Physical activity and sleep: An updated umbrella review of the 2018 Physical Activity Guidelines Advisory Committee report.

Physical activity (PA) is widely considered to improve sleep, but a comprehensive review of the research on this topic has not been performed. In this umbrella review, conducted initially for the 2018 Physical Activity Guidelines for Americans Advisory Committee and updated to reflect more recent research, we examined whether PA enhances sleep outcomes across the lifespan as well as among individuals with sleep disorders. Systematic reviews and meta-analyses were utilized to assess the evidence. We also examined dose-response considerations and whether the association between PA and sleep was moderated by various factors (e.g., timing, sociodemographic characteristics). We found strong evidence that both acute bouts of PA and regular PA improved sleep outcomes. Moderate evidence indicated that longer bouts of PA (both acute and regular) improved sleep, and that the effects of PA on sleep outcomes were generally preserved across adult age groups and sex. Finally, moderate evidence demonstrated that PA improved sleep in adults with insomnia symptoms or obstructive sleep apnea. Several important areas in need of future research were also identified. Overall, the review supported the claim that PA improves sleep, but highlighted gaps that need to be addressed to facilitate more widespread utilization of PA for improving sleep.

Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.

PURPOSE: The implementation and utilization of electronic health records is generating a large volume and variety of data, which are difficult to process using traditional techniques. However, these data could help answer important questions in cancer surveillance and epidemiology research. Artificial intelligence (AI) data processing methods are capable of evaluating large volumes of data, yet current literature on their use in this context of pharmacy informatics is not well characterized. METHODS: A systematic literature review was conducted to evaluate relevant publications within four domains (cancer, pharmacy, AI methods, population science) across PubMed, EMBASE, Scopus, and the Cochrane Library and included all publications indexed between July 17, 2008, and December 31, 2018. The search returned 3,271 publications, which were evaluated for inclusion. RESULTS: There were 36 studies that met criteria for full-text abstraction. Of those, only 45% specifically identified the pharmacy data source, and 55% specified drug agents or drug classes. Multiple AI methods were used; 25% used machine learning (ML), 67% used natural language processing (NLP), and 8% combined ML and NLP. CONCLUSION: This review demonstrates that the application of AI data methods for pharmacy informatics and cancer epidemiology research is expanding. However, the data sources and representations are often missing, challenging study replicability. In addition, there is no consistent format for reporting results, and one of the preferred metrics, F-score, is often missing. There is a resultant need for greater transparency of original data sources and performance of AI methods with pharmacy data to improve the translation of these results into meaningful outcomes.