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首页> 美国卫生研究院文献>Translational Psychiatry >Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series
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Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series

机译：时间序列分析在双相情感障碍情绪波动中的应用以促进治疗创新：一个病例系列

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Treatment innovation for bipolar disorder has been hampered by a lack of techniques to capture a hallmark symptom: ongoing mood instability. Mood swings persist during remission from acute mood episodes and impair daily functioning. The last significant treatment advance remains Lithium (in the 1970s), which aids only the minority of patients. There is no accepted way to establish proof of concept for a new mood-stabilizing treatment. We suggest that combining insights from mood measurement with applied mathematics may provide a step change: repeated daily mood measurement (depression) over a short time frame (1 month) can create individual bipolar mood instability profiles. A time-series approach allows comparison of mood instability pre- and post-treatment. We test a new imagery-focused cognitive therapy treatment approach (MAPP; Mood Action Psychology Programme) targeting a driver of mood instability, and apply these measurement methods in a non-concurrent multiple baseline design case series of 14 patients with bipolar disorder. Weekly mood monitoring and treatment target data improved for the whole sample combined. Time-series analyses of daily mood data, sampled remotely (mobile phone/Internet) for 28 days pre- and post-treatment, demonstrated improvements in individuals' mood stability for 11 of 14 patients. Thus the findings offer preliminary support for a new imagery-focused treatment approach. They also indicate a step in treatment innovation without the requirement for trials in illness episodes or relapse prevention. Importantly, daily measurement offers a description of mood instability at the individual patient level in a clinically meaningful time frame. This costly, chronic and disabling mental illness demands innovation in both treatment approaches (whether pharmacological or psychological) and measurement tool: this work indicates that daily measurements can be used to detect improvement in individual mood stability for treatment innovation (MAPP).
机译：缺乏捕获特征性症状的技术阻碍了躁郁症的治疗创新：持续的情绪不稳定。在急性情绪发作缓解期间，情绪波动持续存在，并损害日常功能。最后一个重要的治疗进展仍然是锂（在1970年代），它仅对少数患者提供帮助。没有公认的方法来建立新的稳定情绪疗法的概念证明。我们建议将来自情绪测量的见解与应用数学相结合可能会提供一个步骤变化：在较短的时间段（1个月）内重复进行每日情绪测量（抑郁）可以创建个人的双相性情绪不稳定状况。时间序列方法可以比较治疗前后的情绪不稳定。我们测试了针对情绪不稳定的驱动因素的以图像为中心的认知治疗新方法（MAPP；情绪行动心理学计划），并将这些测量方法应用于14例双相情感障碍患者的非并行多基线设计案例系列中。整个样本组合的每周情绪监测和治疗目标数据得到改善。对治疗前后28天进行远程采样（手机/互联网）的日常情绪数据进行时间序列分析，结果显示14名患者中有11名患者的情绪稳定性得到了改善。因此，这些发现为以图像为中心的新治疗方法提供了初步支持。它们也表明治疗创新的一步，而无需进行疾病发作或预防复发的试验。重要的是，日常测量可以描述在临床上有意义的时间范围内各个患者水平上的情绪不稳定。这种昂贵，慢性和致残的精神疾病需要在治疗方法（无论是药理学还是心理方法）和测量工具上进行创新：这项工作表明，日常测量可用于检测治疗创新（MAPP）个体情绪稳定性的改善。

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