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Table of Contents
Year : 2022  |  Volume : 11  |  Issue : 2  |  Page : 137-145

Development and psychometric evaluation of the belief and attitude about herbal medicine inventory among iranian patients with cardiovascular disease

1 Nursing Care Research Center (NCRC), School of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, Iran
2 Trauma Nursing Research Centre, Faculty of Nursing and Midwifery, Kashan University of Medical Sciences, Kashan, Iran

Date of Submission13-Jun-2021
Date of Decision22-Oct-2021
Date of Acceptance30-Oct-2021
Date of Web Publication29-Jul-2022

Correspondence Address:
Zahra Sooki
Trauma Nursing Research Centre, Faculty of Nursing and Midwifery, Kashan University of Medical Sciences, Kashan
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/nms.nms_57_21

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Background: Over-the-counter use of herbal products puts patients with cardiovascular diseases (CVDs) at risk for drug interactions. Belief and attitude have significant effects on behavior. Objectives: This study was conducted to develop and evaluate the psychometric properties of the Belief and Attitude about Herbal Medicine Inventory (BAHMI). Methods: In this methodological study, the BAHMI draft was developed based on the approach of Waltz and colleagues and using the existing instruments and the three main theory of planned behavior subscales. After face and content validity assessment, BAHMI construct and concurrent validity were assessed. Accordingly, 200 patients with CVD were consecutively recruited from a heart clinic in Kashan, Iran, 2018. BAHMI reliability was also assessed through the internal consistency and the test–retest methods. Results: The BAHMI draft included 40 items. Seven items were excluded during psychometric evaluation phases. Exploratory factor analysis revealed a five-factor structure for BAHMI which explained 42.636% of the variance of its total score. The correlation coefficient between the scores of BAHMI and Hashem-Dabaghian and colleagues' questionnaire was –0.7 (P < 0.0001). BAHMI mean score was significantly different among patients with different levels of agreement on herbal product use (F = 19.16, P < 0.0001). Cronbach's alpha, intraclass correlation coefficient, standard error of measurement, and smallest detectable change of BAHMI were 0.864, 0.888, ±13.46, and 10.2, respectively. No participant obtained the minimum and maximum possible BAHMI scores. Conclusion: The 33-item BAHMI is a valid and reliable instrument for the assessment of belief and attitude about herbal medicine among patients with CVD. The findings of this study can be used for health policy-making and planning.

Keywords: Attitude, belief, cardiovascular disease, herbal medicine, psychometric evaluation, theory of planned behavior

How to cite this article:
Gholami M, Tagharrobi Z, Sharifi K, Sooki Z. Development and psychometric evaluation of the belief and attitude about herbal medicine inventory among iranian patients with cardiovascular disease. Nurs Midwifery Stud 2022;11:137-45

How to cite this URL:
Gholami M, Tagharrobi Z, Sharifi K, Sooki Z. Development and psychometric evaluation of the belief and attitude about herbal medicine inventory among iranian patients with cardiovascular disease. Nurs Midwifery Stud [serial online] 2022 [cited 2022 Dec 8];11:137-45. Available from: https://www.nmsjournal.com/text.asp?2022/11/2/137/352956

  Introduction Top

Cardiovascular disease (CVD) is the first leading cause of death in the world[1] and in Iran.[2] Patients with CVD usually receive numerous medications.[3] Yet, a large number of them resort to the herbal products (including both medicinal plants and herbal medicines), following the recommendations of lay people and without medical prescriptions.[4],[5] A systematic review reported that the rate of herbal product use among patients with CVD was between 2% and 46% in different countries.[6] This rate is 38%–70.1% in Iran.[7],[8],[9],[10] Simultaneous use of herbal products and conventional cardiac medications may result in drug interactions, which may cause irreparable adverse effects.[11],[12] Therefore, healthcare providers, particularly nurses, need to carefully assess patients for the use of herbal products,[13] identify at-risk patients, and provide them with quality education about appropriate use of herbal products and their potential therapeutic and adverse effects.[14] There are a wide variety of factors behind a behavior such as using herbal products. Belief and attitude are the most important factors affecting the intent to engage in a given behavior and the actual engagement in it.[15],[16] Therefore, belief and attitude assessment can help nurses collect information about patients' health-related behaviors.

Health and belief assessment necessitates appropriate instruments. The most appropriate measurement instruments are those which are developed based on behavior-related theories.[17] One of these theories is the theory of planned behavior (TPB). This theory explains that the most important factors behind engagement in a behavior are intention, attitude, subjective norms, and perceived behavioral control.[17]

Formerly, instruments are developed to assess belief and attitude about herbal medicine among diabetic patients,[18] different ethnic groups and employees,[19] and older adults.[16] Although some of these instruments were TPB-based,[16],[19] none of them targeted patients with CVD. Moreover, studies in Iran into belief and attitude about herbal products were conducted using researcher-made or translated questionnaires which contained limited number of items on belief and attitude[20],[21] and did not cover the different aspects of belief and attitude.[21],[22] In addition, they were conducted on either the general population[20] or patients without non-cardiac conditions.[10],[21],[22],[23] Furthermore, despite the significant effects of culture on belief and attitude, there is no culturally appropriate instrument for the assessment of belief and attitude about herbal product use in Iran. Thus, the present study was conducted to fill these gaps.

  Objectives Top

The aim of this study was the development and psychometric evaluation of Belief and Attitude about Herbal Medicine Inventory (BAHMI) among Iranian patients with CVD.


This methodological study was conducted in 2018 in two phases, namely, BAHMI development and BAHMI psychometric evaluation.

During two study phases, all of the data were gathered by the first author who was a nursing graduate student and had received educations about questionnaire development. The gathered data were reviewed, assessed, and analyzed by the research team during several sessions.

  Phase I: BAHMI development phase Top

The draft of BAHMI was developed using Waltz et al.'s[24] four-step approach. In the first step, the intended concept was identified. In the second step, the aims of measurement were determined in the three main TPB subscales, namely, the measurement of control beliefs, behavioral beliefs, and normative beliefs. These aims were determined based on the definition of belief and attitude in TPB.[17] In the third step, the items of BAHMI were generated through literature search and using the items of the existing relevant instruments. The generated items were combined, refined, revised, and allocated to the aforementioned three subscales. In the fourth step, the wording of the items was revised based on TPB constructs,[25] and the method of item scoring was determined based on the manual for developing TPB-based questionnaires.[25] Item scoring was done on a seven-point scale from “complete disagreement” (scored 1) to “complete agreement” (scored 7). Negatively worded items were reversely scored. Twelve patients with CVD were recruited with maximum variation (respecting their age, gender, education level, and socio-economic status) and interviewed by the first author to more precisely determine the perceived advantages, disadvantages, barriers, and facilitators of using herbal products. Based on the results of the interviews, the BAHMI items were revised and further developed.

  Phase II: BAHMI psychometric evaluation Top

Step I: Face and content validity assessment

BAHMI quantitative and qualitative content validity and quantitative face validity were assessed by 10 experts in nursing, instrument development, complementary and alternative medicine, and health education. For qualitative content validity assessment, the experts were asked to comment on the readability, appropriate wording, adequacy, and scoring of the items and then, the inventory was revised based on their comments. Quantitative content validity was assessed through calculating relaxed content validity ratio (CVRrelaxed)[26] and content validity index (CVI).[27] CVRrelaxed was calculated based on item essentiality[26] and was judged according to Lawshe's table.[27] Item CVI was also calculated based on the relevance, clarity, and simplicity of the items[27] and was judged according to the index of Bausell and Waltz.[24] The total CVI of the inventory (S-CVIaverage) was also calculated through averaging item CVIs.[27]

For quantitative face validity assessment, the experts rated the importance of each item on a five-point scale and then item impact score was calculated.[26] Items with impact scores more than 1.5 were maintained.[26] Qualitative face validity of BAHMI was assessed by 10 patients with CVDs who were different from each other respecting their age, gender, education level, socioeconomic status, and CVD type. Each item was read (by the first author) to them, and they were asked to determine possible ambiguities in it. Items which were ambiguous for them were revised by the research team.

Step II: Construct validity and floor and ceiling effect assessment

BAHMI construct validity was assessed through exploratory factor analysis and the known-groups method. The sample size for exploratory factor analysis is recommended to be 100–300, irrespective of the number of the intended instrument items.[28] Accordingly, we consecutively recruited 200 patients with CVD from the Heart Clinic of Shahid Behesthi Hospital, Kashan, Iran, and asked them to answer BAHMI through face-to-face interviews held by the first author. Sampling was done from April 21 to June 22, 2018. Selection criteria were age over 18, Iranian nationality, ability to communicate verbally, definite medical diagnosis of CVD by a cardiologist, continuous CVD treatments for at least 3 months before the study, having no known cognitive disorders, and consent for participation. Reluctance to stay in the study and refraining from answering BAHMI items were considered as exclusion criteria. Beside BAHMI, recruited patients also answered a demographic questionnaire with nine items on their age, gender, marital status, education level, occupation, monthly income, place of residence, CVD type, CVD diagnosis date, and the length of receiving CVD treatments. Collected data were used for exploratory factor analysis with principal components method and varimax rotation. The number of factors was determined through scree plot and eigenvalues greater than 1. Minimum factor loading was also considered to be 0.4. If an item was loaded on two factors or more, it was allocated to the factor for which it had the greatest factor loading.

BAHMI construct validity was also assessed through the known-groups method. Accordingly, study participants were grouped into seven groups based on their responses to a question about their agreement on using herbal products for the prevention, alleviation, or treatment of CVD-associated problems. This question was scored on a seven-point Likert scale from 1 to 7. Then, these seven groups were compared with each other respecting their BAHMI scores.

Floor and ceiling effects were also assessed.[27] Accordingly, the rate of participants with the minimum and maximum possible BAHMI scores was calculated to determine floor and ceiling effects.

Step III: Concurrent validity assessment

After construct validity assessment, BAHMI concurrent validity was assessed using a questionnaire on attitude about herbal medicine developed by Hashem-Dabaghian et al.[10] They developed their 13-item questionnaire to assess Iranian pregnant women's attitudes toward herbal therapies. The five subscales of their questionnaire are efficacy (4 items), safety (2 items), accessibility and affordability (3 items), preference for taking herbal therapies over conventional therapies (2 items), and desire to use herbal therapies (2 items). Item scoring is done on a five-point Likert scale, resulting in a possible total score of 13–65. Higher scores show more negative attitudes toward using herbal therapies. Hashem-Dabaghian et al.[10] confirmed the acceptable validity and reliability of their questionnaire with a Cronbach's alpha of 0.78. Items 5 and 11 of their questionnaire are specific to pregnant women and hence were adapted in the present study. The content validity of the questionnaire was assessed and confirmed in the present study by seven instructors from Kashan University of Medical Sciences, Kashan, Iran. Its reliability was also confirmed with a Cronbach's alpha of 0.8. Our participants answered this questionnaire and then Spearman's correlation analysis was used to evaluate the correlation between the scores of this questionnaire and the scores of BAHMI.

Step IV: Reliability assessment

The internal consistency of BAHMI and all its subscales was assessed through Cronbach's alpha calculation using the data collected from all participants. For stability assessment, 20 patients were randomly selected from the study sample and were asked to re-answer BAHMI 1 week after their first BAHMI answering. Then, intraclass correlation coefficient (ICC) was calculated to determine the correlation between the test and the retest scores. The standard error measurement of agreement (SEMagreement) was also calculated using the formula shown in [Figure 1], in which SD was the standard deviation of the sum scores of test and retest.[27] After that, the smallest detectable change (SDC) was calculated with a confidence level of 95% using the formula shown in [Figure 1][29]:
Figure 1: SEMagreement and SDC calculations

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  Data analysis Top

Data analysis was done through the SPSS software (v. 16.0). Normality of the data was tested through the Kolmogorov–Smirnov test. Numerical variables were described using central tendency and dispersion measures, whereas categorical variables were described using absolute and relative frequencies. The appropriateness of factor analysis model was tested using the Kaiser–Meyer–Olkin and the Bartlett's tests. Known groups were compared via the one-way analysis of variance. All analyses were done at a significance level of less than 0.05.

  Ethical considerations Top

This study was approved by the Institutional Review Board and the Ethics Committee of Kashan University of Medical Sciences, Kashan, Iran (codes: 26.06.1396.9698 and IR.KAUMS.NUHEPM.REC.1396.15). Sampling was started after obtaining necessary permissions from the aforementioned university and providing them to the nursing office of the study setting. Written informed consent was obtained from all participants, and study instruments were individually completed for each participant in a private room. All participants were ensured that their personal data would be managed confidentially and they could voluntarily withdraw from the study.

  Results Top

Phase I: BAHMI development

The draft of BAHMI contained 40 items in the three subscales of behavioral beliefs, normative beliefs, and control beliefs. These subscales contained 14, 12, and 14 items, respectively.

  Phase II: BAHMI psychometric evaluation Top

Step I: Face and content validity assessment

In this step, some items (such as “Herbal therapies are effective in alleviating or treating CVD symptoms”) were divided into two items. Some items (such as “I trust in the effectiveness of medically prescribed herbal therapies”) overlapped with other items and were excluded, and some items were revised. For instance, the item “The use of herbal products is scientifically approved” was revised as “The use of herbal products is supported by scientific evidence.”

Content validity assessment revealed that two items had CVRrelaxed values less than the minimum critical value for 10 experts, as recommended by Lawshe (i.e. 0.62).[27] Thus, these two items were excluded. The CVI values of all items respecting relevance, clarity, and simplicity were 0.8–1, whereas the minimum acceptable value is 0.78.[24] The S-CVIaverage values of the BAHMI respecting relevance, clarity, and simplicity were 0.93, 0.95, and 0.94, respectively. The impact scores of all items were more than 1.5. None of the items was modified during face validity assessment. Finally, the 38-item BAHMI was subjected to further psychometric evaluation.

Step II: Construct validity and floor and ceiling effect assessment

During the 2-month course of sampling, 235 eligible patients referred to the study setting. However, 30 refused participation and 5 withdrew from it. Thus, data analysis was done on the data collected from 200 patients. On average, they aged 56.91±10.64 years and had been diagnosed with CVD 99.62±108.85 months before the study. Most of them were female (60%), married (91%), and had elementary education (76%). Hypertension and myocardial infarction were, respectively, the most common CVDs among them [Table 1].
Table 1: Participants' characteristics (n=200)

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The results of the KMO test showed sampling adequacy (test value = 0.82), and Bartlett's test showed that the inter-item correlation matrix was appropriate for factor analysis (χ2 = 2522, P < 0.0001). In the exploratory factor analysis, five items (i.e., items 12, 15, 17, 28, and 29) were excluded and five factors were extracted which labeled behavioral beliefs, normative beliefs, perceived personal barriers, perceived general barriers, and reality-based beliefs. These factors explained 42.636% of the total variance of the BAHMI total score [Table 2] and [Table 3]. Scree plot also confirmed that BAHMI contained five factors with eigenvalues more than 1 [Figure 2].
Table 2: Eigenvalues and the amount of variance explained by each of the extracted factors

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Table 3: Items, factors, and item factor loadings of BAHMI

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Figure 2: Scree plot

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In the known-groups method, the results of the one-way analysis of variance revealed at least one significant difference among the seven predetermined groups, respecting the mean score of the 33-item BAHMI (F = 19.16, P < 0.0001). [Table 4] shows the results of the Gabriel post hoc test for pairwise between-group comparisons.
Table 4: Comparison of BAHMI scores among patients with different levels of agreement on herbal product use (n = 200)

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In floor and ceiling effect assessment, no one possessed the minimum and the maximum possible BAHMI scores (i.e., 33 and 231).

Step III: Concurrent validity assessment

The mean scores of BAHMI and Hashemi-Dabaghian and colleagues' questionnaire were 153.55±27.23 and 25.89±7.95, respectively. Spearman's correlation analysis revealed that the scores of these two instruments were significantly correlated (r = –0.7, P < 0.0001).

Step IV: Reliability assessment

The Cronbach's alpha value of BAHMI was 0.864, whereas the Cronbach's alpha values of its behavioral beliefs, normative beliefs, perceived personal barriers, perceived general barriers, and reality-based beliefs were 0.868, 0.825, 0.647, 0.561, and 0.177, respectively. Test–retest stability assessment revealed an ICC of 0.888 (95% CI: 0.72–0.96, P < 0.001). SEMagreement was ±13.46 and SDC was 10.2 with a confidence level of 0.95.

  Discussion Top

Findings revealed that with acceptable validity and reliability in the target population, the 33-item BAHMI can determine the score of belief and attitude about using herbal products in the range of 33–231.

The draft of BAHMI was developed based on TPB as well as using Waltz et al.'s approach and the manual for developing TPB-based questionnaires[25]; thus, compared with other instruments in this area,[10],[20],[22],[23] BAHMI covers a wider range of beliefs and attitudes. Theory-based instrument development helps generate more appropriate items and guarantees the validity of the intended instrument.[24],[25]

The content validity of BAHMI was assessed by experts in different specialties. S-CVIaverage value for all three criteria of relevance, simplicity, and clarity was more than 0.90. Content validity assessment is a key component of instrument development.[29] It ensures as wide as possible coverage of all important aspects of the intended concept. The higher the experience and the knowledge of the experts who assess content validity, the sounder the process of content validity assessment will be.[24] Moreover, an S-CVIaverage of more than 0.9 is considered as great content validity. Study findings also confirmed the face validity of BAHMI. Face validity, which refers to the understandability of the items of an instrument for the target population, improves precision of data collection.[29]

Exploratory factor analysis revealed that BAHMI consisted of five factors, namely, behavioral beliefs, normative beliefs, perceived personal barriers, perceived general barriers, and reality-based beliefs. These factors explained around 43% of the total variance of BAHMI score. The greatest contribution to this variance was related to factors such as behavioral beliefs (13.72%) and normative beliefs (11.42%). Moreover, the other factors each explained more than 5% of the variance. These findings confirm the construct validity of BAHMI. It is believed that each of the extracted factors should explain at least 5% of the variance of the total score.[29] An earlier study into the development of an instrument for assessing attitude and belief about complementary and alternative medicine found that the instrument consisted of the three factors of expected benefits, perceived barriers, and subjective norms, which explained 57% of the total variance of the attitude and belief score.[30] These three factors are almost the same as the four factors of BAHMI. In two other studies, although construct validity was not assessed using factor analysis, the subscales of their belief and attitude instruments were similar to BAHMI subscales.[10],[19] Waltz et al.[24] considered construct validity assessment essential and beneficial to all measurement instruments and noted that its main focus is on the consistency between the items of the intended instrument and the underlying theory and concept. As a strength of the present study, our findings revealed that the extracted factors were consistent with the concept of belief and attitude about herbal product use.

Known-groups comparison in the present study indicated significant difference among patients with different levels of agreement on using herbal medicine respecting their BAHMI scores. This finding denotes that BAHMI successfully differentiates patients from different levels of agreement on herbal medicine use and hence confirms its construct validity. In the known-groups method, the intended instrument is applied to people who are different from each other respecting a certain attribute; hence, significant difference among them respecting the score of the intended instrument confirms its construct validity.[31]

Concurrent validity assessment also revealed a significant correlation (r = –0.7) between the scores of BAHMI and the instrument developed in an earlier study.[10] A correlation coefficient of 0.7 is acceptable for concurrent validity confirmation.[29] Furthermore, BAHMI does not have floor and ceiling effects and has appropriate items for the assessment of belief and attitude about herbal product use. If more than 15% of the respondents of an instrument obtained its minimum or maximum possible score or both, that instrument is considered to have floor or ceiling effects or both. The existence of either of these effects denotes inadequate content validity.[29]

Cronbach's alpha of BAHMI was 0.864; this is consistent with the results of some related scales[16] and is higher than the coefficient of some scales, such as the questionnaire developed by Hashem-Dabaghian et al.[10] Cronbach's alpha values greater than 0.7[27] or 0.8[24] are indicative of great internal consistency. Of course, the reality-based beliefs subscale of BAHMI had a low Cronbach's alpha[27] and hence this subscale cannot be used independently from the other BAHMI subscales. This subscale pertains to realities and reality-based beliefs, which may require respondent to adopt conservative attitude towards herbal products; hence, its low Cronbach's alpha is justifiable.

We also found that test–retest ICC was 0.888. A correlation coefficient more than 0.7 is acceptable.[29] The test–retest method tests the stability and repeatability of the scores of a given instrument and is among the methods for reliability assessment.[24],[27] Therefore, the high test–retest ICC in the present study implies the high reliability of BAHMI.

Study findings showed that SEMagreement and SDC were equal to ±13.46 and 10.2, respectively. Low SEMagreement values support the stability of the intended instrument.[29],[32] Given the possible range of the total BAHMI score (i.e., 33–231), an SEMagreement of ±13.46 confirms the stability and the repeatability of the data collected using BAHMI and hence confirms its reliability.

Among the strength of the study were instrument development based on TPB as well as the diversity of the study participants respecting their demographic characteristics and CVD conditions. The main study limitation was sampling from a single heart clinic. Application of BAHMI to patients with other chronic conditions in different settings can provide more reliable data about its generalizability. Moreover, confirmatory factor analysis for BAHMI is recommended.

  Conclusion Top

The 33-item BAHMI is a valid and reliable instrument and can be used for the assessment of belief and attitude about herbal product use among patients with CVD. Through using BAHMI, the healthcare providers, particularly nurses, can identify patients who are at risk of consuming herbal products. Then, they can provide them with quality education about appropriate use of herbal products and their potential therapeutic and adverse effects.

  Acknowledgments Top

The authors express their gratitude toward the Research and Technology Administration of Kashan University of Medical Sciences, all study participants, and Dr. Fattaneh Hashem-Dabbaghian.

  Financial support and sponsorship Top

This article came from the Master's thesis of the first author in medical-surgical nursing. The thesis had been approved and financially supported by the Research and Technology Administration of Kashan University of Medical Sciences, Kashan, Iran [codes: 26.06.1396.9698].

  Conflicts of interest Top

No conflict of interest associated with this work.

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  [Figure 1], [Figure 2]

  [Table 1], [Table 2], [Table 3], [Table 4]


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