In light of individuals' increasing concern regarding their physical health, mobile health applications (mHealth Apps) have gained popularity in recent years as important tools for addressing health problems. However, users find it challenging to choose appropriate mHealth Apps, as these Apps incorporate diverse behavior change techniques (BCTs), and their individual behavioral intervention effects on users vary. This study proposes a novel BCT-based mHealth App recommendation method to suggest suitable mHealth Apps to users. Specifically, we encode mHealth Apps to obtain information on the BCT adopted by the Apps. Based on the combination of BCTs in each mHealth App and its usage information, we construct a User-BCT matrix to represent users' preferences concerning BCTs. We also construct a user profile for each user, which considers their characteristics related to BCTs. Next, we build a prediction model that links each user's profile to BCTs, and use the AdaBoost algorithm to predict suitable BCTs for a target user. Finally, we recommend mHealth Apps with the highest BCT-matching levels to a target user. We also investigate the performance of the proposed method using a real dataset. The experimental results demonstrate the advantages of the proposed method.

鉴于个人对其身体健康的日益关注，近年来，移动健康应用程序（mHealth Apps）作为解决健康问题的重要工具而日益流行。但是，用户发现选择合适的mHealth应用程序具有挑战性，因为这些应用程序包含了多种行为改变技术（BCT），并且它们对用户的行为干预效果也各不相同。这项研究提出了一种新颖的基于BCT的mHealth App推荐方法，以向用户推荐合适的mHealth App。具体来说，我们对mHealth Apps进行编码，以获取有关Apps采用的BCT的信息。基于每个mHealth App中BCT的组合及其使用信息，我们构造了一个User-BCT矩阵来表示用户对BCT的偏好。我们还为每个用户构造一个用户个人资料，考虑其与BCT相关的特征。接下来，我们建立一个将每个用户的个人资料链接到BCT的预测模型，并使用AdaBoost算法为目标用户预测合适的BCT。最后，我们向目标用户推荐具有最高BCT匹配级别的mHealth应用程序。我们还研究了使用实际数据集提出的方法的性能。实验结果证明了该方法的优点。