Self Efficacy

Self-Efficacy In CHF Statistics

In the current context of health care resources in both limited and increasingly in demand, patients with chronic illnesses can contribute to their own stance and self treatment. Self-management includes all the tasks that a person with a chronic disease or more can do to live a proper, including acquiring the confidence needed to take charge and manage the medical and emotional his or her disease. Recent data suggest that patients who come to manage themselves effectively enjoy better health outcomes and less use of the health care system. Health literacy plays a crucial role in the self-management of patients with chronic diseases. Indeed, to manage themselves, people with chronic or long-term need to be able to understand and evaluate the health information that is communicated to them, often in the context of a complex medical regimen. They must also know how to plan, change their lifestyles and make informed decisions. Finally, they must understand how to access care when they need it. This paper discusses the self management of patients with congestive heart failure with three different ways to provide CHF education that have been described as effective in the literature. With three interventions throughout the year, the data was collected from 150 patients, to understand the importance of self management in patients suffering from chronic diseases such as congestive heart failure.

To analyse the data, different set of tests were run using the variables from the data set. The data was collected through different means of communication, depending upon the availability of the patients. The three intervention groups included the ones who received monthly follow up via telephone, second group received monthly text or email follow-up and the third group received monthly in-person follow-up. Table 1 shows the frequency distribution of the intervention group of the data.

Table 1

One of three intervention group (1 = monthly telephone follow-up, 2 = monthly text or email follow-up, and 3 = monthly in-person follow-up)

Frequency Percent Valid Percent Cumulative Percent
Valid monthly telephone follow-up 48 32.0 32.0 32.0
monthly text or email follow-up 50 33.3 33.3 65.3
monthly in-person follow-up 52 34.7 34.7 100.0
Total 150 100.0 100.0

 

The frequency table shows the percent of respondents who were part of the different groups of interventions throughout the research. As per the table, it can be observed that about 32.0% of the respondents were contacted through telephone for their monthly follow-ups with 33.3% of the respondents receiving follow-ups through emails along with the 34.7% of the patients who received monthly in-person follow-up. A visual representation of the distribution of data presented in Table 1 is showed below in figure 1.1 in the form of a histogram of the variable TreatmentGroup. The normality of the curve on the histogram shows that the data that is being analysed here is normal and it represents the standard deviation of the data which is equal to 0.819.

Figure 1.1

Also, running the frequency of variable SignficantOther which represented the presence of significant other during initial education revealed that about 56.7% of the patients were not accompanied by their partners during the time of initial education about self management during the fight against their disease. Table 2 shows the frequency table of the variable followed by the histogram representation in Figure 2.1. In both cases the data is normal:

Figure 2:

Presence of significant other during initial education

Y Total

Frequency Percent Valid Percent Cumulative Percent
Valid n 85 56.7 56.7 56.7
65 43.3 43.3 100.0
150 100.0 100.0

Figure 2.1

The descriptive stats of the data describe the central tendencies. To calculate the central tendency it means to calculate the mean, median or mode of the given data. The mode is the number which is most repeated in the data, hence in the case of provided data the mode is 3 which represents white race therefore making the most patients being white however on the other hand, the mode for variable gender of patients represents that there is a higher percentage of male patients therefore making 2 the mode which represents male. Figure 3.1 and 3.2 present the mode values of race and gender of the patients.

Figure 3.1

Statistics

Race of patient

N Valid 150
Missing 0
Mode 3

Figure 3.1

Statistics 

Gender of the patient

N Valid 150
Missing 0
Mode 2

The reason why only mode is calculated for both variables is because both of these variables are nominal, as a nominal variable represents categories with no intrinsic ranking. Nevertheless, it is also important to analyse the dispersion of the values from the mean value of a variable along with the variance and the range of dispersion. Standard deviation helps determine whether the data is good and reliable. A lower number means that the data collected is reliable and a high value shows that the data are too variable and unreliable. The standard deviation of the variable Significant_Other represents the dispersion is low with only 0.0497 S.D value. The variance represents the value of 0.247 in figure 4.1. This low percentage shows that the data is not varied and is spread closely. Moreover, in Figure 4.2 the second variable, Patients_Race shows somewhat similar stats, with only 0.683 value of standard deviation and 0.466, hence making it in significant.

Figure 4.1

Statistics

Presence of significant other during initial education

Missing

N Valid 150
0
Std. Deviation .497
Variance .247
Range 1

 Figure 4.2

 Statistics

 Race of patient

Missing

N Valid 150
0
Std. Deviation .683
Variance .466
Range 2

After analysing the data, it was hypothesized that the two variables that measure the Presence of significant other during initial education and patient’s gender have a relationship as female partners tend to be more supportive when their significant others are going through a medical procedure.  Therefore it was hypothesized that male patients will have a higher number of YES responses to the variable Significant_Other as their partners would have been present at the time of their initial education. Therefore, to prove whether the hypothesis is correct or incorrect a scatter plot was formed with the two variables. Figure 5 shows the scatter plot of the two variables. Through this plot it can be observed that the hypothesis is proven wrong. The dots on the plot are significantly scattered making it impossible to connect or form a line. Hence, it has been proved that there is no correlation between the two variables of gender and significant other with R Sq Linear as low as 0.008.

Figure 5

Subsequent to analysing the relationship between significant other and gender of the patient, another hypothesis was formed regarding the gender of the patient and self efficacy at 12 months. The hypothesis suggested that female tend to be stronger emotionally as compared to men therefore the rate of self efficacy of woman will be higher by the end of the research as compared to men. To prove the hypothesis, a correlation was performed between the variables which presented the following statistics shown in Figure 6.

Figure 6

Correlations

Sig. (2-tailed) N Sig. (2-tailed) N

Gender of the patient measurement of self efficacy at 12 months
Gender of the patient Pearson Correlation 1 -.046
.574
150 150
measurement of self efficacy at 12 months Pearson Correlation -.046 1
.574
150 150

Figure 6 shows that the value of Pearson Correlation between the two variable is lower than 0.05. This means that there is a weak relationship between your two variables; therefore changes in one variable are not correlated with changes in the second variable. If our Pearson’s r were 0.01, we could conclude that our variables were not strongly correlated. This means that as one variable increases in value, the second variable decreases in value. The negative correlation concludes that the two variables are indirectly proportional to each other. Moreover, the Sig. (2-tailed) value is as high as .574 which proves that the changes that will occur indirectly between the two variables will not be statistically significant. Hence, the hypothesis assumed is proven incorrect.

Moreover, it was hypothesized that the two variable of intervention group and the race of the patients have no connectedness what so ever and there was no correlation to what the race of the patient was and how they received their follow-ups. To analyse the hypothesis a t-test was run between the two variables which revealed the statistics shown in Figure 7:

Figure 7

Paired Samples Test

Mean

Upper

one of three intervention group (1 = monthly telephone follow-up, 2 = monthly text or email follow-up, and 3 = monthly in-person follow-up)  – race of patient

-.673

1.114

.091

-.853

-.494

-7.401

149

.000

Paired Differences t df Sig. (2-tailed)
Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference
Lower
Pair 1

What we want to observe in this chart is the value of Sig. (2-tailed) value. If the value is lower than 0.005 then our hypothesis is accepted however if the value is greater than 0.005 then our hypothesis is rejected. In the light of the calculated data it can be observed that the hypothesis is correct as the value Sig. (2-tailed) is lower than 0.005 i.e. 0.000.

At the end, the data was analysed by running ANOVA test. The descriptives table (see below) provides some very useful descriptive statistics, including the mean, standard deviation and 95% confidence intervals for the dependent variable (Time) for each group of the respondents receiving different form of follow-ups. Discussing the test of homogeneity of variances it is observed that the Sig. Value is 0.053 which is higher than 0.05 therefore the assumption of homogeneity of variance is met. Coming on to the ANOVA table, therefore the Sig. Value should be lower than 0.05 to conclude it as significant. The value in the ANOVA table is lower than 0.05 (p=0.00) therefore, there is a statistically significant difference between the two variables.

Descriptives

one of three intervention group (1 = monthly telephone follow-up, 2 = monthly text or email follow-up, and 3 = monthly in-person follow-up)

Lower Bound

2

2.00

.000

.000

2.00

2.00

2

2

N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum
Upper Bound
0
1 3 2.33 1.155 .667 -.54 5.20 1 3
2 22 2.64 .658 .140 2.34 2.93 1 3
3 24 2.42 .830 .169 2.07 2.77 1 3
4 20 2.30 .801 .179 1.92 2.68 1 3
5 22 2.05 .785 .167 1.70 2.39 1 3
6 10 1.80 .632 .200 1.35 2.25 1 3
7 17 1.41 .618 .150 1.09 1.73 1 3
8 15 1.53 .516 .133 1.25 1.82 1 2
9 9 1.44 .527 .176 1.04 1.85 1 2
10 5 1.40 .548 .245 .72 2.08 1 2
11 1 1.00 . . . . 1 1
Total 150 2.03 .819 .067 1.89 2.16 1 3

Test of Homogeneity of Variances

one of three intervention group (1 = monthly telephone follow-up, 2 = monthly text or email follow-up, and 3 = monthly in-person follow-up)

Levene Statistic df1 df2 Sig.
1.876(a) 10 138 .053

a  Groups with only one case are ignored in computing the test of homogeneity of variance for one of three intervention group (1 = monthly telephone follow-up, 2 = monthly text or email follow-up, and 3 = monthly in-person follow-up) .

ANOVA

 one of three intervention group (1 = monthly telephone follow-up, 2 = monthly text or email follow-up, and 3 = monthly in-person follow-up)

Sum of Squares df Mean Square F Sig.
Between Groups 30.275 11 2.752 5.456 .000
Within Groups 69.619 138 .504
Total 99.893 149

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