Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient’s daily routine (e.g., “studying”, “at work”, “working out”). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations.
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机译:Litchfield心理咨询模型以哲学为基础,将精神健康服务应用程序Lift Up UP,旨在提供简单,实用的建议,以帮助个人和员工应对日常的精神健康挑战,并将用户与现有的精神健康专业人员联系起来。 Lift me UP将使用先进的技术来:•协助患者评估过程•监控和支持日常工作•将用户推荐给可用的心理健康专家•与市场上的任何产品相比,创造独特的定制体验。