BackgroundQuality of life has become an important indicator for assessing the health care of adolescents. This study aimed to explore the relationship between negative life events and quality of life in adolescents and the potential mediating roles of resilience and social support.MethodsA stratified cluster sampling technique was used to select 3,860 adolescents as study participants. The Adolescent Self-Rating Life Events Checklist, the Resilience Scale for Chinese Adolescents, the Social Support Rating Scale, and the Adolescent Quality of Life Scale were used by participants to rate their negative life events, resilience, social support, quality of life, respectively. The correlations between study variables were analyzed by the Pearson correlation analyses. The AMOS 26.0 software was used to explore the mediating roles of resilience and social support in negative life events and quality of life.ResultsThere was a negative correlation between negative life events and quality of life (β=-0.745, P < 0.05); resilience and social support played an important mediating role in the relationship between negative life events and quality of life (βResilience = −0.287, P < 0.05; βSocial support = −0.124, P < 0.05). The emotional adjustment dimension of resilience (β = −0.285, P < 0.05) and the subjective support dimension of social support (β = −0.100, P < 0.05) played the largest mediating roles, respectively.ConclusionNegative life events were negatively correlated with adolescents' quality of life. Strengthening resilience and social support is expected to weaken and reduce the adverse effects of negative life events on adolescents and further maintain and improve their quality of life.
Background Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults. Methods We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults’ living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models’ performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model. Results The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models’ performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur. Conclusions Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.
This study aimed to explore the psychological status and influencing factors of men who have sex with men (MSM) during the stable period of the COVID-19 epidemic, to provide a reference for the mental health counseling of MSM, and to provide a scientific basis for this group to actively respond to public health emergencies. A cross-sectional survey was conducted on the demographic characteristics, epidemic experiences, risk perception, and COVID-19-related attitudes of MSM in western China, and MSM anxiety and depression were assessed by using the Anxiety Self-Rating Scale and the Center for Epidemiological Studies Depression (CES-D) Scale. The incidences of MSM anxiety and depression in the post-COVID-19 epidemic period are 21.7% and 38.0%, respectively. Logistic regression analysis showed that in terms of anxiety, high controllability of the epidemic (OR = 0.7616) is a protective factor. Thinking that they are more susceptible to COVID-19 (OR = 1.6168) and worrying about another outbreak of the epidemic (OR = 1.4793) are risk factors. In terms of depression, being able to protect themselves from being infected with COVID-19 (OR = 0.6280) is a protective factor. The role of anal sex as “0”/“0.5,” and believing that they are more susceptible to COVID-19 (OR = 1.3408) are risk factors. The sudden outbreak affected the psychological state of MSM and even caused negative feelings of anxiety and depression. These findings suggest that prevention and education should be strengthened, and effective intervention measures should be taken as soon as possible, to improve the mental health of MSM.
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