Meta-analysis of cognitive performance in fibromyalgia

Introduction:

Fibromyalgia is a condition with symptoms of pain, physical function difficulties, and emotional problems, but is also characterized by complaints of poor cognition (often called ‘FibroFog’). Over the last two decades, a number of studies have examined cognitive differences between individuals with and without fibromyalgia. The purpose of the current study was to conduct a quantitative synthesis of these differences across multiple cognitive domains.

Methods:

Following Cochrane guidelines, we identified 37 eligible studies for analysis where persons with fibromyalgia (total n = 964) were compared to participants from age-matched control groups without fibromyalgia (total n = 1,025) on a range of neuropsychological measures. Group differences between persons with fibromyalgia and healthy controls were examined for cognitive domains including processing speed, long- and short-term memory, and executive functions (inhibitory control, set shifting, updating, and accessing). Random-effect meta-analyses were conducted to determine effect sizes for these differences in cognitive performance.

Results:

Fibromyalgia was significantly and negatively associated with performance on all domains of cognitive function. The largest effect size was found for inhibitory control (g = 0.61), followed by memory (g = 0.51 for short-term, 0.50 for long-term memory). The smallest cognitive difference between those with fibromyalgia and controls was for set shifting (g = 0.30).

Conclusion:

These findings support the hypothesis that the self-reported cognitive impact of fibromyalgia is also found in objective neuropsychological measures. Routine screening for cognitive dysfunction in those with fibromyalgia may be warranted in addition to assessment of the traditional fibromyalgia symptoms.

Keywords: Fibromyalgia, Chronic Pain, Cognition, Executive Function, Memory, Meta-analysis

Theoretical Foundations for the Current Meta-Analysis

Studies examining the cognitive difficulties in fibromyalgia and other chronic pain conditions have hypothesized compromised attention systems due to shared networks with pain (Cánovas et al., 2009 Correa, Miro, Martinez, Sanchez, & Lupianez, 2011; Glass et al., 2011). Eccleston and Crombez (1999) proposed that pain is an obstacle for efficient cognitive processing. In this model, attention leads to selection of information salient to cognitive goals, which are triggered by internal and external stimuli. Attentional resources, however, are limited, and cognitive goals must be selected by their degree of saliency. Due to the evolutionary mechanism of pain (i.e., escape and survival), pain-processing is prioritized at the cost of attentional resources for other goal-related behavior. Consequently, Eccleson and Crombez. (1999) theorized that constant goal disruption by pain may cause cognitive difficulties throughout the different stages of the information processing model. Applying this theory, the current study tested for cognitive difficulties in fibromyalgia throughout information processing domains using a model incorporating long-standing theories of working memory (Atkinson & Shiffrin, 1968; Baddeley & Hitch, 1974) and executive functioning (Miyake et al., 2000).

The cognitive model guiding the current study describes the mechanics of information processing, which begins at sensory perception and ends with a behavioral response. In this model, attended information is initially processed through sensory registries, and low-priority or task-irrelevant information is filtered out. Relevant information is then stored into short-term memory, which is limited and held very briefly in systems specific to auditory-verbal and visuospatial information. Rehearsal and elaboration can then move information into long-term memory to be stored and recalled for an unlimited amount of time. The storage of relevant information, however, is not sufficient to achieve goal-related behavior. Instead, a central executive cognitively controls information to produce an appropriate response; such control is possible through specific, though related sets of executive functions within the central executive.

Executive functions are higher-level cognitive processes implemented in goal creation, planning, and response execution (Lezak, Howieson, & Loring, 1983; Lezak, 2004). Miyake et al. (2000) provided evidence for separate but non-orthogonal components of the central executive, falling into the subcategories of inhibition, set shifting, and updating, which are moderately interrelated. Inhibiting involves purposive prevention of interference from prepotent responses (responses previously reinforced and associated with stimuli; Barkley, 2013); shifting involves changing mental sets to provide an appropriate response; and updating involves adding and deleting representations in working memory by goal relevance. Recently, Fisk and Sharpe (2004) have expanded on the work of Miyake et al. (2000) with the addition of a new executive function known as accessing (often measured as verbal fluency), which involves executive action to retrieve information from long-term memory. Assessing individual executive functions may add additional insight into the integrity of the central executive in fibromyalgia.

The current study examines differences between persons with fibromyalgia and healthy adults on cognitive function including processing speed, short-term memory, long-term memory, and executive functions. The potential moderating role of age is also examined as older adults with fibromyalgia may show greater cognitive difficulties due to age-related declines.

Data sources

The current study employed a systematic search of the literature using the Cochrane Collaborations (Higgins & Green, 2011) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher, Liberati, Tetzlaff, & Altman, 2009). Pubmed, PsychInfo, and OVID databases were searched (from January 1950 to July 2015) using Boolean terms including “fibromyalgia” and words describing cognition: (“fibromyalgia” AND “attention,”); (“fibromyalgia” AND “cognition”); (“fibromyalgia” AND “cognitive”); (“fibromyalgia” AND “executive”); (“fibromyalgia” AND “memory”); (“fibromyalgia” AND neuropsychological”). Identified articles were then indexed by database and key terms.

Study selection and inclusion

Two steps were involved in study article selection (see Figure 1). The first step involved two independent reviewers (TB, MB) screening all possible citations to filter out articles which did not relate to fibromyalgia and cognition based on information from titles and abstracts. The key terms searched in the databases provided 2,355 citations, and after removing duplicates (n = 882), there were 1,472 articles to be screened. After review, 1,367 articles were excluded based on irrelevance to the meta-analysis focus (fibromyalgia and task-measured cognitive performance). For example, 28.9% (n = 396) involved diagnosis and treatment/interventions for fibromyalgia, 28.9% (n = 396) did not pertain to objective task-measured cognition, 19.2% (n = 263) were not peer-reviewed empirical articles (e.g., reviews, commentaries), 12.5% (n = 171) did not specifically pertain to fibromyalgia, and 8.2% (n = 112) related to neurophysiological aspects of fibromyalgia with no cognitive performance measures. Other reasons for exclusion included the use of animals, no control group, or the article was only available as an abstract (less than 1% for each). A total of 105 studies were deemed eligible for inclusion in the next step of screening, with high inter-rater reliability above chance agreement (Kappa = .78, p < .001).

The second step involved applying the following inclusion criteria to each study: (1) The study was not characterized by comorbid conditions that included any neurological condition with cognitive effects (including all forms of dementia, neural degeneration or demyelinating disorders, epilepsy, sleep disorders, ADHD) in the control group or in the patient group (other than a primary diagnosis of fibromyalgia); (2) the study looked at objective task-measured cognitive performance; (3) the study did not assess cognition with solely a mental status exam or other screening measure; (4) the study was peer-reviewed; (5) the study included a healthy age-matched control group; (6) the data were not related to the effects of a drug or treatment intervention (baseline comparisons only); (7) the paper was not a review or conference abstract; (8) the study included human participants and did not investigate animals; (9) the study did not make use of emotion-inducing stimuli (i.e., did not use negative or pain-related stimuli) during cognitive tasks, which includes the use of non-neutral (positive or negative) stimuli; (10) the article was available in English; and (11) the study pertained to fibromyalgia specifically (not aggregated with other pain conditions).

Of the 105 articles included in this step of eligibility, 39 studies were initially found to meet the criteria while 66 articles were excluded (98% reviewer agreement). Disagreements were settled by discussion between MB and TB as well as consulting co-authors. Further inspection led to the exclusion of two additional articles, one which was not fully available in English, and one which did not have an age-matched control group (total excluded = 68). The primary reasons for article exclusion were as follows: of the 68 studies excluded, 25 (36.76%) did not include a healthy (age-matched) control group, 16 (23.53%) did not look specifically at cognitive performance, 10 (14.71%) were not full-text research studies (e.g., review, commentary, conference abstract), 6 (8.82%) did not pertain to fibromyalgia specifically, 3 (4.41%) were not peer-reviewed, 4 (5.88%) were not available in English, 1 (1.47%) included emotional stimuli (induced pain), and 3 (4.41%) only assessed cognition using a mental status exam or other screening measure. This process left 37 eligible studies for data extraction and subsequent analyses. It is important to note that these numbers reflect primary reasons for exclusion, as many of the excluded articles failed to meet multiple inclusion criteria. For this step of selecting the 37 of 105 potential articles, there was a high inter-rater reliability (Kappa = .96, p < .001). All included studies with descriptive information can be seen in Table 1.

Data extraction

Data were extracted by two independent reviewers (TB, MB) using a standardized form (Berryman et al., 2013) tailored to current needs. Data extraction included keywords and the location of the study (Europe, North America, etc.). Next, data regarding sample size, participant gender, participant age, and other demographic information were examined. Data was collected for each cognitive task, including the name of the test, the cognitive domain assessed, and the means and standard deviations of scores in the fibromyalgia and the healthy control group. Moreover, score means, standard deviations, sample sizes, sample mean age, t-statistics, and p-values were recorded to compute effect sizes. For each measure, the domain that the measure most directly assessed was determined by study description and initial review (e.g., Stroop – inhibition), scoring type (response time, accuracy, or errors), as well as primary stimuli type (verbal auditory versus visuospatial).

Data synthesis

Outcome measures from each study were synthesized into cognitive domains of interest guided by established theoretical cognitive models (Atkinson & Shiffrin, 1968; Baddeley & Hitch, 1974; Miyake et al., 2000). These included processing speed, short-term and long-term memory, and executive functions of selective attention/response inhibition, set shifting, working memory/updating, and accessing. Because cognitive tasks can be measured by accuracy (higher scores indicating better performance), errors (more errors indicating worse performance) or response time (higher scores indicating worse performance), effect sizes were transformed so that higher effect sizes indicated worse performance (expected direction).

Data pooling

For each group comparison, effect sizes were converted into standardized mean differences using Hedges’ g, calculated from group means and pooled standard deviations for each cognitive task or directly from Cohen’s d. Hedges’ g statistic was calculated using OpenMeta software (Wallace et al., 2012). In cases where standard errors were reported, standard deviations were calculated by multiplying the standard error by the square root of the sample size for each group. Hedges’ g is similar to Cohen’s d and can be interpreted as how many standard deviations the fibromyalgia group is performing above or below the healthy control group. Unlike Cohen’s d, Hedges’ g removes the bias of small and unequal sample sizes, which may overestimate group differences. Effect sizes for Hedges’ g are equivalent to that of Cohen’s d, where small, medium, and large effects correspond to values of .20, .50, and .80 (Cohen, 1988). An estimate of Hedges’ g is considered significant if 0 is not included in the 95% confidence interval (Nakagawa & Cuthill, 2007).

Data were collected from independent samples to meet the assumption of independence in our statistical analyses, allowing only one effect size for a cognitive domain to be collected from each study. Thus, reviewers decided on which measures of a certain domain were most valid for inclusion (had high construct validity) based on current neuropsychological assessment texts and research (Lezak, 2004; Strauss, Sherman, & Spreen, 2006) and discussion with co-authors. In many cases, studies used multiple measures of the same domains, for which composite Hedges’ gs were calculated to provide an optimal estimate. For example, a study using 2 measures of processing speed would provide 2 corresponding effect sizes for that domain, which were then averaged to make a composite effect size for processing to meet the assumption of independence. In the current study, random-effects meta-analysis (Borenstein, Hedges, Higgins, & Rothstein, 2010) was then used to provide pooled Hedges’ gs for each cognitive domain, along with 95% confidence intervals and a measure of between-study heterogeneity (I²).

Risk of bias assessment

Risk of bias was assessed using guidelines provided by the Cochrane Collaboration (Higgins & Green, 2011). Each article was strictly assessed on the criteria provided, including domains of selection bias (biased from non-random generation of groups, non-comparable recruitment), attrition bias (bias due to amount, nature, or handling of missing data), reporting bias (bias due to selective outcome reporting), and other bias (bias due to other sources identified by author reviewers). Other important concerns derived from reviews were noted, such as study limitations and accurate use and interpretation of neuropsychological measures. To handle bias, any studies that were at high risk were not included in analyses.

Statistical analysis

Analysis was conducted using OpenMeta software (Wallace et al., 2012) using the random-effects model with the DerSimonian method (DerSimonian & Laird, 1986). Random-effects analysis was used following the logic that each study might have its own true effect size due to study-specific characteristics with the goal of the current study to provide an estimate for the average of these true standard mean differences and not one true value for these differences. Furthermore, random effect models were used to obtain measures of heterogeneity known as I², which specifies the percent of variability in the effect size across studies. In addition to the pooled standard mean differences (Hedges’ g), 95% confidence intervals, I² estimates, and forest plots for each cognitive domain were produced.

Characteristics of included studies

Of the 37 studies analyzed, 26 were based in Europe (71.1%), 9 in North America (23.7%), and 2 (5.3%) in Asia. Regarding participant characteristics, the mean age for participants with fibromyalgia across these studies was 47.85 (SD = 8.23), and the mean age for the healthy control samples was 45.99 (SD = 8.83). The average percent of females in the fibromyalgia samples was 96.4%, and 94.5% in the healthy control groups. Either years of education or highest education level was recorded from each study. Using studies with available data on years of education (15 studies), the average education for participants with fibromyalgia was 12.93 years (SD = 3.61), and 13.34 years (SD = 2.68) for participants in the healthy control groups. A total of 964 participants with fibromyalgia and 1,025 age-matched participants without fibromyalgia were included from these studies. Characteristics of all studies can be seen in Table 1.

Regarding bias, five studies showed unclear selection bias due to a lack information on recruitment, two had unclear selection bias due to attrition bias (unclear handling of missing data), and three had unclear reporting biases due to incomplete output reporting. Because none of the studies showed a high risk of bias, all studies were used for subsequent analyses. Meta-analytical results described by cognitive domain below are also summarized in Table 2.

Processing Speed

Information processing speed, typically defined as the average time elapsed from stimuli presentation to a task-appropriate response, was measured in 10 studies using diverse measures. The main measures involved the speed at which one completed a simple task or the number of accurately completed problems within an allotted time. These measures included the Digit Symbol Substitution task (Wechsler, 1997), Pattern/Letter/Number Comparison (Salthouse & Babcock, 1991), and Trail Making Task Part A (Reitan, 1958), as well as congruent conditions during the Attention Network Task-Interactions (ANT-I; Callejas, Lupiánez, & Tudela, 2004) and Stroop task (Stroop, 1935). Measures also included reaction times on a lab-developed mental arithmetic task (Montoro, Duschek, Muñoz Ladrón de Guevara, Fernández-Serrano, & Reyes del Paso, 2015), a cued reaction time task (Montoro et al., 2015), and reaction time on the Psychomotor Vigilance Task (Dinges & Powell, 1985). Looking at measures of response speed (14 outcome measures), there was a larger effect size (average g = .59 than for measures of accuracy (4 outcome measures; average g = .49). However, as seen in Figure 2, the random-effects model for processing speed showed a significant and medium-sized difference between those with fibromyalgia and healthy control groups using both measures of response time and accuracy (g = .46, 95% CI: .25 to .67), with no significant between-study heterogeneity (I² = 39.08%, p = .097).

Memory

Executive Functions

Effects of Sample Age

To test the possibility that higher sample age may lead to greater group differences, standard mean differences were meta-regressed on sample age for all cognitive domains with at least ten or more studies. Sample age was significantly associated with differences in long-term memory (b = −.03, 95% CI: −.05 to −.002). No significant associations were seen for short-term memory (b = −.02, −.040 to .002, p = .071), inhibitory control (p = .767), updating (p = .662), or processing speed (p = .521). Approximately 70% of differences in long-term memory, and 57% of differences in short-term memory were accounted for by the fibromyalgia sample age. Overall, studies with higher sample ages showed smaller differences between the fibromyalgia and healthy control participants on measures of long-term memory (i.e., delayed recall/recognition).

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