The Ayurvedic Anger Assessment Scale: An integrative approach for measuring anger in adults

Abstract

Background

The prevalence of psychosomatic disorders associated with emotional instability has risen considerably in recent years. Among the different negative emotions, anger stands out as the most intense and prototypical. When excessive and poorly regulated, it serves as a significant contributing factor in the pathogenesis of various psychosomatic disorders.

Objective

This study aims to develop and systematically validate an integrated Ayurvedic Anger Assessment Scale (AAAS) for the adult population based on Ayurvedic, Indian philosophical, and contemporary psychological concepts, and this manuscript discusses its psychometric validation.

Materials & methods

An extensive literature review and expert consultations were conducted for item generation. A panel of 10 experts evaluated content and face validity. Subsequently, pre-testing was carried out with 15 healthy individuals to assess internal consistency and test–retest reliability. Construct validity was further assessed by administering the questionnaire to a sample of 105 individuals.

Results

Content Validity Index (I-CVI) of the items ranged from 0.50 to 1.00. Given that the panel consisted of ten experts, a minimum acceptable CVI value of 0.78 was applied. Accordingly, seven items with CVI values below this threshold were excluded, and the Scale-level Content Validity Index (S-CVI) was subsequently calculated, yielding a value of 0.91. The Content Validity Ratio (CVR) across items ranged from 0.20 to 1.00. Reliability analysis demonstrated strong internal consistency, with a Cronbach's alpha of 0.944. Test–retest reliability, assessed using the Pearson correlation coefficient, produced values ranging from 0.70 to 1.00 (p < 0.01) across all items. Exploratory factor analysis (EFA) using principal component analysis extracted nine components with eigenvalues >1.

Conclusion

This scale adopts an integrative approach to assessing the intensity of expressed anger, including negative consequences in the form of anger rumination. The development and validation process has been comprehensive, with significant psychometric properties inspiring future researchers to undertake similar studies.

1. Introduction

Ayurveda is an ancient science of life with a long history, and its fundamental principles remain relevant even today. These principles establish Ayurveda as a person-centred medicine that provides guidance for primary prevention, diagnosis, and therapeutics [1]. Dharaniya vega, or natural urges, is one such concept explained in Ayurveda, which is linked to various emotional states of mind and addresses the preventive aspects of health promotion. Emotions, in general, can be categorised as positive and negative emotions. Unpleasant emotions like fear, anger, and jealousy, which are harmful to the development and well-being of an individual, are termed negative emotions [2]. The different emotions explained under Dharaniya vega come under negative emotions that include greed, fear, anger, vanity, jealousy, and malice [3].

The specific emotion an individual experiences at a particular moment and the nature or intensity of that emotion have been the subject of extensive research [4]. Since the effect of emotions is not easy to analyse objectively, the proper identification and measurement of emotions is always a tough and challenging task. Among the spectrum of negative emotions, anger has been selected as the focal construct of the present study, owing to its intensity and status as a prototypical emotional response. Considering its widespread occurrence and significant psychological and social consequences, the prevalence and correlates of anger have been the subject of empirical investigation in various population-based studies. According to a national survey conducted in the USA during 2015-16, the overall prevalence of inappropriate, intense, or poorly regulated anger was found to be 7.8 % [5]. The findings indicated that this form of intense anger was more prevalent among men and younger adults and was linked to reduced psychosocial functioning. The study also reported significant positive correlations with parental influences, as well as adverse experiences during childhood and adulthood and a strong association with several psychiatric conditions, including bipolar disorder, drug dependence, psychotic disorders, and borderline and schizotypal personality disorders.

The available instruments for measuring anger, such as the Clinical Anger Scale [6], the Novaco Anger Scale [7] and the State Trait Anger Expression Inventory [8], are grounded in Western psychological frameworks and often neglect cultural, philosophical, or holistic health perspectives like those found in non-Western traditions. In the Indian context, few researchers have previously attempted to develop an assessment scale for anger based on Indian classical texts. This scale has been constructed based on the theoretical concepts of kayika (behavioural), vachika (verbal), and manasika (mental) domains of anger [9]. Although the scale is a holistic approach covering all aspects of anger, its applicability is limited as it targets only the adolescent population. Additionally, it does not account for various styles of anger expression, including ruminative tendencies. Furthermore, no data that supports content validity is available for this scale in the published literature.

Thus, the aim of this research work was to develop and systematically validate an integrated Ayurvedic Anger Assessment Scale (AAAS) for the adult population based on Ayurvedic, Indian philosophical, and contemporary psychological concepts, and this manuscript discusses its psychometric validation. Developing such a scale might help assess the level of expressed anger and its related implications and indicate the need for care if it exceeds the normal level. Additionally, understanding the epidemiology of anger might help to enhance public health education and clinical strategies aimed at preventing poorly controlled anger.

2. Methodology

2.1. Item generation

An extensive literature search was done to explore the various references that discuss the concept of Anger in Ayurveda, Indian philosophy, and contemporary psychiatry. After reviewing the classical texts of Ayurveda, it was observed that the description of Anger finds its mention in different contexts as a causative factor, playing a significant role in the pathogenesis of various diseases. In Charaka Samhita (one of the classical texts in Ayurveda), anger is described as a state in which the individual experiences intense internal agitation, accompanied by extreme hate or repulsion [10].In Indian philosophical scriptures, a detailed account of anger is not provided, it is described as an aspect of personality that emerges from the interaction between ego (ahamkara) and the external world [11]. In the works of Acharya Bharatha (5th century), an Indian Philosopher, there is a strong emphasis on the experiential aspect of emotions and anger [12].He mentions insult, abuse, false allegations, and threatening acts of hostility as the determinant causes of anger. According to the Bhagavad Gita, the Indian philosophical and spiritual scripture, anger has significant consequences on a person's well-being. It explains that when an individual focuses on sensory objects, attachment develops. This attachment leads to desire, and from desire, anger emerges. Anger then brings about confusion, causing bewilderment of memory. When memory is affected, intellect is weakened, causing the person to lose control over their actions.

Following an extensive literature review, 48 items were initially pooled. To ensure clarity, relevance, and contextual appropriateness, the list was refined to twenty-eight items after thorough discussions with experts and interviews with patients reporting anger-related issues. Items that were found to be ambiguous or lacking in clarity and relevance were systematically removed during this process. The items were devised under three domains, indicating social, psychological, and physical components. Anger can be triggered by various social factors. Assault, bullying, and other violent acts are thought to be possible outcomes of an intense feeling of anger that has a significant social impact, which can result in poor social conduct or even legal issues for an individual [13]. Therefore, it is crucial to examine the influence of social circumstances on the generation and expression of anger. Anger onset and cognitive appraisal were incorporated as sub-sections to evaluate the social component. Anger onset included items to find the influence of the social environment that may act as a triggering factor to cause anger. Cognitive appraisal refers to an individual's interpretation in response to stimuli in their social environment. This sub-section included items referring to a situation to understand how a person interprets a social circumstance that may or may not cause anger, to distinguish stable personality tendencies from situational emotional responses.

Expression of aggression, behaviour, rumination, and anger regulation were the sub-sections included under the psychological component. Under the expression of aggression sub-section, the items were devised in such a manner to assess the extent and styles of anger expression. The behaviour sub-section included items to identify the range of mannerisms made by an individual when he/she is in the grip of anger. Rumination refers to excessive repetitive thinking about an event or situation that caused anger. A greater tendency to ruminate may increase the risk of engaging in disruptive behaviour. The anger regulation sub-section includes items designed to identify the ability to distance oneself from provocation. And the physical component included various physiological effects categorised as immediate and chronic effects. Activation of the sympathetic nervous system during the experience and expression of anger leads to various physiological changes, such as increased cardiovascular response, higher rate of respiration, etc., and when it becomes habitual, it can have a negative impact on the human body. Thus, this domain was categorised into two sub-sections, including items to identify the immediate and chronic effects of anger. The different domains, their sub-sections, and the initial number of items pooled are summarised in Table 1.

Table 1.

Initial item distribution pattern.

SI no:	Domains	Sub-sections	Number of items
1	Social components	Anger onset	8 items
		Cognitive appraisal	5 items
2	Psychological components	Expression of aggression	1 item with 7 sub-questions
		Behaviour	4 items
		Rumination	4 items
		Anger regulation	5 items
3	Physical components	Physiological effects	1 item with 9 sub-questions

2.2. Scoring

The scale was devised as a self-administered scale, and scoring was given using a 5-point Likert Scale ranging from 0 to 4. Zero is “never,” and four is “always,” except for the anger regulation sub-section, where the score was reversed from 4 being “never” to 0 being “always.”

2.3. Pre-testing

The prepared questionnaire was pretested to evaluate face validity and content validity. Face validity is a subjective assessment of the appearance of items. It reflects the face value of the scale in terms of its relevance. Out of 28 items in the anger scale, seven items were found to be ambiguous and were removed. Content validity pertains to the extent to which the items of an instrument accurately represent the intended construct. Establishing content validity is crucial to ensure the overall credibility and appropriateness of the assessment scale. Content validity is evaluated using the content validity index for items (I-CVI), scale-level content validity (S-CVI), and the content validity ratio (CVR).

To assess face validity and content validity, the draft was sent to a panel of 10 experts, including psychologists, subject specialists in tool development, academicians, and clinicians. Experts were chosen based on their professional experience, recognised academic or clinical achievements in their fields, and prior involvement in tool development or validation. The varied backgrounds ensured that the scale was appraised not only for its theoretical validity but also for its practical relevance and cultural suitability.

A proforma was designed and submitted along with the draft for evaluation, suggestions, and remarks on each item of the scale. The experts were requested to add their opinions against each question regarding the domain's content, clarity, understandability, layout, relevance, and overall agreement. The experts evaluated the relevance of each item based on a five-point scale. The Content Validity Index for Items (I-CVI), Scale-level Content Validity Index (S-CVI), and Content Validity Ratio (CVR) were computed to indicate content validity. The items that received a low value were removed.

2.4. Reliability

Reliability is the degree to which a scale yields the same findings on subsequent attempts, given that the construct being examined is stable over time. Reliability was assessed by checking the internal consistency and test-retest reliability. Internal consistency was assessed using Cronbach's alpha test [14]. Test-retest Reliability was determined using the Pearson correlation coefficient after administering the tool to 15 subjects twice over a gap of ten days [15]. The analysis was done using the statistical software SPSS version 26.0 (developed by IBM, Chicago, Illinois, United States). The methodology is summarised in Fig. 2.

Fig. 2.

Study methodology.

2.5. Exploratory factor analysis

After preliminary validation, the scale was subjected to exploratory factor analysis (EFA). Exploratory Factor Analysis (EFA) is a statistical technique to establish construct validity. It is used to identify the underlying structure or dimensions within a set of observed variables. This helps in refining the scale by revealing redundant, irrelevant, or poorly performing items. For this, the scale was administered to a large sample. The sample size was calculated based on the sample-to-variable ratio [16]. The ratio of sample to variable is 5:1(Bryant & Yarnold, 1995; David Garson,2008). After the initial validation, the questionnaire had 21 items; hence, a sample size of 105 was desirable. A convenience sampling approach was employed to recruit study participants through community outreach and institutional settings. A brief screening interview was conducted to assess eligibility. This included collecting basic demographic data and evaluating the individual's ability to comprehend and respond to the questionnaire. Healthy individuals satisfying the inclusion and exclusion criteria were recruited for the study. Participants were included in the study irrespective of gender, religion, occupation, and socio-economic status. Conditions that incapacitated the participant from responding to the questionnaire and psychiatric illnesses were excluded from the study. Individuals who were not willing to participate in the study were also excluded. The demographic characteristics of the study participants are given in Table 2.

Table 2.

Demographic characteristics of the study participants (n = 105).

Parameters	Percentage
Age (years)
19–29	41.0 %
30–39	39.0 %
40 & above	20.0 %
Gender
Male	72.4 %
Female	27.6 %
Occupation
Employed	74.85 %
Unemployed	25.15 %

Prior ethical approval was obtained from the institutional ethics committee (IEC-AIIA/2021-P125). Informed consent was obtained in person through consent forms. All participants were given a participant information sheet.

3. Results

The face validity of the scale was confirmed by proper response options, and it was found to be satisfactory. The I-CVI, S-CVI, and CVR were computed to indicate the content validity. The I-CVI of the items ranged from 0.50 to 1.00. As the number of experts was ten, the acceptable CVI value was 0.78 [17]. Thus, after analysis, seven items that received a value less than 0.78 were deleted, and the S-CVI of the scale was computed. The obtained value was 0.91. The content validity index of items and the Scale level content validity index are given in Table 3. The CVR for different items ranged from 0.2 to 1. The acceptable CVR value for ten experts is 0.62. Seven items that received a value less than 0.62 were deleted. The content validity ratio (CVR) of items is given in Table 4. Cronbach's alpha demonstrated a value of 0.944, indicating an excellent level of reliability and the Test-retest reliability yielded a score >0.70 (P < 0.01) for all items.

Table 3.

Content validity index.

SI NO	Item content validity index I-CVI: (agreed item)/(number of experts)	Number of questions
1	0.5–0.6	7 questions
2	0.7–0.8	7 questions
3	0.9–1	14 questions
S-CVI/Average: (sum of I-CVI scores)/(number of items) = 0. 914

Table 4.

Content validity ratio.

SI NO	Content Validity Ratio	Number of questions
1	0.2–0.7	7 questions
2	0.7–0.8	10 questions
3	0.9–1	11 questions

CVR: [ne- (N/2)]/[N/2] = 0.65.

ne = number of panel members indicating essential.

N = Total number of panel members.

Exploratory Factor Analysis (EFA) was employed to evaluate the construct validity of the tool by examining the interrelationships among variables and identifying the number of underlying factors influencing them [18]. Factor analysis is a statistical approach used to uncover patterns of association within a large set of variables. For factor extraction, Principal Component Analysis (PCA) was chosen, as it is the most widely used method in exploratory studies to perform factor analysis. The strength of PCA lies in its ability to capture the maximum amount of variance in the dataset by transforming the original variables into a smaller set of uncorrelated components. This makes it highly effective for data reduction and for providing a clear preliminary understanding of factor structure. Although common factor extraction methods focus specifically on modelling the shared variance to reveal latent constructs, PCA was preferred in this study because it offers a straightforward, computationally efficient approach that ensures maximum variance explanation, thereby facilitating an initial exploration of the tool's dimensionality.

The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.787, indicating an adequate sample size. Bartlett's test of sphericity was significant (p < 0.001); hence, the data were suitable for factor analysis. According to the Eigenvalue rule by Kaiser, those components whose Eigenvalue is > 1 were retained [19]. It gives the amount of variance in the data explained by each component. After factor extraction, nine components had an Eigenvalue greater than 1. The scree plot shows the graphical representation of Eigenvalues. Eigenvalues are plotted on the Y axis and the components on the X axis (Fig. 3). The percentage of the variance of each component in the scale and the commonality of each item were acquired from the principal component analysis. The cumulative variance accounted for 9 factors, was 71.30 % (Table 5). After factor extraction, factor rotation was done. Factor loading is a criterion used to delete items after factor rotation [20]. In this study, varimax rotation was done to maximise the variance explained by each factor independently and thereby get a better interpretable model. Varimax rotation with the Kaiser Normalisation method was selected in this study, assuming that the factors are uncorrelated.

Fig. 3.

Scree plot.

Table 5.

Total variance explained.

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	10.320	29.484	29.484	10.320	29.484	29.484	5.071	14.490	14.490
2	3.548	10.137	39.621	3.548	10.137	39.621	3.502	10.006	24.496
3	2.711	7.744	47.365	2.711	7.744	47.365	3.347	9.562	34.058
4	2.047	5.848	53.214	2.047	5.848	53.214	3.227	9.219	43.277
5	1.555	4.444	57.657	1.555	4.444	57.657	2.714	7.754	51.031
6	1.386	3.960	61.617	1.386	3.960	61.617	2.050	5.858	56.889
7	1.254	3.582	65.200	1.254	3.582	65.200	1.932	5.521	62.410
8	1.113	3.180	68.380	1.113	3.180	68.380	1.618	4.622	67.032
9	1.024	2.925	71.305	1.024	2.925	71.305	1.495	4.273	71.305

A rotated component matrix (RCM) is the key output of a rotated solution. RCM displays a matrix of coefficients and their correlation (loading) with the underlying factors [21]. The outcome of RCM is given in Table 6. Items having a communality greater than 0.4 are considered eligible. In the present study, the cut-off point for each loading was taken as 0.5. The item loaded on the wrong factor was deleted. If loaded on more factors, the item was retained under the factor where it was loaded high. Based on this, principal item deletion was done one at a time. Each time an item was deleted, internal consistency was repeated to ensure the reliability was not compromised. The analysis was repeated, dropping the poorly loaded items, and finally, a 15-item model was derived by dropping six items. Thus, the final instrument consists of 15 items, where item 5 has seven sub-items relating to aggressive behaviour and item 15 has nine sub-items relating to immediate and chronic physiological effects of anger. The clinical cut-off score categorises the severity of anger issues and guides the need for intervention. A cut-off of >78 indicates severe anger issues, scores between 39 and 77 reflect a moderate condition, and scores <38 suggest a mild condition. Higher scores correspond to poorer anger regulation, which often necessitates external support or intervention. The total of all the sub-items was also included to get the final score. The final scale was tested for internal consistency with Cronbach's alpha, and the score received was 0.926. In the end, the nine factors were labelled to convey the category of items (Fig. 1).

Table 6.

Rotated component matrix.

Item number	Items	Component
		1	2	3	4	5	6	7	8	9
1	Criticism					0.786
2	Made to wait					0.813
3	Insult					0.722
4	Spilling coffee								0.755
5a	Use of abusive words									0.736
5b	Throw objects	0.736
5c	Slam the door	0.756
5d	Harming self	0.792
5e	Harming others	0.854
5f	Clench teeth	0.695
5g	Punch on wall	0.802
6	Argument						0.772
7	Staring						0.691
8	Aggression on the recall of anger outburst events							0.532
9	Unable to forgive							0.647
10	Feel like taking revenge							0.753
11	Avoiding situation			0.769
12	Not showing it out			0.795
13	Understanding situation			0.762
14	Use of techniques			0.818
15a	Body temperature				0.648
15b	Rapid breath				0.807
15c	Heartbeat				0.782
15d	Sweat				0.666
15e	Body tremble		0.561
15f	Headache		0.702
15g	Restlessness		0.743
15h	Appetite		0.782
15i	Sleep		0.830

Fig. 1.

Labelling of factors with the items included.

4. Discussion

The Anger Assessment Scale is relevant within the Ayurvedic framework, as it builds upon previous work on the adolescent population by extending its applicability to the adult population. This scale was developed by adapting concepts from Ayurveda, Indian philosophy and contemporary psychology. Further, an effort was made to review all the existing scales that assess anger to bridge the gaps in the available research pool.

Although anger is seen as a normal or fundamental emotion, it is crucial to understand when it acts as a disease's primary cause. In the classical Ayurvedic text Sushruta Samhita, it is mentioned that anger can be assessed by the tendency of abhidroha (insult or injury), i.e., a person's intention to harm others due to aggression or hostility the person holds in his mind [22]. Meanwhile, anger rumination, as mentioned in contemporary science, is the recurrent negative thinking about past experiences of anger, which can prolong and intensify the anger reaction [23]. Therefore, a person who ruminates on hostile thoughts can have a detrimental effect on their health, and on a deeper level, it might act as a causative factor for many diseases. While drafting the items, a distinct sub-section on rumination and expression of aggression was provided within the psychological domain, aimed at capturing aggressive and hostile thoughts. That helps in assessing the extent of anger expression that an individual experiences.

Under the sub-section of expression of anger, we have tried to include all three components of anger expression, namely, verbal/linguistic (languages/words used), non-verbal (facial expression/gestures), and paralinguistic (vocalisations like tone and loudness) [24]. Interviews with individuals who reported suffering from anger issues with various educational and socio-economic backgrounds helped to devise this section of the domain with clarity during the development process and added greater significance to the scale.

To select an optimum combination of items, keeping an initial pool of items minimum twice as long as the desired final scale is recommended [25]. The initial pool of items generated had 48 items. After the initial discussion, those items that were not adequately measuring the construct were removed, and the final proforma, sent to the experts for validation, had 28 items. After the initial validation phase, seven items that received a low content validity index and content validity ratio were deleted from the questionnaire. The items' low score was presumably the result of inadequate framing or layout. Pre-testing facilitated the keeping of those items that reflected the domain under study [26]. With twenty-one items, a reliability check was done, and the key strength of the devised scale is strong internal consistency and test-retest reliability.

Before the large-sample study, a cognitive interview was conducted to refine the scale [27]. Several items were modified based on the insights from the cognitive interview to improve understandability and clarity. Based on the variable-to-sample ratio, the study sample selected for analysing construct validity consisted of 105 healthy individuals, including both male and female participants of varying ages. The questionnaire's completion time was short, and the response rates were satisfactory with no missing data. The self-administered nature of the scale mitigated the social desirability bias of some items, such as “harming others” or “using abusive words”.

After Factor extraction, 9 Components were generated through Principal Component Analysis [PCA], accounting for 71 % of the variability. Factor loading was taken at 0.5, which tends to check the association between a particular item and a factor. Six items have been removed from the questionnaire based on factor loading. After removing the six items, the internal consistency was checked and verified.

Based on factor loading, the factors were labelled as a means to communicate the category of items. The items loaded to factor 1 were named as aggressive behaviour, as this behaviour can threaten or intimidate others and violate social norms. Factor 2 was named “Chronic Effects of Anger,” as it includes items that relate to the symptoms seen in people with persistent anger or those with high trait anger. Factor 3 contains items to assess how well a person manages their anger, and this factor is named anger management. Factor 4 was named as the immediate effects of anger; the items loaded under this factor might help to assess the immediate physiological effects of anger. Factor 5 includes items to find the causes of anger in an individual and is named as triggering factors. Factor 6 is named “Individual Response,” as this factor loads items relating to how an individual responds to various triggering factors. The items included under this factor do not fall under disruptive behaviour, but are likely to lead to poor interpersonal relationships, feelings of isolation, and loneliness. Factor 7 includes items to find the ruminative behaviour and is named persistent anger. Only one item was loaded to factor 8, and it was named as a social circumstance, as this item was related to an external condition that may or may not cause anger in an individual. Similarly, only one item was loaded to factor 9. The item included under factor 9 is “use of abusive words”; this is named as verbal expression of anger, as using abusive words is a disruptive form of expression of anger.

When mapping the theoretical domain to the final factor structure, factor 1 included items from the ‘expression of aggression’ sub-section, falling within the theoretical domain of the psychological component. Factors 2 and 4 had items from the theoretical domain of the physical component, including ‘chronic and immediate effects’ of anger, respectively. Factor 3 comprised items from the sub-section of ‘anger regulation’ of the psychological component. Factor 5 loaded items from the sub-section ‘anger onset’ of the social component. Factor 6 included items of the ‘behaviour’ sub-section. Factor 7 loaded items from the ‘rumination’ sub-section, while factor 8 loaded a single item from the ‘cognitive appraisal’ sub-section. One of the items from the sub-section ‘expression of aggression’ from the psychological component was loaded to factor 9 separately. Notably, factors 8 and 9 have loaded only a single item, indicating that these factors are weak psychometrically and necessitate the need to broaden these domains in future revalidation. These single-item factors were retained in the present study owing to their significance in identifying diverse styles of anger expression. However, it is marked as provisional considering its weak psychometric properties.

It is also essential to discuss that the expression of anger differs with respect to gender and cultural background. However, this study was limited in exploring these variables as the sample was drawn from a specific geographical area and had a gender imbalance, with 72.4 % male and 27.6 % female participants. While considering the cultural variability in anger expression, perception, and management, the Ayurvedic Anger Assessment Scale holds significant potential for cross-cultural adaptation and translation. To ensure its broader applicability, systematic translation procedures, including forward and backward translation, expert review, and pilot testing have to be done. Cultural adaptation should also involve evaluating semantic, idiomatic, experiential, and conceptual equivalence to maintain the scale's validity across diverse populations. Incorporating culturally relevant expressions and context-specific triggers of anger will enhance the scale's sensitivity and reliability. Furthermore, psychometric validation in each cultural context is essential to confirm consistency in factor structure, reliability, and validity.

As expected, we found a positive association between anger regulation and reduced expression of anger in the population sample. Moreover, the findings suggest that healthy individuals in the study appeared to manage anger more constructively by recognising triggers and employing distraction techniques. Future research could investigate whether individuals with chronic diseases exhibit differences in their anger responses, particularly in terms of intensity and frequency.

The scale employs a multidimensional approach to the construct, with distinct domains that facilitate the differentiation between anger regulation and anger expression. Further study is required to find the positive significant correlation of anger expression with non-communicable diseases and other psychiatric disorders. Our research found that verbal anger expression and aggression had a significant correlation with anger rumination. Acting out to express anger tends to worsen the situation and escalate aggression. Given that an increasing number of patients with higher anger dispositions are reporting to clinical settings, it is essential to formally recognise this as a clinical problem and address its consequences. Continued use and replication of the psychometric properties are further recommended to establish its applicability and usefulness in different settings, particularly among individuals with higher anger dispositions, such as prison inmates or psychiatric patients.

5. Conclusion

This scale is an integrative approach to predict the intensity of observed aggressive behaviour, including negative consequences in the form of anger rumination in the adult population. The development and validation process has been comprehensive, with strong psychometric properties inspiring future researchers to undertake similar studies. This study had certain limitations, most notably the absence of criterion validity assessment, which is a crucial component in the process of scale development. To address this, further validation is necessary by comparing the scale against established and validated anger assessment tools such as the State-Trait Anger Expression Inventory (STAXI-2), the Novaco Anger Scale, or the Holistic Adolescent Anger Assessment Scale. Since the present study involved only preliminary validation, additional research involving individuals with a higher predisposition to anger is essential. This would enable the evaluation of sensitivity, specificity, and predictive values (both positive and negative). Moreover, the relatively small sample size in the current study highlights the need for replication with larger and more diverse populations to strengthen the generalisability of the findings. The present study emphasises the value of integrating Ayurvedic principles with contemporary scientific disciplines through a translational research approach. It illustrates the potential for selected Ayurvedic concepts to contribute meaningfully to modern scientific inquiry and healthcare, considering their relevance and applicability.

Author contribution

AR: Conceptualization, Methodology/study design, Writing- original draft, Writing-review and editing, GS: Conceptualization, Methodology/study design, Supervision, Writing-review and editing MK: Conceptualization, Methodology/study design, Supervision, Writing-review and editing AK: Conceptualization, Formal analysis, Resources, Data curation, Writing-review and editing RR: Formal analysis, Resources, Data curation, Writing-review and editing, SH: Formal analysis, Resources, Data curation, Writing-review and editing, Visualisation.

Declaration of generative AI in scientific writing

During the preparation of this work, we have used Grammarly in order to improve the language and readability of the content. After using this tool/service, we have reviewed and edited the content as needed and take full responsibility for the content of the publication.

Funding sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary data to this article: