Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Dec 1.
Published in final edited form as: Musculoskeletal Care. 2023 Jun 4;21(4):1090–1097. doi: 10.1002/msc.1789

Modifiable physical and behavioural factors associated with widespread pain in older adults with radiographic evidence of knee osteoarthritis

Burcu Aydemir 1, Lutfiyya N Muhammad 3, Jing Song 1, Alison H Chang 2, Dorothy D Dunlop 1, Rowland W Chang 1,3,4, Yvonne C Lee 1,3
PMCID: PMC10714439  NIHMSID: NIHMS1949705  PMID: 37271894

Abstract

Objective.

To identify modifiable physical and behavioural factors associated with widespread pain (WSP) in older adults with radiographic evidence of knee osteoarthritis (OA).

Methods.

Cross-sectional initial visit data of participants with radiographic knee OA (Kellgren-Lawrence grade of ≥ 2) from the Osteoarthritis Initiative Study were analyzed. WSP was defined as pain on both sides of the body, above and below the waist, and in the axial skeleton. Time (hrs/d) spent participating in sitting and moderate-strenuous physical activities were calculated from the Physical Activity Scale for the Elderly questionnaire. Physical function was quantified using gait speed and the chair stand test. Restless sleep was assessed using an item on the CES-D Scale. Logistic regression models were constructed to examine the strength of the associations between primary exposures and WSP in unadjusted and adjusted analyses.

Results.

Among the 2637 participants (mean age 62.6 years, 58.6% female), 16.8% met the criteria for WSP. All primary measures of interest were related to WSP in unadjusted analyses. In adjusted multivariable analysis, slow gait speed (adjusted odds ratio [aOR] 1.43; 95% CI 1.01, 2.02), lower chair stand rate (aOR 0.98; 95% CI 0.97–0.99), and restless sleep (aOR 1.61; 95% CI 1.25–2.08) maintained significant associations with WSP.

Conclusion.

Poor sleep behaviours and low physical function capacity are associated with WSP in adults with radiographic knee OA. These findings highlight the importance of assessing sleep, physical function, and pain distribution in this population. Interventions to improve physical function and sleep behaviours should be investigated as potential strategies to mitigate WSP.

Keywords: functional performance, osteoarthritis, physical activity, sleep, widespread pain

1. Introduction

The knee joint is the most common site for lower limb osteoarthritis (OA) and a leading cause of disability in older adults (Cross et al., 2014; Felson et al., 2000). Although much attention is given to localized, joint-specific OA pain and its debilitating effects, growing evidence suggests that diffuse musculoskeletal pain is common in this population. For instance, there is a high prevalence of individuals with knee OA who report pain across multiple body sites or experience widespread pain (WSP) (Carlesso et al., 2017; Felson et al., 2017; Perruccio et al., 2012; Vina et al., 2020; White et al., 2011).

WSP is defined as pain on both sides of the body, above and below the waist, and in the axial skeleton (Wolfe et al., 1990). In the general population, 1 in 10 adults is affected with WSP (Mansfield et al., 2016), and high psychological distress (McBeth et al., 2001), poor sleep, low physical activity (McBeth, Lacey, & Wilkie, 2014), and reduced physical functioning (Eggermont et al., 2009) have been identified as modifiable physical and behavioural factors contributing to the development of WSP. Many of these factors have also been linked with knee symptoms (Allen et al., 2008; Chang et al., 2020; Thomas et al., 2008; Wise et al., 2010); however, the association between these factors and WSP in adults with radiographic knee OA is not well understood.

What is apparent is that adults with knee OA and WSP report poor OA related pain outcomes (Neogi et al., 2010; Riddle & Stratford, 2014), and an increase in painful body sites can drastically increase functional limitations over time (Kamaleri et al., 2008). Furthermore, preoperative presence of WSP increases the risk of unfavorable clinical pain outcomes post total knee arthroplasty (Vina et al., 2020). Individuals with WSP are also more likely to report higher pain severity and analgesic use pre- and post-total knee arthroplasty (Brummett et al., 2013; Hoogeboom et al., 2012).

Once established, WSP is more likely to persist than resolve (Papageorgiou, Silman, & Macfarlane, 2002), making it vital to pinpoint and treat early. The purpose of this investigation was to identify physical and behavioural lifestyle factors associated with WSP in a large group of older adults with radiographic knee OA. Identification of modifiable factors related to WSP in the broader non-surgical OA population may provide necessary information to improve assessments and interventions aimed at preventing WSP, leading to improvements in short- and long-term outcomes for adults with knee OA.

2. Methods

2.1. Study design and participants.

This is a secondary analysis of data collected at the initial visit from the Osteoarthritis Initiative (OAI) between 2004 and 2006. The OAI was an observational study that enroled male and female participants ages 45–79 years with or at risk of knee OA from 4 clinical sites: Baltimore, Maryland; Pittsburgh, Pennsylvania; Pawtucket, Rhode Island; and Columbus, Ohio. Primary objectives of the OAI were to study the natural history of OA and identify risk and prognostic factors for knee OA. The current study included a subsample of 2637 participants with radiographic evidence of knee OA (Kellgren-Lawrence (KL) grade of ≥ 2) in one or both knee(s). This was determined by bilateral radiographic evaluations of the knee obtained for all participants in weight-bearing posteroanterior and lateral fixed-flexion view and evaluated independently by two experienced readers. Approval from each participating site’s institutional review board was obtained. All participants enrolled in the study provided written informed consent. Further details of the study, protocols, and available data are publicly available at https://nda.nih.gov/oai/.

2.2. Outcome measure.

Widespread pain (WSP) was defined similarly to previous studies of knee OA (Carlesso et al., 2017; Riddle & Stratford, 2014) including studies in the OAI (Vina et al., 2020), which adapted the criteria set by the American College of Rheumatology (Wolfe et al., 1990). In brief, to meet the criteria for WSP, participants had to report pain in at least one body site above and below the waist, on the right and left side of the body, and in the axial region. Information regarding the presence of symptoms in various sites of the body was gathered at the initial OAI visit from a homunculus figure and joint specific questionnaires (back, hip, and knee). On the homunculus figure, participants were asked to indicate locations of pain, aching, or stiffness on most days during the past month. The joint specific questionnaires similarly addressed symptoms in the past 30 days, except for the hip-specific questions, which inquired about symptoms on most days for at least 1 month during the past 12 months. Participants were classified as either meeting these criteria for WSP (presence) or not (absence).

2.3. Primary exposures.

Time spent per day (hrs/d) participating in sitting activities (e.g., reading, handcrafts, watching TV) and moderate-strenuous activities (e.g., dancing, skating, jogging, swimming) were calculated from items within the leisure activity domain on the Physical Activity Scale for the Elderly (PASE) Questionnaire. The PASE collects information on the frequency (days/week) and amount of time (hours/day) spent performing different categories of activities over the past 7 days (Washburn et al., 1999). Each activity is scored for frequency using a 4-point scale ranging from 0 to 3, where 0 = Never and 3 = Often (5–7 days); and for hours per day using a 4-point scale ranging from 1 to 4, where 1= less than one and 4 = greater than four. For each of the activities included, the mid-point of given ranges in frequency and amount were used to calculate approximate average hours/day engaged in each activity (sitting, moderate, and strenuous). Then hours per day were calculated for each specific activity item (frequency*hours/7 days). For the moderate-strenuous activity time, the sum of separately calculated averages from the questions pertaining to “Moderate sport/recreation” and “Strenuous sport/recreation” activities were calculated to describe the total daily hours spent in moderate and strenuous activities.

Physical function was objectively quantified using gait speed (20-meter walk) and the chair stand test. Gait speed was measured using a timed 20-meter walk and computed into a binary measure to represent slow gait speed (< 1 meter/second), which is indicative of disability in older adults (Fritz & Lusardi, 2009). The rate of performance on the 5-times sit-to-stand test was utilized to assess lower extremity strength and endurance. Participants are instructed to stand from a chair then sit back down a total of 5 times as quickly and safely as possible with their arms folded across their chest. The total time required to complete 5 repetitions was measured with a stopwatch. The rate of performance on the chair-stand test was expressed as the number of stands per minute calculated from the time required to complete 5 chair stands.

Restless sleep during the past week was assessed using an item on the Centers for Epidemiologic Studies Depression (CES-D) scale (Radloff, 1977). This item asks participants to recall how often in the past week their sleep was restless with four response options ranging from ‘rarely or none (1–2 days)’ to ‘most or all of the time (5–7 days)’. The presence of restless sleep in this study was defined as reporting at least 3 or more days (Song et al., 2021).

2.4. Covariates.

Relevant demographic and health-related factors were gathered from baseline data and adjusted for in the multivariable analyses. Demographic factors included age in years, sex (male, female), self-reported race (White, Black or African American, Other), and level of education (college and above, less than or equal to high school). Health-related factors included body mass index calculated by height and weight (BMI [kg/m2]; normal < 25, overweight 25–29.9, obese ≥ 30), radiographic KL grade greater than 2 (yes/no), self-reported previous knee injury (yes/no), and presence of comorbidity (0 or ≥ 1) determined using the modified Charlson Comorbidity Index (Katz et al., 1996). General mental health was assessed as a binary measure with the mental component summary score (cutoff Z-score > 50) of the Medical Outcome Study Short Form (SF-12), a valid and reliable measure for health status (Ware, Kosinski, & Keller, 1996).

2.5. Statistical analysis.

Distributions, frequencies, and appropriate assumptions were checked prior to all analyses. For all participant descriptive characteristics, we calculated percentages for categorical variables and mean and SD for continuous variables. To evaluate the associations between modifiable physical and behavioural factors and WSP, logistic regression models were constructed. First unadjusted logistic regression models were constructed to examine the relationship between each of the following independent variables: sitting activity time (hrs/day), moderate-strenuous activity time (hrs/day), slow gait speed (< 1 m/s), chair stand rate (count/min), and restless sleep (≥ 3 days), with the dependent variable: WSP (presence/absence). Odds ratios (OR) and 95% confidence intervals (CI) were used to summarize logistic regression findings. Adjusted odds ratios (aOR), controlling for demographic (age, sex, race, and education level) and health related factors (BMI, KL grade, previous knee injury, comorbidity, and mental component summary score) were estimated using a multivariable logistic regression model that included all independent variables. To address the possible influence of symptomatic knee OA on the observed relationships, we conducted an analysis stratified by whether people reported frequent knee symptoms. Frequent knee symptoms was defined based on response (yes/no) to a question about whether participants experienced pain, aching, or stiffness on most days for at least one month during the past one year in at least one knee. All statistical analyses were performed using R.

3. Results

3.1. Sample Characteristics.

Participant descriptive characteristics for the entire cohort are presented in Table 1. Of the 2637 participants included in the analyses, the mean age was 62.6 ± 9.0 years, and 16.8% met the criteria for WSP. The sample was predominantly female (58.6%), white (77%), obese (44.5%), and with an education level of college or above (81.8%). Over half (52%) of the sample reported frequent knee symptoms or answered yes to the question regarding pain, aching or stiffness in either knee on most days for at least one month during the past one year.

Table 1.

Participant characteristics of overall sample and according to widespread pain (WSP) presence.

Exposure No WSP
(n = 2193)		 WSP
(n = 444)		 Overall
(n = 2637)
Age, y, mean (SD) 62.6 (9.1) 62.4 (8.6) 62.6 (9.0)
Sex (Female), % 56.0 71.2 58.6
Race, %
 White 78.0 72.1 77.0
 Black or African American 19.2 25.5 20.3
 Other 2.8 2.5 2.7
Education (College and above), % 82.7 77.5 81.8
BMI category, %
 Normal (< 25) 16.9 15.1 16.6
 Overweight (25–29.9) 39.4 36.3 38.9
 Obese (> 30) 43.6 48.6 44.5
Symptomatic knee OA (yes), % 47.7 73.2 52.0
KL grade > 2 (yes), % 47.4 46.4 47.3
History of Knee Injury (yes), % 48.5 51.4 49.0
Comorbidity ≥ 1, % 25.3 32.7 26.5
Gait speed < 1 m/s, % 6.9 14.4 8.2
Chair Stand rate, mean (SD) 28.0 (10.7) 24.3 (10.8) 27.4 (10.8)
Restless sleep ≥ 3 days/wk, % 15.4 27.3 17.4
Sitting Activity, hrs/d, mean (SD) 2.8 (1.2) 2.9 (1.3) 2.8 (1.2)
Moderate-Strenuous Activity, hrs/d, mean (SD) 0.30 (0.62) 0.21 (0.52) 0.28 (0.61)
SF-12 MCS > 50a 78.4 64.6 76.1
Open in a new tab

Abbreviations: BMI, body mass index [kg/m2]. SF-12 MCS, Short Form 12 mental component summary.

a

Z-score, scores above 50 indicate better mental health compared to general population mean (50).

3.2. Associations between primary factors and WSP.

In the unadjusted univariable models, more hours spent per day participating in sitting activities, less hours per day spent participating in moderate-vigorous activities, slow gait speed, lower chair stand rate, and restless sleep were each independently associated with WSP (Table 2). In the multivariable analysis, after adjusting for the effects of all measures including covariates, only female sex, slow gait speed, lower chair stand rate, restless sleep, and worse mental health remained significant predictors of meeting the criteria for WSP (Table 3). In analyses stratified by symptomatic knee OA status, results were the same except slow gait speed was only associated with WSP in the symptomatic knee group (aOR 1.61; 95% CI 1.09–2.39; Supplementary Table S1).

Table 2.

Unadjusted univariable analysis of associations between primary exposures and widespread pain in overall sample (n = 2637).

Primary Exposures Odds Ratio 95% CI P value
Gait speed < 1 m/s
 No Ref - -
 Yes 2.28 (1.67, 3.11) < 0.001
Chair Stand/min 0.97 (0.96, 0.98) < 0.001
Restless sleep ≥ 3 days/wk
 No Ref - -
 Yes 2.06 (1.62, 2.62) < 0.001
Sitting Activity (hrs/d) 1.11 (1.03, 1.21) 0.011
Moderate-Strenuous Activity (hrs/d) 0.75 (0.61, 0.92) 0.007
Open in a new tab

Abbreviations: CI, confidence interval.

Table 3.

Adjusted multivariable analysis of associations with widespread pain including all primary exposures and covariates (n = 2637).

Exposures Odds Ratio 95% CI P value
Age (y) 1.00 (0.98, 1.01) 0.540
Sex
 Male Ref - -
 Female 1.71 (1.35, 2.16) < 0.001
Race
 White Ref - -
 Other 1.01 (0.78, 1.30) 0.968
Education
 College and above Ref - -
 High school or less 1.09 (0.83, 1.42) 0.543
BMI category (kg/m2)
 Normal (< 25) Ref - -
 Overweight (25–29.9) 1.05 (0.76, 1.45) 0.783
 Obese (> 30) 0.99 (0.72, 1.36) 0.936
KL grade > 2
 No (= 2) Ref - -
 Yes (3 or 4) 1.00 (0.80, 1.24) 0.962
History of Knee Injury
 No Ref - -
 Yes 1.21 (0.97, 1.50) 0.091
Comorbidity ≥ 1
 No Ref - -
 Yes 1.24 (0.99, 1.57) 0.067
SF-12 MCS > 50
 No Ref - -
 Yes 0.66 (0.52, 0.84) < 0.001
Gait speed < 1 m/s
 No Ref - -
 Yes 1.43 (1.01, 2.02) 0.046
Chair Stand/min 0.98 (0.97, 0.99) < 0.001
Restless sleep ≥ 3 days/wk
 No Ref - -
 Yes 1.61 (1.25, 2.08) < 0.001
Sitting Activity (hrs/d) 1.09 (1.00, 1.19) 0.057
Moderate-Strenuous Activity (hrs/d) 0.89 (0.73, 1.09) 0.263
Open in a new tab

Abbreviations: CI, confidence interval; BMI, body mass index; SF-12 MCS, Short Form 12 mental component summary.

4. Discussion

The purpose of this cross-sectional investigation was to identify modifiable physical and behavioural factors associated with the presence of WSP in a large cohort of older adults with radiographic evidence of knee OA. Measures of physical activity, physical function, and restless sleep were associated with WSP in unadjusted analyses. Following adjustments for demographic and health factors including covariates, only slow gait speed, lower chair stand rate, and self-reported restless sleep (≥ 3 days) remained significantly associated with the presence of WSP.

Previous population-based reports have linked perception based self-report measures of functional limitations to WSP (Kamaleri et al., 2008; McBeth et al., 2014), but, to the best of our knowledge, no studies have examined the relationships between performance-based measures of physical function and WSP in patients with radiographic knee OA. Measures of gait speed and chair stand performance assess physical capacity to cope with performing common everyday tasks (e.g., walking, sit-to-stand transfer). These tests focus on lower body functional capacity, specifically related to mobility, balance, strength and endurance. As opposed to self-report measures of physical function that assess an individual’s perceived level of functional ability, performance-based measures are valuable in determining actual physical performance abilities or functional limitations.

Results of the stratified analysis suggest that the association between slow gait speed and WSP was driven by those with frequent knee symptoms. A possible reason for this could be that any limitations in movement, due to symptoms or joint pathology, may reduce movement quality and efficiency. This could in turn affect the capacity of an individual to perform at a higher level (walk faster). Our findings align with a report demonstrating that people with or at risk of knee OA and coexisting WSP tend to have consistent knee pain, which is related to worse self-reported physical function (Neogi et al., 2010). Additionally, walking is an activity that involves whole body movement patterns; thus, pain across other body sites may potentially contribute to slower and restrained joint motions. It is probable WSP is a trait that fluctuates over time and there is a complex interplay between multiple physical and behavioural factors. Future work is needed to gain a better understanding of the interplay between these factors and the manifestation of WSP.

Restless sleep (≥ 3 days/week) was another modifiable factor that maintained a persistent association with WSP after adjusting for other factors in the multivariable model in this study. This finding is similar to previous reports from population-based prospective studies that observed a relationship between sleep problems with the onset (Aili et al., 2018; McBeth et al., 2014) and resolution (Davies et al., 2008) of WSP in the general population. A possible explanation for the observed relationship could be that sleep may regulate mood and emotional brain-state to maintain affective stability (Yoo et al., 2007). Affective distress or emotional dysfunction is commonly observed in people with fibromyalgia, a prototypical WSP condition, who also present with extensive sleep problems (Abad, Sarinas, & Guilleminault, 2008; Thieme, Turk, & Flor, 2004). Moreover, getting a good night’s rest may help improve mood, which may ultimately influence overall pain perception (Hamilton, Catley, & Karlson, 2007). Poor sleep behaviours can also enhance pain sensitivity (Onen et al., 2001) that may reflect central sensitization or a hyperactive central nervous system that can abnormally amplify pain and sensory processing (Clauw & Hassett, 2017). Several studies using quantitative pain assessment methods have observed the presence of pain sensitivity across multiple body sites in patients with lower extremity OA (Bajaj et al., 2001; Finan et al., 2013; Imamura et al., 2008; Lee et al., 2011; Neogi et al., 2015). Together these studies imply that sleep behaviours may play an important neurobiological role in the manifestation of WSP among people with knee OA. However, the possible mechanisms underlying poor sleep behaviours and pain are complex and still not well understood. More longitudinal work needs to be done to determine the long-term effects of sleep behaviours on the spread of pain. It may be beneficial to include quantitative measures, such as quantitative sensory testing, in future longitudinal investigations to assess potential changes in underlying central mechanisms.

Assessing and monitoring the spatial extent of pain can be clinically useful for developing attainable treatment plans, especially considering that patient-reported measures of knee pain may not always be reliable and can be influenced by co-existing pain in other body regions (Riddle & Perera, 2020). This is most likely because OA patients may have a hard time differentiating their knee pain from pain across other body sites (Gooberman-Hill et al., 2007). Mobility and strength in the upper body is equally important to supplement activities of daily living, especially in people with lower extremity pain. For example, individuals with pain in their lower body (e.g., knee, hip) may use the upper extremities for balance and support when rising from a chair and ascending or descending stairs. Many activities of daily living involve whole body movements; therefore, protective adaptations to WSP may give rise to greater functional limitations and maladaptive behaviours. This could potentially lead to the avoidance of a wider range of daily activities over time. This could explain why people with knee OA who have WSP demonstrate different characteristics of knee pain (Neogi et al., 2010; Schiphof et al., 2013) and function (White et al., 2011) over time compared to those without WSP. Well-designed longitudinal investigations focused on monitoring changes in the spatial extent of pain are needed to develop a more comprehensive temporal understanding of the transitional stages of WSP and whole-body functional limitations.

A major strength of this study is the large representative sample of individuals with well-characterized evidence of radiographic knee OA and data on spatial pain distribution. Limitations of this study include the cross-sectional and observational nature of our analysis. Therefore, we cannot infer causal relationships. Pain can fluctuate over short periods of time which may impact the recall of pain occurrence especially when experienced across multiple body sites. As such, a well-designed prospective study evaluating these relationships frequently overtime would better characterize longitudinal relationships. Next, our study examined self-report measures of activity and sleep behaviours, which may differ from quantitative measures. To support these findings, these relationships could be evaluated utilizing actigraphy. Lastly, WSP involves a complex interplay of physical, psychological, and disease-specific factors; therefore, it is possible that other measures not included in our analyses may be more strongly related to WSP.

5. Conclusion

This cross-sectional secondary analysis demonstrates that physical function capacity and restless sleep (≥ 3 days/wk) are important modifiable factors associated with WSP in older adults with radiographic evidence of knee OA. Our findings suggest that screening patients for WSP and implementing whole-body therapeutic interventions to maintain or improve physical function early in the disease state may have useful clinical implications that deserve further study. In addition, maintaining an appropriate balance between good quality rest and activity behaviours may be key for mitigating WSP.

Supplementary Material

Supplemental Table S1

Acknowledgements:

This work was supported in part by the National Institute of Arthritis and Musculoskeletal and Skin Diseases [grant no. P30-AR072579, R01-AR054155, T32-AR007611, K24-AR080840, and R01-AR064850,].

Conflict of Interest Statement:

No author has any potential conflict of interest as related to this work. Dr. Lee discloses research support from Pfizer, receipt of medical writing support from Sanofi and Eli Lilly, and stock ownership in Cigna-Express Scripts; no other financial disclosures were reported by the authors of this article.

Footnotes

Ethics Statement: The Osteoarthritis Initiative (OAI) data are publicly available at https://nda.nih.gov/oai/. Each site obtained respective IRB approval, and all participants gave written informed consent.

Data Availability Statement:

The data underlying this article are available upon access request from the National Institute of Mental Health Data Archive (NDA) website [https://nda.nih.gov].

References

  1. Abad VC, Sarinas PSA, & Guilleminault C (2008). Sleep and rheumatologic disorders. Sleep Medicine Reviews, 12(3), 211–228. doi: 10.1016/j.smrv.2007.09.001 [DOI] [PubMed] [Google Scholar]
  2. Aili K, Andersson M, Bremander A, Haglund E, Larsson I, & Bergman S (2018). Sleep problems and fatigue as predictors for the onset of chronic widespread pain over a 5- and 18-year perspective. BMC Musculoskeletal Disorders, 19, 390. doi: 10.1186/s12891-018-2310-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Allen KD, Renner JB, Devellis B, Helmick CG, & Jordan JM (2008). Osteoarthritis and sleep: The Johnston County Osteoarthritis Project. The Journal of Rheumatology, 35(6), 1102–1107. [PMC free article] [PubMed] [Google Scholar]
  4. Bajaj P, Bajaj P, Graven-Nielsen T, & Arendt-Nielsen L (2001). Osteoarthritis and its association with muscle hyperalgesia: An experimental controlled study. PAIN, 93(2), 107–114. doi: 10.1016/S0304-3959(01)00300-1 [DOI] [PubMed] [Google Scholar]
  5. Brummett CM, Janda AM, Schueller CM, Tsodikov A, Morris M, Williams DA, & Clauw DJ (2013). Survey criteria for fibromyalgia independently predict increased postoperative opioid consumption after lower-extremity joint arthroplasty: A prospective, observational cohort study. Anesthesiology, 119(6), 1434–1443. doi: 10.1097/ALN.0b013e3182a8eb1f [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Carlesso LC, Niu J, Segal NA, Frey-Law LA, Lewis CE, Nevitt MC, & Neogi T (2017). The Effect of Widespread Pain on Knee Pain Worsening, Incident Knee Osteoarthritis (OA), and Incident Knee Pain: The Multicenter OA (MOST) Study. The Journal of Rheumatology, 44(4), 493–498. doi: 10.3899/jrheum.160853 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chang AH, Song J, Lee J, Chang RW, Semanik PA, & Dunlop DD (2020). Proportion and associated factors of meeting the 2018 Physical Activity Guidelines for Americans in adults with or at risk for knee osteoarthritis. Osteoarthritis and Cartilage, 28(6), 774–781. doi: 10.1016/j.joca.2020.03.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Clauw DJ, & Hassett AL (2017). The role of centralised pain in osteoarthritis. Clinical and Experimental Rheumatology, 35 Suppl 107(5), 79–84. [PubMed] [Google Scholar]
  9. Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, … March L (2014). The global burden of hip and knee osteoarthritis: Estimates from the Global Burden of Disease 2010 study. Annals of the Rheumatic Diseases, 73(7), 1323–1330. doi: 10.1136/annrheumdis-2013-204763 [DOI] [PubMed] [Google Scholar]
  10. Davies KA, Macfarlane GJ, Nicholl BI, Dickens C, Morriss R, Ray D, & McBeth J (2008). Restorative sleep predicts the resolution of chronic widespread pain: Results from the EPIFUND study. Rheumatology, 47(12), 1809–1813. doi: 10.1093/rheumatology/ken389 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Eggermont LHP, Bean JF, Guralnik JM, & Leveille SG (2009). Comparing Pain Severity Versus Pain Location in the MOBILIZE Boston Study: Chronic Pain and Lower Extremity Function*. The Journals of Gerontology: Series A, 64A(7), 763–770. doi: 10.1093/gerona/glp016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Felson DT, Lawrence RC, Dieppe PA, Hirsch R, Helmick CG, Jordan JM, … Fries JF (2000). Osteoarthritis: New insights. Part 1: the disease and its risk factors. Annals of Internal Medicine, 133(8), 635–646. doi: 10.7326/0003-4819-133-8-200010170-00016 [DOI] [PubMed] [Google Scholar]
  13. Felson David T., Niu J, Quinn EK, Neogi T, Lewis C, Lewis CE, … LaValley M (2017). Multiple Nonspecific Sites of Joint Pain Outside the Knees Develop in Persons With Knee Pain. Arthritis & Rheumatology, 69(2), 335–342. doi: 10.1002/art.39848 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Finan PH, Buenaver LF, Bounds SC, Hussain S, Park RJ, Haque UJ, … Smith MT (2013). Discordance between pain and radiographic severity in knee osteoarthritis: Findings from quantitative sensory testing of central sensitization. Arthritis & Rheumatism, 65(2), 363–372. doi: 10.1002/art.34646 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Fritz S, & Lusardi M (2009). White Paper: “Walking Speed: the Sixth Vital Sign”. Journal of Geriatric Physical Therapy, 32(2), 2–5. [PubMed] [Google Scholar]
  16. Gooberman-Hill R, Woolhead G, MacKichan F, Ayis S, Williams S, & Dieppe P (2007). Assessing chronic joint pain: Lessons from a focus group study. Arthritis and Rheumatism, 57(4), 666–671. doi: 10.1002/art.22681 [DOI] [PubMed] [Google Scholar]
  17. Hamilton NA, Catley D, & Karlson C (2007). Sleep and the affective response to stress and pain. Health Psychology, 26, 288–295. doi: 10.1037/0278-6133.26.3.288 [DOI] [PubMed] [Google Scholar]
  18. Hoogeboom TJ, den Broeder AA, Swierstra BA, de Bie RA, & van den Ende CHM (2012). Joint-pain comorbidity, health status, and medication use in hip and knee osteoarthritis: A cross-sectional study. Arthritis Care & Research, 64(1), 54–58. doi: 10.1002/acr.20647 [DOI] [PubMed] [Google Scholar]
  19. Imamura M, Imamura ST, Kaziyama HHS, Targino RA, Hsing WT, De Souza LPM, … Camanho GL (2008). Impact of nervous system hyperalgesia on pain, disability, and quality of life in patients with knee osteoarthritis: A controlled analysis. Arthritis Care & Research, 59(10), 1424–1431. doi: 10.1002/art.24120 [DOI] [PubMed] [Google Scholar]
  20. Kamaleri Y, Natvig B, Ihlebaek CM, & Bruusgaard D (2008). Localized or widespread musculoskeletal pain: Does it matter? PAIN, 138(1), 41–46. doi: 10.1016/j.pain.2007.11.002 [DOI] [PubMed] [Google Scholar]
  21. Katz JN, Chang LC, Sangha O, Fossel AH, & Bates DW (1996). Can comorbidity be measured by questionnaire rather than medical record review? Medical Care, 34(1), 73–84. doi: 10.1097/00005650-199601000-00006 [DOI] [PubMed] [Google Scholar]
  22. Lee YC, Lu B, Bathon JM, Haythornthwaite JA, Smith MT, Page GG, & Edwards RR (2011). Pain sensitivity and pain reactivity in osteoarthritis. Arthritis Care & Research, 63(3), 320–327. doi: 10.1002/acr.20373 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Mansfield KE, Sim J, Jordan JL, & Jordan KP (2016). A systematic review and meta-analysis of the prevalence of chronic widespread pain in the general population. Pain, 157(1), 55–64. doi: 10.1097/j.pain.0000000000000314 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. McBeth J, Macfarlane GJ, Hunt IM, & Silman AJ (2001). Risk factors for persistent chronic widespread pain: A community‐based study. Rheumatology, 40(1), 95–101. doi: 10.1093/rheumatology/40.1.95 [DOI] [PubMed] [Google Scholar]
  25. McBeth John, Lacey RJ, & Wilkie R (2014). Predictors of New-Onset Widespread Pain in Older Adults: Results From a Population-Based Prospective Cohort Study in the UK. Arthritis & Rheumatology, 66(3), 757–767. doi: 10.1002/art.38284 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Neogi T, Frey-Law L, Scholz J, Niu J, Arendt-Nielsen L, Woolf C, … Multicenter Osteoarthritis (MOST) Study. (2015). Sensitivity and sensitisation in relation to pain severity in knee osteoarthritis: Trait or state? Annals of the Rheumatic Diseases, 74(4), 682–688. doi: 10.1136/annrheumdis-2013-204191 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Neogi T, Nevitt MC, Yang M, Curtis JR, Torner J, & Felson DT (2010). Consistency of Knee Pain: Correlates and Association with Function. Osteoarthritis and Cartilage, 18(10), 1250–1255. doi: 10.1016/j.joca.2010.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Onen SH, Alloui A, Gross A, Eschallier A, & Dubray C (2001). The effects of total sleep deprivation, selective sleep interruption and sleep recovery on pain tolerance thresholds in healthy subjects. Journal of Sleep Research, 10(1), 35–42. doi: 10.1046/j.1365-2869.2001.00240.x [DOI] [PubMed] [Google Scholar]
  29. Papageorgiou AC, Silman AJ, & Macfarlane GJ (2002). Chronic widespread pain in the population: A seven year follow up study. Annals of the Rheumatic Diseases, 61(12), 1071–1074. doi: 10.1136/ard.61.12.1071 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Perruccio AV, Power JD, Evans HMK, Mahomed SR, Gandhi R, Mahomed NN, & Davis AM (2012). Multiple joint involvement in total knee replacement for osteoarthritis: Effects on patient-reported outcomes. Arthritis Care & Research, 64(6), 838–846. doi: 10.1002/acr.21629 [DOI] [PubMed] [Google Scholar]
  31. Radloff LS (1977). The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Applied Psychological Measurement, 1(3), 385–401. doi: 10.1177/014662167700100306 [DOI] [Google Scholar]
  32. Riddle DL, & Perera RA (2020). The WOMAC Pain Scale and Crosstalk From Co-occurring Pain Sites in People With Knee Pain: A Causal Modeling Study. Physical Therapy, 100(10), 1872–1881. doi: 10.1093/ptj/pzaa098 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Riddle DL, & Stratford PW (2014). Knee pain during daily tasks, knee osteoarthritis severity, and widespread pain. Physical Therapy, 94(4), 490–498. doi: 10.2522/ptj.20130331 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Schiphof D, Kerkhof HJM, Damen J, de Klerk BM, Hofman A, Koes BW, … Bierma-Zeinstra SMA (2013). Factors for pain in patients with different grades of knee osteoarthritis. Arthritis Care & Research, 65(5), 695–702. doi: 10.1002/acr.21886 [DOI] [PubMed] [Google Scholar]
  35. Song J, Lee J, Lee YC, Chang AH, Semanik PA, Chang RW, … Dunlop DD (2021). Sleep Disturbance Trajectories in Osteoarthritis. Journal of Clinical Rheumatology, 27(8), e440–e445. doi: 10.1097/RHU.0000000000001512 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Thieme K, Turk DC, & Flor H (2004). Comorbid depression and anxiety in fibromyalgia syndrome: Relationship to somatic and psychosocial variables. Psychosomatic Medicine, 66(6), 837–844. doi: 10.1097/01.psy.0000146329.63158.40 [DOI] [PubMed] [Google Scholar]
  37. Thomas E, Peat G, Mallen C, Wood L, Lacey R, Duncan R, & Croft P (2008). Predicting the course of functional limitation among older adults with knee pain: Do local signs, symptoms and radiographs add anything to general indicators? Annals of the Rheumatic Diseases, 67(10), 1390–1398. doi: 10.1136/ard.2007.080945 [DOI] [PubMed] [Google Scholar]
  38. Vina ER, Ran D, Ashbeck EL, & Kwoh CK (2020). Widespread Pain Is Associated with Increased Risk of No Clinical Improvement After TKA in Women. Clinical Orthopaedics and Related Research, 478(7), 1453. doi: 10.1097/CORR.0000000000001001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Ware JE, Kosinski M, & Keller SD (1996). A 12-Item Short-Form Health Survey: Construction of Scales and Preliminary Tests of Reliability and Validity. Medical Care, 34(3), 220–233. [DOI] [PubMed] [Google Scholar]
  40. Washburn RA, McAuley E, Katula J, Mihalko SL, & Boileau RA (1999). The Physical Activity Scale for the Elderly (PASE): Evidence for Validity. Journal of Clinical Epidemiology, 52(7), 643–651. doi: 10.1016/S0895-4356(99)00049-9 [DOI] [PubMed] [Google Scholar]
  41. White DK, Felson DT, Niu J, Nevitt MC, Lewis CE, Torner JC, & Neogi T (2011). Reasons for Functional Decline Despite Reductions in Knee Pain: The Multicenter Osteoarthritis Study. Physical Therapy, 91(12), 1849–1856. doi: 10.2522/ptj.20100385 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wise BL, Niu J, Zhang Y, Wang N, Jordan JM, Choy E, & Hunter DJ (2010). Psychological factors and their relation to osteoarthritis pain. Osteoarthritis and Cartilage, 18(7), 883–887. doi: 10.1016/j.joca.2009.11.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Wolfe F, Smythe HA, Yunus MB, Bennett RM, Bombardier C, Goldenberg DL, … Sheon RP (1990). The american college of rheumatology 1990 criteria for the classification of fibromyalgia. Arthritis & Rheumatism, 33(2), 160–172. doi: 10.1002/art.1780330203 [DOI] [PubMed] [Google Scholar]
  44. Yoo S-S, Gujar N, Hu P, Jolesz FA, & Walker MP (2007). The human emotional brain without sleep—A prefrontal amygdala disconnect. Current Biology, 17(20), R877–R878. doi: 10.1016/j.cub.2007.08.007 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Table S1
NIHMS1949705-supplement-Supplemental_Table_S1.docx (18.8KB, docx)

Data Availability Statement

The data underlying this article are available upon access request from the National Institute of Mental Health Data Archive (NDA) website [https://nda.nih.gov].

RESOURCES