Branch OraLee H, Rikhy Mohit, Auster-Gussman Lisa A, Lockwood Kimberly G, Graham Sarah A
Lark Technologies, Inc, Mountain View, CA, United States.
JMIR Form Res. 2022 Oct 27;6(10):e38215. doi: 10.2196/38215.
Home blood pressure (BP) monitoring is recommended for people with hypertension; however, meta-analyses have demonstrated that BP improvements are related to additional coaching support in combination with self-monitoring, with little or no effect of self-monitoring alone. High-contact coaching requires substantial resources and may be difficult to deliver via human coaching models.
This observational study assessed changes in BP and body weight following participation in a fully digital program called Lark Hypertension Care with coaching powered by artificial intelligence (AI).
Participants (N=864) had a baseline systolic BP (SBP) ≥120 mm Hg, provided their baseline body weight, and had reached at least their third month in the program. The primary outcome was the change in SBP at 3 and 6 months, with secondary outcomes of change in body weight and associations of changes in SBP and body weight with participant demographics, characteristics, and program engagement.
By month 3, there was a significant drop of -5.4 mm Hg (95% CI -6.5 to -4.3; P<.001) in mean SBP from baseline. BP did not change significantly (ie, the SBP drop maintained) from 3 to 6 months for participants who provided readings at both time points (P=.49). Half of the participants achieved a clinically meaningful drop of ≥5 mm Hg by month 3 (178/349, 51.0%) and month 6 (98/199, 49.2%). The magnitude of the drop depended on starting SBP. Participants classified as hypertension stage 2 had the largest mean drop in SBP of -12.4 mm Hg (SE 1.2 mm Hg) by month 3 and -13.0 mm Hg (SE 1.6 mm Hg) by month 6; participants classified as hypertension stage 1 lowered by -5.2 mm Hg (SE 0.8) mm Hg by month 3 and -7.3 mm Hg (SE 1.3 mm Hg) by month 6; participants classified as elevated lowered by -1.1 mm Hg (SE 0.7 mm Hg) by month 3 but did not drop by month 6. Starting SBP (β=.11; P<.001), percent weight change (β=-.36; P=.02), and initial BMI (β=-.56; P<.001) were significantly associated with the likelihood of lowering SBP ≥5 mm Hg by month 3. Percent weight change acted as a mediator of the relationship between program engagement and drop in SBP. The bootstrapped unstandardized indirect effect was -0.0024 (95% CI -0.0052 to 0; P=.002).
A hypertension care program with coaching powered by AI was associated with a clinically meaningful reduction in SBP following 3 and 6 months of program participation. Percent weight change was significantly associated with the likelihood of achieving a ≥5 mm Hg drop in SBP. An AI-powered solution may offer a scalable approach to helping individuals with hypertension achieve clinically meaningful reductions in their BP and associated risk of cardiovascular disease and other serious adverse outcomes via healthy lifestyle changes such as weight loss.
对于高血压患者，建议进行家庭血压监测；然而，荟萃分析表明，血压改善与自我监测相结合的额外指导支持有关，单独的自我监测几乎没有效果或没有效果。高接触式指导需要大量资源，并且通过人工指导模式可能难以提供。
这项观察性研究评估了参与名为Lark高血压护理的全数字项目（由人工智能提供指导）后血压和体重的变化。
参与者（N = 864）的基线收缩压（SBP）≥120 mmHg，提供了他们的基线体重，并且在该项目中至少达到了第三个月。主要结局是3个月和6个月时SBP的变化，次要结局是体重变化以及SBP和体重变化与参与者人口统计学、特征和项目参与度的关联。
到第3个月时，平均SBP较基线显著下降了-5.4 mmHg（95% CI -6.5至-4.3；P <.001）。对于在两个时间点都提供读数的参与者，血压从3个月到6个月没有显著变化（即SBP下降保持）（P =.49）。一半的参与者在第3个月（178/349，51.0%）和第6个月（98/199，49.2%）实现了临床上有意义的下降≥5 mmHg。下降幅度取决于起始SBP。被分类为高血压2期的参与者在第3个月时SBP平均下降幅度最大，为-12.4 mmHg（标准误1.2 mmHg），在第6个月时为-13.0 mmHg（标准误1.6 mmHg）；被分类为高血压1期的参与者在第3个月时下降了-5.2 mmHg（标准误0.8 mmHg），在第6个月时下降了-7.3 mmHg（标准误1.3 mmHg）；被分类为血压升高的参与者在第3个月时下降了-1.1 mmHg（标准误0.7 mmHg），但在第6个月时没有下降。起始SBP（β =.11；P <.001）、体重变化百分比（β = -.36；P =.02）和初始BMI（β = -.56；P <.001）与到第3个月时SBP降低≥5 mmHg的可能性显著相关。体重变化百分比是项目参与度与SBP下降之间关系的中介变量。自抽样的非标准化间接效应为-0.0024（95% CI -0.0052至0；P =.002）。
一个由人工智能提供指导的高血压护理项目与项目参与3个月和6个月后临床上有意义的SBP降低相关。体重变化百分比与SBP下降≥5 mmHg的可能性显著相关。一个由人工智能驱动的解决方案可能提供一种可扩展的方法，通过体重减轻等健康生活方式改变，帮助高血压患者实现临床上有意义的血压降低以及降低心血管疾病和其他严重不良结局的相关风险。
