Evidence that specific personal relationships help regulate depressive symptoms and related constructs among people with probable major depressive disorder

ABSTRACT

Introduction

Relational regulation theory describes how social network members (providers of regulation) help people (recipients of regulation) regulate their effect, actions and thoughts through mostly ordinary social interaction. Regulation is relational when the ability of a provider to regulate a recipient is an emergent property of the dyad and not a stable property of the provider or recipient. Research in predominantly well samples has found that dyads evoked affect and self-relevant thought in recipients. The present research examined whether such effects occurred among people with probable major depressive disorder (MDD).

Methods

A national, internet sample of 2058 US residents was screened for probable MDD. Depressed recipients (N = 152) rated their experience of depression-related constructs when with or thinking about specific providers.

Results

Recipients’ reports of affect and thought varied strongly depending on the dyad they were with or thinking about. These effects occurred for depressive symptoms, positive and negative affect, self-esteem, negative automatic thoughts, hopelessness, excessive reassurance-seeking, reappraisal and emotion suppression. Dyads that evoked depression-related experiences were seen by participants as unsupportive and as evoking conflict.

Conclusion

Relational regulation appears to occur among people with MDD which provides new insights about interpersonal processes in depression.

KEYWORDS:

• Major depressive disorder
• relational regulation theory
• social relations model
• cognitive theory of depression
• emotion regulation
• interpersonal theory

Disclosure statement

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

Notes

1 Internal consistency reliabilities were estimated from variance components designs that modeled perceiver, dyad and item effects. The design was Dyads nested within Perceivers × Items. Formulas were ${\alpha }_{\mathrm{Per}}={\hat{\sigma }}_{\mathrm{Per}}^2/({\hat{\sigma }}_{\mathrm{Per}}^2+{\hat{\sigma }}_{\mathrm{Per}x\mathrm{It}\mathrm{e}m/\mathrm{ni}}^2)$ and ${\alpha }_{\mathrm{Dyadic}}={\hat{\sigma }}_{\mathrm{Dyad}}^2/({\hat{\sigma }}_{\mathrm{Dyad}}^2+{\hat{\sigma }}_{\mathrm{Dyad}x\mathrm{It}\mathrm{e}m/n_i}^2)$. To reduce the size and complexity of the design, two aggregates of items served as the two levels of the Items factor. Thus, this is a form of split half reliability.