The Urdu speaking participants understood and replied to the questions related to socio-demographic questions. The researcher, for the purpose of analysis, derived the variables which were related to cultural differences in the interpretation. The questions related to “socio-demographic” in the questionnaire are presented in Table 1.
Patients’ demographic and characteristics
The characteristics of the patients related to diabetes and demographic are presented in Table 2 and used further for statistical analysis. In relation to age, 41.5% participants were aged between 40–49 years, 44.5% participants were aged between 50–59 years and 14% participants were aged between 60–65 years. For statistical purposes, only two categories of age were considered (40 - 59 years, ˂ 60) and (60–65 years, ≥ 60). The table 2 presents equal number of participants in each group (males and females). Table 2 shows that 33% of participants had no formal education and 70% had a yearly income of less than 120,000 Pakistani Rupees (< US$1200). Sixty-one percent (61%) of the recruited sample had been diagnosed with Type 2 diabetes for more than eight years. The blood glucose testing records indicated that only 9% of the participants had blood glucose level (HbA1c) in range recommended by the American Diabetes Association (19).
In order to explore the self-management practices of the participants, the self-management activities were divided into two categories: satisfactory practice and the unsatisfactory practice. The Table 3 shows that the two activities were least practiced namely the testing of blood glucose levels (n = 178, 89% _4 days/week) and the foot care (n = 145, 73% _ 2 days/week). The other activities such as medication (n = 130, 65% = 7 days/week), diet (n = 148, 74% _ 3 days/week), and exercise (n = 127, 63% _ 3 days/week) found to be using self-management activities adequately.
Summary of Diabetes Self-Care Activities (U-SDSCA)
The table 2 above presents patients’ characteristics and using these variables in table 2, descriptive statistics was calculated for the independent and dependent variables including the frequencies for binary and categorical exposure variables of interest. It was also decided to include in the analysis the medication sub-scale extension of the U-SDSCA as medication self-care plays an important role in the self-management outcome.
The Urdu-version of SDSCA
The Table 4 shows the descriptive outcomes of Urdu-version of SDSCA items, the patients showed low to medium levels of self-management activities which are below the guidelines of ADB (19). The activities refer to the sub-scale of the instrument to record how many days patients performed the specified self-care activity, based on a seven-day interval. The minimum number of days is “0” while the maximum is “7”. It may be seen from the Table 4, that the items 1 ad 2, which represents healthy eating plan and eating plan over the past month, the means were in the middle of the scale between 1–7 (mean = 3.36, σ = 2.08; mean = 3.33, σ = 1.71).
The results for Items 3 and 4 on the exercise sub-scale give mixed information—for 20 minutes exercise, the mean was 2.97 with σ = 1.43 as compared to the mean value of 2.59, σ = 0.80 for specific exercise. The items 5 and 6 represents blood glucose testing which showed overall low means results (mean = 1.95, σ = 0.76; mean = 1.66, σ = 0.56). This indicates that patients did not adhere to blood glucose testing plans and recommendations. The main reason may be that the blood glucose testing strips are very expensive and patients are not in a position to buy these.
The items 7 and 8 of Table 4 represents the means for foot-care which also indicate low responses (mean = 1.87, σ = 1.63; mean = 1.84, σ = 0.86). The patients’ smoking status, recorded as Yes or No, women in this sub-sample (n = 100) informed that they don’t smoke and only 5 male participants out of 100 male participants (5 %) indicated at the time of data collection that they were smokers but not smoking regularly. Therefore, smoking variable has been removed from any further analysis due its non-significance.
The Table 5 presents the means for the instrument U-SDSCA sub-scales. The self-management activities are displayed from the least activity to the most practiced activity: blood glucose testing (mean: 2.25 ± σ = 1.11); foot-care activities (mean: 2.28± σ = 0.45); exercise activities (mean: 2.94 ± σ = 0.82); diet activities (mean: 3.40 ± σ = 1.06); and medication (mean: 6.17 ± σ = 1.18).
Table 6 provides the Urdu-version of SDSCA sub-scale categories for the participants whose practices were within the recommended guidelines of ADA (19) were coded as “1”, and those who were not were coded as “2”.
The association between the patients’ characteristics and the self-management activities in relation to the sub-scales means was investigated using independent t-test with two tailed significance level. Information from Table 2 were utilized to see the differences of the different exposure (independent) variables such as Age, Sex, formal education, income, diabetes Time and blood glucose (HbA1c) with the participants self-management activities such as medication adherence, diet, exercise, foot care and blood glucose monitoring as displayed in Table 5.
In previous analysis (table 5), the high mean value of the medication subscale (Mean: 6.17 ± 1.18 SD) suggested that people with diabetes in the rural area of Pakistan rely on medications to control their blood glucose level. A close review of independent sample t-test results in Table 7 on medication sub-scales for exposure variables age and sex revealed that adherence to medication was most evident among older participants (age ≥ 60 years) with (diff = -.306, p = 0.20) as compared to the participants with age group < 60 years. In addition, female participants have shown better outcome in relation to medication adherence (diff =.15, p =.370) as compared to male participants. The overall results of independent t-test on medication sub-scales are shown in Table 7.
It may be observed from the independent sample t test outcomes in Table 8 that there is a difference in dietary practices between the genders and blood glucose control. The table 8 shows that females are more likely to adhere to an appropriate diet than males (Diff.09; p =.548) and participants whose blood glucose was controlled also scored higher in dietary practice compared to those whose blood glucose was uncontrolled (Diff.29; p =.253). The overall results of independent sample t-test on diet sub-scales are shown in table 8. These differences are not statistically significant as p-vales >0.05.
It may be observed from the independent sample t test outcome in Table 9 that there is a difference on exercise practices between the genders and blood glucose control. The table 9 shows that younger participants (< 60 years of age) are more likely to exercise than older participants (mean 3.28 vs mean 2.89). The means difference is almost statistically significant at p<0.05 (p = 0.056). The participants diagnosed with type 2 diabetes for less than eight years seem to undertake regular exercise than those who diagnosed for a longer time period (diff.241; p =.328). The overall results of independent sample t-test on exercise sub-scales are shown in Table 9.
Blood Glucose Monitoring Sub-scale
Table 10 shows that male participants are less inclined to do blood glucose monitoring than female participants (Diff =.22; p =.160). The participants who had a lower income tested blood glucose less often than participants who had a higher income (Diff = -.598; p =.003) which is statistically significant (p ˂ 0.05). The overall results of independent sample t-test on blood glucose monitoring sub-scales are shown in Table 10.
Multivariate Regression Analysis
The multivariate regression analysis was carried out between the independent variables or predictor variables using participants’ characteristics and their total self-management score and the results are given in Table 11. The participants’ characteristics accounted for 21% of the variability in the total self-management score (R² = 0.211). In addition, women were more inclined to undertake appropriate diabetes self-management activities (β.302; p =.000).
The other statistically significant associations were between income and the level of glucose control (HbA1c). The participants with higher income (> 120,000 Pak Rs) were more likely to undertake appropriate diabetes self-care activities (β =.118; p =.050) than those with lower income. This was also reflected on blood glucose monitoring as participants with uncontrolled glucose level (HbA1c >7%) were unlikely to undertake appropriate diabetes care activities than those with controlled glucose levels (β = -.119; p =.051).
As mentioned previously, the main reason may be the high cost of glucose testing strips which is beyond the reach of participants with low income. The other independent variables related to patients’ characteristics did not have much impact on the total self-management activities.