Seeing the "Big" Picture: Big Data Methods for Exploring Relationships Between Usage, Language, and Outcome in Internet Intervention Data.
Seeing the "Big" Picture: Big Data Methods for Exploring Relationships Between Usage, Language, and Outcome in Internet Intervention Data.
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DOI：
10.2196/jmir.5725
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发表时间：
2016-08-31
影响因子：
7.4
通讯作者：
Parks AC
中科院分区：
文献类型：
--
作者：
Carpenter J;Crutchley P;Zilca RD;Schwartz HA;Smith LK;Cobb AM;Parks AC
评估市场上已有的互联网干预措施的效力既带来了挑战，也带来了机遇。虽然可能有大量的、通常是前所未有的数据（数十万，有时数百万参与者，具有高维度的评估变量），但这些数据本质上是观察性的，部分是非结构化的（例如，自由文本、图像、传感器数据），不包括用于比较的自然对照组，并且通常表现出高损耗率。因此，需要新的方法来使用这些现有的数据，并获得新的见解，可以增强传统的小组随机对照试验。我们的目标是展示新兴的大数据方法如何帮助探索有关互联网健康干预的有效性和过程的问题。我们从一个名为Happify的健康网站和应用程序的用户群中提取数据。为了探索有效性，多层次模型专注于人内变化，探讨了在152，747名用户的样本中，更大的使用量是否预示着更高的幸福感。此外，为了探索伴随改进的底层过程，我们分析了10，818名用户的语言，这些用户具有足够的自由文本响应量和平台使用时间。从这个自由文本构建的主题模型提供了基于语言的相关性的个人用户改善的结果的措施，提供洞察用户所经历的有益的基本过程。在积极情绪方面，用户平均每周提高1.38分（SE 0.01，t122，455=113.60，P<0.001，95%CI 1.36-1.41），8周内提高了27%。在给定的个体用户中，更多的使用预测更多的积极情绪，更少的使用预测更少的积极情绪（估计值0.09，SE 0.01，t6047=9.15，P= 0.001，95%CI 0.07 - 0.12）。这一估计预测，一个给定的用户将报告积极情绪1.26点高后，两周内，当他们使用Happify每天比一周内，当他们没有使用它在所有。在高度参与的用户中，200个自动聚类的主题显示出随着时间的推移对幸福感变化的显著影响（校正P<.001），说明了在参与干预措施时，哪些主题可能比其他主题更有益。特别是，随着时间的推移，与解决消极思想和感受有关的主题与改善相关。通过对自然主义大数据的观察分析，我们可以探索使用互联网幸福干预的人群中使用和幸福感之间的关系，并对伴随它的潜在机制提供新的见解。通过利用大数据来支持这些新类型的分析，我们可以从新的角度探索干预的运作，并利用浮出水面的洞察力来反馈到干预中，并在未来进一步改进。
Assessing the efficacy of Internet interventions that are already in the market introduces both challenges and opportunities. While vast, often unprecedented amounts of data may be available (hundreds of thousands, and sometimes millions of participants with high dimensions of assessed variables), the data are observational in nature, are partly unstructured (eg, free text, images, sensor data), do not include a natural control group to be used for comparison, and typically exhibit high attrition rates. New approaches are therefore needed to use these existing data and derive new insights that can augment traditional smaller-group randomized controlled trials. Our objective was to demonstrate how emerging big data approaches can help explore questions about the effectiveness and process of an Internet well-being intervention. We drew data from the user base of a well-being website and app called Happify. To explore effectiveness, multilevel models focusing on within-person variation explored whether greater usage predicted higher well-being in a sample of 152,747 users. In addition, to explore the underlying processes that accompany improvement, we analyzed language for 10,818 users who had a sufficient volume of free-text response and timespan of platform usage. A topic model constructed from this free text provided language-based correlates of individual user improvement in outcome measures, providing insights into the beneficial underlying processes experienced by users. On a measure of positive emotion, the average user improved 1.38 points per week (SE 0.01, t122,455=113.60, P<.001, 95% CI 1.36–1.41), about a 27% increase over 8 weeks. Within a given individual user, more usage predicted more positive emotion and less usage predicted less positive emotion (estimate 0.09, SE 0.01, t6047=9.15, P=.001, 95% CI .07–.12). This estimate predicted that a given user would report positive emotion 1.26 points higher after a 2-week period when they used Happify daily than during a week when they didn’t use it at all. Among highly engaged users, 200 automatically clustered topics showed a significant (corrected P<.001) effect on change in well-being over time, illustrating which topics may be more beneficial than others when engaging with the interventions. In particular, topics that are related to addressing negative thoughts and feelings were correlated with improvement over time. Using observational analyses on naturalistic big data, we can explore the relationship between usage and well-being among people using an Internet well-being intervention and provide new insights into the underlying mechanisms that accompany it. By leveraging big data to power these new types of analyses, we can explore the workings of an intervention from new angles, and harness the insights that surface to feed back into the intervention and improve it further in the future.
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影响因子：
7.4
作者：
Mohr, David C.;Schueller, Stephen M.;Rashidi, Parisa
通讯作者：
Rashidi, Parisa
影响因子：
4.8
作者：
Munoz, Ricardo F.;Bunge, Eduardo L.;Perez-Stable, Eliseo J.
通讯作者：
Perez-Stable, Eliseo J.
影响因子：
4.6
作者：
Layous, Kristin;Nelson, S. Katherine;Lyubomirsky, Sonja
通讯作者：
Lyubomirsky, Sonja
DOI：
10.1097/jom.0000000000000209
发表时间：
2014-07-01
影响因子：
3.2
作者：
Aikens, Kimberly A.;Astin, John;Bodnar, Catherine M.
通讯作者：
Bodnar, Catherine M.
影响因子：
7.4
作者：
Mohr, David C.;Schueller, Stephen M.;Cheung, Ken
通讯作者：
Cheung, Ken