Research report
Predictors of outcome of group and internet-based cognitive behavior therapy

https://doi.org/10.1016/j.jad.2007.05.001Get rights and content

Abstract

Background

Little is known about which participant characteristics determine the effectiveness of various types of cognitive behavior therapy for sub-threshold depression. The aim of this study was to investigate which characteristics predict treatment outcome of group and internet-based interventions for sub-threshold depression, with a special focus on (i) the five main personality factors, and (ii) their different predictive power in the different types of treatment.

Methods

Eighty-five women and 45 men (mean age = 55 years, S.D. = 4.4) were randomly assigned to a group treatment and an internet-based treatment. The outcome measure was the difference between pre-treatment and post-treatment BDI scores. Analyses of Covariance were conducted to examine which participant characteristics could predict outcome for the two different types of treatment.

Results

Higher baseline BDI scores (F(1,111) = 52.88, p < .01), female gender (F(1,111) = 6.45, p = .01), and lower neuroticism scores (F(1,111) = 7.24, p = .01) predicted better outcome after both treatments. In the group intervention, participants with higher altruism scores improved significantly more after treatment (F(1,111) = 3.94, p = .05) compared to the internet-based condition.

Limitations

Axis-II disorders were not considered; the study assessed personality traits rather than personality disorders.

Conclusions

Outcomes of different types of cognitive behavior therapy for sub-threshold depression are partly predicted by different participant characteristics. Neuroticism was associated with worse outcomes in both types of treatment, while altruism seems to be exclusively related to more favorable outcomes in the group treatment.

Introduction

Depression is a major health problem. Yet, despite its high prevalence, probably fewer than 20% of people with depression are detected and treated (Cole and Dendukuri, 2003). People with sub-threshold depression represent an important group, but they generally do not receive treatment. Despite having symptoms of depression, they do not meet DSM-IV criteria for major depression (Cuijpers and Smit, 2004). People with sub-threshold depression have an increased risk of developing depression (Cuijpers and Smit, 2004, Cuijpers et al., 2006) and, more importantly, sub-threshold depression has serious effects on well-being and psychosocial functioning (Rapaport and Judd, 1998). In fact, in their psychosocial functioning, people with sub-threshold depression are quite similar to those diagnosed with major depression (Gotlib et al., 1995). They experience nearly the same degree of impairment as those diagnosed with major depression in terms of health, functioning, and disability (Wagner et al., 2000). Furthermore the costs of sub-threshold depression are comparable, although lower, to the costs of major depression; about two thirds of the per capita of major depression (Cuijpers et al., 2007).

Cognitive behavior therapy has been proven to be effective in treating sub-threshold depression, (Willemse et al., 2004), and there are currently many different forms: e.g., individual, group, and internet-based cognitive behavior therapy. However, little is known about which participant characteristics determine the effectiveness of the various forms of cognitive behavior therapy for sub-threshold depression; even less is known about the relatively recent internet-based therapy.

For traditional individual and group cognitive behavior therapy, pre-treatment severity, previous episodes of depression, and marital status have been shown to be important predictors of treatment outcome (Hoberman et al., 1988, Neimeyer and Weiss, 1990, Jarrett et al., 1991, Thase et al., 1994, Elkin et al., 1995, Hamilton and Dobson, 2002, Andersson et al., 2004).

Although gender differences in treatment outcome have rarely been found (Hoberman et al., 1988, Neimeyer and Weiss, 1990, Jarrett et al., 1991, Thase et al., 1994), men attended significantly fewer individual cognitive behavior therapy sessions than women (Thase et al., 1994). Since internet-based self-help and group cognitive behavior therapy have very different adherence rates (Spek et al., 2007), it seems important to control for gender, as there might be differences in participation and, consequently, in treatment outcome.

Similarly, although there is little evidence to suggest that educational level is a predictor of response to cognitive behavior therapy in general (Hoberman et al., 1988, Neimeyer and Weiss, 1990, Jarrett et al., 1991), it might be a predictor for treatment outcome for internet-based self-help, since study skills and experience with computers could well affect this condition.

Little is known about the value of the “Big Five” personality characteristics (Costa and McCrae, 1992) in predicting treatment outcomes of cognitive behavior therapy for sub-threshold depression and major depression. In a recent review of thirteen, mostly antidepressant, treatment outcome studies of major depression, high neuroticism scores were shown to be associated with worse outcome (Mulder, 2002). Extraversion has also been associated with treatment outcome for major depression: Zuckerman et al. (1980) found that higher pre-treatment extraversion scores predicted better social adjustment at one year follow-up. In a study on personality traits in a large sample of outpatients with mood and anxiety disorder exhibiting differing patterns of comorbidity, it was found that neuroticism, extraversion and agreeableness differed considerably in subjects with one disorder compared with subjects with more disorders (Cuijpers et al., 2005a). The other two personality factors, openness and conscientiousness, do not appear to have a predictive value for cognitive behavior therapy outcome; however, this might be different for internet-based CBT.

The aim of this study was to investigate which participant characteristics predict treatment outcome for group and internet-based interventions of sub-threshold depression with a special focus on (i) the five main personality factors, and (ii) their potentially different predictive power in the two treatment modalities.

We expected that personality factors would predict treatment outcome. We hypothesized that, because of the different form of the treatments, different predictors would be relevant for the two interventions.

Section snippets

Participants

Participants born between 1930 and 1955 were recruited by advertisements in free regional newspapers. Furthermore, with the help of the Municipal Health Care Service of the city of Eindhoven, we sent personal letters to invite people to participate in the study. The letters (n = 15,697) were sent in cohorts to all residents of Eindhoven born between 1949 and 1955. Only this younger subgroup was invited by letter, because they were more likely to be eligible for inclusion in the study. We knew

Results

Preliminary analysis checked for normality and computed descriptive statistics. All variables were found to be distributed acceptably close to normal.

Participants who dropped out after randomization, but before the start of the intervention, did not differ from participants who started treatment on most characteristics: type of treatment (group CBT versus internet-based CBT), gender, age, having a partner, employment status, previous depressive episodes, EDS screening scores, and NEO-FFI

Discussion

This study investigated the influence of the five main personality factors on treatment outcome of cognitive behavioral therapy interventions for older adults with sub-threshold depression (having symptoms of depression, but not enough to meet DSM-IV criteria for major depression). We found a negative association between outcome of cognitive behavior therapy for sub-threshold depression and neuroticism, and a positive association between group cognitive therapy outcome and altruism.

Educational

Acknowledgements

This study was supported by ZON-MW, The Netherlands Organization for Health Research and Development.

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