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Emotion
Article
License: publisher-specific, author manuscript
Data sources: UnpayWall
Emotion
Article . 2020
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Optimizing stress: An integrated intervention for regulating stress responses.

Authors: Alia J. Crum; Jeremy P. Jamieson; Modupe Akinola;

Optimizing stress: An integrated intervention for regulating stress responses.

Abstract

The dominant cultural valuation of stress is that it is "bad for me." This valuation leads to regulatory goals of reducing or avoiding stress. In this article, we propose an alternative approach-stress optimization-which integrates theory and research on stress mindset (e.g., Crum, Salovey, & Achor, 2013) and stress reappraisal (e.g., Jamieson, Mendes, Blackstock, & Schmader, 2010) interventions. We further integrate these theories with the extended process model of emotion regulation (Gross, 2015). In so doing, we explain how altering second-level valuation systems-shifting the valuation of stress from "is bad for me" to "can be good for me"-fundamentally changes the overarching goal of stress regulation from reducing stress to optimizing stress responses to achieve valued goals. With this optimization goal in mind, individuals are invited to flexibly identify, select, and engage in specific regulation tactics (e.g., situation selection, attentional control, cognitive change, and response modulation) in ways that help them achieve valued ends as opposed to merely reducing or avoiding stressful experiences. We discuss definitions and issues related to key terms including stress, stressors, stress responses, and stress regulation and outline a research agenda for testing this new integrated theory as an intervention. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Related Organizations
Keywords

Humans, Stress, Psychological

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
127
Top 1%
Top 10%
Top 1%
hybrid