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Identifying predictor factors of adherence in patients with multiple sclerosis, Tehran MS society 2013.

INTRODUCTION

The disease multiple sclerosis which is called MS is a chronic disease of nerve system which involves parts of brain and spinal cord. The MS patients may lose some of their capabilities due to this disease. This disease mostly involves young, smart and active members of society. The cause of the disease is not known completely and no precise cure is suggested for it. However, some information about disease, knowing the personal aspect of the patients and supportive actions can be effective in bringing the patients back to the active life). The symptoms are different considering the place and intensity of attacks. Symptom intensity can cause attacks to be better or worse. Intensity of symptoms can last for days, weeks or months and they can be worst. The return of the disease is common. Although it is possible that the disease periods are without improvement and attacks are increased.

Research Background:

MS disease is one of the diseases related to central nerve system. Millen is a combination of fat which act as a cover for nerves and facilitates the fast transform of the nervous messages. The feature of MS is inflammation, millen and gliose. The disease is forerunner. Damages of MS emerge in different times and places in central nerve system. 350000 of people in United States and 2.5 million all over the world have MS. In western societies MS is the second most common cause of neurologic disability. Appearance of MS can be from benignant to the fast speed which makes basic changes in life style of the patients. A study in 2013 in America about the relationship between constant and varied following for post treatment changes in MS patients showed that evaluation and constant adherence of the patients in taking medicine significantly decreased the treatment adherence. A study in 2014 about predicting the life quality of MS patients based on disease perception was done on 100 patients. The results showed that disease perception predicts physical and mental dimensions.

Statement of the Problem:

MS is one of the chronic and disabling diseases which cause a lot of disabilities in young and middle age patients. This disease is common among young adults and appears with various pathologic signs in central nerve system. There is no precise statistics about the disease in Iran but the MS society stated that MS patients in Iran are about 70000. The disease has different treatments but it is still one of the most disabling diseases which affect different aspects of human life and specially their life quality. Based on the importance of treatment adherence and the studies which have been done about not adherence, it is clear that mental-social factors are effective in adherence. So, the mental aspects are investigated in this research.

Social support: it is related to the actions taken for an upset person by family members, friends and colleagues and these actions include helping tools, social-emotional help and informational help. Self-efficacy: it includes the beliefes or judgments of a person and his abilities in doing the tasks and responsibilities. With regard to the issues raised, are the characteristics on adherence to MS treatment effective?

The research aims to identify predictors of adherence and non-adherence to treatment in patients with multiple sclerosis.

Research hypothesis:

There are significant differences between the independent variables (self-efficacy, personality traits, positive and negative affect, satisfaction with services, social support, anxiety, quality of life, depression, and medications feedback MS) in a predictable and non-predictable group dependence.

There are significant differences between self-efficacy and adherence and non-adherence to treatment in multiple sclerosis patients.

Descriptive findings:

To learn more about this the study population, some demographic characteristics collected by demographic questionnaire are presented and discussed using tables and graphs.

Gender:

As it is seen in Table 1-4, out of 120 respondents, 5/17% are male respondents and 5/82% are female. Therefore, the proportion of female is more than male respondents.

Age group:

The age of subjects was calculated per year and an average and standard deviation based on the questionnaire. Respondents' age average as 90/31 years old and a standard deviation is 45/8. The mean age of the sample shows that most patients are at a young age. The minimum age of the respondents was 17 years old and the maximum age is 58 years old.

In order to obtain better results in this case, the age of the respondents was classified into different categories. As it is seen in Table 3-4, maximum age groups are less than 25 years. So that, 32 (7/26 percent) of respondents are under 25 years of age. 3/23 percent of respondents are between 30 and 35 years, 7/21% between 25 to 30 years, 5/12% between 40 to 45 years and 5/12% are between 35 to 40 years old. The age group over 45 years is the least abundant with 9 patients (5/7 per cent).

Low numbers of each interval are in the same category and the top number is on the next category.

Marital Status:

Table (4-4) among respondents under review, 65 (2/54 percent) were married and 48 (40%) were single. Also 4.2 of respondents are divorced, 8/0 and 8 percent are widowed. Most of the respondents are married.

Married respondents in this sample were asked about their age at marriage. The respondents' average age at marriage was 2. The minimum age at marriage among married was 11 years and the maximum age was 31 years. The standard deviation represents that the difference in age at marriage is almost 4 years.

Age of divorced participants at the time of divorce was 83/35. Minimum age at separation was 26 years and maximum was 47 years.

The findings related to the research hypotheses:

First hypothesis: variables of positive and negative affect, anxiety, physical and mental quality of life, social support, depression, neuroticism, extraversion, flexibility, agreeableness, consciousness, self-efficacy and patient feedback to physicians (independent variables) can predict patient feeling about treatment (dependent variable).

In the stepwise regression, the independent variables in entered the model and removed from the model at each phase. The following table is a summary of the variables and the variables removed out of the model.

As seen in the above table, stepwise regression has two phases. Firstly, the physical life is the first variable because of greater correlation with the dependent variable (feeling sick to treatment) and positive variable affect entered into the regression model as the second variable. So, from 14 independent variables, only two variables of life and positive affect physical performance entered the models and other variables remained outside of the model.

The summary of the fitted regression model are seen in tables' below. As it can be seen, the multiple correlation coefficients, [R.sup.2] Determination coefficient and modified correlation [R.sub.adj.sup.2] and standard error are represented. It should also be mentioned that the obtained output should always be based on findings and final digits of the model.

Thus, it can be stated that the correlation of feeling about treatment in a linear combination of the variables entered in row 1 (physical life) in the equation is equal to 0.297. The resulting coefficient of determination equals to 0.088 and the coefficient of determination equals to the discounted 0.080. More than 8% of the variance feeling about treatment can be explained and justified through the independent variable of life and physical functioning, and the rest belongs to other variables.

The correlation of feeling about treatment in a linear combination of the second row, when the variables of physical performance and positive feeling enter into the model is o,346. The adjusted coefficient of determination is 0.105, which explained and justified 10.5% of the variance in patient treatment by combining a sense of life and positive and affect physical performance variables.

The table above shows the significance of the determination coefficient. In fact, when we want to know that the coefficient is significant or not, variance analysis is applied. So, F of the model for physical performance with the average variance regression to the left variance is 11.397 and significance level of 0.001. It shows that there is a linear correlation between the physical performance and patients feelings about treatment. In other words, regression is significant.

In addition the F statistics, when the emotional factor enters the model is 7.96 and the level of significance is 0.001. So, there is a linear relationship between linear combination of the physical performance and positive emotions and feeling of the patient about treatment. In other words, regression is significant.

The related statistics are shown in the table below.

Table 4-52: Regression coefficient.

Model                   Non-standard    Standard       t       sig
                        coefficient    coefficient

                        B       SD        Beta

1          width      3.665    0.320                 11.451   0.000
         Physical     -0.017   0.005      0.297      -3.376   0.001
        performance

2          width      4.511    0.519                 80.688   0.000
         Physical     -0.013   0.005     -0.233      -2.531   0.013
        performance

         Positive     -0.032   0.015     -0.189      -2.052   0.042
          emotion


Table shows that there is significant relationship between independent physical performance and positive emotion in the model and also shows that the second model is significant and equals 4.511 and coefficient of physical performance of life and positive emotion as -0.233 and -0.189. So, the following model can be introduced as regression model:

Patient's feeling about treatment = 4.511 - 0.233 x (physical performance of life) - 0.189 x (positive emotion).

How much is the predictor role between the variables of self-efficacy, personal features, positive and negative emotion, service satisfaction, social support, anxiety, feedback, life quality and depression in group adherence of patients?

Recognition analysis is used for studying the question.

As, it is seen in table 4-70, there is a significant difference among the equality test of family support (f = 4.48, p = 0.3), mental health (f = 0.94, p = 0.01), vitality (f = 7.47, p = 0.007), and lack of activity due to mental problems (p = 0.002, F = 10.00), physical problems (f = 6.67, p = 0.01), physical performance (p = 0.03, f = 4.75), physical dimension (f = 6.31, p = 0.01), mental dimension (f = 8/80 , p = 0.004), extroversion (f = 4.81, p = 0.03), consciousness (p = 0.01, F = 6/14) an depression (f = 4.67, p = 0.033). But there is no significant difference in other variables.

According to table 4-71, the activity limitation due to mental problems with F = 10.004 is significant at the level of 0.002 and is one of the most important variable of the research in recognizing the people attachment to adherence or non-adherence at first stage.

Conclusion:

In the preset study demographic, depression, personal characteristics, self-efficacy, patients' satisfaction, negative and positive emotion, anxiety, life quality, social support and feedback of the MS patients is studied in treatment adherence.

According to the findings of the study, it is concluded that : there is a significant difference between the self-efficacy, personal characteristics, positive and negative emotion, service satisfaction, social support, anxiety, life quality, depression, feedback and MS medicines in predicting and not predicting the group adherence.

The hypothesis is tested by the multivariate variance analysis and based on the indexed of variance analysis in adherence and non-adherence with significant level of 0.03 has significant difference with at least one of the variables. So, it can be concluded that there is a significant and positive relationship between all the variables.

It can be concluded that the variables of positive and negative emotion, anxiety, physical and mental quality of life, social support, depression, extroversion and others. It is found that there is a linear significant relationship between the physical life performance and positive emotion with the patients feeling about treatment. Jundarhan and colleagues (2002) found that physical problems have the highest effect on life quality of the MS patients.

ARTICLE INFO

Article history:

Received 15 April 2014

Received in revised form 22 May 2014

Accepted 25 May 2014

Available online 15 June 2014

REFERENCES

[1] Ackerman , K.D., Heyman.

[2] Baum, A., 2002. Stressful life events precede Exacerbation of multiple sclerosis. Psychosom Med, 64(6): 916-20.

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[4] Costello, K., P. Kennedy, J. Scanzillo, 2008. Recognizing nonadherence in patients with multiple sclerosis and maintaining treatment adherence in the long term. Medscape J Med, 10(9): 225.

[5] Diener, E., R.A. Emmons, 1984. The independence of positive and negative affect. Journal of Personality and social psychology, 47: 1105-1117.

[6] Drentea, P., O.J. Clay, D.L. Roth, M.S. Mittelman, 2006. Predictors of Improvement in Social Support, Social Sciences & Medicine, 63: 957- 67.

[7] Dyment , D.A., G.C. Ebers, A.D. Sadovink, 2004. Genetics of multiple sclerosis . Lancet Neurol, 3: 104-110.

[8] Jolly, J.B., M.J. Dyek, T.A. Kramer, J.N. Wherry, 1994. Cognitive-content specificity improved discriminations of anxious and depressive symptoms . Journal of Abnormal Psychology, 103: 544-552.

[9] Judicibus, MA., MP. McCabe, 2007. The impact of financial costs of multiple sclerosis on quality of life. Int J Behav Med, 14(1): 3-11.

[10] Kantarci, O.H., B.G. Weinshenker, 2005. Natural history of multiple sclerosis . Neurol Clin , 23(1): 17-38.

[11] Kurt, D., DD. Thomas, AG. Barbour, 2002. Stress role in multiple sclerosis in women . Departments of Psychiatry. Neurology psychology at the University of Pittsburgh . Washington, USA.

[12] Kurtzke, JF., 2000. Epidemiology of multiple sclerosis , Does this really point toward anetiology? Neurol Sci., 21(6): 383-403.

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[15] Zwibel, H., G. Pardo, Sh. Smith, D. Denney, M. Oleen-Burkey, 2011. A multicenter study of the predictors of adherence to self-injected glatiramer acetate for treatment of relapsing-remitting multiple sclerosis . J Neurol, 258: 402-411.

(1) Mona Nemati, (2) Seyed Mousa Golestaneh, (3) yousef dehghani

(1) Department of psychology, Bushehr Science and Research Branch, Islamic Azad University, Bushehr,Iran.

(2) Asistant professor, Department of psychology, Persian Gulf University, Bushehr, Iran.

(3) Asistant professor, Department of psychology, Persian Gulf University, Bushehr, Iran.

Corresponding Author: Seyed Mousa Golestaneh, Asistant professor, Department of psychology, Persian Gulf University, Bushehr, Iran,

E-mail: Mgolestaneh@yahoo.com

Table 1-4: Gender frequency of the respondents.

         frequency   percent frequency

male     21          17.5
female               82.5
Total    120         100.0

Table 2-4: the statistics related to group age.

Number of   Min age   Max age   Mean age   SD
responses

120         17        58        31.90      8.45

Table 3-4: frequency of respondents' age

               frequency   Frequency
                           percent     percent

Less than 25   32          26.7        26.7
25-30          26          21 7        48.3
30-35          28          23.3        71.7
35-40          10          8.3         80.0
40-45          15          12.5        92.5
More than 45   V           7.5         100.0
Total          120         100.0

Table 4-4: Frequency of marital state among the respondents.

            frequency   frequency percent

Single      48          40.0
married     65          54.2
separated   1           0.8
Divorced    5           4.2
widowed     1           OS
Total       120         120

Table 4.5: Statistics relating to marriage among married participants.

Number of   Min age   Max age   Mean age   SD
responses

71          11        31        21.49      4.07

Table 4.6: Shows the statistics related to age at the time of
separation between the divorced and separated respondents.

Number of   Min age   Max age   Mean age   KD
responses

6           26        47        35.83      7.27

Table 4-49: Variables entered and removed from the regression model.

model     Entered      Removed         Method
         variables    variables

1        Physical                 <= .050enterance
        performance               >= .100 removal

2        Positive                 <= .050enterance
          emotion                 >= .100 removal

Table 4-5: Summary of model information.

row   Correlation   Determination     Modified      Measured
      coefficient    coefficient    determination      SD
                                     coefficient

1        0.297          0.088           0.080        1.203
2        0.346          0.120           0.105        1.187

row              Change statistics

      square     F      df   df    F sig
        R

1     0.088    11.397   1    118   0.001
2      0.32    4.212    1    117   0.042

Table 4-51: ANOVA

row   coefficients   squares   df     Mean      F       sig
                                     square

1      regression    14.497     1    16.497   11.397   0.001
        residual     170.803   118   1.447
         total       187.300   119
2      regression    22.433     2    11.216   7.960    0.001
        residual     164.867   117   1.409
         total       187.300   119

Table 4-70: The mean equality of the groups.

Predictor variable     Wilkes     F     DF1   DF2     P
                       lambda

Positive emotion        0.98    1.89     1    118   0.17
Negative emotion        1.00    0.001    1    118   0.97
Hidden anxiety          0.99    0.51     1    118   0.58
Obvious anxiety         0.99    0.30     1    118   0.99
Total anxiety           1.00    0.00     1    118   0.99
Family support          0.96    4.48     1    118   0.03
Friends support         0.99    0.22     1    118   0.63
Others support          0.99    0.91     1    118   0.34
Total support           0.98    1.26     1    118   0.26
Physical pain           0.97    2.92     1    118   0.09
Social pain             1.00    0.00     1    118   0.09
Mental health           0.94    6.81     1    118   0.01
Total health            0.99    0.52     1    118   0.47
vitality                0.94    7.47     1    118   0.007
Limitation due          0.92    10.00    1    118   0.002
to mental problems

Limitation due to       0.94    6.76     1    118   0.01
physical problems

Physical performance    0.96    4.75     1    118   0.01
Physical dimension      0.94    6.31     1    118   0.01
Mental dimension        0.93    8.80     1    118   0.004
Self-efficacy           0.98    2.13     1    118   0.14
Moodiness               0.99    0.15     1    118   0.69
Extroversion            0.96    4.81     1    118   0.03
openness                0.98    1.70     1    118   0.19
Acceptability           1.00    0.004    1    118   0.94
Consciousness           0.95    6.14     1    118   0.01
Beck depression         0.96    4.76     1    118   0.03
Feedback                0.98    1.35     1    118   0.24
Patient satisfaction    0.98    1.37     1    118   0.24

Table 4-71: Input/output variable.

First step   Entered variable    F value    Sig

1            Limitation due to   10.004    0.002
              mental problems

Fig. 1-4: Frequency diagram of Respondents by Gender.

female   82.5
male     17.5

Note: Table made from pie chart.
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Author:Nemati, Mona; Golestaneh, Seyed Mousa; Dehghani, Yousef
Publication:Advances in Environmental Biology
Article Type:Report
Geographic Code:7IRAN
Date:Jun 20, 2014
Words:3091
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