新冠疫情下行動健康照護模式介入對健康行為及代謝症候群之影響──以台灣計程車駕駛為例

Effects of mHealth model on health behavior and metabolic syndrome during COVID-19 epidemic: A case study of taxi drivers in Taiwan

目標：計程車駕駛是罹患代謝症候群的高風險群，加上近三年受新冠疫情的影響，本研究想透過行動健康照護模式的介入，以手機等行動裝置結合社群軟體和相關配套措施，來提升健康行為和改善代謝症候群。方法：本研究設計為準實驗研究，篩選至少有一項代謝症候群的風險因子或BMI≥25的計程車駕駛加入，分派為實驗組與對照組，分別先進行前測包含問卷和生理數值檢測，十二週後進行後測。實驗組以行動健康照護模式介入十二週，結合PRECEDE-PROCEED模式中前傾因素、增強因素和使能因素，內容包括:「代謝症候群線上課程」、「自主健康管理」、「Line線上諮詢與追蹤聯繫」和「設立獎勵機制」四個部分，並使用計步器搭配線上運動課程。本研究主要以廣義估計方程式（GEE）進行統計分析。結果：共完成92位，實驗組48位和對照組44位。介入後能顯著提升計程車駕駛的健康行為（B = 0.22，95% CI = 0.07-0.38）、自我效能（B = 0.18，95% CI = 0.02-0.33）和運動量（t = 2.648，95% CI = 140.6- 1042.3）。結論：針對代謝症候群的高風險群，特別是在新冠疫情期間，行動健康照護模式不但可降低感染風險，並可以作為推廣代謝症候群衛生教育的一種方式。

Objectives: Taxi drivers are a high-risk population for metabolic syndrome. This study thus developed a mobile health-care model (mHealth) that integrates mobile phones with social media and various supporting strategies to promote healthy behaviors and address metabolic syndrome. Methods: A quasi-experimental design was adopted. Taxi drivers with at least one risk factor for metabolic syndrome or body mass index≥25 were recruited and divided into an experimental group and a control group. A pretest comprising questionnaires and physiological value tests were conducted, and a posttest was conducted after 12 weeks of intervention. For 12 weeks, the experimental group used the mHealth model, which incorporated the predisposing factor, reinforcing factor, and enabling factor of the PRECEDE-PROCEED model. The model’s content included the components of Metabolic Syndrome Online Course, Self-Health Management, Line Online Consultation and Contact Tracing, and Reward Mechanism. Additionally, a pedometer was used for online exercise courses. Generalized estimating equations were primarily used for statistical analysis. Results: In total, 92 participants completed this study (48 experimental group participants and 44 control group participants). The intervention yielded significant improvements in healthy behavior (β= 0.22, 95% confidence interval [CI] = 0.07–0.38), self-efficacy (β= 0.18, 95% CI = 0.02–0.33) and physical activity (t = 2.648, 95% CI = 140.6–1042.3). Conclusions: For high-risk populations with metabolic syndrome, especially those profoundly affected by the COVID-19 pandemic, the mHealth model can not only reduce their risk of infection but also can be a way to improve metabolic syndrome.