Scherer Emily A, Ben-Zeev Dror, Li Zhigang, Kane John M
Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Hanover, NH, United States.
Psychiatry, Neurology, and Neuroscience, Hofstra Northwell School of Medicine, Hepstead, NY, United States.
JMIR Mhealth Uhealth. 2017 Jan 12;5(1):e1. doi: 10.2196/mhealth.6474.
Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in this context are necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention.
The objectives of this paper are to discuss the utility of the joint modeling approach in the analysis of longitudinal engagement data in mHealth research and to illustrate the application of this approach using data from an mHealth intervention designed to support illness management among people with schizophrenia.
Engagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to a naïve model that does not account for the relationship between dropout and engagement.
The joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app 1 day more per week was associated with a 23% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model.
Naïve longitudinal models that do not account for informative missingness in mHealth data may produce biased results. Joint models provide a way to model intensively collected engagement outcomes while simultaneously accounting for the relationship between engagement and missing data in mHealth intervention research.
评估对干预措施的参与度是理解其疗效的关键组成部分。随着对移动健康（mHealth）领域行为干预措施开发的兴趣日益浓厚，在此背景下评估参与度的适当方法必不可少。为评估移动健康干预措施而收集的数据通常比基于诊所的干预措施收集的数据频率高得多。此外，参与度的缺失数据与参与度水平密切相关，导致可能出现信息性缺失。因此，在分析对移动健康干预措施的参与度时，需要能够适应密集收集的数据并能考虑信息性缺失的模型，以进行无偏推断。
本文的目的是讨论联合建模方法在移动健康研究纵向参与度数据分析中的效用，并使用一项旨在支持精神分裂症患者疾病管理的移动健康干预措施的数据来说明该方法的应用。
分析了一项旨在支持精神分裂症患者疾病管理的移动健康干预措施评估中的参与度数据。将联合模型应用于纵向参与度结果和退出时间，以对参与度结果进行无偏推断。将结果与未考虑退出与参与度之间关系的简单模型进行比较。
联合模型显示参与度与降低退出风险之间存在密切关系。每周多使用1天移动健康应用程序与退出风险降低23%相关（P<.001）。与简单模型相比，使用联合模型时参与度随时间的下降更为陡峭。
未考虑移动健康数据中信息性缺失的简单纵向模型可能会产生有偏差的结果。联合模型提供了一种方法来对密集收集的参与度结果进行建模，同时考虑移动健康干预研究中参与度与缺失数据之间的关系。
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