[논문]대학생의 건강정보이해능력, 건강관리 자기효능감, 건강행위 간의 관계

김미정; 양인숙

doi:10.22156/cs4smb.2022.12.01.045

[국내논문] 대학생의 건강정보이해능력, 건강관리 자기효능감, 건강행위 간의 관계
The Association of Functional Health Literacy and Health Self-Efficacy with Health Behaviors among University Students 원문보기

융합정보논문지 = Journal of Convergence for Information Technology, v.12 no.1, 2022년, pp.45 - 54

초록
AI-Helper

본 연구는 대학생의 건강행위에 영향을 미치는 요인을 규명하기 위해 시도되었으며, 횡단적 서술적 조사 연구 방법으로 2020년 5월부터 6월까지 161명의 참여자를 대상으로 수행되었다. 건강정보이해능력, 건강관리 자기효능감 및 흡연, 음주, 식습관, 신체활동, 비만도의 건강행위 자료를 수집하였다. 대학생의 건강정보이해능력과 건강관리 자기효능감은 각각 평균 10.14±1.39점, 3.96±0.60점이었고, 대상자의 9.9%가 흡연자, 23.0%가 문제음주자, 96.9%가 식습관의 모니터링을 요하는 자, 63.4%가 신체활동이 낮거나 중간 정도인 자, 29.8%가 과체중 또는 비만인 자였다. 성별은 흡연에, 건강정보이해능력은 식습관에, 그리고 성별과 건강관리 자기효능감은 신체활동에 유의한 요인으로 나타났다. 성별과 건강정보이해능력, 건강관리 자기효능감을 고려하여 대학생의 건강행위를 증진시킬 수 있는 방안이 모색되어야 하겠다.

Abstract ▼ AI-Helper

The purpose of this study was to identify affecting factors on health behaviors among university students. A cross-sectional study was conducted with a sample of 161 participants between May and June 2020. The authors assessed functional health literacy, health self-efficacy, and health behaviors. Mean score of functional health literacy and health self-efficacy was 10.14±1.39 and 3.96±0.60, respectively. Of the subjects, 9.9% were smokers, 23.0% were problem drinking, 96.9% were those who needed monitoring of their eating habits, 63.4% were those with low or moderate physical activity, and 29.8% were those who were overweight or obesity. Gender and functional health literacy had an effect on smoking and eating habits, respectively. Gender and health self-efficacy were affecting factors on physical activity. Researchers should be sought strategies to promote health behavior considering gender, functional health literacy and health self-efficacy.

주제어

표/그림 (4)

표 Table 1. General characteristics of subjects (N=161)
표 Table 2. Functional health literacy, health self-efficacy, and health behaviors (N=161)
표 Table 3. Health behaviors according to general characteristics of subjects (N=161)
표 Table 4. Logistic Regression Predicting Likelihood of Health Behaviors (N=161)

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