Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations. Method: Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors. Result: In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system’s ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations. Conclusion: The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.

中文翻译：

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使用可穿戴设备和机器学习实现个性化生活方式建议以改善血压


背景：血压（BP）是人类健康的重要指标，并且受生活方式因素（如活动和睡眠因素）的影响很大。然而，每种生活方式因素对血压的影响程度尚不清楚，并且可能因人而异。我们的目标是调查血压与生活方式因素之间的关系，并提供个性化和精确的血压改善建议，而不是目前一般生活方式建议的做法。方法：我们提出的系统包括使用家用血压监测器和可穿戴活动跟踪器的自动数据收集以及特征工程技术来处理时间序列数据并增强可解释性。我们提出了带有基于 Shapley 值的特征选择的随机森林，以提供个性化 BP 建模和顶级生活方式因素识别，以及随后基于顶级因素生成的精确推荐。结果：我们与加州大学圣地亚哥分校健康中心和 Altman 临床与转化研究所合作进行了一项临床研究，将我们的系统连续三个月应用于 25 名血压升高或 I 期高血压的患者。我们的研究结果验证了我们的系统能够提供准确的个性化血压模型并识别个体之间差异很大的主要特征。我们还在随机对照实验中验证了个性化推荐的有效性。接受建议后，实验组受试者的收缩压和舒张压分别降低了 3.8 和 2.3，而没有建议的受试者分别降低了 0.3 和 0.9。 结论：该研究证明了使用可穿戴设备和机器学习来开发个性化模型和精确的生活方式建议以改善血压的潜力。