针对高血压事件的用户的数字干预信息的影响:回顾性现实世界研究
Yifat Fundoiano-Hershcovitz1, Inbar Breuer Asher1, Marilyn D Ritholz2
1DarioHealth, Hatarshish 5, North Industrial Park, Caesarea, Israel, 972-47704044.
JMIR cardio
|November 26, 2025
概括
数字健康干预措施有效地管理高血压. 数据驱动的推动改善了高血压用户的血压 (BP),并保持了正常血压用户的稳定性,显示了可扩展的自我护理的希望.
科学领域:
- 数字健康数字健康
- 心血管健康 心血管健康
- 公共卫生 公共卫生
背景情况:
- 高血压管理是一个公共卫生挑战,低血压 (BP) 控制率.
- 需要可扩展的数字健康干预措施来改善BP意识,行为改变和临床结果.
- 关于数字干预对BP及其机制的影响,现实世界的证据有限.
研究的目的:
- 为了评估数字干预的有效性,使用数据驱动的数据对月平均BP水平进行推敲.
- 评估高血压和正常血压队列的干预前后血压变化.
- 将干预效应与对照组进行比较.
主要方法:
- 从数字健康平台上对408名用户进行了回顾性现实世界队列研究.
- 倾向性得分匹配在高BP和正常BP队列中比较了干预用户和非用户.
- 各个混合效应模型分析了每月平均血压轨迹,并对生活方式活动适度进行了简单的斜率分析.
主要成果:
- 在高血压队列中,干预组与对照组 (B=-1.06;P=.007) 相比,干预组显示SBP显著下降 (B=-2.09;P<.001).
- 较高的生活方式活动水平与干预组中较大的SBP减少相关 (B=-5.27;P<.001).
- 在正常血压队列中,干预组保持稳定的血压 (B=-0.39;P=.27),而对照组则增加 (B=0.69;P=.03).
结论:
- 数据驱动的推动通过数字平台提高高BP个体的BP结果,并保持正常BP个体的稳定性.
- 这些发现支持将个性化的数字干预措施纳入高血压管理.
- 积极的信息传递,行为参与和用户赋权是长期高血压结果的关键.
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