Torous John, Staples Patrick, Slaters Linda, Adams Jared, Sandoval Luis, Onnela J P, Keshavan Matcheri
Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
Clin Schizophr Relat Psychoses. 2017 Aug 4. doi: 10.3371/CSRP.JTPS.071317.
INTRODUCTION: Despite growing interest in smartphone apps for schizophrenia, little is known about how these apps are utilized in the real world. Understanding how app users are engaging with these tools outside of the confines of traditional clinical studies offers an important information on who is most likely to use apps and what type of data they are willing to share. METHODS: The Schizophrenia and Related Disorders Alliance of America, in partnership with Self Care Catalyst, has created a smartphone app for schizophrenia that is free and publically available on both Apple iTunes and Google Android Play stores. We analyzed user engagement data from this app across its medication tracking, mood tracking, and symptom tracking features from August 16 2015 to January 1 2017 using the R programming language. We included all registered app users in our analysis with reported ages less than 100. RESULTS: We analyzed a total of 43,451 mood, medication and symptom entries from 622 registered users, and excluded a single patient with a reported age of 114. Seventy one percent of the 622 users tried the mood-tracking feature at least once, 49% the symptom tracking feature, and 36% the medication-tracking feature. The mean number of uses of the mood feature was two, the symptom feature 10, and the medication feature 14. However, a small subset of users were very engaged with the app and the top 10 users for each feature accounted for 35% or greater of all entries for that feature. We find that user engagement follows a power law distribution for each feature, and this fit was largely invariant when stratifying for age or gender. DISCUSSION: Engagement with this app for schizophrenia was overall low, but similar to prior naturalistic studies for mental health app use in other diseases. The low rate of engagement in naturalistic settings, compared to higher rates of use in clinical studies, suggests the importance of clinical involvement as one factor in driving engagement for mental health apps. Power law relationships suggest strongly skewed user engagement, with a small subset of users accounting for the majority of substantial engagements. There is a need for further research on app engagement in schizophrenia.
引言：尽管人们对用于精神分裂症的智能手机应用程序越来越感兴趣，但对于这些应用程序在现实世界中的使用情况却知之甚少。了解应用程序用户在传统临床研究范围之外如何使用这些工具，能提供关于谁最有可能使用应用程序以及他们愿意分享何种类型数据的重要信息。 方法：美国精神分裂症及相关障碍联盟与自我护理促进组织合作，开发了一款用于精神分裂症的智能手机应用程序，该程序在苹果iTunes和谷歌安卓应用商店均可免费获取。我们使用R编程语言分析了该应用程序在2015年8月16日至2017年1月1日期间的用药跟踪、情绪跟踪和症状跟踪功能的用户参与数据。我们将所有年龄小于100岁且有报告的注册应用程序用户纳入分析。 结果：我们共分析了622名注册用户的43451条情绪、用药和症状记录，并排除了一名报告年龄为114岁的患者。622名用户中，71%至少尝试过一次情绪跟踪功能，49%尝试过症状跟踪功能，36%尝试过用药跟踪功能。情绪功能的平均使用次数为2次，症状功能为10次，用药功能为14次。然而，一小部分用户与该应用程序的互动非常频繁，每个功能的前10名用户占该功能所有记录的35%或更多。我们发现，每个功能的用户参与度遵循幂律分布，并且在按年龄或性别分层时，这种拟合在很大程度上是不变的。 讨论：这款精神分裂症应用程序的总体参与度较低，但与先前关于其他疾病心理健康应用程序使用情况的自然主义研究相似。与临床研究中较高的使用率相比，自然环境中的低参与率表明临床参与作为推动心理健康应用程序参与度的一个因素的重要性。幂律关系表明用户参与度严重失衡，一小部分用户占大量参与的大多数。需要对精神分裂症应用程序的参与度进行进一步研究。
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