Harnessing context sensing to develop a mobile intervention for depression.
Harnessing context sensing to develop a mobile intervention for depression.
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DOI：
10.2196/jmir.1838
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发表时间：
2011-08-12
影响因子：
7.4
通讯作者：
Mohr DC
中科院分区：
文献类型：
--
作者：
Burns MN;Begale M;Duffecy J;Gergle D;Karr CJ;Giangrande E;Mohr DC
移动的电话传感器可用于开发上下文感知系统，该系统可自动检测患者何时需要帮助。移动的电话还可以提供生态瞬时干预，在出现问题的情况下提供有针对性的援助。然而，这些方法尚未用于治疗重度抑郁症。本研究的目的是调查Mobilyze！的技术可行性、功能可靠性和患者满意度，基于移动的电话和互联网的干预，包括生态瞬时干预和环境感知。我们开发了一个移动的电话应用程序和支持架构，其中机器学习模型（即学习器）基于至少38个并发电话传感器值（例如，全球定位系统，环境光，最近的通话）预测患者的情绪，情感，认知/动机状态，活动，环境背景和社会背景。该网站包括说明患者自我报告状态之间相关性的反馈图，以及教授患者行为激活概念的教学法和工具。与临床医生的简短电话和电子邮件被用来促进依从性。我们在一项单臂试点研究中招募了8名患有重度抑郁症的成年人接受Mobilyze！并完成为期8周的临床评估。通过学习者预测分类上下文状态（例如，位置），实现了有希望的准确率（60%至91%）。对于状态等级（如情绪），预测能力差。参与者对手机应用程序感到满意，自我报告的抑郁症状（betaweek = -.82，P < .001，符合协议科恩d = 3.43）和抑郁症状的访谈测量（betaweek = -.81，P < .001，符合协议科恩d = 3.55）显著改善。参与者也变得不太可能符合重性抑郁障碍的诊断标准（bweek = -.65，P = .03，符合方案缓解率= 85.71%）。共病焦虑症状也有所减少（β-week = -.71，P < .001，符合方案Cohen d = 2.58）。移动！是一种可扩展的、可行的干预措施，并有初步的疗效证据。据我们所知，这是第一次对单极抑郁症进行生态瞬时干预，也是第一次尝试使用上下文感知来识别与心理健康相关的状态。讨论了有关技术功能、数据挖掘和软件开发过程的几个经验教训。 Clinicaltrials.gov NCT 01107041; http://clinicaltrials.gov/ct2/show/NCT01107041（由WebCite在http：//www.webcitation.org/60CVjPH0n上存档）
Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder. The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing. We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients’ mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients’ self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks. Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (betaweek = –.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (betaweek = –.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (bweek = –.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (betaweek = –.71, P < .001, per-protocol Cohen d = 2.58). Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed. Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n)
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影响因子：
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作者：
Kessler, RC;Berglund, P;Walters, EE
通讯作者：
Walters, EE
影响因子：
5.5
作者：
Collins, Linda M.;Murphy, Susan A.;Strecher, Victor
通讯作者：
Strecher, Victor
影响因子：
1.1
作者：
Freund, Y;Schapire, RE
通讯作者：
Schapire, RE
DOI：
10.1007/978-3-540-24646-6_1
发表时间：
2004-01-01
期刊：
PERVASIVE COMPUTING, PROCEEDINGS
影响因子：
--
作者：
Bao, L;Intille, SS
通讯作者：
Intille, SS
DOI：
10.1007/3-540-45014-9_1
发表时间：
2000-01-01
期刊：
MULTIPLE CLASSIFIER SYSTEMS
影响因子：
--
作者：
Dietterich, TG
通讯作者：
Dietterich, TG