Methodology – Page 10 – American ME and CFS Society

Effects of Time Frame on the Recall Reliability of CFS Symptoms

Abstract:

This study serves as an investigation of the reliability of symptom data as reported by individuals with chronic fatigue syndrome (CFS), across three recall time frames (the past week, the past month, and the past 6 months), and at two assessment points (with 1 week in between each assessment). Multilevel model analyses were used to determine the optimal recall time frame, in terms of test -retest reliability, for each of the Fukuda et al. (1994) case defining symptoms.

Results suggested that the optimal time frame for reliably reporting CFS symptoms was six months for sore throat, lymph node pain, muscle pain, post-exertional malaise, headaches, memory/concentration difficulties, and unrefreshing sleep. For joint pain, the optimal time frame was one month. Researchers who are interested in the assessment of CFS symptoms need to take recall time frame into account, especially when the intended goal is to standardize and improve the methods used to reliably and accurately diagnose this complex illness.

Source: Evans M, Jason LA. Effects of Time Frame on the Recall Reliability of CFS Symptoms. Eval Health Prof. 2015 Sep;38(3):367-81. doi: 10.1177/0163278713497014. Epub 2013 Sep 23. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874064/ (Full article)

Multiscale analysis of heart rate variability in non-stationary environments

Abstract:

Heart rate variability (HRV) is highly non-stationary, even if no perturbing influences can be identified during the recording of the data. The non-stationarity becomes more profound when HRV data are measured in intrinsically non-stationary environments, such as social stress. In general, HRV data measured in such situations are more difficult to analyze than those measured in constant environments.

In this paper, we analyze HRV data measured during a social stress test using two multiscale approaches, the adaptive fractal analysis (AFA) and scale-dependent Lyapunov exponent (SDLE), for the purpose of uncovering differences in HRV between chronic fatigue syndrome (CFS) patients and their matched-controls.

CFS is a debilitating, heterogeneous illness with no known biomarker. HRV has shown some promise recently as a non-invasive measure of subtle physiological disturbances and trauma that are otherwise difficult to assess. If the HRV in persons with CFS are significantly different from their healthy controls, then certain cardiac irregularities may constitute good candidate biomarkers for CFS.

Our multiscale analyses show that there are notable differences in HRV between CFS and their matched controls before a social stress test, but these differences seem to diminish during the test. These analyses illustrate that the two employed multiscale approaches could be useful for the analysis of HRV measured in various environments, both stationary and non-stationary.

Source: Gao J, Gurbaxani BM, Hu J, Heilman KJ, Emanuele Ii VA, Lewis GF, Davila M, Unger ER, Lin JM. Multiscale analysis of heart rate variability in non-stationary environments. Front Physiol. 2013 May 30;4:119. doi: 10.3389/fphys.2013.00119. ECollection 2013. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3667239/ (Full article)

A Chronic Fatigue Syndrome (CFS) severity score based on case designation criteria

Abstract:

BACKGROUND: Chronic Fatigue Syndrome case designation criteria are scored as physicians’ subjective, nominal interpretations of patient fatigue, pain (headaches, myalgia, arthralgia, sore throat and lymph nodes), cognitive dysfunction, sleep and exertional exhaustion.

METHODS: Subjects self-reported symptoms using an anchored ordinal scale of 0 (no symptom), 1 (trivial complaints), 2 (mild), 3 (moderate), and 4 (severe). Fatigue of 3 or 4 distinguished “Fatigued” from “Not Fatigued” subjects. The sum of the 8(Sum8) ancillary criteria was tested as a proxy for fatigue. All subjects had history and physical examinations to exclude medical fatigue, and ensure categorization as healthy or CFS subjects.

RESULTS: Fatigued subjects were divided into CFS with ≥4 symptoms or Chronic Idiopathic Fatigue (CIF) with ≤3 symptoms. ROC of Sum8 for CFS and Not Fatigued subjects generated a threshold of 14 (specificity=0.934; sensitivity=0.928). CFS (n=256) and CIF (n=55) criteria were refined to include Sum8≥14 and ≤13, respectively. Not Fatigued subjects had highly skewed Sum8 responses. Healthy Controls (HC; n=269) were defined by fatigue≤2 and Sum8≤13. Those with Sum8≥14 were defined as CFS-Like With Insufficient Fatigue Syndrome (CFSLWIFS; n=20). Sum8 and Fatigue were highly correlated (R(2)=0.977; Cronbach’s alpha=0.924) indicating an intimate relationship between symptom constructs. Cluster analysis suggested 4 clades each in CFS and HC. Translational utility was inferred from the clustering of proteomics from cerebrospinal fluid.

CONCLUSIONS: Plotting Fatigue severity versus Sum8 produced an internally consistent classifying system. This is a necessary step for translating symptom profiles into fatigue phenotypes and their pathophysiological mechanisms.

Source: Baraniuk JN, Adewuyi O, Merck SJ, Ali M, Ravindran MK, Timbol CR, Rayhan R, Zheng Y, Le U, Esteitie R, Petrie KN. A Chronic Fatigue Syndrome (CFS) severity score based on case designation criteria. Am J Transl Res. 2013;5(1):53-68. Epub 2013 Jan 21. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3560481/ (Full article)

Predictors of Fatigue among Patients with Chronic Fatigue Syndrome

Abstract:

Activity logs involve patients writing down their activities over one or more days. Several studies have found these data collection instruments to accurately describe activities of patients with chronic fatigue syndrome (CFS). The purpose of this study was to utilize the repeated measures available on the ACTRE to evaluate predictors of fatigue at a given timepoint.

A random intercept model was tested with the following variables predicting current fatigue: past fatigue (30 mins. prior), current category of activity (e.g., resting, work, recreation, etc.), past category of activity (30 mins. prior), the interaction of past fatigue and past activity, and TH2/TH1 immune shift. These findings and others suggest that activity logs can provide investigators and clinicians with valuable sources of data for understanding patterns of behavior and activity among patients with CFS.

Source: Jason LA, Brown M, Evans M, Brown A. Predictors of Fatigue among Patients with Chronic Fatigue Syndrome. J Hum Behav Soc Environ. 2012 Oct 1;22(7):822-833. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3955704/ (Full article)

Subjective sleep quality and daytime sleepiness in a large sample of patients with chronic fatigue syndrome (CFS)

Abstract:

Chronic fatigue syndrome (CFS) is characterised by incapacitating fatigue in combination with a number of minor criteria, including unrefreshing sleep without further specifications, in the absence of psychiatric and internal disease. As little data exist on subjective sleep quality and daytime sleepiness, these parameters were assessed in a large sample of CFS patients.

Consecutive patients with a diagnosis of CFS in a tertiary referral centre filled out the Fatigue Questionnaire (FQ), Medical Outcomes Study 36-Item Short Form Health Survey (MOS SF-36), Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI). Inclusion comprised 415 individuals (mean age 40.5 yr, SD 7.9, range 18-64; 86% female). Mean FQ (26.90; SD 4.04), mean Global Physical Health from the MOS SF-36 (29.30; SD 12.25) and Global Mental Health from the MOS SF-36 (49.62; SD 18.31) scores corresponded with literature data for similar CFS samples. High mean ESS (10.51; SD 5.52) and global PSQI (10.17; SD 4.02) were observed. No significant relationship was found between ESS and global PSQI.

In contrast, regression analysis demonstrated a significant cubic relation between ESS and ‘PSQI without daytime dysfunction’. A subgroup (n=69) with an insomnia-like phenotype low ESS (<5), high PSQI (mean 11.51; SD 3.86) was observed. The assessment of subjective sleep quality and daytime sleepiness in a large sample of CFS patients indicated high mean PSQI and ESS values. ESS and ‘PSQI without daytime dysfunction’ were inversely related at the spectral ends of ESS. A distinct subgroup with clinical features of insomnia was identified.

Source: Mariman A, Vogelaers D, Hanoulle I, Delesie L, Pevernagie D. Subjective sleep quality and daytime sleepiness in a large sample of patients with chronic fatigue syndrome (CFS). Acta Clin Belg. 2012 Jan-Feb;67(1):19-24. https://www.ncbi.nlm.nih.gov/pubmed/22480034

Minimum data elements for research reports on CFS

Abstract:

Chronic fatigue syndrome (CFS) is a debilitating condition that has received increasing attention from researchers in the past decade. However, it has become difficult to compare data collected in different laboratories due to the variability in basic information regarding descriptions of sampling methods, patient characteristics, and clinical assessments. The issue of variability in CFS research was recently highlighted at the NIH’s 2011 State of the Knowledge of CFS meeting prompting researchers to consider the critical information that should be included in CFS research reports.

To address this problem, we present our consensus on the minimum data elements that should be included in all CFS research reports, along with additional elements that are currently being evaluated in specific research studies that show promise as important patient descriptors for subgrouping of CFS. These recommendations are intended to improve the consistency of reported methods and the interpretability of reported results. Adherence to minimum standards and increased reporting consistency will allow for better comparisons among published CFS articles, provide guidance for future research and foster the generation of knowledge that can directly benefit the patient.

Source: Jason LA, Unger ER, Dimitrakoff JD, Fagin AP, Houghton M, Cook DB, Marshall GD Jr, Klimas N, Snell C. Minimum data elements for research reports on CFS. Brain Behav Immun. 2012 Mar;26(3):401-6. doi: 10.1016/j.bbi.2012.01.014. Epub 2012 Jan 28. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4643273/ (Full article)

Validation of the three-factor model of the PSQI in a large sample of chronic fatigue syndrome (CFS) patients

Abstract:

OBJECTIVE: To evaluate whether a 3-factor model of the Pittsburgh Sleep Quality Index (PSQI) scale would fit the constellation of sleep disturbances in patients with a diagnosis of chronic fatigue syndrome (CFS).

METHODS: Consecutive CFS patients filled out the PSQI. Scores from this self-report questionnaire were examined with exploratory and confirmatory factor analysis (CFA).

RESULTS: 413 CFS patients were included for analysis in this study. CFA showed that the 7 PSQI component scores clustered into the 3 factors reported by Cole et al. (2006), i.e. Sleep Efficiency, Perceived Sleep Quality and Daily Disturbances. In contrast with the single-factor and all 2-factor models, all factor loadings were significant, and all goodness-of-fit values were acceptable.

CONCLUSION: In CFS, the PSQI operates as a 3-factor scoring model as initially seen in healthy and depressed older adults. The separation into 3 discrete factors suggests the limited usefulness of the global PSQI as a single factor for the assessment of subjective sleep quality, as also evidenced by a low Cronbach’s alpha (0.64) in this patient sample.

Source: Mariman A, Vogelaers D, Hanoulle I, Delesie L, Tobback E, Pevernagie D. Validation of the three-factor model of the PSQI in a large sample of chronic fatigue syndrome (CFS) patients. J Psychosom Res. 2012 Feb;72(2):111-3. doi: 10.1016/j.jpsychores.2011.11.004. Epub 2011 Dec 22. https://www.ncbi.nlm.nih.gov/pubmed/22281451

Factor analysis of the Beck Depression Inventory-II with patients with chronic fatigue syndrome

Abstract:

This study examined the properties of the Beck Depression Inventory-II (BDI-II) in a sample of 111 patients with chronic fatigue syndrome (CFS). Exploratory factor analysis identified two factors. The mean score for the Somatic-Affective factor was significantly higher than the Cognitive factor. Convergent and discriminant validity were assessed for BDI-II total score, the two factor scores, and the BDI for Primary Care (BDI-PC). The BDI-PC and Cognitive factor demonstrated superior validity. Results suggest patients endorse BDI-II somatic items that overlap with CFS symptoms at a high rate. Factor scores should be evaluated separately, or the BDI-PC should be utilized with this population.

Source: Brown M, Kaplan C, Jason L. Factor analysis of the Beck Depression Inventory-II with patients with chronic fatigue syndrome. J Health Psychol. 2012 Sep;17(6):799-808. doi: 10.1177/1359105311424470. Epub 2011 Nov 21. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655435/ (Full article)

Fatigue Scales and Chronic Fatigue Syndrome: Issues of Sensitivity and Specificity

Abstract:

Few studies have explored issues of sensitivity and specificity for using the fatigue construct to identify patients meeting chronic fatigue syndrome (CFS) criteria. In this article, we examine the sensitivity and specificity of several fatigue scales that have attempted to define severe fatigue within CFS. Using Receiver Operating Characteristic (ROC) curve analysis, we found most scales and sub-scales had either significant specificity and/or sensitivity problems.

However, the post-exertional subscale of the ME/CFS Fatigue Types Questionnaire (Jason, Jessen, et al., 2009) was the most promising in terms of specificity and sensitivity. Among the more traditional fatigue scales, Krupp, LaRocca, Muir-Nash, and Steinberg’s (1989) Fatigue Severity Scale had the best ability to differentiate CFS from healthy controls. Selecting questions, scales and cut off points to measure fatigue must be done with extreme care in order to successfully identify CFS cases.

Source: Jason LA, Evans M, Brown M, Porter N, Brown A, Hunnell J, Anderson V, Lerch A. Fatigue Scales and Chronic Fatigue Syndrome: Issues of Sensitivity and Specificity. Disabil Stud Q. 2011 Winter;31(1). Pii: 1375. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3181109/ (Full article)

Measuring disability in patients with chronic fatigue syndrome: reliability and validity of the Work and Social Adjustment Scale

Abstract:

BACKGROUND: Disability is a defining feature of chronic conditions, and it is an increasingly used measure of therapy effectiveness. The Work and Social Adjustment Scale (WSAS) is a simple and clear measure of disability. Although the scale is widely used, no study has yet investigated its psychometric properties in patients with chronic fatigue syndrome (CFS).

METHODS: Data from two samples of patients were used, one from a multicenter randomized controlled clinical trial of treatments for CFS (n =639) and the other from a clinic that specializes in CFS (n=384). All patients completed the WSAS as well as other measures.

RESULTS: Internal consistency and the Spearman-Brown split-half coefficient values indicated that the scale is reliable. CFS patients who had comorbid diagnoses of depression, anxiety or fibromyalgia had higher WSAS scores. High levels of disability were associated with high number of physical symptoms, severe fatigue, depression, anxiety, poor sleep quality and poor physical fitness, with correlation coefficients ranging between 0.41 and 0.11. Lower scores on the WSAS were modestly associated with better physical functioning as well as higher levels of physical capacity as assessed by a walking test. Sensitivity to change was evaluated in a subgroup of patients who had undergone a course of cognitive behavioral therapy. Disability significantly decreased after therapy and remained stable at follow-ups.

CONCLUSION: The WSAS is a reliable and valid assessment tool for disability in patients with CFS.

Source: Cella M, Sharpe M, Chalder T. Measuring disability in patients with chronic fatigue syndrome: reliability and validity of the Work and Social Adjustment Scale. J Psychosom Res. 2011 Sep;71(3):124-8. doi: 10.1016/j.jpsychores.2011.02.009. Epub 2011 Apr 3. https://www.ncbi.nlm.nih.gov/pubmed/21843745