Aims: To assess the association of sleep factors (sleep duration, trouble sleeping, sleep disorder) and combined sleep behaviours with the risk of clinically relevant depression (CRD). Methods: A total of 17 859 participants (8806 males and 9053 females) aged 20–79 years from the National Health and Nutrition Examination Survey (NHANES) 2007–2014 waves were included. Sleep duration, trouble sleeping and sleep disorder were asked in the home by trained interviewers using the Computer-Assisted Personal Interviewing (CAPI) system. The combined sleep behaviours were referred to as ‘sleep patterns (healthy, intermediate and poor)’, with a ‘healthy sleep pattern’ defined as sleeping 7–9 h per night with no self-reported trouble sleeping or sleep disorders. And intermediate and poor sleep patterns indicated 1 and 2–3 sleep problems, respectively. Weighted logistic regression was performed to evaluate the association of sleep factors and sleep patterns with the risk of depressive symptoms. Results: The total prevalence of CRD was 9.5% among the 17 859 participants analysed, with females having almost twice as frequency than males. Compared to normal sleep duration (7–9 h), both short and long sleep duration were linked with a higher risk of CRD (short sleep: OR: 1.66, 95% CI: 1.39–1.98; long sleep: OR: 2.75, 95% CI: 1.93–3.92). The self-reported sleep complaints, whether trouble sleeping or sleep disorder, were significantly related with CRD (trouble sleeping: OR: 3.04, 95% CI: 2.59–3.56; sleep disorder: OR: 1.83, 95% CI: 1.44–2.34). Furthermore, the correlations appeared to be higher for individuals with poor sleep pattern (OR: 5.98, 95% CI: 4.91–7.29). Conclusions: In this national representative survey, it was shown that there was a dose-response relationship between sleep patterns and CRD.

Background: To evaluate the association of sleep factors (sleep duration, self-reported trouble sleeping, diagnosed sleep disorder) and combined sleep behaviors with the risk of hypertension. Methods: We analyzed 12,166 adults aged 30–79 years who participated in the 2007–2014 National Health and Nutrition Examination Survey. Sleep duration, self-reported trouble sleeping and sleep disorders were collected using a standardized questionnaire. We included three sleep factors (sleep duration, self-reported trouble sleeping and sleep disorder) to generate an overall sleep score, ranging from 0 to 3. We then defined the sleep pattern as “healthy sleep pattern” (overall sleep score = 3), “intermediate sleep pattern” (overall sleep score = 2), and “poor sleep pattern” (0 ≤ overall sleep score ≤ 1) based on the overall sleep score. The definition of hypertension was based on self-reported antihypertensive medication use or biological measurement (systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg). We used weighted logistic regression models to investigate the associations between sleep and hypertension. Results: The overall prevalence of hypertension was 37.8%. A short sleep duration (OR = 1.20, 95% CI: 1.08 to 1.33, p = 0.001), self-reported trouble sleeping (OR = 1.45, 95% CI: 1.28 to 1.65, p < 0.001) and sleep disorder (OR = 1.33, 95% CI: 1.07 to 1.66, p = 0.012) were related to the risk of hypertension. Poor sleep patterns were closely correlated with the risk of hypertension (OR = 1.90, 95% CI: 1.62 to 2.24). Conclusions: Participants with poor sleep patterns were associated with an increased risk for hypertension.

Knee osteoarthritis (KOA) is common in elderly people, causes pain, loss of physical functioning, and disability. This was a two-arm, superiority, quasi-experimental trial. The aim of this study was to evaluate the effectiveness of a home-based exercise intervention (HBEI) to reduce KOA symptoms and improve the physical functioning of elderly patients. A total of 171 elderly patients (60 years of age or older) with KOA were recruited from four community centers. Patients from two community centers were randomly assigned to the intervention group (IG) and the other two centers were randomly assigned to the control group (CG). Participants in the IG received a 12-week HBEI (including four 2-h sessions supervised by a physiotherapist and fortnightly telephone support) combined with health education, while patients in the CG only received health education. The participants and physiotherapists were aware of the group assignment and alternative treatment components, but the study’s hypotheses were not disclosed to the participants. Pain intensity, joint stiffness, lower-limb muscle strength, balance, mobility, and quality of life were measured before and after the intervention by the same blinded assessor. A total of 171 patients (IG: n = 84, CG: n = 87) were enrolled. Data were obtained from 141 patients with an average age of 68 (range, 60–86 years) who completed the 12-week study (IG: n = 71, CG: n = 70). No significant group differences were found in any outcome measures at baseline. At week 12, the pretest/posttest changes 3significant between-group differences in decreases in pain intensity (− 1.60 (CI, − 2.75 to − 0.58)) and stiffness (− 0.79 (CI, − 1.37 to − 0.21)), with the IG exhibiting significantly larger improvements on both measures than the CG. The IG also showed significantly greater improvements on all the secondary outcomes than the CG did. HBEI may be effective for relieving KOA symptoms, increasing the physical functioning, and improving quality of life in community-dwelling KOA elderly patients. A large randomized controlled trial with long-term follow-up is needed to confirm these findings. Chinese Clinical Trails Registry number ChiCTR1800017026 (retrospectively registered). Registered 9 July 2018.

Background: Knee osteoarthritis (KOA) provides many challenges on the healthcare system. However, few studies have reported the epidemiology, particularly in a large population. Our study aimed to estimate the prevalence, incidence, trends, and patterns of diagnosed KOA in China. Methods: This was a longitudinal study. We used health insurance claims of 17.7 million adults from 2008–2017 to identify people with KOA. Trends in prevalence and incidence were analyzed using joinpoint regression. Results: We identified 2,447,990 people with KOA in Beijing, 60% of which were women. The 10-year average age-standardized prevalence and incidence of KOA was, respectively, 4.6% and 25.2 per 1000 person-years. Prevalence increased with age, surging after 55 years old. The average crude prevalence was 13.2% for people over 55 years old. The prevalence showed an increasing trend from 2008 to 2017, including a period of rapid rise from 2008 to 2011 (p < 0.05); the increase in prevalence was greatest in people under 35 years old (p < 0.05). Conclusion: Our analyses showed that the annual prevalence rate of KOA increased significantly from 2008 to 2017 in China. We need to increase our attention to women and the elderly over 55 years old, and also be alert to the younger trend of incidence of KOA.

Objectives: We aimed to develop a model for predicting the 4-year risk of knee osteoarthritis (KOA) based on survey data obtained via a random, nationwide sample of Chinese individuals.Methods: Data was analyzed from 8193 middle-aged and older adults included in the China Health and Retirement Longitudinal Study (CHARLS). The incident of symptomatic KOA was defined as participants who were free of symptomatic KOA at baseline (CHARLS2011) and diagnosed with symptomatic KOA at the 4-year follow-up (CHARLS2015). The effects of potential predictors on the incident of KOA were estimated using logistic regression models and the final model was internally validated using the bootstrapping technique. Model performance was assessed based on discrimination—area under the receiver operating characteristic curve (AUC)—and calibration.Results: A total of 815 incidents of KOA were identified at the 4-year follow-up, resulting in a cumulative incidence of approximately 9.95%. The final multivariable model included age, sex, waist circumference, residential area, difficulty with activities of daily living (ADLs)/instrumental activities of daily living (IADLs), history of hip fracture, depressive symptoms, number of chronic comorbidities, self-rated health status, and level of moderate physical activity (MPA). The risk model showed good discrimination with AUC = 0.719 (95% confidence interval [CI] 0.700–0.737) and optimism-corrected AUC = 0.712 after bootstrap validation. A satisfactory agreement was observed between the observed and predicted probability of incident symptomatic KOA. And a simple clinical score model was developed for quantifying the risk of KOA.Conclusion: Our prediction model may aid the early identification of individuals at the greatest risk of developing KOA within 4 years.