Diabetes Self-Management in the Digital Health Era: A Concept Analysis Using Natural Language Processing

Diabetes Self-Management in the Digital Health Era: A Concept Analysis Using Natural Language Processing | 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 Article Diabetes Self-Management in the Digital Health Era: A Concept Analysis Using Natural Language Processing Donghwan Lee, Aderonke Kareem, Solhee Shin, Leila Ledbetter, Aaliyah Alvin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8304580/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Diabetes self-management is increasingly shaped by digital health technologies, yet existing conceptual definitions do not fully reflect this evolving landscape. This study aimed to refine the concept of diabetes self-management in the digital health era by applying Rodgers’ evolutionary method integrated with Natural Language Processing (NLP). Ninety-seven studies were analyzed through a dual-phase design combining manual review and computational clustering of sentence-level embeddings. The analysis identified core attributes, including daily behavioral routines, cognitive and psychological processes, multilevel social support, personalization, digital health integration, and dynamic patient–provider relationships. Antecedents encompassed physical and psychological demands, emotional readiness, access to education and support, resource and technology availability, and health and digital literacy. Consequences included improved clinical outcomes, enhanced quality of life, empowerment, and reduced healthcare burden. Integrating manual and NLP-based approaches strengthened conceptual clarity and provided a contemporary framework for understanding diabetes self-management within digitally enabled patient-centered care. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Diabetes self-management Digital health Natural language processing Concept analysis Sentence embeddings Figures Figure 1 Figure 2 INTRODUCTION Chronic illness extends beyond physiological symptoms, shaping the lived experiences of individuals and families while imposing significant burdens at personal, societal, and systemic levels, including nearly three-quarters of global deaths, increased disability, and rising healthcare costs 1–4 . In response, self-management has become central to chronic illness care, as individuals must take active roles in navigating daily life, sustaining quality of life, and making health-related decisions outside clinical settings 5,6 . Among chronic illnesses, type 2 diabetes mellitus is especially significant. Type 2 diabetes is one of the most prevalent and burdensome conditions globally, and its successful management depends almost entirely on effective self-management 7 . Individuals must engage in a continuous “juggling act” of balancing medical tasks with emotional and social well-being 8 . More than 90% of diabetes care occurs outside of clinical settings and is carried out by individuals themselves 9,10 . Yet despite its central role, sustaining effective diabetes self-management remains a persistent challenge 11 . In response to the need for more accessible and personalized self-management support, digital health has emerged as a promising approach for individuals with type 2 diabetes. Digital health technologies, including mobile health applications, telehealth platforms, wearable devices, and remote monitoring tools, offer new opportunities to overcome barriers to care, such as transportation limitations and geographic isolation 12,13 . These tools enable individuals to track health metrics like blood glucose, blood pressure, weight, physical activity, and medications, access educational resources, and communicate with providers in real-time, fostering informed decision-making and proactive diabetes self-management 14,15 . Building on these advances, the mid-2010s marked the beginning of a distinct digital health era. This period was characterized by the adoption of foundational interoperability standards, such as Fast Healthcare Interoperability Resources (FHIR), and the widespread implementation of certified electronic health records (EHRs), which have accelerated digital innovation and data exchange in healthcare 16 . This turning point was internationally recognized in the 2015 United Nations General Assembly, which emphasized the transformative potential of information and communications technology in expanding equitable access to care 17,18 . The COVID-19 pandemic further catalyzed these developments, accelerating telemedicine and remote patient monitoring as essential components of chronic illness management 19 . Digital health tools have since become increasingly integrated into formal care models, supporting real-time adjustments to care plans and enhancing provider–patient communication through integration with platforms like EHRs 20,21 . Together, these advances underscore digital health as a key enabler of patient-centered, data-driven self-management strategies for individuals living with type 2 diabetes. As digital health becomes increasingly embedded in healthcare delivery, technology is reshaping traditional care practices and becoming a routine component of diabetes management 22 . This evolving role of digital health underscores the need to revisit and refine the concept of diabetes self-management. Defining and refining concepts is essential for scientific progress, as they serve as the building blocks of theory, provide the language for studying phenomena, and ensure that research remains systematically linked to real-world problems 23 . According to Rodgers 24 , concepts are not static; they evolve through use and in response to emerging challenges. In this context, diabetes self-management must be refined to reflect the transformative influence of digital health on how individuals engage with and manage type 2 diabetes. Therefore, this study aims to refine the concept of diabetes self-management in the digital health era, addressing the complexities and shifting needs of individuals within this rapidly changing healthcare landscape. To capture this evolving concept, this study extends Rodgers’ evolutionary method by integrating Natural Language Processing (NLP) techniques. Traditional concept analysis relies on inductive reasoning and manual review of selected literature, which, while rigorous, is constrained by sample size and researcher subjectivity. In contrast, NLP enables the systematic examination of large textual corpora, uncovering patterns in language use, contextual meanings, and emergent themes that may remain hidden in conventional approaches 25,26 . This computational extension not only increases scalability and reproducibility but also introduces novel analytic dimensions by bridging qualitative theory-building with data-driven methods. In doing so, this study extends concept analysis beyond its traditional boundaries by introducing methodological innovation and providing a contemporary, data-informed understanding of diabetes self-management in the digital health era. RESULTS The final literature search, conducted on April 14, 2025, yielded 2,375 records. Complete search strategies, including database-specific date ranges and filters, are provided in Supplementary Table 1. After duplicate removal (n=362), 2,013 records were screened for eligibility. During the full-text review, 280 articles were examined by an independent reviewer. Studies were excluded for reasons such as outcomes not aligned with the review focus (n=10), ineligible study design (n=16), ineligible population (n=10), or not conceptually relevant to diabetes self-management (n=147). After applying the inclusion and exclusion criteria, 97 studies were retained for analysis. Non-English papers were not deliberately excluded, although none met the inclusion criteria at the full-text level. The final dataset included studies conducted across 23 countries. The most common study designs were qualitative studies (n=25), randomized controlled trials (n=21), and development-focused evaluations such as pilot, feasibility, or usability studies. Less common designs included cross-sectional surveys, mixed methods approaches, and secondary data analyses. Detailed study characteristics, including country, study design, population, and the designated review phase for each article (Phase 1 or Phase 2), are presented in Supplementary Table 2, and the overall screening and selection process is illustrated in the PRISMA flow diagram (Figure 1). Phase 1: Manual Concept Analysis Approximately 25% of the included studies (n=24) were randomly selected and manually reviewed by the three independent reviewers. Inter-rater reliability analysis demonstrated substantial agreement (Fleiss’ κ=0.80, 95% CI [0.79, 0.81]), confirming consistency in the identification of attributes, antecedents, and consequences of diabetes self-management in the digital health era. Table 1 summarizes how each reviewer independently identified and categorized relevant text segments across the attributes, antecedents, and consequences. Table 2 presents the integrated findings, combining the reviewers’ finalized manual classifications with the results of the NLP-based analysis. The complete independent reviewer classifications, along with the comparative alignment used to evaluate agreement and generate the reliability statistics, are provided in Supplementary Table 3. Table 1. Summary of Manual Concept Analysis Findings Across Three Independent Reviewers Dimension Reviewer A Reviewer B Reviewer C Attributes Health Engagement (Diabetes) Digital Literacy Executive Competence Patient and Clinician Communication Complexity & Continuity Self-Regulation & Motivation Educational / Supportive System & Social Interaction Digital Health–Driven Autonomy & Engagement Daily Behavioral and Lifestyle Components Cognitive and Psychological Determinants of Self-Management Social and Environmental Context Personalization and Person-Centered Care Digital Health for Diabetes Self-Management Dynamic Patient–Provider Relationship Antecedents Motivation and Self-efficacy Access to Technology Individual Factors Patient Clinician Factors Complexity and Disease Burden Self-Regulation and Motivation Educational / Supportive System & Social Interaction Autonomy and Active Engagement in Digital Self-Management Demands of Diabetes Self-Management Emotional Readiness for Change Access to Education and Ongoing Support Access to Resources and Technology Consequences Health Outcomes Psychological Outcomes Social Outcomes Complexity & Continuity Self-Regulation & Motivation Educational / Supportive System & Social Interaction Digital Health–Driven Autonomy & Engagement Clinical and Health Outcomes Quality of Life and Psychosocial Well-being Empowerment, Autonomy, and Motivation Long-Term Maintenance and Sustainability System-Level and Economic Implications Table 2. Summary of Findings from Manual and NLP-based Concept Analyses Dimension Phase 1. Manual Concept Analysis Phase 2. NLP-Based Concept Analysis Attributes Daily Behavioral and Lifestyle Components Daily Self-Management Activities Lifestyle Change as an Ongoing Process Cognitive and Psychological Factors of Self-Management Knowledge and Skills Self-Efficacy, Competence, and Motivation Psychological and Identity Dimensions Social and Environmental Context Addressing Social Determinants of Health Multilevel Social Support: Family, Community, and Digital Platforms Personalization and Person-Centered Care Digital Health for Diabetes Self-Management Digitalizing Health for Monitoring and Decision Support Digital Literacy User Experience Interoperability, Access, and Infrastructure Dynamic Patient–Provider Relationship Professional Support in the Digital Era Digital Health-Driven Patient-Provider Communication Self-Management as the Core of Diabetes Care Centrality of Self-Management in Diabetes Care Behavioral and Lifestyle Components of Self-Management Necessity of Lifestyle Modification and Ongoing Self-Management Patient-Centered Support and Empowerment Individualized Feedback and Patient-Centered Care Engagement, Adherence, and Empowerment Education and Support as Essential Enablers Peer-Enabled Support and Learning through Online Communities Digital Health and Technology Integration in Diabetes Self-Management Comprehensive Monitoring and Datafication of Self-Management Accessibility and Empowerment through Digital Health Technologies Evaluation and Appropriateness of Mobile Apps Strengths and Limitations of Mobile Health Apps User-Centered Design and Personalization Contextual and System-Level Considerations Chronic and Global Burden of Diabetes Patient Characteristics and Disease Context as Influential Factors Antecedents Demands of Diabetes Self-Management Physical Demands Psychological Demands Emotional Readiness for Change Access to Education and Support Access to Resources and Technology Patient and Disease Characteristics Access to Guidance, Education, and Structured Support Access to Digital Health Technology Health Literacy and Digital Literacy Linkages Between Self-Management and Healthcare Providers Consequences Clinical and Health Outcomes Quality of Life and Psychosocial Well-being Empowerment, Autonomy, and Motivation System-Level and Economic Implications Healthier Behaviors and Clinical Outcomes Enhanced Knowledge and Self-Awareness Empowerment, Self-Efficacy, and Motivation Reduced Healthcare Burden Attributes The manual analysis identified six attributes of diabetes self-management in the digital health era. First, daily behavioral and lifestyle components encompassed routine activities such as blood glucose monitoring, dietary adjustments, medication adherence, and sustained lifestyle changes, reflecting the ongoing and complex nature of diabetes care. Second, cognitive and psychological factors included knowledge, skills, motivation, and self-efficacy, highlighting the importance of patient confidence and emotional well-being in sustaining long-term engagement. Third, the social and environmental context underscored the role of social determinants of health and multilevel social support across family, community, and digital platforms in shaping self-management behaviors. Fourth, personalization and person-centered care emphasized tailored education, adaptive strategies, and alignment with individual values and preferences. Fifth, digital health emerged as a defining attribute, encompassing remote monitoring, decision support, digital literacy, user-friendly experiences, and infrastructural considerations for interoperability and access. Finally, a dynamic patient–provider relationship was central, where digital tools facilitated digital health–driven communication that enhances, rather than replaces, professional support. Antecedents Four major antecedents were identified. Demands of diabetes self-management captured both the physical (e.g., diet, exercise, insulin use, continuous monitoring) and psychological burdens (e.g., depression, distress, stigma) associated with daily disease management. Emotional readiness for change reflected motivational processes, autonomy, self-efficacy, and relatedness, which were essential for adopting and sustaining self-care behaviors. Access to education and ongoing support were consistently emphasized, with diabetes self-management education and structured follow-up highlighted as critical to skill development and sustained engagement. Lastly, access to resources and technology represented the enabling role of mobile apps, devices, and digital platforms, while also acknowledging challenges such as connectivity issues and digital inequities. Consequences Four categories of consequences were identified. Clinical and health outcomes included improved glycemic control, reduced complications, and prevention of comorbidities. Quality of life and psychosocial well-being reflected reduced distress, improved mental health, and greater life satisfaction, although glycemic improvement did not always translate into psychological benefits. Empowerment, autonomy, and motivation highlighted how self-management enhanced confidence, decision-making, and long-term adherence. Finally, system-level and economic implications underscored reduced healthcare utilization and costs, while also recognizing the broader social and productivity benefits of effective self-management. Phase 2: Natural Language Processing-Based Concept Analysis A total of 73 articles comprising 16,218 sentences were processed through the NLP pipeline. Cluster analysis showed that the corpus was optimally described by six clusters. One of the six clusters was thematically centered on diabetes self-management and contained 3,308 sentences. Sub-clustering analysis within this cluster identified five distinct sub-clusters, averaging approximately 600 sentences each. From each of the five sub-clusters, 30 representative sentences (~5%) were selected to identify the attributes, antecedents, and consequences of the concept. Figure 2 visualizes the two-stage clustering process by projecting the sentence-level SBERT embeddings into a two-dimensional space using UMAP. Panel (a) shows the six primary clusters derived from the full corpus, and Panel (b) displays the five sub-clusters within the diabetes self-management–focused cluster. Attributes A broad set of attributes was identified. Self-management as the core of diabetes care was reinforced, emphasizing that daily behavioral and lifestyle activities such as diet regulation, physical activity, medication adherence, and ongoing monitoring are indispensable for effective disease management. Patient-centered support and empowerment were highlighted through individualized feedback, real-time prompts, and tools that fostered engagement, adherence, and motivation. Education and support remained central, with digital platforms extending structured guidance beyond traditional care. Peer-enabled support and online communities emerged as valuable spaces for social learning, knowledge exchange, and emotional encouragement. Digital health and technology integration encompassed comprehensive monitoring and datafication, as well as the accessibility and empowerment made possible by digital platforms. This attribute also captured the importance of systematic evaluation of mobile apps, recognition of their strengths and limitations, and the need for user-centered design and personalization for diabetes self-management engagement. Finally, contextual and system-level considerations were evident, including recognition of the chronic and global burden of diabetes, the necessity of sustained lifestyle modification, and the influence of patient and disease characteristics on the feasibility and effectiveness of self-management. Antecedents Five categories of antecedents were identified. First, patient and disease characteristics such as demographics, psychosocial factors, comorbidities, and disease trajectory shaped individual capacity for engagement in self-management. Access to guidance, education, and structured support was foundational for building the skills and knowledge necessary for effective self-management. Access to digital health technology included the use of mobile apps, glucose-monitoring devices, and online resources that facilitate daily care. Health literacy and digital literacy were critical in enabling patients to interpret data, set goals, and act on feedback. Finally, linkages with healthcare providers underscored the role of professional oversight and integration of patient-generated data into clinical care. Consequences Four consequence categories were observed. Healthier behaviors and clinical outcomes were the most frequently reported, with digital interventions improving adherence to treatment regimens, lifestyle changes, and glycemic control. Enhanced knowledge and self-awareness reflected the ability of patients to understand the relationship between behaviors and outcomes, strengthening self-management capacity. Empowerment, self-efficacy, and motivation captured the psychological benefits of digital health engagement, including greater confidence, autonomy, and social connectedness. Finally, reduced healthcare burden highlighted the broader system-level effects of effective self-management, including fewer complications, decreased hospitalizations, and lower healthcare costs, particularly when digital tools extend access to underserved populations. A summary of the identified attributes, antecedents, and consequences identified in the NLP-based concept analysis is listed in Table 2. DISCUSSION The purpose of this study was to refine the concept of diabetes self-management in the context of the digital health era. Drawing on 97 studies analyzed through both manual review and NLP-based approaches, we systematically identified the attributes, antecedents, and consequences of the concept. Integrating findings from both approaches further provides an opportunity to update and clarify the definition of diabetes self-management within today’s digital health landscape. In the digital health era, diabetes self-management can be understood through a set of defining attributes that describe how individuals navigate and sustain daily care. The findings show that behavioral elements are central to diabetes self-management. Daily routines such as medication adherence, dietary regulation, physical activity, and glucose self-monitoring continue to form the foundation of effective management 27 – 29 . Importantly, these behaviors are not static but involve continuous, lifelong adaptation to individual health conditions, personal demands, and environmental circumstances 30 – 32 . Beyond behaviors, cognitive and psychological factors shape the capacity to initiate and sustain self-management. Knowledge and skills, competence, motivation, and self-efficacy enable individuals to begin and maintain lifelong self-management practices 31 , 33 . These factors also foster constructive psychological adaptation, helping individuals live with chronic conditions while cultivating positive attitudes 34 . Promoting these psychological dimensions can strengthen a healthy social identity 35 , 36 , support the pursuit of personal goals and values 9 , and enhance proactive, sustained engagement in self-management 37 . Social and environmental contexts shape diabetes self-management for everyone, yet systemic inequities create disproportionate barriers for racial and ethnic minorities and socioeconomically marginalized groups 36 – 38 . These contexts influence individuals’ capacity to engage in daily management by shaping access to resources, supportive environments, and opportunities for care. However, the lived realities of individuals affected by structural inequities reveal persistent gaps in how digital health is approached and implemented 36 . These gaps include limited tailoring of digital tools to diverse users’ needs, uneven engagement among racially and socioeconomically marginalized groups, and design features that may unintentionally reinforce stigma or require resources not equally available 36 , 37 , 39 . Addressing these gaps requires demonstrating how digital health tools can actively mitigate, rather than reinforce, existing disparities. Within this broader landscape, multilevel social support encompassing family, community, and digital platforms plays a crucial role in enabling sustained self-management. In the digital health era, online communities and virtual platforms have expanded forms of social connection, extending support networks beyond geographic and temporal boundaries 10 , 40 . Digital health technologies have become central attributes of self-management. These technologies enable continuous monitoring, tracking, and visualization of physiological data and integrate with daily routines to deliver real-time feedback, personalized recommendations, and improved communication with providers 40 – 42 . By fostering adaptive, patient-centered care, these technologies strengthen self-management capacity and confidence 27 , 31 . Another important dimension of digital health is the user experience. Sustained self-management engagement using digital health depends on both cognitive understanding and emotional factors, such as trust, satisfaction, and perceived usefulness of technology, alongside considerations such as privacy, digital literacy, and fit with daily life 43 , 44 . Accordingly, intuitive design, personalization are essential for sustaining adoption and maximizing the benefits of digital health tools 45 . Mobile applications are particularly prominent in diabetes care, offering features such as tracking, monitoring, education, and communication 46 – 48 . However, their potential is often limited by challenges of integration, comprehensiveness, and evidence-based design, underscoring the need for systematic evaluation of app content and features to ensure reliability, safety, and clinical relevance 46 , 49 , 50 . Finally, digital health technologies are reshaping individuals’ roles in diabetes care, enabling them to act as active partners in decision-making rather than passive recipients. Through real-time monitoring, data interpretation, and enhanced communication with providers, individuals gain greater capacity to engage continuously in managing their health 27 , 51 . This shift supports timely feedback, shared decision-making, and a more collaborative and dynamic relationship among individuals, providers, and technologies in diabetes self-management 31 , 43 , 52 . Beyond these core attributes, diabetes self-management also depends on a set of antecedents that provide the necessary conditions for individuals’ readiness and capacity to engage effectively in daily care. A central foundation is the balance between the physical and psychological demands of managing a chronic illness 30 , 53 . Physical demands include frequent glucose monitoring, medication administration, and ongoing lifestyle decisions 9 , 30 , 42 , 54 . Psychological demands require adapting to chronic illness, maintaining a constructive identity, and managing distress, depression, anxiety, or stigma 34 – 36 . Emotional readiness—including motivation, autonomy, competence, and relatedness—further supports sustained engagement 37 , 51 . Education and ongoing support are also essential antecedents. Self-management education improves clinical outcomes when paired with continued reinforcement through regular contact with healthcare providers or peer networks, which helps embed coping strategies and behavior change into daily routines 30 , 31 , 52 . In the digital health era, access to resources and technology has become increasingly important. Mobile apps, wearable devices, and telehealth platforms expand opportunities for monitoring, education, and remote support 41 , 45 , 55 . However, disparities in connectivity, digital literacy, and socioeconomic resources may limit uptake 38 , 43 , underscoring the need for inclusive, user-centered design 37 , 45 . Health literacy and digital literacy are crucial antecedents that enable individuals to interpret health information, navigate digital tools, and translate knowledge into action, such as goal setting and making informed decisions 56 – 58 . Finally, linkages with healthcare providers remain indispensable. Patient-generated data, such as glucose readings, dietary logs, and physical activity records, can be integrated into clinical encounters, allowing providers to tailor counseling and treatment 47 , 59 . Digital platforms support this integration through continuous communication, strengthening patient–provider partnerships and promoting personalized care 60 , 61 . Building on these antecedents, effective diabetes self-management gives rise to a range of clinical, psychosocial, and system-level consequences. Consistent engagement improves glycemic control, reduces complications, and lowers the risk of comorbidities 31 , 40 , 41 . Self-management also influences quality of life and psychosocial well-being by reducing distress, enhancing coping, and strengthening daily functioning 33 , 34 . Empowerment and autonomy are central consequences, with self-efficacy supporting sustained behavior change 29 ; digital tools further promote confidence and motivation when they facilitate reflection and decision-making 45 , 51 . Sustaining self-management requires ongoing reinforcement 40 . User-centered digital interventions and continuous support can help maintain engagement and contribute to fewer hospitalizations, lower healthcare expenditures, and reduced system burden 32 , 55 . Conversely, inadequate self-management increases clinical risks and costs, underscoring the need for scalable and equitable strategies to promote long-term success 32 , 41 . At the methodological level, the integration of manual review and NLP-based analysis demonstrates a growing convergence between interpretive and computational approaches in literature synthesis. NLP has been increasingly adopted to address the limitations of manual literature reviews—such as labor intensity, susceptibility to bias, and limited scalability—by organizing large bodies of evidence more efficiently and objectively 62 – 64 . Recent work also shows that NLP can enhance search precision by identifying conceptual similarities that traditional keyword methods may overlook, thereby improving transparency and reproducibility 65 . Despite these advantages, most NLP applications still focus on describing linguistic patterns rather than advancing theoretical understanding. As Liu et al. 66 argue, generating conceptual insight requires interpreting how identified topics are related to one another. From this perspective, NLP augments rather than replaces human reasoning by enabling researchers to manage large textual datasets and reveal the underlying relational patterns among concepts that support conceptual development 66 . In this study, the dual-phase design combines the contextual depth of manual analysis with the broad analytic coverage of computational methods. The manual review phase captured the contextual and relational aspects of diabetes self-management, while the NLP-based phase expanded insights across a much larger corpus. Together, these approaches demonstrate how integrating manual and computational techniques can strengthen conceptual clarity and analytic depth. The refined concept of diabetes self-management in the digital health era also carries important implications for practice, policy, and research. In clinical practice, digital health tools should be patient-centered, user-friendly, and adaptable to individual needs, functioning as extensions of professional care rather than substitutes. Such tools can reinforce education, provide personalized feedback, and strengthen therapeutic relationships 51 , 52 . At the policy level, integrating digital health into chronic illness management requires a strong equity focus. Addressing disparities such as those in broadband access, device availability, and digital literacy is critical to prevent widening gaps in care. Policies that support interoperability, privacy, and responsible data governance are also essential to ensure safe and meaningful use of patient-generated data 37 , 38 , 45 . From a research perspective, this study offers a conceptual foundation that warrants empirical testing and further theoretical refinement. Future work should examine the attributes, antecedents, and consequences across diverse populations and chronic conditions, incorporating the lived experiences of patients and caregivers. Longitudinal and intervention-based studies will be particularly valuable in assessing how digital health–integrated self-management translates into sustained behavior change, improved outcomes, and system-level efficiencies. Despite these contributions, several limitations should be acknowledged. Both manual and NLP-based analysis required researcher interpretation, which, while systematic, cannot fully eliminate subjectivity. Furthermore, the NLP-based concept analysis used in this study represents, to our best knowledge, the first attempt to integrate such computational methods into Rodgers’ evolutionary concept analysis. At present, there are no established methodological standards or unified frameworks for applying NLP to concept analysis. The NLP-based phase relied on sentence embeddings and clustering techniques to identify thematic patterns across a large corpus. While this high-dimensional representation enabled systematic organization and pattern detection, it also raises questions about whether such computational abstraction can fully capture the nuanced, context-dependent meanings central to conceptual inquiry. These methodological uncertainties underscore the need for further refinement, validation, and consensus building as computational techniques become more widely integrated into health science research. Digital health is reshaping diabetes self-management by strengthening engagement, supporting early detection, and enabling real-time decision-making 67 . Using Rodgers’ evolutionary method integrated with NLP, this study reconceptualized diabetes self-management as a multidimensional process shaped by behavioral, psychological, social, and technological factors. Digital health functions as an essential component of this process by facilitating continuous monitoring, personalized feedback, and stronger patient–provider connections. By combining qualitative and computational approaches, this study generated a more comprehensive conceptual understanding of diabetes self-management in the digital era. This refined conceptual foundation provides a contemporary direction for future research and offers guidance for practice and policy efforts that aim to promote equitable, technology-enabled chronic illness care. METHODS Data Collection A comprehensive literature search was conducted in three electronic databases, including MEDLINE (via PubMed), CINAHL (via EBSCOhost), and IEEE Xplore Digital Library, to identify peer-reviewed studies related to type 2 diabetes, digital health, and self-management. The search strategy was developed by a professional medical librarian in consultation with the author team. Keywords and subject headings representing the three core concepts were iteratively refined, validated against a set of benchmark articles, and peer-reviewed by a second independent librarian to ensure completeness and reproducibility. The search process followed established methodological guidelines for comprehensive literature reviews and concept analyses 24 , 68 . The search was limited to studies published between 2015 and 2025. Studies were considered eligible if they (1) focused on adults aged 18 years or older with type 2 diabetes, (2) addressed self-management as a primary or secondary focus, (3) examined the role or application of digital health technologies such as telemedicine, mobile health, wearable devices, or remote monitoring, and (4) reported empirical findings or protocol-level methodological details. All search results were imported into Covidence (Veritas Health Innovation, Melbourne, Australia) for citation screening and data management. Title, abstract, and full-text screening were conducted according to the predefined inclusion and exclusion criteria. During the full-text review, studies were excluded if they were review articles, conference abstracts, meeting reports, or retracted papers. Studies were also excluded if they focused on individuals with gestational or type 1 diabetes, included mixed samples of type 1 and type 2 diabetes without stratified results, or did not specify the diabetes type. Outcomes were considered ineligible when studies focused exclusively on a single behavioral or psychological construct (e.g., foot care behavior, self-efficacy, or blood glucose monitoring frequency) or that measured only healthcare utilization metrics (e.g., clinic visits or encounter counts) without addressing broader self-management processes. The screening process was documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram 68 . Study Design This study was guided by Rodgers’ evolutionary method of concept analysis and implemented using a two-phase design that integrated traditional manual review with computational approaches. Phase 1 consisted of a manual literature-based concept analysis in which approximately 25% of the studies (n = 24) were randomly selected for in-depth review. This proportion was chosen to ensure both feasibility and analytic rigor, consistent with Rodgers’ recommendation that at least 20% of the available literature be reviewed to achieve a meaningful analysis 24 . In phase 2, NLP techniques were applied to the remaining 75% of articles (n = 73) to systematically organize and structure the corpus—that is, the full collection of article texts used for the computational analysis. This approach ensured representativeness and scalability while maintaining alignment with Rodgers’ framework. The attributes, antecedents, and consequences identified in both Phases were subsequently synthesized to refine the conceptual understanding of diabetes self-management in the digital health era. Rodgers’ Evolutionary Method and Data Extraction The Rodgers’ evolutionary method includes six key steps 24 : (1) identifying the focal concept and related terms; (2) defining the realm of inquiry; (3) extracting attributes, antecedents, and consequences; (4) analyzing conceptual characteristics; (5) incorporating exemplars, if appropriate; and (6) determining implications for conceptual refinement and future research. Full-text articles were imported into Microsoft Word and reviewed in their entirety for data immersion. Relevant sentences and paragraphs were identified as discrete text segments and deductively categorized according to Rodgers’ framework into attributes, antecedents, and consequences. Individual article analyses were then synthesized to identify recurring patterns and reorganized under Rodgers’ analytical framework to refine the conceptual boundaries. Three expert reviewers independently conducted Phase 1 analysis. They identified and categorized attributes, antecedents, and consequences of diabetes self-management within the digital health context. Inter-rater reliability was assessed using Fleiss’ kappa. To prepare the data for this analysis, the reviewers’ classifications were brought together, and each identified text segment was treated as a discrete analytic unit. For each unit, the sentences supporting the reviewers’ classifications were examined to assess whether the reviewers interpreted and categorized the conceptual element consistently. The patterns of agreement and divergence across reviewers’ categorical assignments were then used to calculate Fleiss’ kappa and its 95% confidence interval. After completing the reliability assessment, the reviewers met to discuss discrepancies, clarify conceptual boundaries, and refine the analytic framework through consensus to ensure interpretive coherence. Natural Language Processing-Based Analysis NLP techniques were applied to the full-text articles assigned to the computational analysis phase, comprising approximately 75% of the total sample. Text was automatically extracted from PDF files and converted into a machine-readable format. All analyses were conducted using Python (version 3.11.2; Python Software Foundation, Wilmington, DE, USA) and open-source libraries for text preprocessing and modeling. Extracted text was preprocessed to correct extraction artifacts (e.g., line breaks, hyphenation), standardize terminology, and remove extraneous symbols. Sentence segmentation was performed with spaCy (an open-source Python library for advanced natural language processing), and split tokens were corrected using pyenchant (a spellchecking Python library). Sentence embeddings (i.e., numerical representation that captures the semantic meaning of the sentence) were generated with SentenceTransformers (SBERT, all-MiniLM-L6-v2 ), an open-source Python framework for sentence, text, and image embedding, and stored with per-file mappings. K-means clustering was applied to identify thematic clusters, with the number of clusters optimized using the elbow method and silhouette scores. To support visual interpretation, Uniform Manifold Approximation and Projection (UMAP) was used to create two-dimensional representations of the high-dimensional embeddings. For clusters that were thematically relevant to the concept of diabetes self-management, sentences and their corresponding embeddings were exported for detailed qualitative review. Sub-clustering was then performed within these clusters to group similar elements together by major topics and capture specific conceptual themes 69 . From each sub-cluster, a set of representative sentences was selected by ranking the cosine similarity between each sentence embedding and its cluster centroids. Approximately 5% of the total sentence count per sub-cluster was retained for in-depth review. Although there is no established standard for determining the number of representative sentences in concept analysis using NLP, this proportion was determined through iterative testing to achieve data saturation, ensuring that additional sentences beyond this threshold did not yield new thematic insights. These representative sentences provided the textual basis for applying Rodgers’ framework to identify attributes, antecedents, and consequences of diabetes self-management. Declarations DATA AVAILABILITY The data supporting this study are derived from published articles included in the concept analysis and are publicly accessible through the respective journals and databases. A full list of the 97 included studies, along with their descriptive characteristics and phase assignments, is provided in Supplementary Table 2. The extracted text corpora used for NLP processing can be made available from the corresponding author upon reasonable request. CODE AVAILABILITY The full set of custom scripts used in this study is available in the publicly accessible GitHub repository Diabetes-Self-Management-in-the-Digital-Health-Era (https://github.com/hanlee6461/Diabetes-Self-Management-in-the-Digital-Health-Era). The repository includes code for all analytic steps, including preprocessing, sentence embedding generation, clustering, sub-clustering, and UMAP visualization AUTHOR CONTRIBUTIONS D.L. conceptualized the study, led the NLP-based analyses, and drafted the manuscript. A.K. and S.S. conducted the manual concept analysis and contributed to data interpretation. L.L. and A.A. assisted with search strategy development and screening procedures. R.J.S. and K.G. provided methodological guidance, supervised the study, and critically revised the manuscript. All authors reviewed and approved the final manuscript. CLINICAL TRIAL NUMBER Not applicable. HUMAN ETHICS AND CONSENT TO PARTICIPATE DECLARATIONS This study did not involve human participants, and therefore, ethics approval and informed consent were not required. FUNDING This study received no external funding. COMPETING INTERESTS The authors declare no financial or non-financial competing interests. References Benavidez, G. A., Zahnd, W. E., Hung, P. & Eberth, J. M. Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area. Prev Chronic Dis 21 , E14 (2024). Hacker, K. The Burden of Chronic Disease. Mayo Clin Proc Innov Qual Outcomes 8 , 112–119 (2024). Larsen, P. D. Lubkin’s Chronic Illness: Impact and Intervention . (Jones & Bartlett Learning, Burlington, Massachusetts, 2023). Thomas, S. A. et al. 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BetaMe: impact of a comprehensive digital health programme on HbA1c and weight at 12 months for people with diabetes and pre-diabetes: study protocol for a randomised controlled trial. Trials 19 , 161 (2018). De Frel, D. L. et al. Participatory Development of an Integrated, eHealth-Supported, Educational Care Pathway (Diabetes Box) for People With Type 2 Diabetes: Development and Usability Study. JMIR Hum Factors 11 , e45055 (2024). Marini, C. et al. Opening the Black Box of an mHealth Patient-Reported Outcome Tool for Diabetes Self-Management: Interview Study Among Patients With Type 2 Diabetes. JMIR Form Res 7 , e47811 (2023). Lewinski, A. A. et al. Perceptions of Using Multiple Mobile Health Devices to Support Self‐Management Among Adults With Type 2 Diabetes: A Qualitative Descriptive Study. J of Nursing Scholarship 53 , 643–652 (2021). Torbjørnsen, A., Småstuen, M. C., Jenum, A. K., Årsand, E. & Ribu, L. Acceptability of an mHealth App Intervention for Persons With Type 2 Diabetes and its Associations With Initial Self-Management: Randomized Controlled Trial. JMIR Mhealth Uhealth 6 , e125 (2018). Jeffrey, B. et al. Mobile phone applications and their use in the self-management of Type 2 Diabetes Mellitus: a qualitative study among app users and non-app users. Diabetol Metab Syndr 11 , 84 (2019). Izahar, S. et al. Content Analysis of Mobile Health Applications on Diabetes Mellitus. Front. Endocrinol. 8 , 318 (2017). Kim, Y., Lee, H. & Seo, J. M. Integrated Diabetes Self-Management Program Using Smartphone Application: A Randomized Controlled Trial. West J Nurs Res 44 , 383–394 (2022). LeSeure, P., Chin, E. & Zhang, S. A Culturally Sensitive Mobile App (DiaFriend) to Improve Self-Care in Patients With Type 2 Diabetes: Development Study. JMIR Diabetes 9 , e63393 (2024). Basilico, A., Marceglia, S., Bonacina, S. & Pinciroli, F. Advising patients on selecting trustful apps for diabetes self-care. Computers in Biology and Medicine 71 , 86–96 (2016). Bults, M., Van Leersum, C. M., Olthuis, T. J. J., Bekhuis, R. E. M. & Den Ouden, M. E. M. Mobile Health Apps for the Control and Self-management of Type 2 Diabetes Mellitus: Qualitative Study on Users’ Acceptability and Acceptance. JMIR Diabetes 8 , e41076 (2023). Lie, S. S., Karlsen, B., Graue, M. & Oftedal, B. The influence of an eHealth intervention for adults with type 2 diabetes on the patient–nurse relationship: a qualitative study. Scandinavian Caring Sciences 33 , 741–749 (2019). Krall, J. S., Childs, B. & Mehrotra, N. Mobile Applications to Support Diabetes Self-Management Education: Patient Experiences and Provider Perspectives. J Diabetes Sci Technol 17 , 1206–1211 (2023). Shippee, N. D., Shah, N. D., May, C. R., Mair, F. S. & Montori, V. M. Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice. J Clin Epidemiol 65 , 1041–1051 (2012). Karlsen, B. et al. Assessment of a web-based Guided Self-Determination intervention for adults with type 2 diabetes in general practice: a study protocol. BMJ Open 6 , e013026 (2016). Gosak, L., Pajnkihar, M. & Stiglic, G. The Impact of Mobile Health Use on the Self-care of Patients With Type 2 Diabetes: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 11 , e31652 (2022). Kelly, L., Jenkinson, C. & Morley, D. Experiences of Using Web-Based and Mobile Technologies to Support Self-Management of Type 2 Diabetes: Qualitative Study. JMIR Diabetes 3 , e9 (2018). Maharaj, A., Lim, D., Murphy, R. & Serlachius, A. Comparing Two Commercially Available Diabetes Apps to Explore Challenges in User Engagement: Randomized Controlled Feasibility Study. JMIR Form Res 5 , e25151 (2021). Woldamanuel, Y. et al. Perspectives on Promoting Physical Activity Using eHealth in Primary Care by Health Care Professionals and Individuals With Prediabetes and Type 2 Diabetes: Qualitative Study. JMIR Diabetes 8 , e39474 (2023). Dsouza, S. M., Venne, J., Shetty, S. & Brand, H. Identification of challenges and leveraging mHealth technology, with need-based solutions to empower self-management in type 2 diabetes: a qualitative study. Diabetol Metab Syndr 16 , 182 (2024). Ayre, J. et al. Factors for Supporting Primary Care Physician Engagement With Patient Apps for Type 2 Diabetes Self-Management That Link to Primary Care: Interview Study. JMIR Mhealth Uhealth 7 , e11885 (2019). Xia, S.-F. et al. Web-Based TangPlan and WeChat Combination to Support Self-management for Patients With Type 2 Diabetes: Randomized Controlled Trial. JMIR Mhealth Uhealth 10 , e30571 (2022). Martin, C. & Hood, D. The Use of Natural Language Processing in Literature Reviews . https://www.axtria.com (2024). Orel, E. et al. An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study. J Med Internet Res 25 , e39736 (2023). Pugliese, S., Giannetti, V. & Banerjee, S. How to conduct efficient and objective literature reviews using natural language processing: A step-by-step guide for marketing researchers. Psychology & Marketing 41 , 427–441 (2024). Kwabena, A. E., Wiafe, O.-B., John, B.-D., Bernard, A. & Boateng, F. A. F. An automated method for developing search strategies for systematic review using Natural Language Processing (NLP). MethodsX 10 , 101935 (2023). Liu, D., Thomas, M. A. & Li, Y. Natural language processing enhanced literature reviews. Journal of Information Technology 40 , 414–440 (2025). Shaw, R. J. Wearable Health Technologies Are on the Rise. NEJM Catalyst 6 , (2025). Rethlefsen, M. L. et al. PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Syst Rev 10 , 39 (2021). Nielsen, F. Hierarchical Clustering. in Introduction to HPC with MPI for Data Science (ed. Nielsen, F.) 195–211 (Springer International Publishing, Cham, 2016). doi:10.1007/978-3-319-21903-5_8. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 11 Jan, 2026 Reviewers agreed at journal 11 Jan, 2026 Reviews received at journal 10 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 29 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Submission checks completed at journal 10 Dec, 2025 First submitted to journal 08 Dec, 2025 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. 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1","display":"","copyAsset":false,"role":"figure","size":134149,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the Article Selection\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8304580/v1/26e13341c1a4afe4dd5259ee.png"},{"id":99341354,"identity":"6068504b-3a33-4076-a51c-249259d696bf","added_by":"auto","created_at":"2026-01-01 06:21:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":345392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-Stage K-Means Clustering with UMAP Visualization of Sentence-Level Embeddings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote.\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eHigh-dimensional sentence embeddings generated with SBERT were projected into a two-dimensional space using UMAP to support visual interpretation.\u003cbr\u003e\n \u003cstrong\u003ea)\u003c/strong\u003e The entire corpus was grouped into six clusters: Cluster 0 (Digital Health Usability \u0026amp; Development), Cluster 1 (Research Design \u0026amp; Procedures), Cluster 2 (Intervention Effectiveness), Cluster 3 (Diabetes Self-Management Processes), Cluster 4 (Patient Support Mechanisms), and Cluster 5 (Patient Perceptions \u0026amp; Technology Acceptance).\u003cbr\u003e\n \u003cstrong\u003eb) \u003c/strong\u003eCluster 3 was further examined through sub-clustering (k=5) to identify thematic subgroups: Sub-cluster 0 (Self-Monitoring \u0026amp; Personalized Feedback), Sub-cluster 1 (Foundational Self-Management Behaviors \u0026amp; Education), Sub-cluster 2 (Peer Support \u0026amp; Social Learning), Sub-cluster 3 (Diabetes Burden \u0026amp; Clinical Context), and Sub-cluster 4 (Digital App Features \u0026amp; Intervention Design).\u003cbr\u003e\nBoth clustering steps used elbow and silhouette scores to determine optimal cluster counts.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8304580/v1/fe3ea801b9808cf9abcdecb6.png"},{"id":99801727,"identity":"670bb92a-565d-4ac6-a9a8-7b415cef5b43","added_by":"auto","created_at":"2026-01-08 14:07:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1862157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8304580/v1/3f968e37-7941-4295-881d-ba30aefddfeb.pdf"},{"id":99341360,"identity":"de8d2944-b972-433c-9ba3-eda736c85968","added_by":"auto","created_at":"2026-01-01 06:21:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":192479,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8304580/v1/cb1a59933906fc686d432e10.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diabetes Self-Management in the Digital Health Era: A Concept Analysis Using Natural Language Processing","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChronic illness extends beyond physiological symptoms, shaping the lived experiences of individuals and families while imposing significant burdens at personal, societal, and systemic levels, including nearly three-quarters of global deaths, increased disability, and rising healthcare costs\u003csup\u003e1–4\u003c/sup\u003e. In response, self-management has become central to chronic illness care, as individuals must take active roles in navigating daily life, sustaining quality of life, and making health-related decisions outside clinical settings\u003csup\u003e5,6\u003c/sup\u003e. Among chronic illnesses, type 2 diabetes mellitus is especially significant. Type 2 diabetes is one of the most prevalent and burdensome conditions globally, and its successful management depends almost entirely on effective self-management\u003csup\u003e7\u003c/sup\u003e. Individuals must engage in a continuous “juggling act” of balancing medical tasks with emotional and social well-being\u003csup\u003e8\u003c/sup\u003e. More than 90% of diabetes care occurs outside of clinical settings and is carried out by individuals themselves\u003csup\u003e9,10\u003c/sup\u003e. Yet despite its central role, sustaining effective diabetes self-management remains a persistent challenge\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn response to the need for more accessible and personalized self-management support, digital health has emerged as a promising approach for individuals with type 2 diabetes. Digital health technologies, including mobile health applications, telehealth platforms, wearable devices, and remote monitoring tools, offer new opportunities to overcome barriers to care, such as transportation limitations and geographic isolation\u003csup\u003e12,13\u003c/sup\u003e. These tools enable individuals to track health metrics like blood glucose, blood pressure, weight, physical activity, and medications, access educational resources, and communicate with providers in real-time, fostering informed decision-making and proactive diabetes self-management\u003csup\u003e14,15\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBuilding on these advances, the mid-2010s marked the beginning of a distinct digital health era. This period was characterized by the adoption of foundational interoperability standards, such as Fast Healthcare Interoperability Resources (FHIR), and the widespread implementation of certified electronic health records (EHRs), which have accelerated digital innovation and data exchange in healthcare\u003csup\u003e16\u003c/sup\u003e. This turning point was internationally recognized in the 2015 United Nations General Assembly, which emphasized the transformative potential of information and communications technology in expanding equitable access to care\u003csup\u003e17,18\u003c/sup\u003e. The COVID-19 pandemic further catalyzed these developments, accelerating telemedicine and remote patient monitoring as essential components of chronic illness management\u003csup\u003e19\u003c/sup\u003e. Digital health tools have since become increasingly integrated into formal care models, supporting real-time adjustments to care plans and enhancing provider–patient communication through integration with platforms like EHRs\u003csup\u003e20,21\u003c/sup\u003e. Together, these advances underscore digital health as a key enabler of patient-centered, data-driven self-management strategies for individuals living with type 2 diabetes.\u003c/p\u003e\n\u003cp\u003eAs digital health becomes increasingly embedded in healthcare delivery, technology is reshaping traditional care practices and becoming a routine component of diabetes management\u003csup\u003e22\u003c/sup\u003e. This evolving role of digital health underscores the need to revisit and refine the concept of diabetes self-management. Defining and refining concepts is essential for scientific progress, as they serve as the building blocks of theory, provide the language for studying phenomena, and ensure that research remains systematically linked to real-world problems\u003csup\u003e23\u003c/sup\u003e. According to Rodgers\u003csup\u003e24\u003c/sup\u003e, concepts are not static; they evolve through use and in response to emerging challenges. In this context, diabetes self-management must be refined to reflect the transformative influence of digital health on how individuals engage with and manage type 2 diabetes. Therefore, this study aims to refine the concept of diabetes self-management in the digital health era, addressing the complexities and shifting needs of individuals within this rapidly changing healthcare landscape.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo capture this evolving concept, this study extends Rodgers’ evolutionary method by integrating Natural Language Processing (NLP) techniques. Traditional concept analysis relies on inductive reasoning and manual review of selected literature, which, while rigorous, is constrained by sample size and researcher subjectivity. In contrast, NLP enables the systematic examination of large textual corpora, uncovering patterns in language use, contextual meanings, and emergent themes that may remain hidden in conventional approaches\u003csup\u003e25,26\u003c/sup\u003e. This computational extension not only increases scalability and reproducibility but also introduces novel analytic dimensions by bridging qualitative theory-building with data-driven methods. In doing so, this study extends concept analysis beyond its traditional boundaries by introducing methodological innovation and providing a contemporary, data-informed understanding of diabetes self-management in the digital health era.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe final literature search, conducted on April 14, 2025, yielded 2,375 records. Complete search strategies, including database-specific date ranges and filters, are provided in Supplementary Table 1. After duplicate removal (n=362), 2,013 records were screened for eligibility. During the full-text review, 280 articles were examined by an independent reviewer. Studies were excluded for reasons such as outcomes not aligned with the review focus (n=10), ineligible study design (n=16), ineligible population (n=10), or not conceptually relevant to diabetes self-management (n=147).\u003c/p\u003e\n\u003cp\u003eAfter applying the inclusion and exclusion criteria, 97 studies were retained for analysis. Non-English papers were not deliberately excluded, although none met the inclusion criteria at the full-text level. The final dataset included studies conducted across 23 countries. The most common study designs were qualitative studies (n=25), randomized controlled trials (n=21), and development-focused evaluations such as pilot, feasibility, or usability studies. Less common designs included cross-sectional surveys, mixed methods approaches, and secondary data analyses. Detailed study characteristics, including country, study design, population, and the designated review phase for each article (Phase 1 or Phase 2), are presented in Supplementary Table 2, and the overall screening and selection process is illustrated in the PRISMA flow diagram (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhase 1: Manual Concept Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 25% of the included studies (n=24) were randomly selected and manually reviewed by the three independent reviewers. Inter-rater reliability analysis demonstrated substantial agreement (Fleiss\u0026rsquo; \u0026kappa;=0.80, 95% CI [0.79, 0.81]), confirming consistency in the identification of attributes, antecedents, and consequences of diabetes self-management in the digital health era. Table 1 summarizes how each reviewer independently identified and categorized relevant text segments across the attributes, antecedents, and consequences. Table 2 presents the integrated findings, combining the reviewers\u0026rsquo; finalized manual classifications with the results of the NLP-based analysis. The complete independent reviewer classifications, along with the comparative alignment used to evaluate agreement and generate the reliability statistics, are provided in Supplementary Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Summary of Manual Concept Analysis Findings Across Three Independent Reviewers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReviewer A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReviewer B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReviewer C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttributes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHealth Engagement (Diabetes)\u003c/li\u003e\n \u003cli\u003eDigital Literacy\u003c/li\u003e\n \u003cli\u003eExecutive Competence\u003c/li\u003e\n \u003cli\u003ePatient and Clinician Communication\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eComplexity \u0026amp; Continuity\u003c/li\u003e\n \u003cli\u003eSelf-Regulation \u0026amp; Motivation\u003c/li\u003e\n \u003cli\u003eEducational / Supportive System \u0026amp; Social Interaction\u003c/li\u003e\n \u003cli\u003eDigital Health\u0026ndash;Driven Autonomy \u0026amp; Engagement\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eDaily Behavioral and Lifestyle Components\u003c/li\u003e\n \u003cli\u003eCognitive and Psychological Determinants of Self-Management\u003c/li\u003e\n \u003cli\u003eSocial and Environmental Context\u003c/li\u003e\n \u003cli\u003ePersonalization and Person-Centered Care\u003c/li\u003e\n \u003cli\u003eDigital Health for Diabetes Self-Management\u003c/li\u003e\n \u003cli\u003eDynamic Patient\u0026ndash;Provider Relationship\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntecedents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eMotivation and Self-efficacy\u003c/li\u003e\n \u003cli\u003eAccess to Technology\u003c/li\u003e\n \u003cli\u003eIndividual Factors\u003c/li\u003e\n \u003cli\u003ePatient Clinician Factors\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eComplexity and Disease Burden\u003c/li\u003e\n \u003cli\u003eSelf-Regulation and Motivation\u003c/li\u003e\n \u003cli\u003eEducational / Supportive System \u0026amp; Social Interaction\u003c/li\u003e\n \u003cli\u003eAutonomy and Active Engagement in Digital Self-Management\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eDemands of Diabetes Self-Management\u003c/li\u003e\n \u003cli\u003eEmotional Readiness for Change\u003c/li\u003e\n \u003cli\u003eAccess to Education and Ongoing Support\u003c/li\u003e\n \u003cli\u003eAccess to Resources and Technology\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHealth Outcomes\u003c/li\u003e\n \u003cli\u003ePsychological Outcomes\u003c/li\u003e\n \u003cli\u003eSocial Outcomes\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eComplexity \u0026amp; Continuity\u003c/li\u003e\n \u003cli\u003eSelf-Regulation \u0026amp; Motivation\u003c/li\u003e\n \u003cli\u003eEducational / Supportive System \u0026amp; Social Interaction\u003c/li\u003e\n \u003cli\u003eDigital Health\u0026ndash;Driven Autonomy \u0026amp; Engagement\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5918%;\"\u003e\n \u003cul\u003e\n \u003cli\u003eClinical and Health Outcomes\u003c/li\u003e\n \u003cli\u003eQuality of Life and Psychosocial Well-being\u003c/li\u003e\n \u003cli\u003eEmpowerment, Autonomy, and Motivation\u003c/li\u003e\n \u003cli\u003eLong-Term Maintenance and Sustainability\u003c/li\u003e\n \u003cli\u003eSystem-Level and Economic Implications\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Summary of Findings from Manual and NLP-based Concept Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase 1. Manual Concept Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.9184%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase 2. NLP-Based Concept Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttributes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8571%;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003eDaily Behavioral and Lifestyle Components\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eDaily Self-Management Activities\u003c/li\u003e\n \u003cli\u003eLifestyle Change as an Ongoing Process\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCognitive and Psychological Factors of Self-Management\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eKnowledge and Skills\u003c/li\u003e\n \u003cli\u003eSelf-Efficacy, Competence, and Motivation\u003c/li\u003e\n \u003cli\u003ePsychological and Identity Dimensions\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSocial and Environmental Context\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eAddressing Social Determinants of Health\u003c/li\u003e\n \u003cli\u003eMultilevel Social Support: Family, Community, and Digital Platforms\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePersonalization and Person-Centered Care\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDigital Health for Diabetes Self-Management\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eDigitalizing Health for Monitoring and Decision Support\u003c/li\u003e\n \u003cli\u003eDigital Literacy\u003c/li\u003e\n \u003cli\u003eUser Experience\u003c/li\u003e\n \u003cli\u003eInteroperability, Access, and Infrastructure\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDynamic Patient\u0026ndash;Provider Relationship\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eProfessional Support in the Digital Era\u003c/li\u003e\n \u003cli\u003eDigital Health-Driven Patient-Provider Communication\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.9184%;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003eSelf-Management as the Core of Diabetes Care\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eCentrality of Self-Management in Diabetes Care\u003c/li\u003e\n \u003cli\u003eBehavioral and Lifestyle Components of Self-Management\u003c/li\u003e\n \u003cli\u003eNecessity of Lifestyle Modification and Ongoing Self-Management\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePatient-Centered Support and Empowerment\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eIndividualized Feedback and Patient-Centered Care\u003c/li\u003e\n \u003cli\u003eEngagement, Adherence, and Empowerment\u003c/li\u003e\n \u003cli\u003eEducation and Support as Essential Enablers\u003c/li\u003e\n \u003cli\u003ePeer-Enabled Support and Learning through Online Communities\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDigital Health and Technology Integration in Diabetes Self-Management\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eComprehensive Monitoring and Datafication of Self-Management\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAccessibility and Empowerment through Digital Health Technologies\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEvaluation and Appropriateness of Mobile Apps\u003c/li\u003e\n \u003cli\u003eStrengths and Limitations of Mobile Health Apps\u003c/li\u003e\n \u003cli\u003eUser-Centered Design and Personalization\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eContextual and System-Level Considerations\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eChronic and Global Burden of Diabetes\u003c/li\u003e\n \u003cli\u003ePatient Characteristics and Disease Context as Influential Factors\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntecedents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8571%;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003eDemands of Diabetes Self-Management\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003ePhysical Demands\u003c/li\u003e\n \u003cli\u003ePsychological Demands\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEmotional Readiness for Change\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAccess to Education and Support\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAccess to Resources and Technology\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.9184%;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003ePatient and Disease Characteristics\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAccess to Guidance, Education, and Structured Support\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAccess to Digital Health Technology\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHealth Literacy and Digital Literacy\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLinkages Between Self-Management and Healthcare Providers\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.8571%;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003eClinical and Health Outcomes\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eQuality of Life and Psychosocial Well-being\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEmpowerment, Autonomy, and Motivation\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSystem-Level and Economic Implications\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.9184%;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003eHealthier Behaviors and Clinical Outcomes\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEnhanced Knowledge and Self-Awareness\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEmpowerment, Self-Efficacy, and Motivation\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eReduced Healthcare Burden\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAttributes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manual analysis identified six attributes of diabetes self-management in the digital health era. First, daily behavioral and lifestyle components encompassed routine activities such as blood glucose monitoring, dietary adjustments, medication adherence, and sustained lifestyle changes, reflecting the ongoing and complex nature of diabetes care. Second, cognitive and psychological factors included knowledge, skills, motivation, and self-efficacy, highlighting the importance of patient confidence and emotional well-being in sustaining long-term engagement. Third, the social and environmental context underscored the role of social determinants of health and multilevel social support across family, community, and digital platforms in shaping self-management behaviors. Fourth, personalization and person-centered care emphasized tailored education, adaptive strategies, and alignment with individual values and preferences. Fifth, digital health emerged as a defining attribute, encompassing remote monitoring, decision support, digital literacy, user-friendly experiences, and infrastructural considerations for interoperability and access. Finally, a dynamic patient\u0026ndash;provider relationship was central, where digital tools facilitated digital health\u0026ndash;driven communication that enhances, rather than replaces, professional support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAntecedents\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour major antecedents were identified. Demands of diabetes self-management captured both the physical (e.g., diet, exercise, insulin use, continuous monitoring) and psychological burdens (e.g., depression, distress, stigma) associated with daily disease management. Emotional readiness for change reflected motivational processes, autonomy, self-efficacy, and relatedness, which were essential for adopting and sustaining self-care behaviors. Access to education and ongoing support were consistently emphasized, with diabetes self-management education and structured follow-up highlighted as critical to skill development and sustained engagement. Lastly, access to resources and technology represented the enabling role of mobile apps, devices, and digital platforms, while also acknowledging challenges such as connectivity issues and digital inequities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsequences\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour categories of consequences were identified. Clinical and health outcomes included improved glycemic control, reduced complications, and prevention of comorbidities. Quality of life and psychosocial well-being reflected reduced distress, improved mental health, and greater life satisfaction, although glycemic improvement did not always translate into psychological benefits. Empowerment, autonomy, and motivation highlighted how self-management enhanced confidence, decision-making, and long-term adherence. Finally, system-level and economic implications underscored reduced healthcare utilization and costs, while also recognizing the broader social and productivity benefits of effective self-management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhase 2: Natural Language Processing-Based Concept Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 73 articles comprising 16,218 sentences were processed through the NLP pipeline. Cluster analysis showed that the corpus was optimally described by six clusters. One of the six clusters was thematically centered on diabetes self-management and contained 3,308 sentences. Sub-clustering analysis within this cluster identified five distinct sub-clusters, averaging approximately 600 sentences each. From each of the five sub-clusters, 30 representative sentences (~5%) were selected to identify the attributes, antecedents, and consequences of the concept. Figure 2 visualizes the two-stage clustering process by projecting the sentence-level SBERT embeddings into a two-dimensional space using UMAP. Panel (a) shows the six primary clusters derived from the full corpus, and Panel (b) displays the five sub-clusters within the diabetes self-management\u0026ndash;focused cluster.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAttributes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA broad set of attributes was identified. Self-management as the core of diabetes care was reinforced, emphasizing that daily behavioral and lifestyle activities such as diet regulation, physical activity, medication adherence, and ongoing monitoring are indispensable for effective disease management. Patient-centered support and empowerment were highlighted through individualized feedback, real-time prompts, and tools that fostered engagement, adherence, and motivation. Education and support remained central, with digital platforms extending structured guidance beyond traditional care. Peer-enabled support and online communities emerged as valuable spaces for social learning, knowledge exchange, and emotional encouragement. Digital health and technology integration encompassed comprehensive monitoring and datafication, as well as the accessibility and empowerment made possible by digital platforms. This attribute also captured the importance of systematic evaluation of mobile apps, recognition of their strengths and limitations, and the need for user-centered design and personalization for diabetes self-management engagement. Finally, contextual and system-level considerations were evident, including recognition of the chronic and global burden of diabetes, the necessity of sustained lifestyle modification, and the influence of patient and disease characteristics on the feasibility and effectiveness of self-management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAntecedents\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive categories of antecedents were identified. First, patient and disease characteristics such as demographics, psychosocial factors, comorbidities, and disease trajectory shaped individual capacity for engagement in self-management. Access to guidance, education, and structured support was foundational for building the skills and knowledge necessary for effective self-management. Access to digital health technology included the use of mobile apps, glucose-monitoring devices, and online resources that facilitate daily care. Health literacy and digital literacy were critical in enabling patients to interpret data, set goals, and act on feedback. Finally, linkages with healthcare providers underscored the role of professional oversight and integration of patient-generated data into clinical care.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsequences\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour consequence categories were observed. Healthier behaviors and clinical outcomes were the most frequently reported, with digital interventions improving adherence to treatment regimens, lifestyle changes, and glycemic control. Enhanced knowledge and self-awareness reflected the ability of patients to understand the relationship between behaviors and outcomes, strengthening self-management capacity. Empowerment, self-efficacy, and motivation captured the psychological benefits of digital health engagement, including greater confidence, autonomy, and social connectedness. Finally, reduced healthcare burden highlighted the broader system-level effects of effective self-management, including fewer complications, decreased hospitalizations, and lower healthcare costs, particularly when digital tools extend access to underserved populations.\u003c/p\u003e\n\u003cp\u003eA summary of the identified attributes, antecedents, and consequences identified in the NLP-based concept analysis is listed in Table 2.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe purpose of this study was to refine the concept of diabetes self-management in the context of the digital health era. Drawing on 97 studies analyzed through both manual review and NLP-based approaches, we systematically identified the attributes, antecedents, and consequences of the concept. Integrating findings from both approaches further provides an opportunity to update and clarify the definition of diabetes self-management within today’s digital health landscape.\u003c/p\u003e \u003cp\u003eIn the digital health era, diabetes self-management can be understood through a set of defining attributes that describe how individuals navigate and sustain daily care. The findings show that behavioral elements are central to diabetes self-management. Daily routines such as medication adherence, dietary regulation, physical activity, and glucose self-monitoring continue to form the foundation of effective management\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Importantly, these behaviors are not static but involve continuous, lifelong adaptation to individual health conditions, personal demands, and environmental circumstances\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e–\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Beyond behaviors, cognitive and psychological factors shape the capacity to initiate and sustain self-management. Knowledge and skills, competence, motivation, and self-efficacy enable individuals to begin and maintain lifelong self-management practices\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These factors also foster constructive psychological adaptation, helping individuals live with chronic conditions while cultivating positive attitudes\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Promoting these psychological dimensions can strengthen a healthy social identity\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, support the pursuit of personal goals and values\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and enhance proactive, sustained engagement in self-management\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSocial and environmental contexts shape diabetes self-management for everyone, yet systemic inequities create disproportionate barriers for racial and ethnic minorities and socioeconomically marginalized groups\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e–\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These contexts influence individuals’ capacity to engage in daily management by shaping access to resources, supportive environments, and opportunities for care. However, the lived realities of individuals affected by structural inequities reveal persistent gaps in how digital health is approached and implemented\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. These gaps include limited tailoring of digital tools to diverse users’ needs, uneven engagement among racially and socioeconomically marginalized groups, and design features that may unintentionally reinforce stigma or require resources not equally available \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Addressing these gaps requires demonstrating how digital health tools can actively mitigate, rather than reinforce, existing disparities. Within this broader landscape, multilevel social support encompassing family, community, and digital platforms plays a crucial role in enabling sustained self-management. In the digital health era, online communities and virtual platforms have expanded forms of social connection, extending support networks beyond geographic and temporal boundaries\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDigital health technologies have become central attributes of self-management. These technologies enable continuous monitoring, tracking, and visualization of physiological data and integrate with daily routines to deliver real-time feedback, personalized recommendations, and improved communication with providers\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e–\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. By fostering adaptive, patient-centered care, these technologies strengthen self-management capacity and confidence\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Another important dimension of digital health is the user experience. Sustained self-management engagement using digital health depends on both cognitive understanding and emotional factors, such as trust, satisfaction, and perceived usefulness of technology, alongside considerations such as privacy, digital literacy, and fit with daily life \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Accordingly, intuitive design, personalization are essential for sustaining adoption and maximizing the benefits of digital health tools\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Mobile applications are particularly prominent in diabetes care, offering features such as tracking, monitoring, education, and communication\u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. However, their potential is often limited by challenges of integration, comprehensiveness, and evidence-based design, underscoring the need for systematic evaluation of app content and features to ensure reliability, safety, and clinical relevance\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, digital health technologies are reshaping individuals’ roles in diabetes care, enabling them to act as active partners in decision-making rather than passive recipients. Through real-time monitoring, data interpretation, and enhanced communication with providers, individuals gain greater capacity to engage continuously in managing their health\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. This shift supports timely feedback, shared decision-making, and a more collaborative and dynamic relationship among individuals, providers, and technologies in diabetes self-management\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBeyond these core attributes, diabetes self-management also depends on a set of antecedents that provide the necessary conditions for individuals’ readiness and capacity to engage effectively in daily care. A central foundation is the balance between the physical and psychological demands of managing a chronic illness\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Physical demands include frequent glucose monitoring, medication administration, and ongoing lifestyle decisions\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Psychological demands require adapting to chronic illness, maintaining a constructive identity, and managing distress, depression, anxiety, or stigma\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Emotional readiness—including motivation, autonomy, competence, and relatedness—further supports sustained engagement\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Education and ongoing support are also essential antecedents. Self-management education improves clinical outcomes when paired with continued reinforcement through regular contact with healthcare providers or peer networks, which helps embed coping strategies and behavior change into daily routines\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the digital health era, access to resources and technology has become increasingly important. Mobile apps, wearable devices, and telehealth platforms expand opportunities for monitoring, education, and remote support\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. However, disparities in connectivity, digital literacy, and socioeconomic resources may limit uptake\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, underscoring the need for inclusive, user-centered design\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Health literacy and digital literacy are crucial antecedents that enable individuals to interpret health information, navigate digital tools, and translate knowledge into action, such as goal setting and making informed decisions\u003csup\u003e\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e–\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Finally, linkages with healthcare providers remain indispensable. Patient-generated data, such as glucose readings, dietary logs, and physical activity records, can be integrated into clinical encounters, allowing providers to tailor counseling and treatment\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Digital platforms support this integration through continuous communication, strengthening patient–provider partnerships and promoting personalized care\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBuilding on these antecedents, effective diabetes self-management gives rise to a range of clinical, psychosocial, and system-level consequences. Consistent engagement improves glycemic control, reduces complications, and lowers the risk of comorbidities\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Self-management also influences quality of life and psychosocial well-being by reducing distress, enhancing coping, and strengthening daily functioning\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Empowerment and autonomy are central consequences, with self-efficacy supporting sustained behavior change\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e; digital tools further promote confidence and motivation when they facilitate reflection and decision-making\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Sustaining self-management requires ongoing reinforcement\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. User-centered digital interventions and continuous support can help maintain engagement and contribute to fewer hospitalizations, lower healthcare expenditures, and reduced system burden\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Conversely, inadequate self-management increases clinical risks and costs, underscoring the need for scalable and equitable strategies to promote long-term success\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the methodological level, the integration of manual review and NLP-based analysis demonstrates a growing convergence between interpretive and computational approaches in literature synthesis. NLP has been increasingly adopted to address the limitations of manual literature reviews—such as labor intensity, susceptibility to bias, and limited scalability—by organizing large bodies of evidence more efficiently and objectively\u003csup\u003e\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e–\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Recent work also shows that NLP can enhance search precision by identifying conceptual similarities that traditional keyword methods may overlook, thereby improving transparency and reproducibility\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite these advantages, most NLP applications still focus on describing linguistic patterns rather than advancing theoretical understanding. As Liu et al.\u003csup\u003e66\u003c/sup\u003e argue, generating conceptual insight requires interpreting how identified topics are related to one another. From this perspective, NLP augments rather than replaces human reasoning by enabling researchers to manage large textual datasets and reveal the underlying relational patterns among concepts that support conceptual development\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. In this study, the dual-phase design combines the contextual depth of manual analysis with the broad analytic coverage of computational methods. The manual review phase captured the contextual and relational aspects of diabetes self-management, while the NLP-based phase expanded insights across a much larger corpus. Together, these approaches demonstrate how integrating manual and computational techniques can strengthen conceptual clarity and analytic depth.\u003c/p\u003e \u003cp\u003eThe refined concept of diabetes self-management in the digital health era also carries important implications for practice, policy, and research. In clinical practice, digital health tools should be patient-centered, user-friendly, and adaptable to individual needs, functioning as extensions of professional care rather than substitutes. Such tools can reinforce education, provide personalized feedback, and strengthen therapeutic relationships\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. At the policy level, integrating digital health into chronic illness management requires a strong equity focus. Addressing disparities such as those in broadband access, device availability, and digital literacy is critical to prevent widening gaps in care. Policies that support interoperability, privacy, and responsible data governance are also essential to ensure safe and meaningful use of patient-generated data\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. From a research perspective, this study offers a conceptual foundation that warrants empirical testing and further theoretical refinement. Future work should examine the attributes, antecedents, and consequences across diverse populations and chronic conditions, incorporating the lived experiences of patients and caregivers. Longitudinal and intervention-based studies will be particularly valuable in assessing how digital health–integrated self-management translates into sustained behavior change, improved outcomes, and system-level efficiencies.\u003c/p\u003e \u003cp\u003eDespite these contributions, several limitations should be acknowledged. Both manual and NLP-based analysis required researcher interpretation, which, while systematic, cannot fully eliminate subjectivity. Furthermore, the NLP-based concept analysis used in this study represents, to our best knowledge, the first attempt to integrate such computational methods into Rodgers’ evolutionary concept analysis. At present, there are no established methodological standards or unified frameworks for applying NLP to concept analysis. The NLP-based phase relied on sentence embeddings and clustering techniques to identify thematic patterns across a large corpus. While this high-dimensional representation enabled systematic organization and pattern detection, it also raises questions about whether such computational abstraction can fully capture the nuanced, context-dependent meanings central to conceptual inquiry. These methodological uncertainties underscore the need for further refinement, validation, and consensus building as computational techniques become more widely integrated into health science research.\u003c/p\u003e \u003cp\u003eDigital health is reshaping diabetes self-management by strengthening engagement, supporting early detection, and enabling real-time decision-making\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Using Rodgers’ evolutionary method integrated with NLP, this study reconceptualized diabetes self-management as a multidimensional process shaped by behavioral, psychological, social, and technological factors. Digital health functions as an essential component of this process by facilitating continuous monitoring, personalized feedback, and stronger patient–provider connections. By combining qualitative and computational approaches, this study generated a more comprehensive conceptual understanding of diabetes self-management in the digital era. This refined conceptual foundation provides a contemporary direction for future research and offers guidance for practice and policy efforts that aim to promote equitable, technology-enabled chronic illness care.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"METHODS","content":"\u003ch2\u003eData Collection\u003c/h2\u003e\u003cp\u003eA comprehensive literature search was conducted in three electronic databases, including MEDLINE (via PubMed), CINAHL (via EBSCOhost), and IEEE Xplore Digital Library, to identify peer-reviewed studies related to type 2 diabetes, digital health, and self-management. The search strategy was developed by a professional medical librarian in consultation with the author team. Keywords and subject headings representing the three core concepts were iteratively refined, validated against a set of benchmark articles, and peer-reviewed by a second independent librarian to ensure completeness and reproducibility. The search process followed established methodological guidelines for comprehensive literature reviews and concept analyses\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The search was limited to studies published between 2015 and 2025.\u003c/p\u003e\u003cp\u003eStudies were considered eligible if they (1) focused on adults aged 18 years or older with type 2 diabetes, (2) addressed self-management as a primary or secondary focus, (3) examined the role or application of digital health technologies such as telemedicine, mobile health, wearable devices, or remote monitoring, and (4) reported empirical findings or protocol-level methodological details.\u003c/p\u003e\u003cp\u003eAll search results were imported into Covidence (Veritas Health Innovation, Melbourne, Australia) for citation screening and data management. Title, abstract, and full-text screening were conducted according to the predefined inclusion and exclusion criteria. During the full-text review, studies were excluded if they were review articles, conference abstracts, meeting reports, or retracted papers. Studies were also excluded if they focused on individuals with gestational or type 1 diabetes, included mixed samples of type 1 and type 2 diabetes without stratified results, or did not specify the diabetes type. Outcomes were considered ineligible when studies focused exclusively on a single behavioral or psychological construct (e.g., foot care behavior, self-efficacy, or blood glucose monitoring frequency) or that measured only healthcare utilization metrics (e.g., clinic visits or encounter counts) without addressing broader self-management processes. The screening process was documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis study was guided by Rodgers’ evolutionary method of concept analysis and implemented using a two-phase design that integrated traditional manual review with computational approaches. Phase 1 consisted of a manual literature-based concept analysis in which approximately 25% of the studies (n = 24) were randomly selected for in-depth review. This proportion was chosen to ensure both feasibility and analytic rigor, consistent with Rodgers’ recommendation that at least 20% of the available literature be reviewed to achieve a meaningful analysis\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In phase 2, NLP techniques were applied to the remaining 75% of articles (n = 73) to systematically organize and structure the corpus—that is, the full collection of article texts used for the computational analysis. This approach ensured representativeness and scalability while maintaining alignment with Rodgers’ framework. The attributes, antecedents, and consequences identified in both Phases were subsequently synthesized to refine the conceptual understanding of diabetes self-management in the digital health era.\u003c/p\u003e\u003ch2\u003eRodgers’ Evolutionary Method and Data Extraction\u003c/h2\u003e\u003cp\u003eThe Rodgers’ evolutionary method includes six key steps\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e: (1) identifying the focal concept and related terms; (2) defining the realm of inquiry; (3) extracting attributes, antecedents, and consequences; (4) analyzing conceptual characteristics; (5) incorporating exemplars, if appropriate; and (6) determining implications for conceptual refinement and future research.\u003c/p\u003e\u003cp\u003eFull-text articles were imported into Microsoft Word and reviewed in their entirety for data immersion. Relevant sentences and paragraphs were identified as discrete text segments and deductively categorized according to Rodgers’ framework into attributes, antecedents, and consequences. Individual article analyses were then synthesized to identify recurring patterns and reorganized under Rodgers’ analytical framework to refine the conceptual boundaries.\u003c/p\u003e\u003cp\u003eThree expert reviewers independently conducted Phase 1 analysis. They identified and categorized attributes, antecedents, and consequences of diabetes self-management within the digital health context. Inter-rater reliability was assessed using Fleiss’ kappa. To prepare the data for this analysis, the reviewers’ classifications were brought together, and each identified text segment was treated as a discrete analytic unit. For each unit, the sentences supporting the reviewers’ classifications were examined to assess whether the reviewers interpreted and categorized the conceptual element consistently. The patterns of agreement and divergence across reviewers’ categorical assignments were then used to calculate Fleiss’ kappa and its 95% confidence interval. After completing the reliability assessment, the reviewers met to discuss discrepancies, clarify conceptual boundaries, and refine the analytic framework through consensus to ensure interpretive coherence.\u003c/p\u003e\u003ch2\u003eNatural Language Processing-Based Analysis\u003c/h2\u003e\u003cp\u003eNLP techniques were applied to the full-text articles assigned to the computational analysis phase, comprising approximately 75% of the total sample. Text was automatically extracted from PDF files and converted into a machine-readable format. All analyses were conducted using Python (version 3.11.2; Python Software Foundation, Wilmington, DE, USA) and open-source libraries for text preprocessing and modeling. Extracted text was preprocessed to correct extraction artifacts (e.g., line breaks, hyphenation), standardize terminology, and remove extraneous symbols. Sentence segmentation was performed with \u003cem\u003espaCy\u003c/em\u003e (an open-source Python library for advanced natural language processing), and split tokens were corrected using \u003cem\u003epyenchant\u003c/em\u003e (a spellchecking Python library). Sentence embeddings (i.e., numerical representation that captures the semantic meaning of the sentence) were generated with \u003cem\u003eSentenceTransformers\u003c/em\u003e (SBERT, \u003cem\u003eall-MiniLM-L6-v2\u003c/em\u003e), an open-source Python framework for sentence, text, and image embedding, and stored with per-file mappings. K-means clustering was applied to identify thematic clusters, with the number of clusters optimized using the elbow method and silhouette scores. To support visual interpretation, Uniform Manifold Approximation and Projection (UMAP) was used to create two-dimensional representations of the high-dimensional embeddings.\u003c/p\u003e\u003cp\u003eFor clusters that were thematically relevant to the concept of diabetes self-management, sentences and their corresponding embeddings were exported for detailed qualitative review. Sub-clustering was then performed within these clusters to group similar elements together by major topics and capture specific conceptual themes\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. From each sub-cluster, a set of representative sentences was selected by ranking the cosine similarity between each sentence embedding and its cluster centroids. Approximately 5% of the total sentence count per sub-cluster was retained for in-depth review. Although there is no established standard for determining the number of representative sentences in concept analysis using NLP, this proportion was determined through iterative testing to achieve data saturation, ensuring that additional sentences beyond this threshold did not yield new thematic insights. These representative sentences provided the textual basis for applying Rodgers’ framework to identify attributes, antecedents, and consequences of diabetes self-management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study are derived from published articles included in the concept analysis and are publicly accessible through the respective journals and databases. A full list of the 97 included studies, along with their descriptive characteristics and phase assignments, is provided in Supplementary Table 2. The extracted text corpora used for NLP processing can be made available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCODE AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe full set of custom scripts used in this study is available in the publicly accessible GitHub repository \u003cem\u003eDiabetes-Self-Management-in-the-Digital-Health-Era\u003c/em\u003e (https://github.com/hanlee6461/Diabetes-Self-Management-in-the-Digital-Health-Era). The repository includes code for all analytic steps, including preprocessing, sentence embedding generation, clustering, sub-clustering, and UMAP visualization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.L. conceptualized the study, led the NLP-based analyses, and drafted the manuscript. A.K. and S.S. conducted the manual concept analysis and contributed to data interpretation. L.L. and A.A. assisted with search strategy development and screening procedures. R.J.S. and K.G. provided methodological guidance, supervised the study, and critically revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHUMAN ETHICS AND CONSENT TO PARTICIPATE DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human participants, and therefore, ethics approval and informed consent were not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial or non-financial competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBenavidez, G. A., Zahnd, W. E., Hung, P. \u0026amp; Eberth, J. M. 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Acceptability of an mHealth App Intervention for Persons With Type 2 Diabetes and its Associations With Initial Self-Management: Randomized Controlled Trial. \u003cem\u003eJMIR Mhealth Uhealth\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e125 (2018).\u003c/li\u003e\n \u003cli\u003eJeffrey, B. \u003cem\u003eet al.\u003c/em\u003e Mobile phone applications and their use in the self-management of Type 2 Diabetes Mellitus: a qualitative study among app users and non-app users. \u003cem\u003eDiabetol Metab Syndr\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 84 (2019).\u003c/li\u003e\n \u003cli\u003eIzahar, S. \u003cem\u003eet al.\u003c/em\u003e Content Analysis of Mobile Health Applications on Diabetes Mellitus. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 318 (2017).\u003c/li\u003e\n \u003cli\u003eKim, Y., Lee, H. \u0026amp; Seo, J. M. Integrated Diabetes Self-Management Program Using Smartphone Application: A Randomized Controlled Trial. \u003cem\u003eWest J Nurs Res\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 383\u0026ndash;394 (2022).\u003c/li\u003e\n \u003cli\u003eLeSeure, P., Chin, E. \u0026amp; Zhang, S. A Culturally Sensitive Mobile App (DiaFriend) to Improve Self-Care in Patients With Type 2 Diabetes: Development Study. \u003cem\u003eJMIR Diabetes\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e63393 (2024).\u003c/li\u003e\n \u003cli\u003eBasilico, A., Marceglia, S., Bonacina, S. \u0026amp; Pinciroli, F. Advising patients on selecting trustful apps for diabetes self-care. \u003cem\u003eComputers in Biology and Medicine\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 86\u0026ndash;96 (2016).\u003c/li\u003e\n \u003cli\u003eBults, M., Van Leersum, C. M., Olthuis, T. J. J., Bekhuis, R. E. M. \u0026amp; Den Ouden, M. E. M. Mobile Health Apps for the Control and Self-management of Type 2 Diabetes Mellitus: Qualitative Study on Users\u0026rsquo; Acceptability and Acceptance. \u003cem\u003eJMIR Diabetes\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e41076 (2023).\u003c/li\u003e\n \u003cli\u003eLie, S. S., Karlsen, B., Graue, M. \u0026amp; Oftedal, B. The influence of an eHealth intervention for adults with type 2 diabetes on the patient\u0026ndash;nurse relationship: a qualitative study. \u003cem\u003eScandinavian Caring Sciences\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 741\u0026ndash;749 (2019).\u003c/li\u003e\n \u003cli\u003eKrall, J. S., Childs, B. \u0026amp; Mehrotra, N. Mobile Applications to Support Diabetes Self-Management Education: Patient Experiences and Provider Perspectives. \u003cem\u003eJ Diabetes Sci Technol\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 1206\u0026ndash;1211 (2023).\u003c/li\u003e\n \u003cli\u003eShippee, N. D., Shah, N. D., May, C. R., Mair, F. S. \u0026amp; Montori, V. M. Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice. \u003cem\u003eJ Clin Epidemiol\u003c/em\u003e \u003cstrong\u003e65\u003c/strong\u003e, 1041\u0026ndash;1051 (2012).\u003c/li\u003e\n \u003cli\u003eKarlsen, B. \u003cem\u003eet al.\u003c/em\u003e Assessment of a web-based Guided Self-Determination intervention for adults with type 2 diabetes in general practice: a study protocol. \u003cem\u003eBMJ Open\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e013026 (2016).\u003c/li\u003e\n \u003cli\u003eGosak, L., Pajnkihar, M. \u0026amp; Stiglic, G. The Impact of Mobile Health Use on the Self-care of Patients With Type 2 Diabetes: Protocol for a Randomized Controlled Trial. \u003cem\u003eJMIR Res Protoc\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, e31652 (2022).\u003c/li\u003e\n \u003cli\u003eKelly, L., Jenkinson, C. \u0026amp; Morley, D. Experiences of Using Web-Based and Mobile Technologies to Support Self-Management of Type 2 Diabetes: Qualitative Study. \u003cem\u003eJMIR Diabetes\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, e9 (2018).\u003c/li\u003e\n \u003cli\u003eMaharaj, A., Lim, D., Murphy, R. \u0026amp; Serlachius, A. Comparing Two Commercially Available Diabetes Apps to Explore Challenges in User Engagement: Randomized Controlled Feasibility Study. \u003cem\u003eJMIR Form Res\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e25151 (2021).\u003c/li\u003e\n \u003cli\u003eWoldamanuel, Y. \u003cem\u003eet al.\u003c/em\u003e Perspectives on Promoting Physical Activity Using eHealth in Primary Care by Health Care Professionals and Individuals With Prediabetes and Type 2 Diabetes: Qualitative Study. \u003cem\u003eJMIR Diabetes\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e39474 (2023).\u003c/li\u003e\n \u003cli\u003eDsouza, S. M., Venne, J., Shetty, S. \u0026amp; Brand, H. 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How to conduct efficient and objective literature reviews using natural language processing: A step-by-step guide for marketing researchers. \u003cem\u003ePsychology \u0026amp; Marketing\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 427\u0026ndash;441 (2024).\u003c/li\u003e\n \u003cli\u003eKwabena, A. E., Wiafe, O.-B., John, B.-D., Bernard, A. \u0026amp; Boateng, F. A. F. An automated method for developing search strategies for systematic review using Natural Language Processing (NLP). \u003cem\u003eMethodsX\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 101935 (2023).\u003c/li\u003e\n \u003cli\u003eLiu, D., Thomas, M. A. \u0026amp; Li, Y. Natural language processing enhanced literature reviews. \u003cem\u003eJournal of Information Technology\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 414\u0026ndash;440 (2025).\u003c/li\u003e\n \u003cli\u003eShaw, R. J. Wearable Health Technologies Are on the Rise. \u003cem\u003eNEJM Catalyst\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, (2025).\u003c/li\u003e\n \u003cli\u003eRethlefsen, M. L. \u003cem\u003eet al.\u003c/em\u003e PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. \u003cem\u003eSyst Rev\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 39 (2021).\u003c/li\u003e\n \u003cli\u003eNielsen, F. Hierarchical Clustering. in \u003cem\u003eIntroduction to HPC with MPI for Data Science\u003c/em\u003e (ed. Nielsen, F.) 195\u0026ndash;211 (Springer International Publishing, Cham, 2016). doi:10.1007/978-3-319-21903-5_8.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes self-management, Digital health, Natural language processing, Concept analysis, Sentence embeddings","lastPublishedDoi":"10.21203/rs.3.rs-8304580/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8304580/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiabetes self-management is increasingly shaped by digital health technologies, yet existing conceptual definitions do not fully reflect this evolving landscape. This study aimed to refine the concept of diabetes self-management in the digital health era by applying Rodgers\u0026rsquo; evolutionary method integrated with Natural Language Processing (NLP). Ninety-seven studies were analyzed through a dual-phase design combining manual review and computational clustering of sentence-level embeddings. The analysis identified core attributes, including daily behavioral routines, cognitive and psychological processes, multilevel social support, personalization, digital health integration, and dynamic patient\u0026ndash;provider relationships. Antecedents encompassed physical and psychological demands, emotional readiness, access to education and support, resource and technology availability, and health and digital literacy. Consequences included improved clinical outcomes, enhanced quality of life, empowerment, and reduced healthcare burden. Integrating manual and NLP-based approaches strengthened conceptual clarity and provided a contemporary framework for understanding diabetes self-management within digitally enabled patient-centered care.\u003c/p\u003e","manuscriptTitle":"Diabetes Self-Management in the Digital Health Era: A Concept Analysis Using Natural Language Processing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-01 06:21:10","doi":"10.21203/rs.3.rs-8304580/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T19:59:22+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"320552470912152292813625220509440112341","date":"2026-01-12T20:41:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T03:27:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101447769577687875131425658849181259906","date":"2026-01-12T02:47:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49716842959196808330618615721619426473","date":"2026-01-11T09:59:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T18:23:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259038657805274904822838222914636006390","date":"2026-01-07T13:08:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-29T10:18:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T19:05:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-10T05:39:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Digital Medicine","date":"2025-12-08T07:00:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e6d068c-988f-48b9-88ce-e95606279add","owner":[],"postedDate":"January 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60341962,"name":"Health sciences/Health care"},{"id":60341963,"name":"Biological sciences/Psychology"},{"id":60341964,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-21T14:53:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-01 06:21:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8304580","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8304580","identity":"rs-8304580","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}