# “Does getting older signal improved mood repair for people with early-onset mood disorder histories? A longitudinal study of outcomes and mechanisms across middle age.”

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $579,953

## Abstract

Abstract
This application responds to RFA-MH-17-405 Adult Maturational Changes and Dysfunctions in Emotion
Regulation. As the RFA notes, aging is associated (for most people) with increasing emotional well-being,
emotional stability, and a positivity bias. While these maturational changes are believed to reflect improved
emotion regulation skills (like better ways of attenuating sad mood), the literature on emotion regulation
strategies and age is decidedly equivocal. Further, little is known about when maturational changes may occur
given the scarcity of longitudinal studies and the use of designs, which contrast highly divergent age groups.
And it is not known how psychopathology affects presumed adult maturational changes in affect-related skills.
We propose to build on a unique sample of Ss, heterogeneous with respect to risk of depressive disorder, who
(in prior research studies) repeatedly reported during ages 18-35+ on their emotional well-being and how they
attenuate sadness/distress (mood repair); their autonomic nervous system (ANS) functioning associated with
affect processing was also assessed via peripheral measures. We propose to re-assess n=225 with histories
of diagnosed depression and n=200 with no histories of depression once more, and thus extend the data base
up to age 59 years, covering middle-age. We will identify latent trajectories of trait mood repair from ages 18
to 59 years and determine its correlates and the effects of personality, treatment exposure and ANS
physiologic functioning on class membership. We also will examine the ability to repair mood in the laboratory
via two adaptive cognitive regulation strategies: attention-refocusing and neutral reappraisal. Finally, we will
use a novel experimental procedure (the Cognitive Effort Discounting Paradigm or COG-ED), which quantifies
the subjective cost of cognitive effort (involved in cost-benefit computations to perform a task), to examine the
impact of affective load on effort-based decision making. We will test several clearly articulated hypotheses
about : i) the effects of depressive illness history and sex on positive maturational effects in mood repair with
age, ii) the predictive/moderating values of personality and treatment exposure on age-related mood repair
changes, iii) the relations of latent-class mood repair trajectories to sex and shifts in ANS functioning over time;
iv) the predictive value of latent-class mood repair trajectories for mood repair success in the laboratory; v)
ever-depressed and never-depressed group-related differences on the COG-ED, and vi) the relations of COG-
ED performance to mood repair trajectory membership and lab-based mood repair performance via attention
refocusing. Our study will fill several gaps in the literature on emotion regulation and aging. It also represents
the first step to assess the extent to which a neuro-economic approach to decision making reflects a
mechanism that may explain mood repair choices. Findings ...

## Key facts

- **NIH application ID:** 9923732
- **Project number:** 5R01MH113214-04
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** MARIA KOVACS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $579,953
- **Award type:** 5
- **Project period:** 2017-07-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9923732

## Citation

> US National Institutes of Health, RePORTER application 9923732, “Does getting older signal improved mood repair for people with early-onset mood disorder histories? A longitudinal study of outcomes and mechanisms across middle age.” (5R01MH113214-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9923732. Licensed CC0.

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