H1N1 Preventive Health Behaviors in a University Setting

Abstract

Background: When H1N1 emerged in 2009, institutions of higher education were immediately faced with questions about how best to protect their community from the virus, yet limited information existed to help predict student preventive behaviors. Methods: The authors surveyed students at a large urban university in November 2009 to better understand how students perceived their susceptibility to and the severity of H1N1, which preventive behaviors they engaged in, and if policies impacted their preventive health decisions. Results: Preventive health behavior messaging had a mixed impact on students. Students made simple behavior changes to protect themselves from H1N1, especially if they perceived a high personal risk of contracting H1N1. Although policies were instituted to enable students to avoid classes when ill, almost no student self-isolated for the entire duration of their illness. Conclusions: These findings can help inform future decision making in a university setting to best influence preventive health behaviors.

How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms

“…As the moderation coefficient for both constructs was positive, higher risk perception and civic attitudes were associated with a stronger effect of perceived efficacy on compliance. Regarding risk perception, this result is in line with previous studies reporting a strong association between risk perception and changes in (or maintenance of) health behaviors in a variety of contexts, including epidemic/pandemics [26][27][28]71]. With respect to civic attitudes, this is a novel finding of our study, as such attitudes have not previously been studied in the context of pandemic behavioral responses.…”

“…Research on past epidemics/pandemics (e.g., H1N1) has not only shown risk perception to be a key driver of health behaviors, but it has also consistently found an association between risk perception and precautionary behaviors [ 26 , 27 , 28 ], even though a meta-analysis highlighted that this association involves only small effect sizes [ 25 ], especially with respect to perceived severity ( r = 0.16). In the context of health psychology, significant interactions between risk perception and self-efficacy have been found; for example, research has identified an association between motivation to think about cardiovascular disease, use of health information, and knowledge acquisition [ 29 ].…”

Willingness to Self-Isolate When Facing a Pandemic Risk: Model, Empirical Test, and Policy Recommendations

“…According to the self-reported behavioral intention regarding the H1N1 influenza of university students in southwestern US, Mas et al (2012) claimed that an array of issues may influence students' decision to self-isolate, including interpersonal, academic, environmental, and social factors [20]; however, their analysis lacks an empirical basis. Risk perception has been widely established as a significant predictor of engagement in preventive health behaviors, including SI [21]; those who report being unfamiliar with the term "pandemic influenza," male respondents, and employed people who are not able to work from home have been found to be less willing to comply [22]. A survey in two counties in North Carolina showed that 50% of households with children under 18 and 65% of working adults reported the ability to comply with SI at home for 7-10 days if recommended to do so by the authorities [23].…”

Social Media Usage and Influenza Beliefs, Risk Perceptions and Behavioral Intentions Among Students at a University in Southeastern US

“…For example, a recent online survey on Human Papilloma Virus vaccination uptake in a university in Michigan has attained a response rate of 19% (192/1000) (Navalpakam, Dany, & Hajj Hussein, 2016). A study on preventive behaviors against pandemic influenza in 2009 in a university in Washington DC had a response rate of 17% (819/4900) (Katz, May, Sanza, Johnston, & Petinaux, 2012). However, some health-related surveys in university settings with high response rates generally considers a special population with certain restrictions, such as medical students (Afonso et al, 2017;Giri, Bangal, & Phalke, 2013), or the administration of the questionnaire was carried out during an event in which a large group of students were gathered (Kongnyuy et al, 2007), or they were recruited around a student healthcare center (Okafor, Hu, & Cook, 2015).…”