Background: While the challenges of COVID-19 are still unfolding, the enhancement of protective behavior remains a top priority in global health care. However, current behavior-promoting strategies may be inefficient without first identifying the individuals with lower engagement in protective behavior and the associating factors.
Objective: This study aimed to identify individuals with and potential contributing factors to low engagement in protective behavior during the COVID-19 pandemic.
Methods: This is a causal-comparative study. A theory-based web-based survey was used to investigate individuals' protective behavior and potential associating factors. During June 2020, the distribution of the survey was targeted to 3 areas: Taiwan, Japan, and North America. Based on the theory of the health belief model (HBM), the survey collected participants' various perceptions toward COVID-19 and a collection of protective behaviors. In addition to the descriptive analysis, cluster analysis, ANOVA, and Fisher exact and chi-square tests were used.
Results: A total of 384 responses were analyzed. More than half of the respondents lived in Taiwan, followed by Japan, then North America. The respondents were grouped into 3 clusters according to their engagement level in all protective behaviors. These 3 clusters were significantly different from each other in terms of the participants' sex, residency, perceived barriers, self-efficacy, and cues of action.
Conclusions: This study used an HBM-based questionnaire to assess protective behaviors against COVID-19 and the associated factors across multiple countries. The findings indicate significant differences in various HBM concepts among individuals with varying levels of behavioral engagement.
Keywords: COVID; COVID-19; attitude; attitudes; causal; causal comparative; health belief model; infection control; infectious disease; opinion; opinions; pandemic; prevention; protective; protective behavior; public health; public safety; survey; surveys.
©Chia-Chun Tang, Hsi Chen, Shao-Yu Tsai, Wei-Wen Wu. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 19.12.2023.