Lockwood Kimberly G, Pitter Viveka, Kulkarni Priya R, Graham Sarah A, Auster-Gussman Lisa A, Branch OraLee H
Clinical Research, Lark Health, Mountain View, California, United States of America.
Data Science, Lark Health, Mountain View, California, United States of America.
PLOS Digit Health. 2023 Jul 31;2(7):e0000303. doi: 10.1371/journal.pdig.0000303. eCollection 2023 Jul.
Digital health programs can play a key role in supporting lifestyle changes to prevent and reduce cardiovascular disease (CVD) risk. A key concern for new programs is understanding who is interested in participating. Thus, the primary objective of this study was to utilize electronic health records (EHR) to predict interest in a digital health app called Lark Heart Health. Because prior studies indicate that males are less likely to utilize prevention-focused digital health programs, secondary analyses assessed sex differences in recruitment and enrollment. Data were drawn from an ongoing pilot study of the Heart Health program, which provides digital health behavior coaching and surveys for CVD prevention. EHR data were used to predict whether potential program participants who received a study recruitment email showed interest in the program by "clicking through" on the email to learn more. Primary objective analyses used backward elimination regression and eXtreme Gradient Boost modeling. Recruitment emails were sent to 8,649 patients with available EHR data; 1,092 showed interest (i.e., clicked through) and 345 chose to participate in the study. EHR variables that predicted higher odds of showing interest were higher body mass index (BMI), fewer elevated lab values, lower HbA1c, non-smoking status, and identifying as White. Secondary objective analyses showed that, males and females showed similar program interest and were equally represented throughout recruitment and enrollment. In summary, BMI, elevated lab values, HbA1c, smoking status, and race emerged as key predictors of program interest; conversely, sex, age, CVD history, history of chronic health issues, and medication use did not predict program interest. We also found no sex differences in the recruitment and enrollment process for this program. These insights can aid in refining digital health tools to best serve those interested, as well as highlight groups who may benefit from behavioral intervention tools promoted by additional recruitment efforts tailored to their interest.
数字健康项目在支持生活方式改变以预防和降低心血管疾病（CVD）风险方面可以发挥关键作用。新项目的一个关键问题是了解谁有兴趣参与。因此，本研究的主要目的是利用电子健康记录（EHR）来预测对一款名为Lark心脏健康的数字健康应用程序的兴趣。由于先前的研究表明男性不太可能使用以预防为重点的数字健康项目，二次分析评估了招募和注册过程中的性别差异。数据来自正在进行的心脏健康项目试点研究，该项目为心血管疾病预防提供数字健康行为指导和调查。EHR数据用于预测收到研究招募邮件的潜在项目参与者是否通过“点击”邮件以了解更多信息来表明对该项目感兴趣。主要目标分析使用向后消除回归和极端梯度提升建模。向8649名有可用EHR数据的患者发送了招募邮件；1092人表示感兴趣（即点击），345人选择参与研究。预测显示出更高兴趣几率的EHR变量包括更高的体重指数（BMI）、更少的实验室值升高、更低的糖化血红蛋白（HbA1c）、非吸烟状态以及白人身份。二次目标分析表明，男性和女性对项目的兴趣相似，并且在整个招募和注册过程中的代表性相同。总之，BMI、实验室值升高、HbA1c、吸烟状态和种族成为项目兴趣的关键预测因素；相反，性别、年龄、心血管疾病史、慢性健康问题史和药物使用情况并不能预测项目兴趣。我们还发现该项目的招募和注册过程中没有性别差异。这些见解有助于优化数字健康工具，以更好地服务于感兴趣的人群，同时突出那些可能从针对其兴趣的额外招募努力所推广的行为干预工具中受益的群体。
