Racial/ethnic differences in sleep quality among older adults with osteoarthritis

Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients

Knee osteoarthritis (KOA) combined with depressive symptoms is prevalent and leads to poor outcomes and significant financial burdens. However, practical tools for identifying at-risk patients remain limited. A robust prediction model is needed to address this gap. This study aims to develop and validate a predictive model to identify KOA patients at risk of developing depressive symptoms. The China Health and Retirement Longitudinal Survey (CHARLS) data were used for model development and the Osteoarthritis Initiative (OAI) for external validation. 18 potential predictors were selected using LASSO regression. 4 machine learning models-logistic regression, decision tree, random forest, and artificial neural network-were developed. Model performance was assessed using the area under the operating characteristic curve (AUC), calibration curves, and decision curve analysis. The most important features were extracted from the optimal model on external validation. A total of 469 individuals were included, with 70% used for training and 30% for testing. The random forest model achieved the best performance, with an AUC of 0.928 in the test set, outperforming logistic regression (AUC 0.622), decision tree (AUC 0.611), and neural network models (AUC 0.868). External validation revealed an AUC of 0.877 (95% CI: 0.864-0.889) for the adjusted random forest model. Pain severity was the most significant predictor, followed by the five-time sit-to-stand test (FTSST) and sleep problems. This study is the first in China to apply a predictive model for depressive symptoms in KOA patients, offering a practical tool for early risk identification using routinely available data.

The interplay between symptoms of insomnia and pain in people with osteoarthritis: A narrative review of the current evidence

Osteoarthritis (OA) is a leading cause of disability worldwide and clinical pain is the major symptom of OA. This clinical OA-related pain is firmly associated with symptoms of insomnia, which are reported in up to 81% of people with OA. Since understanding the association between both symptoms is critical for their appropriate management, this narrative review synthesizes the existing evidence in people with OA on i) the mechanisms underlying the association between insomnia symptoms and clinical OA-related pain, and ii) the effectiveness of conservative non-pharmacological treatments on insomnia symptoms and clinical OA-related pain. The evidence available identifies depressive symptoms, pain catastrophizing and pain self-efficacy as mechanisms partially explaining the cross-sectional association between insomnia symptoms and pain in people with OA. Furthermore, in comparison to treatments without a specific insomnia intervention, the ones including an insomnia intervention appear more effective for improving insomnia symptoms, but not for reducing clinical OA-related pain. However, at a within-person level, treatment-related positive effects on insomnia symptoms are associated with a long-term pain reduction. Future longitudinal prospective studies offering fundamental insights into neurobiological and psychosocial mechanisms explaining the association between insomnia symptoms and clinical OA-related pain will enable the development of effective treatments targeting both symptoms.

The relationship between sleep quality, neck pain, shoulder pain and disability, physical activity, and health perception among middle-aged women: a cross-sectional study

BACKGROUND

Sleep quality is an important physical requirement for a healthy life, and good sleep quality has been recognized as a significant component in physical and mental health and well-being. The purpose of this study was to identify the factors that affect sleep quality as well as the relationship between sleep quality and neck pain, shoulder pain and disability, physical activity, and health perception.

METHODS

We conducted surveys on 494 women between the age of 35 and 64 years. The study evaluated neck pain, shoulder pain and disability, physical activity, self-health perception and sleep quality with self-reported questionnaires in middle-aged women. Data were analyzed using SPSS 23.0.

RESULTS

The results showed that the more severe the neck pain and shoulder pain and disability, the worse the sleep quality was in middle-aged women and the better the health perception, the lower the sleep quality score was, indicating good sleep quality. Shoulder pain, self-perceived task difficulty, and health perception were identified as variables that affected the sleep quality in middle-aged women. The explanatory power of the model in explaining sleep quality was 22.9%.

CONCLUSIONS

Worsened shoulder pain, self-perceived task difficulty, and negative health perception can affect poor sleep quality; therefore, it is necessary to develop health interventions for pain management and emotional and social support for improving daily sleep quality. To improve the sleep quality in middle-aged women, healthcare workers should consider the subjects' pain and functional disability, in accordance with their health perception.

Applications of Wearable Technology in a Real-Life Setting in People with Knee Osteoarthritis: A Systematic Scoping Review

With the growing number of people affected by osteoarthritis, wearable technology may enable the provision of care outside a traditional clinical setting and thus transform how healthcare is delivered for this patient group. Here, we mapped the available empirical evidence on the utilization of wearable technology in a real-world setting in people with knee osteoarthritis. From an analysis of 68 studies, we found that the use of accelerometers for physical activity assessment is the most prevalent mode of use of wearable technology in this population. We identify low technical complexity and cost, ability to connect with a healthcare professional, and consistency in the analysis of the data as the most critical facilitators for the feasibility of using wearable technology in a real-world setting. To fully realize the clinical potential of wearable technology for people with knee osteoarthritis, this review highlights the need for more research employing wearables for information sharing and treatment, increased inter-study consistency through standardization and improved reporting, and increased representation of vulnerable populations.

Momentary Reports of Fatigue Predict Physical Activity Level: Wrist, Waist, and Combined Accelerometry

Objective: Fatigue is commonly reported by persons with osteoarthritis (OA) and predicts worse functioning and decreased activity. The current research used a combination of wrist and waist accelerometry along with experience sampling methodology to examine the relationship between reports of fatigue and subsequent physical activity among older adults with knee OA. Method: Two hundred one participants completed an interview followed by a 1-week period during which their activity was monitored and they reported symptoms of pain and fatigue. Multilevel models examined within-subjects versus between-subjects patterns of symptoms and their association with physical activity. Results: Fatigue was the most consistent predictor of lowered physical activity (β = −20.83, p < .001). Although wrist-worn actigraphs produced higher averaged activity counts than did waist actigraphs ( t = 34.68, p < .001), multilevel models showed consistent results regardless of placement. Discussion: Fatigue was a consistent predictor of lowered activity regardless of actigraph location.

Relationship between objectively measured lifestyle factors and health factors in patients with knee osteoarthritis: The STROBE Study

The purpose of this study was to investigate the association between objectively-measured lifestyle factors and health factors in patients with knee osteoarthritis (OA).In this cross-sectional study, 52 patients with knee OA were examined. Lifestyle factors were measured using a wearable smartwatch (step counts, walking distance, calorie consumption, sleep hours) and by self-report (eating speed). Body mass index (BMI), waist circumference, blood pressure, muscle strength of knee extensor and hip abductor, knee pain, symptoms, daily living function, sports recreation function, quality of life by knee injury and OA outcome score (KOOS) were measured to obtain data on health factors. Correlations and regression analysis were used to analyze the relationship between lifestyle factors and health factors.KOOS subscales (pain, symptom, daily living function) and hip abductor strength were positively correlated with daily step count, which was the only independently contributing lifestyle factor. Additionally, knee pain duration and diastolic blood pressure were negatively correlated with daily step count. BMI and waist circumference showed no correlation with physical activity data, but were negatively correlated with sleep duration and eating speed.The findings of this study contribute to expanding the knowledge on how lifestyle habits of older patients with knee OA contribute to their health status. Daily step counts were associated with knee OA-related pain, symptom, function in daily living, duration of knee pain, blood pressure, and strength of hip abductor. BMI and waist circumference were associated with sleep duration and eating speed.

Racial-Ethnic Differences in Osteoarthritis Pain and Disability: A Meta-Analysis

Osteoarthritis (OA), a leading cause of disability and pain, affects 32.5 million Americans, producing tremendous economic burden. Although some findings suggest that racial/ethnic minorities experience increased OA pain severity, other studies have shown conflicting results. This meta-analysis examined differences in clinical pain severity between African Americans (AAs) and non-Hispanic whites with OA. Articles were initially identified between October 1 and 5, 2016, and updated May 30, 2018, using PubMed, Web of Science, PsycINFO, and the Cochrane Library Database. Eligibility included English-language peer-reviewed articles comparing clinical pain severity in adult black/AA and non-Hispanic white/Caucasian patients with OA. Nonduplicate article abstracts (N = 1,194) were screened by 4 reviewers, 224 articles underwent full-text review, and 61 articles reported effect sizes of pain severity stratified by race. Forest plots of the standard mean difference showed higher pain severity in AAs for studies using the Western Ontario and McMasters Universities Osteoarthritis Index (0.57; 95% confidence interval [CI], 0.54-0.61) and non-Western Ontario and McMasters Universities Osteoarthritis Index studies (0.35, 95% CI, 0.23-0.47). AAs also showed higher self-reported disability (0.38, 95% CI, 0.22-0.54) and poorer performance testing (-0.58, 95% CI, -0.72 to -0.44). Clinical pain severity and disability in OA is higher among AAs and future studies should explore the reasons for these differences to improve pain management. PERSPECTIVE: This meta-analysis shows that differences exist in clinical pain severity, functional limitations, and poor performance between AAs and non-Hispanic whites with OA. This research may lead to a better understanding of racial/ethnic differences in OA-related pain.

Sex Differences in Sleep Duration among Older Adults with Self-Reported Diagnosis of Arthritis: National Health and Nutrition Examination Survey, 2009-2012

Objective

Sleep is restorative, essential, and beneficial to health. Prevalences of some diseases have been associated with sleep duration. There are few studies in the literature on the relationship of sleep duration and arthritis stratified by sex in older adults. The purpose of this research is to investigate sleep duration among older adults in the United States who have self-reported diagnosis of arthritis.

Methods

A cross-sectional study design was used. The data source was the National Health and Nutrition Examination 2009-2010 and 2011-2012. Self-reported diagnosis of arthritis and sleep duration were the variables of interest.

Results

There were 4,888 participants, aged 50 years and above, of whom 41.6% self-reported having a diagnosis of arthritis, and 60.6% were female. Of the people who had a self-reported diagnosis of arthritis, 15.2% reported sleeping 2-5 hours as compared with 10.9% of the people who did not have a self-reported diagnosis of arthritis (P = .0004). In bivariate analysis of self-reported diagnosis of arthritis and sleep stratified by sex, there were significantly more people with self-reported diagnosis of arthritis who slept 2-5 hours for both women (P = 0.0192) and men (P = 0.0231). The overall relationship remained significant in adjusted overall logistic regression comparing for self-reported diagnosis of arthritis for 2-5 hours of sleep (with 6-7 hours of sleep as the reference) (odds ratio: 1.35 [95% CI: 1.08, 1.70; P = 0.0103]); however, when the data were stratified by sex, the association failed to reach significance.

Conclusion

In this analysis of noninstitutionalized older adults in the United States, the prevalence of a self-reported diagnosis of arthritis was associated with shorter sleep duration in the overall analyses, but the association failed to reach significance when stratified by sex.