Use of Passive Sensing in Psychotherapy Studies in Late Life: A Pilot Example, Opportunities and Challenges.
Use of Passive Sensing in Psychotherapy Studies in Late Life: A Pilot Example, Opportunities and Challenges.
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DOI：
10.3389/fpsyt.2021.732773
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发表时间：
2021
影响因子：
4.7
通讯作者：
Sirey JA
中科院分区：
文献类型：
--
作者：
Lee J;Solomonov N;Banerjee S;Alexopoulos GS;Sirey JA
老年抑郁症是异质性的，患者的病程随时间而变化。大多数心理治疗研究仅使用定期管理的自我报告量表来测量活动水平和症状。这些量表可能无法捕获治疗期间的颗粒变化。我们介绍了潜在的效用被动传感数据收集与智能手机评估波动的日常功能在真实的时间在心理治疗老年虐待受害者的晚年抑郁症。据我们所知，这是第一次对抑郁的老年虐待受害者进行被动感知的调查。我们目前的数据从三个受害者谁收到了9周的干预作为试点随机对照试验的一部分，并显示抑郁症状显着减少（减少50%）。使用智能手机，我们跟踪了参与者每天解锁智能手机的次数，在家里花费的时间，在谈话中花费的时间，以及治疗过程中的步数。在摄入时、第6周和第9周收集抑郁症状和行为激活的独立评估。数据显示，患者水平的活动水平在治疗期间波动，与自我报告的行为激活相对应。我们展示了被动感知数据如何扩大我们对老年虐待中晚年抑郁症异质性表现的理解。我们说明了如何与被动传感和主观措施测量的活动水平的变化轨迹可以同时跟踪随着时间的推移。我们概述了被动传感数据收集在未来的研究中应用的挑战和潜在的解决方案，使用新的先进的统计建模，如人工智能算法，更大的样本。
Late-life depression is heterogenous and patients vary in disease course over time. Most psychotherapy studies measure activity levels and symptoms solely using self-report scales, administered periodically. These scales may not capture granular changes during treatment. We introduce the potential utility of passive sensing data collected with smartphone to assess fluctuations in daily functioning in real time during psychotherapy for late life depression in elder abuse victims. To our knowledge, this is the first investigation of passive sensing among depressed elder abuse victims. We present data from three victims who received a 9-week intervention as part of a pilot randomized controlled trial and showed a significant decrease in depressive symptoms (50% reduction). Using a smartphone, we tracked participants' daily number of smartphone unlocks, time spent at home, time spent in conversation, and step count over treatment. Independent assessment of depressive symptoms and behavioral activation were collected at intake, Weeks 6 and 9. Data revealed patient-level fluctuations in activity level over treatment, corresponding with self-reported behavioral activation. We demonstrate how passive sensing data could expand our understanding of heterogenous presentations of late-life depression among elder abuse. We illustrate how trajectories of change in activity levels as measured with passive sensing and subjective measures can be tracked concurrently over time. We outline challenges and potential solutions for application of passive sensing data collection in future studies with larger samples using novel advanced statistical modeling, such as artificial intelligence algorithms.
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DOI：
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