背景:互联网的普及和移动设备的快速发展导致在线获取健康相关信息的倾向和机会增加。电子健康素养量表(eHEALS),广泛用于测量电子健康素养,评估个人的搜索能力,理解,评价,并使用电子健康信息。然而,eHEALS多因子模型的中文版还有待验证,在台湾,很少探索eHEALS与大学生健康促进生活方式(HPLP)之间的相关性。
目的:本研究旨在检查拟合度,有效性,和中国eHEALS多因素模型的可靠性,并阐明eHEALS对大学生HPLP的预测作用。
方法:台北的大学生,台湾的首都,被招募,和406份有效问卷,包括社会人口统计学特征,eheals,并收集HPLP反应。进行验证性因素分析以验证中国eHEALS。独立样本t检验,单向方差分析,我们进行了多元线性回归分析,以检验社会人口统计学变量与HPLP之间的关系.进行了Pearson乘积矩相关和二元逻辑回归分析,以确定eHEALS对HPLP的预测作用。
结果:中国eHEALS在搜索中表现出最佳拟合,用法,和评估三因素模型(比较拟合指数=0.991,塔克-刘易斯指数=0.984,近似均方根误差=0.062),并证实了其有效性和可靠性。大学生eHEALS平均得分为3.17/4.00(SD0.48)分,评价分量表得分最低(平均3.08,SD0.56分).此外,有重要的性行为,机构导向,每日阅读时间,每日屏幕时间,主要信息频道,HPLP和感知健康状况差异:男性参与者(t404=2.346,P=0.02),参加普通大学(T404=2.564,P=0.01),每日读数≥1小时(F2,403=17.618,P<.001),那些每天在移动设备或计算机上花费少于3小时的人(F2,403=7.148,P<.001),那些从他人获取信息的人(t404=3.892,P<.001),健康状态良好的患者(F2,403=24.366,P<.001)得分明显较高。在调整社会人口统计学变量后,eHEALS评分仍然是HPLP的独立预测因子.与eHEALS分数相对较高的学生相比,eHEALS评分相对较低的患者HPLP阴性风险为3.37倍(调整后比值比[OR]=3.37,95%CI1.49-7.61),这可以解释14.7%-24.4%的方差(Cox-SnellR2=0.147,NagelkerkeR2=0.244,P=.004)。
结论:台北大学生的电子健康素养还有改善的空间。eHEALS可用于筛选需要HPLP改进的学生,从而提供适当的电子健康素养培训计划,特别是那些针对评估素养的人。此外,本研究中使用的中国eHEALS的三因素模型导致更明确的量表内容,从而增加了该量表在健康促进研究中的实用性和适用性。
BACKGROUND: The popularization of the internet and rapid development of mobile devices have led to an increased inclination and opportunities to obtain health-related information online. The eHealth Literacy Scale (eHEALS), widely used for measuring eHealth literacy, assesses an individual\'s ability to search, understand, appraise, and use eHealth information. However, the Chinese version of the eHEALS multiple-factor model remains to be validated, and the correlation between eHEALS and the health-promoting lifestyle profile (HPLP) among university students is rarely explored in Taiwan.
OBJECTIVE: This study aimed to examine the fit, validity, and reliability of the Chinese eHEALS multiple-factor model and to clarify the predictive effects of eHEALS on the HPLP among university students.
METHODS: University students in Taipei, the capital of Taiwan, were recruited, and 406 valid questionnaires including sociodemographic characteristics, eHEALS, and HPLP responses were collected. Confirmatory factor analysis was performed to validate the Chinese eHEALS. Independent sample t test, 1-way ANOVA, and multiple linear regression analyses were conducted to examine the relationship between sociodemographic variables and the HPLP. Pearson product-moment correlation and binary logistic regression analyses were performed to ascertain the predictive effects of eHEALS on the HPLP.
RESULTS: The Chinese eHEALS exhibited an optimal fit when delineated into the search, usage, and evaluation 3-factor model (comparative fit index=0.991, Tucker-Lewis index=0.984, root mean square error of approximation=0.062), and its validity and reliability were confirmed. The mean eHEALS score of university students was 3.17/4.00 (SD 0.48) points, and the score for the evaluation subscale was the lowest (mean 3.08, SD 0.56 points). Furthermore, there were significant sex, institution orientation, daily reading time, daily screen time, primary information channel, and perceived health status differences in the HPLP: male participants (t404=2.346, P=.02), participants attending general university (t404=2.564, P=.01), those reading ≥1 hour daily (F2,403=17.618, P<.001), those spending <3 hours on mobile devices or computers daily (F2,403=7.148, P<.001), those acquiring information from others (t404=3.892, P<.001), and those with a good perceived health status (F2,403=24.366, P<.001) had a significantly higher score. After adjusting for sociodemographic variables, the eHEALS score remained an independent predictor of the HPLP. Compared to students with relatively high eHEALS scores, those with relatively low eHEALS scores had a 3.37 times risk of a negative HPLP (adjusted odds ratio [OR]=3.37, 95% CI 1.49-7.61), which could explain 14.7%-24.4% of the variance (Cox-Snell R2=0.147, Nagelkerke R2=0.244, P=.004).
CONCLUSIONS: There is room for improvement in eHealth literacy among university students in Taipei. eHEALS may be used to screen students who require HPLP improvement, thereby providing appropriate eHealth literacy training programs, particularly those targeting evaluation literacy. Additionally, the 3-factor model of the Chinese eHEALS used in this study results in more definite scale content, thus increasing the practicality and applicability of this scale in health-promoting studies.