Development and Validation of the Self-Perceived Anhedonia Scale for Adults: A Novel Tool for Assessing Pleasure Deficits

Abstract

Background: The present study focuses on the development and validation of the Self-Perceived Anhedonia Scale for Adults (SPAS-A), a novel instrument designed to assess pleasure deficits across multiple dimensions of anhedonia, including social, physical, cognitive, and emotional facets. The study aimed to establish the psychometric properties of the scale, including its reliability and validity. Materials and methods: All the data were collected from a sample of 600 participants between February 2024 and November 2024 via Google Forms. Results: Factorial analysis, including Exploratory Factor Analysis and Principal Component Analysis, revealed a four-factor structure, accounting for 72.4% of the total variance, which demonstrated a coherent and multidimensional representation of anhedonia. The reliability of the scale was further supported by high Cronbach’s Alpha values for each subscale, with social anhedonia showing an exceptional value of 0.916, followed by emotional (0.905), cognitive (0.900), and physical (0.873) anhedonia. Conclusions: These findings indicate that SPAS-A is a reliable and valid tool for assessing pleasure deficits in adults, providing a comprehensive measure of anhedonia that can be utilized in both clinical and research settings. Future studies could benefit from longitudinal designs and more diverse samples to better understand the temporal and cultural variability of anhedonia.

1. Introduction

Anhedonia, a diminished ability to experience pleasure [1,2], is a complex phenomenon that extends beyond psychiatric diagnoses, manifesting across various conditions and states of well-being. While often linked to clinical disorders such as depression [3,4], bipolar disorder [2,5], or obsessive–compulsive disorder (OCD) [6], it can also emerge in individuals without a formal psychiatric diagnosis [7]. Factors like chronic fatigue, burnout, or life stressors may precipitate this state, reflecting the intricate interplay between emotional, cognitive, and physiological dimensions of mental health [8,9]. For instance, recurrent depressive disorder prominently features anhedonia as a core symptom, with individuals frequently reporting a pervasive loss of interest in previously enjoyable activities [3,10].

Similarly, those with panic disorder [11] may experience emotional numbing due to persistent anxiety, while emotional dysregulation [12,13] is another contributor to this debilitating symptom.

The role of anhedonia is equally notable in conditions like substance use disorders [14] and sleep disturbances, where disruptions in the brain’s reward system and emotional regulation mechanisms diminish the capacity for pleasure [15]. Eating disorders, such as anorexia and bulimia [16], further highlight the psychosomatic dimensions of anhedonia, influenced by self-critical thoughts and co-occurring emotional distress. Such findings underscore the necessity of a nuanced approach to understanding anhedonia, considering both clinical and subclinical dimensions to enhance diagnostic accuracy and therapeutic interventions.

The emergence of novel psychological tools for assessing complex emotional and behavioral traits has significantly advanced the field of mental health research. The Self-Perceived Anhedonia Scale for Adults (SPAS-A) was developed to address the critical need for precise measurement of pleasure deficits—anhedonia. This scale aims to bridge the gap between subjective self-assessment and clinical evaluation, offering a reliable and valid measure of self-perceived anhedonia in adults.

Building upon prior theoretical frameworks, the SPAS-A incorporates contemporary insights into affective neuroscience and psychometrics [17]. The validation process involved factor analysis, test–retest reliability, and criterion validity to ensure its robustness.

The development of SPAS-A also addresses the need for more nuanced tools that align self-report measures with neurobiological findings [18,19]. By addressing the subjective experience of pleasure deficits, this scale provides a useful resource for clinicians and researchers aiming to enhance diagnostic precision and treatment efficacy. This article delves into the conceptualization, methodological rigor, and clinical applicability of the SPAS-A.

The conceptual underpinnings of the SPAS-A are rooted in affective neuroscience and cognitive–behavioral theories, providing a multifaceted understanding of pleasure deficits. While anhedonia has been extensively studied, many existing tools have often failed to capture the subjective nuances of the experience. Traditional instruments frequently prioritize objective behavioral markers or neuroimaging data, but overlook the individual’s self-perception, which is crucial for comprehensive assessment.

The development process of SPAS-A involved careful design, supported by quantitative research. Initial pilot studies explored the semantic clarity and relevance of scale items, and subsequent empirical testing incorporated diverse demographic cohorts to ensure inclusivity and generalizability. A cornerstone of the validation process was confirmatory factor analysis which confirmed the scale’s internal consistency and dimensional structure.

The SPAS-A provides four distinct subscales: social anhedonia (SA), physical anhedonia (PA), cognitive anhedonia (CA), and emotional anhedonia (EA), enabling a multidimensional understanding of anhedonia in both clinical and research settings.

SA refers to the diminished capacity to derive pleasure from social interactions and relationships [20,21,22]. We found in the literature that SA is particularly relevant in disorders such as schizophrenia, where impairments in social functioning are prevalent [23,24]. The SPAS-A incorporates items that assess an individual’s enjoyment of social activities, connection with peers, and engagement in social networks.

PA measures the lack of pleasure derived from sensory or bodily experiences, such as food, touch, or physical activity [25,26,27]. This subscale has strong implications for understanding anhedonia in conditions like depression and substance use disorders [28,29], where physical pleasure is frequently blunted.

CA pertains to the inability to derive pleasure from intellectual or mental engagement, such as reading, problem-solving, or creative pursuits [30]. This subscale reflects research linking cognitive pleasure deficits with both major depressive disorder and anxiety disorders.

EA is characterized by a reduced capacity to experience positive emotions or joy from typically pleasurable events [31]. This dimension is crucial in differentiating anhedonia from related constructs like apathy or general mood disturbances. The emotional subscale evaluates both the intensity and frequency of positive emotional experiences. In Romania, while mental health research has progressed, significant gaps remain in understanding and treating anhedonia. This phenomenon is particularly noted in burnout, depression, and other mood disorders among healthcare workers, educators, and public sector employees. Some researchers [32] observed that in Parkinson’s disease, apathy and anhedonia may occur together or independently, with clinical scales like the Snaith–Hamilton Pleasure Scale (SHAPS) for anhedonia and the Apathy Evaluation Scale (AES), the Dimensional Apathy Scale (DAS), or the Lille Apathy Rating Scale (LARS) for apathy proving effective for rapid assessment. Their findings also indicated correlations between neurophysiological measures and clinical scores, suggesting that combining such tools with instruments like the SPAS-A could enhance diagnostic precision and support earlier, tailored interventions. Integrating these approaches could help bridge current research gaps and improve the management of anhedonia in Romania. In developing SPAS-A, we aimed to create a tool that goes beyond the limitations of existing measures, offering a holistic perspective on anhedonia. This paradigm shift not only enhances the accuracy of assessments but also supports more personalized therapeutic strategies. Looking ahead, the SPAS-A has the potential to be integrated into digital health platforms, increasing its accessibility and reach. Additionally, future research could explore the scale’s predictive validity in longitudinal studies, further solidifying its role in understanding and addressing anhedonia in various psychological conditions.

• Objectives

The primary objective of this study was to develop and validate a self-report instrument to measure self-perceived anhedonia across four dimensions: social, physical, cognitive, and emotional. Additional aims included the following:

• Establishing the factorial structure and internal consistency of the scale through exploratory and confirmatory factor analyses.
• Evaluating the scale’s reliability and stability through repeated measures and descriptive analyses.

• Hypotheses

• The SPAS-A indicates a clear four-factor structure corresponding to its subscales: social, physical, cognitive, and emotional anhedonia.
• The SPAS-A exhibits high internal consistency, with Cronbach’s Alpha values exceeding 0.80 for all subscales, and strong reliability over time as evidenced by test–retest measures with an intraclass correlation coefficient greater than 0.75.

2. Materials and Methods

The methodological framework for developing and validating the SPAS-A was designed to ensure psychometric rigor and clinical applicability. This section outlines the step-by-step approach undertaken in scale development, including the generation of items, validation procedures, and statistical analyses, while also presenting the underlying objectives and hypotheses guiding the research.

Participants (N = 600) were recruited from diverse demographic and clinical backgrounds, including both community and clinical populations, to ensure the scale’s validity across settings. Inclusion criteria required participants to be adults aged 18–65 years, fluent in the Romanian language, and capable of providing informed consent. Exclusion criteria included severe cognitive impairment or active psychosis that might hinder accurate self-reporting.

Items were generated based on a comprehensive review of existing anhedonia literature, clinical expertise [7,33,34,35,36], and extensive input from patients in the SHAPS [37,38]. An initial pool of 30 items was reviewed by a panel of psychologists and psychiatrists for content relevance, clarity, and cultural appropriateness. This iterative process reduced the pool to 20 items distributed across the two hypothesized subscales.

Using a 5-point Likert scale where responses range from 1 (“never”) to 5 (“always”), individuals provide ratings for specific items, and the global score is calculated by summing these responses. Global scores between 20 and 40 indicate an absence of self-perceived anhedonia, scores from 41 to 80 reflect moderate self-perceived anhedonia, and a score between 81 and 100 represents pronounced self-perceived anhedonia.

A pilot study involving 98 participants was conducted using a convenience sampling method to evaluate the semantic clarity, readability, and initial internal consistency of the scale. Based on participant feedback, minor revisions were made to the item wording and response format. Following these revisions, the revised scale was administered to a larger sample (N = 600) selected through stratified random sampling to ensure diverse representation across key demographic groups. This larger sample was used to evaluate its psychometric properties.

To refine the scale, a criterion-based approach was employed to reduce the original set of 30 items to 20 items. Items were selected based on their contribution to the overall construct, ensuring they were both conceptually relevant and exhibited high factor loadings in Exploratory Factor Analysis.

Exploratory and confirmatory factor analyses were carried out using the same sample (N = 600). These analyses helped confirm the scale’s structural validity and provided a basis for further refinement.

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the National Teaching Center for Children’s Neurorehabilitation “Dr. Nicolae Robanescu” in Bucharest, Romania (protocol code 41, date of approval 3 January 2024).

Statistical Analysis

The statistical analysis was conducted in multiple stages to assess the psychometric properties of the SPAS-A. Data analysis was performed using IBM SPSS 27 and AMOS software v27 for Structural Equation Modeling (SEM).

• Exploratory Factor Analysis (EFA) was performed to identify the underlying factor structure of the scale. Principal Component Analysis (PCA) with Varimax rotation was used to reveal the distinct factors, and the analysis revealed a clear four-factor structure—social, emotional, cognitive, and physical anhedonia.
• Confirmatory Factor Analysis (CFA) was conducted to validate the hypothesized four-factor model. The model fit was assessed using fit indices including the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR).
• SEM was used to explore the relationships among the latent factors, confirming their independence after rotation. The model also examined how the latent factors interrelate, and the correlations among them were found to be moderate, supporting the complexity of anhedonia as a multidimensional construct.
• Reliability testing was conducted in two stages to evaluate the internal consistency of the scale. Initially, Cronbach’s Alpha was calculated on the control sample, revealing strong internal consistency across all subscales, with values exceeding the 0.7 threshold. These preliminary results provided robust evidence of the scale’s reliability.
• Finally, descriptive statistics were analyzed, including means and standard deviations of items across all dimensions, providing evidence of the scale’s sensitivity to variations in anhedonia symptoms.

All participants provided informed consent, which was obtained through their completion of the questionnaire via Google Forms. Participants were assured of their anonymity and the confidentiality of their responses. Data were collected online to ensure accessibility and convenience for participants.

3. Results

3.1. Participant Characteristics and Anhedonia Levels

The study sample consisted of 600 participants, with the majority identifying as female (55.2%, n = 331) and the remainder as male (44.8%, n = 269), as shown in

Table 1. Distribution of demographic, clinical, and anhedonia characteristics (N = 600).

Category	Subcategory	Frequency (N)	Percent (%)	Mean (±SD)	Min Score	Max Score
Gender	Male	269	44.8	-	-	-
	Female	331	55.2	-	-	-
Age	18–24 years	157	26.2	-	-	-
	25–34 years	113	18.8	-	-	-
	35–44 years	235	39.2	-	-	-
	45–54 years	95	15.8	-	-	-
Pathology	No psychiatric diagnosis	231	38.5	-	-	-
	Recurrent depressive disorder	80	13.3	-	-	-
	Obsessive–compulsive disorder (OCD)	75	12.5	-	-	-
	Panic disorder	43	7.2	-	-	-
	Attention-deficit/hyperactivity disorder	36	6.0	-	-	-
	Substance use disorder	35	5.8	-	-	-
	Sleep disorders	35	5.8	-	-	-
	Eating disorders	36	6.0	-	-	-
	Bipolar disorder	29	4.8	-	-	-
Anhedonia scores	Social	-	-	12.45 ± 4.63	5	25
	Physical	-	-	13.62 ± 4.97	5	25
	Cognitive	-	-	12.89 ± 5.02	5	25
	Emotional	-	-	13.74 ± 5.04	5	25
Global Level	Absence of self-perceived anhedonia	154	25.7	-	20	40
	Moderate self-perceived anhedonia	429	71.5	-	41	80
	Pronounced self-perceived anhedonia	17	2.8	-	81	100

. Participants’ ages were distributed across four categories, with the largest proportion aged 35–44 years (39.2%, n = 235), followed by those aged 18–24 years (26.2%, n = 157), 25–34 years (18.8%, n = 113), and 45–54 years (15.8%, n = 95). In terms of clinical characteristics, 38.5% (n = 231) of participants reported no psychiatric diagnosis. Among those with a diagnosis, the most common conditions were recurrent depressive disorder (13.3%, n = 80) and obsessive–compulsive disorder (12.5%, n = 75). Other reported conditions included panic disorder (7.2%, n = 43), attention-deficit/hyperactivity disorder (6.0%, n = 36), substance use disorders (5.8%, n = 35), sleep disorders (5.8%, n = 35), eating disorders (6.0%, n = 36), and bipolar disorder (4.8%, n = 29). The assessment of anhedonia revealed variability across four dimensions, with mean scores (±standard deviation) as follows: SA (12.45 ± 4.63), PA (13.62 ± 4.97), CA (12.89 ± 5.02), and EA (13.74 ± 5.04).

For global score, a large proportion of participants (71.5%, n = 429) reported moderate self-perceived anhedonia, while 25.7% (n = 154) reported no self-perceived anhedonia, and a smaller percentage (2.8%, n = 17) reported pronounced self-perceived anhedonia. These data highlight the varied levels of anhedonia experienced across the sample and provide an understanding of its distribution in relation to both demographic and clinical characteristics.

3.2. Exploratory Factor Analysis (EFA)—Principal Component Analysis (PCA) and Varimax Rotation

EFA was conducted using PCA with Varimax rotation to examine the underlying factor structure of the anhedonia dimensions in the study. This statistical approach was employed to identify potential latent factors that explain the observed patterns of correlations among the items. PCA, a technique used to reduce the dimensionality of data, helps in identifying the principal components that account for the maximum variance in the data. Varimax rotation was applied to maximize the variance of squared loadings of a factor, allowing for a clearer interpretation of the factors.

The correlation coefficients for each dimension (

Table 2. (a) Correlations analysis for anhedonia dimensions and items. (b) Item-level correlations for all dimensions.

(a)
Dimension						Items		Range of Correlation Coefficients (r)					Significance (p)				Highest Correlation (Item)						Value
Social Anhedonia (SA)						Q1–Q5		0.653–0.888					<0.001				Q2 (Group difficulties)						0.888
Physical Anhedonia (PA)						Q6–Q10		0.460–0.823					<0.001				Q6 (Exhaustion)						0.823
Cognitive Anhedonia (CA)						Q11–Q15		0.546–0.883					<0.001				Q13 (Lack of meaning)						0.883
Emotional Anhedonia (EA)						Q16–Q20		0.567–0.869					<0.001				Q20 (Anticipation of pleasure)						0.869
(b)
Item/Dimension	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	SA	PA	CA	EA
Correlation Coefficient	1.000	0.743 **	0.693 **	0.671 **	0.653 **	0.734 **	0.706 **	0.681 **	0.464 **	0.451 **	0.590 **	0.607 **	0.696 **	0.546 **	0.562 **	0.754 **	0.567 **	0.589 **	0.663 **	0.674 **	0.860 **	0.826 **	0.771 **	0.866 **
Significance (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Correlation Coefficient	0.000	1.000	0.706 **	0.670 **	0.707 **	0.823 **	1.000	0.729 **	0.460 **	0.444 **	0.644 **	0.667 **	0.723 **	0.711 **	0.733 **	0.685 **	0.656 **	0.634 **	0.724 **	0.869 **	0.888 **	0.844 **	0.851 **	0.854 **
Significance (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.00	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Correlation Coefficient	0.000	0.000	1.000	0.652 **	0.713 **	0.844 **	0.844 **	1.000	0.549 **	0.487 **	0.607 **	0.711 **	1.000	0.711 **	0.723 **	0.618 **	0.634 **	1.000	0.784 **	0.866 **	0.866 **	0.883 **	0.794 **	0.853 **
Significance (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

** denotes a correlation that is statistically significant at the 0.01 level (2-tailed).

a) range from moderate to high, all of which are statistically significant with p-values less than 0.001. The highest correlation for social anhedonia is found for Item Q2 (group difficulties), with a value of 0.888. For physical anhedonia, Item Q6 (exhaustion) has the highest correlation at 0.823. In the case of cognitive anhedonia, the highest correlation occurs for Item Q13 (lack of meaning), with a value of 0.883. Lastly, the highest correlation for emotional anhedonia is seen in Item Q20 (anticipation of pleasure), with a value of 0.869. SA = social anhedonia; PA = physical anhedonia; CA = cognitive anhedonia; EA = emotional anhedonia. The coefficients (Table 2b) demonstrate significant positive correlations between items within each dimension. These correlations indicate that the items within each anhedonia dimension are strongly related to one another, with the highest correlation observed for the items that are most central to each dimension, as detailed in the previous table.

These analyses examine the relationships between individual self-reported items assessing anhedonia dimensions and their respective composite scores.

All individual items show significant positive correlations with the composite SA score, ranging from r = 0.844 (Q4: emotional disconnection from the community) to r = 0.888 (Q2: inability to be part of social groups). The strongest individual correlation is observed between the item on feeling unable to be part of social groups (Q2) and the social anhedonia score (r = 0.888, p < 0.001).

The correlations related to PA indicate that items assessing exhaustion (Q6), vitality (Q7), and energy (Q8) show strong correlations with the physical anhedonia score (r = 0.823 to r = 0.844, p < 0.001). Additionally, the inability to savor food (Q10) is also strongly correlated with the physical anhedonia score (r = 0.766, p < 0.001), while the lack of pleasure in eating (Q9) shows a slightly lower but still significant correlation (r = 0.783, p < 0.001).

CA items also exhibit strong positive correlations with their composite score. The highest correlation is observed for Q13 (inability to find meaning in experiences) with r = 0.883 (p < 0.001). Other notable correlations include Q14 (inability to appreciate beauty or complexity, r = 0.856, p < 0.001) and Q12 (lack of intellectual curiosity, r = 0.851, p < 0.001).

All items within EA are significantly correlated with the composite score. The strongest correlation is observed for Q20 (inability to anticipate pleasure or joy, r = 0.869, p < 0.001). Other items with strong correlations include Q16 (inability to deeply experience positive emotions, r = 0.866, p < 0.001) and Q19 (inability to identify or express emotions, r = 0.853, p < 0.001).

Across all domains, the consistent strength and significance of the correlations highlight the coherence of the individual items with their respective anhedonia dimensions, underscoring the validity of these composite measures in capturing distinct facets of anhedonia.

Descriptive statistics highlight patterns across the variables of interest, with mean values ranging from 2.44 to 3.03. Intellectual difficulties, such as challenges in focusing enough to read or learn, showed the highest average frequency (M = 3.03, SD = 1.135), closely followed by perceptions of a lack of vitality in daily activities (M = 2.98, SD = 1.161). Social isolation and difficulties savoring food were among the less frequently reported experiences, with respective mean values of 2.44 (SD = 1.228) and 2.46 (SD = 1.213). These findings indicate that intellectual and physical dimensions of anhedonia might manifest more prominently within this sample compared to social and sensory domains. Standard deviations were consistent across items, suggesting moderate variability in participant responses.

Sampling adequacy and sphericity were assessed using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity (

Table 3. Summary of descriptive statistics, sampling adequacy, communalities, and variance explained.

Category	Details
Descriptive Statistics
Items (Q1–Q20)	Mean: 2.44–3.03; Std. Deviation: 1.116–1.279
Highest Mean Item	Q11 (Challenges Focusing): Mean = 3.03, SD = 1.135
Lowest Mean Item	Q3 (Social Isolation): Mean = 2.44, SD = 1.228
Sampling Adequacy
KMO Measure	0.900 (Excellent)
Bartlett’s Test	χ2(190) = 8690.476, p < 0.001
Communalities
Highest Communality Item	Q13 (Lack of Meaning): 0.787
Lowest Communality Item	Q10 (Savoring Food): 0.572
Principal Component Analysis
Number of Components	4
Total Variance Explained	72.4%
Variance by Components
Component 1	Initial Eigenvalue: 8.485; % of Variance: 42.4%
Component 2	Initial Eigenvalue: 2.206; % of Variance: 11.0%
Component 3	Initial Eigenvalue: 2.035; % of Variance: 10.2%
Component 4	Initial Eigenvalue: 1.755; % of Variance: 8.8%
Post-Rotation Variance	Balanced variance across components: Component 1 = 19.3%; Component 2 = 19.2%; Component 3 = 17.5%; Component 4 = 16.3%.

). The KMO value of 0.900 indicates excellent sampling adequacy, confirming that the dataset is well suited for factor analysis. Bartlett’s test yielded a highly significant result (χ²(190) = 8690.476, p < 0.001), supporting the suitability of the correlation matrix for this type of analysis.

Communalities reported in Table 3 provide insights into the proportion of variance explained for each item by the extracted components. Items assessing PA, such as energy levels for physical activities and vitality, showed consistently high communalities (e.g., 0.764 and 0.765, respectively). Similarly, CA items, such as lack of intellectual curiosity and inability to find meaning, were well represented by the extracted components, with communalities of 0.732 and 0.787, respectively. Items related to sensory and social dimensions also demonstrated substantial communalities, further confirming the robust representation of these variables within the analysis.

The Principal Component Analysis, detailed in Table 3, identified four components that collectively accounted for 72.4% of the total variance. The first component explained 42.4% of the variance and primarily captured broad aspects of anhedonia, integrating emotional, social, and cognitive dimensions. Subsequent components further delineated specific domains of anhedonia. The second component accounted for an additional 11.0% of the variance, followed by the third and fourth components explaining 10.2% and 8.8%, respectively. Post-rotation, the distribution of variance became more balanced across components, with the first and second components explaining approximately 19% of the variance each, while the third and fourth accounted for 17.5% and 16.3%, respectively. This refinement underscores the multidimensional nature of anhedonia as represented in this sample.

Table 4. Summary of component analysis: unrotated matrix, rotated matrix, and component transformation matrix.

Component	1 (Social Anhedonia)	2 (Emotional Anhedonia)	3 (Physical Anhedonia)	4 (Cognitive Anhedonia)	Description
Q1	0.676	−0.516			Socially isolated
Q2	0.635	−0.596			Unable to be part of social groups
Q3	0.651	−0.572			Rejected by others during social activities
Q4	0.673	−0.521			Emotionally disconnected from the community around you
Q5	0.630	−0.574			Lacking in social support
Q6	0.548		0.623		Exhausted even after adequate sleep
Q7	0.553		0.543		Lacking vitality in daily activities
Q8	0.590		0.540		Lacking energy to enjoy physical activities
Q9	0.640				No pleasure in eating
Q10	0.681				Unable to savor food
Q11	0.569			0.730	Unable to focus enough to read or learn
Q12	0.634			−0.525	Lacking intellectual curiosity
Q13	0.663			−0.527	Unable to find meaning or significance in what you learn or experience
Q14	0.738				Unable to appreciate the beauty or complexity of things around you
Q15	0.732				Unable to use your creativity or imagination
Q16	0.697				Unable to deeply experience positive emotions
Q17	0.682				Unable to share positive emotions with others
Q18	0.608				Unable to manage negative emotions effectively
Q19	0.672				Unable to identify or express your emotions
Q20	0.713				Unable to anticipate pleasure or joy
Component Matrix (Rotated)
Q1	0.804				Socially isolated
Q2	0.854				Unable to be part of social groups
Q3	0.823				Rejected by others during social activities
Q4	0.794				Emotionally disconnected from the community around you
Q5	0.829				Lacking in social support
Q6			0.842		Exhausted even after adequate sleep
Q7			0.841		Lacking vitality in daily activities
Q8			0.836		Lacking energy to enjoy physical activities
Q9			0.642		No pleasure in eating
Q10			0.581		Unable to savor food
Q11				0.730	Unable to focus enough to read or learn
Q12				0.813	Lacking intellectual curiosity
Q13				0.842	Unable to find meaning or significance in what you learn or experience
Q14				0.748	Unable to appreciate the beauty or complexity of things around you
Q15				0.771	Unable to use your creativity or imagination
Q16		0.794			Unable to deeply experience positive emotions
Q17		0.802			Unable to share positive emotions with others
Q18		0.730			Unable to manage negative emotions effectively
Q19		0.822			Unable to identify or express your emotions
Q20		0.817			Unable to anticipate pleasure or joy
Component Transformation Matrix
1	0.514	0.540	0.501	0.439
2	−0.842	0.174	0.367	0.354
3	0.138	−0.646	−0.097	0.744
4	−0.084	0.510	−0.778	0.357

The table above presents the factor loadings for each of the 20 questions (Q1 to Q20) across the four components of anhedonia. The values indicate the strength of the association between each question and the respective component, with higher values reflecting a stronger relationship.

presents the unrotated component matrix, highlighting strong loadings for variables reflecting social and emotional isolation on the first component. For instance, Q1 shows a loading of 0.676, and Q2 has a loading of 0.635. These results suggest that the first component captures a broad dimension of social and emotional challenges. PA items, such as Q6 (0.623) and Q7 (0.543), predominantly load onto the second component, indicating a distinct physical depletion factor.

Table 4 also presents the rotated component matrix, which provides a clearer factor structure through Varimax rotation. The first component groups items related to social isolation, with high loadings observed for Q1 (0.804) and Q2 (0.854). The second component predominantly captures EA, with strong loadings for Q16 (0.794) and Q17 (0.802). The third component reflects CA, with items such as Q12 (0.813) and Q13 (0.842) loading strongly. The fourth component encompasses items related to PA, demonstrated by Q6 (0.842) and Q7 (0.841).

Finally, Table 4 provides the component transformation matrix, illustrating the relationships between the extracted components post-rotation. These results confirm the orthogonal nature of the Varimax rotation, which ensures that the components are independent of each other, enhancing interpretability. The analysis underscores the multidimensional nature of anhedonia, with distinct factors capturing social, emotional, cognitive, and physical dimensions.

3.3. Structural Equation Modeling (SEM)

SEM was used to examine the relationships between the factors of anhedonia and the associated scale items as detailed in

Table 5. Models’ info.

Estimation Method	DWLS
Optimization method	NLMINB
Number of observations	600
Free parameters	106
Standard errors	Robust
Scaled test	Mean adjusted, scaled, and shifted
Converged	TRUE
Iterations	53
Model	F1 = ~Q1 + Q2 + Q3 + Q4 + Q5
	F2 = ~Q6 + Q7 + Q8 + Q9 + Q10
	F3 = ~Q11 + Q12 + Q13 + Q14 + Q15
	F4 = ~Q16 + Q17 + Q18 + Q19 + Q20
	F1~~F2 F1~~F3 F1~~F4 F2~~F1 F2~~F3 F2~~F4 F3~~F1 F3~~F4 F4~~F1 F4~~F2 F4~~F3 F3~~F2

Note: Model set-up: lavaan → lav_partable_flat(): duplicated elements in model syntax have been ignored: “F1~~F2”, “F1~~F3”, “F2~~F3”, “F1~~F4”, “F2~~F4”, “F3~~F4”; variables (Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10, Q11, Q12, Q13, Q14, Q15, Q16, Q17, Q18, Q19, Q20) have been changed to ordered type.

. SEM helped explore how the different dimensions of anhedonia (social, physical, cognitive, and emotional) are measured by specific questions in each scale. Table 5 provides the model setup and key parameters, including the estimation method, optimization process, and relationships between the latent variables and observed items.

The model fits (

Table 6. Model fit indices and comparisons.

Model Type	X2	df	p	SRMR	RMSEA	Lower	Upper	RMSEA p	CFI	TLI	NNFI	RNI	NFI	RFI	IFI	PNFI
User Model	1046	164	<0.001	0.065	0.095	0.089	0.100	<0.001	0.989	0.987	0.987	0.989	0.987	0.985	0.989	0.852
Baseline Model	80,534	190	<0.001	-	-	-	-	-	-	-	-	-	-	-	-	-
Scaled User	1277	164	<0.001	0.055	0.106	0.101	0.112	<0.001	-	-	-	-	-	-	-	-
Scaled Baseline	20,830	190	<0.001	-	-	-	-	-	-	-	-	-	-	-	-	-
Classical	-	-	-	0.065	0.095	0.089	0.100	<0.001	-	-	-	-	-	-	-	-
Robust	-	-	-	0.055	0.139	0.132	0.146	<0.001	-	-	-	-	-	-	-	-

) provide insights into the performance and robustness of the hypothesized structural model. The chi-square statistic (X²) for the User Model is significant (X² = 1046, df = 164, p < 0.001), indicating a lack of perfect fit; however, this is expected in large sample sizes. The Standardized Root Mean Square Residual (SRMR = 0.065) and Root Mean Square Error of Approximation (RMSEA = 0.095, 90% CI [0.089, 0.100], RMSEA p < 0.001) suggest an acceptable but not excellent fit. The Comparative Fit Index (CFI = 0.989), Tucker–Lewis index (TLI = 0.987), normed fit index (NFI = 0.987), and other incremental fit indices (RNI = 0.989; IFI = 0.989) demonstrate strong model performance, exceeding the commonly accepted thresholds of 0.90. The parsimony normed fit index (PNFI = 0.852) highlights the model’s balance between goodness-of-fit and parsimony. Comparisons with the Baseline Model (X² = 80,534, df = 190, p < 0.001) underscore the superior fit of the User Model. Scaled comparisons further corroborate these findings, with the Scaled User Model yielding SRMR = 0.055 and RMSEA = 0.106, while the Scaled Baseline Model shows considerably worse fit. Classical and robust estimations support the model’s consistency, reinforcing the validity of the presented findings.

The measurement model (

Table 7. Measurement model, variances, covariances, and intercepts.

Latent/ Variable	Observed/Variable 1	Observed/Variable 2	Estimate	SE	95% CI Lower	95% CI Upper	β	z	p
F1	Q1		1.000	0.0000	1.000	1.000	0.867
	Q2		1.003	0.0172	0.970	1.037	0.869	58.2	<0.001
	Q3		1.007	0.0172	0.973	1.040	0.872	58.6	<0.001
	Q4		0.999	0.0167	0.966	1.032	0.866	59.7	<0.001
	Q5		0.966	0.0166	0.933	0.998	0.837	58.2	<0.001
F2	Q6		1.000	0.0000	1.000	1.000	0.804
	Q7		1.027	0.0227	0.982	1.071	0.825	45.3	<0.001
	Q8		1.016	0.0224	0.972	1.060	0.816	45.3	<0.001
	Q9		1.076	0.0243	1.028	1.123	0.865	44.3	<0.001
	Q10		1.106	0.0236	1.060	1.152	0.889	46.9	<0.001
F3	Q11		1.000	0.0000	1.000	1.000	0.714
	Q12		1.117	0.0333	1.052	1.182	0.797	33.5	<0.001
	Q13		1.198	0.0353	1.129	1.267	0.855	33.9	<0.001
	Q14		1.257	0.0348	1.189	1.325	0.897	36.1	<0.001
	Q15		1.254	0.0355	1.184	1.324	0.895	35.3	<0.001
F4	Q16		1.000	0.0000	1.000	1.000	0.879
	Q17		0.985	0.0184	0.949	1.021	0.866	53.4	<0.001
	Q18		0.854	0.0233	0.809	0.900	0.751	36.6	<0.001
	Q19		0.968	0.0201	0.928	1.007	0.850	48.2	<0.001
	Q20		1.006	0.0172	0.972	1.039	0.884	58.5	<0.001
Covariances	F1	F2	0.349	0.0263	0.298	0.401	0.501	13.3	<0.001
	F1	F3	0.324	0.0233	0.279	0.370	0.525	13.9	<0.001
	F1	F4	0.403	0.0249	0.355	0.452	0.530	16.2	<0.001
	F2	F3	0.319	0.0246	0.271	0.367	0.557	13.0	<0.001
	F2	F4	0.361	0.0228	0.317	0.406	0.511	15.9	<0.001
	F3	F4	0.378	0.0241	0.330	0.425	0.602	15.7	<0.001
Variances	F1		0.751	0.0195	0.713	0.789	1.000	38.5	<0.001
	F2		0.646	0.0232	0.601	0.692	1.000	27.8	<0.001
	F3		0.509	0.0284	0.454	0.565	1.000	18.0	<0.001
	F4		0.773	0.0210	0.731	0.814	1.000	36.8	<0.001
Intercepts	Q1–Q20, F1–F4		0.000	0.000	0.000	0.000

) provides detailed estimates for variances, covariances, and intercepts across latent and observed variables. Each latent factor is operationalized through observed indicators, with strong and statistically significant factor loadings, as evidenced by high β values and z-scores (p < 0.001). For Factor 1 (F1), the loadings range from 0.837 (Q5) to 0.872 (Q3), reflecting a robust alignment between the latent construct and its indicators. Factor 2 (F2) demonstrates similarly strong loadings, peaking at 0.889 (Q10). Factor 3 (F3) exhibits loadings between 0.714 (Q11) and 0.897 (Q14), while Factor 4 (F4) ranges from 0.751 (Q18) to 0.884 (Q20), maintaining strong and reliable associations across constructs.

Covariance estimates highlight moderate to strong interrelations among factors, with coefficients ranging from 0.319 (F2–F3) to 0.403 (F1–F4). These values indicate partial shared variance while preserving the theoretical distinction of each construct. Variance estimates for F1 (0.751), F2 (0.646), and other factors reinforce their significance, each achieving high z-scores (p < 0.001), further affirming the robustness of the measurement model. These findings provide compelling evidence for the reliability and validity of the model, establishing a solid foundation for subsequent analyses.

The SEM presented was estimated using the Diagonally Weighted Least Squares (DWLS) method, a robust approach particularly suitable for ordinal data. The optimization was carried out using the NLMINB algorithm, which effectively handles complex parameter estimation. The dataset comprises 600 observations, providing a substantial sample size for model estimation. The model includes 106 free parameters, and standard errors were computed using the conventional standard approach. The estimation successfully converged after 53 iterations, indicating that the optimization process reached a stable solution without divergence or estimation issues.

The structure of the model involves four latent factors: F1, F2, F3, and F4. Each of these latent factors is defined by five observed indicators. Specifically, F1 is measured by the indicators Q1 through Q5, F2 is measured by Q6 through Q10, F3 by Q11 through Q15, and F4 by Q16 through Q20. These relationships are specified using reflective measurement models, meaning that the indicators are presumed to be influenced by their underlying latent constructs.

The model also accounts for correlations among all latent factors, as indicated by the covariance terms specified between each pair of factors. For example, the relationships between F1 and F2, F1 and F3, F2 and F3, and so on, are explicitly modeled. These covariance terms allow for the examination of conceptual interrelations between the factors and contribute to the overall fit of the model. During the model set-up, duplicated covariance specifications were automatically removed to ensure parsimony and avoid redundancy. This adjustment does not affect the theoretical interpretation but streamlines the computational process.

An important aspect of the model preparation was the transformation of the observed variables Q1 through Q20 into an ordered type, recognizing their ordinal nature. This step ensures consistency with the DWLS estimation method, which assumes that the observed variables are ordered categorically. By coercing the variables into an ordinal format, the analysis aligns with the assumptions of the estimation process, improving the validity and interpretability of the results.

Overall, the model is methodologically sound, with a clear definition of latent constructs and their observed indicators, a comprehensive treatment of factor interrelationships through covariance terms, and an appropriate handling of data type to ensure alignment with the chosen estimation method. The successful convergence of the model further supports its statistical robustness, providing a strong foundation for subsequent analyses and interpretations.

The SEM presented in the diagram offers an in-depth examination of the latent constructs underlying the measured variables, effectively addressing the psychometric properties of the instrument. The model comprises four distinct latent factors, labeled F1 through F4, each defined by five observed variables (Q1 to Q20). These latent variables represent unobservable psychological constructs, operationalized through their corresponding observed indicators, which are depicted as rectangles.

The factor loadings, represented by the arrows connecting the latent factors to the observed variables, exhibit robust values across all constructs. For Factor 1 (F1), the loadings range from 0.97 to 1.01, indicating a high level of reliability in the observed variables Q1 to Q5 as indicators of this latent construct. Similarly, Factor 2 (F2) is measured by Q6 to Q10, with loadings between 1.02 and 1.11, demonstrating a consistently strong relationship. Factor 3 (F3) is characterized by loadings of 1.12 to 1.26 on indicators Q11 to Q15, marking the strongest associations within the model. Lastly, Factor 4 (F4) shows loadings between 0.85 and 1.01 on Q16 to Q20, confirming a solid linkage between the latent construct and its measured variables.

The model depicts the relationships between the higher-order global construct (global anhedonia score, Glbl_) and its four sub-dimensions: social anhedonia (F1), physical anhedonia (F2), cognitive anhedonia (F3), and emotional anhedonia (F4). Each sub-dimension is linked to specific observed variables (Q1-Q20) as shown in Figure 1. Solid lines indicate direct relationships, with standardized regression weights and factor loadings displayed along the paths. Dashed lines represent weaker or less significant pathways. The green hues of the arrows and numerals correspond to the strength of the relationships, with darker tones and thicker lines denoting higher standardized coefficients, hence stronger associations. Conversely, paler arrows and thinner paths reflect lower weights, underscoring weaker explanatory power or statis-tical relevance.

The global anhedonia score shows the strongest relationship with physical anhedonia (F2, 0.82), followed by moderate relationships with social anhedonia (F1, 0.63) and emotional anhedonia (F4, 0.61). The weakest link is observed with cognitive anhedonia (F3, 0.32).

Among the sub-dimensions, the factor loadings of observed variables demonstrate varying levels of contribution. For instance, Q8 and Q9 display relatively lower contributions to physical anhedonia (F2), while Q12 and Q11 strongly contribute to cognitive anhedonia (F3).

The structural model demonstrates correlations among the four latent factors, with coefficients such as 0.61 (Glbl_-F4), 0.32 (Glbl_-F3), and 0.82 (Glbl_-F2) indicating varying strengths of relationships between the global construct and its sub-dimensions. These correlations suggest that while the latent factors share variance with the global anhedonia score, they also maintain sufficient independence to support their conceptual distinction as unique constructs.

Residual variances for the observed indicators, represented by error terms (displayed in green beside the observed variables), account for the variance not explained by their respective latent constructs.

The model’s configuration aligns with theoretical assumptions, exhibiting strong factor loadings and well-specified residuals. For instance, social anhedonia (F1) demonstrates factor loadings ranging from 0.82 to 0.85 for Q1 to Q5. Physical anhedonia (F2) exhibits loadings from 0.68 to 0.87 for Q6 to Q10. Cognitive anhedonia (F3) shows loadings between 0.66 and 0.86 for Q11 to Q15. Lastly, emotional anhedonia (F4) is characterized by loadings between 0.75 and 0.85 for Q16 to Q20.

3.4. Reliability Analysis: Cronbach’s Alpha

To evaluate the internal consistency of the anhedonia subscales within the SPAS-A framework, a reliability analysis was conducted using Cronbach’s alpha coefficients. Descriptive statistics for item performance and scale-level metrics are summarized in

Table 8. Reliability and item statistics for anhedonia scales.

Statistic	Social Anhedonia (SA)	Physical Anhedonia (PA)	Cognitive Anhedonia (CA)	Emotional Anhedonia (EA)
Cronbach’s Alpha	0.916	0.873	0.900	0.905
Cronbach’s Alpha (Standardized Items)	0.916	0.874	0.899	0.905
Number of Items	5	5	5	5
Item Means	2.558	2.46–2.98	2.63–3.03	2.53–2.88
Item Standard Deviation	1.169–1.239	1.161–1.279	1.135–1.281	1.116–1.194
Inter-Item Correlation (Range)	0.646–0.736	0.466–0.779	0.543–0.729	0.568–0.748
Scale Mean	12.79	13.77	13.74	13.22
Scale Variance	26.835	24.124	26.059	23.867
Scale Standard Deviation	5.180	4.912	5.105	4.885
Corrected Item–Total Correlations (Range)	0.772–0.807	0.658–0.744	0.650–0.803	0.684–0.785
Cronbach’s Alpha if Item Deleted (Range)	0.892–0.899	0.836–0.856	0.866–0.898	0.879–0.900

.

The reliability analysis of the instrument reveals high internal consistency across its subscales, reflecting strong reliability in measuring the different dimensions of anhedonia. Specifically, Cronbach’s Alpha values for the four subscales—social, physical, cognitive, and emotional anhedonia—are consistently above the recommended threshold of 0.7, indicating robust scale reliability.

SA achieved a Cronbach’s Alpha of 0.916, demonstrating excellent internal consistency, with item–total correlations ranging from 0.772 to 0.807 and a relatively narrow range of Cronbach’s Alpha if the item was deleted (0.892 to 0.899).

PA showed a slightly lower but still satisfactory Cronbach’s Alpha of 0.873, with inter-item correlations ranging from 0.466 to 0.779. This scale also exhibited good corrected item–total correlations (0.658 to 0.744), suggesting its reliability for capturing physical pleasure deficits.

CA’s Cronbach’s Alpha of 0.900 further supports the scale’s strong internal consistency, with item–total correlations between 0.650 and 0.803.

EA demonstrated a Cronbach’s Alpha of 0.905, signifying high reliability, with corrected item–total correlations ranging from 0.684 to 0.785.

Across all subscales, item means, and standard deviations reflect a consistent trend of moderate levels of anhedonia symptoms, providing evidence of the scale’s sensitivity to variations in pleasure deficits. These findings validate the SPAS-A as a reliable tool for assessing pleasure deficits across social, physical, cognitive, and emotional dimensions in adults.

4. Discussion

The study included a large sample of 600 participants, with a diverse age range and a notable representation of psychiatric diagnoses, which enhances the generalizability and clinical relevance of the findings. EFA, using PCA and Varimax rotation, revealed a clear structure with four distinct factors—social, emotional, cognitive, and physical anhedonia—demonstrating the multidimensional nature of anhedonia. The high correlation between individual items and their respective composite scores across all dimensions highlights the internal consistency and validity of the scale. Additionally, the strength and significance of the correlations between the individual items and their composite scores underscore the robustness of the construct being measured. The factor structure revealed that social and emotional dimensions of anhedonia were most prominently grouped together, while cognitive and physical dimensions were separated into distinct factors, reflecting their unique manifestations. These findings align with previous research, suggesting that anhedonia is not a monolithic construct but rather a complex interplay of different domains. The analysis of descriptive statistics further revealed that cognitive and physical aspects of anhedonia were more frequently reported in this sample, which may reflect the specific characteristics of the population or the nature of the disorders under investigation. The use of SEM provided additional insights into the relationships between the latent factors, confirming the independence of the factors after rotation and offering a more nuanced understanding of the underlying structure of anhedonia. The model’s successful convergence further validates the robustness of the analytical approach, reinforcing the credibility of the results. Overall, the study demonstrates the utility of the SPAS-A as a valid and reliable measure of anhedonia, providing valuable insights for clinical practice and future research on pleasure deficits in various psychological conditions.

The SEM presented in this study effectively underscores the psychometric soundness of the SPAS-A, illustrating how the observed variables robustly define the four latent factors. The strong factor loadings confirm the reliability of the instrument, ensuring that each observed variable is a meaningful indicator of its corresponding latent construct. The correlation coefficients among the latent factors further suggest moderate interrelations, highlighting the complexity of anhedonia as a multidimensional construct, while maintaining the uniqueness of each factor. The inclusion of residual variances adds an important layer of realism to the model, acknowledging that while the latent constructs explain significant portions of the observed variables, there is still variance that is not captured, emphasizing the nuanced nature of psychological measurement.

The theoretical implications of these findings are far-reaching, as they contribute to a deeper understanding of anhedonia as a multifaceted experience that affects individuals across various psychological conditions. By distinguishing the different dimensions of anhedonia—social, emotional, cognitive, and physical—the SPAS-A provides a more comprehensive framework for studying pleasure deficits. This multidimensional approach allows researchers and clinicians to better understand how different aspects of anhedonia interact and how they may manifest in different populations, providing a nuanced perspective on this complex psychological phenomenon. Furthermore, the study’s findings suggest that interventions for anhedonia should be tailored to address the specific domains of impairment, which may vary depending on the individual’s symptoms.

Additionally, the reliability analysis, supported by the high Cronbach’s Alpha values for all subscales, offers compelling evidence for the internal consistency of the scale. The values consistently exceed the threshold of 0.7, indicating that each subscale is robust in measuring its respective dimension of anhedonia. The fact that Cronbach’s Alpha values are close to 1.0, particularly for the social (0.916), cognitive (0.900), and emotional (0.905) anhedonia scales, further demonstrates the reliability of these subscales. The physical anhedonia scale, while showing a slightly lower Cronbach’s Alpha of 0.873, still indicates strong reliability, particularly when coupled with the positive item–total correlations and moderate inter-item correlations. This consistency across different types of anhedonia is critical for establishing the scale as a comprehensive tool for assessing pleasure deficits.

From a practical standpoint, the reliability and internal consistency of the SPAS-A make it an effective tool for use in both clinical and research settings. Its ability to measure the diverse aspects of anhedonia offers a unique opportunity for clinicians to assess the severity and scope of pleasure deficits in individuals with various psychiatric conditions. This can guide the development of personalized treatment plans, ensuring that interventions target the specific areas of anhedonia that are most relevant to the patient’s experience. Additionally, the scale’s strong psychometric properties make it a valuable resource for future studies examining the relationship between anhedonia and other psychological conditions, as well as for exploring the impact of treatment interventions on anhedonia over time.

Given the diverse ways in which anhedonia can manifest, treatment strategies should be tailored to the particular dimensions that are most affected in each individual. For instance, individuals primarily experiencing social anhedonia may benefit from interventions aimed at enhancing social motivation and engagement, such as social skills training or behavioral activation therapies that encourage positive social interactions. Those with pronounced emotional anhedonia may require approaches that focus on increasing emotional responsiveness, such as affective retraining or mindfulness-based therapies designed to enhance emotional awareness and regulation. Cognitive anhedonia, which impacts motivation and decision-making, may be more effectively addressed through cognitive–behavioral strategies that help individuals reframe their expectations of pleasure and reinforce goal-directed behaviors. Meanwhile, interventions targeting physical anhedonia might involve sensory-based therapies, such as exposure to pleasurable stimuli or activities designed to enhance bodily awareness and enjoyment.

The item means and standard deviations provide further evidence of the scale’s sensitivity to variation in anhedonia symptoms, with moderate levels of reported symptoms across dimensions. This suggests that the tool can effectively capture a range of anhedonia experiences within the adult population, including those with clinical conditions. The balance between high reliability and sensitivity underscores the scale’s utility in both clinical and research settings, particularly for exploring the diverse manifestations of anhedonia.

Taken together, the combination of Structural Equation Modeling and reliability analysis provides robust evidence for the psychometric properties of the SPAS-A. Taken together, the integrated application of Structural Equation Modeling (SEM) and reliability analysis offers compelling and methodologically robust evidence supporting the psychometric soundness of the SPAS-A. The scale’s ability to capture distinct dimensions of anhedonia with high internal consistency, supported by a solid factor structure, reinforces its potential as a valuable instrument for assessing pleasure deficits in adults. As such, the scale stands poised for broader application in psychological research and clinical practice, contributing to a deeper understanding of anhedonia and its implications across different psychological conditions.

In clinical practice, we anticipate that SPAS-A will have multiple uses. As a diagnostic aid, it offers a nuanced view of anhedonia, facilitating early intervention in disorders such as major depressive disorder and post-traumatic stress disorder. Additionally, its integration into therapeutic contexts enables real-time monitoring of treatment efficacy, aligning self-reported outcomes with physiological and behavioral markers. Its potential to enhance patient–clinician communication further underscores its value, filling the gaps in understanding that often impede effective care.

Our scale, the SPAS-A, distinguishes itself from other anhedonia assessment tools through its multidimensional structure, its ability to capture distinct yet interrelated domains of pleasure deficits, and its broad applicability across different clinical populations. Unlike existing scales that often focus on a single aspect of anhedonia, the SPAS-A offers a four-factor structure, encompassing social anhedonia, physical anhedonia, cognitive anhedonia, and emotional anhedonia. This comprehensive framework enables a more nuanced understanding of how anhedonia manifests across different psychological conditions. SPAS-A assesses the diminished capacity to derive pleasure from social interactions and relationships, a feature highly relevant in conditions such as schizophrenia and autism spectrum disorders, where social withdrawal and interpersonal difficulties are common. Unlike traditional scales [35,39] which often measure only the presence or absence of social engagement, the SPAS-A evaluates the subjective enjoyment of social activities, peer connection, and participation in social networks. Regarding physical anhedonia (PA), this subscale measures the lack of pleasure derived from sensory and bodily experiences, such as eating, touch, and physical activity. Unlike the Revised Physical Anhedonia Scale (RPAS) [40], which primarily focuses on sensory deficits, the SPAS-A provides a broader evaluation of physical pleasure loss, making it particularly useful for studying depression, substance use disorders, and neurodegenerative conditions where sensory pleasure is frequently diminished. Cognitive anhedonia (CA), a unique feature of the SPAS-A, evaluates the inability to experience pleasure from intellectual and mental activities, such as reading, problem-solving, or engaging in creative tasks. This dimension is not captured in existing anhedonia measures but is highly relevant in major depressive disorder and anxiety disorders, where individuals often report a loss of interest in cognitively stimulating experiences. The emotional subscale, emotional anhedonia (EA), measures both the intensity and frequency of positive emotional experiences, distinguishing anhedonia from related constructs like apathy or general mood disturbances. Unlike scales that assess anhedonia in broad terms [35,41], the SPAS-A evaluates emotional responsiveness across various pleasurable events, providing a more precise differentiation between anhedonia and general emotional blunting. Moreover, SPAS-A is designed to be applicable across a wide range of psychiatric and neurological conditions, including major depressive disorder, schizophrenia, anxiety disorders, substance use disorders, and Parkinson’s disease. Its multidimensional nature allows clinicians and researchers to identify which specific aspects of anhedonia are impaired, enabling more targeted interventions.

Many widely used anhedonia scales, such as the Snaith–Hamilton Pleasure Scale (SHAPS) or Temporal Experience of Pleasure Scale (TEPS) [35], focus primarily on anticipatory vs. consummatory pleasure [42] without distinguishing between social, physical, cognitive, and emotional domains. By contrast, the SPAS-A integrates these elements within its four subscales, providing a more detailed and clinically relevant assessment.

Limitations and Future Research Directions

While our study provides valuable insights into the multidimensional construct of anhedonia, several methodological and conceptual must be acknowledged, which will guide the trajectory of future research.

One primary limitation of our study pertains to the sample composition, which, despite its relevance to the study objectives, may constrain the generalizability of the findings to broader populations. Future investigations should prioritize the validation of the SPAS-A within diverse clinical and non-clinical cohorts, ensuring its applicability across various psychopathological spectra. Expanding research to encompass a wider array of psychiatric conditions, including schizophrenia, major depressive disorder, anxiety disorders, and neurodegenerative pathologies, will be instrumental in refining the external validity of the scale and elucidating its relevance across different clinical profiles.

The cross-sectional design of our study does not allow us to draw conclusions about how anhedonia evolves over time. A longitudinal approach will be necessary to assess changes in anhedonia, particularly in response to treatment, and to explore whether specific subtypes —social, physical, cognitive, or emotional—of anhedonia predict long-term functional outcomes.

Our study primarily relies on self-report questionnaires, which are subject to biases such as social desirability and introspective limitations, which may impact the accuracy of self-perceived anhedonia severity. Future studies will integrate objective measures, such as behavioral tasks assessing reward processing or neuroimaging techniques, to provide a more comprehensive understanding of anhedonia.

Although the SPAS-A distinguishes between different subtypes of anhedonia, it remains important to further establish its discriminant validity relative to apathy, anergia, and general mood disturbances. Systematic analyses of the relationships between SPAS-A subscale scores and other psychological dimensions are necessary to refine the specificity of the instrument and ensure its ability to measure anhedonia as a distinct and clinically meaningful construct.

While the SPAS-A provides a robust framework for assessing anhedonia, its clinical applicability in predicting treatment response remains to be explored. Future research needs to investigate how different anhedonia dimensions relate to pharmacological, psychological, and neurostimulation interventions, with the goal of enhancing personalized treatment strategies. By addressing these research directions, we aim to enhance the understanding of anhedonia’s complex nature, improve diagnostic accuracy, and contribute to the development of more effective, tailored interventions that better address the needs of individuals experiencing this debilitating condition.

5. Conclusions

The study provides valuable insights into the prevalence and multidimensional nature of anhedonia, revealing variability across social, physical, cognitive, and emotional domains.

The results highlight SPAS-A as a reliable and valid tool for assessing pleasure deficits across multiple domains, with significant implications for clinical practice and research. These findings emphasize the importance of addressing the distinct dimensions of anhedonia in therapeutic interventions, suggesting that a multidimensional approach can improve treatment outcomes for individuals with varying forms of anhedonia. By offering a deeper understanding of the condition, clinicians can better tailor interventions to individual needs, thereby enhancing therapeutic efficacy. Despite the robust psychometric properties of the SPAS-A, further validation in diverse clinical populations is recommended to confirm its utility across a wider range of psychiatric conditions.