The Relationship Between Physical Activity and Mobile Phone Addiction Among Adolescents and Young Adults: Systematic Review and Meta-analysis of Observational Studies.

BACKGROUND: Previous studies have reported a potential negative correlation between physical activity (PA) and mobile phone addiction (MPA) among adolescents and young adults. To date, the strength of this correlation has not been well characterized. OBJECTIVE: This review and meta-analysis aimed to synthesize available empirical studies to examine the correlations between PA and MPA among adolescents and young adults. We also explored several potential moderators, including time of data collection, country or region, and type of population, associated with the relationship between PA and MPA. METHODS: Four electronic databases (PubMed, Scopus, PsycINFO, and Web of Science) were searched from database inception to March 2022 to identify relevant studies. The pooled Pearson correlation coefficients and their corresponding 95% CIs for the relationship between PA and MPA were calculated using the inverse variance method. The methodological quality of the included cross-sectional studies was determined based on the Joanna Briggs Institute appraisal checklist. The study conformed to the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analyses) guidelines. RESULTS: In total, 892 relevant articles were identified, of which 22 were selected based on the inclusion and exclusion criteria. The final meta-analysis included 17 of the 22 studies. Results of random effects modeling revealed a moderate correlation between PA and MPA among adolescents and young adults (summary r=-0.243, P<.001). Sensitivity and publication bias analyses further demonstrated the robustness of our results. All the included studies were scored as high quality with a low risk of bias. Subgroup analysis further indicated that none of the hypothesized moderators (time of data collection, country or region, and type of population) significantly affected the relationship between PA and MPA, as confirmed by the mixed effects analysis. In addition, in the data collection subgroups, medium effect sizes were obtained for data collected before COVID-19 (r=-0.333, P<.001) and data collected during COVID-19 (r=-0.207, P<.001). In subgroup analyses for country or region, the correlation coefficient for China and other developing regions showed a similarly moderate effect size (r=-0.201, P<.001 and r= -0.217, P<.001, respectively). However, the effect sizes for developed regions were not significant (r=-0.446, P=.39). In a subgroup analysis based on the type of population, we found that the effect size for young adults was moderate (r=-0.250, P<.001). However, that of adolescents was not significant (r=-0.129, P=.24). CONCLUSIONS: Our results demonstrate a moderately negative relationship between PA and MPA among young adults. The strength of this relationship was not influenced by the time of data collection, country or region, or type of population.

Epidemic management and control through risk-dependent individual contact interventions.

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model.

The coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists' subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on developing a CT-based radiomics model to assess whether COVID-19 patients are in the early, progressive, severe, or absorption stages of the disease. We retrospectively analyzed the CT images of 284 COVID-19 patients. All of the patients were divided into four groups (0-3): early (n = 75), progressive (n = 58), severe (n = 75), and absorption (n = 76) groups, according to the progression of the disease and the CT features. Meanwhile, they were split randomly to training and test datasets with the fixed ratio of 7 : 3 in each category. Thirty-eight radiomic features were nominated from 1688 radiomic features after using select K-best method and the ElasticNet algorithm. On this basis, a support vector machine (SVM) classifier was trained to build this model. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of various models. The precision, recall, and f 1-score of the classification model of macro- and microaverage were 0.82, 0.82, 0.81, 0.81, 0.81, and 0.81 for the training dataset and 0.75, 0.73, 0.73, 0.72, 0.72, and 0.72 for the test dataset. The AUCs for groups 0, 1, 2, and 3 on the training dataset were 0.99, 0.97, 0.96, and 0.93, and the microaverage AUC was 0.97 with a macroaverage AUC of 0.97. On the test dataset, AUCs for each group were 0.97, 0.86, 0.83, and 0.89 and the microaverage AUC was 0.89 with a macroaverage AUC of 0.90. The CT-based radiomics model proved efficacious in assessing the severity of COVID-19.

Investigation of 100 SARS-CoV-2 infected families in Wuhan: Transmission patterns and follow-up.

BACKGROUND: To prevent the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strict control of person-to-person transmission is essential. Family transmission is the most common route of transmission; however, family transmission patterns and outcomes are not well understood. METHODS: We enrolled confirmed cases discharged from Wuhan Zhuankou Fangcang Shelter Hospital from February 17, 2020 to March 8, 2020 along with the family members they had contact with, to evaluate baseline characteristics, family transmission patterns and outcomes. The follow-up period lasted until May 8, 2020. RESULTS: This study evaluated 369 participants, which included 100 patients admitted to the shelter hospital and the family members they had contact with. Family transmission occurred in 62% of household, with 190 cases confirmed to have SARS-CoV-2 infection. There were eight patterns of family transmission, and spousal transmission (44/83, 53.0%) was the most common pattern, especially in the middle-age generation group (35/83, 42.2%). The homes of the families in which all members were infected had a smaller per capita area than those of other families (29.1 ± 11.89 cm2 vs 41.0 ± 19.70 cm2, respectively, P = 0.037), and the per capita area was negatively associated with the number of infected family members (R = -0.097, P = 0.048). Of the 190 confirmed cases, the 113 mild or moderate cases were monitored in fangcang (including Wuhan Zhuankou Fangcang and other fangcang), and the 59 severe cases were treated at designated hospitals. By the end of follow-up, 185 patients recovered and returned home after completing at least 14 days of isolation at the community quarantine center, four died in hospitals, and one died at home before hospitalization. Interestingly, four patients had positive nucleic acid test results after previous negative results, though none of these patients were re-hospitalized, and none of their close contacts reported an infection. CONCLUSIONS: Our data found eight family transmission patterns, of which spousal transmission was the most common. Some patients were also found to have positive test results during follow-up.