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A clinical and laboratory investigation of 141 patients fulfilling the American-European consensus criteria of pSS was undertaken in the period May 2004 to April 2005. Median time since diagnosis was 5.5 years. Examinations included the fatigue questionnaires: fatigue severity scale (FSS), fatigue visual analogue scale (VAS), functional assessment of chronic illness therapy - fatigue (FACIT-F) and medical outcome study short form-36 (SF-36) vitality, which were repeated in a follow-up investigation in January and February 2010.
Standard haematological and immunological tests were carried out, including antinuclear antibodies (ANA), anti-SSA and anti-Sjögren's syndrome B antigen (anti-SSB) and IgG. These laboratory tests were performed in the routine hospital laboratory. ANA, anti-SSA and anti-SSB were analysed by ELISA. Anti-SSA and -SSB statuses were classified dichotomously; other serum and blood laboratory values had continuous values. Lip biopsy focus score was recorded from the medical files. Serum cytokines were previously analysed at our laboratory [18]. The assay comprises analyses of 25 cytokines:
Among fatigue instruments used in rheumatic disease studies, including pSS, is the Fatigue Severity Scale (FSS), Functional Assessment of Chronic Illness Therapy - Fatigue (FACIT-F) and different VAS variants. FSS assesses functional issues during the preceding two weeks [19]. FACIT-F is a general fatigue measure with emphasis on daily life function [20]. SF-36 assesses different health aspects during the preceding four weeks [21]. The vitality domain of SF-36 has been used as a proxy measure of fatigue in several conditions. FSS and fatigue VAS are positive scales in that higher values mean higher fatigue levels, while FACIT-F and vitality have the opposite direction. In the present study, Norwegian versions of FSS, fatigue VAS, FACIT-F and SF-36 vitality were used. The SF-36 mental health domain was also recorded and included in the analyses to account for possible depression bias. Regarding fatigue VAS, patients were asked: "How have you experienced fatigue (tretthet; that is, "tiredness") during last week?", and the anchors were 0 mm = tiredness is no problem, and 100 mm = tiredness is a big problem. The questionnaires were initially completed face-to-face in connection with the clinical investigation in 2004 to 2005. At follow-up, postal questionnaires were sent to the trial participants in January and February 2010. Three patients were deceased and one had emigrated to an unknown location, thus 137 patients were sent questionnaires at this time. Patients not responding also received a postal reminder and new questionnaires. The study was approved by the Regional Committee for Medical and Health Research Ethics, and informed consent was obtained from all participants.
The difference in fatigue measures between follow-up and baseline was computed. To assess whether or not fatigue changed over time, paired t-tests were applied. Fatigue was compared with the following clinical and laboratory variables: serum cytokine concentrations, CRP, serum IgG, ANA, anti-SSA, anti-SSB, blood sedimentation rate, and Schirmer's test, UWS, focus score, VAS assessments of eye and mouth dryness, and pain. Comparisons with baseline fatigue were performed using Spearman's rank coefficient (rho). Regarding change in fatigue over time, these variables were compared with continuous fatigue differences (using Spearman's rho), and dichotomised differences (increased fatigue or not, using the Mann-Whitney U test). Associations between dichotomous variables were calculated using Fisher's exact test or McNemar's test. Hierarchical multiple linear regression was used to assess the ability of clinical and laboratory control measures to predict fatigue change over time, over and above any effect of socio-demographic factors. Two-sided P-values were computed, and P-values below 0.05 were considered statistically significant. Analyses were performed using PASW Statistics 18.0 (SPSS Inc., Chicago, IL, USA) and Prism 5 (GraphPad Software Inc., San Diego, CA, USA). Power calculations were performed using PS 3.0 [22].
Hierarchical multiple linear regression was used to assess the ability of baseline clinical and laboratory control measures to predict fatigue change over time (Tables 3 and 4). Age, gender and education level were entered at Step 1 (education level had values from 1 = elementary school to 5 = university), Schirmer's test and UWS at Step 2, and focus score, anti-SSA/SSB, IgG and the SF-36 MH subscore at Step 3. The dependent variable was change in fatigue, and the model was repeated for all fatigue measures (change in FSS, FACIT-F, fatigue VAS and vitality). Step 1 (socio-demographic factors) predicted change in vitality, but no other partial model, and no final model, showed any significant prediction (Tables 3 and 4). The analysis was also performed with baseline mouth dryness, eye dryness and pain VAS added to Step 2, with unaltered results.
KH participated in data collection, performed the statistical analyses and drafted the manuscript. IB participated in statistical analyses and in drafting the manuscript. JGB, RJ and AIB conceived of the study, participated in the data collection and helped to draft the manuscript. All authors read and approved the final manuscript.
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The Godin Leisure-Time Exercise questionnaire scores is a reliable, simple, and effective measure of physical activity level. This questionnaire enables the assessment of self-reported leisure-time physical activity and gives information on the number of times one engages in mild, moderate, or strenuous leisure-time physical activity for durations of at least 15 min within a typical 7-day period19. A score is calculated by multiplying activity frequency ratings by a Metabolic Equivalent of Task coefficient for each type of activity. Resulting values were then summed to give a leisure score index (LSI) expressed in arbitrary units where higher scores represent higher levels of physical activity. To our knowledge, this questionnaire has been validated in English but not in French. A non-validated French translation of the Godin Leisure-Time Exercise questionnaire has thus been used in the present experiment, as it has been done in other studies with French participants (e.g.20).
Age, sex, type of ICU admission, SAPS II, SAPS II-modified score (i.e. SAPS II without considering the age), Glasgow score, treatments received in ICU, duration of mechanical ventilation, uses of extracorporeal membrane oxygenation or renal replacement therapy, duration of ICU stay and time since ICU discharge were obtained from each ICU databases. We used the year of discharge (i.e. 2013, 2014, 2015, 2016, 2017 and 2018) to perform our statistical analysis as this variable fit best with our model (see below). We also considered the number of months from hospital discharge to give a more detailed description of the relationship between fatigue prevalence and time since discharge.
All data generated or analyzed during this study are included in this published article (and its Supplementary Information files). The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
G.Y.M. acquired the utilized funding. All authors designed the study. R.S. and P.I. mailed all the questionnaires to the ICU patients. All authors managed the database. J.M., R.S. and P.I. analyzed and interpreted the obtained data. J.M. performed the statistical analysis. J.M. and P.I. prepared the first draft of the manuscript. All authors reviewed the manuscript. All authors participated in the revision process of the manuscript and therefore vouch for the integrity and accuracy of the presented data as well as the process, which lead to the presented data. All authors read and approved the final manuscript.
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In the present study, the aim was to gather background information on the development of the FACIT-Sp, as well as subsequent empirical data from studies that have employed the measure as primary or secondary predictors or as outcome variables. The scope was not intended to be an exhaustive review of every study that uses the FACIT-Sp in any capacity. Rather, the inclusion criteria were studies that employed the FACIT-Sp as an integral component where results enhanced our understanding of the FACIT-Sp, of spiritual well-being within chronic illness populations, or both. To this end, studies with relatively small samples that significantly lacked generalizability were excluded, as were studies that included the FACIT-Sp in a supplementary fashion. Also, reporting of statistical significance has been limited to data that has directly impacted implications for the FACIT-Sp. Finally, while several measures of spiritual well-being exist, the focus of this review is on spiritual well-being as assessed by the FACIT-Sp. 2ff7e9595c
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