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农村老年人生命质量及其影响因素分析

Quality of life and its influencing factors among rural elderly residents

  • 摘要: 目的 了解农村地区老年人生命质量现状, 并分析其影响因素。方法 采用分层整群抽样方法, 选取四川省广元市农村地区老年人704名为研究对象, 问卷调查农村老年人生命质量状况, 通过结构方程模型分析其影响因素。结果 调查对象生命质量综合平均得分为(59.66±22.08)分, 其中躯体健康总评(PCS)为(54.36±25.09)分, 心理健康总评(MCS)为(64.96±23.49)分;老年人生命质量男性优于女性(t=6.971, P<0.05), 与家人居住者优于独居者(t=-1.974, P<0.05), 生活能自理者优于不能完全自理者(t=8.093, P<0.05), 未患慢性疾病者优于患慢性疾病者(t=-7.232, P<0.05), 经常参加体育锻炼者优于不参加体育锻炼者(t=2.872, P<0.05)。不同年龄(F=1.568, P<0.05)、文化程度(F=17.01, P<0.05)、经济状况(F=52.07, P<0.05)的老年人生命质量评分差异均有统计学意义;结构方程模型各变量回归系数性别为-8.364, 年龄为0.449, 经济状况为-8.815, 社会支持为5.907、体育锻炼为-25.287, 慢性病患病情况为8.777, 自理能力为-7.696, 均会对农村老年人的生命质量产生直接或间接影响。结论 四川农村地区老年人生命质量较差, 性别、年龄、经济状况、社会支持、慢性病患病情况等因素均可影响其生命质量。

     

    Abstract: Objective To examine the quality of life (QOL) and its influencing factors among rural elderly residents in Sichuan province.Methods With stratified cluster sampling, 704 rural elderly residents aged ≥ 60 years in Guangyuan municipality of Sichuan province were surveyed face-to-face with questionnaires.Structural equation model (SEM) was used in data analysis.Results The mean of QOL summary score was 59.66±22.08, with mean scores of 54.36±25.09 for physical component summary (PCS) and 64.96±23.49 for mental component summary (MCS).Compared to the residents of female, living alone, with chronic illness, and not taking part in physical exercise, those of male, living with family members, without chronic illness, and taking part in physical exercise had a better QOL (all P<0.05).There were significant differences in QOL among the residents with different age, education, and living conditions(P<0.05).The results of SEM showed that gender(-8.364, P<0.05), age(0.449, P<0.05), economic status(-8.815, P<0.05), social support(5.90, P<0.05), chronic illness(8.777, P<0.05), and physical exercise(-25.287, P<0.05) had direct or indirect influence on the QOL of the residents(all P<0.05).Conclusion The QOL of rural elderly residents in Guangyuan municipality of Sichuan province was poor and influenced by gender, age, economic status, social support, chronic illness conditions, and other factors.

     

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