Results from 16 studies on the correlation between conscientiousness and medication adherence.

dat.molloy2014

Format

The data frame contains the following columns:

authorscharacterstudy authors
yearnumericpublication year
ninumericsample size of the study
rinumericobserved correlation
controlscharacternumber of variables controlled for
designcharacterwhether a cross-sectional or prospective design was used
a_measurecharactertype of adherence measure (self-report or other)
c_measurecharactertype of conscientiousness measure (NEO or other)
meanagenumericmean age of the sample
qualitynumericmethodological quality

Details

Conscientiousness, one of the big-5 personality traits, can be defined as “socially prescribed impulse control that facilitates task- and goal-directed behaviour, such as thinking before acting, delaying gratification, following norms and rules and planning, organising and prioritising tasks” (John & Srivastava, 1999). Conscientiousness has been shown to be related to a number of health-related behaviors (e.g., tobacco/alcohol/drug use, diet and activity patterns, risky behaviors). A recent meta-analysis by Molloy et al. (2014) examined to what extent conscientiousness is related to medication adherence, that is, the extent to which (typically chronically ill) patients follow a prescribed medication regimen (e.g., taking a daily dose of a cholesterol lowering drug in patients with high LDL serum cholesterol levels). The results from the 16 studies included in this meta-analysis are provided in this dataset.

Variable a_measure indicates whether adherence was measured based on self-reports or a more ‘objective’ measure (e.g., electronic monitoring of pill bottle openings, pill counts). Variable c_measure indicates whether conscientiousness was measured with some version of the NEO personality inventory or some other scale. Methodological quality was scored by the authors on a 1 to 4 scale with higher scores indicating higher quality (see article for details on how this score was derived).

Source

Molloy, G. J., O'Carroll, R. E., & Ferguson, E. (2014). Conscientiousness and medication adherence: A meta-analysis. Annals of Behavioral Medicine, 47(1), 92--101. https://doi.org/10.1007/s12160-013-9524-4

References

John, O. P., & Srivastava, S. (1999). The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (2nd ed., pp. 102-138). New York: Guilford Press.

Examples

### copy data into 'dat' and examine data
dat <- dat.molloy2014
dat[-c(5:6)]
#>                authors year  ni     ri   a_measure c_measure meanage quality
#> 1      Axelsson et al. 2009 109  0.187 self-report     other   22.00       1
#> 2      Axelsson et al. 2011 749  0.162 self-report       NEO   53.59       1
#> 3         Bruce et al. 2010  55  0.340       other       NEO   43.36       2
#> 4   Christensen et al. 1999 107  0.320 self-report     other   41.70       1
#> 5  Christensen & Smith 1995  72  0.270       other       NEO   46.39       2
#> 6         Cohen et al. 2004  65  0.000       other       NEO   41.20       2
#> 7       Dobbels et al. 2005 174  0.175 self-report       NEO   52.30       1
#> 8        Ediger et al. 2007 326  0.050 self-report       NEO   41.00       3
#> 9         Insel et al. 2006  58  0.260       other     other   77.00       2
#> 10       Jerant et al. 2011 771  0.010       other       NEO   78.60       3
#> 11        Moran et al. 1997  56 -0.090       other       NEO   57.20       2
#> 12   O'Cleirigh et al. 2007  91  0.370 self-report       NEO   37.90       2
#> 13       Penedo et al. 2003 116  0.000 self-report       NEO   39.20       1
#> 14        Quine et al. 2012 537  0.150 self-report     other   69.00       2
#> 15      Stilley et al. 2004 158  0.240       other       NEO   46.20       3
#> 16 Wiebe & Christensen 1997  65  0.040       other       NEO   56.00       1

# \dontrun{

### load metafor package
library(metafor)

### calculate r-to-z transformed correlations and corresponding sampling variances
dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat, slab=paste(authors, year, sep=", "))
dat[-c(5:6)]
#> 
#>                authors year  ni     ri   a_measure c_measure meanage quality      yi     vi 
#> 1      Axelsson et al. 2009 109  0.187 self-report     other   22.00       1  0.1892 0.0094 
#> 2      Axelsson et al. 2011 749  0.162 self-report       NEO   53.59       1  0.1634 0.0013 
#> 3         Bruce et al. 2010  55  0.340       other       NEO   43.36       2  0.3541 0.0192 
#> 4   Christensen et al. 1999 107  0.320 self-report     other   41.70       1  0.3316 0.0096 
#> 5  Christensen & Smith 1995  72  0.270       other       NEO   46.39       2  0.2769 0.0145 
#> 6         Cohen et al. 2004  65  0.000       other       NEO   41.20       2  0.0000 0.0161 
#> 7       Dobbels et al. 2005 174  0.175 self-report       NEO   52.30       1  0.1768 0.0058 
#> 8        Ediger et al. 2007 326  0.050 self-report       NEO   41.00       3  0.0500 0.0031 
#> 9         Insel et al. 2006  58  0.260       other     other   77.00       2  0.2661 0.0182 
#> 10       Jerant et al. 2011 771  0.010       other       NEO   78.60       3  0.0100 0.0013 
#> 11        Moran et al. 1997  56 -0.090       other       NEO   57.20       2 -0.0902 0.0189 
#> 12   O'Cleirigh et al. 2007  91  0.370 self-report       NEO   37.90       2  0.3884 0.0114 
#> 13       Penedo et al. 2003 116  0.000 self-report       NEO   39.20       1  0.0000 0.0088 
#> 14        Quine et al. 2012 537  0.150 self-report     other   69.00       2  0.1511 0.0019 
#> 15      Stilley et al. 2004 158  0.240       other       NEO   46.20       3  0.2448 0.0065 
#> 16 Wiebe & Christensen 1997  65  0.040       other       NEO   56.00       1  0.0400 0.0161 
#> 

### meta-analysis of the transformed correlations using a random-effects model
res <- rma(yi, vi, data=dat)
res
#> 
#> Random-Effects Model (k = 16; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0055)
#> tau (square root of estimated tau^2 value):      0.0901
#> I^2 (total heterogeneity / total variability):   61.73%
#> H^2 (total variability / sampling variability):  2.61
#> 
#> Test for Heterogeneity:
#> Q(df = 15) = 38.1595, p-val = 0.0009
#> 
#> Model Results:
#> 
#> estimate      se    zval    pval   ci.lb   ci.ub     ​ 
#>   0.1499  0.0316  4.7501  <.0001  0.0881  0.2118  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### average correlation with 95% CI
predict(res, digits=3, transf=transf.ztor)
#> 
#>   pred ci.lb ci.ub  pi.lb pi.ub 
#>  0.149 0.088 0.209 -0.037 0.325 
#> 

### forest plot
forest(res, addpred=TRUE, xlim=c(-1.6,1.6), atransf=transf.ztor,
       at=transf.rtoz(c(-.4,-.2,0,.2,.4,.6)), digits=c(2,1), cex=.8,
       header="Author(s), Year")


### funnel plot
funnel(res)


# }