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Is low positive emotionality a specific risk factor for depression? A meta-analysis of longitudinal studies.
Open accessAbstract
Depression is well known to share a negative cross-sectional relationship with personality constructs defined by positive emotion (positive affect, extraversion, behavioral activation). These Positive Emotionality (PE) constructs have been proposed to represent stable temperamental risk factors for depression, not merely current mood state. These constructs have also been proposed to increase risk specifically for depression, relative to anxiety. We performed a meta-analysis of longitudinal studies to examine the relationship of PE to depression (59 effect sizes) and anxiety (26 effect sizes). In cross-sectional analyses, PE constructs were negatively associated with depression (r = -.34) and anxiety (r = -.24). PE constructs also prospectively predicted depression (r = -.26) and anxiety (r = -.19). These relationships remained statistically significant, but were markedly attenuated, when baseline levels of depression (β = -.08) and anxiety (β = -.06) were controlled. Moreover, depression and anxiety were equally strong predictors of subsequent changes in PE (β = -.07 and -.09, respectively). These findings are consistent with theoretical accounts of low PE as a temperamental vulnerability for depression, but suggest that the prospective relationship of PE to depression may be weaker and less specific than previously assumed. (PsycINFO Database Record
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“Deficits in positive affect are predictive and characteristic of certain anxiety disorders (Khazanov & Ruscio, 2016; Kotov, Gamez, Schmidt, & Watson, 2010), as well as depression (e.g., Mineka, Watson, & Clark, 1998).”
From Examining Positive and Negative Affect as Outcomes and Moderators of Cognitive-Behavioral Therapy and Acceptance and Commitment Therapy for Social Anxiety Disorder. (2019) · cites this paper as Khazanov & Ruscio, 2016
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“These findings are paralleled with results harnessing longitudinal datasets, experimental designs, and structural equation modelling methods (Brown, Chorpita, & Barlow, 1998; Kashdan, Weeks, & Savostyanova, 2011; Khazanov & Ruscio, 2016; Sewart et al., 2019).”
From Examining Positive and Negative Affect as Outcomes and Moderators of Cognitive-Behavioral Therapy and Acceptance and Commitment Therapy for Social Anxiety Disorder. (2019) · cites this paper as Khazanov & Ruscio, 2016
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“In contrast, meta-analytic data indicate that positive emotionality (e.g., positive affect, extraversion, and behavioral activation) decreases risk for depression16.”
From Psychobiological factors of resilience and depression in late life (2019) · cites this paper as [16]
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Metadata
- Authors
- Khazanov, G. K., & Ruscio, A. M.
- Venue
- Psychological Bulletin
- Published
- 2016-09
- Volume
- 142
- Issue
- 9
- Pages
- 991-1015
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- PMC
- PMC5110375
- OpenAlex
- W2473686504
- Cited by
- 240
- References
- 179
- Citations by year
- 2026: 21 · 2025: 25 · 2024: 19 · 2023: 20 · 2022: 37 · 2021: 21 · 2020: 59 · 2019: 23 · 2018: 12 · 2017: 2 · 2016: 1
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Psychological Bulletin (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01 MH094425)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Clinical Psychology › Personality Traits and Psychology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- positive affectivity, depression, anxiety, positive affect, extraversion, behavioral activation, prospective prediction
- MeSH terms
- Anxiety, Anxiety Disorders, Cross-Sectional Studies, Depression, Depressive Disorder, Emotions, Humans, Longitudinal Studies, Personality, Risk Factors, Temperament
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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