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Fatigue in Myasthenia Gravis patients.

Abstract: The purposes of this study were to validate psychometric properties of the Myasthenia Gravis Fatigue Scale (MGFS) and to use the MGFS to study the relationship of selected demographic and clinical variables to fatigue in a myasthenia gravis (MG) patient population. The MGFS, an existing scale, was developed to measure fatigue severity in MG patients. A convenience sample of 67 MG patients was approached on return visit to the Neuromuscular Disease Clinic. After giving consent, participants filled out four questionnaires: the MGFS, the Chalder Fatigue Scale, the Center for Epidemiologic Studies-Depression Scale, and a Demographic and Clinical Data Form. Participants' muscle weakness was evaluated using the Modified Quantitative MG Score for Disease Severity assessment form (13 muscles tested). Three days after the clinic visit, a copy of the MGFS was mailed to participants to allow retest at 5 to 7 days after initial testing. Findings showed that the MGFS demonstrated adequate internal consistency and test-retest reliability. In addition, the fatigue severity (MGFS) score correlated moderately with depression. We adjusted for the effect of depression and noted a relationship between the fatigue score and two of nine selected demographic and clinical characteristics--activity restriction and number of years since diagnosis. These results lay the groundwork for further investigation of fatigue in MG patients and identification of mitigating or coping methods.


Fatigue, a subjective experience that ranges from tiredness to exhaustion, is a common complaint in both ill and healthy people (Aaronson et al., 1999) and is especially common in those with neurological disorders and cancer. Fatigue may negatively affect a person's ability to perform physical or mental activity as well as quality of life, and it has been shown to be associated with poor outcomes (Amato et al., 2001; Hwang, Chang, Cogswell, & Basimis, 2002; Michael, 2002).

Pathological fatigue, in contrast to normal fatigue, does not subside with rest and is characterized by a feeling of tiredness before activity, lack of energy to complete tasks, exhaustion after usual activity, or all of the above. Fatigue has been found to correlate with physical and psychological parameters in patients with multiple sclerosis, chronic fatigue syndrome, HIV infection, and AIDS (Breitbart, McDonald, Rosenfeld, Monkman, & Passik, 1998; Ford, Trigwell, & Johnson, 1998; O'Dell, Meighen, & Riggs, 1996; Packer, Foster, & Brouwer, 1997; Vercoulen et al., 1997; Walker, McGown, Jantos, & Anson, 1997). There is only one published study on the correlation between physical and psychological factors and fatigue in myasthenia gravis (MG) patients. The current study was designed to investigate these relationships using an instrument adapted to measure fatigue in MG patients.


Grohar-Murray, Becker, Ricci, Polak, and Danehy (1994), in an unpublished study, surveyed 250 MG patients regarding the characteristics of their fatigue, such as duration, time of occurrence, and exacerbating factors, as well as self-care management techniques. Fatigue severity was measured by the Myasthenia Gravis Fatigue Scale (MGFS), an adaptation of an instrument used for multiple sclerosis patients. Results revealed that the women with MG experienced more severe fatigue than the men. Also, level of physical activity was found to correlate with fatigue severity (r = .61). Another study by the same group showed that low-impact aerobic exercise such as walking, swimming, and running helped MG patients relieve their fatigue (Grohar-Murray, Becker, Reilly, & Ricci, 1998). However, there was not a significant difference in fatigue score, as measured by MGFS, between MG patients who reported that aerobic exercise relieved fatigue and those who reported that aerobic exercise did not relieve their fatigue. In the same study, women reported more fatigue than men in various age categories, at each level of functional status measured, and in the aerobic exercise group. Age category did not significantly affect fatigue score.

Paul, Cohen, Goldstein, and Gilchrist (2000) administered a cognitive intervention to study fatigue and the effect of fatigue on quality of life in MG patients (n = 28) and control participants (n = 24). The results showed that the MG patient group demonstrated significantly greater cognitive (mental) and physical fatigue than the control group (p < .05) both before and after the intervention.

Fatigue has been measured with various instruments, mainly, visual analog scales and questionnaires with Likert-scale format, including the Checklist of Individual Strength-Fatigue (van der Werf et al., 1998; Vercoulen et al., 1996, 1997), the Fatigue Severity Scale (Packer, Sauriol, & Brouwer, 1994), the Chalder Fatigue Scale (Ford et al., 1998), the Fatigue Assessment Inventory (O'Dell et al., 1996), the Piper Fatigue Scale (Cupler, Otero, Hench, Luciano, & Dalakas, 1996; O'Dell et al., 1996), and the Multidimensional Assessment of Fatigue (Schwartz, Coulthard-Morris, & Zeng, 1996). However, these instruments are not specific for measuring fatigue in the MG patient population.

Grohar-Murray, Sears, Hubsky, and Becker (1994) combined and modified two unpublished questionnaires, which were used to measure fatigue in multiple sclerosis patients, for use with MG patients. They have done preliminary psychometric studies on MGFS, the 26-item adapted instrument, but only parts of their data have been published (Grohar-Murray et al., 1998). In the current study, additional data on psychometric parameters for the MGFS were collected to further its usefulness as a fatigue scale for MG patients.

The specific purposes of this study were to (a) validate psychometric properties of MGFS and (b) use the MGFS to study the relationship of selected demographic and clinical variables to fatigue in a MG patient population. Among the demographic and clinical factors chosen, age, gender, and physical activity had been investigated by Grohar-Murray, Becker, et al. (1994) and Grohar-Murray, Sears, et al. (1994). Body mass index (BMI), number of medications taken daily, duration of MG, history of thymectomy, and a disease severity score were expected to relate to fatigue, although there are no studies on these factors in MG patients. Depression has been reported to correlate with fatigue in both healthy persons and those with multiple sclerosis (Lee, Lentz, Taylor, Mitchell, & Woods, 1994; Libbus, Baker, Osgood, Phillips, & Valentine, 1995; Schwartz et al., 1996), systemic lupus erythematosus (Krupp, LaRocca, Muir-Nash, & Steinberg, 1989; Wang, Gladman, & Urowitz, 1998), and HIV/AIDS (O'Dell et al., 1996; Walker et al., 1997). Therefore, this variable was also included in the current study.



Sixty-seven patients who were making a return visit to the Neuromuscular Disease Clinic at the University of Alabama at Birmingham Medical Center agreed to participate in the study. To be eligible they were (a) diagnosed with either ocular MG or generalized MG; (b) able to read, write, and understand English; (c) between the ages of 19 and 70 years; and (d) not subject to any other neuromuscular or other debilitating disease such as heart failure or hemolytic anemia. Protection of participants was ensured by approval of the study by the University of Alabama at Birmingham Institutional Review Board, recruitment of participants through their physician, and signed consent forms.


Four questionnaires were used to measure or record participants' fatigue, depression, and selected demographic and clinical characteristics. In addition, a protocol recommended by Task Force of the Medical Scientific Advisory Board of the Myasthenia Gravis Foundation of America (Task Force, 2000) was used to assess muscle weakness.

Myasthenia Gravis Fatigue Scale. The MGFS was designed to measure fatigue severity for MG patients (Grohar-Murray, Sears, et al., 1994). It contains 26 items that fall within three subscales: 9 items measure perception of fatigue (MGP), 8 items measure the task avoidance behaviors resulting from fatigue (MGT), and 9 items measure the observable motor signs or symptoms resulting from fatigue (MGM). Items on the MGFS are answered with a 5-point Likert scale (5 = always, 4 = usually, 3 = frequently, 2 = sometimes, and 1 = never). The MGFS score is then computed by summing the ratings on items; possible scores range from 26 to 130, with higher scores indicating greater fatigue.

Content validity indexes of MGFS, determined by a panel of experts, were .90 and .98. Internal consistency and test-retest reliability of the MGFS have been examined with both MG patients and volunteers without MG. Test-retest reliability of the MG group and the control group was determined, with correlation coefficient of .85 for each group. Internal consistency of the MGFS was calculated, with alpha coefficients of .89 for the overall scale, and .80, .70, and .92 for MGP, MGT, and MGM, respectively (Grohar-Murray et al., 1998). Construct validity of the MGFS was investigated using a group of MG patients and a group of normal adults. The difference in scores was significant, p = .001 (Grohar-Murray et al.).

Chalder Fatigue Scale (CFS). The CFS was chosen as an additional fatigue assessment because it measures both physical and mental aspects of fatigue, is relatively easy to administer, and has excellent psychometric properties. The CFS is designed to measure fatigue severity and can be used with both hospital and community populations (Chalder et al., 1993). It is contains 14 items, 8 of which measure physical fatigue and 6 of which measure mental fatigue. Each item has four options: (a) better than usual, (b) no more than usual, (c) worse than usual, and (d) much worse than usual. A bimodal response system is commonly used to score this questionnaire (0 = better than usual or no more than usual; 1 = worse than usual or much worse than usual). With this method of scoring, possible scores range from 0 to 14. High scores indicate greater fatigue. The total scale and subscale scores were calculated by Cronbach's alpha. Internal consistencies of the total scale and physical fatigue and mental fatigue subscales were evidenced by alpha coefficients of .88 to .90, .845, and .821, respectively (Chalder et al.). In the current study, alpha coefficients of .937, .916, and .908, respectively, were found.

Center for Epidemiologic Studies-Depression (CES-D) Scale. The CES-D scale is designed to assess the frequency of occurrence of depressive symptoms during the past week (Radloff, 1977). It consists of 20 items that describe characteristics of depression. A 4-point Likert scale is used to indicate frequency. The score can be computed by summing the ratings on items; possible scores range from 0 to 60. A score of 16 or greater indicates clinical depression. The CES-D has adequate psychometric properties, with alpha coefficient between .84 and .90 (Radloff). Alpha coefficient of the CES-D scale, as used in the current study, was .906.

Demographic and Clinical Data Form. A Demographic and Clinical Data Form was developed by the investigator. It was used to record selected demographic and clinical characteristics, such as age, gender, body mass index (BMI), current medications, and history of thymectomy for the participants.

Modified Quantitative MG Score for Disease Severity (MQMG). The Quantitative Myasthenia Gravis Score (QMG) instrument was developed by Barohn et al. (1998) to assess muscle weakness in MG patients. This instrument was derived from a previous instrument, the Quantified Myasthenia Gravis Strength Score (Tindall et al., 1987; Tindall, Phillips, Rollins, Wells, & Hall, 1993). The QMG comprises 13 physical tests of different muscle groups involved in head lifting, eye movement, facial expression, swallowing, speech, arm and leg movement, hand grip, and ventilation. It takes approximately 30 minutes to complete the 13-item QMG (Wolfe et al., 1999). Performance on each item is scored on a 4-point Likert scale (0 = no weakness to 3 = severe weakness). Therefore, the scores could range from 0 to 39. Higher scores indicate greater muscle weakness or disease severity. Interrater reliability of the QMG was calculated using two to five raters for each of 9 participants (5 with MG, 4 without MG). It was calculated as the average of standard deviations of each participant's scores, which was 1.34 out of 39. QMG provides a precise quantitative muscle weakness score. The Task Force of the Medical Scientific Advisory Board of the Myasthenia Gravis Foundation of America (2000) has recommended that QMG be used in all prospective studies of therapy for MG patients.

To make the QMG safer and more convenient for use in the current study, two of the 13 items were modified. For one item, instead of asking the participant to swallow 4 oz of water and observing possible choking, the investigator asked the participant about his or her response to swallowing liquids. The spirometer commonly used in the clinic was used, rather than the suggested spirometer, for another item. The modified QMG used in the current study was named the Modified Quantitative MG Score for Disease Severity (MQMG). Internal consistency calculation in the current study yielded an alpha coefficient of .865.


Patients on a return visit to the Neuromuscular Disease Clinic were given an explanation of the study via letter from their clinic physician and a verbal explanation by the investigator. Those who consented to participate were asked to fill out the four questionnaires: the Demographic and Clinical Data form, CFS, MGFS, and CES-D scale. The investigator was present in the clinic waiting room or examination room to assist with the questionnaires, if needed. Some information for the Demographic and Clinical Data form was obtained from medical records. As each participant completed the questionnaires, the investigator examined each participant's muscle weakness using the MQMG protocol.

Three days after the clinic visit, a new copy of the MGFS, along with a self-addressed, stamped envelope, was mailed to each participant to allow retest within 5 to 7 days of initial testing. A telephone call to remind the participant to return his or her questionnaire was made on the fifth day after the initial visit. Sixty-seven patients participated over an 8-month period.

Data Analysis

The Statistical Package for the Social Science (SPSS) version 10.0 was used to analyze the data. Pearson's product-moment correlation coefficient was calculated to determine test-retest reliability of the MGFS, and Cronbach's alpha calculation was employed to examine internal consistency of the MGFS and other appropriate instruments used in the study. Multiple regression analysis was used to examine the relationship between fatigue score, as measured by MGFS, and selected demographic and clinical variables, controlling for appropriate covariables. Post hoc power analysis for multiple regression was performed by using the method of Cohen and Cohen (1977).


Sample Description

Participants ranged in age from 24 to 67 years (M = 48.4 years) and were approximately evenly divided by sex (Table 1). The majority were Caucasian (70.1%), were overweight or obese as indicated by BMI (77.6%), and had generalized MG (92.5%). Almost half of the participants were diagnosed with MG within the past 5 years. Approximately 69% reported other health conditions. The most common health problem was hypertension (44.8%), followed by diabetes mellitus (19.4%), and respiratory problems (14.9%). As part of MG treatment, pyridostigmine (Mestinon) was the most commonly prescribed drug, followed by prednisone and azathioprine (Imuran) or another immunosuppressive drug. About half of the participants had undergone thymectomy. About a third were fully active, 58.2% reported that they were able to do self-care, light housework, or office work but were restricted in strenuous physical activity, and 9.0% were restricted even in the ability to walk.

Psychometric Properties of the MGFS

The initial administration of the MGFS for 67 participants was used to examine the internal consistency of the instrument. The coefficient alpha scores for MGFS and its three subscales were excellent, ranging from .850 to .934, as shown in Table 2.

For the 1-week retest of the MGFS, 49 of the 67 participants mailed the questionnaire back within 7 days of the initial test, 16 did not return the questionnaire, and 2 returned the questionnaire too late for it to be analyzed. Therefore, test-retest reliability was examined by comparing scores from 49 participants on initial testing and repeat testing 5 to 7 days later. The result showed high test-retest reliability (r = .872, p < .001).

Fatigue and Selected Demographic and Clinical Variables

Multiple regression analyses with five covariables (age, gender, BMI, number of years since diagnosis, and history of thymectomy) were performed to examine the relationships between fatigue, as measured by MGFS, and disease severity (MQMG) score; CFS scores for physical, mental, and total fatigue; and depression (CES-D) score. The resulting bivariate correlations are shown in Table 3. MGFS fatigue score correlated positively (r = .313 to .515) with all fatigue and depression scores. The correlation with disease severity score approached statistical significance (r = .237, p = .056).

Stepwise multiple regression analysis with depression as a covariable was then used to assess the relationship of nine selected demographic and clinical variables to fatigue score. The nine variables were age, gender, BMI, number of years since diagnosis, number of current medications, other morbidity, history of thymectomy, activity restriction, and disease severity. To control for the effect of depression, that variable was forced into the regression model at the initial step, and the nine independent variables were then allowed to enter the model stepwise using alpha = .20 as a criterion to enter the equation and alpha = .25 as the exclusion criterion. Examination of residual analyses revealed two influential outliers. Therefore, the analyses were repeated with the exclusion of the two respective participants (n = 65).

A summary of the stepwise multiple regression analysis for the MGFS with 65 participants is presented in Table 4. As shown in the table, in the initial step, with depression as a covariable in the regression model, statistical significance was obtained and [R.sup.2] = .370. The independent variables were then allowed to enter the model stepwise. The first independent variable to enter was activity restriction ([R.sup.2] change = .191), followed by number of years since diagnosis ([R.sup.2] change = .025). In the overall regression model, 59% of the variance in the MGFS score was accounted for by depression, activity restriction, and number of years since diagnosis. Depression accounted for 37% of the variance in fatigue score. Activity restriction and number of years since diagnosis explained 19% and 3% of the variance, respectively. Post hoc power analysis for this multiple regression analysis yielded a power (1-[beta]) of .94, indicating that the sample size was sufficient to detect the absence of correlation between fatigue and variables that did not enter the model.


The MGFS demonstrated adequate internal consistency. The current findings strongly support the previously described results from preliminary psychometric testing published by Grohar-Murray et al. (1998). The test-retest reliability of the MGFS within a 1-week time period was high (r = .872) and was almost identical to the 2-week test-retest results obtained by Grohar-Murray et al. On inspection of items included in the MGFS before use in the current study, it was noted that one item is difficult to interpret. Item 14 contains the statement "Rest relieves my fatigue." This item is scored on a scale with anchors 1 (never) to 5 (always). A score of 5 could be interpreted as indicating either a high or a low level of fatigue. To determine whether differing interpretations of this item would affect the internal consistency of the instrument, Cronbach's alpha calculation was performed for total MGFS and the MGT subscale (which included item 14) using forward and reverse scoring of item 14. The results showed minimal differences in Cronbach's alpha, a difference of .003 for the total MGFS and .006 for the MGT subscale. Therefore, it was concluded that the interpretation of this item did not substantially alter the results of the MGFS. Thus, item 14 was retained and scored forward as in the original instrument for the remaining analyses.

It was surprising that the correlation between the fatigue (MGFS) and disease severity (MQMG) scores was low and only approached statistical significance (r = .237, p = .056), because the MGFS score indicates physical fatigue and the MQMG score indicates muscle weakness. The fact that the correlation approached significance indicated that it most likely would become significant if the number of participants were increased. Another explanation for the lack of significant correlation is that the MGFS is a self-report questionnaire, and participants responded to the MGFS by considering their overall, rather than momentary, fatigue. On the other hand, the muscle weakness score was the actual score for muscle weakness at the moment of testing. In addition, the time that muscle weakness was tested (i.e., morning or afternoon) and duration of action of the common drug pyridostigmine could affect the degree of muscle weakness. Note that when simple correlation between the MGFS score and MQMG was run, without the inclusion of the five covariables, there was a significant correlation between these two scores (r = .296, p < .05).

Low positive correlations were found between the MGFS score and the CFS scores for physical, mental, and total fatigue. There may be several reasons for the fact that the correlations for these fatigue scales were low. Although the MGFS and CFS are both designed to measure severity of fatigue, the MGFS is designed for MG patients, whereas the CFS is not designed for persons with any specific disease. Also, a 5-point Likert scale rating is used for responses on the MGFS, whereas participants respond to CFS items on a 4-point Likert scale, and in the majority of reported studies, these responses are recoded to produce a bimodal rating system (yes or no). To determine whether use of the bimodal rating system affected the results, the correlation analysis was rerun using the 4-point Likert scale for the CFS. The results showed only a small increase in the correlation coefficients between the MGFS score and the CFS scores for physical, mental, and total fatigue (r = .457, .390, and .463, respectively).

Low correlation between the MGFS and CFS may also be due to differences in emphasis. The MGFS contains three subscales and the CFS, two subscales. On inspection of specific items, MGP (fatigue perception) and MGT (task avoidance) subscales of the MGFS seem to be most comparable to the physical fatigue subscale of the CFS. When the analyses were rerun using the combined score of the MGP and MGT subscales rather than the total MGFS score, the correlation results were improved. Correlation coefficients between the combined MGP and MGT score and the CFS scores for physical, mental, and total fatigue (using the 4-point CFS responses) were .495, .459, and .518, respectively. The improvement in correlation results indicated that the bimodal rating system of the CFS and inclusion of the MGM (motor sign or symptoms) subscale of MGFS may have been factors that decreased the correlation between scores on the two fatigue scales. However, even with deletion of these two factors, the greatest correlation coefficient seen between the two fatigue assessments was only moderate (r = .518).

Depression has been reported to be correlated with fatigue in both healthy people and patients. The current findings showed a moderate correlation between depression score and the MGFS score (r = .515), after controlling for the effect of five covariables (Table 3). This is not surprising because other studies that compared fatigue and depression in various populations, yielded correlations between .17 and .82 (Andrykowski, Curran, & Lightner, 1998; Dzurec, Hoover, & Fields, 2002; Ford et al., 1998; Lee et al., 1994; Libbus et al., 1995; O'Dell et al., 1996; Schwartz et al., 1996; Walker et al., 1997).

When nine other variables that might affect fatigue were examined, depression score was used as a covariable because of its moderate correlation with fatigue score. The regression analysis results showed that, after controlling for the effect of depression, 22% of the variance in fatigue score was explained by two variables. The best predictor of fatigue severity was activity restriction, followed by number of years since diagnosis. In the current study, the zero-order correlation between MGFS score and activity restriction was moderate, r = .576, p < .001, which was consistent with the study of Grohar-Murray, Becker, et al. (1994). For 250 MG patients, Grohar-Murray, Becker, et al. reported a moderate correlation between fatigue severity score and their variable called level of functional status (r = .61); this level of functional status variable comprised the same items as activity restriction in the current study. It is not surprising that, as fatigue scores increased, MG patients were less physically active. Similarly, as the duration of disease increased, the severity of fatigue increased also.

Of the 65 participants in the current study, 60 had generalized MG and 5 had ocular MG. It is possible that the 5 ocular MG patients may have confounded the regression results, because activity restriction might be different between these 5 and the 60 generalized MG patients. Therefore, the stepwise regression analysis was rerun with the 60 generalized MG patients only. The resulting regression model was similar to the regression model for all 65 participants ([R.sup.2] = .580, p < .001). Thus, it can be concluded that type of MG did not confound the regression analysis results.


In this study, activity restriction was found to be the best predictor for fatigue severity. Note that the majority of the participants were overweight or obese (BMI [greater than or equal to] 25), which may be due to the effect of steroids taken as part of the MG treatment or the decreased physical activity resulting from MG fatigue, or both. Indeed, obesity may be a contributing factor to the decrease in physical activity. Although BMI showed no correlation with the MGFS score, it did correlate with activity restriction (r = .289, p < .05). It would follow that as a patient's BMI increases, activity restriction is increased, and the patient experiences more fatigue. The link between BMI, activity restriction, and fatigue severity needs further investigation. Study results support nurses' efforts to educate patients about BMI and management of excess weight and obesity, particularly patients with MG. Results also support the use of depression assessment in MG patients to determine whether treatment is necessary, because fatigue and depression are correlated.

To increase the generalizability of results, replication of the study is needed with in other geographic areas and participants in various stages of disease severity according to MG Foundation of America (MGFA) Clinical Classification. Further studies might take into account other physiological and psychological variables, such as hemoglobin level, anti-acetylcholine receptor antibody level, use of dietary supplements and herbal medications, and social support efforts, all of which may influence the fatigue severity level. A longitudinal study following MG patients for a period of time to ascertain whether their individual level of fatigue severity, activity restriction, and disease severity change over time would be interesting, as well as a survey of methods used to cope with fatigue.


Results of this study indicate that the MGFS has good internal consistency and test-retest reliability, validating the initial psychometric properties of Grohar-Murray et al. (1998). In the presence of five demographic and clinical covariables, the correlation between the MGFS fatigue score and the disease severity (muscle weakness) score approached statistical significance, but was not significant. The MGFS score correlated moderately with depression and at a low to moderate level with subscales of the CFS, another instrument used to measure fatigue. The results from stepwise multiple regression analysis, controlling for depression, demonstrated that of nine selected demographic and clinical variables, the best predictor of fatigue severity was activity restriction. BMI, although not correlating significantly with fatigue score, does correlate positively with activity restriction. Knowledge of the relationship between fatigue severity and physiological/psychological factors such as activity restriction and depression will help healthcare providers provide comprehensive care to their MG patients, resulting in improving patients' quality of life. It also will lay the groundwork for further investigations.
Table 1. Sample Demographic and Clinical
Characteristics of MGFS Study Participants (N = 67)

Characteristics Percentage Mean Deviation

Age (range 24-67 years) 48.4 12.44

 Male 47.8
 Female 52.2

 White 70.1
 Black 29.9

Body Mass Index 31.8 6.75
 (range 21.4-53.5)
 [less than or equal to] 24.99 22.4
 25-29.99 26.9
 [greater than or equal to] 30 50.7

Type of MG
 Ocular MG 7.5
 Generalized MG 92.5

Years Since MG Diagnosis
 (range 1-37 years)
 [less than or equal to] 5 46.2
 >5 and [less than or equal to] 10 25.4
 >10 28.4

Other Health Conditions 68.7

 Total number taken 4.4 2.76
 (range 1-15)
 Pyridostigmine 82.0
 Prednisone 61.2
 Azathioprine/other 32.8
 immunosuppressive drug

 Yes 50.7
 No 49.3

Level of Activity
 Fully active 32.8
 Restricted in activity 58.2
 Needed help with walking 9.0

Table 2. Internal Consistencies of MGFS and Its
Subscales (N = 67)

 No. of Cronbach's
Instrument Items Alpha

Myasthenia Gravis Fatigue Scale Total Scale 26 .934
Myasthenia Gravis Perception Subscale 9 .879
Myasthenia Gravis Task Avoidance Subscale 8 .855
Myasthenia Gravis Motor Sign and 9 .850
 Symptom Subscale

Table 3. Correlation Matrix for MGFS, MQMG, CFS-Phys, CFS-Ment,
CFS-Total, and CES-D, Adjusted for Five Demographic and Clinical
Variables (a)


MQMG .237 --
CFS-Phys .391 *** .070 --
CFS-Ment .313 ** .155 .487 ****
CFS-Total .411 *** .114 .894 ****
CES-D .515 **** .305 *** .298 **

 CFS-Ment CFS-Total CES-D

CFS-Ment --
CFS-Total .800 **** --
CES-D .493 **** .436 **** --

Key: CES-D = Center for Epidemiologic Studies-Depression Scale

CFS-Ment = Chalder Fatigue Scale for mental fatigue

CFS-Phys = Chalder Fatigue Scale for physical fatigue

CFS-Total = Chalder Fatigue Scale for total fatigue score

MGFS = Myasthenia Gravis Fatigue Scale

MQMG = Modified Quantitative MG Score for Disease Severity.

(a) Age, gender, body mass index, number of years since diagnosis,
history of thymectomy

** p < .05

*** p < .01

**** p < .001

Table 4. Summary of Stepwise Multiple Regression Analysis for
Myasthenia Gravis Fatigue Scale Score

Model Variables Entered (a) [R.sup.2] df F change

Covariable Depression .370 1, 63 37.02 ****
Stepwise Activity restriction .191 1, 62 29.96 ****
Independent Number of years .025 1, 61 3.625 *
 Variables since diagnosis

Model B SE B [beta]

Covariable .874 .169 .454
Stepwise 15.875 3.00 .464
Independent .386 .203 .157

(a) Variables that did not enter the regression model: age, gender,
body mass index, number of current medications, other morbidity,
history of thymectomy, and disease severity (Modified Quantitative MG
Score for Disease Severity).

* p < .l

**** p < .001


This study was a part of dissertation research (Waraluk Kittiwatanapaisan) and was supported by the Katherine Donohoe Nursing Research Fellowship, MGFA.


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Questions or comments about this article may be directed to: Dorothy K. Gauthier, PhD RN, by phone at 205/934-6578 or by email at She is an associate professor of nursing in the University of Alabama School of Nursing at the University of Alabama at Birmingham.

Waraluk Kittiwatanapaisan, DSN RN, is an instructor in the fundamentals of nursing department at Khon Kaen University, Khon Kaen, Thailand.

Anne M. Williams, PhD RN, is an assistant professor of nursing in the School of Nursing at the University of Alabama at Birmingham.

Shin J. Oh, MD, is a professor in the department of neurology at the University of Alabama at Birmingham.

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Author:Kittiwatanapaisan, Waraluk; Gauthier, Dorothy K.; Williams, Anne M.; Oh, Shin J.
Publication:Journal of Neuroscience Nursing
Date:Apr 1, 2003
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