Department of Psychology, National University of Singapore, Singapore, Singapore.
Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
JMIR Mhealth Uhealth. 2023 May 22;11:e44685. doi: 10.2196/44685.
Microrandomized trials (MRTs) have emerged as the gold standard for the development and evaluation of multicomponent, adaptive mobile health (mHealth) interventions. However, not much is known about the state of participant engagement measurement in MRTs of mHealth interventions.
In this scoping review, we aimed to quantify the proportion of existing or planned MRTs of mHealth interventions to date that have assessed (or have planned to assess) engagement. In addition, for the trials that have explicitly assessed (or have planned to assess) engagement, we aimed to investigate how engagement has been operationalized and to identify the factors that have been studied as determinants of engagement in MRTs of mHealth interventions.
We conducted a broad search for MRTs of mHealth interventions in 5 databases and manually searched preprint servers and trial registries. Study characteristics of each included evidence source were extracted. We coded and categorized these data to identify how engagement has been operationalized and which determinants, moderators, and covariates have been assessed in existing MRTs.
Our database and manual search yielded 22 eligible evidence sources. Most of these studies (14/22, 64%) were designed to evaluate the effects of intervention components. The median sample size of the included MRTs was 110.5. At least 1 explicit measure of engagement was included in 91% (20/22) of the included MRTs. We found that objective measures such as system usage data (16/20, 80%) and sensor data (7/20, 35%) are the most common methods of measuring engagement. All studies included at least 1 measure of the physical facet of engagement, but the affective and cognitive facets of engagement have largely been neglected (only measured by 1 study each). Most studies measured engagement with the mHealth intervention (Little e) and not with the health behavior of interest (Big E). Only 6 (30%) of the 20 studies that measured engagement assessed the determinants of engagement in MRTs of mHealth interventions; notification-related variables were the most common determinants of engagement assessed (4/6, 67% studies). Of the 6 studies, 3 (50%) examined the moderators of participant engagement-2 studies investigated time-related moderators exclusively, and 1 study planned to investigate a comprehensive set of physiological and psychosocial moderators in addition to time-related moderators.
Although the measurement of participant engagement in MRTs of mHealth interventions is prevalent, there is a need for future trials to diversify the measurement of engagement. There is also a need for researchers to address the lack of attention to how engagement is determined and moderated. We hope that by mapping the state of engagement measurement in existing MRTs of mHealth interventions, this review will encourage researchers to pay more attention to these issues when planning for engagement measurement in future trials.
微随机试验(MRT)已成为开发和评估多成分、适应性移动健康(mHealth)干预措施的金标准。然而,对于 mHealth 干预措施的 MRT 中参与者参与度的测量状态,我们知之甚少。
在本次范围界定综述中,我们旨在量化迄今为止评估(或计划评估)参与度的 mHealth 干预措施的现有或计划 MRT 的比例。此外,对于明确评估(或计划评估)参与度的试验,我们旨在研究如何对参与度进行操作化,并确定作为 mHealth 干预措施的 MRT 中参与度决定因素的研究因素。
我们在 5 个数据库中广泛搜索了 mHealth 干预措施的 MRT,并手动搜索了预印本服务器和试验登记处。提取每个纳入证据来源的研究特征。我们对这些数据进行编码和分类,以确定参与度是如何操作化的,以及在现有的 MRT 中评估了哪些决定因素、调节剂和协变量。
我们的数据库和手动搜索共产生了 22 项符合条件的证据来源。这些研究中大多数(14/22,64%)旨在评估干预措施组成部分的效果。纳入的 MRT 的中位样本量为 110.5。在纳入的 MRT 中,至少有 1 项明确的参与度测量,占 91%(20/22)。我们发现,系统使用数据(16/20,80%)和传感器数据(7/20,35%)等客观测量方法是测量参与度最常见的方法。所有研究都至少包括了 1 项对参与度的身体方面的测量,但情感和认知方面的参与度很大程度上被忽视了(每个方面都只有 1 项研究进行了测量)。大多数研究测量了 mHealth 干预措施的参与度(Little e),而不是所关注的健康行为的参与度(Big E)。在测量参与度的 20 项研究中,只有 6 项(30%)评估了 mHealth 干预措施的 MRT 中参与度的决定因素;通知相关变量是评估最多的参与度决定因素(67%的研究)。在这 6 项研究中,有 3 项(50%)研究了参与者参与度的调节剂——2 项研究专门研究了时间相关的调节剂,1 项研究计划除时间相关的调节剂外,还研究全面的生理和心理社会调节剂。
尽管 mHealth 干预措施的 MRT 中参与者参与度的测量很普遍,但未来的试验需要多样化参与度的测量。研究人员还需要解决参与度如何确定和调节的问题。我们希望通过描绘 mHealth 干预措施的现有 MRT 中参与度测量的状态,本综述将鼓励研究人员在未来的试验中规划参与度测量时更加关注这些问题。