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Hedonic capacity in a Chinese community population sample: psychometric properties of the dimensional anhedonia rating scale (DARS) and exploration of gender differences

BMC Psychology volume 13, Article number: 410 (2025) Cite this article

Abstract

Background

A reduction in the hedonic capacity, that is, anhedonia, is closely associated with the onset of depression, which negatively affects mental health and life satisfaction. However, simple and comprehensive tools to assess hedonic capacity in community-dwelling adults are lacking.

Methods

An online survey was conducted from March to April 2023, recruiting 678 community-dwelling adults. We assessed the psychometric properties of the Chinese version of the Dimensional Anhedonia Rating Scale (DARS) and used multivariate linear regression to examine gender differences in hedonic capacity and its contributing factors.

Results

The Chinese version of the DARS demonstrated excellent reliability and validity in the non-clinical sample. Higher education levels and being female were associated with better hedonic capacity, while sleep disturbances and living alone were linked to lower hedonic capacity. Additionally, gender differences were found in the factors affecting hedonic capacity: for males, living alone and sleep disturbances had a negative impact, whereas for females, higher education levels were positively associated with hedonic capacity.

Limitations

There was a lack of structured diagnoses among community samples.

Conclusion

The Chinese version of the DARS is an effective tool for assessing hedonic capacity, i.e. screening for depression in community-dwelling populations. The observed gender differences in hedonic capacity imply that future mental health services could be more effective if adapted based on gender.

Peer Review reports

Introduction

The healthy hedonic capacity significantly influences individuals’ experience of happiness and mental well-being [1].It supports people in dealing with stress, overcoming life’s challenges, and fostering more harmonious social relationships. Impaired hedonic capacity might affect a person’s ability to experience or appreciate life’s pleasures [2].Impaired hedonic capacity, a hallmark of anhedonia, is also considered a common symptom across many psychiatric disorders [3,4,5,6,7,8,9]. Anhedonia signifies not only a reduced capacity to experience pleasure but also encompasses impairments in areas such as reward anticipation, motivation, reward learning, and decision-making. Psychiatric diseases characterized by anhedonia typically exhibit a more extensive impairment of hedonic capacity [10].Additionally, it indicates increased severity of the illness [11–12],an increased likelihood of suicide [13–14],a poorer response to treatment [15],and a more extensive psychosocial damage [16–17].

Hedonic capacity varies across individuals, those with reduced hedonic capacity may be more susceptible to depression [2, 18]. Clinical researches have indicated that reduced hedonic capacity may serve as a predictor of psychosis or Major depression disorder (MDD) and is linked to adverse social behaviors [19,20,21]. Nevertheless, the proportion of individuals in China who actively seek mental health care remains low [22],leading to missed opportunities for professional screening among the general population. Therefore, an anhedonia rating scale that integrates neuropsychological, behavioral, and self-report components would be an ideal assessment tool with broad applicability [23].

The healthy hedonic capacity is modulated by the complicated reward processing, which can be categorized into following components: desire/interest (the inclination to be rewarded), anticipated rewards (the anticipation or pleasure derived from them), effort (the actual calculation of the reward and the exertion put in) and consummatory pleasure (enjoy) [10, 24–25]. Dysfunction of any part of the process may lead to anhedonia [2]. The early anhedonia rating scales were primarily self-assessment instruments that measured an individual’s level of consummatory pleasure experienced in recent or current days. Among them, the Snaith-Hamilton Pleasure Scale (SHAPS) has been widely validated in clinical and non-clinical individuals because of its wide applicability and limited cultural bias, and it is generally considered one of the most reliable methods for assessing anhedonia in patients with MDD [18, 25,26,27].

Moreover, the Dimensional Anhedonia Rating Scale (DARS) is a useful supplement to the assessment components of hedonic capacity. It was developed to measure not only consummatory pleasure but also the three other specific components of interest/ desire, anticipated rewards, and effort [10].The DARS has been primarily utilized in the evaluation of MDD in China [28].Current evidence suggests that hedonic capacity varies across individuals, with differences forming a spectrum that extends from healthy individuals to those with subthreshold depression and MDD. The DARS has been validated as a reliable instrument for detecting subtle changes in an individual’s ability to experience pleasure in non-clinical adolescent sample [29]. Its good design has led to its widespread use across diverse populations in multiple countries [2, 10, 28,29,30]. However, the psychometric properties of the Chinese version of the DARS has not been examined in non-clinical adults’ sample. In addition, DARS may be more appropriate than other self-report depression scales for screening adults at high risk of depression in the community sample because of its reduced stigma.

To address these gaps in the field, this study sought to evaluate the psychometric properties of the DARS in community-dwelling adults. Notably, studies suggest that there are emotional differences between men and women [31], but results on gender differences in hedonic capacity are mixed [32,33,34]. Therefore, the second aim of this study was to explore whether gender influences individual hedonic capacity and to identify the factors underlying gender differences in hedonic capacity.

Methods

Participants

This study aimed to assess pleasure measured by the DARS scale and explore factors influencing hedonic capacity in a sample of community population Chinese adults. An online survey was conducted between March and April 2023 via WeChat, including a self-designed questionnaire and psychological status scales. Participants who gave informed consent and completed the survey received a small voucher. Eligibility criteria included being at least 18 years old, literate, with no history of neurological or psychiatric disorders, and a native Chinese speaker [2, 18, 35].The study was approved by the Ethics Committee of Peking University Sixth Hospital.

Sample size calculating

The sample size estimation for this study was based on the number of scale items and psychometric analysis techniques. Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were employed, with sample size requirements considered for each approach. According to Thompson [36],CFA requires a sample size that is 10–20 times the number of observed variables. Additionally, Comrey [37] and Cattell [38] recommended that EFA requires a minimum sample size of over 500 cases. Given that the DARS scale contains 17 items, we applied a factor of 20 to the number of observed items for calculation, resulting in a CFA sample size of 17 × 20 = 340. Therefore, a roughly sample size of 500 was deemed sufficient for the psychometric analysis in this study.

Instruments and procedures

The hedonic capacity of the community-dwelling adult sample in this study was assessed using the Chinese version of the DARS [28, 39].The DARS is a 17-item self-report instrument that evaluates four types of rewards (pastimes/hobbies, foods/drinks, social activities, and sensory experiences) and four components of reward processing (desire/interest, anticipated reward, effort and consummatory pleasure) [10].Participants identified 2–3 personalized reward experiences for each reward type (e.g., listing at least two favorite foods/drinks). They then responded to standardized questions based on their experiences to assess their current desire, effort, and level of consummatory pleasure (e.g., “I want these foods/drinks”). Each question was rated on a 5-point Likert scale ranging from “not at all” (0) to “very much” (4). Total scores ranged from 0 to 68, with higher scores indicating greater hedonic capacity, i.e., lower levels of anhedonia. The scale has been validated among both depressed and healthy individuals in Canada and Germany [2, 10],and has demonstrated excellent reliability and validity among depressed individuals in China (Cronbach’s α = 0.95 ~ 0.97) [28, 39].

The Chinese version of the SHAPS was also used to measure hedonic capacity as a gold standard in this study. The SHAPS is a 14-item self-report instrument that evaluates hedonic experiences across four domains: interest/pastimes, social interaction, sensory experiences, and food/drink. Each item is rated on a 4-point Likert scale (1 = Definitely Agree, 2 = Agree, 3 = Disagree, 4 = Definitely Disagree).Total scores range from 14 to 56, with higher scores indicating lower hedonic capacity, or greater anhedonia [40].The Chinese version of the SHAPS has demonstrated good internal consistency in both clinical and non-clinical samples, with Cronbach’s α values of 0.91 and 0.90, respectively [27, 40].

The Patient Health Questionnaire-9 (PHQ-9) was used to assess depressive symptoms [41].The PHQ-9 is a self-report depression scale included in the Primary Care Evaluation of Mental Disorders (PRIME-MD) diagnostic tool [42].This scale evaluates the severity and frequency of depressive symptoms experienced over the past two weeks, with each item rated from 0 (“not at all”) to 3 (“nearly every day”).Total scores range from 0 to 27, with higher scores indicating more severe depressive symptoms [41].The Chinese version of the PHQ-9 has been extensively used in clinical samples, as well as in community and healthy populations, to assess depressive symptoms, demonstrating strong reliability and validity [43,44,45].

The Generalized Anxiety Disorder-7 (GAD-7) scale was used in this study to assess the severity and frequency of anxiety symptoms experienced by participants over the past two weeks [46].According to the DSM-IV-TR [47], the GAD-7 captures the most essential diagnostic criteria for generalized anxiety disorder. Each item on the GAD-7 is rated on a 4-point scale: 0 (“not at all”), 1 (“several days”), 2 (“more than half the days”), and 3 (“nearly every day”).Total scores range from 0 to 21, with higher scores indicating more severe anxiety symptoms [46].The GAD-7 has been extensively validated in primary care settings and large samples of the general population [48,49,50].

The self-designed questionnaire included demographic characteristics, work pressure, dwelling state and sleep disturbance (The ‘sleep disturbance’ item in the questionnaire was assessed through the following question: ‘Do you experience difficulty falling asleep on a daily basis? ), etc.

Statistical analyses

Validity and reliability

The structure of the Chinese version of the DARS was evaluated using EFA with PROMAX rotation, following the methodology used in previous studies on the Spanish version of the DARS [30].Factor loadings with an absolute value of 0.5 or higher were considered to have a significant impact on the overall explained variance [51].The suitability of the data for factor analysis was confirmed by the Kaiser-Meyer-Olkin (KMO) measure.

The factor structure of the DARS was evaluated using CFA with robust maximum likelihood estimation. The criteria for a robust model included indices such as the followings: a chi-square/degrees of freedom ratio (≤ 3 indicates a strong fit); and fit indices such as the goodness-of-fit index (GFI) [52], comparative fit index (CFI) [53],Tucker–Lewis index (TLI) [54],and normed fit index (NFI) [55].Values for these indices approaching 1 indicate a strong model fit (> 0.90, acceptable; > 0.95, good; > 0.97, very good) [56].Additionally, the root-mean-square error of approximation (RMSEA) within the range of 0 to 0.08 signifies a good fit [56].

Internal reliability was assessed using Cronbach’s alpha, with values above 0.8 indicating strong reliability [57–58]. Construct reliability (CR) was calculated to assess the consistency of the variables in representing the latent constructs. Additionally, the Omega coefficient (ω) was calculated to further evaluate reliability, particularly to address the potential underestimation of true reliability by Cronbach’s alpha [59]. Convergent validity between the total DARS score, its subscales, and the SHAPS score was examined using Pearson correlation analysis. Average variance extracted (AVE) were also calculated to assess discriminant validity.

Factors influencing hedonic capacity and gender differences

Spearman correlation analysis was initially conducted to identify demographic variables that showed a significant association with the total DARS score; these were selected as pre-identified variables. To examine the impact of demographic characteristics on hedonic capacity across different gender groups, multiple linear regression analyses were performed using the total DARS score as the dependent variable, with variables entered using the enter method. This approach was used to identify characteristics that were independently associated with hedonic capacity. Variables with a P-value < 0.05 were considered significantly related to hedonic capacity.

All analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 23.0 and AMOS version 26.0 (SPSS Inc., Chicago, IL, USA). Descriptive analyses for categorical variables were presented as rates (percentages). To enhance the presentation of the data, all quantitative variables were reported as mean ± standard deviation, and either a t-test or a nonparametric test was applied based on the characteristics of the data. Unless otherwise specified, a significance level of P < 0.05 was used for all statistical analyses.

Results

Demographic and the distribution of clinical characteristics

A total of 760 completed questionnaires were collected. Participants were instructed to answer the questions as accurately and truthfully as possible. However, 82 questionnaires were excluded due to the “landmine question.” (Description of the landmine question: Participants were asked to recall the year they started primary school, and responses that were clearly illogical were filtered out.) As a result, 678 questionnaires were deemed suitable for statistical analysis.

The community sample included participants aged 18 to 83 years, with the majority being female (361 women, representing 53.2% of the total sample). Among the participants, 274 (40.4%) had completed college or higher education, 445 (65.6%) were married, and 188 (27.7%) reported sleep disturbances. Gender differences were observed in hedonic capacity (women are better), as indicated by the DARS scale and its subscales. However, no significant differences were found between males and females in terms of general demographic factors such as age, education level, or symptoms of anxiety and depression (Table 1).

Table 1 Demographics and clinical characteristics of all subjects (n = 678)
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Validity and reliability

In our sample, the KMO value was 0.962, and Bartlett’s test of sphericity yielded a P-value of less than 0.001. Factor analysis revealed that the communalities for the 17 items of the Chinese version of the DARS ranged from 0.698 to 0.976 (Table S1). Moreover, the four-factor structure identified closely matched that of the original revision [10] (Fig. 1).The fit indices indicated a satisfactory to excellent fit for the four-factor model (GFI = 0.929, CFI = 0.979, NFI = 0.972, TLI = 0.975, RMSEA = 0.061, χ2/df = 3.544).As expected, all items loaded well above the 0.80 threshold, supporting the validity of the four-factor model. The total variance explained by the four principal components was 86.1% (Table S1).

Fig. 1
figure 1

The confirmatory factor analysis of the Chinese version of the DARS. A/B/C/D represents the four subscales of DARS. (A, PH, Pastimes/Hobbies; B, FD, Food/Drinks; C, SA, Social activities; D, SE, Sensory experiences). All items loaded well above the 0.8 threshold. The fit indices indicated that the four-factor model demonstrated a satisfactory to good fit (GFI = 0.929, CFI = 0.979, NFI = 0.972, TLI = 0.975, RMSEA = 0.061, χ2/df = 3.544). All the DARS components are given as statements relating to their respective domains. The specific explanation is as follows: A1, “I would enjoy these activities”; A2, “I would spend time doing these activities”; A3, “I want to do these activities”; A4, “These activities would interest me”; B1, “I would make an effort to get/make these foods/drinks”; B2, “I would enjoy these foods/drinks”; B3, “I want to have these foods/drinks”; B4, “I would eat as much of these foods as I could”; C1, “Spending time doing these things would make me happy”; C2, “I would be interested in doing things that involve other people”; C3, “I would be the one to plan these activities”; C4, “I would actively participate in these social activities”; D1, “I would actively seek out these experiences”; D2, “I get excited thinking about these experiences”; D3, “If I were to have these experiences I would savor every moment”; D4, “I want to have these experiences”; D5, “I would make an effort to spend time having these experiences”

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The correlation heatmaps provided empirical support for the relationships between each subscale of the DARS and its constituent items, as well as their strong correlations with the overall SHAPS score. Specifically, the correlation between the total DARS score and SHAPS is -0.69, while the correlations between the four DARS subscales and SHAPS range from − 0.53 to -0.68(Fig. 2).The AVE scores for all constructs ranged from 0.75 to 0.87, exceeding the 0.50 threshold [60].Standardized factor loadings for all items ranged from 0.82 to 0.94, surpassing the 0.7 criterion [61].Construct reliability ranged from 0.92 to 0.96, exceeding the required minimum of 0.70 [60] (Table 2).These results indicate that the DARS demonstrates good convergent validity and CR (Table 2).The DARS also showed strong discriminant validity, as evidenced by the fact that the average variance extracted for each domain in our sample was greater than the corresponding inter-item correlation coefficient (i.e., the diagonal value exceeded the off-diagonal values) [60] (Table 3).

Fig. 2
figure 2

Correlation matrix heatmap of 17 items of DARS, four subscales and SHAPS total score. A/B/C/D represents the four subscales of DARS. (A, Pastimes/Hobbies; B, Food/Drinks; C, Social activities; D, Sensory experiences); Red indicates a negative correlation, blue indicates a positive correlation, and darker shades represent stronger correlations

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Table 2 Convergent validity of Chinese version of the DARS
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Table 3 Discriminant validity of Chinese version of the DARS
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The internal consistency of the total score on the DARS was strong (Table 4), with Cronbach’s alpha and Omega ω coefficients both reaching 0.971, well above the 0.80 threshold [57–58]. The Cronbach’s alpha and Omega ω coefficients for the four subscales, categorized by reward type, were all greater than 0.92. Additionally, analysis of the changes in the scale coefficient after item deletion revealed that removing any item did not lead to an increase in the alpha coefficient of the scale (Table 4).

Table 4 Internal reliability of Chinese version of the DARS
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Factors that influence hedonic capacity in different genders

In the full sample, Spearman correlation analysis revealed significant associations between hedonic capacity (DARS total score) and several sample characteristics, including gender, work stress, sleep disturbances, education level, living situation, and symptoms of anxiety and depression (Tables S2-S3). A subsequent multiple linear regression analysis showed that hedonic capacity was significantly associated with gender, sleep disturbances, education level, and living situation. Specifically, females exhibited a 2.796-unit increase in hedonic capacity compared to males (β = 0.087, 95% CI [0.407, 5.186]).Individuals with sleep disturbances had a 3.356-unit decrease in hedonic capacity compared to those with normal sleep patterns (β = -0.093, 95% CI [-6.288, -0.425]).Those with at least a bachelor’s degree experienced a 4.196-unit increase in hedonic capacity compared to individuals with less education (β = 0.128, 95% CI [1.755, 6.636]).Furthermore, people living alone had a 5.828-unit decrease in hedonic capacity compared to those living with others (β = -0.122, 95% CI [-9.345, -2.312]) (Table S4, Fig. 3A).

Fig. 3
figure 3

Analysis of the elements that influence hedonic state in different populations. (A) Factors that influence the hedonic functional state in all observed samples; (B) Factors that influence the hedonic functional state in all observed female samples; (C) Factors that influence the hedonic functional state in all observed male samples. *P<0.05; **P<0.01

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Subsequent gender-based subgroup analyses were performed, and separate multiple linear regression models were developed for each gender to examine the factors influencing hedonic capacity. Among females, those with at least a college degree reported a 4.501-unit higher hedonic capacity (β = 0.145, 95% CI [1.286, 7.715]) compared to females with lower educational levels (Tables S5-S6, Fig. 3B). In males, sleep disturbances were associated with a 4.968-unit decrease in hedonic capacity (β = -0.137, 95% CI [-9.460, -0.475]) compared to those with normal sleep. Additionally, males who lived alone had a 6.533-unit lower hedonic capacity (β = -0.142, 95% CI [-11.492, -1.573]) than those who did not live alone (Tables S7-S8, Fig. 3C).

Discussion

This study aimed to evaluate the psychometric properties of the Chinese version of the DARS in the community-dwelling adults’ sample. Similar to the original English version, the Chinese DARS demonstrated a stable and reliable four-factor structure encompassing pastimes/hobbies, food/drink, social activities, and sensory experiences [10].It also showed strong convergent and discriminant validity, along with high internal consistency. Our findings suggested that demographic factors influenced hedonic capacity: it was generally higher among females and those with higher education levels, while sleep disturbances and living alone were associated with reduced hedonic capacity. For males, these challenges had a more pronounced negative effect. In contrast, education positively impacted the hedonic capacity of females.

CFA results indicated that the four-factor model had an acceptable to good fit, with significant item loadings, supporting its suitability for the sample of Chinese community-dwelling adults [2]. Our findings confirmed that the DARS maintains a robust four-factor structure. The DARS has also proven effective for assessing anhedonia in various countries, including Canada, Spain, Germany, and China [2, 10, 29–30, 39],demonstrating measurement invariance across ages and cultures. This may be due to the DARS requiring personalized examples, minimizing age-related bias [62].This study confirmed previous findings [2, 10, 29–30, 39], showing high internal consistency for the Chinese DARS in both total and subscale scores (Cronbach’s α = 0.89–0.97).The DARS showed a strong negative correlation with the SHAPS (r = -0.69),consistent with the original and Spanish versions in clinical samples [10, 30] (Fig. 2).All components had AVE scores above 0.50 [60],confirming strong convergent validity. These results support the DARS as a reliable tool for assessing hedonic capacity in the Chinese community-dwelling adults.

Previous studies have shown that hedonic capacity can decrease even in the general population [26].While most research has focused on clinical samples of patients with schizophrenia or depression, it is important to recognize that the general population may also exhibit “subthreshold” psychiatric symptoms to some extent [33].In the general population, reduced hedonic capacity has been linked to poor social functioning in adulthood and an increased risk of developing depression [63].The DARS is widely regarded as a reliable tool for detecting subtle changes in hedonic capacity in the general population, people with subthreshold depression, and patients diagnosed with MDD [11–12].Many individuals in the general population may not receive a formal psychiatric diagnosis due to various factors, including stigma, discrimination, lack of cultural knowledge, etc. There is an urgent need for comprehensive screening, intervention, and prevention efforts for high-risk groups [64].Given the strengths of the DARS, it can serve as a key instrument for identifying individuals at high risk of depression within the Chinese community, as it effectively captures subtle variations in hedonic capacity.

In our subsequent multiple linear regression analyses, we found no correlations between anxiety and depressive symptoms and hedonic capacity. These findings are consistent with the Research Domain Criteria (RDoC) framework and previous studies [28, 65].Furthermore, we identified key factors that enhance our understanding of the drivers of hedonic capacity. Higher levels of education and being female have a positive influence on hedonic capacity, a finding that is consistent with results from other psychological studies. However, a few studies suggest the opposite view, with factors like women’s underground economic status and aging potentially affecting the consistency of the findings. Moreover, the stability of the results might be influenced by the relevance of the assessment scale used [34]. Despite this, females consistently report higher levels of happiness than males across all educational levels [66].This disparity continues to widen with higher educational attainment, as females experience greater subjective well-being as their education level increases [66].It has been shown that males and females differ in how they process past experiences and information. Specifically, females are more likely to experience happiness through meaningful social interactions, while males tend to derive happiness from engaging in pleasurable activities alone [67].These gender differences in hedonic capacity can be explained by these factors. Two key characteristics that negatively impact hedonic capacity are sleep disturbance and living in solitude. Previous studies have demonstrated that sleep disturbance is often linked to reduced activation of brain regions involved in the reward system, both during the reception and anticipation of rewards. This reduced activation can also lead to less effort in seeking rewards [68–69].Living alone can lead to social isolation, which has become increasingly common in today’s society. Social isolation can severely affect an individual’s physical and emotional well-being, potentially leading to higher suicide rates and lower overall quality of life and longevity [70–71].Social isolation may also increase male susceptibility to reduced hedonic capacity by triggering inflammatory responses in the body [72].However, females tend to be less affected by this phenomenon due to their greater ability to integrate social resources [73].These behavioral differences may also be linked to the societal organizations, social structures, and traditional social roles that males and females occupy [67].While relying on subjective self-report measures limits definitive conclusions, these preliminary findings suggest a potential pathway for identifying reduced hedonic capacity in the general population.

Nevertheless, it is important to acknowledge several limitations. This study was a cross-sectional analysis that relied primarily on online measurements. The use of a single assessment, rather than a combination of methods such as clinical evaluation, behavioral tests and self-reports, affects the validity of the results [18]. In summary, given its reliance on data from a single online assessment, further validation of our conclusions with larger, more diverse samples is needed.

Conclusion

In summary, our study confirmed the robust psychometric properties of the Chinese version of the DARS in a community-dwelling adult sample from China. It also offers a reliable alternative method for assessing hedonic capacity, which encompasses four distinct domains: pastimes/hobbies, foods/drinks, social activities, and sensory experiences. Our findings revealed significant gender differences in the decline of hedonic capacity, suggesting that gender-specific mental health services should be considered in the future.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

DARS:

Dimensional Anhedonia Rating Scale

MDD:

Major depression disorder

SHAPS:

Snaith-Hamilton Pleasure Scale

EFA:

Exploratory factor analysis

CFA:

Confirmatory factor analysis

PHQ-9:

The Patient Health Questionnaire-9

PRIME-MD:

Primary Care Evaluation of Mental Disorders

GAD-7:

Generalized Anxiety Disorder-7

KMO:

Kaiser-Meyer-Olkin

GFI:

goodness-of-fit index

CFI:

Comparative fit index

TLI:

Tucker–Lewis index

NFI:

Normed fit index

RMSEA:

Root-mean-square error of approximation

CR:

Construct reliability

AVE:

Average variance extracted

SPSS:

Statistical Package for the Social Sciences

CI:

Confidence Interval

RDoC:

Research Domain Criteria

References

  1. Kringelbach ML, Berridge KC. The affective core of emotion: linking pleasure, subjective Well-Being, and optimal metastability in the brain. Emot Review: J Int Soc Res Emot. 2017;9(3):191–9.

    Article  Google Scholar 

  2. Wellan SA, Daniels A, Walter H. State anhedonia in young healthy adults: psychometric properties of the German dimensional anhedonia rating scale (DARS) and effects of the COVID-19 pandemic. Front Psychol. 2021;12:682824.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Association AP. Diagnostic and statistical manual of mental disorders. 5th ed. American Psychiatric Association; 2013.

  4. Organization WH. International classification of diseases. 11th ed. World Health Organization; 2018.

  5. Fletcher K, Parker G, Paterson A, Fava M, Iosifescu D, Pizzagalli DA. Anhedonia in melancholic and non-melancholic depressive disorders. J Affect Disord. 2015;184:81–8.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wang J, Huang J, Yang XH, Lui SS, Cheung EF, Chan RC. Anhedonia in schizophrenia: deficits in both motivation and hedonic capacity. Schizophr Res. 2015;168(1–2):465–74.

    Article  PubMed  Google Scholar 

  7. Garfield JB, Lubman DI, Yücel M. Anhedonia in substance use disorders: a systematic review of its nature, course and clinical correlates. Aust N Z J Psychiatry. 2014;48(1):36–51.

    Article  PubMed  Google Scholar 

  8. Utz KS, Martini M, Mrochen A, Lambrecht V, Süß P, Renner B, Freiherr J, Schenk T, Winkler J, Marxreiter F. A multisensory deficit in the perception of pleasantness in Parkinson’s disease. J Parkinson’s Disease. 2021;11(4):2035–45.

    Article  Google Scholar 

  9. Assogna F, Cravello L, Caltagirone C, Spalletta G. Anhedonia in Parkinson’s disease: a systematic review of the literature. Mov Disorders: Official J Mov Disorder Soc. 2011;26(10):1825–34.

    Article  Google Scholar 

  10. Rizvi SJ, Quilty LC, Sproule BA, Cyriac A, Michael Bagby R, Kennedy SH. Development and validation of the dimensional anhedonia rating scale (DARS) in a community sample and individuals with major depression. Psychiatry Res. 2015;229(1–2):109–19.

    Article  PubMed  Google Scholar 

  11. Gong L, Yin Y, He C, Ye Q, Bai F, Yuan Y, Zhang H, Lv L, Zhang H, Xie C, et al. Disrupted reward circuits is associated with cognitive deficits and depression severity in major depressive disorder. J Psychiatr Res. 2017;84:9–17.

    Article  PubMed  Google Scholar 

  12. Pelizza L, Ferrari A. Anhedonia in schizophrenia and major depression: state or trait? Ann Gen Psychiatry. 2009;8:22.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lynch CJ, Gunning FM, Liston C. Causes and consequences of diagnostic heterogeneity in depression: paths to discovering novel biological depression subtypes. Biol Psychiatry. 2020;88(1):83–94.

    Article  PubMed  Google Scholar 

  14. Ducasse D, Loas G, Dassa D, Gramaglia C, Zeppegno P, Guillaume S, Olié E, Courtet P. Anhedonia is associated with suicidal ideation independently of depression: A meta-analysis. Depress Anxiety. 2018;35(5):382–92.

    Article  PubMed  Google Scholar 

  15. Uher R, Perlis RH, Henigsberg N, Zobel A, Rietschel M, Mors O, Hauser J, Dernovsek MZ, Souery D, Bajs M, et al. Depression symptom dimensions as predictors of antidepressant treatment outcome: replicable evidence for interest-activity symptoms. Psychol Med. 2012;42(5):967–80.

    Article  PubMed  Google Scholar 

  16. Vinckier F, Gourion D, Mouchabac S. Anhedonia predicts poor psychosocial functioning: results from a large cohort of patients treated for major depressive disorder by general practitioners. Eur Psychiatry: J Association Eur Psychiatrists. 2017;44:1–8.

    Article  Google Scholar 

  17. Whitton AE, Kumar P, Treadway MT, Rutherford AV, Ironside ML, Foti D, Fitzmaurice G, Du F, Pizzagalli DA. Distinct profiles of anhedonia and reward processing and their prospective associations with quality of life among individuals with mood disorders. Mol Psychiatry 2023.

  18. Martino I, Santangelo G, Moschella D, Marino L, Servidio R, Augimeri A, Costabile A, Capoderose G, Cerasa A. Assessment of Snaith-Hamilton pleasure scale (SHAPS): the dimension of anhedonia in Italian healthy sample. Neurol Sciences: Official J Italian Neurol Soc Italian Soc Clin Neurophysiol. 2018;39(4):657–61.

    Article  Google Scholar 

  19. Leboyer M, Bellivier F, Nosten-Bertrand M, Jouvent R, Pauls D, Mallet J. Psychiatric genetics: search for phenotypes. Trends Neurosci. 1998;21(3):102–5.

    Article  PubMed  Google Scholar 

  20. Hasler G, Drevets WC, Manji HK, Charney DS. Discovering endophenotypes for major depression. Neuropsychopharmacology: Official Publication Am Coll Neuropsychopharmacol. 2004;29(10):1765–81.

    Article  Google Scholar 

  21. Pizzagalli DA. Depression, stress, and anhedonia: toward a synthesis and integrated model. Ann Rev Clin Psychol. 2014;10:393–423.

    Article  Google Scholar 

  22. Lu J, Xu X, Huang Y, Li T, Ma C, Xu G, Yin H, Xu X, Ma Y, Wang L, et al. Prevalence of depressive disorders and treatment in China: a cross-sectional epidemiological study. Lancet Psychiatry. 2021;8(11):981–90.

    Article  PubMed  Google Scholar 

  23. Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 2013;11:126.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Husain M, Roiser JP. Neuroscience of apathy and anhedonia: a transdiagnostic approach. Nat Rev Neurosci. 2018;19(8):470–84.

    Article  PubMed  Google Scholar 

  25. Rizvi SJ, Pizzagalli DA, Sproule BA, Kennedy SH. Assessing anhedonia in depression: potentials and pitfalls. Neurosci Biobehav Rev. 2016;65:21–35.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Franken IH, Rassin E, Muris P. The assessment of anhedonia in clinical and non-clinical populations: further validation of the Snaith-Hamilton pleasure scale (SHAPS). J Affect Disord. 2007;99(1–3):83–9.

    Article  PubMed  Google Scholar 

  27. Zhang P, Zhang N, Fang S, He J, Fan L, Luo X, Zhang J, Xiong Y, Luo F, Wang X, et al. Factor structure and measurement invariance of the Chinese version of the Snaith-Hamilton pleasure scale (SHAPS) in Non-clinical and clinical populations. J Affect Disord. 2021;281:759–66.

    Article  PubMed  Google Scholar 

  28. Lin J, Su Y, Rizvi SJ, Jagoda J, Li J, Wu Y, Dai Y, Zhang Y, Kennedy SH, Si T. Define and characterize the anhedonia in major depressive disorder: an explorative study. J Affect Disord. 2022;313:235–42.

    Article  PubMed  Google Scholar 

  29. Xiao WY, Zhu RZ, Lv ZX, Fan J, Yi ZH, Chen J, Ju K, Yan C. Psychometric properties of the dimensional anhedonia rating scale in Chinese typical, subthreshold, and clinically depressed adolescents. PsyCh J. 2022;11(3):344–55.

    Article  PubMed  Google Scholar 

  30. Arrua-Duarte E, Migoya-Borja M, Barrigón ML, Barahona I, Delgado-Gomez D, Courtet P, Aroca F, Rizvi SJ, Kennedy SH, Quilty LC, et al. Spanish adaptation of the dimensional anhedonia rating scale (DARS). J Affect Disord. 2019;245:702–7.

    Article  PubMed  Google Scholar 

  31. Prentice F, Hobson H, Spooner R, Murphy J. Gender differences in interoceptive accuracy and emotional ability: an explanation for incompatible findings. Neurosci Biobehav Rev. 2022;141:104808.

    Article  PubMed  Google Scholar 

  32. Dodell-Feder D, Germine L. Epidemiological dimensions of social anhedonia. Clin Psychol Sci. 2018;6(5):735–43.

    Article  Google Scholar 

  33. Chan RC, Wang Y, Yan C, Zhao Q, McGrath J, Hsi X, Stone WS. A study of trait anhedonia in non-clinical Chinese samples: evidence from the Chapman scales for physical and social anhedonia. PLoS ONE. 2012;7(4):e34275.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Hui CLM, Ko WT, Chang WC, Lee EHM, Chan SKW, Chen EYH. Clinical and functional correlates of financially deprived women with first-episode psychosis. Early Interv Psychiat. 2019;13(3):639–45.

    Article  Google Scholar 

  35. Trøstheim M, Eikemo M, Meir R, Hansen I, Paul E, Kroll SL, Garland EL, Leknes S. Assessment of anhedonia in adults with and without mental illness: A systematic review and Meta-analysis. JAMA Netw Open. 2020;3(8):e2013233.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Thompson B. The Ten Commandments of Good Structural Equation Modeling Behavior: A User-Friendly, Introductory Primer on SEM. 1998.

  37. Comreys AJNY. A first course in factor analysis Academic Press. 1973.

  38. Cattell R. 1978, The Scientific Use of Factor Analysis in Behavioral and Life Sciences. New York: Plenum Press. 1978.

  39. Jia H, Wang L, An J, Kong Q, Qiao H, Pan C, Si T. Reliability and validity of the Chinese version of dimensional anhedonia rating scale of depression in China. Chin J Psychiatry. 2020;5(3):216–20.

    Google Scholar 

  40. Liu WH, Wang LZ, Zhu YH, Li MH, Chan RC. Clinical utility of the Snaith-Hamilton-Pleasure scale in the Chinese settings. BMC Psychiatry. 2012;12:184.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary care evaluation of mental disorders. Patient Health Questionnaire Jama. 1999;282(18):1737–44.

    PubMed  Google Scholar 

  43. Sun Y, Kong Z, Song Y, Liu J, Wang X. The validity and reliability of the PHQ-9 on screening of depression in neurology: a cross sectional study. BMC Psychiatry. 2022;22(1):98.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Liu ZW, Yu Y, Hu M, Liu HM, Zhou L, Xiao SY. PHQ-9 and PHQ-2 for screening depression in Chinese rural elderly. PLoS ONE. 2016;11(3):e0151042.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Wang W, Bian Q, Zhao Y, Li X, Wang W, Du J, Zhang G, Zhou Q, Zhao M. Reliability and validity of the Chinese version of the patient health questionnaire (PHQ-9) in the general population. Gen Hosp Psychiatry. 2014;36(5):539–44.

    Article  PubMed  Google Scholar 

  46. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7.

    Article  PubMed  Google Scholar 

  47. Pub AAJAP. Diagnostic and statistical manual of mental disorders: DSM-IV-TR. 2000, 157.

  48. Kroenke K, Spitzer RL, Williams JB, Monahan PO, Löwe B. Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann Intern Med. 2007;146(5):317–25.

    Article  PubMed  Google Scholar 

  49. Löwe B, Decker O, Müller S, Brähler E, Schellberg D, Herzog W, Herzberg PY. Validation and standardization of the generalized anxiety disorder screener (GAD-7) in the general population. Med Care. 2008;46(3):266–74.

    Article  PubMed  Google Scholar 

  50. Zhou Y, Xu J, Rief W. Are comparisons of mental disorders between Chinese and German students possible? An examination of measurement invariance for the PHQ-15, PHQ-9 and GAD-7. BMC Psychiatry. 2020;20(1):480.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Costello AB. Osborne JJPa, research, evaluation: best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. 2005, 10(1):7.

  52. Shevlin M, Miles JNJP. differences I: Effects of sample size, model specification and factor loadings on the GFI in confirmatory factor analysis. 1998, 25(1):85–90.

  53. Bentler, PMJPb. Comparative fit indexes in structural models. 1990, 107(2):238.

  54. Tucker LR, Lewis CJP. A reliability coefficient for maximum likelihood factor analysis. 1973, 38(1):1–10.

  55. Bentler P, Bonett DJPB. Significance tests and goodness-of-Fit in analysis of covariance structures significance tests and goodness of fit in the analysis of covariance structures. August. 2014.

  56. Brown TAJBr. Confirmatory factor analysis of the Penn state worry questionnaire: multiple factors or method effects? Therapy. 2003;41(12):1411–26.

    Google Scholar 

  57. Nunnally J. Psychometric theory, 2nd eda: New York: McGraw-Hill; 1978.

  58. Carmines EG, Zeller RA. Reliability and validity assessment. Sage; 1979.

  59. Dunn TJ, Baguley T, Brunsden V. From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation. British journal of psychology (London, England: 1953) 2014, 105(3):399–412.

  60. Fornell C, Larcker DFJJ. Evaluating structural equation models with unobservable variables and measurement error. 1981, 18(1):39–50.

  61. Hair JF. Multivariate data analysis. 2009.

  62. Sullivan-Toole H, DePasque S, Holt-Gosselin B, Galván A. Worth working for: the influence of effort costs on teens’ choices during a novel decision making game. Dev Cogn Neurosci. 2019;37:100652.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Franken IH, Van Strien JW, Nijs IM. Effect of hedonic tone on event-related potential measures of cognitive processing. Psychiatry Res. 2006;142(2–3):233–9.

    Article  PubMed  Google Scholar 

  64. Chen EY, Wong GH, Lam MM, Chiu CP, Hui CL. Real-world implementation of early intervention in psychosis: resources, funding models and evidence-based practice. World Psychiatry: Official J World Psychiatric Association (WPA). 2008;7(3):163–4.

    Article  Google Scholar 

  65. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, Wang P. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167(7):748–51.

    Article  PubMed  Google Scholar 

  66. Yang J, Zeng Y, Wang XJFE. The gender happiness gap in China: composition effect or coefficient effect? Fem Econ. 2024;30(1):70–105.

    Article  Google Scholar 

  67. Brakus JJ, Chen W, Schmitt B, Zarantonello LJP. Marketing: experiences and happiness: the role of gender. 2022, 39(8):1646–59.

  68. Holm SM, Forbes EE, Ryan ND, Phillips ML, Tarr JA, Dahl RE. Reward-related brain function and sleep in pre/early pubertal and mid/late pubertal adolescents. J Adolesc Health: Official Publication Soc Adolesc Med. 2009;45(4):326–34.

    Article  Google Scholar 

  69. Engle-Friedman M, Palencar V, Riela S. Sleep and effort in adolescent athletes. J Child Health Care: Professionals Working Child Hosp Community. 2010;14(2):131–41.

    Article  Google Scholar 

  70. Umberson D, Lin Z, Cha HJJH, Behavior S. Gender and social isolation across the life course. 2022, 63(3):319–335.

  71. Holt-Lunstad JJARPH. Social connection as a public health issue: the evidence and a systemic framework for prioritizing the social in social determinants of health. 2022, 43:193–213.

  72. Yang YC, McClintock MK, Kozloski M, Li TJJ. Social isolation and adult mortality: the role of chronic inflammation and sex differences. J Health Social Behav. 2013;54(2):183–203.

    Article  Google Scholar 

  73. Taylor SE, Klein LC, Lewis BP, Gruenewald TL, Gurung RA, Updegraff JA. Biobehavioral responses to stress in females: Tend-and-befriend, not fight-or-flight. 2002.

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Acknowledgements

Not applicable.

Funding

This study was supported by grants from Capital Medical Development Research Fund (2022-1-4111), and National Natural Science Foundation of China (No. 82171529, 82371530).

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Author notes

  1. Qi Wang and Yankun Wu contributed equally to this work.

Authors and Affiliations

  1. Hebei Provincial Mental Health Center, Baoding, 071000, Hebei, China

    Qi Wang, Yunshu Zhang & Keqing Li

  2. NHC Key Laboratory of Mental Health (Peking University), Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China

    Yankun Wu, Yun-Ai Su & Tianmei Si

  3. The Sixth Clinical Medical College of Hebei University, Baoding, 071000, Hebei, China

    Qi Wang, Yunshu Zhang & Keqing Li

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Contributions

TMS, YAS, YKW, and QW conceived and coordinated the study, designed, performed, and analysed the experiments, and wrote the paper. YKW, QW, YAS, YSZ, and KQL carried out the data collection and coordinated the project. TMS, YAS, YSZ, and KQL contributed data interpretation and revisions to the paper. All authors reviewed the results and approved the final version of the manuscript.

Corresponding authors

Correspondence to Yun-Ai Su or Tianmei Si.

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This study was approved by the Ethics Committee of Peking University Sixth Hospital and was conducted in strict accordance with the Declaration of Helsinki. All participants provided informed consent online before the formal survey.

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Wang, Q., Wu, Y., Zhang, Y. et al. Hedonic capacity in a Chinese community population sample: psychometric properties of the dimensional anhedonia rating scale (DARS) and exploration of gender differences. BMC Psychol 13, 410 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40359-025-02740-x

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Keywords

BMC Psychology

ISSN: 2050-7283

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