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Dating Applications, Sexual Behaviors, and Attitudes of College Students in Brazil's Legal Amazon.

Maycon Klerystton B TavaresRomulo L P de MeloBianca F da RochaDébora J AndradeDanielle R EvangelistaMárcia C T S PeresLeonardo R BaldaçaraThiago DeSouza-VieiraElisangela V AssisJosé Bruno Nunes Ferreira Silva
Published in: International journal of environmental research and public health (2020)
Although dating applications (apps) have become popular among young adults, there is a dearth of information regarding the sexual health implications among Brazilian college students. This study examined risky sexual behavior and attitudes of dating app users, based on their sex in Brazil's Legal Amazon. Three hundred and fifty-nine students reported their sociodemographic data, dating app use, and sexual behaviors and attitudes through self-administered questionnaires. Bivariate analyses and analysis of variance (ANOVA) with Bonferroni post-hoc tests were performed. Dating app use was reported by 238 (66.3%) subjects, most of whom had an encounter and sex with a casual partner. Women frequently requested condom use. Trust in one's partner or having repeated encounters were the main reasons for engaging in risky sexual behavior. Men had a greater number of sexual partners and less protective attitudes. Sexual health awareness by apps was not reported by 97% of women, and most of them were not tested for sexually transmitted infections. A positive attitude toward sexual health was not a predictor of safe sex. Important similarities and differences regarding risky sexual behaviors and attitudes were observed between the sexes, many of which correlated with increased sexual vulnerability during the sexual encounters arranged through the dating apps. This cross-sectional study supports efforts on sexual health promotion and sexual education implementation in the face of growing usage of apps among young adults for sexual matters.
Keyphrases
  • mental health
  • young adults
  • healthcare
  • primary care
  • type diabetes
  • pregnant women
  • health information
  • health promotion
  • skeletal muscle
  • machine learning
  • big data
  • pregnancy outcomes
  • data analysis