Critical pathways and factors of non-health outcomes affecting self-care behaviors in patients with type 2 diabetes and their differences in patient preference in medical decision-making

DOI: https://doi.org/10.21203/rs.2.21066/v1

Abstract

Background

The effects of patient sustained self-care behaviors (SCB) on glycemic control are even greater than the effects of medical treatment, indicating the value of identifying the factors that influence SCB. To date, these factors have not been placed in one model to clarify the critical path affecting SCB. The aims of this study were to explore the relationships of these factors and the differences by decision-making preference.

Methods

A cross-sectional study was conducted among outpatients with type 2 diabetes at a regional teaching hospital. Purposive sampling was adopted to recruit 316 eligible patients via self-administered questionnaires. Partial least squares structural equation modeling was used for analysis.

Results S

ignificant direct pathways were identified from health literacy (HL) to self-efficacy (SE), patient empowerment (PE), and SCB; from SE to SCB; and from PE to SCB. Indirect pathways were from HL to SCB via SE or PE. The pathway from HL to SE was significantly stronger in those preferring shared decision-making than in those who preferred physician decision-making.

Conclusions

HL is a critical factor in improving SCB in patients with type 2 diabetes, and the pathway from HL to SE differed the most by patient preference for making decisions. Therefore, developing an effective promotion strategy and application tools to improve the overall HL of diabetic is the cornerstone of enhancing SCB in this population.

Introduction

Diabetes is a life-long chronic condition that leads to serious consequences. Over 425 million people worldwide are currently living with diabetes [1], with 1.6 million deaths directly attributed to diabetes each year [2]. Therefore, the World Health Organization (WHO) calls on countries to prevent or delay the onset of diabetes by promoting a healthy lifestyle, to reduce the risk of diabetes and effectively manage the disease to decrease the rates of complications and mortality [3].

To effectively control the progression of diabetes, prevent long-term complications, and promote quality of life, patients with diabetes need effective self-care behaviors (SCB) [4], which have an even greater impact than medical treatment [5]. SCB refers to “decisions and actions that an individual can take to cope with a health problem or to improve his or her health” [6]; these would include regular physical activity, a healthy and balanced diet, self-monitoring of blood glucose, and compliance with medication. Effective self-care behaviors are necessary to maintaining optimal HbA1C levels [7], particularly in patients with type 2 diabetes [8, 9]. In recent years, there has been a call for health care evaluation to move beyond measuring health outcomes, to also consider the value of non-health outcomes, such as empowerment, psychosocial outcomes, and quality of life [10]. Therefore, clarifying the impact of these non-health outcomes on SCB is an urgent issue.

Health literacy (HL) is defined as “the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions” [11]. It is increasingly recognized as an important modifiable psychosocial factor in the self-management behaviors of patients with type 2 diabetes [12], and is also theorized to be an important non-clinical factor in decreasing the risk of adverse outcomes in diabetes [13]. HL is considered to be positively correlated with SCB [12, 14, 15], and can also affect SCB via self-efficacy [16, 17]. Therefore, HL is widely used in studying patient-related health behaviors [18].

Another factor well known to be associated with SCB in patients with diabetes is self-efficacy (SE), which is “the belief in one’s capacity to organize and execute the courses of action required to manage a prospective situation” [19]. SE is the main factor that directly affects health behaviors [20]. Patients with high SE have better compliance with SCB [21], both general SE and disease-specific SE can affect SCB [22]. To improve the SCB of patients with diabetes, high SE is necessary.

Diabetes self-management is a complex lifelong journey whose prerequisites for success are the patient’s active and responsible participation in the process. Patients must be internally motivated rather than externally motivated [23]. Patient empowerment (PE) is a process designed to facilitate self-directed behavior change [24]. Therefore, PE is another widely researched determinant of healthy behaviors [18]. The WHO defines empowerment as “a process through which people gain greater control over decisions and actions affecting their health” [25], which demonstrates that PE “doesn’t mean ‘giving’ people power. Rather it’s about ‘enabling’ them to recognize and use their power” [25]. The empowerment approach has been proven to improve SE and SCB [26]. Patients with different health literacy levels may respond differently to PE. The Health Empowerment Model [27] proposes that PE is deeply interwoven with HL to affect the health outcomes of patients. Findings of the interactions between PE and HL in terms of health outcomes in patients are not consistent [28].

In summary, patients with diabetes require a high level of responsibility and promise to implement a new lifestyle of SCB [29]. HL, SE, and PE all directly or indirectly affect SCB, but they have not yet been integrated into one model to clarify their pathways of influence on SCB or their relationship to patient preference in terms of medical decision-making. This study used partial least squares structural equation modeling (PLS-SEM) to analyze these relationships. The aims of the current study were to: 1) investigate the pathways between HL, SE, and PE on SCB in patients with type 2 diabetes; and 2) compare the differences in these pathways by preference in making medical decisions. These findings may identify key pathways to provide a more effective reference for professional interventions, and improve the SCB of patients with type 2 diabetes, thereby helping them achieve effective control of their blood glucose and avoid the complications associated with uncontrolled disease.

Methods

Study participants

The study was conducted with the participation of outpatients who were diagnosed with type 2 diabetes for more than 1 year (primary diagnosis included up to three diagnostic codes in the International Classification of Diseases, Ninth Revision, Clinical Modification: 250) upon visiting the Department of Metabolism of a regional teaching hospital in Hsinchu City, Taiwan. Purposive sampling was adopted to recruit eligible patients with consent during all clinic sessions from June through September 2017. Researchers described the purpose of this study briefly prior before acquiring written informed consent and distributing the questionnaires. A total of 372 questionnaires were distributed, and 316 completed self-administered questionnaires were collected, for a rate of valid questionnaires of 85%. The questionnaires include the scales of HL, SE, PE, and SCB, and the patient's preference in making medical decisions.

Research scale design

Data were derived from patients’ self-administered questionnaires. The questionnaire for this study was developed with reference to pre-existing validated scales and was reviewed by an expert panel whose members included a specialist physician, dietitian, and health educator. The scale items were selected for applicability and ease of administration. The details of each scale are as follows.

The HL scale was developed with reference to the Functional Communicative and Critical Health Literacy (FCCHL) scale developed by Ishikawa et al. [30]. The scale consists of 14 items in three dimensions: functional health literacy (FHL), 5 items; interactive health literacy (IHL), 5 items; and critical health literacy (CHL), 4 items. Each item was scored on a 5-point Likert scale (from 1 = strongly disagree to 5 = strongly agree). Mean scores of HL were obtained by summing the 14 item scores and dividing by the number of items, with higher scores indicating higher HL. Reliability analysis of the scales revealed Cronbach’s α = 0.82.

The SE scale used in this study was developed based on the Diabetes Self-Efficacy Scale [31] and Perceived Diabetes Self-Management Scale [32], which included 19 items on a 5-point Likert scale (from 1 = strongly disagree to 5 = strongly agree). It had a Cronbach’s α = 0.83 for reliability.

The PE scale used in this study referred to the Chinese Diabetes Empowerment Process Scale [33], which included 15 items with 5-point Likert scale (from 1 = strongly disagree to 5 = strongly agree). It had a Cronbach’s α = 0.95 for reliability.

The SCB scale used in this study was developed with reference to the Diabetes Self-Management Questionnaire [34], containing 14 items on 5-point Likert scale (from 1 = strongly disagree to 5 = strongly agree). The Cronbach’s α = 0.83 for reliability.

The patient's preference decision scale was based on the Patient’s Role Preference in Decision-Making questionnaire [35]. Patients selected their preference for making medical decisions, selecting one of these five items: 1 = like to make treatment decisions on their own; 2 = like to make treatment decisions on their own after listening to physician’s opinion; 3 = like to make treatment decisions together with the physician; 4 = like physician to make treatment decisions after talking to the patient; and 5 = like physician to make treatment decisions alone. Upon analysis, patients were categorized into three groups: patient decision-making (answers 1 and 2), shared decision making (SDM) (answer 3), and physician decision-making (answers 4 and 5).

Data analysis

The PLS-SEM incorporates into canonical correlation concepts the important statistical analysis techniques of regression analysis, principal component analysis, and path analysis. PLS-SEM can be applied to Mediation and Moderation analysis, and has gained universal attention in the field of health care [36]. It can not only help the researcher handle measurement problems of variable reduction but also addresses the structural problems of predicting and interpreting relationships in the research hypothesis. In interpreting the relationship of the latent variable to the dependent variable in regression analysis, this type of analysis is not affected by multi-collinearity, being distribution-free, requires only a small sample size, and can be applied to either formative or reflective measurements [37]. PLS-SEM was used to investigate the pathways between HL, SE, and PE on SCB in patients with type 2 diabetes and multiple group analysis was used to compare the differences in these pathways by decision-making preference. Descriptive statistics, including mean, standard deviations, and frequency, were used to analyze the distribution of patient characteristics, HL, SE, PE, SCB, and decision preferences. SmartPLS 3.0 (Institute of Operations Management and Organizations, University of Hamburg, Germany) was used for data analysis.

Results

Study participant characteristics

Among the 316 participants, 39.6% were male and 60.4% were female. Those aged ≥ 65 years accounted for 63.3% of the study population; 44.7% had an elementary school education and 22.0% had a high school education. In terms of decision-making preferences, 56.3% preferred physician decision-making and 43.7% preferred SDM. The scales were scored on 5-point Likert scale, with average values ranging from 3.42 to 4.03, indicating that participants had a more positive attitude towards each scale, especially PE (Table 1).

Table 1
Participant’s characteristics and average value of each scale
 
N
%
Mean
SD
  
N
%
Mean
SD
Sex
        
Preference of SDM
      
Male
125
39.6
    
SDM
138
43.7
    
Female
191
60.4
    
Physician decision
178
56.3
    
Age
        
Educational
        
≦54
45
14.2
    
Elementary school
136
44.7
    
55–64
71
22.5
    
Middle school
54
17.8
    
65–74
103
32.6
    
High school
67
22.0
    
≧75
97
30.7
    
College or high
47
15.5
    
HL
316
  
3.42
0.47
PE
316
  
4.03
0.48
SE
316
  
3.82
0.47
SCB
316
  
3.66
0.52
HL: health literacy; SE: self-efficacy; PE: patient empowerment; SCB: self-care behaviors。

Reliability and validity

In this study results were analyzed by measurement mode. The factor loadings of each dimension were between 0.592 and 0.939 (Fig. 1). The factor loading of FHL was 0.339. According to Hair et al. [38], the factor loading of each dimension is recommended to be ≥ 0.5, so the FHL dimension was excluded from measurement mode analysis. Table 2 shows that the Cronbach's α and Composite Reliability (CR) values of each dimension were greater than 0.7, indicating good construct reliability and high internal consistency. The Average Variance Extracted (AVE) was greater than 0.5, showing convergent validity. Fornell and Larcker’s test shows that the correlation coefficient was lower than the value of the diagonal element (√AVB), indicating that the measurement mode had the required discriminant validity [39].

Table 2
Reliability, convergent, and discriminant validity of measurement model
Construct
correlations
Cronbach’s α
CR
AVE
HL
SE
PE
SCB
HL
0.938
      
0.863
0.936
0.880
SE
0.516
0.764
    
0.754
0.846
0.584
PE
0.450
0.638
0.853
  
0.876
0.914
0.727
SCB
0.499
0.646
0.539
0.755
0.762
0.840
0.570
CR: composite reliability; AVE: Average variance extracted

Structural model

To clarify the path of HL, SE, and PE on SCB, this study applied the PLS Bootstrap method with 5,000 resamplings to obtain inference statistics. As shown in Table 3, HL had a direct positive effect on SE (path coefficient 0.516, t value = 8.755, P < 0.001), PE (path coefficient 0.450, t value = 9.749, P < 0.001), and SCB (path coefficient 0.197, t value = 3.705, P < 0.001). SE had a direct positive effect on SCB (path coefficient 0.433, t value = 6.526, P < 0.001) and PE had a direct positive effect on SCB (path coefficient 0.174, t value = 2.890, P < 0.01). HL also indirectly influenced SCB via SE and PE (for SE: path coefficient 0.223, t value = 4.766, P < 0.001; for PE: path coefficient 0.079, t value = 2.890, P < 0.01). The pathways are shown in Fig. 1.

Table 3
Direct and indirect effects, and multiple group analysis
Path
Path coefficient
t value
P value
Multiple group analysis
SDM /physician decision
Direct effect
      
Path coefficient
P value
HL→SE
0.516
8.755
0.000
0.249
0.008
HL→PE
0.450
9.749
0.000
0.144
0.061
HL→SCB
0.197
3.705
0.000
0.171
0.919
SE→SCB
0.433
6.526
0.000
0.012
0.453
PE→SCB
0.174
2.890
0.004
0.060
0.322
Indirect effect
          
HL→SE→SCB
0.223
4.766
0.000
    
HL→PE→SCB
0.079
2.890
0.004
    

Pathways differences by preference in medical decision-making

To compare the pathways differences in terms of preference decisions (SDM and physician decision), Hensler et al. [40] pointed out that Measurement Invariance Assessment is necessary, which includes three steps: configural invariance, compositional invariance, and the equality of composite mean values and variances. Using the Measurement Invariance Assessment in SmartPLS usually automatically establishes configural invariance (Step 1). Table 4 shows the correlation coefficient of Step 2. In Step 3, the P value of both the mean value and the variance were greater than 0.05, which is not significant. These results show that the measurement invariance was established for the scales when measuring different groups.

Table 4
Results of the measurement invariance assessment
Construct
Step 2
Step 3
 
Correlation coefficient
P value
Variance
P value
Mean value
P value
HL
1.000
0.508
0.155
0.567
0.179
0.125
SE
0.996
0.141
0.383
0.088
-0.086
0.397
PE
0.998
0.447
0.029
0.883
-0.064
0.545
SCB
0.997
0.508
0.160
0.578
-0.069
0.520

Next, multi-group analysis was used to compare the pathways between the SDM and the physician decision-making groups. In the five paths shown in Fig. 1, the path of HL directly positively affecting SE was significantly stronger for the SDM group than for the physician decision group (Table 3, path coefficient 0.249, P < 0.01).

Discussion

This study is the first to place HL, SE, PE, and SCB in patients with type 2 diabetes in the same model to explore their pathways used PLS-SEM, and their differences in terms of the patient preference for decision-making.

These findings clarify the relationships of HL, SE, and PE to SCB, which are: HL directly positively influences SE and SCB, and SE directly positively influences SCB. HL can also indirectly influence SCB through SE. These relationships were consistent with previous researches [7, 16, 17]. Patients with higher HL can better promote their own health-related behaviors, and they may feel more confident in their ability to complete SCB [41, 42]. In particular, IHL and CHL have a greater impact on SE than FHL [7]. The more patients can enhance their SE, the more they may feel empowered to handle their situation [43], so HL plays an important role in the impact of SCB, and SE is also an important predictor of SCB [22].

This study also further clarified the effect of HL and PE on SCB, which is: HL directly positively influences PE, and PE directly positively influences SCB. HL can also indirectly influence SCB through PE. Studies have shown that HL and PE are deeply interwoven [21], and each independently affects SCB [44], but limited HL is a threat to PE and self-management [45]. Wang et al. [28] proposed that PE may promote SCB in patients with high IHL and CHL, but may have no effect on SCB in patients with low communicative and critical health literacy (CCHL). Obviously, no matter patients are empowered externally or internally, these empowerment may sustained only when patients have adequate HL. Increasing HL is an antecedent of PE [46–48]. Strengthening PE without adequate HL may lead patients to harm their health condition by making uninformed decisions, and HL plays a bigger role than PE in determining health status [18].

In this study, the mean PE was 4.03 and the mean SE was 3.82, indicating that patients tended to have more PE than SE. The combined care plans for patients with type 2 diabetes under the National Health Insurance System in Taiwan encourages patients to be empowered by health providers, so PE and SE are generally high. However, the mean of the HL scale was 3.42, indicating that patients’ HL was obviously insufficient, and the mean of the SCB scale (3.66) was not high as well. Although these patients had the beliefs and actions to perform health behaviors and wanted to control their own health behaviors, they still felt a strong sense of powerlessness. Therefore, the self-management behaviors of the patients relied too heavily on the health care system to take active responsibility for SCB. The mindset of these patients must be changed, and their self-improvement in HL is the cornerstone by which to promote SCB.

In terms of the differences in preference in decision-making (SDM vs physician decision), HL directly positively affected SE in the five paths of Fig. 1 significantly more for the SDM group than for the physician decision group. Because HL can improve the ability of the patient to perform SCB, the patient then is better able to participate in SDM [49], further influencing clinical decisions. Patients will be more confident to take on self-management when they have more health-related knowledge, feel they can seek out resources and applications, and have positive interactions with health care professionals. As a result, the self-efficacy of patients is also improved. The study by Brabers et al. [49] showed that HL was associated with patient involvement in SDM, especially CHL. Patients participating in SDM have an increased commitment to health behaviors [50] and greater awareness and confidence to start their treatment [51]. Also, because patients with HL are more likely to play an active role in clinical decision-making, patients with HL are much more likely to show behavioral change [52]. Because HL involves all aspects of health care, such as prevention, screening, diagnosis, and treatment, it is considered the basis of the health care delivery system [53].

Limitations of this study include the fact that participant inclusion was based on patient consent, which may have introduced selection bias into the study sample. This study sample of a single regional hospital may not be generalizable. Also, the sample included only outpatients with type 2 diabetes; those with other types of diabetes or more advanced disease may have different outcomes. Further study is needed to explore the specific factors that influence HL, in order to develop an effective promotion strategy and the associated application tools. Overall, improving the HL of patients with diabetes can enhance their self-care ability.

Conclusions

This study provides insight into HL as a critical factor in improving the SCB of patients with type 2 diabetes, and clarified the pathways between HL, SE, and PE on SCB. HL was found to have a direct positive effect on SE, PE, and SCB. HL also had an indirectly positive influence in SCB through SE and PE. Separately, SE and PE each directly positively affected SCB. In the SDM group, HL directly positively affected SE significantly more strongly than in the physician decision group. Therefore, the development of an effective promotion strategy and application tools to improve the overall HL of patients with type 2 diabetes is the cornerstone of enhancing SCB to improve health and reduce complications in this patient population.

Abbreviations

SCB: Self-care behaviors; HL: Health literacy; SE: Self-efficacy; PE: Patient empowerment; WHO: World Health Organization; HbA1C: Glycated Hemoglobin; PLS-SEM: Partial least squares structural equation modeling; FCCHL: Functional Communicative and Critical Health Literacy; FHL: Functional health literacy; IHL: Interactive health literacy; CHL: Critical health literacy; SDM: Shared decision making; CR: Composite Reliability; AVE: Average Variance Extracted; CCHL: Communicative and critical health literacy

Declarations

Acknowledgements

The authors would like to thank the participants who agreed to self-administered questionnaire.

Ethics approval and consent to participate

This study was approved by the review board of National Taiwan University Hospital Hsin-Chu Branch

Availability of data and materials

The data was collected from the patient's self-administered questionnaire with patient consent during the current study. The datasets are not publicly available, but are accessible from the corresponding author on reasonable request.

Consent for publication

Not applicable

Competing interests

The authors declare they have no competing interests.

Funding

This study was supported by grants from the National Taiwan University Hospital,

Hsin-Chu Branch, (107-HCH038).

Authors' contributions

MJW took the lead on the design of the study, the analysis of the data and the writing of the manuscript. HML conducted data analysis and data interpretation. LCH involved in the study design and assisted with analysis. YTL participated in the study design and the collection of data. All authors read and approved the final version of the manuscript.

Author details

  1. Department of Secretariat, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
  2. Department of Healthcare Management, Yuanpei University of Medical Technology, Hsinchu, Taiwan
  3. Department of Business Administration, Minghsin University of Science and Technology, Hsinchu, Taiwan.
  4. Department of Public Health, China Medical University, Taichung, Taiwan
  5. Department of Development and Planning, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan

References

  1. Diabetes concerns every family [cited 2019 Mar. 24]. Available from: https://www.idf.org/e-library/epidemiology-research/54-our-activities/455-world-diabetes-day-2018-19.html
  2. 6 million death are directly attributed to diabetes each year [cited 2019 Mar. 24]. Available from: https://www.who.int/diabetes/en/
  3. World Health Day 2016: Beat diabetes [2016 Jun 13]. Available from: http://www.who.int/campaigns/world-health-day/2016/en/
  4. American Diabetes Association. Standards of medical care in diabetes—2010. Diabetes Care. 2010; 33: S11–S61.
  5. Anderson RM, Funnell MM, Aikens JE, Krein SL, Fitzgerald JT, Nwankwo R et al. Evaluating the efficacy of an empowermentbased self-management consultant intervention: results of a twoyear randomized controlled trial. Ther Patient Educ. 2009; 1: 3–11.
  6. Self-Care Behavior [cited 2019 Oct 30]. Available from: https://www.encyclopedia.com/education/encyclopedias-almanacs-transcripts-and- maps/self-care-behavior
  7. Lee YJ, Shin SJ, Wang RH, Lin KD, Lee YL, Wang YH. Pathways of empowerment perceptions, health literacy, self-efficacy, and self-care behaviors to glycemic control in patients with type 2 diabetes mellitus. Patient Educ Couns.2016; 9 (2):287-94.
  8. Gao J, Wang J, Zheng P, Harrdorfer R, Kegler MC, Zhu Y, Fu H, Effects of self-care, self-efficacy, social support on glycemic control in adults with type 2 diabetes. BMC Fam. Pract. 2013;14: 66–
  9. Walker RJ, Gebregziabher M, Martin-Harris B, Egede LE, Relationships between social determinants of health and process and outcomes in adults with type 2 diabetes: validation of a conceptual framework. BMC Endocr. Disord. 2014;14: 82–
  10. McAllister M, Dunn G, Payne K, Davies L, Todd C. Patient empowerment: the need to consider it as a measurable patient-reported outcome for chronic conditions. BMC Health Serv Res.2012;12:157.
  11. Nielsen-Bohlman L, Panzer AM, Kindig DA. Health Literacy: A Prescription to End Confusion. Washington (DC): Institute of Medicine of the National Academies; 2004.
  12. Bailey SC, Brega AG, Crutchfield TM, lasy T, Herr H, Kaphingst K. et al., Update on health literacy and diabetes. Diabetes Educ. 2014;40:581–
  13. Health Literacy: A Prescription to End Confusion. The National Academies Press; 2004.
  14. Brega AG, Ang A, Vega. et al. Mechanisms underlying the relationship between health literacy and glycemic control in American Indians and Alaska Natives. Patient Educ. Couns. 2012; 88 (1): 61–
  15. Al Sayah F, Majumdar SR, Egede LE, Johnson JA. Associations between health literacy and health outcomes in a predominantly low-income african american population with type 2 diabetes. J Health Commun.2015;20(5):581-8.
  16. Bohanny W, Wu SF, Liu CY, Yeh SH, Tsay SL, Wang TJ, Health literacy, self-efficacy, and self-care behaviors in patients with type 2 diabetes mellitus. J. Am. Assoc. Nurse Pract. 2013; 25 (9): 495–
  17. Sarkar U, Fisher L, Schillinger D, Is self-efficacy associated with diabetes self-management across race/ethnicity and health literacy? Diabetes Care. 2006; 29 (4): 823–
  18. Náfrádi L, Nakamoto K, Csabai M, Papp-Zipernovszky O, Schulz PJ. An empirical test of the Health Empowerment Model: Does patient empowerment moderate the effect of health literacy on health status? Patient Educ Couns.2018;101(3):511-517.
  19. Bandura A. Self-efficacy in changing societies. New York, NY: Cambridge University;1995.
  20. Bandura A. Health promotion by social cognitive means. Health Education & Behavio 2004; 31(2):143-164.
  21. Reisi M, Mostafavi F, Javadzade H, Mahaki B, Tavassoli E, & Sharifirad G. Impact of health literacy, self-efficacy, and outcome expectations on adherence to self-care behaviors in Ira­nians with type 2 diabetes. Oman Medical Journal. 2016; 31(1): 52-59.
  22. King DK, Glasgow RE, Toobert DJ, Strycker LA, Estabrooks PA, Osuna D, & Faber AJ. Self-efficacy, problem solving, and social-environmental support are associated with dia­betes self-management behaviors. Diabetes Care. 2010;33(4): 751-753.
  23. Ahola AJ, Groop PH. Barriers to self-management of diabetes. Diabet Med.2013 ;30 (4):413-20.
  24. Anderson RM, Funnell MM, Patient empowerment: myths and misconceptions, Patient Educ Couns. 2010;79 (3): 277–
  25. Patient empowerment and health care [cited 2019 Feb. 8]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK144022/
  26. Chen MF, Wang RH, Lin KC, Hsu HY, Chen SW, Efficacy of an empowerment program for Taiwanese patients with type 2 diabetes: a randomized controlled trial, Appl. Nurs. Res. 2015; 28(4):366-73.
  27. Schulz PJ, Nakamoto K, Health literacy and patient empowerment in health communication: the importance of separating conjoined twins, Patient Educ. Couns. 2013; 90: 4–
  28. Wang RH, Hsu HC, Lee YJ, Shin SJ, Lin KD, An LW. Patient empowerment interacts with health literacy to associate with subsequent self-management behaviors in patients with type 2 diabetes: A prospective study in Taiwan. Patient Educ Couns.2016; 99(10):1626-31.
  29. American Diabetes Association. 6. Glycemic targets. Diabetes care. 2015; 38(Supplement 1): S33-40.
  30. Ishikawa H, Takeuchi T, Yano E. Measuring functional, communicative, and critical health literacy among diabetic patients. Diabetes Care. 2008;31(5):874–879.
  31. Rapley P, Passmore A, & Phillips M. Review of the psychometric properties of the diabetes self-efficacy scale: Australian longitudinal study. Nursing & Health Sciences.2003; 5: 289-97.
  32. Wallston KA, Rothman RL, & Cherrington A. Psychometric properties of the perceived diabetes self-management scale (PDSMS). J Behav Med.2007; 30: 395-401.
  33. Chen MF et al. Diabetes Empowerment Process Scale: development and psychometric testing of the Chinese version. J Adv Nurs.2011; 67(1):204-14.
  34. Schmitt A, Gahr A, Hermanns N, Kulzer B, Huber J, Haak T. The Diabetes Self- Management Questionnaire (DSMQ): development and evaluation of an instrument to assess diabetes self-care activities associated with glycaemic control. Health Qual Life Outcomes. 2013;11:13
  35. Bruera E, Sweeney C, Calder K, Palmer L, Benisch-Tolley S. Patient prefer-ences versus physician perceptions of treatment decisions in cancer care. J Clin Oncol.2001; 19: 2883–5.
  36. Waddimba AC, Beckman HB, Mahoney TL, Burgess JF Jr. The Moderating Effect of Job Satisfaction on Physicians' Motivation to Adhere to Financially Incentivized Clinical Practice Guidelines. Med Care Res Rev.2017;74(2):148-177.
  37. Pirouz, D.M. An Overview of Partial Least Squares. 2006 [cited 2018 Feb. 26]. Available from: http://ssrn.com/abstract= 1631359.
  38. Hair Jr. JF, Black WC, Babin BJ, & Anderson RE. Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. 2009.
  39. Fornell, C, & Larcker, DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 1981; 18: 39-50.
  40. Henseler J, Ringle CM, & Sarstedt M. Testing measurement invariance of composites using partial least squares. International Marketing Review. 2016; 33(3):405-431.
  41. Hou SI. Health Education Theoretical Concepts, Effective Strategies and Core Competencies. Health Promot Pract. 2014;15(5):619–621 .
  42. Diviani N, Camerini AL, Reinholz D, Galfetti A, Schulz PJ. Health literacy, health empowerment and health information search in the field of MMR vaccination: a crosssectional study protocol. BMJ Open. 2012;2(6):e002162.
  43. Jaensson M, Dahlberg K, Nilsson U, Stenberg E. The impact of self-efficacy and health literacy on outcome after bariatric surgery in Sweden: a protocol for a prospective longitudinal mixed-methods study. BMJ Open. 2019; 9(5):27272. 
  44. Camerini AL, Schulz PJ, Health literacy and patient empowerment: separating con-joined twins in the context of chronic low back pain. PLoS One. 2015;10: e0118032.
  45. Abdullah A, Liew SM, Salim H, Ng CJ, Chinna K. Prevalence of limited health literacy among patients with type 2 diabetes mellitus: A systematic review. PLoS One.2019 ;14(5):e0216402.
  46. Porr C, Drummond J, Richter S. Health literacy as an empowerment tool for low-income mothers. Fam Community Health.2006; 29 (4):328-35.
  47. Castro EM, Van Regenmortel T, Vanhaecht K, Sermeus W, Van Hecke A. Patient empowerment, patient participation and patient-centeredness in hospital care: A concept analysis based on a literature review. Patient Educ Couns. 2016; 99(12):1923-1939.
  48. van Dam HA, van der Horst F, van den Borne B, Ryckman R, Crebolder H. Provider- patient interaction in diabetes care: effects on patient self-care and outcomes. A systematic review. Patient Educ Couns.2003;51(1):17-28.
  49. Brabers AE, Rademakers JJ, Groenewegen PP, van Dijk L, de Jong JD. What role does health literacy play in patients' involvement in medical decisionmaking? PloS one. 2017;12(3):e0173316
  50. AsadiJanati N, Vahdat S, Yazdan Panah A. Studying patient participation in controlling postoperative infection in orthognathic surgery in BouAli Hospital, Tehran in 2012-2013. Medical Sciences Journal. 2014; 24(3):189-93.
  51. Ghiyasvandian S, Mousavizadeh Sn, Dehghan Nayeri N, Haghani H. The Effect of Decision Aid's Pakage in Selected Treatment by Patients with Early Stage Breast Cancer and Decision making Outcomes. Journal of Fasa University of Medical Sciences. 2013;3(3):271-9.
  52. Khosravi A, Ahmadzadeh Kh, Arastoopoor Sh, R. T. Health Literacy Levels of Diabetic Patients Referred to Shiraz Health Centers and Its Effective Factors. Health Inf Manage. 2015; 12(2):194-205.
  53. Torres RY, Marks R. Relationships among health literacy, knowledge about hormone therapy, selfefficacy, and decision-making among postmenopausal health. Journal of health communication. 2009;14(1): 43-55.