Knee Pain Progression Research Articles

Association between folate intake and radiographic progression, pain function scores in subjects with radiographic knee osteoarthritis: Data from the osteoarthritis initiative.

Folate has an important role in the functioning of the musculoskeletal system, including modulation of inflammation, immunity, cartilage regeneration, prevention of osteoporosis, and maintenance of muscle strength, but evidence on the association between folate intake and knee pain, functional scores, and radiographic progression in patients with knee osteoarthritis (OA) is still limited. Our population-based cohort was extracted from the osteoarthritis initiative (OAI), focusing on individuals with prevalent radiographic knee OA (with a Kellgren-Lawrence score ≥2). Folate consumption was determined using the food frequency questionnaire. Data regarding the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and radiographic readings were collected over 48 months. We analyzed the compiled data using generalized additive mixed models. Our cohort consisted of 1472 OA patients (626 men and 846 women, mean [SD] age 62.35 [8.92]). At the 48-month follow-up, we observed a significant correlation between higher folate intake and a slower progression of knee pain and functional scores, as evidenced by a statistically significant decrease in the WOMAC total score, WOMAC pain subscale score, and WOMAC function/disability subscale score (p < .05). The fully adjusted models estimated a reduction of -0.028 points per 50 μg/1000 kcal of daily folate intake on the WOMAC pain subscale, -0.117 points on the WOMAC function subscale, and -0.160 points on the total WOMAC scale. Furthermore, our nonparametric fit analysis suggested that a higher intake of folate might decelerate the radiographic progression of OA. Stratified analyses indicated that an increase in folate consumption might particularly benefit men, older adults, overweight and obese individuals, and those with a higher dietary fiber intake. Higher folate intake is correlated with improved knee function and reduced pain in patients with knee OA and might deter the radiographic progression of OA. The benefits appear to be more pronounced in men, older adults, overweight and obese individuals, and those with a higher dietary fiber intake.

Serum levels of hydroxylated metabolites of arachidonic acid cross-sectionally and longitudinally predict knee pain progression: an observational cohort study

ObjectiveTo examine associations between serum oxylipins, which regulate tissue repair and pain signalling, and knee pain/radiographic osteoarthritis (OA) at baseline and knee pain at 3 year follow-up. MethodBaseline, and 3 year follow-up, knee pain phenotypes were assessed from 154 participants in the Knee Pain in the Community Cohort (KPIC) study. Serum and radiographic Kellgren and Lawrence (KL) and Nottingham line drawing atlas (NLDA) OA scores were collected at baseline. Oxylipin levels were quantified using liquid chromatography coupled with mass spectrometry (LC-MS/MS). Associations were measured by linear regression and Receiver Operator Characteristics (ROC). ResultsSerum levels of 8,9-epoxyeicosatrienoic acid (EET) (β(95%CI)=1.809(-0.71-2.91)), 14,15-dihydroxyeicosatrienoic acid (DHET) (β(95%CI)=0.827(0.34-1.31)), and 12-hydroxyeicosatetraenoic acid (β(95%CI)=4.090(1.92-6.26)) and anandamide (β(95%CI)=3.060(1.35-4.77)) were cross-sectionally associated with current self-reported knee pain scores (NRS item 3, average pain). Serum levels of 9- (β(95%CI)=0.467(0.18-0.75)) & 15-hydroxyeicosatetraenoic acid (β(95%CI)=0.759(0.29-1.22)), 14-hydroxydocosahexaenoic acid (β(95%CI)=0.483(0.24-0.73)), and the ratio of 8,9-EET:DHET (β(95%CI)=0.510(0.19-0.82)) were cross-sectionally associated with K/L scores. Baseline serum concentrations of 8,9-EET (β(95%CI)=2.166(0.89-3.44)), 5,6-DHET (β(95%CI)=152.179(69.39-234.97)), and 5-HETE (β(95%CI)=1.724(0.677-2.77) showed positive longitudinal associations with follow-up knee pain scores (NRS item 3, average pain). Combined serum 8,9-EET and 5-HETE concentration showed the strongest longitudinal association (β(95%CI)=1.156(0.54-1.77) with pain scores at 3 years, and ROC curves distinguished between participants with no pain and high pain scores at follow-up (AUC(95%CI)=0.71(0.61-0.82)). ConclusionsSerum levels of a combination of hydroxylated metabolites of arachidonic acid may have prognostic utility for knee pain, providing a potential novel approach to identify people who are more likely to have debilitating pain in the future.

Mapping the course to recovery: a prospective study on the anatomic distribution of early postoperative pain after total knee arthroplasty

IntroductionEarly postoperative pain following total knee arthroplasty significantly impacts outcomes and patient satisfaction. However, the characteristics and sources of early pain after total knee arthroplasty remain unclear. Therefore, the purpose of this study was to determine the anatomic distribution and course of postoperative pain in the acute and subacute period following total knee arthroplasty.MethodsA prospective observational study of primary, elective unilateral total knee arthroplasty cases was conducted at our academic tertiary care medical center from January 2021 to September 2021. Preoperative variables were extracted from institutional electronic medical records. Postoperatively, patients utilized a knee pain map to identify the two locations with the most significant pain and rated it using the visual analog scale (VAS). The data were collected on day 0, at 2 weeks, 2 months, and 6 months after operation.ResultsThis study included 112 patients, with 6% of patients having no pain at postoperative day 0, 22% at 2 weeks, 46% at 2 months, and 86% at 6 months after operation. In those who reported pain, the VAS score (mean ± standard deviation) was 5.8 ± 2.4 on postoperative day 0 and decreased at each follow-up time point (5.4 ± 2.3 at 2 weeks, 3.9 ± 2.2 at 2 months, and 3.8 ± 2.7 at 6 months). The majority of patients were able to identify distinct loci of pain. The most common early pain loci were patellae, thigh, and medial joint line, and this distribution dissipated by 6 months.ConclusionAt 2 postoperative weeks, pain was primarily at the medial joint, and at 6 months postoperatively, pain was more likely to be at the lateral joint. No relationship was found between pain at six months and pain scores or location at postoperative day 0 or 2 weeks. Understanding the distribution and progression of knee pain following total knee arthroplasty may benefit patient education and targeted interventions.Level of EvidenceLevel II, prospective observational study

Deep learning applications in osteoarthritis imaging.

Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI. A variety of DL methods have been developed for detecting and grading the severity of knee OA and various features of knee OA on X-rays using standardized classification systems with diagnostic performance similar to human readers. Multiple DL approaches have been described for fully automated segmentation of cartilage and other knee tissues and have achieved higher segmentation accuracy than currently used methods with substantial reductions in segmentation times. Various DL models analyzing baseline X-rays and MRI have been developed for OA risk assessment. These models have shown high diagnostic performance for predicting a wide variety of OA outcomes, including the incidence and progression of radiographic knee OA, the presence and progression of knee pain, and future total knee replacement. The preliminary results of DL applications in OA imaging have been encouraging. However, many DL techniques require further technical refinement to maximize diagnostic performance. Furthermore, the generalizability of DL approaches needs to be further investigated in prospective studies using large image datasets acquired at different institutions with different imaging hardware before they can be implemented in clinical practice and research studies.

Automated machine learning-based prediction of the progression of knee pain, functional decline, and incidence of knee osteoarthritis in individuals at high risk of knee osteoarthritis: Data from the osteoarthritis initiative study.

This study aimed to examine the performance of machine learning models in predicting the progression of knee pain, functional decline, and incidence of knee osteoarthritis (OA) in high-risk individuals, with automated machine learning (AutoML) being used to automate the prediction process. There were four stages in the process of our AutoML-integrated prediction. Stage 1-Data preparation: The data of 3200 eligible individuals in the Osteoarthritis Initiative (OAI) study who were considered at high risk of knee OA at the baseline visit were extracted and used. Specifically, 1094 variables from the OAI study were used to predict the changes in knee pain, physical function, and incidence of knee OA (i.e. the first occurrence of frequent knee symptoms and definite tibial osteophytes (Kellgren and Lawrence grade ≥2)) over a 9-year period. Stage 2-Model training: The AutoML approach was used to automatically train nine widely used machine learning (ML) models. Stage 3-Model testing: The AutoML approach was used to automatically test the performance of the ML models. Stage 4-Selection of important input variables: The AutoML approach automated the process of computing the importance scores of all input variables and identifying the most important ones, using the technique of permutation feature importance. Using the AutoML approach, the weighted ensemble model and the CatBoost model showed the best performance among all nine ML models. For the prediction of each outcome in each year, the five most important input variables were identified, most of which were obtained from self-reported questionnaire surveys and radiographic imaging reports. The AutoML approach has shown potential in automating the process of using ML models to predict long-term changes in knee OA-related outcomes. Its use could support the deployment of ML solutions, facilitating the provision of personalized interventions to prevent the deterioration of knee health and incident knee OA.

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis (OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-m walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique (“super learning”) to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain. Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR 0.80 (0.64–1.00) and aOR 0.79 (0.66–0.95), respectively].

Determining a Threshold of Medial Meniscal Extrusion for Prediction of Knee Pain and Cartilage Damage Progression Over 4 Years: Data From the Osteoarthritis Initiative.

BACKGROUND. The extent of medial meniscal extrusion (MME) that is associated with structural and symptomatic progression of knee osteoarthritis has not been defined yet. OBJECTIVE. The purpose of our study was to investigate MRI-based thresholds of MME that are associated with structural progression of knee degenerative disease and symptoms over a period of 4 years. METHODS. We studied 328 knees of 235 participants that were randomly selected from the Osteoarthritis Initiative cohort. MME was quantified on coronal sections of intermediate-weighted MRI sequences obtained at 3 T. Knee pain and cartilage abnormalities were measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scale and the cartilage whole-organ MRI score (WORMS). General estimating equations with logistic regression models were used to correlate baseline MME and changes in pain (WOMAC) and cartilage damage (WORMS). ROC analyses were performed to determine the area under the ROC curve (AUROC). Individual thresholds were determined by maximizing the product of sensitivity and specificity. RESULTS. The AUROC for predicting progression of knee pain, medial compartment cartilage damage, and medial tibial cartilage damage were 0.71, 0.70, and 0.72, respectively, and the individual thresholds for MME were 2.5, 2.7, and 2.8 mm. A single threshold of 2.5 mm was determined by maximizing the mean of the product of sensitivity and specificity of the three outcome variables (knee pain progression, medial compartmental cartilage damage progression, and medial tibial cartilage damage progression). CONCLUSION. MME was associated with knee pain and cartilage damage progression over 4 years. A single threshold of 2.5 mm was found to be the most useful threshold for predicting knee pain, medial compartment cartilage damage progression, and tibial cartilage damage progression over 4 years. CLINICAL IMPACT. This threshold could be used to standardize the diagnostic criterion of extrusion and to better characterize the risk for subsequent structural and symptomatic progression of knee osteoarthritis.

Psychological Predictors of Perceived Age and Chronic Pain Impact in Individuals With and Without Knee Osteoarthritis

Chronological age is a risk factor in chronic pain; however, aging research supports the premise that physical and psychological health may better predict perceived age. Given the lack of evidence on perceived age in the context of chronic pain, the current study presents novel findings about the relationship between perceived age, chronic pain impact, and psychological function in adults with and without knee osteoarthritis. This secondary analysis was part of an ongoing multisite observational cohort study to understand the progression of knee pain and disability. Community-dwelling adults (N=227) ages 45+ completed measures of trait resilience, trait positive and negative affect, pain catastrophizing, subjective perceptions of age, and the Graded Chronic Pain Scale. On average, participants reported feeling 10 years younger than their chronological age; however, this effect was attenuated in individuals reporting high-impact pain. Lower perceived age was associated with lower pain impact (low pain/low disability), while higher perceived age correlated with higher pain impact (high pain/high disability) and more adverse psychological effects. Using hierarchical linear regression, high-impact pain and positive affect emerged as statistically significant predictors of perceived age, whereas no differences were observed among trait resilience, negative affect, or pain catastrophizing. These findings highlight the importance of a biopsychosocial approach in understanding the intersection between psychological and physical factors associated with chronic pain. Addressing negative self-perceptions of aging, while simultaneously augmenting positive affect, through psychological therapies may mitigate pain and disability.

Deep learning approach to predict pain progression in knee osteoarthritis

Purpose: Pain progression in individuals with knee osteoarthritis (OA) is poorly understood and difficult to predict. Developing improved methods for predicting the progression of knee pain could identify individuals at high risk for pain progression who would be best suited for early intervention. This study was performed to determine the feasibility of a deep learning (DL) approach to predict knee pain progression in subjects in the Osteoarthritis Initiative (OAI) through analysis of demographic and radiographic risk factors and baseline knee radiographs. Methods: The study group consisted of 2000 subjects randomly selected from the OAI with Kellgren Lawrence (KL) grades 1, 2, or 3 pre-radiographic OA or radiographic OA of the knee at baseline. 1000 subjects showed pain progression and 1000 subjects did not show pain progression over a 60-month follow-up period. Pain progression was defined according to the Foundation of the National Institute of Health OA Biomarker Consortium Project as a 9 point or greater increase in WOMAC score (0-100 scale) between 2 or more time-points from 24-month to 60-months follow-up. The study group was randomly divided into a training dataset (742 subjects with and 758 subjects without pain progression) used to train the models, a validation dataset (108 subjects with and 92 subjects without pain progression) used to choose the most optimal models, and a hold-out test dataset (150 subjects with and 150 subjects without pain progression) used to evaluate the diagnostic performance of the models. An artificial neural network (ANN) was used to create a clinical model to provide a confidence score for predicting pain progression using demographic and radiographic risk factors including baseline age, gender, race, body mass index (BMI), history of knee injury, KL grade, and tibiofemoral angle. A DL model consisted of two separate deep convolutional neural networks (YOLO and DenseNet). The first network (YOLO) was used to crop regions of interest around each individual knee joint on the standing bilateral posterior-anterior knee radiographs. The second classification network (DenseNet) provided a confidence score for predicting pain progression based on DL analysis of the cropped knee images. A combined model was created by integrating both clinical data and DL analysis of the baseline knee radiographs. The combined model used DenseNet to extract radiograph information as a feature vector which was further concatenated with the demographic and radiographic risk factors data vector. The combined feature vector was then fed to a fully connected network for joint model training. Receiver operation characteristic (ROC) and area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate the diagnostic performance of the clinical, DL, and combined clinical and DL models for predicting pain progression. Delong’s method was used to compare the AUCs of the different models. The optimal sensitivity and specificity of the models were determined using the Youden index. Results: Figure 1 shows the ROC curves for the clinical, DL, and combined clinical and DL models for predicting pain progression. Table 1 shows the AUCs and the optimal sensitivity and specificity of the models. The clinical model had the lowest AUC of 0.644 with 61.7% sensitivity and 68.9% specificity for predicting pain progression. The DL model had AUCs of 0.753, which was significantly higher (p<0.05) than the AUC of the clinical model. The DL model also had higher (p<0.05) sensitivity and specificity than the clinical model for predicting pain progression. The combined clinical and DL model had the highest AUC of 0.804 with 75.2% sensitivity and 76.2% specificity for predicting pain progression. The AUC of the combined clinical and DL model was significantly higher (p<0.001) than the AUCs of the clinical and DL model, indicating greater overall diagnostic performance for predicting pain progression. Conclusions: Our study demonstrated the feasibility of a combined clinical and DL model for predicting the progression of knee pain through analysis of demographic and radiographic risk factors and baseline knee radiographs. The combined clinical and DL model showed a significant improvement in diagnostic performance for predicting knee pain progression when compared to the clinical model and DL model alone.Tabled 1AUCs and optimal sensitivity and specificity of the models for predicting knee pain progression.AUC (95% CI)Sensitivity (95% CI)Specificity (95% CI)Clinical Model0.644 (0.587 - 0.698)61.74 (53.4 - 69.6)68.87 (60.8 - 76.2)DL model0.753 (0.700 - 0.801)65.77 (57.6 - 73.3)73.51 (65.7 - 80.4)Combined Clinical and DL model0.804 (0.754 - 0.847)75.17 (67.4 - 81.9)76.16 (68.6 - 82.7) Open table in a new tab

Contribution of central and peripheral risk factors to prevalence, incidence and progression of knee pain: a community-based cohort study

SummaryAimTo explore risk factors that may influence knee pain (KP) through central or peripheral mechanisms.MethodsA questionnaire-based prospective community cohort study with KP defined as pain in or around a knee on most days for at least a month. Baseline prevalence, and one year incidence and progression (KP worsening) were examined. Central (e.g., Pain Catastrophizing Scale (PCS)) and peripheral (e.g., significant injury) risk factors were examined. Adjusted odds ratio (OR) and 95% confidence interval (CI) were calculated using logistic regression. Proportional risk contribution (PRC) was estimated using receiver-operator-characteristic (ROC) analysis.ResultsOf 9506 baseline participants, 4288 (45%) had KP (men 1826; women, 2462). KP incidence was 12% (men 11%, women 13%), and KP progression 19% (men 16%, women 21%) at one year. While both central and peripheral factors contributed to prevalence, central factors contributed more to progression, and peripheral factors more to incidence of KP. For example, although PCS (OR 2.06, 95% CI 1.88–2.25) and injury (5.62, 4.92–6.42) associated with KP prevalence, PCS associated with progression (2.27, 1.83–2.83) but not incidence (1.14, 0.86–1.52), whereas injury more strongly associated with incidence (69.27, 24.15–198.7) than progression (2.52, 1.48–4.30). The PRC of central and peripheral factors were 19% and 23% for prevalence, 14% and 29% for incidence, and 29% and 5% for progression, respectively.ConclusionsBoth central and peripheral risk factors influence KP but relative contributions may differ in terms of development (mainly peripheral) and progression (mainly central). Further study of such relative contributions may inform primary and secondary prevention strategies.

Residual knee pain and functional outcome following total knee arthroplasty in osteoarthritic patients

Total knee arthroplasty (TKA) is a successful and safe elective operation in managing patients with severe osteoarthritis of the knee. However, the presence of residual knee pain (RKP) post-TKA can adversely affect patient satisfaction and functional outcome. Hence, the aim of this paper is to identify the incidence, progression of knee pain, functional outcome post-TKA and possible predictive factors for the development of RKP post-TKA. A retrospective review of 357 patients was conducted with a minimum follow-up period of 2 years. Predictive factors reviewed include the patients' demographics, co-morbidities, type of implants and patellar management. For functional outcome, the patient's Knee Society Score (KSS) and Western Ontario and McMaster Universities Index of Osteoarthritis scores were analysed. To determine presence and severity of RKP, the sub-score for pain in the KSS was utilised. In total, 31.1 and 28.9% of the patients were found to have RKP at 1 and 2 years, respectively, though their functional outcome scores continued improving from 1 year. Ischaemic heart disease (IHD) patients were more likely to have RKP and poorer functional outcome at 1 year. Males and patients with posterior-stabilised implants were found to have better functional outcome at 1 and 2 years, respectively. Almost a third of the patients continued to have RKP at 2 years post-TKA, with factors such as gender, presence of IHD and implant type significantly associated with the development of RKP and/or poorer functional outcome scores. By recognising the incidence and predictive factors for RKP, physicians will be able to better manage their patients' expectations and optimise their pre-morbid status pre-operatively. III.

Review of: Effect of Vitamin D Supplementation on Progression of Knee Pain and Cartilage Volume Loss in Patients with Symptomatic Osteoarthritis: A Randomized Controlled Trial

Practice PearlOsteoarthritis is a widespread and significant disorder. Correction of vitamin D deficiency in patients with significant osteoarthritis of the knee did not result in improvement of pain symptoms or in preservation of cartilage in the knee space.

OP0316 The Association Between Childhood Obesity Measures and Adulthood Knee Pain, Stiffness and Physical Dysfunction: A 25-Year Cohort Study

BackgroundObesity has been associated with knee pain in both children and adults.[1, 2] However, no study has investigated the long-term association between childhood weight and knee symptoms assessed using WOMAC...

Predictors for the progression of knee pain in subjects with early symptomatic knee osteoarthritis - five year results from the CHECK study

Predictors for the progression of knee pain in subjects with early symptomatic knee osteoarthritis - five year results from the CHECK study

Effect of Vitamin D Supplementation on Progression of Knee Pain and Cartilage Volume Loss in Patients With Symptomatic Osteoarthritis

Knee osteoarthritis (OA), a disorder of cartilage and periarticular bone, is a public health problem without effective medical treatments. Some studies have suggested that vitamin D may protect against structural progression. To determine whether vitamin D supplementation reduces symptom and structural progression of knee OA. A 2-year randomized, placebo-controlled, double-blind, clinical trial involving 146 participants with symptomatic knee OA (mean age, 62.4 years [SD, 8.5]; 57 women [61%], 115 white race [79%]). Patients were enrolled at Tufts Medical Center in Boston between March 2006 and June 2009. Participants were randomized to receive either placebo or oral cholecalciferol, 2000 IU/d, with dose escalation to elevate serum levels to more than 36 ng/mL. Primary outcomes were knee pain severity (Western Ontario and McMaster Universities [WOMAC] pain scale, 0-20: 0, no pain; 20, extreme pain), and cartilage volume loss measured by magnetic resonance imaging. Secondary end points included physical function, knee function (WOMAC function scale, 0-68: 0, no difficulty; 68, extreme difficulty), cartilage thickness, bone marrow lesions, and radiographic joint space width. Eighty-five percent of the participants completed the study. Serum 25-hydroxyvitamin D levels increased by a mean 16.1 ng/mL (95% CI, 13.7 to 18.6) in the treatment group and by a mean 2.1 mg/mL (95% CI, 0.5 to 3.7) (P < .001) in the placebo group. Baseline knee pain was slightly worse in the treatment group (mean, 6.9; 95% CI, 6.0 to 7.7) than in the placebo group (mean, 5.8; 95% CI, 5.0 to 6.6) (P = .08). Baseline knee function was significantly worse in the treatment group (mean, 22.7; 95% CI, 19.8 to 25.6) than in the placebo group (mean, 18.5; 95% CI, 15.8 to 21.2) (P = .04). Knee pain decreased in both groups by a mean -2.31 (95% CI, -3.24 to -1.38) in the treatment group and -1.46 (95% CI, -2.33 to -0.60) in the placebo group, with no significant differences at any time. The percentage of cartilage volume decreased by the same extent in both groups (mean, -4.30; 95% CI, -5.48 to -3.12 vs mean, -4.25; 95% CI, -6.12 to -2.39) (P = .96). There were no differences in any of the secondary clinical end points. Vitamin D supplementation for 2 years at a dose sufficient to elevate 25-hydroxyvitamin D plasma levels to higher than 36 ng/mL, when compared with placebo, did not reduce knee pain or cartilage volume loss in patients with symptomatic knee OA. clinicaltrials.gov Identifier: NCT00306774.

Physiotherapy Program through Home Visits for Community-Dwelling Elderly Japanese Women with Mild Knee Pain

Knee pain is extremely common among the elderly, particularly women. Hence, there is an urgent need for applicable community-based intervention models for halting the progression of knee pain and related disabilities in elderly women. We aimed to assess the efficacy of home-visit physiotherapy as a new intervention model. This non-randomized 5-month-long controlled trial enrolled elderly community-dwelling women (aged 60-83 years) with mild knee pain. The intervention consisted of two home visits by a physiotherapist, with instructions on routinely performing muscle-strengthening exercises at home and implementing simple environmental modifications when necessary. Outcome measures were assessed at baseline and 5 months later. The primary outcomes were measured as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and a newly devised seiza-style sitting score. People in Japan, especially elderly women, are accustomed to seiza-style sitting that involves kneeling on one's lower legs while resting the buttocks on the heels. The secondary outcomes included quadriceps isometric strength, knee alignment in the frontal and sagittal planes, and passive knee extension range. Forty-two subjects (20 in the intervention group and 22 in the control group) completed the study. At baseline, characteristics and the primary outcomes did not significantly differ between the 2 groups. At 5 months, the primary outcomes improved significantly in the intervention group. Estimated differences in the change from baseline for each outcome between the 2 groups were computed, adjusting for outcome variables imbalanced at baseline. Even after the adjustment, the home-visit physiotherapy regimen provides favorable improvement in the seiza-style sitting score.

Effect of shoe heel height on vastus medialis and vastus lateralis electromyographic activity during sit to stand

BackgroundIt has been proposed that high-heeled shoes may contribute to the development and progression of knee pain. However, surprisingly little research has been carried out on how shoe heel height affects muscle activity around the knee joint. The purpose of this study was to investigate the effect of differing heel height on the electromyographic (EMG) activity in vastus medialis (VM) and vastus lateralis (VL) during a sit to stand activity. This was an exploratory study to inform future research.MethodsA repeated measures design was used. Twenty five healthy females carried out a standardised sit to stand activity under 4 conditions; barefoot, and with heel wedges of 1, 3, and 5 cm in height. EMG activity was recorded from VM and VL during the activity. Data were analysed using 1 × 4 repeated measures ANOVA.ResultsAverage rectified EMG activity differed with heel height in both VM (F2.2, 51.7 = 5.24, p < 0.01), and VL (F3, 72 = 5.32, p < 0.01). However the VM: VL EMG ratio was not significantly different between conditions (F3, 72 = 0.61, p = 0.609).ConclusionWe found that as heel height increased, there was an increase in EMG activity in both VM and VL, but no change in the relative EMG intensity of VM and VL as measured by the VM: VL ratio. This showed that no VM: VL imbalance was elicited. This study provides information that will inform future research on how heel height affects muscle activity around the knee joint.

Predictors of onset and progression of knee pain in adults living in the community. A prospective study

To investigate determinants of the onset and progression of knee pain in a population-based sample of people aged > or = 50 yrs. Prospective cohort study of 2982 people registered with three general practices in North Staffordshire, UK. Using questionnaire surveys at baseline and 3 yrs, demographic, knee-related and general health factors were assessed for their relationship with onset of new knee pain, and progression from non-severe to severe knee pain. Response rates were 77% (baseline) and 75% (follow-up). Baseline factors significantly associated with onset of knee pain were knee injury [odds ratio (OR) 1.6, 95% CI 1.2, 2.2], depression (OR 1.4, 95% CI 1.1, 1.8), widespread pain (OR 1.5, 95% CI 1.1, 1.9 compared with no pain) and younger age. Onset of severe knee pain was associated most strongly with obesity (OR 2.9, 95% CI 1.7, 5.1) and physical limitations (OR 2.5, 95% CI 1.5, 4.1), and with widespread pain, older age, female gender and comorbidity. The strongest independent predictors of progression from non-severe to severe knee pain were chronicity (OR 3.1, 95% CI 2.1, 4.6), previous use of health care (OR 2.2, 95% CI 1.5, 3.3) and obesity (OR 2.1, 95% CI 1.2, 3.6). In addition to a focus on obesity, there is potential for primary prevention of knee pain by tackling knee injuries and treating depression. Other factors are likely to determine whether the knee pain then progresses. An area for future research is the ineffectiveness of current health care in halting or reversing progression of knee pain at a population level.

Disabling knee pain--another consequence of obesity: results from a prospective cohort study.

BackgroundObesity is linked to knee osteoarthritis (OA) and knee pain. These are disabling problems that are more prevalent in older adults. No prospective study has estimated the impact of excess weight avoidance on the occurrence of knee pain in the general older population. The aim of this study was to investigate the influence of overweight and obesity on the onset and progression of knee pain and disability in older adults living in the community.MethodsA prospective cohort study of people aged 50 and over registered with three general practices in North Staffordshire, UK. 5784 people who had responded to a survey in March 2000 were mailed a follow-up questionnaire in March 2003. The main outcome measures were self-reported knee pain and severe knee pain and disability at 3 years measured by the Western Ontario and McMaster Universities Osteoarthritis index.ResultsAdjusted response to follow-up was 75%. Among responders with no knee pain at baseline, obesity predicted onset of severe knee pain (relative risk 2.8; 95% CI 1.8, 4.5 compared to normal body mass index (BMI) category). Considering overweight and obese categories together, 19% of new cases of severe knee pain over a 3-year period could potentially be avoided by a one-category shift downwards in BMI; this includes almost half of the new cases that arose in the obese group.ConclusionObesity accounts for a substantial proportion of severe disabling knee pain. As knee pain is a common disabling condition in older adults living in the community, effective public health interventions about avoidance of excess weight could have a major impact on future lower limb disability in older adults.