Attrition of older adults in web-based health interventions: Survival analysis within an observational cohort study

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Marian ZM Hurmuz-Bodde, Stephanie M Jansen-Kosterink, Hermie J Hermens, Lex van Velsen
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引用次数: 0

Abstract

To identify demographics and personal motivation types that predict dropping out of eHealth interventions among older adults. We conducted an observational cohort study. Participants completed a pre-test questionnaire and got access to an eHealth intervention, called Stranded, for 4 weeks. With survival and Cox-regression analyses, demographics and types of personal motivation were identified that affect drop-out. Ninety older adults started using Stranded. 45.6% participants continued their use for 4 weeks. 32.2% dropped out in the first week and 22.2% dropped out in the second or third week. The final multivariate Cox-regression model which predicts drop-out, consisted of the variables: perceived computer skills and level of external regulation. Predicting the chance of dropping out of an eHealth intervention is possible by using level of self-perceived computer skills and level of external regulation (externally controlled rewards or punishments direct behaviour). Anticipating to these factors can improve eHealth adoption.
网络健康干预中老年人的流失:观察性队列研究中的生存分析
目的:确定可预测老年人退出电子健康干预的人口统计学特征和个人动机类型。我们开展了一项观察性队列研究。参与者填写了一份测试前问卷,并接受了为期 4 周的名为 "搁浅"(Stranded)的电子健康干预。通过生存分析和考克斯回归分析,确定了影响退出的人口统计学因素和个人动机类型。90 名老年人开始使用 Stranded。45.6%的参与者持续使用了 4 周。32.2%的人在第一周退出,22.2%的人在第二周或第三周退出。预测退出的最终多元考克斯回归模型由以下变量组成:感知的计算机技能和外部调节水平。通过使用自我感觉的计算机技能水平和外部调节水平(外部控制的奖惩行为),可以预测退出电子健康干预的几率。对这些因素进行预测可以提高电子保健的采用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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