Division of Health Services and Outcomes Research, Children's Mercy Kansas City, Kansas City, MO, United States.
Departments of Biobehavioral Health and Medicine, The Pennsylvania State University, University Park, PA, United States.
JMIR Mhealth Uhealth. 2021 Jul 26;9(7):e23303. doi: 10.2196/23303.
With the growing interest in mobile health (mHealth), behavioral medicine researchers are increasingly conducting intervention studies that use mobile technology (eg, to support healthy behavior change). Such studies' scientific premises are often sound, yet there is a dearth of implementational data on which to base mHealth research methodologies. Notably, mHealth approaches must be designed to be acceptable to research participants to support meaningful engagement, but little empirical data about design factors influencing acceptability in such studies exist.
This study aims to evaluate the impact of two common design factors in mHealth intervention research-requiring multiple devices (eg, a study smartphone and wrist sensor) relative to requiring a single device and providing individually tailored feedback as opposed to generic content-on reported participant acceptability.
A diverse US adult convenience sample (female: 104/255, 40.8%; White: 208/255, 81.6%; aged 18-74 years) was recruited to complete a web-based experiment. A 2×2 factorial design (number of devices×nature of feedback) was used. A learning module explaining the necessary concepts (eg, behavior change interventions, acceptability, and tailored content) was presented, followed by four vignettes (representing each factorial cell) that were presented to participants in a random order. The vignettes each described a hypothetical mHealth intervention study featuring different combinations of the two design factors (requiring a single device vs multiple devices and providing tailored vs generic content). Participants rated acceptability dimensions (interest, benefit, enjoyment, utility, confidence, difficulty, and overall likelihood of participating) for each study presented.
Reported interest, benefit, enjoyment, confidence in completing study requirements, and perceived utility were each significantly higher for studies featuring tailored (vs generic) content, and the overall estimate of the likelihood of participation was significantly higher. Ratings of interest, benefit, and perceived utility were significantly higher for studies requiring multiple devices (vs a single device); however, multiple device studies also had significantly lower ratings of confidence in completing study requirements, and participation was seen as more difficult and was associated with a lower estimated likelihood of participation. The two factors did not exhibit any evidence of statistical interactions in any of the outcomes tested.
The results suggest that potential research participants are sensitive to mHealth design factors. These mHealth intervention design factors may be important for initial perceptions of acceptability (in research or clinical settings). This, in turn, may be associated with participant (eg, self) selection processes, differential compliance with study or treatment processes, or retention over time.
随着移动医疗(mHealth)兴趣的增长,行为医学研究人员越来越多地开展使用移动技术的干预研究(例如,支持健康行为改变)。此类研究的科学前提通常是合理的,但缺乏实施数据来为 mHealth 研究方法提供依据。值得注意的是,mHealth 方法必须设计为能够被研究参与者接受,以支持有意义的参与,但在这些研究中,关于影响可接受性的设计因素的实证数据很少。
本研究旨在评估 mHealth 干预研究中两种常见设计因素(需要多种设备,例如研究用智能手机和腕部传感器,而不是单一设备,以及提供个性化反馈,而不是通用内容)对报告的参与者可接受性的影响。
本研究招募了美国不同的成年便利样本(女性:104/255,40.8%;白人:208/255,81.6%;年龄 18-74 岁)完成基于网络的实验。采用 2×2 因子设计(设备数量×反馈性质)。参与者首先学习一个解释必要概念(例如行为改变干预、可接受性和个性化内容)的学习模块,然后按照随机顺序呈现四个虚拟案例(代表每个因子单元)。每个虚拟案例都描述了一个具有不同设计因素组合(使用单一设备与多种设备,提供个性化与通用内容)的假设 mHealth 干预研究。参与者对呈现的每项研究的可接受性维度(兴趣、益处、享受、效用、信心、难度和总体参与可能性)进行评分。
报告的兴趣、益处、享受、对完成研究要求的信心和感知效用对于具有个性化(与通用)内容的研究均显著更高,总体参与可能性的估计也显著更高。对于需要多种设备(而不是单一设备)的研究,对兴趣、益处和感知效用的评分较高;然而,对于多设备研究,对完成研究要求的信心评分较低,且参与被视为更加困难,参与的可能性估计也较低。在测试的任何结果中,这两个因素都没有表现出任何统计学交互作用的证据。
结果表明,潜在的研究参与者对 mHealth 设计因素很敏感。这些 mHealth 干预设计因素可能对可接受性的初步认知(在研究或临床环境中)很重要。这反过来又可能与参与者(例如自我)选择过程、对研究或治疗过程的遵从性差异或随时间保留率有关。