Abstract
Objectives
The study aimed to compare the psychological and physical characteristics of older adults with knee osteoarthritis (OA) vs those of adults with chronic low back pain (CLBP) and to identify psychological and physical predictors of function as measured by gait speed.
Design
Secondary data analysis.
Method and Patients
Eighty-eight older adults with advanced knee OA and 200 with CLBP who had participated in separate randomized controlled trials were selected for this study.
Measures
Inclusion criteria for both trials included age ≥65 and pain of at least moderate intensity that occurred daily or almost every day for at least the previous 3 months. Psychological constructs (catastrophizing, fear avoidance, self-efficacy, depression, affective distress) and physical measures (comorbid medical conditions, pain duration, pain severity, pain related interference, self-rated health) were obtained.
Results
Subjects with CLBP had slower gait (0.88 m/s vs 0.96 m/s, P = 0.002) and more comorbid conditions than subjects with knee pain (mean 3.36 vs 1.97, P < 0.001). All the psychological measures were significantly worse in the CLBP group except the Multidimensional Pain Inventory–Affective Distress score. Self-efficacy, pain severity, and medical comorbidity burden were associated with slower gait regardless of the location of the pain.
Conclusions
Older adults with chronic pain may have distinct psychological and physical profiles that differentially impact gait speed. These findings suggest that not all pain conditions are the same in their psychological and physical characteristics and may need to be taken into consideration when developing treatment plans.
Keywords: Low Back Pain, Knee Pain, Physical Function, Psychosocial Factors
Introduction
It is estimated that up to 50% of older adults live with chronic pain [1]. Chronic low back pain (CLBP) and knee osteoarthritis (OA) predominate as the causes of pain. Low back pain (LBP) is the most frequently reported musculoskeletal problem among older adults [2]. The 1991–2002 Medicare data indicate that visits for LBP increased by 131.7% [3]. Approximately 4.3 million older Americans live with symptomatic knee OA [4], and it is one of the leading causes of disability for older adults [5]. Yet treatment for chronic pain in these patients remains inadequate. Reasons vary from the increasing adverse events associated with commonly used medications such as opioids and nonsteroidal anti-inflammatory agents to not addressing the psychological factors that worsen pain.
Assessment of psychological function is crucial in the patient with chronic pain. Such factors include depression, perceptions of pain self-efficacy, and fear of movement-related pain. In younger patients with CLBP, self-efficacy has been found to correlate significantly with post-treatment pain ratings, tolerance for physical activities, performance of isokinetic endurance tasks, use of pain medication, and work status [6–8]. Studies with mixed-aged patients suggest that pain location may be differentially associated with these factors [9,10]. The psychological profile of older adults with chronic back or knee pain, two commonly seen conditions, has not been well described; therefore, improved understanding of the psychology of older adults with these conditions may provide useful information for the clinician managing these patients.
The weak association between pain-associated pathology and physical function in patients with chronic pain has long been recognized and has motivated researchers to look into the influence of psychosocial factors on physical disability. While psychological factors play an important role in the experience of chronic pain, their impact on the physical function of older adults with chronic pain is not well understood. There is evidence for an association as Tiedemann et al. found that the depression, anxiety, and vitality scores of the Short-Form 12 Health Status Questionnaire were predictors of gait speed among 668 mostly pain-free older adults [11], and Reid et al. found that depressive symptoms were a risk factor for disabling musculoskeletal pain among community-dwelling older adults [12]. However, the psychological factors that may be associated with physical function in the older patient with chronic back or knee pain are not known.
Therefore, our study had two purposes. The first goal was to compare the psychological and physical characteristics of older adults with chronic knee OA with those with CLBP. The second goal was to identify psychological and physical associations of function in patients with these two chronic pain conditions.
Methods
Participants
We performed secondary data analysis from two National Institutes of Health (NIH)-funded clinical trials conducted at the University of Pittsburgh Pain Evaluation and Treatment Institute. The first study consisted of 88 English-speaking, community-dwelling older adults with advanced knee OA who had participated in a randomized controlled trial (RCT) to determine the efficacy of periosteal stimulation therapy [13]. The second study consisted of 200 English-speaking, community-dwelling older adults who had participated in an RCT of periosteal electrical nerve stimulation for the treatment of CLBP. We were able to compare these conditions across studies because the inclusion and exclusion criteria regarding age, pain severity, pain duration, and cognitive status were the same in both studies. Thus, the participants were similar except for the location of their chronic pain.
Inclusion criteria for both studies included age of ≥65 years and pain of at least moderate intensity that occurred daily or almost every day for at least the previous 3 months. Pain was measured in both groups with the pain thermometer [14]. For the enrollment of older adults with advanced knee OA to be ensured, plain radiographs needed to be Kellgren–Lawrence grade 2, 3, or 4. Exclusion criteria included prior knee surgery, non-OA arthridites, large knee effusion or severe mechanical knee instability, and corticosteroid or hyaluronic acid injection during the prior 3 months. For the CLBP study, exclusion criteria included a history of back surgery or trauma, known spinal pathology other than osteoarthritis, symptomatic severe spinal stenosis, prominent radicular pain, and red flags that indicated the possibility of serious underlying illness such as the presence of fever or significant unintentional weight loss. Additionally, subjects in both studies were excluded for cognitive impairment if the Folstein Mini-Mental State Examination was <24 adjusted for age and education. Participants were also excluded if they had pain in other body parts that was more severe than their CLBP or chronic knee pain. All participants provided written informed consent. The study was approved by the University of Pittsburgh Institutional Review Board.
Characteristics of the study sample by pain type are shown in Table 1. Those with CLBP were slightly older, more educated, and more overweight (body mass index [BMI] 25–29). The majority of patients with knee OA (80%) had a Kellgren–Lawrence score of 4.
Table 1.
Characteristics of the study population by pain type
| Characteristics | Knee Pain (N = 88) | Chronic Low Back Pain (N = 200) | P Value* |
|---|---|---|---|
| Age, mean ± SD | 71.5 ± 5.4 | 73.9 ± 5.8 | 0.001 |
| Age categories (%) | 0.09 | ||
| 65–74 | 70.5 | 56.5 | |
| 75–84 | 27.3 | 39.5 | |
| 85+ | 2.3 | 4.0 | |
| Gender (%) | 0.8 | ||
| Female | 54.5 | 57.0 | |
| Male | 45.5 | 43.0 | |
| Ethnicity (%) | 0.108 | ||
| White | 95.35 | 89.5 | |
| African American | 4.65 | 10.5 | |
| Educational level (%)† | 0.002 | ||
| Low | 34.1 | 32.5 | |
| Medium | 17.0 | 36.0 | |
| High | 48.9 | 31.5 | |
| Marital status (%) | 0.8 | ||
| Single | 3.4 | 6.0 | |
| Separated or divorced | 9.1 | 11.5 | |
| Married | 62.5 | 58.0 | |
| Widowed | 25.0 | 24.5 | |
| BMI categories (%) | 0.005 | ||
| Normal (<25 kg/m2) | 15.9 | 23.0 | |
| Overweight (25–30 kg/m2) | 30.7 | 44.0 | |
| Obese (≥30 kg/m2) | 53.4 | 33.0 |
Two samples t-test for continuous variables, Chi-Square or Fisher’s exact test for categorical variables.
Low: includes ≤9th, 10th–12th, and high school graduate; Medium: includes technical school and some college; High: includes college degree and masters or above.
SD = standard deviation; BMI = body mass index.
Measures
Demographic data included age, gender, race/ethnicity, educational level, marital status, and standard BMI. The independent variables for this study included measures of psychological and physical characteristics, initially to characterize each pain group and then to examine their association with physical performance as measured by gait speed (the dependent variable). We chose this as the dependent variable because gait speed has been found to be a strong indicator of disability and dependence in older adults [15–17]. Consequently, it has become a routine measure of function [18] and treatment outcome in older adults [19].
Psychological Measures
Coping Strategies Questionnaire–Catastrophizing Scale
It assesses cognitive coping, which has been shown to significantly contribute to disability in younger adults with chronic pain [20].
Fear Avoidance and Beliefs Questionnaire
It evaluates fear of movement-related pain and has been shown to predict self-reported disability in subjects with CLBP [21,22].
Chronic Pain Self-Efficacy Scale (CPSS)
It has been demonstrated to predict task performance and pain treatment outcome [23,24]. Self-efficacy is an individual’s judgment regarding perception of their ability to perform different tasks in different situations.
Geriatric Depression Scale (GDS)
It measures depression, which has been shown to be strongly associated with chronic pain [25].
Multidimensional Pain Inventory (MPI)
It contains scales designed to measure pain intensity, interference with daily activities as the result of pain, and affective distress [26].
Physical Measures
Gait Speed (m/s)
It is a timed assessment of usual walking speed and used routinely as an outcome measure in studies of older adults because of its ability to predict disability and dependence [15,16]. In the knee study, the distanced walked was 4 m, and, in the CLBP study, the distance was 50 ft. Both methods have been validated [16,27].
Cumulative Illness Rating Scale (CIRS)
It is a self-report measure used to capture comorbid conditions [28]. It reviews 14 systems (such as cardiac and gastrointestinal). It has previously been suggested that the number of medical diagnoses unrelated to pain contribute independently to compromised functional status in the older adult [29]. We excluded the musculoskeletal system when we scored the CIRS, as this would have added a point to their score.
Self-Rated Health Score
It has a strong association with morbidity and mortality in older adults [29,30] as well as an association with pain intensity in community-dwelling elders [31].
McGill Pain Questionnaire-Short Form (MPQ)
It is a widely used, validated, and reliable generic pain measure that has been used in an older population. It describes sensory and affective responses to pain [32].
Statistical Analysis
Chi-squared tests and t-tests were used to compare patients with knee OA and CLBP with respect to demographics, psychological, and physical measures. Average gait speed comparisons by demographic categories were carried out by using analysis of variance. Linear regression (correlation) was used to assess the relationship between gait speed and the psychological and physical measures. If the associated P value was < 0.10, that measure was considered statistically viable for inclusion in the multivariable predictor model. For the final prediction model of gait speed, we used backward stepwise linear regression. All analyses were carried out by using Stata statistical software, version 10.0 (StataCorp Inc., College Station, TX).
Results
Psychological and Physical Characteristics of Both Groups
The results for the physical measures obtained in both groups are shown in Table 2. Subjects with CLBP had significantly slower gait speed (0.88 m/s vs 0.96 m/s, P = 0.002) and significantly more comorbid conditions than subjects with chronic knee pain (3.36 vs 1.97, P < 0.001). The two groups were not significantly different in pain duration or in measures of pain, except for the MPI–Pain Severity Score that was slightly higher in the CLBP group. The psychological measures by pain type are shown in Table 3. The CLBP group scored significantly worse on all measures except the MPI–Affective Distress score.
Table 2.
Physical and pain measures by pain type
| Characteristics | N | Knee Osteoarthritis | N | Chronic Low Back Pain | Effect Size* | P Value† |
|---|---|---|---|---|---|---|
| Gait speed (m/s) | 88 | 199 | 0.39 | 0.002 | ||
| Mean ± SD | 0.96 ± 0.22 | 0.88 ± 0.21 | ||||
| Median | 0.97 | 0.88 | ||||
| CIRS‡ (range: 0–13) | 88 | 198 | −0.80 | <0.001 | ||
| Mean ± SD | 1.97 ± 1.27 | 3.36 ± 1.76 | ||||
| Median | 2 | 3 | ||||
| Self-rated health score‡ (range: 1–5) | 88 | 200 | 0.67 | <0.001 | ||
| Mean ± SD | 4.11 ± 0.74 | 3.59 ± 0.75 | ||||
| Median | 4.4 | 4 | ||||
| Pain duration (years) | 87 | 197 | −0.35 | 0.14‡ | ||
| Mean ± SD | 7.94 ± 7.36 | 12.55 ± 14.65 | ||||
| Median | 5 | 7 | ||||
| Total MPQ Score‡ (range: 0–45) | 88 | 200 | 0.030 | 0.82 | ||
| Mean ± SD | 12.28 ± 8.33 | 12.06 ± 7.36 | ||||
| Median | 10 | 10 | ||||
| MPQ VAS Score‡ (range: 0–100 mm) | 87 | 200 | −0.10 | 0.44 | ||
| Mean ± SD | 24.46 ± 22.22 | 26.53 ± 20.57 | ||||
| Median | 22 | 25.5 | ||||
| MPI–Pain Severity Score‡ (range: 0–6) | 88 | 200 | −0.39 | 0.002 | ||
| Mean ± SD | 2.23 ± 1.08 | 2.68 ± 1.16 | ||||
| Median | 2 | 2.5 | ||||
| MPI–Pain-Related Interference‡ (range: 0–6) | 88 | 200 | −0.15 | 0.24 | ||
| Mean ± SD | 2.03 ± 1.60 | 2.26 ± 1.46 | ||||
| Median | 2 | 2 |
Effect size = difference between means/pooled standard deviation.
Two samples t-test.
Total CIRS = Total Cumulative Illness Rating Scale score, does not include musculoskeletal system, the higher the score, the greater the number of comorbid conditions, Self-Rated Health Score; the higher the score the better the evaluation; MPQ = McGill Pain Questionnaire; the higher the score the worse the pain; MPQ VAS = McGill Pain Questionnaire Visual Analog Scale; the higher the score the worse the pain; MPI = Multidimensional Pain Inventory; the higher the score the worse the pain.
SD = standard deviation.
Table 3.
Psychological measures by pain type
| Characteristics | N | Knee Pain | N | Chronic Low-Back Pain | Effect Size* | P Value† |
|---|---|---|---|---|---|---|
| CSQ-Catastrophizing Scale‡ (range 0–6) | 88 | 200 | −0.32 | 0.005 | ||
| Mean ± SD | 0.46 ± 0.73 | 0.77 ± 1.04 | ||||
| Median | 0 | 0.3 | ||||
| FAB‡ (range: 0–6) | 88 | 200 | −0.29 | 0.02 | ||
| Mean ± SD | 2.36 ± 1.44 | 2.82 ± 1.58 | ||||
| Median | 2.25 | 2.75 | ||||
| CPSS Score‡ (range: 0–100) | 88 | 78.18 ± 13.3 | 200 | 72.87 ± 17.33 | 0.32 | 0.005 |
| Mean ± SD | 8 | 75.3 | ||||
| Median | 80.9 | |||||
| Total GDS Score‡ (range: 0–30) | 88 | 200 | −0.29 | 0.01 | ||
| Mean ± SD | 3.26 ± 3.80 | 4.54 ± 4.52 | ||||
| Median | 2 | 3 | ||||
| MPI–affective distress‡ (range: 0–6) | 88 | 200 | 0.08 | 0.5 | ||
| Mean ± SD | 1.63 ± 1.19 | 1.52 ± 1.44 | ||||
| Median | 1.5 | 1 |
Effect size = difference between means/pooled SD.
Two samples t-test.
Total CSQ = Cognitive Strategies Questionnaire (the higher the score, the more the catastrophizing); FAB = Fear Avoidance and Beliefs Questionnaire (the higher the score, the more fear related to activity); CPSS = Chronic Pain Self-Efficacy Scale; GDS = Geriatric Depression Scale (the higher the score, the more the depression); MPI = Multidimensional Pain Inventory (the higher the score, the more the affective distress); PSQ = Pittsburgh Sleep Questionnaire.
SD = standard deviation.
Comparison of Both Groups by Gait Speed
The comparison of gait speed for different patient characteristics by whether they have knee pain or CLBP is shown in Table 4. This table shows which characteristics should be included in the model that are associated with gait speed and whether or not an interaction with pain area should also be included. For both groups, gait speed decreased in females and being widowed, and with age and lower educational level. While BMI category was not significantly different, we included it as a covariate in subsequent gait speed prediction models for face validity and its association with physical performance tests [33].
Table 4.
Characteristics of pain type by gait speed (m/s)
| Gait Speed | Gait Speed | |||||||
|---|---|---|---|---|---|---|---|---|
| Knee Osteoarthritis Patients |
Chronic Low Back Pain Patients |
|||||||
| Characteristics | N | Mean ± SD | Median | P Value* | N | Mean ± SD | Median | P Value* |
| Age categories | ||||||||
| 65–74 | 62 | 1.03 ± 0.19 | 1.03 | <0.001 | 113 | 0.94 ± 0.19 | 0.92 | <0.001 |
| 75–84 | 24 | 0.80 ± 0.17 | 0.79 | 79 | 0.81 ± 0.19 | 0.84 | ||
| 85+ | 2 | 0.57 ± 0.10 | 0.57 | 8 | 0.66 ± 0.30 | 0.67 | ||
| Gender | ||||||||
| Female | 48 | 0.92 ± 0.24 | 0.90 | 0.025 | 114 | 0.82 ± 0.20 | 0.82 | <0.001 |
| Male | 40 | 1.02 ± 0.17 | 1.02 | 86 | 0.96 ± 0.18 | 0.95 | ||
| Ethnicity | ||||||||
| White | 82 | 0.96 ± 0.21 | 0.97 | 0.762 | 179 | 0.89 ± 0.20 | 0.89 | 0.071 |
| African American | 4 | 0.99 ± 0.32 | 1.07 | 21 | 0.80 ± 0.23 | 0.83 | ||
| Educational level† | ||||||||
| Low | 30 | 0.84 ± 0.20 | 0.84 | 0.001 | 65 | 0.81 ± 0.20 | 0.84 | <0.001 |
| Medium | 15 | 0.99 ± 0.18 | 1.02 | 72 | 0.87 ± 0.20 | 0.84 | ||
| High | 43 | 1.03 ± 0.21 | 1.03 | 63 | 0.96 ± 0.21 | 0.98 | ||
| Marital status | ||||||||
| Single | 3 | 1.12 ± 0.13 | 1.05 | 0.022 | 12 | 0.94 ± 0.24 | 0.88 | 0.003 |
| Separate or divorced | 8 | 0.99 ± 0.16 | 0.99 | 23 | 0.86 ± 0.24 | 0.82 | ||
| Married | 55 | 0.99 ± 0.20 | 1.00 | 116 | 0.91 ± 0.19 | 0.90 | ||
| Widowed | 22 | 0.85 ± 0.24 | 0.84 | 49 | 0.79 ± 0.21 | 0.82 | ||
| BMI categories | ||||||||
| Normal (<25 kg/m2) | 14 | 0.98 ± 0.28 | 0.93 | 0.079 | 46 | 0.94 ± 0.18 | 0.91 | 0.089 |
| Overweight (25–30 kg/m2) | 27 | 1.03 ± 0.22 | 1.07 | 88 | 0.86 ± 0.22 | 0.88 | ||
| Obese (≥30 kg/m2) | 47 | 0.92 ± 0.18 | 0.93 | 66 | 0.85 ± 0.20 | 0.84 | ||
One way ANOVA.
Low: includes ≤9th, 10th–12th and high school graduate; Medium: includes technical school and some college; High: includes college degree and masters or above.
SD = standard deviation; BMI = body mass index.
Regression Results
The results of the univariate linear regression analysis of gait speed for either the knee pain or CLBP group by the psychological or physical variables are shown in Table 5. For patients with knee pain, the MPQ total score and the MPI–Pain-Related Interference were significant (P = 0.025 and P = 0.017, respectively). For patients with CLBP, the CIRS, MPI–Pain Severity, and MPI–Pain-Related Interference remained significant (P = 0.038, P < 0.001, P < 0.001, respectively). Regarding the psychological variables, most were significantly correlated with gait speed. The variables with the highest correlations were the CPSS (knee: r = 0.49, CLBP: r = 0.44) and GDS scores for the patients with knee pain (r = −0.42).
Table 5.
Univariate linear regression of gait speed (m/s) by psychological and physical measures for each pain type
| Knee Osteoarthritis |
Chronic Low Back Pain |
|||||
|---|---|---|---|---|---|---|
| Characteristics | β (se [β]) | r* | P Value | β (se [β]) | r* | P Value |
| CSQ-Catastrophizing Scale | −0.0842 (0.0306) | −0.28 | 0.007 | −0.0334 (0.0140) | −0.17 | 0.018 |
| FAB | −0.0349 (0.0158) | −0.23 | 0.030 | −0.0312 (0.0091) | −0.24 | 0.001 |
| CPSS Score | 0.0079 (0.0015) | 0.49 | <0.001 | 0.0053 (0.0008) | 0.44 | <0.001 |
| Total GDS Score | −0.0236 (0.0056) | −0.42 | 0.000 | −0.0057 (0.0032) | −0.12 | 0.079 |
| MPI–affective distress | −0.0240 (0.0193) | −0.30 | 0.217 | −0.0106 (0.0102) | −0.07 | 0.300 |
| CIRS | −0.0165 (0.0182) | −0.10 | 0.368 | −0.0174 (0.0083) | −0.15 | 0.038 |
| Self-Rated Health Score | 0.0791 (0.0302) | 0.27 | 0.011 | 0.0719 (0.0190) | 0.26 | 0.000 |
| Pain duration (years) | 0.0012 (0.0032) | 0.04 | 0.709 | 0.0006 (0.0010) | 0.04 | 0.575 |
| Total MPQ Score | −0.0062 (0.0027) | −0.24 | 0.025 | −0.0026 (0.0020) | −0.09 | 0.202 |
| MPQ VAS Score | −0.0001 (0.0011) | −0.00 | 0.984 | −0.0013 (0.0007) | −0.13 | 0.077 |
| MPI–Pain Severity Score | −0.0353 (0.0213) | −0.18 | 0.101 | −0.0591 (0.0120) | −0.33 | <0.001 |
| MPI–Pain-Related Interference | −0.0343 (0.0141) | −0.25 | 0.017 | −0.0416 (0.0097) | −0.29 | <0.001 |
Pearson correlation coefficient.
CSQ = Cognitive Strategies Questionnaire; FAB = Fear Avoidance and Beliefs Questionnaire; CPSS = Chronic Pain Self-Efficacy Scale; GDS = Geriatric Depression Scale; MPI = Multidimensional Pain Inventory; CIRS = Cumulative Illness Rating Scale; MPQ VAS = McGill Pain Questionnaire Visual Analog Scale.
Before determining the best prediction model, for those knee pain and CLBP significant univariate measures (P < 0.1), we decided to assess whether the interaction of pain group with each of the other measures was a significant predictor of gait speed. Regression models of gait speed were run with pain group, the measure, and the group × measure interaction term as predictors. Only the GDS score demonstrated a significant interaction. This means that, in individuals with high GDS scores, gait speed was significantly lower for the participants with knee pain than those with back pain. When the GDS score indicated no depressive symptoms, the participants with knee pain are associated with faster gait speeds. However, it should be noted that there were two participants with high GDS values that highly influenced the results, and, when removed from analysis, the interaction was no longer significant. Consequently, we chose not to include the interaction term when developing the full model described below.
Finally, to see which combination of measures would result in the best prediction model, we then ran multiple regression analyses on the psychological and physical variables that remained significant in the univariate analysis (as well as BMI) and added the study group (0 = knee, 1 = CLBP). Results are reported by using standardized beta coefficients to reveal the relative importance of each predictor in the model. Controlling for age, education, BMI, and gender (each remained in the model after backward elimination), the significant predictors remaining included the MPI–Pain Severity Score (β = −0.1420), CPSS (β = 0.2768), and CIRS (dichotomized) (β = −0.0894). The resultant model had a R2 = 0.43, indicating that 43% of the variability in gait speed could be explained by the model.
Discussion
This is the first study to find that patients with CLBP and painful knee OA have different psychological and physical profiles, with the strain of illness more evident in older adults with CLBP. We also found that self-efficacy, pain intensity, and comorbidity burden are associated with gait speed regardless of whether older adults have CLBP or chronic knee pain.
Self-efficacy, pain severity, and increasing comorbidities were associated with gait speed regardless of the location of the pain. Thus, increased self-efficacy was associated with faster gait speed in both groups of patients, while increased pain intensity and number of comorbid conditions were associated with slower gait speed. Self-efficacy has emerged as an important psychological trait that predicts improvement in disability and chronic pain in treatment programs [8,34]. As self-efficacy correlated with gait speed independent of pain location, it suggests that self-efficacy is also an important psychological trait that may impact physical function in older adults with chronic pain. Our findings are consistent with Maly et al. [35] who found that self-efficacy beliefs mediated walking performance among older adults with knee OA.
The physical and psychological profiles of older adults with chronic knee pain vs patients with CLBP suggest that the latter bear a significantly higher burden of illness. We have found this to be true in our clinical practice, but this is the first study to our knowledge that shows a difference among older adults with different chronic pain conditions. These data suggest that pain syndromes may vary in their psychological impact, and this should be taken into account when treating patients. A summary question that we have recommended using when interviewing patients is “Has pain interfered with your energy, mood, personality, or relationships with other people?” [36]. The possibility that the psychological profile of patients may vary by pain condition has been looked at in younger populations. For example, among patients with acute LBP and acute shoulder pain, LBP was more strongly associated with catastrophizing and fear-avoidance beliefs than acute shoulder pain [9]. Another study found that patients with acute LBP were found to be more depressed than patients with acute jaw pain [10]. Among patients with chronic pain, affective distress was present in all the conditions studied but more pronounced in patients with cervical pain than in those with lumbar or lower extremity pain [37].
Differential expression of psychological characteristics in different pain conditions may result in the application of interventions that appear to be ineffective when, in reality, the effects are attenuated because of the inclusion of heterogeneous pain conditions [38]. This is illustrated by two recent treatment studies, one by Kroenke et al. [39], who found that patients with heterogeneous pain conditions and comorbid depression showed improvement in their depression but not their pain, and one by Lin et al. [40], who found that patients with arthritis as the cause of their chronic pain showed improvement in both depression and pain. This suggests that clinical trials of chronic pain and comorbid depression need to account for the type of pain condition present, and the differential responsiveness of different pain conditions as well as their associated comorbidity could lead to different treatment outcomes depending upon the mix of pain conditions studied. Our results support this possibility as the psychological profiles of older patients with knee OA and CLBP differ.
The strengths of our analysis include the detailed physical, psychological, and clinical data on patients with knee OA and CLBP, two conditions commonly seen in older adults. We were able to compare the two groups across studies because the inclusion and exclusion criteria for pain intensity, frequency, and duration were the same for both studies, thus minimizing nonrandomization bias. We used gait speed as our outcome variable, which is a well-known and validated performance measure in older adults. Our study is limited by its cross-sectional design; therefore, the direction of causality cannot be inferred from our analyses. The measures obtained showed a modest range of scores, indicating that participants were fairly high functioning both physically and psychologically. For example, a gait speed of ≥1 m/s is associated with a lower risk for health-related outcomes among normally functioning older adults [41], and, in our sample, the gait speed was 0.96 m/s for participants with knee OA and 0.88 m/s for the CLBP group. As the study participants were higher functioning, the results may not be generalizable to more frail older adults. Additionally, as participants with knee OA had advanced disease (the majority [80%] had a Kellgren–Lawrence score of 4 on their x-ray), the results may not be applicable to patients with knee OA with less severe disease. There was also lack of diversity in race and ethnicity. We cannot exclude radiographic knee OA in the CLBP group. There may have been motivational or fear of failing differences between the two groups to account for the difference in gait speed.
In conclusion, we found that older adults with chronic pain have distinct psychological and physical profiles, with the burden of illness appearing greater for patients with CLBP. Additionally, self-efficacy, pain intensity, and comorbid condition burden were associated with gait speed in both groups. These findings bring to light the presence of distinct psychological characteristics in patients with different types of pain conditions, which may differentially impact function and in turn should be taken into consideration when health professionals develop treatment plans.
Acknowledgments
This research was supported by research grants (R01AG018299, R21AG24288) from the National Center for Complementary and Alternative Medicine and the National Institute on Aging, National Institutes of Health. During the time of this work, Drs. Morone and Karp were funded by the NIH Roadmap Multidisciplinary Clinical Research Career Development Award Grant (1KL2RR024154-03) from the NIH. This publication was also made possible by Grant Number UL1RR024153 from the National Center for Research Resources (NCRR), a component of the NIH and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Dr. Lynch was supported by grant T32 AG 021885-05 from the NIH.
References
- 1.Gibson SJ. IASP global year against pain in older persons: Highlighting the current status and future perspectives in geriatric pain. Expert Rev Neurother. 2007;7:627–35. doi: 10.1586/14737175.7.6.627. [DOI] [PubMed] [Google Scholar]
- 2.Bressler HB, Keyes WJ, Rochon PA, Badley E. The prevalence of low back pain in the elderly. A systematic review of the literature. Spine. 1999;24:1813–9. doi: 10.1097/00007632-199909010-00011. [DOI] [PubMed] [Google Scholar]
- 3.Weiner DK, Kim YS, Bonino P, Wang T. Low back pain in older adults: Are we utilizing healthcare resources wisely? Pain Med. 2006;7:143–50. doi: 10.1111/j.1526-4637.2006.00112.x. [DOI] [PubMed] [Google Scholar]
- 4.Dillon CF, Rasch EK, Gu Q, Hirsch R. Prevalence of knee osteoarthritis in the United States: Arthritis data from the Third National Health and Nutrition Examination Survey 1991–94. J Rheumatol. 2006;33:2271–9. [PubMed] [Google Scholar]
- 5.Guccione AA, Felson DT, Anderson JJ, et al. The effects of specific medical conditions on the functional limitations of elders in the Framingham Study. Am J Public Health. 1994;84:351–8. doi: 10.2105/ajph.84.3.351. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Dolce JJ, Crocker MF, Moletteire C, Doleys DM. Exercise quotas, anticipatory concern and self-efficacy expectancies in chronic pain: A preliminary report. Pain. 1986;24:365–72. doi: 10.1016/0304-3959(86)90122-3. [DOI] [PubMed] [Google Scholar]
- 7.Kores RC, Murphy WD, Rosenthal TL, Elias DB, North WC. Predicting outcome of chronic pain treatment via a modified self-efficacy scale. Behav Res Ther. 1990;28:165–9. doi: 10.1016/0005-7967(90)90029-i. [DOI] [PubMed] [Google Scholar]
- 8.Dolce JJ. Self-efficacy and disability beliefs in behavioral treatment of pain. Behav Res Ther. 1987;25:289–99. doi: 10.1016/0005-7967(87)90007-6. [DOI] [PubMed] [Google Scholar]
- 9.Van Der Windt DA, Kuijpers T, Jellema P, Van Der Heijden GJ, Bouter LM. Do psychological factors predict outcome in both low-back pain and shoulder pain? Ann Rheum Dis. 2007;66:313–9. doi: 10.1136/ard.2006.053553. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Edwards D, Gatchel R, Adams L, Stowell AW. Emotional distress and medication use in two acute pain populations: Jaw and low back. Pain Pract. 2006;6:242–53. doi: 10.1111/j.1533-2500.2006.00093.x. [DOI] [PubMed] [Google Scholar]
- 11.Tiedemann A, Sherrington C, Lord SR. Physiological and psychological predictors of walking speed in older community-dwelling people. Gerontology. 2005;51:390–5. doi: 10.1159/000088703. [DOI] [PubMed] [Google Scholar]
- 12.Reid MC, Williams CS, Gill TM. The relationship between psychological factors and disabling musculoskeletal pain in community-dwelling older persons. J Am Geriatr Soc. 2003;51:1092–8. doi: 10.1046/j.1532-5415.2003.51357.x. [DOI] [PubMed] [Google Scholar]
- 13.Weiner DK, Rudy TE, Morone N, Glick R, Kwoh CK. Efficacy of periosteal stimulation therapy for the treatment of osteoarthritis-associated chronic knee pain: An initial controlled clinical trial. J Am Geriatr Soc. 2007;55:1541–7. doi: 10.1111/j.1532-5415.2007.01314.x. [DOI] [PubMed] [Google Scholar]
- 14.Roland M, Morris R. A study of the natural history of back pain. Part I: Development of a reliable and sensitive measure of disability in low-back pain. Spine. 1983;8:141–4. doi: 10.1097/00007632-198303000-00004. [DOI] [PubMed] [Google Scholar]
- 15.Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49:M85–94. doi: 10.1093/geronj/49.2.m85. [DOI] [PubMed] [Google Scholar]
- 16.Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent disability: Consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci. 2000;55:M221–31. doi: 10.1093/gerona/55.4.m221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Judge JO, Schechtman K, Cress E. The relationship between physical performance measures and independence in instrumental activities of daily living. The FICSIT Group. Frailty and injury: Cooperative studies of intervention trials. J Am Geriatr Soc. 1996;44:1332–41. doi: 10.1111/j.1532-5415.1996.tb01404.x. [DOI] [PubMed] [Google Scholar]
- 18.Potter JM, Evans AL, Duncan G. Gait speed and activities of daily living function in geriatric patients. Arch Phys Med Rehabil. 1995;76:997–9. doi: 10.1016/s0003-9993(95)81036-6. [DOI] [PubMed] [Google Scholar]
- 19.Marks R. Reliability and validity of self-paced walking time measures for knee osteoarthritis. Arthritis Care Res. 1994;7:50–3. doi: 10.1002/art.1790070111. [DOI] [PubMed] [Google Scholar]
- 20.Rosenstiel AK, Keefe FJ. The use of coping strategies in chronic low back pain patients: Relationship to patient characteristics and current adjustment. Pain. 1983;17:33–44. doi: 10.1016/0304-3959(83)90125-2. [DOI] [PubMed] [Google Scholar]
- 21.Waddell G, Newton M, Henderson I, Somerville D, Main CJ. A Fear-Avoidance Beliefs Questionnaire (FABQ) and the role of fear-avoidance beliefs in chronic low back pain and disability. Pain. 1993;52:157–68. doi: 10.1016/0304-3959(93)90127-B. [DOI] [PubMed] [Google Scholar]
- 22.Crombez G, Vlaeyen JW, Heuts PH, Lysens R. Pain-related fear is more disabling than pain itself: Evidence on the role of pain-related fear in chronic back pain disability. Pain. 1999;80:329–39. doi: 10.1016/s0304-3959(98)00229-2. [DOI] [PubMed] [Google Scholar]
- 23.Bandura A. Recycling misconceptions of perceived self-efficacy. Cognit Ther Res. 1984;8(3):231–55. [Google Scholar]
- 24.Anderson KO, Dowds BN, Pelletz RE, Edwards WT, Peeters-Asdourian C. Development and initial validation of a scale to measure self-efficacy beliefs in patients with chronic pain. Pain. 1995;63:77–84. doi: 10.1016/0304-3959(95)00021-J. [DOI] [PubMed] [Google Scholar]
- 25.Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1982;17:37–49. doi: 10.1016/0022-3956(82)90033-4. [DOI] [PubMed] [Google Scholar]
- 26.Kerns RD, Turk DC, Rudy TE. The West Haven-Yale Multidimensional Pain Inventory (WHYMPI) Pain. 1985;23:345–56. doi: 10.1016/0304-3959(85)90004-1. [DOI] [PubMed] [Google Scholar]
- 27.Brown M, Sinacore DR, Binder EF, Kohrt WM. Physical and performance measures for the identification of mild to moderate frailty. J Gerontol A Biol Sci Med Sci. 2000;55:M350–5. doi: 10.1093/gerona/55.6.m350. [DOI] [PubMed] [Google Scholar]
- 28.Linn BS, Linn MW, Gurel L. Cumulative illness rating scale. J Am Geriatr Soc. 1968;16:622–6. doi: 10.1111/j.1532-5415.1968.tb02103.x. [DOI] [PubMed] [Google Scholar]
- 29.Farrell MJ, Gerontol M, Gibson SJ, Helme RD. The effect of medical status on the activity level of older pain clinic patients. J Am Geriatr Soc. 1995;43:102–7. doi: 10.1111/j.1532-5415.1995.tb06373.x. [DOI] [PubMed] [Google Scholar]
- 30.Idler EL, Benyamini Y. Self-rated health and mortality: A review of twenty-seven community studies. J Health Soc Behav. 1997;38:21–37. [PubMed] [Google Scholar]
- 31.Reyes-Gibby CC, Aday L, Cleeland C. Impact of pain on self-rated health in the community-dwelling older adults. Pain. 2002;95:75–82. doi: 10.1016/s0304-3959(01)00375-x. [DOI] [PubMed] [Google Scholar]
- 32.Melzack R. The short-form McGill Pain Questionnaire. Pain. 1987;30:191–7. doi: 10.1016/0304-3959(87)91074-8. [DOI] [PubMed] [Google Scholar]
- 33.Sternfeld B, Ngo L, Satariano WA, Tager IB. Associations of body composition with physical performance and self-reported functional limitation in elderly men and women. Am J Epidemiol. 2002;156:110–21. doi: 10.1093/aje/kwf023. [DOI] [PubMed] [Google Scholar]
- 34.Marks R, Allegrante JP, Lorig K. A review and synthesis of research evidence for self-efficacy-enhancing interventions for reducing chronic disability: Implications for health education practice (part I) Health Promot Pract. 2005;6:37–43. doi: 10.1177/1524839904266790. [DOI] [PubMed] [Google Scholar]
- 35.Maly MR, Costigan PA, Olney SJ. Self-efficacy mediates walking performance in older adults with knee osteoarthritis. J Gerontol A Biol Sci Med Sci. 2007;62:1142–6. doi: 10.1093/gerona/62.10.1142. [DOI] [PubMed] [Google Scholar]
- 36.Weiner DK, Herr K. Comprehensive assessment & interdisciplinary treatment planning: An integrative overview. In: Weiner DK, Herr K, Rudy TE, editors. Persistent Pain in Older Adults: An Interdisciplinary Guide for Treatment. New York: Springer Publishing Company; 2002. pp. 19–57. [Google Scholar]
- 37.Porter-Moffitt S, Gatchel RJ, Robinson RC, et al. Biopsychosocial profiles of different pain diagnostic groups. J Pain. 2006;7:308–18. doi: 10.1016/j.jpain.2005.12.003. [DOI] [PubMed] [Google Scholar]
- 38.Gureje O. Treating chronic pain in the context of comorbid depression. Pain. 2008;134:3–4. doi: 10.1016/j.pain.2007.10.029. [DOI] [PubMed] [Google Scholar]
- 39.Kroenke K, Shen J, Oxman TE, Williams JW, Jr, Dietrich AJ. Impact of pain on the outcomes of depression treatment: Results from the RESPECT trial. Pain. 2008;134:209–15. doi: 10.1016/j.pain.2007.09.021. [DOI] [PubMed] [Google Scholar]
- 40.Lin EH, Katon W, Von Korff M, et al. Effect of improving depression care on pain and functional outcomes among older adults with arthritis: A randomized controlled trial. JAMA. 2003;290:2428–34. doi: 10.1001/jama.290.18.2428. [DOI] [PubMed] [Google Scholar]
- 41.Cesari M, Kritchevsky SB, Penninx BW, et al. Prognostic value of usual gait speed in well-functioning older people—Results from the Health, Aging and Body Composition Study. J Am Geriatr Soc. 2005;53:1675–80. doi: 10.1111/j.1532-5415.2005.53501.x. [DOI] [PubMed] [Google Scholar]