Outcomes of Asthma Self – Management Program

The structured interview was administered to 138 asthmatic patients. One hundred seven patients returned the questionnaires (response, 78%). Fifty-nine percent of this final group of patients expressed the desire to participate in the program (n = 63). Reasons for declining participation were, in order of importance as determined by the number of patients who offered the reason: lack of time, distance to the hospital, no symptoms and therefore no need to participate, and lack of interest in the program.

The principal component analysis performed on the interview questions resulted in four components explaining 61% of the variance of the interview questions (Table 1). Interview questions 2, 6, 9, and 10 had to be excluded because of recurrent low loadings on components or high loadings on isolated components.

The attitude questions loaded on two distinct components: a component “personal benefits” and a component “general benefits.” Cronbach a was high for the personal benefits and general benefits scales (0.80 and 0.76, respectively) and somewhat lower for the selfefficacy scale (0.50), reflecting the diverse nature of barriers patients perceive to participating. Overall, the principal component analysis results showed that interview questions 1, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 16, and 17 had factor loadings > 0.50 and therefore measured ASE in an adequate way. The four components resulting from the analysis were included in the regression model as proximal factors.

Characteristics of the participants and nonparticipants are described in Table 2. Even if the mean differences between the two groups of patients were all in the expected direction, this difference was significant for some of the characteristics only as indicated with significance levels in Table 2.

The Pearson correlations between the significant group means differences were investigated to decide on the distal factors to include in the model (Table 3). Because AQLQ scores correlated with two other factors, we decided to exclude AQLQ results from the model to avoid multicollinearity in the regression analysis, whereas we kept education level in the analysis because previous research had repeatedly demonstrated its importance. Because of the low correlation between PEF scores and previous hospitalizations, both factors were included resulting in a final regression model (Fig 1, bottom, b) with the four components from Table 1, education level, PEF scores, previous hospitalizations, and ASC scores (Table 2).

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Asthma

Determinants of Intending To Participate in Our Asthma Program: Logistic Regression Analysis

We performed two logistic regression analyses. The first analysis examined the predictive power of the distal factors on intending to participate in the asthma program. The second analysis examined the predictive power of the distal and proximal factors together on the patients intention. This last analysis was performed to evaluate the relative importance of the distal factors when proximal factors were added to the model. Results are shown in Table 4.

Education level was a significant predictor of the intention to participate. Higher educated asthmatic patients were more likely to participate in the selfmanagement program than lower educated asthmatic patients. Neither previous hospitalizations due to an asthma exacerbation or PEF scores had a predictive value for the intention to participate. The ASC score was a significant predictor of the intention to participate. This means that patients who experienced more intense symptoms the 2 weeks before recruitment were more likely to express the intention to participate. When social cognitive variables were added to the model, they carried the weight of the prediction and ASC lost its significant predictive value. Significant predictors of intention behavior were education level, perceiving personal benefits, self-efficacy and social influence. Perceiving more personal benefits in participation in the program (having a more positive attitude), having fewer barriers to participate (having higher self-efficacy expectations), and experiencing more social influence for better self-care made a patient more likely to intent to participate in our program. Believing in the general benefits of the program had no predictive value for the intention to participate.

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Table 1—Principal Component Analysis Results of the Interview Questions

QuestionNo. Topic Components
PersonalBenefit GeneralBenefit SelfEfficacy SocialInfluence
1 The asthma program would be useful to me. 0.68t 0.01 – 0.15 0.33
5 I would like to participate in the program to be more involved with my asthma treatment. 0.80t 0.18 – 0.26 – 0.01
7 I would like to participate in the program to learn more about my asthma. 0.78t 0.21 – 0.15 – 0.06
11 I would like to participate in the program to obtain a reduction of my asthma complaints. 0.80t 0.09 – 0.03 – 0.02
13 I believe that the program influences the asthma control of the participants. 0.08 0.81t 0.04 0.21
14 I believe that patients get more involved with their asthma treatment when participating in the asthma education program. 0.13 0.78t – 0.24 – 0.21
15 I think that patients learn more about their asthma when participating in the asthma program. 0.39 0.56t – 0.26 – 0.30
16 I think that asthma complaints decrease in patients participating in the asthma education program offered. 0.12 0.72t 0.12 0.04
3 I do not think I have time to participate. – 0.09 0.11 0.62t – 0.32
4 I live too far away from the hospital to participate in the program. – 0.32 – 0.01 0.65t – 0.07
8 I think participating would be too expensive. 0.06 – 0.37 0.62t 0.17
12 The fact that the program consists of some group sessions impedes me to participate. – 0.15 0.01 0.51t 0.09
17 Social influence 0.04 0.03 – 0.01 0.85t

Table 2—Characteristics of Participants and Nonparticipants

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Characteristics Participants (n = 63) Nonparticipants (n = 44) Group Comparisons
Age, yr 42 42
Male gender, % 38 30
Marital status, % Married 81 75
Unmarried 15 25
Divorced 4 0
Highest education level, % College or university 44 24 X2 testi
High school 52 67
Primary school 4 9
Duration of asthma, yr 18 13
FEV1, % predicted 83 90
FVC, % predicted 102 104
PEF, % predicted 82 91 One-way analysis of variance!
Previous hospitalizations, % 54 29 X2 testi
Personal benefits scale 13.9 (2.3) 11.4(1.9) Mann-Whitney U testj
General benefits scale 12.6(1.5) 12 (2.1) Mann-Whitney U testi
Social influence total score 2.8 (2.8) 1.8 (2.2)
Self-efficacy scale 16.7(1.3) 14(1.9) Mann-Whitney U testj
AQLQ 133 (31) 148 (32) Mann-Whitney U testi
ASC 118(70) 86 (74) Mann-Whitney U testi
KASE-AQ: knowledge 12 (2.9) 11 (2.8)
KASE-AQ: attitude 75 (7.8) 76 (8.8)
KASE-AQ: self-efficacy 69 (13.3) 74 (12.3)
PANAS: negative affectivity 22 (7.7) 20 (7.9)
PANAS: positive affectivity 31 (7.2) 33 (6.6)

Table 3—Pearson Correlations Between Demographic, Clinical, and Questionnaire Data

Education PEF Previous AQLQ ASC
Variables Level Scores Hospitalizations Scores Scores
Education level 1
PEF scores 0.02 1
Previous – 0.06 – 0.19* 1
hospitalizations
AQLQ scores 0.20* 0.17 – 0.18 1
ASC scores – 0.16 0.07 0.05 – 0.72i 1

 

Table 4—Predictors of Intending To Participate in the Asthma Program

Variables Regression Analysis 1, OR (95% Confidence Interval) Regression Analysis 2, OR (95% Confidence Interval)
Distal factors*
Education level 1.8 (1.1—2.7)j 2.7 (1.3-5.6)J
Previous 2.1 (0.9—5.1) 2.1 (0.5-8.1)
hospitalizations
ASC score 1.007 (1.001-1.01)i 1.004 (0.9-1.01)
PEF score 0.97 (0.9-1.002) 0.98 (0.9-1.02)
Proximal factors*
Personal benefits 7.6 (2.4-12.5)§
General benefits 0.7 (0.3-1.7)
Self-efficacy 12.5 (5.2-19.3)§
Social influence 3.3 (1.3-8.4)i
R2, % 23§ 72§