آموزش رفتاردرمانی شناختی

Abstract Background and objectives Progress toward establishing treatments for mental disorders has been good, particularly for cognitive behavior therapy (CBT). However, there is considerable room for improvement. The goal of this study was to begin the process of investigating the potential for improving treatment outcome via improving our understanding of learning processes. Methods Individuals diagnosed with major depressive disorder (N = 20) participated in three computer-delivered CBT lessons for depression. Indices of learning were taken after each lesson, during three phone calls over the week following the lesson, and one week later. These were: (a) whether the participant thought about the lesson, (b) whether the participant applied the lesson, and (c) whether the participant generalized the lesson. Based on a predetermined list of therapy points (i.e., distinct ideas and principles), all participant responses were coded for the number of therapy points they thought about, applied, or generalized following each lesson.

Progress toward establishing cognitive behavioral treatments (CBT) for most mental disorders has been good (Layard & Clark, 2014). However, the effect sizes can be small to moderate, gains may not persist, and there are patients who derive little or no benefit (Lambert, 2011, Rey et al., 2011 and Vittengl et al., 2007). We propose to investigate a possible novel pathway to improving treatment outcome via improving our understanding of learning processes in the context of a CBT session. There is good reason to believe that learning from a CBT treatment session is non-optimal. Indeed, cognitive psychologists have demonstrated that the odds are really stacked against learning, applying and generalizing new knowledge in the context of formal instruction. This is the transfer of learning problem (Detterman and Sternberg, 1993, Gick and Holyoak, 1983 and Thorndike, 1932). People are often able to encode, recall, and recognize information, but there are multiple empirical demonstrations that people largely fail to apply the material that was learned in similar situations that differ only in surface features (Day and Goldstone, 2012 and Gick and Holyoak, 1983). While we know that learning in the context of therapy can be improved by applying basic lessons from cognitive psychology (Harvey et al., 2014), fostering successful transfer is far from trivial and is an ongoing topic of research (e.g., Andersson et al., 2012, Leberman et al., 2006, Mestre, 2005, Rohrer et al., 2010 and Scogin et al., 1998). In the present study, we seek to document the extent to which the material covered in a CBT session is thought about, applied and generalized to situations outside the session. Our rationale for focusing on major depressive disorder is that depression is one of the most prevalent disorders and a leading cause of disability worldwide (World Health Organization, 2004). A significant proportion of patients don't recover (Judd et al., 2000 and Solomon et al., 2000). Of those who do recover, the majority relapse (Solomon et al., 2000). Hence, there is a need for innovation focused on improving treatment for depression. Also, depression is characterized by neuropsychological impairments, such as memory and attention (Behnken et al., 2010, Campbell and MacQueen, 2004, MacQueen et al., 2003 and Videbech and Ravnkilde, 2004), and these are associated with poorer outcome (Majer et al., 2004). Hence, depression is a good candidate for studying processes of learning CBT. We focus on computer-delivered CBT for depression as it has been well studied and enables careful experimental control of the content provided relative to therapist-delivered CBT. Moreover, there is evidence that computerized CBT for depression can reduce symptoms of depression in both efficacy and effectiveness trials with medium to large effect sizes (Andersson and Cuijpers, 2009 and Andrews et al., 2010), even 6 or more months following treatment (Andersson et al., 2005, Andersson et al., 2013, Andrews et al., 2010 and Spek et al., 2008). Computer-delivered CBT is also acceptable to patients (Andrews et al., 2010) and patients receiving computerized therapy also adhere to treatment recommendations just as much as patients in in-person therapy (van Ballegooijen et al., 2014). Despite these impressive outcomes, there is room to optimize these programs. Computer-delivered CBT for depression has high dropout rates (Andersson et al., 2005 and Andersson and Cuijpers, 2009), although these are lower in therapist-guided computer-based treatments than in unguided treatments (Andersson, 2014). However, computer-delivered CBT modules are well suited for dissemination and can help close the gap between research and practice (Kazdin & Blase, 2011) since they cost less than standard treatment and allow for a consistent standard of content provided (McCrone et al., 2004 and Proudfoot, 2004). Hence, computer-delivered CBT is a good platform for studying the learning of CBT. The present study was designed to begin the process of investigating learning across three computer-delivered CBT lessons for depression. We included an assessment of three indices of learning: (a) whether the participant thought about the CBT lesson, (b) whether the participant applied the CBT lesson and (c) whether the participant generalized the CBT lesson. The first aim was to document the proportion of participants who displayed each of the three indices of learning. The hypothesis tested was that transfer of learning of the CBT lessons would be poor. The second aim was to investigate the association between the three indices of learning and depression scores one week later. The hypothesis tested was that participants who exhibited better learning would also exhibit reduced depression symptomatology one week later.

3. Results 3.1. Thoughts As evident in Table 2, the majority of participants reported that they thought about the CBT lesson content after each lesson. Following the CBT lessons, an average of 73.33–85.00% of participants reported having thought about the lesson in the past 24 hours and they reported thinking about the lesson on average 1.91 to 2.99 times. As evident in Table 2, between 50.83 and 65.00% of participants’ thoughts about lesson content were accurate. Table 2. Summary of learning measures. Measure Number of times % Of Participants % Accuracy Thought After Lesson 1 2.99 (1.96) 85.00% (22.06%) 62.50% (27.51%) After Lesson 2 2.31 (1.47) 80.00% (33.05%) 65.00% (31.83%) After Lesson 3 1.91 (1.58) 73.33% (32.17%) 50.83% (35.03%) Application After Lesson 1 2.20 (1.20) 55.00% (29.12%) 42.50% (29.36%) After Lesson 2 2.55 (1.54) 63.75% (38.45%) 48.75% (34.86%) After Lesson 3 1.85 (1.35) 50.83% (35.96%) 36.25% (36.20%) Generalization – Cognitiona After Lesson 1 0.53 (0.60) 50.00% (51.30%) N/A After Lesson 2 0.75 (0.50) 80.00% (41.04%) N/A After Lesson 3 0.85 (0.61) 85.00% (36.64%) N/A Generalization – Behaviora After Lesson 1 1.08 (0.82) 78.90% (41.89%) N/A After Lesson 2 1.35 (0.69) 95.00% (22.36%) N/A After Lesson 3 1.58 (0.59) 95.00% (22.36%) N/A Note. Mean (Standard Deviation) presented. % of Participants denotes the percentage of participants who reported thinking abut or applying the lesson since the last lesson at least 1 time. % Accuracy refers to the percentage of participants who accurately thought about or applied the lesson at least one time. N/A = Generalization already takes accuracy into account. a Generalization range is 0–2. Table options 3.2. Application Also evident in Table 2, the majority of participants reported applying the therapy points following each of the lessons. Specifically, an average of 50.83–63.75% of participants reported applying what they learned in the lesson during the past 24 hours. An average of 36.25–48.75% of the therapy points that participants reported having applied following each lesson were accurate. 3.3. Generalization At most, it was possible to generalize twice. As evident in Table 2, on average, participants accurately generalized cognitively between 0.53 and 0.85 times. On average, participants generalized behaviorally between 1.08 and 1.58 times. 3.4. Learning and depression symptoms one week later Table 3 presents two-tailed Pearson correlations between the learning measure and IDS-SR taken at the beginning of the subsequent lesson. The only significant associations were for generalization. Specifically, 10 of the 18 correlations reached significance at the 0.05 level or lower. Even when taking into account only accurate thoughts and application of lesson content, there were no significant correlations between these learning measures and clinical outcome one week later. Table 3. Correlations between learning measures and clinical outcome (IDS-SR). Measure IDS-SR before lesson 2 IDS-SR before lesson 3 IDS-SR before lesson 4 Thought After Lesson 1 .04 −.03 .05 After Lesson 2 −.02 .03 .11 After Lesson 3 .06 .12 .07 Application After Lesson 1 .21 .02 .01 After Lesson 2 .07 .02 .01 After Lesson 3 .00 −.19 −.30 Generalization - Cognition After Lesson 1 −.52* −.59** −.63** After Lesson 2 −.17 −.31 −.36 After Lesson 3 −.41 −.54* −.61** Generalization - Behavior After Lesson 1 −.46* −.45 −.51* After Lesson 2 −.34 −.52* −.56* After Lesson 3 −.38 −.49* −.36 Note. *p < .05. **p < .01.