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一款用于评估饮酒行为的智能手机应用程序:开发、依从性和反应性。

A Smartphone App to Assess Alcohol Consumption Behavior: Development, Compliance, and Reactivity.

机构信息

Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Australia.

Melbourne eResearch Group, School of Computing and Information Services, University of Melbourne, Parkville, Australia.

出版信息

JMIR Mhealth Uhealth. 2019 Mar 25;7(3):e11157. doi: 10.2196/11157.

Abstract

BACKGROUND

There are disadvantages-largely related to cost, participant burden, and missing data-associated with traditional electronic methods of assessing drinking behavior in real time. This potentially diminishes some of the advantages-namely, enhanced sample size and diversity-typically attributed to these methods. Download of smartphone apps to participants' own phones might preserve these advantages. However, to date, few researchers have detailed the process involved in developing custom-built apps for use in the experimental arena or explored methodological concerns regarding compliance and reactivity.

OBJECTIVE

The aim of this study was to describe the process used to guide the development of a custom-built smartphone app designed to capture alcohol intake behavior in the healthy population. Methodological issues related to compliance with and reactivity to app study protocols were examined. Specifically, we sought to investigate whether hazard and nonhazard drinkers would be equally compliant. We also explored whether reactivity in the form of a decrease in drinking or reduced responding ("yes") to drinking behavior would emerge as a function of hazard or nonhazard group status.

METHODS

An iterative development process that included elements typical of agile software design guided the creation of the CNLab-A app. Healthy individuals used the app to record alcohol consumption behavior each day for 21 days. Submissions were either event- or notification-contingent. We considered the size and diversity of the sample, and assessed the data for evidence of app protocol compliance and reactivity as a function of hazard and nonhazard drinker status.

RESULTS

CNLab-A yielded a large and diverse sample (N=671, mean age 23.12). On average, participants submitted data on 20.27 (SD 1.88) out of 21 days (96.5%, 20.27/21). Both hazard and nonhazard drinkers were highly compliant with app protocols. There were no differences between groups in terms of number of days of app use (P=.49) or average number of app responses (P=.54). Linear growth analyses revealed hazardous drinkers decreased their alcohol intake by 0.80 standard drinks over the 21-day experimental period. There was no change to the drinking of nonhazard individuals. Both hazard and nonhazard drinkers showed a slight decrease in responding ("yes") to drinking behavior over the same period.

CONCLUSIONS

Smartphone apps participants download to their own phones are effective and methodologically sound means of obtaining alcohol consumption information for research purposes. Although further investigation is required, such apps might, in future, allow for a more thorough examination of the antecedents and consequences of drinking behavior.

摘要

背景

传统的实时电子方法评估饮酒行为存在诸多弊端,主要与成本、参与者负担和数据缺失有关。这在一定程度上削弱了这些方法通常具有的一些优势,例如增加样本量和多样性。将智能手机应用程序下载到参与者自己的手机上可能会保留这些优势。然而,迄今为止,很少有研究人员详细描述过为实验领域开发定制应用程序所涉及的过程,也没有探讨过与应用程序研究协议的依从性和反应性相关的方法学问题。

目的

本研究旨在描述指导开发用于健康人群的定制智能手机应用程序以获取饮酒行为信息的过程。研究了与应用程序研究协议的依从性和反应性相关的方法学问题。具体而言,我们试图调查危险和非危险饮酒者的依从性是否相当。我们还探讨了危险或非危险饮酒者身份是否会导致饮酒行为减少或对饮酒行为的反应减少(“是”)。

方法

迭代开发过程包括敏捷软件开发设计的典型元素,指导了 CNLab-A 应用程序的创建。健康个体每天使用该应用程序记录 21 天的饮酒行为。提交内容要么是事件触发的,要么是通知触发的。我们考虑了样本的大小和多样性,并评估了数据是否存在应用程序协议依从性和反应性的证据,这些证据与危险和非危险饮酒者的身份有关。

结果

CNLab-A 产生了一个庞大而多样化的样本(N=671,平均年龄 23.12 岁)。参与者平均提交了 21 天中的 20.27 天(SD 1.88)数据(96.5%,20.27/21)。危险和非危险饮酒者都非常遵守应用程序协议。在使用应用程序的天数(P=.49)或平均应用程序响应次数(P=.54)方面,两组之间没有差异。线性增长分析显示,危险饮酒者在 21 天的实验期间减少了 0.80 标准饮品的饮酒量。非危险饮酒者的饮酒量没有变化。在同一时期,危险和非危险饮酒者对饮酒行为的反应(“是”)略有减少。

结论

参与者自行下载的智能手机应用程序是一种有效的、方法合理的获取酒精消费信息的研究手段。尽管还需要进一步研究,但在未来,此类应用程序可能会允许更彻底地研究饮酒行为的前因后果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcda/6452287/5bf2be7c9045/mhealth_v7i3e11157_fig1.jpg

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