Cambridge Cognition, Cambridge, United Kingdom.
Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
JMIR Ment Health. 2024 May 31;11:e46895. doi: 10.2196/46895.
Cognitive symptoms are an underrecognized aspect of depression that are often untreated. High-frequency cognitive assessment holds promise for improving disease and treatment monitoring. Although we have previously found it feasible to remotely assess cognition and mood in this capacity, further work is needed to ascertain the optimal methodology to implement and synthesize these techniques.
The objective of this study was to examine (1) longitudinal changes in mood, cognition, activity levels, and heart rate over 6 weeks; (2) diurnal and weekday-related changes; and (3) co-occurrence of fluctuations between mood, cognitive function, and activity.
A total of 30 adults with current mild-moderate depression stabilized on antidepressant monotherapy responded to testing delivered through an Apple Watch (Apple Inc) for 6 weeks. Outcome measures included cognitive function, assessed with 3 brief n-back tasks daily; self-reported depressed mood, assessed once daily; daily total step count; and average heart rate. Change over a 6-week duration, diurnal and day-of-week variations, and covariation between outcome measures were examined using nonlinear and multilevel models.
Participants showed initial improvement in the Cognition Kit N-Back performance, followed by a learning plateau. Performance reached 90% of individual learning levels on average 10 days after study onset. N-back performance was typically better earlier and later in the day, and step counts were lower at the beginning and end of each week. Higher step counts overall were associated with faster n-back learning, and an increased daily step count was associated with better mood on the same (P<.001) and following day (P=.02). Daily n-back performance covaried with self-reported mood after participants reached their learning plateau (P=.01).
The current results support the feasibility and sensitivity of high-frequency cognitive assessments for disease and treatment monitoring in patients with depression. Methods to model the individual plateau in task learning can be used as a sensitive approach to better characterize changes in behavior and improve the clinical relevance of cognitive data. Wearable technology allows assessment of activity levels, which may influence both cognition and mood.
认知症状是抑郁症未被充分认识的一个方面，且往往得不到治疗。高频认知评估有望改善疾病监测和治疗监测。尽管我们之前已经发现，以这种能力远程评估认知和情绪是可行的，但还需要进一步的工作来确定实施和综合这些技术的最佳方法。
本研究旨在：（1）在 6 周内检查情绪、认知、活动水平和心率的纵向变化；（2）检测日间和工作日相关的变化；（3）检测情绪、认知功能和活动之间波动的同时发生情况。
30 名正在服用抗抑郁药单药治疗且病情稳定的成年人患有当前轻度至中度抑郁症，他们通过 Apple Watch（Apple Inc.）接受了为期 6 周的测试。评估指标包括：每天使用 3 次简短 n-back 任务进行认知功能评估；每天进行一次自我报告的抑郁情绪评估；每天的总步数；以及平均心率。使用非线性和多层次模型来检查 6 周内的变化、日间和周内的变化以及评估指标之间的协同变化。
参与者在认知能力测试套件 n-back 表现上表现出最初的改善，随后达到学习高原。平均而言，在研究开始后 10 天达到个人学习水平的 90%。n-back 表现通常在一天中的早些时候和晚些时候更好，而在每周的开始和结束时步数较低。总的来说，较高的步数与 n-back 学习速度更快有关，而每天增加的步数与当天和次日的情绪更好有关（P＜0.001）和第二天（P=.02）。当参与者达到学习高原后，每日 n-back 表现与自我报告的情绪相互关联（P=.01）。
目前的结果支持使用高频认知评估来监测抑郁症患者的疾病和治疗的可行性和敏感性。可以使用模拟任务学习中个体高原的方法作为一种敏感方法，以更好地描述行为变化并提高认知数据的临床相关性。可穿戴技术允许评估活动水平，这可能会影响认知和情绪。
