# Affective forecasting dynamics as an early intervention target in depression: evidence from ecological monitoring and temporal network analysis

## TL;DR

This study explores how people with depression forecast emotions differently, using daily tracking and heart rate data to find early warning signs and potential treatment targets.

## Contribution

The study introduces temporal network analysis and ecological monitoring to uncover novel psycho-physiological mechanisms of affective forecasting deficits in depression.

## Key findings

- Dysphoric individuals reported lower anticipatory, experienced, and consummatory emotional valence compared to non-dysphoric individuals.
- Temporal network density was higher in dysphoric individuals, indicating more rigid emotional dynamics.
- Heart rate variability predicted anticipatory valence, and affective forecasting dynamics predicted subsequent depression.

## Abstract

Deficits in affective forecasting are closely associated with the development and maintenance of depression. While previous research has shown that emotional fluctuations and future thinking are prevalent in daily life, little is known about the psycho-physiological mechanisms of affective forecasting deficits in relation to specific daily events in depression. Methods: Dysphoric (N = 40) and non-dysphoric (N = 60) individuals completed assessments of their anticipatory emotions, experienced emotions, and consummatory emotions for specific daily events 3 times a day for consecutive 14 days. Heart rate variability (HRV) data was collected using customized smartwatches. Temporal network analysis was used to estimate time-lagged associations among anticipatory, experienced and consummatory emotions.

Dysphoric individuals reported significantly lower levels of anticipatory, experienced, and consummatory valence compared to non-dysphoric individuals. Furthermore, distinct patterns emerged in the temporal networks of anticipatory, experienced, and consummatory emotions between the dysphoric and non-dysphoric groups. Network density was considerably higher in dysphoric individuals than in non-dysphoric individuals. In addition, HRV was predictive of anticipatory valence across all participants. Moreover, the dynamic associations between anticipatory and experienced emotions predicted subsequent depression, even after accounting for baseline depressive symptoms.

Our findings reveal provide novel insights into the psycho-physiological mechanisms of affective forecasting deficits in depression, with several clinical implications: (1) dysfunctional affective forecasting dynamics may serve as salient early warning signs and sensitive predictors of depression; and (2) improving the flexibility of affective forecasting may be a promising target for addressing depression.