Archives of Physical Medicine and Rehabilitation
Volume 90, Issue 11 , Pages 1874-1879, November 2009

The Relationship of Self-Reported Pain and Functional Impairment to Gait Mechanics in Overweight and Obese Persons With Knee Osteoarthritis

  • Mary Beth Nebel, BSE

      Affiliations

    • Department of Biological Anthropology and Anatomy, Duke University, Durham, NC
    • Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC
    • ,
  • Ershela L. Sims, PhD

      Affiliations

    • Department of Biological Anthropology and Anatomy, Duke University, Durham, NC
    • Corresponding Author InformationCorrespondence to Ershela L. Sims, PhD, 203 Biological Science Building, Box 90383, Durham, NC 27708
    • ,
  • Francis J. Keefe, PhD

      Affiliations

    • Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
    • ,
  • Virginia B. Kraus, MD, PhD

      Affiliations

    • Department of Rheumatology and Immunology, Duke University, Durham, NC
    • ,
  • Farshid Guilak, PhD

      Affiliations

    • Department of Surgery, Duke University, Durham, NC
    • ,
  • David S. Caldwell, MD

      Affiliations

    • Department of Rheumatology and Immunology, Duke University, Durham, NC
    • ,
  • Jennifer J. Pells, PhD

      Affiliations

    • Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
    • ,
  • Robin Queen, PhD

      Affiliations

    • Department of Surgery, Duke University, Durham, NC
    • Michael W. Krzyzewski Human Performance Lab, Duke University, Durham, NC
    • ,
  • Daniel Schmitt, PhD

      Affiliations

    • Department of Biological Anthropology and Anatomy, Duke University, Durham, NC

Article Outline

Abstract 

Nebel MB, Sims EL, Keefe FJ, Kraus VB, Guilak F, Caldwell DS, Pells JJ, Queen R, Schmitt D. The relationship of self-reported pain and functional impairment to gait mechanics in overweight and obese persons with knee osteoarthritis.

Objective

To examine the degree to which 2 commonly used measures of pain and disability, the Arthritis Impact Measurement Scales (AIMS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), relate to objective gait measurements.

Design

A descriptive study of the influence of self-reported pain and perceived functional impairment on gait mechanics in osteoarthritic adults.

Setting

A university clinical research laboratory.

Participants

Overweight/obese adults with radiographic knee osteoarthritis (OA) as well as pain and disability associated with the disease (N=179).

Interventions

Not applicable.

Main Outcome Measures

The AIMS and WOMAC were administered to determine self-report measures of pain and disability. Speed, stride length, support time, knee angle, and peak vertical force (PVF) were determined from 3-dimensional kinematic and kinetic data collected on subjects walking at self-selected normal and fast speeds. Anthropometric data and radiographic levels of OA were also collected.

Results

Pearson correlation analysis showed that the AIMS physical disability score was inversely correlated with speed, stride length, and knee range of motion at both speeds and PVF at the fast speed. The WOMAC function score was inversely correlated with speed and stride length at both speeds and with PVF at fast speed. The WOMAC pain score was inversely correlated with speed and PVF at the fast speed. Regression analysis revealed that the AIMS physical disability score and body mass index accounted for the greatest variation in speed at the normal speed. Overall, AIMS physical disability and WOMAC function explained a larger proportion of variance in gait mechanics than radiographic measures of OA disease severity.

Conclusions

Taken together, the results suggest that the AIMS physical disability and WOMAC function scores are associated with some important measures of gait impairment.

Key Words: Gait, Joint diseases, Osteoarthritis, Pain, Rehabilitation

List of Abbreviations: AIMS, Arthritis Impact Measurement Scales, BMI, body mass index, K/L, Kellgren/Lawrence, KROM, knee range of motion, OA, osteoarthritis, PVF, peak vertical force, RDS, radiographic disease severity, SL, stride length, WOMAC, Western Ontario and McMaster Universities Osteoarthritis Index

 

OSTEOARTHRITIS IS ONE OF the most prevalent rheumatic diseases, affecting the knees of up to 37.4% of adults over the age of 60.1 Knee OA is the most common cause of disability in community-dwelling elderly adults.2 Pain in the affected joint is one of the most frequent complaints of patients suffering from OA and is commonly accompanied by decreased patient mobility and increased stiffness.3, 4 OA sufferers are often unable to execute activities of daily living such as walking up stairs or standing from a seated position, and they may also be unable to complete the physical activities prescribed as part of the treatment plan for their OA disease. Over the past 2 decades, clinicians and researchers have increasingly relied on self-report measures to assess pain, psychologic disability, and physical disability in OA patients. Among the most widely used self-report measures are the AIMS and the WOMAC questionnaires. These standardized self-report instruments are convenient, disease specific, and increasingly being incorporated into clinical practice and research studies.

The most reliable, direct, and objective way to assess movement disability in OA patients is to document gait patterns during walking. Many studies have compared gait patterns in individuals with knee OA with those of healthy controls.5, 6, 7, 8 These studies have generally found that OA patients tend to walk at slower speeds than healthy subjects and tend to exhibit limited KROM compared with controls.7, 8 In addition, patients with knee OA have been shown to demonstrate both altered ground reaction forces and shorter stride lengths compared with age-matched controls.6, 9, 10

The degree to which a patient's description of his/her own pain and disability relates to actual limitations in gait is poorly understood. Although researchers typically use a variety of self-report instruments for evaluating pain, psychologic disability, and physical disability including gait difficulties, few studies have attempted to correlate these datasets and explore their relationship quantitatively. In addition, many OA patients have comorbid conditions including obesity that affect not only disease progression but may also affect their psychologic well-being and gait, independent of the actual extent of OA in their joints.11, 12, 13 People with OA who are overweight (25≤BMI≤29) or obese (BMI≥30) are more likely to experience increased levels of pain.14 Understanding the relationship between OA, self-reported pain, and disability measures and gait is critically important to developing a full understanding of the effect that OA has on a patient's life, the progression of the disease for individual patients, and effective pathways for intervention.

Based on the model that gait disability is influenced not only by the degree of OA and obesity but also by a person's level and perception of pain, measuring a person's self-reported level of disability and pain has become accepted.15 Although some research has focused on biomechanic aspects of disablement in knee OA6, 7, 8, 16, 17 and other studies have explored psychosocial influences on function,11, 13, 18, 19, 20, 21 to date, the interplay of all these variables and gait disability has not been investigated in a large sample. Determining which aspects of gait mechanics have clinical relevance will greatly advance our understanding of this disease and our ability to establish efficacious treatment strategies for OA patients. Thus, the primary aim of this study was to examine which self-reported measures of pain and disability collected via the AIMS and WOMAC relate to objective performance measures, determined by using gait kinetics and kinematics in obese patients with knee OA of varying radiographic degrees. In patients who have knee OA, gait patterns may also be influenced by age, RDS, and BMI.22, 23 Furthermore, overweight and obese patients with OA are likely to experience pain with movement, which may have a negative influence on physical function as manifested through gait mechanics. This was an interesting aspect of the present study that was focused on a sample of overweight and obese OA patients. Thus, a secondary aim of this study was to evaluate the contributions of self-report measures of pain and disability as well as RDS and BMI to gait patterns of people with knee OA.

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Methods 

Subjects 

A total of 179 patients (43 men, 136 women) with radiographic OA in at least 1 knee (152 bilateral and 27 unilateral) and persistent knee pain participated in this study. All participants in this study were recruited as part of an ongoing study (OA Life) testing the separate and combined effects of a lifestyle behavioral weight-management intervention and pain-coping skills training intervention for obese OA patients. All participants signed an informed consent form approved by the Duke Medical Center Institutional Review Board. To be included in the study, patients had to be overweight or obese (BMI between 25 and 42kg/m2), meet the American College of Rheumatology criteria for symptomatic knee OA, have chronic knee pain, and have no other weight-bearing joint affected by OA on the basis of clinical examination. Exclusion criteria included a significant medical condition that would increase the risk of an adverse experience (eg, myocardial infarction), patients already involved in regular exercise, an abnormal cardiac response to exercise, a non-OA inflammatory anthropathy, morbid obesity, and regular use of corticosteroids. Weight-bearing, fixed-flexion (30°) posterior-anterior radiographs of both knees were taken with the SynaFlexer X-ray positioning frame.24,a The knee x-rays were graded by an experienced reader for OA severity on the basis of the K/L grading system (0-4 scale).25 For patients with bilateral knee OA, the knee with the higher K/L grade was recorded as the most-affected limb. If both limbs had the same K/L grade, the right limb was used as the most-affected limb. This most-affected limb was the limb used in all data analyses.

Self-Report Measures of Pain and Disability 

Two standardized instruments were used to assess self-reports of pain and disability. First, the AIMS was used to assess pain, physical disability, and psychologic disability. The range of scores on the AIMS scales is 0 to 10, with 0 representing good health status and 10 representing poor health status. Research26 has supported the reliability of the AIMS and found it is valid when used with different types of arthritis, with a range of social and demographic groupings and in different clinical settings.

Second, the WOMAC Version VA3.1 was used to assess self-reports of pain, stiffness, and physical function. The WOMAC OA index used in this study was a visual analog scale that consisted of 3 subscales that assessed pain (5 questions), stiffness (2 questions), and physical function (17 questions). The reliability and validity of this index has been supported by previous research.27 The range of scores on each of these subscales was between 0 and 100mm, with higher WOMAC scores reflecting a worse condition.27

Gait Parameters 

Reflective markers were placed at the superior aspect of the L5-sacral interface as well as bilaterally at the following landmarks: acromion process, lateral epicondyle of the humerus, wrist, anterior superior iliac spine, thigh, lateral knee (at the joint line), shank, lateral malleolus, calcaneus, and foot (2nd webspace). In addition, markers were placed bilaterally on the medial femoral condyle and medial malleolus for the identification of joint centers during the collection of a static trial. Once the static trial was completed, the medial markers were removed. In preparation for data collection, patients practiced walking along a 30-m walkway at 2 differentiable and consistent self-selected speeds: the speed at which they normally perform their daily walking activities (normal) and the maximum speed they felt comfortable achieving (fast). These 2 speeds were chosen in order to get a sense of the speed at which the participants are most comfortable and to see how their gait mechanics change when they were presented with a challenge. Average normal and fast gait speed was determined from the average walking speed obtained during the 3 practice trials at each speed (normal and fast). Gait speed was measured by using 2 wireless infrared photocell timing devicesb positioned 5m apart. After the practice trials, 3-dimensional kinematic data were collected at 60Hz by using a motion analysis systemc while patients completed 5 walking trials at each speed. Time-synchronized ground reaction force data were collected by using 2 AMTI force platesd at a sampling rate of 1200Hz. Variability in walking speed for each speed was restricted to ±5% of their average walking speed; trials outside of this range or trials during which the subject did not contact at least 1 of the force plates cleanly were repeated. The raw motion capture data were smoothed by using a 4th–order, recursive Butterworth filter with a 6-Hz cutoff frequency. Three trials at each speed in which all of the reflective markers could be identified and in which the subject had clean contact with the force plates were used for analysis. Spatiotemporal variables (speed and SL) and PVF and KROM across an entire gait cycle were computed by using OrthoTrak 6.3.c SL data were normalized to subject height, whereas ground reaction force data were normalized to body weight. The aforementioned gait variables were chosen as dependent variables based on findings from prior research. For example, OA patients walk slower, have shorter stride lengths, and have a smaller KROM than their counterparts without OA.6, 10, 28, 29 In addition, researchers30, 31, 32 have found that leg stiffness is proportional to ground reaction force; with less compliant gait producing a higher PVF.30, 31, 32 Therefore, we wanted to examine how self-reported stiffness actually reflects true stiffness as manifested through KROM and PVF.

Statistical Analysis 

Statistical analysis was performed by using SPSS (version 12.0.1 for Windowse). Correlation analyses were performed to examine the associations between self-reported pain and disability assessed by using the AIMS and WOMAC scales, demographics (age, gender, race), BMI, RDS, and gait measures (speed, SL, KROM, PVF) at each speed. Independent variables that were related to the gait measures (P<.05) were retained for further analyses.33

Stepwise regression analyses were used to determine the contributions of perceived pain and functional impairment to variance in gait mechanics.18, 34 In each regression analysis, the self-report measures of pain and disability, demographics, BMI, and RDS were entered as independent variables. The subscales of the AIMS and WOMAC were considered separately during all analyses. The current study had adequate power for regression analysis; there were at least 15 cases per predictor.34

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Results 

Descriptive data and means and standard deviations for the pain and disability measures for the study participants are described in table 1. When walking at their normal speed, the study participants showed a mean walking speed of 1.106±0.191m/s. When walking at the fast speed, the study participants had a mean walking speed of 1.52±0.298m/s. Table 2 gives detailed information regarding the gait mechanics at both speeds.

Table 1. Descriptive Subject Characteristics, and Pain and Disability Measures
Mean ± SD
Age (y)57.9±10.0
BMI (kg/m2)34.3±4.4
Height (m)1.67±0.07
Weight (kg)95.58±15.5
AIMS Pain subscale (0–10)5.80±1.83
AIMS Physical Disability subscale (0–10)1.67±1.19
AIMS Psychological subscale (0–10)2.90±1.55
WOMAC Pain subscale (0–100)45.11±19.4
WOMAC Function subscale (0–100)47.42±19.1
WOMAC Stiffness subscale (0–100)56.35±24.2
Table 2. Gait Mechanics
NormalFast
Speed (m/s)1.106±.1911.52±.298
Stride length (statures).729±.101.823±.151
Knee range of motion (°)57.75±8.2959.97±8.96
Peak vertical GRF (BW)1.05±.09461.16±.143

NOTE. All values listed as mean ± SD.

Abbreviation: BW, body weight.

As can be seen in table 3, for the normal speed condition, correlation analysis revealed that BMI, RDS, and AIMS physical disability were correlated with the majority of the gait variables. WOMAC function was correlated with gait speed and SL, whereas the AIMS pain score was also correlated with speed. Thus, when walking at their normal speed, the OA patients in this study reporting problems with physical function and more disability were more likely to walk slower, take shorter strides, and have a smaller KROM.

Table 3. Correlations Between Self-Report Measures, BMI, RDS, and Gait Variables at the Normal Speed
BMIRDSWOMAC FunctionAIMS PhysicalAIMS Pain
Speed (m/s)−.28†−.17⁎−.24†−.29†−.16⁎
Stride length (statures)−.29†−.17⁎−.20†−.22†NS
KROM (°)−.25†−.26†NS−.21†NS
PVF (BW)−.28†−.26†NSNSNS

Abbreviations: BW, body weight; NS, nonsignificant correlation.

⁎Significant correlation, P<.05.

†Significant correlation, P<.01.

As can be seen in table 4, there was a number of significant correlations between the self-report measures of pain and disability variables and the gait parameters at the fast walking speed. Significant inverse correlations were found between the WOMAC function score and speed, SL, and PVF. In addition, the AIMS physical disability score was also found to be inversely correlated with each of the 4 dependent variables. Taken together, these findings indicate that, when participants were asked to walk fast, those who reported higher levels of physical disability and functional limitations were more likely to do so at a slower speed, with smaller strides, and with a more limited KROM.

Table 4. Correlations Between Self-Report Measures, Demographics, BMI, RDS, and Gait Variables at the Fast Speed
AgeRaceSexBMIRDSWOMAC FunctionWOMAC StiffnessWOMAC PainAIMS PhysicalAIMS PainAIMS Psych
Speed (m/s)NS0.18⁎−.21†−.23†−.16⁎−.28†NS−.20⁎−.28†−.16⁎−.18⁎
Stride length (statures)−.15⁎NSNS−.28†−.17⁎−.26†NSNS−.26†NSNS
KROM (°)NSNSNS−.25†−.24†NSNSNS−.26†NSNS
PVF (BW)NSNS−.21⁎−.42†−.23†−.31†−.22†−.27†−.24†NSNS

Abbreviations: BW, body weight; NS, nonsignificant correlation.

⁎Significant correlation, P<.05.

†Significant correlation, P<.01.

The stepwise regression analysis (table 5) revealed that AIMS physical disability score and BMI explained the highest proportions of variance in the gait parameters at the normal walking speed. Radiographic disease severity also accounted for small portions of variance in speed, KROM, and PVF. The regression analyses conducted for the fast walking speed revealed that the WOMAC function score explained a significant proportion of variance in speed (10%) and SL (4%). The AIMS physical disability score explained a significant proportion of variance in KROM (10%), SL (7%), and PVF (4%). BMI also explained a significant proportion of variance in PVF (20%), SL (9%), speed (5%), and KROM (5%). Disease severity (RDS) explained a significant proportion of variance in KROM (9%) and PVF (3%).

Table 5. Contribution of Self-Report Measures, BMI, and RDS to Variation in Gait Parameters
R2R2 ChangeβP
Normal
Gait speed
AIMS physical disability.09.09−.328<.001
BMI.19.10−.253.001
Radiographic disease severity.22.03−.164.029
Stride length
BMI.09.09−.296<.001
AIMS physical disability.15.06−.239.002
Age.17.03−.163.034
KROM
BMI.08.08−.204.013
Radiographic disease severity.13.05−.320<.001
AIMS physical disability.19.06−.248.002
PVF
BMI.08.08−.264.002
Radiographic disease severity.13.05−.212.013
Fast
Gait speed
WOMAC function.10.10−.169.042
Age.15.05−.339<.001
BMI.20.05−.221.003
AIMS physical disability.24.04−.203.008
Race.27.03−.195.014
AIMS psychological disability.30.03−.175.028
Stride length
BMI.09.09−.263.001
AIMS physical disability.17.07−.212.006
Age.20.03−.235.002
WOMAC function.24.04−.230.004
KROM
AIMS physical disability.10.10−.220.007
Radiographic disease severity.18.09−.267.001
BMI.23.05−.326<.001
PVF
BMI.20.20−.385<.001
Radiographic disease severity.23.03−.209.010
AIMS physical disability.26.04−.192.010

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Discussion 

Consistent with previous research, OA patients in this study walked more slowly,7, 35 took shorter strides,35 exhibited lower PVFs,35 and limited KROM6, 7 compared with normative values from the literature. Also, in agreement with previous research, RDS explained some of the variation in gait patterns.36, 37

The primary question addressed in this study was whether self-report measures of perceived pain and functional limitation (AIMS and WOMAC measures) were correlated with objective measures of gait in obese persons with OA. Based on the concept that gait impairment is influenced not only by the degree of OA and obesity but also by a person's level and perception of pain, measuring a person's self-reported level of pain and functional limitation has become accepted.15 Although the AIMS and WOMAC questionnaires provide convenient assessment methods, it is unknown whether answers given by patients on these questionnaires accurately reflect gait impairment. The results obtained in this study showed that self-report measures of functional impairment were significantly correlated with gait speed, indicating that OA subjects who find it more difficult to complete daily activities and consider themselves more functionally limited walk more slowly. In addition, self-reported physical disability and function contributed to between 10% and 14% of variance in gait speed. Moreover, previously reported evidence suggests that obese persons prefer to walk more slowly than their normal-weight peers,38, 39 making the relationships observed in the current study more impressive because they were apparent even after controlling for variables that are considered to be important in explaining gait speed in OA patients (BMI, RDS).

Pain levels experienced by OA patients are believed to be a significant contributor to reduced walking speed in patients with knee OA compared with healthy controls.40 In concurrence, this study determined that patients who reported more pain on the AIMS pain subscale also walked more slowly when asked to walk faster than their normal walking speed. Furthermore patients who reported more pain via the WOMAC pain scale walked more slowly and exhibited lower PVF.

The data presented in this article also show that in this large sample variation in several important gait parameters is more strongly influenced by perception of physical impairments than by RDS. The findings from the regression analyses suggest that the influence of a patient's perception of his/her physical limitations on gait variance appears to increase with increasing stress on the locomotor system. At the fast speed, a self-report of functional impairment was significantly correlated with each of the gait parameters except for SL. At the fast speed, the WOMAC function score was the strongest predictor of variance in movement speed and SL, whereas the AIMS physical disability score was the strongest predictor variance in KROM and the second strongest predictor of variance in SL and PVF. In interpreting these findings, it is important to keep in mind that the predictive utility of self-report measures in explaining gait may be underestimated because measures of functional impairment were entered into the regression only after controlling for other variables (BMI, disease severity) that are important in understanding gait mechanics.

The results of correlation analyses suggested that, at the fast speed, the strength of the relationship between perceived physical limitation, as measured by the WOMAC function score, and walking speed tended to increase. In an investigation of knee biomechanics of moderately severe knee OA, Landry et al41 did not observe that walking faster enhanced or revealed any additional biomechanic differences between the OA and control groups. It may be that the patients with moderate disability in this study continued to use the same gait strategies at the fast speed as they did at their normal speed as the Landry data would suggest but that the strategies of the more severely disabled patients in the present study failed under the higher stress condition because the biomechanic benefit of walking more slowly has been shown to be highly patient specific and vary with disease severity.42

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Conclusions 

The purpose of this study was to see how self-report measures of pain and disability relate to objective measures of physical performance (gait mechanics). The results showed that the predictors included in our study accounted for approximately 20% to 30% of the variance in gait mechanics. Our findings serve as an objective validation that specific measures within the AIMS and WOMAC reflect gait impairments. WOMAC function, WOMAC pain, and AIMS physical disability all track limitations in speed and SL, whereas AIMS physical disability also tracks limited KROM when the locomotor system is under greater stress. In addition, these measures explained variance in certain gait parameters beyond the variance accounted for by RDS or by BMI. This is a significant finding because it highlights the importance of recognizing and addressing a patient's own perception of pain and functional impairment to understand and to treat OA. However, because these self-report measures of pain and disability only account for a portion of the variance in the aberrant gait patterns of OA sufferers, perhaps something else is driving the OA patients to report the levels of pain, stiffness, and disability they are experiencing as a result of their OA disease. Maybe pain cognitions such as pain catastrophizing, pain-related fear, or self-efficacy are influencing the subject's appraisal of his/her pain and symptoms, thereby causing his/her self-report measures to reflect his/her true level of physical function. Future research should be conducted to determine what else is contributing to the altered gait mechanics. The authors also suggest that future work should be conducted to look at the effects of interventions designed to reduce pain and disability (eg, weight loss or pain coping skills interventions) on gait mechanics.

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Acknowledgments 

The authors would like to thank Matthew Williams, BS, Paul Riordan, BS, and Jessica Tischner, PhD for their assistance with data collection, data analysis, and statistical analysis.

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References 

  1. 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;33:2271–2279
  2. 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;84:351–358
  3. Martin DF. Pathomechanics of knee osteoarthritis. Med Sci Sports Exerc. 1994;26:1429–1434
  4. Sharma L, Cahue S, Song J, Hayes K, Pai YC, Dunlop D. Physical functioning over three years in knee osteoarthritis: role of psychosocial, local mechanical, and neuromuscular factors. Arthritis Rheum. 2003;48:3359–3370
  5. Messier SP. Osteoarthritis of the knee and associated factors of age and obesity: effects on gait. Med Sci Sports Exerc. 1994;26:1446–1452
  6. Baliunas AJ, Hurwitz DE, Ryals AB, et al. Increased knee joint loads during walking are present in subjects with knee osteoarthritis. Osteoarthritis Cartilage. 2002;10:573–579
  7. Brinkmann JR, Perry J. Rate and range of knee motion during ambulation in healthy and arthritic subjects. Phys Ther. 1985;65:1055–1060
  8. Stauffer RN, Chao EY, Gyory AN. Biomechanical gait analysis of the diseased knee joint. Clin Orthop Relat Res. 1977;(126):246–255
  9. Al-Zahrani KS, Bakheit AM. A study of the gait characteristics of patients with chronic osteoarthritis of the knee. Disabil Rehabil. 2002;24:275–280
  10. Messier SP, Loeser RF, Hoover JL, Semble EL, Wise CM. Osteoarthritis of the knee: effects on gait, strength, and flexibility. Arch Phys Med Rehabil. 1992;73:29–36
  11. Summers MN, Haley WE, Reveille JD, Alarcon GS. Radiographic assessment and psychologic variables as predictors of pain and functional impairment in osteoarthritis of the knee or hip. Arthritis Rheum. 1988;31:204–209
  12. Ettinger WH, Davis MA, Neuhaus JM, Mallon KP. Long-term physical functioning in persons with knee osteoarthritis from NHANES (I: Effects of comorbid medical conditions). J Clin Epidemiol. 1994;47:809–815
  13. Focht BC, Rejeski WJ, Ambrosius WT, Katula JA, Messier SP. Exercise, self-efficacy, and mobility performance in overweight and obese older adults with knee osteoarthritis. Arthritis Rheum. 2005;53:659–665
  14. Wadden TA, Foster GD. Behavioral treatment of obesity. Med Clin North Am. 2000;84:441–461
  15. Pollard B, Johnston M, Dixon D. Theoretical framework and methodological development of common subjective health outcome measures in osteoarthritis: a critical review. Health Qual Life Outcomes. 2007;5:14
  16. Hurwitz DE, Ryals AB, Case JP, Block JA, Andriacchi TP. The knee adduction moment during gait in subjects with knee osteoarthritis is more closely correlated with static alignment than radiographic disease severity, toe out angle and pain. J Orthop Res. 2002;20:101–107
  17. Mundermann A, Dyrby CO, Andriacchi TP. Secondary gait changes in patients with medial compartment knee osteoarthritis: increased load at the ankle, knee, and hip during walking. Arthritis Rheum. 2005;52:2835–2844
  18. Maly MR, Costigan PA, Olney SJ. Contribution of psychosocial and mechanical variables to physical performance measures in knee osteoarthritis. Phys Ther. 2005;85:1318–1328
  19. Rejeski WJ, Craven T, Ettinger WH, McFarlane M, Shumaker S. Self-efficacy and pain in disability with osteoarthritis of the knee. J Gerontol B Psychol Sci Soc Sci. 1996;51:P24–P29
  20. Rejeski WJ, Miller ME, Foy C, Messier S, Rapp S. Self-efficacy and the progression of functional limitations and self-reported disability in older adults with knee pain. J Gerontol B Psychol Sci Soc Sci. 2001;56:S261–S265
  21. Stratford PW, Kennedy DM. Performance measures were necessary to obtain a complete picture of osteoarthritic patients. J Clin Epidemiol. 2006;59:160–167
  22. Cooper C, Snow S, McAlindon TE, et al. Risk factors for the incidence and progression of radiographic knee osteoarthritis. Arthritis Rheum. 2000;43:995–1000
  23. Hannan M, Felson D, Pincus T. Analysis of the discordance between radiographic changes in knee pain in osteoarthritis of the knee. J Rheumatol. 2000;27:1513–1517
  24. Peterfy C, Li J, Siam S, Duryea J, Lynch J, Miaux Y. Comparison of fixed-flexion positioning with fluoroscopic semi-flexed positioning for quantifying radiographic joint-space width in the knee: test-retest reproducibility. Skeletal Radiol. 2003;32:128–132
  25. Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16:494–502
  26. Meenan RF, Gertman PM, Mason JH, Dunaif R. The arthritis impact measurement scales (Further investigations of a health status measure). Arthritis Rheum. 1982;25:1048–1053
  27. Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15:1833–1840
  28. Brinkmann JJ, Perry JJ. Rate and range of knee motion during ambulation in healthy and arthritic subjects. Phys Ther. 1985;65:1055–1060
  29. Messier SP, DeVita P, Cowan RE, Seay J, Young HC, Marsh AP. Do older adults with knee osteoarthritis place greater loads on the knee during gait? (A preliminary study). Arch Phys Med Rehabil. 2005;86:703–709
  30. Li Y, Crompton RH, Alexander RM, Günther MM, Wang W. Characteristics of ground reaction forces in normal and chimpanzee-like bipedal walking by humans. Folia Primatol (Basel). 1996;66:137–159
  31. Schmitt D. Insights into the evolution of human bipedalism from experimental studies of humans and other primates. J Exp Biol. 2003;206:1437–1448
  32. Yaguramaki N, Nishizawa J, Adachi K, Endo B. The relationship between posture and external force in walking. Anthropol Sci. 1995;103:117–140
  33. Somers TJ, Keefe FJ, Pells JJ, et al. Pain catastrophizing and pain-related fear in osteoarthritis patients: relationships to pain and disability. J Pain Symptom Manage. 2009;37:863–872
  34. Field A. Discovering statistics using SPSS. 2nd ed.. London: Sage Publications; 2005;
  35. Weidenhielm L. Kinetic and kinematic characteristics of gait in patients with medial knee arthrosis. Acta Orthop Scand. 2003;74:369
  36. Sims E, Nebel M, Keefe K. Severity of knee osteoarthritis and its effect on gait mechanics in walking. Proceedings of the American Society of Biomechanics Annual Meeting. 2006;Blacksburg, VA
  37. Thorp LE, Sumner DR, Block JA, Moisio KC, Shott S, Wimmer MA. Knee joint loading differs in individuals with mild compared with moderate medial knee osteoarthritis. Arthritis Rheum. 2006;54:3842–3849
  38. DeVita P, Hortobagyi T. Obesity is not associated with increased knee joint torque and power during level walking. J Biomech. 2003;36:1355–1362
  39. Mattsson E, Evers Larsson U, Rossner S. Is walking too exhausting for obese women?. Int J Obes Relat Metab Disord. 1997;21:380–386
  40. Robon MJ, Perell KL, Fang M, Guererro E. The relationship between ankle plantar flexor muscle moments and knee compressive forces in subjects with and without pain. Clin Biomech (Bristol, Avon). 2000;15:522–527
  41. Landry SC, McKean KA, Hubley-Kozey CL, Stanish WD, Deluzio KJ. Knee biomechanics of moderate OA patients measured during gait at a self-selected and fast walking speed. J Biomech. 2007;40:1754–1761
  42. Munderman A, Dyrby C, Hurwitz DE, Sharma L, Andriacchi T. Potential strategies to reduce medial compartment loading in patients with knee osteoarthritis of varying severity: reduced walking speed [published erratum appears in Arthritis Rheum 2004;50:4073]. Arthritis Rheum. 2004;50:1172–1178
  • a SynaFlexer X-ray positioning frame; Synarc, 575 Market St, 17th Fl, San Francisco, CA 94105.
  • b Brower Timing Systems, 12660 South Fort St, Ste 102, Draper, UT 84020.
  • c Motion Analysis Corp, 3617 Westwind Blvd, Santa Rosa, CA 95403.
  • d AMTI force plates; Advanced Medical Technologies Inc, 176 Waltham St, Watertown, MA 02472-4800.
  • e Version 12.0.1; SPSS, Inc, 233 S. Wacker Dr, 11th Fl, Chicago, IL 60606.

 Supported by the National Institutes of Health (grant no. AR50245).

 No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated.

 Reprints are not available from the author.

PII: S0003-9993(09)00670-4

doi:10.1016/j.apmr.2009.07.010

Archives of Physical Medicine and Rehabilitation
Volume 90, Issue 11 , Pages 1874-1879, November 2009

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