Introduction: In research and treatment of mood disorders, “euthymia” traditionally denotes the absence of clinically significant mood disturbance. A newer, expanded definition of euthymia also includes positive affect and psychological well-being. Objective: We aimed to test this comprehensive model of euthymia and estimate the coherence and predictive power of each factor in the model. Methods: Community-dwelling adults (N = 601), including both mental health outpatients and non-patients at high risk for personality pathology, completed a battery of interviews and questionnaires at time 1. Most (n = 497) were reassessed on average 8 months later (time 2). We modeled euthymia using standard mood, personality, and psychosocial functioning assessments rather than measures designed specifically for euthymia. Results: The hypothesized model of euthymia was supported by confirmatory factor analysis: specific measures loaded on three lower order factors (mood disturbance, positive affect, and psychological well-being) that reflected general euthymia at time 1. Each factor (general euthymia plus lower order factors) demonstrated moderately strong concurrent (time 1) and predictive (time 1–2) correlations with outcomes, including employment status, income, mental health treatment consumption, and disability. Compared to positive affect and psychological well-being, mood disturbance had stronger incremental (i.e., nonoverlapping) relations with these outcomes. Conclusions: Support for a comprehensive model of euthymia reinforces efforts to improve assessment and treatment of mood and other disorders. Beyond dampening of psychological distress, euthymia-informed treatment goals encompass full recovery, including enjoyment and meaning in life.

Euthymia traditionally denotes the absence of clinically significant mood disturbance in the context of mood disorders (e.g., major depressive disorder [MDD], bipolar disorder). Beyond lack of mood disturbance, Fava, Bech, and Guidi expanded the definition of euthymia to encompass positive affect and psychological well-being [1, 3]. To the extent that this broad conceptualization of euthymia is valid, assessment and targeting of euthymia may expand treatment goals beyond dampening psychological distress to encompass enjoyment and meaning in life, consistent with patients’ conceptualizations of a “cure” for mood disorders [4] and closer to the clinical concept of recovery [5].

From a clinimetric perspective that complements standard diagnostic taxonomies and supports clinical decision making, Fava and colleagues conceptualized euthymia as a broad unitary construct [1, 3]. Relevant to euthymia, mood disturbances include both depression (e.g., negative affect and hopelessness) and mania (e.g., elevated, expansive, or irritable mood and rapidly shifting thoughts) [6]. Further, positive affect involves pleasant activation (e.g., energy, enthusiasm, optimism), subjective well-being, and life satisfaction [2, 3, 7]. Finally, psychological well-being in euthymia involves balance in flexibility, consistency, and stress resistance dimensions. This balance supports intrapersonal (e.g., tolerance of distress and frustration, integrated sense of self that facilitates adaptive behavior) and interpersonal (e.g., successfully fulfilling social roles, maintaining positive social relationships) functioning [8, 9], which align conceptually with personality functioning in the DSM-5 Alternative Model of Personality Disorder [6, 10].

The current study evaluated this comprehensive model of euthymia in community-dwelling adults, including both mental health outpatients and non-patients at risk for psychopathology. We hypothesized that euthymia arises from (lack of) mood disturbance, positive affect, and psychological well-being, measured by relevant questionnaires and interviews (see Fig. 1). We also compared the validity of general euthymia and its components via their relations with conceptually distinct outcomes (e.g., unemployment, psychiatric treatment consumption, disability). We modeled euthymia using standard mood, personality, and psychosocial functioning assessments rather than measures designed specifically for euthymia. Thus, our findings also speak to the generalizability of the euthymia construct.

Fig. 1.

Model of euthymia. All factor loadings are standardized and significant at p < 0.001.

Fig. 1.

Model of euthymia. All factor loadings are standardized and significant at p < 0.001.

Close modal

Data were drawn from a research program designed to improve theory and measurement of personality pathology and functioning [11, 12]. The Institutional Review Board of the University of Notre Dame approved the study protocol, number 17-12-4289. All participants provided written informed consent.

Participants (N = 601) were 301 mental health outpatients plus 300 community-dwelling adults at risk for personality pathology. Participants were ≥18 years old; responded to interviews and questionnaires in English; and did not have dementia, intellectual disability, or active psychosis. Most outpatients were referred by a community mental health center and local practitioners; a few were recruited from the University of Notre Dame community. At-risk adults were recruited via random dialing of landline and cellular telephone numbers in the greater South Bend, Indiana area, and a few from the study’s pilot phase [11]. Participants not currently in mental health treatment scored ≥2 on the Iowa Personality Disorder Screen [13] (see the online suppl. File for additional information about the measures; for all online suppl. material, see www.karger.com/doi/10.1159/000529784).

Participants completed assessments over 1–2 days at a research facility. Nearly all participants completed questionnaires alone using a computer; research team members assisted a few (e.g., due to poor eyesight). Trained researchers conducted structured interviews. Participants consented to return for additional assessment approximately 6 months later. Researchers used multiple methods to attempt to contact all participants for reassessment.

We modeled euthymia using three measures each of mood disturbance, positive affect, and psychological well-being at time 1. We measured mood disturbance using MDD and persistent depressive disorder (PDD) symptom counts from the Mini-International Neuropsychiatric Interview [14] (adapted to DSM-5 with the author’s permission) and the Inventory of Depression and Anxiety Symptoms-II (IDAS-II) dysphoria scale [15]. We did not consider mania because of the infrequency of bipolar disorder in the sample. Positive affect measures included the IDAS-II well-being scale, Satisfaction with Life Scale [16], and the general satisfaction scale from the WHO Quality of Life-Brief Form [17]. Finally, (low) psychological well-being measures were non-coping and non-cooperativeness scales derived from the Measure of Disordered Personality Functioning (Parker et al. [18] 2004) and the total score from a 24-item version [11, 19] of the Psychological Well-Being scales [20].

Outcomes were measured at time 1 and again at time 2, an average of 8 months later, with a background information questionnaire and the World Health Organization Disability Assessment Schedule (WHODAS 2.0) [21]. From participants’ choices among employment status descriptors, we coded 1 for “unemployed” or “disabled, ” or 0 for “employed,” “self-employed,” “student,” “homemaker,” or “retired.” Participants reported their current yearly family income in $10,000 increments from <$10,000 to ≥$60,000; we coded (1) <$10,000; (2) $10,000–19,999; (3) $20,000–39,999; (4) $40,000–59,999; and (5) ≥$60,000, by collapsing rarely selected ranges. We coded three variables 1 (yes) or 0 (no) from participants’ reports of whether they were “now in mental health treatment,” had “ever been hospitalized for emotional or behavioral problems,” and “regularly take any medication prescribed by a doctor” for a “psychological condition.” Finally, we analyzed the WHODAS 2.0 total score, assessing behavioral limitations in important domains (e.g., communication, transportation, social participation, work tasks), plus an activities of daily living subscale (e.g., dressing, bathing, eating, walking) [11].

We tested the euthymia model in Figure 1 with confirmatory factor analysis using the lavaan package [22] in R software [23]. We modeled euthymia using measures taken at time 1 and computed regression-based factor scores. We correlated time 1 euthymia scores with outcomes measured at time 1 and 2 to gauge concurrent and predictive validity, respectively. We examined both zero-order and partial correlations to clarify the validity of each lower order factor.

Participants (N = 601) were M = 46 (SD = 13) years old; 57% were women; 21% were black, 68% white, and 11% other racial/ethnic groups. Regarding current mood disorders, 30% met criteria for MDD, 19% for PDD, and 2% for bipolar disorder. Descriptive statistics and correlations among the euthymia-relevant measures appear in online supplement Table S1.

We tested the euthymia model shown in Figure 1 using confirmatory factor analysis. Specific measures loaded on three lower order factors (mood disturbance, positive affect, and psychological well-being), which formed a general euthymia factor. The measures’ residuals were independent with one exception. Allowing the interview variables’ (MDD and PDD symptoms) residuals to correlate (0.28) improved fit slightly (∼0.01) on the following indices. The final model fit adequately considering the comparative fit index (0.97), Tucker-Lewis index (0.96), root mean square error of approximation (0.08), and standardized root mean square residual (0.04). All factor loadings were substantive (>|0.30|) and statistically significant, p < 0.001.

From this model, we computed lower order (mood disturbance, positive affect, psychological well-being) and general euthymia factor scores. Table 1 displays concurrent (time 1) and predictive (time 1–2) correlations of euthymia scores with unemployment, income, mental health treatment consumption, and disability measures. The concurrent (0.24) and predictive (0.28) correlations were similar in strength (median |r| values). General euthymia (0.27), mood disturbance (0.28), positive affect (0.25), and psychological well-being (0.27) factors each correlated significantly with all measured outcomes.

Table 1.

Descriptive statistics and correlations of outcome variables with euthymia factor scores

MSDRangeEuthymia factor score correlations
general euthymiamood disturbancepositive affectpsychological well-being
Concurrent outcomes 
 History of psychiatric hospitalization 0.39 0.49 0–1 −0.22 0.25 −0.20 −0.22 
 Unemployed or disabled 0.48 0.50 0–1 −0.26 0.25 −0.25 −0.26 
 Income level 2.52 1.42 1–5 0.23 −0.23 0.23 0.23 
 Receiving mental health treatment 0.47 0.50 0–1 −0.18 0.22 −0.17 −0.18 
 Taking prescription psychiatric medication 0.55 0.50 0–1 −0.23 0.28 −0.21 −0.23 
 WHODAS 2.0 total 2.05 0.69 1–5 −0.60 0.62 −0.58 −0.60 
  Activities of daily living 1.66 0.74 1–5 −0.40 0.43 −0.39 −0.40 
Predictive outcomes 
 History of psychiatric hospitalization 0.38 0.49 0–1 −0.18 0.22 −0.16 −0.18 
 Unemployed or disabled 0.43 0.50 0–1 −0.27 0.27 −0.27 −0.27 
 Income level 2.53 1.42 1–5 0.18 −0.20 0.18 0.18 
 Receiving mental health treatment 0.44 0.50 0–1 −0.28 0.32 −0.26 −0.28 
 Taking prescription psychiatric medication 0.51 0.50 0–1 −0.28 0.33 −0.25 −0.28 
 WHODAS 2.0 total 2.00 0.74 1–5 −0.50 0.55 −0.47 −0.50 
  Activities of daily living 1.68 0.77 1–5 −0.31 0.35 −0.29 −0.31 
MSDRangeEuthymia factor score correlations
general euthymiamood disturbancepositive affectpsychological well-being
Concurrent outcomes 
 History of psychiatric hospitalization 0.39 0.49 0–1 −0.22 0.25 −0.20 −0.22 
 Unemployed or disabled 0.48 0.50 0–1 −0.26 0.25 −0.25 −0.26 
 Income level 2.52 1.42 1–5 0.23 −0.23 0.23 0.23 
 Receiving mental health treatment 0.47 0.50 0–1 −0.18 0.22 −0.17 −0.18 
 Taking prescription psychiatric medication 0.55 0.50 0–1 −0.23 0.28 −0.21 −0.23 
 WHODAS 2.0 total 2.05 0.69 1–5 −0.60 0.62 −0.58 −0.60 
  Activities of daily living 1.66 0.74 1–5 −0.40 0.43 −0.39 −0.40 
Predictive outcomes 
 History of psychiatric hospitalization 0.38 0.49 0–1 −0.18 0.22 −0.16 −0.18 
 Unemployed or disabled 0.43 0.50 0–1 −0.27 0.27 −0.27 −0.27 
 Income level 2.53 1.42 1–5 0.18 −0.20 0.18 0.18 
 Receiving mental health treatment 0.44 0.50 0–1 −0.28 0.32 −0.26 −0.28 
 Taking prescription psychiatric medication 0.51 0.50 0–1 −0.28 0.33 −0.25 −0.28 
 WHODAS 2.0 total 2.00 0.74 1–5 −0.50 0.55 −0.47 −0.50 
  Activities of daily living 1.68 0.77 1–5 −0.31 0.35 −0.29 −0.31 

Concurrent n = 601, except hospitalization n = 600, and income n = 599. Predictive n = 497, except hospitalization and income n = 496. For dichotomous variables, 0 = no, 1 = yes.

All Spearman’s correlations p < 0.001, two-tailed.

WHODAS 2.0, World Health Organization Disability Assessment Schedule 2.0.

Partial correlations for each lower order factor controlled the other two lower order factors (see Table 2). Concurrent (0.05) and predictive (0.05) partial correlations were similar (median |r| values). However, mood disturbance (0.17) showed generally stronger unique relations to outcomes than did positive affect (0.04) or psychological well-being (0.02).

Table 2.

Partial correlations of euthymia lower order factor scores with outcome variables

Euthymia factor score
mood disturbancepositive affectpsychological well-being
Concurrent outcomes 
 History of psychiatric hospitalization 0.14 0.08 −0.06 
 Unemployed or disabled 0.06 −0.04 −0.01 
 Income level −0.05 0.04 0.01 
 Receiving mental health treatment 0.14 0.00 0.04 
 Taking prescription psychiatric medication 0.21 0.04 0.05 
 WHODAS 2.0 total 0.28 −0.05 −0.01 
  Activities of daily living 0.19 −0.05 0.04 
Predictive outcomes 
 History of psychiatric hospitalization 0.15 0.07 −0.02 
 Unemployed or disabled 0.07 −0.07 0.02 
 Income level −0.10 0.04 −0.05 
 Receiving mental health treatment 0.19 0.03 0.01 
 Taking prescription psychiatric medication 0.23 0.07 0.01 
 WHODAS 2.0 total 0.31 0.04 −0.01 
  Activities of daily living 0.21 0.01 0.04 
Euthymia factor score
mood disturbancepositive affectpsychological well-being
Concurrent outcomes 
 History of psychiatric hospitalization 0.14 0.08 −0.06 
 Unemployed or disabled 0.06 −0.04 −0.01 
 Income level −0.05 0.04 0.01 
 Receiving mental health treatment 0.14 0.00 0.04 
 Taking prescription psychiatric medication 0.21 0.04 0.05 
 WHODAS 2.0 total 0.28 −0.05 −0.01 
  Activities of daily living 0.19 −0.05 0.04 
Predictive outcomes 
 History of psychiatric hospitalization 0.15 0.07 −0.02 
 Unemployed or disabled 0.07 −0.07 0.02 
 Income level −0.10 0.04 −0.05 
 Receiving mental health treatment 0.19 0.03 0.01 
 Taking prescription psychiatric medication 0.23 0.07 0.01 
 WHODAS 2.0 total 0.31 0.04 −0.01 
  Activities of daily living 0.21 0.01 0.04 

Concurrent n = 601, except hospitalization n = 600, and income n = 599. Predictive n = 497, except hospitalization and income n = 496. For dichotomous variables, 0 = no, 1 = yes. The partial Spearman’s correlations for each factor (mood disturbance, positive affect, psychological well-being) control for the other two factors. Correlations ≥ |0.09|, p < 0.05, ≥ |0.12|, p < 0.01, two-tailed; significant rs are bolded.

WHODAS 2.0, World Health Organization Disability Assessment Schedule 2.0.

We found clear support for a comprehensive model of euthymia arising from (lack of) mood disturbance, positive affect, and psychological well-being [1, 3]. The model fit well, and each factor (general euthymia plus the three lower order factors) demonstrated moderately strong concurrent and predictive correlations with objective outcomes (e.g., unemployment, income, mental health treatment consumption, disability).

Support for the comprehensive euthymia model reinforces the importance of psychological well-being and resilience as treatment targets [4, 24] and conceptualization of mental health as more than the absence of mental illness or distress [25]. However, many depression treatments, including antidepressant medication and cognitive behavioral therapy, decrease negative affect more than they increase optimal functioning or positive affect [26, 27]. Augmenting cognitive behavioral therapy with behavioral activation, well-being, and other positive-affect interventions may improve patient outcomes [28, 31].

Well-Being Therapy, moreover, targets euthymia specifically [32, 34]. This short-term manualized psychotherapy utilizes self-monitoring, homework, and patient-therapist interactions over 8–20 sessions. Patients identify periods of psychological well-being, clarify thoughts and behaviors that interrupt well-being, and develop skills to modulate well-being and pursue euthymia. Well-Being Therapy has improved patient outcomes when applied in combination (e.g., with cognitive behavioral therapy) or sequence (e.g., after antidepressant medication) with other depression treatments and can also be used alone [32, 34].

Our results also suggest that traditional emphasis on lack of mood disturbance in euthymia has some merit. Mood disturbance, compared to positive affect or psychological well-being, showed the strongest incremental relations to outcomes. Consequently, if resources allow measurement of only one aspect of euthymia, mood disturbance may be the most important. However, our outcome variables more often referenced poor functioning (e.g., unemployment, disability) that may align with mood disturbance. Future research using outcome variables reflecting positive mental health [35, 36] or achievements (e.g., career advancement, relationship development) might find stronger unique relations with positive affect and psychological well-being [37].

Several limitations temper our conclusions. The current measures were not developed to assess euthymia specifically. Clinimetric tools designed to measure euthymia include the self-report Euthymia Scale and the Clinical Interview for Euthymia [1, 3]. Arguably, however, a robust euthymia construct would not be measure-dependent and could be observed from multiple perspectives. Our results thus speak to the strength of the euthymia construct. In addition, treatment of depression was the context in which Fava and colleagues developed their euthymia model, but only a minority of the current participants had mood disorders. Further, because bipolar disorder was rare in our sample, we did not analyze mania-related scales. Future research in a bipolar disorder clinic, for example, might advance our preliminary findings in important ways. Emergence of the hypothesized euthymia construct in a mixed outpatient/high-risk community sample again highlights the generalizability of this model.

We observed an empirical structure consistent with Fava and colleagues’ comprehensive model of euthymia [1, 3]. General euthymia and three mid-level euthymia factors (lack of mood disturbance, positive affect, and psychological well-being) demonstrated significant concurrent and predictive correlations with important outcome variables. These findings strengthen the empirical base for the broad euthymia model, support its use to measure psychosocial functioning rigorously, and extend treatment goals for mood disorders to encompass full recovery [31], including positive affect, psychological well-being, and resilience [3, 34].

The study protocol was reviewed and approved by the Institutional Review Board of the University of Notre Dame, approval number 17-12-4289. All participants provided written informed consent. This research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki and the American Psychological Association Ethical Principles of Psychologists and Code of Conduct.

Dr. Vittengl is a paid reviewer for UpToDate. Dr. Jarrett is a paid consultant to the NIH, NIMH, and UpToDate. Dr. Clark and Dr. Ro have no financial interests related to topics covered to declare.

This report was supported by a grant R01-MH083830 to Lee Anna Clark, Ph.D. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health (NIMH) or the National Institutes of Health (NIH).

Jeffrey Vittengl conceptualized the research question, analyzed the data, and wrote the manuscript. Robin Jarrett conceptualized the research question and wrote the manuscript. Eunyoe Ro collected the data, conceptualized the research question, and wrote the manuscript. Lee Anna Clark secured the study funding, collected and managed the data, conceptualized the research question, and wrote the manuscript.

The data presented in this article are currently available only upon request, for the purpose of verifying that the results reported are veridical. Please address data requests to Dr. Clark, la.clark@nd.edu. The dataset will be made publicly available after publication of the primary articles concerning the research questions that the data were collected to address. There is not yet a timeline for when this might be.

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