生存分析用于预测健身应用程序用户流失率
Monika Zakrzewska1, Oscar Bastidas-Jossa1, Amaia Mendez-Zorrilla1
1eVIDA Research Group, University of Deusto, Bilbao, Spain.
mHealth
|November 10, 2025
概括
在健身应用程序中保留用户是具有挑战性的. 生存分析确定了影响流失的因素,如影响流失的性别和活动水平,参数模型显示了改善参与度的强有力的预测性能.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 行为科学 行为科学
背景情况:
- 健身应用程序被广泛采用,以促进身体活动和健康的生活方式.
- 长期的用户参与是一个重大挑战,在采用后的几周内,学率很高.
- 在数字健身应用程序中现有的流失预测研究是有限的,通常使用基本的统计模型.
研究的目的:
- 在健身应用程序中使用生存分析技术分析用户流失率.
- 确定导致用户退出数字健身应用的关键因素.
- 评估生存分析对预测用户流失时间和改进保留策略的适用性.
主要方法:
- 对3034名使用Mammoth Hunters健身应用程序的用户的数据进行了生存分析.
- 使用多种生存分析方法,包括卡普兰-梅尔,参数模型和治愈分数模型.
- 模型的性能被用像平均绝对误差和一致性指数这样的指标来评估.
主要成果:
- 根据性别,年龄,活动水平和培训频率观察到保留率的显著差异.
- 男人,老年用户和训练频率较高的人表现出更长的参与度.
- LogNormal参数模型为用户流失提供了最佳的预测性能.
结论:
- 生存和治愈模型为健身应用程序中的用户流失动态提供了宝贵的见解.
- 识别关键因素可以帮助开发人员增强个性化,降低学率,提高用户保留率.
- 先进的建模可以通过改进的数字健身平台来支持可持续的健康结果.
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