Kwasny Mary J, Schueller Stephen M, Lattie Emily, Gray Elizabeth L, Mohr David C
Division of Biostatistics, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
JMIR Ment Health. 2019 Mar 21;6(3):e11572. doi: 10.2196/11572.
IntelliCare is a mental health app platform with 14 apps that are elemental, simple and brief to use, and eclectic. Although a variety of apps may improve engagement, leading to better outcomes, they may require navigation aids such as recommender systems that can quickly direct a person to a useful app.
As the first step toward developing navigation and recommender tools, this study explored app-use patterns across the IntelliCare platform and their relationship with depression and anxiety outcomes.
This is a secondary analysis of the IntelliCare Field Trial, which recruited people with depression or anxiety. Participants of the trial received 8 weeks of coaching, primarily by text, and weekly random recommendations for apps. App-use metrics included frequency and lifetime use. Depression and anxiety, measured using the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, respectively, were assessed at baseline and end of treatment. Cluster analysis was utilized to determine patterns of app use; ordinal logistic regression models and log-rank tests were used to determine if these use metrics alone, or in combination, predicted improvement or remission in depression or anxiety.
The analysis included 96 people who generally followed recommendations to download and try new apps each week. Apps were clustered into 5 groups: Thinking (apps that targeted or relied on thinking), Calming (relaxation and insomnia), Checklists (apps that used checklists), Activity (behavioral activation and activity), and Other. Both overall frequency of use and lifetime use predicted response for depression and anxiety. The Thinking, Calming, and Checklist clusters were associated with improvement in depression and anxiety, and the Activity cluster was associated with improvement in Anxiety only. However, the use of clusters was less strongly associated with improvement than individual app use.
Participants in the field trial remained engaged with a suite of apps for the full 8 weeks of the trial. App-use patterns did fall into clusters, suggesting that some knowledge about the use of one app may be useful in selecting another app that the person is more likely to use and may help suggest apps based on baseline symptomology and personal preference.
IntelliCare是一个心理健康应用程序平台，有14款应用程序，这些应用程序基本、简单且易于使用，兼具综合性。尽管各种各样的应用程序可能会提高参与度，从而带来更好的效果，但它们可能需要导航辅助工具，比如推荐系统，以便能迅速引导用户找到有用的应用程序。
作为开发导航和推荐工具的第一步，本研究探索了IntelliCare平台上的应用程序使用模式及其与抑郁和焦虑结果的关系。
这是对IntelliCare现场试验的二次分析，该试验招募了患有抑郁症或焦虑症的人群。试验参与者接受了为期8周的指导，主要通过短信进行，并且每周会收到应用程序的随机推荐。应用程序使用指标包括使用频率和终身使用情况。分别使用患者健康问卷-9和广泛性焦虑障碍-7量表测量抑郁和焦虑，在基线和治疗结束时进行评估。采用聚类分析来确定应用程序的使用模式；使用有序逻辑回归模型和对数秩检验来确定这些使用指标单独或综合起来是否能预测抑郁或焦虑的改善或缓解情况。
分析纳入了96人，他们通常遵循每周下载并尝试新应用程序的推荐。应用程序被聚类为5组：思维类（针对或依赖思维的应用程序）、平静类（放松和失眠应用程序）、清单类（使用清单的应用程序）、活动类（行为激活和活动应用程序）以及其他类。总体使用频率和终身使用情况都能预测抑郁和焦虑的反应。思维类、平静类和清单类聚类与抑郁和焦虑的改善相关，而活动类聚类仅与焦虑的改善相关。然而，聚类的使用与改善的关联程度不如单个应用程序的使用。
现场试验的参与者在整个8周试验期间都持续使用了一套应用程序。应用程序使用模式确实可以聚类，这表明关于一款应用程序使用的一些知识可能有助于选择用户更可能使用的另一款应用程序，并且可能有助于根据基线症状和个人偏好推荐应用程序。
