Development and Psychometric Evaluation of the Heart Failure Treatment Adherence Questionnaire (HFAQ) for Military Personnel and Their Family: A Mixed, Exploratory, and Sequential Study

authors:

avatar Amirhossein Akbarzadeh ORCID 1 , 2 , avatar Armin Zareiyan ORCID 3 , avatar Saeideh Ghaffarifar ORCID 2 , avatar Younes Ghelich 4 , *

Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran
Medical Education Research Center, Health Management and Safety Promotion Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran
Public Health Department, Health in Disaster & Emergencies Department, Nursing Faculty, AJA University of Medical Sciences, Tehran, Iran
Department of Cardiology, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran

how to cite: Akbarzadeh A, Zareiyan A, Ghaffarifar S, Ghelich Y. Development and Psychometric Evaluation of the Heart Failure Treatment Adherence Questionnaire (HFAQ) for Military Personnel and Their Family: A Mixed, Exploratory, and Sequential Study. J Arch Mil Med.10(2):e127116. doi: 10.5812/jamm-127116.

Abstract

Background:

Heart failure (HF) is an important public health issue whose prevalence, hospitalization rate, and costs have increased over the years. Treatment adherence is the behavior of the patient in taking medications, correcting diet, and ability to change lifestyle with the recommendations of the health care provider. Poor treatment adherence contributes to worsening disease outcomes. Currently, according to the literature, no specific instrument is available to measure treatment adherence of HF patients.

Objectives:

The aim of this study was to develop and evaluate the heart failure treatment adherence questionnaire (HFAQ) for military personnel and their family.

Methods:

Items were generated by literature review, patient field interviews, and expert opinions. Content validity was assessed by measuring CVR and CVI, while face validity was evaluated by measuring the impact score of each item. The research team performed EGA, bootEGA, and CFA to assess the construct validity, and reliability was assessed by measuring ICC and Cronbach’s alpha.

Results:

Item pool with 86 items was generated consisting of 45 medications, 10 lifestyles, nine diet items, and 22 common items between these three categories. Forty-three items with CVR score lower than 0.62 were removed from the HFAQ. All remained items had acceptable face validity. Cronbach’s alpha and ICC of HFAQ were reported as 0.73 and 0.97, respectively. EGA results represented a four-dimension model for HFAQ with a relatively narrow confidence interval (CI 95% [3.769, 4.231]), and the stability of items, in constructs, ranged from 0.96 to 1. Goodness-of-fit results was reported as χ2 = 535.657, df = 293, χ2/df = 1.828, P-value < .001, CFI = 0.851, and RMSEA = 0.050.

Conclusions:

HFAQ is the first treatment adherence questionnaire developed specifically for assessing treatment adherence of HF patients and is a valid and reliable 26-item questionnaire that evaluates patients' treatment adherence in three main contexts of medication, physical activity, and diet. HFAQ has four dimensions of health literacy, social and economic, barrier, and patient-provider relationship, which can be used as an intervention for improving treatment outcomes and disease burden.

1. Background

Heart failure (HF) is defined as a ‘clinical syndrome consisting of dyspnea, malaise, swelling, and/or decreased exercise capacity due to the loss of compensation for cardiac pumping function due to structural and/or functional abnormalities of the heart (1). Its incidence ranges from 100 to 900 cases per 100,000 people each year (2), and the prevalence is estimated to be two percent of the total population (3). HF is an important public health issue, and its prevalence, hospitalization rate, and costs increase over the years (4). It is responsible for 9.91 million years lost due to disability (YLDs). In addition, it’s estimated that the annual expenditure for HF is approximately 346 billion USD (5).

The incidence and prevalence of HF were not studied in military personnel, but some studies reported earlier onset of coronary artery disease in military personnel (6), which is a predisposing factor of HF (7).

According to the World Health Organization (WHO), improving treatment adherence levels may be more effective than improving current treatment (8). Treatment adherence is the behavior of patients in taking medications, correcting diet, and ability to change lifestyle with the recommendations of the health care provider (8). Poor treatment adherence contributes to the worsening of disease conditions and leads to an increase in morbidity, mortality, direct and indirect health care costs, and disease burden (9). Evidence supports low treatment adherence in patients with chronic disease (10). Treatment adherence among patients with cardiovascular diseases (CVDs) is 57 percent in 24 months period and decreases by 0.15 percent monthly, leading to low treatment adherence and CVD stage progression in the following years (11); While more complex treatment regimens require multiple drug assumptions, lifestyle modification, and diet changes were failed more (12). Most treatment adherence questionnaires can be used as an effective tool for improving care efficacy and treatment outcome (13).

Many studies identified several factors that influence treatment adherence of individuals, including patient's beliefs, health literacy, motivation, disease severity, psychiatric and cognitive condition, ethnicity, cultural differences, occupation, and changes in daily life. Cost, side effects, the total number of pills, and complexity in administration of medications also play a major role in determining treatment adherence (14-25).

To the best of our knowledge, currently, there is no specific instrument for measuring treatment adherence of HF patients in the literature review. The goal of this study was to develop and validate a novel instrument for measuring treatment adherence of HF patients. The results of this study can help health care providers and patients to detect reasons for treatment adherence failure in the early stages. This study also intends to assess the psychometric properties of the preliminary version of this questionnaire on military personnel and their families.

2. Objectives

To develop and validate a novel instrument for measuring treatment adherence of HF patients.

3. Methods

3.1. Participants, Sampling, and Design

This methodological psychometric exploratory study was conducted on military personnel and their family as key participants. Maximum variation sampling was used to reach a good representation of the community and reflect multidimensional aspects of HF within individuals with various perspectives, beliefs, behaviors, disease severities, etiologies, situations, and other differences, so forth (26). Participants were selected from both outpatient (private clinics) and inpatient (Madani hospital of Tabriz) settings. Written informed consent was obtained from all participants before entering the study and after a comprehensive introduction to the study protocol. The inclusion criteria were (1) Confirmed cases of HF; (2) Adults older than 18 years old; (3) Ability to understand and speak in Persian; (4) Stable psychiatric condition; and (5) History of treatment for at least six months. Participants who refused to fill the form and those who partially filled the form were excluded from the study.

3.2. Phase I Item Generation

Items were generated by assessing multiple resources to reach acceptable coverage of various contents from literature reviews, patient field interviews, and expert opinions.

3.2.1. Step1-Literature Review

The research team reviewed 62 qualitative and quantitative self-reporting questionnaires using the following keywords: ‘treatment adherence and compliance’ and ‘medication adherence and compliance. The search was performed for the period after the 1990s to gather factors that influence treatment adherence.

3.2.2. Step2-Patient Field Interview

Patients were asked about difficulties that they face in performing treatment plans, including medication taking, lifestyle, and diet modifications.

First, a total of 30 semi-structured interviews were performed with 30 HF patients. Participants were selected from different age groups, socioeconomic status, literacy, disease severity, employment, and marital status to set a representative sample.

Second. The research team conducted a quick survey on 50 HF patients to explain three main reasons for their non-adherence.

3.2.3. Step 3-Expert Panel Opinion

An expert panel was formed to suggest additional items and summarize all items that were gathered in previous steps. The expert panel included four cardiologists, three cardiology nurses, one health education specialist, one psychiatrist, and one general practitioner, which had 3 - 26 years of experience in their careers.

3.3. Phase II Validity Analysis

3.3.1. Step 1-Content Validity

Content validity is a prerequisite for measuring other types of validities and has a critical role in the development of the new instrument. Content validity was assessed by measuring the content validity ratio (CVR) and content validity index (CVI) (27).

CVR is a common method for measuring content validity, which is originally established by Lawshe. This ratio was used in various studies for quantitative assessment of content validity by collecting expert scores about the relevance of each item.

The research team invited three HF fellows, four cardiologists, one cardiology nurse, one health education professor, and one psychiatrist for CVR assessment. Experts scored the importance of each item using a three-item Likert (1 = not necessary; 2 = Useful but not necessary; 3 = Essential). ‘Essential’ score was determined as acceptable. Scores were analyzed by the Lawshe formula, and items with CVR score lower than 0.62 were removed (28).

CVI is a method to assess the content representativeness of each item by expert scoring. Lynn provided a standard for acceptable CVI by the number of rating experts. Acceptable CVI in the 10 experts scoring condition is 0.78 (29).

The research team invited two HF fellows, four general cardiologists, two cardiology nurses, one health education professor, and one psychiatrist for CVI assessment. Experts scored the relevance of items using a four-item Likert (1 = Not relevant; 2 = Somewhat relevant; 3 = Highly relevant, 4 = Completely relevant). CVI was measured by dividing the number of experts, which gave scores of 3 and 4 into the total number of experts (30). Items with CVI score lower than 0.78 were revised.

3.3.2. Step 2-Face Validity

Face validity is an extent to measure which each research item concludes the conceptual domain of underlying content or not (31). Face validity of HFAQ was evaluated by measuring the impact score (IS) of each item by an expert panel consisting four HF fellows, three cardiologists, one cardiology nurse, one health education professor, and one psychiatrist. A five-item Likert was used (1 = Strongly appropriate; 2 = Appropriate but needs small changes; 3 = Appropriate but needs intermediate changes; 4 = Appropriate but needs significant changes; 5 = Inappropriate). Items with an impact score lower than 1.5 were removed (32).

3.3.3. Step 3-Construct Validity

The research team performed exploratory graph analysis (EGA), bootEGA, and confirmatory factor analysis (CFA) to assess the construct validity. Also, 360 individuals participated in this part of the study, eight participants refused to fill the forms, and 20 partially filled the forms, indicating a response rate of 92.2%. The demographic characteristics of participants are mentioned in Table 1.

Table 1. Demographic Characteristic of Participants a
CharacteristicItem Generation Phase, N = 30Construct Validity, N = 332Reliability, N = 33
Mean age62.32 ± 10.2260.92 ± 9.7268.76 ± 11.08
Gender
Female14 (46.7)175 (52.7)18 (54.5)
Male16 (53.3)157 (47.3)15 (45.5)
Marital status
Married26 (86.7)277 (83.4)25 (75.6)
Single 4 (13.3)55 (16.6)8 (24.4)
Educational status
illiterate2 (6.7)23 (6.9)5 (15.2)
Below diploma6 (20.0)73 (22.0)8 (24.2)
Diploma 10 (33.3)102 (30.7)9 (27.3)
Bachelors8 (26.7)102 (30.7)6 (18.1)
Master4 (13.3)31 (9.4)5 (15.2)
PhD0 (0.0)1 (0.3)0 (0.0)
Occupational status
Unemployed6 (20)96 (28.9)6 (18.2)
Employed12 (40.0)113 (34.0)15 (45.5)
Self-employed4 (13.3)48 (14.5)5 (15.1)
Retired8 (26.7)75 (22.6)7 (21.2)

EGA is an alternative and novel approach that determines complex relation between items and scorings to establish hypothetically acceptable constructs. Also, EGA is a better and more accurate tool to assess the dimensionality of questionnaires rather than traditional methods like exploratory factor analysis (33). Each item of the questionnaire, as a random variable, implies a node in the network psychometric perspective, while EGA identifies the dimension of constructs by investigating the connection of nodes and utilizing the inverse of variance-covariance matrix (34, 35). Hypothetically, each item of the questionnaire might correlate with other items, this connection is identified as edges or links. An edge indicates a partial correlation coefficient, which identifies the strength of association between nodes (36). A partial correlation network is interpreted using the walktrap algorithm, which analyzes distances via random walks (37). BootEGA with a parametric approach, as a complementary tool for assessing internal consistency, was utilized to evaluate the structural consistency and dimensionality of structures. In this method, EGA creates a network of nodes and edges and generates new replicate data until the desired number of bootstraps is reached (e.g., 500). The research team used descriptive statistics of EGA, such as median number, standard error, confidence interval of dimensions, lower and upper confidence interval around the median, and lower and upper quantile, to evaluate the stability of dimensions (38). CFA was utilized, and goodness-of-fit indices, such as chi-square, degree of freedom (df), P-value, comparative fit index (CFI), and root mean square error of approximation (RMSEA), were measured to confirm the convergent and discriminant validity of the constructs. The ratio of chi-square to df is the preferred measure to assess fitness between hypothesized model and data, which a ratio of two or lower was considered as a great fit (39). RMSEA lower than 0.08, CFI values higher than 0.9, and P-value lower than 0.05 were indicated as good fit standards (40).

3.4. Phase III Reliability Analysis

The reliability of HFAQ was assessed by measuring the Intraclass correlation coefficient (ICC) and Cronbach’s alpha. Thirty-three individuals were engaged randomly in reliability assessment, and the interval between test and re-test was two weeks. Cronbach’s alpha, which measures internal consistency, was used to evaluate construct reliability. Values higher than 0.9 were indicated as well, values in the range of 0.7 - 0.9 were indicated as adequate, and values lower than 0.7 were indicated as inappropriate internal consistency (41). ICC was used to assess the reproducibility and stability of data in the test-retest method, and values higher than 0.6 were implied as acceptable (42).

3.5. Ethical Considerations

Ethical approval was obtained from the ethical committee of AJA University of medical sciences before the commencement of the study. All participants were informed, and those who agreed to participate filled out the informed consent form. Participation in the study had no harm to participants, and all of them had the right to withdraw from any part of the study without taking any consequences. The collected information remained confidential, and the HFAQ questionnaire only focused on needed information.

3.6. Statistical Analysis

Data analysis was administered using statistical package for social sciences (SPSS) version 26. Data of EGA was utilized using EGAnet package version 0.9.8; hence, CFA was analyzed by performing CFA Fit of EGA Structure code by R software version 4.1.2. The validity and reliability of HFAQ were determined by applying specific statistical methods, which are described in the following sections.

4. Results

4.1. Step 1-Item Generation Results

In the first step, 88 potential items were listed by literature review in three main domains of medication, lifestyle, and diet. In the second step, 24 potential items were listed by patient field interview, and finally, expert panel suggested 10 new potential items and gathered all the 122 items for generating HFAQ's item pool. Also, 11 items with similar themes were merged, 12 items were deleted due to lack of enough value in assessing treatment adherence of HF patients, and 13 items were removed because of low relevancy and suitability in measuring scale. Item pool with 86 items was generated with 45 medications, ten lifestyles, nine diet items, and 22 common items between these three categories. Item pool of HFAQ was categorized into four main domains and 15 sub-domains. The main domains were belief, behavior, barrier, and the patient-provider relationship. Sub-domains were understanding of disease and treatment, cognitive and psychiatric properties, self-management competency, outcome expectancies, situational perception, ethnicity, side effects/harms of medication, poly-pharmacy, occupation, changes in daily life, the complexity of treatment, availability of effective treatment, cost, support, medication-taking discipline, disease severity, stigma, and treatment satisfaction.

4.2. Step 2-Validity Results

4.2.1. Content Validity

All 86 items were scored by two separate expert panels to measure the CVR and CVI scores of each item. CVR score of items varied from -0.6 to 1. Also, 27 items had a score of 0.8, and 16 items were scored as 1; while 43 items with CVR scores lower than 0.62 were removed. CVI score of items varied in the range of 0.5 to 1. Also, 21 items had a score of 0.8, 14 items were scored 0.9, and 6 items were scored 1; while two items with a CVI score lower than 0.79 were revised.

4.2.2. Face Validity

The expert panel analyzed the face validity of the remained items (n = 43). The impact score of items was more than 1.5 and varied from 2.4 to 4.8. Six items had slight structural changes, and all of the items were understandable.

4.2.3. Construct Validity

Data was collected from 332 questionnaires that were filled out, and 500 bootstraps were reached to assess the construct validity of the HFAQ. In the first step, EGA was conducted to specify questionnaire dimensions, which represented a five-factor model (Figure 1). The stability of constructs was evaluated by checking the descriptive statistics. Based on the results, the mean of the five dimensions mirrors the empirical EGA with a relatively narrow confidence interval (CI 95% [4.083, 5.916]).

Structural consistency was measured by assessing the dimensional stability of HFAQ items across all bootstrap replicate samples. Based on the results, the dimensional stability of constructs was 0.91, 0.81, 0.17, 0.23, and 1.0, respectively, which indicated significant instability in dimensions 3 and 4. Afterward, the research team clarified unstable items by investigating item stability values across each dimension in the replicate bootstrap samples (Appendix 2). Unstable items were reported as Q10, Q15, Q17, Q19, Q22, Q23, Q31, Q33, and Q34. These items were associated with their theoretical dimensions; however, they share a significant conceptual similarity, which forms a separate dimension. Multidimensional items can reduce the stability of dimensions; thus, the research team removed these items to reach good dimension consistency.

In the second step, EGA was conducted to specify dimensions after removing nine unstable items. EGA results represented a five-dimension model, in which construct 4 with two items (i.e., Q29 and Q30) had a replication value of 0.42 (Figure 2). The research team removed these items to reach good dimension consistency.

Item stability of HFAQ after removing the nine unstable items.

Finally, EGA was conducted after removing 11 items. EGA results represented a four-dimension model for HFAQ (Figure 3), as (C1)- Health Literacy, (C2)- Barriers, (C3)- Social and Economic, and (C4)- Patient-Provider Relationship. The number of items in constructs was 6, 9, 4, and 7 items, respectively. Based on the descriptive statistics of dimensions across all bootstrap replicate samples results, the mean of four dimensions mirrored empirical EGA with a relatively narrow confidence interval (CI 95% [3.769, 4.231]) (Table 2). Stability of items in constructs ranged from 0.96 to 1, which represented a good construct consistency (Figure 4).

Dimensionality results of EGA for the HFAQ after the eleven unstable items were removed.
Table 2. Descriptive Statistics of HFAQ Dimensions Across All Bootstrap Replicate Samples
N BootsMedian DimSE DimCI DimLower CIUpper CILower QuantileUpper Quantile
50040.1180.2313.7694.23144
Item stability of HFAQ after removing the eleven unstable items.

CFA was performed to assess the correlation of hypothesized model by measuring goodness-of-fit indices (Figure 5). The results are reported as χ2 = 535.657, df = 293, χ2/df = 1.828, P-value < .001, CFI = 0.851, and RMSEA = 0.050. Great fit to data was expressed by RMSEA, P-value, and Ratio of chi-square to df; hence, CFI was lower than 0.9, representing moderate fit to data.

4.3. Reliability Results

4.3.1. Step 1- Internal Consistency Results

Cronbach’s alpha of the 43-item final item pool was 0.58, which is lower than 0.7. The research team removed six items before the conduction of EGA. Final Cronbach’s alpha was assessed after the EGA results, representing a value of 0.73. Cronbach’s alpha of constructs was 0.70, 0.73, 0.60, and 0.86, respectively. Health literacy, barrier, patient-provider relationship constructs, and HFAQ entirely had acceptable internal consistency; however, social and economic constructs had moderate internal consistency.

4.3.2. Step 2- Stability Results

ICC of the entire questionnaire was 0.97, while ICC values of each construct were reported as 0.89, 0.94, 0.71, and 0.95, respectively. All ICC values were higher than 0.6, demonstrating good stability of HFAQ items and constructs over time.

5. Discussion

Heart failure is a major health problem for patients, their families, and health systems worldwide (43). Ageing of communities’ results in increasing prevalence and incidence of HF, which induces a severe burden on health systems (44). Improving treatment adherence could lead to better disease outcomes (45). To the best of our knowledge, HFAQ is the first questionnaire focused on assessing treatment adherence of HF patients.

Based on the results, all items were loaded into the patient-provider relationship, social and economic, health literacy, and barrier constructs. To the best of our knowledge, the acceptable value of internal consistency in the EGA method is controversial among studies. Generally, there is no established acceptable value; however, an expert panel can set a range according to the condition of each scale development procedure (38). Some studies defined 0.75 or above as an acceptable standard (46). The findings of this study indicated a frequency of 0.986 for bootstraps, which represents a high re-creating of the random re-occurrence of constructs.

Health literacy was one construct of HFAQ, which contains six items with dimensional stability of 0.9. Schonfeld et al. assessed the association between self-reported health literacy and treatment adherence by reviewing nine studies. Six studies reported significant and positive effects, while two studies reported positive but non-significant and one reported mixed results about the association between health literacy and treatment adherence, indicating the significant and principal importance of health literacy in measuring treatment adherence (47). Suhail et al. reported a significant association between health literacy and treatment adherence of patients with ischemic heart disease (48).

The patient-provider relationship was one of the constructs of this study with good stability. Wu et al. reviewed three general CVDs and three heart failure-specific studies to assess the importance of the patient-provider relationship and identified factors that affect the adherence of HF patients. Based on this study, both patients and physicians must have good relationships to reach acceptable treatment adherence (49). Many studies supported the robustness of this concept on treatment adherence of patients. Zschocke et al. developed the topical therapy adherence questionnaire in three domains, which “knowledge, communication, and relationship with a physician”, with seven items, were one of the constructs. This construct was considered to have a significant influence on treatment adherence, with item-total correlation ranged from 0.58 to 0.92 (50). Based on the HFAQ’s results, seven items were loaded in the patient-provider relationship construct with replication values ranging from 0.96 to 1.00, indicating the high stability of this construct (46).

WHO identified socioeconomic status as an important predictor of adherence and a risk factor for poor adherence, which needs to be improved by fundamental interventions? Also, socioeconomic factors were identified specifically as the underlying cause of poor adherence in hypertensive patients (8). Gast et al. studied 21 systematic reviews to identify factors that influence the adherence of adults with chronic physical disease. This study reported a social gradient in treatment adherence behavior, and also socioeconomic status had a positive impact on adherence of CVD patients. The social and economic construct of HFAQ had four items, which represented 0.96 - 0.99 replication and 0.67 correlation with the patient-provider relationship construct. The results declared the important role of physician interventions in decreasing the consequences of low socioeconomic status on treatment adherence.

George et al. (23) identified influencing concepts of treatment adherence as two general constructs of belief and behavior; hence, factors that distort treatment adherence were called the ‘general concept of barriers’ (30). However, Matza et al. identified ‘barriers’ to treatment adherence as an independent construct (51). In the process of developing HFAQ, the research team gathered data from multiple domains of belief, behavior, and barriers. Similarly, EGA results categorized nine items with barrier concept to one construct; thus, the research team decided to consider the barriers of treatment adherence as a separate construct rather than a general concept.

Content and face validity of HFAQ was assessed by measuring CVR, CVI, and impact score. Forty-three items (50%) with a CVR score lower than 0.62 (Lawshe table limit for 10 examiners) were removed. Two items had a CVI score lower than 0.79, while the impact score of all items was more than 1.5. Dehghan et al. developed a 35-items coronary artery disease treatment adherence scale. In this study, two items (3.6%) had a CVR score lower than 0.51 (Lawshe table limit for 14 examiners). All CVI values were higher than 0.79, while four items were removed due to the low impact score (52). The CVI value and impact scores of the two studies were similar, while in the process of developing HFAQ, more items were removed, which might be due to the higher number of items in the primary item pool or different expectancies and standards of expert examiners.

The reliability of HFAQ was assessed by measuring internal consistency and test-retest reliability. Cronbach's alpha was 0.73, and ICC was measured as 0.97. Ma et al. developed a treatment adherence questionnaire for patients with hypertension, which Cronbach’s alpha was reported as 0.86 and ICC as 0.82. Both studies reached acceptable internal consistency due to Cronbach’s alpha value higher than 0.6 - 0.7 and acceptable test-retest reliability according to ICC value higher than 0.6.

5.1. Conclusions

HFAQ is the first treatment adherence questionnaire developed specifically for assessing treatment adherence of heart failure patients and is a valid and reliable 26-item questionnaire which intends to evaluate treatment adherence in three main contexts of medication, physical activity, and diet. Treatment adherence factors were extracted from three conceptual frameworks of belief, barrier, and behavior, which led to the development of four dimensions of health literacy, social and economic, barrier, and the patient-provider relationship. HFAQ can be used as an intervention for improving treatment outcomes and disease burden. HFAQ was developed as a primary tool for emphasizing the importance of treatment adherence among heart failure patients as a chronic disease. Eventually, the research team hopes that the findings will help other researchers to develop more comprehensive and advanced treatment adherence questionnaires for heart failure patients.

5.2. Limitations

The limitations of this study can be classified as follow. Items were generated using the literature review, focus group discussion with 30 patient, and expert opinions (n = 10). Increasing the number of patients and experts would help researchers to achieve a richer item pool. Also, gathering data from friends and family of patients can be helpful. The HFAQ was developed to assess treatment adherence of military personnel and their families, while developing a general questionnaire for heart failure patients requires a wide diversity of participants in beliefs, ethnicity, occupation, socioeconomic level, and health literacy fields. A small sample of participants was gathered for questionnaire development, while implementing HFAQ on more patients may provide different results.

References

  • 1.

    Tsutsui H, Isobe M, Ito H, Ito H, Okumura K, Ono M, et al. JCS 2017/JHFS 2017 Guideline on Diagnosis and Treatment of Acute and Chronic Heart Failure- Digest Version. Circ J. 2019;83(10):2084-184. doi: 10.1253/circj.CJ-19-0342. [PubMed: 31511439].

  • 2.

    Ziaeian B, Fonarow GC. Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13(6):368-78. doi: 10.1038/nrcardio.2016.25. [PubMed: 26935038]. [PubMed Central: PMC4868779].

  • 3.

    Ponikowski P, Anker SD, AlHabib KF, Cowie MR, Force TL, Hu S, et al. Heart failure: preventing disease and death worldwide. ESC Heart Fail. 2014;1(1):4-25. doi: 10.1002/ehf2.12005. [PubMed: 28834669].

  • 4.

    Lund LH, Savarese G. Global Public Health Burden of Heart Failure. Card Fail Rev. 2017;3(1):7. doi: 10.15420/cfr.2016:25:2.

  • 5.

    Lippi G, Sanchis-Gomar F. Global epidemiology and future trends of heart failure. AME Med J. 2020;5:15. doi: 10.21037/amj.2020.03.03.

  • 6.

    Khodaee Ataloo N. [The Effect of Military Occupational Stress on the Age Incidence of Cardiovascular Diseases]. Paramed Sci Mil Health. 2017;12(1):7-11. Persian.

  • 7.

    Zannad F. Rising incidence of heart failure demands action. Lancet. 2018;391(10120):518-9. doi: 10.1016/s0140-6736(17)32873-8.

  • 8.

    Sabaté E. Adherence to long-term therapies: evidence for action. Geneva: World Health Organization; 2003.

  • 9.

    Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353(5):487-97. doi: 10.1056/NEJMra050100. [PubMed: 16079372].

  • 10.

    Brown MT, Bussell JK. Medication adherence: WHO cares? Mayo Clin Proc. 2011;86(4):304-14. doi: 10.4065/mcp.2010.0575. [PubMed: 21389250]. [PubMed Central: PMC3068890].

  • 11.

    Naderi SH, Bestwick JP, Wald DS. Adherence to drugs that prevent cardiovascular disease: meta-analysis on 376,162 patients. Am J Med. 2012;125(9):882-7. doi: 10.1016/j.amjmed.2011.12.013. [PubMed: 22748400].

  • 12.

    Martin LR, Williams SL, Haskard KB, Dimatteo MR. The challenge of patient adherence. Ther Clin Risk Manag. 2005;1(3):189-99. [PubMed: 18360559]. [PubMed Central: PMC1661624].

  • 13.

    Weingarten SR, Henning JM, Badamgarav E, Knight K, Hasselblad V, Gano AJ, et al. Interventions used in disease management programmes for patients with chronic illness-which ones work? Meta-analysis of published reports. BMJ. 2002;325(7370):925. doi: 10.1136/bmj.325.7370.925. [PubMed: 12399340]. [PubMed Central: PMC130055].

  • 14.

    Duong M, Piroth L, Grappin M, Forte F, Peytavin G, Buisson M, et al. Evaluation of the Patient Medication Adherence Questionnaire as a tool for self-reported adherence assessment in HIV-infected patients on antiretroviral regimens. HIV Clin Trials. 2001;2(2):128-35. doi: 10.1310/M3JR-G390-LXCM-F62G. [PubMed: 11590521].

  • 15.

    Anghel LA, Farcas AM, Oprean RN. An overview of the common methods used to measure treatment adherence. Med Pharm Rep. 2019;92(2):117-22. doi: 10.15386/mpr-1201. [PubMed: 31086837]. [PubMed Central: PMC6510353].

  • 16.

    Ahmadizadeh MJ, Ebadi A, Sirati Nir M, Tavallaii A, Sharif Nia H, Lotfi MS. Development and psychometric evaluation of the Treatment Adherence Questionnaire for Patients with Combat Post-traumatic Stress Disorder. Patient Prefer Adherence. 2019;13:419-30. doi: 10.2147/PPA.S175353. [PubMed: 30962678]. [PubMed Central: PMC6434911].

  • 17.

    Amico KR, Fisher WA, Cornman DH, Shuper PA, Redding CG, Konkle-Parker DJ, et al. Visual analog scale of ART adherence: association with 3-day self-report and adherence barriers. J Acquir Immune Defic Syndr. 2006;42(4):455-9. doi: 10.1097/01.qai.0000225020.73760.c2. [PubMed: 16810111].

  • 18.

    Willey C, Redding C, Stafford J, Garfield F, Geletko S, Flanigan T, et al. Stages of change for adherence with medication regimens for chronic disease: development and validation of a measure. Clin Ther. 2000;22(7):858-71. doi: 10.1016/s0149-2918(00)80058-2. [PubMed: 10945512].

  • 19.

    Clayton CD, Veach J, Macfadden W, Haskins J, Docherty JP, Lindenmayer JP. Assessment of clinician awareness of nonadherence using a new structured rating scale. J Psychiatr Pract. 2010;16(3):164-9. doi: 10.1097/01.pra.0000375712.85454.c6. [PubMed: 20485104].

  • 20.

    Goh SSL, Lai PSM, Liew SM, Tan KM, Chung WW, Chua SS. Development of a PATIENT-Medication Adherence Instrument (P-MAI) and a HEALTHCARE PROFESSIONAL-Medication Adherence Instrument (H-MAI) using the nominal group technique. PLoS One. 2020;15(11). e0242051. doi: 10.1371/journal.pone.0242051. [PubMed: 33175871]. [PubMed Central: PMC7657514].

  • 21.

    Fernandez S, Chaplin W, Schoenthaler AM, Ogedegbe G. Revision and validation of the medication adherence self-efficacy scale (MASES) in hypertensive African Americans. J Behav Med. 2008;31(6):453-62. doi: 10.1007/s10865-008-9170-7. [PubMed: 18784996]. [PubMed Central: PMC3763496].

  • 22.

    Unni EJ, Sternbach N, Goren A. Using the Medication Adherence Reasons Scale (MAR-Scale) to identify the reasons for non-adherence across multiple disease conditions. Patient Prefer Adherence. 2019;13:993-1004. doi: 10.2147/PPA.S205359. [PubMed: 31308635]. [PubMed Central: PMC6612984].

  • 23.

    George J, Mackinnon A, Kong DC, Stewart K. Development and validation of the Beliefs and Behaviour Questionnaire (BBQ). Patient Educ Couns. 2006;64(1-3):50-60. doi: 10.1016/j.pec.2005.11.010. [PubMed: 16843634].

  • 24.

    Unni EJ, Farris KB. Development of a new scale to measure self-reported medication nonadherence. Res Social Adm Pharm. 2015;11(3):e133-43. doi: 10.1016/j.sapharm.2009.06.005. [PubMed: 21272524].

  • 25.

    Shalansky SJ, Levy AR, Ignaszewski AP. Self-reported Morisky score for identifying nonadherence with cardiovascular medications. Ann Pharmacother. 2004;38(9):1363-8. doi: 10.1345/aph.1E071. [PubMed: 15238622].

  • 26.

    AncaVitcu EL, Vitcu L, Marcu A. Multi-stage maximum variation sampling in health promotion programs’ evaluation. J Prev Med. 2007;15(14):5-18.

  • 27.

    Slocumb EM, Cole FL. A practical approach to content validation. Appl Nurs Res. 1991;4(4):192-5. doi: 10.1016/s0897-1897(05)80097-7.

  • 28.

    Polit DF, Beck CT. The content validity index: are you sure you know what's being reported? Critique and recommendations. Res Nurs Health. 2006;29(5):489-97. doi: 10.1002/nur.20147. [PubMed: 16977646].

  • 29.

    Almanasreh E, Moles R, Chen TF. Evaluation of methods used for estimating content validity. Res Social Adm Pharm. 2019;15(2):214-21. doi: 10.1016/j.sapharm.2018.03.066. [PubMed: 29606610].

  • 30.

    Polit DF, Beck CT, Owen SV. Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health. 2007;30(4):459-67. doi: 10.1002/nur.20199. [PubMed: 17654487].

  • 31.

    Sangoseni O, Hellman M, Hill C. Development and Validation of a Questionnaire to Assess the Effect of Online Learning on Behaviors, Attitudes, and Clinical Practices of Physical Therapists in the United States Regarding Evidenced-based Clinical Practice. Internet J Allied Health Sci Pract. 2013;11(12). doi: 10.46743/1540-580x/2013.1439.

  • 32.

    Hajizadeh E, Asghari M. [Statistical methods and analyses in health and biosciences a research methodological approach]. Tehran: Jahade Daneshgahi Publications; 2011. p. 395-410. Persian.

  • 33.

    Christensen AP, Golino H, Silvia PJ. A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires. Eur J Pers. 2020;34(6):1095-108. doi: 10.1002/per.2265.

  • 34.

    Golino HF, Epskamp S. Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS One. 2017;12(6). e0174035. doi: 10.1371/journal.pone.0174035. [PubMed: 28594839]. [PubMed Central: PMC5465941].

  • 35.

    Golino H, Shi D, Christensen AP, Garrido LE, Nieto MD, Sadana R, et al. Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychol Methods. 2020;25(3):292-320. doi: 10.1037/met0000255. [PubMed: 32191105]. [PubMed Central: PMC7244378].

  • 36.

    Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23(4):617-34. doi: 10.1037/met0000167. [PubMed: 29595293].

  • 37.

    Pons P, Latapy M. Computing Communities in Large Networks Using Random Walks. Comput Inf Sci. 2005;3733:284-93. doi: 10.1007/11569596_31.

  • 38.

    Christensen AP, Golino H. Estimating the Stability of Psychological Dimensions via Bootstrap Exploratory Graph Analysis: A Monte Carlo Simulation and Tutorial. Psych. 2021;3(3):479-500. doi: 10.3390/psych3030032.

  • 39.

    Alavi M, Visentin DC, Thapa DK, Hunt GE, Watson R, Cleary M. Chi-square for model fit in confirmatory factor analysis. J Adv Nurs. 2020;76(9):2209-11. doi: 10.1111/jan.14399. [PubMed: 32323338].

  • 40.

    Hox JJ. Confirmatory Factor Analysis. In: Barnes JC, Forde DR, editors. The Encyclopedia of Research Methods in Criminology and Criminal Justice. John Wiley & Sons, Inc; 2021. p. 830-2.

  • 41.

    Mahler C, Hermann K, Horne R, Ludt S, Haefeli WE, Szecsenyi J, et al. Assessing reported adherence to pharmacological treatment recommendations. Translation and evaluation of the Medication Adherence Report Scale (MARS) in Germany. J Eval Clin Pract. 2010;16:574-9. doi: 10.1111/j.1365-2753.2009.01169.x.

  • 42.

    Hallgren KA. Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. Tutor Quant Methods Psychol. 2012;8(1):23-34. doi: 10.20982/tqmp.08.1.p023. [PubMed: 22833776]. [PubMed Central: PMC3402032].

  • 43.

    Groenewegen A, Rutten FH, Mosterd A, Hoes AW. Epidemiology of heart failure. Eur J Heart Fail. 2020;22(8):1342-56. doi: 10.1002/ejhf.1858. [PubMed: 32483830]. [PubMed Central: PMC7540043].

  • 44.

    Cook C, Cole G, Asaria P, Jabbour R, Francis DP. The annual global economic burden of heart failure. Int J Cardiol. 2014;171(3):368-76. doi: 10.1016/j.ijcard.2013.12.028. [PubMed: 24398230].

  • 45.

    Ghulam Ishaq RRG. Cost and Benefits Analysis of Treatment Adherence in End-Stage Renal Disease Patients. J Behav Sci. 2021;31.

  • 46.

    Golino H, Lillard AS, Becker I, Christensen AP. Investigating the Structure of the Children's Concentration and Empathy Scale Using Exploratory Graph Analysis. Psychol Test Adapt Dev. 2021:1-15. doi: 10.1027/2698-1866/a000008.

  • 47.

    Schonfeld MS, Pfisterer-Heise S, Bergelt C. Self-reported health literacy and medication adherence in older adults: a systematic review. BMJ Open. 2021;11(12). e056307. doi: 10.1136/bmjopen-2021-056307. [PubMed: 34916329]. [PubMed Central: PMC8679075].

  • 48.

    Suhail M, Saeed H, Saleem Z, Younas S, Hashmi FK, Rasool F, et al. Association of health literacy and medication adherence with health-related quality of life (HRQoL) in patients with ischemic heart disease. Health Qual Life Outcomes. 2021;19(1):118. doi: 10.1186/s12955-021-01761-5. [PubMed: 33849547]. [PubMed Central: PMC8045399].

  • 49.

    Wu JR, Moser DK, Lennie TA, Burkhart PV. Medication adherence in patients who have heart failure: a review of the literature. Nurs Clin North Am. 2008;43(1):133-53. vii-viii. doi: 10.1016/j.cnur.2007.10.006. [PubMed: 18249229].

  • 50.

    Zschocke I, Mrowietz U, Lotzin A, Karakasili E, Reich K. Assessing adherence factors in patients under topical treatment: development of the Topical Therapy Adherence Questionnaire (TTAQ). Arch Dermatol Res. 2014;306(3):287-97. doi: 10.1007/s00403-014-1446-x. [PubMed: 24509981]. [PubMed Central: PMC3955139].

  • 51.

    Matza LS, Park J, Coyne KS, Skinner EP, Malley KG, Wolever RQ. Derivation and validation of the ASK-12 adherence barrier survey. Ann Pharmacother. 2009;43(10):1621-30. doi: 10.1345/aph.1M174. [PubMed: 19776298].

  • 52.

    Dehghan Nayeri N, Yadegary MA, Seylani K, Navab E. Development and Psychometric Evaluation of Coronary Artery Disease Treatment Adherence Scale. Cardiol Ther. 2019;8(1):103-15. doi: 10.1007/s40119-019-0135-4. [PubMed: 30972558]. [PubMed Central: PMC6525209].

Copyright © 2022, Journal of Archives in Military Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.