Association of pain with frequency and ... - Wiley Online Library

Arthritis Care & Research Vol. 63, No. 7, July 2011, pp 991–997 DOI 10.1002/acr.20476 © 2011, American College of Rheumatology

ORIGINAL ARTICLE

Association of Pain With Frequency and Magnitude of Knee Loading in Knee Osteoarthritis SHAWN M. ROBBINS,1 TREVOR B. BIRMINGHAM,2 JACK P. CALLAGHAN,3 GARETH R. JONES,4 BERT M. CHESWORTH,2 AND MONICA R. MALY5

Objective. Although the relationship between pain and the magnitude of medial knee loading has been previously studied, the contribution of frequency of loading has not. The objective of this study was to determine whether the addition of loading frequency (steps/day) to loading magnitude (knee adduction moment [KAM] impulse) helps explain variance in knee pain in people with knee osteoarthritis (OA). Methods. Participants were adults with symptomatic knee OA with radiographic signs in the medial knee compartment (n ⴝ 38, 10 women). Pain was measured using the pain subscale of the Knee Injury and Osteoarthritis Outcome Score. Participants wore an accelerometer for 1 week to determine the average number of steps/day. The external KAM impulse was calculated from 3-dimensional gait analysis as participants ambulated at self-selected speeds. Knee extensor strength was measured with an isokinetic dynamometer. Linear regression was used to examine the relationship between pain and steps/day after controlling for the KAM impulse, knee extensor strength, and body mass index (BMI). Results. After controlling for BMI (R2 ⴝ 0.02), knee extensor strength (R2change ⴝ 0.26, P < 0.05), and KAM impulse (R2change ⴝ 0.11, P < 0.05), steps/day contributed an additional 9% of variance in pain (P < 0.05). This model accounted for a total of 49% of the variance in pain (F[4,33] ⴝ 7.77, P < 0.05). Conclusion. Increased knee loading frequency and magnitude were associated with increased pain. Objective measures of loading frequency should be considered when investigating the incidence and progression of knee OA.

INTRODUCTION Knee osteoarthritis (OA) is a prevalent condition resulting in pain and altered neuromuscular performance, including weakness and impaired mobility (1,2). Pain is a priority for people with knee OA, and much effort and money is invested in managing knee pain using interventions such Supported by the Natural Sciences and Engineering Research Council of Canada (grant 353715) and the Fowler Kennedy Sports Medicine Clinic. Dr. Robbins’ training was supported in part by the Joint Motion Program, a CIHR Training Program in Musculoskeletal Health Research and Leadership, and by the Physiotherapy Foundation of Canada through an Ann Collins Whitmore Memorial Award. 1 Shawn M. Robbins, PT, PhD: University of Western Ontario, London, Ontario, and Dalhousie University, Halifax, Nova Scotia, Canada; 2Trevor B. Birmingham, PT, PhD, Bert M. Chesworth, PT, PhD: University of Western Ontario, London, Ontario, Canada; 3Jack P. Callaghan, PhD: University of Waterloo, Waterloo, Ontario, Canada; 4Gareth R. Jones, PhD: University of British Columbia Okanagan, Kelowna, British Columbia, Canada; 5Monica R. Maly, PT, PhD: McMaster University, Hamilton, Ontario, Canada. Address correspondence to Monica R. Maly, PT, PhD, Room 435 IAHS, School of Rehabilitation Science, McMaster University, 1400 Main Street West, Hamilton, Ontario, L8S 1C7 Canada. E-mail: mmaly@mcmaster.ca. Submitted for publication September 11, 2010; accepted in revised form March 18, 2011.

as medication and surgery (3,4). To manage this symptom appropriately, it is important to understand the multitude of factors that influence pain. Advancing age and female sex are related to higher pain intensities experienced by people with knee OA (5). Mechanical factors including body mass index (BMI), knee extensor strength, and gait kinetics, also relate to pain (6,7). Among people with knee OA, those with a higher BMI report significantly more intense knee pain (7). Increased isokinetic knee extensor strength was associated with lower pain intensity in participants with knee OA (6). Measures of knee joint loading, specifically the external knee adduction moment (KAM), have previously shown a relationship to pain in this population, suggesting pain may play a role in modulating knee joint loads (8). The KAM is a measure of joint loading and is used extensively as a proxy for medial compartment loading in the knee with evidence of high validity and reliability (8 –12). Studies examining the relationship between the KAM and pain in people with knee OA have produced conflicting results. Pain may be a protective mechanism that assists in limiting knee load (13). Pain reduction from medications coincided with increased KAM, but these results could be partially explained by concurrent increases in gait speed (14,15). A study that investigated the peak KAM before and after lidocaine injections in participants with 991

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Significance & Innovations ●

Among people with knee osteoarthritis, those who report higher pain intensity and frequency also demonstrate greater loading frequency due to an active lifestyle, and a larger knee adduction moment impulse, denoting greater magnitude and duration of knee loading during a step.

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The frequency of knee joint loading explains some variance in pain that is not captured by measures of the magnitude of knee joint loading alone.

knee OA found no change in the peak KAM when gait speed was controlled (16). Cross-sectional studies examining the relationship between pain and the KAM in participants with knee OA provide limited clarification. Participants with symptomatic knee OA had greater KAM peak and impulse values compared to asymptomatic participants with knee OA (17). Another study found an inverse relationship between pain severity and peak KAM (8), while other work found no relationship between these variables (18). Differences in study methods, including the type of pain scale and study sample, might account for these inconsistencies. It may be unreasonable to expect a relationship between the pain experienced during activities of daily living and a biomechanical measure reflecting an instant in time. The KAM captures the magnitude of loading during a single step and does not include information about the frequency of loading that occurs during daily ambulatory activity. Considering that the properties of articular cartilage and bone vary depending on the frequency or duration of loading (19,20), measures that represent repetitive or prolonged loading would add insight into the role of knee loading in the study of OA. For instance, cyclic compressive loading of rat bones decreased their strength, defined as resistance to bending, and a dose-response relationship between compressive forces and increased damage was demonstrated (20). Also, subchondral osteoblasts from porcine models exposed to cyclic stresses modified the metabolism of chrondrocytes, resulting in decreased collagen synthesis (21). Further evidence for considering loading frequency is evident in studies examining the relationship between the risk of knee OA development and physical activity. A dose-response relationship exists between physical activity and risk for developing knee OA (22). Men with knee OA (n ⫽ 295) were more likely to be exposed to a high frequency of occupational kneeling, squatting, heavy lifting, and carrying (odds ratio [OR] 2.4) than healthy men (n ⫽ 327) (23). A history of regular sports participation increased the risk of incident knee OA (OR 3.2) in 354 participants over 5 years (24). In contrast, other studies have demonstrated no relationship between physical activity and incidence of knee OA (25,26). Methodologic inconsistencies, including differences in the selfreport physical activity measures and group differences, partly explain these contradictory findings (27). Objective measures of physical activity, including pedometers and

accelerometers, may be useful in identifying a relationship between loading repetition and knee OA incidence or progression. Also, it remains unclear whether loading frequency shares any relationship with the sequelae of knee OA, including pain. Loading frequency may explain additional variance in pain not captured by measures of knee loading magnitude alone. This study examined the relationship between pain and loading frequency in addition to other mechanical factors that have shown a previous relationship to pain. Specifically, the purpose of this study was to determine whether the addition of loading frequency (steps/day) to loading magnitude (the KAM) helps explain variance in knee pain in people with knee OA. It was hypothesized that loading frequency would explain a significant portion of variance in pain, after controlling for BMI, knee extensor strength, and magnitude of knee load (the KAM).

PATIENTS AND METHODS Study design and sample. This cross-sectional study was conducted from October 2008 to February 2010 in a community in southwestern Ontario, Canada. Participation required a physician diagnosis of clinical and radiographic knee OA consistent with the criteria described by the American College of Rheumatology (28), with the most severe radiographic involvement in the medial compartment. In brief, these criteria required knee pain, age ⬎18 years, radiographic evidence of osteophytes, and any one of the following: age ⬎50 years, morning stiffness ⬍30 minutes, or crepitus with active range of motion of the knee. A Kellgren Lawrence (K/L) scale score of knee OA disease severity (between 2 and 4 on a radiograph) determined by one of the investigators (SMR) was required to represent mild to severe knee OA (29). For radiographs, participants stood such that their patellae, centered on the femoral condyles, were in the frontal plane (30). The inclusion criteria also required participants to be between ages 40 and 80 years and report knee pain within the last week. Exclusion criteria included the following: lower extremity joint replacement, previous knee ligament injury, lower extremity surgery or trauma within the last 12 months, knee injections within the last 3 months, gait aid or brace used for mobility, and neurologic or cardiovascular conditions that limit mobility (e.g., Parkinson’s disease, emphysema). Forty participants were recruited for the study. However, the data from 2 participants were dropped from the final analysis. One participant was unable to complete data collection due to time limitations, and the other reported she previously had a cerebral vascular accident after being enrolled in the study. The characteristics for the remaining 38 participants (10 women) are provided in Table 1. The sample included 47%, 37%, and 16% of participants with mild, moderate, and severe knee OA, respectively, as determined by K/L scale scores. In those participants with bilateral radiographic knee OA (n ⫽ 29), the knee with the highest intensity of symptoms reported by the participant was chosen as the study leg. The study was approved by the University of Western Ontario Research Ethics Board and written informed con-

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Table 1. Descriptive statistics for the demographic, dependent, and independent variables for the study sample (n ⴝ 38, 10 women)* Variable

Mean ⴞ SD

Median

Range

Age, years Height, meters Body mass, kg BMI, kg/m2 Gait speed, meters/second KOOS-pain, scale 0–100 Knee extensor strength, Nm KAM impulse, Nm ⫻ seconds KAM impulse, %BW ⫻ Ht ⫻ seconds Steps/day†

54 ⫾ 7 1.75 ⫾ 0.10 93.26 ⫾ 16.64 30.35 ⫾ 4.19 1.12 ⫾ 0.19 56 ⫾ 16 123.96 ⫾ 48.31 22.70 ⫾ 10.11 1.39 ⫾ 0.44 3,568 ⫾ 1,374

53 1.78 92.40 29.94 1.13 58 127.37 21.31 1.35 3,307

43–72 1.50–1.88 58.45–128.23 20.77–41.21 0.79–1.46 25–83 38.12–215.13 5.88–62.75 0.49–2.80 1,363–7,057

* BMI ⫽ body mass index; KOOS-pain ⫽ pain subscale of the Knee Injury and Osteoarthritis Outcome Score (lower scores represent severe/frequent pain); Nm ⫽ Newton meters; KAM ⫽ external knee adduction moment; BW ⫽ body weight; Ht ⫽ height. † By the study leg only.

sent was obtained from the participants prior to study enrollment. Pain. Pain was the dependent variable and was measured using the pain subscale of the Knee Injury and Osteoarthritis Outcome Score (KOOS-pain) (31). This subscale consists of 9 items that ask the participants to rate pain frequency and intensity on a scale of 0 (none) to 4 (extreme) over the last week. Eight of the items consist of questions rating pain intensity during different movements or activities, and the ninth item relates to pain frequency. The subscale was transformed to a 0 –100 scale, where 100 ⫽ no pain and 0 ⫽ severe pain. The KOOS-pain subscale previously demonstrated test–retest reliability (intraclass correlation coefficient [ICC2,1] 0.85) and construct validity with the body pain subscale of the Short Form 36 health survey (r ⫽ 0.46) (31). Knee extensor strength. Concentric knee extensor strength was represented by peak knee extensor torque measured using an isokinetic dynamometer (Biodex System 3 Pro, Biodex Medical Systems). Participants were seated with the hip in 90o of flexion and performed 2 submaximal and 2 maximal practice trials to become accustomed to testing. The range of motion during testing was restricted from 10 –90 degrees of knee flexion to avoid discomfort at end range. Data collection included 5 maximum effort trials for concentric knee extension at 60o/ second. The 3 highest values attained were averaged together and presented in units of Newton meters (Nm). This method has produced data with excellent test–retest reliability in participants with knee OA in our laboratory (ICC2,1 0.93) (32). BMI was calculated as body mass divided by height squared (kg/m2). Loading magnitude. Kinematic and kinetic gait data were collected with an 8-camera, 3-dimensional motion capture system (Motion Analysis Corporation) using a 60 Hz sample rate and a synchronized floor-mounted force plate (Model OR6-5, Advanced Mechanical Technology) using a 1,200 Hz sample rate. Twenty-two reflective mark-

ers (12 mm diameter) were attached to the participants according to a modified Helen Hayes maker configuration (33). The participants stood statically on the force plate with additional markers on the medial knees and ankles to determine joint centers and calculate body mass. Functional hip joint centers were calculated as the participants actively flexed, extended, abducted, and adducted each hip (34). Participants then ambulated barefoot at a selfselected speed across an 8-meter long capture area after completing 2 warm-up trials. Five trials were captured with the participants striking the force plate with the study leg. Participants completed testing while barefoot in order to limit the effect of shoes on the KAM (35). Gait data processing included filtering marker data with a fourthorder zero-lag Butterworth filter at 6 Hz cutoff frequency using commercial software (EvART 4.0, Motion Analysis Corporation). The KAM waveform was generated using inverse dynamics with a fixed tibia coordinate system using commercial software, and the KAM was not normalized to body mass or height (Orthotrak 6.2.4, Motion Analysis Corporation). The KAM impulse for each gait trial was calculated by integrating the stance phase portion of the KAM waveform using a custom-written software program (Matlab 7.0.1, Mathworks). The mean KAM impulse from the 5 trials for each participant, in units of Nm ⫻ seconds, was calculated and represented the magnitude of knee loading. The KAM impulse, as opposed to the peak value, was chosen as the measure of knee loading magnitude because it incorporates the KAM magnitude and duration for the entire stance phase. The KAM impulse also demonstrates a relationship with pain in people with knee OA (17). KAM impulse values in non-normalized units of Nm ⫻ seconds were used in the analysis, as opposed to normalized to body mass and/or height, because normalizing the KAM impulse might divert attention from the absolute mechanical loading at the knee (36). However, the descriptive statistics for the non-normalized (Nm ⫻ seconds) and normalized to body weight (BW) and height (Ht) (%BW ⫻ Ht ⫻ seconds) KAM impulse were calculated to allow comparisons with other studies.

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Table 2. Pearson’s correlation coefficients (2-tailed P values) between the dependent and independent variables* Variable

KOOS-pain

BMI

Knee extensor strength

KAM impulse

Steps/day

KOOS-pain BMI Knee extensor strength KAM impulse Steps/day

–

⫺0.15 (0.38) –

0.49 (⬍ 0.01) ⫺0.11 (0.51) –

⫺0.22 (0.18) 0.41 (0.01) 0.29 (0.07) –

⫺0.33 (0.04) ⫺0.11 (0.52) ⫺0.18 (0.28) ⫺0.17 (0.31) –

* KOOS-pain ⫽ pain subscale of the Knee Injury and Osteoarthritis Outcome Score (lower scores represent severe pain); BMI ⫽ body mass index; KAM ⫽ external knee adduction moment.

height. Knee extensor strength was entered in the second step. KAM impulse and steps/day were entered in the third and fourth steps, respectively. The additional explained variance provided by the KAM impulse or steps/ day was considered statistically significant if the P value was less than 0.05. Both histograms of the residuals and plots of the residuals against the predicted values of the dependent variables were utilized to examine the appropriateness of the regression analysis. Diagnostic statistics, including tolerance and variance inflation factors, examined the colinearity between independent variables. All analyses were completed using SPSS software, version 17.0.

Loading frequency. Steps/day was measured using a unidimensional accelerometer (GT1M, ActiGraph) with an epoch of 60 seconds (37). The epoch represents the time for which a sum of the steps taken during that time period is recorded by the accelerometer. Participants wore the accelerometer on their waist at the mid-axillary line of their study leg for 7 consecutive days. The participants wore the accelerometer during waking hours and removed it when sleeping, bathing, or swimming. Participants were telephoned or e-mailed twice during the week to facilitate compliance. Days on which the participants did not wear the device for at least 12 hours were excluded, and a minimum of 5 of the 7 days were required for determining mean steps/day, regardless of which days (i.e., weekday or weekend). Data from the participants that did not meet these conditions were not analyzed. The step counts from the accelerometer were then halved because the device records the number of steps taken by both legs, and only the steps taken by the study leg were required. The steps/ day for the study leg was calculated for each day and averaged together to produce the mean steps/day over the week and represented knee loading frequency.

RESULTS Descriptive statistics for the dependent and independent variables are provided in Table 1. Participants wore the accelerometers for a mean of 6.5 days for 14.8 hours/day capturing a total of 246 days. Twenty days were excluded from the final analysis because the participants did not wear the accelerometer for at least 12 hours on those days. All participants wore the accelerometer for at least 5 days for a minimum of 12 hours/day; therefore, no participants were excluded from the final analysis for noncompliance. The Pearson’s correlation coefficients among the independent and dependent variables are provided in Table 2. For the first step of the regression analysis, 2% of the variance in KOOS-pain was explained by BMI (Table 3). Knee extensor strength was added in the second step and significantly increased the combined explained variance by 26% (Table 3). Lower knee extensor strength values were associated with increased pain and represented by lower KOOS-pain scores. KAM impulse significantly accounted for an additional 11% of the explained variance when entered in step 3 (Table 3). Higher KAM impulse

Statistical analysis. Descriptive statistics, including means and SDs, were calculated for the demographic, dependent, and independent variables and gait speed. Accelerometer compliance was examined by calculating the mean number of days and hours of monitoring for the study sample. Pearson’s correlation coefficients examined the bivariate relationships between the variables. A hypothesis-driven, sequential forward linear regression analysis was completed. KOOS-pain was the dependent variable and BMI, knee extensor strength, KAM impulse, and steps/day were entered as the independent variables. BMI was entered in the first step of the regression model to control for the effects of body mass and

Table 3. Summary of linear regression analysis for the dependent variable, KOOS-pain* Step

Analysis

R

R

Adjusted R2

1 2 3 4

BMI BMI, knee extensor strength BMI, knee extensor strength, KAM impulse BMI, knee extensor strength, KAM impulse, steps/day

0.15 0.53 0.63 0.70

0.02 0.28 0.39 0.49

⫺0.01 0.24 0.34 0.42

2

R2 change

F change

P†

– 0.26 0.11 0.09

– 12.84 6.18 5.84

– ⬍ 0.01 0.02 0.02

* KOOS-pain ⫽ pain subscale of the Knee Injury and Osteoarthritis Outcome Score; BMI ⫽ body mass index; KAM ⫽ external knee adduction moment. † Represents the P value for the F change statistics.

Loading Frequency and Magnitude in Knee OA Pain values were associated with increased pain. Finally, steps/ day significantly accounted for an additional 9% of the explained variance when entered in step 4 (Table 3). Higher steps/day values were associated with increased pain. Together, BMI (standardized coefficient [␤] ⫽ ⫺0.07, P ⫽ 0.61), knee extensor strength (␤ ⫽ 0.57, P ⬍ 0.05), KAM impulse (␤ ⫽ ⫺0.41, P ⬍ 0.05), and steps/day (␤ ⫽ ⫺0.31, P ⬍ 0.05) significantly accounted for a total of 49% of the variance in KOOS-pain (F[4,33] ⫽ 7.77, P ⬍ 0.05). Examination of the residuals demonstrated the appropriateness of the linear regression analysis. Residuals were normally distributed and evidence was consistent with linear relationships. The plots indicated consistent variance in the residuals, and therefore they did not depend on the values of the predicted dependent variables. Diagnostic statistics also revealed no colinearity between independent variables.

DISCUSSION The number of steps completed each day, a measure of loading frequency, and the KAM impulse, a proxy for loading in the medial knee compartment, each explained a significant portion of variance in pain in people with knee OA. Greater loading repetition and greater medial loading magnitude were associated with increased pain. Loading repetition could incite pain related to knee OA through a variety of mechanisms. In vitro studies show that loading frequency alters articular cartilage and subchondral bone. The tibial articular cartilage from sheep knees following meniscectomy developed high strain centrally during only one hour of cyclic loading, and the cartilage remained deformed and dehydrated after loading (38). These findings from in vitro work provide potential pathways for the development of pain in people with knee OA. The current study demonstrates the importance of loading frequency or repetition in the pain experience of people with knee OA. In this study, participants that were more active experienced more pain. The participants might have remained physically active despite the pain due to life demands (i.e., work, childcare) or due to a desire to continue with physically active hobbies. Previous qualitative research supports this hypothesis. People with arthritis reported that they remained active despite the pain associated with arthritis in order to meet social expectations (39). A quantitative study also supports the relationship between pain and physical activity (40). Increased pain severity was associated with increased physical activity levels, and the researchers surmised that participants with pain remained active in an attempt to control their pain levels (40). Further research is required to investigate the foundation of the relationship between pain and loading frequency, including the reasons for remaining active despite pain. This finding is important considering that guidelines recommend that patients with OA should increase their physical activity levels in order to obtain the health benefits associated with physical activity (22). We do not suggest that people with knee OA should decrease their physical activity to control knee pain. Instead, the present results suggest that when introducing interventions aimed at in-

995 creasing physical activity, we must pay careful attention to the types of activities used and the symptoms among those people with knee OA. For instance, strengthening exercise programs have shown to decrease the pain of patients with knee OA, while concurrently increasing physical activity levels (41). Previous cross-sectional studies examining the relationship between loading magnitude, using the peak value of the KAM, and pain have demonstrated minimal or nonexistent relationships (18,42). Differences in the sample (where previous work included older participants and a greater proportion of women) and differences in pain measures might account for the discrepancies between the previous work and the current study. However, other studies support the current findings (8,43). Although the relationship is not strong, pain is a likely mediator of knee loading in people with knee OA. Further support is provided in studies examining the influence of pain relief on the KAM in participants with knee OA (14,15). However, because gait speed remains an issue in these studies, additional studies examining whether pain is a mediator of loading is warranted. Although the KAM impulse and steps/day accounted for relatively small magnitudes of variance in KOOS-pain scores (at 11% and 9%, respectively), it is important to note that pain is difficult to understand given the multitude of factors that influence this construct (7). Previous studies have found correlations between pain and peak KAM in the magnitude of r ⫽ 0.14 – 0.33 (8,18,42), similar to the values reported in the current study. Additionally, 51% of the variance in pain was not accounted for by BMI, knee extensor strength, KAM impulse, and steps/day in the current study. Other important factors, including psychosocial variables such as depressive symptoms and anxiety, likely contributed to the 51% of unexplained variance (44,45). Regardless, knee mechanics and physical activity play a role in the pain experience, and modifications to knee loading provide potential opportunities to limit pain. Different factors, including footwear and gait speed, influence the KAM and therefore potentially the results of this study (35,46). Participants ambulated without shoes during testing to limit the effect of shoes on the KAM, although many individuals might perform the majority of their daily ambulation while wearing shoes. Self-selected footwear has shown to increase the KAM in people with knee OA, and therefore our values could represent an underestimatior of the KAM (35,47). Additionally, participants completed testing at a self-selected speed to match their normal gait speed; however, we cannot confirm that these speeds closely match, and a person’s gait speed likely varies throughout a given day. Increases in gait speed have shown to decrease the KAM impulse but increase the peak KAM value (46). Further analysis revealed that including gait speed in the regression did not significantly add to the explained variance in pain (R2change ⫽ 0.02, P ⫽ 0.33), and the KAM impulse (R2change ⫽ 0.09, P ⬍ 0.05) and steps/day (R2change ⫽ 0.10, P ⬍ 0.05) still significantly explained the variance in pain. A larger proportion of women in the current sample would have increased the generalizability of the results.

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The majority of the study sample was recruited from a tertiary care center that specializes in orthopedics, including surgery, and the sample represents the patients referred to this center. Error in the calculation of the KAM impulse could have resulted from movement of the surface markers placed on the skin in relation to the underlying bone. This is relevant considering the majority of participants was overweight or obese and would have additional layers of soft tissue between the bone and surface markers. Increasing the sample size would have also permitted the investigation of the influence of other variables on pain such as disease severity. Another limitation is the use of pain medications was not standardized, and it is unclear if this influenced the results. Twenty-three of the participants reported they regularly used pain medications. However, post hoc analysis revealed the regular use of pain medication, which was self-reported on a demographic form, did not significantly correlate with the dependent or independent variables (r ⫽ ⫺0.12 to 0.16). Also, there were no significant differences in KOOS-pain scores, analyzed with an independent t-test, between those who took or did not take pain medication (t ⫽ ⫺0.07, P ⫽ 0.95). In summary, measures of knee loading frequency and magnitude, specifically steps/day and KAM impulse, influence the pain experienced by participants with knee OA. Using objective measures, future research should examine the role that physical activity and ambulatory mechanics have on the incidence and progression of knee OA. A further understanding of these relationships would assist in developing physical activity guidelines for people with knee OA. This would ensure that physical activity programs for knee OA populations were safe, effective, and could be conducted without an increase in symptoms or risk of disease progression. Additionally, the trade-off between loading frequency and magnitude should be investigated. Specifically, a decrease in loading magnitude, possibly through surgical intervention, gait modification, or orthotics use, might allow patients with knee OA to be more physically active, thereby leading to an increase in loading frequency without worsening symptoms or disease progression.

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AUTHOR CONTRIBUTIONS All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. Maly 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 conception and design. Robbins, Birmingham, Callaghan, Maly. Acquisition of data. Robbins, Birmingham, Jones, Maly. Analysis and interpretation of data. Robbins, Birmingham, Callaghan, Chesworth, Maly.

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