Clients’ and therapists’ parasympathetic interpersonal and intrapersonal regulation dynamics during psychotherapy for depression

Abstract

Objective

The literature on affective regulation in psychotherapy has traditionally relied on explicit client self-report measures. However, both clients’ and therapists’ affect fluctuate moment-to-moment during a session, highlighting the need for more implicit and continuous indices to better understand these dynamics. This study examined parasympathetic interpersonal and intrapersonal regulation dynamics between therapists and clients with Major Depressive Disorder during Supportive-Expressive Therapy.

Method

Data were collected from 52 dyads across five preselected sessions, using the Respiratory Sinus Arrhythmia (RSA) index. We employed a longitudinal Actor-Partner Interdependence Model, with clients self-reporting their functioning level before and after each session, as the moderator.

Results

Therapists’ RSA at one time point negatively associated with clients’ RSA at the next, and vice-versa, indicating interpersonal regulation. Clients’ RSA at one time point was positively associated with their RSA at the next, indicating intrapersonal regulation. However, only interpersonal regulation was significantly moderated by clients’ pre-to-post session functioning. Specifically, sessions where clients led positive dyadic RSA associations showed greater improvement in clients’ functioning than those led by therapists.

Conclusion

Physiological interpersonal regulation, measured by RSA, may be a catalyst for change in depression treatment. Therapists who are responsive to clients’ arousal levels may help clients improve their functioning.

Keywords:

• depression
• affect regulation dynamics
• parasympathetic nervous system
• respiratory sinus arrhythmia (RSA)

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1 Level-1 and Level-2 random effects were only modeled for the intercepts since the model did not converge by adding random effects for slopes.

2 We used Preacher et al.’s (2006) computational tool for probing interaction effects in MLM analyses.

Additional information

Funding

This work was supported by Israel Science Foundation: [Grant Number 2466/21].