), sleep quality (PSQI) and IBD activity (SCCAI > 5, HBI > 5). Covariates were included in the model such as age, IBD subtype and BMI over 25. A four-profile solution based was chosen (see supplementary Table 1, entropy was adequate at 0.82).

The latent profiles (See Fig. 1; Table 2) were named as follows: the low fatigue profile (23%) – encompassing mild levels of fatigue and low levels of depression and anxiety; the poor mental health profile representing the smallest group (14%) characterised by severe anxiety and depression; the active IBD profile (31%) with high levels of IBD activity and associated poor sleep quality, but only mild-moderate mental health impairment. Finally, there was the at-risk profile (33%), being the largest profile, with mild levels of depression and anxiety, and moderate levels of fatigue.

Fig. 1
figure 1

Latent profiles of determinants of fatigue. Figure illustrates the characteristics of each profile based on reported anxiety, depression, IBD activity, sleep quality and fatigue levels. A minority were in the poor mental health profile (14%), with the majority in the at risk profile (33%), with similar proportion in the active IBD profile (33%). Scores have been normalised by highest possible response for each score

Table 2 Mean values in each latent profile – with interpretation based on established cut offs. IBD (inflammatory bowel disease) activity refers to the proportion with clinically active IBD. Sleep quality via the Pittsburgh Sleep Quality Index. Depression via Patient Health Questionnaire 9 scoring. Anxiety via the Generalised anxiety disorder − 7 score. Fatigue by the Functional assessment of chronic illness measurement system fatigue score

As age increased there was a decreased probability of measurement of the higher fatigue and mental health profiles and decreased probability of membership in the lower fatigue profiles (see covariate plotting in Fig. 2). No significant change in profile membership was seen with IBD subtype (see supplementary Fig. 1).

Fig. 2
figure 2

Latent profiles of determinants of fatigue. Age (covariate in latent model) plotted against each latent profile – low fatigue, at risk, active IBD, poor mental health

Female gender, opioid usage and obesity were associated with membership of higher fatigue profiles (multinomial regression with low fatigue profile as base see Table 3). Age over 40 was associated with decreased likelihood of membership in the poor mental health profile. Current smoking status was associated with increased likelihood of being in the poor mental health profile and the at-risk profile but not in the active IBD profile. Corticosteroid usage was associated with increased likelihood of membership in the poor mental health class. No differences were seen with IBD subtype, IBD duration, or any biologic or immunomodulator usage.

Table 3 Multinomial regression analyses with relative risk ratio reported relative to low fatigue profile. IBD (inflammatory bowel disease)


For the first time in the IBD literature this study used latent profile analysis to distinguish four fatigue profiles, differing by sleep quality, IBD activity, depression, and anxiety. The higher fatigue profiles were associated with opioid usage, younger age, female gender, corticosteroid usage and obesity. Depression and anxiety were closed related across the different profiles, similarly IBD activity and sleep quality remained related across the different profiles. The profile with the highest fatigue scores saw poor sleep, IBD activity and depression present in at least moderate severity.

The importance of mental conditions was highlighted by this data with moderate-high levels of depression and anxiety seen in the class with a high probability of severe fatigue. This may in part be a physiological consequence of the neurological effects of active IBD associated inflammatory cytokines [30, 31]. There are likely bidirectional relationships between fatigue and mental health conditions, and mental conditions and IBD activity [32, 33] making causation difficult to assess. Sleep disturbance has also been associated with worse depression or anxiety.

There was a profile referred to as ‘active IBD’ that had high proportion of active IBD and poor sleep quality with low-moderate anxiety/depression scores. Clinically active IBD certainly influences sleep quality and perhaps addressing IBD activity in those in this profile will lead to improvement in both aspects and reduce the likelihood of severe fatigue. Our initial hypothesis was incorrect – there was no profile with significant levels of fatigue and low IBD activity. IBD activity in a way mirrored fatigue scores. It is important to note here that this is clinical IBD activity rather than objective IBD activity (calprotectin/endoscopy based), and consequently may relate to IBS related symptoms that are common in people with inactive IBD [34]. These IBS-like symptoms can often be influenced by other factors such as depression or anxiety.

Females were more likely to be in the higher fatigue and mental health profiles. Fatigue is more commonly seen in females [35] although in IBD populations gender differences in fatigue have been mixed [1]. Similarly, depression and anxiety are more common in females [36,37,38] which perhaps explains the observed associations with the profiles seen here. Variance in profile membership was seen with age but not with IBD duration.

Corticosteroid usage was more common in the poor mental health class. This may relate to the medications influence on mental health and to its usage – generally in those with clinically active IBD. The association between corticosteroids and high levels of fatigue may be due to its association with clinically active IBD. Opioid usage, and in particular opioid misuse, has been related to fatigued, perhaps due to associated sedation and have also been associated with more severe IBD [39,40,41].

The reported causes of fatigue in IBD are many and varied with current approaches suggesting considering causes in isolation with approaches varying from considering causes sequentially or in parallel [2, 12]. The data here suggests that the common causes of fatigue frequently coexist – for example IBD activity and sleep were closely related. The authors would suggest that those presenting to IBD clinic with severe fatigue be screened for depression and evaluated for active IBD before pursuing other possible aetiologies.

Limitations of this study include selection bias a result of the use of an online questionnaire that may attract people with fatigue or sleep problems. Similarly, the form of survey and method of recruitment is likely responsible for the predominantly female cohort. The proportion of participants with Crohn’s disease was above that present in Australian prevalence data [42]. Reporting bias may also be significant, noting a study of people with Crohn’s disease reported worse sleep quality than that observed by objective measures [43]. Data on other medical conditions study participants may have that may influence fatigue, such as heart failure, was not available. There is no gold standard measure for choosing a latent profile measure – here we used statistical measures of model performance along with model interpretability and relevance to the previous literature.

The absence of an objective measure of IBD activity is also considered a limitation. A more valid approach would be to incorporate measures such as faecal calprotectin or endoscopic activity to define objective disease activity in addition to patient reported disease activity. Understanding the associations between fatigue profile membership and objective and subjective IBD activity would be valuable. Similarly, the inclusion of socioeconomic data in the model or subsequent analysis may also be valuable. Given the nature of data collection there was no opportunity to assess for anaemia that been associated with fatigue [6]. However, as others have noted [1] anaemia was not associated with fatigue in numerous cross-sectional studies, and hence its lack of inclusion in the model here is not considered a significant limitation [44,45,46].

Reviewing the plot of the latent profiles (see Fig. 2) one may see that the ‘low fatigue’ and ‘at risk’ profiles are in some areas parallel – suggest that this may represent different severities of the same profile referred to as the Salsa effect [47]. However, the authors would note that the ‘at risk’ profile has a sharper rise in IBD activity and fatigue – suggesting that perhaps the increase in IBD activity leads to greater fatigue and a comparatively smaller increase in anxiety, depression, and sleep quality scores – and would argue that this does not represent simply the ‘low fatigue’ profile at a greater severity.

It would be valuable to assess how fatigue profiles change over time, alongside influencing factors and the prognostic relevance of fatigue on IBD outcomes. Current evidence suggests that fatigue remains stable in the majority of IBD patients over time [13]. The latent profiles of fatigue defined in this study add granularity to factors associated with fatigue in IBD patients, adding further opportunities to address these debilitating and prevalent symptoms.


Latent profile analysis identified four profiles differentiated by levels of fatigue. The observed profiles suggest that the common risk factors for fatigue in IBD will typically co-exist. The association between depression and fatigue underlines the importance of screening for depression during IBD clinic. Attention should also be given to other factors associated with higher fatigue profiles such as obesity, opioid usage and corticosteroid usage. Further research should consider changes in fatigue profiles over time.

Data availability

The data underlying this article are available upon request to Dr Alex Barnes at


  1. D’Silva A, Fox D, Nasser Y et al. Prevalence and risk factors for fatigue in adults with inflammatory bowel disease: a systematic review with Meta-analysis. Clin Gastroenterol Hepatol. 2021.

  2. Borren NZ, van der Woude CJ, Ananthakrishnan AN. Fatigue in IBD: epidemiology, pathophysiology and management. Nat Rev Gastroenterol Hepatol. 2019;16(4):247–59.

    Article  CAS  PubMed  Google Scholar 

  3. van Langenberg DR, Gibson PR. Systematic review: fatigue in inflammatory bowel disease. Aliment Pharmacol Ther. 2010;32(2):131–43.

    Article  PubMed  Google Scholar 

  4. Chavarría C, Casanova MJ, Chaparro M, et al. Prevalence and factors Associated with fatigue in patients with inflammatory bowel disease: a Multicentre Study. J Crohn’s Colitis. 2019;13(8):996–1002.

    Article  Google Scholar 

  5. Frigstad S, Høivik M, Jahnsen J, et al. Fatigue is not associated with vitamin D deficiency in inflammatory bowel disease patients. World J Gastroenterol. 2018;24(29):3293–301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hashash J, Ramos-Rivers C, Youk A, et al. Quality of Sleep and Coexistent Psychopathology have significant impact on fatigue burden in patients with inflammatory bowel disease. J Clin Gastroenterol. 2018;52(5):423–30.

    Article  PubMed  Google Scholar 

  7. Huppertz-Hauss G, Høivik ML, Jelsness-Jørgensen LP, et al. Fatigue in a population-based cohort of patients with inflammatory bowel disease 20 years after diagnosis: the IBSEN study. Scand J Gastroenterol. 2017;52(3):351–8.

    Article  PubMed  Google Scholar 

  8. Villoria A, García V, Dosal A, et al. Fatigue in out-patients with inflammatory bowel disease: prevalence and predictive factors. PLoS ONE. 2017;12(7):e0181435.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Arnett SV, Clark IA. Inflammatory fatigue and sickness behaviour - lessons for the diagnosis and management of chronic fatigue syndrome. J Affect Disord. 2012;141(2–3):130–42.

    Article  CAS  PubMed  Google Scholar 

  10. He X, Ng M, Choi K, et al. Synergistic interactions among fatigue, Sleep Disturbance, and Depression in Women with breast Cancer: a cross-sectional study. Oncol Nurs Forum. 2022;49(3):243–54.

    Article  PubMed  Google Scholar 

  11. Fiorentino L, Rissling M, Liu L, Ancoli-Israel S. The Symptom cluster of sleep, fatigue and depressive symptoms in breast Cancer patients: severity of the Problem and Treatment options. Drug Discov Today Dis Models. 2011;8(4):167–73.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Nocerino A, Nguyen A, Agrawal M, Mone A, Lakhani K, Swaminath A. Fatigue in Inflammatory Bowel diseases: etiologies and management. Adv Ther. 2020;37(1):97–112.

    Article  PubMed  Google Scholar 

  13. Klusmann B, Fleer J, Tovote KA, et al. Trajectories of fatigue in inflammatory bowel disease. Inflamm Bowel Dis. 2021;27(12):1919–30.

    Article  PubMed  Google Scholar 

  14. Conley S, Proctor DD, Jeon S, Sandler RS, Redeker NS. Symptom clusters in adults with inflammatory bowel disease. Res Nurs Health. 2017;40(5):424–34.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Black CJ, Yiannakou Y, Guthrie EA, West R, Houghton LA, Ford AC. A Novel Method to Classify and Subgroup patients with IBS based on gastrointestinal symptoms and psychological profiles. Am J Gastroenterol. 2021;116(2):372–81.

    Article  PubMed  Google Scholar 

  16. Polster AV, Palsson OS, Törnblom H, et al. Subgroups of IBS patients are characterized by specific, reproducible profiles of GI and non-GI symptoms and report differences in healthcare utilization: a population-based study. Neurogastroenterol Motil. 2019;31(1):e13483.

    Article  PubMed  Google Scholar 

  17. Riggott C, Fairbrass KM, Black CJ, Gracie DJ, Ford AC. Novel symptom clusters predict disease impact and healthcare utilisation in inflammatory bowel disease: prospective longitudinal follow-up study. Alimentary pharmacology & therapeutics.; 2023.

  18. Tinsley A, Macklin EA, Korzenik JR, Sands BE. Validation of the functional assessment of chronic illness therapy-fatigue (FACIT-F) in patients with inflammatory bowel disease. Aliment Pharmacol Ther. 2011;34(11–12):1328–36.

    Article  CAS  PubMed  Google Scholar 

  19. Saraiva S, Cortez-Pinto J, Barosa R, et al. Evaluation of fatigue in inflammatory bowel disease - a useful tool in daily practice. Scand J Gastroenterol. 2019;54(4):465–70.

    Article  PubMed  Google Scholar 

  20. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Pyschiatry Res. 1989;28(2):193–213.

    Article  CAS  Google Scholar 

  21. Harvey RF, Bradshaw JM. A simple index of Crohn’s-disease activity. Lancet. 1980;8(1):514.

    Article  Google Scholar 

  22. Bennebroek Evertsz F, Hoeks CC, Nieuwkerk PT, et al. Development of the patient Harvey Bradshaw index and a comparison with a clinician-based Harvey Bradshaw index assessment of Crohn’s disease activity. J Clin Gastroenterol. 2013;47(10):850–6.

    Article  PubMed  Google Scholar 

  23. Walmsley RS, Ayres RC, Pounder RE, Allan RN. A simple clinical colitis activity index. Gut. 1998;43(1):29–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bennebroek Evertsz’ F, Nieuwkerk PT, Stokkers PCF, et al. The patient simple clinical colitis activity index (P-SCCAI) can detect ulcerative colitis (UC) disease activity in remission: a comparison of the P-SCCAI with clinician-based SCCAI and biological markers. J Crohn’s Colitis. 2013;7(11):890–900.

    Article  Google Scholar 

  25. Marín-Jiménez I, Nos P, Domènech E, et al. Diagnostic performance of the simple clinical colitis activity index self-administered online at home by patients with Ulcerative Colitis: CRONICA-UC Study. Official J Am Coll Gastroenterol | ACG. 2016;111(2):261–8.

    Article  Google Scholar 

  26. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7.

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lanza S, Dziak J, Huang L, Wagner A, Collins L. LCA Stata plugin users’ guide. Published 2018. Accessed 2023.

  29. Weller B, Bowen N, Faubert S. Latent class analysis: a guide to best practice. J Black Psychol 2020;46.

  30. Felger JC, Lotrich FE. Inflammatory cytokines in depression: neurobiological mechanisms and therapeutic implications. Neuroscience. 2013;246:199–229.

    Article  CAS  PubMed  Google Scholar 

  31. Frank P, Jokela M, Batty GD, Cadar D, Steptoe A, Kivimäki M. Association between Systemic Inflammation and individual symptoms of Depression: a pooled analysis of 15 Population-based Cohort studies. Am J Psychiatry. 2021;178(12):1107–18.

    Article  PubMed  Google Scholar 

  32. Gaines LS, Slaughter JC, Schwartz DA, et al. Does reverse causality underlie the temporal relationship between Depression and Crohn’s Disease? Inflamm Bowel Dis. 2020;26(3):423–8.

    Article  PubMed  Google Scholar 

  33. Bisgaard TH, Allin KH, Elmahdi R, Jess T. The bidirectional risk of inflammatory bowel disease and anxiety or depression: a systematic review and meta-analysis. Gen Hosp Psychiatry. 2023;83:109–16.

    Article  PubMed  Google Scholar 

  34. Ozer M, Bengi G, Colak R, Cengiz O, Akpinar H. Prevalence of irritable bowel syndrome-like symptoms using Rome IV criteria in patients with inactive inflammatory bowel disease and relation with quality of life. Med (Baltim). 2020;99(19):e20067.

    Article  Google Scholar 

  35. Fuhrer R, Wessely S. The epidemiology of fatigue and depression: a French primary-care study. Psychol Med. 1995;25(5):895–905.

    Article  CAS  PubMed  Google Scholar 

  36. Weinberger AH, Gbedemah M, Martinez AM, Nash D, Galea S, Goodwin RD. Trends in depression prevalence in the USA from 2005 to 2015: widening disparities in vulnerable groups. Psychol Med. 2018;48(8):1308–15.

    Article  CAS  PubMed  Google Scholar 

  37. Pedersen CB, Mors O, Bertelsen A, et al. A comprehensive nationwide study of the incidence rate and lifetime risk for treated mental disorders. JAMA Psychiatry. 2014;71(5):573–81.

    Article  PubMed  Google Scholar 

  38. Wittchen HU, Zhao S, Kessler RC, Eaton WW. DSM-III-R generalized anxiety disorder in the National Comorbidity Survey. Arch Gen Psychiatry. 1994;51(5):355–64.

    Article  CAS  PubMed  Google Scholar 

  39. Hanson KA, Loftus EV Jr., Harmsen WS, Diehl NN, Zinsmeister AR, Sandborn WJ. Clinical features and outcome of patients with inflammatory bowel disease who use narcotics: a case-control study. Inflamm Bowel Dis. 2009;15(5):772–7.

    Article  PubMed  Google Scholar 

  40. Sanford D, Thornley P, Teriaky A, Chande N, Gregor J. Opioid use is associated with decreased quality of life in patients with Crohn’s disease. Saudi J Gastroenterol. 2014;20(3):182–7.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Targownik LE, Nugent Z, Singh H, Bugden S, Bernstein CN. The prevalence and predictors of opioid use in inflammatory bowel disease: a population-based analysis. Am J Gastroenterol. 2014;109(10):1613–20.

    Article  CAS  PubMed  Google Scholar 

  42. Busingye D, Pollack A, Chidwick K. Prevalence of inflammatory bowel disease in the Australian general practice population: a cross-sectional study. PLoS ONE. 2021;16(5):e0252458.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Iskandar H, Linan E, Patel A, et al. Self-reported sleep disturbance in Crohn’s disease is not confirmed by objective sleep measures. Sci Rep. 2020;10(1):1980.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Aluzaite K, Al-Mandhari R, Osborne H, et al. Detailed Multi-dimensional Assessment of fatigue in inflammatory bowel disease. Inflamm Intest Dis. 2019;3(4):192–201.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Vogelaar L, van’t Spijker A, van Tilburg AJ, Kuipers EJ, Timman R, van der Woude CJ. Determinants of fatigue in Crohn’s disease patients. Eur J Gastroenterol Hepatol. 2013;25(2):246–51.

    Article  PubMed  Google Scholar 

  46. Hashash JG, Ramos-Rivers C, Youk A, et al. Quality of Sleep and Coexistent Psychopathology have significant impact on fatigue burden in patients with inflammatory bowel disease. J Clin Gastroenterol. 2018;52(5):423–30.

    Article  PubMed  Google Scholar 

  47. Sinha P, Calfee CS, Delucchi KL. Practitioner’s guide to latent class analysis: methodological considerations and common pitfalls. Crit Care Med. 2021;49(1):e63–79.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


No funding was received for this work.

Author information

Authors and Affiliations



AB was responsible for study concept and design, and data acquisition. AB and TO were responsible for data analysis. AB and RB were responsible for data interpretation. All authors were responsible for revision of the manuscript.

Corresponding author

Correspondence to Alex Barnes.

Ethics declarations

Ethics approval and consent to participate

Ethics approval for this study was obtained from the Southern Adelaide Human Research Ethics Committee (203.20) and informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

Jane M Andrews: Speakers fees, and Ad Boards from: Abbott, AbbVie, Allergan, Anatara, AstraZeneca, Bayer, BMS 2020, Celegene, Celltrion, Falk, Ferring, Gilead, Hospira, Im-muninc, ImmunsanT, Janssen, MSD, Nestle, Novartis, Progen-ity, Pfizer, Sandoz, Shire, Takeda, Vifor, RAH research Fund, The Hospital Research Fund 2020-2022, The Helmsley Trust 2020-2023. Reme Mountifield: Speakers fees, and Ad Boards from: Abbott, AbbVie, Allergan, Anatara, AstraZeneca, Bayer, BMS 2020, Celegene, Celltrion, Falk, Ferring, Gilead, Hospira, Im-muninc, ImmunsanT, Janssen, MSD, Nestle, Novartis, Progen-ity, Pfizer, Sandoz, Shire, Takeda. Rob V Bryant: has received Grant/Research support/Speaker fees (all paid to employer for research support): AbbVie, Ferring, Janssen, Shire, Takeda, Emerge Health; shareholder in BiomebankNo conflict of interest: Alex Barnes, Sutapa Mukherjee, Paul Spizzo, Barbara Toson.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barnes, A., Toson, B., Bryant, R. et al. Latent profiles of fatigue in inflammatory bowel disease. BMC Gastroenterol 24, 148 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: