A Cross-Sectional Study Comparing Flourishing and Quality of Life as Indicators of Psychological Well-Being in Adults

Abstract

Flourishing and quality of life are related constructs often used to measure overall well-being, but it is unclear whether they capture the same underlying facets of well-being or provide complementary information. This study evaluated the relationship between a multidimensional flourishing measure (i.e., the Harvard Human Flourishing Program Measure; FM) and a commonly used quality of life instrument (i.e., the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form, Q-LES-SF). Analysis was performed on a sample of 302 adult participants recruited using the online survey platform Prolific. Participants completed the FM and Q-LES-SF, as well as the Patient Health Questionnaire-9 (PHQ-9; depression), Generalized Anxiety Disorder-7 (GAD-7), Alcohol Use Disorders Identification Test (AUDIT), and the PROMIS Smoking/Nicotine Dependence scale. Pearson correlations were used to assess associations between variables, Fisher’s z-tests compared correlation magnitudes, and linear regressions tested incremental validity. FM and Q-LES-SF scores were strongly positively correlated (r = 0.81, p < .001). Both FM and Q-LES-SF showed significant negative correlations with PHQ-9 (rs = −0.69, −0.74), GAD-7 (rs = −0.65, −0.66), and AUDIT (rs = −0.17, −0.18, respectively) scores (ps < .001), with no significant differences between correlation magnitudes. Adding FM as a predictor alongside QL significantly improved the prediction of PHQ-9 and GAD-7 scores but not AUDIT or cigarette dependence scores. These findings are a first step in understanding the convergent and incremental validity of FM and QL and if the separate research programs for each measure may be mutually informative.

Keywords

well-being flourishing quality of life psychometrics positive psychology

Introduction

Flourishing has recently gained prominence as a framework for assessing overall well-being. In 2021, Harvard University launched the Global Flourishing Study, a longitudinal investigation of 200,000 participants across 22 countries, building on the growing interest in understanding overall well-being on a global scale (Crabtree et al., 2021). This heightened interest aligns with the broader goals of positive psychology and public health to move beyond the mere absence of disease and better understand and promote well-being (Trudel-Fitzgerald et al., 2019). Flourishing is generally conceptualized as a state in which “all aspects of a person’s life are good” (VanderWeele & Johnson, 2025b). The two most well-known scales for measuring flourishing are the Flourishing Scale (Diener et al., 2010) and Human Flourishing Program’s Flourishing Measure (VanderWeele, 2017). The latter assesses well-being across six domains: happiness and life satisfaction, physical and mental health, meaning and purpose, character and virtue, social relationships, and financial stability.

A related concept for capturing well-being is quality of life. Quality of life evaluates life satisfaction across various domains, such as physical health, social relationships, and daily activities (Burckhardt & Anderson, 2003). Thus, although both quality of life and flourishing instruments intend to assess holistic well-being, they do so with different theoretical emphases: quality of life is often rooted in subjective life satisfaction within key domains (e.g., physical health, social connection) whereas flourishing extends beyond satisfaction to include purpose, virtue, and other elements of human thriving (VanderWeele & Johnson, 2025a).

Numerous instruments exist to measure quality of life, varying in length and focus. Common examples include the Quality of Life Scale (Flanagan, 1978), McGill Quality of Life Questionnaire – Expanded (Robin Cohen et al., 2019), Health-Related Quality of Life Questionnaire (HRQOL; CDC, 2022), World Health Organization Quality of Life Instrument (WHOQOL-BREF; WHO, 2024), Global Quality of Life Scale (Burckhardt & Anderson, 2003), and the Quality of Life Enjoyment and Satisfaction Questionnaire – Short Form or Q-LES-SF (Endicott et al., 1993; Stevanovic, 2011) used in the present study. These measures differ in their inclusion of domains like spirituality, their length, and complexity and their intended usage (e.g., general population surveys versus clinical trials) (Hyland, 2003).

To date, it remains unclear to what extent flourishing and quality of life reflect overlapping versus distinct constructs. Despite their different nomenclature and theoretical underpinnings, the empirical distinctions between them are not well established. There is a growing literature examining how flourishing and quality of life relate to mental health and health behaviors, but it has largely considered these constructs separately. Quality of life has well-documented associations with mental health: for example, low quality of life is linked to higher levels of anxiety and depression (Defar et al., 2023; Wilmer et al., 2021), increased likelihood of smoking initiation and reduced odds of smoking cessation (Goldenberg et al., 2014), and alcohol use disorder (Dayal & Kaloiya, 2024; Lu et al., 2022). Fewer studies have examined flourishing in relation to mental health and health behaviors, though some early evidence indicates that low flourishing is associated with mood and anxiety disorders (Schotanus-Dijkstra et al., 2017) and greater marijuana use (Rhew et al., 2021). Interestingly, there is also some evidence to suggest that flourishing improves with recovery from alcohol use disorder (Makin et al., 2022; McGaffin et al., 2015). However, findings have not been entirely consistent, as two studies found no association between flourishing and substance use (Low, 2011; Schotanus-Dijkstra et al., 2017). Additionally, one found a negative association between flourishing and marijuana use, but not alcohol consumption (Rhew et al., 2021).

In summary, flourishing and quality of life have known links to mental health and substance use, yet it is not established whether one of these well-being constructs is more strongly related to such outcomes and whether both offer unique underlying variance related to well-being. No prior research has directly compared a comprehensive flourishing and quality of life measure in the same sample. Gaining insight into these associations could clarify if flourishing and quality of life measures provide complementary information about well-being. Such knowledge may inform whether the separate research programs centered on each construct can be integrated or interpreted together.

The present study examines the convergent validity between a flourishing measure (i.e., the FM) and a quality of life measure (i.e., the Q-LES-SF) within a single sample. We examine their correlations and compare how each relates to depression, anxiety, alcohol use, and smoking. We also test whether each measure provides unique predictive value when included in combined regression models. These analyses aim to assess how FM and quality of life (as measured by the Q-LES-SF) function as distinct yet complementary indicators of well-being.

Methods

Prolific Sample and Data Collection

Prolific (Prolific, 2024) was used to recruit and collect study participant data. The final sample consisted of 302 adults after quality control exclusions (see below). Inclusion criteria required participants to be 18 years or older, reside in the U.S., and use a Prolific-accessible electronic device (e.g., laptop, desktop, or tablet). Participants were stratified so that age, sex, and ethnicity demographics mirrored the national population as determined by the U.S. Census Bureau. Demographic information is listed in Table 1.

Table 1.

Participant Characteristics

	N/mean (SD)	Percent/observed range	Scale range
Sex
Male	142	47.19%
Female	159	52.81%
Age
19-28	58	19.67%
29-38	56	18.36%
39-48	54	17.70%
49-58	56	18.36%
59-68	60	19.67%
69-78	18	6.23%
Hispanic ethnicity	18	5.94%
Race
White	232	76.57%
Black	40	13.20%
Asian	19	6.27%
Pacific Islander	1	0.33%
Mixed race	8	2.64%
Other	3	0.99%
Marital status
Married	124	40.92%
Divorced	48	15.84%
Separated	2	0.66%
Single	121	39.93%
Widowed	8	2.64%
Pretax household income
< $15,000	21	6.93%
$15,000-29,999	55	18.15%
$30,000-44,999	39	12.87%
$45,000-59,999	37	12.21%
$60,000-74,999	43	14.19%
$75,000-89,999	22	7.26%
$90,000-104,999	27	8.91%
$105,000-119,999	20	6.60%
> $120,000	39	12.87%
Employment status
Full time	133	43.89%
Part time	77	25.41%
Retired or disability	42	13.86%
Not employed	51	16.83%
Years of education	15.31 (2.59)	4 – 25
Key study variables
Flourishing	75.06 (22.71)	7 – 118	7 – 120
Quality of life	0.67 (0.19)	0.11 – 0.98	0 – 1.00
AUDIT*	5.96 (4.88)	2 – 25	0 – 40
PROMIS smoking dependence*	11.10 (4.76)	4 – 20	4 – 20
PROMIS E-cigarette dependence*	9.18 (4.49)	4 – 19	4 – 19
PHQ-9	5.75 (6.13)	0 – 27	0 – 27
GAD-7	5.22 (5.64)	0 – 21	0 – 21

Note. *n = subset of sample.

Participants completed multiple self-report assessments, including the Q-LES-SF (Endicott et al., 1993; Stevanovic, 2011), a 16-item measure of past-week life satisfaction and the FM (VanderWeele, 2017), a 12-item assessment of flourishing and well-being. The Q-LES-SF specifically asks participants to rate their satisfaction with physical health, economic stability, social relationships and ability to function over the past two weeks. FM includes questions related to six domains: meaning and purpose in life, happiness and life satisfaction, character and virtue, physical and mental health, close social relationships, and financial and material stability. Participants also completed the Alcohol Use Disorder Identification Test (AUDIT; (Higgins-Biddle & Babor, 2018), a 10-item screening tool for alcohol consumption and risky alcohol-associated behaviors; Patient Reported Outcomes Measurement Information System (PROMIS) E-cigarette: Nicotine Dependence for Daily and Nondaily Smokers – Short Form 4a and Smoking: Nicotine Dependence for Daily and Nondaily Smokers – Short Form 4a (Shadel et al., 2014), two 4-item assessments of E-cigarette and smoking dependence; Patient Health Questionnaire-9 (Kroenke et al., 2001), a 9-item screening measure for depression; and the Generalized Anxiety Disorder-7 (Spitzer et al., 2006), a 7-item screening measure for anxiety. Two attention check items were embedded in the Prolific battery instructing participants to choose a specific answer choice. Participants who answered either check incorrectly or did not complete at least 50% of the survey were excluded from analyses (n = 20). All assessments were completed using Inquisit software (Inquisit 5, 2016) and are listed in Supplemental File 2. The compensation rate was initially $6 for the first two pilot participants and then increased to $8 for all subsequent participants. This study was reviewed by the Uniformed Services University Human Research Protections Program and determined to be exempt from Institutional Review Board oversight. This manuscript was prepared with consideration of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (Cuschieri, 2019).

Data Analysis

Data were analyzed using R, version 4.4.1 (R Core Team, 2023). Cronbach’s alpha was calculated to determine internal consistency. Shapiro-Wilk tests were used to assess normality. Pearson’s correlation coefficients were calculated to assess bivariate correlations between FM, Q-LES-SF, and each health outcome (AUDIT, PROMIS, PHQ-9, and GAD-7). Fisher’s z-test was then used to determine whether there were significant differences between correlation coefficients. To determine whether FM improved model fit, linear regression analyses were first performed with Q-LES-SF and each dependent variable individually. FM was then added to each model. To assess model fit, we calculated the difference in Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values between models containing only Q-LES-SF and models containing both Q-LES-SF and FM as predictors. Additionally, ANOVA was utilized to compare explained variance between models. This process was repeated for each individual dependent variable. An alpha value of 0.05 was used for all analyses. Furthermore, all analyses used pairwise deletion, so that if the participant was missing data on either of the variables used in the analysis, they were dropped from that analysis, but included in all others where data was available. With 302 participants in the primary analytic sample, we were powered to detect correlations of 0.16 with 80% power and alpha of 0.05.

Results

Participant Characteristics

Demographic characteristics of the sample are presented in Table 1. Briefly, participants ranged in age from 19 to 78 years (mean age = 45.4, SD = 15.4), with a roughly even gender split (47% male, 53% female). While diverse, the sample overall tended to have lower levels of mental health symptoms or substance abuse. Although Shapiro-Wilk test indicated that multiple variables deviated from normality (Table S2), Spearman correlations were highly similar to Pearson correlations (Table S3). We therefore retained Pearson correlations in the primary analyses due to their interpretability and consistency with prior literature.

FM and Q-LES-SF

Both FM and Q-LES-SF demonstrated high internal consistency in this sample (Cronbach’s α = 0.91 for FM, 0.94 for Q-LES-SF). On average, participants reported moderately high levels of well-being: the mean FM score was 75.06 (SD = 22.71) on the 0–120 flourishing scale, and the mean Q-LES-SF score was 0.67 (SD = 0.19) when expressed as proportion of the maximum (67% satisfaction). There was a statistically significant strong positive correlation between FM and Q-LES-SF, (r = .81, p ≤ 2.2e-16). The scatterplot between FM and Q-LES-SF is shown in Figure 1.

Figure 1.

Flourishing and Quality of Life. There was a strong, positive correlation between FM and Q-LES-SF scores, r = .81, p ≤ 2.2e-16

AUDIT

AUDIT scores were only calculated for individuals who reported alcohol consumption (n = 211). Cronbach’s α for the AUDIT was 0.82. There was a negative correlation between FM and AUDIT scores (r = −0.17, p = 0.013; Figure 2(A)). Similarly, there was a negative correlation between Q-LES-SF and AUDIT (r = −0.18, p = 0.008; Figure 2(B)). A Fisher’s z-test determined these two correlation coefficients were not significantly different from each other (z = −0.12, p = 0.91).

Figure 2.

Flourishing and Quality of Life with Mental Health Measures. Dep. = dependence. (A) Flourishing and Problematic Drinking, r = −0.17, p = 0.013. (B) Quality of Life and Problematic Drinking, r = −0.18, p = 0.008. (C) Flourishing and Smoking Dependence, r = −.23, p = 0.15. (D) Quality of Life and Smoking Dependence, r = −0.41, p = 0.008. (E) Flourishing and E-Cigarette Dependence, r = −.11, p = 0.50. (F) Quality of Life and E-Cigarette Dependence, r = −0.34, p = 0.03. (G) Flourishing and Depression, r = −0.69, p ≤ 2.2e-16. (H) Quality of Life and Depression, r = −0.74, p ≤ 2.2e-16. (I) Flourishing and Anxiety r = −0.65, p ≤ 2.2e-16. (J) Quality of Life and Anxiety, r = −0.66, p ≤ 2.2e-16. Overall, higher flourishing and quality of life as measured by the FM and Q-LES-SF, respectively, were similarly associated with lower depression, anxiety, and substance dependence

PROMIS Nicotine Dependence

There was a significant negative correlation with ever smoking and FM (r = −0.13, p = 0.027), but not Q-LES-SF (r = −.10, p = 0.09). Among participants who reported smoking cigarettes in the last 30 days (n = 43), there was a significant negative correlation between smoking dependence and Q-LES-SF (r = −0.41, p = 0.008; Figure 2(D)), but not FM (r = −.23, p = 0.15; Figure 2(C)). There was no significant correlation between lifetime E-cigarette use and QL or FM (ps > .05). However, among participants who reported E-cigarette use in the last 30 days (n = 40), there was a significant negative correlation between E-cigarette nicotine dependence and Q-LES-SF (r = −0.34, p = 0.03; Figure 2(F)), but not FM (r = −.11, p = 0.50; Figure 2(E)).

PHQ-9

Cronbach’s α for the PHQ-9 was 0.91. There was a negative correlation between FM and PHQ-9 scores (r = −0.69, p ≤ 2.2e-16; Figure 2(G)). Similarly, there was a negative correlation between Q-LES-SF and PHQ-9 (r = −0.74, p ≤ 2.2e-16; Figure 2(H)). A Fisher’s z-test determined these two correlation coefficients were not significantly different from each other (z = −1.06, p = 0.29).

GAD-7

Cronbach’s α for the GAD-7 was 0.94. There was a negative correlation between FM and GAD-7 scores (r = −0.65, p ≤ 2.2e-16; Figure 2I). Similarly, there was a negative correlation between Q-LES-SF and GAD-7 (r = −0.66, p ≤ 2.2e-16; Figure 2(J)). A Fisher’s z-test determined these two correlation coefficients were not significantly different from each other (z = −0.27, p = 0.78).

Model Fit Analyses

Model comparison statistics indicated that adding flourishing (FM) to quality of life (Q-LES-SF) improved model fit for depression and anxiety outcomes, as well as E-cigarette dependence. Specifically, AIC and BIC values were lower for models including both FM and Q-LES-SF when predicting PHQ-9, GAD-7, and E-cigarette dependence (PROMIS) scores, suggesting that FM provides incremental predictive value beyond Q-LES-SF. Addition of FM to a linear model raised the adjusted R² of GAD-7 from 0.43 to 0.47 and of PHQ-9 from 0.54 to 0.57. In contrast, AIC and BIC values did not improve for alcohol use (AUDIT) and smoking dependence (PROMIS), indicating no additional benefit of including FM. ANOVA model comparisons confirmed these patterns, with FM significantly improving the prediction of PHQ-9, GAD-7, and E-cigarette dependence (p = 1.85 × 10⁻⁵, 6.72 × 10⁻⁶, and 0.04, respectively), but not AUDIT (p = 0.51) or smoking dependence (p = 0.26). Full model fit statistics are reported in Table 2; full regression coefficients are available in Table S1. To ensure robustness, model fit analyses were re-run with the addition of covariates sex, age, and income (Table S4). These analyses revealed an identical pattern, where FM provided statistically significant incremental predictive value for PHQ-9, GAD-7, and E-cigarette dependence, but not for AUDIT or cigarette smoking dependence.

Table 2.

Model Fit Statistics

	AIC	BIC	Adjusted R²	p-value
GAD-7
Model 1	680.1526	691.2639	0.43	6.72e-06
Model 2	661.6408	676.4559	0.47	6.72e-06
PHQ-9
Model 1	623.2842	634.3955	0.54	1.85e-05
Model 2	606.7323	621.5474	0.57	1.85e-05
AUDIT
Model 1	596.9977	607.0532	0.027	0.51
Model 2	598.5623	611.9698	0.024	0.51
PROMIS smoking dependence
Model 1	236.9043	242.0450	0.15	0.26
Model 2	237.4941	244.3484	0.15	0.26
PROMIS E-cigarette dependence
Model 1	238.3006	243.3672	0.10	0.04
Model 2	235.7192	242.4747	0.12	0.04

Note. Model 1: Quality of life; Model 2: Quality of life and Flourishing; p-value for difference in explained variance.

Discussion

The present study aimed to assess the convergent and incremental validity of two known measures of well-being, flourishing and quality of life. This study showed high levels of similarity but non-redundancy among the FM and Q-LES-SF. Specifically, FM and Q-LES-SF were very strongly correlated (r = .81) and similarly negatively associated with all measures of alcohol use and mental health, with mixed results relating to ever smoking, smoking, and E-cigarette dependence. Additionally, adding FM led to a modest but statistically significant improvement in explaining variance in depression, anxiety, and E-cigarette dependence (though not for alcohol use or smoking). This suggests that although FM and QL are closely related indicators of well-being, FM may encompass additional dimensions that are particularly relevant for mental health outcomes. Interestingly, FM was positively associated with E-cigarette dependence when controlling for QL despite negative univariate association, indicating a potential suppression effect. The residual variance unique to FM may not be negatively associated, or even weakly associated, with E-cigarette use. Additionally, the increase in R² for predicting E-cigarette was particularly modest and may be simply due to measurement error.

This study is the first to our knowledge to directly compare the Harvard flourishing scale with a multi-item quality of life scale. A recent study found significant positive correlation (r = 0.75) between FM and a unidimensional measurement of quality of life (Shiba et al., 2022). Our finding of an ∼0.8 correlation between FM and Q-LES-SF is consistent with those observations, reiterating the notion that flourishing shares a large common core with general life satisfaction or quality of life measures. In our data, individuals with elevated anxiety and depression symptoms or higher substance use tended to report both lower flourishing and lower quality of life in tandem, which is not surprising given that both constructs encompass content related to mood, functioning, and life satisfaction. At the same time, the fact that FM accounted for modest additional variance in depression and anxiety suggests that it taps into aspects of well-being not fully measured by Q-LES-SF. One interpretation is that FM includes items that place greater emphasis on meaning,purpose and character than Q-LES-SF. For instance, someone may be relatively satisfied with their life circumstances yet still feel a lack of deeper meaning or self-actualization. The Q-LES-SF is likely also more context and situation dependent, as it specifically asks individuals to consider aspects of their life satisfaction within the last week. Finally, the inclusion of subscales of FM that focus on higher-level introspection (such as meaning in life, character, and virtue) may be a significant distinguisher for predicting mental health in a sample such as ours that was relatively healthy, educated and had high levels of employment. Because FM also includes these questions in addition to those more associated with day-to-day well-being, it may have wider applicability in psychological research that specifically focuses on mental health.

There are multiple limitations specific to this study. While this sample was stratified to mirror age, sex, and ethnic demographic representation of U.S. Census Bureau data, this sample was predominantly healthy, which skewed data towards healthier scores on all variables. Thus, it is unclear how these results would generalize to clinical populations with lower Q-LES-SF or FM scores and greater endorsement of mental health and substance use problems. Participants were recruited through the online platform Prolific, and prior studies have found some discrepancies between users of online surveys and the general population, including demographic differences and lower subjective well-being (Stone et al., 2019). Although a recent study found that Prolific users provided relatively high-quality data compared to other crowdsourced respondents (Douglas et al., 2023), the usage of this type of sample may limit generalizability. Additionally, the cross-sectional nature of this study limits its ability to make conclusions on causality or examine the trajectory of these associations over time, and the statistical methods used primarily address convergent and discriminant validity. Thus, we are unable to comment specifically on distinguishing between construct overlap, which would require analyses of latent variables. Some analyses, particularly those involving cigarette and E-cigarette dependence, were performed on a significantly smaller subset of participants, limiting power. Finally, our focus was on one particular flourishing scale and one particular quality of life scale—results may differ with other instruments. Quality of life can be measured in more domain-specific ways (e.g., health-related quality of life in medical studies) and flourishing can also be assessed with different scales (such as Diener’s Flourishing Scale (Diener et al., 2010)). The general pattern of strong overlap might hold broadly, but the exact degree of complementarity could vary with the tools and contexts.

Conclusion

This study provides early evidence indicating that flourishing and quality of life are highly correlated constructs and account for similar levels of variance related to well-being. However, because of FM’s propensity to improve model fit when predicting GAD-7, PHQ-9, and some substance-related scores, it likely captures broader aspects of well-being than a QL measure such as Q-LES-SF alone. This suggests that FM may be an improvement in the measurement of wellbeing or be able to provide insight on functioning across multiple domains. Future studies with clinical and non-US populations, additional measures, and longitudinal analyses are needed to provide further quantitative evidence for the distinctiveness of FM from QL and other measures of well-being.

Supplemental Material

Supplemental material - A Cross-Sectional Study Comparing Flourishing and Quality of Life as Indicators of Psychological Well-Being in Adults

Supplemental material for A Cross-Sectional Study Comparing Flourishing and Quality of Life as Indicators of Psychological Well-Being in Adults by Michael Setzer, Samantha G. Malone, Ravi A. Iyer, Emma L. Winterlind, and Joshua C. Gray in Psychological Reports

Supplemental Material

Supplemental material - A Cross-Sectional Study Comparing Flourishing and Quality of Life as Indicators of Psychological Well-Being in Adults

Footnotes

Author note

The authors agree to this author order

ORCID iD

Michael R. Setzer

Ethical Considerations

This study was deemed exempt from the Uniformed Services University IRB, and all individuals consented to participate in the survey.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Support provided by National Institute on Alcohol Abuse and Alcoholism (R01-AA030041) and the Department of Defense (HU0001-22-2-0066).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data and materials used for this study are available upon reasonable request of the corresponding author.

Disclaimer

The opinions and assertions herein are those of the authors and do not necessarily reflect the official views of the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. Moreover, the opinions and assertions herein do not necessarily reflect the official views of the Department of Defense, Uniformed Services University, the National Institute on Alcohol Abuse and Alcoholism, the US Government, and do not imply endorsement by the Federal Government.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Michael R. Setzer: Michael R. Setzer is a doctoral candidate in Medical and Clinical Psychology at the Uniformed Services University.

Samantha G. Malone: Samantha Malone holds a PhD in Experimental Psychology from the University of Kentucky and has an extensive background in neuroscience, biostatistics, and NIH-funded research. She completed her postdoctoral training at the Uniformed Services University under the mentorship of Dr. Joshua Gray and currently serves as a Medical Writer at Precision AQ, where she develops evidence-based medical communications

Ravi A. Iyer: Ravi Iyer is a sophomore at Cornell University's Charles H. Dyson School of Applied Economics and Management, concentrating in finance. He currently works in private equity at Small Enterprise Assistance Funds (SEAF).

Emma L. Winterlind: Emma Winterlind is a third-year doctoral student in the Clinical Psychology program at the University of Wyoming. Her research primarily focuses on cognitive mechanisms surrounding the initiation and maintenance of substance use behaviors among young adults.

Joshua C. Gray: Joshua C. Gray, PhD, is an Associate Professor in the Department of Medical and Clinical Psychology at the Uniformed Services University of the Health Sciences. His research focuses on addiction, psychiatric genetics, health services, and the development of novel treatments for substance use disorders.

References

Burckhardt

C. S.

Anderson

K. L.

(2003). The quality of life scale (QOLS): Reliability, validity, and utilization. Health and Quality of Life Outcomes, 1(1), 60. https://doi.org/10.1186/1477-7525-1-60

Crabtree

English

Johnson

Ritter

VanderWeele

(2021). Global flourishing study: Questionnaire development report: Gallup Inc.

Cuschieri

(2019). The STROBE guidelines. Saudi Journal of Anaesthesia, 13(5), S31–S34. https://doi.org/10.4103/sja.SJA_543_18

Dayal

Kaloiya

G. S.

(2024). Quality of life in alcohol use disorder: Exploration of predictive factors in a cross-sectional study. Psychiatry International, 5(1), 101–120. https://doi.org/10.3390/psychiatryint5010008

Defar

Abraham

Reta

Deribe

Jisso

Yeheyis

Kebede

K. M.

Beyene

Ayalew

(2023). Health related quality of life among people with mental illness: The role of socio-clinical characteristics and level of functional disability. Frontiers in Public Health, 11(1), 1134032. https://doi.org/10.3389/fpubh.2023.1134032

Diener

Wirtz

Tov

Kim-Prieto

Choi

Oishi

Biswas-Diener

(2010). New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research, 97(2), 143–156. https://doi.org/10.1007/s11205-009-9493-y

Douglas

B. D.

Ewell

P. J.

Brauer

(2023). Data quality in online human-subjects research: Comparisons between MTurk, prolific, CloudResearch, qualtrics, and SONA. PLoS One, 18(3 March), e0279720. https://doi.org/10.1371/journal.pone.0279720

Endicott

Nee

Harrison

Blumenthal

(1993). Quality of life enjoyment and satisfaction questionnaire: A new measure. Psychopharmacology Bulletin, 29(2), 321–326.

Flanagan

J. C.

(1978). A research approach to improving our quality of life. American Psychologist, 33(2), 138–147. https://doi.org/10.1037/0003-066X.33.2.138

10.

Goldenberg

Danovitch

IsHak

W. W.

(2014). Quality of life and smoking: Quality of life and smoking. American Journal on Addictions, 23(6), 540–562. https://doi.org/10.1111/j.1521-0391.2014.12148.x

11.

Higgins-Biddle

J. C.

Babor

T. F.

(2018). A review of the alcohol use disorders identification test (AUDIT), AUDIT-C, and USAUDIT for screening in the United States: Past issues and future directions. American Journal of Drug and Alcohol Abuse, 44(Number 6), 578–586. https://doi.org/10.1080/00952990.2018.1456545

12.

Hyland

M. E.

(2003). A brief guide to the selection of quality of life instrument. Health and Quality of Life Outcomes, 1(1), 24. https://doi.org/10.1186/1477-7525-1-24

13.

Inquisit 5 . (2016). Inquisit software. https://www.millisecond.com

14.

Kroenke

Spitzer

R. L.

Williams

J. B. W.

(2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x

15.

Low

K. G.

(2011). Flourishing, substance use, and engagement in students entering college: A preliminary study. Journal of American College Health, 59(6), 555–561. https://doi.org/10.1080/07448481.2011.563432

16.

Yang

Cui

(2022). Alcohol use disorder and its association with quality of life and mortality in Chinese male adults: A population-based cohort study. BMC Public Health, 22(1), 789. https://doi.org/10.1186/s12889-022-13146-4

17.

Makin

Allen

Carson

Bush

Merrifield

(2022). Light at the end of the bottle: Flourishing in people recovering from alcohol problems. Journal of Substance Use, 27(1), 107–114. https://doi.org/10.1080/14659891.2021.1905092

18.

McGaffin

B. J.

Deane

F. P.

Kelly

P. J.

Ciarrochi

(2015). Flourishing, languishing and moderate mental health: Prevalence and change in mental health during recovery from drug and alcohol problems. Addiction Research and Theory, 23(5), 351–360. https://doi.org/10.3109/16066359.2015.1019346

19.

Prolific . (2024). Prolific. https://www.prolific.com

20.

R Core Team . (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing.

21.

Rhew

I. C.

Cadigan

J. M.

Lee

C. M.

(2021). Marijuana, but not alcohol, use frequency associated with greater loneliness, psychological distress, and less flourishing among young adults. Drug and Alcohol Dependence, 218(1), Article 108404. https://doi.org/10.1016/j.drugalcdep.2020.108404

22.

Robin Cohen

Russell

L. B.

Leis

Shahidi

Porterfield

Kuhl

D. R.

Gadermann

A. M.

Sawatzky

(2019). More comprehensively measuring quality of life in life-threatening illness: The McGill quality of life questionnaire - Expanded. BMC Palliative Care, 18(1), 92. https://doi.org/10.1186/s12904-019-0473-y

23.

Schotanus-Dijkstra

Ten Have

Lamers

S. M. A.

de Graaf

Bohlmeijer

E. T.

(2017). The longitudinal relationship between flourishing mental health and incident mood, anxiety and substance use disorders. The European Journal of Public Health, 27(3), 563–568. https://doi.org/10.1093/eurpub/ckw202

24.

Shadel

W. G.

Edelen

M. O.

Tucker

J. S.

Stucky

B. D.

Hansen

Cai

(2014). Development of the PROMIS® nicotine dependence item banks. Nicotine & Tobacco Research, 16(Suppl_3), S190–S201. https://doi.org/10.1093/ntr/ntu032

25.

Shiba

Cowden

R. G.

Gonzalez

Lee

M. T.

Lomas

Lai

A. Y.

VanderWeele

T. J.

(2022). Global trends of mean and inequality in multidimensional wellbeing: Analysis of 1.2 million individuals from 162 Countries, 2009–2019. Frontiers in Public Health, 10(1), 824960. https://doi.org/10.3389/fpubh.2022.824960

26.

Spitzer

R. L.

Kroenke

Williams

J. B. W.

Löwe

(2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. https://doi.org/10.1001/archinte.166.10.1092

27.

Stevanovic

(2011). Quality of life enjoyment and satisfaction questionnaire - Short form for quality of life assessments in clinical practice: A psychometric study. Journal of Psychiatric and Mental Health Nursing, 18(8), 744–750. https://doi.org/10.1111/j.1365-2850.2011.01735.x

28.

Stone

A. A.

Walentynowicz

Schneider

Junghaenel

D. U.

Wen

C. K.

(2019). MTurk participants have substantially lower evaluative subjective well-being than other survey participants. Computers in Human Behavior, 94(1), 1–8. https://doi.org/10.1016/j.chb.2018.12.042

29.

Trudel-Fitzgerald

Millstein

R. A.

Von Hippel

Howe

C. J.

Tomasso

L. P.

Wagner

G. R.

Vanderweele

T. J.

(2019). Psychological well-being as part of the public health debate? Insight into dimensions, interventions, and policy. BMC Public Health, 19(1), 1712. https://doi.org/10.1186/s12889-019-8029-x

30.

VanderWeele

T. J.

(2017). On the promotion of human flourishing. Proceedings of the National Academy of Sciences, 114(31), 8148–8156. https://doi.org/10.1073/pnas.1702996114

31.

VanderWeele

T. J.

Johnson

B. R.

(2025a). Multidimensional versus unidimensional approaches to well-being. Nature Human Behaviour, 9(5), 857–863. https://doi.org/10.1038/s41562-025-02187-5

32.

VanderWeele

T. J.

Johnson

B. R.

(2025b). Why we need to measure people’s well-being - Lessons from a global survey. Nature, 641(8061), 34. https://doi.org/10.1038/d41586-025-01254-1

33.

Wilmer

M. T.

Anderson

Reynolds

(2021). Correlates of quality of life in anxiety disorders: Review of recent research. Current Psychiatry Reports, 23(11), 77. https://doi.org/10.1007/s11920-021-01290-4

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