Digital Health Intervention Finds Relationship Between Anxiety, Depression Symptoms

In a 12‐week, therapist‐supported, smartphone‐delivered digital health intervention for symptoms of anxiety and depression, researchers found that symptoms of the 2 conditions both overlap and fluctuate together.

The intervention also demonstrated that anxiety symptoms predicted later depressive symptoms more strongly, compared with depressive symptoms predicting anxiety symptoms.

These findings were published in Journal of Clinical Psychology.

The study included 290 participants who were mostly female (79%) with a mean (SD) age of 39.64 (10.25) years. The study authors noted that more than half (54%) of patients self‐reported using psychotropic medication. Additionally, all patients scored at least a 5 on either the Patient Health Questionnaire (PHQ‐9) or Generalized Anxiety Questionnaire (GAD‐7).

The smartphone-based intervention included a pre‐specified sequence of evidenced‐based modules integrating components of mindfulness‐based stress reduction and cognitive therapy, cognitive behavioral therapy, and heart rate variability biofeedback (HRVB) training.

The authors used linear mixed models to analyze both the concurrent and lead-lag relationships between anxiety and depression.

In 1 hypothesis, the authors predicted that an increase in anxiety would be linked to an increase in depression within the current week. In a second hypothesis, they also predicted that an increase in anxiety in the prior week would be followed by an increase in depression in the current week.

In support of the first prediction, the authors found that higher levels of anxiety during the current biweekly assessment were associated with greater depressive symptoms during the current biweekly assessment.

In support of the second hypothesis, higher anxiety levels during the prior biweekly assessment were linked to greater depressive symptoms during the current biweekly assessment.

“Our results, limited by a lack of a comparison group, are consistent with the day‐to‐day data in that anxiety symptoms at time t–1 were a stronger predictor of depressive symptoms at time t than depressive symptoms at time t–1 predicting anxiety levels at time t,” the authors said.

The authors also noted these results “are far from conclusive but do give rise to a number of questions,” adding that future studies would benefit from analyzing the effects of anxiety mediators and moderators on subsequent depressive symptoms and certain emotional effects of anxiety reduction.

“Third, it would be important to examine specific GAD‐7 items that predict subsequent reductions in depression, such as being nervous and on edge, worrying about a number of events, difficulty controlling the worry, difficulty relaxing, irritability, and fear of a negative event occurring in the future,” they added.

Additionally, due to the relationship between anxiety and depression, altering weekly treatment approaches based on changing anxiety and depression levels may benefit patients more than the structured approach taken in this particular intervention.

“For example, if a patient’s anxiety is higher than depression on a particular week, the patient may benefit from psychoeducation on the cyclical nature of anxiety and depression, and from collaborating with the clinician to develop skills to mitigate a possible upcoming deterioration of mood,” the authors suggested.

Reference

Allende S, Forman-Hoffman VL, Goldin PR. Examining the temporal dynamics of anxiety and depressive symptoms during a therapist-supported, smartphone-based intervention for depression: longitudinal observational study. J Clin Psychol. Published online June 10, 2022. doi:10.1002/jclp.23401