Department of Occupational Therapy, California State University, Dominguez Hills, 1000 East Victoria Street, Carson, CA 90747, USA.
Am J Occup Ther. 2011 May-Jun;65(3):329-37. doi: 10.5014/ajot.2011.000505.
We explored personal factors that can predict health-related lifestyles of community-dwelling older adults. A convenience sample of 253 older adults was recruited to complete the Health Enhancement Lifestyle Profile (HELP), a comprehensive measure of health-promoting behaviors. Data were analyzed through univariate correlational/comparative statistics followed by stepwise multiple regression analysis to determine significant predictor variables for different aspects of health-related lifestyle. Personal health conditions, including the number of chronic diseases or impairments and self-rated health, were two strong predictors for the HELP (R2 = .571, p < .0001). Demographic characteristics, including age, gender, race, education, and employment status, also demonstrated varied degrees of capability for predicting the different HELP scales (e.g., Exercise, Diet, Leisure). When developing individualized plans for older adults in community settings, occupational therapists should consider the clients' strengths and vulnerabilities potentially derived from personal health factors and demographic attributes to yield more effective lifestyle interventions.
我们探讨了能够预测社区居住的老年人健康相关生活方式的个人因素。我们招募了 253 名老年人作为便利样本，让他们完成健康促进生活方式概况表（HELP），这是一项对促进健康行为的全面衡量标准。通过单变量相关/比较统计分析，以及逐步多元回归分析，确定健康相关生活方式不同方面的显著预测变量。个人健康状况，包括慢性疾病或损伤的数量以及自我评估的健康状况，是 HELP 的两个重要预测因素（R2 =.571，p <.0001）。人口统计学特征，包括年龄、性别、种族、教育程度和就业状况，也显示出在预测 HELP 不同量表方面的不同能力（例如，运动、饮食、休闲）。在为社区环境中的老年人制定个性化计划时，职业治疗师应考虑到患者的优势和劣势，这些优势和劣势可能源自个人健康因素和人口统计属性，从而使生活方式干预更加有效。
