Department of Public Health, Faculty of Liberal Arts, Krirk University, Bangkok 10220, Thailand.
Department of Curriculum and Instruction, Faculty of Education, Chulalongkorn University, Bangkok 10330, Thailand.
Nutrients. 2022 Dec 25;15(1):101. doi: 10.3390/nu15010101.
This study aimed to develop and test a causal relationship among perceived self-efficacy (PSE), health literacy (HL), access to COVID-19 preventive material (ACPM), social networks (SN), and health-promoting behaviors (HPBs). Multistage stratified random sampling was used to recruit 250 older adults with noncommunicable diseases (NCDs) from Thai urban and rural communities. The data were collected with self-reported questionnaires. Data analyses used descriptive statistics and structural equation modeling. The results indicated that participants in urban communities had higher PSE, ACPM, HL, SN, and HPBs than rural participants. The fitness parameters of the modified model (χ2 = 71.936, df = 58, p-value = 0.103, χ2/df = 1.240; root mean square error of approximation (RMSEA) = 0.031; standardized root mean square residual (SRMR) = 0.042; goodness of fit index (GFI) = 0.964; normed-fit index (NFI) = 0.964; comparative fit index (CFI) = 0.993) indicated its suitability as the research model. HPBs were directly positively influenced by PSE (β = 0.40, p < 0.001), ACPM (β = 0.24, p < 0.001), HL (β = 0.19, p < 0.01), and SN (β = 0.01, p < 0.05). Therefore, taking all predicting variables together could explain 81.0% of the variance in HPBs. Multidisciplinary healthcare teams could use these findings to establish proper interventions or healthcare activities to increase HPBs among older adults, particularly in this era of the “new normal”.
本研究旨在发展和检验感知自我效能（PSE）、健康素养（HL）、获得 COVID-19 预防材料（ACPM）、社交网络（SN）和促进健康行为（HPBs）之间的因果关系。采用多阶段分层随机抽样方法，从泰国城乡社区招募了 250 名患有非传染性疾病（NCDs）的老年人。数据通过自报问卷收集。数据分析采用描述性统计和结构方程模型。结果表明，城市社区的参与者比农村参与者具有更高的 PSE、ACPM、HL、SN 和 HPBs。修正模型的拟合参数（χ2 = 71.936，df = 58，p 值 = 0.103，χ2/df = 1.240；近似均方根误差（RMSEA）= 0.031；标准化均方根残差（SRMR）= 0.042；拟合优度指数（GFI）= 0.964；归一化拟合指数（NFI）= 0.964；比较拟合指数（CFI）= 0.993）表明其适合作为研究模型。HPBs 直接受到 PSE（β=0.40，p < 0.001）、ACPM（β=0.24，p < 0.001）、HL（β=0.19，p < 0.01）和 SN（β=0.01，p < 0.05）的正向影响。因此，综合所有预测变量，可以解释 81.0%的 HPBs 方差。多学科医疗团队可以利用这些发现，为老年人制定适当的干预措施或医疗保健活动，特别是在这个“新常态”时代，以提高他们的 HPBs。
