Digital measures of activity and motivation impact depression and anxiety in the real world

Abstract

Mood and anxiety disorders are highly comorbid, with symptom severity varying over time. Individuals with and without these disorders completed 30-days of ecological momentary assessment (EMAs) of depression, anxiety and distress, developed based on the established Mood and Anxiety Symptom Questionnaire (MASQ). These electronic MASQ (eMASQ) EMAs were collected alongside novel intrinsic and extrinsic motivation EMAs, and physical/digital activity measures (steps/screentime) across N = 70-101 participants. Each eMASQ-EMA significantly related to its corresponding MASQ measure. Extrinsic and intrinsic motivation negatively related to each eMASQ-EMA and had the greatest influence on patients' overall symptom profile. Physical, but not digital activity, was negatively associated with concurrent and 1-week lagged anxiety and depression, highlighting the temporally delayed benefits of physical activity on depression and anxiety symptoms in psychiatric groups. Collectively, this study suggests cognitive constructs related to drive and physical activity, may be useful in predicting continuous and transient psychiatric symptoms in the real-world.

Fig. 1. Flowchart illustrating phases of data preprocessing and participants included in each set of analyses.

Bold text indicates datasets in which analyses were performed. HC Healthy Control group, MA Mood/Anxiety disorder group. Created in BioRender. Beltran, J. (2025) https://BioRender.com/p43h413.

Fig. 2. Study Adherence between groups.

a Box plots illustrate the percentage of days out of the 30-day requirement on which participants completed at least one survey. b Line graph illustrates the percentage of subjects who completed at least one survey on a given day out of the total number enrolled to determine study adherence. Error bars standard deviation of a proportion, HC Healthy Control group, MA Mood/Anxiety disorder group.

Fig. 3. Results from assessing the reliability of eMASQ-EMAs for anxiety, distress and depression.

a–c Scatter plots showcasing significant relationships between the in-lab MASQ for Anxious Arousal, General Distress, and Anhedonic Depression against anxiety, distress, and depression eMASQ-EMA scores alongside their score distributions. Individual data points represent survey responses per participant. d Lollipop plots depicting results from regression models assessing the relationship between the in-lab MASQ for Anxiety, Distress and Depression and their corresponding eMASQ-EMAs in the full cohort (HC + MA). MASQ total scores represent the sum of each MASQ subscale and were correlated against the sum of each eMASQ-EMA. In the full cohort, significant associations were found between each MASQ subscale and its corresponding eMASQ-EMA demonstrating the reliability of these scales (N = 80, β’s > 0.0792, t’s > 2.28, p’s < 0.05). HC Healthy Control group, MA Mood/Anxiety disorder group, MASQ Mood and Anxiety Symptom Questionnaire.

Fig. 4. Regression results illustrating marginal effects plots of the predicted values of anxiety, distress and depression severity across different levels of intrinsic and extrinsic motivation upon adjusting for covariates.

Zero-inflated Poisson models for each anxiety/distress/depression eMASQ-EMA demonstrated there were main effects of intrinsic motivation (a–c) (N = 101, IRRs > 0.82, p’s < 0.001) and extrinsic motivation (d–f) (N = 101, IRRs > 0.88, p’s ≤ 0.001) on each eMASQ-EMA. There were also significant interactions between group and intrinsic motivation on anxiety/distress/depression (IRRs > 1.08, p’s < 0.001) and group and extrinsic motivation on depression (N = 101, IRR = 1.06, p = 0.018). Data points are based on marginal effects from the fitted ZIP models using the GLMMadaptive package’s ‘effectPlotData‘ function in R. HC Healthy Control group, MA Mood/Anxiety disorder group.

Fig. 5. Regression results illustrating marginal effects plots of the predicted values of anxiety, distress and depression severity across steps taken per day upon adjusting for covariates.

Zero-inflated Poisson models demonstrated a main effect of steps on anxiety (a), distress (b) and depression (c) (N = 70, IRRs > 0.87, p’s ≤ 0.05). Across all three measures, participants in the MA group experience greater symptom severity (N = 70, IRRs > 2, p’s < 0.001) in comparison to HC. Meanwhile, only in the anxiety model was there an effect of sex such that females demonstrate higher anxiety scores than males, after accounting for step counts. Steps data were scaled within-participants. Data points are based on marginal effects from the fitted ZIP models using the GLMMadaptive package’s ‘effectPlotData‘ function in R. HC Healthy Control group, MA Mood/Anxiety disorder group.

Fig. 6. The D_EPNA.

a The correlation influence of node j on the pair of nodes i and k is defined as the difference between the correlation, C(i,k), and the partial correlation, PC(i,k | j). The partial correlation coefficient captures the effect (or contribution) of one node on the correlation between a pair of nodes. When this coefficient is large, it means a significant fraction of the correlation between a pair of nodes can be explained by the effect of a third node (Jacob et al., 2019). b A dependency matrix is created by calculating the partial correlation effect for each node on all other pairwise correlations in the network. The total influence of node j on node i, D(i,j) is defined as the average influence of node j on the correlations C(i,k), over all nodes k. The node dependencies define a dependency matrix D, whose (i,j) element is the influence of node j on node i. c The ‘Influencing Degree’ of node j is defined as the sum of the influence of node j on all other nodes i. The larger this measure is, the greater its impact on all other connections in the network and the more likely it is to generate the information flow in the network. d A graph visualization based on the pair-wise dependency connectivity matrix. The graph is color-coded according to its influencing degree. Pair-wise nodes with dependencies that were significantly different between the two groups at the p < 0.05 (FDR corrected) level are plotted as edges. Each edge is color-coded according to the t-test sign as light or dark gray with the arrows representing the direction of influence.

Fig. 7. D_EPNA results.

a A network illustration and graph visualization of the ‘influencing degree’ of symptoms in the MA group against healthy controls. Each region is color-coded according to the t statistic value from the t-test between the ‘Influencing Degree’ of the two groups. All pair-wise ROIs with connections, significant at the p < 0.05 level, are plotted as edges. Each edge is color-coded according to the t-test sign as light or dark gray with the arrows representing the direction of influence. b The nodes’ averaged ‘Influencing Degree’ and (c) ‘Influenced Degree’. The total influence of both extrinsic and intrinsic motivation was significantly higher among the MA group compared to healthy controls. *p < 0.05, **p < 0.05 FDR corrected. HC Healthy Control group, MA Mood/Anxiety disorder group.