Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.
Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.
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机器学习分析，以确定数字行为表型的参与和健康结果的有效性的mHealth干预肥胖：随机对照试验。
DOI：
10.2196/27218
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发表时间：
2021-06-24
影响因子：
7.4
通讯作者：
Choi HJ
中科院分区：
文献类型：
--
作者：
Kim M;Yang J;Ahn WY;Choi HJ
数字医疗保健社区已被敦促通过分析多维数字表型来提高参与度和临床结果。本研究旨在使用机器学习方法来研究多变量表型在预测数字认知行为疗法的参与率和健康结果方面的表现。我们利用了通过有效的心理问卷评估的传统表型和来自移动应用程序的时间序列数据中的多维数字表型，该移动应用程序包括45名接受为期8周的数字认知行为治疗的参与者。我们进行了机器学习分析，以区分重要的特征。订阅率越高，8周(r=−0.59；P<；.001)和24周(r=−0.52；P=.001)的体重减轻程度越高。应用机器学习方法，对传统表型的自尊较低，对数字表型的应用内激励措施较高，这通常会解释参与度和健康结果。此外，16种类型的数字表型(即较少摄入高卡路里食物和晚间零食，以及较高的与导师的互动频率)预测了参与率(平均R2 0.416，SD 0.006)。短期体重变化的预测(平均R2 0.382，SD 0.015)与13种不同的数字表型(即较少摄入高热量食物和碳水化合物，较高摄入低热量食物)相关。最后，8项数字表型指标(即较低的碳水化合物和晚间零食摄入量以及较高的动力)与长期体重变化相关(平均R2为0.590，SD为0.011)。我们的发现成功地展示了多种心理结构，如情绪、认知、行为和动机表型，是如何使用机器学习方法解释数字干预的机制和临床效果的。因此，我们的研究设计了一个可解释的数字表型模型，包括干预前和干预期间动机的多个方面，预测参与和临床疗效。这一研究路线可能会为高级预防和个性化数字疗法的发展提供启示。ClinicalTrials.gov NCT03465306；https://clinicaltrials.gov/ct2/show/NCT03465306
The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. This study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy. We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics. A higher engagement rate was associated with higher weight loss at 8 weeks (r=−0.59; P<.001) and 24 weeks (r=−0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R2 0.416, SD 0.006). The prediction of short-term weight change (mean R2 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R2 0.590, SD 0.011). Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306
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DOI：
10.1109/jproc.2015.2501978
发表时间：
2016-02
期刊：
Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
影响因子：
--
作者：
Johnson AE;Ghassemi MM;Nemati S;Niehaus KE;Clifton DA;Clifford GD
通讯作者：
Clifford GD
影响因子：
6.6
作者：
Hidalgo-Mazzei, Diego;Mateu, Ainoa;Colom, Francesc
通讯作者：
Colom, Francesc
影响因子：
4.8
作者：
Guay, F;Vallerand, RJ;Blanchard, C
通讯作者：
Blanchard, C
影响因子：
--
作者：
Juresa, Vesna;Musil, Vera;Petrovic, Davor
通讯作者：
Petrovic, Davor
影响因子：
5
作者：
Kim, Meelim;Kim, Youngin;Choi, Hyung Jin
通讯作者：
Choi, Hyung Jin