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日本新型冠状病毒大流行期间社区居住老年人跌倒的危险因素:一项前瞻性队列研究。

Risk Factors for Falls in Community-Dwelling Older Adults During the Novel Coronavirus Pandemic in Japan: A Prospective Cohort Study.

作者信息

Murayama Akihiko, Higuchi Daisuke, Saida Kosuke, Tanaka Shigeya, Shinohara Tomoyuki

机构信息

Department of Physical Therapy, Faculty of Rehabilitation, Gunma University of Health and Welfare, Maebashi Plaza Genki 21 6-7F, 2-12-1 Hon-machi, Maebashi-shi 371-0023, Gunma, Japan.

Department of Physical Therapy, Faculty of Health Care, Takasaki University of Health and Welfare, 27 Naka Orui-machi, Takasaki-shi 370-0033, Gunma, Japan.

出版信息

Int J Environ Res Public Health. 2024 Nov 30;21(12):1603. doi: 10.3390/ijerph21121603.

Abstract

This study aimed to test the hypothesis that knowledge derived from indirect assessments can be used to identify fall risk factors during a period of social distancing. A baseline survey of 1953 community-dwelling older adults was conducted in May 2020, with a follow-up survey performed in May 2023 to assess the situation 3 years later. In total, 339 individuals were followed from baseline to follow-up. Baseline age, sex, Questionnaire for Change of Life, Frailty Screening Index, and Questionnaire for Medical Checkup of Old-Old (QMCOO) scores and subscales were used to determine fall predictors. In addition, history of falls in the past year was assessed at follow-up (outcome). The participants were categorized into fall (n = 78) and non-fall (n = 261) groups. Using binary logistic regression analysis, items that showed significant differences in a between-group comparison were analyzed, and age and history of falls, which were sub-items of the QMCOO, were identified as predictors of falls. Although special assessments may be required during periods of social distancing, we believe that it is important for these assessments to continue being performed as they are performed during normal times.

摘要

本研究旨在检验以下假设

在社交距离期间,从间接评估中获得的知识可用于识别跌倒风险因素。2020年5月对1953名社区居住的老年人进行了基线调查,并于2023年5月进行了随访调查,以评估3年后的情况。总共有339人从基线到随访进行了跟踪。基线年龄、性别、生活变化问卷、衰弱筛查指数以及高龄老人健康体检问卷(QMCOO)得分和子量表被用于确定跌倒预测因素。此外,在随访时评估过去一年的跌倒史(结果)。参与者被分为跌倒组(n = 78)和非跌倒组(n = 261)。使用二元逻辑回归分析,对组间比较显示出显著差异的项目进行分析,并且将QMCOO的子项目年龄和跌倒史确定为跌倒的预测因素。尽管在社交距离期间可能需要进行特殊评估,但我们认为这些评估继续像正常时期那样进行很重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf2e/11675169/fa4bd6c2e992/ijerph-21-01603-g001.jpg

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