Explore the vast range of titles available.

With the advent of IoT technology in education, understanding its impact on physical education is crucial. This study investigates how the acceptance of wearable IoT devices influences the physical education results of college freshmen. It posits that user acceptance plays a mediating role in the effectiveness of these devices in enhancing physical performance metrics. The study enrolled 150 first-year students from Guangdong University of Finance & Economics, divided equally into an experimental group and a control group. Participants in the experimental group were provided with ‘Xiaomi 8’ smart bracelets to be worn during physical education classes. The study spanned six weeks, focusing on assessing various physical performance metrics and the acceptance of the wearable technology. The data analysis involved comparing the physical performance of both groups and conducting regression analyses to evaluate the mediation effect of acceptance. Results indicated significant improvements in physical performance metrics in the experimental group, as evidenced by the Standardized Mean Differences (SMD). Notably, enhancements were observed in short-distance speed and aerobic endurance. The direct impact of wearable IoT devices on physical performance accounted for 66.4% variance, which increased to 84.1% upon incorporating acceptance as a mediator. These findings suggest that the effectiveness of wearable technology in physical education is significantly influenced by students’ acceptance. The study concludes that wearable IoT devices can effectively enhance physical education outcomes among college students, with user acceptance playing a crucial mediating role. This underscores the importance of considering user acceptance in the integration of technology in educational settings. The findings provide valuable insights for educators and technologists in designing and implementing technology-integrated curricula.

Over the past decade, wearable sensors for fall detection have gained significant attention due to their potential in improving the safety of elderly users and reducing fall-related injuries. This review employs a network-based visualization approach to analyze research trends, key technologies, and collaborative networks. Using studies from SCI- and SSCI-indexed journals from 2015 to 2024, we analyzed 582 articles and 65 reviews with CiteSpace, revealing a significant rise in research on wearable sensors for fall detection. Additionally, we reviewed various datasets and machine learning techniques, from traditional methods to advanced deep learning frameworks, which demonstrate high accuracies, F1 scores, sensitivities, and specificities in controlled settings. This review provides a comprehensive overview of the progress and emerging trends, offering a foundation for future advancements in wearable fall detection systems.

During the COVID-19 pandemic, people living with HIV (PLWH) were considered to be at risk of worse COVID-19 outcomes once infected. However, the existing evidence is inconsistent. This systematic review and meta-analysis aimed to compare the risk of SARS-CoV-2 infection, severe COVID-19 symptoms, and mortality among PLWH and patients without HIV. The articles included studies published in PubMed, Medline, Embase, and Cochrane between December 1, 2019, and December 1, 2021. We included the original studies published in English focusing on observational studies assessing the risk of SARS-CoV-2 infection, severe COVID-19 symptoms, and mortality among PLWH. Four independent reviewers extracted data. STrengthening the Reporting of OBservational studies in Epidemiology-Modified (STROBE-M) checklist was used for quality assessment. For the results with heterogeneity I >75%, a random-effects model was employed. Otherwise, a fixed-effects model was used. The risk of SARS-CoV-2 infection, severe COVID-19 symptoms, and mortality were compared with and without HIV. We included a total of 32 studies and 71,779,737 study samples, of whom 797,564 (1.11%) were PLWH. Compared with COVID-19 patients without HIV infection, PLWH had comparable risk of SARS-CoV-2 infection (adjusted Risk Ratio=1.07, 95% CI: 0.53-2.16, I 96%, study n=6, n=20,199,805) and risk of developing severe COVID-19 symptoms (aRR=1.06, 95% CI: 0.97-1.16, I 75%, n=10, n=2,243,370). PLWH, if infected with SARS-CoV-2, were found to have an increased risk of mortality compared with people without HIV (aRR=1.30, 95% CI: 1.09-1.56, I 76%, study n=16, n=71,032,659). This finding was consistent across different subgroup analyses. PLWH are at increased risk of COVID-19 related mortality once infected. The local health system should, on the one hand, strengthen COVID-19 prevention and clinical management among PLWH to avoid infection and, on the other hand, sustain the HIV care continuum for PLWH for HIV management.

China has low seasonal influenza vaccination rates among priority populations. In this study, we aimed to evaluate a pay-it-forward strategy to increase influenza vaccine uptake in rural, suburban, and urban settings in China. We performed a quasi-experimental pragmatic trial to examine the effectiveness of a pay-it-forward intervention (a free influenza vaccine and an opportunity to donate financially to support vaccination of other individuals) to increase influenza vaccine uptake compared with standard-of-care user-paid vaccination among children (aged between 6 months and 8 years) and older people (≥60 years) in China. Recruitment took place in the standard-of-care group until the expected sample size was reached and then in the pay-it-forward group in primary care clinics from a rural site (Yangshan), a suburban site (Zengcheng), and an urban site (Tianhe). Participants were introduced to the influenza vaccine by project staff using a pamphlet about influenza vaccination and were either asked to pay out-of-pocket at the standard market price (US$8·5–23·2; standard-of-care group) or to donate any amount anonymously (pay-it-forward group). Participants had to be eligible to receive an influenza vaccine and to have not received an influenza vaccine in the past year. The primary outcome was vaccine uptake. Secondary outcomes were vaccine confidence and costs (from the health-care provider perspective). Regression methods compared influenza vaccine uptake and vaccine confidence between the two groups. This trial is registered with ChiCTR, ChiCTR2000040048. From Sept 21, 2020, to March 3, 2021, 300 enrolees were recruited from patients visiting three primary care clinics. 55 (37%) of 150 people in the standard-of-care group (40 [53%] of 75 children and 15 [20%] of 75 older adults) and 111 (74%) of 150 in the pay-it-forward group (66 [88%] of 75 children and 45 [60%] of 75 older adults) received an influenza vaccine. People in the pay-it-forward group were more likely to receive an influenza vaccine compared with those in the standard-of-care group (adjusted odds ratio [aOR] 6·7 [95% CI 2·7–16·6] among children and 5·0 [2·3–10·8] among older adults). People in the pay-it-forward group had greater confidence in vaccine safety (aOR 2·2 [95% CI 1·2–3·9]), importance (3·1 [1·6–5·9]), and effectiveness (3·1 [1·7–5·7]). In the pay-it-forward group, 107 (96%) of 111 participants donated money for subsequent vaccinations. The pay-it-forward group had a lower economic cost (calculated as the cost without subtraction of donations) per person vaccinated (US$45·60) than did the standard-of-care group ($64·67). The pay-it-forward intervention seemed to be effective in improving influenza vaccine uptake and community engagement. Our data have implications for prosocial interventions to enhance influenza vaccine uptake in countries where influenza vaccines are available for a fee. Bill & Melinda Gates Foundation and the UK National Institute for Health Research.

Antimicrobial resistance (AMR) in Enterobacterales poses serious public health, agricultural, and environmental threats. In Southeast Asia, a coordinated “One Health” approach is lacking, and fragmented evidence hampers targeted interventions. This study systematically quantify and analyse AMR prevalence across human, animal, and environmental sectors in Southeast Asia by conducting a meta-analysis of 137 observational studies from 2013 to 2023. We found that Ceftriaxone resistance in E. coli was highest in human samples (49.3%, 95% CI: 37.3–61.3; N  = 2,640), followed by environmental (37.1%, 95% CI: 8.4–72.2; N  = 288) and animal sources (11.2%, 95% CI: 1.6–27.9; N  = 923). In humans, meropenem resistance was 13.0% in K. pneumonia e (95% CI: 2.0–31.3; N  = 7,803) and 1.4% in E. coli (95% CI: 0.1–4.4; N  = 13,696). Resistance increased over time in human ( p  = 0.009) and animal sectors ( p  = 0.004). bla CTX-M and bla TEM were reported across all sectors. This synthesis also highlights a critical evidence gap: most studies focused on Thailand (67) and Vietnam (42). Samples came mostly from animals (62) and humans (59), with limited multi-sector studies. Only one study assessed all four sectors (human, animal, environment, food). Our study reveals an escalating AMR crisis alongside critical research gaps across Southeast Asia. Future efforts must therefore strengthen both integrated surveillance to understand transmission and regional health systems to implement effective One Health action.

Introduction Social network strategies, in which social networks are utilized to influence individuals or communities, are increasingly being used to deliver human immunodeficiency virus (HIV) interventions to key populations. We summarized and critically assessed existing research on the effectiveness of social network strategies in promoting HIV self‐testing (HIVST). Methods Using search terms related to social network interventions and HIVST, we searched five databases for trials published between 1st January 2010 and 30th June 2023. Outcomes included uptake of HIV testing, HIV prevalence and linkage to antiretroviral therapy (ART) or HIV care. We used network meta‐analysis to assess the uptake of HIV testing through social network strategies compared with control methods. A pairwise meta‐analysis of studies with a comparison arm that reported outcomes was performed to assess relative risks (RR) and their corresponding 95% confidence intervals (CI). Results Among the 4496 manuscripts identified, 39 studies fulfilled the inclusion criteria, including one quasi‐experimental study, 22 randomized controlled trials and 16 observational studies. Networks HIVST testing was organized by peers (distributed to known peers, 15 studies), partners (distributed to their sexual partners, 16 studies) and peer educators (distributed to unknown peers, 8 studies). Among social networks, simulating the possibilities of ranking position, peer distribution had the highest uptake of HIV testing (84% probability), followed by partner distribution (80% probability) and peer educator distribution (74% probability). Pairwise meta‐analysis showed that peer distribution (RR 2.29, 95% CI 1.54−3.39, 5 studies) and partner distribution (RR 1.76, 95% CI 1.50−2.07, 10 studies) also increased the probability of detecting HIV reactivity during testing within the key population when compared to the control. Discussion All of the three social network distribution strategies enhanced the uptake of HIV testing compared to standard facility‐based testing. Linkage to ART or HIV care remained comparable to facility‐based testing across the three HIVST distribution strategies. Conclusions Network‐based HIVST distribution is considered effective in augmenting HIV testing rates and reaching marginalized populations compared to facility‐based testing. These strategies can be integrated with the existing HIV care services, to fill the testing gap among key populations globally. PROSPERO Number CRD42022361782

Background: Meningococcal B (MenB) vaccination offers protection against invasive meningococcal disease and moderate cross-protection against gonorrhea. However, little is known about coverage and behavioral correlates among men who have sex with men (MSM) in China. This study assessed self-reported MenB vaccination uptake and its associations with sociodemographic and behavioral factors. Methods: We conducted a nationwide cross-sectional survey among 1022 MSM recruited via community-based organizations and online platforms. Vaccination status and recent sexual behaviors were self-reported. Logistic regression identified correlates of uptake, and latent class analysis (LCA) examined behavioral profiles. Results: Participants had a mean age of 29.6 years; most were unmarried (87.7%) and nearly 90% had a college degree or above. Overall, 21.7% reported receiving MenB vaccination. Uptake was positively associated with condomless anal intercourse (aOR = 1.57, 95% CI: 1.08–2.31), group sex (occasionally: aOR = 1.63, 95% CI: 1.01–2.64; frequently: aOR = 3.86, 95% CI: 1.85–8.04), and female partners in the past six months (aOR = 3.69, 95% CI: 2.25–6.10). MSM with multiple casual male partners were less likely to be vaccinated (aOR = 0.55, 95% CI: 0.32–0.93). LCA identified heterogeneous subgroups; notably, the “multi-partner and proactive” group, with high pre-exposure prophylaxis against HIV infection awareness and frequent STI testing, showed low uptake (13.4%). Conclusions: MenB vaccination coverage among MSM in China remained suboptimal. Uptake differed across behavioral subgroups, underscoring the need for stratified, context-specific strategies to inform future vaccine introduction.

HIV self-testing (HIVST) has been rapidly scaled up and additional strategies further expand testing uptake. Secondary distribution involves people (defined as \"indexes\") applying for multiple kits and subsequently sharing them with people (defined as \"alters\") in their social networks. However, identifying key influencers is difficult. This study aimed to develop an innovative ensemble machine learning approach to identify key influencers among Chinese men who have sex with men (MSM) for secondary distribution of HIVST kits. We defined three types of key influencers: (1) key distributors who can distribute more kits, (2) key promoters who can contribute to finding first-time testing alters, and (3) key detectors who can help to find positive alters. Four machine learning models (logistic regression, support vector machine, decision tree, and random forest) were trained to identify key influencers. An ensemble learning algorithm was adopted to combine these 4 models. For comparison with our machine learning models, self-evaluated leadership scales were used as the human identification approach. Four metrics for performance evaluation, including accuracy, precision, recall, and F -score, were used to evaluate the machine learning models and the human identification approach. Simulation experiments were carried out to validate our approach. We included 309 indexes (our sample size) who were eligible and applied for multiple test kits; they distributed these kits to 269 alters. We compared the performance of the machine learning classification and ensemble learning models with that of the human identification approach based on leadership self-evaluated scales in terms of the 2 nearest cutoffs. Our approach outperformed human identification (based on the cutoff of the self-reported scales), exceeding by an average accuracy of 11.0%, could distribute 18.2% (95% CI 9.9%-26.5%) more kits, and find 13.6% (95% CI 1.9%-25.3%) more first-time testing alters and 12.0% (95% CI -14.7% to 38.7%) more positive-testing alters. Our approach could also increase the simulated intervention's efficiency by 17.7% (95% CI -3.5% to 38.8%) compared to that of human identification. We built machine learning models to identify key influencers among Chinese MSM who were more likely to engage in secondary distribution of HIVST kits. Chinese Clinical Trial Registry (ChiCTR) ChiCTR1900025433; https://www.chictr.org.cn/showproj.html?proj=42001.

Background Estimating the population sizes of key populations(people who inject drugs, men who have sex with men, transgender persons, and commercial sex workers) is critical for understanding the overall Human Immunodeficiency Virus burden. This scoping review aims to synthesize existing methods for population size estimation among key populations, and provide recommendations for future application of the existing methods. Methods Relevant studies published from 1st January 2000 to 4th August 2020 and related to key population size estimation were retrieved and 120 of 688 studies were assessed. After reading the full texts, 81 studies were further excluded. Therefore, 39 studies were included in this scoping review. Estimation methods included five digital methods, one in-person method, and four hybrid methods. Finding We summarized and organized the methods for population size estimateion into the following five categories: methods based on independent samples (including capture-recapture method and multiplier method), methods based on population counting (including Delphi method and mapping method), methods based on the official report (including workbook method), methods based on social network (including respondent-driven sampling method and network scale-up method) and methods based on data-driven technologies (Bayesian estimation method, Stochastic simulation method, and Laska, Meisner, and Siegel estimation method). Thirty-six (92%) articles were published after 2010 and 23 (59%) used multiple methods. Among the articles published after 2010, 11 in high-income countries and 28 in low-income countries. A total of 10 estimated the size of commercial sex workers, 14 focused on men who have sex with men, and 10 focused on people who inject drugs. Conclusions There was no gold standard for population size estimation. Among 120 studies that were related to population size estimation of key populations, the most commonly used population estimation method is the multiplier method (26/120 studies). Every method has its strengths and biases. In recent years, novel methods based on data-driven technologies such as Bayesian estimation have been developed and applied in many surveys.