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Original Contribution |

Chronic Musculoskeletal Pain and the Occurrence of Falls in an Older Population FREE

Suzanne G. Leveille, PhD, RN; Richard N. Jones, ScD; Dan K. Kiely, MPH; Jeffrey M. Hausdorff, PhD; Robert H. Shmerling, MD; Jack M. Guralnik, PhD, MD; Douglas P. Kiel, MD; Lewis A. Lipsitz, MD; Jonathan F. Bean, MD
[+] Author Affiliations

Author Affiliations: Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center (Drs Leveille, Jones, Shmerling, Kiel, and Lipsitz); College of Nursing and Health Sciences, University of Massachusetts-Boston (Dr Leveille); Harvard Medical School (Drs Leveille, Jones, Kiel, Hausdorff, Shmerling, Lipsitz and Bean; Institute for Aging Research, Hebrew SeniorLife (Drs Jones, Kiel, and Lipsitz and Mr Kiely); and Spaulding Rehabilitation Hospital (Dr Bean), Boston, Massachusetts; Department of Neurology, Tel-Aviv Sourasky Medical Center, and Department of Physical Therapy, Tel Aviv University (Dr Hausdorff), Tel Aviv, Israel; and Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland (Dr Guralnik).


JAMA. 2009;302(20):2214-2221. doi:10.1001/jama.2009.1738.
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Published online

Context Chronic pain is a major contributor to disability in older adults; however, the potential role of chronic pain as a risk factor for falls is poorly understood.

Objective To determine whether chronic musculoskeletal pain is associated with an increased occurrence of falls in a cohort of community-living older adults.

Design, Setting, and Participants The Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) Boston Study is a population-based longitudinal study of falls involving 749 adults aged 70 years and older. Participants were enrolled from September 2005 through January 2008.

Main Outcome Measure Participants recorded falls on monthly calendar postcards mailed to the study center during an 18-month period.

Results There were 1029 falls reported during the follow-up. A report of 2 or more locations of musculoskeletal pain at baseline was associated with greater occurrence of falls. The age-adjusted rates of falls per person-year were 1.18 (95% confidence interval [CI], 1.13-1.23) for the 300 participants with 2 or more sites of joint pain, 0.90 (95% CI, 0.87-0.92) for the 181 participants with single-site pain, and 0.78 (95% CI, 0.74-0.81) for the 267 participants with no joint pain. Similarly, more severe or disabling pain at baseline was associated with higher fall rates (P < .05). The association persisted after adjusting for multiple confounders and fall risk factors. The greatest risk for falls was observed in persons who had 2 or more pain sites (adjusted rate ratio [RR], 1.53; 95% CI, 1.17-1.99), and those in the highest tertiles of pain severity (adjusted RR, 1.53; 95% CI, 1.12-2.08) and pain interference with activities (adjusted RR, 1.53; 95%CI, 1.15-2.05), compared with their peers with no pain or those in the lowest tertiles of pain scores.

Conclusions Chronic pain measured according to number of locations, severity, or pain interference with daily activities was associated with greater risk of falls in older adults.

Figures in this Article

Falls rank among the 10 leading causes of death in older adults in the United States, resulting in more than $19 billion in health care costs annually.1,2 Despite a growing body of scientific evidence supporting associations between a number of risk factors and falls,3 efforts to translate these findings into effective fall prevention strategies have been limited.4 Perhaps one contributing factor to the limited success of multifactorial fall prevention efforts may be that some major causes of falls in older persons continue to elude researchers.

Few reports have examined chronic pain as a risk for falls in older adults,57 and none has prospectively examined multiple pain sites in relation to fall risk in the general population of older adults living in the community. Pain contributes to functional decline and muscle weakness and is associated with mobility limitations that could predispose to falls.810 In addition, neurocognitive deficits observed in elderly fallers11 are not unlike the mild cognitive deficits observed in older adults with chronic back pain,12 supporting the possibility of a central-mediated pathway whereby pain contributes to falls. Given the high prevalence of chronic pain coupled with the problem of undertreatment of chronic pain in older patients,13 it is reasonable to surmise that chronic pain could be an important contributor to falls. The Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) Boston Study (MBS) used a longitudinal cohort design to explore a set of risk factors for falls that are generally more challenging to measure with the hope of identifying new targets for fall prevention.

Study participants were women and men aged 70 years and older living in the community in Boston and nearby suburbs. Recruitment and enrollment took place from September 2005 to January 2008 within a defined geographic area bounded by a 5-mile radius from the Institute for Aging Research at the Hebrew Rehabilitation Center (HRC) in Boston. The sampling area was chosen to capture a diverse urban and suburban population, to increase likelihood of recognition of the study center, and to minimize transportation burden. Details of the study methods were published previously.14,15

Initial eligibility was based on age 70 years or older, ability to walk 20 feet without personal assistance, ability to communicate in English, and the expectation of staying in the area for 2 years. Following the initial recruitment visit, study staff contacted prospective enrollees by telephone to confirm eligibility and schedule the baseline home and clinic visits. During the home visit, written informed consent was obtained and participants were screened and excluded for moderate or severe cognitive impairment using the Mini-Mental State Examination (MMSE score, <18).16,17 All protocols for the study and consent procedures were approved by the institutional review boards of the HRC and collaborating institutions.

Falls Assessments

A fall was defined as unintentionally coming to rest on the ground or other lower level not as a result of a major intrinsic event (eg, myocardial infarction, stroke, or seizure) or an overwhelming external hazard (eg, hit by a vehicle).18 During the home visit, participants were instructed to complete and return monthly falls-calendar postcards. On the postcards, participants were to record an F for each fall on the day it occurred and an N on days when no fall occurred. This approach has been well-validated for use in epidemiological cohort studies.19 Research staff monitored the return of the calendars and on any given month, approximately one-third of participants were called for missing or incomplete calendars. Falls were assessed for up to 18 months through April 2009.

Chronic Pain Assessment

Pain was assessed according to location, overall pain severity and pain interference with daily activities, encompassing key dimensions for pain assessment recommended by the American Geriatrics Society.20 We used a 13-item joint pain questionnaire to assess chronic musculoskeletal pain in hands and wrists, shoulders, back, chest, hips, knees, and feet.21 This measure was previously associated with decline in physical function in older women.10,22 Chronic pain in each site was based on participant's report that pain was present in the previous month and present for at least 3 months in the previous year. Chest pain associated with angina was excluded, based on an algorithm used to classify angina from the Rose questionnaire23 and use of nitrates.

We classified chronic joint pain as follows: (1) pain in 2 or more locations (referred to as polyarticular pain), (2) pain in a single location, and (3) no pain. We also developed a second set of pain location measures according to each specific joint site. For example, knee pain was classified as (1) pain in the knee(s) and 1 or more other joint locations, (2) pain in the knee(s) only, and (3) no knee pain. We used 2 subscales of the Brief Pain Inventory (BPI), the 4-item pain severity subscale, and the 7-item BPI pain interference scale.24 The BPI, which measures pain in general without reference to location, was originally developed for use in cancer patients but has been validated for use in nonmalignant pain.25,26

Pain was also assessed monthly during follow-up using a single pain–rating question on the monthly fall postcards. The question, from the well-validated 36-Item Medical Outcomes Study Short Form Health Survey, was stated as follows: “In the past month, how much bodily pain have you had?” response options were “none, very mild, mild, moderate, severe, and very severe.”27

Sociodemographics, Chronic Conditions, and Fall Risk Factors

Sociodemographic characteristics assessed in the home interview included age, sex, race (self-identified), and years of education. Race was included because our prior work found that Black race was associated with polyarticular pain.22 Cognitive status was assessed using the MMSE, scored 0-30.17 We used the validated Physical Activity Scale for the Elderly (PASE) to measure physical activity in the previous week.28 Participants were asked about physician-diagnosed major medical conditions. Heart disease included report of heart attack, congestive heart failure, angina, pacemaker, or cardiac arrhythmia. Other self-reported diagnoses included stroke, Parkinson disease, rheumatoid arthritis, and spinal stenosis or disk disease. Peripheral neuropathy was assessed using Semmes-Weinstein monofilament testing.29 Peripheral arterial disease was defined using an algorithm, based on an ankle-arm index of less than 0.90, and the Rose Intermittent Claudication questionnaire.23 Diabetes was defined using an algorithm based on self-reported diabetes, use of antidiabetic medications, and laboratory measures from the baseline clinic visit including random glucose (≥200 mg/dL, to convert to mmol/L, multiply by 0.0555) and hemoglobin A1c (>7%). American College of Rheumatology (ACR) clinical criteria for osteoarthritis of the hand and knee30,31 were assessed in the clinic examination by experienced nurses trained by the study rheumatologist (R.H.S.). Depression was assessed using the Eaton method based on a modification of the 20-item Centers for Epidemiologic Studies Depression scale.32,33 Distant vision was measured at 10-feet using a letter chart, the Good-Lite Chart Model 600A. Body mass index (calculated as weight in kilograms divided by height in meters squared) was calculated from measured height and weight. Standing balance was scored using 4 timed tests (side-by-side, semi-tandem, tandem, and 1-leg stands).34 For the timed chair stands test, participants were asked to fold their arms across their chest and stand up and down from a chair 5 times as quickly as possible.34 Gait speed was based on the shortest time of 2 trials of a usual-paced 4-meter walk.

Medications

During the home visit, the interviewer recorded use of all prescription and over-the-counter medications taken in the previous 2 weeks. Active ingredients of medications were coded according to the Iowa Drug Information System ingredient codes.35 Analgesic medications included opioid and nonopioid analgesics and daily use was determined from dose and frequency information. Daily or less than daily use of 325 mg or less of aspirin, probable antithrombotic therapy, was not included as an analgesic. Psychotherapeutic agents, including sedative, hypnotic, anxiolytic, antidepressant, and antipsychotic medications, were categorized as use of 2 or more daily, 1 daily, nondaily use, and no use.

Analysis

We planned to enroll 800 participants to have 648 evaluable participants at the end of follow-up, accounting for possible attrition. Assuming the annual occurrence of falls, estimated at 30%,36 follows a Poisson process, we expected to have 85% power to detect a difference as small as 20% between those with polyarticular pain compared with those with no pain, using a χ2 test with continuity correction and significance level of 0.05.

In our analyses, we tested both the association between baseline pain measures and risk of falls over the 18-month follow-up and the short-term relationship between pain measured each month and risk of falls in the subsequent month. We used descriptive statistics and χ21 tests to describe prevalence of baseline characteristics and fall risk factors according to musculoskeletal pain categories (none, single site, polyarticular). Age-adjusted fall rates and 95% confidence intervals (CIs) were calculated using the direct method, applying the crude age-specific rates to the age distribution of the cohort.37

Statistical models were performed using total number of falls (as a count variable) per total follow-up time for each participant, yielding multivariable-adjusted rate ratios (RRs) and 95% CIs. Using the Poisson distribution for fall counts assumes that the mean equals the variance and this assumption typically does not hold because the variance is often much higher than the mean. To correct for this overdispersion, which can result in underestimates of standard errors and overestimates of χ2 statistics, we used negative binomial regression models with an offset variable for log total years of follow-up. We examined 3 domains of baseline chronic pain in relation to fall risk: pain location (none, single joint site, polyarticular), severity (tertiles of the BPI pain severity subscale), and interference (tertiles of the BPI pain interference subscale). In addition, we performed a similar analysis using site-specific pain measures. There was very little missing information in the baseline measures and no single covariate had more than 2.4% missing. In the fully adjusted models that included all covariates, only 5.6% of records (n = 42) were excluded for missing information. Analyses were performed using SAS software version 9.1 (SAS Institute Inc, Cary, North Carolina).

To evaluate the association between monthly pain ratings and risk for falls in the subsequent month during the 18-month follow-up, we performed pooled logistic regression models. Using an approach described previously, each month of follow-up for each participant is a separate observation in the data set, which assumes that within-participant observations are independent and risk of falls in relation to pain is unchanged over time.38,39 The logistic regression models, generating odds ratios (ORs), were adjusted for baseline covariates used in the fully adjusted negative binomial models previously described. Because of the small numbers who reported very severe pain on the monthly pain rating, we grouped severe and very severe pain ratings.

From a random sample comprising 5655 households within the target area, recruitment staff confirmed that 4319 persons aged 70 years and older resided at the sampled addresses. Of these, 1610 were ineligible, 1916 were of unknown eligibility (including refusal to complete screening), 44 persons were eligible but did not complete the interview, and 749 were eligible and completed the baseline home interview and clinic examination. Ineligibility was most commonly related to language, poor health, mobility, and cognitive status.

To determine the response rate among those eligible to participate, which was 53%, we applied our observed eligibility rate (33%) to estimate the proportion of those we contacted whose eligibility was unknown would have been eligible to participate (American Association of Public Opinion Research40). Participants were younger than nonparticipants with a mean (SD) of 78 (5) years vs 79 (7) years (P < .001) and more likely to be white, non-Hispanic (81% vs 77%, P = .02) but no more likely to be women (63% vs 64%, P = .81).

At baseline, 40% of participants reported chronic polyarticular pain. Another 24% reported chronic pain in only 1 joint area. The number of musculoskeletal pain locations was highly correlated with the tertile classifications of both BPI pain severity and pain interference (r = 0.55 for each). The 2 BPI subscales also were highly correlated (r = 0.70). Older adults who had polyarticular pain were more likely to be women, have fewer years of education, be obese, have fallen in the previous year, and have poorer performance in tests of balance and mobility (Table 1). Medical conditions associated with chronic musculoskeletal pain included spinal stenosis or disk disease, hand and knee osteoarthritis, rheumatoid arthritis, depression, peripheral arterial disease, and heart disease (Table 2).

Table Graphic Jump LocationTable 1. Baseline Characteristics According to Chronic Musculoskeletal Pain Categoriesa
Table Graphic Jump LocationTable 2. Baseline Medical Conditions According to Pain Categories

Overall, 76% of participants completed the 18 monthly calendars, 90% completed 15 or more monthly calendars, and 94% completed at least 12 monthly calendars. On average, 98% of falls calendar information was completed each month either by returned postcards or by telephone; specifically, the proportions of completed calendars at 6, 9, 12, and 18 months were 97%, 97%, 98%, and 98%, respectively, among persons currently enrolled at each time point. A total of 1029 falls were reported by the 749 participants during and up to 18 months of follow-up. Four hundred five participants (55%) fell at least once during the follow-up. Older persons who had chronic pain, whether measured by location, severity, or pain interference with activities had higher rates of falls during follow-up compared with those who had no pain (P < .05, Figure).

Place holder to copy figure label and caption
Figure. Age-Adjusted Fall Rates According to Pain Measures
Graphic Jump Location

Age-adjusted rates and 95% confidence intervals (presented as error bars) were derived using the direct method adjusted to the age distribution of the study cohort. Categories for joint pain are 1, no pain; 2, single-site pain; and 3, multisite pain; the Brief Pain Inventory (BPI) pain severity categories are 1, 0 to 0.99; 2, 1.0 to 3.25; and 3, 3.26 to 10.0; and the BPI pain interference categories are 1, 0; 2, 0.1 to 1.19; and 3, 2 to 10. P<.001 comparing the highest with the lowest category in the joint pain analysis; P<.05 in comparing the highest with the lowest category in the BPI pain severity analysis; and P<.01 when comparing the highest with the lowest category in the BPI pain interference analysis.

After multivariable adjustment for chronic conditions and fall risk factors, each measure of chronic pain continued to be independently associated with increased occurrence of falls (Table 3). Adjustment for balance and mobility performance, use of psychotherapeutic medications, and, in subsequent models, adjustment for use of analgesics and clinical criteria for osteoarthritis of the hand and knee had little influence on the RRs. When we adjusted for history of falls, the association with each pain measure was attenuated but remained significant (eTable). We found no evidence of an interaction between musculoskeletal pain and use of daily analgesics in relation to falls (test for interaction, P = .78).

Table Graphic Jump LocationTable 3. Occurrence of Falls According to Baseline Pain Measures

We considered individual musculoskeletal sites alone or in combination with other sites of pain in relation to falls. For each site of joint pain, risk of falls increased only when polyarticular pain was present (Table 4). The single exception was back pain, which was not associated with an increased rate of falls compared with persons without pain.

Table Graphic Jump LocationTable 4. Occurrence of Falls According to Pain Sitesa

In about one-third of the monthly postcards, participants rated their pain on average for the month as moderate to very severe. We observed a strong graded relationship in the short term between pain severity ratings each month with risk for falls in the subsequent month (Table 5). For example, among persons who reported severe or very severe pain for any given month on their calendar postcard, there was a 77% increased likelihood for a fall in the subsequent month compared with those who reported no pain (multivariable adjusted OR, 1.77; 95% CI, 1.32-2.38). Persons reporting even very mild pain also had an elevated risk of falling in any given month (adjusted OR, 1.36; 95% CI, 1.08-1.71). Further adjustment for baseline pain status led to only a modest attenuation of the association with no change in the significance of the findings.

Table Graphic Jump LocationTable 5. Adjusted Odds Ratios for Falls in the Subsequent Month According to Monthly Pain Ratingsa

Both chronic pain and falls were very common in our study population. Our results provide strong and consistent evidence that chronic musculoskeletal pain, regardless of the measure used, is associated with increased risk of falls in a general population of community-living older adults. The effect was observed using chronic pain assessed at baseline predicting falls over 18 months and, more immediately, in monthly pain ratings predicting falls in the subsequent month. Pain may be a marker for underlying pathology or treatments that could contribute to falls, such as spinal stenosis, osteoarthritis with deformities, or sedating medications. However, when we adjusted for these potentially confounding factors, pain remained a strong independent risk factor for falls.

Possible underlying mechanisms for the pain-falls relationship can be grouped into 3 categories: local joint pathology, neuromuscular effects of pain, and central mechanisms, whereby pain interferes with cognition or executive function. Osteoarthritis is the main disease process contributing to joint pain in older adults. Polyarticular pain, as defined in our study, may represent a generalized arthritic process. Findings regarding risk of falls from arthritis are generally weak or inconclusive, possibly related to varying definitions of arthritis.41 Knee pain but not clinically diagnosed knee osteoarthritis, was associated with increased fall risk in older trial participants.7 In our analyses, the association between pain and falls was independent of clinically assessed hand and knee osteoarthritis, as well as mobility performance. However, we cannot be certain that unmeasured joint pathology could be a contributing factor to the observed associations.

Neuromuscular effects of pain could lead to leg muscle weakness or slowed neuromuscular responses to an impending fall. Muscle weakness could arise from lack of physical activity or from a direct effect of pain on muscle, referred to as reflex muscle inhibition.42 Another factor may be gait alterations or adaptations to chronic pain that lead to instability and subsequent balance impairments.

Chronic pain may serve as a distractor or, in some way, interfere with cognitive activity needed to prevent a fall. Successful avoidance or interruptions of a fall typically requires a cognitively mediated physical maneuver. Recent imaging studies provide evidence that patients with chronic pain exhibit changes in both structure and function of the brain consistent with changes observed through neuropsychological testing.43,44 Patients with chronic pain show poorer executive function and decreased attentional resources compared with healthy controls.45 Attention has also been associated with gait changes and fall risk.4648 A cognitively mediated pathway would be consistent with our finding of similar fall risk with pain in the upper or lower extremities.

We did not observe a lower rate of falls among analgesic users, contrary to our previous study, which found that analgesic users had lower fall risk than nonusers among women with pain.6 Benefits of analgesic use may have been more evident among disabled women than in the higher functioning MBS cohort. Analgesic use is sometimes thought to contribute to falls; however, underuse of analgesics also could contribute to falls. This question deserves further study using an experimental design.

Mobility limitations and history of falls are among the strongest predictors of falls.3 The observed association between pain and falls was independent of mobility function. Including falls that occurred in the year before baseline in our models was likely an overadjustment for chronic pain defined also in reference to the past year (lasting ≥3 months in the past year). Thus, according to our hypothesis, chronic pain in the previous year would likely contribute to falls in the previous year. We did not control for depression because pain and depression were highly correlated in the MBS cohort, similar to other cohorts.12,22 Nevertheless, this may be an important consideration for future investigations.

Although we studied fall risk prospectively, we cannot exclude the possibilities that baseline pain was a consequence of previous falls or that pain-related pathology was the underlying cause of the falls. We adjusted our models for comorbid conditions including clinical evidence of osteoarthritis without any substantive change in the pain-falls relationship. Strengths of this study include the population-based design, the extensive assessment of fall risk factors and possible confounders, the monthly falls ascertainment with little missing information, and the assessment of pain in several complimentary ways. Our results may be generalizable to the population of mobile older adults living independently in the community without significant cognitive difficulties.

The findings provide evidence suggesting that the common complaint of the aches and pains of old age is related to a greater hazard than previously thought. Daily discomfort may accompany not only difficulties in performing daily activities but equally as important may be a risk for falls and possibly fall-related injuries in the older population. The significance of this work is in the identification of chronic pain as an overlooked and potentially important risk factor for falls in older adults. A randomized controlled trial is needed to determine whether improved pain control could reduce risk for falls among older patients with chronic pain.

Corresponding Author: Suzanne G. Leveille, PhD, RN, College of Nursing and Health Sciences, University of Massachusetts Boston, 100 Morrissey Blvd, Boston, MA 02125-3393 (suzanne.leveille@umb.edu).

Author Contributions: Dr Leveille had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Leveille, Shmerling, Guralnik, Lipsitz, Bean.

Acquisition of data: Leveille, Jones, Kiel, Lipsitz, Bean.

Analysis and interpretation of data: Leveille, Jones, Kiely, Hausdorff, Guralnik, Kiely, Lipsitz, Bean.

Drafting of the manuscript: Leveille, Jones, Kiely, Lipsitz.

Critical revision of the manuscript for important intellectual content: Leveille, Jones, Kiely, Hausdorff, Shmerling, Guralnik, Kiel, Bean.

Statistical analysis: Leveille, Jones, Kiely, Guralnik.

Obtained funding: Leveille, Kiel, Lipsitz.

Administrative, technical, or material support: Lipsitz, Bean.

Study supervision: Leveille, Lipsitz, Bean.

Financial Disclosures: Dr Kiel reports receiving grant support from Pfizer, Amgen, Merck, Novartis, and Hologic; consulting income from Amgen, Novartis, Merck, GlaxoSmithKline, Lilly, Procter & Gamble, Philips Lifeline, and Wyeth; and speakers' honoraria from Novartis, Merck GlaxoSmithKline, and Lilly.

Funding/Support: This research was supported by grant P01AG004390 from the National Institute on Aging Research Nursing Home Program Project. Dr Guralnik's time was funded by the Intramural Research Program, National Institute on Aging, NIH. The coding of the medication data for the MOBILIZE Boston Study was supported by an unrestricted grant from Pfizer Inc, none of which was to support salary, stipends, or other funding except for salaries for research staff who were not involved in this article.

Role of the Sponsor: Dr Guralnik, an employee of the National Institute on Aging, had no role in the decision to fund this research. He participated in the design of the study, interpretation of the analysis, and preparation of the manuscript. No employees of Pfizer Inc had any role in the conduct of this research or in the coding of the medication data.

Disclaimer: The views expressed in this article are the authors' and do not reflect those of the study sponsors.

Additional Contributions: We thank the MOBILIZE Boston research team and study participants for their time, effort, and dedication.

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Stewart AL, Hays RD, Ware JE Jr. The MOS short-form general health survey: reliability and validity in a patient population.  Med Care. 1988;26(7):724-735
PubMed   |  Link to Article
Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation.  J Clin Epidemiol. 1993;46(2):153-162
PubMed   |  Link to Article
Perkins BA, Olaleye D, Zinman B, Bril V. Simple screening tests for peripheral neuropathy in the diabetes clinic.  Diabetes Care. 2001;24(2):250-256
PubMed   |  Link to Article
Altman R, Alarcon G, Appelrouth D,  et al.  The American College of Rheumatology criteria for the classification and reporting of osteoarthritis of the hand.  Arthritis Rheum. 1990;33(11):1601-1610
PubMed   |  Link to Article
Altman R, Asch E, Bloch D,  et al; Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association.  Development of criteria for the classification and reporting of osteoarthritis: classification of osteoarthritis of the knee.  Arthritis Rheum. 1986;29(8):1039-1049
PubMed   |  Link to Article
Eaton WW, Muntaner C, Smith C, Tien A, Ybarra M. Center for Epidemiologic Studies Depression Scale: review and revision (CESD and CESD–R). In: Maruish ME, ed. The Use of Psychological Testing for Treatment Planning and Outcomes Assessment. Vol 3. Mahwah, NJ: Lawrence Erlbaum Assoc Inc; 2004
Radloff L. The CES-D Scale: a self report depresion scale for research in the general population.  Appl Psychol Meas. 1977;1:385-401
Link to Article
Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability.  N Engl J Med. 1995;332(9):556-561
PubMed   |  Link to Article
Pahor M, Chrischilles EA, Guralnik JM, Brown SL, Wallace RB, Carbonin P. Drug data coding and analysis in epidemiologic studies.  Eur J Epidemiol. 1994;10(4):405-411
PubMed   |  Link to Article
Tinetti ME. Clinical practice: preventing falls in elderly persons.  N Engl J Med. 2003;348(1):42-49
PubMed   |  Link to Article
Curtin LR, Klein RJ. Direct standardization (age-adjusted death rates). Healthy People 2000 Statistical Notes. March 6 1995:1-10. http://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf. Accessed October 28, 2009
Cupples LA, D'Agostino RB, Anderson K, Kannel WB. Comparison of baseline and repeated measure covariate techniques in the Framingham Heart Study.  Stat Med. 1988;7(1-2):205-222
PubMed   |  Link to Article
D'Agostino RB, Lee ML, Belanger AJ, Cupples LA, Anderson K, Kannel WB. Relation of pooled logistic regression to time dependent Cox regression analysis: the Framingham Heart Study.  Stat Med. 1990;9(12):1501-1515
PubMed   |  Link to Article
American Association for Public Opinion Research.  Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. 5th ed. Lenexa, KS: AAPOR; 2008
Leveille SG. Musculoskeletal pain and risk for falls in older adults?  J Pain Manag. 2009;1(4):329-338
Graven-Nielsen T, Lund H, Arendt-Nielsen L, Danneskiold-Samsøe B, Bliddal H. Inhibition of maximal voluntary contraction force by experimantal muscle pain: A centrally mediated mechanism.  Muscle Nerve. 2002;26(5):708-712
PubMed   |  Link to Article
Apkarian AV, Baliki MN, Geha PY. Towards a theory of chronic pain.  Prog Neurobiol. 2009;87(2):81-97
PubMed   |  Link to Article
Neugebauer V, Galhardo V, Maione S, Mackey SC. Forebrain pain mechanisms.  Brain Res Brain Res Rev. 2009;60(1):226-242
PubMed   |  Link to Article
Eccleston C, Crombez G. Pain demands attention: a cognitive-affective model of the interruptive function of pain.  Psychol Bull. 1999;125(3):356-366
PubMed   |  Link to Article
Ben-Itzhak R, Giladi N, Gruendlinger L, Hausdorff JM. Can methylphenidate reduce fall risk in community-living older adults? a double-blind, single-dose cross-over study.  J Am Geriatr Soc. 2008;56(4):695-700
PubMed   |  Link to Article
Springer S, Giladi N, Peretz C, Yogev G, Simon ES, Hausdorff JM. Dual-tasking effects on gait variability: the role of aging, falls, and executive function.  Mov Disord. 2006;21(7):950-957
PubMed   |  Link to Article
Ble A, Volpato S, Zuliani G,  et al.  Executive function correlates with walking speed in older persons: the InCHIANTI study.  J Am Geriatr Soc. 2005;53(3):410-415
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure. Age-Adjusted Fall Rates According to Pain Measures
Graphic Jump Location

Age-adjusted rates and 95% confidence intervals (presented as error bars) were derived using the direct method adjusted to the age distribution of the study cohort. Categories for joint pain are 1, no pain; 2, single-site pain; and 3, multisite pain; the Brief Pain Inventory (BPI) pain severity categories are 1, 0 to 0.99; 2, 1.0 to 3.25; and 3, 3.26 to 10.0; and the BPI pain interference categories are 1, 0; 2, 0.1 to 1.19; and 3, 2 to 10. P<.001 comparing the highest with the lowest category in the joint pain analysis; P<.05 in comparing the highest with the lowest category in the BPI pain severity analysis; and P<.01 when comparing the highest with the lowest category in the BPI pain interference analysis.

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics According to Chronic Musculoskeletal Pain Categoriesa
Table Graphic Jump LocationTable 2. Baseline Medical Conditions According to Pain Categories
Table Graphic Jump LocationTable 3. Occurrence of Falls According to Baseline Pain Measures
Table Graphic Jump LocationTable 4. Occurrence of Falls According to Pain Sitesa
Table Graphic Jump LocationTable 5. Adjusted Odds Ratios for Falls in the Subsequent Month According to Monthly Pain Ratingsa

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Stewart AL, Hays RD, Ware JE Jr. The MOS short-form general health survey: reliability and validity in a patient population.  Med Care. 1988;26(7):724-735
PubMed   |  Link to Article
Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation.  J Clin Epidemiol. 1993;46(2):153-162
PubMed   |  Link to Article
Perkins BA, Olaleye D, Zinman B, Bril V. Simple screening tests for peripheral neuropathy in the diabetes clinic.  Diabetes Care. 2001;24(2):250-256
PubMed   |  Link to Article
Altman R, Alarcon G, Appelrouth D,  et al.  The American College of Rheumatology criteria for the classification and reporting of osteoarthritis of the hand.  Arthritis Rheum. 1990;33(11):1601-1610
PubMed   |  Link to Article
Altman R, Asch E, Bloch D,  et al; Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association.  Development of criteria for the classification and reporting of osteoarthritis: classification of osteoarthritis of the knee.  Arthritis Rheum. 1986;29(8):1039-1049
PubMed   |  Link to Article
Eaton WW, Muntaner C, Smith C, Tien A, Ybarra M. Center for Epidemiologic Studies Depression Scale: review and revision (CESD and CESD–R). In: Maruish ME, ed. The Use of Psychological Testing for Treatment Planning and Outcomes Assessment. Vol 3. Mahwah, NJ: Lawrence Erlbaum Assoc Inc; 2004
Radloff L. The CES-D Scale: a self report depresion scale for research in the general population.  Appl Psychol Meas. 1977;1:385-401
Link to Article
Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability.  N Engl J Med. 1995;332(9):556-561
PubMed   |  Link to Article
Pahor M, Chrischilles EA, Guralnik JM, Brown SL, Wallace RB, Carbonin P. Drug data coding and analysis in epidemiologic studies.  Eur J Epidemiol. 1994;10(4):405-411
PubMed   |  Link to Article
Tinetti ME. Clinical practice: preventing falls in elderly persons.  N Engl J Med. 2003;348(1):42-49
PubMed   |  Link to Article
Curtin LR, Klein RJ. Direct standardization (age-adjusted death rates). Healthy People 2000 Statistical Notes. March 6 1995:1-10. http://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf. Accessed October 28, 2009
Cupples LA, D'Agostino RB, Anderson K, Kannel WB. Comparison of baseline and repeated measure covariate techniques in the Framingham Heart Study.  Stat Med. 1988;7(1-2):205-222
PubMed   |  Link to Article
D'Agostino RB, Lee ML, Belanger AJ, Cupples LA, Anderson K, Kannel WB. Relation of pooled logistic regression to time dependent Cox regression analysis: the Framingham Heart Study.  Stat Med. 1990;9(12):1501-1515
PubMed   |  Link to Article
American Association for Public Opinion Research.  Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. 5th ed. Lenexa, KS: AAPOR; 2008
Leveille SG. Musculoskeletal pain and risk for falls in older adults?  J Pain Manag. 2009;1(4):329-338
Graven-Nielsen T, Lund H, Arendt-Nielsen L, Danneskiold-Samsøe B, Bliddal H. Inhibition of maximal voluntary contraction force by experimantal muscle pain: A centrally mediated mechanism.  Muscle Nerve. 2002;26(5):708-712
PubMed   |  Link to Article
Apkarian AV, Baliki MN, Geha PY. Towards a theory of chronic pain.  Prog Neurobiol. 2009;87(2):81-97
PubMed   |  Link to Article
Neugebauer V, Galhardo V, Maione S, Mackey SC. Forebrain pain mechanisms.  Brain Res Brain Res Rev. 2009;60(1):226-242
PubMed   |  Link to Article
Eccleston C, Crombez G. Pain demands attention: a cognitive-affective model of the interruptive function of pain.  Psychol Bull. 1999;125(3):356-366
PubMed   |  Link to Article
Ben-Itzhak R, Giladi N, Gruendlinger L, Hausdorff JM. Can methylphenidate reduce fall risk in community-living older adults? a double-blind, single-dose cross-over study.  J Am Geriatr Soc. 2008;56(4):695-700
PubMed   |  Link to Article
Springer S, Giladi N, Peretz C, Yogev G, Simon ES, Hausdorff JM. Dual-tasking effects on gait variability: the role of aging, falls, and executive function.  Mov Disord. 2006;21(7):950-957
PubMed   |  Link to Article
Ble A, Volpato S, Zuliani G,  et al.  Executive function correlates with walking speed in older persons: the InCHIANTI study.  J Am Geriatr Soc. 2005;53(3):410-415
PubMed   |  Link to Article
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