Oakley-Girvan Ingrid, Lavista Juan M, Miller Yasamin, Davis Sharon, Acle Carlos, Hancock Jeffrey, Nelson Lorene M
Public Health Institute, Oakland, CA, United States.
Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Stanford, CA, United States.
JMIR Form Res. 2019 Jan 11;3(1):e10246. doi: 10.2196/10246.
Risk factors, including limited exercise, poor sleep, smoking, and alcohol and drug use, if mitigated early, can improve long-term health. Risk prevalence has traditionally been measured using methods that now have diminished participation rates. With >75% of American citizens owning smartphones, new data collection methods using mobile apps can be evaluated.
The objective of our study was to describe the development, implementation, and evaluation of a mobile device-based survey system for behavioral risk assessment. Specifically, we evaluated its feasibility, usability, acceptability, and validity.
We enrolled 536 students from 3 Vermont State Colleges. Iterative mobile app development incorporated focus groups, extensive testing, and the following 4 app versions: iOS standard, iOS gamified, Android standard, and Android gamified. We aimed to capture survey data, paradata, and ambient data such as geolocation. Using 3 separate surveys, we asked a total of 27 questions that included demographic characteristics, behavioral health, and questions regarding the app's usability and survey process.
Planned enrollment was exceeded in just a few days. There were 1392 "hits" to the landing page where the app could be downloaded. Excluding known project testers and others not part of the study population, 670 participants downloadeded the SHAPE app. Of those, 94.9% of participants (636/670) agreed to participate by providing in-app consent. Of the 636 who provided consent, 84.3% (536/636) were deemed eligible for the study. The majority of eligible respondents completed the initial survey (459/536, 85.6%), whereas 29.9% (160/536) completed the second survey and 28.5% (153/536) completed the third survey. The SHAPE survey obtained 414 participants on the behavioral risk items in survey 1, which is nearly double the 209 participants who completed the traditional Vermont College Health Survey in 2014. SHAPE survey responses were consistent with the traditionally collected Vermont College Health Survey data.
This study provides data highlighting the potential for mobile apps to improve population-based health, including an assessment of recruitment methods, burden and response rapidity, and future adaptations. Although gamification and monetary rewards were relatively unimportant to this study population, item response theory may be technologically feasible to reduce individual survey burden. Additional data collected by smartphones, such as geolocation, could be important in additional analysis, such as neighborhood characteristics and their impact on behavioral risk factors. Mobile tools that offer rapid adaptation for specific populations may improve research data collection for primary prevention and could be used to improve engagement and health outcomes.
风险因素，包括运动受限、睡眠不佳、吸烟以及酗酒和吸毒等，若能早期加以缓解，可改善长期健康状况。传统上，风险患病率是通过参与率如今已降低的方法来衡量的。鉴于超过75%的美国公民拥有智能手机，可对使用移动应用程序的新数据收集方法进行评估。
我们研究的目的是描述一种用于行为风险评估的基于移动设备的调查系统的开发、实施和评估。具体而言，我们评估了其可行性、可用性、可接受性和有效性。
我们招募了来自佛蒙特州三所州立学院的536名学生。迭代式移动应用程序开发纳入了焦点小组、广泛测试以及以下4个应用程序版本：iOS标准版、iOS游戏化版、安卓标准版和安卓游戏化版。我们旨在获取调查数据、辅助数据以及诸如地理位置等环境数据。通过3项独立调查，我们总共提出了27个问题，涵盖人口统计学特征、行为健康以及有关应用程序可用性和调查过程的问题。
短短几天内就超过了计划招募人数。应用程序可下载的着陆页有1392次“点击”。排除已知的项目测试人员和其他不属于研究人群的人员后，670名参与者下载了SHAPE应用程序。其中，94.9%的参与者（636/670）通过在应用程序内提供同意书表示同意参与。在提供同意书的636人中，84.3%（536/636）被认为符合研究资格。大多数符合资格的受访者完成了初始调查（459/536，85.6%），而29.9%（160/536）完成了第二次调查，28.5%（153/536）完成了第三次调查。SHAPE调查在调查1的行为风险项目上获得了414名参与者的数据，这几乎是2014年完成传统佛蒙特学院健康调查的209名参与者的两倍。SHAPE调查的回答与传统收集的佛蒙特学院健康调查数据一致。
本研究提供的数据突出了移动应用程序在改善基于人群的健康方面的潜力，包括对招募方法、负担和响应速度的评估以及未来的适应性调整。尽管游戏化和金钱奖励对本研究人群相对不太重要，但项目反应理论在技术上可能可行，可减轻个体调查负担。智能手机收集的其他数据，如地理位置，在诸如邻里特征及其对行为风险因素的影响等额外分析中可能很重要。能够针对特定人群快速进行调整的移动工具可能会改善初级预防的研究数据收集，并可用于提高参与度和改善健康结果。
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