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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Med Inform Decis Mak . 2026 Mar 11;26:125. doi: 10.1186/s12911-026-03426-z Search in PMC Search in PubMed View in NLM Catalog Add to search Patient decision aid–supported shared decision making and short-term decision-making outcomes in type 2 diabetes: a prospective observational comparative study Su-Han Hsu Su-Han Hsu 1 Department of Pharmacy, Taipei City Hospital Yangming Branch, Taipei, Taiwan 2 School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, 11031 Taiwan 4 Department of Exercise and Health Sciences, University of Taipei, Taipei, Taiwan Find articles by Su-Han Hsu 1, 2, 4, # , Cheng-Hsuan Chiang Cheng-Hsuan Chiang 1 Department of Pharmacy, Taipei City Hospital Yangming Branch, Taipei, Taiwan 2 School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, 11031 Taiwan Find articles by Cheng-Hsuan Chiang 1, 2, # , Chia-Hui Lin Chia-Hui Lin 1 Department of Pharmacy, Taipei City Hospital Yangming Branch, Taipei, Taiwan 3 Department of Pharmacy and Master Program, Tajen University, Pingtung, Taiwan 4 Department of Exercise and Health Sciences, University of Taipei, Taipei, Taiwan Find articles by Chia-Hui Lin 1, 3, 4 , Shih-Horng Huang Shih-Horng Huang 5 Department of Surgery, Division of General Surgery, Fu Jen Catholic University Hospital, New Taipei City, Taiwan Find articles by Shih-Horng Huang 5 , Joseph Jordan Keller Joseph Jordan Keller 6 Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA USA 7 Department of Psychiatry, Children’s Hospital and Regional Medical Center, Seattle, WA USA Find articles by Joseph Jordan Keller 6, 7 , Li-Hsuan Wang Li-Hsuan Wang 2 School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, 11031 Taiwan 8 Department of Pharmacy, Taipei Medical University Hospital, Taipei, Taiwan Find articles by Li-Hsuan Wang 2, 8, ✉, # , Kung-Pei Tang Kung-Pei Tang 9 Department of Education and Humanities in Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan 10 Department of Early Childhood and Family Education, College of Education, National Taipei University of Education, Taipei City, 106 Taiwan Find articles by Kung-Pei Tang 9, 10, ✉, # Author information Article notes Copyright and License information 1 Department of Pharmacy, Taipei City Hospital Yangming Branch, Taipei, Taiwan 2 School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, 11031 Taiwan 3 Department of Pharmacy and Master Program, Tajen University, Pingtung, Taiwan 4 Department of Exercise and Health Sciences, University of Taipei, Taipei, Taiwan 5 Department of Surgery, Division of General Surgery, Fu Jen Catholic University Hospital, New Taipei City, Taiwan 6 Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA USA 7 Department of Psychiatry, Children’s Hospital and Regional Medical Center, Seattle, WA USA 8 Department of Pharmacy, Taipei Medical University Hospital, Taipei, Taiwan 9 Department of Education and Humanities in Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan 10 Department of Early Childhood and Family Education, College of Education, National Taipei University of Education, Taipei City, 106 Taiwan ✉ Corresponding author. # Contributed equally. Received 2025 Nov 29; Accepted 2026 Mar 5; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13088564 PMID: 41814312 Abstract Background Our study utilized patient decision aids (PDA) to explore the influence of shared decision making (SDM) on type 2 diabetes patients’ intention to use subcutaneous antidiabetic agents. Methods A prospective observational comparative study was conducted involving 249 patients with type 2 diabetes who were referred by physicians and subsequently interviewed by pharmacists across different clinics. Patients were classified into two parallel groups based on routine clinical practice. A patient decision aid (PDA) entitled “Type 2 Diabetes: Oral or Subcutaneous Antidiabetic Agents for My Diabetes Control” was developed for this study. Data collection focused on patients’ intention to initiate subcutaneous antidiabetic agents, post-test knowledge scores, and satisfaction with clinical visits. These outcomes were compared between the SDM group, which received pharmacist-facilitated shared decision making using a PDA, and the PDA self-completion (control) group, which received usual care and completed the PDA by self-review. Results A total of 249 patients were included (SDM group n = 123; control group n = 126). The SDM group had higher baseline disease severity indicators. After adjustment, the SDM group demonstrated higher decision-making (adjusted mean difference 0.59, 95% CI 0.17–1.00), knowledge (0.84, 95% CI 0.59–1.09), and satisfaction (1.86, 95% CI 0.98–2.73) scores compared with controls. Conclusions PDA-supported shared decision making was associated with higher short-term decision-making intentions, knowledge, and satisfaction among patients with type 2 diabetes. These findings suggest that PDAs may support patients in considering injectable treatment options, although conclusions regarding treatment initiation or clinical outcomes require further longitudinal evaluation. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-026-03426-z. Keywords: Diabetes mellitus, type 2; Decision making, shared; Decision support techniques; Patient satisfaction Background Diabetes and diabetes care According to the International Diabetes Federation (IDF) [ 1 ], reported that 11.1% of the adult population (20–79 years) has diabetes. The IDF estimates that by 2050, diabetes will affect around 853 million adults worldwide—equivalent to one in eight adults—marking a 46% rise compared to current figures. Although global healthcare expenditure on diabetes exceeded 760 billion USD in 2019, there were still 4.2 million deaths worldwide in the same year because of diabetes or its related complications. The issue of diabetes is a global problem that cannot be ignored. On the basis of the 2019 Taiwan Diabetes Annual Report [ 2 ], the age-standardized incidence rate of diabetes in Taiwan was approximately 0.6–0.65% per year, with a higher prevalence in males than females. Based on the population distribution in Taiwan, there was approximately 150,000 to 160,000 new diabetes cases annually. The population with diabetes in Taiwan has been steadily increasing, with the proportion of individuals aged 75 and above continuously rising. The aging population in Taiwan is one of the key factors contributing to the increasing incidence of diabetes. From 840,000 people in 2000 to 2.18 million in 2014, diabetes has become one of the major challenges for public health and the healthcare system. Numerous large-scale clinical studies have confirmed that blood glucose control in diabetic patients can effectively reduce the risk of cardiovascular diseases and mortality. A clinical trial published in 2000 demonstrated that for patients with type 2 diabetes, a 1% reduction in HbA1c was associated with a 14% decrease in the risk of myocardial infarction, a 21% reduction in diabetes-related mortality, and a 37% reduction in complications related to peripheral vascular disease [ 3 ]. Therefore, the primary care goal for diabetic patients is blood glucose control, which also includes managing blood pressure, HbA1c, blood lipids, and microalbuminuria. In addition to pharmacological treatment, non-pharmacological interventions are widely used in clinical practice as a means to help achieve the blood glucose control targets for patients. Since 2000, systematic reviews and meta-analyses have been conducted to research the effects of educational interventions on blood glucose control in diabetic patients. A meta-analysis published in 2004, primarily based on Western studies, found that face-to-face teaching, using the cognitive reframing teaching method combined with practice, could reduce patients’ HbA1c by 0.32% [ 4 ]. Additionally, a meta-analysis published in 2016 focused on the Chinese population also found that educational interventions could reduce patients’ HbA1c. Furthermore, long-term continuous educational interventions resulted in a more significant reduction in HbA1c levels [ 5 ]. With the adjustment and integration of health education models and content, it has gradually evolved into the current Diabetes Self-Management Education and Support (DSMES). The American Diabetes Association mentions in publicly available data that many large studies have found that using DSMES as an intervention for diabetes patients can reduce HbA1c by approximately 1% [ 6 , 7 ]. In addition, DSMES interventions have demonstrated various clinical effects, such as a 34% reduction in overall mortality [ 8 ], a decrease in diabetes-related complications [ 9 , 10 ], improved quality of life for patients [ 11 ], and a reduction in the distress and stress [ 12 ] associated with diabetes. Beyond the clinical benefits, cost-effectiveness analysis studies also suggest that DSMES interventions for diabetes patients can reduce hospitalization-related costs and lower expenditure related to complications. The research findings support the cost-effectiveness of this intervention [ 12 ]. DSMES emphasizes personalization and a patient-centered approach, enabling patients to understand diabetes-related information. Afterward, healthcare providers support patients in making their treatment decisions, thereby increasing patients’ self-care and problem-solving abilities. The content of DSMES-based patient intervention education includes assessments of nutrition, physical activity, smoking cessation, psychological and social factors, as well as nursing-related evaluations. Patient-centered individualized improvement plans and relevant educations are provided [ 13 ]. Since 1996, Taiwan’s Department of Health has introduced the shared care concept for diabetes, based on the UK experience. This approach involves the sharing and integration of resources from various healthcare institutions and interdisciplinary medical professionals, establishing a patient-centered shared care model. The shared care model was initially implemented in Yilan County and gradually expanded to other counties and cities. By 2002, it was rolled out nationwide in Taiwan. Over the years of development, Taiwan’s shared care model has focused on DSMES to manage diabetes care and has successfully become a model in the worldwide. Shared decision-making and decision-making support tools SDM originated in the United States, where the 1982 patient-centered care initiative emphasized shared well-being and active patient involvement. SDM is a patient-centered clinical healthcare process that integrates knowledge, communication, and respect to support medical decisions aligned with patient values and professional guidance [ 14 , 15 ]. In recent years, with support from the Ministry of Health and Welfare and the National Health Insurance Administration, SDM has become increasingly integrated into clinical practice in Taiwan. Medical decisions are typically categorized into four quadrants based on risk levels and certainty. SDM is particularly suitable for medical situations with low certainty. The expected functions of SDM include: (1) promoting patient involvement in decisions, (2) clarifying and communicating patient values, (3) avoiding directive recommendations, and (4) supporting informed, value-consistent choices. Ultimately, SDM aims to achieve mutual understanding both cognitively and emotionally through information exchange, fostering a consensus based on trust [ 16 ]. Decision support tools used in SDM must involve at least two decision-makers, shared contribution to treatment decisions, smooth communication between both parties, and a mutually agreed-upon outcome [ 17 ]. These tools, known as Patient Decision Aids (PDAs), help reduce information asymmetry and support feasible, informed choices by both patients and providers. The effectiveness of PDA is often evaluated using short-term cognitive outcomes such as knowledge. Systematic reviews and meta-analyses consistently identify knowledge change as a primary outcome. Satisfaction is also recognized as a key metric in tool evaluation [ 18 – 20 ]. Diabetes-related patient decision aid Research on the association between SDM and clinical outcomes in diabetic patients remains limited. However, patients with type 2 diabetes mellitus (T2DM) who participated in SDM showed a statistically significant reduction in glycated hemoglobin levels compared to those receiving usual care, potentially lowering the risk of diabetes-related complications [ 21 ].In addition, a systematic meta-analysis published in 2019 analyzed 15 randomized trials to assess the effects of PDAs used by diabetic patients in SDM. The results found that the group using PDAs performed better in reducing conflicts, increasing cognition, and participating in SDM [ 22 ]. With advancements in medical science, a wide variety of hypoglycemic agents are available in Taiwan. Based on their administration methods, these agents are primarily classified as oral or subcutaneous, with further subdivisions based on mechanisms of action. Furthermore, these categories can be subdivided based on their specific mechanisms of action. In recent years, the usage patterns of various hypoglycemic agents have undergone slight changes. However, 2019 Taiwan Diabetes Annual Report [ 2 ] reported that, despite an increase in insulin usage, its adoption rate remains considerably lower than that observed in Europe and the United States. This disparity may have an impact on the overall glycemic control rates among diabetic patients. Another subcutaneous antidiabetic agent, the glucagon-like peptide-1 (GLP-1) receptor agonist, was introduced to the Taiwanese market in 2011. While its usage has steadily increased over the years, its market share was only 0.48% in 2014. Although comparative data on the international usage of GLP-1 receptor agonists remains limited, the aforementioned report indicates that newer hypoglycemic agents still require ongoing promotion in Taiwan. Consequently, it can be inferred that the utilization rate of subcutaneous antidiabetic agent among diabetic patients in Taiwan is lower than that observed in Europe and the United States. Our study aims to evaluate the application of SDM " Type 2 diabetes: Oral or subcutaneous antidiabetic agents for my diabetes control " on patients’ willingness to adopt subcutaneous antidiabetic agent and assess short-term decision-related outcomes among patients with T2DM. Methods Subjects This study was designed as a prospective observational comparative study conducted in a real-world clinical setting, using convenience sampling. The study was conducted in the spring of 2021. Patients were included if they had T2DM; were aged 20 years or older; were taking oral hypoglycemic agents (OHAs); and were not using subcutaneous antidiabetic agent, such as insulin or GLP-1 receptor agonists (GLP-1 RAs). The exclusion criteria included the inability to read Chinese, hearing impairment (deafness or severe hearing loss), visual impairment, or the inability to communicate in Mandarin, English, Taiwanese, or any language understood by healthcare professionals facilitating SDM. Procedures The developed PDA titled “Type 2 diabetes: Oral or subcutaneous antidiabetic agents for my diabetes control” was implemented for patient recruitment in the Taipei regional hospital [ 23 ]. The study was designed as a prospective observational comparative study in a real-world clinical setting. (Fig. 1 ) Based on routine clinical judgment, physicians identified patients who were referred for pharmacist-facilitated shared decision making using a patient decision aid (PDA). Patients who received pharmacist-facilitated SDM constituted the SDM group, whereas those who received usual medical consultation and treatment constituted the PDA self-completion (control) group. Participants in the control group completed the same questionnaires as the SDM group but did not undergo a structured SDM process. The outcome of our study compared intention to use subcutaneous antidiabetic agent, post-test knowledge scores, and patient satisfaction between the two groups. Fig. 1. Open in a new tab Participant flow in a prospective observational comparative study Measurement tool Our study utilized the PDA as a research recruitment tool was adapted from a previously validated instrument published by Hsu et al. (2023) [ 23 ], which is publicly accessible. The PDA was designed as a shared decision-making tool to support discussions regarding the use of subcutaneous antidiabetic agents among patients with type 2 diabetes. The PDA consisted of two parts: (1) a brochure illustrating the pros and cons of OHAs and subcutaneous antidiabetic agents pertaining to outcome, adverse effects, and ease of use and (2) a questionnaire to identify the personal values of patients with T2DM and their attitudes toward subcutaneous antidiabetic agents. In the SDM group, the PDA was delivered through a one-on-one shared decision-making session facilitated by a pharmacist during routine outpatient visits. Each session followed a standardized format and lasted approximately 20 min on average, ensuring consistency in the SDM process across participants. Participants in the control group were provided with the same PDA materials for self-review but did not engage in a pharmacist-facilitated SDM session. Both groups completed identical questionnaires following the consultation. Decision-making was assessed using items rated on a 5-point Likert scale (1 = strongly disagree and 5 = strongly agree) was used to represent decision-making score. The higher score means patient’s positive attitude to use subcutaneous antidiabetic agent. The PDA included four items to assess participants’ post-test knowledge of SDM. Each correct response scored one point, while incorrect or “do not know” answers scored zero. The total score, ranging from 0 to 4, reflected the participant’s post-test knowledge level. Internal consistency of the knowledge items, assessed using the Kuder–Richardson Formula 20 (KR-20), was 0.44. Satisfaction score was measured using 5-point Likert scale, where higher scores indicated greater knowledge and higher satisfaction with the SDM. The satisfaction scale demonstrated excellent internal consistency (Cronbach’s α = 0.92). The demographic variables included age, gender, education level, year of diabetes mellitus diagnosis, blood sugar control status, number of OHAs currently being taken, BMI, self-monitoring of blood glucose frequency. Statistical methods The independent samples t-test was used to analyze differences between two groups based on demographic variables, including age, current blood glucose control status, number of oral hypoglycemic agents (OHAs) currently being taken, height, and weight. The mean and standard deviation were also calculated. The descriptive statistics of categorical variables, such as gender, educational level, duration of diabetes, and frequency of self-monitoring of blood glucose, were analyzed using the chi-square test, with group percentages reported. Given the nonrandomized design and observed baseline imbalances between groups, analysis of covariance (ANCOVA) was performed to compare post-intervention outcomes between the SDM and control groups while adjusting for clinically relevant baseline covariates. Covariates were selected a priori based on their potential association with both physician assignment to pharmacist-facilitated SDM and study outcomes, and included age, sex, baseline HbA1c, fasting glucose, and number of oral antidiabetic agents. For the post-test knowledge outcome, pre-test knowledge score was additionally included to account for baseline differences in knowledge. The decision-making score was prespecified as the primary outcome, with post-test knowledge and satisfaction treated as secondary outcomes. The significance level (α) was set at 0.05 for all statistical tests. Analyses were performed using complete-case analysis, and cases with missing data were excluded listwise. Statistical analyses were conducted using PASW Statistics Version 19.0 (SPSS, Chicago, IL, USA). Hypotheses Our study compared the differences between the SDM group and the control group receiving usual care. The following hypotheses were formulated: H1: Patients exposed to pharmacist-facilitated SDM were hypothesized to demonstrate higher decision-making scores compared to those who do not receive SDM. H2: Patients exposed to pharmacist-facilitated SDM were hypothesized to demonstrate higher post-test knowledge scores compared to those who do not receive SDM. H3: Patients exposed to pharmacist-facilitated SDM were hypothesized to demonstrate higher satisfaction with the medical intervention compared to those who do not receive SDM. Results A total of 249 patients were enrolled (SDM n = 123; control n = 126). For adjusted analyses, complete-case ANCOVA was performed, resulting in an analytic sample of 227 participants; 22 were excluded due to missing covariate data. Missingness was primarily due to unavailable laboratory data in routine clinical records. The baseline characteristics of both groups are shown in Table 1 . The mean age in the SDM group and the control group was 62.85 years (SD = 13.121 years) and 65.83 years (SD = 12.267 years) respectively. The proportion of males in the SDM group and the control group was 53.7% and 49.2% separately. The majority of patients reported senior high school or university as their highest level of education. Approximately 40% of patients had been living with diabetes for over 10 years. The average HbA1c in the SDM group and the control group was 7.76% (SD = 1.462%) and 7.08% (SD = 1.309%) respectively. The average number of oral hypoglycemic agents (OHAs) taken in the SDM group and the control group was 2.47 and 1.75 separately. Table 1. Baseline characteristics (demographic variables) between the SDM and control groups Variable SDM groups ( N = 123) PDA self-completion groups ( N = 126) T test Chi-square test P value Age 62.85 (13.121) 65.83 (12.267) 1.857 0.065 Gender 0.494 0.482 Male 66 (53.7) 62 (49.2) Female 57 (46.3) 64 (50.8) Education level 8.365 0.079 None 7 (5.7) 0 (0) Elementary 22 (17.9) 23 (18.3) Junior high 13 (10.6) 19 (15.1) Senior high 31 (25.2) 35 (27.8) University 50 (40.7) 49 (38.9) Time since diabetes diagnosis a 0.777 0.855 < 1 year 14 (11.4) 14 (11.2) 1–5 years 33 (26.8) 28 (22.4) 5–10 years 30 (24.4) 31 (24.8) ≥ 10 years 46 (37.7) 52 (41.6) HbA1C (%) b 7.76 (1.462) 7.08 (1.309) 3.711 < 0.001 Glucose AC (mg/dL) 152.60 (43.232) 133.88 (51.509) 3.006 0.003 Numbers of concurrent oral hypoglycemic agents 2.47 (1.058) 1.75 (0.817) 5.998 < 0.001 Height (cm) c 162.88 (9.102) 161.62 (8.415) 1.134 0.258 Weight (kg) d 69.92 (15.434) 66.36 (13.271) 1.948 0.053 BMI 26.36 (5.518) 25.28 (3.905) 1.784 0.077 Self-monitoring of blood glucose frequency 13.412 0.009 At least once a day 6 (4.9) 18 (14.3) At least once a week 26 (21.1) 31 (24.6) At least once a month 11 (8.9) 15 (11.9) Less than once a month 14 (11.4) 20 (15.9) No monitoring 66 (53.7) 42 (33.3) Pre-test knowledge score 1.63 (1.18) 1.86 (1.30) 1.470 0.143 Open in a new tab Continuous variables were analyzed using the independent samples t-test, while categorical variables were analyzed using the Chi-square test. a 1 missing value for time since diabetes diagnosis. b 22 missing values for HbA1c. c 1 missing value for height. d 1 missing value for weight There were no significant differences between the two groups in terms of age, gender, educational level, year of diabetes diagnosis, height, weight, body mass index (BMI), and pre-test knowledge. The SDM group had higher mean levels of HbA1C (%) (7.76 vs. 7.08, p < 0.001), fasting blood glucose (mg/dL) (152.60 vs. 133.88, p = 0.003), the number of currently used oral hypoglycemic agents (2.47 vs. 1.75, p < 0.001), and self-monitoring of blood glucose frequency ( p = 0.009) compared to the control group. The results of SDM questionnaire are presented in Table 2 . After adjustment for age, sex, baseline HbA1c, fasting glucose, number of oral antidiabetic agents, and baseline pre-test knowledge score (for the post-test knowledge outcome), patients in the SDM group demonstrated higher post-intervention scores across all outcomes compared with the control group. The adjusted mean decision-making score was 2.77 (95% CI 2.50–3.03) in the SDM group and 2.18 (95% CI 1.88–2.48) in the control group, with an adjusted mean difference of 0.59 ( p = 0.007). For post-test knowledge, the adjusted mean score was 3.41 (95% CI 3.24–3.58) in the SDM group compared with 2.57 (95% CI 2.38–2.75) in the control group, corresponding to an adjusted mean difference of 0.84 ( p < 0.001). The adjusted mean satisfaction score was also higher in the SDM group (18.05, 95% CI 17.44–18.65) than in the control group (16.19, 95% CI 15.53–16.86), with an adjusted mean difference of 1.86 ( p < 0.001). Unadjusted post-test outcomes are presented in Supplementary Table S1 . Table 2. Adjusted post-intervention decision-making, knowledge, and satisfaction scores by study group SDM group Adjusted mean (95% CI) PDA self-completion group Adjusted mean (95% CI) Adjusted mean difference P value Decision-making score 2.765 (2.495–3.034) 2.180 (1.882–2.478) 0.585 0.007 Post-test knowledge score 3.411 (3.242–3.580) 2.567 (2.380–2.754) 0.844 < 0.001 Satisfaction score 18.046 (17.443–18.646) 16.191 (15.525–16.857) 1.855 < 0.001 Open in a new tab Values are adjusted marginal means estimated using ANCOVA, adjusting for age, sex, baseline HbA1c, fasting glucose, number of oral antidiabetic agents, and Pre-test knowledge score (for the Post-test knowledge outcome only). Analyses were based on complete cases included in the ANCOVA models ( n = 227) Discussion The findings of our study support the research hypotheses, confirming that patients who engaged in SDM with their healthcare providers exhibited significantly higher decision-making scores, post-test knowledge scores, and satisfaction levels compared to those who did not participate in SDM. A higher decision-making score indicates a stronger behavioral intention among patients with type 2 diabetes to use insulin pens for blood glucose control. Behavioral intention reflects the degree of motivation to perform a specific behavior, and it serves as a psychological predictor of actual behavioral performance [ 24 ]. Therefore, a higher decision-making score suggests a greater intention to use insulin pens, which may be correlated with patients’ actual adoption of this method for managing their condition. The PDAs can assist patients in linking their treatment attitudes and behavioral intentions. A study conducted in Taiwan has shown that the use of PDAs is significantly associated with both the stages of behavior change and levels of HbA1c, suggesting that PDAs may facilitate to more informed decision-making and better glycemic control [ 25 ]. In a study conducted in Japan, healthcare provider interventions, which explained how insulin could reduce the burden on the body and lower comorbidities, which increased patients’ willingness to accept subcutaneous antidiabetic agent [ 26 ]. Patients’ knowledge of insulin is also associated to their use of the medication. A study in South African [ 27 ] revealed that individuals lacked familiarity with insulin and were unable to use it effectively. In the study conducted in Oman [ 28 ], patients expressed fear and distrust towards insulin due to concerns about potential long-term side effects, weight gain, and a lack of basic understanding of insulin therapy. Furthermore, a meta-analysis conducted in the United Kingdom [ 29 ] highlighted that patients’ lack of knowledge and misconceptions about insulin, as well as the perception that insulin use indicated a more advanced stage of the disease, were key factors contributing to their reluctance and concerns about initiating insulin therapy. A cross-sectional study in Uganda [ 30 ] also shows that willingness to use subcutaneous antidiabetic agent is closely linked to patients’ knowledge and perceptions of these medications. Patients with misconceptions or inadequate understanding of subcutaneous antidiabetic agents are more likely to show lower willingness to use such treatment options, which, in the context of our study, is reflected in lower decision-making scores. In addition to achieving higher scores in decision-making score and post-test knowledge assessments, the SDM group in our study also reported greater satisfaction compared to the control group. Satisfaction can contribute to a reduction in doctor-patient conflicts, enhance participation in SDM, and serve as an indicator of a stronger doctor-patient relationship [ 31 ]. Moreover, it is essential for patients and physicians to collaboratively discuss treatment strategies, with patient satisfaction and preferences playing a critical role in this process. A systematic review conducted in the United States [ 32 ] reported that patients preferred and valued SDM and wanted to make decisions in collaboration with the physicians. The Dutch expert, Merel M Ruissen, made this suggestion in 2021. An understanding of, and responsiveness to, the individual preferences of patients with T2DM is crucial in improving the quality of provided care [ 33 ]. However, SDM also has some drawbacks. A systematic review conducted in the United States [ 32 ] highlighted that limited consultation time was identified as one of the most frequently cited barriers among context-related factors in the included studies, particularly in resource-constrained settings with a high patient-to-provider ratio. While SDM can be beneficial to patients’ disease control to a certain extent, it may be too time-consuming and cause problems such as the allocation of medical personnel. Patient decision aids and SDM processes should include discussions on cost and value. American expert Mary C. Politi [ 34 ] outlines how to conduct a cost-effectiveness analysis of SDM. In the future, there will be a need to increase cost awareness in medical decision making and develop cost-effectiveness models in 2023. The main limitation of our study is its nonrandomized observational design. The nonrandomized observational design introduces the possibility of residual confounding, despite adjustment for key baseline clinical characteristics using ANCOVA. Group assignment was determined by physician judgment, which may be influenced by unmeasured factors related to disease severity or patient readiness. Although covariate adjustment was applied, residual confounding related to physician-guided group allocation may remain, and causal inference cannot be established. Furthermore, the mean age of the participants in this study was comparable to the age distribution of the general population of patients with type 2 diabetes in Taiwan [ 2 ]. This enhances the relevance of our findings to routine clinical practice in similar healthcare settings. Nevertheless, decision-making processes, treatment preferences, and engagement with patient decision aids may differ across age groups, particularly among older adults who may face additional physical, cognitive, or practical challenges when considering injectable therapies. This may impede generalizability and the extrapolation of the research results. Moreover, the implementation and effectiveness of PDA-supported shared decision making may vary across healthcare settings with different patient demographics, clinical workflows, and resource availability. These factors were not specifically examined in the present study. Therefore, further studies involving older patient populations and diverse clinical settings are warranted to better assess the broader applicability of PDA-supported shared decision making in diabetes care. Besides, the decision-making score in our study can’t be directly interpreted as an indication of whether subcutaneous antidiabetic agents should be adopted, nor can they provide insights into the potential impact on HbA1C control in patients with type 2 diabetes. A long-term longitudinal study should be conducted to better reveal a connection between the decision result and the control of blood glucose, thus expounding the substantive effect of the use of SDM interventions. In particular, the strength of our study lies in its use of a prospective observational comparative study to examine the association between PDA-supported SDM and short-term decision-related outcomes. Through performing SDM in clinical practice, one can understand a patient’s attitude with PDA. Our findings indicate that the PDA has a positive effect, as reflected in associated with decision-making score, higher post-test knowledge scores, and greater satisfaction scores. These results underscore the potential for healthcare providers to effectively implement this PDA in real-world clinical settings. Conclusions Patients with type 2 diabetes who have been treated with multiple oral hypoglycemic agents and remain poorly controlled may represent a population in whom patient decision aids are particularly relevant. In this study, exposure to PDA-supported shared decision making was associated with higher short-term decision-making intentions, knowledge, and satisfaction, rather than actual initiation of injectable therapy or improvements in glycemic outcomes. These findings suggest that PDA-supported SDM may help patients more clearly consider injectable treatment options within the decision-making process. However, conclusions regarding treatment initiation, timing, or clinical effectiveness cannot be drawn. Future longitudinal studies are warranted to determine whether improvements in short-term intentions translate into sustained treatment changes and clinical outcomes. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (16KB, docx) Acknowledgements The authors sincerely thank all participating patients and the pharmacists from the Department of Pharmacy, Taipei Medical University Hospital, and the Department of Pharmacy, Taipei City Hospital–Yangming Branch, Taipei, Taiwan, for their invaluable assistance. We are also grateful to Kuan-Yang Chen and Yi-Chun Chiu for their technical and administrative support throughout the study. Finally, we extend our appreciation to the anonymous reviewers for their insightful comments, which substantially improved the quality of this manuscript. Abbreviations HbA1c Glycated Hemoglobin BMI Body Mass Index DSMES Diabetes Self-Management Education and Support GLP-1 Glucagon-like Peptide-1 GLP-1 RA Glucagon-like Peptide-1 Receptor Agonist IDF International Diabetes Federation OHA Oral Hypoglycemic Agent PASW Predictive Analytics Software PDA Patient Decision Aid SD Standard Deviation SDM Shared Decision-Making SPSS Statistical Package for the Social Sciences T2DM Type 2 Diabetes Mellitus UK United Kingdom USA United States of America USD United States Dollar Author contributions Su-Han Hsu: Conceptualization, methodology, formal analysis, and writing – review & editing. Cheng-Hsuan Chiang: Conceptualization, methodology, and writing – review & editing. Chia-Hui Lin: Investigation and data curation. Shih-Horng Huang: Investigation and data curation. Joseph Jordan Keller: Investigation and data curation. Li-Hsuan Wang: Conceptualization, methodology, formal analysis, writing – original draft, writing – review & editing, and supervision. Kung-Pei Tang: Investigation and data curation. Funding This work was financially supported by the Taipei Medical University Hospital (112TMUH-P-05). The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Data availability Data are available on reasonable request. Declarations Human ethics and consent to participate The study protocol was reviewed and approved by the Joint Institutional Review Board of Taipei Medical University (TMU-JIRB No.: N202101079). The ethics committee approved the use of verbal informed consent because the study involved minimal risk and no personally identifiable information was collected, which was obtained from all participants prior to data collection. The verbal consent procedure was conducted by trained research staff following the JIRB-approved script and protocol. All procedures followed the ethical standards of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Su-Han Hsu and Cheng-Hsuan Chiang are both first authors and contributed equally to this work. Li-Hsuan Wang and Kung-Pei Tang contributed equally to this work. Contributor Information Li-Hsuan Wang, Email: [email protected]. Kung-Pei Tang, Email: [email protected]. References 1. International Diabetes Federation. Diabetes facts & figures. Brussels, Belgium: International Diabetes Federation. 2025. https://idf.org/about-diabetes/diabetes-facts-figures/ . Accessed Nov 8 2025. 2. Du SD. Taiwan Diabetes Yearbook 2019: Type 2 Diabetes. 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Supplementary Materials Supplementary Material 1 (16KB, docx) Data Availability Statement Data are available on reasonable request. 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