Obesity and other modifiable factors for physical inactivity measured by accelerometer in adults with knee osteoarthritis

Authors


  • ClinicalTrials.gov identifier: NCT0080171.

  • The findings and conclusions herein are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the Osteoarthritis Initiative.

Abstract

Objective

To investigate the public health impact of obesity and other modifiable risk factors related to physical inactivity in adults with knee osteoarthritis (OA).

Methods

The frequency of inactivity as defined by the US Department of Health and Human Services was assessed from objective accelerometer monitoring of 1,089 participants (ages 49–84 years) with radiographic knee OA during the Osteoarthritis Initiative 48-month visit (2008–2010). The relationship between modifiable factors (weight status, dietary fat, fiber, smoking, depressive symptoms, knee function, knee pain, and knee confidence) with inactivity was assessed using odds ratios (ORs) and attributable fractions (AFs), controlling for descriptive factors (age, sex, race, education, lives alone, employment, frequent knee symptoms, and comorbidity).

Results

Almost half (48.9%) of participants with knee OA were inactive. Being overweight (OR 1.8, 95% confidence interval [95% CI] 1.2–2.5) or obese (OR 3.9, 95% CI 2.6–5.7), having inadequate dietary fiber intake (OR 1.6, 95% CI 1.2–2.2), severe knee dysfunction (OR 1.9, 95% CI 1.3–2.8), and severe pain (OR 1.7, 95% CI 1.1–2.5) were significantly related to inactivity, controlling for descriptive factors. Modifiable factors with significant average AFs were being overweight or obese (AF 23.8%, 95% CI 10.5–38.6%) and inadequate dietary fiber (AF 12.1%, 95% CI 0.1–24.5%), controlling for all factors.

Conclusion

Being obese or overweight, the quality of the diet, severe pain, and severe dysfunction are significantly associated with physical inactivity in adults with knee OA. All components should be considered in designing physical activity interventions that target arthritis populations with low activity levels.

INTRODUCTION

Arthritis is a growing public health problem in the US, affecting almost 50 million adults (1). The prevalence of activity limitations attributable to arthritis is more than 21 million. Osteoarthritis (OA) affecting the knee is currently a leading cause of disability in adults (2–4). It is widely recognized that physical activity offers important benefits to persons with arthritis, including knee OA. Physical activity programs can reduce pain, improve physical performance, reduce depressive symptoms, and prevent or delay disability in knee OA (5–8). In addition to disease-specific benefits, randomized clinical trials show that physical activity can improve muscle strength and increase aerobic capacity, flexibility, and strength (9–11). Recent federal guidelines now include people with arthritis in the physical activity recommendations to promote these benefits (12).

Despite important health benefits from being physically active, persons with arthritis are particularly inactive and are at risk for poor health outcomes (13, 14). In a national US survey, 44% of persons with arthritis were classified as inactive (i.e., reporting no sustained 10-minute periods of moderate-to-vigorous [MV] physical activity in a week) compared to 36% of adults without arthritis (15). Physical inactivity may account for an estimated 21% of activity limitations attributed to arthritis (16). Furthermore, physical inactivity threatens full participation in both employment opportunities and independent community living, as well as leads to increased health care costs (17).

Identifying predictors of inactivity is important to develop public health interventions aimed at reducing inactivity. Therefore, this study simultaneously investigated risk factors that are modifiable and related to inactivity to identify strategic targets for public health intervention. For example, knee pain and function are commonly viewed as barriers to being physically active for adults with knee OA, and significant associations between inactivity were reported in the National Health Interview Survey (NHIS) (15) and the Canadian National Population Health Survey (NPHS) (18) studies. Similarly, being overweight, depressive symptoms, and smoking are associated with inactivity (7, 19, 20), and knee confidence is implicated by its strong association with physical function (21). Literature from the general population shows that low fiber intake has been associated with low physical activity levels (22).

There are limited reports on objectively measured physical activity that examine characteristics of inactivity. The purpose of this study was to identify modifiable risk factors that may increase the frequency of physical inactivity among adults with knee OA and to calculate the attributable fraction (AF) of modifiable risk factors that account for the excess physical inactivity in this sample.

Significance & Innovations

  • We investigated the potential public health impact of modifiable factors including being obese/overweight, low-fiber diet, inadequate dietary fat, smoking status, depressive symptoms, severe knee pain, and severe knee dysfunction related to physical inactivity in adults with knee osteoarthritis.

  • The public health importance of each modifiable risk factor on physical inactivity was estimated using the attributable fraction.

  • Modifiable factors significantly associated with physical inactivity (being obese/overweight, the quality of the diet, severe knee pain, and severe knee dysfunction) should be considered in developing physical activity interventions that target arthritis populations with low activity levels.

PATIENTS AND METHODS

Study population.

This physical activity study evaluated a subcohort from the Osteoarthritis Initiative (OAI), a prospective study investigating risk factors and biomarkers associated with the development and progression of knee OA in adults ages 45–79 years at enrollment, with or at high risk to develop knee OA. Annual OAI evaluations began in 2004 at 4 clinical sites (Baltimore, Maryland; Columbus, Ohio; Pittsburgh, Pennsylvania; and Pawtucket, Rhode Island) and are currently ongoing (http://www.oai.ucsf.edu/datarelease/About.asp). Internal review board approval was obtained at each of the participating sites. Each participant provided written informed consent. The OAI excluded individuals with rheumatoid or inflammatory arthritis; severe joint space narrowing in both knees on the baseline knee radiograph or unilateral total knee replacement and severe joint space narrowing in the other knee; bilateral total knee replacement or plans to have bilateral knee replacement in the next 3 years; inability to undergo a 3.0T magnetic resonance imaging examination of the knee because of contraindications; positive pregnancy test; inability to provide a blood sample; use of ambulatory aides other than a single straight cane for more than 50% of the time during ambulation; comorbid conditions that might interfere with the ability to participate in a 4-year study; or current participation in a double-blind randomized trial. All OAI participants underwent knee radiography at baseline. The radiographic acquisition protocol may be found at http://www.oai.ucsf.edu/datarelease/OperationsManuals.asp. Baseline films were assessed by clinic readers for the Osteoarthritis Research Society International (OARSI) atlas grades of tibiofemoral osteophytes and joint space narrowing (23). The baseline visit identified 2,679 participants with radiographic evidence of knee OA (i.e., definite or osteophyte grade of ≥1, according to the OARSI atlas) in one or both knees from the total OAI enrollment of 4,796 persons in 2004–2005.

Accelerometer monitoring of physical activity study was offered to a subcohort of OAI participants at the 48-month followup visit between 2008 and 2010. A total of 2,127 persons consented to participate in accelerometer monitoring, representing 78.4% of the 2,712 eligible subcohort participants (another 1,543 OAI participants had visits that preceded the accelerometer study start date and 541 were deceased or withdrew from the OAI study or did not return for the 48-month visit). This report included only the 1,223 participants with baseline radiographic knee OA as shown in Figure 1. Accelerometer and 48-month visit data were merged with OAI public data (24) containing information on participant characteristics.

Figure 1.

Flow chart of analytical sample OAI participants in accelerometer study. OA = osteoarthritis.

Outcome: physical activity measures.

Physical activity was measured using a GT1M Actigraph accelerometer, a small uniaxial accelerometer that measures vertical accelerations (25). Uniaxial accelerometer validation studies against whole-body indirect calorimetry showed high correlation with metabolic equivalent (r = 0.93) and total energy expenditure (r = 0.93) (26). The accuracy (27) and test–retest reliability (28) of Actigraph accelerometers under field conditions are established in many populations, including persons with OA (29). Accelerometers output an activity count, which is the weighted sum of the number of accelerations measured over 1-minute periods, where the weights are proportional to the magnitude of measured acceleration.

Trained research personnel gave participants uniform scripted instructions to wear the unit on a belt at the natural waistline on the right hip in line with the right axilla upon arising in the morning and continuously until retiring at night, except during water activities, for 7 consecutive days. Participants maintained a daily log to record time spent in water and cycling activities, which may not be fully captured by accelerometers. Accelerometer data were analytically filtered using methodology validated in adults with rheumatic disease (30–32). Periods of non-wear were defined as ≥90 minutes with zero activity counts (allowing for 2 interrupted minutes with counts <100) (31).

Accelerometer data included ≥4 valid days for each participant. A valid day of monitoring was defined as ≥10 wear hours in a 24-hour period (30). Total daily minutes of MV physical activity were calculated using methodology from the National Institutes of Health (counts ≥2,020/minute occurring in bouts lasting ≥10 minutes, with allowance for interruptions of 1 or 2 minutes below the MV threshold) (30). Each person was classified according to the US Department of Health and Human Services (DHHS) physical activity guidelines as meets recommendations (≥150 minutes of MV activity bouts/week in bouts lasting at least 10 minutes), low active (10–149 minutes of MV bouts/week), or inactive (zero minutes of MV activity bouts/week) (12).

Modifiable risk factors.

Modifiable general health factors included body mass index (BMI) and depressive symptoms. BMI was calculated from measured height (m2) and weight (kg). Persons were classified as normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25.0–29.9 kg/m2), or obese (BMI ≥30 kg/m2). High depressive symptoms were assessed by a score ≥16 from the Center for Epidemiologic Studies Depression Scale (CES-D) (33). Knee-specific factors included knee function, knee pain, and knee confidence. Self-reported current knee function and pain in the past 7 days were obtained from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Likert version 3.1, modified in the OAI (34). Person-level scores used the maximum WOMAC value of the 2 knees. For the purpose of analysis, we classified WOMAC knee function symptoms based on tertiles as follows: no dysfunction (bottom tertile) = 0; moderate dysfunction = 0.1–11.2; and severe dysfunction (top tertile) = 11.3–68. Similarly, WOMAC knee pain was classified using tertiles as no pain (bottom tertile) = 0; moderate pain = 0.1–3.9; and severe pain (top tertile) = 4–20. Knee confidence was assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS) (35) question, “How much are you troubled with lack of confidence in your knees?” with possible responses of not at all, mildly, moderately, severely, and extremely.

Modifiable behavior factors included smoking status and dietary intake. Smoking status was dichotomized as current smoking versus not current smoking or missing. Dietary intake was assessed at baseline using the Block 2000 systematic nutrition assessment (36, 37). For dietary intake variables, we focused on fat intake and dietary fiber intake as they represent the 2 extremes of low- and high-calorie density of food. Dietary factors were dichotomized as high fat (≥35% of daily calories) versus adequate/low fat intake, and as inadequate (<20 gm/day) versus adequate fiber intake (38, 39). Additional nutritional factors measured by the Block 2000 were only weakly associated with inactivity status and therefore were not considered relevant as modifiable factors. The questionnaires can be obtained from the publically available OAI web site (http://www.oai.ucsf.edu/datarelease/OperationsManuals.asp).

Descriptive factors.

Covariates were measured at the OAI 48-month visit except where noted. Descriptive covariates included sociodemographics and prior health factors. Sociodemographic baseline factors included race, age, sex, living status, education, and employment (at 24-month visit). Descriptive prior health factors included frequent knee symptoms, comorbidity, and the report of a total knee replacement prior to enrollment. Frequent knee symptoms were ascertained from a positive response to the question, “During the past 12 months, have you had pain, aching, or stiffness in or around your right/left knee on most days for at least one month?” Comorbidities were assessed by the modified Charlson comorbidity index (40) at the 24-month visit.

Statistical analysis.

Univariate analyses of baseline trend effects across physical activity levels were evaluated by a Mantel-Haenszel test for ordinal categories. A chi-square test for overall differences was applied to nominal variables. Modifiable factors associated with physical inactivity were evaluated by logistic regression, controlling for all descriptive and modifiable factors; an associated 95% confidence interval (95% CI) that falls above 1 indicates a significant association. Further analyses added interaction terms between sex and each modifiable factor to logistic regression models.

The AF related to inactivity was estimated for each modifiable risk factor (41). The sample AF estimates “excess” inactivity based on the risk factor frequency and its association with inactivity. The term “excess” conceptually refers to the reduction in the outcome that would occur if the risk factor were removed from the population. In a cross-sectional sample, the AF is the potential reduction in the outcome (e.g., inactivity) if the risk factor was totally absent (e.g., no obesity) (42, 43), but it does not imply cause and effect. An average AF (AAF) accounts for individuals with multiple factors, estimating the excess proportion of the outcome that can be attributed to any of the designated risk factors (44). The AAF is usually lower than the sum of individual AFs because an outcome is typically attributable to more than one risk factor. The AF estimates were assessed using Poisson regression with robust error variance and the sample prevalence of the modifiable risk factors employing SAS, version 9.2, with SAS macro in 2010–2011 (45, 46).

RESULTS

A total of 1,223 persons, ages 49–84 years, with radiographic knee OA participated in physical activity measurement using accelerometers at the 48-month OAI visit. At the baseline OAI visit, 67.1% of these had definite joint space narrowing equivalent of a Kellgren/Lawrence grade of 3 or 4 (47). Participants in this study had similar baseline age (62.0 years versus 62.8 years) and BMI (29.2 kg/m2 versus 29.8 kg/m2) compared to the nonparticipating OAI radiographic knee OA cohort, but were more frequently male (44.9% versus 38.9%) and white (81.0% versus 75.3%), with slightly less pain (mean WOMAC pain score 3.5 versus 4.6). Of the participants, 1,111 (90.8%) had at least 4 valid days of accelerometer monitoring data; a total of 1,089 participants (all but 2%) had complete covariate data.

These 1,089 adults with knee OA had a mean age of 66.1 years, were primarily female (54.8%), white (83.7%), working (53.9%), did not live alone (78.3%), and had post high-school education (84.9%). Few smoked (5.7%) or had high depressive symptoms (11.5%), but substantial portions reported high fat intake (46.3%), inadequate dietary fiber (79.2%), some limitations in knee function (69.1%), knee pain (68.6%), some trouble with lack of knee confidence (53.2%), and were overweight/obese (79.0%). In regard to physical activity, 48.9% (39.8% of men and 56.3% of women) were inactive, demonstrating no 10-minute bouts of MV activity during the week of monitoring. Only 10.2% attained recommended activity levels.

Descriptive and modifiable characteristics of this sample by physical activity characteristics are shown in Table 1. All descriptive factors were significantly associated with physical activity levels. Among modifiable factors, greater levels of physical inactivity were significantly associated with greater prevalence of overweight/obesity, inadequate dietary fiber intake, greater WOMAC knee dysfunction, and greater WOMAC knee pain.

Table 1. Physical activity distribution of adults with radiographic knee OA (n = 1,089)*
 No.Inactive, %Low active, %Meets reccomendations, %P for trend
  • *

    OA = osteoarthritis; BMI = body mass index; CES-D = Center for Epidemiologic Studies Depression Scale; WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index.

  • Mantel-Haenszel chi-square test for trend (1df) except for race, sex, and living arrangement comparisons, which used chi-square test for overall differences.

  • Includes 1 person with BMI below the normal range.

  • §

    WOMAC function tertiles based on maximum of left and right knees: no dysfunction (bottom tertile) = 0; moderate dysfunction = 0.1–11.2; and severe dysfunction (top tertile) = 11.3–68.

  • WOMAC pain tertiles based on maximum of left and right knees: no pain (bottom tertile) = 0; moderate pain = 0.1–3.9; and severe pain (top tertile) = 4–20.

Descriptive factors     
 Overall1,08948.941.010.2
 Age, years     
  49–5930033.754.312.0< 0.0001
  60–6937442.845.711.5 
  70–7941565.327.07.7 
 Male49239.847.213.0< 0.0001
 Female59756.335.97.9 
 Race/ethnicity     
  White91146.941.311.9< 0.0001
  Nonwhite17859.039.31.7 
 Live alone     
  Yes23658.133.18.90.0061
  No85346.343.110.6 
 Education     
  Post high-school92545.543.411.1< 0.0001
  High-school or less16467.727.44.9 
 Employed     
  Working58740.647.412.1< 0.0001
  Not working46458.633.08.4 
  Missing/other3857.939.52.6 
 Chronic knee pain     
  No57646.940.113.00.0114
  Yes51351.141.97.0 
 Total knee replacement prior to enrollment     
  No1,02068.127.54.40.0012
  Yes6947.641.910.6 
 Comorbidity score     
  078244.144.311.6< 0.0001
  1–226261.132.46.5 
  ≥34560.033.36.7 
Modifiable factors     
 General health factors     
  BMI normal (18.5–24.9 kg/m2)22935.846.317.9< 0.0001
  BMI overweight (25.0–29.9 kg/m2)44144.043.512.5 
  BMI obese (≥30 kg/m2)41961.135.33.6 
  Depressive symptoms     
   Not high96448.241.010.80.0948
   High (CES-D ≥16)12553.640.85.6 
Health behaviors     
 Fat intake     
  Adequate/low58547.441.211.50.1394
  High50450.640.78.7 
 Dietary fiber intake     
  Adequate22740.545.414.10.0018
  Low86251.037.89.2 
 Smoker     
  No1,02748.741.210.10.8391
  Yes/missing6251.637.111.3 
Knee-specific health factors     
 WOMAC dysfunction§     
  None33742.143.914.0< 0.0001
  Moderate (middle tertile)37846.041.512.4 
  Severe (top tertile)37457.837.74.6 
 WOMAC knee pain     
  None34243.342.714.0< 0.0001
  Moderate (middle tertile)36447.540.711.8 
  Severe (top tertile)38355.139.75.2 
 Knee confidence     
  Not troubled51048.839.611.60.2006
  Mild/moderately troubled53047.643.29.3 
  Severely/extremely troubled4963.330.66.1 

The association of modifiable factors with inactivity quantified as odds ratios (ORs) is summarized in Table 2. Modifiable factors significantly related to inactivity were being overweight (OR 1.8, 95% CI 1.2–2.5) or obese (OR 3.9, 95% CI 2.6–5.7), inadequate dietary fiber intake (OR 1.6, 95% CI 1.2–2.2), severe dysfunction (OR 1.9, 95% CI 1.3–2.8), and severe pain (OR 1.7, 95% CI 1.1–2.5) after controlling for descriptive factors. Being overweight (OR 1.8, 95% CI 1.2–2.5) or obese (OR 3.7, 95% CI 2.5–5.4) and inadequate dietary fiber intake (OR 1.5, 95% CI 1.1–2.1) were significantly related to inactivity when controlling for all descriptive and modifiable factors.

Table 2. Modifiable health factor ORs for physical inactivity in adults with radiographic knee OA (n = 1,089)*
Modifiable health factorsNo.Inactive, %Unadjusted, OR (95% CI)Adjusted for descriptive factors, OR (95% CI)Adjusted for descriptive/other modifiable factors, OR (95% CI)
  • *

    ORs = odds ratios; OA = osteoarthritis; 95% CI = 95% confidence interval; BMI = body mass index; WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index.

  • OR adjusted for descriptive factors (age, sex, race, living status, education, employment, chronic knee pain, total knee replacement, and comorbidities) with associated 95% CI.

  • OR adjusted for all descriptive and other modifiable factors with associated 95% CI.

  • §

    Includes 1 person with BMI below the normal range. BMI ranges are 18.5–24.9 kg/m2 for normal, 25.0–29.9 kg/m2 for overweight, and ≥30 kg/m2 for obese.

  • WOMAC function tertiles based on maximum of left and right knees: no dysfunction (bottom tertile) = 0; moderate dysfunction = 0.1–11.2; and severe dysfunction (top tertile) = 11.3–68.

  • #

    WOMAC pain tertiles based on maximum of left and right knees: no pain (bottom tertile) = 0; moderate pain = 0.1–3.9; and severe pain (top tertile) = 4–20.

Health behaviors     
 Fat intake     
  Adequate/low58547.4ReferenceReferenceReference
  High50450.61.1 (0.9–1.4)1.3 (0.9–1.6)1.0 (0.8–1.3)
 Dietary fiber intake     
  Adequate22740.5ReferenceReferenceReference
  Inadequate86251.01.5 (1.1–2.1)1.6 (1.2–2.2)1.5 (1.1–2.1)
 Smoker     
  No1,02748.7ReferenceReferenceReference
  Yes/missing6251.61.1 (0.7–1.9)1.4 (0.8–2.5)1.4 (0.8–2.5)
General health factors     
 Weight status§     
  BMI normal22935.8ReferenceReferenceReference
  BMI overweight44144.01.4 (1.0–2.0)1.8 (1.2–2.5)1.8 (1.2–2.5)
  BMI obese41961.12.8 (2.0–3.9)3.9 (2.6–5.7)3.7 (2.5–5.4)
 Depressive symptoms     
  Not high96448.2ReferenceReferenceReference
  High12553.61.2 (0.9–1.8)1.2 (0.8–1.8)1.0 (0.7–1.6)
Knee-specific factors     
 WOMAC dysfunction     
  None33742.1ReferenceReferenceReference
  Moderate (middle tertile)37846.01.2 (0.9–1.6)1.3 (0.9–1.8)1.1 (0.7–1.7)
  Severe (top tertile)37457.81.9 (1.4–2.5)1.9 (1.3–2.8)1.7 (0.9–3.1)
 WOMAC knee pain#     
  None34243.3ReferenceReferenceReference
  Moderate (middle tertile)36447.51.2 (0.9–1.6)1.4 (0.9–1.9)1.3 (0.8–2.0)
  Severe (top tertile)38355.11.6 (1.2–2.2)1.7 (1.1–2.5)1.1 (0.6–2.0)
 Knee confidence     
  Not troubled51048.8ReferenceReferenceReference
  Mildly/moderately troubled53047.61.0 (0.7–1.2)1.0 (0.7–1.3)0.8 (0.6–1.1)
  Severely/extremely troubled4963.31.8 (1.0–3.3)1.8 (0.9–3.6)1.4 (0.6–2.9)

The influence of modifiable factors on inactivity is further examined from a public health perspective. Figure 2 presents the modifiable factor AAFs adjusted for descriptive and other modifiable factors. The AAF accounts for the fact that some individuals have multiple modifiable risk factors. For example, among overweight/obese adults in this study, 80.2% had low-fiber diets and 70.8% had knee pain. The AAF model includes the significant modifiable factors based on the bivariate relationship between inactivity using logistic regression, i.e., dietary fiber intake, weight status, knee function, and knee pain. Being overweight/obese had a statistically significant 23.8% (95% CI 10.5–38.6%) relationship to excess inactivity. Inadequate dietary fiber was significantly related to another 12.1% (95% CI 0.1–24.5%). Together, being obese/overweight and having a low-fiber diet accounted for 35.9% of excess inactivity in these adults with knee OA. Remaining modifiable factors were related to <7% excess inactivity and were not significant.

Figure 2.

Adjusted average attributable fraction of inactivity for modifiable factors; adjusted for descriptive factors (age, sex, race, living status, education, employment, chronic knee pain, total knee replacement, comorbidities) and all modifiable factors (obese/overweight, inadequate dietary fiber, knee dysfunction, knee pain, high-fat diet, smoking, high depressive symptoms, or being troubled by knee confidence). ∗∗ = aggregated contribution of remaining modifiable risk factors (high-fat diet, smoking, high depressive symptoms, or being troubled by knee confidence).

DISCUSSION

This study contributes to public health efforts to improve health outcomes of persons with arthritis by examining the association of potentially modifiable risk factors associated with physical inactivity in a large cohort of adults with radiographic evidence of knee OA. An important strength of this study is the objective assessment of physical inactivity. Only 1 of 10 adults with knee OA had recommended physical activity levels. Notably, more than one-third of men and half the women were completely inactive, doing no sustained MV activity that lasted ≥10 minutes in a week. Modifiable factors significantly associated with inactivity were being overweight/obese and consuming a diet with inadequate fiber, the report of severe knee limitations, and severe knee pain. More than 23.8% of excess inactivity was related to being overweight/obese, and another 12.1% was related to inadequate dietary fiber consumption after accounting for other descriptive and modifiable factors.

A low level of physical activity among adults with arthritis is a recognized public health concern. However, assessing the magnitude of the problem has been a challenge due to differing methods for assessing physical activity. Earlier studies that relied on self-reported physical activity levels estimate that 23.8–57.8% of adults with arthritis in the US are inactive (13, 15, 16, 18, 19, 48). Imbedded into these estimates are differences related to the self-report of physical activity and how inactivity was defined. Inactivity was defined by no reported leisure time activity (19, 48), <10 minutes/week MV leisure activities (13), <3 sessions/month lasting ≥15 minutes of activities associated with moderate intensity energy expenditure (16, 18), and no reported activities lasting ≥10 minutes (15). In this study, the definition of physical inactivity is anchored on the federal DHHS definition and is assessed by objective accelerometer monitoring.

Modifiable factors were evaluated from 2 perspectives. The first perspective identifies factors associated with physical inactivity at the level of the individual. Modifiable factors significantly associated with inactivity based on adjusted ORs were obesity, knee pain, knee dysfunction, and dietary fiber intake. Earlier studies on adults reporting an arthritis diagnosis (18, 19) found a significant relationship between inactivity and being overweight, but a 2002 NHIS study did not find a significant association (15). These reports evaluate broader arthritis populations than the current study and rely on self-report to assess inactivity and weight status. Self-report is related to underreporting of weight (49) and overreporting of physical activity amount/intensity (50). It is not known how reporting accuracy may influence the apparent association between inactivity and weight status, but measurement issues may contribute to the mixed findings across earlier studies.

Significant associations between inactivity and self-reported knee dysfunction (OR 1.9) and pain (OR 1.7) were in line with findings from the NHIS (15) and Canadian NPHS (18) studies. We also found a significant association between inactivity and inadequate fiber intake (OR 1.6). In the general adult population, low fiber intake has been associated with low physical activity levels (22, 51). This association may partially reflect the association of a low-fiber diet, representing low intake of fruits, vegetables, whole grains, and high consumption of refined carbohydrates/sugars related to snacking and a sedentary lifestyle (52, 53). Thus, a low-fiber diet may be a marker for unhealthy behaviors that include inactivity and high fat/high sugar snacking.

A second public health perspective examined the influence of each modifiable factor on inactivity by estimating the AAF for the sample. The sample AAF has public health relevance because the metric incorporates population criteria related to the risk factor prevalence plus its association with the outcome. Recognizing that many individuals had multiple modifiable risk factors (e.g., 80.2% of overweight/obese adults had low-fiber diets and 70.8% reported pain), we examined the simultaneous effect of all modifiable risk factors on inactivity. While pain (AAF 6.2%) and dysfunction (AAF 2.6%) are associated with lower levels of inactivity, being obese/overweight (AAF 23.8%) and inadequate dietary fiber (AAF 12.1%) explain a significant and larger proportion of inactivity. The results reinforce the contribution of excess weight and poor diet.

Pain and poor function are commonly viewed as barriers to being physically active for adults with knee OA (15, 18, 54). These findings indicate that being overweight/obese and an unhealthy diet are also important to consider. There is evidence that higher BMI is related to greater knee pain and poor function in adults with knee OA (55–58). In turn, high levels of pain are associated with binge eating (59). If obesity due to poor dietary patterns contributes to knee pain and resulting poor function through mechanical stress due to excess weight on the joint, then obesity supported by poor dietary choices may contribute to the relationship between knee pain and inactivity. However, randomized controlled trials show that exercise is safe and effective for overweight/obese adults with OA (58). Taken together, these results support incorporating weight loss and diet modification into interventions designed to promote health benefits from physical activity.

There are limitations to acknowledge in the present study. Accelerometers do not provide information on the type of the physical activity (e.g., household, transportation), information which may be helpful in targeting interventions. The accelerometer used in this study cannot capture water activities and may underestimate upper body movement or activities such as cycling. Diary information showed a median of 0 minutes/day spent in water and cycling activities, so the potential underestimate of inactivity is negligible. Radiographic data on knee joint damage and dietary information were only available from baseline, 4 years prior to the current study. Because joint damage does not improve over time and people with subsequent knee replacements were excluded, radiographic verification remains valid. Dietary fiber intake tends to remain stable or decrease slightly over a 3- to 4-year period, as demonstrated by control groups from large nutritional trials (60, 61); a potential underestimate of inadequate fiber intake would likely understate the strength of its relationship with inactivity found in our analyses. Self-reported data are commonly subject to social desirability bias and recall bias. However, to minimize biases, we have used validated questionnaires such as Block 2000 for dietary variables, the CES-D for depressive symptoms, the WOMAC for knee function and pain, and the KOOS for knee confidence. Our results will be strengthened if participants overreported dietary fiber intake as we would have underestimated the effects of dietary fiber. Finally, causality cannot be inferred from these cross-sectional data. These limitations must be balanced against the substantial strengths of this study, which include the large sample size, clinical measures of height and weight as opposed to self-reports, radiographic verification of knee OA, and the age and sex diversity of this OA cohort. An important strength of this study is that the federal DHHS definition of inactivity (12) based on objective accelerometer monitoring was used.

Despite substantial health benefits related to physical activity, adults with radiographic knee OA were particularly inactive. A substantial 48.9% of adults with knee OA were classified as inactive, demonstrating no 10-minute episodes of MV activity in a week. There is a critical need to intensify public health efforts to reduce physical inactivity among adults with knee OA. Being obese/overweight, the quality of the diet, severe pain, severe dysfunction, and levels of physical activity are interrelated in adults with knee OA. One cannot hope to improve physical activity patterns in adults with knee OA without consideration for weight management, diet, and OA pain and disability, as all may affect successful achievement of activity goals. All components should be considered in developing physical activity interventions that target arthritis populations with low activity levels.

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. Lee 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. Lee, Song, Chang, van Horn, Bathon, Jackson, Mysiw, Nevitt, Dunlop.

Acquisition of data. Lee, Song, Bathon, Eaton, Hochberg, Jackson, Kwoh, Mysiw, Nevitt.

Analysis and interpretation of data. Lee, Song, Hootman, Semanik, Chang, Sharma, van Horn, Bathon, Eaton, Jackson, Kwoh, Nevitt, Dunlop.

ROLE OF THE STUDY SPONSOR

Merck Research Laboratories, Novartis Pharmaceuticals, GlaxoSmithKline, and Pfizer had no role in the study design or in the collection, analysis, or interpretation of the data, the writing of the manuscript, or the decision to submit the manuscript for publication. Publication of this article was not contingent upon approval by Merck Research Laboratories, Novartis Pharmaceuticals, GlaxoSmithKline, and Pfizer.

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