Search for: All records

Incidence of sexually transmitted infections (STIs) is rising sharply in the United States. Between 2014 and 2019, incidence among men and women has increased by 62.8% and 21.4%, respectively, with an estimated 68 million Americans contracting an STI in 2018.a Some human behaviors impacting the expanding STI epidemic are unprotected sex and multiple sexual partners.b Increasing dating app usage has been postulated as a driver for increases in the numbers of people engaging in these behaviors. Using the proposed model, it is estimated that both STI incidence and prevalence for females and males have increased annually by 9%–15% between 2015 and 2019 due to dating apps usage, and that STI incidence and prevalence will continue to increase in the future. The model is also used to assess the possible benefit of in-app prevention campaigns. 

The decision process of engaging or disengaging automation has been termed reliance on automation, and it has been widely analyzed as a summary measure of automation usage rather than a dynamic measure. We provide a framework for defining temporal reliance dynamics and apply it to a data-set from a previous study. Our findings show that (1) the higher the reliability of an automated system, the larger the reliance over time; and (2) more workload created by the automation type does not significantly affect the operators’ reliance dynamics in high-reliability systems, but it does produce greater reliance in low-reliability systems. Furthermore, on average, operators with low performance make fewer decision changes and prefer to stick to their decision of using automation even if it is not performing well. Operators with high performance, on average, have a higher frequency of decision change, and therefore, their automation usage periods are shorter.