create the summary of the findings "Methods and Materials/Patients
The statistical population included all members of SCI
Association of Guilan Province (N=133). Based on Krejcie and
Morgan Table [18], the sample size was determined to be 97.
All of the participants signed informed written consent before
participation. This study has been approved by Guilan University
institutional review board. As mentioned in the results' section,
power analysis in general linear model was used to assess the
adequacy of the sample size in regression analysis. The patients
aged over 16 suffering from SCI at least in the previous six
months were selected as the sample, and those who were born
with SCI were excluded from the study. To collect demographic
information, an author-made instrument was used. This
instrument consisted of age, gender, educational attainment, the
time elapsed from the injury occurrence, type of injury, bedsore,
the use of a urinary catheter, and the use of diapers, nurse, and
wheelchair. Other data collection instruments used in the present
study are as follows:
-The spinal cord injury quality-of-life-23 questionnaire
(SCIQL-23): This 23-item questionnaire has been developed
to assess health-related quality of life in SCI patients [19]. This
instrument has presented an excellent reliability and its alpha
coefficient has been reported to be more than 0.80 [20]. The
Farsi Version of this questionnaire, with an acceptable content
validity, has been tested on a sample of SCI patients in Iran
[21]. The last item of SCIQL-23 deals with the measurement of
Global Quality of Life (GQOL). The other 22 items are divided
into three subscales of function (FUBC) (physical and social
limitations), mood (MOOD) (psychological status of patients),
and injury-associated problems (PROB) (level of autonomy
and other issues related to the injury). All scores of these parts,
except for GQOL, are converted to achieve a scale with a range
of 0 to 100. Higher scores on FUNC, PROB, and MOOD parts
indicate weaker levels of quality of life [19,21].
-Beck Depression Inventory-Short Form (BDI-SF): This
questionnaire includes 13 sets of items that each of them
represents a state in patients. The score of each item ranges
between 0 and 3 and the total score will be between 0 and 39
[22]. However, it should be noted that a score of less than 4
indicates possible denial of depression, good pretense, so do
scores less than the usual level even for healthy people. In
addition, very high scores, even among severely depressed
ones, suggest a possible exaggeration of depression. The main
advantage of the short-form of this inventory is that it takes
about five minutes to fill it out. Beck et al. [23], by reviewing the
studies which had used this instrument, found that its reliability
coefficient using the test-retest method ranges between 0.48
and 0.86 depending on the interval between the frequency of
the test runs and the studied population. On the other hand,
based Hamilton Psychiatric Rating Scale for Depression
(HRSD), Zung Self-Rating Scale, MMPI Depression Scale, and
SCL-90, it has been shown that the mean correlation of BDI-SF
is more than 0.60 [24]. Reliability and validity coefficients of
this inventory in Iran have been reported to be 0.78 and 0.70-
0.90, respectively [25].
Procedure
Out of the 97 subjects of this study, 38 patients were
evaluated in SCI Association of Guilan Province in Rasht,
27 patients who were not able to visit the Association were
examined at their homes, and others were invited to Welfare
Administration of Rasht to complete the sample size and
evaluating them. First, SCI patients were visited by a physician
and then referred to a psychologist in order to fill out the
questionnaires. Before the beginning of the study, the subjects
were assured that their information will be kept confidential
and anonymous.
Statistical analyses
The obtained data were statistically analyzed using
SPSS-20 at a significance level of P<0.05. In order to
assess the relationship between distance variables, Pearson
correlation coefficient was used. In addition, independent
student t-test was used to compare scores of quality of life in
terms of demographic variables, damages, and demographic
features. Finally, after removal of demographic confounders,
hierarchical regression analysis was used to determine the role
of depressed mood in the prediction of quality of life scores.
Results
The gender distribution of subjects showed that 71 patients
(73.2%) were male and 26 patients (26.8%) were female. In
terms of marital status, 33 (34%), 51 (52%), 5 (5%), and 1
(3.1%) subjects were single, married, divorced, and widowed,
respectively. Other subjects did not answer the question
related to marital status. The highest and the lowest frequency
of educational attainment were related to high school
diploma (29.9%) and academic or elementary degree (6.1%),
respectively. In terms of occupation, 64 subjects (65.9%) were
unemployed and 11 (11.3%), 9 (9.3%), and 4 (4.1%) subjects
were self-employed, housewife, and clerk, respectively. Table
1 shows the frequency distribution of SCI patients to the global
quality of life level.
The study of the last item of SCIQL-23 showed that 18.5%
of patients were dissatisfied with their quality of life and
only 5% of them expressed their satisfaction in this regard.
In this study, to determine the main role of depressed mood
symptoms on the quality of life, it was necessary to control
all demographic confounders and the disease characteristics
which were significantly associated with the criterion variable
in hierarchical regression analyses. Hence, Pearson correlation
analyses were performed to examine the relationship between
distance variables (Table 2).
According to table 2, the mean age of subjects was
39.03±11.09. In addition, the mean duration of injury
and hospitalization were obtained as 126.39±104.01 and
47.82±50.55 days, respectively. Correlation coefficients
between the studied variables indicated that quality of life score
is only related to the duration of injury (P<0.05). This means
that the score of quality of life increases (lower levels of quality
IrJNS. 2017;2(4) IrJNS. 2016;2(1) 1511
Quality of Life in Spinal Cord Injury
Table 1. Frequency Distribution of Satisfaction with Quality of Life among SCI Patients (n=97)
Variable Satisfaction Frequency %
Global Quality of Life (GQOL)
Very Dissatisfied 18 18.5
Dissatisfied 36 37.1
Satisfied 38 39.2
Very Satisfied 5 5.1
Table 2. Correlation Coefficients Matrix of Some Demographic Variables and Quality of Life in SCI Patients (n=97)
Variables Mean Sandard
Deviation 1 2 3 4 5 6 7 8
1-T_score (total) for SCIQL-23 50.00 10.00 1
2-QoL _FUNC 13.88 7.33 0.923** 1
3-QoL_MooD 7.45 4.74 0.880** 0.698** 1
4-QoL_PROB 12.02 4.35 0.855** 0.664** 0.699** 1
5-Current Age (year) 39.03 11.09 0.105 0.126 0.060 0.075 1
6-Lesion Age (year) 28.34 11.55 0.150 0.136 0.137 0.128 0.677** 1
7-Lesion Duration (month) 126.39 104.01 -0.202* -0.168 -0.209* -0.170 0.329** -0.343** 1
8-Length of Hospitalization (days) 47.82 50.55 0.244 0.275 -0.025 0.311 0.085 -0.276 0.389 1
* p<0.05, **p<0.01
of life) as the duration of injury prolongs. On the other hand, no
significant relationship was found between quality of life and
age, age at the injury occurrence, and duration of hospitalization.
However, an inverse correlation was observed between the
current age of the patient and age at the time of injury (r=-0.67)
and injury duration (r=-0.32), which is statistically significant
(P<0.05). An inverse correlation was also found between age
at the time of injury and injury duration (r=-0.34). Table 3
presents the comparison of total scores of quality of life among
SCI patients in terms of demographic variables and the disease
characteristics. In this part, the subjects who had provided
unknown responses to demographic questions were excluded
from the analysis.
According to the results of tables 2 and 3, it was shown
that there is a significant difference between the studied groups
in terms of injury duration, gender, the state of paralysis, and
the use of nurse, wheelchair, catheter, and diaper (P≤0.05). As
shown in table 3, it can be found that female and tetraplegic
patients, patients with bedsores, and those who use nurse,
wheelchair, catheter, and diaper experience a poorer quality of
life. Therefore, these variables which presented a significant
relationship with the total score of SCIQL-23 in univariate
analyses were regarded as confounding variables and their
values were controlled in hierarchical regression analysis.
Before doing the main statistical analyses, the normality of
the criterion variable in regression analysis of the total score
of SCIQL-23 was examined using the histogram diagram
and comparing it with the normal curve (Figure 1). Before
performing this analysis, the data related to eight subjects
were excluded from statistical analysis because of the
missing information and the loss of more than 20% of their
questionnaire’s information.
Figure 1 shows that standardized regression residuals of
the quality of life scores follow a normal distribution. Normal
P-P diagram (Figure 2) also indicates that observed cumulative
probability or the points representing standardized residuals
of the criterion variable are close to the normal line. Thus, it
can be concluded that the criterion variable data are slightly
deviated from the normal distribution.
The results of Durbin-Watson test (DW=1.988) also
demonstrated that the assumption of independence of errors
for the regression analysis is established. In addition, power
analysis was used in order to determine the adequacy of the
sample size (n=97). The results of the general linear model
showed that the test power for the main variable (depressed
mood) is over 0.80 which suggests the adequacy of sample
size for regression analysis. Table 4 presents a summary of
regression model before and after controlling the confounding
variables within models 1 and 2.
The results shown in table 4 indicate that R2 and adjusted
R2 coefficients of the model improved after controlling the
confounding variables, as about 70% of changes in quality
of life can be explained by depressed mood in the second or
adjusted model. In addition, regression coefficient (R) for the
relationship between total score of quality of life and depressed
mood in the adjusted model was obtained to be at least 0.65
which, in any case, represents a strong correlation between
these two variables.
The results of hierarchical regression analysis in table 5
demonstrated that, after controlling the confounding variables,
depressed mood has a significant relationship with the total
score of quality of life and predicts its values (F=23.77,
P<0.0001). Signs of beta coefficients can be interpreted like
the matrix of correlation coefficients obtained in this study.
Nevertheless, it can be stated that standard deviation of
SCIQL-23 score of SCI patients increases by 0.66 with one
unit of increase in standard deviation of depressed mood score.
This means that quality of life of SCI patients declines with the
increase in depressed mood scores."