Descriptive Statistics
Frequencies and percentages. A majority of the participants were female (n = 165,
84.2%). The age of participants heavily varied between 36 to 40 years (n = 40, 20.4%) and 41 to 45 years (n = 48, 24.5%). The ethnicity of the participants was predominantly White (n = 159, 81.1%). Most of the participants were married (n = 101, 51.5%). The highest level of education varied between primary/vocational education (n = 49, 25.0%), secondary education (n = 53, 27.0%), and higher education (n = 94, 48.0%). Many of the participants were employed (n = 85, 43.4%). Roughly one quarter of the sample could not work or had physical challenges (n = 53, 27.0%). Table 1 shows the frequencies and percentages of the demographics.
Table 1
Frequency Table for Demographic Variables
Variable
|
n
|
%
|
Gender
Male
|
31
|
15.8
|
Female
|
165
|
84.2
|
Age
18-25
|
11
|
5.6
|
26-30
|
21
|
10.7
|
31-35
|
37
|
18.9
|
36-40
|
40
|
20.4
|
41-45
|
48
|
24.5
|
46-50
|
39
|
19.9
|
Ethnicity
Caucasian/White
|
159
|
81.1
|
African American/Black
|
18
|
9.2
|
Asian
|
4
|
2.0
|
Hispanic/Latino
|
5
|
2.6
|
Native American or Alaskan Native
|
4
|
2.0
|
More than one ethnicity
|
4
|
2.0
|
Other
|
2
|
1.0
|
Marital status
Single
|
58
|
29.6
|
Married
|
101
|
51.5
|
Separated/divorced/widowed
|
37
|
18.9
|
Highest degree or level of school completed
Primary/vocational
|
49
|
25.0
|
Secondary
|
53
|
27.0
|
Higher
|
94
|
48.0
|
Current employment status
Employed
|
85
|
43.4
|
Self-employed
|
5
|
2.6
|
Unemployed but looking for work
|
17
|
8.7
|
Unemployed but not currently looking for work
|
20
|
10.2
|
Student
|
6
|
3.1
|
Retired
|
10
|
5.1
|
Unable to work or physically challenged
|
53
|
27.0
|
Note. Due to rounding errors, percentages may not equal 100%.
A majority of the sample had a relapsing-remitting form of MS (n = 155, 79.1%). Anxiety, depression, and hypertension were the most prevalent comorbidities. Many participants had one comorbidity (n = 53, 27.0%), two comorbidities (n = 54, 27.6%), or three comorbidities (n = 22, 11.2%). Several participants lacked any comorbidities (n = 60, 30.6%). In regard to condition severity, most of the participants had normal symptom severity (n = 43, 21.9%), mild disability (n = 48, 24.5%), moderate disability (n = 43, 21.4%), or gait disability (n = 38, 19.4%). The pain intensity for the participants was approximately even in distribution from 0 (no pain) to 8 (severe pain). Table 2 presents the frequencies and percentages for the nominal level variables.
Table 2
Frequency Table for Nominal Variables
Variable
|
n
|
%
|
Type of MS
Relapsing-remitting
|
155
|
79.1
|
Primary progressive
|
9
|
4.6
|
Secondary progressive
|
5
|
2.6
|
Progressive-relapsing
|
7
|
3.6
|
Don’t know
|
20
|
10.2
|
Anxiety disorder diagnosis
Yes
|
81
|
41.3
|
No
|
115
|
58.7
|
Lung trouble diagnosis
Yes
|
4
|
2.0
|
No
|
192
|
98.0
|
High cholesterol diagnosis
Yes
|
23
|
11.7
|
No
|
173
|
88.3
|
Diabetes mellitus diagnosis
Yes
|
6
|
3.1
|
No
|
190
|
96.9
|
Depression diagnosis
Yes
|
103
|
52.6
|
No
|
93
|
47.4
|
Hypertension
Yes
|
41
|
20.9
|
No
|
155
|
79.1
|
Comorbidity frequency
0
|
60
|
30.6
|
1
|
53
|
27.0
|
2
|
54
|
27.6
|
3
|
22
|
11.2
|
4
|
4
|
2.0
|
5
|
3
|
1.5
|
Condition severity
|
|
|
Normal: I may have some mild symptoms, mostly sensory due to MS but they do not limit my activity. If I do have an attack, I return to normal when the attack has passed.
|
43
|
21.9
|
Mild disability: I have some noticeable symptoms from my MS but they are minor and have only a small effect on my lifestyle.
|
48
|
24.5
|
Moderate disability: I don’t have any limitations in my walking ability. However, I do have significant problems due to MS that limit daily activities in other ways.
|
42
|
21.4
|
Gait disability: MS does interfere with my activities, especially my walking. I can work a full day, but athletic or physically demanding activities are more difficult than they used to be. I usually don’t need a cane or other assistance to walk.
|
38
|
19.4
|
Early cane: I use a cane or a single crutch or some other form of support (such as touching a wall or leaning on someone’s arm) for walking all the time or part of the time, especially when walking outside. I think I can walk 25 feet in 20 seconds without a cane or crutch. I always need some assistance (cane or crutch) if I want to walk as far as three blocks.
|
7
|
3.6
|
Late cane: To be able to walk 25 feet, I have to have a cane, crutch, or someone to hold onto. I can get around the house or other buildings by holding onto furniture or touching the walls for support. I may use a scooter or wheelchair if I want to go greater distances.
|
9
|
4.6
|
Bilateral support: To be able to walk as far as 25 feet, I must have two canes or crutches or a walker. I may use a scooter or wheelchair for longer distances.
|
5
|
2.6
|
Wheelchair/scooter: My main method of mobility is a wheelchair. I may be able to stand and/or take one or two steps, but I can’t walk 25 feet, even with crutches or a walker.
|
4
|
2.0
|
Bedridden: Unable to sit in a wheelchair for more than one hour
|
0
|
0.0
|
Pain intensity
|
|
|
0 (no pain)
|
15
|
7.7
|
1 (mild)
|
20
|
10.2
|
2 (mild)
|
18
|
9.2
|
3 (mild)
|
20
|
10.2
|
4 (moderate)
|
11
|
5.6
|
5 (moderate)
|
18
|
9.2
|
6 (moderate)
|
42
|
21.4
|
7 (severe)
|
21
|
10.7
|
8 (severe)
|
24
|
12.2
|
9 (severe)
|
4
|
2.0
|
10 (severe)
|
3
|
1.5
|
Summary statistics. Comorbidity frequency ranged from 0.00 to 5.00, with M = 1.32 and SD = 1.17. Condition severity ranged from 0.00 to 7.00, with M = 1.92 and SD = 1.67. Pain intensity ranged from 0.00 to 10.00, with M = 4.63 and SD = 2.68. Fatigue scores ranged from 1.00 to 7.00, with M = 5.39 and SD = 1.41. Cognitive deficits scores ranged from 0.07 to 3.80, with M = 1.94 and SD = 0.87. Self-management scores ranged from 2.79 to 5.00, with M = 3.89 and SD = 0.43. QoL scores ranged from 21.76 to 95.49, with M = 56.37 and SD = 16.07.
Reliability. The researcher examined Cronbach’s alpha values for the series of items making up the FSS, the MSNQ, the MSSM-R, and the QoL scale. The results for all four scales met the acceptable threshold for internal consistency (α > .70). The other three instruments consisted of one survey item; therefore, the researcher could not conduct a reliability analysis. Table 3 presents the findings of the descriptive statistics for the variables of interest.
Table 3
Summary Statistics for Interval Variables
Variable
|
Min.
|
Max.
|
M
|
SD
|
Skew
|
Kurtosis
|
# of
items
|
Α
|
Comorbidity
|
0.00
|
5.00
|
1.32
|
1.17
|
0.69
|
0.15
|
1
|
–
|
Condition severity
|
0.00
|
7.00
|
1.92
|
1.67
|
0.99
|
0.77
|
1
|
–
|
Symptomatology Pain
|
0.00
|
10.00
|
4.63
|
2.68
|
-0.20
|
-1.08
|
1
|
|
Fatigue
|
1.00
|
7.00
|
5.39
|
1.41
|
-1.12
|
0.68
|
9
|
.93
|
Cognitive deficits
|
0.07
|
3.80
|
1.94
|
0.87
|
-0.19
|
-0.82
|
15
|
.95
|
Self-management
|
2.79
|
5.00
|
3.89
|
0.43
|
-0.22
|
-0.34
|
24
|
.77
|
QoL
|
21.76
|
95.49
|
56.37
|
16.07
|
0.27
|
-0.65
|
31
|
.92
|
Normality. The researcher examined the skewness and kurtosis values to test the normality assumption. The researcher used the following criteria to assess normality: skew between -2.0 and 2.0 and kurtosis between -7.0 and 7.0 (Kline, 2010). The values for skew and kurtosis fell within the acceptable range. Further assessment of the normality assumption occurred through use of the Kolmogorov-Smirnov test and the Shapiro-Wilk test. The researcher concluded that the findings of the tests were not statistically significant for self-management and QoL; therefore, these variables met the assumption. The findings of the Kolmogorov-Smirnov test and Shapiro-Wilk test, however, were not significant for the remaining variables (p < .05). Comorbidity, condition severity, pain intensity, fatigue, and cognitive deficits did not meet the assumption. Stevens (2009) suggested that samples with sums of 50 or more observations often approximate to normality. With a total sample of 196 participants, the current researcher assumed that the distributions approximated toward normality. Table 4 presents the findings of the Kolmogorov-Smirnov and Shapiro-Wilk tests.
Table 4
Kolmogorov-Smirnov and Shapiro-Wilk Tests
Variable
|
Kolmogorov-Smirnov test
|
Shapiro-Wilk test
|
Statistic p
|
Statistic p
|
Comorbidity
|
0.18
|
<.001
|
0.88
|
<.001
|
Condition severity
|
0.17
|
<.001
|
0.89
|
<.001
|
Symptomatology Pain
|
0.18
|
<.001
|
0.94
|
<.001
|
Fatigue
|
0.14
|
<.001
|
0.89
|
<.001
|
Cognitive deficits
|
0.07
|
<.001
|
0.98
|
<.001
|
Self-management
|
0.06
|
.200
|
0.99
|
.227
|
Quality of life
|
0.06
|
.200
|
0.99
|
.258
|
Bivariate Analysis
The researcher performed bivariate analyses to identify which predictor variables have a statistically significant relationship with the dependent variable (see Table 5). The findings revealed that comorbidity, condition severity, symptomatology (pain, fatigue, and cognitive deficits), and self-management had significant relationships with QoL. Comorbidity, condition severity, and symptomatology were inversely associated with QoL, while self-management was positively associated with QoL.
Table 5
Pearson Correlations for Bivariate Relationships
Source
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
1. Comorbidity
|
1.00
|
|
|
|
|
|
|
2. Condition severity
|
.13
|
1.00
|
|
|
|
|
|
3. Pain intensity
|
.33**
|
.41**
|
1.00
|
|
|
|
|
4. Fatigue
|
.33**
|
.29**
|
.51**
|
1.00
|
|
|
|
5. Cognitive defects
|
.39**
|
.20**
|
.42**
|
.54**/
|
1.00
|
|
|
6. Self-management
|
-.30**
|
-.09
|
-.08
|
-.08
|
-.33*
|
1.00
|
|
7. Quality of life
|
-.45**
|
-.32**
|
-.40**
|
-.42**
|
-.56**
|
.48**
|
1.00
|
Notes. *Denotes relationship is significant at α = .05.
**Denotes relationship is significant at α = .01.
Facilitators/Barriers and Quality of Life
The researcher used a multiple linear regression to examine the predictive relationship between comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) on QoL. The predictor variables corresponded to comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits). The continuous criterion variable corresponded to QoL.
Prior to analysis, the researcher assessed the assumptions of normality, homoscedasticity, and absence of multicollinearity. The results met the assumption of normality for the residuals due to the data closely following the normality trend line (see Figure 2). The assumption of homoscedasticity underwent visual assessment through a residuals plot. The results met the assumption due to a nonrecurring pattern appearing in the plot (see Figure 3). The results met the assumption of multicollinearity due to the variance inflation factors (VIF) being below 10 (see Table 6).
The results of the overall model of the multiple linear regression were statistically significant (F[5, 190] = 27.63, p < .001, R2 = .421). This suggests that a significant relationship exists between comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) and QoL. The R2 value suggests that approximately 42.1% of the variance in QoL can be explained by the predictors.
Comorbidity (t = -4.00, p < .001) was a significant predictor in the model, suggesting that with every one-unit increase in comorbidity scores, QoL scores decreased by approximately 3.38 units. Condition severity (t = -2.79, p = .006) was a significant predictor in the model, suggesting that with every one-unit increase in condition severity scores, QoL scores decreased by approximately 1.63 units. Cognitive deficits (t = -5.29, p < .001) was a significant predictor in the model, suggesting that with every one-unit increase in cognitive deficits scores, QoL scores decreased by approximately 6.68 units.
Table 6
Multiple Linear Regression With Comorbidity, Condition Severity, and Symptomatology (Pain,
Fatigue, and Cognitive Deficits) Predicting Quality of Life
Source
|
B
|
SE
|
β
|
t
|
p
|
VIF
|
Comorbidity
|
-3.38
|
0.85
|
-.25
|
-4.00
|
<.001
|
1.24
|
Condition severity
|
-1.63
|
0.58
|
-.17
|
-2.79
|
.006
|
1.21
|
Pain intensity
|
-0.40
|
0.42
|
-.07
|
-0.96
|
.340
|
1.59
|
Fatigue
|
-0.72
|
0.81
|
-.06
|
-0.89
|
.373
|
1.66
|
Cognitive deficits
|
-6.68
|
1.26
|
-.36
|
-5.29
|
<.001
|
1.55
|
Note. F(5, 190) = 27.63, p < .001, R2 = .421.
Facilitators/Barriers and Self-Management
The researcher conducted a multiple linear regression to examine the predictive relationship of comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) on self-management. A criterion in this regression model was the mediating variable.
Prior to analysis, the researcher assessed assumptions of normality for the residuals, homoscedasticity, and absence of multicollinearity. The assessment met assumption of normality for the residuals due to the data closely following the normality trend line (see Figure 4). This visual assessment of assumption of homoscedasticity featured a residuals plot. The assessment met the assumption due to a nonrecurring pattern appearing in the plot (see Figure 5). The assessment met assumption of multicollinearity due to the VIF being below 10 (see Table 7).
The results of the overall model of the multiple linear regression were statistically significant, (F[5, 190] = 7.99, p < .001, R2 = .174). This suggests that a significant relationship exists between comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) and self-management. The R2 value suggests that approximately 17.4% of the variance in self-management can be explained by the predictors.
Comorbidity (t = -3.23, p < .001) was a significant predictor in the model, suggesting that with every one-unit increase in comorbidity scores, self-management scores decreased by approximately 0.09 units. Cognitive deficits (t = -4.28, p < .001) was a significant predictor in the model, suggesting that with every one-unit increase in cognitive deficits scores, self-management scores decreased by approximately 0.17 units. Table 7 depicts the results of the multiple linear regression.
Table 7
Multiple Linear Regression with Comorbidity, Condition Severity, and Symptomatology (Pain,
Fatigue, and Cognitive Deficits) Predicting Self-Management
Source
|
B
|
SE
|
β
|
t
|
p
|
VIF
|
Comorbidity
|
-0.09
|
0.03
|
-.24
|
-3.23
|
.001
|
1.24
|
Condition severity
|
-0.02
|
0.02
|
-.07
|
-0.98
|
.328
|
1.21
|
Pain intensity
|
0.02
|
0.01
|
.09
|
1.11
|
.267
|
1.59
|
Fatigue
|
0.05
|
0.03
|
.16
|
1.88
|
.062
|
1.66
|
Cognitive deficits
|
-0.17
|
0.04
|
-.35
|
-4.28
|
<.001
|
1.55
|
Note. F(5, 190) = 7.99, p < .001, R2 = .174.
Self-Management and Quality of Life
The researcher conducted a linear regression to examine the predictive relationship of self-management on QoL. Prior to analysis, the researcher assessed the assumptions of normality for the residuals and homoscedasticity. The assessment met the assumption of normality for the residuals due to the data closely following the normality trend line (see Figure 6). The assessment met the assumption of homoscedasticity with a visual assessment through a residuals plot. The assessment met assumption due to a nonrecurring pattern appearing in the plot (see Figure 7).
The results of the overall model of the linear regression indicated a lack of statistical significance (F[1, 194] = 58.56, p < .001, R2 = .232), suggesting that a significant relationship between self-management and QoL exists. The R2 value suggests that approximately 23.2% of the variance in QoL can be explained by self-management. Self-management (t = 7.65, p < .001) was a significant predictor in the model, suggesting that with every one-unit increase in self-management, QoL scores increased by approximately 18.10 units. Table 8 presents the results of the linear regression.
Table 8
Linear Regression with Self-Management Predicting Quality of Life
Source
|
B
|
SE
|
β
|
T
|
p
|
Self-management
|
18.10
|
2.37
|
.48
|
7.65
|
<.001
|
Note. F(1, 194) = 58.56, p < .001, R2 = .232.
Mediating Effects of Self-Management
The researcher conducted a Baron and Kenny mediation analysis to assess whether self-management significantly mediates the effects of comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) on QoL in adults with MS. The researcher conducted three regressions to assess for mediation. For mediation to be supported, the results were required to meet four conditions:
- A relationship must exist between the independent variables (comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) and the dependent variable (QoL).
- A relationship must exist between the independent variables (comorbidity, condition severity, and symptomatology [pain, fatigue, and cognitive deficits]) and the mediator variable (self-management).
- A relationship must exist between the mediator variable (self-management) and the dependent variable (QoL) while in the presence of the independent variables (comorbidity, condition severity, and symptomatology [pain, fatigue, and cognitive deficits]).
- The independent variables (comorbidity, condition severity, and symptomatology [pain, fatigue, and cognitive deficits]) should not be significant predictors of the dependent variable (QoL) in the presence of the mediator variable (self-management).
First, the study included a regression conducted with comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) predicting QoL (dependent variable). The results of the regression were significant (F(5, 190) = 27.63, p < .001). Comorbidity, condition severity, and cognitive deficits were significant predictors in the regression model. The results partially met the first item of the Baron and Kenny method.
Second, the regression with comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) predicting self-management (dependent variable) occurred. The results of the regression were significant (F(1, 190) = 7.99, p < .001). Comorbidity and cognitive deficits were significant predictors in the regression model. The results partially met the second item of the Baron and Kenny method.
Finally, the multiple linear regression conducted with comorbidity, condition severity, and symptomatology (pain, fatigue, and cognitive deficits) and self-management (mediator) predicting QoL (dependent variable) occurred. The results of the regression were significant (F(6, 189) = 31.98, p < .001). Comorbidity, condition severity, cognitive deficits, and self-management were significant predictors in the regression model. With self-management being significant in the regression model, the results met the third item of the Baron and Kenny method. The predictor variables that were significant in the first regression model were also significant in the third regression. In addition, no drastic reduction or increase in the beta values occurred.
The results did not meet the fourth item of the Baron and Kenny method. Therefore, the results support partial mediation for self-management on the relationship between comorbidity, condition severity, and cognitive deficits on QoL. The results did not support mediation for self-management on the relationship between pain intensity and fatigue on QoL. Table 9 shows the results of the regressions.
Table 9
Regression Results with Self-Management Mediating the Relationship Between Comorbidity,
Condition Severity, and Symptomatology (Pain, Fatigue, and Cognitive Deficits) and QoL
Dependent
|
Independent
|
B
|
SE
|
β
|
t
|
p
|
VIF
|
Regression 1:
QoL
|
Comorbidity
|
-3.38
|
0.85
|
-.25
|
-4.00
|
<.00
1
|
1.24
|
|
Condition severity
|
-1.63
|
0.58
|
-.17
|
-2.79
|
.006
|
1.21
|
|
Pain intensity
|
-0.40
|
0.42
|
-.07
|
-0.96
|
.340
|
1.59
|
|
Fatigue
|
-0.72
|
0.81
|
-.06
|
-0.89
|
.373
|
1.66
|
|
Cognitive deficits
|
-6.68
|
1.26
|
-.36
|
-5.29
|
<.00
1
|
1.55
|
Regression 2:
Self-management
|
Comorbidity
|
-0.09
|
.03
|
-.24
|
-3.23
|
.001
|
1.24
|
|
Condition severity
|
-0.02
|
.02
|
-.07
|
-0.98
|
.328
|
1.21
|
|
Pain intensity
|
0.02
|
.01
|
.09
|
1.11
|
.267
|
1.59
|
|
Fatigue
|
0.05
|
.03
|
.16
|
1.88
|
.062
|
1.66
|
|
Cognitive deficits
|
-0.17
|
.04
|
-.35
|
-4.28
|
<.00
1
|
1.55
|
Regression 3:
QoL
|
Comorbidity
|
-2.35
|
0.81
|
-.17
|
-2.92
|
.004
|
1.31
|
|
Condition severity
|
-1.41
|
0.54
|
-.15
|
-2.60
|
.010
|
1.22
|
|
Pain intensity
|
-0.58
|
0.39
|
-.10
|
-1.48
|
.141
|
1.60
|
|
Fatigue
|
-1.30
|
0.76
|
-.11
|
-1.71
|
.089
|
1.69
|
|
Cognitive deficits
|
-4.64
|
1.23
|
-.25
|
-3.78
|
<.00
1
|
1.70
|
|
Self-management
|
11.90
|
2.12
|
.32
|
5.62
|
<.00
1
|
1.21
|
Notes. First regression: F(5, 190) = 27.63, p < .001, R2 = .421.
Second regression: F(5, 190) = 7.99, p < .001, R2 = .174.
Third regression: F(6,189) = 31.98, p < .001, R2 = .504.