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

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 (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). 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 intrinsic motivation significantly influenced steps (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.

Digital navigators to implement smartphone and digital tools in care.

While smartphone apps and other digital health tools have the clear potential to increase both quality of and access to care, actual successful implementation remains limited. Challenges often encountered in seeking to use apps in care include selecting safe/effective tools, spending clinical time troubleshooting technology instead of discussing health matters, and lack of time to check and review constant streams of data these digital tools can produce. In this 'From Research to Clinical Practice' piece, we focus on how a new care team member, the digital navigator, can help overcome these barriers through conducting evidence-based app evaluation to help in selecting the right apps, troubleshooting technology outside of visits to improve the therapeutic alliance during, and finally summarizing digital data to facilitate clinical care that focus on actionable data.

New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices.

Mobile and connected devices like smartphones and wearable sensors can facilitate the collection of novel naturalistic and longitudinal data relevant to psychiatry at both the personal and population level. The National Institute of Mental Health's Research Domain Criteria framework offers a useful roadmap to organize, guide and lead new digital phenotyping data towards research discoveries and clinical advances.