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BACKGROUND

Mood and anxiety disorders are highly prevalent and comorbid worldwide, with variability in symptom severity that fluctuates over time. Digital phenotyping, a growing field that aims to characterize clinical, cognitive and behavioral features via personal digital devices, enables continuous quantification of symptom severity in the real world, and in real-time.

METHODS

In this study, N=114 individuals with a mood or anxiety disorder (MA) or healthy controls (HC) were enrolled and completed 30-days of ecological momentary assessments (EMA) of symptom severity. Novel real-world measures of anxiety, distress and depression were developed based on the established Mood and Anxiety Symptom Questionnaire (MASQ). The full MASQ was also completed in the laboratory (in-lab). Additional EMA measures related to extrinsic and intrinsic motivation, and passive activity data were also collected over the same 30-days. Mixed-effects models adjusting for time and individual tested the association between real-world symptom severity EMA and the corresponding full MASQ sub-scores. A graph theory neural network model (DNA) was applied to all data to estimate symptom interactions.

RESULTS

There was overall good adherence over 30-days (MA=69.5%, HC=71.2% completion), with no group difference (t=0.874, p=0.386). Real-world measures of anxiety/distress/depression were associated with their corresponding MASQ measure within the MA group (t's > 2.33, p's < 0.024). Physical activity (steps) was negatively associated with real-world distress and depression (IRRs > 0.93, p's ≤ 0.05). Both intrinsic and extrinsic motivation were negatively associated with real-world distress/depression (IRR's > 0.82, p's < 0.001). DNA revealed that both extrinsic and intrinsic motivation significantly influenced other symptom severity measures to a greater extent in the MA group compared to the HC group (extrinsic/intrinsic motivation: t = 2.62, p < 0.02, q FDR < 0.05, Cohen's = 0.76; t = 2.69, p < 0.01, q FDR < 0.05, Cohen's = 0.78 respectively), and that intrinsic motivation significantly influenced steps (t = 3.24, p < 0.003, q FDR < 0.05, Cohen's = 0.94).

CONCLUSIONS

Novel real-world measures of anxiety, distress and depression significantly related to their corresponding established in-lab measures of these symptom domains in individuals with mood and anxiety disorders. Novel, exploratory measures of extrinsic and intrinsic motivation also significantly related to real-world mood and anxiety symptoms and had the greatest influencing degree on patients' overall symptom profile. This suggests that measures of cognitive constructs related to drive and activity may be useful in characterizing phenotypes in the real-world.

BACKGROUND

METHODS

RESULTS

CONCLUSIONS

背景

情绪和焦虑障碍在全球范围内高度流行且常合并出现，症状严重程度随时间波动。数字表型分析是一个不断发展的领域，旨在通过个人数字设备来描述临床、认知和行为特征，能够在现实世界中实时持续量化症状严重程度。

方法

在本研究中，招募了N = 114名患有情绪或焦虑障碍（MA）的个体或健康对照（HC），并完成了30天的症状严重程度生态瞬时评估（EMA）。基于已有的情绪和焦虑症状问卷（MASQ）开发了新的现实世界焦虑、痛苦和抑郁测量方法。完整的MASQ也在实验室中完成（实验室测量）。在相同的30天内还收集了与外在和内在动机相关的额外EMA测量数据以及被动活动数据。调整时间和个体因素的混合效应模型测试了现实世界症状严重程度EMA与相应的完整MASQ子分数之间的关联。将图论神经网络模型（DNA）应用于所有数据以估计症状相互作用。

结果

在30天内总体依从性良好（MA组完成率 = 69.5%，HC组完成率 = 71.2%），两组之间无差异（t = 0.874，p = 0.386）。MA组中，现实世界的焦虑/痛苦/抑郁测量与相应的MASQ测量相关（t值 > 2.33，p值 < 0.024）。身体活动（步数）与现实世界的痛苦和抑郁呈负相关（IRR > 0.93，p值 ≤ 0.05）。外在和内在动机均与现实世界的痛苦/抑郁呈负相关（IRR > 0.82，p值 < 0.001）。DNA显示，与HC组相比，外在和内在动机在MA组中对其他症状严重程度测量的影响程度更大（外在/内在动机：t = 2.62，p < 0.02，q FDR < 0.05，Cohen's d = 0.76；t = 2.69，p < 0.01，q FDR < 0.05，Cohen's d = 0.78），并且内在动机对步数有显著影响（t = 3.24，p < 0.003，q FDR < 0.05，Cohen's d = 0.94）。