Volume 26, Issue 7 pp. 708-716
ORIGINAL ARTICLE
Open Access

Cognitive predictors of response to interpersonal and social rhythm therapy in mood disorders

Samantha J. Groves

Corresponding Author

Samantha J. Groves

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

Specialist Mental Health Services, Canterbury District Health Board, Christchurch, New Zealand

Correspondence

Samantha J. Groves, Department of Psychological Medicine, University of Otago, PO BOX 4345 Christchurch, New Zealand.

Email: [email protected]

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Katie M. Douglas

Katie M. Douglas

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

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Marie T. Crowe

Marie T. Crowe

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

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Maree Inder

Maree Inder

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

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Jenny Jordan

Jenny Jordan

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

Specialist Mental Health Services, Canterbury District Health Board, Christchurch, New Zealand

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Dave Carlyle

Dave Carlyle

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

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Ben Beaglehole

Ben Beaglehole

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

Specialist Mental Health Services, Canterbury District Health Board, Christchurch, New Zealand

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Roger Mulder

Roger Mulder

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

Specialist Mental Health Services, Canterbury District Health Board, Christchurch, New Zealand

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Cameron Lacey

Cameron Lacey

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

Department of Māori/Indigenous Health Innovation, Māori Indigenous Health Institute, University of Otago, Christchurch, New Zealand

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Sue Luty

Sue Luty

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

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Kate Eggleston

Kate Eggleston

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

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Chris Frampton

Chris Frampton

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

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Christopher R. Bowie

Christopher R. Bowie

Department of Psychology, Queens University, Kingston, Ontario, Canada

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Richard J. Porter

Richard J. Porter

Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

Specialist Mental Health Services, Canterbury District Health Board, Christchurch, New Zealand

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First published: 31 July 2024
Citations: 1

Abstract

Background

There has been increasing interest in examining the potential moderating effects that cognitive functioning has on treatment outcome in bipolar disorder (BD) and major depressive disorder (MDD). Therefore, the aim of this exploratory study was to examine the relationship between baseline cognitive function and treatment outcome in individuals with mood disorders who completed 12 months of interpersonal and social rhythm therapy (IPSRT), and were randomised to receive adjunctive cognitive remediation (CR) or no additional intervention.

Methods

Fifty-eight patients with mood disorders (BD, n = 36, MDD, n = 22), who were randomised to IPSRT-CR or IPSRT, underwent cognitive testing at baseline and completed follow-up mood measures after 12 months. General linear modelling was used to examine the relationship between baseline cognitive function (both objective and subjective) and change in mood symptom burden, and functioning, from baseline to treatment-end.

Results

Poorer baseline attention/executive function was associated with less change in mood symptom burden, particularly depressive symptoms, at treatment-end. Additionally, slower psychomotor speed at baseline was associated with less improvement in mania symptom burden. Subjective cognitive function at baseline was not related to change in mood symptom burden at treatment-end, and neither objective nor subjective cognitive function was associated with functional outcome.

Limitations

Due to the exploratory nature of the study, there was no correction for multiple comparisons.

Conclusion

Aspects of objective cognitive function were associated with treatment outcomes following psychotherapy. Further large-scale research is required to examine the role that cognitive function may have in determining various aspects of mood disorder recovery.

1 INTRODUCTION

It is well-established that a substantial proportion of individuals with mood disorders have objective cognitive impairment,1, 2 with impairment present across a range of cognitive domains, both in episode and euthymia.3-6 Cognitive impairment is linked to poorer functional outcomes7-9 and an increased likelihood of episode recurrence.10, 11 In this context, there has been increasing interest in examining the potential moderating effects that cognitive functioning has on treatment outcomes in bipolar disorder (BD) and major depressive disorder (MDD). A greater understanding of cognitive predictors of treatment response may help delineate particular individuals who require more intensive intervention to improve aspects of cognition.

A systematic review has demonstrated that certain domains of cognitive function may be associated with treatment outcomes in mood disorder populations, particularly attention/executive function, and processing speed.12 Much of this research has focused on predictors of pharmacotherapy response. Few studies have examined this issue in relation to psychotherapy. The psychotherapy response literature in both BD and MDD reports that the most convincing evidence is for a relationship between poorer baseline verbal learning and memory performance and less improvement in mood symptomatology post-treatment.13-16 However, not all studies have found this,17-19 and other cognitive domains have also shown predictive utility, for example, processing speed16, 19, 20 and executive function.15, 16, 21 Literature on cognitive predictors of functional outcome is even more sparse, with little consistency around which cognitive domains are predictive.14, 19, 22-24 For example, Deckersbach et al.14 found that poorer performance on tasks of executive function and memory at baseline predicted poorer functional outcomes following cognitive remediation (CR) in BD. In contrast, a comparison of CR versus treatment as usual in BD showed a relationship between poorer cognitive performance, including executive function and psychomotor speed, at baseline and greater changes in psychosocial functioning at treatment-end.24 Further research is therefore required to examine this issue fully.

In consideration of the paucity of research examining cognitive predictors of treatment outcome following psychotherapy, the aim of this exploratory analysis was to examine associations between baseline cognitive function and treatment outcome in individuals with mood disorders receiving 12 months of Interpersonal and Social Rhythm Therapy (IPSRT). In contrast to most of the literature, both mood and functional outcomes were examined in the current study. A measure of mood symptom burden as opposed to a cross-sectional measure of mood symptom severity was employed as the former has been identified as a strong predictor of patient functioning and distress.25, 26 Whilst a cross-sectional measure assesses mood symptomatology at a single time point, the Longitudinal Interval Follow-up Evaluation (LIFE)27 interview assesses mood state retrospectively over a period of time (usually 6 months), yielding a number of measures including an overall score which is indicative of overall burden of mood symptoms. While the LIFE is a retrospective interview, we have recently shown a good correlation with concurrently measured mood symptoms.28

Primary outcomes from the original randomised controlled trial (RCT), including change in cognitive function, are reported elsewhere.29 In brief, one of the key findings was that the addition of CR to IPSRT resulted in less improvement in mood symptoms and psychosocial functioning at treatment-end. The primary goal of this analysis was to examine the association between baseline global objective cognitive function and mood symptom burden, as defined by the LIFE at treatment-end (12 months). In addition, we examined the relationship between (a) individual domains of objective cognitive function performance at baseline and mood symptom burden and functional outcome at treatment-end, and (b) subjective cognitive performance at baseline and mood symptom burden and functional outcome at treatment-end. Baseline cognitive scores were standardised according to performance of a healthy control group which also gave an easily interpreted statistic reflecting the degree of impairment of patients at baseline.

2 METHODS

2.1 Trial design

The current study was part of a larger RCT comparing IPSRT and CR (IPSRT-CR) with IPSRT alone for individuals with mood disorders.29 The RCT received ethical approval from Northern A Health and Disability Ethics Committee, New Zealand (ref: 16/NTA/64) and was prospectively registered on the Australian and New Zealand Clinical Trial Register (ref: ACTRN12616001328460) on 23 September 2016. In comparison with the parent study, in which there were several research assessment time-points, and the statistical approach was the last observation carried forward, the current study focused on data from baseline and treatment-end (12 months), with all participants having completed the trial. A healthy control group was also included in the current study.

2.2 Participants

The mood disorder sample was recruited from publicly funded adult Specialist Mental Health Services (Canterbury District Health Board) in Christchurch, New Zealand, with treating teams referring patients to the trial following discharge from services. To be eligible for study inclusion, a primary diagnosis of BD (I, II or Other Specified) or MDD was required, which was assigned during the recent treatment episode. Additionally, patients had to be within 3 months of discharge from adult Specialist Mental Health Services, aged between 18 and 65 years, and have subjective cognitive difficulties as determined by self or the referring psychiatrist. Subjective cognitive difficulties were assessed with a single yes/no question (i.e. ‘are you having any difficulties with concentration, memory or decision making?’). Exclusion criteria included current severe substance use disorder, schizophrenia or schizoaffective disorder, neurodegenerative disease, history of severe brain injury (loss of consciousness for more than 1 hour), having had a previous course of IPSRT in the last 18 months or CR intervention or electroconvulsive therapy in the past 6 months, concurrent engagement in psychotherapy and not being able to communicate in the English language.

Healthy control participants were recruited via flyers posted around public places in Christchurch and on various websites. Attempts were made to match the patient and healthy control groups by age, gender and premorbid IQ at a group level. Healthy control participants were aged between 18 and 65 years with no current or historical diagnosed mental health problems as determined by the Mini-International Neuropsychiatric Interview (M.I.N.I30). Exclusion criteria for healthy control participants were the same as the patient sample. Written informed consent was obtained from all individuals before participating in the study.

2.3 Clinical assessment

Following informed consent and prior to randomisation, a research nurse administered the Structured Clinical Interview for DSM-5 Research Version (SCID-5-RV31) to each patient. Mood symptom severity was rated using the clinician-administered and self-report versions of the Quick Inventory of Depressive Symptomatology (QIDS-C and QIDS-SR32), and the Young Mania Rating Scale (YMRS33). The QIDS-C and YMRS were administered every 6 months, whereas the QIDS-SR was completed every patient visit.

Mood symptom burden (LIFE27) and general functioning (Functioning Assessment Short Test, FAST34) were assessed by a blinded assessor via telephone. Both were conducted at baseline and at 6 months and 12 months. For the LIFE, scores reflected mood symptom burden over the previous 6 months. Patients were rated on a 1–6 scale, where 1 = no symptoms, 2 = residual symptoms, 3 = partial remission, 4 = does not meet DSM criteria but has major symptoms or impairment, 5 = meets definite DSM criteria for an ‘episode’, 6 = fulfils definite criteria for an ‘episode’ with the presence of either psychotic symptoms or extreme impairment in functioning.

Patients also completed a measure of psychosocial functioning (Social Adjustment Scale, SAS35) at baseline. The QIDS-C, QIDS-SR, YMRS, LIFE, FAST and SAS were also completed at 12-month follow-up. Healthy control participants did not complete any clinical measures.

2.4 Cognitive assessment

Patients and healthy control participants underwent a broad battery of objective cognitive tests at baseline assessment which was conducted by an assessor blinded to treatment arm. The cognitive testing battery included: Rey Auditory Verbal Learning Test (RAVLT36), Groton Maze Learning Test (GMLT37), Controlled Oral Word Association Test (COWAT38), Delis-Kaplan Executive Function System (D-KEFS) Category Fluency,39 D-KEFS Category Switching,39 Digit Span Test,40 Timed Chase Test,37 the Identification Test37 and the National Adult Reading Test (NART41). In addition to the cognitive testing battery, all participants completed a self-report questionnaire examining subjective cognitive function (Cognitive Complaints in Bipolar Disorder Rating Assessment, COBRA42).

2.5 Psychotherapy

Following randomisation, sequentially numbered envelopes were stored in a locked cabinet by an independent research coordinator and given to therapists after the pre-treatment assessment was completed. IPSRT was based on the manual devised by Frank43 but adapted by experienced IPSRT therapists to be used for individuals across the mood disorder spectrum.29 IPSRT combines interpersonal psychotherapy with a focus on stabilising social rhythms or routines in a person's life. IPSRT was conducted over a period of 12 months (weekly for the first 10–12 weeks, fortnightly for 4 months, and monthly thereafter), however, therapy frequency could be increased according to clinical need. The CR intervention was delivered according to a manual developed specifically for the study,44 in collaboration with Professor Christopher Bowie (Queen's University, Canada). The key components of CR involved psychoeducation about cognitive impairment in mood disorders, repetitive practise of computerised cognitive exercises and strategy coaching, and discussion of how cognitive strategies could transfer to daily life. For the combined IPSRT-CR intervention, CR was integrated into IPSRT sessions from approximately session 5 and continued for 12 sessions, with the CR taking approximately 20–30 min of the 60-min therapy session. Patients were also asked to complete at least three practise sessions of computerised cognitive practise at home per week. The same six clinicians who administered IPSRT also delivered the CR intervention. Further details are in Douglas et al.29

2.6 Medication management

Participants were accepted into the trial on any medication regime. Six consultant psychiatrists provided medication management, using the Royal Australian and New Zealand College of Psychiatrist clinical practice guidelines for mood disorders45 and clinical judgement to inform decision making. The psychiatrist saw each patient at study entry, 6 and 12 months, and when clinically indicated.

2.7 Statistical analyses

Analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 28 for Windows.46 In keeping with the design of the parent study, treatment arm was included as a fixed factor.

Baseline scores on all cognitive tests and the COBRA were converted to z-scores, calculated as the difference from the mean of the healthy control group at baseline, divided by the standard deviation of the healthy control group at baseline. A positive z-score reflected better performance. The objective cognitive test variables were then grouped into four a priori domains (see Table 1). Z-scores for each domain were calculated as the mean z-score of the individual cognitive variables within that domain. A global cognition z-score was also calculated based on the mean of the cognitive domain z-scores for each participant.

TABLE 1. Test variables in each domain.
Domain Variables
Psychomotor speed Timed Chase Test—Moves per second
Identification Test—Speed
Attention/executive function Identification Test—Total errors
Controlled Oral Word Association Test—Total words
Category Fluency—Total words
Category Switching—Total words
Digit Span—Total forward
Digit Span—Total backward
Digit Span—Span forward
Digit Span—Span backward
Visuospatial learning and memory Groton Maze Learning Test—Total errors trials 1–5
Groton Maze Learning Test—Total errors delayed recall
Verbal learning and memory Rey Auditory Verbal Learning Test—Total words trials 1–5
Rey Auditory Verbal Learning Test—List B

Rey Auditory Verbal Learning Test

Total words trial 6 Rey Auditory Verbal Learning Test

Delayed recall

Z-scores for global cognition, the four cognitive domains, and the COBRA, were then used to examine cognitive predictors of treatment response (as measured by changes on the LIFE, FAST and SAS from baseline to treatment-end) using general linear modelling. Change scores on the LIFE, FAST and SAS were calculated such that a positive score reflected an improvement in mood or functioning. Whilst calculating a reliable change index or entering baseline and follow-up data as ‘time’ factors in the models were potential options for analysis, it was decided that a change score was more readily able to be used when drawing comparisons between other studies. Demographic and clinical variables that were hypothesised to influence change in mood and functioning were examined via Pearson correlations for continuous variables and independent t-tests for non-continuous data. Variables examined included age, gender, diagnosis (BD and MDD), age of onset, therapy type (IPSRT-CR or IPSRT), substance use disorder (present or absent), comorbid anxiety disorder (present or absent), baseline scores on the YMRS, QIDS-C, QIDS-SR, SAS and LIFE (affective disturbance, depression and mania scores), and medication. The possible influence of medication was examined by dividing medication into (1) lithium, (2) anticonvulsants, (3) antidepressants and (4) antipsychotics (5) benzodiazepines, that is, as five variables coded No/Yes.

An initial general linear model for each outcome variable was constructed. In order to ensure that all possible moderators were considered, demographic and clinical covariates were entered if they were associated with the outcome variable at a significance threshold of p < 0.1. Each of the baseline cognitive variables (global cognition, cognitive domain scores, and COBRA) were entered individually into these models to determine their independent association with treatment response. Due to the exploratory nature of the current study, there was no correction made for multiple comparisons. In the sections that follow, only the significant effects from each model will be reported.

3 RESULTS

Of the 68 mood disorder participants who entered the study, 58 completed treatment and follow-up testing (IPSRT = 31, IPSRT-CR = 27). Forty-eight healthy control participants were also recruited (see Table 2). The mean age of the mood disorder sample was 36.3 years (SD 12.3), and 34 were female (59%). Thirty-six (62%) individuals had a diagnosis of BD (17 BD I, 18 BD II and 1 BD NOS) and 22 (38%) had a diagnosis of MDD. Thirty-four (59%) were in full or partial remission at baseline, and 24 (41%) were in episode. The mean QIDS-C score was 7.66 ± 5.5 (range: 0–20).

TABLE 2. Demographic and clinical characteristics at baseline.
Mood disorder sample Healthy control sample p
Age (years) 36.3 (12.3) 36.4 (16.4) 0.98
Gender (female: male) 34:24 36:12 0.08
Estimated verbal IQ (NART) 106.1 (6.7) 107.1 (7.3) 0.50
Mood disorder diagnosis (BD 1:BD II:BD NOS:MDD) 17:18:1:22
Depression severity (QIDS-C) 7.66 (5.5)
Mania severity (YMRS) 1.36 (2.4)
Longitudinal depression symptoms (LIFE) 2.1 (1.4)
Longitudinal mania symptoms (LIFE) 0.2 (0.4)
Longitudinal affective disturbance (LIFE) 2.26 (1.3)
General functioning (FAST) 18.4 (12.1)
Psychosocial functioning (SAS) 2.4 (0.5)
  • Abbreviations: BD, bipolar disorder; BD NOS, bipolar disorder not otherwise specified; COBRA, Cognitive Complaints in Bipolar Disorder Rating Assessment; FAST, Functional Assessment Short Test; LIFE, Longitudinal Interview Follow-up Evaluation; MDD, Major Depressive Disorder; NART, National Adult Reading Test; QIDS-C, Quick Inventory for Depressive Symptomatology—Clinician Rated; SAS, Social Adjustment Scale; YMRS, Young Mania Rating Scale.
  • a The LIFE assessed mood symptoms in the 6-month period prior to the study.
  • * p ≤ 0.05.

The mean (SD) z-scores for each domain representing an effect size difference between the mood disorder participants were: global cognition = −0.29 ± 0.56, verbal learning and memory = −0.37 ± 0.73, visuospatial learning and memory = −0.48 ± 1.12, attention/executive function = −0.43 ± 0.69 and psychomotor speed = 0.14 ± 0.94. Values for individual variables are provided in Table S1. The mean (SD) z-score for the COBRA was −2.10 ± 1.59.

Several demographic and clinical variables were associated with change in LIFE, FAST and SAS scores from pre- to post-treatment at p < 0.1 and were entered into the respective general linear models (see Table S2). These were:
  1. Change in LIFE affective disturbance: treatment arm, baseline LIFE affective disturbance and depression scores, baseline FAST score and baseline antidepressant use.
  2. Change in LIFE depression score: gender, treatment arm, baseline LIFE affective disturbance and depression scores, baseline FAST score and baseline antidepressant use.
  3. Change in LIFE mania score: gender, baseline LIFE mania score, baseline FAST score, presence of substance use disorder and anxiety disorder.
  4. Change in FAST score: treatment arm, baseline QIDS-SR score, baseline LIFE affective disturbance and depression scores, baseline FAST and SAS scores, comorbid anxiety disorder and baseline lithium use.
  5. Change in SAS score: treatment arm, baseline QIDS-SR score, baseline LIFE depression score, and baseline SAS score.

3.1 Association between baseline global cognition and mood symptom burden

Results of the general linear model analyses are presented in Table 3, with full models provided in Table S3. No association was found between baseline global cognition and subsequent burden of mood symptoms as measured by the LIFE: affective disturbance (F(1,55) = 0.11, p = 0.75), depression (F(1,54) = 0.009, p = 0.92) and mania (F(1,53) = 0.57, p = 0.46).

TABLE 3. Relationship between baseline cognitive function and change in mood symptom burden and general functioning following psychotherapy.
LIFE affective disturbance LIFE depression LIFE mania FAST SAS
Global cognition 0.11 (0.75) 0.01 (0.92) 0.57 (0.46) 0.08 (0.78) 1.05 (0.31)
Verbal learning and memory 0.72 (0.40) 0.00 (0.97) 0.83 (0.37) 0.07 (0.79) 0.95 (0.33)
Visuospatial learning and memory 0.61 (0.44) 0.32 (0.58) 0.15 (0.71) 0.29 (0.59) 1.40 (0.24)
Attention/executive function 4.23 (0.04) 5.89 (0.02) 0.36 (0.55) 2.60 (0.11) 0.31 (0.58)
Psychomotor speed 0.11 (0.74) 0.44 (0.51) 4.57 (0.04) 0.74 (0.39) 1.81 (0.19)
COBRA 1.79 (0.19) 1.06 (0.31) 0.83 (0.37) 2.52 (0.12) 0.63 (0.43)
  • Abbreviations: COBRA, Cognitive Complaints in Bipolar Disorder Rating Assessment; FAST, Functioning Assessment Short Test; LIFE, Longitudinal Interview Follow-up Evaluation; SAS, Social Adjustment Score.
  • a The LIFE assessed mood symptoms in the final 6 months of treatment.
  • b All cognitive variables are z-scores, derived from the mean and standard deviation of the healthy control group at baseline.
  • c Results from ANCOVA, presented as F (p). p ≤ 0.05.
  • Degrees of freedom ranging from 1.52 to 1.55.

3.2 Association between baseline objective cognitive domains and mood symptom burden

Results of the general linear models are presented in Table 3. Across the entire mood disorder sample, baseline attention/executive function was significantly associated with change in LIFE affective disturbance (F(1,54) = 4.23, p = 0.04). More specifically, worse attention/executive function performance at baseline was associated with less change in the total burden of mood symptoms pre- to post-treatment (regression coefficient = 0.46). The only other variable that remained significant in the final model was baseline LIFE affective disturbance (F(1,54) = 33.11, p = <0.001), whereby worse baseline scores on the LIFE were associated with greater improvement at treatment-end. Baseline attention/executive function performance was also significantly associated with change in LIFE depression scores (F(1,54) = 5.89, p = 0.02), with worse attention/executive function performance being associated with less improvement in depressive symptomology following treatment (regression coefficient = 0.48). Other significant variables in the final model were baseline LIFE depression symptoms (F(1,54) = 41.19, p = <0.001) and treatment arm (F(1,54) = 5.48, p = 0.02). More specifically, worse scores on the LIFE at baseline and randomisation to the IPSRT group were associated with greater improvement. Finally, baseline psychomotor speed performance was significantly associated with change in LIFE mania scores (F(1,54) = 4.25, p = 0.04), where slower psychomotor speed performance at baseline was associated with less improvement in mania symptoms by treatment-end (regression coefficient = 0.15). Other demographic and clinical variables that remained significant in the final model were baseline LIFE mania symptoms (F(1,54) = 54.53, p = <0.001) and the presence of a substance use disorder (F(1,54) = 13.39, p = <0.001), whereby worse baseline mania symptoms and comorbid substance use disorder were associated with greater improvement post-treatment. No other cognitive domains were significantly associated with mood score change.

3.3 Association between baseline subjective cognitive function and mood symptom burden

No association was found between baseline COBRA scores and subsequent burden of mood symptoms as measured by the LIFE: affective disturbance (F(1,55) = 0.18, p = 0.19), depression (F(1,54) = 1.06, p = 0.31) and mania (F(1,53) = 083, p = 0.37).

3.4 Association between baseline global cognition and change in functioning

Results of the general linear model analyses for functional measures are presented in Table 3. No association was found between baseline global cognition and change in general functioning as measured by the FAST (F(1,53) = 0.08, p = 0.78), or change in psychosocial functioning as measured by the SAS (F(1,54) = 1.05, p = 0.31).

3.5 Association between baseline objective cognitive domains and change in functioning

No cognitive domains were associated with change in FAST scores or change in SAS scores from pre- to post-treatment (see Table 3).

3.6 Association between baseline subjective cognitive function and change in functioning

No association was found between baseline COBRA scores and change on the FAST (F(1,53) = 2.52, p = 0.12) or SAS (F(1,53) = 0.63, p = 0.43) at treatment-end (see Table 3).

4 DISCUSSION

The aim of the current analysis was to examine associations between baseline cognitive function and change in mood symptom burden and functioning following a 12-month course of IPSRT. We found that baseline global objective cognitive function was not predictive of change in mood symptom burden post-treatment. In analyses of individual cognitive domains, poorer baseline attention/executive function was associated with less change in total mood symptom burden, and burden of depression symptoms, following psychotherapy. Slower baseline psychomotor speed was also associated with less change in burden of mania symptoms by treatment-end.

The participants with mood disorders were recruited on discharge from Specialist Mental Health Services. In New Zealand, only people with severe mental illness are seen by Specialist Mental Health Services, with most others being treated in primary care. Specialist Mental Health Services include inpatient care and community mental health care. Patients cared for by community mental health teams are admitted to the teams from inpatient care or direct from primary care. Patients are discharged from community mental health teams after what is usually a short (<3 months) period of stabilisation, but not necessarily after an episode has resolved. In this study, approximately 40% of patients were still in-episode. This is therefore a study of people with severe or complicated illness, at a stage of partial treatment following an acute episode of mood disturbance.

The finding of an association between poorer baseline attention/executive performance and less change in mood symptom burden at treatment-end, particularly depression symptoms, is consistent with prior research examining cognitive predictors of treatment response.12 There are several possible explanations for such a finding. First, it is possible that poorer attention/executive function performance serves as a proxy for dysfunction within the fronto-limbic circuits. Attention/executive function is sub-served by areas within the prefrontal cortex, with aberrant neural activity having been found in these brain regions in mood disorder populations.47 Attenuated prefrontal control over hyperactive limbic and subcortical structures (e.g. amygdala) may lead to the negative affective biases and perseverative thinking patterns that are common in mood disorders, with such cognitive processes believed to contribute to the development and maintenance of mood symptomatology.48, 49 Thus, poorer attention/executive function performance may highlight key pathological processes that serve to perpetuate mood symptoms and impair response to treatment.

Second, it is possible that impairment in attention/executive function might impinge on an individual's ability to effectively engage in psychotherapy and thus, derive as much benefit. Individuals in the current study underwent 12 months of psychotherapy with or without CR, which will have required a level of cognitive function necessary for the assimilation and implementation of core concepts and strategies. Impairments in attention/executive function may make it particularly difficult for individuals to engage in therapy, for example, due to difficulty attending to content discussed or lack of cognitive flexibility limiting the ability to transfer strategies learnt in therapy to real life.

There was no association between baseline subjective cognitive performance and change in mood symptomatology following therapy. This was somewhat surprising as it has been shown that subjective cognitive impairment may be related to factors such as depressive symptom severity and increased self-referential processing, albeit cross-sectionally.50-52 To our knowledge, no previous studies have examined the predictive value of subjective cognitive complaints and treatment outcomes in mood disorder populations. Thus, further research is required to examine this relationship more fully.

No significant relationships were found between baseline cognitive performance (both objective and subjective) and changes in functional outcome as measured by the FAST and SAS. This is despite the well-substantiated link between cognitive function and functional disability in mood disorders.7-9, 53, 54 We are aware of five studies that have examined the relationship between baseline cognitive function and functional outcome following psychotherapy, and there has been little consistency regarding which cognitive domains are predictive or the directionality of the associations found.14, 19, 22-24 This may relate to the difficulties in defining and measuring functional outcome.55 Based on current available evidence, it is apparent that the relationship between baseline cognitive function and psychosocial functioning following treatment remains unclear.

Across all three significant models, higher mood symptom severity at baseline was associated with greater improvement in mood symptom burden at treatment-end. This is not surprising given there would have been greater scope for improvement in this regard, albeit such an association may not have been found in a more clinically ill sample. For the association between baseline attention/executive function performance and change in LIFE depression scores, those in the IPSRT group showed greater improvement in depressive symptoms at treatment-end compared with the IPSRT-CR group. As noted above, the original outcome paper showed that those receiving adjunctive CR had less improvement in mood symptoms and psychosocial functioning following treatment.29

Psychomotor speed was also associated with treatment outcome, whereby slower psychomotor speed at baseline was associated with less improvement in LIFE mania symptoms post-treatment. This was moderated by the presence of a concurrent substance use disorder, which was associated with a greater reduction in mania symptoms at treatment-end. These findings should be interpreted cautiously. Baseline mania scores were low, giving little scope for change, and there was also very little difference between controls and mood-disordered patients in psychomotor speed. In this context, the clinical significance is unclear and as we note in the limitations section, there is the risk of Type 1 error in this exploratory analysis. It is possible that improved management of the substance use disorder via therapy provided greater scope for improving mood symptoms, particularly mania, as they commonly co-occur. Alternatively, participants may have learnt alternative strategies during therapy to manage their mania symptoms, leading to a reduction in substance use and overall symptomatic improvement. A systematic review suggests equal, if not better, outcomes for individuals with comorbid BD and substance use disorder receiving psychotherapy,56 while in our previous studies using IPSRT, there was no evidence that current substance use disorder impaired response.57

The current analysis has several strengths. First, few studies have examined baseline cognitive predictors of mood and general functioning with psychotherapy across the mood disorder spectrum. Second, the current analysis examined change in mood symptom burden (both syndromal and subsyndromal) over the 12-month treatment period, which may be more sensitive to change than the more commonly used outcome of relapse and/or remission. Moreover, research suggests that patients with mood disorders can experience significant periods in subsyndromal mood states,58-60 with ongoing mood symptom burden having been identified as an important determinant of functioning and a significant contributor to patient distress.25, 26 To this end, employing a longitudinal measure of mood disturbance may be more clinically meaningful for patients and may also provide a more accurate depiction of an individual's symptoms and course than a cross-sectional measure. Finally, predictors of treatment outcome were examined in a diverse group of individuals, particularly regarding clinical characteristics, thereby increasing the generalisability of study findings.

The current analysis also has several limitations. First, the sample was heterogeneous regarding multiple clinical characteristics including mood disorder diagnosis, medications and mood state at baseline. While this heterogeneity may be positive for a pragmatic clinical trial generating generalisable clinical results, it reduces the likelihood of providing clear data on predictors, particularly with a relatively small sample. Second, sample size is clearly a limitation. To mitigate this, the data were analysed together for both treatment arms, using treatment arm as a fixed factor in analysis. Using the whole sample increased power to examine factors operating across both groups but did not of course compensate for low power in examining predictors specific to those receiving CR. Thirdly, no correction was made for multiple comparisons. Whilst we attempted to mitigate this issue by using a priori-defined domains for statistical analysis to reduce the number of comparisons, the lack of correction could mean that some findings are due to chance. Finally, as the original trial was designed to be a pragmatic clinical trial with minimal inclusion/exclusion criteria, we only recruited patients who had subjectively assessed cognitive difficulties as measured by a single yes/no question. We were unable to examine the correlation between this single-item question and more in-depth measures of subjective cognitive impairment such as the COBRA as all participants were reporting cognitive difficulties. Additionally, whilst the correlation between subjective and objective cognitive difficulties is relatively small in MDD and BD,51, 52 this inclusion criterion may have reduced the range of cognitive difficulties in our sample, excluding patients with superior cognitive functioning and limiting the predictive power of our analyses.

5 CONCLUSIONS

This exploratory analysis found evidence that poorer baseline attention/executive function performance was associated with less change in mood symptom burden, particularly depressive symptoms, following 12 months of psychotherapy. Slower baseline psychomotor function was also associated with less change in mania symptomatology at treatment-end. The finding that poorer attention/executive function was associated with less improvement is consistent with the emerging literature on cognitive predictors of treatment response.12 In this study, cognitive rehabilitation methods were added to psychotherapy at the beginning of treatment, partly in the expectation that this would improve cognitive functioning including attention/executive function. However, not only was CR ineffective in improving attention/executive function,29 but those receiving IPSRT alone had greater improvement in mood and psychosocial functioning at treatment-end. Thus, while targeting these cognitive functions with CR in order to improve treatment outcomes may appear to be a logical conclusion from our data, the original study did not find evidence of this. The reasons for this have been more extensively discussed in our previous report.29 Further large-scale studies are required to examine the role that baseline cognitive function has in determining various aspects of mood disorder recovery.

ACKNOWLEDGEMENTS

The authors acknowledge the crucial roles of Bridget Kimber, Rachel Day-Brown (research nurses), Emily Douglas (research assistant) and Andrea Bartram (data manager) in this study. Open access publishing facilitated by University of Otago, as part of the Wiley - University of Otago agreement via the Council of Australian University Librarians.

    CONFLICT OF INTEREST STATEMENT

    KD, CB and RP use software provided free-of-charge by Scientific Brain Training Pro for Cognitive Remediation trials. RP has received support for travel to educational meetings from Servier and Lundbeck. CB has grant support from Lundbeck, Takeda and Pfizer and has received consulting fees from Boehringer Ingelheim, Pfizer, Lundbeck.

    DATA AVAILABILITY STATEMENT

    The data that support the findings of this study are available from the corresponding author, SG, upon reasonable request.

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