A goal of 10,000 steps per day is widely advocated, but there is little evidence to support that goal. Our purpose was to examine the dose–response relationships between step count and all-cause mortality and cardiovascular disease risk. Cochrane Central Register of Controlled Trials, EMBASE, OVID, PubMed, Scopus, and Web of Science databases were systematically searched for studies published before July 9, 2021, that evaluated the association between daily steps and at least 1 outcome. Sixteen publications (12 related to all-cause mortality, 5 related to cardiovascular disease; and 1 article contained 2 outcomes: both all-cause death and cardiovascular events) were eligible for inclusion in the meta-analysis. There was evidence of a nonlinear dose–response relationship between step count and risk of all-cause mortality or cardiovascular disease (p = 0.002 and p = 0.014 for nonlinearity, respectively). When we restricted the analyses to accelerometer-based studies, the third quartile had a 40.36% lower risk of all-cause mortality and a 35.05% lower risk of cardiovascular event than the first quartile (all-cause mortality: Q1 = 4183 steps/day, Q3 = 8959 steps/day; cardiovascular event: Q1 = 3500 steps/day, Q3 = 9500 steps/day; respectively). Our meta-analysis suggests inverse associations between higher step count and risk of premature death and cardiovascular events in middle-aged and older adults, with nonlinear dose–response patterns.

Background: An aging population poses significant challenges to health care in China. Health management has been implemented to reduce the costs of care, raise health service utilization, increase health knowledge and improve quality of life. Several studies have tried to verify the effectiveness of health management in achieving these goals worldwide. However, there have been insufficient randomized control trials (RCTs) to draw reliable conclusions. The few small-scale studies conducted in China include mostly the general population rather than the elderly. Our study is designed to evaluate the impact of community-based health management on the health of the elderly through an RCT in Nanjing, China. Methods: Two thousand four hundred participants, aged 60 or older and who gave informed consent, were randomly allocated 1:1 into management and control groups, the randomization schedule was concealed from community health service center staff until allocation. Community-based health management was applied in the former while the latter was only given usual care. After 18 months, three categories of variables (subjective grading health indices, objective health indices and health service utilization) were measured based on a questionnaire, clinical monitoring and diagnostic measurements. Differences between the two groups were assessed before and after the intervention and analyzed with t-test, χ²-test, and multiple regression analysis. Results: Compared with the control group, the management group demonstrated improvement on the following variables (P<0.01): health knowledge score, self-evaluated psychological conditions, overall self-evaluated health conditions, diet score, physical activity duration per week, regular blood pressure monitoring, waist-to-hip ratio, systolic blood pressure and fasting blood sugar. The number of outpatient clinic visits did not differ significantly (P=0.60) between the two groups before intervention, while after intervention it was smaller in the management group than in the control group (P<0.01). However, the number of hospital admissions in the preceding 6 months was not different between the two groups even after intervention (P=0.36). Multiple regression analysis showed that gender, age, education level, chronic disease status and self-evaluated psychological conditions were important factors affecting health knowledge score, BMI, and waist-to-hip ratio. Conclusion: Community-based health management improved both subjective grading health indices, objective health indices and decreased the number of outpatient clinic visits, demonstrating effectiveness in improving elderly health. Trial registration: ChiCTR-OCH-11001716

Objectives Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis. Methods Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites. Results Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively. Conclusions ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models. PROSPERO registration number CRD42022346896.