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Fatigue in type 1 diabetes, prevalence, predictors and comparison with the background population
Accepted Manuscript Fatigue in type 1 diabetes, prevalence, predictors and comparison with the background population Øystein Jensen, Tomm Bernklev, Charlotte Gibbs, Ragnar Bekkhus Moe, Dag Hofsø, Lars-Petter Jelsness-Jørgensen PII: DOI: Reference:
S0168-8227(18)30004-4 https://doi.org/10.1016/j.diabres.2018.06.012 DIAB 7423
To appear in:
Diabetes Research and Clinical Practice
Received Date: Revised Date: Accepted Date:
3 January 2018 29 May 2018 13 June 2018
Please cite this article as: O. Jensen, T. Bernklev, C. Gibbs, R. Bekkhus Moe, D. Hofsø, L-P. Jelsness-Jørgensen, Fatigue in type 1 diabetes, prevalence, predictors and comparison with the background population, Diabetes Research and Clinical Practice (2018), doi: https://doi.org/10.1016/j.diabres.2018.06.012
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Fatigue in type 1 diabetes, prevalence, predictors and comparison with the background population.
Øystein Jensena,b, Tomm Bernklevc,b, Charlotte Gibbsd, Ragnar Bekkhus Moee, Dag Hofsøf Lars-Petter Jelsness-Jørgensena,e a
Østfold University College, Dept of Health and Welfare, Pb 770, 1757 Halden, Norway,
b
Institute of Clinical Medicine, University of Oslo, Klaus Torgårds vei 3, 0372 Oslo,
Norway, c Vestfold Hospital Trust, Research and Development, Halfdan Wilhelmsens alle 17, 3116 Tønsberg, d Telemark Hospital Trust, Dept of Endocrinology, Ulefossvegen 55, 3710 Skien, Norway, e Østfold Hospital Trust, Dept of Internal Medicine, Kalnesveien 300, 1714 Grålum, Norway, f Vestfold Hospital Trust, Morbid Obesity Centre and Section of Endocrinology, Halfdan Wilhelmsens alle 17, 3116 Tønsberg. Corresponding author: Lars-Petter Jelsness-Jørgensen, Østfold University College, Faculty of Health and Social Studies Tel.: +47-69608836. E-mail address: [email protected]. E-mail addresses: [email protected] (Ø.Jensen), [email protected] (T.Bernklev), [email protected] (C. Gibbs), [email protected] (R. Moe), [email protected] (D.Hofsø), [email protected] (LP. Jelsness-Jørgensen). Funding sources: This study was supported by unrestricted grants from Østfold University College, Telemark, Østfold and Vestfold Hospital Trust.
Declarations of interest: None
ABSTRACT Aim: Fatigue is scarcely studied in type 1 diabetes (T1D), and the aims were to investigate its prevalence compared to the background population, potential associations, and to validate the Fatigue Questionnaire (FQ) in type 1 diabetes. Methods: Persons with T1D were recruited from three outpatient clinics in Norway. Fatigue was measured using the FQ, and FQ data from the Norwegian background population were used for comparison. Socio-demographic and clinical variables were obtained by self-report, clinical investigation, medical records and laboratory tests. Results: Of 332 eligible patients, 288 (87%) were included. Mean age was 44.65/44.95 years (SD 13.34/13.38) for females/males, respectively. Total fatigue (TF) was 15.31 (SD 5.51) compared to 12.2 (SD 4.0) in the background population (p <0.001). HADS ≥8, current menstruation, increased leukocytes and sleep problems were associated with increased TF. Chronic fatigue (CF) was reported in 26.4% compared to 11% in the background population (p <0.001). HADS ≥8, increased time since diagnosis and decreased sleep quality were associated with CF. The validity, internal consistency and repeatability of the FQ was confirmed.
Conclusions: Fatigue was more common in T1D than in the background population, and associated with increased anxiety, depression and sleep problems. The FQ demonstrated satisfactory psychometric properties. Keywords: Chronic fatigue, Fatigue, Total fatigue, Type 1 diabetes
INTRODUCTION Type 1 diabetes (T1D) is characterized by a selective destruction of pancreatic beta cells that usually leads to absolute insulin deficiency, with a peak incidence in childhood and adulthood [1, 2]. T1D may affect people with diabetes negatively, particularly due to the risk of developing long-term side effects such as retinopathy, neuropathy, nephropathy and macrovascular complications [2]. Several studies have also shown that T1D is associated with impaired health-related quality of life (HRQoL) and increased symptom burden, e.g., higher levels of depression and anxiety compared to the background population [3, 4]. Fatigue is associated with a wide variety of medical conditions [5]. The perception of fatigue is subjective and may be defined as “an overwhelming, debilitating, and sustained sense of exhaustion that decreases one's ability to carry out daily activities, including the ability to work effectively and to function at one's usual level in family or social roles” [6, 7]. Fatigue can affect almost all people throughout life, but in most cases, the condition is transient and relieved by rest [8]. In some cases, however, symptoms persist for a prolonged period. Although people with T1D frequently report fatigue in daily follow-up, clinical studies are either lacking or of low quality [9]. In a systematic review of the literature on fatigue in
T1D, Jensen et al. [9] were only able to identify one study with high quality [10]. However, even in that study, several methodological limitations were identified. For instance, data on HbA1c levels were collected through medical records. Therefore, these data and data on fatigue may have been collected too far apart in time, allowing clinically significant changes to occur. Moreover, data on blood glucose levels were only investigated in a subset of 68 patients. A vast majority of the studies reviewed by Jensen et al. [9] had not included clinical and socio-demographic data, which makes it impossible to draw any conclusions on potential associations between fatigue, clinical and socio-demographic data in T1D. Such information could broaden our knowledge and understanding of fatigue symptoms in these people with diabetes. Furthermore, factors identified from studies on fatigue in other populations, such as, e.g., vitamin D, ferritin or C-reactive protein levels (CRP), have not yet been studied in T1D. The primary aim of this study was to determine the prevalence of fatigue in T1D compared to the background population. The secondary aims were to identify socio-demographic and clinical factors that may influence fatigue, as well as to investigate the validity, reliability and sensitivity to change of the Fatigue Questionnaire (FQ) in T1D. MATERIALS AND METHODS Patients In this cross-sectional, descriptive study, people diagnosed with T1D who were 18 years or older were consecutively included during routine follow-up at three diabetes outpatient clinics in the southeastern part of Norway from May 2015 to November 2016. Patients were excluded if they were unable to provide written informed consent.
Ethics The study was performed in accordance with the principles of the Helsinki Declaration and approved by the Regional Committee for medical and health research ethics (ref.nr. 2012/845). All patients were informed about the study orally, and written informed consent was obtained from all patients before inclusion. At each center, a senior endocrinologist was responsible for the study (local PI). Data collection A standardized inclusion procedure was followed at each center. This procedure included baseline collection of socio-demographic, clinical, laboratory and patient reported outcome data. Moreover, this procedure enabled patients to fill out the questionnaires in peace and quiet at the hospital outpatient clinic. Clinical data were collected during physical examination, from medical records and laboratory tests. Diabetes complications were defined as having one or more of either nephropathy, retinopathy or neuropathy. Nephropathy was defined based on the albumin/kreatinin-ratio (U-AKR) > 30 mg/mmol. The presence of retinopathy had to be verified by a specialist in ophthalmology and includes non‐ proliferative, proliferative and maculopathy. Neuropathy was defined as sensory loss by use of 10g monofilament on the great toe and on the plantar aspect of the first, second and third metatarsal of each lower extremity, in accordance with the recommendations of the Norwegian Diabetes Guidelines. Comorbidity was assessed using the Nord-Trøndelag Health Study (HUNT) comorbidity questionnaire and collected based on medical records and patient selfreport. Female patients also self-reported menstruation status at time of baseline assessment. Foot ulcers were classified according to the Wagner classification [11].
In addition to clinical data, self-reported data on civil status, educational level, physical activity, smoking habits and work status were collected. Work was dichotomized into either working (including full-time employee, apprentice/ trainee, part-time employee, partially disabled, partial sick leave or student) or not working (including full disability, home resident, full time sick leave full time or retired). Information regarding fatigue was collected with the Fatigue Questionnaire (FQ) [12]. To investigate the test-retest reliability of the FQ, all patients were invited to fill out the FQ questionnaire a second time, 4-6 weeks following baseline assessments. In addition, patients self-reported their perceived disease state using a question with three potential answers: “Compared to last time you completed the questionnaire, how do you evaluate your condition today? (i) unchanged (ii) improved, or (iii) deteriorated”. An invitation to participate in the retest was sent by mail, and patients were asked to complete the questionnaires and return them in a pre-stamped envelope. In addition, sleep problems were measured using question six in the Basic Nordic Sleep Questionnaire (BNSQ) [13]. This question measures how well the patient has slept during the past three months. The BNSQ is scored on a five-point Likert scale, on which a higher score indicates poor sleep quality. In the current study, the item on sleep quality from the BNSQ was dichotomized into “no sleep problems” (scores <4) or “sleep problems” (scores ≥4). The Fatigue Questionnaire (FQ) The FQ was originally developed by Chalder et al. [12] and consists of 11 questions that can be divided into two dimensions: physical fatigue (PF; 7 items) and mental fatigue (MF; 4 items). Four response options are used (0= better than usual, 1= no more than usual, 2= worse than usual, and 3= much worse than usual). Higher scores imply higher levels of fatigue.
Combining the scores of PF and MF produces a score for total fatigue (TF), with a maximum score of 33. Two additional questions cover the duration and extent of fatigue [12]. The FQ scores can be dichotomized, where original scores of 0 or 1 are scored 0 and original scores of 2 and 3 are scored 1. Chronic fatigue (CF) is defined as a dichotomized score ≥4 and a duration ≥6 months [12, 14]. The FQ has been translated into Norwegian and validated [14], and studies have demonstrated that the questionnaire has stable psychometric properties across populations [8]. The Norwegian version of the FQ has not been validated in patients with type 1 diabetes. The FQ was chosen over other fatigue questionnaires since it is designed to measure the presence of chronic fatigue. Moreover, background population FQ data was available for comparison. Hospital Anxiety and Depression Scale Anxiety and depression were measured using the Hospital Anxiety and Depression Scale (HADS) [15]. The HADS is a self-assessment instrument that is divided into two scales, measuring symptoms of anxiety (A) and depression (D). Each scale has seven items that are scored on a four-point Likert scale (0-3). Higher scores imply many symptoms. In accordance with recommendations in the literature, a cut-off value of HADS-A or HADS-D ≥ 8 was used to identify borderline cases of anxiety and depression [16, 17]. Short Form 36 (SF-36) The SF-36 is a generic HRQOL questionnaire designed to assess functional status, well-being, and general perception of health. The questionnaire consists of 36 items transformed into 8 dimensions: physical functioning (10 items), bodily pain (2 items), vitality or energy level (4 items), social functioning (2 items), mental health (5 items), general health perceptions (5 items), role limitation due to physical problems (4 items), and role limitations due to personal or emotional problems (3 items). An
additional item (HT) reports health transitions over the past year. The questionnaire has been translated into Norwegian and validated [18]. The SF-36 has previously been validated among people with diabetes [19]. Fatigue data from the Norwegian background population Fatigue data from the Norwegian background population were collected by Loge et al. [14]. A random sample of 3500 Norwegians aged 19-80 years, drawn from the National Register by the Norwegian Government Computer Center and representative of the entire Norwegian population, was investigated. After two reminders, 2287 questionnaires were eligible for evaluation. Statistical Analysis To assess the characteristics of the sample, we used descriptive analysis and frequencies. Independent samples t-tests and chi-square statistics were used to examine the differences in fatigue distribution and socio-demographic and clinical variables between the background population and patients with type 1 diabetes. To assess possible associations between clinical and socio-demographic data and fatigue scores, t-tests for binary variables and chi-square and bivariate correlation analyses were used. Variables with a significance level of less than 0.2 in univariate analysis were included in the multivariate regression analyses. For total fatigue (TF), linear regression analysis was used, while logistic regression was used for chronic fatigue (CF).
Convergent validity was calculated using binary correlation analysis (Spearman’s rho) between the FQ and the SF-36 dimensions. In general, we hypothesized that increased fatigue would correlate negatively with all dimensions of the SF-36. Based on the semantic construct, we furthermore hypothesized that PF would correlate most strongly with the SF-36
dimensions vitality (VT), general health (GH), role physical (RP) and social functioning (SF), while MF would correlate most strongly with vitality and mental health (MH). Known-group validity was tested by comparing fatigue scores in patients with or without diabetes-related complications and comorbidity. Internal consistency reliability was determined by Cronbach’s alpha, while test-retest reliability was measured using the intraclass correlation coefficient (ICC) among patients who reported an unchanged condition from baseline to retest. Sensitivity to change was investigated comparing fatigue scores (paired t-tests) in patients who reported either symptom deterioration or improvement from baseline to retest. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS version 23, IBM Corporation, Somers New York, USA).
RESULTS Out of 332 patients eligible for inclusion, 288 (87%) gave informed consent and participated. Socio-demographic and clinical data are presented in Table 1. A total of 74 patients (25.8 %) were registered as not working, divided into 35 (47.3 %) on full disability pension, four home residents (5.4 %), 14 (18.9 %) on fulltime sick leave and 21 (28.4 %) retirees.
Fatigue in type 1 diabetes compared to the background population Compared to the background population, people with T1D had a statistically significantly higher prevalence of CF as well as dimensional (PF/MF) and TF scores. CF was equally distributed between genders: 29.6% in females and 22.8% in males (p =0.228). Differences in FQ scores are presented in Table 2.
Associations between total fatigue, socio-demographic and clinical factors Several factors were statistically significantly associated with total fatigue in univariate, but not multivariate, analysis (Table 3). Vitamin D deficiency (<50 nmol/L), thyroid-stimulating hormone (TSH), CRP, glucose and HbA1c were not associated with TF.
Associations between Chronic fatigue and clinical factors Even though several variables were associated with chronic fatigue in the univariate analysis, merely increased time since diagnosis, sleep problems, HADS-A ≥8 and HADS-D ≥8, remained significant in the multivariate regression analysis (Table 4). When stratifying for borderline anxiety or depression, 43 patients (15%) with HADS-A and 29 patients (10%) with HADS-D with scores of eight or above reported CF, while 32 patients (11%) with HADS-A and 43 patients (15%) with HADS-D below 8 reported CF. In those with a combined HADSD and HADS-A below 8, a total of 21 patients (7.3%) reported CF (Supplementary table S1).
Validity, reliability and sensitivity to change of the FQ Calculation of convergent validity through correlations between the FQ dimensions and the SF-36 dimensions revealed negative correlations with all SF-36 dimensions. Physical fatigue was moderately to highly correlated with RP, GH, VT, SF and MH of the SF-36 (-.546 to .760). Mental fatigue was moderately correlated with the vitality dimension in SF-36 but also to SF and MH (-.466 to -.510) (Table 5). Known-group validity showed that the mean physical fatigue score was 10.74 vs 9.20 (p =0.004), and the mean mental fatigue score was 5.12 vs 4.55 (p =0.036) for those with or without complications/comorbidity. The internal consistency reliability showed a Cronbach’s alpha of 0.90 for TF, 0.89 for PF, and 0.78 for MF. A total of 70.5% (203/288) patients participated in the test-retest. Of these, seven had not reported their health condition and were consequently treated as missing,
80% (156/203) reported an unchanged condition, 10% (n =19) reported improvement, and 11% (n =21) reported deterioration. In the 156 patients reporting unchanged conditions, the ICC for TF, PF and MF between baseline and follow-up was 0.79, 0.73 and 0.76, respectively. Values for those people with a perceived change in health condition are presented in Supplementary table S2.
DISCUSSION In the current study, we identified that fatigue symptoms, including chronic fatigue, were significantly elevated in people with type 1 diabetes, compared to the Norwegian background population. Symptoms of anxiety and depression, as well as sleep problems, were significantly associated with fatigue in these people. In total, 26.4% of people with type 1 diabetes reported chronic fatigue, a figure that is more than doubled compared to the background population [14]. Compared to other chronic diseases, the figure aligns with those of ulcerative colitis and Crohn’s disease but is lower than in patients with cancer, multiple sclerosis and rheumatoid arthritis [20]. Prevalence values of fatigue are dependent on the measurement tools used, as well as the corresponding cut-off values and types of fatigue. Even though prior studies have reported that fatigue affects 23%-42% [10, 21, 22] of people with type 1 diabetes, this finding is hampered by the use of questionnaires not specifically designed to measure fatigue or even not validated for that purpose [9]. Another important factor that may influence prevalence values is the study population at hand. Indeed, authors of prior studies in this field have emphasized that patient inclusion may have been biased due to a selection of patients with more complex disease [10, 21]. In contrast to the consecutive inclusion in the present study, patients in the studies by Goedendorp et.al and Menting et.al [10, 21] were randomly selected out of an outpatient cohort of 831 type 1 diabetes patients.
For total and chronic fatigue, gender, employment status, marital status and smoking were significantly associated with fatigue in the univariate analysis. Physical activity was also significantly associated with total fatigue in the univariate analysis, but none of these associations were present in multivariate analysis. It may look like epidemiological factors in addition to sleep and age have less importance than clinical factors in this population, and this result is found in other studies [10, 21]. In the present study, younger age was only associated with chronic fatigue in the univariate analysis, in contrast to the findings by Goedendorp et.al [10]. Moreover, age is not consistently associated with fatigue in other chronic diseases [9]. Results regarding the association between gender and fatigue have also been different across studies [14]. Segerstedt et.al [23] reported that females had more fatigue than men, except for mental fatigue, but this study has been considered to have medium quality [9] and is in contrast to the present and other studies [10, 21] measuring fatigue and type 1 diabetes. Factors such as vitamin D and iron deficiency have been linked to fatigue in other studies [24, 25]. Vitamin D deficiency causes muscle weakness [26]. In iron deficiency, many symptoms, such as fatigue and exhaustion, are nonspecific [27]. In the present study, vitamin D deficiency was not associated with either total or chronic fatigue. Furthermore, both ferritin and Hb were associated with total fatigue in univariate, but not in multivariate, linear regression analysis. Even though we observed a significant association between HbA1c and fatigue in the univariate analysis, these associations were not present in the multivariate analysis. Both Goedendorp et.al [10] and Menting et.al [21] observed a significant association between HbA1c and fatigue, but these studies have retrieved HbA1c from medical records, while the present study analyzed HbA1c levels at the same time as the questionnaire were completed. The fact that blood tests and fatigue data are collected at the same point of time may reduce the risk of clinically significant changes to occur in patients.
Plasma glucose was also associated with fatigue in the univariate analysis. Goedendorp et.al [10] found a weak relationship between blood glucose and chronic fatigue; however, whether this finding is based on uni- or multivariate analyses is unclear. In contrast to Goedendorp et.al [10], we found that increased disease duration was associated with chronic fatigue in the multivariate logistic regression analysis. In diabetes, the relationship between disease duration and the development of complications are well known [28]. However, diabetes-related complications, such as retinopathy, nephropathy and coronary heart disease, as well as comorbidity, were not found to be associated with total and chronic fatigue in multivariate regression analysis. These factors were also reported by Goedendorp et.al [10], and in addition, they reported that neuropathy was associated with chronic fatigue, while Menting et.al [21] found that patients with numbness in the feet and a higher number of diabetes complications had more severe fatigue over time. The reason for the discrepancy between the current and prior studies may potentially be related to different ways of how data on complications/comorbidity were obtained, as well as differences in the study populations. In both Goedendorp et.al and Menting et.al [10, 21], the diabetes duration of included patients was six and nine years longer than in the present study, respectively. This prolonged disease duration may also contribute to the observed differences. An increased level of white blood cells (WBC) was associated with total fatigue, irrespective of gender. It might of course be speculated that this finding is due to low-grade inflammation, but at the same time, we did not observe a significant association between total fatigue and CRP levels. Menstruation was also significantly associated with increased total fatigue, which is in line with studies finding that such symptoms may occur as part of the menstrual cycle [29, 30]. Both total and chronic fatigue were associated with increased depression scores, which have been observed in prior studies of fatigue [10, 21, 31]. Goedendorp et.al [10] found that
the number of people with type 1 diabetes who reported chronic fatigue was higher in those with clinically relevant depressive symptoms than in those without. Of course, living with depressive symptoms may result in a lack of energy. However, Goedendorp et.al [10] found that 31% reported chronic fatigue without any depressive symptoms. Similar findings were observed in the present study, in which 14.9% of patients reported chronic fatigue without having symptoms of depression (HADS-D <8). Consequently, there does not seem to be a 100% overlap between depressive symptoms and fatigue; rather, both may coexist and be separate entities. It is, however, beyond the scope of this paper to make any conclusions on causality. A high level of anxiety (HADS-A score ≥8) was also associated with a high total fatigue score as well as increased prevalence of chronic fatigue. Anxiety can cause a host of rapid, stressful thoughts, and there is limited research on how anxiety and fatigue are interconnected in people with type 1 diabetes, which of course should be further investigated. However, our findings indicate that it may be important to screen patients for such symptoms in order to provide adequate treatment for fatigue. Sleep problems were statistically significantly associated with total as well as chronic fatigue. This association has also been reported in several other studies [8, 10, 32, 33] but is in contrast to the findings of Menting et.al [21], who found that fewer sleep disturbances were associated with greater fatigue severity. Increased awareness of the contribution of sleep problems to the experience of fatigue is warranted, and future studies should investigate if, e.g., addressing sleep hygiene in clinical follow-up could decrease fatigue symptoms. Correlations between the dimensions of the FQ and SF-36 demonstrated good convergent validity. Moreover, known-group validity demonstrated that the FQ was able to distinguish between patients with and without complications/comorbidity. Internal consistency reliability displayed acceptable to excellent values, and test-retest reliability
revealed moderate to good ICCs (All ICCs > 0.70)[33, 34]. The sample size needed in testretest analysis has been the subject of some debate [35]. In the present study, all patients were invited to participate in the retest, and 2/3 of those included at baseline answered and returned the questionnaires after 4-6 weeks. This test-retest interval was chosen to avoid a potential recall bias [34]. Furthermore, a central aspect of a patient-reported outcome measures is the ability to respond to relevant changes in a particular condition. Our results indicate that the FQ is able to capture these changes, since physical and mental fatigue increased in patients who reported disease deterioration and decreased in patients who reported disease improvement. The number of patients reporting either deterioration or improvement in this study was generally low and should thus be interpreted with caution and investigated prospectively [34]. This study has several strengths, such as a relatively large sample size, the homogenous inclusion, laboratory tests taken the same day as questionnaires were completed, comparison with background population data, and the use of a valid, reliable and responsive measurement tool. However, there are also some limitations. First, the cross-sectional design cannot address any causal mechanisms. The relatively small numbers of people with type 1 diabetes who reported deterioration or improvement at the time of retest may have introduced a type II error. Even though a consecutive recruitment procedure was undertaken, we cannot exclude the risk of a potential recruitment bias. Another limitation is the fact that we do not have any data on those who did not participate in the study. Consequently, it remains unknown if these people differed significantly from those included. Furthermore, we cannot conclude that the population included in this study is entirely representative of the adult Norwegian type 1 diabetes population. While the proportion of women included in our study seem to be somewhat higher than figures reported in the Norwegian Diabetes
Register (NDR), 52.8 % compared to 46-47 %, average age (2017) for both females and males seem to be comparable (female NDR - 44.3 years vs. current study 44.65 years, male NDR - 43.7 years, vs. current study 44.9 years). Moreover, median values of HbA1c in NDR and our study is quite comparable (7.8 %, vs 8.1 %). However, it is important to emphasize that not all hospitals and outpatient clinics report to the NDR, which indeed is the fact for one of the centers in the current study. The background data on fatigue in the Norwegian population was published in 1998 and it might be speculated if the level of fatigue has changed during the last 20 years. Indeed, updated fatigue data from the Norwegian background population has been collected and is under publication planning. However, based on personal communication with dr. Jon Håvard Loge (Principal investigator), the currently unpublished fatigue data seem not to differ significantly between 1998 and 2018.
CONCLUSION Fatigue, both total and chronic, was significantly more common in people with type 1 diabetes than in the background population. Disease duration, borderline anxiety, depression and sleep problems were associated with fatigue, of which the first two factors have not previously been identified in clinical studies. Further studies are needed to address causal relationships. The Fatigue Questionnaire demonstrated satisfactory psychometric properties.
Acknowledgements The authors would like to express their gratitude to all participating patients; the diabetes nurses: Ellen S. Holte, Nina Eikanger and Janne B. Lønne (Telemark Hospital Trust), Anne M. Johansen, Ellen Fjeldstad, Jorun M. Wahlberg, Annfrid Blystad, Peggy M. Karlsen
(Østfold Hospital Trust), Merethe Westberg, and Synnøve Cunningham (Vestfold Hospital Trust); and participating physicians at all three hospitals: Bjarne Mella, Torgunn Huseby, Gunvor Hovland, Synne Frønæs, and Trine T Heggenes (Østfold Hospital Trust).
Funding This study was supported by research grants from the Østfold University College, Telemark, Østfold - and the Vestfold Hospital Trust. The funding sources had no involvement in the study design, analysis and interpretation of the data, or writing the paper.
Author contributions All authors of this paper have made substantial contributions according to the ICMJE. In particular, Øystein Jensen, Tomm Bernklev and Lars-Petter Jelsness-Jørgensen contributed to the study design, data analysis and interpretation of data. Charlotte Gibbs, Ragnar Bekkhus Moe and Dag Hofsø were the local PIs and were responsible for recruitment at the centers. Furthermore, all authors drafted the work, revised it critically for intellectual content, and approved the final version of the manuscript. Declaration of interest None to declare.
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Cappellini, M.D., et al., Iron deficiency across chronic inflammatory conditions: International expert opinion on definition, diagnosis, and management. Am J Hematol, 2017. 92(10): p. 1068-1078. Diabetes, C., I. Complications Trial/Epidemiology of Diabetes, and G. Complications Research, Modern-day clinical course of type 1 diabetes mellitus after 30 years' duration: The diabetes control and complications trial/epidemiology of diabetes interventions and complications and pittsburgh epidemiology of diabetes complications experience (19832005). Archives of Internal Medicine, 2009. 169(14): p. 1307-1316. Sharma, P., et al., Problems related to menstruation amongst adolescent girls. Indian J Pediatr, 2008. 75(2): p. 125-9. Farage, M.A., T.W. Osborn, and A.B. MacLean, Cognitive, sensory, and emotional changes associated with the menstrual cycle: a review. Archives of Gynecology and Obstetrics, 2008. 278(4): p. 299. Visser, M.R. and E.M. Smets, Fatigue, depression and quality of life in cancer patients: how are they related? Support Care Cancer, 1998. 6(2): p. 101-8. Beiske, A.G., et al., Fatigue in Parkinson's disease: prevalence and associated factors. Mov Disord, 2010. 25(14): p. 2456-60. Opheim, R., et al., Fatigue interference with daily living among patients with inflammatory bowel disease. Qual Life Res, 2014. 23(2): p. 707-17. Fayers, P. and D. Machin, Quality of Life The Assessment, Analysis and Interpretation of Patient-reported Outcomes. 2007, Wiley,: Hoboken. p. 1 online resource (568 p.). Jelsness-Jorgensen, L.P., et al., Validity, Reliability, and Responsiveness of the Brief Pain Inventory in Inflammatory Bowel Disease. Can J Gastroenterol Hepatol, 2016. 2016: p. 5624261.
Table 1. Socio-demographic and clinical data of included patients
Age - mean (SD) years Age range (min-max) Civil status (missing, n=1) Single Married/cohabitant Divorced/separated Widow/widower Educational level Lower education (SD) Higher education (SD) Employment (missing, n=1) Working Not working Smoking (yes) (missing, n=2) Diabetes duration - mean (SD) BMI - mean (SD) Diabetes complications (in numbers) Neuropathy Retinopathy Nephropathy Comorbidity mr_and sr_ yes (in number) Diabetic foot ulcer: yes/no Hb (ref: 11.5-15.0) - mean (SD) (missing, n=2) WBC (ref: 3.5-11.0) - mean (SD) (missing, n=3) CRP (ref: 0-10) - mean (SD) (missing, n=7) Serum ferritin (ref: 10-200) - mean (SD) (missing, n=9) Plasma glucose (ref: 4.0-6.3)* - mean (SD) (missing, n=2) HbA1c (ref: 4.3-6.1) - mean (%) (missing, n=2) mmol/mol (SD)
Female (52.8%) (n = 152) 44.65 (13.34) 18-76
Male (47.2%) (n =136) 44.95 (13.38) 18-80
25 121 4 2
13 110 11 1
78 (27.1) 74 (25.7)
79 (27.4) 57 (19.8)
0.51
108 44 35 23.41 (13.3) 26.50 (4.70)
105 30 33 22.46 (13.2) 26.83 (4.38)
0.22 0.78 0.544 0.556
18 40 10 106 5/148 13.32 (1.09) 6.68 (2.03) 5.21 (5.65) 66.57 (56.41)
20 45 10 71 6/129 15.00 (1.05) 6.31 (1.80) 3.40 (2.14) 133.94 (75.06)
0.475 0.210 0.797 0.002 0.760 0.000 0.109 0.001 0.000
10.06 (4.56)
0.435
9.63 (4.84)
p value
0.85
0.73
8.2 % 8.1 % 0.602 66 mmol/mol 65 mmol/mol (13.1) (13.9) 25 OH Vit-D (ref: >50) (SD) (missing, n=16) 66.55 (20.55) 62.46 (20.27) 0.101 Table legends and abbreviations: SD, standard deviation; lower education, =< university/college; higher education, college or university; mr, medical records; sr, self-reported; Diabetic foot ulcer measured by Wagner classification; Reference values appear in parentheses following blood tests; WBC, white blood cells. *Not fasting
Table 2. Fatigue Questionnaire dimensions: Physical Fatigue, Mental Fatigue, Total Fatigue, and Chronic Fatigue scores among patients with type 1 diabetes compared to the
Norwegian background population. Fatigue Questionnaire Type 1 Diabetes dimensions (n = 288)
Background population (n = 2287)
p-value
Unadjusted Adjusted* Physical Fatigue – mean (SD) 10.34 (3.95) 10.33 (3.84) 7.9 (3.1) p <0.001 Mental Fatigue – mean (SD) 4.97 (2.04) 4.98 (2.03) 4.3 (1.4) p <0.001 Total Fatigue – mean (SD) 15.31 (5.51) 15.31 (5.39) 12.2 (4.0) p <0.001 Chronic Fatigue 76/288 (26.4%) 260/2287 (11%) p <0.001 Table legends and abbreviations: SD, Standard deviations; *Values adjusted for gender and education; CF is reported in total numbers of included patients and percent.
Table 3. Univariate and Multivariate Linear Regression Analysis of Factors associated with Total Fatigue scores
Univariate Factor
β
p
β
Multivariate p
Adj r2
Thrombocytes 0.116 0.051 Ferritin -0.111 0.064 Hb -0.089 0.133 ASAT -0.079 0.184 Comorbidity_any 0.195 0.001 Gender -0.172 0.003 Activity (ref no) -0.167 0.005 Not in relationship (ref in) 0.082 0.165 Unemployed (ref employed) 0.109 0.065 Smoking (ref no) 0.094 0.115 Nephropathy 0.106 0.072 Coronary Heart Disease 0.103 0.081 Hypothyreosis 0.108 0.068 Graves’ disease 0.090 0.126 Insulin pump (ref no) 0.049 0.094 Menstruation 0.106 0.192 0.161 0.015 .384 WBC 0.161 0.006 0.190 0.004 Sleep problems 0.317 0.000 0.199 0.004 HADS-A ≥ 8 0.422 0.000 0.319 <0.001 HADS-D ≥ 8 0.452 0.000 0.278 <0.001 Table legends and abbreviations: ASAT, aspartate amino transferase; WBC, white blood cells; HADS, Hospital Anxiety and Depression Scale; HADS-A, HADS-Anxiety; HADS-D, HADSDepression.
Table 4. Univariate and Multivariate logistic regression analysis of factors associated with Chronic Fatigue Univariate Multivariate
FACTORS OR 95% CI p-value OR 95% CI p-value HbA1c 1.23 0.95-1.60 0.12 BMI 1.05 1.00-1.11 0.07 Unemployed (ref employed) 1.45 0.83-2.54 0.19 Retinopathy 1.59 0.91-2.77 0.11 Age < 40 (ref older) 2.05 1.11-3.86 0.03 Not in relationship (ref in) 1.57 0.84-2.94 0.16 Gender (female) 1.42 0.84-2.42 0.19 Thrombocytes 1.00 1.000-1.008 0.04 Coronary heart disease 2.24 0.90-5.55 0.08 Sleeping problems (ref no) 4.46 2.56-7.76 0.00 3.47 1.84- 6.54 <0.001 Time since diagnosis 1.02 1.00-1.04 0.02 1.04 1.01- 1.06 0.003 HADS-A ≥ 8 4.93 2.80-8.70 <0.001 2.76 1.42- 5.37 0.003 HADS-D ≥ 8 7.07 3.60-13.85 <0.001 4.78 2.21-10.34 <0.001 Table legends and abbreviations: HbA1c, Haemoglobin A1c, BMI, Body Mass Index; HADS-A; Hospital Anxiety and Depression Scale., HADS-D; Hospital Anxiety and Depression Scale-Depression.
Table 5. Correlation (Spearman’s rho) between the Fatigue Questionnaire and the Short Form- 36 dimensions. SF-36 dimensions Fatigue Questionnaire dimensions
Physical Fatigue Mental Fatigue Physical Functioning -,478 -,320 Role Physical -,341 -,564 Bodily Pain -,380 -,296 General Health -,413 -,588 Vitality -,760 -,510 Social Functioning -,467 -,639 Role Emotional -,429 -,336 Mental Health -,466 -,546 Table legends and abbreviations: All values are significant with a <0.001. Scores in the range 0.50-0.80 are in boldface.
Total Fatigue -,461 -,541 -,374 -,582 -,756 -,641 -,429 -,576 p-value
Highlights
Fatigue was significantly more common in type 1 diabetes mellitus than in the background population. Out of the several clinical and socio-demographic factors investigated, increased levels of anxiety, depression and sleep problems displayed the highest association with fatigue. The Fatigue Questionnaire is a valid and reliable tool for the measurement of fatigue in type 1 diabetes mellitus.
S0168-8227(18)30004-4 https://doi.org/10.1016/j.diabres.2018.06.012 DIAB 7423
To appear in:
Diabetes Research and Clinical Practice
Received Date: Revised Date: Accepted Date:
3 January 2018 29 May 2018 13 June 2018
Please cite this article as: O. Jensen, T. Bernklev, C. Gibbs, R. Bekkhus Moe, D. Hofsø, L-P. Jelsness-Jørgensen, Fatigue in type 1 diabetes, prevalence, predictors and comparison with the background population, Diabetes Research and Clinical Practice (2018), doi: https://doi.org/10.1016/j.diabres.2018.06.012
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Fatigue in type 1 diabetes, prevalence, predictors and comparison with the background population.
Øystein Jensena,b, Tomm Bernklevc,b, Charlotte Gibbsd, Ragnar Bekkhus Moee, Dag Hofsøf Lars-Petter Jelsness-Jørgensena,e a
Østfold University College, Dept of Health and Welfare, Pb 770, 1757 Halden, Norway,
b
Institute of Clinical Medicine, University of Oslo, Klaus Torgårds vei 3, 0372 Oslo,
Norway, c Vestfold Hospital Trust, Research and Development, Halfdan Wilhelmsens alle 17, 3116 Tønsberg, d Telemark Hospital Trust, Dept of Endocrinology, Ulefossvegen 55, 3710 Skien, Norway, e Østfold Hospital Trust, Dept of Internal Medicine, Kalnesveien 300, 1714 Grålum, Norway, f Vestfold Hospital Trust, Morbid Obesity Centre and Section of Endocrinology, Halfdan Wilhelmsens alle 17, 3116 Tønsberg. Corresponding author: Lars-Petter Jelsness-Jørgensen, Østfold University College, Faculty of Health and Social Studies Tel.: +47-69608836. E-mail address: [email protected]. E-mail addresses: [email protected] (Ø.Jensen), [email protected] (T.Bernklev), [email protected] (C. Gibbs), [email protected] (R. Moe), [email protected] (D.Hofsø), [email protected] (LP. Jelsness-Jørgensen). Funding sources: This study was supported by unrestricted grants from Østfold University College, Telemark, Østfold and Vestfold Hospital Trust.
Declarations of interest: None
ABSTRACT Aim: Fatigue is scarcely studied in type 1 diabetes (T1D), and the aims were to investigate its prevalence compared to the background population, potential associations, and to validate the Fatigue Questionnaire (FQ) in type 1 diabetes. Methods: Persons with T1D were recruited from three outpatient clinics in Norway. Fatigue was measured using the FQ, and FQ data from the Norwegian background population were used for comparison. Socio-demographic and clinical variables were obtained by self-report, clinical investigation, medical records and laboratory tests. Results: Of 332 eligible patients, 288 (87%) were included. Mean age was 44.65/44.95 years (SD 13.34/13.38) for females/males, respectively. Total fatigue (TF) was 15.31 (SD 5.51) compared to 12.2 (SD 4.0) in the background population (p <0.001). HADS ≥8, current menstruation, increased leukocytes and sleep problems were associated with increased TF. Chronic fatigue (CF) was reported in 26.4% compared to 11% in the background population (p <0.001). HADS ≥8, increased time since diagnosis and decreased sleep quality were associated with CF. The validity, internal consistency and repeatability of the FQ was confirmed.
Conclusions: Fatigue was more common in T1D than in the background population, and associated with increased anxiety, depression and sleep problems. The FQ demonstrated satisfactory psychometric properties. Keywords: Chronic fatigue, Fatigue, Total fatigue, Type 1 diabetes
INTRODUCTION Type 1 diabetes (T1D) is characterized by a selective destruction of pancreatic beta cells that usually leads to absolute insulin deficiency, with a peak incidence in childhood and adulthood [1, 2]. T1D may affect people with diabetes negatively, particularly due to the risk of developing long-term side effects such as retinopathy, neuropathy, nephropathy and macrovascular complications [2]. Several studies have also shown that T1D is associated with impaired health-related quality of life (HRQoL) and increased symptom burden, e.g., higher levels of depression and anxiety compared to the background population [3, 4]. Fatigue is associated with a wide variety of medical conditions [5]. The perception of fatigue is subjective and may be defined as “an overwhelming, debilitating, and sustained sense of exhaustion that decreases one's ability to carry out daily activities, including the ability to work effectively and to function at one's usual level in family or social roles” [6, 7]. Fatigue can affect almost all people throughout life, but in most cases, the condition is transient and relieved by rest [8]. In some cases, however, symptoms persist for a prolonged period. Although people with T1D frequently report fatigue in daily follow-up, clinical studies are either lacking or of low quality [9]. In a systematic review of the literature on fatigue in
T1D, Jensen et al. [9] were only able to identify one study with high quality [10]. However, even in that study, several methodological limitations were identified. For instance, data on HbA1c levels were collected through medical records. Therefore, these data and data on fatigue may have been collected too far apart in time, allowing clinically significant changes to occur. Moreover, data on blood glucose levels were only investigated in a subset of 68 patients. A vast majority of the studies reviewed by Jensen et al. [9] had not included clinical and socio-demographic data, which makes it impossible to draw any conclusions on potential associations between fatigue, clinical and socio-demographic data in T1D. Such information could broaden our knowledge and understanding of fatigue symptoms in these people with diabetes. Furthermore, factors identified from studies on fatigue in other populations, such as, e.g., vitamin D, ferritin or C-reactive protein levels (CRP), have not yet been studied in T1D. The primary aim of this study was to determine the prevalence of fatigue in T1D compared to the background population. The secondary aims were to identify socio-demographic and clinical factors that may influence fatigue, as well as to investigate the validity, reliability and sensitivity to change of the Fatigue Questionnaire (FQ) in T1D. MATERIALS AND METHODS Patients In this cross-sectional, descriptive study, people diagnosed with T1D who were 18 years or older were consecutively included during routine follow-up at three diabetes outpatient clinics in the southeastern part of Norway from May 2015 to November 2016. Patients were excluded if they were unable to provide written informed consent.
Ethics The study was performed in accordance with the principles of the Helsinki Declaration and approved by the Regional Committee for medical and health research ethics (ref.nr. 2012/845). All patients were informed about the study orally, and written informed consent was obtained from all patients before inclusion. At each center, a senior endocrinologist was responsible for the study (local PI). Data collection A standardized inclusion procedure was followed at each center. This procedure included baseline collection of socio-demographic, clinical, laboratory and patient reported outcome data. Moreover, this procedure enabled patients to fill out the questionnaires in peace and quiet at the hospital outpatient clinic. Clinical data were collected during physical examination, from medical records and laboratory tests. Diabetes complications were defined as having one or more of either nephropathy, retinopathy or neuropathy. Nephropathy was defined based on the albumin/kreatinin-ratio (U-AKR) > 30 mg/mmol. The presence of retinopathy had to be verified by a specialist in ophthalmology and includes non‐ proliferative, proliferative and maculopathy. Neuropathy was defined as sensory loss by use of 10g monofilament on the great toe and on the plantar aspect of the first, second and third metatarsal of each lower extremity, in accordance with the recommendations of the Norwegian Diabetes Guidelines. Comorbidity was assessed using the Nord-Trøndelag Health Study (HUNT) comorbidity questionnaire and collected based on medical records and patient selfreport. Female patients also self-reported menstruation status at time of baseline assessment. Foot ulcers were classified according to the Wagner classification [11].
In addition to clinical data, self-reported data on civil status, educational level, physical activity, smoking habits and work status were collected. Work was dichotomized into either working (including full-time employee, apprentice/ trainee, part-time employee, partially disabled, partial sick leave or student) or not working (including full disability, home resident, full time sick leave full time or retired). Information regarding fatigue was collected with the Fatigue Questionnaire (FQ) [12]. To investigate the test-retest reliability of the FQ, all patients were invited to fill out the FQ questionnaire a second time, 4-6 weeks following baseline assessments. In addition, patients self-reported their perceived disease state using a question with three potential answers: “Compared to last time you completed the questionnaire, how do you evaluate your condition today? (i) unchanged (ii) improved, or (iii) deteriorated”. An invitation to participate in the retest was sent by mail, and patients were asked to complete the questionnaires and return them in a pre-stamped envelope. In addition, sleep problems were measured using question six in the Basic Nordic Sleep Questionnaire (BNSQ) [13]. This question measures how well the patient has slept during the past three months. The BNSQ is scored on a five-point Likert scale, on which a higher score indicates poor sleep quality. In the current study, the item on sleep quality from the BNSQ was dichotomized into “no sleep problems” (scores <4) or “sleep problems” (scores ≥4). The Fatigue Questionnaire (FQ) The FQ was originally developed by Chalder et al. [12] and consists of 11 questions that can be divided into two dimensions: physical fatigue (PF; 7 items) and mental fatigue (MF; 4 items). Four response options are used (0= better than usual, 1= no more than usual, 2= worse than usual, and 3= much worse than usual). Higher scores imply higher levels of fatigue.
Combining the scores of PF and MF produces a score for total fatigue (TF), with a maximum score of 33. Two additional questions cover the duration and extent of fatigue [12]. The FQ scores can be dichotomized, where original scores of 0 or 1 are scored 0 and original scores of 2 and 3 are scored 1. Chronic fatigue (CF) is defined as a dichotomized score ≥4 and a duration ≥6 months [12, 14]. The FQ has been translated into Norwegian and validated [14], and studies have demonstrated that the questionnaire has stable psychometric properties across populations [8]. The Norwegian version of the FQ has not been validated in patients with type 1 diabetes. The FQ was chosen over other fatigue questionnaires since it is designed to measure the presence of chronic fatigue. Moreover, background population FQ data was available for comparison. Hospital Anxiety and Depression Scale Anxiety and depression were measured using the Hospital Anxiety and Depression Scale (HADS) [15]. The HADS is a self-assessment instrument that is divided into two scales, measuring symptoms of anxiety (A) and depression (D). Each scale has seven items that are scored on a four-point Likert scale (0-3). Higher scores imply many symptoms. In accordance with recommendations in the literature, a cut-off value of HADS-A or HADS-D ≥ 8 was used to identify borderline cases of anxiety and depression [16, 17]. Short Form 36 (SF-36) The SF-36 is a generic HRQOL questionnaire designed to assess functional status, well-being, and general perception of health. The questionnaire consists of 36 items transformed into 8 dimensions: physical functioning (10 items), bodily pain (2 items), vitality or energy level (4 items), social functioning (2 items), mental health (5 items), general health perceptions (5 items), role limitation due to physical problems (4 items), and role limitations due to personal or emotional problems (3 items). An
additional item (HT) reports health transitions over the past year. The questionnaire has been translated into Norwegian and validated [18]. The SF-36 has previously been validated among people with diabetes [19]. Fatigue data from the Norwegian background population Fatigue data from the Norwegian background population were collected by Loge et al. [14]. A random sample of 3500 Norwegians aged 19-80 years, drawn from the National Register by the Norwegian Government Computer Center and representative of the entire Norwegian population, was investigated. After two reminders, 2287 questionnaires were eligible for evaluation. Statistical Analysis To assess the characteristics of the sample, we used descriptive analysis and frequencies. Independent samples t-tests and chi-square statistics were used to examine the differences in fatigue distribution and socio-demographic and clinical variables between the background population and patients with type 1 diabetes. To assess possible associations between clinical and socio-demographic data and fatigue scores, t-tests for binary variables and chi-square and bivariate correlation analyses were used. Variables with a significance level of less than 0.2 in univariate analysis were included in the multivariate regression analyses. For total fatigue (TF), linear regression analysis was used, while logistic regression was used for chronic fatigue (CF).
Convergent validity was calculated using binary correlation analysis (Spearman’s rho) between the FQ and the SF-36 dimensions. In general, we hypothesized that increased fatigue would correlate negatively with all dimensions of the SF-36. Based on the semantic construct, we furthermore hypothesized that PF would correlate most strongly with the SF-36
dimensions vitality (VT), general health (GH), role physical (RP) and social functioning (SF), while MF would correlate most strongly with vitality and mental health (MH). Known-group validity was tested by comparing fatigue scores in patients with or without diabetes-related complications and comorbidity. Internal consistency reliability was determined by Cronbach’s alpha, while test-retest reliability was measured using the intraclass correlation coefficient (ICC) among patients who reported an unchanged condition from baseline to retest. Sensitivity to change was investigated comparing fatigue scores (paired t-tests) in patients who reported either symptom deterioration or improvement from baseline to retest. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS version 23, IBM Corporation, Somers New York, USA).
RESULTS Out of 332 patients eligible for inclusion, 288 (87%) gave informed consent and participated. Socio-demographic and clinical data are presented in Table 1. A total of 74 patients (25.8 %) were registered as not working, divided into 35 (47.3 %) on full disability pension, four home residents (5.4 %), 14 (18.9 %) on fulltime sick leave and 21 (28.4 %) retirees.
Fatigue in type 1 diabetes compared to the background population Compared to the background population, people with T1D had a statistically significantly higher prevalence of CF as well as dimensional (PF/MF) and TF scores. CF was equally distributed between genders: 29.6% in females and 22.8% in males (p =0.228). Differences in FQ scores are presented in Table 2.
Associations between total fatigue, socio-demographic and clinical factors Several factors were statistically significantly associated with total fatigue in univariate, but not multivariate, analysis (Table 3). Vitamin D deficiency (<50 nmol/L), thyroid-stimulating hormone (TSH), CRP, glucose and HbA1c were not associated with TF.
Associations between Chronic fatigue and clinical factors Even though several variables were associated with chronic fatigue in the univariate analysis, merely increased time since diagnosis, sleep problems, HADS-A ≥8 and HADS-D ≥8, remained significant in the multivariate regression analysis (Table 4). When stratifying for borderline anxiety or depression, 43 patients (15%) with HADS-A and 29 patients (10%) with HADS-D with scores of eight or above reported CF, while 32 patients (11%) with HADS-A and 43 patients (15%) with HADS-D below 8 reported CF. In those with a combined HADSD and HADS-A below 8, a total of 21 patients (7.3%) reported CF (Supplementary table S1).
Validity, reliability and sensitivity to change of the FQ Calculation of convergent validity through correlations between the FQ dimensions and the SF-36 dimensions revealed negative correlations with all SF-36 dimensions. Physical fatigue was moderately to highly correlated with RP, GH, VT, SF and MH of the SF-36 (-.546 to .760). Mental fatigue was moderately correlated with the vitality dimension in SF-36 but also to SF and MH (-.466 to -.510) (Table 5). Known-group validity showed that the mean physical fatigue score was 10.74 vs 9.20 (p =0.004), and the mean mental fatigue score was 5.12 vs 4.55 (p =0.036) for those with or without complications/comorbidity. The internal consistency reliability showed a Cronbach’s alpha of 0.90 for TF, 0.89 for PF, and 0.78 for MF. A total of 70.5% (203/288) patients participated in the test-retest. Of these, seven had not reported their health condition and were consequently treated as missing,
80% (156/203) reported an unchanged condition, 10% (n =19) reported improvement, and 11% (n =21) reported deterioration. In the 156 patients reporting unchanged conditions, the ICC for TF, PF and MF between baseline and follow-up was 0.79, 0.73 and 0.76, respectively. Values for those people with a perceived change in health condition are presented in Supplementary table S2.
DISCUSSION In the current study, we identified that fatigue symptoms, including chronic fatigue, were significantly elevated in people with type 1 diabetes, compared to the Norwegian background population. Symptoms of anxiety and depression, as well as sleep problems, were significantly associated with fatigue in these people. In total, 26.4% of people with type 1 diabetes reported chronic fatigue, a figure that is more than doubled compared to the background population [14]. Compared to other chronic diseases, the figure aligns with those of ulcerative colitis and Crohn’s disease but is lower than in patients with cancer, multiple sclerosis and rheumatoid arthritis [20]. Prevalence values of fatigue are dependent on the measurement tools used, as well as the corresponding cut-off values and types of fatigue. Even though prior studies have reported that fatigue affects 23%-42% [10, 21, 22] of people with type 1 diabetes, this finding is hampered by the use of questionnaires not specifically designed to measure fatigue or even not validated for that purpose [9]. Another important factor that may influence prevalence values is the study population at hand. Indeed, authors of prior studies in this field have emphasized that patient inclusion may have been biased due to a selection of patients with more complex disease [10, 21]. In contrast to the consecutive inclusion in the present study, patients in the studies by Goedendorp et.al and Menting et.al [10, 21] were randomly selected out of an outpatient cohort of 831 type 1 diabetes patients.
For total and chronic fatigue, gender, employment status, marital status and smoking were significantly associated with fatigue in the univariate analysis. Physical activity was also significantly associated with total fatigue in the univariate analysis, but none of these associations were present in multivariate analysis. It may look like epidemiological factors in addition to sleep and age have less importance than clinical factors in this population, and this result is found in other studies [10, 21]. In the present study, younger age was only associated with chronic fatigue in the univariate analysis, in contrast to the findings by Goedendorp et.al [10]. Moreover, age is not consistently associated with fatigue in other chronic diseases [9]. Results regarding the association between gender and fatigue have also been different across studies [14]. Segerstedt et.al [23] reported that females had more fatigue than men, except for mental fatigue, but this study has been considered to have medium quality [9] and is in contrast to the present and other studies [10, 21] measuring fatigue and type 1 diabetes. Factors such as vitamin D and iron deficiency have been linked to fatigue in other studies [24, 25]. Vitamin D deficiency causes muscle weakness [26]. In iron deficiency, many symptoms, such as fatigue and exhaustion, are nonspecific [27]. In the present study, vitamin D deficiency was not associated with either total or chronic fatigue. Furthermore, both ferritin and Hb were associated with total fatigue in univariate, but not in multivariate, linear regression analysis. Even though we observed a significant association between HbA1c and fatigue in the univariate analysis, these associations were not present in the multivariate analysis. Both Goedendorp et.al [10] and Menting et.al [21] observed a significant association between HbA1c and fatigue, but these studies have retrieved HbA1c from medical records, while the present study analyzed HbA1c levels at the same time as the questionnaire were completed. The fact that blood tests and fatigue data are collected at the same point of time may reduce the risk of clinically significant changes to occur in patients.
Plasma glucose was also associated with fatigue in the univariate analysis. Goedendorp et.al [10] found a weak relationship between blood glucose and chronic fatigue; however, whether this finding is based on uni- or multivariate analyses is unclear. In contrast to Goedendorp et.al [10], we found that increased disease duration was associated with chronic fatigue in the multivariate logistic regression analysis. In diabetes, the relationship between disease duration and the development of complications are well known [28]. However, diabetes-related complications, such as retinopathy, nephropathy and coronary heart disease, as well as comorbidity, were not found to be associated with total and chronic fatigue in multivariate regression analysis. These factors were also reported by Goedendorp et.al [10], and in addition, they reported that neuropathy was associated with chronic fatigue, while Menting et.al [21] found that patients with numbness in the feet and a higher number of diabetes complications had more severe fatigue over time. The reason for the discrepancy between the current and prior studies may potentially be related to different ways of how data on complications/comorbidity were obtained, as well as differences in the study populations. In both Goedendorp et.al and Menting et.al [10, 21], the diabetes duration of included patients was six and nine years longer than in the present study, respectively. This prolonged disease duration may also contribute to the observed differences. An increased level of white blood cells (WBC) was associated with total fatigue, irrespective of gender. It might of course be speculated that this finding is due to low-grade inflammation, but at the same time, we did not observe a significant association between total fatigue and CRP levels. Menstruation was also significantly associated with increased total fatigue, which is in line with studies finding that such symptoms may occur as part of the menstrual cycle [29, 30]. Both total and chronic fatigue were associated with increased depression scores, which have been observed in prior studies of fatigue [10, 21, 31]. Goedendorp et.al [10] found that
the number of people with type 1 diabetes who reported chronic fatigue was higher in those with clinically relevant depressive symptoms than in those without. Of course, living with depressive symptoms may result in a lack of energy. However, Goedendorp et.al [10] found that 31% reported chronic fatigue without any depressive symptoms. Similar findings were observed in the present study, in which 14.9% of patients reported chronic fatigue without having symptoms of depression (HADS-D <8). Consequently, there does not seem to be a 100% overlap between depressive symptoms and fatigue; rather, both may coexist and be separate entities. It is, however, beyond the scope of this paper to make any conclusions on causality. A high level of anxiety (HADS-A score ≥8) was also associated with a high total fatigue score as well as increased prevalence of chronic fatigue. Anxiety can cause a host of rapid, stressful thoughts, and there is limited research on how anxiety and fatigue are interconnected in people with type 1 diabetes, which of course should be further investigated. However, our findings indicate that it may be important to screen patients for such symptoms in order to provide adequate treatment for fatigue. Sleep problems were statistically significantly associated with total as well as chronic fatigue. This association has also been reported in several other studies [8, 10, 32, 33] but is in contrast to the findings of Menting et.al [21], who found that fewer sleep disturbances were associated with greater fatigue severity. Increased awareness of the contribution of sleep problems to the experience of fatigue is warranted, and future studies should investigate if, e.g., addressing sleep hygiene in clinical follow-up could decrease fatigue symptoms. Correlations between the dimensions of the FQ and SF-36 demonstrated good convergent validity. Moreover, known-group validity demonstrated that the FQ was able to distinguish between patients with and without complications/comorbidity. Internal consistency reliability displayed acceptable to excellent values, and test-retest reliability
revealed moderate to good ICCs (All ICCs > 0.70)[33, 34]. The sample size needed in testretest analysis has been the subject of some debate [35]. In the present study, all patients were invited to participate in the retest, and 2/3 of those included at baseline answered and returned the questionnaires after 4-6 weeks. This test-retest interval was chosen to avoid a potential recall bias [34]. Furthermore, a central aspect of a patient-reported outcome measures is the ability to respond to relevant changes in a particular condition. Our results indicate that the FQ is able to capture these changes, since physical and mental fatigue increased in patients who reported disease deterioration and decreased in patients who reported disease improvement. The number of patients reporting either deterioration or improvement in this study was generally low and should thus be interpreted with caution and investigated prospectively [34]. This study has several strengths, such as a relatively large sample size, the homogenous inclusion, laboratory tests taken the same day as questionnaires were completed, comparison with background population data, and the use of a valid, reliable and responsive measurement tool. However, there are also some limitations. First, the cross-sectional design cannot address any causal mechanisms. The relatively small numbers of people with type 1 diabetes who reported deterioration or improvement at the time of retest may have introduced a type II error. Even though a consecutive recruitment procedure was undertaken, we cannot exclude the risk of a potential recruitment bias. Another limitation is the fact that we do not have any data on those who did not participate in the study. Consequently, it remains unknown if these people differed significantly from those included. Furthermore, we cannot conclude that the population included in this study is entirely representative of the adult Norwegian type 1 diabetes population. While the proportion of women included in our study seem to be somewhat higher than figures reported in the Norwegian Diabetes
Register (NDR), 52.8 % compared to 46-47 %, average age (2017) for both females and males seem to be comparable (female NDR - 44.3 years vs. current study 44.65 years, male NDR - 43.7 years, vs. current study 44.9 years). Moreover, median values of HbA1c in NDR and our study is quite comparable (7.8 %, vs 8.1 %). However, it is important to emphasize that not all hospitals and outpatient clinics report to the NDR, which indeed is the fact for one of the centers in the current study. The background data on fatigue in the Norwegian population was published in 1998 and it might be speculated if the level of fatigue has changed during the last 20 years. Indeed, updated fatigue data from the Norwegian background population has been collected and is under publication planning. However, based on personal communication with dr. Jon Håvard Loge (Principal investigator), the currently unpublished fatigue data seem not to differ significantly between 1998 and 2018.
CONCLUSION Fatigue, both total and chronic, was significantly more common in people with type 1 diabetes than in the background population. Disease duration, borderline anxiety, depression and sleep problems were associated with fatigue, of which the first two factors have not previously been identified in clinical studies. Further studies are needed to address causal relationships. The Fatigue Questionnaire demonstrated satisfactory psychometric properties.
Acknowledgements The authors would like to express their gratitude to all participating patients; the diabetes nurses: Ellen S. Holte, Nina Eikanger and Janne B. Lønne (Telemark Hospital Trust), Anne M. Johansen, Ellen Fjeldstad, Jorun M. Wahlberg, Annfrid Blystad, Peggy M. Karlsen
(Østfold Hospital Trust), Merethe Westberg, and Synnøve Cunningham (Vestfold Hospital Trust); and participating physicians at all three hospitals: Bjarne Mella, Torgunn Huseby, Gunvor Hovland, Synne Frønæs, and Trine T Heggenes (Østfold Hospital Trust).
Funding This study was supported by research grants from the Østfold University College, Telemark, Østfold - and the Vestfold Hospital Trust. The funding sources had no involvement in the study design, analysis and interpretation of the data, or writing the paper.
Author contributions All authors of this paper have made substantial contributions according to the ICMJE. In particular, Øystein Jensen, Tomm Bernklev and Lars-Petter Jelsness-Jørgensen contributed to the study design, data analysis and interpretation of data. Charlotte Gibbs, Ragnar Bekkhus Moe and Dag Hofsø were the local PIs and were responsible for recruitment at the centers. Furthermore, all authors drafted the work, revised it critically for intellectual content, and approved the final version of the manuscript. Declaration of interest None to declare.
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Table 1. Socio-demographic and clinical data of included patients
Age - mean (SD) years Age range (min-max) Civil status (missing, n=1) Single Married/cohabitant Divorced/separated Widow/widower Educational level Lower education (SD) Higher education (SD) Employment (missing, n=1) Working Not working Smoking (yes) (missing, n=2) Diabetes duration - mean (SD) BMI - mean (SD) Diabetes complications (in numbers) Neuropathy Retinopathy Nephropathy Comorbidity mr_and sr_ yes (in number) Diabetic foot ulcer: yes/no Hb (ref: 11.5-15.0) - mean (SD) (missing, n=2) WBC (ref: 3.5-11.0) - mean (SD) (missing, n=3) CRP (ref: 0-10) - mean (SD) (missing, n=7) Serum ferritin (ref: 10-200) - mean (SD) (missing, n=9) Plasma glucose (ref: 4.0-6.3)* - mean (SD) (missing, n=2) HbA1c (ref: 4.3-6.1) - mean (%) (missing, n=2) mmol/mol (SD)
Female (52.8%) (n = 152) 44.65 (13.34) 18-76
Male (47.2%) (n =136) 44.95 (13.38) 18-80
25 121 4 2
13 110 11 1
78 (27.1) 74 (25.7)
79 (27.4) 57 (19.8)
0.51
108 44 35 23.41 (13.3) 26.50 (4.70)
105 30 33 22.46 (13.2) 26.83 (4.38)
0.22 0.78 0.544 0.556
18 40 10 106 5/148 13.32 (1.09) 6.68 (2.03) 5.21 (5.65) 66.57 (56.41)
20 45 10 71 6/129 15.00 (1.05) 6.31 (1.80) 3.40 (2.14) 133.94 (75.06)
0.475 0.210 0.797 0.002 0.760 0.000 0.109 0.001 0.000
10.06 (4.56)
0.435
9.63 (4.84)
p value
0.85
0.73
8.2 % 8.1 % 0.602 66 mmol/mol 65 mmol/mol (13.1) (13.9) 25 OH Vit-D (ref: >50) (SD) (missing, n=16) 66.55 (20.55) 62.46 (20.27) 0.101 Table legends and abbreviations: SD, standard deviation; lower education, =< university/college; higher education, college or university; mr, medical records; sr, self-reported; Diabetic foot ulcer measured by Wagner classification; Reference values appear in parentheses following blood tests; WBC, white blood cells. *Not fasting
Table 2. Fatigue Questionnaire dimensions: Physical Fatigue, Mental Fatigue, Total Fatigue, and Chronic Fatigue scores among patients with type 1 diabetes compared to the
Norwegian background population. Fatigue Questionnaire Type 1 Diabetes dimensions (n = 288)
Background population (n = 2287)
p-value
Unadjusted Adjusted* Physical Fatigue – mean (SD) 10.34 (3.95) 10.33 (3.84) 7.9 (3.1) p <0.001 Mental Fatigue – mean (SD) 4.97 (2.04) 4.98 (2.03) 4.3 (1.4) p <0.001 Total Fatigue – mean (SD) 15.31 (5.51) 15.31 (5.39) 12.2 (4.0) p <0.001 Chronic Fatigue 76/288 (26.4%) 260/2287 (11%) p <0.001 Table legends and abbreviations: SD, Standard deviations; *Values adjusted for gender and education; CF is reported in total numbers of included patients and percent.
Table 3. Univariate and Multivariate Linear Regression Analysis of Factors associated with Total Fatigue scores
Univariate Factor
β
p
β
Multivariate p
Adj r2
Thrombocytes 0.116 0.051 Ferritin -0.111 0.064 Hb -0.089 0.133 ASAT -0.079 0.184 Comorbidity_any 0.195 0.001 Gender -0.172 0.003 Activity (ref no) -0.167 0.005 Not in relationship (ref in) 0.082 0.165 Unemployed (ref employed) 0.109 0.065 Smoking (ref no) 0.094 0.115 Nephropathy 0.106 0.072 Coronary Heart Disease 0.103 0.081 Hypothyreosis 0.108 0.068 Graves’ disease 0.090 0.126 Insulin pump (ref no) 0.049 0.094 Menstruation 0.106 0.192 0.161 0.015 .384 WBC 0.161 0.006 0.190 0.004 Sleep problems 0.317 0.000 0.199 0.004 HADS-A ≥ 8 0.422 0.000 0.319 <0.001 HADS-D ≥ 8 0.452 0.000 0.278 <0.001 Table legends and abbreviations: ASAT, aspartate amino transferase; WBC, white blood cells; HADS, Hospital Anxiety and Depression Scale; HADS-A, HADS-Anxiety; HADS-D, HADSDepression.
Table 4. Univariate and Multivariate logistic regression analysis of factors associated with Chronic Fatigue Univariate Multivariate
FACTORS OR 95% CI p-value OR 95% CI p-value HbA1c 1.23 0.95-1.60 0.12 BMI 1.05 1.00-1.11 0.07 Unemployed (ref employed) 1.45 0.83-2.54 0.19 Retinopathy 1.59 0.91-2.77 0.11 Age < 40 (ref older) 2.05 1.11-3.86 0.03 Not in relationship (ref in) 1.57 0.84-2.94 0.16 Gender (female) 1.42 0.84-2.42 0.19 Thrombocytes 1.00 1.000-1.008 0.04 Coronary heart disease 2.24 0.90-5.55 0.08 Sleeping problems (ref no) 4.46 2.56-7.76 0.00 3.47 1.84- 6.54 <0.001 Time since diagnosis 1.02 1.00-1.04 0.02 1.04 1.01- 1.06 0.003 HADS-A ≥ 8 4.93 2.80-8.70 <0.001 2.76 1.42- 5.37 0.003 HADS-D ≥ 8 7.07 3.60-13.85 <0.001 4.78 2.21-10.34 <0.001 Table legends and abbreviations: HbA1c, Haemoglobin A1c, BMI, Body Mass Index; HADS-A; Hospital Anxiety and Depression Scale., HADS-D; Hospital Anxiety and Depression Scale-Depression.
Table 5. Correlation (Spearman’s rho) between the Fatigue Questionnaire and the Short Form- 36 dimensions. SF-36 dimensions Fatigue Questionnaire dimensions
Physical Fatigue Mental Fatigue Physical Functioning -,478 -,320 Role Physical -,341 -,564 Bodily Pain -,380 -,296 General Health -,413 -,588 Vitality -,760 -,510 Social Functioning -,467 -,639 Role Emotional -,429 -,336 Mental Health -,466 -,546 Table legends and abbreviations: All values are significant with a <0.001. Scores in the range 0.50-0.80 are in boldface.
Total Fatigue -,461 -,541 -,374 -,582 -,756 -,641 -,429 -,576 p-value
Highlights
Fatigue was significantly more common in type 1 diabetes mellitus than in the background population. Out of the several clinical and socio-demographic factors investigated, increased levels of anxiety, depression and sleep problems displayed the highest association with fatigue. The Fatigue Questionnaire is a valid and reliable tool for the measurement of fatigue in type 1 diabetes mellitus.