Сравнительное исследование распространенности усталости у лиц с заболеваниями: систематический обзор и мета-анализ.

Introduction

Fatigue is one of the most frequent complaints not only among subjects with diseases, like 50% of cancer patients 1 , but also in the general population, approximately 16% 2 . Short-term fatigue can be a predictable physiological response to intense activity and typically does not affect daily life 3 . However, pathological fatigue, especially when chronic and uncontrolled, leads to a poor quality of life (QoL) and impairs daily efficiency 4 , 5 .

Many factors affect the prevalence of fatigue, including age, sex, ethnicity, occupation, and psychological condition 5 – 8 . For example, females are known to be more susceptible to cancer-related fatigue (CRF), with a prevalence about 2.1 times higher than that of males 9 . Health-related factors such as sleep quality, obesity, nutritional status, stress, and illness further influence the frequency and severity of fatigue 10 – 12 . Among these, the presence of a disorder or disease is the primary determinants of the prevalence and severity of fatigue; for instance, approximately 70% and 82% of individuals with irritable bowel syndrome (IBS) and fibromyalgia, respectively, experience fatigue 13 , 14 . Additionally, other disease-related factors, such as malnutrition in patients with inflammatory bowel disease (IBD), also affect prevalence of fatigue 15 .

Disease-related fatigue is not only a primary symptom of many disorders but also a factor that influences the clinical course of the disease, including its outcomes. Increased fatigue has been associated with the progression of dialysis initiation in patients with chronic kidney disease 16 , and with increased mortality in individuals with chronic obstructive pulmonary disease (COPD) 17 . Moreover, fatigue has been linked to the prognosis of diseases, with potentially higher recurrence rates of stroke and breast cancer among patients reporting more frequent or severe fatigue 18 , 19 . Therefore, assessing the presence and severity of fatigue is important in the management of patients, particularly those with chronic illnesses 20 , 21 .

To date, although numerous studies have examined the prevalence of fatigue in individuals with specific diseases, there is a lack of research comparing the prevalence and severity of fatigue across a diverse range of conditions. Therefore, this study aims to conduct a comprehensive comparison of fatigue prevalence and its clinical features across various diseases.

Methods

Study design

To investigate the prevalence of fatigue in subjects with various diseases worldwide, research papers containing data on fatigue prevalence in diseased individuals were systematically reviewed and analyzed. This systematic review has been registered in the PROSPERO database (CRD42021270490) and was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Data sources and keywords

Data were collected from two databases (PubMed and the Cochrane Library) and searches were conducted for publications available through March 31st, 2021. The search keywords included ‘fatigue’ [MeSH term] and 'prevalence.' The search terms used were "Fatigue [Title] AND Prevalence [Title/Abstract]" OR "Fatigue [Title] AND Frequency [Title/Abstract]" in both PubMed and the Cochrane Library. All languages were included.

Eligibility criteria

Studies were screened using the following inclusion criteria: (1) studies including patients diagnosed with a specific disease in International Classification of Diseases-10 (ICD-10), (2) studies reporting fatigue prevalence from cross-sectional or longitudinal designs, (3) studies with fatigue prevalence data obtained through fatigue-specific assessment tools or physician interviews. The exclusion criteria were as follows: (1) studies reporting lifetime prevalence of fatigue, (2) studies focusing on a specific fatigue subdomain, such as only physical or mental, (3) studies on fatigue induction after specific medical procedures, (4) studies on myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) prevalence, (5) studies in patients with cancer, (6) studies with fewer than 30 participants, (7) review articles, (8) duplicate articles, and (9) articles without full texts. All languages were included.

Review process and data extraction

Initially, three authors (NHP, EUK, JHY) searched and screened all titles and abstracts retrieved. The full texts of eligible studies were then independently reviewed and cross-checked by these authors. In the event of disagreement, discussions were held among the authors, with the final decision made by the corresponding author (Professor Son CG).

Data extracted from each study included the first author’s name, study title, study type (cross-sectional, longitudinal), number of participants (total, male, female), classification of diseases (by ICD-10), diagnosed disease, fatigue prevalence, severity levels of fatigue (moderate, severe), fatigue diagnostic method (questionnaire, interview), fatigue assessment tools, publication year, and the continent/country where the study was conducted.

Data coding and synthesis

Regarding the longitudinal study, only the first observed data were selected. We synthesized the fatigue prevalence without differentiation of ‘chronic’, ‘general’ or ‘severe’ fatigue. On the other hand, we produced the severity-related data using only studies presenting simultaneously ‘moderate’ and ‘severe’ fatigue prevalence (each exclusively) and synthesized ‘moderate to severe’ prevalence by combining these. If a study contained three levels of fatigue—‘mild or no,’ ‘moderate,’ ‘severe’—fatigue data. we excluded data on ‘mild or no’ fatigue.

Diseases were classified according to organ systems or ICD-10 into 12 categories: nervous system, musculoskeletal system, infectious diseases, immune system, digestive system, genitourinary system, respiratory system, circulatory system, endocrine/metabolic system, transplantation, mental/behavioral diseases, and others.

Study quality assessment

The quality of each included article was independently assessed by two investigators using the Newcastle–Ottawa Scale (NOS) adapted for cross-sectional and cohort studies. The assessment of each article’s quality was based on three categories: participant selection, comparability of study controls, and appropriateness of outcome. A NOS scores were categorized as high (> 7), moderate (5–7), or low (< 5). Any discrepancies in scoring were resolved through discussions among the four investigators.

Meta-analysis

We conducted a meta-analysis to calculate the mean prevalence and 95% confidence intervals (95% CI). To improve statistical analysis, the prevalence from each study was log-transformed, and the pooled estimates were then converted back to the original prevalence scale. Additionally, the prevalence of fatigue without transformation was also analyzed to test the robustness of the results. To explain the potentially high interstudy heterogeneity, the pooled outcome measures and their corresponding 95% CI were calculated using a random-effects model fitted with the restricted maximum likelihood estimator. The I 2 statistic was also used to evaluate the degree of heterogeneity between studies.

All analyses were performed using the “meta” package (by Guido Schwarzer) in R version 4.2.1. Statistical significance was determined using hypothesis testing to analyze differences between the groups. In all analyses, meta-regression was applied, and p-values < 0.05 were considered statistically significant.

Result

Characteristics of the included studies

Of the 3,152 articles initially screened in PubMed and Cochrane, 214 articles (233 sets of prevalence data) met our eligibility criteria. These comprised 175 cross-sectional studies (193 data) and 39 longitudinal studies (40 data) (Fig. 1 ). In total, 102,024 participants (438 ± 1,421 participants) with 88 different types of individual diseases were enrolled. Of these, data for 78 sets (involving 52,082 participants, with 21,079 males and 31,003 females) included gender information (Table 1 , Fig. 2 ). According to the study quality assessment using the NOS scale, 85.0% (182 out of 214 articles) scored 5 or higher, indicating that their study quality is at least medium.

Thirty-eight articles (39 data, with 10,389 participants) reported information on fatigue severity (‘moderate’ and ‘severe’). The major studies (214 data) assessed the prevalence of fatigue (including its severity) using 28 types of self-reported, questionnaire-based measurement tools. These included the Fatigue Severity Scale (FSS, 59 data), Chalder Fatigue Questionnaire (CFQ, 18 data), Checklist Individual Strength (CIS, 18 data), Fatigue Impact Scale (FIS, 14 data), Fatigue Assessment Scale (FAS, 11 data), and Multidimensional Fatigue Inventory (MFI, 10 data), among others. Approximately 71% of the studies (165 data) were conducted since 2011, with 74% of the studies originating from Europe (126 data) and North America (47 data) respectively (Table 1 ).

Comparison of fatigue prevalence by organ-system

An overall prevalence of 102,024 participants (233 data for 88 individual diseases) was 49.4% [95% CI 46.9 -52.1] (Fig. 2 , Table 2 ). According to the International Classification of Diseases-10 (ICD-10), patients with mental/behavioral diseases reported the highest fatigue prevalence at 65.9% [95% CI 54.5–79.6], followed by those with transplantation (56.8% [95% CI 42.7–75.5]) and with diseases of the respiratory system (56.3% [95% CI 43.8–72.5]). Meanwhile patients diseases in circulatory system showed the lowest fatigue prevalence at 34.7% [95% CI 24.5–49.2), respectively (Fig. 2 ). The differences in fatigue prevalence among organ system diseases were statistically significant across the systems (p < 0.001).

Comparison of fatigue prevalence by individual disease

Regarding fatigue associated with 88 individual diseases (total 102,024 participants), subjects with five diseases showed a fatigue prevalence of over 90%; osteogenesis imperfecta (96.4% [95% CI: 73.9–100]), whiplash-associated disorders (93.8% [95% CI: 65.6–100]), postural orthostatic tachycardia syndrome (POTS, 93.6% [95% CI: 69.7–100]), gastroparesis (92.3% [95% CI: 78.4–100]), and pulmonary hypertension (90.0% [95% CI: 74.5–100]). In contrast, the lowest prevalence was observed in patients with Lyme disease (9.0% [95% CI: 4.7–17.3]). Among diseases with the three largest participant numbers (over 10,000 subjects), patients with multiple sclerosis (20 data, 14,480 subjects) had a fatigue prevalence of 60.2% [95% CI 55.1–65.6], while patients with HIV infection (11 data, 16,147 subjects) and heart failure (3 data, 12,715 subjects) presented relatively low prevalence of 38.2% [95% CI: 24.7–58.9] and 38.3% [95% CI: 28.0–52.5], respectively (Table 2 ). The differences in fatigue prevalence related to individual diseases were statistically significant (p < 0.001).

Comparison of fatigue prevalence by gender

Among 52,082 participants (21,079 males and 31,003 females) provided with gender information, 73 data for 29 diseases (51,676 participants: 20,869 males and 30,807 females) met our criteria (existence of gender-specific information for ≥ 100 participants) for gender-related analysis. The total fatigue prevalence was female-dominant at a ratio of 1.15-fold, with rates of 43.8% [95% CI: 39.3–48.8] for males and 50.3% [95% CI: 46.1–54.9] for females. Subjects with 23 diseases (79.3%) exhibited a female-dominant pattern, most notably in patients with chronic low back pain (1.77-fold), giardiasis (1.60-fold), spinal cord injury (1.60-fold), and EBV infection (1.50-fold). Conversely, psoriatic arthritis demonstrated the most male-dominant fatigue prevalence pattern at 0.68-fold (based on one RCT) (Fig. 3 ).

Comparison of fatigue prevalence by severity

Thirty-nine data (10,389 participants) reported prevalence rate by severity information (moderate versus severe exclusively) for 28 individual diseases. The overall fatigue prevalence was 55.3% [95% CI; 49.6–61.6], while the fatigue severity ratio of ‘severe’ to ‘moderate’ was 0.87, respectively. Patients suffering from Pompe disease (6.1-folds), epilepsy (2.8-folds), pulmonary hypertension (2.0-folds) and systemic lupus erythematosus (SLE, 2.0-folds) reported over 2-folds ‘severe’ to ‘moderate’ ratio, respectively (Fig. 4 ).

Discussion

From 214 articles reporting 233 data across 88 individual diseases (involving 102,024 participants) in 44 countries, we herein systematically produced the characteristics of fatigue prevalence among diseased subjects worldwide. The pooled prevalence rate of fatigue was 48.6% (data not shown), which is nearly the same as that found in meta-analysis data (49.4% [95% CI 46.9–52.1]) (Fig. 2 , Table 2 ). These results suggest that approximately half of patients with illnesses experience clinically significant fatigue. This prevalence rate is nearly three times higher than the 16.4% (95% CI, 13.6–19.9) observed in the general population of 623,624 individuals 2 .

Among disease-related fatigue, CRF is most well-known. Two recent systematic reviews reported CRF prevalence rates of 49.0% and 52.0% 22 , 23 , which are nearly the same as our current data across 88 types of diseases. In our current results, patients with mental or behavioral disorders showed the highest prevalence of fatigue at 65.9% [95% CI 54.5–79.6] (Fig. 2 ), with rates derived from patients suffering from functional movement disorders (71.7%), major depressive disorder (MDD, 64.7%), and attention deficit hyperactivity disorder (ADHD, 61.8%), respectively (Table 2 ). These three disorders belonged in psychiatric problems, which originate from brain. Structural and functional changes in the brain are known to be an important contributor to development of notable fatigue, such as 93.6% in patients with postural orthostatic tachycardia syndrome (POTS), 73.3% in brain injury and 69.9% in chronic headache (Table 2 ). In addition, patients with multiple sclerosis (14,480 participants) and Parkinson’s disease (2,521 participant) presented 60.2% and 54.3% of fatigue prevalence, as already known as diseases leading to high rates of fatigue 24 , 25 . Long COVID has gained attention due to its overlap with many clinical features of ME/CFS, a well-known complex, multisystem neuroimmune disease 26 , 27 . In our data, 47.9% of subjects with COVID-19 infection reported fatigue (Table 2 ). The brain-related prominent fatigue symptom was evident in our previous CRF data, showing the highest prevalence (52.5%) among patients with brain tumors compared to eight other cancer types 1 .

Among 88 different diseases, patients with five conditions showed a fatigue prevalence exceeding 90%. The highest prevalence was observed in osteogenesis imperfecta (96.4%), followed by whiplash-associated disorders (93.8%), POTS (93.6%), gastroparesis (92.3%), and pulmonary hypertension (90.0%), respectively (Table 2 ). Osteogenesis imperfecta is a common hereditary connective tissue disorder characterized by extremely fragile bones and pain, with a prevalence of approximately 90% 28 . Fatigue is often recognized as a cluster of symptoms that accompany other issues, such as pain, depression, anxiety, or sleep disturbances. In particular, fatigue and pain mutually influence each other, and these symptoms often co-occur in connective tissue disorders 29 , 30 . Both fibromyalgia and systemic lupus erythematosus (SLE) are typical pain-presenting connective tissue diseases, while they showed high fatigue prevalence rates as 82.2% and 63.4% (Table 2 ). Regarding Whiplash-associated disorders (93.8% prevalence of fatigue) and gastroparesis (92.3% prevalence of fatigue), both conditions are typically associated with severe pain and a tendency for dysfunction without identifiable lesions or structural pathology in the majority of affected individuals 31 , 32 . One cohort study reported that among 3,230 individuals with fatigue, approximately 65% (1,967 subjects) had a co-symptom of pain, depression, or both of them 33 . Another study reported that the presence particularly of fatigue affected both the physical and mental aspects of health-related QoL, contrast to pain affecting physical-dominant QoL 34 .

In our present meta-analyses, some data were calculated from a relatively small number of studies or participants, with an average of 438 ± 1,421 individuals (Table 1 ). However, data derived from sixteen diseases enrolled over 1,000 participants, including subjects with HIV infection (16,147 participants), multiple sclerosis (14,480 participants), heart failure (12,715 participants), rheumatoid arthritis (8,251 participants), and primary immunodeficiency (2,366 participants), respectively (Table 2 ). Individuals with primary immunodeficiency (18.2%) or acquired immunodeficiency (HIV infection, 38.2%) showed relatively low fatigue prevalence. Additionally, patients with heart failure had a 38.3% prevalence of fatigue, which is inconsistent with its impact on health-related status (Table 2 ). These observations suggest that disease-related fatigue may depend more on the presence of specific conditions than on the overall severity of the disease 35 . An analysis from 1,696 patients with chronic diseases revealed that fatigue severity was notably influenced by pain, sleep disturbance, and physical dysfunction, and female sex 36 . In fact, pain is not the primary symptom for most individuals with primary or acquired immunodeficiency without advanced complications, as well as those with heart failure 37 , 38 .

On the other hand, fatigue prevalence is affected by many factors, including gender, obesity, age, ethnicity, occupation, sleep quality, and psychological condition 5 – 8 , 12 . Our data showed the high frequent rate of fatigue (59.1%) in subjects with obesity (Table 2 ). Other groups have indicated obesity as a contributor to fatigue in the general population as well as in diseased subjects such as those post-stroke and even in ME/CFS 39 – 41 . Females are generally more prone to fatigue than males, with our previous studies indicating a 1.5-fold increase 2 , 42 . Most data from our studies on 29 different diseases showed a female-dominant or equivalent pattern of fatigue prevalence, with a few exceptions (Fig. 3 ). Among the studies included in our analysis, subjects with low back pain showed the most female-predominant fatigue (1.8-fold, 17.3% vs. 30.7%) 43 , while subjects with psoriatic arthritis presented a male-predominant fatigue rate (0.7-fold, 40.9% vs. 27.7%), respectively 44 . However, we must exercise caution or acknowledge limitations regarding the male-dominant fatigue prevalence in subjects with psoriatic arthritis, because another recent study presented reverse data 45 .

The medical impact of fatigue relies on its severity and/or duration. Therefore, the guidelines for the management of fatigue recommend assessing the severity of fatigue, not just the presence of fatigue 35 . In general, the medical concern focuses on the moderate to severe level of fatigue in the clinical field 46 . When we analyzed data from 39 studies (10,389 participants across 28 diseases) containing information on the prevalence of severity-informed fatigue, the total prevalence rate of ‘moderate to severe’ fatigue was 55.3% [95% CI 49.6–61.6] (Table 2 ). The top 4 diseases showed over 80% prevalence of ‘moderate to severe’ level of fatigue, such as gastroparesis (92.3%), pulmonary hypertension (90.0%), chronic obstructive pulmonary disease (COPD, 83.2%) and multiple sclerosis (80.0%). Meanwhile the highest prevalence of severe fatigue (67.1%) was observed in individuals with Pompe disease (Fig. 4 ). Prominent fatigue has been well recognized as an important feature of late-onset Pompe disease, which shows progressive weakness in the heart and skeletal muscles, caused by mutations in a gene responsible for breaking down glycogen 47 . The second-highest rate of severe fatigue, found in 60.0% of patients with pulmonary hypertension, is likely related to the core symptom of dyspnea, given that pulmonary hypertension is a potentially fatal disease 48 .

We herein analyzed the overall comparison of fatigue prevalence in subjects with diseases or disorders. However, our study has some limitations such as the small number of studies and participants in certain diseases, and the lack of data on risk factors for fatigue and fatigue severity. The disease-related fatigue can also be influenced by the stage of the disease and therapeutic interventions. The CRF prevalence varies significantly depending on the treatment phase, with ‘moderate to severe’ fatigue reported in 30.8% of patients before treatment compared to 46.1% during treatment. This variation is why we excluded CRF prevalence data from the present study. Additionally, we found that the prevalence of fatigue in inflammatory bowel disease increased 1.7-fold during the ‘active phase’ compared to the ‘remission phase’ 49 . Another issue is the applications of different fatigue-assessment tools and inconsistent cut-off for prevalence and severity of fatigue in each study. Accordingly, we require to consider the carefulness in interpretations of the present data related to these limitations.

There is no established standard treatment for chronic, severe fatigue associated with specific diseases 50 . Moreover, physicians often underestimate the importance of fatigue, creating barriers to effective communication about this symptom. For example, one study reported that approximately 66% of cancer patients had never discussed their fatigue with their doctor 51 . Fatigue, particularly during treatment, is associated with reduced treatment compliance 52 . Given the limitations of using medications to manage fatigue, non-pharmacological treatments are increasingly recommended 53 . Therefore, we emphasize the need for clearer assessment and differentiation of fatigue characteristics among patients with diseased conditions. Our current data provide a comparative reference for healthcare providers to make patient-specific decisions on fatigue management.

Conclusion

The present results represent the first comprehensive showcase of the comparative features of fatigue prevalence and severity among subjects with 88 different diseases. Our findings indicate that approximately half of the patients suffering from an illness report clinically significant fatigue, with a prevalence rate of 49.4% [95% CI 46.9–52.1], along with a slight female dominance (43.5% vs. 49.8%), but a notable female predominance (1.8-fold) in patients with low back pain. According to the ICD-10 classification, fatigue prevalence rates were highest in patients with mental/behavioral diseases (65.9% [95% CI 54.9–79.6]) and lowest among those with circulatory system diseases (34.7% [95% CI 24.5–49.2]). The top disease groups exhibiting moderate to severe levels of fatigue included gastroparesis (92.3%), pulmonary hypertension (90.0%), chronic obstructive pulmonary disease (COPD, 83.2%), and multiple sclerosis (80.0%). Our findings offer valuable reference data for future research on fatigue and for the management of patients experiencing fatigue.

Supplementary Information

Supplementary Information.

Author contributions

NHP, YEK and JHY had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. YCA conducted statistical analysis. NHP, EJL and CGS designed the study and drafted the manuscript. BJP contributed to manuscript revisions and submission process. CGS obtained the funding and supervised the whole process of this study. All authors read and approved the final manuscript.

Funding

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2018R1A6A1A03025221) and Bio&Medical Technology Development Program (NRF-2022M3A9E4095338).

Availability of data and materials

Data and materials are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-74683-z.

References

1. Kang, Y. E. et al. Prevalence of cancer-related fatigue based on severity: a systematic review and meta-analysis. Sci. Rep. 13 (1), 12815 (2023).

2. Yoon, J. H. et al. The demographic features of fatigue in the general population worldwide: a systematic review and meta-analysis. Front. Public Health. 11 , 1192121 (2023).

3. Kluger, B. M., Krupp, L. B. & Enoka, R. M. Fatigue and fatigability in neurologic illnesses: proposal for a unified taxonomy. Neurology. 80 (4), 409–416 (2013).

4. Landmark-Hoyvik, H. et al. The genetics and epigenetics of fatigue. PM R. 2 (5), 456–465 (2010).

5. Ghajarzadeh, M. et al. Fatigue in multiple sclerosis: relationship with disease duration, physical disability, disease pattern, age and sex. Acta Neurol. Belg. 113 (4), 411–414 (2013).

6. Zielinski, M. R., Systrom, D. M. & Rose, N. R. Fatigue, sleep, and autoimmune and related disorders. Front. Immunol. 10 , 1827 (2019).

7. Bazazan, A. et al. Physical and psychological job demands and fatigue experience among offshore workers. Heliyon. 9 (6), e16441 (2023).

8. Dinos, S. et al. A systematic review of chronic fatigue, its syndromes and ethnicity: prevalence, severity, co-morbidity and coping. Int. J. Epidemiol. 38 (6), 1554–1570 (2009).

9. Ma, Y. et al. Prevalence and risk factors of cancer-related fatigue: A systematic review and meta-analysis. Int. J. Nurs. Stud. 111 , 103707 (2020).

10. Doerr, J. M. et al. Reciprocal relationship between acute stress and acute fatigue in everyday life in a sample of university students. Biol. Psychol. 110 , 42–49 (2015).

11. Fortier-Brochu, E., Beaulieu-Bonneau, S., Ivers, H. & Morin, C. M. Relations between sleep, fatigue, and health-related quality of life in individuals with insomnia. J. Psychosom. Res. 69 (5), 475–483 (2010).

12. Resnick, H. E., Carter, E. A., Aloia, M. & Phillips, B. Cross-sectional relationship of reported fatigue to obesity, diet, and physical activity: results from the third national health and nutrition examination survey. J. Clin. Sleep Med. 2 (2), 163–169 (2006).

13. Frandemark, A., Jakobsson Ung, E., Tornblom, H., Simren, M. & Jakobsson, S. Fatigue: a distressing symptom for patients with irritable bowel syndrome. Neurogastroenterol. Motil. 10.1111/nmo.12898 (2017).

14. Overman, C. L., Kool, M. B., Da Silva, J. A. & Geenen, R. The prevalence of severe fatigue in rheumatic diseases: an international study. Clin. Rheumatol. 35 (2), 409–415 (2016).

15. Borren, N. Z., van der Woude, C. J. & Ananthakrishnan, A. N. Fatigue in IBD: Epidemiology, pathophysiology and management. Nat. Rev. Gastroenterol. Hepatol. 16 (4), 247–259 (2019).

16. Gregg, L. P. et al. Fatigue in nondialysis chronic kidney disease: Correlates and association with kidney outcomes. Am. J. Nephrol. 50 (1), 37–47 (2019).

17. Andersson, M., Stridsman, C., Ronmark, E., Lindberg, A. & Emtner, M. Physical activity and fatigue in chronic obstructive pulmonary disease - A population based study. Respir. Med. 109 (8), 1048–1057 (2015).

18. Glader, E. L., Stegmayr, B. & Asplund, K. Poststroke fatigue: a 2-year follow-up study of stroke patients in Sweden. Stroke. 33 (5), 1327–1333 (2002).

19. Ruiz-Casado, A., Alvarez-Bustos, A., de Pedro, C. G., Mendez-Otero, M. & Romero-Elias, M. Cancer-related fatigue in breast cancer survivors: A review. Clin. Breast Cancer. 21 (1), 10–25 (2021).

20. Krupp, L. B., LaRocca, N. G., Muir-Nash, J. & Steinberg, A. D. The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch. Neurol. 46 (10), 1121–1123 (1989).

21. Langston, B., Armes, J., Levy, A., Tidey, E. & Ream, E. The prevalence and severity of fatigue in men with prostate cancer: a systematic review of the literature. Support Care Cancer. 21 (6), 1761–1771 (2013).

22. Maqbali. M. A. Cancer-related fatigue: An overview. Br. J. Nurs. (2021).

23. Ma, Y. et al. Prevalence and risk factors of cancer-related fatigue: A systematic review and meta-analysis. Int. J. Nurs. Stud. 10.1016/j.ijnurstu.2020.103707 (2020).

24. Barbi, C. et al. Brain structural and functional alterations in multiple sclerosis-related fatigue: A systematic review. Neurol. Int. 14 (2), 506–535 (2022).

25. Siciliano, M. et al. Fatigue in Parkinson’s disease: A systematic review and meta-analysis. Mov. Disord. 33 (11), 1712–1723 (2018).

26. Lim, E. J. & Son, C. G. Review of case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). J. Transl. Med. 18 (1), 289 (2020).

27. Wong, T. L. & Weitzer, D. J. Long COVID and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)-A systemic review and comparison of clinical presentation and symptomatology. Medicina (Kaunas). 57 (5), 418 (2021).

28. Arponen, H., Waltimo-Siren, J., Valta, H. & Makitie, O. Fatigue and disturbances of sleep in patients with osteogenesis imperfecta - a cross-sectional questionnaire study. BMC Musculoskelet. Disord. 19 (1), 3 (2018).

29. Molvaer, A. K. et al. Metabolic predictors of pain, fatigue, depression and quality of life in people with long-term type 1 diabetes-the Dialong study. Diabet. Med. 40 (3), e15009 (2023).

30. Wohland, H., Aringer, M. & Leuchten, N. Physical exercise is associated with less fatigue, less pain and better sleep in patients with systemic lupus erythematosus. Clin. Exp. Rheumatol. 10.55563/clinexprheumatol/az3pkn (2023).

31. McClune, T., Burton, A. K. & Waddell, G. Whiplash associated disorders: A review of the literature to guide patient information and advice. Emerg. Med. J. 19 (6), 499–506 (2002).

32. Mandarino, F. V. et al. Imaging in gastroparesis: exploring innovative diagnostic approaches, symptoms, and treatment. Life (Basel). 13 (8), 1743 (2023).

33. Reyes-Gibby, C. C., Aday, L. A., Anderson, K. O., Mendoza, T. R. & Cleeland, C. S. Pain, depression, and fatigue in community-dwelling adults with and without a history of cancer. J. Pain Symptom Manage. 32 (2), 118–128 (2006).

34. Baek, Y., Jung, K., Kim, H. & Lee, S. Association between fatigue, pain, digestive problems, and sleep disturbances and individuals’ health-related quality of life: A nationwide survey in South Korea. Health Qual. Life Outcomes. 18 (1), 159 (2020).

35. Goertz, Y. M. J. et al. Fatigue in patients with chronic disease: results from the population-based Lifelines Cohort Study. Sci. Rep. 11 (1), 20977 (2021).

36. Menting, J. et al. Is fatigue a disease-specific or generic symptom in chronic medical conditions?. Health Psychol. 37 (6), 530–543 (2018).

37. Hu, Y. et al. Progress in pathological and therapeutic research of HIV-related neuropathic pain. Cell. Mol. Neurobiol. 43 (7), 3343–3373 (2023).

38. Godfrey, C., Harrison, M. B., Medves, J. & Tranmer, J. E. The symptom of pain with heart failure: a systematic review. J. Card. Fail. 12 (4), 307–313 (2006).

39. Norris, T., Hawton, K., Hamilton-Shield, J. & Crawley, E. Obesity in adolescents with chronic fatigue syndrome: an observational study. Arch. Dis. Child. 102 (1), 35–39 (2017).

40. Lim, W., Hong, S., Nelesen, R. & Dimsdale, J. E. The association of obesity, cytokine levels, and depressive symptoms with diverse measures of fatigue in healthy subjects. Arch Intern. Med. 165 (8), 910–915 (2005).

41. Gu, M. et al. Obesity and poststroke fatigue: A 2-year longitudinal study. Neurol. Ther. 10 (2), 955–969 (2021).

42. Kang, Y. E. et al. Prevalence of cancer-related fatigue based on severity: a systematic review and meta-analysis. Sci. Rep. 10.1038/s41598-023-39046-0 (2023).

43. Salvetti Mde, G., Pimenta, C. A., Braga, P. E. & McGillion, M. Prevalence of fatigue and associated factors in chronic low back pain patients. Rev. Lat. Am. Enfermagem. 21 , 12–19 (2013).

44. Tan, J. S. Q., Fong, W., Kwan, Y. H. & Leung, Y. Y. Prevalence and variables associated with fatigue in psoriatic arthritis: a cross-sectional study. Rheumatol. Int. 40 (11), 1825–1834 (2020).

45. Coates, L. C. et al. Sex-specific differences in patients with psoriatic arthritis: A systematic review. J. Rheumatol. 50 (4), 488–496 (2023).

46. Wang, X. S. et al. Prevalence and characteristics of moderate to severe fatigue: a multicenter study in cancer patients and survivors. Cancer. 120 (3), 425–432 (2014).

47. Hagemans, M. L. et al. Fatigue: an important feature of late-onset Pompe disease. J. Neurol. 254 (7), 941–945 (2007).

48. Tartavoulle, T. M. et al. Multidimensional fatigue in pulmonary hypertension: prevalence, severity and predictors. ERJ Open Res. 10.1183/23120541.00079-2017 (2018).

49. Kim, Y. J., Lee, S. G., Lee, J. S., Choi, Y. J. & Son, C. G. Comparative characteristics of fatigue in irritable bowel syndrome and inflammatory bowel disease: A systematic review and meta-analysis. J. Psychosom. Res. 177 , 111589 (2024).

50. Pae, C. U. et al. Pharmacological treatment of chronic fatigue syndrome: focusing on the role of antidepressants. Expert Opin. Pharmacother. 10 (10), 1561–1570 (2009).

51. Passik, S. D. et al. Patient-related barriers to fatigue communication: initial validation of the fatigue management barriers questionnaire. J. Pain Symptom Manage. 24 (5), 481–493 (2002).

52. Poort, H. et al. cognitive behavioral therapy for treatment-related fatigue in chronic myeloid leukemia patients on tyrosine kinase inhibitors: A mixed-method study. J. Clin. Psychol. Med. Settings. 26 (4), 440–448 (2019).

53. NCGC. Pharmacological interventions: Myalgic encephalomyelitis (or encephalopathy) / chronic fatigue syndrome: diagnosis and management: Evidence review F. London (2021).

Associated Data

Supplementary Materials

Supplementary Information.

Data Availability Statement

Data and materials are available from the corresponding author upon reasonable request.