Studies on the Effectiveness of Self-Management Education and Regular Medical Review for Adults with Asthma
Results from 15 trials examining the effectiveness of self-management education and regular medical review for adults with asthma.
dat.gibson2002
The data frame contains the following columns:
author | character |
first author of study |
year | numeric |
publication year |
n1i | numeric |
number of participants in the intervention group |
m1i | numeric |
mean number of days off work/school in the intervention group |
sd1i | numeric |
standard deviation of the number of days off work/school in the intervention group |
n2i | numeric |
number of participants in the control/comparison group |
m2i | numeric |
mean number of days off work/school in the control/comparison group |
sd2i | numeric |
standard deviation of the number of days off work/school in the control/comparison group |
ai | numeric |
number of participants who had one or more days off work/school in the intervention group |
bi | numeric |
number of participants who no days off work/school in the intervention group |
ci | numeric |
number of participants who had one or more days off work/school in the control/comparison group |
di | numeric |
number of participants who no days off work/school in the control/comparison group |
type | numeric |
numeric code for the intervention type (see below) |
Asthma management guidelines typically recommend for patients to receive education and regular medical review. While self-management programs have been shown to increase patient knowledge, it is less clear to what extent they actually impact health outcomes. The systematic review by Gibson et al. (2002) examined the effectiveness of self-management education and regular medical review for adults with asthma. In each study, participants receiving a certain management intervention were compared against those in a control/comparison group with respect to a variety of health outcomes. One of the outcomes examined in a number of studies was the number of days off work/school.
The majority of studies reporting this outcome provided means and standard deviations allowing a meta-analysis of standardized mean differences. Seven studies also reported the number of participants who had one or more days off work/school in each group. These studies could be meta-analyzed using, for example, (log) risk ratios. Finally, one could also consider a combined analysis based on standardized mean differences computed from the means and standard deviations where available and using probit transformed risk differences (which also provide estimates of the standardized mean difference) for the remaining studies.
Some degree of patient education was provided in all studies. In addition, the type
variable indicates what additional intervention components were included in each study:
optimal self-management (writing action plan, self-monitoring, regular medical review),
self-monitoring and regular medical review,
self-monitoring only,
regular medical review only,
written action plan only.
Gibson, P. G., Powell, H., Wilson, A., Abramson, M. J., Haywood, P., Bauman, A., et al. (2002). Self-management education and regular practitioner review for adults with asthma. Cochrane Database of Systematic Reviews, 3.
### copy data into 'dat' and examine data dat <- dat.gibson2002 dat ### fixed-effects model analysis of the standardized mean differences dat <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat) res <- rma(yi, vi, data=dat, method="FE") print(res, digits=2) ### fixed-effects model analysis of the (log) risk ratios dat <- escalc(measure="RR", ai=ai, bi=bi, ci=ci, di=di, data=dat) res <- rma(yi, vi, data=dat, method="FE") print(res, digits=2) predict(res, transf=exp, digits=2) ### fixed-effects model analysis of the standardized mean differences and the probit transformed ### risk differences (which also provide estimates of the standardized mean difference) dat <- escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat) dat <- escalc(measure="PBIT", ai=ai, bi=bi, ci=ci, di=di, data=dat, replace=FALSE) dat res <- rma(yi, vi, data=dat, method="FE") print(res, digits=2)
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