Gender Differences in the Association Between Screen Time and Depression.

Introduction: Previous research has shown that screen time is associated with depression, especially in children. Some evidence further suggests that the association may be stronger in women than in men, although findings are inconclusive. This cross-sectional study examines the association between screen time and depression in representative U.S. adults, stratified by gender. Methods: This study used data from the 2015-2016 National Health and Nutrition Examination Survey; analysis was conducted in 2023. Screen time was partitioned into 3 categories-0-2 hours, 3-4 hours, and >4 hours-and included TV and computer time. Depression was defined as a Patient Health Questionnaire score ≥10. TV time and computer time were also analyzed as separate exposures. A multivariable logistic regression model examined the association between screen time and depression. Results: Results showed that there was a significant interaction between gender and screen time. An association between the highest screen time exposure group and depression was observed for women (>4 hours per day: OR=3.09; 95% CI=1.68, 5.70). The type of screen time affected the relationship, with TV showing a stronger association than computer time. There were no significant associations in men across all exposure groups. Conclusions: Further research is needed to determine whether higher levels of screen time, especially TV, may be a depression risk marker for women but not men.

FILER: a framework for harmonizing and querying large-scale functional genomics knowledge.

Querying massive functional genomic and annotation data collections, linking and summarizing the query results across data sources/data types are important steps in high-throughput genomic and genetic analytical workflows. However, these steps are made difficult by the heterogeneity and breadth of data sources, experimental assays, biological conditions/tissues/cell types and file formats. FILER (FunctIonaL gEnomics Repository) is a framework for querying large-scale genomics knowledge with a large, curated integrated catalog of harmonized functional genomic and annotation data coupled with a scalable genomic search and querying interface. FILER uniquely provides: (i) streamlined access to >50 000 harmonized, annotated genomic datasets across >20 integrated data sources, >1100 tissues/cell types and >20 experimental assays; (ii) a scalable genomic querying interface; and (iii) ability to analyze and annotate user's experimental data. This rich resource spans >17 billion GRCh37/hg19 and GRCh38/hg38 genomic records. Our benchmark querying 7 × 109 hg19 FILER records shows FILER is highly scalable, with a sub-linear 32-fold increase in querying time when increasing the number of queries 1000-fold from 1000 to 1 000 000 intervals. Together, these features facilitate reproducible research and streamline integrating/querying large-scale genomic data within analyses/workflows. FILER can be deployed on cloud or local servers (https://bitbucket.org/wanglab-upenn/FILER) for integration with custom pipelines and is freely available (https://lisanwanglab.org/FILER).