- Research
- Open access
- Published:
Perceived cognitive loss, symptomology, and psychological well-being with bipolar disorder
International Journal of Bipolar Disorders volume 12, Article number: 34 (2024)
Abstract
Background
Adults with bipolar disorder (BD) commonly present with cognitive deficits. Many also report subjective or perceived cognitive failures. For this study, we identified four distinct clusters of adults with BD on the basis of both BD symptoms (depression and hypo/mania) and perceived cognitive errors (i.e., forgetfulness, distractibility, false triggering). We hypothesized that participants reporting more BD symptoms and cognitive errors would report lower psychological well-being (i.e., self-efficacy, life scheme, life satisfaction). A second objective was to determine if and how clusters differed in terms of BD related factors (e.g., subtypes, sleep, medications) and sociodemographic differences such as age of participants. From the BADAS (Bipolar Affective Disorder and older Adults) Study, we identified 281adults with BD (M = 44.27 years of age, range 19–81), recruited via social media.
Results
All clusters significantly differed across all grouping variables except symptoms of hypo/mania due to low frequency. Across clusters, perceived cognitive failures and BD symptoms increased in lockstep; that is, those reporting more cognitive errors also reported significantly higher symptoms of both depression and hypo/mania. As hypothesized, they also reported significantly lower psychological well-being.
Conclusions
Age did not significantly differ across clusters in contrast to existing research in which cognitive loss is objectively measured. That is, perceived cognitive errors are significantly associated with lower psychological well-being for both young and older adults with BD.
Introduction
Adults with bipolar disorder (BD) commonly present with both objective and subjective or perceived cognitive deficits. Research indicates considerable variability in cognitive loss, with deficits most commonly observed in older adults with BD (Montejo et al. 2022a, b, c); however, associations are not strong between self-reported, subjective reports and objective cognitive deficits as measured using structured assessment batteries (Cullen et al. 2016; Miskowiak et al. 2018). That is, self-reported cognitive errors are a poor proxy for objective cognitive loss. Various explanations for this discrepancy have been suggested, yet reasons remain unclear. Irrespective, perceived cognitive errors directly and indirectly affect mental health (O’Rourke et al. 2017), and have been found to affect psychosocial functioning and quality of life for those with various neurological and psychiatric conditions (Groenman et al. 2022) including BD (O’Rourke et al. 2017).
Previous BD research has identified three naturally-occurring groups or clusters of objectively measured cognitive deficits (i.e., intact, selective impairment, & global severe impairment (Burdick et al. 2005; Lima et al. 2019; Montejo et al. 2022a, b, c; Solé et al. 2016). This study is the first to identify clusters of BD symptoms and perceived or subjectively-reported cognitive errors (i.e., forgetfulness, distractibility, false triggering). In addition, we identify differences in psychological well-being across naturally occurring groups of adults with BD (i.e., self-efficacy, life scheme, life satisfaction). And we compare BD related factors and sociodemographic features across clusters. For instance, older age was assumed to be significantly associated with severity of reported cognitive errors.
BD and cognitive functioning
BD is a chronic and episodic mental health condition characterized by mood variability. Symptoms and episodes of depression and mania or hypomania (i.e., hypo/mania) emerge across the life span, sometimes together (i.e., mixed symptom presentation (Grande et al. 2016)). Lifetime prevalence of BD is about 1–3% worldwide (Merikangas et al. 2011). And BD is highly heritable, with estimates from twin and family studies suggesting 60–85% heritability between first-degree relatives (Johansson et al. 2019; Smoller and Finn 2003).
BD is also a leading cause of disability worldwide due, in part, to early onset in most cases (i.e., late adolescence to late 20’s), chronicity and impact on employment and relationships (Yerushalmi et al. 2021; Alonso et al. 2011; Tohen et al. 2000; Zarate et al. 2000; Martinez-Aran et al. 2007). Moreover, BD has the highest rate of completed suicides across psychiatric disorders (Plans et al. 2019). Fortunately, pharmacotherapy is effective for most with BD, yet subthreshold symptoms commonly persist (Fountoulakis et al. 2012). In addition to mood symptoms, cognitive loss is commonly reported (Arts et al. 2008).
Cognitive impairment and decline with BD is the focus of growing research and clinical interest (Montejo et al. 2022a, b, c; Lin et al. 2019). This includes both perceived or subjectively reported cognitive impairment and objective measurement (i.e., neurocognitive testing). Existing research suggests that primary cognitive deficits with BD include verbal memory, speed/reaction time, attention and executive functioning (Cullen et al. 2016; Latalova et al. 2011), evident during BD mood episodes and also when euthymic (Thompson et al. 2005).
However, there is high variability in cognitive loss across and within samples when cognition is objectively measured, ranging between 5 − 57% for executive functions, 9 − 51% for working memory, 23 − 44% for speed/reaction time and 8 − 42% for verbal memory (Cullen et al. 2016). Other studies suggest that euthymic persons with BD have significant cognitive impairment, yet many perform within age-adjusted normal parameters (Sparding et al. 2017).
One hypothesis that may explain these discrepancies is the neuroprogression model of cognitive decline which contends that recurrent psychosis (which occurs in manic states) disrupts homeostasis between inflammatory mechanisms, oxidative processes and neuroprotective mechanisms; these effects cause neural death and are cumulative (Serafini et al. 2021). Cognitive decline is associated with BD chronicity (Cardoso et al. 2015), meaning that more acute mood episodes and number of hospitalizations predict more pronounced cognitive loss. These deficits are thought to become self-evident and clinically manifest in middle age, accelerating age-related cognitive decline into later life (Szmulewicz et al. 2020). Therefore, age is likely associated with cognitive decline though findings are inconsistent (Montejo et al. 2022a, b, c; Robinson and Nicol Ferrier 2006). Some evidence supports the neuroprogression model (Serafini et al. 2021; Robinson and Nicol Ferrier 2006) but meta-analyses provide little evidence of progressive cognitive decline (Samamé et al. 2014, 2022). Nor have the effects of cognitive deficits on psychological well-being and quality of life with BD been fully examined.
Cognitive deficits are reliably measured by objective neuropsychological test batteries and brain imaging (i.e., fMRI, identified lesions) whereas perceived cognitive errors are measured by self-report. Of note, perceived and objectively measured cognitive deficits with BD are not strongly correlated (Miskowiak et al. 2018; Jensen et al. 2015). Some studies found no significant associations (Werf-Eldering et al. 2011) yet others reported that perceived cognitive deficits are moderately but not strongly correlated with objective measures of memory, attention, and executive function (Martinez-Aran et al. 2005). This discrepancy may be due to depressive symptoms, poor medication adherence, comorbid psychiatric conditions, and metacognitive capacity (Miskowiak et al. 2018).
Though perceived cognitive errors are a poor proxy for objective cognitive deficits, this does not negate the importance of the former. For instance, perceived or subjective cognitive errors may better reflect change from premorbid functioning, not fully captured by objective tests (Forcada et al. 2015). It has also been suggested that objective tests lack ecological validity relative to aspects of cognition necessary for daily living (Mohn and Rund 2016; Elst et al. 2008). In fact, perceived cognitive deficits are strongly associated with distress and poor psychosocial functioning with various psychological and neurological conditions (Groenman et al. 2022). Perceived cognition is also significantly associated with quality of life with Alzheimer disease in contrast to cognition as objectively measured (Naglie et al. 2011). Similarly, subjective memory impairment predicts future depression among healthy older adults (Heun and Hein 2005).
Self-reported cognitive deficits with BD are associated with various psychosocial difficulties including inability to work, live independently, participate in leisure activities, and form and maintain social relationships (Demant et al. 2015). Though poor proxies of objective cognitive deficits (Burdick et al., 2005, Svendsen et al., 2012), perceived cognitive errors directly and indirectly affect mental health with BD (O’Rourke et al. 2017) as patients with cognitive complaints report more suicide ideation and attempts (i.e., self-harm (Luo et al. 2020; Lima et al. 2018)).
Perceived cognitive errors are associated with depressive symptoms while symptomatic and euthymic (O’Rourke et al. 2017). And frequency and severity of depressive mood episodes are a significant predictor of cognitive decline. It remains unclear however how various perceived cognitive errors differentially affect psychological well-being. Do effects accumulate over time (e.g., years since BD diagnosis, age), and are specific BD symptoms (i.e., depression vs. hypo/mania) associated with certain cognitive deficits?
Despite extensive research examining the neuropsychology of BD, many questions remain. One major question pertains to the considerable heterogeneity in cognitive function with BD (Montejo et al. 2022a, b, c). Using hierarchical cluster analyses performed on standardized test scores, previous BD research has identified distinct subpopulations: those cognitively intact (31-43%), those with selective cognitive impairment (28-46%) and a notable minority with global severe impairment (12-39%) (Lima et al. 2019; Montejo et al. 2022c; Millett and Burdick 2021). Intelligence (i.e., IQ), early versus late BD onset, years of educations and number of mood episodes significantly differ across clusters, but associations between age and cognitive decline with BD remain unclear (Lima et al. 2019; Montejo et al. 2022a, b, c; Millett and Burdick 2021). Nor does subjective cognitive loss significantly differ across clusters (Lima et al. 2019). Moreover, few studies address the proportion and heterogeneity of perceived cognitive failures (Lima et al. 2019; Grover et al. 2023). This underscores the need for a closer and separate look at perceived cognition in order to better understand its heterogeneity, the mechanisms accounting for it, and associations with BD symptoms and well-being.
Based on existing research, we similarly assumed that distinct clusters would form based on various types of subjective or perceived cognitive errors and BD symptoms (both depression and hypo/mania). Two to five cluster solutions were considered based on interpretability, cluster size, with statistically significant differences in cognitive errors and BD symptoms across clusters. We hypothesized that psychological well-being would significantly differ across clusters; that is, groups reporting more cognitive errors would report lower psychological well-being. We further hypothesized that those who report lower BD symptoms would report lower cognitive deficits, and greater psychological well-being.
A secondary objective was to compare BD related factors and sociodemographic variables across cluster. For instance, we assumed that those diagnosed decades ago and treated over many years would report more cognitive errors (i.e., older adults with BD). And in accord with the neuroprogression model, we assumed that cognitive errors are associated with age. That is, that those diagnosed decades ago and treated over many years would report more cognitive errors (i.e., older adults with BD).
Methods
Participant recruitment and online data collection
For this study, we identified 281 young and older adults with BD recruited for the BADAS (Bipolar Affective Disorder and older Adults) Study (Yerushalmi et al. 2021; O’Rourke et al. 2021a, b, c). Participants provided responses online to measures of cognitive loss, BD symptomology (both depression and hypo/mania) and psychological well-being. They also reported BD related factors (e.g., medications, BD subtype), sociodemographic factors such as age, and behavioral factors (i.e., sleep, alcohol consumption).
BADAS participants were recruited over 19 days using social media advertising targeted to adults with BD (Cohen et al. 2023). By clicking on advertisements appearing within Facebook newsfeeds, participants were directed to an online form that specified study inclusion criteria (King et al. 2014). Thereafter, they completed a series of counterbalanced online questionnaires hosted on a secure university https server; responses were encrypted before transmission (King et al. 2016).
Machine-generated algorithms calculated by social media platforms like Facebook are unique not so much for their sensitivity but specificity (i.e., exclusion of those who do not have BD). That is, participants recruited via social media are not representative of the population (Mühlhoff and Willem 2023); instead, they may be more symptomatic than outpatients attending mood disorder clinics (O’Rourke et al. 2021a, b, c), yet we can be confident these are persons with BD as only Facebook users with BD received recruitment notices (King et al. 2014; Pollock Star et al. 2022). In previous research with BADAS participants, BD diagnoses were confirmed in clinical interviews (Stalman et al. 2018; O’Rourke et al. 2016) and corroborated by proxy informants (i.e., cohabiting partners (Yerushalmi et al. 2021).
Instruments
The BDSx (Bipolar Disorder Symptom Scale) was developed to briefly measure symptoms of both depression and hypo/mania (hypomania + mania = hypo/mania: continuum where the point of transition is not always apparent (O’Rourke et al. 2016, 2018a, b). Respondents indicate the degree to which each of 20 mood adjectives corresponds to how they feel at that moment on a Likert scale ranging from not at all (0) to a lot (2). Though not designed as a screening measure, sensitivity for the depression subscale is high at 88% (6+, 76% specificity) but lower at 57% for the hypo/mania subscale (5+, 90% specificity (Osher et al. 2020).
Internal consistency of BDSx responses has been reported as α = 0.91 for the depression subscale (Cohen et al. 2023) but lower for the hypo/mania subscale at α = 0.76 (Kraun et al. 2020). This 4-factor model appears stable over time (O’Rourke et al. 2016), with young and older adults with BD (O’Rourke et al. 2018a, b) and across BD subtypes (O’Rourke et al. 2021a, b, c). Construct validity is supported by research with cohabiting partners of persons with BD as affrontive symptoms of mania (e.g., furious, disgusted, argumentative) have the greatest negative impact on partners, especially when physically together (i.e., same GPS coordinates (Yerushalmi et al. 2021).
The Cognitive Failures Questionnaire (CFQ (Broadbent et al. 1982) measures subjective or perceived failures in perception, memory, and motor function (e.g., “Do you often forget people’s names?”; “Do you lose your temper then regret it?”; “Do you bump into people?”). Responses to each of 17 items are reported along a Likert scale ranging from never (0) to very often (4). CFQ responses have been validated with general samples (Rast et al. 2009) and older adults with BD, 0.80 < α < 0.88 (O’Rourke et al. 2021c). Responses to the CFQ measure three distinct factors: Forgetfulness, distractibility and false triggering (i.e., interrupted processing of cognitive and motor actions (Rast et al. 2009).
The Spiritual Index of Well-Being (SiWB) was developed to measure health-related quality of life (QoL (Daaleman et al. 2004). Responses to each of 12 items are reported along a Likert scale ranging from strongly agree (1) to strongly disagree (5). The SiWB does not measure religiosity, but self-efficacy (α = 0.86) and life scheme, α = 0.86 (López et al. 2009). Test-retest reliability over 2-weeks has been reported as r = .78. Good internal consistency has been reported with various adult populations, 0.84 < α < 0.90 (Daaleman and Frey 2004).
The Satisfaction with Life Scale (SWLS (Diener et al. 1985) measures quality of life based on person-specific criteria. Participants respond to 5 questions with response alternatives ranging from very strongly disagree (1) to very strongly agree (7). Good internal consistency has been reported with older adults (α = 0.82) (Heisel and Flett 2016), including older adults with BD, α = 0.89 (O’Rourke et al. 2018b). Research indicates that depression is associated with lower SWLS, but unrelated to hypo/manic symptoms of BD (Meyer et al. 2004).
Pittsburgh Sleep Quality Index (PSQI) is a 19-item self-rated questionnaire that assesses perceived sleep quality (Buysse et al. 1989). Responses are reported along 4-point Likert scales. PSQI items measure sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each of these domains differs significantly between euthymic persons with BD and control participants (Saunders et al. 2013). PSQI responses are also significantly correlated with cognitive loss associated with BD (Russo et al. 2015). Construct validity of PSQI responses has been demonstrated using objective indices of sleep disturbance (i.e., actigraphy (Geoffroy et al. 2014). Prospective research suggests that the PSQI predicts BD mood episode recurrence even after controlling for residual mood symptoms (Cretu et al. 2016).
Alcohol Use Disorders Identification Test (AUDIT) is a self-report scale initially developed to measure alcohol misuse (Babor and Grant 1989). Responses are reported along a 5-point (8 items) and 3-point Likert scales (2 items). Internal consistency of AUDIT responses ranges from 0.74 < α < 0.96, and α = 0.86 with BD (O’Rourke et al. 2018a, b). Concurrent validity has been demonstrated in comparison to the Michigan Alcoholism Screening Test, r = .77 (Allen et al. 1997). Research with the AUDIT demonstrates that cognitive loss is greater for BD patients with concomitant alcohol use disorders (Levy et al. 2008).
A socio-demographic questionnaire was constructed for the BADAS Study to collect descriptive and mental health information (e.g., psychotropic medication). Participants indicated their country of residence (drop-down menu), number of years of education, work or occupation, current employment, and relationship status. At recruitment, they were asked if they had BD, and subtype if known; at Time 2, they were asked if they had been diagnosed with BD by a clinician (e.g., psychiatrist), and date of BD diagnosis (month, year). We included only those who indicated that they both had BD, and had been diagnosed with BD.
Analytic strategy
Cluster analysis was performed using Ward’s method, consistent with previous BD research (Lima et al. 2019; Montejo et al. 2022a, b, c; Millett and Burdick 2021). Clusters were formed based on three cognitive errors (i.e., forgetfulness, distractibility, false triggering) and four BDSx factors (i.e., cognitive Sx depression, somatic Sx depression, elation/loss of insight, affrontive Sx hypo/mania). Analysis of Variance (ANOVA) compared clusters by age, education, number of prescribed psychotropic medications, employment, years since BD diagnosis, alcohol misuse and sleep quality.
Multivariate analyses of variance (MANOVA) were performed to confirm the distinctiveness of each cluster, compare sociodemographic features (e.g., age, medication), and to compare psychological well-being across clusters (i.e., self-efficacy, life scheme, life satisfaction). We assumed that those who reported fewer cognitive errors and lower BD symptoms would report greater psychological well-being.
Results
Our sample was composed of 199 women, 81 men and 1 person of undefined gender (N = 281), ranging from 19 to 81 years of age. Participants were 44.27 years of age on average (SD = 13.59), they were diagnosed with BD 10.01 years ago (SD = 8.69, range = 1–46) and completed 12.51 years of education (SD = 6.10, range = 1–26). Linear interpolation was performed to impute for missing scale responses (less than 2%, missing at random). Participants took 28.25 min on average to complete online questionnaires (SD = 25.83).
Most participants lived in North America (38% Canada, 31% USA), with 15% from the U.K, 6% from Australia/New Zealand, and 5% from Ireland. In total, participants from 13 countries provided responses. The majority were Caucasian (91%) or mixed race (5%). Most (51%) were married or otherwise coupled, 14% were separated, 30% were single and 3.2% were widowed. Among those who specified a BD subtype, the plurality (42%) reported BD II, 29% BD NOS (not otherwise specified), 25% BD I, and 5% with cyclothymia. Most (64%) participants reported comorbid mental health conditions, with generalized anxiety disorder (n = 27) and post-traumatic stress disorder (n = 21) reported most often. See Table 1.
On average, participants were currently prescribed 2.35 psychotropic medications (SD = 1.66) to treat BD and other mental health conditions. More precisely, most participants indicated that they were currently prescribed 1 or more mood stabilizer (n = 165, 59%) and 1 or more antidepressant (n = 180, 64%); smaller numbers listed 1 or more anxiolytic (n = 110, 39%) and 1 or more antipsychotic (n = 107, 38%). By category, lithium (n = 233), bupropion (n = 131), clonazepam (n = 168), and quetiapine (n = 208) were the medications most listed by participants (i.e., mood stabilizer, antidepressant, anxiolytic, and antipsychotic, respectively).
Cognition/BD symptom clusters
Hierarchical cluster analysis revealed distinct groups based on perceived cognitive errors and BD symptoms (both depression and hypo/mania). Participants appear to cluster in four distinct subgroups computed using squared Euclidean distance. MANOVA results indicate a significant multivariate effect across clusters by cognitive failures and BD symptoms, Wilk’s λ = 0.11, p < .01. With 37 participants in the smallest of four clusters, our sample is sufficient to identify medium to large effect sizes (where α = 0.80, d = 0.80) (Cohen 2016).
Of note, symptom levels are somewhat greater than the BDSx depressive cut-off score of 6 + for both of the comparatively euthymic clusters, M = 7.57 & M = 7.58. In contrast, response levels were well above cut-off for both symptomatic clusters, M = 15.15 & M = 14.60. Hypo/mania response levels are elevated for both symptomatic clusters, M = 5.18 & M = 6.36. And hypo/mania response levels are below the 5 + cut-off score for euthymic clusters, M = 2.73 & M = 2.99 (Osher et al. 2020).
Based on reported cognitive errors and BD symptomology, the smallest cluster was labeled low symptoms and low cognitive errors, ↓Sx + ↓cog; n = 37, 13.1%; these participants appeared largely euthymic and reported few perceived cognitive errors. The largest cluster is characterized by low symptoms and moderate cognitive errors, ↓Sx + ∼ cog; n = 104, 37.01%; these participants reported some cognitive loss though largely euthymic. The next cluster is characterized by high symptoms and moderate cognitive errors, ↑Sx + ∼ cog; n = 62, 22.06%. And the final cluster is characterized by both high symptoms and high cognitive errors, ↑Sx + ↑ cog; n = 78, 27.7%. Across clusters, perceived cognitive errors and BD symptoms largely increase in lockstep, both depression and hypo/mania. See Fig. 1.
Cognitive Errors Across Clusters: Distractibility, Forgetfulness and False Triggering. ↓Sx + ↓cog = low BD Sx – low cognitive errors. ↓Sx + ∼ cog = low BD Sx – moderate cognitive errors. ↑Sx + ∼ cog = high BD Sx – moderate cognitive errors. ↑Sx + ↑cog = high BD Sx – high cognitive errors. Note: y-axis = mean cognitive errors (Cognitive Failures Questionnaire), each cluster
All four clusters significantly differed for each of the three cognitive errors, Wilk’s λ = 0.16, p < .01. Moreover, post hoc analyses indicate that all clusters differed from all others for distractibility (F [3,277] = 329.96, p < .01), forgetfulness (F [3,277] = 173.47, p < .01) and false triggering, F [3,277] = 120.61, p < .01. Differences across clusters, however, were less pronounced for BD symptoms. See Table 2.
For instance, those in the two comparatively euthymic clusters (↓Sx + ↓cog & ↓Sx + ∼ cog) differed in neither depressive nor hypo/manic symptoms. The two symptomatic clusters (↑Sx + ∼ cog & ↑Sx + ↑ cog) did not differ in depressive symptoms (neither cognitive nor somatic symptoms) nor affrontive symptoms of hypo/mania. See Fig. 2.
Affective Symptoms Across Clusters: Depression and Hypo/mania Note: y-axis = mean BDSx response levels across clusters, both depression and hypo/mania.↓Sx + ↓cog = low BD Sx – low cognitive errors.↓Sx + ∼ cog = low BD Sx – moderate cognitive errors. ↑Sx + ∼ cog = high BD Sx – moderate cognitive errors.
However, both euthymic clusters differ from both symptomatic clusters in affrontive symptoms of hypo/mania, cognitive and somatic symptoms of depression. Similarly, the two symptomatic clusters differ from both euthymic clusters for these three BD symptoms. Of note, elation/loss of insight differs only between the two symptomatic clusters. In other words, symptomatic clusters are high in cognitive errors and depressive symptoms (↑Sx + ∼ cog) and high in cognitive errors, depressive symptoms and hypo/mania, ↑Sx + ↑ cog.
Between cluster comparisons – sociodemographic features
There were no significant differences across clusters in education (F [3, 276] = 1.35; p = .26), relationship status (χ (Cullen et al. 2016) [df = 21] = 16.68, p = .73), gender (χ (Cullen et al. 2016) [df = 6] = 4.99, p = .55), ethnicity (χ (Cullen et al. 2016) [df = 18] = 11.01, p = .89) or country of residence, χ (Cullen et al. 2016) (df = 51) = 57.88, p = .24. Nor do clusters differ by BD subtype, χ (Cullen et al. 2016) [df = 9] = 10.58, p = .31. However, participants in the two relatively euthymic clusters were prescribed significantly fewer psychotropic medications than both symptomatic clusters, F(3,77) = 5.09, p < .01.
There were differences across clusters in reported sleep quality, F [3, 277] = 8.10, p < .01; yet post-hoc comparisons indicate that this difference is only between the high Sx – high CE cluster (↑Sx + ↑ cog) and all others; the other three do not significantly differ. Significant differences in alcohol misuse were also observed across clusters, F [3, 277] = 3.02; p = .03; here again, reported consumption was highest for the high Sx – high CE cluster (↑Sx + ↑ cog), significantly higher than both comparatively euthymic clusters (↓Sx + ↓cog & ↓Sx + ∼ cog), but not the high Sx – moderate CE cluster (↑Sx + ∼ cog).
Contrary to assumption, age did not significantly differ across clusters, (F [3, 277] = 0.30, p = .83) despite the wide age range of participants recruited for the BADAS study (19 to 81 years); those reporting more symptoms and cognitive errors were no older than those reporting few or any. And though duration of BD diagnosis significantly differed across clusters (F [3, 256] = 8.66, p < .01), those in the high Sx - high CE cluster (↑Sx + ↑ cog) were, in fact, diagnosed more recently with BD 7.43 years ago on average (SD = 6.24) in contrast to those in the low Sx - low CE cluster, ↓Sx + ↓cog, n = 260; M = 15.91 (SD = 12.09).
Neither result supports the neuroprogression model of cognitive decline, as older age and longer duration of diagnosis are generally correlated with number of BD mood episodes. Not only is age consistent across clusters, but those reporting fewer BD symptoms and cognitive errors were diagnosed twice as long ago on average as those reporting highest levels of symptoms and cognitive errors. This result is opposite than predicted by the neuroprogression model.
Comparison between clusters – psychological well-being
MANOVA revealed that all clusters significantly differed for each of the three measures of psychological well- being, Wilk’s λ = 0.75, p < .01. That is, post-hoc tests indicate that all clusters significantly differ from all others in self-efficacy (F [3,277] = 23.33, p < .01), life scheme (F [3,277] = 15.20, p < .01), and life satisfaction, F [3,277] = 17.21, p < .01. The low Sx - low CE group (↓Sx + ↓cog) reported highest self-efficacy, followed by low Sx - moderate CE group (↓Sx + ∼ cog), moderate Sx - moderate CE group (∼Sx + ∼ cog) and high Sx - high CE group (↑Sx + ↑ cog). Self-efficacy significantly differed across all groups. See Table 3.
A similar inverse pattern between greater psychological well-being and BD lower symptoms/cognitive errors was observed also for life scheme and life satisfaction. However, the low to moderate clusters did not significantly differ for life scheme, and neither the low-moderate nor moderate-high clusters differed for life satisfaction. Nonetheless, as perceived cognitive loss and BD symptoms increase, psychological well-being declines. See Fig. 3.
Psychological well-being across clusters Note: y-axis = mean response levels for well-being measures, each cluster ↓Sx + ↓cog = low BD Sx – low cognitive errors. ↓Sx + ∼ cog = low BD Sx – moderate cognitive errors. ↑Sx + ∼ cog = high BD Sx – moderate cognitive errors. ↑Sx + ↑cog = high BD Sx – high cognitive errors
Discussion
For this study, we categorized BD patients with varying levels of perceived cognitive failures and BD symptomology (both depression and hypo/mania). Using hierarchical cluster analyses, we identified four distinct clusters within two groupings. The comparatively euthymic clusters are: low Sx - low CE (low symptoms, few cognitive errors); and low Sx - moderate CE clusters (low symptoms, moderate cognitive errors). The symptomatic clusters are: high Sx - moderate CE (moderate cognitive errors, low hypo/mania, high depression); and high Sx – high CE clusters (high cognitive errors, high depressive symptoms, high hypo/mania). Differences between clusters are greater for cognitive errors than BD symptoms, and greater between- than within-groupings (i.e., the two euthymic clusters more similar than both symptomatic clusters, and vice versa).
Our four cluster solution based on perceived cognitive errors (and BD symptoms) is distinct from previous BD research in which cognition was objectively measured. Each of these prior studies has proposed a three cluster solution: An intact cluster (no difference between BD and control groups); a selective deficits cluster with circumscribed deficits in specific domains; and a global deficits cluster with widespread deficits across domains (Burdick et al. 2005; Lima et al. 2019; Montejo et al. 2022a, b, c; Solé et al. 2016). Though the number of clusters differ, we similarly found that perceived cognitive errors increase along a continuum; cognitive loss appears progressive and incremental, not circumscribed to limited domains or specific BD symptoms.
Instead, perceived cognitive failures and BD symptomology increase largely in lockstep across clusters. That is, as perceived cognitive loss increases, so too BD symptoms, both depression and hypo/mania. And as we hypothesized, BD symptoms and cognitive errors are inversely associated with psychological well-being. As cognitive errors and BD symptoms increase, psychological well-being declines. Those reporting the highest symptom levels and cognitive errors were prescribed more psychotropic medications, reported poor sleep quality and alcohol misuse.
Previous research reports that depressive symptoms are closely linked to perceived cognitive loss (Demant et al. 2015; Lima et al. 2018; Miskowiak et al. 2012) suggesting that perceived cognitive loss may reflect depression and not cognition. Yet residual depressive symptoms between mood episodes do not fully explain these deficits (Luo et al. 2020; Toyoshima et al. 2019); moreover, reported that depressive symptoms do not appear to moderate associations between subjective cognition and psychosocial functioning (Luo et al. 2020).
As our results suggest, hypo/mania along with depressive symptoms account for variance in perceived cognitive errors. Yet only patients in the final cluster reported significantly higher elation/loss of insight. Depressive symptoms were similar between clusters 3 and 4; only in the latter were classic mania symptoms elevated (e.g., talkativeness, euphoria). And BD patients in this cluster reported highest levels of all cognitive errors (i.e., forgetfulness, distractibility, false triggering). Some studies suggest that persons reporting hypo/mania tend to underestimate cognitive deficits due to increased energy and heightened productivity (Lin et al. 2019; Siegel-Ramsay et al. 2022). By contrast, our findings suggest that symptoms of hypo/mania are also associated with cognitive errors. Depressive symptoms alone do not fully account for perceived cognitive errors (Lima et al. 2018).
Contrary to our hypothesis, age did not differ across clusters. Both young and older adults with BD reported similar levels of perceived cognitive errors. According to some research and the neuroprogression hypothesis, cognitive decline with BD is associated with age; that is, older adults exhibit comparatively poor cognitive performance (Serafini et al. 2021). By contrast, we found no association between age and perceived cognitive errors consistent with other studies (Grover et al. 2023). In fact, patients who had lived with a BD diagnosis longest reported fewer cognitive errors and higher psychological well-being (Lima et al. 2018). We might speculate that those who lived longest with BD have learned to cope with their condition and learned to manage mood episodes (e.g., consistent exercise, diet, social support). This finding is important since it might indicate that intervention (e.g., psychotherapy) can be effective, especially in first years of diagnosis, when patients are learning to cope with the condition.
Limitations and directions for future study
The present study has various limitations. First, data are cross-sectional limiting our ability to make causal conclusions regarding the direction of influence between BD symptoms, cognitive errors and well-being. Associations between constructs may well be longitudinal, bidirectional and cross-lagged. For instance, medication and sleep quality examined above as dependent or outcome variables, significantly differ across clusters. Yet both are known to impact cognition and may significantly increase perceived cognitive errors. Mediation and regression analyses should be conducted in future research to better understand the strength and direction of associations over time.
Nor did we include a healthy comparison sample or employ objective measurement. BD symptoms, cognitive errors and psychological well-being were each measured by self-report alone; neither responses nor BD diagnoses were clinically corroborated. Future research should measure cognition objectively (e.g., standardized neurocognitive battery) and perceived or subjective cognitive errors along with psychological well-being.
Additionally, participants for this study were recruited online and may be more symptomatic than BD outpatients supported in the community (O’Rourke et al. 2021a, b, c). The results of this study should be replicated with BD participants recruited via more traditional measures (e.g., memory disorders clinics). This should include factors such as substance misuse (not just alcohol), known to adversely affect cognition with BD (Levy et al. 2008).
In summary, our findings suggest that adults with BD who report more perceived cognitive errors and BD symptoms also report lower psychological well-being (i.e., self-efficacy, life scheme, life satisfaction). This did not differ between young and older adults with BD. To the contrary, time since BD diagnosis appears to be inversely associated with cognitive errors. These findings underscore the importance of subjective or perceived cognition; perceived cognitive errors appear significantly and inversely associated with psychological well-being with BD.
How should clinicians understand perceived cognitive errors reported by patients? If reported while euthymic, impacting functional ability (e.g., employment) or quality of life, a neurocognitive assessment may be warranted. Yet perceived cognitive errors may be a stronger proxy for BD symptoms not cognitive decline. The results of this study suggest perceived cognitive errors are associated with symptoms of both depression and hypo/mania.
Data availability
Anonymized data are available from the corresponding author on reasonable request.
References
Allen JP, Litten RZ, Fertig JB, Babor T. A review of research on the Alcohol Use disorders Identification Test (AUDIT). Alcohol Clin Exp Res. 1997;21(4):613–9.
Alonso J, Petukhova M, Vilagut G, Chatterji S, Heeringa S, Üstün TB, Alhamzawi AO, Viana MC, Angermeyer M, Bromet E, Bruffaerts R. Days out of role due to common physical and mental conditions: results from the WHO World Mental Health surveys. Mol Psychiatry. 2011;16(12):1234–46.
Arts B, Jabben N, Krabbendam L, Van Os J. Meta-analyses of cognitive functioning in euthymic bipolar patients and their first-degree relatives. Psychol Med. 2008;38(6):771–85.
Babor TF, Grant M. From clinical research to secondary prevention: International collaboration in the development of the Alcohol disorders Identification Test (AUDIT). Alcohol Health Res World. 1989;13(4):371–5.
Broadbent DE, Cooper PF, FitzGerald P, Parkes KR. The cognitive failures questionnaire (CFQ) and its correlates. Br J Clin Psychol. 1982;21(1):1–6.
Burdick KE, Endick CJ, Goldberg JF. Assessing cognitive deficits in bipolar disorder: are self-reports valid? Psychiatry Res. 2005;136(1):43–50.
Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.
Canham SL, Mahmood A, Stalman MN, King D, O’Rourke N. Personal theories of substance use among middle-aged and older adults with bipolar disorder. Aging Ment Health. 2018;22(6):813–8. https://doi.org/10.1080/13607863.2017.1299689.
Cardoso T, Bauer IE, Meyer TD, Kapczinski F, Soares JC. Neuroprogression and cognitive functioning in bipolar disorder: a systematic review. Curr Psychiatry Rep. 2015;17:1–24.
Cohen J. A power primer. In: Kazdin AE, editor. Methodological issues and strategies in clinical research. Washington: American Psychological Association; 2016. pp. 279–84. https://doi.org/10.1037/14805-018.
Cohen B, Sixsmith A, Star AP, Haglili O, O’Rourke N. Direct and indirect predictors of Medication Adherence with bipolar disorder: path analysis. JMIR Form Res. 2023;7(1):e44059. https://doi.org/10.2196/44059.
Cretu JB, Culver JL, Goffin KC, Shah S, Ketter TA. Sleep, residual mood symptoms, and time to relapse in recovered patients with bipolar disorder. J Affect Disord. 2016;190:162–6.
Cullen B, Ward J, Graham NA, Deary IJ, Pell JP, Smith DJ, Evans JJ. Prevalence and correlates of cognitive impairment in euthymic adults with bipolar disorder: a systematic review. J Affect Disord. 2016;205:165–81.
Daaleman TP, Frey BB. The spirituality index of well-being: a new instrument for health-related quality-of-life research. Ann Fam Med. 2004;2(5):499–503.
Daaleman TP, Perera S, Studenski SA. Religion, spirituality, and health status in geriatric outpatients. Ann Fam Med. 2004;2(1):49–53.
Demant KM, Vinberg M, Kessing LV, Miskowiak KW. Assessment of subjective and objective cognitive function in bipolar disorder: correlations, predictors and the relation to psychosocial function. Psychiatry Res. 2015;229(1–2):565–71.
Diener ED, Emmons RA, Larsen RJ, Griffin S. The satisfaction with Life Scale. J Pers Assess. 1985;49(1):71–5.
Forcada I, Mur M, Mora E, Vieta E, Bartrés-Faz D, Portella MJ. The influence of cognitive reserve on psychosocial and neuropsychological functioning in bipolar disorder. Eur Neuropsychopharmacol. 2015;25(2):214–22.
Fountoulakis KN, Kasper S, Andreassen O, Blier P, Okasha A, Severus E, Vieta E. Efficacy of pharmacotherapy in bipolar disorder: a report by the WPA section on pharmacopsychiatry. Eur Arch Psychiatry Clin Neurosci. 2012;262(1):1–48.
Geoffroy PA, Boudebesse C, Bellivier F, Lajnef M, Henry C, Leboyer M, Scott J, Etain B. Sleep in remitted bipolar disorder: a naturalistic case-control study using actigraphy. J Affect Disord. 2014;158:1–7.
Grande I, Berk M, Birmaher B. Vieta. Bipolar disorder. Lancet. 2016;387:1561–72.
Groenman AP, van der Werf S, Geurts HM. Subjective cognition in adults with common psychiatric classifications; a systematic review. Psychiatry Res. 2022;308:114374.
Grover S, Avasthi A, Chakravarty R, Dan A, Chakraborty K, Neogi R, Desouza A, Nayak O, Praharaj S, Menon V, Deep R. Subjective cognitive deficits and its correlates among patients with bipolar disorder: findings from the bipolar disorder course and outcome study from India (BiD-CoIN study). Indian J Psychiatry. 2023;65(3):345–55.
Heisel MJ, Flett GL. Investigating the psychometric properties of the geriatric suicide ideation scale (GSIS) among community-residing older adults. Aging Ment Health. 2016;20(2):208–21.
Heun R, Hein S. Risk factors of major depression in the elderly. Eur Psychiatry. 2005;20(3):199–204.
Jensen JH, Støttrup MM, Nayberg E, Knorr U, Ullum H, Purdon SE, Kessing LV, Miskowiak KW. Optimising screening for cognitive dysfunction in bipolar disorder: validation and evaluation of objective and subjective tools. J Affect Disord. 2015;187:10–9.
Johansson V, Kuja-Halkola R, Cannon TD, Hultman CM, Hedman AM. A population-based heritability estimate of bipolar disorder in a Swedish twin sample. Psychiatry Res. 2019;278:180–7.
King DB, O’Rourke N, DeLongis A. Social media recruitment and online data collection: a beginner’s guide and best practices for accessing low-prevalence and hard-to-reach populations. Can Psychol. 2014;55(4):240–9.
King DB, Sixsmith A, Shahir HY, Sadeghi M, Razmara M, O’Rourke N. An ecological momentary sampling tool for movement patterns and psychiatric symptom variability: a pilot study. Gerontechnol. 2016;14(2):105–9.
Kraun L, O’Rourke N, Osher Y, Bersudsky Y, Belotherkovsky D, Bachner YG. Is the 6-item, self‐report HAM‐D an effective depression screening measure with bipolar disorder? Perspect Psychiatr Care. 2020;56(4):900–4.
Latalova K, Prasko J, Diveky T, Velartova H. Cognitive impairment in bipolar disorder. Biomed Pap Med Fac Univ Palacky Olomouc. 2011;155(1):19–26.
Levy B, Monzani BA, Stephansky MR, Weiss RD. Neurocognitive impairment in patients with co-occurring bipolar disorder and alcohol dependence upon discharge from inpatient care. Psychiatry Res. 2008;161(1):28–35.
Lima FM, Cardoso TA, Serafim SD, Martins DS, Solé B, Martínez-Arán A, Vieta E, Rosa AR. Validity and reliability of the Cognitive complaints in Bipolar Disorder Rating Assessment (COBRA) in Brazilian bipolar patients. Trends Psychiatry Psychother. 2018;40:170–8.
Lima F, Rabelo-da-Ponte FD, Bücker J, Czepielewski L, Hasse-Sousa M, Telesca R, Solé B, Reinares M, Vieta E, Rosa AR. Identifying cognitive subgroups in bipolar disorder: a cluster analysis. J Affect Disord. 2019;246:252–61.
Lin X, Lu D, Huang Z, Chen W, Luo X, Zhu Y. The associations between subjective and objective cognitive functioning across manic or hypomanic, depressed, and euthymic states in Chinese bipolar patients. J Affect Disord. 2019;249:73–81.
López AJ, McCaffrey R, Quinn Griffin MT, Fitzpatrick JJ. Spiritual well-being and practices among women with gynecologic cancer. Oncol Nurs Forum. 2009;36(3):300–5.
Luo X, Zhu Y, Lu D, Zong K, Lin X. Subjective cognitive dysfunction in patients with bipolar disorder: the prevalence, related factors and effects on predicting psychosocial functioning and suicidal ideation. Psychiatry Res. 2020;284:112669.
Martinez-Aran A, Vieta E, Colom F, Torrent C, Reinares M, Goikolea JM, Benabarre A, Comes M, Sanchez-Moreno J. Do cognitive complaints in euthymic bipolar patients reflect objective cognitive impairment? Psychother Psychosom. 2005;74(5):295–302.
Martinez-Aran A, Vieta E, Torrent C, Sanchez‐Moreno J, Goikolea JM, Salamero M, Malhi GS, Gonzalez‐Pinto A, Daban C, Alvarez‐Grandi S, Fountoulakis K. Functional outcome in bipolar disorder: the role of clinical and cognitive factors. Bipolar Disord. 2007;9(1–2):103–13.
Merikangas KR, Jin R, He JP, Kessler RC, Lee S, Sampson NA, Viana MC, Andrade LH, Hu C, Karam EG, Ladea M. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch Gen Psychiatry. 2011;68(3):241–51.
Meyer C, Rumpf HJ, Hapke U, John U. Impact of psychiatric disorders in the general population: satisfaction with life and the influence of comorbidity and disorder duration. Soc Psychiatry Psychiatr Epidemiol. 2004;39:435–41.
Millett CE, Burdick KE. Defining heterogeneous cognitive trajectories in bipolar disorder: a perspective. Harv Rev Psychiatry. 2021;29(4):298–302.
Miskowiak K, Vinberg M, Christensen EM, Kessing LV. Is there a difference in subjective experience of cognitive function in patients with unipolar disorder versus bipolar disorder? Nordic J Psychiatry. 2012;66(6):389–95.
Miskowiak KW, Burdick KE, Martinez-Aran A, Bonnin CM, Bowie CR, Carvalho AF, Gallagher P, Lafer B, López‐Jaramillo C, Sumiyoshi T, McIntyre RS. Assessing and addressing cognitive impairment in bipolar disorder: the International Society for Bipolar Disorders Targeting Cognition Task Force recommendations for clinicians. Bipolar Disord. 2018;20(3):184–94.
Mohn C, Rund BR. Neurocognitive profile in major depressive disorders: relationship to symptom level and subjective memory complaints. BMC Psychiatry. 2016;16(1):1–6.
Montejo L, Torrent C, Jimenez E, Martínez-Arán A, Blumberg HP, Burdick KE, Chen P, Dols A, Eyler LT, Forester BP, Gatchel JR. Cognition in older adults with bipolar disorder: an ISBD task force systematic review and meta‐analysis based on a comprehensive neuropsychological assessment. Bipolar Disord. 2022a;24(2):115–36.
Montejo L, Jiménez E, Solé B, Murru A, Arbelo N, Benabarre A, Valentí M, Clougher D, Rodríguez MA, Borràs R, Martínez-Arán A. Identifying neurocognitive heterogeneity in older adults with bipolar disorder: a cluster analysis. J Affect Disord. 2022b;298:522–31.
Montejo L, Solé B, Jiménez E, Borràs R, Clougher D, Reinares M, Portella MJ, Martinez-Aran A, Vieta E, del Mar Bonnín C, Torrent C. Aging in bipolar disorder: cognitive performance and clinical factors based on an adulthood-lifespan perspective. J Affect Disord. 2022c;312:292–302.
Mühlhoff R, Willem T. Social media advertising for clinical studies: ethical and data protection implications of online targeting. Big Data Soc. 2023;10(1):20539517231156127. https://doi.org/10.1177/2053951723115612.
Naglie G, Hogan DB, Krahn M, Beattie BL, Black SE, MacKnight C, Freedman M, Patterson C, Borrie M, Bergman H, Byszewski A. Predictors of patient self-ratings of quality of life in Alzheimer disease: cross-sectional results from the Canadian Alzheimer’s Disease Quality of Life Study. Am J Geriatr Psychiatry. 2011;19(10):881–90.
O’Rourke N, Sixsmith A, King DB, Yaghoubi-Shahir H, Canham SL, BADAS Study Team. Development and validation of the BDSx: a brief measure of mood and symptom variability for use with adults with bipolar disorder. Int J Bipolar Disord. 2016;4:1–8.
O’Rourke N, Heisel MJ, Canham SL, Sixsmith A, BADAS Study Team. Predictors of suicide ideation among older adults with bipolar disorder. PLoS ONE. 2017;12(11):e0187632.
O’Rourke N, Bachner YG, Canham SL, Sixsmith A, BADAS Study Team. Measurement equivalence of the BDSx scale with young and older adults with bipolar disorder. Psychiatry Res. 2018a;263:245–9.
O’Rourke N, Heisel MJ, Canham SL, Sixsmith A, Yaghoubi-Shahir H, King DB. Psychometric validation of the geriatric suicide ideation scale (GSIS) among older adults with bipolar disorder. Aging Ment Health. 2018b;22(6):794–801.
O’Rourke N, Sixsmith A, BADAS Study Team. Ecological momentary assessment of mood and movement with bipolar disorder over time: participant recruitment and efficacy of study methods. Int J Meth Psychiatr Res. 2021a;30(4):e1895.
O’Rourke N, Sixsmith A, Michael T, Bachner YG. Is the 4-factor model of symptomology equivalent across bipolar disorder subtypes? Int J Bipolar Disord. 2021b;9(1):1–9. https://doi.org/10.1186/s40345-021-00229-1.
O’Rourke N, Sixsmith A, Kirshner G, Osher Y. Perceived cognitive failures and quality of life for older adults with bipolar disorder. J Affect Disord. 2021c;287:433–40. https://doi.org/10.1016/j.jad.2021.03.024.
Osher Y, Bersudsky Y, O’Rourke N, Belotherkovsky D, Bachner YG. Clinical validation of the BDSx scale with bipolar disorder outpatients. Archiv Psychiatric Nurs. 2020;34(1):49–52.
Passos IC, Mwangi B, Vieta E, Berk M, Kapczinski F. Areas of controversy in neuroprogression in bipolar disorder. Acta Psychiatr Scand. 2016;134(2):91–103.
Plans L, Barrot C, Nieto E, Rios J, Schulze TG, Papiol S, Mitjans M, Vieta E, Benabarre A. Association between completed suicide and bipolar disorder: a systematic review of the literature. J Affect Disord. 2019;242:111–22.
Pollock Star A, Bachner YG, Cohen B, Haglili O, O’Rourke N. Social media use and well-being with bipolar disorder during the COVID-19 pandemic: path analysis. JMIR Form Res. 2022;6(8):e39519. https://doi.org/10.2196/39519.
Rast P, Zimprich D, Van Boxtel M, Jolles J. Factor structure and measurement invariance of the cognitive failures questionnaire across the adult life span. Assess. 2009;16(2):145–58.
Robinson LJ, Nicol Ferrier I. Evolution of cognitive impairment in bipolar disorder: a systematic review of cross-sectional evidence. Bipolar Disord. 2006;8(2):103–16.
Russo M, Mahon K, Shanahan M, Ramjas E, Solon C, Purcell SM, Burdick KE. The relationship between sleep quality and neurocognition in bipolar disorder. J Affect Disord. 2015;187:156–62.
Samamé C, Martino DJ, Strejilevich SA. Longitudinal course of cognitive deficits in bipolar disorder: a meta-analytic study. J Affect Disord. 2014;164:130–8.
Samamé C, Cattaneo BL, Richaud MC, Strejilevich S, Aprahamian I. The long-term course of cognition in bipolar disorder: a systematic review and meta-analysis of patient-control differences in test-score changes. Psychol Med. 2022;52(2):217–28.
Saunders EF, Novick DM, Fernandez-Mendoza J, Kamali M, Ryan KA, Langenecker SA, Gelenberg AJ, McInnis MG. Sleep quality during euthymia in bipolar disorder: the role of clinical features, personality traits, and stressful life events. Int J Bipolar Disord. 2013;1(1):1–2.
Serafini G, Pardini M, Monacelli F, Orso B, Girtler N, Brugnolo A, Amore M, Nobili F, Disease Management Team on Dementia of the IRCCS Ospedale Policlinico San Martino. Neuroprogression as an illness trajectory in bipolar disorder: a selective review of the current literature. Brain Sci. 2021;11(2):276. https://doi.org/10.3390/brainsci11020276.
Siegel-Ramsay JE, Wu B, Bond M, Spelber D, Chiang KS, di Scalea TL, Collier SJ, Smith T, Nunez L, Fuller E, Strakowski SM. Subjective cognitive impairment and its relationship to sleep impairment, anxiety severity and depressive symptoms in individuals with bipolar disorder. J Affect Disord Rep. 2022;10:100442.
Smoller JW, Finn CT. Family, twin, and adoption studies of bipolar disorder. In: American Journal of Medical Genetics Part C: Seminars in Medical Genetics. 2003;123(1). Hoboken: Wiley. pp. 48–58.
Solé B, Jiménez E, Torrent C, del Mar Bonnin C, Torres I, Reinares M, Priego Á, Salamero M, Colom F, Varo C, Vieta E. Cognitive variability in bipolar II disorder: who is cognitively impaired and who is preserved. Bipolar Dis. 2016;18(3):288–99.
Sparding T, Silander K, Pålsson E, Östlind J, Ekman CJ, Sellgren CM, Joas E, Hansen S, Landén M. Classification of cognitive performance in bipolar disorder. Cog Neuropsychiatry. 2017;22(5):407–21.
Stalman MN, Canham SL, Mahmood A, King D, O’Rourke N. Aspects of control and substance use among middle-aged and older adults with bipolar disorder. Int J Ment Health Nurs. 2018;27(2):833–40. https://doi.org/10.1111/inm.12371.
Svendsen AM, Kessing LV, Munkholm K, Vinberg M, Miskowiak KW. Is there an association between subjective and objective measures of cognitive function in patients with affective disorders? Nordic J Psychiatry. 2012;66(4):248–53.
Szmulewicz A, Valerio MP, Martino DJ. Longitudinal analysis of cognitive performances in recent-onset and late‐life bipolar disorder: a systematic review and meta‐analysis. Bipolar Disord. 2020;22(1):28–37.
Thompson JM, Gallagher P, Hughes JH, Watson S, Gray JM, Ferrier IN, Young AH. Neurocognitive impairment in euthymic patients with bipolar affective disorder. Br J Psychiatry. 2005;186(1):32–40.
Tohen M, Hennen J, Zarate CM Jr, Baldessarini RJ, Strakowski SM, Stoll AL, Faedda GL, Suppes T, Gebre-Medhin P, Cohen BM. Two-year syndromal and functional recovery in 219 cases of first-episode major affective disorder with psychotic features. Am J Psychiatry. 2000;157(2):220–8.
Toyoshima K, Kako Y, Toyomaki A, Shimizu Y, Tanaka T, Nakagawa S, Inoue T, Martinez-Aran A, Vieta E, Kusumi I. Associations between cognitive impairment and quality of life in euthymic bipolar patients. Psychiatry Res. 2019;271:510–5.
Van der Elst W, Van Boxtel MP, Van Breukelen GJ, Jolles J. A large-scale cross-sectional and longitudinal study into the ecological validity of neuropsychological test measures in neurologically intact people. Arch Clin Neuropsychol. 2008;23(7–8):787–800.
van der Werf-Eldering MJ, Burger H, Jabben N, Holthausen EA, Aleman A, Nolen WA. Is the lack of association between cognitive complaints and objective cognitive functioning in patients with bipolar disorder moderated by depressive symptoms? J Affect Disord. 2011;130(1–2):306–11.
Yerushalmi M, Sixsmith A, Pollock Star A, King DB, O’Rourke N. Ecological momentary assessment of bipolar disorder symptoms and partner affect: longitudinal pilot study. JMIR Form Res. 2021;5(9):e30472.
Zarate CA, Tohen M, Land M, Cavanagh S. Functional impairment and cognition in bipolar disorder. Psychiatr Q. 2000;71:309–29.
Author information
Authors and Affiliations
Contributions
Conceptualization, H.G., A.S. and N.O’R.; Methodology, A.S. and NO’R.; Formal analysis, H.G. and N.O’R.; Writing—original draft, H.G., A.P.S., M.S. and N.O’R.; Writing—review & editing, H.G., A.P.S., M.S. and NO’R.; Supervision, A.S. and N.O.; Project administration, A.S. and N.O’R.
Corresponding author
Ethics declarations
Ethics approval
The BADAS Study conformed to the Principles of Human Rights, as adopted by the World Medical Association at the 18th WMA General Assembly, Helsinki Finland (June, 1964) and subsequently amended at the 64th WMA General Assembly, Fortaleza Brazil (October, 2013). As stated on the study splashpage, respondents indicated consent to participate by clicking to proceed. They were not required to provide identifying information aside from an email address if they wished. One randomly selected participant received a $500 lottery prize. The BADAS Study received approval from the Human Research Ethics Committee, Simon Fraser University, Burnaby (BC), Canada (#2014s0375). Support for the BADAS study provided by the Age-Well Network of Centres for Excellence [CRP 2015-WP6.2; O’Rourke] and Canadian Institutes of Health Research, Institute of Aging [RN 134209–259022] awarded to Drs. O’Rourke and Sixsmith. Portions of this study were presented at the International Psychogeriatric Association Meeting (September, 2024) Buenos Aires, Argentina.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Haglili, O., Sixsmith, A., Star, A.P. et al. Perceived cognitive loss, symptomology, and psychological well-being with bipolar disorder. Int J Bipolar Disord 12, 34 (2024). https://doi.org/10.1186/s40345-024-00357-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s40345-024-00357-4