Hsiung Der-Yun, Liu Chia-Wei, Cheng Pi-Chen, Ma Wei-Fen
Department of Public Health, China Medical University, Taichung, Taiwan; Department of Nursing, Hungkuang University, Taichung, Taiwan.
Department of Nursing, Central Taiwan University of Science and Technology, Taichung, Taiwan.
Appl Nurs Res. 2015 May;28(2):72-7. doi: 10.1016/j.apnr.2014.12.001. Epub 2014 Dec 17.
This study aimed to develop non-invasive assessment indicators for predicting the risk of metabolic syndrome. A cross-sectional study design with 154 convenient subjects recruited from the family clinics was used for this study. Physical assessment sheet, lifestyle profile, the heart rate variability assessment and standard blood sample tests were used to measure variables. The subjects were categorized into four groups based on the number of factors meeting the criteria for metabolic syndrome. After excluding invasive blood tests, the results of multivariate logistic regression identified non-invasive assessment (blood pressure, body mass index and very lower frequency of heart rate variability) were the significantly predictors of the risks of metabolic syndrome. When invasive blood test cannot be performed, community health care providers can use the non-invasive physical assessments to predict the risk of early-stage metabolic syndrome, consequently enabling them to implement related health education and interventions.
本研究旨在开发用于预测代谢综合征风险的非侵入性评估指标。本研究采用横断面研究设计，从家庭诊所招募了154名方便选取的受试者。使用身体评估表、生活方式概况、心率变异性评估和标准血液样本检测来测量变量。根据符合代谢综合征标准的因素数量，将受试者分为四组。在排除侵入性血液检测后，多变量逻辑回归结果确定非侵入性评估（血压、体重指数和心率变异性的极低频率）是代谢综合征风险的显著预测指标。当无法进行侵入性血液检测时，社区医疗保健提供者可以使用非侵入性身体评估来预测早期代谢综合征的风险，从而使他们能够实施相关的健康教育和干预措施。
