اثربخشی، پاسخ و ترک رفتاردرمانی دیالکتیکی برای اختلال شخصیت مرزی در تنظیمات بیماران بستری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|30106||2013||6 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Behaviour Research and Therapy, Volume 51, Issue 8, August 2013, Pages 411–416
To examine the effectiveness of dialectical behavior therapy for inpatients with borderline personality disorder (BPD), small sample sizes and, predominantly, tests of statistical significance have been used so far. We studied 1423 consecutively admitted individuals with BPD, who were seeking a 3-month inpatient treatment. They completed the Borderline Symptom List (BSL) as the main outcome measure, and other self-rating measures at pre- and post-treatment. Therapy outcome was defined in three ways: effect size (ES), response based on the reliable change index, and remission compared to the general population symptom level. Non-parametric conditional inference trees were used to predict dropouts. In the pre-post comparison of the BSL, the ES was 0.54 (95% CI: 0.49–0.59). The response rate was 45%; 31% remained unchanged, and 11% deteriorated. Approximately 15% showed a symptom level equivalent to that of the general population. A further 10% of participants dropped out. A predictive impact on dropout was demonstrated by substance use disorders and a younger age at pre-treatment. In future research, follow-up assessments should be conducted to investigate the extent to which response and remission rates at post-treatment remain stable over time. A consistent definition of response appears to be essential for cross-study and cross-methodological comparisons.
Dialectical behavior therapy (DBT; Linehan, 1993a, 1993b) is currently the most frequently investigated psychosocial intervention for borderline personality disorder (BPD). The four core elements of DBT are individual therapy, which takes place once a week; weekly skills training within the group; telephone coaching by the individual therapist; and supervision for the therapeutic team (Linehan, 1993a, 1993b). The treatment concept was originally conceived on an outpatient basis, but has been adapted to the inpatient setting (Swenson, Sanderson, Duilt, & Linehan, 2001). The short- and long-term effectiveness of inpatient DBT was shown by various work groups (Bohus et al., 2004; Fassbinder et al., 2007; Höschel, 2006; Kleindienst et al., 2008; Kröger et al., 2006; Simpson et al., 2004). For inpatient DBT, moderate to large effect sizes emerged at the end of treatment with regard to self-reported, general, or depressive symptom severity (ES = 0.56 to 0.84 and ES = 0.59 to 1.90, respectively), and large effect sizes were found with regard to psychosocial functioning as rated by others (ES = 0.80–1.33). However, the results of these studies are based on relatively small samples (N = 20 to N = 50), which, moreover, were treated in university establishments. To date, mean value comparisons and effect sizes as a benchmark for assessing the effectiveness of a treatment are predominant in the publications on DBT, whereas the clinical significance enables an individual assessment of the change status (cf. Jacobson, Roberts, Berns & McGlinchey, 1999). Using the parameters of clinical significance, it can be determined whether, at post-treatment, a patient has reliably deteriorated or improved (response), or whether the symptom level has adjusted to that of a clinically unimpaired sample (remission). Only in one completer sample (N = 31) was the clinical significance indicated in addition to the ES of 0.84 following a three-month inpatient DBT treatment: According to this, 42% of patients at post-treatment ( Bohus et al., 2004) and 50% within 21 months after the end of therapy ( Kleindienst et al., 2008) were remitted in terms of general symptom strain. One of the main aims of DBT is to lower dropout rates (Linehan, 1993a), even though no significant difference in the mean dropout rates between DBT (24.7%) and control conditions (27.3%) was found in a meta-analysis (Kliem, Kröger, & Kosfelder, 2010). In the face of these (partially) high dropout rates, ranging from 4.2% to 61.1% (SD = 15.6%), it seemed to be important to identify characteristics that are associated with discontinuation of treatment. To the best of our knowledge, four studies have examined differences compared to completers and predictive factors for inpatients who dropped out of DBT ( Bohus et al., 2004; Kröger et al., 2006; Perroud, Uher, Dieben, Nicastro, & Huguelet, 2010; Rüsch et al., 2008). While no differences in any aspect were found between completers and dropouts in the Bohus et al. (2004) and Kröger et al. (2006) studies, dropouts in the Rüsch et al. (2008) study reported more trait anxiety, fewer lifetime suicide attempts, and higher experiential avoidance (without error correction for multiple testing). The latter two characteristics were both confirmed in a stepwise logistic regression as dependent variables for dropout. However, lower education was found to be the only predictive characteristic in the Perroud et al. (2010) study, which did not include those characteristics (i.e., lifetime suicide attempts and experiential avoidance) that were found in the Rüsch et al. (2008) study. These results were based on small sample sizes, ranging from 40 to 60 mostly female participants, with the exception of the Perroud et al. (2010) study, with 447 participants. In addition, low dropout rates were reported, ranging from 12% to 25.8%, with the exception of the Rüsch et al. (2008) study, with 46%. Therefore, sample sizes and dropout rates made it difficult to find any differences between completers and dropouts due to a lack of statistical power. The use of a regression analysis in the Rüsch et al. (2008) study implies a larger sample size than 60 participants, and requires a confirmation in a cross-validation analysis. Since individuals with specific co-occurring mental disorders were excluded (e.g., anorexia nervosa, substance use disorders, Bohus et al., 2004; Rüsch et al., 2008), these conditions could not be included in the analyses, even though they might also be suggested as risk factors for a discontinuation of treatment (Kröger et al., 2010; Linehan et al., 2002). Hence, results need to be confirmed and expanded in further analyses, which should be based on larger sample sizes with fewer exclusion criteria. The aim of the current study is, therefore, to use a large consecutive sample of patients admitted to a 3-month DBT program in order to draw on various parameters for assessing its effectiveness regarding disorder-specific symptom strain and further complaints. For this purpose, in particular, effect sizes should be calculated in comparison to the clinical significance through the RCI method (Jacobson & Truax, 1991). Moreover, a further aim is to identify predictors of discontinuation of therapy.
نتیجه گیری انگلیسی
Non-parametric conditional inference trees (C-Trees; Hothorn, Hornik, & Zeileis, 2006; Strobl, Malley, & Tutz, 2009) based on the principle of recursive partitioning were applied to analyze associations between pre-treatment measurements and risk of dropout. An exact permutation test will assess the strength of the association between response and input variable (Strasser & Weber, 1999). Since permutation tests derive the p-values from sample-specific permutation distributions of the test statistics, only p-values are reported. In the following analysis, gender, age, social, educational, and employment status, treatment history, as well as the level of mean pre-treatment scores of outcome measures (BSL, BDI, GSI, and GAF, respectively), co-occurring mental disorders, and psychosocial stress factors were selected for testing the association with dropout (no = 0; yes = 1). The R package “party” (a laboratory for recursive partitioning; Hothorn, Hornik, Strobl, & Zeileis, 2011) was used for this analysis.