Relationships between depression, anxiety, and motivation in the real-world: Effects of physical activity and screentime

Jacqueline M Beltran, Yael Jacob, Marishka Mehta, Tasnim Hossain, Abigail Adams, Samantha Fontaine, John Torous, Catherine A McDonough, Matthew Johnson, Andrew Delgado, James W Murrough, Laurel S Morris
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Abstract

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 (DEPNA) 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(58)=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 (IRR's > 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). DEPNA 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(46) = 2.62, p < 0.02, q FDR < 0.05, Cohen's d = 0.76; t(46) = 2.69, p < 0.01, q FDR < 0.05, Cohen's d = 0.78 respectively), and that steps significantly influenced intrinsic motivation (t(46) = 3.24, p < 0.003, q FDR < 0.05, Cohen's d = 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.
现实世界中抑郁、焦虑和动机之间的关系:体育活动和屏幕时间的影响
背景:情绪障碍和焦虑症在全球范围内发病率很高,并发症多,症状严重程度随时间变化而波动。数字表型技术(Digital phenotyping)是一个不断发展的领域,旨在通过个人数字设备描述临床、认知和行为特征,可在现实世界中对症状严重程度进行持续、实时的量化。研究方法本研究共招募了 114 名情绪或焦虑障碍(MA)患者或健康对照组(HC),并完成了 30 天的症状严重程度生态瞬间评估(EMA)。在已制定的情绪和焦虑症状问卷(MASQ)的基础上,开发了新的焦虑、痛苦和抑郁的真实世界测量方法。完整的 MASQ 也在实验室内完成(in-lab)。在同样的 30 天内,还收集了与外在和内在动机相关的其他 EMA 测量以及被动活动数据。对时间和个体进行调整的混合效应模型测试了真实世界症状严重程度 EMA 与相应的 MASQ 总分之间的关联。图论神经网络模型(DEPNA)适用于所有数据,以估计症状之间的相互作用。结果:30 天内的总体坚持率良好(MA=69.5%,HC=71.2%),没有组间差异(t(58)=0.874, p=0.386)。在 MA 组中,焦虑/压力/抑郁的真实世界测量与相应的 MASQ 测量相关(t's > 2.33,p's < 0.024)。体力活动(步数)与现实世界中的焦虑和抑郁呈负相关(IRR为0.93,P≤0.05)。内在动机和外在动机均与现实世界的苦恼/抑郁呈负相关(IRR 为 0.82,P 为 0.001)。DEPNA 显示,与 HC 组相比,外在动机和内在动机对 MA 组其他症状严重程度的影响更大(外在/内在动机:t(46) = 2.62,p < 0.02,q FDR <0.05,Cohen's d = 0.76;t(46) = 2.69,p <0.01,q FDR <0.05,Cohen's d = 0.78),步骤显著影响内在动机(t(46) = 3.24,p <0.003,q FDR <0.05,Cohen's d = 0.94)。结论新的真实世界焦虑、苦恼和抑郁测量结果与实验室内已确定的情绪和焦虑症患者这些症状领域的相应测量结果有显著相关性。外在动机和内在动机的新探索性测量也与真实世界中的情绪和焦虑症状有显著相关性,并且对患者的整体症状特征影响最大。这表明,与驱动力和活动相关的认知结构测量可能有助于描述真实世界中的表型特征。
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