Existing studies reporting on the levels of physical fitness among college students used relatively few fitness tests as a reflection of physical fitness, which could not comprehensively evaluate the ...levels of physical fitness. Thus, the current study aimed to investigate the cross-sectional relationship between body mass index (BMI) and a physical fitness index (PFI) based on six indicators of fitness in Chinese college students.
Anthropometric measurements and six measures of physical fitness (Vital capacity, 50-m sprint, sit and reach, standing long jump, 800/1000-m run, pull-up/bent-leg sit-up) were measured. BMI was calculated to classify individuals into underweight, normal weight, overweight, and obesity groups. Z-scores based on sex-specific mean and standard deviation were calculated, and the sum of z-scores for the six fitness tests was used as a PFI. Three models (a linear regression model, polynomial regression model with a second-order BMI term and a restricted cubic spline regression model) were fitted to discuss the potential relation between BMI and PFI. We compared the models using Akaike Information Criterion (AIC) and R square.
Totally, 8548 freshmen from the years 2014 to 2016 in a medical college completed the physical fitness tests. There was a decreasing trend of physical fitness index from the years 2014 to 2016 (P for trend < 0.01). More male than female students were overweight or obese (23.5% vs. 11.9%), but more female than male students were normal weight (74.7% vs. 64.8%). A restricted cubic spline regression model was superior to linear and polynomial regression model with lower AIC and higher R square.
The relationships between BMI and PFI in college students were non-linear. Underweight, overweight and obese students had poorer performance in physical fitness index than normal weight students. Future prospective, longitudinal cohort studies to identify the causal relations and potential mechanism in a good manner are required.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
The association between sarcopenia and mild cognitive impairment (MCI) among elderly adults in China remains unclear. The present study aimed to examine the association based on a ...nationally representative large‐scale survey.
Methods
The study used two waves of data from China Health and Retirement Longitudinal Study (CHARLS) in 2015 and 2018. All subjects met the inclusion criteria were classified based on Asia Working Group for Sarcopenia 2019 criteria. Aging‐associated cognitive decline is used to define MCI, and cognitive function is measured based on four dimensions: orientation, computation, memory, and drawing. OLS and logistic regression model were conducted to analyse the cross‐sectional association between sarcopenia and different cognitive functions. Logistic regression model was conducted to analyse the longitudinal association between sarcopenia and MCI.
Results
Totally, 5715 participants aged over 60 years (43.8% women; mean age 67.3 ± 6.0 years) were enrolled in a cross‐sectional association study in 2015, and further 2982 elderly adults were followed up in 2018. During the period, sarcopenia and possible sarcopenia increased from 8.5% to 29.6%. Scores of cognitive and four dimensions (orientation, computation, memory, and drawing) exhibited a decreasing trend from non‐sarcopenia to sarcopenia (P < 0.001). In the fully adjusted OLS regression model, scores of four dimensions were lower in possible sarcopenia and sarcopenia groups when compared with the non‐sarcopenia group (P < 0.05) respectively. The incidence of MCI was 10.1%, 16.5%, and 24.2% for non‐sarcopenia, possible sarcopenia, and sarcopenia groups from 2015 to 2018, with a significantly statistical difference (P < 0.001). Logistic regression model revealed an odds ratio of 1.43 95% confidence interval (CI): 1.06–1.91, P = 0.017 for the possible sarcopenia group and 1.72 (95% CI: 1.04–2.85, P = 0.035) for sarcopenia group when compared with the non‐sarcopenia group.
Conclusions
Sarcopenia is associated with worse cognitive impairment, which provided new evidence for a strong association that warrants further research into mechanistic insights.
Amajor challenge in transforming development to inclusive, sustainable pathways is the pervasive and persistent trade-off between provisioning services (e.g., agricultural production) on the one hand ...and regulating services (e.g., water purification, flood control) and biodiversity conservation on the other. We report on an application of China’s new Ecological Development Strategy, now being formally tested and refined for subsequent scaling nationwide, which aims to mitigate and even eliminate these trade-offs. Our focus is the Ecosystem Function Conservation Area of Hainan Island, a rural, tropical region where expansion of rubber plantations has driven extensive loss of natural forest and its vital benefits to people. We explored both the biophysical and the socioeconomic options for achieving simultaneous improvements in product provision, regulating services, biodiversity, and livelihoods. We quantified historic trade-offs between rubber production and vital regulating services, finding that, over the past 20 y (1998–2017), there was a 72.2% increase in rubber plantation area, leading to decreases in soil retention (17.8%), water purification reduced retention of nitrogen (56.3%) and phosphorus (27.4%), flood mitigation (21.9%), carbon sequestration (1.7%), and habitat for biodiversity (6.9%). Using scenario analyses, we identified a two-pronged strategy that would significantly reduce these trade-offs, enhancing regulating services and biodiversity, while simultaneously diversifying and increasing product provision and improving livelihoods. This general approach to analyzing product provision, regulating services, biodiversity, and livelihoods has applicability in rural landscapes across China, South and Southeast Asia, and beyond.
Evidence regarding environmental factors associated with disease severity of COVID-19 remained scarce. This study aimed to investigate the association of residential greenness exposure with COVID-19 ...severity applying a retrospective cross-sectional study in Wuhan, China. We included 30,253 COVID-19 cases aged over 45 years from January 1 to February 27, 2020. Residential greenness was quantitatively assessed using normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). A multilevel generalized linear model using Poisson regression was implemented to analyze the association between greenness exposure and disease severity of COVID-19, after adjusting for potential covariates. A linear exposure-response relationship was found between greenness and COVID-19 severity. In the adjusted model, one 0.1 unit increase of NDVI and EVI in the 1000-m buffer radius was significantly associated with a 7.6% (95% confidence interval (CI): 4.0%, 11.1%) and 10.0% (95% CI: 5.1%, 14.7%) reduction of the prevalence of COVID-19 severity, respectively. The effect of residential greenness seemed to be more pronounced among participants with lower population density and economic levels. Air pollutants mediated 0.82~12.08% of the greenness and COVID-19 severity association, particularly to nitrogen dioxide. Sensitivity analyses suggested the robustness of the results. Our findings suggested that residential greenness exposure was beneficial to reduce the prevalence of COVID-19 severity.
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•The study is the first to discuss the associations of greenness with COVID-19 severity.•Greenness was associated with reduction of the prevalence of COVID-19 severity.•The exposure-response relation between greenness and COVID-19 severity was linear.•Air pollutants mediated the associations of greenness with COVID-19 severity.
Prognostics and health management allows us to predict the remaining useful life (RUL) of machinery, which is important in reducing maintenance costs and downtime, and even preventing casualties. ...Bearing faults account for a large proportion of machine faults. To predict the RUL of bearings, health indicators that represent the degeneration state are extracted based on the Hilbert-Huang transform and selected according to Spearman's coefficient. A model-based particle filter method is then used to track the degradation state. The unknown parameters in the nonlinear system are updated by a new method of logarithmic linear recursive least squares. A recursive maximum likelihood estimation algorithm is introduced to learn the noise in the system, and an innovative parameter fusion technique based on normalized partial derivative weights is used. Finally, the RUL of the bearings is predicted. The proposed method is validated using data from the test platform PRONOSTIA.
Medical image segmentation plays an important role in many clinical medicines, such as medical diagnosis and computer-assisted treatment. However, due to the large quality differences, variable ...lesion areas and their complex shapes, medical image segmentation is a very challenging task. However, most of the recent deep learning methods ignore the global context information as well as the receptive fields of pixels and do not consider the reuse of pixel features during the feature extraction stage. In this paper, we propose DGFAU-Net, an encoder–decoder structured 2D segmentation model, to overcome the shortcomings aforementioned. In the encoder, DenseNet and AtrousCNN networks are leveraged to extract image features. The DenseNet network is mainly used to achieve the reuse of pixel features, and AtrousCNN is utilized to enhance the receptive field of pixels. In the decoder, two modules, global feature attention upsample (GFAU) and pyramid pooling attention squeeze-excitation (PPASE), are proposed. GFAU combines low-level and high-level features to generate new features with richer information for improving the perceptions of global contextual information of pixels. PPASE employs a multi-scale pooling layer to enhance the pixel’s acceptance field. In addition, Focal loss is used to balance the difference between samples of the lesion and non-lesioned areas. Extensive experiments are conducted on one local dataset and two public datasets, which are the local dataset of MRI images of carotid plaque, DRIVE vessel segmentation dataset and CHASE_DB1 vessel segmentation dataset, and the experimental results demonstrate the effectiveness of our proposed model.
Previous epidemiological studies on the association between short-term exposure to particulate matter (PM) with hospital admission in major cities in China were limited to shorter study periods or a ...single hospital. The aim of this ecological study based on a 12.5-year time series was to investigate the association of short-term exposure to PM with aerodynamic diameter ≤ 2.5 μm (PM
) and aerodynamic diameter ≤ 10 μm (PM
) with hospital admissions for respiratory diseases.
Daily hospital admissions data were from the Shanghai Medical Insurance System for the period January 1, 2008 to July 31, 2020. We estimated the percentage change with its 95% confidence interval (CI) for each 10 μg/m
increase in the level of PM
and PM
after adjustment for calendar time, day of the week, public holidays, and meteorological factors applying a generalized additive model with a quasi-Poisson distribution.
There were 1,960,361 hospital admissions for respiratory diseases in Shanghai during the study period. A 10 μg/m
increase in the level of each class of PM was associated with increased total respiratory diseases when the lag time was 0 day (PM
: 0.755%; 95% CI: 0.422, 1.089%; PM
: 0.250%; 95% CI: 0.042, 0.459%). The PM
and PM
levels also had positive associations with admissions for COPD, asthma, and pneumonia. Stratified analyses demonstrated stronger effects in patients more than 45 years old and during the cold season. Total respiratory diseases increased linearly with PM concentration from 0 to 100 μg/m
, and increased more slowly at higher PM concentrations.
This time-series study suggests that short-term exposure to PM increased the risk for hospital admission for respiratory diseases, even at low concentrations. These findings suggest that reducing atmospheric PM concentrations may reduce hospital admissions for respiratory diseases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Functional disability and multimorbidity are common among older people. However, little is known about the relationship between functional disability and different multimorbidity combinations. We ...aimed to identify multimorbidity patterns and explore the associations between these patterns and functional disability.
We investigated a multi-stage random sample of 1871 participants aged ≥60 years and covered by long-term care insurance in Shanghai, China. Multimorbidity was defined as the simultaneous presence of two or more chronic diseases in an individual. Participants completed scales to assess basic and instrumental activities of daily living (BADL and IADL, respectively). Multimorbidity patterns were identified via exploratory factor analysis. Binary logistic regression models were used to determine adjusted associations between functional disability and number and patterns of multimorbidity.
Multimorbidity was present in 74.3% of participants. The prevalence of BADL disability was 50.7% and that of IADL disability was 90.7%. There was a strong association between multimorbidity and disability. We identified three multimorbidity patterns: musculoskeletal, cardio-metabolic, and mental-degenerative diseases. The cardio-metabolic disease pattern was associated with both BADL (OR 1.28, 95%CI 1.16-1.41) and IADL (OR 1.41, 95%CI 1.19-1.68) disability. The mental-degenerative disease pattern was associated with BADL disability (OR 1.55, 95%CI 1.40-1.72).
Multimorbidity and functional disability are highly prevalent among older people covered by long-term care insurance in Shanghai, and distinct multimorbidity patterns are differentially associated with functional disability. Appropriate long-term healthcare and prevention strategies for older people may help reduce multimorbidity, maintain functional ability, and improve health-related quality of life.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
ObjectivesThere are limited data on the relationship between sleep duration and possible sarcopenia. Hence, this study aimed to investigate the associations of sleep duration with possible sarcopenia ...and its defining components based on the China Health and Retirement Longitudinal Study (CHARLS).DesignA retrospective cohort study.SettingThis study was conducted on participants aged over 45 years applying the 2011 baseline and 2015 follow-up survey from CHARLS covering 450 villages, 150 counties and 28 provinces.ParticipantsData from 5036 individuals (2568 men and 2468 women) free of possible sarcopenia at baseline were analysed.Primary and secondary outcome measuresThe dose-response relationship between sleep duration and possible sarcopenia.ResultsDuring 4 years of follow-up, 964 (19.14%) participants developed possible sarcopenia. Compared with participants who slept 6–8 hours per night, those with shorter sleep duration (<6 hours per night) were independently associated with 22% (OR, 1.22; 95% CI, 1.04 to 1.44) increased risk of developing possible sarcopenia and 27% (OR, 1.27; 95% CI, 1.04 to 1.57) increased risk of developing low handgrip strength after controlling for potential confounders. Long sleep duration (>8 hours per night) was not significantly associated with incident possible sarcopenia. The plots of restricted cubic splines exhibited an atypical inverse J-shaped association between sleep duration and possible sarcopenia. Subgroup analysis showed a stronger association between sleep duration and possible sarcopenia in participants aged 45–59 years and composed of male populations.ConclusionsShort sleep duration was a potential risk factor for possible sarcopenia and low handgrip strength. The improvement of sleep duration should be considered a target in early preventive and administrative strategies against the development of handgrip strength decline and further reduced the occurrence of sarcopenia.
Residential greenness exposure has been linked to a number of physical and mental disorders. Nevertheless, evidence on the association between greenness and geriatric depression was limited and ...focused on developed countries. This study was aimed to investigate whether the relationship between residential greenness exposure and geriatric depression exists among the elderly with long-term care insurance (LTCI) in Shanghai, China. In 2018, a total of 1066 LTCI elderly from a cross-sectional survey completed a questionnaire in Shanghai. Residential greenness indicators, including normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI), were calculated from the Landsat 8 imagery data in different buffers (100-m, 300-m, and 500-m). Mediation analysis by perceived social support was conducted to explore potential mechanisms underlying the associations. In the fully adjusted model, one IQR increase of NDVI and SAVI in the 300-m buffer size was associated with an 11.9% (PR: 0.881, 95% CI: 0.795, 0.977) and 14.7% (PR: 0.853, 95% CI: 0.766, 0.949) lower prevalence of geriatric depression, respectively. Stronger association was observed in the elderly with lower education level, living in non-central area, and lower family monthly income. Perceived social support significantly mediated 40.4% of the total effect for NDVI 300-m buffer and 40.3% for SAVI 300-m buffer to the greenness-depression association, respectively. Our results indicate the importance of residential greenness exposure to geriatric depression, especially for the elderly with lower education level, living in non-central area, and lower family monthly income. Perceived social support might mediate the association. Well-designed longitudinal studies are warranted to confirm our findings and investigate the underlying mechanisms.
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