Joint Display Study for Prediabetes Prevention in Urban Contexts and Behavioral Change of Thailand: A Mixed Methods Study

Joint Display Study for Prediabetes Prevention in Urban Contexts and Behavioral Change of Thailand: A Mixed Methods Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Joint Display Study for Prediabetes Prevention in Urban Contexts and Behavioral Change of Thailand: A Mixed Methods Study Nittaya Sukchaisong, Araya Chiangkhong, Chavanant Sumanasrethakul, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7091035/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Type 2 diabetes mellitus (T2DM) is usually a lifelong condition that importance for global health problems. The management of prediabetes in the urban contexts is a priority. To examine the effects of nine intervention functions including education, training, environmental restructuring, enablement, restriction, coercion, modelling, incentivisation, and persuasion on self-care behaviors for glycemic control in individuals with prediabetes using a mixed methods approach. Methods A convergent mixed methods design was employed. Quantitative data were collected from 790 individuals with prediabetes using a self-reported questionnaire based on the Behavior Change Wheel (BCW). Multiple linear regression (enter method) was used to assess the relationship between intervention functions and self-care behavior. Qualitative data were gathered via semi-structured interviews with 25 purposively sampled participants and analyzed using thematic analysis. Findings were integrated using a joint display approach guided by Guetterman, Fetters, and Creswell. Results Eight of the nine intervention functions significantly predicted of self-care behavior (p < .01), with the strongest effects observed for enablement (β = .520), education (β = .492), and training (β = .421). Coercion (β = –.003, p = .786) showed no significant association. Thematic analysis revealed convergence with the quantitative findings, highlighting themes such as skill-building, peer and provider support, and motivational narratives. The joint display demonstrated alignment between perceived influences and actual behavior, supporting the need for multicomponent interventions. Conclusions This study highlights the multifactorial nature of self-care behavior change among individuals with prediabetes. Tailored interventions should prioritize enablement, education, and training while avoiding coercive strategies. Nurses are well-positioned to implement theory-based, culturally responsive approaches that reflect patients' real-life capabilities, opportunities, and motivations. Clinical trial number: Not applicable. Prediabetic State Health Behavior Self Care Type 2 Diabetes Prevention Urban Contexts Mixed Methods Study Background Type 2 diabetes mellitus (T2DM) poses a significant global health challenge, with its prevalence escalating in both developed and developing countries [ 1 ]. Lifestyle-related risk factors such as unhealthy diets, physical inactivity, and obesity are major contributors to this epidemic. Prediabetes, a precursor state characterized by elevated blood glucose levels not yet meeting diagnostic thresholds for diabetes, represents a critical window for prevention. Without effective interventions, individuals with prediabetes are at increased risk of progressing to T2DM and related complications, including cardiovascular disease and premature mortality [ 2 , 3 ]. Substantial evidence supports the efficacy of lifestyle interventions-including dietary modification, physical activity, and weight control-in reducing the risk of diabetes by up to 58% [ 4 ]. However, adherence to such behavioral recommendations remains suboptimal due to competing life priorities, insufficient social support, and limited access to resources [ 5 ]. Moreover, sociocultural and environmental influences complicate behavior change efforts in real-world settings, particularly in urban populations where access to care and health literacy may vary widely. The Capability, Opportunity, and Motivation–Behavior (COM-B) model provides a robust framework for analyzing the drivers of health behavior change [ 6 ]. This model integrates psychological and physical capabilities, physical and social opportunities, and both reflective and automatic motivational processes. The Behavior Change Wheel (BCW), which builds upon the COM-B model, identifies nine core intervention functions-such as education, persuasion, enablement, and environmental restructuring-that link behavioral diagnosis to appropriate strategies for change [ 7 ]. Although previous studies have applied COM-B and BCW frameworks to chronic disease management, relatively few have examined how individuals with prediabetes perceive and respond to intervention functions in everyday life. This study uniquely applies the COM-B framework and BCW in a mixed-methods design, combining qualitative experiences with quantitative perceptions of intervention functions. This integrative approach aims to inform the development of contextually relevant, tailored strategies for diabetes prevention. Given their proximity to at-risk populations and frequent roles in lifestyle counseling, nurses are well positioned to implement these theory-informed, context-sensitive interventions and play a pivotal role in translating behavioral science into practice. Methods This study employed a sequential exploratory mixed methods design, consisting of two phases: Phase one is a qualitative case study and followed phase two is a quantitative cross-sectional study. The Capability, Opportunity, and Motivation–Behavior (COM-B) model served as the guiding theoretical framework for both phases to explain behavioral determinants of self-care for type 2 diabetes prevention among individuals with prediabetes. Phase 1: Qualitative Phase Design and Setting A case study qualitative design was used to explore self-care behaviors among individuals with prediabetes in urban Bangkok, Thailand. The COM-B model structured both the interview guide and thematic analysis to examine behavioral drivers in the domains of capability, opportunity, and motivation. Participants and Sampling Participants were purposively recruited from community health centers in Dusit District, Bangkok, Thailand. There are 25 participants and eligibility criteria included: (1) adults aged ≥ 35 years; (2) diagnosed with prediabetes at least six months prior using fasting plasma glucose (FPG 100 – 125 mg/dL); (3) BMI ≥ 23 kg/m²; and (4) ability to communicate in Thai and provide informed consent. Individuals with a history of diabetes, cognitive impairment, or current enrollment in diabetes-related interventions were excluded. Sampling aimed to include individuals who had either successfully delayed diabetes progression or developed T2DM, enabling comparative insights into divergent behavioral outcomes. Recruitment was facilitated through referrals from local health workers. Data Collection Data were collected through in-depth semi-structured interviews conducted by the lead researcher. The interview guide was developed based on COM-B domains, reviewed by three behavioral science experts, and piloted for clarity. Each interview lasted 45–60 minutes, was audio-recorded, transcribed verbatim, and verified through member checking. Field notes were maintained to document contextual insights. Data Analysis and Rigor Semi-structured interviews were conducted in Thai and subsequently translated into English for analysis. Quotes were translated and verified for conceptual equivalence by bilingual experts to ensure the integrity and accuracy of participants’ meanings. Data were analyzed using Braun and Clarke’s six-step thematic analysis [ 8 ], integrating both deductive coding from COM-B constructs and inductive theme generation. An iterative and reflexive approach guided manual coding and theme development. To ensure rigor, credibility was enhanced through peer debriefing among three research team members, and member checking was performed with selected participants to validate interpretations. Confirmability was supported by an audit trail and reflexive journaling. Data saturation was achieved after 25 interviews when no new themes emerged. Phase 2: Quantitative Phase Design and Instrument Development This phase employed a cross-sectional survey to quantitatively examine the influence of intervention functions on self-care behaviors for glycemic management among working-age adults with prediabetes in urban settings. The instrument was developed through an integrative process combining theoretical guidance from the Behavior Change Wheel (BCW) framework [ 6 ] with empirical insights derived from Phase 1 qualitative findings. To ensure contextual relevance and cultural sensitivity, each of the nine intervention functions-education, training, persuasion, incentivisation, coercion, modelling, enablement, environmental restructuring, and restriction-was systematically mapped to key themes that emerged from participants’ narratives. Section 1: Perceptions of Intervention Functions Each intervention function was operationalized through five self-report items, yielding a total of 45 items. These items reflected participants’ perceptions of how each intervention function was experienced in the context of urban life and glycemic self-care. All responses were rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), referencing behaviors or experiences within the past month. Section 2: Self-Care Behaviors for Glycemic Management The outcome variable was assessed using ten items that measured self-care behaviors aimed at managing blood glucose levels. These items included practical actions such as selecting low-glycemic foods, portion control, reading nutrition labels, avoiding sugary snacks, and engaging in regular physical activity. Items in this section were also rated on the same 5-point Likert scale, using the same reference timeframe. To ensure content validity, three experts in behavioral science and qualitative methodology assessed each item using the Index of Item–Objective Congruence (IOC). Items with IOC values below 0.50 were revised or removed. A pilot test with 30 individuals from the target population was conducted to evaluate item clarity, feasibility of administration, and preliminary internal consistency. To assess construct validity, Confirmatory Factor Analysis (CFA) was conducted separately for each section using maximum likelihood estimation. Section 1 , CFA supported a nine-factor structure corresponding to the nine BCW intervention functions. The model demonstrated good fit: χ² = 2075, df = 909, p < .001; CFI = 0.936; TLI = 0.930; RMSEA = 0.0403 (90% CI = 0.0380–0.0426); and SRMR = 0.0282. Standardized factor loadings ranged from 0.617 to 0.969 (p < .001), confirming that all items adequately reflected their respective latent constructs. Composite Reliability (CR) values ranged from 0.834 to 0.932, while Average Variance Extracted (AVE) values met or exceeded the recommended threshold of 0.50 for all but one construct, indicating acceptable convergent validity and internal consistency. Section 2 , CFA supported a unidimensional model with excellent fit indices: χ² = 50.6, df = 35, p = .043; CFI = 0.986; TLI = 0.982; RMSEA = 0.0238 (90% CI = 0.00451–0.0374); and SRMR = 0.0231. All standardized factor loadings ranged from 0.603 to 0.821 and were statistically significant, indicating that the ten items coherently represented the latent construct of glycemic self-care behaviors. Given these findings, the instrument was deemed psychometrically sound, valid, and reliable for assessing both perceptions of intervention strategies (Section 1) and behavioral adherence outcomes (Section 2) among urban working-age individuals with prediabetes. The complete English-language version of both scales is provided in Supplementary File: Additional File 1. Participants and Sampling Participants were recruited from Vajira Hospital and its affiliated community health centers in urban Bangkok, Thailand. A stratified random sampling method was used to ensure diversity across age, gender, and education levels. Eligible participants were Thai adults aged ≥ 35 years, diagnosed with prediabetes for at least six months but not more than two years, with fasting plasma glucose levels between 100 and 125 mg/dL, in accordance with the American Diabetes Association (ADA) criteria [ 9 ]. Additional criteria included BMI ≥ 23 kg/m², ability to communicate in Thai, and provision of written informed consent. Exclusion criteria comprised a prior diagnosis of type 2 diabetes, pregnancy, significant cognitive or psychiatric impairment, or concurrent participation in other diabetes prevention programs. Recruitment was coordinated by trained health personnel who reviewed outpatient records and contacted eligible individuals through community-based clinics. All participants were provided with detailed study information and gave written informed consent prior to enrollment. Sample Size Calculation The required sample size was estimated using G*Power version 3.1.9.4 for linear multiple regression analysis (fixed model, R² deviation from zero), based on an effect size (f²) of 0.02 as suggested by Cohen [ 10 ], with α = 0.05, power = 0.80, and nine predictors. This yielded a minimum sample size of 790 participants for detecting moderate-to-large effects in exploratory behavioral studies [ 11 ]. Data Collection Data were collected through self-administered paper-based questionnaires distributed during outpatient clinic visits and community outreach activities. Participants completed the questionnaires in designated private areas, with support from trained research assistants available to clarify item meanings if needed. The average completion time was 15–20 minutes. All responses were anonymized and coded for analysis. Data were double-entered into a secured database and cross-verified to ensure accuracy prior to statistical analysis. Statistical Analysis Descriptive statistics were used to summarize demographic characteristics and study variables. Multiple linear regression (Enter method) was performed to assess the relationship between the nine intervention functions and self-care behavior for glycemic management. All predictors were entered simultaneously to reflect the theoretical structure of the model [ 6 , 12 , 13 ]. Assumptions for regression were evaluated as follows: linearity and homoscedasticity were confirmed via residual plots; normality of residuals was assessed using histograms and Q–Q plots; independence of errors was supported by a Durbin–Watson statistic of 2.03 [ 14 ]. Multicollinearity was not present (VIFs = 1.01–1.03) [ 15 ], and no influential outliers were identified (Cook’s distance < 0.01) [ 16 ]. Results Phase 1 Qualitative Results: Thematic Analysis Based on the COM-B Framework This study analyzed data obtained from in-depth interviews with 25 participants, focusing on self-care experiences related to type 2 diabetes mellitus (T2DM) prevention among individuals with prediabetes. Using the COM-B framework, the results are categorized into three major themes: Capability, Opportunity, and Motivation. These categories provide a comprehensive understanding of behavioral determinants and offer actionable insights for intervention development. Participant Demographics and Characteristics The participants included 15 females (66.67%) and 10 males (33.33%), aged between 35 and 59 years, with a mean age of 38.51 years. Educational backgrounds varied, with 13.33% having completed primary education, 20.00% secondary education, 53.33% holding bachelor’s degrees, and 13.33% having graduate degrees. Based on clinical follow-up, some participants successfully delayed diabetes progression, while others developed T2DM. Informants were assigned anonymized codes to ensure confidentiality. Each code consisted of a letter indicating gender (F = female, M = male), followed by a two-digit number representing the participant’s unique sequence in the study (e.g., F03, M08). In parentheses, we noted whether the individual successfully delayed the progression of prediabetes (“Delayed progression”) or developed type 2 diabetes during the observation period (“Developed diabetes”). This coding system facilitated comparative analysis across subgroups while preserving participant anonymity. Theme I: Capability Subtheme 1.1: Psychological Capability - Awareness and Knowledge Participants with greater awareness and understanding of prediabetes demonstrated stronger adherence to preventive behaviors. Their knowledge enabled them to perceive risks and implement lifestyle changes proactively. F03 (Delayed progression): “When I started learning more about prediabetes, I understood that small changes could make a big difference.” M08 (Delayed progression): “Knowing I was at risk made me realize I needed to act before it was too late.” Conversely, those with limited awareness often underestimated the condition: F15 (Developed diabetes): “I thought being at risk wasn’t a big deal, so I didn’t prioritize making changes.” Subtheme 1.2: Physical Capability - Skills and Stamina Physical capability, including practical skills and physical endurance, played a key role in behavior maintenance. F09 (Delayed progression): “I started cooking my meals to control sugar intake and avoided ordering fast food.” M05 (Delayed progression): “I began exercising every morning, and it has become part of my routine.” In contrast, fatigue hindered sustained behavior: F10 (Developed diabetes): “My body would get tired too quickly, and I couldn’t keep up with the exercises.” Theme II: Opportunity Subtheme 2.1: Physical Opportunity - Access to Resources Participants with access to health-promoting resources were more successful in lifestyle modification. M02 (Delayed progression): “The gym near my workplace made it easy for me to exercise regularly.” F08 (Delayed progression): “Attending community health workshops helped me learn practical tips for managing my prediabetes.” Limited access was a common barrier: F02 (Developed diabetes): “The grocery store near my house didn’t have many fresh vegetables or whole-grain options.” Subtheme 2.2: Social Opportunity - Family and Peer Support Supportive relationships reinforced behavioral changes. F01 (Delayed progression): “Having my partner join me on morning walks made it easier to stay consistent.” M09 (Delayed progression): “My friends and I joined a workout group, and it kept me accountable.” In contrast, lack of support impaired motivation: M01 (Developed diabetes): “I didn’t have anyone to share my struggles with, and it felt like I was managing everything on my own.” Theme III: Motivation Subtheme 3.1: Reflective Motivation - Goal-Setting and Planning Clear goals and structured plans supported sustained change. F05 (Delayed progression): “Breaking my goals into smaller steps... made the process feel manageable and motivating.” M06 (Delayed progression): “The doctor provided a clear plan, which gave me direction in managing my health.” Others struggled due to stress or competing demands: M03 (Developed diabetes): “Even when I started with good intentions, my routine often fell apart when unexpected things happened.” Subtheme 3.2: Automatic Motivation - Emotional Triggers and Habits Participants who managed emotional triggers and habits well were more successful in sustaining behavior. F08 (Delayed progression): “Joining a support group helped me replace old habits with healthier ones.” Emotional eating and stress disrupted behavior in others: F07 (Developed diabetes): “I planned to cut out sugary drinks completely, but I found it hard to resist.” Phase 2 Quantitative Results In the quantitative phase of the study, a total of 790 individuals diagnosed with prediabetes participated. The participants had a mean age of 45.2 years (SD = 6.7), with 62% identifying as female and 38% as male. The average body mass index (BMI) was 27.3 kg/m² (SD = 3.8), ranging from 23.0 to 35.7 kg/m². The mean fasting blood sugar (FBS) level was 110.7 mg/dL (SD = 6.4), which falls within the diagnostic criteria for prediabetes (100–125 mg/dL). In addition, the average glycated hemoglobin (HbA1c) level was 6.1% (SD = 0.3). With regard to behavioral variables, the average self-care behavior score for glycemic management was 29.74 (SD = 7.65) out of a possible score of 50, indicating a moderate level of self-management practices among the participants. The perceived support from the nine intervention functions, each assessed on a 5-point Likert scale and reported as a sum score ranging from 5 to 25, varied in intensity. The highest mean score was found in the function of restriction (M = 20.56, SD = 3.00), followed by incentivisation (M = 15.46, SD = 6.30), education (M = 15.28, SD = 6.13), and training (M = 15.11, SD = 6.36). Environmental restructuring and enablement showed similar levels of perceived support, with mean scores of 15.05 (SD = 6.19) and 14.87 (SD = 6.28), respectively. Slightly lower mean scores were observed for persuasion (M = 14.77, SD = 6.19) and modelling (M = 14.60, SD = 6.01). The lowest perceived support was found in the coercion domain (M = 9.31, SD = 2.79). A multiple linear regression analysis was conducted to investigate the predictive power of nine intervention functions, grounded in the Behavior Change Wheel (BCW) framework, on self-care behavior for glycemic management. The analysis included 790 participants with prediabetes, and all predictors were simultaneously entered using the enter method. The overall model was statistically significant, F(9, 780) = 752.629, p < .001, and explained a substantial proportion of the variance in the dependent variable (R² = .897, Adjusted R² = .895). The standard error of the estimate was 2.475, indicating good model precision. Preliminary diagnostics confirmed that the assumptions of multiple regression were met. There was no evidence of multicollinearity, as all variance inflation factor (VIF) values ranged from 1.006 to 1.027, well below the conventional threshold of 10. Tolerance values were above .974, indicating acceptable levels of independence among predictors. Additionally, the residuals showed no signs of autocorrelation. All predictors were statistically significant, except one. Enablement had the strongest standardized effect (β = .520, t = 45.10, p < .001), followed by Education (β = .492, t = 42.50, p < .001) and Training (β = .421, t = 36.12, p < .001). Additional significant predictors included Modelling (β = .366, t = 31.43, p < .001), Persuasion (β = .294, t = 25.35, p < .001), and Environmental Restructuring (β = .147, t = 12.66, p < .001). Predictors with smaller yet statistically significant effects included Incentivisation (β = .121, t = 10.41, p < .001) and Restriction (β = .037, t = 3.17, p = .002). In contrast, coercion was not a significant predictor (β = –.003, t = − 0.271, p = .786). These findings underscore the predictive validity of BCW-based intervention functions in influencing glycemic self-care behavior and suggest that enablement, education, and training are particularly critical in behavior change strategies for individuals with prediabetes. Table 1 Regression coefficients predicting Self-Care Behaviors for Glycemic Management from COM-B-based intervention functions (Enter method) Predictor B SE β t p 95% CI for B Tolerance VIF (Constant) -15.745 .918 -17.151 < .001 [ -17.547, -13.943] - - Education .614 .014 .492 42.499 < .001 [ .585, .642] .989 1.011 Training .506 .014 .421 36.120 < .001 [.479, .534] .974 1.027 Enablement .634 .014 .520 45.103 < .001 [ .607,.662] .994 1.006 Modelling .466 .015 .366 31.427 < .001 [ .437,.495] .978 1.023 Persuasion .364 .014 .294 25.353 < .001 [ .335, .392] .983 1.017 Environmental Restructuring .181 .014 .147 12.661 < .001 [ .153, .209] .988 1.012 Incentivisation .147 .014 .121 10.410 < .001 [ .119, .175] .977 1.024 Restriction .094 .030 .037 3.168 .002 [ .036, .152] .981 1.019 Coercion − .009 .032 − .003 − .271 .786 [− .071, .054] .986 1.014 R² of 0.897, Adjusted R² of 0.896, (F(9, 780) = 752.63, p < .001) Joint Display: Integration of Qualitative and Quantitative Findings To synthesize the evidence from both strands of this sequential mixed methods study, a joint display matrix was developed to integrate qualitative insights from Phase 1 with quantitative results from Phase 2. This integration was guided by the COM-B model and Behavior Change Wheel (BCW) framework, providing a theory-driven structure to interpret the results. The joint display enabled a deeper understanding of how statistically significant predictors aligned with participants’ lived experiences in practicing diabetes-preventive behaviors. As illustrated in Table 2 , Joint Display of Quantitative and Qualitative Findings Based on the COM-B and BCW Frameworks , the three most influential intervention functions—Enablement, Education, and Training-emerged as both thematically dominant in qualitative data and statistically robust in the regression analysis (β = .520, .492, and .421, respectively). These functions were closely associated with enhancing psychological and physical capability, a core domain in the COM-B model. Participants frequently emphasized the importance of feeling supported, gaining relevant knowledge, and developing practical skills: “Knowing someone supports me makes it easier to stick to my routine.” (F06, delayed progression) “When I learned more about prediabetes, I realized I had to change.” (F03, delayed progression) “I needed to practice healthy cooking with someone first.” (M05, delayed progression) A second cluster of moderately strong predictors—Modelling, Persuasion, and Restriction—reflected social and motivational enablers of behavior change. These functions were linked to exposure to positive role models, persuasive communication from trusted figures, and the implementation of household dietary norms: “Seeing others succeed made me try harder.” (M09, delayed progression) “Doctors telling stories about success moved me to act.” (F08, delayed progression) “Avoiding certain foods became easier with family rules.” (F06, delayed progression) The remaining three functions—Environmental Restructuring, Incentivisation, and Coercion—demonstrated comparatively lower or non-significant predictive contributions (β = .147, .121, and –.003, respectively). While Environmental Restructuring and Incentivisation were statistically significant, Coercion was not (p = .786). Qualitative accounts suggested that these functions were more context-dependent and variably effective depending on structural constraints and personal circumstances: “Healthy food wasn’t available where I live.” (F02, developed diabetes) “Fear of getting diabetes forced me to change.” (M10, developed diabetes) “Getting praise made me proud of myself.” (F11, delayed progression) Overall, the integration of qualitative and quantitative findings revealed a substantial degree of convergence between the theoretical underpinnings of the COM-B model, participants’ experiential narratives, and the statistical associations derived from regression analysis. This triangulation not only reinforces the conceptual robustness of the study but also offers practical guidance for intervention development. Specifically, the findings support the formulation of comprehensive, multi-component behavior change interventions that simultaneously enhance individuals’ capability through targeted strategies such as enablement, education, and hands-on training; bolster motivation through mechanisms including modelling, persuasive communication, and incentivisation; and address structural and contextual barriers by optimizing environmental restructuring and implementing appropriate restrictions. Such alignment across data sources underscores the importance of designing behavior change programs that are theoretically grounded, empirically validated, and contextually relevant to the lived experiences of individuals at risk of type 2 diabetes. Table 2 Joint Display of Quantitative and Qualitative Findings Based on COM-B and BCW Frameworks Intervention Function Quantitative Significance (β, p-value) Qualitative Illustration Integration Insight Enablement β = 0.537, p < .001 “Knowing someone supports me makes it easier to stick to my routine.” (F06) Strong convergence; perceived as both emotional and instrumental support [ 6 ]. Education β = 0.519, p < .001 “When I learned more about prediabetes, I realized I had to change.” (F03) Convergent; knowledge is foundational for initiating behavior [ 17 ]. Training β = 0.411, p < .001 “I needed to practice healthy cooking with someone first.” (M05) Complementary; hands-on training enhances confidence and capability [ 18 ]. Modelling β = 0.366, p < .001 “Seeing others succeed made me try harder.” (M09) Convergent; role models motivate behavior change through social learning [ 20 ]. Persuasion β = 0.294, p < .001 “Doctors telling stories about success moved me to act.” (F08) Complementary; narratives enhance reflective motivation [ 18 ]. Environmental Restructuring β = 0.147, p < .001 “Healthy food wasn’t available where I live.” (F02) Convergent; environmental cues support sustainable habits [ 24 ]. Incentivisation β = 0.121, p < .001 “Getting praise made me proud of myself.” (F11) Complementary; tangible rewards reinforce behavior through external motivation. [ 22 ] Restriction β = 0.037, p = .002 “Avoiding certain foods became easier with family rules.” (F06) Expansive; social reinforcement strengthens rule-based behavior [ 21 ]. Coercion β = − 0.003, p = .786 “Fear of getting diabetes forced me to change.” (M10) Limited alignment; fear-based approaches may prompt short-term change [ 23 ]. Note : This joint display was constructed following the guidance of Guetterman, Fetters, & Creswell (2021) to integrate quantitative and qualitative results through a matrix format. The 'Integration Insight' column reflects the derived meta-inferences, supporting the use of mixed methods integration to inform behavior change interventions. Discussion This study provides a robust synthesis of behavioral determinants influencing self-care among individuals with prediabetes, leveraging mixed methods integration through the COM-B model and Behavior Change Wheel (BCW). The convergence of qualitative and quantitative findings highlights the practical utility of theory-informed approaches in developing targeted nursing interventions. Enablement, education, and training emerged as the most influential intervention functions, evidenced both by high regression coefficients and consistent thematic support. These components reflect the central tenet of the COM-B model, where behavior arises from the interplay between capability, opportunity, and motivation [ 6 ]. For nursing practice, these findings emphasize the importance of tailored educational content, skill-building, and psychosocial support to empower individuals with prediabetes. Prior research confirms that culturally adapted, nurse-led programs can significantly enhance self-care adherence [ 17 – 19 ]. Nurses working in primary and community health settings are uniquely positioned to assess behavioral readiness and deliver individualized interventions. Strengthening nurses’ competencies in behavioral theory- particularly in COM-B and BCW-can enhance intervention fidelity and responsiveness to diverse patient needs. Modelling, persuasion, and restriction demonstrated moderate influence, particularly through mechanisms of social learning and normative reinforcement. These strategies, when embedded in peer-group education or home-based care, have potential to enhance motivation and accountability [ 20 , 21 ]. Environmental restructuring and incentivisation showed smaller but significant effects. Notably, the influence of incentivisation aligns with literature suggesting that tangible rewards can support short-term adherence without undermining intrinsic motivation, particularly when aligned with personal goals [ 22 ]. In contrast, coercion was not a significant predictor, reinforcing evidence that fear-based messaging-if not paired with efficacy-enhancing strategies-can lead to disengagement or psychological reactance [ 23 – 25 ]. This has particular relevance in urban Thai populations, where high stress and exposure to unhealthy cues may diminish the effectiveness of punitive or fear-based approaches. Amid these challenges, nurse-led interventions supported by community engagement or digital platforms offer promising avenues. Evidence suggests that technology-enhanced programs delivered by trained nurses can improve behavioral outcomes in populations at risk of diabetes [ 26 ]. Finally, the use of a joint display matrix enriched the interpretive process by aligning theoretical constructs with empirical evidence and lived experiences. The high explanatory power of the regression model (R² = 0.896) affirms the utility of the nine intervention functions as a modular framework for designing nursing interventions. This underscores the value of mixed methods designs in producing actionable, context-sensitive, and theory-driven insights for nursing science [ 27 , 28 ]. Conclusion This mixed methods study highlights the multifactorial nature of self-care behavior change in individuals with prediabetes, guided by the COM-B model and Behavior Change Wheel (BCW). Integration of qualitative and quantitative findings revealed enablement, education, and training as the most influential intervention functions, consistently supported by both lived experiences and statistical analysis. Community and primary care nurses are well-positioned to deliver culturally responsive interventions that enhance capability, opportunity, and motivation. Emphasis should be placed on skill-building, knowledge enhancement, and psychosocial support, while coercive strategies should be avoided due to limited effectiveness. The joint display approach enriched the interpretation of behavioral determinants and provided actionable insights for practice. These findings support the development of multi-component, theory-based nursing interventions tailored to urban populations at risk for type 2 diabetes. Future research should examine long-term outcomes and test these strategies in broader contexts. Limitations This study did not incorporate longitudinal follow-up to evaluate sustained behavioral outcomes or biochemical progression from prediabetes to type 2 diabetes. Consequently, the long-term efficacy of the intervention remains uncertain. Future research should employ prospective cohort designs or randomized controlled trials with extended follow-up periods to rigorously assess durability of behavior change and clinical impact over time. Abbreviations T2DM Type 2 diabetes mellitus COM-B Capability, Opportunity, and Motivation-Behavior BCW Behavior Change Wheel Declarations Acknowledgements The authors sincerely thank all participants for sharing their experiences. We are also grateful to the Faculty of Medicine Vajira Hospital and Kuakarun Faculty of Nursing for their institutional support. Special thanks to the research assistants and content experts for their valuable input, and to our colleagues and mentors for their constructive feedback. Language editing assistance was provided by Paperpal, an AI-powered academic writing tool. All content and interpretations were reviewed and approved by the authors. Authors’ Contributions Conceptualization: Araya Chiangkhong (A.C.) and Nittaya Sukchaisong (N.S.); Methodology: A.C.; Validation: A.C., N.S., and Kanokporn Imsakul (K.I.); Formal analysis: A.C.; Investigation: A.C.; Writing – original draft preparation: A.C.; Writing – review and editing: N.S. and Chavanant Sumanasrethakul (C.S.); Visualization: A.C.; Supervision: N.S.; Project administration: A.C.; Funding acquisition: N.S. All authors have read and approved the final manuscript. Funding This research project was supported by the Thailand Science Research and Innovation (TSRI) under Contract No. FRB670080/0468, as part of the research program titled “Life Style Modification Model for Preventing T2DM in Adults with Prediabetes: Dusit Model.” Availability of data and materials Data can be made available from the corresponding author on reasonable request. Ethical Approval and consent to participate This study was approved by the Research Ethics Committee of the Faculty of Medicine Vajira Hospital, Navamindradhiraj University (COA 051/2567, dated March 19, 2024). The research was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. Privacy and confidentiality were strictly maintained. All participants provided written informed consent, and pseudonyms were used to ensure anonymity in all reports. Informed consent was obtained from all participants. The confidentiality of the data and the anonymity and privacy of participants were preserved at all times. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Author details 1 Kuakarun Faculty of Nursing, Navamindradhiraj University, Bangkok 10300, Thailand 2 Faculty of Medicine Vajira Hospital, Navaminradhiraj University, Bangkok 10300, Thailand *Corresponding author e-mail: [email protected] References World Health Organization. Global report on diabetes. Geneva: World Health Organization; 2020. Schlesinger S, Neuenschwander M, Barbaresko J, et al. Prediabetes and risk of mortality, diabetes-related complications and comorbidities: umbrella review of meta-analyses of prospective studies. Diabetologia. 2022;65(2):275–85. 10.1007/s00125-021-05592-3 . Glechner A, Keuchel L, Affengruber L, et al. Effects of lifestyle changes on adults with prediabetes: a systematic review and meta-analysis. Prim Care Diabetes. 2018;12(5):393–408. 10.1016/j.pcd.2018.07.003 . Uusitupa M, Khan TA, Viguiliouk E, et al. Prevention of type 2 diabetes by lifestyle changes: a systematic review and meta-analysis. Nutrients. 2019;11(11):2611. 10.3390/nu11112611 . Odglun Y, Sranacharoenpong K, Nirdnoy N. Effects of a culturally tailored diabetes prevention program for at-risk Thai Muslim people in semi-urban areas. J Health Res. 2023;37(4):192–200. https://doi.org/10.56808/2586-940X.1026 . Michie S, Johnston M, Carey R. (2016). Behavior Change Techniques. In M. Gellman, & J. R. Turner, editors, Encyclopedia of Behavioral Medicine (pp. 1–8). Springer. https://doi.org/10.1007/978-1-4614-6439-6_1661-2 Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement science: IS. 2011;6:42. https://doi.org/10.1186/1748-5908-6-42 . Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Res Psychol. 2006;3(2):77–101. https://doi.org/10.1191/1478088706qp063oa . American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes—2024. Diabetes Care. 2024;47(Supplement1):S19–29. https://doi.org/10.2337/dc24-S002 . Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale (NJ): Lawrence Erlbaum Associates; 1988. Faul F, Erdfelder E, Buchner A, Lang A-G. Statistical power analyses using GPower 3.1: Tests for correlation and regression analyses*. Behav Res Methods. 2009;41(4):1149–60. https://doi.org/10.3758/BRM.41.4.1149 . Field AP. (2018) Discovering Statistics Using IBM SPSS Statistics. 5th Edition, Sage, Newbury Park. Tabachnick BG, Fidell LS. (2019). Using Multivariate Statistics (7th ed.). Pearson. Durbin J. and G. S. Watson. Testing for Serial Correlation in Least Squares Regression: I. Biometrika , vol. 37, no. 3/4, 1950, pp. 409–28. JSTOR , https://doi.org/10.2307/2332391 . Accessed 10 June 2025. Hair JF, Babin BJ, Anderson RE, Black WC. Multivariate Data Analysis. 8th ed. England: Pearson Prentice; 2019. Stevens JP. (2009) Applied Multivariate Statistics for the Social Sciences. 5th Edition, Routledge, New York. Rasaiah J, et al. The effectiveness of a community-based type 2 diabetes prevention program in vulnerable populations: A realist review. Prev Med Rep. 2022;26:101734. https://doi.org/10.1016/j.pmedr.2022.101734 . Wali H, et al. Health system interventions for adults with type 2 diabetes in low- and middle-income countries: A realist review. BMJ Global Health. 2021;6(7):e005428. https://doi.org/10.1136/bmjgh-2021-005428 . Sherifali D, da Silva LP, Dewan P, et al. Peer Support for Type 2 Diabetes Management in Low- and Middle-Income Countries (LMICs): A Scoping Review. Glob Heart. 2024;19(1):20. https://doi.org/10.5334/gh.1299 . Published 2024 Feb 20. Werfalli M, Raubenheimer PJ, Engel M, et al. The effectiveness of peer and community health worker-led self-management support programs for improving diabetes health-related outcomes in adults in low- and-middle-income countries: a systematic review. Syst Rev. 2020;9:133. https://doi.org/10.1186/s13643-020-01377-8 . Gyawali B, Bloch J, Vaidya A, Kallestrup P. Community-based interventions for prevention of Type 2 diabetes in low- and middle-income countries: a systematic review. Health Promot Int. 2019;34(6):1218–30. https://doi.org/10.1093/heapro/day081 . Vlaev I, King D, Darzi A, et al. Changing health behaviors using financial incentives: a review from behavioral economics. BMC Public Health. 2019;19:1059. https://doi.org/10.1186/s12889-019-7407-8 . Witte K, Allen M. A meta-analysis of fear appeals: implications for effective public health campaigns. Health Educ Behav. 2000;27(5):591–615. 10.1177/109019810002700506 . Tannenbaum MB, Hepler J, Zimmerman RS, et al. Appealing to fear: A meta-analysis of fear appeal effectiveness and theories. Psychol Bull. 2015;141(6):1178–204. 10.1037/a0039729 . Sarker A, Das R, Ether S, et al. Non-pharmacological interventions for the prevention of type 2 diabetes mellitus in low and middle-income countries: protocol for a systematic review and meta-analysis of randomized controlled trials. Syst Rev. 2020;9:288. https://doi.org/10.1186/s13643-020-01550-z . Lee JH, et al. Short-term effects of the Internet-based Korea Diabetes Prevention Study: 6-month results of a community-based randomized controlled trial. Diabetes Metab J. 2021;45(6):960–5. Guetterman TC, Fetters MD, Creswell JW. Integrating quantitative and qualitative results in health science mixed methods research through joint displays. Ann Fam Med. 2015;13(6):554–61. https://doi.org/10.1370/afm.1865 . Creswell JW, Plano Clark VL. Designing and conducting mixed methods research. 3rd ed. Thousand Oaks (CA): SAGE; 2018. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFileQuestionnaireIFandSelfCarePrediabetes.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7091035","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513677457,"identity":"fd49b888-d505-4c9e-96a5-3c883c238521","order_by":0,"name":"Nittaya Sukchaisong","email":"","orcid":"","institution":"Navamindradhiraj University","correspondingAuthor":false,"prefix":"","firstName":"Nittaya","middleName":"","lastName":"Sukchaisong","suffix":""},{"id":513677458,"identity":"2b669855-afd4-45c2-9cd8-e6e5f54645f8","order_by":1,"name":"Araya Chiangkhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYNCCCjjrALFazsAYCcRqYWwjRQv/7MPPJH7OuxNtcID54QfGH3cIa5E4l2Ym2bvtWe6GA2zGEgwJz4hw1BkGMwnebYeBWhjMgA47TFiH/Bn2b5J/54C0sH8jTovBGR4zad4GkBYeIm0xPMNTbC1z7HDuzMM8xRIJaURokTvDvvHmm5rDuX3H2zd++GBDhBYgYJEAU8xAnECUBqDaD0QqHAWjYBSMgpEKACimPSeCJIcfAAAAAElFTkSuQmCC","orcid":"","institution":"Navamindradhiraj University","correspondingAuthor":true,"prefix":"","firstName":"Araya","middleName":"","lastName":"Chiangkhong","suffix":""},{"id":513677459,"identity":"b00fbeb9-503a-42dc-aa04-09a399f6fcfc","order_by":2,"name":"Chavanant Sumanasrethakul","email":"","orcid":"","institution":"Navamindradhiraj University","correspondingAuthor":false,"prefix":"","firstName":"Chavanant","middleName":"","lastName":"Sumanasrethakul","suffix":""},{"id":513677460,"identity":"361a599c-aebd-460d-a4ee-e3eb1cf58f80","order_by":3,"name":"Kanokporn Imsakul","email":"","orcid":"","institution":"Navamindradhiraj University","correspondingAuthor":false,"prefix":"","firstName":"Kanokporn","middleName":"","lastName":"Imsakul","suffix":""}],"badges":[],"createdAt":"2025-07-10 09:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7091035/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7091035/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96604131,"identity":"571c339e-2bb9-422b-9324-d679a1798fc3","added_by":"auto","created_at":"2025-11-24 09:12:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":931953,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7091035/v1/db88acf2-32e5-4a04-b1fc-ad862503dada.pdf"},{"id":91368471,"identity":"799806b3-880f-4455-bc7b-316552857293","added_by":"auto","created_at":"2025-09-15 18:09:37","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29856,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileQuestionnaireIFandSelfCarePrediabetes.docx","url":"https://assets-eu.researchsquare.com/files/rs-7091035/v1/137eb21ea78494a6742f827f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint Display Study for Prediabetes Prevention in Urban Contexts and Behavioral Change of Thailand: A Mixed Methods Study","fulltext":[{"header":"Background","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) poses a significant global health challenge, with its prevalence escalating in both developed and developing countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lifestyle-related risk factors such as unhealthy diets, physical inactivity, and obesity are major contributors to this epidemic. Prediabetes, a precursor state characterized by elevated blood glucose levels not yet meeting diagnostic thresholds for diabetes, represents a critical window for prevention. Without effective interventions, individuals with prediabetes are at increased risk of progressing to T2DM and related complications, including cardiovascular disease and premature mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSubstantial evidence supports the efficacy of lifestyle interventions-including dietary modification, physical activity, and weight control-in reducing the risk of diabetes by up to 58% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, adherence to such behavioral recommendations remains suboptimal due to competing life priorities, insufficient social support, and limited access to resources [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, sociocultural and environmental influences complicate behavior change efforts in real-world settings, particularly in urban populations where access to care and health literacy may vary widely.\u003c/p\u003e\u003cp\u003eThe Capability, Opportunity, and Motivation\u0026ndash;Behavior (COM-B) model provides a robust framework for analyzing the drivers of health behavior change [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This model integrates psychological and physical capabilities, physical and social opportunities, and both reflective and automatic motivational processes. The Behavior Change Wheel (BCW), which builds upon the COM-B model, identifies nine core intervention functions-such as education, persuasion, enablement, and environmental restructuring-that link behavioral diagnosis to appropriate strategies for change [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough previous studies have applied COM-B and BCW frameworks to chronic disease management, relatively few have examined how individuals with prediabetes perceive and respond to intervention functions in everyday life. This study uniquely applies the COM-B framework and BCW in a mixed-methods design, combining qualitative experiences with quantitative perceptions of intervention functions. This integrative approach aims to inform the development of contextually relevant, tailored strategies for diabetes prevention. Given their proximity to at-risk populations and frequent roles in lifestyle counseling, nurses are well positioned to implement these theory-informed, context-sensitive interventions and play a pivotal role in translating behavioral science into practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study employed a sequential exploratory mixed methods design, consisting of two phases: Phase one is a qualitative case study and followed phase two is a quantitative cross-sectional study. The Capability, Opportunity, and Motivation\u0026ndash;Behavior (COM-B) model served as the guiding theoretical framework for both phases to explain behavioral determinants of self-care for type 2 diabetes prevention among individuals with prediabetes.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhase 1: Qualitative Phase\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign and Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA case study qualitative design was used to explore self-care behaviors among individuals with prediabetes in urban Bangkok, Thailand. The COM-B model structured both the interview guide and thematic analysis to examine behavioral drivers in the domains of capability, opportunity, and motivation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants and Sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Participants were purposively recruited from community health centers in Dusit District, Bangkok, Thailand. There are 25 participants and eligibility criteria included: (1) adults aged\u0026thinsp;\u0026ge;\u0026thinsp;35 years; (2) diagnosed with prediabetes at least six months prior using fasting plasma glucose (FPG 100\u003cb\u003e\u0026ndash;\u003c/b\u003e125 mg/dL); (3) BMI\u0026thinsp;\u0026ge;\u0026thinsp;23 kg/m\u0026sup2;; and (4) ability to communicate in Thai and provide informed consent. Individuals with a history of diabetes, cognitive impairment, or current enrollment in diabetes-related interventions were excluded.\u003c/p\u003e\u003cp\u003eSampling aimed to include individuals who had either successfully delayed diabetes progression or developed T2DM, enabling comparative insights into divergent behavioral outcomes. Recruitment was facilitated through referrals from local health workers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were collected through in-depth semi-structured interviews conducted by the lead researcher. The interview guide was developed based on COM-B domains, reviewed by three behavioral science experts, and piloted for clarity. Each interview lasted 45\u0026ndash;60 minutes, was audio-recorded, transcribed verbatim, and verified through member checking. Field notes were maintained to document contextual insights.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Analysis and Rigor\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSemi-structured interviews were conducted in Thai and subsequently translated into English for analysis. Quotes were translated and verified for conceptual equivalence by bilingual experts to ensure the integrity and accuracy of participants\u0026rsquo; meanings. Data were analyzed using Braun and Clarke\u0026rsquo;s six-step thematic analysis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], integrating both deductive coding from COM-B constructs and inductive theme generation. An iterative and reflexive approach guided manual coding and theme development. To ensure rigor, credibility was enhanced through peer debriefing among three research team members, and member checking was performed with selected participants to validate interpretations. Confirmability was supported by an audit trail and reflexive journaling. Data saturation was achieved after 25 interviews when no new themes emerged.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhase 2: Quantitative Phase\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign and Instrument Development\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis phase employed a cross-sectional survey to quantitatively examine the influence of intervention functions on self-care behaviors for glycemic management among working-age adults with prediabetes in urban settings. The instrument was developed through an integrative process combining theoretical guidance from the Behavior Change Wheel (BCW) framework [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] with empirical insights derived from Phase 1 qualitative findings. To ensure contextual relevance and cultural sensitivity, each of the nine intervention functions-education, training, persuasion, incentivisation, coercion, modelling, enablement, environmental restructuring, and restriction-was systematically mapped to key themes that emerged from participants\u0026rsquo; narratives.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSection 1: Perceptions of Intervention Functions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEach intervention function was operationalized through five self-report items, yielding a total of 45 items. These items reflected participants\u0026rsquo; perceptions of how each intervention function was experienced in the context of urban life and glycemic self-care. All responses were rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), referencing behaviors or experiences within the past month.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSection 2: Self-Care Behaviors for Glycemic Management\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe outcome variable was assessed using ten items that measured self-care behaviors aimed at managing blood glucose levels. These items included practical actions such as selecting low-glycemic foods, portion control, reading nutrition labels, avoiding sugary snacks, and engaging in regular physical activity. Items in this section were also rated on the same 5-point Likert scale, using the same reference timeframe.\u003c/p\u003e\u003cp\u003eTo ensure content validity, three experts in behavioral science and qualitative methodology assessed each item using the Index of Item\u0026ndash;Objective Congruence (IOC). Items with IOC values below 0.50 were revised or removed. A pilot test with 30 individuals from the target population was conducted to evaluate item clarity, feasibility of administration, and preliminary internal consistency.\u003c/p\u003e\u003cp\u003eTo assess construct validity, Confirmatory Factor Analysis (CFA) was conducted separately for each section using maximum likelihood estimation.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSection 1\u003c/b\u003e, CFA supported a nine-factor structure corresponding to the nine BCW intervention functions. The model demonstrated good fit: χ\u0026sup2; = 2075, df\u0026thinsp;=\u0026thinsp;909, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; CFI\u0026thinsp;=\u0026thinsp;0.936; TLI\u0026thinsp;=\u0026thinsp;0.930; RMSEA\u0026thinsp;=\u0026thinsp;0.0403 (90% CI\u0026thinsp;=\u0026thinsp;0.0380\u0026ndash;0.0426); and SRMR\u0026thinsp;=\u0026thinsp;0.0282. Standardized factor loadings ranged from 0.617 to 0.969 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), confirming that all items adequately reflected their respective latent constructs. Composite Reliability (CR) values ranged from 0.834 to 0.932, while Average Variance Extracted (AVE) values met or exceeded the recommended threshold of 0.50 for all but one construct, indicating acceptable convergent validity and internal consistency.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSection 2\u003c/b\u003e, CFA supported a unidimensional model with excellent fit indices: χ\u0026sup2; = 50.6, df\u0026thinsp;=\u0026thinsp;35, p\u0026thinsp;=\u0026thinsp;.043; CFI\u0026thinsp;=\u0026thinsp;0.986; TLI\u0026thinsp;=\u0026thinsp;0.982; RMSEA\u0026thinsp;=\u0026thinsp;0.0238 (90% CI\u0026thinsp;=\u0026thinsp;0.00451\u0026ndash;0.0374); and SRMR\u0026thinsp;=\u0026thinsp;0.0231. All standardized factor loadings ranged from 0.603 to 0.821 and were statistically significant, indicating that the ten items coherently represented the latent construct of glycemic self-care behaviors.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eGiven these findings, the instrument was deemed psychometrically sound, valid, and reliable for assessing both perceptions of intervention strategies (Section 1) and behavioral adherence outcomes (Section 2) among urban working-age individuals with prediabetes. The complete English-language version of both scales is provided in Supplementary File: Additional File 1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants and Sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Participants were recruited from Vajira Hospital and its affiliated community health centers in urban Bangkok, Thailand. A stratified random sampling method was used to ensure diversity across age, gender, and education levels. Eligible participants were Thai adults aged\u0026thinsp;\u0026ge;\u0026thinsp;35 years, diagnosed with prediabetes for at least six months but not more than two years, with fasting plasma glucose levels between 100 and 125 mg/dL, in accordance with the American Diabetes Association (ADA) criteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additional criteria included BMI\u0026thinsp;\u0026ge;\u0026thinsp;23 kg/m\u0026sup2;, ability to communicate in Thai, and provision of written informed consent.\u003c/p\u003e\u003cp\u003eExclusion criteria comprised a prior diagnosis of type 2 diabetes, pregnancy, significant cognitive or psychiatric impairment, or concurrent participation in other diabetes prevention programs. Recruitment was coordinated by trained health personnel who reviewed outpatient records and contacted eligible individuals through community-based clinics. All participants were provided with detailed study information and gave written informed consent prior to enrollment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Size Calculation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe required sample size was estimated using G*Power version 3.1.9.4 for linear multiple regression analysis (fixed model, R\u0026sup2; deviation from zero), based on an effect size (f\u0026sup2;) of 0.02 as suggested by Cohen [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], with α\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.80, and nine predictors. This yielded a minimum sample size of 790 participants for detecting moderate-to-large effects in exploratory behavioral studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were collected through self-administered paper-based questionnaires distributed during outpatient clinic visits and community outreach activities. Participants completed the questionnaires in designated private areas, with support from trained research assistants available to clarify item meanings if needed. The average completion time was 15\u0026ndash;20 minutes. All responses were anonymized and coded for analysis. Data were double-entered into a secured database and cross-verified to ensure accuracy prior to statistical analysis.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to summarize demographic characteristics and study variables. Multiple linear regression (Enter method) was performed to assess the relationship between the nine intervention functions and self-care behavior for glycemic management. All predictors were entered simultaneously to reflect the theoretical structure of the model [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Assumptions for regression were evaluated as follows: linearity and homoscedasticity were confirmed via residual plots; normality of residuals was assessed using histograms and Q\u0026ndash;Q plots; independence of errors was supported by a Durbin\u0026ndash;Watson statistic of 2.03 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Multicollinearity was not present (VIFs\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.03) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and no influential outliers were identified (Cook\u0026rsquo;s distance\u0026thinsp;\u0026lt;\u0026thinsp;0.01) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePhase 1 Qualitative Results: Thematic Analysis Based on the COM-B Framework\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study analyzed data obtained from in-depth interviews with 25 participants, focusing on self-care experiences related to type 2 diabetes mellitus (T2DM) prevention among individuals with prediabetes. Using the COM-B framework, the results are categorized into three major themes: Capability, Opportunity, and Motivation. These categories provide a comprehensive understanding of behavioral determinants and offer actionable insights for intervention development.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipant Demographics and Characteristics\u003c/b\u003e The participants included 15 females (66.67%) and 10 males (33.33%), aged between 35 and 59 years, with a mean age of 38.51 years. Educational backgrounds varied, with 13.33% having completed primary education, 20.00% secondary education, 53.33% holding bachelor\u0026rsquo;s degrees, and 13.33% having graduate degrees. Based on clinical follow-up, some participants successfully delayed diabetes progression, while others developed T2DM.\u003c/p\u003e\u003cp\u003eInformants were assigned anonymized codes to ensure confidentiality. Each code consisted of a letter indicating gender (F\u0026thinsp;=\u0026thinsp;female, M\u0026thinsp;=\u0026thinsp;male), followed by a two-digit number representing the participant\u0026rsquo;s unique sequence in the study (e.g., F03, M08). In parentheses, we noted whether the individual successfully delayed the progression of prediabetes (\u0026ldquo;Delayed progression\u0026rdquo;) or developed type 2 diabetes during the observation period (\u0026ldquo;Developed diabetes\u0026rdquo;). This coding system facilitated comparative analysis across subgroups while preserving participant anonymity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheme I: Capability\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSubtheme 1.1: Psychological Capability - Awareness and Knowledge\u003c/em\u003e Participants with greater awareness and understanding of prediabetes demonstrated stronger adherence to preventive behaviors. Their knowledge enabled them to perceive risks and implement lifestyle changes proactively.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF03 (Delayed progression): \u0026ldquo;When I started learning more about prediabetes, I understood that small changes could make a big difference.\u0026rdquo;\u003c/p\u003e\u003cp\u003eM08 (Delayed progression): \u0026ldquo;Knowing I was at risk made me realize I needed to act before it was too late.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eConversely, those with limited awareness often underestimated the condition:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF15 (Developed diabetes): \u0026ldquo;I thought being at risk wasn\u0026rsquo;t a big deal, so I didn\u0026rsquo;t prioritize making changes.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSubtheme 1.2: Physical Capability - Skills and Stamina\u003c/em\u003e Physical capability, including practical skills and physical endurance, played a key role in behavior maintenance.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF09 (Delayed progression): \u0026ldquo;I started cooking my meals to control sugar intake and avoided ordering fast food.\u0026rdquo;\u003c/p\u003e\u003cp\u003eM05 (Delayed progression): \u0026ldquo;I began exercising every morning, and it has become part of my routine.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn contrast, fatigue hindered sustained behavior:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF10 (Developed diabetes): \u0026ldquo;My body would get tired too quickly, and I couldn\u0026rsquo;t keep up with the exercises.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheme II: Opportunity\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSubtheme 2.1: Physical Opportunity - Access to Resources\u003c/em\u003e Participants with access to health-promoting resources were more successful in lifestyle modification.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eM02 (Delayed progression): \u0026ldquo;The gym near my workplace made it easy for me to exercise regularly.\u0026rdquo;\u003c/p\u003e\u003cp\u003eF08 (Delayed progression): \u0026ldquo;Attending community health workshops helped me learn practical tips for managing my prediabetes.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLimited access was a common barrier:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF02 (Developed diabetes): \u0026ldquo;The grocery store near my house didn\u0026rsquo;t have many fresh vegetables or whole-grain options.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSubtheme 2.2: Social Opportunity - Family and Peer Support\u003c/em\u003e Supportive relationships reinforced behavioral changes.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF01 (Delayed progression): \u0026ldquo;Having my partner join me on morning walks made it easier to stay consistent.\u0026rdquo;\u003c/p\u003e\u003cp\u003eM09 (Delayed progression): \u0026ldquo;My friends and I joined a workout group, and it kept me accountable.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn contrast, lack of support impaired motivation:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eM01 (Developed diabetes): \u0026ldquo;I didn\u0026rsquo;t have anyone to share my struggles with, and it felt like I was managing everything on my own.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheme III: Motivation\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSubtheme 3.1: Reflective Motivation - Goal-Setting and Planning\u003c/em\u003e Clear goals and structured plans supported sustained change.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF05 (Delayed progression): \u0026ldquo;Breaking my goals into smaller steps... made the process feel manageable and motivating.\u0026rdquo;\u003c/p\u003e\u003cp\u003eM06 (Delayed progression): \u0026ldquo;The doctor provided a clear plan, which gave me direction in managing my health.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOthers struggled due to stress or competing demands:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eM03 (Developed diabetes): \u0026ldquo;Even when I started with good intentions, my routine often fell apart when unexpected things happened.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSubtheme 3.2: Automatic Motivation - Emotional Triggers and Habits\u003c/em\u003e Participants who managed emotional triggers and habits well were more successful in sustaining behavior.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF08 (Delayed progression): \u0026ldquo;Joining a support group helped me replace old habits with healthier ones.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEmotional eating and stress disrupted behavior in others:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eF07 (Developed diabetes): \u0026ldquo;I planned to cut out sugary drinks completely, but I found it hard to resist.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhase 2 Quantitative Results\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the quantitative phase of the study, a total of 790 individuals diagnosed with prediabetes participated. The participants had a mean age of 45.2 years (SD\u0026thinsp;=\u0026thinsp;6.7), with 62% identifying as female and 38% as male. The average body mass index (BMI) was 27.3 kg/m\u0026sup2; (SD\u0026thinsp;=\u0026thinsp;3.8), ranging from 23.0 to 35.7 kg/m\u0026sup2;. The mean fasting blood sugar (FBS) level was 110.7 mg/dL (SD\u0026thinsp;=\u0026thinsp;6.4), which falls within the diagnostic criteria for prediabetes (100\u0026ndash;125 mg/dL). In addition, the average glycated hemoglobin (HbA1c) level was 6.1% (SD\u0026thinsp;=\u0026thinsp;0.3).\u003c/p\u003e\u003cp\u003eWith regard to behavioral variables, the average self-care behavior score for glycemic management was 29.74 (SD\u0026thinsp;=\u0026thinsp;7.65) out of a possible score of 50, indicating a moderate level of self-management practices among the participants.\u003c/p\u003e\u003cp\u003eThe perceived support from the nine intervention functions, each assessed on a 5-point Likert scale and reported as a sum score ranging from 5 to 25, varied in intensity. The highest mean score was found in the function of restriction (M\u0026thinsp;=\u0026thinsp;20.56, SD\u0026thinsp;=\u0026thinsp;3.00), followed by incentivisation (M\u0026thinsp;=\u0026thinsp;15.46, SD\u0026thinsp;=\u0026thinsp;6.30), education (M\u0026thinsp;=\u0026thinsp;15.28, SD\u0026thinsp;=\u0026thinsp;6.13), and training (M\u0026thinsp;=\u0026thinsp;15.11, SD\u0026thinsp;=\u0026thinsp;6.36). Environmental restructuring and enablement showed similar levels of perceived support, with mean scores of 15.05 (SD\u0026thinsp;=\u0026thinsp;6.19) and 14.87 (SD\u0026thinsp;=\u0026thinsp;6.28), respectively. Slightly lower mean scores were observed for persuasion (M\u0026thinsp;=\u0026thinsp;14.77, SD\u0026thinsp;=\u0026thinsp;6.19) and modelling (M\u0026thinsp;=\u0026thinsp;14.60, SD\u0026thinsp;=\u0026thinsp;6.01). The lowest perceived support was found in the coercion domain (M\u0026thinsp;=\u0026thinsp;9.31, SD\u0026thinsp;=\u0026thinsp;2.79).\u003c/p\u003e\u003cp\u003eA multiple linear regression analysis was conducted to investigate the predictive power of nine intervention functions, grounded in the Behavior Change Wheel (BCW) framework, on self-care behavior for glycemic management. The analysis included 790 participants with prediabetes, and all predictors were simultaneously entered using the enter method. The overall model was statistically significant, F(9, 780)\u0026thinsp;=\u0026thinsp;752.629, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, and explained a substantial proportion of the variance in the dependent variable (R\u0026sup2; = .897, Adjusted R\u0026sup2; = .895). The standard error of the estimate was 2.475, indicating good model precision.\u003c/p\u003e\u003cp\u003ePreliminary diagnostics confirmed that the assumptions of multiple regression were met. There was no evidence of multicollinearity, as all variance inflation factor (VIF) values ranged from 1.006 to 1.027, well below the conventional threshold of 10. Tolerance values were above .974, indicating acceptable levels of independence among predictors. Additionally, the residuals showed no signs of autocorrelation.\u003c/p\u003e\u003cp\u003eAll predictors were statistically significant, except one. Enablement had the strongest standardized effect (β\u0026thinsp;=\u0026thinsp;.520, t\u0026thinsp;=\u0026thinsp;45.10, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), followed by Education (β\u0026thinsp;=\u0026thinsp;.492, t\u0026thinsp;=\u0026thinsp;42.50, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Training (β\u0026thinsp;=\u0026thinsp;.421, t\u0026thinsp;=\u0026thinsp;36.12, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additional significant predictors included Modelling (β\u0026thinsp;=\u0026thinsp;.366, t\u0026thinsp;=\u0026thinsp;31.43, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Persuasion (β\u0026thinsp;=\u0026thinsp;.294, t\u0026thinsp;=\u0026thinsp;25.35, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and Environmental Restructuring (β\u0026thinsp;=\u0026thinsp;.147, t\u0026thinsp;=\u0026thinsp;12.66, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Predictors with smaller yet statistically significant effects included Incentivisation (β\u0026thinsp;=\u0026thinsp;.121, t\u0026thinsp;=\u0026thinsp;10.41, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Restriction (β\u0026thinsp;=\u0026thinsp;.037, t\u0026thinsp;=\u0026thinsp;3.17, p\u0026thinsp;=\u0026thinsp;.002). In contrast, coercion was not a significant predictor (β = \u0026ndash;.003, t = \u0026minus;\u0026thinsp;0.271, p\u0026thinsp;=\u0026thinsp;.786). These findings underscore the predictive validity of BCW-based intervention functions in influencing glycemic self-care behavior and suggest that enablement, education, and training are particularly critical in behavior change strategies for individuals with prediabetes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eRegression coefficients predicting Self-Care Behaviors for Glycemic Management from COM-B-based intervention functions (Enter method)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI for B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTolerance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-15.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-17.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[ -17.547, -13.943]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[ .585, .642]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[.479, .534]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnablement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[ .607,.662]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModelling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[ .437,.495]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersuasion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[ .335, .392]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironmental Restructuring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[ .153, .209]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncentivisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[ .119, .175]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRestriction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[ .036, .152]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoercion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u0026minus;\u0026thinsp;.071, .054]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eR\u0026sup2; of 0.897, Adjusted R\u0026sup2; of 0.896, (F(9, 780)\u0026thinsp;=\u0026thinsp;752.63, p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eJoint Display: Integration of Qualitative and Quantitative Findings\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo synthesize the evidence from both strands of this sequential mixed methods study, a joint display matrix was developed to integrate qualitative insights from Phase 1 with quantitative results from Phase 2. This integration was guided by the COM-B model and Behavior Change Wheel (BCW) framework, providing a theory-driven structure to interpret the results. The joint display enabled a deeper understanding of how statistically significant predictors aligned with participants\u0026rsquo; lived experiences in practicing diabetes-preventive behaviors.\u003c/p\u003e\u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cem\u003eJoint Display of Quantitative and Qualitative Findings Based on the COM-B and BCW Frameworks\u003c/em\u003e, the three most influential intervention functions\u0026mdash;Enablement, Education, and Training-emerged as both thematically dominant in qualitative data and statistically robust in the regression analysis (β\u0026thinsp;=\u0026thinsp;.520, .492, and .421, respectively). These functions were closely associated with enhancing psychological and physical capability, a core domain in the COM-B model. Participants frequently emphasized the importance of feeling supported, gaining relevant knowledge, and developing practical skills:\u003c/p\u003e\u003cp\u003e\u0026ldquo;Knowing someone supports me makes it easier to stick to my routine.\u0026rdquo; (F06, delayed progression)\u003c/p\u003e\u003cp\u003e\u0026ldquo;When I learned more about prediabetes, I realized I had to change.\u0026rdquo; (F03, delayed progression)\u003c/p\u003e\u003cp\u003e\u0026ldquo;I needed to practice healthy cooking with someone first.\u0026rdquo; (M05, delayed progression)\u003c/p\u003e\u003cp\u003eA second cluster of moderately strong predictors\u0026mdash;Modelling, Persuasion, and Restriction\u0026mdash;reflected social and motivational enablers of behavior change. These functions were linked to exposure to positive role models, persuasive communication from trusted figures, and the implementation of household dietary norms:\u003c/p\u003e\u003cp\u003e\u0026ldquo;Seeing others succeed made me try harder.\u0026rdquo; (M09, delayed progression)\u003c/p\u003e\u003cp\u003e\u0026ldquo;Doctors telling stories about success moved me to act.\u0026rdquo; (F08, delayed progression)\u003c/p\u003e\u003cp\u003e\u0026ldquo;Avoiding certain foods became easier with family rules.\u0026rdquo; (F06, delayed progression)\u003c/p\u003e\u003cp\u003eThe remaining three functions\u0026mdash;Environmental Restructuring, Incentivisation, and Coercion\u0026mdash;demonstrated comparatively lower or non-significant predictive contributions (β\u0026thinsp;=\u0026thinsp;.147, .121, and \u0026ndash;.003, respectively). While Environmental Restructuring and Incentivisation were statistically significant, Coercion was not (p\u0026thinsp;=\u0026thinsp;.786). Qualitative accounts suggested that these functions were more context-dependent and variably effective depending on structural constraints and personal circumstances:\u003c/p\u003e\u003cp\u003e\u0026ldquo;Healthy food wasn\u0026rsquo;t available where I live.\u0026rdquo; (F02, developed diabetes)\u003c/p\u003e\u003cp\u003e\u0026ldquo;Fear of getting diabetes forced me to change.\u0026rdquo; (M10, developed diabetes)\u003c/p\u003e\u003cp\u003e\u0026ldquo;Getting praise made me proud of myself.\u0026rdquo; (F11, delayed progression)\u003c/p\u003e\u003cp\u003eOverall, the integration of qualitative and quantitative findings revealed a substantial degree of convergence between the theoretical underpinnings of the COM-B model, participants\u0026rsquo; experiential narratives, and the statistical associations derived from regression analysis. This triangulation not only reinforces the conceptual robustness of the study but also offers practical guidance for intervention development. Specifically, the findings support the formulation of comprehensive, multi-component behavior change interventions that simultaneously enhance individuals\u0026rsquo; capability through targeted strategies such as enablement, education, and hands-on training; bolster motivation through mechanisms including modelling, persuasive communication, and incentivisation; and address structural and contextual barriers by optimizing environmental restructuring and implementing appropriate restrictions. Such alignment across data sources underscores the importance of designing behavior change programs that are theoretically grounded, empirically validated, and contextually relevant to the lived experiences of individuals at risk of type 2 diabetes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eJoint Display of Quantitative and Qualitative Findings Based on COM-B and BCW Frameworks\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention Function\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantitative Significance (β, p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQualitative Illustration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntegration Insight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnablement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.537, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;Knowing someone supports me makes it easier to stick to my routine.\u0026rdquo; (F06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStrong convergence; perceived as both emotional and instrumental support [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.519, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;When I learned more about prediabetes, I realized I had to change.\u0026rdquo; (F03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConvergent; knowledge is foundational for initiating behavior [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.411, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;I needed to practice healthy cooking with someone first.\u0026rdquo; (M05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComplementary; hands-on training enhances confidence and capability [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModelling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.366, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;Seeing others succeed made me try harder.\u0026rdquo; (M09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConvergent; role models motivate behavior change through social learning [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersuasion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.294, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;Doctors telling stories about success moved me to act.\u0026rdquo; (F08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComplementary; narratives enhance reflective motivation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironmental Restructuring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.147, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;Healthy food wasn\u0026rsquo;t available where I live.\u0026rdquo; (F02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConvergent; environmental cues support sustainable habits [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncentivisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.121, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;Getting praise made me proud of myself.\u0026rdquo; (F11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComplementary; tangible rewards reinforce behavior through external motivation. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRestriction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.037, p\u0026thinsp;=\u0026thinsp;.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;Avoiding certain foods became easier with family rules.\u0026rdquo; (F06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExpansive; social reinforcement strengthens rule-based behavior [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoercion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ = \u0026minus;\u0026thinsp;0.003, p\u0026thinsp;=\u0026thinsp;.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;Fear of getting diabetes forced me to change.\u0026rdquo; (M10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLimited alignment; fear-based approaches may prompt short-term change [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003eThis joint display was constructed following the guidance of Guetterman, Fetters, \u0026amp; Creswell (2021) to integrate quantitative and qualitative results through a matrix format. The 'Integration Insight' column reflects the derived meta-inferences, supporting the use of mixed methods integration to inform behavior change interventions.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a robust synthesis of behavioral determinants influencing self-care among individuals with prediabetes, leveraging mixed methods integration through the COM-B model and Behavior Change Wheel (BCW). The convergence of qualitative and quantitative findings highlights the practical utility of theory-informed approaches in developing targeted nursing interventions.\u003c/p\u003e\u003cp\u003eEnablement, education, and training emerged as the most influential intervention functions, evidenced both by high regression coefficients and consistent thematic support. These components reflect the central tenet of the COM-B model, where behavior arises from the interplay between capability, opportunity, and motivation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For nursing practice, these findings emphasize the importance of tailored educational content, skill-building, and psychosocial support to empower individuals with prediabetes. Prior research confirms that culturally adapted, nurse-led programs can significantly enhance self-care adherence [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNurses working in primary and community health settings are uniquely positioned to assess behavioral readiness and deliver individualized interventions. Strengthening nurses\u0026rsquo; competencies in behavioral theory- particularly in COM-B and BCW-can enhance intervention fidelity and responsiveness to diverse patient needs.\u003c/p\u003e\u003cp\u003eModelling, persuasion, and restriction demonstrated moderate influence, particularly through mechanisms of social learning and normative reinforcement. These strategies, when embedded in peer-group education or home-based care, have potential to enhance motivation and accountability [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEnvironmental restructuring and incentivisation showed smaller but significant effects. Notably, the influence of incentivisation aligns with literature suggesting that tangible rewards can support short-term adherence without undermining intrinsic motivation, particularly when aligned with personal goals [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast, coercion was not a significant predictor, reinforcing evidence that fear-based messaging-if not paired with efficacy-enhancing strategies-can lead to disengagement or psychological reactance [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This has particular relevance in urban Thai populations, where high stress and exposure to unhealthy cues may diminish the effectiveness of punitive or fear-based approaches.\u003c/p\u003e\u003cp\u003eAmid these challenges, nurse-led interventions supported by community engagement or digital platforms offer promising avenues. Evidence suggests that technology-enhanced programs delivered by trained nurses can improve behavioral outcomes in populations at risk of diabetes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, the use of a joint display matrix enriched the interpretive process by aligning theoretical constructs with empirical evidence and lived experiences. The high explanatory power of the regression model (R\u0026sup2; = 0.896) affirms the utility of the nine intervention functions as a modular framework for designing nursing interventions. This underscores the value of mixed methods designs in producing actionable, context-sensitive, and theory-driven insights for nursing science [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis mixed methods study highlights the multifactorial nature of self-care behavior change in individuals with prediabetes, guided by the COM-B model and Behavior Change Wheel (BCW). Integration of qualitative and quantitative findings revealed enablement, education, and training as the most influential intervention functions, consistently supported by both lived experiences and statistical analysis. Community and primary care nurses are well-positioned to deliver culturally responsive interventions that enhance capability, opportunity, and motivation. Emphasis should be placed on skill-building, knowledge enhancement, and psychosocial support, while coercive strategies should be avoided due to limited effectiveness. The joint display approach enriched the interpretation of behavioral determinants and provided actionable insights for practice. These findings support the development of multi-component, theory-based nursing interventions tailored to urban populations at risk for type 2 diabetes. Future research should examine long-term outcomes and test these strategies in broader contexts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study did not incorporate longitudinal follow-up to evaluate sustained behavioral outcomes or biochemical progression from prediabetes to type 2 diabetes. Consequently, the long-term efficacy of the intervention remains uncertain. Future research should employ prospective cohort designs or randomized controlled trials with extended follow-up periods to rigorously assess durability of behavior change and clinical impact over time.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOM-B\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCapability, Opportunity, and Motivation-Behavior\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBCW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBehavior Change Wheel\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank all participants for sharing their experiences. We are also grateful to the Faculty of Medicine Vajira Hospital and Kuakarun Faculty of Nursing for their institutional support. Special thanks to the research assistants and content experts for their valuable input, and to our colleagues and mentors for their constructive feedback. Language editing assistance was provided by Paperpal, an AI-powered academic writing tool. All content and interpretations were reviewed and approved by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Araya Chiangkhong (A.C.) and Nittaya Sukchaisong (N.S.);\u003cbr\u003e\u0026nbsp;Methodology: A.C.;\u003cbr\u003e\u0026nbsp;Validation: A.C., N.S., and Kanokporn Imsakul (K.I.);\u003cbr\u003e\u0026nbsp;Formal analysis: A.C.;\u003cbr\u003e\u0026nbsp;Investigation: A.C.;\u003cbr\u003e\u0026nbsp;Writing – original draft preparation: A.C.;\u003cbr\u003e\u0026nbsp;Writing – review and editing: N.S. and Chavanant Sumanasrethakul (C.S.);\u003cbr\u003e\u0026nbsp;Visualization: A.C.;\u003cbr\u003e\u0026nbsp;Supervision: N.S.;\u003cbr\u003e\u0026nbsp;Project administration: A.C.;\u003cbr\u003e\u0026nbsp;Funding acquisition: N.S.\u003cbr\u003e\u0026nbsp;All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research project was supported by the Thailand Science Research and Innovation (TSRI) under Contract No. FRB670080/0468, as part of the research program titled \u003cem\u003e“Life Style Modification Model for Preventing T2DM in Adults with Prediabetes: Dusit Model.”\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData can be made available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of the Faculty of Medicine Vajira Hospital, Navamindradhiraj University (COA 051/2567, dated March 19, 2024). The research was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. Privacy and confidentiality were strictly maintained. All participants provided written informed consent, and pseudonyms were used to ensure anonymity in all reports. Informed consent was obtained from all participants. The confidentiality of the data and the anonymity and privacy of participants were preserved at all times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eKuakarun Faculty of Nursing, Navamindradhiraj University, Bangkok 10300, Thailand\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eFaculty of Medicine Vajira Hospital, Navaminradhiraj University, Bangkok 10300, Thailand\u003c/p\u003e\n\u003cp\u003e*Corresponding author e-mail: \u0026nbsp;[email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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Ann Fam Med. 2015;13(6):554\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1370/afm.1865\u003c/span\u003e\u003cspan address=\"10.1370/afm.1865\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCreswell JW, Plano Clark VL. Designing and conducting mixed methods research. 3rd ed. Thousand Oaks (CA): SAGE; 2018.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prediabetic State, Health Behavior, Self Care, Type 2 Diabetes Prevention, Urban Contexts, Mixed Methods Study","lastPublishedDoi":"10.21203/rs.3.rs-7091035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7091035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eType 2 diabetes mellitus (T2DM) is usually a lifelong condition that importance for global health problems. The management of prediabetes in the urban contexts is a priority. To examine the effects of nine intervention functions including education, training, environmental restructuring, enablement, restriction, coercion, modelling, incentivisation, and persuasion on self-care behaviors for glycemic control in individuals with prediabetes using a mixed methods approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA convergent mixed methods design was employed. Quantitative data were collected from 790 individuals with prediabetes using a self-reported questionnaire based on the Behavior Change Wheel (BCW). Multiple linear regression (enter method) was used to assess the relationship between intervention functions and self-care behavior. Qualitative data were gathered via semi-structured interviews with 25 purposively sampled participants and analyzed using thematic analysis. Findings were integrated using a joint display approach guided by Guetterman, Fetters, and Creswell.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEight of the nine intervention functions significantly predicted of self-care behavior (p \u0026lt; .01), with the strongest effects observed for enablement (β = .520), education (β = .492), and training (β = .421). Coercion (β = –.003, p = .786) showed no significant association. Thematic analysis revealed convergence with the quantitative findings, highlighting themes such as skill-building, peer and provider support, and motivational narratives. The joint display demonstrated alignment between perceived influences and actual behavior, supporting the need for multicomponent interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights the multifactorial nature of self-care behavior change among individuals with prediabetes. Tailored interventions should prioritize enablement, education, and training while avoiding coercive strategies. Nurses are well-positioned to implement theory-based, culturally responsive approaches that reflect patients' real-life capabilities, opportunities, and motivations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Joint Display Study for Prediabetes Prevention in Urban Contexts and Behavioral Change of Thailand: A Mixed Methods Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 18:01:32","doi":"10.21203/rs.3.rs-7091035/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8f90f5b-3f6e-409a-8951-00245f130a0e","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-22T09:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-15 18:01:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7091035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7091035","identity":"rs-7091035","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}