Abstract
Objective
The present study aimed to determine whether specific cognitive domains part of the Montreal Cognitive Assessment (MoCA) are significantly lower in community-dwelling older adults with chronic pain compared with older adults without pain and whether these domains would be associated with self-reported pain, disability, and somatosensory function.
Design
Secondary data analysis, cross-sectional.
Setting
University of Florida.
Subjects
Individuals over 60 years old enrolled in the Neuromodulatory Examination of Pain and mobility Across the Lifespan (NEPAL) study were included if they completed the MoCA and other study measures (n = 62). Most participants reported pain on most days during the past three months (63%).
Methods
Subjects underwent a health assessment (HAS) and a quantitative sensory testing (QST) session. Health/medical history, cognitive function and self-reported pain measures were administered during the HAS. Mechanical and thermal detection, and thermal pain thresholds were assessed during the QST session.
Results
Older adults with chronic pain had lower MoCA scores compared with controls on domains of executive function, attention, memory, and language (P < 0.05). The attention and language domains survived adjustments for age, sex, education, depression, and pain duration (P < 0.05). Attention was significantly associated with all pain characteristics including pain intensity and disability, while executive function was associated with mechanical detection (P < 0.05).
Conclusion
Our results support previous findings that individuals with chronic pain tend to show poorer cognitive functioning compared with pain-free controls in domains of attention and executive function. Our findings also extend these findings to community-dwelling older adults, who are already most vulnerable to age-related cognitive declines.
Keywords: Pain, Aging, Cognition, Executive Function
Introduction
Chronic pain affects as many as over 100 million Americans and costs the United States over $635 billion dollars each year [1, 2]. The management of chronic pain is challenging, in part, because it is a multidimensional phenomenon which involves complex and poorly understood neurocognitive mechanisms [3–7]. Previous work has shown that cognitive processes can influence pain [7–11] and that pain, in turn, can interfere with cognitive processes in both healthy individuals and individuals with chronic pain [4, 12]. Moriarty and colleagues proposed numerous ways in which chronic pain itself may induce cognitive impairment, including overloading of attentional resources [13]. Alternatively, cognitive impairment may be a contributor to chronic pain, not just a consequence of it [14, 15]. Indeed, individuals with chronic pain frequently report cognitive issues that interfere with daily functioning [16–19], and individuals with cognitive impairment are more likely to underreport their painful symptoms and be at greater risk for undertreatment of their pain [20]. Although chronic pain affects individuals of all ages, its prevalence and impact are greater in older individuals [1, 21]. Given our growing aging population and the well-documented declines in cognitive function that occur with age, it is important to concurrently assess pain and cognitive function among older adults. An initial step will be to determine whether currently used cognitive screening measures can differentiate cognitive changes associated with important aspects of the pain experience in this vulnerable population.
One screening tool frequently used in both clinical practice and research, is the Montreal Cognitive Assessment (MoCA). The MoCA is designed to assist with the detection of mild cognitive impairment (MCI) and has a sensitivity of 90% and 100% in detecting MCI and Alzheimer’s disease (AD), respectively [22]. Beyond its screening characteristics, the MoCA can be used as an indicator of overall global cognitive ability, as it measures cognitive function across multiple domains, such as attention and visuospatial ability [23, 24]. Furthermore, the MoCA has demonstrated utility in other clinical populations, including vascular disease [25], heart failure [26], brain metastases [27], substance use disorder [28], and Parkinson’s disease [29]. The MoCA is also considered superior to the Mini-Mental Status Exam for screening purposes [22, 30]. However, despite all the research linking pain and cognition, only one study has used the MoCA to explore the relationship between chronic pain and cognitive function [31]. However, this study included a younger cohort (ages 18–60) and only examined the total MoCA scores but did not evaluate the relationship between the MoCA’s various cognitive domains and the pain experience. Thus, further work is specifically needed to determine whether specific aspects of cognitive function assessed by the MoCA are associated with pain in older adults, as they are more likely to experience both pain and cognitive issues compared with other age groups.
Therefore, the present study aimed to determine whether specific cognitive domains of the MoCA are significantly lower in community-dwelling older adults with chronic pain compared with older adults without chronic pain and whether these domains would be associated with self-reported pain, disability, and somatosensory function. We hypothesized that, in a sample of older adults, individuals with chronic pain would have lower scores on MoCA cognitive domains previously associated with pain (i.e., attention and executive function). Finally, we hypothesized that MoCA cognitive domains would be significantly associated with self-reported pain characteristics, pain intensity, and disability during the past six months as well as mechanical and thermal detection thresholds and pain thresholds important for somatosensory function.
Methods
Participants
Community-dwelling older adults (age > 60 years) were recruited as part of an ongoing NIH/NIA-funded study at the University of Florida (NEPAL: Neuromodulatory Examination of Pain and Mobility Across the Lifespan, PI: Cruz-Almeida). The parent study aimed to examine the neurobiological mechanisms underlying age-related differences in pain modulation and their impact on function. Potential participants were recruited through various methods including word-of-mouth, newspaper ads, and fliers and were screened over the phone and in person. Participants were excluded for any of the following: 1) Neurological conditions such as Alzheimer’s and Parkinson’s; 2) uncontrolled hypertension (bp > 150/95 mm Hg), heart failure or past acute myocardial infarctions; 3) significant psychiatric conditions (e.g., major depression, bipolar disorder) or psychiatric conditions requiring hospitalization within the preceding year; 4) chronic opioid use; 5) systemic rheumatic disorders (e.g., fibromyalgia, rheumatoid arthritis); 6) HIV and AIDS; 7) significant anxiety regarding protocol procedures; 8) cognitive impairment (Modified Mini-Mental State Examination (3MS) score ≤ 77) [32]; and 9) difficulties writing or completing any parts of the MoCA. Because the parent study aimed to recruit relatively healthy older adults with variability in pain (pain-free older adults to those experiencing chronic pain) from the community, participants were not recruited for a specific chronic pain condition. All procedures were reviewed and approved by the University of Florida’s Institutional Review Board, and all participants provided informed consent prior to undergoing further screening and any of the experimental procedures. An overview of various aspects of the NEPAL study’s health assessment and quantitative sensory testing sessions has been provided in several publications [33–35], and findings from other sessions are not reported here.
Health Assessment Session
During the health assessment session, a clinical research coordinator obtained written informed consent and reviewed health and pain history information with each participant. The following instruments were also administered during this session to assess self-reported pain and global cognitive function:
Self-Reported Pain
Participants were asked whether they had experienced pain on most days for at least 3 months and, if yes, a pain history interview was performed. A validated body manikin [36, 37] was used to document pain location including anatomical pain sites across the following body regions: head/face, neck, shoulders, arms, hands, chest, stomach, upper and lower back, legs, knees, and feet. Participants were also asked to select their worst pain if they reported more than one pain, and they were further asked about their worst pain duration (in years), as well as their average pain intensity using a numerical rating scale (NRS) [38]. In addition, participants completed the Graded Chronic Pain Scale (GCPS) [39] and the revised Short-form McGill Pain Questionnaire (SF-MPQ-2) to assess the multidimensional aspects of the pain experience. The GCPS has 2 subscales assessing characteristic pain intensity and pain-related disability during the past six months, while the revised SF-MPQ-2 assesses the degree of the intensity with which individuals experienced 22 potential pain symptom descriptors in the past week. The SF-MPQ-2 consists of four scales: 1) Neuropathic (e.g., hot-burning pain, cold-freezing pain, tingling); 2) Continuous (e.g., gnawing pain, aching pain, tender); 3) Intermittent (e.g., shooting pain, stabbing pain, sharp pain); and 4) Affective (tiring-exhausting, sickening, fearful, and punishing-cruel), and summary scores are computed by averaging the ratings of items that comprise each subscale [40, 41].
MoCA
The MoCA is a validated experimenter-administered instrument designed to screen for MCI and dementia and to examine global cognitive function [22]. The MoCA takes approximately 10 minutes to complete and has a maximum possible score of 30. The test is divided into eight domains: visuospatial, executive function, naming, memory, attention, language, abstraction, and orientation. Visuospatial ability is assessed using a clock drawing task and by copying a three-dimensional cube. Executive functions are assessed with an alternation task that involves connecting a line from a number to a letter in ascending order. Naming is assessed by asking the participant to name three animals (lion, camel, rhinoceros). A series of tasks are used to evaluate attention abilities; these tasks include repeating a list of digits in forward and backwards order, a target detection task, as well as a serial subtraction task. Language is evaluated with the repetition of two syntactically complex sentences and a fluency task. Abstraction is assessed with a similarity task. Lastly, orientation to time and place is assessed. For the present study, we used previously developed MoCA subscales assessing multiple cognitive domains [42–44]. The Executive Index Score (EIS) was computed by adding raw values for the digit span forward and backward, letter A tapping, modified Trail-Making Test Part B, letter fluency, clock drawing, serial-seven subtraction, and abstraction. Values for this index score ranged from zero to 13. The Memory Index Score (MIS) was computed by adding the number of words recalled in free delayed recall, category-cued recall, and multiple choice–cued recall multiplied by three, two and one, respectively. Scores for MIS ranged from zero to 15. This scoring method was devised to better elicit and detect an encoding memory deficit. The Visuospatial Index Score (VIS) has scores ranging from zero to seven and was computed by adding the raw scores of the cube copy, clock drawing, and naming. The Language Index Score (LIS) has scores ranging from zero to six and was computed by adding scores for naming, sentence repetition, and letter fluency. The Attention Index Score (AIS) was formed by adding the raw scores for digit span forward and backward, letter A tapping, serial-seven subtraction, sentence repetition, and the words recalled in both immediate recall trials, with a score ranging from zero to 18. The Orientation Index Score (OIS) has scores ranging from zero to six and was formed by adding the points for the orientation section of the MoCA.
Quantitative Sensory Testing (QST) Session
Standardized testing was performed at the thenar eminence and on the first metatarsal head on all participants. An overview of the procedures was given to the participant, and specific instructions were provided right before starting each test. Measurement of a particular type of threshold was first demonstrated, and at least one practice trial was conducted to ensure that subjects understood the testing procedures [33–35]. Vibratory, tactile, and thermal detection and pain threshold measurements were obtained with the TSA-II Neurosensory Analyzer and accompanying software (Medoc Ltd., Ramat Yishai, Israel). The method of limits was used to obtain all detection thresholds [45]. Vibration: Vibratory thresholds for frequencies of 100 Hz were captured with Medoc’s handheld VSA-3000 circular probe with the 1.22 cm2 contact tip. Three trials, separated by approximately 10 seconds each, began at 0 μm and increased at a rate of 0.5 μm/sec until the participant reported feeling the stimulus or until a maximum amplitude of 130 μm was reached. The mean value of the three trials was computed and used as the vibratory detection threshold for each site. Tactile Detection: Tactile detection was assessed using Semmes-Weinstein monofilaments (Touch TestTM Sensory Evaluator, North Coast Medical, Inc., Morgan Hill, CA). Participants were instructed to keep their eyes closed and indicate with a “yes” or a “no” whether they could feel the test stimulus. The method of limits was used for four series at each site. For each descending series of stimulations, averages were computed using the force of the last monofilament to be detected and the force of the first monofilament stimulation that escaped detection. For ascending series, an average was calculated using the force of the last monofilament stimulation that escaped detection and the force of the first monofilament to be detected. Participant bias was assessed by performing “catch trials” which involve mimicking the motion of a stimulation without making contact with the skin. Instances where the participant reported a sensation during a catch trial were removed from the analysis. Whenever the lowest stimulation force of 0.008 g was detected, this value was used as the threshold for that particular series. Whenever the highest stimulation force of 300 g was not detected, this value was used as a ceiling threshold for that series. The arithmetic means of values acquired during the four series were used as the threshold values of tactile detection for each site. Thermal Detection: The TSA-II Neurosensory Analyzer with a 30 x 30 mm thermode was used to deliver thermal stimulation. Every trial started at 32°C and decreased (for cold testing) or increased (for warmth testing) at a rate of 1°C/sec until the subject reported feeling the stimulus or until the stimulus reached the cutoff value (0°C for cold testing and 50°C for warmth testing). Each trial was separated by a gap of approximately 10 seconds. Detection threshold for each modality and each site was then defined as the average of four trials of threshold temperatures (per site). Thermal Pain: Subjects were asked to indicate as soon as a stimulus progressed from “just being cold to being painfully cold” or from “just being hot to being painfully hot.” Every trial started at 32°C and then the temperature was either lowered (for cold pain) or augmented (for heat pain) at a rate of 1°C/sec until the participant reported first experiencing pain or the cutoff value was reached (0°C for cold pain and 50°C for heat pain). Every trial was separated by a gap of approximately 20 seconds. The pain detection threshold was set as the average of three trials at each test site. Pressure Pain: A handheld digital pressure algometer (Algomed, Medoc) was applied with increasing pressure until the participant indicated the pressure changed to a sensation of pain by clicking a button. Pressure pain threshold was applied at a constant rate of 1 kg/sec until participants clicked a button when “first became painful” at two locations in a counterbalanced order: quadriceps and trapezius. Testing was stopped at a maximum pressure of 1000 kPa. This procedure was repeated three times to obtain an average pressure pain threshold for each test site. Due to multicollinearity between experimental modalities, QST variables were z-transformed by test site and by modality, and then they were combined for analysis. Four index scores were generated in total and entered into the subsequent analysis: 1) mechanical detection (vibratory and tactile at hand and foot); 2) thermal detection (reverse cool and warm detection at hand and foot); 3) thermal pain threshold (reverse cold and heat pain at hand and foot); and 4) pressure pain (at trapezius and quadriceps muscles). The present variable groupings were deemed appropriate on the basis of previously published work examining the physiological properties of sensory channels [46].
Statistical Methods
Data were entered by one experimenter and checked for accuracy by a different, blinded experimenter. All analyses were conducted in IBM-SPSS version 26 software for MacOS. Alpha level was set at a probability less than 0.05, and values were presented as mean ± SD unless stated otherwise. Differences in demographic (i.e., age, sex, race, education) and clinical variables (Center for Epidemiological Studies-Depression [CES-D], Total MoCA) by pain groups were assessed using χ2 analysis for categorical variables and independent samples t-tests for continuous variables. We employed a Multivariate Analysis of Variance (MANOVA) procedure to examine pain group differences across the six MoCA subscales. As previous studies have demonstrated associations between MoCA and multiple important covariates, we also applied a Multivariate Analysis of Covariance (MANCOVA) to examine pain group differences across the six MoCA subscales, while controlling for age, sex, education, depressive symptomatology, and pain duration. These variables, although not significantly different between pain groups in our sample, are known to be important contributors to both cognitive performance and the pain experience. To decrease the probability of Type-1 errors, we applied a Bonferroni correction for multiple comparisons in both MANOVA and MANCOVA models. Given that most of the MoCA subscales are non-normally distributed, pain group differences were also assessed using the non-parametric Mann–Whitney U test for independent samples. MoCA subscales that survived Bonferroni corrections were subsequently examined for associations with self-reported pain and QST variables using partial correlations controlling for age, sex, education, depressive symptomatology, and pain duration. Given our small sample size for estimating potentially small effects, we used the percentile bootstrapped confidence intervals for statistical inference based on 5,000 bootstrap samples, as its performance is relatively robust to small sample sizes and potential outliers.
Results
Study Participants
Out of 69 participants that completed the MoCA, there were some individuals with missing items needed to calculate the Attention, Memory, and Executive Function Index Scores for a final sample of 62. The sample was 66% female with a mean age of 71.3 years. Out of the 62 participants, 39 individuals (62.9%) reported experiencing pain on most days during the past three months. These individuals reported on average three distinct pain problems (range 1–5) with the knee/legs (29.5%) and the back (25%) being the most common pain regions reported. On average, pain duration was reported to be around 7.7 years with a wide range (from 4.8 months to over 45 years). There were no statistically significant differences between the groups regarding age, race, educational attainment, and depressive symptomology (P > 0.05). However, sex, GCPS subscales, and total MoCA scores differed significantly between the groups (P < 0.05, Table 1).
Table 1.
Demographic characteristics of our sample of older participants (n = 62)
| No chronic pain | Chronic pain | ||
|---|---|---|---|
| (n = 23) | (n = 39) | Probability | |
| Age (Mean ± SD) | 71.8 ± 6.9 | 71.1 ± 6.1 | 0.665 |
| Race, x2 | 0.417 | ||
| -Non-Hispanic white | 23 | 33 | |
| -African American | 0 | 2 | |
| -Asian/Pacific Islander | 0 | 2 | |
| -Hispanic | 0 | 1 | |
| -Other | 0 | 1 | |
| Education Level, x2 | 0.243 | ||
| -High school degree | 2 | 7 | |
| -Two-year college degree | 4 | 5 | |
| -Four-year college degree | 5 | 8 | |
| -Master’s degree | 3 | 12 | |
| -Doctoral degree | 9 | 7 | |
| Sex, x2 | 0.019 | ||
| -Male | 12 | 9 | |
| -Female | 11 | 30 | |
| Worst Pain Intensity | – | 5.0 ± 2.0 | – |
| Anatomical Pain Sites | – | 3.9 ± 2.4 | – |
| Worst Pain Duration | – | 8.6 ± 12.1 | – |
|
GCPS (Mean ± SD) -Characteristic pain intensity -Pain-related disability |
13.2 ± 19.2 10.3 ± 21.8 |
45.5 ± 21.8 31.3 ± 25.2 |
0.000 0.001 |
| CES-D (Mean ± SD) | 6.7 ± 5.5 | 7.8 ± 5.0 | 0.375 |
| Total MoCA (Mean ± SD) | 27.9 ± 1.5 | 26.2 ± 2.7 | 0.002 |
CES-D = Center for Epidemiological Studies-Depression; GCPS = Graded Chronic Pain Scale; MoCA = Montreal Cognitive Assessment; SD = standard deviation.
Pain Group Differences in MoCA Performance in Older Individuals
The MANOVA revealed a significant main effect of MoCA subscales by pain group (Wilks’ Lambda F (6, 56) = 2.4, P = 0.038) with the Executive Function, Attention, MIS, and LIS surviving post-hoc Bonferroni corrections (P < 0.05, Table 2). Further, the MANCOVA controlling age, sex, education, depressive symptomatology, and pain duration was also significantly different (Wilks’ Lambda F (6, 51) = 3.2, P = 0.010), with only the AIS and the LIS surviving the post-hoc Bonferroni corrections (P < 0.05, Table 2). The non-parametric Mann–Whitney U test for independent samples also suggested there were significant differences between the groups on the AIS (P = 0.002) and LIS (P = 0.034).
Table 2.
Means for MoCA subscale scores along with Bonferroni-corrected probability values
| MoCA subscales (Mean±SD) | No chronic pain (n = 2) | Chronic pain (n = 39) | Bonferroni probability valuea | Age, sex, education, duration, CES-D adjusted Bonferroni probabilityb |
|---|---|---|---|---|
| Executive Index Score | 12.7 ± 0.8 | 12.0 ± 1.6 | 0.047 | 0.082 |
| Attention Index Score | 17.9 ± 0.3 | 16.9 ± 1.6 | 0.003 | 0.001 |
| Memory Index Score | 13.8 ± 1.3 | 12.6 ± 2.9 | 0.048 | 0.083 |
| Visuospatial Index Score | 6.4 ± 0.7 | 6.3 ± 0.9 | 0.695 | 0.935 |
| Language Index Score | 5.6 ± 0.5 | 5.1 ± 0.9 | 0.016 | 0.009 |
| Orientation Index Score | 6.0 ± 0.0 | 5.9 ± 0.3 | 0.184 | 0.148 |
MANOVA procedure.
MANCOVA procedure.
Associations of MoCA Performance with Self-Reported Pain Characteristics
Both self-reported pain intensity (i.e., GCPS: Characteristic in Intensity Scale) and disability (i.e., GCPS: Pain Disability Scale) during the past six months were significantly correlated with the AIS but not with the EIS or the LIS (P > 0.05) adjusting for age, sex, education, depressive symptomatology, and pain duration—associations that were confirmed by statistically significant bias corrected 95% bootstrapped confidence intervals (Table 3). Further, both EIS and AIS, but not LIS, were significantly correlated with all subscales of the SF-MPQ-2 adjusting for age, sex, education, depressive symptomatology, and pain duration—associations that were confirmed by statistically significant bias corrected accelerated 95% bootstrapped confidence intervals (Table 3).
Table 3.
Associations between MoCA subscales and self-reported pain characteristics controlling for age, sex, education, depression and pain duration
| Executive index (r, p, BCa 95% CI) | Attention index (r, p, BCa 95% CI) | Language index (r, p, BCa 95% CI) | |
|---|---|---|---|
| GCPS: Characteristic Pain Intensity | −0.176, 0.191, | −0.321, 0.015, | −0.215, 0.108, |
| −0.443 to 0.132 | −0.505 to −0.126 | −0.464 to 0.060 | |
| GCPS: Pain Disability | −0.097, 0.474, | −0.312, 0.018, | −0.219, 0.102, |
| −0.365 to 0.158 | −0.541 to −0.071 | −0.509 to 0.069 | |
| SF-MPQ-2: Continuous Pain | −0.586, 0.000, | −0.517, 0.000, | −0.206, 0.128, |
| −0.815 to −0.273 | −0.789 to −0.292 | −0.480 to 0.082 | |
| SF-MPQ-2: Intermittent Pain | −0.287, 0.032, | −0.401, 0.002, | −0.207, 0.125, |
| −0.580 to 0.046 | −0.577 to −0.219 | −0.483 to 0.086 | |
| SF-MPQ-2: Neuropathic Pain | −0.468, 0.000, | −0.477, 0.000, | −0.207, 0.125, |
| −0.735 to −0.119 | −0.745 to −0.248 | −0.459 to 0.052 | |
| SF-MPQ-2: Affective Pain | −0.527, 0.000, | −0.449, 0.001, | −0.188, 0.166, |
| −0.759 to −0.197 | −0.730 to −0.221 | −0.453 to 0.084 |
BCa: Bias accelerated bootstrapped 95% confidence intervals.
Associations of MoCA Performance with Somatosensory Function
Only mechanical detection thresholds were significantly associated with the EISs (r = −0.300, P = 0.044, BCa 95% CI: −0.585 to −0.015) controlling for age, sex, education, depressive symptoms, and pain duration. There were no statistically significant associations between MoCA subscales and thermal detection (r = −0.043 to 0.106, P = 0.822 to 0.464), as well as thermal (r = −0.044 to 0.111, P = 0.822 to 0.464) and mechanical pain thresholds (r = −0.043 to 0.106, P = 0.822 to 0.464).
Discussion
The present investigation sought to examine the associations between subdomains of the MoCA, with self-reported pain characteristics and somatosensory function in community-dwelling older individuals without cognitive impairment. Several important findings emerged. First, older adults reporting pain during the past three months on most days had significantly lower MoCA scores on the scales of executive function, attention, memory, and language compared with older controls, with only attention and language scales surviving adjustments for age, sex, education, depression, and pain duration. Second, only the attention scale was highly associated with self-reported pain intensity and disability during the past six months and with all scales of the SF-MPQ-2. Finally, only the executive function scale was significantly associated with mechanical detection thresholds in community-dwelling older individuals.
Previous research has found the MoCA to be useful for cognitive assessment in a variety of clinical populations (Parkinson’s, cerebral small vessel disease, brain metastases, substance abuse disorder, heart failure) [25–29]. However, no studies to date have examined the relationship between the various cognitive domains measured by the MoCA and pain in older adults. Our findings of lower performance on various scales of the MoCA in community-dwelling older adults reporting pain is consistent with existing literature, where acute and chronic pain are reciprocally associated with cognitive performance. Moriarty et al. offers a preliminary model in which pain leads to cognitive impairment via competition for limited neural resources, neuroplasticity or neurochemical mediation [13]. Further, a previous study found that impaired cognition predicted the development of chronic pain after surgery [14]. However, our older participants did not have a cognitive impairment when tested, as this was exclusionary; thus, it would be important to know whether these differences in performance lead to future cognitive impairment. While the direction of the relationship between pain and cognition is not testable in the present work, future longitudinal studies in non-cognitively impaired groups should be conducted to establish the extent to which chronic pain predicts cognitive impairment and vice-versa.
Only the attention subscale of the MoCA was both significantly lower in those individuals reporting pain and was significantly associated with self-reported pain intensity, pain disability, and other pain characteristics (i.e., continuous, neuropathic, affective pain). This is consistent with the idea that the perception of pain is highly dependent on cognitive processes such as attention, where impairment in this or the executive functions that coordinate them [3, 47–49], is likely to contribute to pain intensity and pain-related disability. Further, early deficits in attention may be predictive of cognitive decline years later. For example, in the Berlin Aging Study [50], poor initial performance on measures of attention and executive function were better predictors of which nondemented individuals were diagnosed with AD 2 years later than were tests of episodic memory. It is important to note that the differences by pain status reported here are not likely to represent clinically meaningful impairment and likely reflect the normal limits of inter-individual variability in cognitive function. Future studies are needed to determine whether baseline lower performance within the normal limits of cognitive function increases the susceptibility to pain or vice-versa. Regardless of the specific mechanisms, additional studies are needed using the MoCA in older persons with chronic pain to examine potential clinical implications while testing the directionality of the findings in an older population.
Finally, better performance on the MoCA executive function domain was significantly associated with better detection of mechanical stimuli, but not with other somatosensory function including experimental pain thresholds. This is consistent with a vast literature of cross-sectional and longitudinal studies suggesting that, in older individuals, there is a strengthening of the links among somatosensory and cognitive functions that may point to potential common underlying causes influencing these functions (i.e., deterioration of common neurobiological processes); an increase in resource overlap, competition, and compensatory changes; or a combination of the two [51]. Although from our study we cannot determine why only mechanical thresholds show this association in older adults, these findings support the need to investigate age-related changes in somatosensory function and cognitive function simultaneously in future studies.
Our study is limited by its cross-sectional nature, not allowing for causal associations to be made between pain and cognitive function as measured with the MoCA. Second, our study was not designed nor powered to determine sensitivity or specificity of these MoCA subscales in older persons with chronic pain. Third, our older participants were mostly between the ages of 60 and 79 years, making it impossible to generalize to older adults over 80 years of age. Fourth, our older sample was mainly composed of Non-Hispanic white individuals; thus, results may not generalize to individuals from other racial/ethnic groups, and we did not specifically aim to examine sex differences. Future studies in more diverse samples examining sex differences are needed. Finally, given the extensive medical neurological exclusions of the parent study, our findings may not be representative of older individuals with cognitive impairment or neurological conditions. Nonetheless, our findings suggest that the MoCA may be useful for assessing mild cognitive function differences associated with the experience of pain. Future studies may further advance our understanding of how pain relates to cognition by employing measures that more thoroughly hone in on specific cognitive domains, such as executive function and attention. Further, from a clinical perspective, the MoCA may help identify older individuals with pain that should not be treated with pain medications known to worsen cognitive function (i.e., opioids, tricyclics) to prevent potential future cognitive decline.
Funding: This work was supported by the National Institutes of Health (NIA K01AG048259, R01AG059809, R01AG067757 to YC-A, NIAAA K01AA025306 to EP, NIA K01AG050707 to AJW), the Cognitive Aging & Memory Clinical Translational Program, McKnight Brain Foundation, the Claude D. Pepper Older Americans Independence Center (P30AG028740). All authors report no conflict of interest.
References
- 1.Gereau RW, Sluka KA, Maixner W, et al. A pain research agenda for the 21st century. J Pain 2014;15(12):1203–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Tsang A, Von Korff M, Lee S, et al. Common chronic pain conditions in developed and developing countries: Gender and age differences and comorbidity with depression-anxiety disorders. J Pain 2008;9(10):883–91. [DOI] [PubMed] [Google Scholar]
- 3.Apkarian AV, Bushnell MC, Treede R-D, Zubieta J-K. Human brain mechanisms of pain perception and regulation in health and disease. Eur J Pain 2005;9(4):463. [DOI] [PubMed] [Google Scholar]
- 4.Higgins DM, Martin AM, Baker DG, Vasterling JJ, Risbrough V. The relationship between chronic pain and neurocognitive function: A systematic review. Clin J Pain 2018;34(3):262–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Seminowicz DA, Davis KD. Interactions of pain intensity and cognitive load: The brain stays on task. Cerebral Cortex 2007;17(6):1412–22. [DOI] [PubMed] [Google Scholar]
- 6.Seminowicz DA, Wideman TH, Naso L, et al. Effective treatment of chronic low back pain in humans reverses abnormal brain anatomy and function. J Neurosci 2011;31(20):7540–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wiech K, Ploner M, Tracey I. Neurocognitive aspects of pain perception. Trends Cogn Sci 2008;12(8):306–13. [DOI] [PubMed] [Google Scholar]
- 8.Nakae A, Endo K, Adachi T, et al. The influence of working memory capacity on experimental heat pain. J Pain 2013;14(10):1088–96. [DOI] [PubMed] [Google Scholar]
- 9.Quevedo AS, Coghill RC. Attentional modulation of spatial integration of pain: Evidence for dynamic spatial tuning. J Neurosci 2007;27(43):11635–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ramírez-Maestre C, Esteve R, López AE. Cognitive appraisal and coping in chronic pain patients. Eur J Pain 2008;12(6):749–56. [DOI] [PubMed] [Google Scholar]
- 11.Villemure C, Bushnell MC. Cognitive modulation of pain: How do attention and emotion influence pain processing? Pain 2002;95(3):195–9. [DOI] [PubMed] [Google Scholar]
- 12.Gatzounis R, Schrooten MGS, Crombez G, Vlaeyen JWS. Activity interruptions by pain impair activity resumption, but not more than activity interruptions by other stimuli: An experimental investigation. Pain 2018;159(2):351–8. [DOI] [PubMed] [Google Scholar]
- 13.Moriarty O, McGuire BE, Finn DP. The effect of pain on cognitive function: A review of clinical and preclinical research. Prog Neurobiol 2011;93(3):385–404. [DOI] [PubMed] [Google Scholar]
- 14.Attal N, Masselin-Dubois A, Martinez V, et al. Does cognitive functioning predict chronic pain? Results from a prospective surgical cohort. Brain 2014;137(3):904–17. [DOI] [PubMed] [Google Scholar]
- 15.Solberg Nes L, Roach AR, Segerstrom SC. Executive functions, self-regulation, and chronic pain: A review. Ann Behav Med 2009;37(2):173–83. [DOI] [PubMed] [Google Scholar]
- 16.Jamison RN, Sbrocco T, Parris WC. The influence of problems with concentration and memory on emotional distress and daily activities in chronic pain patients. Int J Psychiatry Med 1989;18(2):183–91. [DOI] [PubMed] [Google Scholar]
- 17.Kewman DG, Vaishampayan N, Zald D, Han B. Cognitive impairment in musculoskeletal pain patients. Int J Psychiatry Med 1991;21(3):253–62. [DOI] [PubMed] [Google Scholar]
- 18.Muñoz M, Esteve R. Reports of memory functioning by patients with chronic pain. Clin J Pain 2005;21(4):287–91. [DOI] [PubMed] [Google Scholar]
- 19.Schnurr RF, MacDonald MR. Memory complaints in chronic pain. Clin J Pain 1995;11(2):103–11. [DOI] [PubMed] [Google Scholar]
- 20.Parmelee PA. Pain in cognitively impaired older persons. Clin Geriatr Med 1996;12(3):473–87. [PubMed] [Google Scholar]
- 21.Patel KV, Guralnik JM, Dansie EJ, Turk DC. Prevalence and impact of pain among older adults in the United States: Findings from the 2011 National Health and Aging Trends Study. Pain 2013;154(12):2649–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Nasreddine ZS, Phillips NA, Bédirian Vér, et al. The montreal cognitive assessment, moca: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53(4):695–9. [DOI] [PubMed] [Google Scholar]
- 23.Koski L, Xie H, Finch L. Measuring cognition in a geriatric outpatient clinic: Rasch analysis of the Montreal Cognitive Assessment. J Geriatr Psychiatry Neurol 2009;22(3):151–60. [DOI] [PubMed] [Google Scholar]
- 24.Koski L, Xie H, Konsztowicz S. Improving precision in the quantification of cognition using the Montreal Cognitive Assessment and the Mini-Mental State Examination. Int Psychogeriatr 2011;23(7):1107–15. [DOI] [PubMed] [Google Scholar]
- 25.Wong A, Xiong YY, Kwan PWL, et al. The validity, reliability and clinical utility of the Hong Kong Montreal Cognitive Assessment (HK-MoCA) in patients with cerebral small vessel disease. Dement Geriatr Cogn Disord 2009;28(1):81–7. [DOI] [PubMed] [Google Scholar]
- 26.Cameron J, Worrall-Carter L, Page K, Riegel B, Lo SK, Stewart S. Does cognitive impairment predict poor self-care in patients with heart failure? Eur J Heart Fail 2010;12(5):508–15. [DOI] [PubMed] [Google Scholar]
- 27.Olson RA, Chhanabhai T, McKenzie M. Feasibility study of the Montreal Cognitive Assessment (MoCA) in patients with brain metastases. Supportive Care in Cancer 2008;16(11):1273–8. [DOI] [PubMed] [Google Scholar]
- 28.Copersino ML, Fals-Stewart W, Fitzmaurice G, Schretlen DJ, Sokoloff J, Weiss RD. Rapid cognitive screening of patients with substance use disorders. Exp Clin Psychopharmacol 2009;17(5):337–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Hoops S, Nazem S, Siderowf AD, et al. Validity of the MoCA and MMSE in the detection of MCI and dementia in Parkinson disease. Neurology 2009;73(21):1738–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Waldron-Perrine B, Axelrod BN. Determining an appropriate cutting score for indication of impairment on the Montreal Cognitive Assessment. Int J Geriatr Psychiatry 2012;27(11):1189–94. [DOI] [PubMed] [Google Scholar]
- 31.Ferreira KDS, Oliver GZ, Thomaz DC, Teixeira CT, Foss MP. Cognitive deficits in chronic pain patients, in a brief screening test, are independent of comorbidities and medication use. Arq Neuropsiquiatr 2016;74(5):361–6. [DOI] [PubMed] [Google Scholar]
- 32.Teng EL, Chui HC. The modified mini-mental state (3MS) examination. J Clin Psychiatry 1987;48(8):314–8. [PubMed] [Google Scholar]
- 33.Cruz-Almeida Y, Fillingim RB, Riley JL, et al. Chronic pain is associated with a brain aging biomarker in community-dwelling older adults. Pain 2019;160(5):1119–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Cruz-Almeida Y, Sinha P, Rani A, Huo Z, Fillingim RB, Foster T. Epigenetic aging is associated with clinical and experimental pain in community-dwelling older adults. Mol Pain 2019;15:174480691987181.1744806919871819. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Lysne P, Cohen R, Hoyos L, Fillingim RB, Riley JL 3rd, Cruz-Almeida Y. Age and pain differences in non-verbal fluency performance: Associations with cortical thickness and subcortical volumes. Exp Gerontol 2019;126:110708. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Cruz-Almeida Y, Martinez-Arizala A, Widerstrom-Noga EG. Chronicity of pain associated with spinal cord injury: A longitudinal analysis. J Rehabil Res Dev 2005;42(5):585–94. [DOI] [PubMed] [Google Scholar]
- 37.Margolis RB, Chibnall JT, Tait RC. Test-retest reliability of the pain drawing instrument. Pain 1988;33(1):49–51. [DOI] [PubMed] [Google Scholar]
- 38.Jensen MP, Karoly P. Self-Report Scales and Procedures for Assessing Pain in Adults. Handbook of Pain Assessment. New York: Guilford; 1992:135–51. [Google Scholar]
- 39.Von Korff M, Ormel J, Keefe FJ, Dworkin SF. Grading the severity of chronic. Pain 1992;50(2):133–49. [DOI] [PubMed] [Google Scholar]
- 40.Dworkin RH, Turk DC, Revicki DA, et al. Development and initial validation of an expanded and revised version of the Short-Form McGill Pain Questionnaire (SF-MPQ-2). Pain 2009;144(1):35–42. [DOI] [PubMed] [Google Scholar]
- 41.Lovejoy TI, Turk DC, Morasco BJ. Evaluation of the psychometric properties of the revised short-form McGill pain questionnaire. J Pain 2012;13(12):1250–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Apolinario D, dos Santos MF, Sassaki E, et al. Normative data for the Montreal Cognitive Assessment (MoCA) and the Memory Index Score (MoCA-MIS) in Brazil: Adjusting the nonlinear effects of education with fractional polynomials. Int J Geriatr Psychiatry 2018;33(7):893–9. [DOI] [PubMed] [Google Scholar]
- 43.Julayanont P, Brousseau M, Chertkow H, Phillips N, Nasreddine ZS. Montreal cognitive assessment memory index score (MoCA-MIS) as a predictor of conversion from mild cognitive impairment to Alzheimer's disease. J Am Geriatr Soc 2014;62(4):679–84. [DOI] [PubMed] [Google Scholar]
- 44.Kaur A, Edland SD, Peavy GM. The MoCA-memory index score: an efficient alternative to paragraph recall for the detection of amnestic mild cognitive impairment. Alzheimer Dis Assoc Disord 2018;32(2):120–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Cruz-Almeida Y, Felix ER, Martinez-Arizala A, Widerström-Noga EG. Decreased spinothalamic and dorsal column medial lemniscus-mediated function is associated with neuropathic pain after spinal cord injury. J Neurotrauma 2012;29(17):2706–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Willis WD, Coggeshall RE. Sensory Mechanisms of the Spinal Cord. Boston, MA: Springer; 2004. [Google Scholar]
- 47.Baddeley AD. Exploring Working Memory: Selected Works of Alan Baddeley. London: Routledge; 2012. [Google Scholar]
- 48.Postle BR. Working memory as an emergent property of the mind and brain. Neuroscience 2006;139(1):23–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Simon CB, Lentz TA, Bishop MD, Riley JL 3rd, Fillingim RB, George SZ. Comparative associations of working memory and pain catastrophizing with chronic low back pain intensity. Phys Ther 2016;96(7):1049–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Rapp MA, Reischies FM. Attention and executive control predict Alzheimer disease in late life: Results from the Berlin Aging Study (BASE). Am J Geriatr Psychiatry 2005;13(2):134–41. [DOI] [PubMed] [Google Scholar]
- 51.Li KZ, Lindenberger U. Relations between aging sensory/sensorimotor and cognitive functions. Neurosci Biobehav Rev 2002;26(7):777–83. [DOI] [PubMed] [Google Scholar]