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Factors that predict change in urinary incontinence in older rural women.

机译:预测老年农村妇女尿失禁改变的因素。

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摘要

Urinary incontinence (UI) is a common condition, with as many as 13 million individuals affected in the United States. Further knowledge of the predisposing factors associated with UI would allow health providers to identify individuals who may benefit from interventions to prevent, manage, or improve UI. In this retrospective research design, secondary analysis was used to examine associations between the factors under study and UI severity as well as a change in UI severity. A sample of 218 older women was initially randomized into either a behavioral management or a control group.; The dependent variable, UI, was operationally defined by severity, as measured by grams of urine loss per 24 hours and by a bladder diary used to assess episodes of urine loss. The independent variables were specific demographic factors, physical health, and health perception factors. Regression methods were used to select models that could be used to predict urine severity at 3 time periods, baseline, 6 months, and 12 months. A mixed-model analysis was used to supplement findings and to confirm results over the 3 time periods.; Model selection at baseline indicated that the strongest predictor of urine loss and episodes was self-perceived severity, followed by ADL index, education, and age. For predictors of baseline episodes, the strongest predictor was baseline severity, followed by the psychosocial subscale and the impact of mouth/eating disorders. At the 6- and 12-month periods, baseline severity was the strongest predictor for both urine loss and episodes. For urine loss in grams, inclusion in the treatment group, age, stroke, and self-health report were also significant predictors, and, for episodes, treatment group, age, treatment/hypertension interaction, and BMI were significant. For predictors of change in severity in urine loss at 6 months, baseline urine loss was again the strong predictor, followed by treatment group, age, and self-health report. The strongest predictor of change in episodes at 6 months was baseline episodes, followed by treatment and age.; The strongest predictor of urine loss at 12 months was 6-month urine loss. For predictors of change in urine loss from baseline to 12 months, perceived health, followed by Treatment x Baseline Urine Loss Interaction were significant. For change in urine loss from 6 to 12 months, the significant predictors included 6-month urine loss, followed by parity. For episodes at 12 months, the strongest predictor was 6-month episodes, and for change in episodes from baseline to 12 months, the treatment group, followed by baseline episodes, age, and perceived health were significant. For change in episodes from 6 to 12 months, 6-month episodes were the strongest predictors. There was a significant treatment effect at all time periods, except for the 12-month episodes and change in urine loss and episodes (6–12 months) outcomes. The mixed model confirmed support for all variables, but the Treatment x Time Interaction at 6 and 12 months was the strongest influence on both urine loss and episodes.; From these findings, the researcher recommended that nursing professionals plan interventions around assessments and focus on the identified factors.
机译:尿失禁(UI)是一种常见疾病,在美国有多达1300万人受到影响。对与UI相关的诱发因素的进一步了解将使卫生服务提供者能够识别可能从干预措施中受益的个体,以预防,管理或改善UI。在此回顾性研究设计中,使用二级分析来检查研究因素与UI严重性以及UI严重性变化之间的关联。最初,将218名老年妇女的样本随机分为行为管理或对照组。因变量UI是根据严重程度在操作上定义的,其严重程度由每24小时的尿液流失克数和用于评估尿液流失发作的膀胱日记来衡量。自变量是特定的人口统计学因素,身体健康和健康感知因素。使用回归方法选择模型,这些模型可用于预测基线,6个月和12个月三个时间段的尿液严重程度。混合模型分析用于补充发现并确认3个时间段的结果。基线时的模型选择表明,尿液流失和发作的最强预测因子是自我感知的严重程度,其次是ADL指数,教育程度和年龄。对于基线发作的预测因素,最强的预测因素是基线严重程度,其次是心理社会量表和口腔/进食障碍的影响。在6个月和12个月期间,基线严重程度是尿液流失和发作的最强预测指标。对于以克计的尿液流失,治疗组的纳入,年龄,中风和自我健康报告也是重要的预测指标,对于发作,治疗组的年龄,治疗/高血压相互作用和BMI也很重要。对于6个月时尿液流失严重程度变化的预测指标,基线尿液流失仍是最重要的预测指标,其次是治疗组,年龄和自我健康报告。 6个月时发作变化的最强预测因子是基线发作,其次是治疗和年龄。在12个月时尿流失最强的预测指标是6个月时尿流失。对于预测从基线到12个月尿液流失变化的预测指标,感知到的健康状况以及随后的“治疗x基线尿液流失交互作用”非常重要。对于6到12个月的尿量变化,重要的预测指标包括6个月的尿量减少,然后进行平价。对于12个月的发作,最强的预测因子是6个月的发作,对于从基线到12个月的发作变化,治疗组,其次是基线发作,年龄和感知的健康状况。对于6个月到12个月的发作变化,6个月发作是最强的预测因素。除了12个月的发作以及尿液流失和发作(6至12个月)的变化以外,在所有时间段都有显着的治疗效果。混合模型证实了对所有变量的支持,但是在6和12个月时的治疗x时间交互作用对尿量和发作的影响最大。根据这些发现,研究人员建议护理专业人员计划围绕评估的干预措施,并着重于确定的因素。

著录项

  • 作者

    Bell-Kotwall, Lorna Marie.;

  • 作者单位

    The University of North Carolina at Chapel Hill.;

  • 授予单位 The University of North Carolina at Chapel Hill.;
  • 学科 Health Sciences Nursing.; Health Sciences Obstetrics and Gynecology.; Health Sciences Health Care Management.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 231 p.
  • 总页数 231
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 预防医学、卫生学;妇幼卫生;预防医学、卫生学;
  • 关键词

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