Pessimism and Homework in CBT for Depression - Wiley Online Library

Pessimism and Homework in CBT for Depression Mieke A. Sachsenweger, Richard B. Fletcher, and Dave Clarke Massey University

Objective: To investigate the moderating effects of attributional style on the relationship between client-rated benefits of homework completion and depression throughout a course of cognitive behavior A total of 28 clients, aged 20 to 65 years, experiencing their first major therapy (CBT). Method: depressive episode completed up to 20 sessions of CBT plus a follow-up session at 2 months. Clients, therapists, and independent observers completed the measures. Multilevel modeling was used for a within-person longitudinal analysis of data. Results: Pessimism slightly moderated the relationship between quantity–quality of homework completion and depression severity over time. Decreases in depression severity were largely independent of level of homework completion. Conclusion: While CBT is effective in reducing depression, pessimistic attributional style can affect the perception that clients bring to the quantity and quality of homework completion. Clinicians could thus focus on dealing C 2015 with pessimistic style for treatment and homework to moderate the severity of depression. Wiley Periodicals, Inc. J. Clin. Psychol. 71:1153–1172, 2015. Keywords: CBT; depression; homework; MLM; pessimism

Cognitive behavior therapy (CBT) is known to be an effective treatment for depression (Anderson et al., 2009; Hollon, Haman, & Brown, 2002), but there is still much research to be done into the exact mechanisms of change in CBT (Garratt, Ingram, Rand, & Sawalani, 2007). Within CBT, studies have demonstrated that homework correlates with increased therapeutic outcome in terms of depression severity, thus being identified as an essential component of CBT (Haarhoff & Kazantzis, 2007). However, even less research has been conducted on the factors influencing homework compliance, in particular, client cognitions and beliefs, even though these are theorized to be the most important influence in homework compliance (Kazantzis, Deane, Ronan, & Lampropoulos, 2005). Attributional style and the reformulated theory of learned helplessness are central explanations in the development and maintenance of depression (Abramson, Seligman, & Teasdale, 1978). Specifically, a pessimistic style or pessimism may be an important predictor of not only severity of depression but also client compliance with the quality and quantity of homework completion (Riso & Thase, 2007). Therefore, the aim of our study is to investigate the effects of clients’ pessimistic attributional style on their levels of depression and homework compliance over a course of CBT. Depression is a widespread and debilitating mental health condition. Its symptoms result in significant personal distress that can affect many areas of people’s lives (Dobson & Dozois, 2008). Relapse rates are high. Approximately 85% of clients will experience a relapse (on average, around four) during their lifetime (Carr & McNulty, 2006). If left untreated these recurrences become longer and more frequent. For clients treated with a combination of medication and therapy, relapse rates can often be reduced to 20%–35% (Carr & McNulty, 2006). The central assumption of CBT is that people’s perceptions of events are what result in their subsequent emotions and behaviors, rather than the events themselves (Beck, Rush, Shaw, & This research was supported by the Massey University School of Psychology, and partially funded by Albany Strategic Research and Lottery Health Research Council grants awarded to Nikolaos Kazantzis, Paul Merrick, Janet Leathem, et al. at Massey University. We wish to thank Margo Munro for her assistance with the research design and grant applications. Please address correspondence to: Richard Fletcher, School of Psychology, Massey University, Albany Campus, Private Bag 102 904, North Shore Mail Centre, New Zealand. E-mail: [email protected] JOURNAL OF CLINICAL PSYCHOLOGY, Vol. 71(12), 1153–1172 (2015) Published online in Wiley Online Library (wileyonlinelibrary.com/journal/jclp).

C 2015 Wiley Periodicals, Inc. DOI: 10.1002/jclp.22227

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Emery, 1979). There is a wide resulting research base supporting the efficacy of CBT; however, it is still not clear what specific techniques play primary roles in producing these results and for whom (Dobson & Dozois, 2001; DeRubeis et al., 1990; Gotlib & Hammen, 1992; Hollon & Beck, 2004; Hundt, Mignogna, Underhill, & Cully, 2013).

Definition of Homework in CBT The use of homework is central to CBT (Haarhoff & Kazantzis, 2007). The behavioral principles of classical and operant conditioning as well as the notions of maintenance and shaping are among the important factors that can help the client to learn and generalize their behaviors into new situations and then adapt and maintain them in the context of external influences (Kazantzis & L’Abate, 2005). Homework further ensures skills can be practiced in a guided manner that can allow therapists and clients to address any obstacles that might arise. Homework provides an environment in which ideas and expectations can be tested to strengthen new, alternative assumptions and core beliefs by gathering new evidence and information (Garland & Scott, 2002; Thase & Callan, 2006). There is, however, a lack of agreement as to what constitutes “homework.” A multitude of terms have been substituted for homework over the years because of the negative connotations that clients can associate with its meaning (Kazantzis, 2005; Kazantzis & L’Abate, 2007; Kazantzis & Ronan, 2006). Even when the term homework is used, the definitions vary. One definition includes criteria such as the activities being empirically supported, tailored to the client, designed collaboratively, and planned in advance among other criteria (Kazantzis, 2005). This includes much of what should be incorporated into homework assignments but is not necessarily so in practice. Another definition states that homework is “any out-of-office activity directed by a therapist and intended to have a therapeutic effect if undertaken during therapy” (Neimeyer, Kazantzis, Kassler, Baker, & Fletcher, 2008, p. 199). Even this definition is questionable; for instance, Kazantzis and Lampropoulos (2002) point out that empirical studies that compare homework to control groups can be complicated by the control group engaging in extra activities without being directed by the therapist. Thus, in our study, homework refers to any task that is carried out by the client between therapy sessions with the intention to have, or contribute towards, a therapeutic effect for the client, that is discussed specifically in therapy and is in line with session content.

Homework Compliance in CBT A meta-analysis of 27 studies by Kazantzis, Deane, and Ronan (2000) examined the effects of homework assignments and compliance on therapeutic outcome in CBT. The effect size for including versus not including homework assignments was small to medium (.36). The weighted average correlation between compliance and outcome for 16 studies was r = .22. The authors concluded that including homework increases therapeutic outcome, and that homework compliance indeed correlates with outcome. Kazantzis, Deane, Ronan, and Lampropoulos (2005) noted there are inconsistent results within the randomized trials investigating the causal nature of homework compliance. Some studies found no effect for homework (e.g., Kornblith, Rehm, O’Hara, & Lamparski, 1983), but these studies were compromised by their inability to keep homework and nonhomework separate (Thase & Callan, 2006) as well as limited ratings of homework compliance (Weck, ¨ Richtberg, Esch, Hofling, & Stangier, 2013). Correlational research has noted more consistent positive correlations between homework compliance and therapeutic outcome (Kazantzis, Deane, Ronan, & Lampropoulos, 2005), but there are several limitations with this research, which we discuss next. Rees, McEvoy, and Nathan (2005) measured the quantity of homework completion at every session but employed a yes/no format instead of a scale detailing the amount. They gathered more detail regarding the specific task of thought diaries–in terms of the number of sections attempted and overall accuracy–and highlighted the importance of including quality measures in future research. Burns and Spangler (2000) had clients rate their homework compliance

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retrospectively at a 12-week evaluation point. The therapists also rated compliance retrospectively, with the aid of notes and records. Retrospective ratings can easily be distorted due to factors that influence objectivity such as the client performing poorly or very well in therapy and problems such as distorted memories. Studies by LeBeau, Davies, Culver, and Craske (2013), and Startup and Edmonds (1994) are examples of research that employed therapist-only ratings of homework compliance, with no client or independent observer ratings. Thus, there were no measures against which to check any possible therapist biases or the accuracy of ratings. Additionally Startup and Edmonds (1994) measured homework at the first two sessions of therapy only and correlated it with final outcome scores, giving little information as to changes over time and overall compliance. Homework compliance is not necessarily stable over time and may decrease over the course of therapy (Gaynor, Lawrence, & Nelson-Gray, 2006). Gaynor et al. (2006) found no correlation with change in the Beck Depression Inventory (BDI-II; Beck, Steer, & Brown 1996) at 3-month follow up. In summary, homework compliance in CBT is generally related to decreases in severity of depression. The collaborative planning of homework between therapist and client is recommended to help motivate the client to implement the homework. The nature of the homework and its meaning for the client need to be clearly defined. What constitutes homework versus nonhomework should be specified. It is important to access both the quality and the quantity of homework completion over time as therapy sessions progress. Client and therapist ratings of compliance should be scaled and multidimensional.

Client, Therapist, and Task Factors Although homework has been shown to correlate with therapy outcome in numerous studies, albeit with some methodological limitations, there is much less research on the role that client, therapist, and task factors play in the implementation of homework (Kazantzis, Deane, Ronan, & Lampropoulos, 2005). These factors include clients’ beliefs and acceptance of treatment rationale (Addis & Jacobson, 2000), difficulty of tasks (Conoley, Padula, Payton, & Daniels, 1994), thought records versus real-life behavioral tasks (Rees et al., 2005), and the consistency of therapists’ behaviors in implementing the task (Bryant, Simons, & Thase, 1999). Other client factors are more theoretically based rather than research based (Kazantzis & L’Abate, 2007; Kazantzis, Deane, Ronan, & Lampropoulos, 2005). For example, Ajzen’s (1985, 1988) theory of planned behavior incorporates the general notion of cost-benefit analyses when considering a homework task. If the perceived costs outweigh the benefits, then a client is much less likely to invest in doing the task (Bjornholdt, 2006; Kazantzis & L’Abate, 2005). According to Ajzen’s (1985, 1988) theory, clients will also evaluate whether they think they have much control over these factors and whether they think they can change or cope with them. Low perceived self-efficacy is likely to result in the client not engaging in the homework task (Kazantzis & L’Abate, 2005). The notion of self-efficacy was emphasized in Bandura’s (1989) work, which posited that past experiences of homework, both personal and vicarious (through others’ experiences), play a role in how much control a client perceives they have over homework. Therapist responses and the client’s emotional experiences also play a role in the development of these beliefs, and the client then forms expectations about future homework tasks and their likely outcomes (Kazantzis & L’Abate, 2005). In particular, Riso and Thase (2007) name hopelessness and helplessness (two of the cornerstones of depression) as “the most severe obstacles to the successful implementation of homework assignments in therapy” (p. 248). Not only can the client predict that the homework task will go badly and be pointless, but they also often fail to engage in collaboratively designing the task, resulting in a cycle of the homework task then becoming harder than it would otherwise have been, reinforcing their original beliefs (Riso & Thase, 2007). The learned helplessness theory (Seligman, 1972) and later formulation of a pessimistic attributional style (Abramson et al., 1978) have drawn important links with vulnerability to depression. Seligman (1972) noted that animals can learn their responses are independent of stimuli, and subsequently display a “learned helplessness” whereby motivation ceases and responses abate. He was among the first to note that depressed individuals similarly often report

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beliefs that their actions will not have an effect on outcomes and thus experience decreased motivation. Abramson et al. (1978) further extended this with the reformulated learned helplessness model, delineating universal helplessness (e.g., an incurable illness that no one can change) versus personal helplessness (e.g., getting bad grades despite trying hard and others getting good grades), which relates to whether they attribute the cause of helplessness to internal or external factors. They also postulated that people make attributions about global versus specific deficits, and stable or chronic versus unstable or transient deficits, defining how broadly and for how long the person expects the deficits to have an effect. Pessimistic people are thought to attribute negative events to internal, stable, and global causes, whereas optimistic people are thought to attribute them to opposite causes: external, unstable, and specific (Gillham, Shatt´e, Reivich, & Seligman, 2001). Pessimistic attributional style has been consistently linked with depressive symptoms, particularly attributing bad events to internal, stable, and global causes (Sweeney, Anderson, & Bailey, 1986). Because of the lack of research on factors influencing homework compliance in general, the importance of helplessness attributions in pessimism, and the link with depression, pessimistic attributional styles rather than helplessness per se were employed as the focus of our research. Pessimistic attributional style was hypothesized in our study to be an important factor in client compliance with homework (e.g., see Riso & Thase, 2007), in part because of the potential perception of the benefits of homework completion as less and the costs as more than they may be in reality. Such perceptions may mean clients are also less likely to complete their homework in the future. As a personality variable, pessimistic attributional style was considered a moderator rather than a mediator variable in our study. A variable functions as a moderator when it alters the direction and/or strength of the effect of a predictor variable such as homework completion on a criterion variable such as depression (Baron & Kenny, 1986). A moderating effect is determined statistically by the presence of an interaction effect of the predictor and the moderator on the criterion. An alternative consideration is that pessimism functions as a mediator in the relationship between homework and depression. A variable functions as a mediator to the extent that it accounts for the relation between the predictor and the criterion (Baron & Kenny, 1986). Whereas moderators specify when certain effects will occur, mediators explain how or why such effects occur. To satisfy Baron and Kenny’s criterion for mediation, homework completion should predict not only depression severity but also pessimistic attributional style, rather than vice versa, which conceptually is unlikely (Riso & Thase, 2007).

The Current Study’s Aims The aim of our study was twofold: First, we addressed many of the methodological limitations that influenced previous research on homework by (a) using client ratings rather than therapist or outside observer ratings (although these were all recorded and compared for assessing the level of agreement as part of a larger study); (b) rating throughout the course of therapy rather than retrospectively; and (c) employing more measures of homework completion than just quantity. Second, we investigated the influence that client factors have on homework compliance and benefit, namely, by focusing on pessimism as a central theory of depression. It was also important for us to investigate the effect that this then had on moderating therapy outcome. The following hypotheses were tested in relation to changes over the course of CBT: H1: Client-rated quantity and quality of homework completion and benefits of completion (pleasure, mastery, and progress) will be negatively related to depression severity over the course of therapy. H2: Pessimistic attributional style will be positively related to depression severity. H3: Pessimistic attributional style will moderate the relationship between homework completion and depression severity.


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Method Participants The sample (N = 28) comprised 18 females and 10 males recruited from public print media advertisements and retained from an initial pool of 261 applicants. The inclusion criteria for our study are as follows: participants are between 18 and 65 years of age; they are presently experiencing their first major depressive episode (MDE), as defined in the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition Text Revision (DSM-IV-TR; American Psychiatric Association, 2000), as their primary difficulty; they are proficient in English; they are not taking any central nervous system acting drugs; they do not meet DSM-IV-TR diagnostic criteria for substance abuse, psychosis, or borderline personality disorder; and they have no imminent risk of self-harm or suicide, that is, they are able to be managed safely with outpatient psychotherapy. The aim was to reduce significantly confounding factors in our study and exclude disorders that require a specific treatment program such as dialectical behavior therapy (Linehan, 1993). We interviewed the applicants via telephone to assess whether they met study criteria; 186 people did not proceed past this stage. The 65 remaining potential applicants completed the Composite International Diagnostic Interview (Robins et al., 1988; see also Peters & Andrews, 1995) and were further assessed by a therapist who subsequently completed the Suitability for Cognitive Therapy Scale (Safran & Segal, 1990). Applicants were further narrowed down based on confirmation of meeting study criteria, and their availability to meet study conditions, such as session attendance. Of the 65 applicants interviewed, 37 did not meet criteria and were given community contacts to pursue and offered a written report. The final sample comprised 28 adults primarily in their 30s or 40s and were of New Zealand European ancestry (see Table 1). Most of the sample had no previous therapy, nor had they ever taken psychotropic medication.

Procedures All participants undertook a 30-minute telephone interview and the narrowed pool of 65 participants then completed the Composite International Diagnostic Interview at the Massey University Centre for Psychology. They were also assessed in person by therapists who used a semistructured interview format to confirm they met the MDE criteria. The participants completed the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996) and the Attributional Style Questionnaire (ASQ; Peterson et al., 1982). We obtained informed consent from each participant. We obtained ethical approval for this study from the New Zealand X Regional Ethics Committee (NXT/06/07/085) Following previous research on CBT practice (Kazantzis, MacEwan, & Dattilio, 2005) suggesting several procedures and measures for optimal delivery and assessment of the effectiveness of CBT for depression, we offered participants 20 sessions of CBT for depression. We video recorded each session to monitor therapist competency in implementing homework and CBT delivery and to obtain independent ratings (e.g., client homework compliance). Therapy was delivered twice per week for the first four weeks, when change is most likely to occur (Tang & DeRubeis, 1999), and once per week thereafter. We provided a follow-up session two months after therapy ended. Instead of following a manual, therapy generally progressed from psychoeducation and behavioral tasks, such as activity scheduling, to more cognitive tasks later in therapy, such as the use of thought records to challenge thinking biases. Formulations were finalized collaboratively around session 10, and behavioral experiments and core belief work followed, with relapse prevention in the final one to two sessions. In this study, we largely focused on enhancing homework completion. Therapists followed a strict protocol for implementing homework: An overview is provided in Guiding Model for Practice (Kazantzis, MacEwan, et al., 2005, pp. 380–400) and outlined in the Homework Adherence and Competence Scale (HAACS; Kazantzis, Wedge, & Dobson, 2005). Broadly, the

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Table 1

Demographics of the Depression Study Participants (N = 28)

Gender Male Female Intake age 20–29 30–39 40–49 50–59 60–65 Ethnicity NZ European Other Marital status Single Dating De Facto Married Divorced Widow(er) Employment Full-time Part-time Other Children 0 1 2 3 4 5 Past therapy 0 months < 1 month 1–3 months 4–6 months 7–9 months Past medication 0 months < 1 month 1–3 months 4–6 months

n

%

10 18

36 64

3 7 9 5 4

11 25 32 18 14

24 4

86 14

4 1 5 11 6 1

14 4 18 39 21 4

6 10 12

21 36 43

8 2 14 2 1 1

29 7 50 7 4 4

18 2 6 1 1

64 7 21 4 4

24 0 3 1

86 0 11 4

Note. NZ = New Zealand. Total percentages may not equal 100 because of rounding.

stages include reviewing, designing, and assigning homework tasks with the client throughout the session. Seven therapists participated in the study as part of their doctoral training in clinical psychology (2008–2009). In addition to their standard training, therapists attended a 1-week CBT training course and a 1-week CBT for depression course. Therapists were also trained specifically in homework administration, which comprised 2 days of homework protocol training (Kazantzis, MacEwan, et al., 2005). Therapists videotaped themselves role-playing homework protocol with a “client” and reviewed and repeated the role-playing until 100% adherence was achieved as per the HAACS.


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Throughout the study, a senior registered clinical psychologist supervised therapists in a weekly group format and monitored competency in CBT using the Cognitive Therapy Scale (Young & Beck, 1980). Although the Cognitive Therapy Scale scores were not recorded over the sessions, they were used to give informal and confidential feedback to the individual therapists. To ensure that competence in homework administration was being maintained, independent observers viewed random sessions, and through supervision, HAACS adherence scores were obtained and discussed.

Measures ASQ (Peterson et al., 1982). The ASQ is a 12-item measure comprising 12 hypothetical events, half of which are positive (e.g., “You become very rich”) and the other half negative (e.g., “You have been looking for a job unsuccessfully for some time”). Clients are asked to write a hypothetical cause for the event and then rank it on three dimensions–internality (“Is it due to me or other people?”), stability (“Will it be present in the future?”), and globality (“Does it influence many areas?”)–using a 7-point scale ranging from 1 (totally due to other people or circumstances) to 7 (totally due to me). Internal reliability for both the positive events subscale (CoPos) and the negative events subscale (CoNeg) was found to be good, α = .75, .72, respectively (Peterson et al., 1982). The CoNeg subscale produced reliabilities of between α = .85 (intake) and α = .62 (2-month follow-up) for our data set, while the CoPos subscale produced reliabilities of between α = .64 (intake) and α = .85 (2-month follow-up). Our study used only the CoNeg score (which will be referred to as pessimism), given that this has been most commonly used in research and linked with depression (Sweeney et al., 1986). Because of the length of the questionnaire and time constraints, the ASQ was completed only at selected sessions: at intake, sessions 5, 8, and 20, and 2-month follow-up, with the more frequent time points earlier in therapy, which is in line with hypothesizing that this is when change most likely occurs (Tang & DeRubeis, 1999). Homework Rating Scale-II (HRS-II; Kazantzis, Deane, & Ronan, 2005). The HRSII is a 12-item measure that assesses the implementation and completion of homework from the previous session. The client and therapist complete the scale at each session, while independent raters complete it at random intervals. We used the client version of the scale because the client’s perspective from first-hand experience is important, which is the focus of our study. Items are rated on a 5-point scale ranging from 0 (I was able to do the activity not at all) to 4 (I was able to do the activity completely). Items 3 (difficulty) and 4 (obstacles) are reverse-scored. The items were developed based on behavioral and cognitive theories of client, therapist, and task factors that have been previously linked with homework completion (Bjornholdt, 2006). The items also cover the phases of designing, assigning and doing, and reviewing homework. The HRS-II is a new measure, and its psychometric properties are yet to be fully established. Kazantzis et al. (2006) reported an alpha of α = .87 for the client version; our study found alphas ranging from α = .71 to α = .91 over the sessions. Factor analysis by Bjornholdt (2006) of the current HRS-II data produced three factors from the 12 items (when rated by clients), which are also referred to in Munro (2006): Factor 1–benefits and completion (quantity and quality items, plus pleasure, mastery, and progress); Factor 2–costs and completion (quantity and quality, plus difficulty and obstacles); and Factor 3–client beliefs (comprehension, rationale, collaboration, specificity, and match with therapy goals). There is one item for quantity (“I was able to do the activity . . . ”), rated on a 5-point scale ranging from 0 (not at all) to 4 (completely), and one item for quality (“I was able to do the activity well . . . ”), rated on a 5-point scale ranging from 0 (not at all) to 4 (extremely). Client beliefs are associated with the design phase of homework; situational factors such as costs of completion taps the “doing” phase of homework, and benefits of completion is thought to be particularly important in the reviewing and client synthesis stage (Bjornholdt, 2006; Kazantzis, Deane, & Ronan, 2005). This final stage of reviewing homework is where client pessimistic attributional style is hypothesized to influence perceived benefits of completion, thus impairing actual benefits

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in relation to treatment response. In our study, Factor 1 results over time produced the only significant correlation with change in depression severity. Therefore, the basic quantity–quality interaction was included in the multilevel model (MLM) and Factor 1 in a second MLM. The other two factors were not examined.

BDI-II (Beck et al., 1996). The BDI-II comprises 21 items measuring the intensity of depressive symptoms (such as loss of pleasure, feelings of guilt, crying, and tiredness) in patients over the past two week period. Scales on each item are worded differently according to the item and range from 0 (e.g., I do not feel sad) to 3 (I am so sad or unhappy that I can’t stand it), with total scores ranging from 0 to 63. Beck et al. (1996) reported high reliabilities, with alphas of α = .92 for a sample of 500 outpatients and α = .93 for a sample of 120 college students. Similar reliability coefficients have been found in later studies and with other populations (e.g., Arnau, Meagher, Norris, & Bramson, 2001; Dozois, Dobson, & Ahnberg, 1998; Osman et al., 1997). The BDI-II was employed at every session in our study (22 time points). Reliability for the BDI-II in the current study was strong, with values all equal to or greater than α = .90. HAACS (Kazantzis, Wedge, et al., 2005). The HAACS is a 19-item scale designed to assess therapists’ competence in reviewing, designing, and assigning homework. Independent raters completed the HAACS (in this case via sessions recorded on DVD). The raters determined whether a therapist was adhering to each item (yes/no), and if yes, they rated therapists on a 7-point scale ranging from 0 (non-adherence/extremely poor) to 6 (excellent). A psychometric review of the HAACS was conducted by Munro (2006), who found the internal consistencies for each of the three areas adequate for the review, design, and assign sections, α = .70, α = .81, and α = .80, respectively. Alphas obtained for the larger adherence and competence domains were α = .77 and α = .81, respectively. Reliability coefficients in the current study ranged from α = .76 to α = .89. An intraclass correlation coefficient (Ciccetti, 1994) was computed for assessing the degree of agreement between the two independent raters on the HAACS scores. When comparing total HAACS scores, the agreement coefficient was .91, almost perfect, according to Montgomery, Graham, Evans, and Fahey (2002). Data Analysis We chose longitudinal MLM analysis because time and session are considered to be nested within each person (Singer & Willett, 2003). Further, MLM deals with moderating effects simultaneously so that we can readily test for the moderating effect of pessimistic attributional style on the homework–depression relationship over time. Data can be conceptualized at two levels: Level 1 variables describe individual (within-person) change over time, and Level 2 variables describe differences in change between different people (Singer & Willett, 2003). MLM allows for analysis of individual growth models rather than group averages over time. In essence this means that trajectories are developed for each individual, and the relationships are derived from both the individual’s data over time (Level 1) and data between individuals (Level 2; Affleck, Zautra, Tennen, & Armeli, 1999; Kwok et al., 2008). This is important because within a study such as this one, clients’ scores vary widely in both their initial depression status and their subsequent rate of change; group trends do not reflect this. When employing longitudinal models for the analysis of change, it is necessary to use at least three time points (Singer & Willett, 2003). Kwok et al. (2008) recommend eight or more as a large number that will provide more reliable estimates. Our study, with 22 time points, more than satisfies this recommendation. Time was treated as a continuous variable. Covariates may be time-variant, which is important because we used the ASQ and the HRS-II in this study. Singer and Willett (2003) suggest three minimum requirements for MLM. The first, as already stated, is that there must be at least three time points of data (our study has 22 time points). The second requirement is to have a reliable outcome measure that changes systematically across time (in this case the BDI-II). Finally, a sensible measurement for time is required. CBT sessions were numbered from 0 (intake) to 20, regardless of actual time between sessions (vs. using days in treatment) because clients missed some sessions and sessions were often spaced farther apart than anticipated. The 2 month


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follow-up was coded as session 21. With MLM it is acceptable to have different numbers of waves for each client (clients complete different numbers of sessions) or unequal spacing of data (clients may not come at regular 1-week intervals; Kwok et al., 2008). Before carrying out analyses, missing values were calculated and replaced using IBM’s SPSS (version 21) expectation–maximization method. Percentages of missing data were minimal (0.17% to 1.75% depending on the measure). However, ASQ data were available only at five time points (intake, sessions 5, 8, and 20, and 2-month follow-up), and so linear interpolation was employed (Roth, 1994). There were two options for managing missing ASQ data: one was to exclude the data, which would mean that sessions in which the ASQ was not completed would be excluded from analyses, so that 16 time points would be excluded; the other option was to employ linear interpolation to extend the ASQ scores over all time points in between measurements (Roth, 1994). We chose this option to enable maximum use of the data for the other variables in the study. Alpha reliability analyses were conducted on the BDI-II, ASQ, HRS-II, and HAACS at each time point, as reported earlier. As per Singer and Willett’s (2003) guidelines, descriptive graphs were produced for each of the variables used in the study. Residual plots of the variables were checked to ensure normal distribution via visual inspection of normal probability plots and scatterplots, in accordance with Tabachnick and Fidell’s (2007) recommendations. Inspection concluded that assumptions of normality, linearity, and homoscedasticity were adequately met, with the exception of the HRS-II items comprising the beliefs factor (which was not directly used in our study). Regression analyses were performed with (a) BDI-II scores over time, (b) HRS-II scores (individual items, total scores, and factor scores) over time, and (c) ASQ pessimism scores over time for each person. Each regression produced an intercept (constant) and slope (Beta) for each client, to reflect the time variant nature of these variables. Simple correlations were performed between the BDI-II intercepts and slopes, and the HRS-II and ASQ intercepts and slopes, as well as between the HRS-II factors and the ASQ pessimism variable. These correlations highlighted significant relationships on which potential predictors for the MLM could be identified. Raw scores were used in the MLM analysis. Quantity and quality of homework completion (intercept and slope of each) were correlated with each other to check the relationship between homework completion at the beginning of therapy and homework completion throughout therapy. Quantity and quality (intercept and slope) were then multiplied together to form an interaction term, which was correlated with the intercepts and slopes of BDI-II scores. The interaction was entered into a MLM. Factor 1 was included in a second MLM (described below). MLM analysis in SPSS uses the MIXED procedure for maximum likelihood estimation, to essentially carry out a series of regressions upon regressions (Singer & Willett, 2003). Model A, the unconditional means model (Singer & Willett, 2003), was specified first, in which BDI-II raw scores were entered into the model alone. This base model showed whether BDI-II scores at the beginning of therapy were significantly different from zero on average (fixed effects), and whether there was enough variance at Levels 1 (within persons, across time) and 2 (between persons) to proceed with further models. Groups were not clustered at Level 2 because time is nested within the individual. BDI-II scores are included from session two to align with the point at which homework completion was able to be monitored from. Model B, the unconditional growth model (Singer & Willett, 2003) was specified next, in which “time” was added to Model A in the form of session codes. If there is no significant effect for time, then there is no change in succeeding models (Singer & Willett, 2003). This model also contained variance components, which showed whether time explained any variance at Level 1 or 2. Models A and B form the base from which to add predictor variables. The aim of adding predictor variables in subsequent models is to explain some of the remaining variance from Model B. Model C is the appropriate time to introduce the main predictor of interest. In our study, two alternative models were built with a different predictor of interest at Model C. Initially this was the quantity–quality homework interaction, traditionally the measure of focus in previous research (Neimeyer et al., 2008). Further, because MLM is an analysis of a variance-based

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Figure 1. Line graph showing all 28 clients’ BDI-II scores from intake to 2-month follow-up. Note. The bold line indicates the average trend.

procedure, interactions are the primary focus of testing, rather than focusing on the quantity and quality variables separately (Neimeyer et al., 2008; Tabachnick & Fidell, 2007). The alternative and second model focused on homework Factor 1 (benefits and completion)–as hypothesized in our study–being closely linked with attributional style. From each of these two models, Model D was then built. In both cases, Model D allowed us to control for pessimistic attributional style (pessimism). Models were not centered because the BDI-II scores do not need to be transformed or scaled when the raw scores are valid and accurate (Singer & Willett, 2003; Tabachnick & Fidell, 2007). BDI-II scores were measured from intake and represented their true scores accurately.

Results Demographic Findings Age correlated significantly with depression (r = −.13, p < .01) and pessimism (r = −.23, p < .01), but not with homework quantity and quality or benefits and completion. Multivariate analysis of variance tested for the significance of differences between males and females on the dependent variables, Wilk’s Lambda = 0.73, F (5, 475) = 34.37, p < .001. Post hoc analysis in multivariate analysis of variance revealed that males had significantly lower mean BDI-II scores (14.49, standard deviation [SD] = 8.06) than females (19.58, SD = 12.71). Differences for pessimism, homework benefits and completion, or homework quantity and quality were not significant. Overall, clients showed a decline in depression severity as measured by the BDI-II (Figure 1). The graphs do not indicate any strong curvilinear trends. There is large variability in both clients’ initial statuses and their rates of change, demonstrating the importance of incorporating a MLM method of analysis rather than relying on group trends.

Correlations Table 2 shows the means and standard deviations of the BDI-II, HRS-II, and ASQ pessimism scores at times when these measures’ data were collected together. No client initial ratings


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Table 2 Means and Standard Deviations of Depression, Homework, and Pessimism Scores Session number

n

BDI-II Mean (SD)

HRS-II (Client) Mean (SD)

ASQ CoNeg Mean (SD)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

28 28 28 28 28 28 28 27 27 27 26 26 25 25 23 22 22 21 21 20 19 19

31.07 (11.09) 27.07 (11.52) 24.90 (11.12) 22.11 (12.04) 24.11 (11.98) 21.29 (13.36) 19.14 (11.11) 19.33 (11.87) 18.57 (11.96) 18.48 (11.81) 17.54 (11.82) 16.62 (10.86) 19.04 (10.91) 15.72 (10.45) 15.65 (10.71) 15.77 (11.08) 15.23 (10.74) 14.98 (9.58) 14.33 (8.77) 12.65 (10.68) 10.74 (8.81) 8.79 (8.48)

– – 29.71 (6.17) 29.03 (7.06) 30.86 (5.56) 29.14 (5.66) 29.68 (5.20) 29.04 (6.13) 30.04 (6.55) 28.58 (6.05) 27.93 (6.32) 29.25 (7.14) 30.29 (6.18) 30.56 (8.01) 32.86 (6.30) 30.20 (7.23) 30.24 (7.61) 33.05 (7.30) 30.07 (7.65) 30.89 (8.95) 33.42 (6.89) 32.41 (6.79)

90.42 (15.06) – – – – 84.72 (13.70) – – 85.44 (13.65) – – – – – – – – – – – 77.43 (9.98) 77.03 (8.71)

Note. SD = standard deviation; CoNeg = negative events subscale. The Beck Depression Inventory (BDI-II) and the Homework Rating Scale (HRS-II) were administered at each cognitive behavior therapy session. The Attributional Style Questionnaire (ASQ) was administered only at intake (0) and at sessions 5, 8, 20, and 21 (2-month follow-up).

(intercepts) of HRS-II items correlated with the BDI-II slope; however, many of the HRS-II item ratings over time (slopes) yielded significant relationships with BDI-II scores over time, reflecting the time-varying nature of homework. When combined into HRS-II factors, only one significant relationship was apparent: The slope of Factor 1 (benefits and completion) correlated significantly with BDI-II change over time (r = −.79, p < .01). When the raw and regressed ASQ scores were correlated with BDI-II intercepts and slopes, only the pessimism variable’s raw scores and intercepts shared significant correlations with the BDI-II intercept. The lack of BDI-II intercept correlations with the pessimism slope suggests that while initial BDI-II scores are related to initial pessimism scores, they do not predict change in pessimism over time. However, there was a negative correlation between BDI-II slope and pessimism raw scores and intercept, which indicates that the higher a persons’ initial pessimism level is, the slower their depression level (BDI-II score) is likely to decrease. The positive correlation of BDI-II slope with pessimism slope indicates that as BDI-II score decreases over time, so do pessimism scores (Table 3).

MLM The sample size of 28 with 546 data points provided sufficient power to undertake MLM longitudinal analysis (Kwok et al., 2008; Singer & Willett, 2003). For both Levels 1 and 2, time is nested within people, such that Level 1 examines change within people and Level 2 measures change between people. The final models for the quality–quantity interaction and Factor 1 are presented in Tables 4 and 5, respectively.

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Table 3 BDI-II Intercept/Slope Correlations With Pessimism Scores Over All Time Points BDI-II Intercept (α)

BDI-II Slope (β)

0.50** 0.61** −0.09

−0.32** −0.44* 0.57**

Pessimism (raw) Pessimism intercept (α) Pessimism slope (β)

Note. BDI-II = Beck Depression Inventory-II; ASQ = Attributional Style Questionnaire. *p < .05. **p < .01.

Table 4 Uncontrolled and Controlled Effects of Homework as Measured by a Quantity–Quality Interaction, With Attributional Style, on Depression Severity Parameter Fixed effects Initial status, π0i

Rate of change, π1i

Intercept

γ00

Interaction

γ01

Pessimism

γ02

Intercept

γ10

Interaction

γ11

Pessimism

γ12

Variance components Level 1 Within person

σ²ε

Level 2

Initial status

σ²0

Rate of change

σ²1

Covariance

σ01

Pseudo R² statistics and goodness-of-fit R²ε R²0 R²1 Deviance AIC BIC

Model A

Model B

Model C

Model D

17.33*** (1.71) -

23.05*** (2.11) −0.80*** (0.10) -

22.31*** (2.19) 0.13 (0.11) −0.66*** (0.11) −0.03* (0.01) -

3.61 (5.46) 0.08 (0.11) 1.36*** (0.36) 1.19** (0.42) −0.02 (0.01) −0.14 (0.03)

48.44*** (3.21) 79.18*** (21.89) -

22.89*** (1.56) 120.77*** (33.26) 0.18** (0.06) −2.74* (1.16)

22.71*** (1.55) 120.14*** (33.09) 0.16** (0.06) −2.56* (1.11)

22.30*** (1.53) 92.04** (26.62) 0.10* (0.04) −1.00 (0.79)

3338.27 3344.27 3356.81

0.53 3039.39 3051.39 3076.47

0.531 0.005 0.111 3034.03 3050.03 3083.47

0.54 0.238 0.444 3003.90 3023.90 3065.65

Note. AIC = Akaike information criterion; BIC = Bayesian information criterion; BDI-II = Beck Depression Inventory; HRS-II = Homework Rating Scale. Model A and B represent BDI-II scores and time, respectively. Model C represents HRS-II quantity–quality interaction. Model D represents Model C controlling for pessimism attributional style. ***p < .001. **p < .01. *p < .05.

The initial status fixed effect, γ00, for Model A indicates the mean BDI-II score for the depression study sample at session two is significantly different from zero. Model B takes the time structure taken into account. The fixed effects rate of change statistic, γ10, under Model B is an estimate of the average change trajectory (slope) of the samples’ BDI-II scores over time.


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Table 5 Uncontrolled and Controlled Effects of Homework as Measured by HRS-II Factor 1, With Attributional Style, on Depression Severity Parameter Fixed effects Initial status, π0i

Rate of change, π1i

Intercept

γ00

Factor 1

γ01

Pessimism

γ02

Intercept

γ10

Factor 1

γ11

Pessimism

γ12

Variance components Level 1 Within person

σ²ε

Level 2

Initial status

σ²0

Rate of change

σ²1

Covariance

σ01

Pseudo R² statistics and goodness-of-fit R²ε R²0 R²1 Deviance AIC BIC

Model A

Model B

Model C

Model D

17.33*** (1.71) -

23.05*** (2.11) −0.80*** (0.10) -

24.74*** (2.39) −0.19 (0.12) −0.76*** (0.15) 0.00 (0.01) -

4.55 (5.57) −0.18 (0.12) 1.43*** (0.36) 1.27** (0.44) −0.01 (0.01) −0.15*** (0.03)

48.44*** (3.21) 79.18*** (21.89) -

22.89*** (1.56) 120.77*** (33.26) 0.18** (0.06) −2.74* (1.16)

22.36*** (1.52) 120.76*** (33.24) 0.18** (0.06) −2.67* (1.14)

21.86*** (1.50) 91.26** (26.34) 0.11* (0.04) −1.00 (0.80)

3338.27 3344.27 3356.81

0.53 3039.39 3051.39 3076.47

0.538 −0.00 −0.00 3034.72 3050.72 3084.18

0.548 0.244 0.389 3002.03 3022.03 3063.81

Note. AIC = Akaike information criterion; BIC = Bayesian information criterion; HRS = Homework Rating Scale. Model A and B are represented for comparison. Model C represents HRS-II Factor 1 (“benefits and completion”). Model D represents Model C controlling for pessimistic attributional style. ***p < .001. **p < .01. *p < .05.

This number is negative and significant, indicating that on average BDI-II scores decrease over time. The variance components of Tables 4 and 5 are, according to Singer and Willett (2003), of most interest. In Model A, the significant positive variance components at both Levels 1 and 2 (σ²ε and σ²0) indicate that there is variance to be explained both within and between people in BDI-II scores. The higher number at Level 2 indicates there is more variation to be explained between people than within. The variance components in Model B demonstrate how much variance the added variable of time has explained. The number has dropped at Level 1, indicating that time explains some variation within people; 53% of variation is explained by adding time (see the pseudo R-square statistic R²ε). However, the variation to be explained between people at Level 2 increases by adding time because the figures for both intercept and rate of change are significant, which is to be expected (Singer & Willett, 2003). The negative and significant covariance statistic indicates that those who have a higher initial (session two) BDI-II score experience a slower rate of change over time. The deviance statistic enables us to compare models of nested data, and from Model A to Model B, this reduces by a large amount, 298.88. This shows that Model B is a superior fit to Model A. Note that the deviance statistic cannot be interpreted with the same certainty for

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Models C or D because the requirement of nested data is not fulfilled. The final two statistics to observe are the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The AIC statistic takes into account the number of parameters in a model (fixed effects and variance components), while the BIC statistic takes this into account as well, plus the sample size. It is clear that both statistics decrease in Model B (the AIC decreases by 292.88 and the BIC decreases by 280.34), providing further evidence that Model B is a superior fit. Table 4 highlights the effects of adding the quantity–quality homework interaction at Model C. Adding homework in this form resulted in a minimal effect for the quantity–quality interaction on BDI-II scores over time, indicated by the near-zero change in variance scores at both Level 1 and Level 2 between Model B and Model C. This means that, over time, people who engage in a higher quantity and quality of homework will experience minimally more change in BDIII scores. Additionally, a loss of significance of the γ11 parameter in Model D indicated that pessimism moderates the minimal homework–depression relationship; therefore, once pessimism is taken into account and controlled for, there is no significant difference in BDI-II scores over time between clients who do no homework and those who do more. This highlights therefore that pessimism is an important consideration regarding its impact on perceived homework completion alone. Table 5 highlights the effects of adding homework Factor 1, which accounts for not only quantity and quality of completion but also perceived benefits of completion. Model C’s intercept parameter of initial status, γ00, shows that the average BDI-II score at session two is still nonzero, with homework taken into account. The initial status homework parameter, γ01, shows the average difference in BDI-II scores at session two between clients scoring 0 on a homework factor and those scoring high on a homework factor. There are no significant effects for any of the HRS-II factors on this parameter. The fixed effects rate of change intercept, γ10, shows that even if clients did not endorse any of the homework items for experienced benefits and completion, the change in BDI-II scores over time still decreased significantly. The final fixed effect, the γ11 parameter, shows no significant differences, indicating that change in BDI-II scores was not related to homework Factor 1. There was little Level 2 variance change when homework was added, as evidenced by the negligible R²0 and R²1 statistics. Factor 1 explained only an additional 0.8% of the variance from Model B (as seen by the R²ε statistic). Thus, it is apparent that homework factors explain very little variation in BDI-II scores within people and none between people. The interpretation of Model D in Table 5 is as follows. The γ00 parameter indicates that when clients score 0 on the pessimism construct, their corresponding session two BDI-II scores are very low, not significantly different from zero. The γ01 parameter again describes the difference in average BDI-II scores between those who score 0 on the homework factor and those who score high on it, after controlling for the effects of attributional style. After adding pessimism these scores remain nonsignificant because there was no effect of homework in Model C to begin with. The significant γ02 parameter shows that after controlling for homework Factor 1, BDI-II initial scores are positively related with pessimism scores (i.e., if BDI-II scores are higher, then pessimism scores will be too). The fixed effect rate of change parameter γ10 shows that if clients are scoring lower on their experience of homework benefits and their pessimism factor, then their BDI-II scores are likely to be lower. The γ11 parameter remains nonsignificant in Model D for pessimism, meaning that after controlling for attributional style, there is still no significant difference in BDI-II rate of change between those scoring low and high on homework benefits (Factor 1). The final fixed effect parameter, γ12, indicates that after controlling for homework benefits, pessimism is negatively associated with rate of change in depression. Therefore, people with higher levels of pessimism experience a slower rate of change in depression. In terms of variance components, pessimism does not provide much change in terms of explaining Level 1 (within-person) variance. However, at Level 2, pessimism explains 24.4% of the variance in BDI-II scores at session two, according to the R²0 value. Pessimism also explains 38.9% of the variance in BDI-II rate of change, according to the R²1 value. HAACS (therapist competence in homework) rankings (Spearman, 1987) were controlled for in a post hoc analysis on Model D, which showed there was no significant effect.


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Discussion Summary of Findings Our study demonstrated that, on average, clients experienced significant decreases in depression severity over the course of therapy, largely independent of level of homework completion. Attributional style was shown to relate to depression severity in that those with higher pessimism scores produced higher levels of initial depression and slower rates of change over time. One of the main results of our study was that there was a minimal relationship between clients’ perceptions of quantity and quality of homework completion and depression over the course of therapy, once many of the limitations of previous studies were addressed. The low strength of this relationship between homework and depression was somewhat in contrast to previous research in this area (e.g., Kazantzis et al., 2000; Kazantzis, Deane, Ronan, & Lampropoulos, 2005; Rees et al., 2005). It may be that when investigating homework in detail, rather than employing averages or limited ratings, the homework effect is diminished. However, when pessimism was added to this model, the quantity–quality effect on depression scores disappeared, indicating that pessimism moderates this relationship to a degree–people higher in pessimism are less likely to self-report benefit from homework quantity or quality of completion. The theory behind completing homework inherently makes sense, and so the finding of a minimal (rather than absent) relationship might serve to demonstrate that the basic principles of homework are still important. Another main result of our study was that there was no significant relationship between homework benefits and completion and depression over the course of therapy. Our study has provided some support for the importance of homework in CBT (Haarhoff & Kazantzis, 2007). Other studies have found that homework had minimal effects (Kazantzis et al., 2000), inconsistent effects (Kazantzis, Deane, Ronan, & Lampropoulos, 2005), and no significant effects (e.g., Kornblith et al., 1983; Weck et al., 2013) on therapeutic outcomes. Our study controlled for some of the factors that might have resulted in inconsistent or limited effects of homework. For example, we used client ratings of homework compliance versus therapist-only ratings (LeBeau et al., 2013; Startup & Edmonds, 1994). Further, we included (a) the interaction of quality with quantity of homework completion (Rees et al., 2005); (b) the concurrent versus retrospective ratings with CBT sessions over time (Burns & Spangler, 2000; Startup & Edmonds, 1994; Weck et al., 2013); and (c) both single items and multiple items to measure quantity–quality of homework completion and benefits, respectively (Bjornholdt, 2006). Consistent with the extension of learned helplessness theory (Seligman, 1972) to pessimistic attributional style (Abramson et al., 1978; Gillham et al., 2001), our study demonstrated that pessimism is related to changes in depression over time (Sweeney et al., 1986) and that it can affect the effect of self-rated quality–quantity of homework on depression in CBT sessions (Riso & Thase, 2007). The latter finding also ties in with Ajzen’s (1985, 1988) theory of planned behavior, in that the potential of benefits of homework completion are perceived as less and the costs as more than they may be in reality (Bjornholdt, 2006; Kazantzis & L’Abate, 2005). There are a number of hypotheses that might explain why the homework effect was so weak. It is possible, for example, that there are many other factors that may be more important in terms of enhancing outcome for depressive symptoms than homework completion, such as the widely researched therapeutic relationship (e.g., Zuroff & Blatt, 2006). Another consideration is that measurement of homework quantitatively might not be capturing clients’ experiences accurately. Also, whether a client completes the assigned task as prescribed has less of an effect than the transfer of learning that occurs in more subjective cognitive shifts and the ensuing generalized behavior changes resulting from therapy. A similar sentiment was expressed by Emily Holmes during an Australian Association for Cognitive Behavioral Therapy expert panel discussion of unresolved issues in homework: “Is it actually doing the homework, or is it setting the homework? . . . . Maybe what would be interesting is not the exact homework . . . but deciding that might be something that would be good to do, and what spontaneously emerges from that . . . . A pertinent issue might not be compliance with homework, but how that homework was collaboratively decided upon” (Kazantzis, Arntz, Borkovec, Holmes & Wade, 2010, p. 127). Another possibility is that a small effect might in fact exist, but the strong focus on homework in the depression study meant that clients were overall engaging in large amounts of homework.

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This might mean that there were not enough clients not regularly completing homework to which to compare results. This is where a randomized controlled study with comparison groups would provide clearer results, although the limitations of these are still significant, in that clients often do tasks on their own even when they are not required to. A final important finding from our study was that clients are clearly rating their homework completion from a different perspective than therapists. Agreement between clients and therapists as well as independent observers was also poor. This was a result also found by Cammin-Nowak et al. (2013). Therefore, these results may be slightly different if run from the therapist or observer perspective, but the fact remains that this would still not be the client’s perception.

Limitations A major limitation of this study is the very small sample size (N = 28). Replication of this study with larger samples should be undertaken to test the hypotheses and explore other relationships among pessimism, homework, and depression. It is important to acknowledge that our study was not a randomized controlled trial and the absence of a control group is notable. This would be an avenue for future researchers to explore. Not all of the measures were administered at each time point. In particular, the ASQ was not measured between sessions 9 and 19. Therapist competence in reviewing, designing, and assigning homework via the HAACS was checked only in the first half of therapy. There was sound rationale for this decision in that most change is hypothesized to occur within the first four weeks of therapy (Tang & DeRubeis, 1999). Additionally, numerous and lengthy measures can be arduous for clients. In addition to the client version of the HRS-II, the therapist version could also be analysed, to not only ascertain the degree of agreement between therapists and clients, but also to trace the changes in both sets of ratings over the therapy sessions. Similarly, therapist competence in reviewing, designing, and assigning homework as well as competence in CBT as measured by the Cognitive Therapy Scale (Young & Beck, 1980) could examine the relationships among pessimism, competencies, and adherence to CBT as the sessions progressed. Single items were used for measuring self-reported quantity and quality of homework compliance. Further development of the scales might result in multiple items to measure quantity and quality of homework compliance (Bjornholdt, 2006; Munro, 2006). Another limitation of the study is that the sample is not representative of the people in the general population who have a major depressive disorder. By screening clients thoroughly for age, number of depressive episodes, and concurrent or past therapy, generalization of findings is limited. This was designed specifically because as an initial investigation, it was desirable to control potential confounding variables. However, replications need to occur with other populations (e.g., clinical and more severe populations). The results could also be replicated with a larger sample size although the number of clients in our study was greater than the average sample size of 19 cited in the Kazantzis et al. (2000) meta-analysis. It was also sufficient for multilevel analysis with 22 time points and 546 data points (Kwok et al., 2008).

Conclusion No one can doubt the importance of therapists discussing clients’ everyday experiences in therapy and relating their learning to outside life. In this regard, homework provides the means for integrating a person’s therapy experiences with their external world. The high focus on homework in the current study means that it is difficult to know what the effects of no focus on homework would have been. However, the positive outcomes in terms of depression scores and attributional style improvements indicate that homework is certainly not harmful–in fact it makes sound theoretical sense that is too important to ignore. The final point to take away from our study is that CBT was effective in reducing depression severity for all clients, an effect that was maintained at follow-up. Additionally, pessimism scores were shown to decrease for the majority of clients over the course of therapy. Therefore, attributional style is a worthy target of change. In particular, it may be beneficial for clinicians


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to concentrate their efforts on pessimistic attributions. Clinicians’ attention to internal, stable, and global attributions of negative events is likely to be beneficial because it provides a means for highlighting these to the client and altering what is a significant underlying factor in the maintenance of depression.

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