Poor Sleep Quality Is Strongly Associated With

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ARTHRITIS & RHEUMATOLOGY Vol. 66, No. 5, May 2014, pp 1388–1394 DOI 10.1002/art.38329 © 2014, American College of Rheumatology

Poor Sleep Quality Is Strongly Associated With Subsequent Pain Intensity in Patients With Acute Low Back Pain Saad M. Alsaadi,1 James H. McAuley,2 Julia M. Hush,3 Serigne Lo,4 Chung-Wei Christine Lin,4 Christopher M. Williams,4 and Christopher G. Maher4 Results. The GEE analysis demonstrated a large effect of poor sleep on subsequent pain intensity, such that for every 1-point decrease in sleep quality (based on a 0–3-point scale), pain intensity (based on a 0–10point scale) increased by 2.08 points (95% confidence interval 1.99ⴚ2.16). This effect was independent of depression and common prognostic factors for low back pain. Conclusion. Sleep quality is strongly related to subsequent pain intensity in patients with acute low back pain. Future research is needed to determine whether targeting sleep improvement contributes to pain reduction.

Objective. Recent research suggests that sleep quality and pain intensity are intimately linked. Although sleep problems are common in patients with low back pain, the effect of sleep quality on the levels of pain intensity is currently unknown. The aim of this study was to investigate the effect of sleep quality on subsequent pain intensity in patients with recent-onset low back pain. Methods. Data on 1,246 patients with acute low back pain were included in the analysis. Sleep quality was assessed using the sleep quality item of the Pittsburgh Sleep Quality Index, scored on a 0–3-point scale, where 0 ⴝ very good sleep quality and 3 ⴝ very bad sleep quality. Pain intensity was assessed on a numerical rating scale (range 0–10). A generalized estimating equation (GEE) analysis modeled with an exchangeable correlation structure was used to examine the relationship between sleep quality and pain intensity. The model further controlled for symptoms of depression and prognostic factors for low back pain.

Low back pain is a major global health problem (1). Despite the expenditure of billions of dollars and the publication of thousands of research articles, low back pain is the leading cause of disability worldwide, and management is only moderately effective (2,3). Epidemiologic studies have shown that a large proportion of patients with low back pain also present with comorbidities such as depression and anxiety, which along with other psychological factors can exacerbate the condition and complicate management (4,5). Interventions based on these findings have led to moderate reductions in pain and disability (6,7). Antidepressant drugs (e.g., heterocyclics and tricyclics) have been shown to significantly reduce pain and improve disability in patients with chronic low back pain, relative to placebo (8,9). Such medications are thought to increase the number of neurotransmitters released at the spinal cord level and reduce the patient’s fear of movement related to low back pain (10). Recently, sleep problems have also been identified as common in patients with low back pain; 50–60% of patients with either acute or chronic low back pain report problems with their sleep (11–13). Despite

The study reported herein used data from a consecutive subset of participants from the PACE study, an investigator-initiated study funded by the National Health and Medical Research Council, Australia, with supplementary industry funding. The current study did not receive any funding. Dr. Maher is recipient of an Australian Research Council Fellowship. 1 Saad M. Alsaadi, PhD: King Fahd University Hospital and the University of Dammam, Khobar, Saudi Arabia; 2James H. McAuley, PhD: Neuroscience Research Australia and University of New South Wales, Sydney, New South Wales, Australia; 3Julia M. Hush, PhD: Macquarie University, Sydney, New South Wales, Australia; 4Serigne Lo, PhD, Chung-Wei Christine Lin, PhD, Christopher M. Williams, PhD, Christopher G. Maher, PhD: The George Institute for Global Health and University of Sydney, Sydney, New South Wales, Australia. Address correspondence to Saad Alsaadi, PhD, Department of Physical Therapy, King Fahd University Hospital, University of Dammam, PO Box 40035, Khobar 31952, Saudi Arabia. E-mail: [email protected]. Submitted for publication February 5, 2013; accepted in revised form December 17, 2013. 1388

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the high prevalence of sleep disturbance in the population of patients with low back pain, research has been limited. Sleep is essential, and its disturbance can lead to serious consequences (14). With regard to pain, recent evidence suggests that sleep can modulate the intensity of pain (15). Experimental studies in healthy volunteers (without pain) have demonstrated that induced sleep deprivation, via either a reduction in sleep duration or disruption of sleep architecture, leads to the development of musculoskeletal pain and increased pain sensitivity to noxious stimuli (16–18). Clinical studies in patients with cancer (19) and patients with rheumatoid arthritis (20) have shown a significant correlation between pain intensity and reported sleep quality. In addition, longitudinal studies have identified a bidirectional relationship between sleep and pain, such that a night of poor sleep quality was followed by a day with more severe pain, which consequently worsened sleep quality during the subsequent night (21,22). Sleep recovery after a period of induced sleep deprivation in healthy volunteers has also been shown to produce an analgesic effect similar to that induced by nonsteroidal antiinflammatory drugs (23). Furthermore, improved sleep quality in patients with painful conditions, such as osteoarthritis (24) and chronic musculoskeletal pain (25), is significantly associated with reductions in pain intensity. Finally, previous research also indicates a strong association between sleep disturbance and the development of musculoskeletal pain. A Finnish study demonstrated that sleep disturbance is a strong predictor of the development of low back pain among adolescents (26). Likewise, a recent study showed that women ages ⱖ45 years who reported sleep problems are 5 times more likely to develop fibromyalgia compared with those who do not report sleep problems (27). Taken together, these findings imply that disturbed sleep has an important influence on pain and may hinder treatment effectiveness. Studies investigating the impact of sleep disturbance on the intensity of low back pain have shown a significant association between the quality of sleep and pain intensity, fatigue, subsequent-day function, and psychological distress (28). It has also been reported that persons with low back pain who have sleep problems and more severe pain (11) are at higher risk of being hospitalized for care of low back pain than those with good sleep quality (29). These findings suggest that poor sleep quality may be associated with exacerbations of low back pain; however, to date, no direct association has been measured over the course of low back pain.

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Both pain intensity and sleep quality fluctuate over time (30,31). As a consequence, identifying the effect of sleep quality on pain intensity in the population of patients with low back pain is severely hampered by the paucity of longitudinal data. Currently, it remains uncertain whether levels of pain intensity are dependent on sleep quality at different time points throughout the course of low back pain. Accordingly, the present study aimed to determine whether poor sleep quality is associated with subsequent increases in pain intensity in patients assessed during an episode of acute low back pain. PATIENTS AND METHODS Study design overview. This study used data from a consecutive subset of participants in the PACE study, a randomized double-dummy, placebo-controlled trial evaluating paracetamol (acetaminophen) for the treatment of acute low back pain. The PACE study methods have been published elsewhere (32). Briefly, the PACE study aimed to enroll 1,650 individuals seeking care for acute low back pain. All participants received guideline-recommended advice and were randomized to receive study medication containing either active paracetamol or placebo. Participants were asked to take the study medication until “recovery from back pain” or for a maximum of 4 weeks after randomization. Followup lasted for 12 weeks. Ethics approval for the PACE study was obtained from the University of Sydney Human Research Ethics Committee. Participants. We used data on all participants in the PACE study who had completed the 12-week followup period prior to the date of this analysis (October 12, 2012). Because the PACE study had not been completed at this time, participants from all treatment groups were used, and investigators remained blinded to group assignment. The inclusion criteria were as follows: primary symptom of pain in the area between the twelfth rib and the buttock crease, with or without leg pain, for ⬍6 weeks; experiencing a new episode of low back pain, preceded by a period of at least 1 month without low back pain, and moderate intensity of pain as measured by an adaptation of item 7 of the Short Form 36 (33), where the original phrasing of the item “how much bodily pain . . .” was changed to “how much back pain . . .” to reflect the study interest in back pain. The exclusion criteria were as follows: low back pain caused by serious spinal pathology (e.g., metastatic, infective, or inflammatory spinal disease, cauda equina syndrome, spinal fracture); receiving recommended doses of analgesics (based on Australian guidelines for dosing of pain medicine) (34); spinal surgery within the preceding 6 months; serious comorbidities preventing treatment with paracetamol (e.g., renal or liver failure); currently receiving psychotropic medication for a mental health condition that remained uncontrolled; or pregnant or intending to become pregnant during the study period. Assessments. A baseline questionnaire was completed during a telephone interview, to measure demographic characteristics, pain intensity, global impression of recovery, disability, sleep quality, and symptoms of depression. All partic-

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ipants were asked to record subsequent weekly outcome data, including pain intensity, global impression of recovery, disability, and sleep quality, in their outcome assessment booklet. Participants could then elect to provide researchers with data over the telephone or via an online database. The mode of data reporting (by telephone or online) could vary for a participant between data collection time points. Sleep assessment. Sleep quality was assessed using the sleep quality item (item 6) of the Pittsburgh Sleep Quality Index (PSQI) (35). The item was modified to evaluate sleep quality over the past 7 days instead of the past 4 weeks. Each participant was asked to rate his/her sleep quality based on the following question: “During the past week, how would you rate your sleep quality overall?” The response categories were as follows: 0 ⫽ very good, 1 ⫽ fairly good, 2 ⫽ fairly bad, and 3 ⫽ very bad. Pain assessment. Participants were asked to rate average pain over the last 24 hours on a 0–10-point numerical rating scale (NRS), where 0 ⫽ no pain and 10 ⫽ worst possible pain. The NRS is widely used to measure pain and has been shown to be valid and reliable (36). Low back pain prognostic factors. The following prognostic factors were collected at baseline and included in the multivariate analysis, because they have been suggested to impact the prognosis for low back pain (37): age, sex, duration of current episode (number of days from the onset of pain to the time of assessment), number of previous episodes, physical disability related to low back pain, feelings of depression, risk of pain persistence, paid compensation, leg pain, paid employment, and number of days of limited activities of daily living. Physical disability was assessed using the Roland– Morris Disability Questionnaire, which is scored from 0 (no disability) to 24 (high disability) (38). Depression was assessed through the participant’s response to the question “How much have you been bothered by feeling depressed in the past 7 days?” on a 0–10-point scale, where 0 ⫽ not at all and 10 ⫽ extremely. The risk of pain persistence was assessed by the participant’s view of the probability of having persistence of current pain, based on the question “How large is the risk that your current pain may become persistent?” on a 0–10-point scale, where 0 ⫽ no risk and 10 ⫽ very large risk. Statistical analysis. Statistical analyses were performed using SPSS version 19. Continuous variables are expressed as the mean ⫾ SD, and categorical variables are expressed as frequencies and percentages. The relationship between pain intensity and sleep quality was evaluated using repeated measurements of pain intensity and sleep quality over 12 weeks. Generalized estimating equation (GEE) models (39) with an exchangeable correlation structure and robust standard errors were used to account for any correlation among the observations in a single subject. GEE models extend generalized linear models by allowing repeated measures. Both variables (pain intensity and sleep quality) are considered to be continuous. GEE regression models with Gaussian distribution were used, because the outcome (pain intensity) was assumed to be continuous. All analyses were conducted at the level of each individual measurement (i.e., baseline and 4 followup time points). In a bivariate analysis, the models were adjusted for symptoms of depression at baseline, and a multivariate analysis was adjusted for intensity of pain at baseline and the previously mentioned

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baseline prognostic factors. All P values were calculated with the 2-tailed Wald test, and the nominal alpha value was set to 5%.

RESULTS Data on 1,246 individuals with acute low back pain were included in the analysis. The demographic and clinical characteristics of the participants are presented in Table 1. The mean ⫾ SD age of the participants was 44.2 ⫾ 15.7 years, and 46% were female. The mean ⫾ SD pain intensity score at baseline was 6.3 ⫾ 1.9 on a 0–10-point NRS, and the mean ⫾ SD duration of low back pain was 9.8 ⫾ 9.8 days. Only 252 (20%) of the participants reported pain radiating to the leg. The mean ⫾ SD score for physical disability, as assessed by the 24-item Roland–Morris Disability Questionnaire, was 13.0 ⫾ 5.4, indicating moderate levels of disability. The mean ⫾ SD score for depression was 3.1 ⫾ 2.9 and that for anxiety associated with the risk of pain persistence was 4.5 ⫾ 2.8, as measured on a 0–10-mm scale, indicating low levels of depression and anxiety. During their first visit, 766 patients (62%) reported that their pain was either unchanged or worse compared with the level at the onset of pain. At baseline, 633 participants (51%) reported that their sleep quality was either very bad or fairly bad. Both sleep quality and pain intensity improved over the 12-week followup period, and 118

Table 1. Baseline demographic and clinical characteristics of the study participants Age, mean ⫾ SD years Duration of current symptoms, mean ⫾ SD days Pain rating, mean ⫾ SD (0–10 scale) No. of previous episodes, mean ⫾ SD Days of reduced activity, mean ⫾ SD Depression rating, mean ⫾ SD (0–10 scale)* Risk of pain persistence rating, mean ⫾ SD (0⫺10 scale) Physical disability rating, mean ⫾ SD (0–24 scale)† Female sex, no. (%) Leg pain, no. (%) Receiving compensation, no. (%) Paid employment, no. (%) Global perceived effect, no. (%) Unchanged Better Worse Sleep quality rating, no. (%) (0–3 scale)‡ Very bad Fairly bad Fairly good Very good

44.2 ⫾ 15.7 9.8 ⫾ 9.8 6.3 ⫾ 1.9 6.7 ⫾ 14.0 3.3 ⫾ 5.3 3.1 ⫾ 2.9 4.5 ⫾ 2.8 13.0 ⫾ 5.4 567 (46) 252 (20) 80 (6) 925 (74) 375 (30) 476 (38) 391 (32) 135 (11) 498 (40) 506 (41) 104 (8)

* Feelings of depression during the past 7 days. † Assessed using the Roland–Morris Disability Questionnaire. ‡ Assessed using the Pittsburgh Sleep Quality Index.

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Table 2. Effect of poor sleep quality on subsequent pain ratings, using a GEE model with an exchangeable correlation*

Intercept ␤ coefficient 95% CI P Sleep quality ␤ coefficient 95% CI P

Model 1†

Model 2‡

Model 3§

1.08 0.97–1.19 0.001

0.90 0.77–1.02 0.001

0.54 0.04–1.03 0.001

2.08 1.99–2.16 ⬍0.001

2.05 1.96–2.14 ⬍0.001

2.00 1.90–2.09 ⬍0.001

* GEE ⫽ generalized estimating equation; 95% CI ⫽ 95% confidence interval. † Dependent variable pain; independent variable sleep. ‡ Dependent variable pain; independent variables sleep and symptoms of depression at baseline. § Dependent variable pain; independent variables sleep, baseline pain, disability, female sex, presence of leg pain, presence of compensation, paid employment, age, duration of current symptoms, number of previous episodes, number of days of reduced activity, and risk of persistence of pain. Figure 1. Prevalence of poor sleep quality from baseline to week 12.

subjects (10%) reported that their sleep quality was either very bad or fairly bad at week 12 (mean ⫾ SD pain intensity score 1.3 ⫾ 2.2). Figures 1 and 2, respectively, show ratings for sleep quality and pain intensity over time. The percentage of missing data for each time point (baseline and 4 followup time points) for both sleep quality and pain intensity was very low (i.e., never

Figure 2. Mean pain intensity from baseline to week 12.

⬎9%). We expected to include 6,230 assessment occasions in the GEE analysis (i.e., 1,246 participants assessed at 5 time points). Due to missing data, 5,831 assessments (93.6%) were included in the final analyses. Full details about the number and ratio of missing data for each variable included in the analysis are available from the corresponding author. The results of the GEE analysis, as shown in Table 2, demonstrated evidence of an association between sleep quality and subsequent pain intensity (P ⬍ 0.001). These findings can be interpreted that for every 1-point decrease in sleep quality (based on a 0–3-point scale), pain intensity (based on a 0–10-point NRS) increased by 2.08 points (95% CI 1.99–2.16). The association between sleep quality and subsequent pain intensity was independent of baseline symptoms of depression (␤ ⫽ 2.05 [95% CI 1.96–2.14]). The strength of the association between sleep quality and subsequent pain also remained after adjusting for important low back pain prognostic factors (age, sex, duration of current episode, number of previous episodes, physical disability, symptoms of depression, risk of persistence of pain, paid compensation, leg pain, paid employment, and days of limited activities of daily living) (␤ ⫽ 2.00 [95% CI 1.90–2.09]). The interpretation of the analysis is that, on average, a patient with a sleep quality score of 0 (very good) on a 0–3-point scale would be expected to have a pain intensity score of 1.08 on a 1–10-point NRS (the intercept), a patient with a sleep quality score of 1 (fairly good) would have a pain intensity score of 3.16, a sleep

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quality score of 2 (fairly bad) would be associated with a pain intensity score of 5.24, and a patient with a sleep quality score of 3 (very bad) would be expected to have a pain intensity score of 7.32. DISCUSSION In this study, we investigated the association between sleep quality and subsequent pain intensity in patients with acute low back pain. The results showed a strong relationship between the quality of a patient’s sleep and subsequent pain intensity that remained of the same magnitude even after controlling for symptoms of depression and important prognostic factors. The effect of poor sleep was large: for every 1-point decrease in sleep quality, pain intensity increased by ⬃2 points. This study demonstrates that sleep quality has a profound impact on the experience of pain intensity. Our data show that the difference in pain intensity among individuals with a favorable sleep quality score (0 on a 0–3-point scale) and those with a very bad sleep quality score (3 on a 0–3-point scale) was ⬃6 points on a 0–10-point NRS. This clearly indicates that untreated sleep problems in individuals with low back pain are likely to hinder pain management. The literature suggests that untreated sleep problems in patients with painful conditions can lead to a vicious circle of pain and poor-quality sleep, which adversely affects an individual’s quality of life, including social integration, physical activity, and emotional well-being (40). In fact, previous studies investigating sleep quality in patients with chronic low back pain have shown sleep quality to be associated with psychological distress and physical disability (41,42). Therefore, it seems prudent that clinicians should consider performing a sleep assessment early in the management of low back pain. Most contemporary treatments of low back pain have disappointingly small treatment effects, and a focus of research in this field is the identification of more effective treatments. Against this background, the results of this study are quite significant. The difference in pain intensity following a fairly good night’s sleep compared with a very poor night’s sleep is ⬃4 points on a 0–10-point NRS. This effect is at least double the size of the effect that is expected for the majority of low back pain treatments, such as spinal manipulative therapy or exercise therapy (43). There is evidence that sleep improvement plays a role in pain reduction of a magnitude similar to that of most common analgesic drugs (23). Clinically, good-quality sleep has been reported to contribute to the resolution of painful conditions (44).

Furthermore, treatment targeting sleep improvement in various pain conditions contributed significantly to the pain reduction. For example, cognitive behavioral therapy targeting insomnia in patients with chronic musculoskeletal pain and arthritic pain has been shown to significantly reduce pain intensity (45). Although the effect size was reported to be small to medium, it persisted for 1 year posttreatment (24). Considering the findings of the current study, future research in patients with low back pain should evaluate whether targeting sleep improvement can contribute to a reduction in pain intensity. The current study has several limitations that must be considered when interpreting the findings. First, the trial had not been completed at the time of this analysis, which makes it impossible to determine the potential effect of active treatment (paracetamol) on the study results. However, because our analyses are withinsubject, we believe that any effects are likely to be small. Second, sleep was subjectively assessed using a single item, the sleep quality item of the PSQI. Thus, it is unknown which aspects of disturbed sleep (e.g., sleep duration, latency, or fragmentation) contributed to the intensity of low back pain. In addition, retrospective evaluation of an individual’s quality of sleep for the past several days has the potential for recall bias that results from memory distortion (46). Given the order in which pain and sleep quality were assessed, it is possible that more intense pain inflated the patient’s recall or perception of sleep quality by focusing on the poor nights of sleep and therefore to underestimate sleep quality over a 7-day period. It is therefore important for future research to evaluate the daily association between sleep disturbance and low back pain, using both subjective (e.g., sleep diary) and objective (e.g., actigraph) assessments of sleep. Finally, sleep quality is a general construct that is difficult to define and complex to measure. A patient’s report of poor sleep quality can be related to factors such as disturbance of sleep parameters (e.g., difficulty falling asleep), psychological distress, tiredness on waking, or impaired daytime functioning (47). In addition, polysomnographic investigation of patients with pain has shown that pain can disturb a patient’s sleep architecture (i.e., sleep cycle), which can also be responsible for not feeling refreshed upon waking, leading to the reporting of poor sleep quality (48). Further research is required to determine the factors that are responsible for the reporting of poor sleep quality by patients with low back pain, so that management of sleep disturbance in these patients can be optimized.

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In summary, this study demonstrates a large effect of poor sleep quality on subsequent pain intensity in patients with acute low back pain. The magnitude of this effect was considerably larger than that of most contemporary treatments for low back pain. The results suggest that research into the role of interventions designed to improve sleep quality in patients with low back pain are warranted. REFERENCES 1. Balague F, Mannion AF, Pellise F, Cedraschi C. Non-specific low back pain. Lancet 2012;379:482–91. 2. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2013;380:2163–96. 3. Henschke N, Ostelo RW, van Tulder MW, Vlaeyen JW, Morley S, Assendelft WJ, et al. Behavioural treatment for chronic low-back pain. Cochrane Database Syst Rev 2010:CD002014. 4. Andersson GB. Epidemiological features of chronic low-back pain. Lancet 1999;354:581–5. 5. Chou R, Shekelle P. Will this patient develop persistent disabling low back pain? JAMA 2010;303:1295–302. 6. Bair MJ, Robinson RL, Katon W, Kroenke K. Depression and pain comorbidity: a literature review. Arch Intern Med 2003;163: 2433–45. 7. Hill JC, Whitehurst DG, Lewis M, Bryan S, Dunn KM, Foster NE, et al. Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial. Lancet 2011;378:1560–71. 8. Salerno SM, Browning R, Jackson JL. The effect of antidepressant treatment on chronic back pain: a meta-analysis. Arch Intern Med 2002;162:19–24. 9. Staiger TO, Gaster B, Sullivan MD, Deyo RA. Systematic review of antidepressants in the treatment of chronic low back pain. Spine 2003;28:2540–5. 10. Verdu B, Decosterd I, Buclin T, Stiefel F, Berney A. Antidepressants for the treatment of chronic pain. Drugs 2008;68:2611–32. 11. Alsaadi SM, McAuley JH, Hush JM, Maher CG. Erratum to: Prevalence of sleep disturbance in patients with low back pain [corrected and republished in Eur Spine J 2012;21:554–60]. Eur Spine J 2011;20:737–43. 12. Tang NK, Wright KJ, Salkovskis PM, Tang NK, Wright KJ, Salkovskis PM. Prevalence and correlates of clinical insomnia co-occurring with chronic back pain. J Sleep Res 2007;16:85–95. 13. Marty M, Rozenberg S, Duplan B, Thomas P, Duquesnoy B, Allaert F. Quality of sleep in patients with chronic low back pain: a case-control study. Eur Spine J 2008;17:839–44. 14. Orzel-Gryglewska J. Consequences of sleep deprivation. Int J Occup Med Environ Health 2010;23:95–114. 15. Haack M, Scott-Sutherland J, Santangelo G, Simpson NS, Sethna N, Mullington JM. Pain sensitivity and modulation in primary insomnia. Eur J Pain 2012;16:522–33. 16. Tiede W, Magerl W, Baumgartner U, Durrer B, Ehlert U, Treede RD. Sleep restriction attenuates amplitudes and attentional modulation of pain-related evoked potentials, but augments pain ratings in healthy volunteers. Pain 2010;148:36–42. 17. Kundermann B, Spernal J, Huber MT, Krieg JC, Lautenbacher S. Sleep deprivation affects thermal pain thresholds but not somatosensory thresholds in healthy volunteers. Psychosom Med 2004;66: 932–7.

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Errata DOI 10.1002/art.38669

In the article by Duval et al in the October 2009 issue of Arthritis & Rheumatism (pages 3038–3048), the primer sequences for the aggrecan gene, listed in the “Chromatin Immunoprecipitation (ChIP) Assay” section of Materials and Methods, were shown incorrectly. The sequences actually used in the study (taken from the human sequence of the promoter of AGC1 gene [accession no. AF031586] and corresponding to nucleotides 60–82 [forward] and 220–240 [reverse]) were as follows: forward 5⬘-AATTTGAAGACCCAGAGACTCGC, reverse 5⬘-GCTACTGTCGGCCACGATTT. DOI 10.1002/art.38673

In the article by Park et al in the December 2013 issue of Arthritis & Rheumatism (pages 3141–3152), the grant number of one of the Korea Healthcare Technology R&D Project grants was shown incorrectly. The statement in the footnotes on the first page of the article should have read, “Supported by the Ministry of Health, Welfare, and Family Affairs (Korea Healthcare Technology R&D Project grants A084026 and A110274).” DOI 10.1002/art.38653

In the article by Kuller et al in the March 2014 issue of Arthritis & Rheumatology (pages 497–507), there were several typographical errors in Table 4, column 5 (age-adjusted death rates per 1,000 person-years in the group of anti-CCP⫺negative patients with DMARD use at baseline). The corrected values for age-adjusted death rates (with 95% confidence intervals in parentheses) are as follows: general health excellent/very good 10.2 (3.2–33.6), general health good 16.7 (9.2–32.6), general health fair/poor 29.8 (14.2–62.8); no diabetes 16.8 (10.4–27.4), diabetes 27.5 (8.6–92.0); no CHD at baseline 16.7 (10.3⫺27.3), CHD at baseline 26.1 (10.2–71.6); physical function score on the SF-36 ⱕ85 17.9 (11.2–29.3), physical function score on the SF-36 ⬎85 13.5 (3.6–56.6); waist circumference ⱕ88 cm 15.5 (8.0–30.3), waist circumference ⬎88 cm 19.8 (11.1–37.7); total METs per week ⬍2.5 20.0 (10.9–36.6), total METs per week 2.5–18.24 16.9 (9.0–32.1), total METs per week ⱖ18.25 18.2 (6.7–50.6); no hypertension 18.2 (10.3–32.5), hypertension 17.5 (8.8–36.4). Additionally, there were some typographical errors in Table 4, column 4 (weighted total in the group of anti-CCP–negative patients with DMARD use at baseline). The corrected values are as follows: general health excellent/very good 64, general health good 125, general health fair/poor 55. Finally, there was a typographical error in Table 1, column 3: the correct 95% confidence interval for the age-adjusted death rate in the group of anti-CCP⫺positive patients with no diabetes was 14.0–22.7. We regret the errors.