Social comparison and aggression: The mediating role of relative deprivation and moderating role of covert narcissism.

Social comparison is a universal social phenomenon that profoundly influences aggressive behaviours among young adults. Based on the general aggression model, this study investigated the relationship between social comparison and aggression, and the mediating role of relative deprivation. To further explore the mechanism underlying this influence, covert narcissism was examined as a moderator in this relationship, based on relative deprivation theory. The results from the current study using a total of 726 Chinese college students showed that social comparison was positively correlated with aggression, which was mediated by relative deprivation. Specifically, more frequent social comparison was associated with higher relative deprivation, which was, in turn, associated with higher aggression. Covert narcissism acted as a moderator in this model. Covert narcissism exacerbated the relationships between social comparison and relative deprivation and relative deprivation and aggression. Specifically, compared to individuals with low levels of covert narcissism, those with high levels of covert narcissism exhibited greater relative deprivation when subjected to the same social comparisons, subsequently displaying increased levels of aggression. This study deepens the understanding of the relationship between social comparison and aggression and provides an intervention direction and a theoretical basis for effectively preventing aggression in young adults.

Design of a Tri-Axial Force Measurement Transducer for Plantar Force Measurements.

Transducers for spatial plantar force measurements have numerous applications in biomechanics, rehabilitation medicine, and gait analysis. In this work, the design of a novel, tri-axial transducer for plantar force measurements was presented. The proposed design could resolve both the normal and the shear forces applied at the foot's sole. The novelty of the design consisted in using a rotating bump to translate the external loads into axial compressive forces which could be measured effectively by conventional pressure sensors. For the prototype presented, multilayer polydimethylsiloxane (PDMS) thin-film capacitive stacks were manufactured and used as sensing units, although in principle the design could be extended to various types of sensors. A quasi-static analytic solution to describe the behavior of the transducer was also derived and used to optimize the design. To characterize the performance of the transducer, a 3 cm diameter, 1 cm tall prototype was manufactured and tested under various combination of shear and normal loading scenarios. The tests confirmed the ability of the transducer to generate strong capacitive signals and measure both the magnitude and direction of the normal and shear loads in the dynamic range of interest.

Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study.

BACKGROUND: Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations. OBJECTIVE: This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns. METHODS: Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis. RESULTS: In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs. CONCLUSIONS: The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.

Predicting non-attendance in hospital outpatient appointments using deep learning approach.

The hospital outpatient non-attendance imposes a substantial financial burden on hospitals and roots in multiple diverse reasons. This research aims to build an advanced predictive model for predicting non-attendance regarding the whole spectrum of probable contributing factors to non-attendance that could be collated from heterogeneous sources including electronic patients records and external non-hospital data. We proposed a new non-attendance prediction model based on deep neural networks and machine learning models. The proposed approach works upon sparse stacked denoising autoencoders (SDAEs) to learn the underlying manifold of data and thereby compacting information and providing a better representation that can be utilised afterwards by other learning models as well. The proposed approach is evaluated over real hospital data and compared with several well-known and scalable machine learning models. The evaluation results reveal the proposed approach with softmax layer and logistic regression outperforms other methods in practice.

Assessing inequality, irregularity, and severity regarding road traffic safety during COVID-19.

COVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for "Hispanic" and "Male" groups; (2) the "hot spots" of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic.

Associations Between the Perceived Severity of the COVID-19 Pandemic, Cyberchondria, Depression, Anxiety, Stress, and Lockdown Experience: Cross-sectional Survey Study.

BACKGROUND: The outbreak of the COVID-19 pandemic has caused great panic among the public, with many people suffering from adverse stress reactions. To control the spread of the pandemic, governments in many countries have imposed lockdown policies. In this unique pandemic context, people can obtain information about pandemic dynamics on the internet. However, searching for health-related information on the internet frequently increases the possibility of individuals being troubled by the information that they find, and consequently, experiencing symptoms of cyberchondria. OBJECTIVE: We aimed to examine the relationships between people's perceived severity of the COVID-19 pandemic and their depression, anxiety, and stress to explore the role of cyberchondria, which, in these relationship mechanisms, is closely related to using the internet. In addition, we also examined the moderating role of lockdown experiences. METHODS: In February 2020, a total of 486 participants were recruited through a web-based platform from areas in China with a large number of infections. We used questionnaires to measure participants' perceived severity of the COVID-19 pandemic, to measure the severity of their cyberchondria, depression, anxiety, and stress symptoms, and to assess their lockdown experiences. Confirmatory factor analysis, exploratory factor analysis, common method bias, descriptive statistical analysis, and correlation analysis were performed, and moderated mediation models were examined. RESULTS: There was a positive association between perceived severity of the COVID-19 pandemic and depression (ß=0.36, t=8.51, P<.001), anxiety (ß=0.41, t=9.84, P<.001), and stress (ß=0.46, t=11.45, P<.001), which were mediated by cyberchondria (ß=0.36, t=8.59, P<.001). The direct effects of perceived severity of the COVID-19 pandemic on anxiety (ß=0.07, t=2.01, P=.045) and stress (ß=0.09, t=2.75, P=.006) and the indirect effects of cyberchondria on depression (ß=0.10, t=2.59, P=.009) and anxiety (ß=0.10, t=2.50, P=.01) were moderated by lockdown experience. CONCLUSIONS: The higher the perceived severity of the COVID-19 pandemic, the more serious individuals' symptoms of depression, anxiety, and stress. In addition, the associations were partially mediated by cyberchondria. Individuals with higher perceived severity of the COVID-19 pandemic were more likely to develop cyberchondria, which aggravated individuals' depression, anxiety, and stress symptoms. Negative lockdown experiences exacerbated the COVID-19 pandemic's impact on mental health.

Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model.

BACKGROUND AND OBJECTIVE: Type 2 diabetes mellitus (T2DM) complications seriously affect the quality of life and could not be cured completely. Actions should be taken for prevention and self-management. Analysis of warning factors is beneficial for patients, on which some previous studies focused. They generally used the professional medical test factors or complete factors to predict and prevent, but it was inconvenient and impractical for patients to self-manage. With this in mind, this study built a Bayesian network (BN) model, from the perspective of diabetic patients' self-management and prevention, to predict six complications of T2DM using the selected warning factors which patients could have access from medical examination. Furthermore, the model was analyzed to explore the relationships between physiological variables and T2DM complications, as well as the complications themselves. The model aims to help patients with T2DM self-manage and prevent themselves from complications. METHODS: The dataset was collected from a well-known data center called the National Health Clinical Center between 1st January 2009 and 31st December 2009. After preprocess and impute the data, a BN model merging expert knowledge was built with Bootstrap and Tabu search algorithm. Markov Blanket (MB) was used to select the warning factors and predict T2DM complications. Moreover, a Bayesian network without prior information (BN-wopi) model learned using 10-fold cross-validation both in structure and in parameters was added to compare with other classifiers learned using 10-fold cross-validation fairly. The warning factors were selected according the structure learned in each fold and were used to predict. Finally, the performance of two BN models using warning features were compared with Naïve Bayes model, Random Forest model, and C5.0 Decision Tree model, which used all features to predict. Besides, the validation parameters of the proposed model were also compared with those in existing studies using some other variables in clinical data or biomedical data to predict T2DM complications. RESULTS: Experimental results indicated that the BN models using warning factors performed statistically better than their counterparts using all other variables in predicting T2DM complications. In addition, the proposed BN model were effective and significant in predicting diabetic nephropathy (DN) (AUC: 0.831), diabetic foot (DF) (AUC: 0.905), diabetic macrovascular complications (DMV) (AUC: 0.753) and diabetic ketoacidosis (DK) (AUC: 0.877) with the selected warning factors compared with other experiments. CONCLUSIONS: The warning factors of DN, DF, DMV, and DK selected by MB in this research might be able to help predict certain T2DM complications effectively, and the proposed BN model might be used as a general tool for prevention, monitoring, and self-management.

Understanding inter-organizational trust among integrated care service provider networks: A perspective on organizational asymmetries.

OBJECTIVE: To explore the factors that influence trust among the integrated healthcare service provider network in the context of seeking combined health and care services in the UK. DATA SOURCES/STUDY SETTING: Primary data were collected from three regional integrated care service provider networks from March 2016 to October 2017. STUDY DESIGN: Explorative qualitative study and inductive methods from emerging findings. DATA COLLECTION/EXTRACTION METHODS: We conducted qualitative semi-structured interviews in three care networks and collected organizational documents from local integration boards from 2016 to 2017. Thematic analysis was performed in three large care networks with hospital staff, local councils, integration boards, and community and voluntary organizations under the NHS England Better Care Fund. PRINCIPAL FINDINGS: Our findings reveal that trust among integrated care service provider networks is influenced by the following factors on various asymmetries: 1) recognition and knowledge asymmetries among care service partners of each other's skills, expertise and capabilities; 2) capacity and financial imbalances within the network; and 3) organizational differences in management, culture and attitudes toward change. CONCLUSION: There is a need to improve competence recognition and capacity imbalances and to foster open minds toward change within networks to build trust to overcome divisions and facilitate integrated services among health and care organizations.