BACKGROUND Bipolar disorder (BD) is a chronic illness with a high recurrence rate. Smartphones can be a useful tool for detecting prodromal symptoms of episode recurrence (through real-time monitoring) and providing options for early intervention between outpatient visits. AIMS The aim of this systematic review is to overview and discuss the studies on the smartphone-based systems that monitor or detect the phase change in BD. We also discuss the challenges concerning predictive modelling. METHODS Published studies were identified through searching the electronic databases. Predictive attributes reflecting illness activity were evaluated including data from patients' self-assessment ratings and objectively measured data collected via smartphone. Articles were reviewed according to PRISMA guidelines. RESULTS Objective data automatically collected using smartphones (voice data from phone calls and smartphone-usage data reflecting social and physical activities) are valid markers of a mood state. The articles surveyed reported accuracies in the range of 67% to 97% in predicting mood status. Various machine learning approaches have been analyzed, however, there is no clear evidence about the superiority of any of the approach. CONCLUSIONS The management of BD could be significantly improved by monitoring of illness activity via smartphone.

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智能手机作为双相情感障碍的监测工具：包括数据分析，机器学习算法和预测建模在内的系统综述。

背景技术躁郁症（BD）是一种具有高复发率的慢性疾病。智能手机可以成为检测发作复发的前兆症状（通过实时监控）并为门诊就诊之间的早期干预提供选择的有用工具。目的本系统综述的目的是概述和讨论基于智能手机的系统的研究，该系统可监视或检测BD的相位变化。我们还将讨论有关预测建模的挑战。方法通过搜索电子数据库确定已发表的研究。评估反映疾病活动的预测属性，包括来自患者自我评估等级的数据以及通过智能手机收集的客观测量数据。根据PRISMA指南对文章进行了审查。结果使用智能手机自动收集的客观数据（来自电话的语音数据以及反映社交和体育活动的智能手机使用数据）是情绪状态的有效标记。接受调查的文章报道了预测情绪状态的准确性在67％到97％之间。已经分析了各种机器学习方法，但是，尚无明显证据证明任何一种方法的优越性。结论通过智能手机监测疾病活动可以大大改善BD的管理。但是，尚无明确证据表明任何方法的优越性。结论通过智能手机监测疾病活动可以大大改善BD的管理。但是，尚无明确证据表明任何方法的优越性。结论通过智能手机监测疾病活动可以大大改善BD的管理。