基于人工智能的自主数字健康干预使用精确生活方式指导对成人高血压患者血压的影响:单臂非随机试验。

Q2 Medicine
JMIR Cardio Pub Date : 2024-05-28 DOI:10.2196/51916
Jared Leitner, Po-Han Chiang, Parag Agnihotri, Sujit Dey
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引用次数: 0

摘要

背景:家庭血压(BP)监测与生活方式指导可有效控制高血压并降低心血管风险。然而,传统的人工生活方式指导模式由于运营成本高和人员要求高而大大限制了其可用性。此外,缺乏对患者生活方式的监测以及临床医生的时间限制也会阻碍对生活方式调整的个性化指导:本研究评估了基于人工智能(AI)的全数字化自主生活方式指导计划对成人高血压患者实现血压控制的效果:参与者参加了一项单臂非随机试验,并在试验中获得了血压监测仪和可穿戴活动追踪器。从这些设备和一个问卷移动应用程序中收集数据,用于训练个性化的机器学习模型,从而通过短信和移动应用程序向参与者提供精准的生活方式指导。主要结果包括:(1) 从基线到 12 周和 24 周期间收缩压和舒张压的变化;(2) 从基线到 12 周和 24 周期间受控、1 期和 2 期高血压类别参与者的百分比变化。次要结果包括:(1)根据数据收集一致性衡量的参与者参与率;(2)临床医生人工外展次数:共有 141 名参与者接受了 24 周的监测。12周时,收缩压和舒张压下降了5.6毫米汞柱(95% CI -7.1至-4.2;PC结论:该研究表明,基于全数字化、自主和人工智能的生活方式指导具有潜力,可使高血压患者的血压得到有意义的改善和高度参与,同时大幅减少临床医生的工作量:ClinicalTrials.gov NCT06337734; https://clinicaltrials.gov/study/NCT06337734。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial.

Background: Home blood pressure (BP) monitoring with lifestyle coaching is effective in managing hypertension and reducing cardiovascular risk. However, traditional manual lifestyle coaching models significantly limit availability due to high operating costs and personnel requirements. Furthermore, the lack of patient lifestyle monitoring and clinician time constraints can prevent personalized coaching on lifestyle modifications.

Objective: This study assesses the effectiveness of a fully digital, autonomous, and artificial intelligence (AI)-based lifestyle coaching program on achieving BP control among adults with hypertension.

Methods: Participants were enrolled in a single-arm nonrandomized trial in which they received a BP monitor and wearable activity tracker. Data were collected from these devices and a questionnaire mobile app, which were used to train personalized machine learning models that enabled precision lifestyle coaching delivered to participants via SMS text messaging and a mobile app. The primary outcomes included (1) the changes in systolic and diastolic BP from baseline to 12 and 24 weeks and (2) the percentage change of participants in the controlled, stage-1, and stage-2 hypertension categories from baseline to 12 and 24 weeks. Secondary outcomes included (1) the participant engagement rate as measured by data collection consistency and (2) the number of manual clinician outreaches.

Results: In total, 141 participants were monitored over 24 weeks. At 12 weeks, systolic and diastolic BP decreased by 5.6 mm Hg (95% CI -7.1 to -4.2; P<.001) and 3.8 mm Hg (95% CI -4.7 to -2.8; P<.001), respectively. Particularly, for participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 9.6 mm Hg (95% CI -12.2 to -6.9; P<.001) and 5.7 mm Hg (95% CI -7.6 to -3.9; P<.001), respectively. At 24 weeks, systolic and diastolic BP decreased by 8.1 mm Hg (95% CI -10.1 to -6.1; P<.001) and 5.1 mm Hg (95% CI -6.2 to -3.9; P<.001), respectively. For participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 14.2 mm Hg (95% CI -17.7 to -10.7; P<.001) and 8.1 mm Hg (95% CI -10.4 to -5.7; P<.001), respectively, at 24 weeks. The percentage of participants with controlled BP increased by 17.2% (22/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The percentage of participants with stage-2 hypertension decreased by 25% (32/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The average weekly participant engagement rate was 92% (SD 3.9%), and only 5.9% (6/102) of the participants required manual outreach over 24 weeks.

Conclusions: The study demonstrates the potential of fully digital, autonomous, and AI-based lifestyle coaching to achieve meaningful BP improvements and high engagement for patients with hypertension while substantially reducing clinician workloads.

Trial registration: ClinicalTrials.gov NCT06337734; https://clinicaltrials.gov/study/NCT06337734.

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来源期刊
JMIR Cardio
JMIR Cardio Computer Science-Computer Science Applications
CiteScore
3.50
自引率
0.00%
发文量
25
审稿时长
12 weeks
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