Characteristics of behavior change techniques in theory-based self-management education and support interventions for adults with type 2 diabetes using digital technology: A scoping review.

Characteristics of behavior change techniques in theory-based self-management education and support interventions for adults with type 2 diabetes using digital technology: A scoping review - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Digit Health . 2026 May 30;12:20552076261443759. doi: 10.1177/20552076261443759 Characteristics of behavior change techniques in theory-based self-management education and support interventions for adults with type 2 diabetes using digital technology: A scoping review EunSeok Cha 1, 2 , Meihua Ji 3, ✉ , Colleen Dawkins 4 , Xiaoyan Qi 3 , Hyesun Jang 1 , Melissa Spezia Faulkner 5 Author information Article notes Copyright and License information 1 College of Nursing, Chungnam National University, Daejeon, South Korea 2 Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA 3 School of Nursing, Capital Medical University, Beijing, China 4 College of Nursing and Health Professions, University of Southern Mississippi, Hattiesburg, MS, USA 5 Byrdine F. Lewis College of Nursing and Health Professions, Georgia State University, Atlanta, GA, USA ✉ Meihua Ji, School of Nursing, Capital Medical University, 10 You-An-Men-Wai Xi-Tou-Tiao, Fengtai District, Beijing 100069, China. Email: mjshouyi@ccmu.edu.cn Received 2025 Dec 29; Revised 2026 Apr 1; Accepted 2026 Apr 5; Collection date 2026 Jan-Dec. © The Author(s) 2026 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/ ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage ). PMC Copyright notice PMCID: PMC13222451  PMID: 42226764 Abstract Objective This scoping review aimed to inform the development of theory-driven, person-centered digital diabetes self-management education and support (DSME/S) programs for patients with type 2 diabetes. Methods The Behavioral Education and Support Techniques (BEST) taxonomy, adapted from the Behavior Change Techniques Taxonomy version 1 (BCTTv1), was used to improve clarity and relevance for DSME/S. Theory-based digital DSME/S intervention studies published between 2016 and 2023 and targeting adults aged ≥20 years were included. Studies were selected and data extracted using the Population, Concept, and Context framework. Theoretical foundations and behavior change domains were analyzed through iterative consensus among the research team. Results Of 5,181 records, 41 studies met inclusion criteria. Most of the participants in the included studies were over 40 years old. Smartphone app–based interventions predominated, accounting for 36.5%, followed by web-based interventions at 34.2%. The ADCES 7 Self-Care Behaviors framework, which underpins the National Standards for DSME/S, was the most frequently identified framework (n=14, 34%). Shaping Knowledge, Goals and Planning, Social Support, and Monitoring were the most frequently applied behavior change domains, reflecting a stronger emphasis on individual-level capabilities than on contextual or system-level changes. No single theory was consistently associated with positive clinical or behavioral outcomes across studies. Conclusions This review identified the theoretical frameworks, behavioral domains and techniques targeted in current digital DSME/S. Future research is needed to evaluate the effectiveness of digital DSME/S interventions across diverse populations and to identify behavior change techniques tailored to specific subgroups, such as individuals with early-onset type 2 diabetes. Keywords: digital health, technology, behavior change techniques, theory-driven intervention, type 2 diabetes, scoping review Introduction Diabetes is a chronic, non-communicable disease that poses a significant threat to global public health. 1 Evidence-based diabetes self-management education and support (DSME/S) programs, which enhance self-management behaviors, play a crucial role in improving health outcomes among individuals with type 2 diabetes (T2D). 1 – 3 A recent meta-analysis reported that randomized clinical trials of behavioral interventions, in general, achieved an average reduction of 0.64% in glycosylated hemoglobin (HbA1c), whereas quasi-experimental studies demonstrated a greater mean reduction of 1.27% (95% CI, −0.63% to 3.17%). 1 Despite the well-established benefits of DSME/S, patient engagement in such programs remains suboptimal. 4 , 5 To improve accessibility, convenience, and cost-effectiveness, digital approaches to DSME/S have gained popularity, with numerous studies reporting favorable outcomes. 2 , 6 – 8 A recent systematic review demonstrated that digital health–based DSME/S interventions effectively enhance diabetes knowledge, improve glycemic control (HbA1c), and promote overall quality of life. 8 These findings highlight the growing need to integrate digital technologies into future diabetes management interventions that are free from temporal and spatial constraints and resilient to situational disruptions such as the COVID-19 pandemic. 9 However, DSME/S using digital technologies requires adequate digital literacy and readiness to engage with technology. 10 For instance, many individuals with early-onset type 2 diabetes who have had substantial exposure of digital technologies from childhood—tend to be more receptive to diverse digital modalities, whereas those with later-onset T2D may prefer hybrid programs combining limited digital features with in-person education. 4 While the latter group often faces challenges such as comorbidities, polypharmacy, cognitive decline, and reduced physical function, 11 , 12 younger individuals with early-onset T2D are more likely to experience emotional distress, fear, and stigma related to obesity and chronic illness following an early diagnosis. 13 – 15 These factors place them in a vulnerable context, 12 increasing the likelihood of inadequate decision-making and poor self-care. 16 , 17 To effectively translate research into practice, digital DSME/S programs should be person-centered and tailored to the behavioral and psychosocial characteristics of each population, applying context-specific behavioral techniques and implementation strategies. 9 , 13 , 14 , 18 Nevertheless, few studies have provided in-depth guidance on how to design such interventions using digital technologies. 13 , 14 , 18 Health behavior models and theories that clarify determinants of behavior and elucidate mechanisms underlying behavior change support researchers in selecting appropriate behavior change techniques and designing effective implementation strategies. 19 , 20 Clinical interventions, however, have traditionally emphasized cognitive capability, offering robust conceptual frameworks for understanding intentional and conscious health-related actions. 21 Yet, rational thinking alone cannot fully account for the complexity of human health behaviors. 16 , 22 , 23 Emotional responses and context-driven decision-making often shape real-world actions, underscoring the need to consider these processes alongside cognitive mechanisms. Nudges —defined as subtle modifications in the environment that guide individuals toward desirable behaviors without restricting their freedom of choice—may be particularly useful for designing person-centered DSME/S interventions, as well as for enhancing individual capability and motivation. 24 , 25 That is, behavioral interventions should address not only individual-level factors but also societal and system-level determinants. 19 , 25 Drawing on lessons from prior behavioral research will be essential to ensure that such interventions are effective, scalable, and sustainable. This scoping review examined the behavioral domains and techniques applied in currently available digital DSME/S interventions to identify gaps and opportunities for theory-informed behavioral strategies leveraging emerging technologies to improve care for individuals with T2D. 17 , 21 , 26 It specifically included interventions that were explicitly grounded in established behavioral theories, with clearly defined theoretical constructs and corresponding measurement strategies, thereby providing a structured basis for identifying and interpreting behavior change components. 21 Theory-driven interventions were considered when their theoretical foundations were explicitly stated and key constructs were operationalized within the intervention design and evaluation. In addition, the ADCES7 Self-Care Behaviors framework was included, as it serves as the foundation for the National Standards for DSME/S established by the American Diabetes Association (ADA) and reflects a theory-informed approach to diabetes self-management. 2 This review extends the earlier work of Kebede et al. (1990–2016), focused on a similar line of inquiry. 27 The aim of this study was to establish a comprehensive knowledge base for developing theory-driven, person-centered digital DSME/S programs, 17 , 28 particularly for the growing population of individuals with early-onset T2D and for older adults living longer with multiple comorbidities. 12 , 13 , 29 Method Framework A scoping review, which explores a broader topic and addresses less specific research questions than a systematic review, 30 was conducted to examine the behavioral techniques previously applied in DSME/S programs that incorporated digital technologies, specifically targeting adults with T2D. The Population, Concept, and Context (PCC) mnemonic was utilized for developing search strategies, selecting articles and structuring this review. 30 – 32 Development and application of the Behavior Education and Support Techniques (BEST) Taxonomy to target diabetes education and support services Health behaviors are influenced by diverse contextual factors; therefore, researchers must understand the theoretical rationale and practical applications of behavior change techniques when designing interventions across various settings. To meet this need, the Behavior Education and Support Techniques (BEST) Taxonomy for diabetes education and support was developed, drawing upon Michie’s Behavior Change Technique Taxonomy v1 (BCTTv1), 33 the Behavior Change Wheel, 25 Designing for Behavior Change, 17 and the resources of Thinking, Fast and Slow 34 and Predictably Irrational, 16 which illuminate how human decision making reflects both rational and systematically irrational processes. Through an iterative approach, the domains within the BCTTv1 and associated taxonomies were carefully examined and reviewed by team members to determine whether or not they were suitable for the contextual application within DSME/S via digital means. Line by line sophisticated scrutiny of the 93 BCTs under BCTTv1 were conducted and determined for retention, elimination or consolidation with other domains. A working sheet was developed to showcase the rational and relevance for retaining or eliminating related BCTs from current domains. As a result, a total of 9 domains with 58 BCTs were developed (see Supplemental Table 1 ), among which some of the BCTs were re-grouped, eliminated, or consolidated based on the phenomena it described and were collated into a more precise, informative and easy to adopt BEST taxonomy applicable to DSME/S using digital technologies. Reasons of the creation and use of the BEST taxonomy Michie et al. developed a taxonomy of 93 behavior change techniques (BCTs) organized into 16 categories through a Delphi-type exercise. This BCTTv1 taxonomy was designed to enhance the understanding of behavioral change techniques by providing a structured hierarchy, making it more useful for behavior change interventions. 33 It has gained international consensus as a standard framework for developing and reporting key components of behavior change interventions targeting various behaviors, such as smoking cessation, dietary modification, and physical exercise. 35 – 37 Despite its widespread adoption, several limitations have been identified, including issues related to its ontological structure, the clarity of labels, definitions, 38 and limited applicability to educational and supportive interventions for non-hospitalized patients. 39 During our scoping review of digital DSME/S interventions, similar challenges were encountered when attempting to classify DSME/S components using BCTTv1 like the following. First, BCTTv1 primarily focuses on psychotherapeutic or psychological interventions, making certain techniques difficult to apply directly within educational settings for individuals with T2D—for example, 14.1 ( Behavior cost ), 14.2 ( Punishment ), and 14.10 ( Remove punishment ). Second, DSME/S interventions primarily aim to modify diabetes self-management behaviors, ultimately influencing biomarkers as clinical outcomes. However, BCTTv1 distinguishes between “Behavioral Goal Setting (1.1)” and “Outcome Goal Setting (1.3)” within the Goals and Planning domain. Similarly, differentiating between domains such as “Comparison of Behavior 6 ” and “Comparison of Outcomes 9 ” becomes problematic because behaviors targeted by DSMES inherently serve as measurable outcomes. Third, many digital interventions now provide technology-driven feedback based on changes in behavioral and clinical outcomes; however, such digital feedback mechanisms were not included as techniques in the BCTTv1. Fourth, techniques aimed at enhancing self-efficacy—such as leveraging past experiences, vicarious experiences, verbal persuasion, and physiological or emotional states—are dispersed across multiple domains in BCTTv1, limiting conceptual coherence. Fifth, the domain names in BCTTv1 emphasize methodological aspects, focusing largely on “how” rather than clarifying “for what purpose” or “when” techniques should be applied, which reduces applicability for diabetes educators and clinical specialists. Moreover, the taxonomy does not differentiate among sources of social support (e.g., family, peers, healthcare providers), despite their distinct roles in DSME/S design. Lastly, BCTTv1 does not adequately address issues of health-literacy or knowledge-behavior gaps, including the integration of theoretical knowledge and practical skills—both critical components of DSME/S. Given that the purpose of this scoping review was to provide behavioral insights applicable to DSME/S using digital technologies, therefore, the BEST Taxonomy was developed to better capture these contextual and practical dimensions (See Table 1 & Supplemental Table 1 ). Table 1. BCTTv1 vs. BEST taxonomy. BCTTv1 BEST Changes of domain Reasons for developing BEST Domain # of BCT Domain # of BEST 1. Goals and planning 9 I. Goals and planning 3 N/A Behavior and outcome goals cannot be indistinguishable in DSMES. 2. Feedback and monitoring 7 II. Monitoring with/without feedback 7 Distinguished between simple tracking and monitoring with feedback from others and self in BEST. Specify whether the monitoring is behavioral or clinical. 3. Social support 3 III. Social support 5 N/A Instead of practical and emotional categories, it was divided into material and emotional social support from healthcare providers and sources of social support (family, peers, and friends) 4. Shaping knowledge 4 IV. Shaping knowledge 8 N/A Divided by health-related information to facilitate behavior change and knowledge related to skill training 5. Natural consequences 6 Deleted N/A Deleted BCTs V1 included information about health, social, environmental, and emotional consequences, as well as the salience of consequences, monitoring of emotional consequences, and anticipated regret in this category. These elements were incorporated into II and IV in BEST 6. Comparison of behaviour 3 V. Comparison of behavior 4 N/A BCTs in this category were renamed because people compare past and present behaviors as well as themselves to others. Additionally, we used more intuitive terminology in BCT r1, such as “social norms” instead of “information about others’ approval.” 7. Associations 8 VI. Making operant condition 10 Changed the domain name to intuitive terminology to better incorporate the themes of BCTs in the category Extensive changes were made in the terminologies of BCT under this categories 8. Repetitions and substitution 7 VII. (behavioral) skill training 5 Changed the domain name to intuitive terminology to better incorporate the themes of BCTs in the category A BCT (e.g., 8.3 Habit Formation) was merged into VII.1 (Behavioral Practice/Rehearsal/Habit Formation) . Additionally , 8.6 Generalization of a Target Behavior in BCT v1 was renamed to VII.4 Translation (Generalization of a Target Behavior), and 8.7 Graded Tasks was changed to VII.5 Graded (Up, Down) Tasks . 9. Comparison of outcomes 3 Deleted N/A In behavioral intervention, outcomes and behavior cannot be indistinguishable; therefore, 6. Comparison of Behavior and 9. Comparison of Outcomes may be redundant. As Michie et al. noted in the supplemental table of their 2013 article, BCTs under 9. Comparison of Outcomes may be overlapped with 5.1, 5.3, and 5.6. Thus, this domain was deleted. 10. Reward and threat 11 VIII. Reward and threat 10 N/A While the domain name is Reward and Threat , only one BCT (10.11. Future Publishment) is included in BCTv1. In BEST, some BCTs were merged due to similar concepts (e.g., 10.4. Social Reward and 10.5. Social Incentive were combined into VIII.4. Social Incentive/Reward), and several BCTs related to threat and punishment (i.e., VIII.5–VIII.9) were added. 11. Regulation 4 Deleted N/A Deleted Reassigned to different domains after careful definition reviews and multiple discussion (i.e.,11.1. pharmacological support to IV.3; 11.2. reduce negative emotions to IV.5; 11.3conserving mental resources to IV.5.; 11.4. paradoxical instructions to VI.4) 12. Antecedents 6 IX. Environmental context and resources 6 Changed the domain name to intuitive terminology to better incorporate the themes of BCTs in the category Newly added IX. 3. Restructuring mind-set in BEST. Deleted 12.5. Adding objects to the environment since IX.1 or IX.2 could cover this. 13. Identify 5 Deleted N/A Deleted After careful review of definitions and multiple team discussions, the BCTs have been reassigned to VIII or IX. For example, 13.1 (Identification of self as a role model) has been moved to VIII.3, while 13.2 (Framing/reframing), 13.3 (Incompatible beliefs), 13.4 (Valued self-identity), and 13.5 (Identity associated with changed behavior) have been moved to IX.3 14. Scheduled consequences 10 Deleted N/A Deleted BCTs in category 14 were merged into the BCTs in VIII in BEST. 15. Self-belief 4 Deleted N/A Deleted BCTs in this category seem to be related to self-efficacy and could be incorporated into V and VII. 16. Covert learning 3 Deleted N/A Deleted The proposed BCTs were associated with imagination, which in turn relates to emotional rewards and threats. Therefore, the corresponding BCTs were reassigned to IV, V, and VIII under different labels Total: 16 Total: 93 Total: 9 Total: 58 - - Open in a new tab A five-stage methodological framework for this scoping review Stage 1: Identifying the research questions Theory-based interventions provide a clear and detailed blueprint that enhances replicability and sustainability across diverse contexts. 17 , 26 In contrast, interventions lacking a theoretical foundation often represent “off-the-shelf” approaches that lack long-term consistency and impact. 26 Accordingly, analyzing behavioral techniques within theory-based DSME/S interventions allows researchers to identify which components require modification and how nudges can be strategically applied to promote action in specific populations and settings. 17 Because diabetes care requires lifelong self-management and affects millions of individuals worldwide, digital DSME/S grounded in sound theoretical frameworks holds great potential for optimizing glycemic control. To ensure both effectiveness and fidelity, a clearly defined blueprint is essential to guide adaptation to contextual needs and target core diabetes self-management behaviors. 17 , 33 Based on these considerations, the following research questions focused on adults with T2D were developed: (1) Which theories or models have been used to design digital DSMES interventions? (2) What study designs were used, with whom, and for how long? (3) How were the interventions delivered and what types of behavioral and educational support techniques (BEST) were applied in DSME/S using digital technologies? and (4) What were the outcomes of the interventions? Stage 2: Identifying relevant studies In June, 2023, seven databases—PubMed, Cumulative Index to Nursing and Allied Health Literature, Web of Science, Google, OVID, Scopus, and PsycINFO—were searched for relevant studies published with the assistance of a medical librarian. MEDLINE was excluded based on the librarian’s advice, as it overlaps with PubMed by approximately 90%. The search strategy was developed using the PCC mnemonic 30 – 32 : Population (P) refers to adults aged 20 years and older with T2D. Concept (C) refers to a theory-based DSME/S intervention aimed at comprehensive diabetes management, encompassing lifestyle modifications, self-monitoring, psychosocial coping, risk reduction, and other essential components-related to self-care. Context (C) focused on theory-based DSME/S interventions employing digital health technologies. In 2001, Eysenbach defined mobile health broadly, encompassing not only “internet medicine” but also virtual medicine leveraging computer technology. 40 Since then, the healthcare sector has expanded beyond internet- and computer-based approaches to incorporate diverse digital technologies, such as telehealth, ehealth, electronic health records, and wearable devices, to reduce health disparities and address social determinants of health. 41 This broader interpretation of digital technology was adopted in the current study. Initial keywords were identified through preliminary searches and discussions with a librarian and research team. The search strategy was further refined through iterative testing to enhance sensitivity and relevance. Synonyms within each domain were linked using “OR,” and the three PCC domains were combined using “AND”. 1. Population: (“type 2 diabetes” OR “T2D” OR “ diabetes”) AND (“adult*”). 2. Concept: ((“Self-management behavior*” AND (“Nudge” OR “marketing” OR “marketing strategies” OR “behavioral policy” OR “public health policy” OR “decision making” OR “Message framing” OR “loss-framed” OR “gain-framed” OR “econs” OR “mass media” OR “media effects” OR “inter-temporal choice” OR “decision making”)) 3. Context: (“Digital”, “eEducation”, “telehealth”, “ehealth”, “mobile-health”, “mhealth”, “eCounseling”, “hybrid”, “online”, “social media”, “chat”, “AI”, “apps”, “web-based”, “chatbot”, “SNS”, “text-message”, “email reminders”, “zoom”, “video-call”) All DSME/S interventions were included. Articles were excluded if they were published in languages other than English or if only the title or abstract was available. Since a scoping review with similar research questions covering relevant publications from January 1990 to June 2016 was already published in 2017, 27 only publications from June 2016 to June 13, 2023, were included. The search strategy was adapted for each database according to its indexing system and search functionalities. In this step, 5,181 publications were retrieved and imported into EndNote version X20 reference software (Clarivate Analytics). The complete search strings for each database are provided in Figure 1 . Figure 1. Open in a new tab Flow diagram for the scoping review process. Stage 3: Study selection In the first stage, two trained undergraduate research assistants independently screened article titles under the supervision of the first author (ESC). Articles deemed clearly relevant were placed in the “yes” folder, whereas those requiring further consideration were placed in an “unsure” folder. The two RAs then cross-checked their decisions to ensure consistency. Subsequently, a master’s-prepared research assistant (HJ) reviewed all articles in both the “yes” and “unsure” folders to confirm eligibility. Finally, ESC conducted a comprehensive re-review of these articles to ensure the accuracy and consistency of the final selection. During this stage, simple protocol papers were excluded because they did not report intervention outcomes. However, published protocols that were associated with included studies were referred to gain insight into how the interventions were developed, including the theoretical frameworks and behavioral techniques applied. Following the same process, abstract reviews were conducted, resulting in 101 articles remaining for full-text review. Three authors (ESC, MJ, CD) reviewed 101 articles to address the research questions. First, two teams consisting of three authors (ESC, MJ, and CD) reviewed the publications to determine if studies (articles) needed to have a theory or framework to be included (Research Question (RQ) 1). As a result, only 47 articles remained. Second, all three authors reassessed the selected articles to determine their suitability for answering Research Questions 2 through 4 (RQ2–RQ4). At this stage, ten articles were identified as five pairs originating from the same study; one article from each pair was retained, resulting in five included studies. Additionally, one article, 42 which was a protocol paper explaining the DSME/S using digital technology, was excluded. Consequently, a total of 41 articles remained for the final review and were analyzed in the current study (See Figure 1 ). Stage 4: Charting of the data Preparation of the data extraction form Based on previous research recommendations, 27 , 32 a data extraction form was developed through multiple discussions among ESC, MJ, and CD. Five articles were independently reviewed by ESC, MJ, and CD using the data extraction form and BCT taxonomy BCTTv1. As described above, several issues with the extraction form were identified including confusion regarding the use of BCTTv1. In response, ESC prepared a preliminary version of the BEST, and MJ and CD re-reviewed the five articles using the updated form. Iterative Zoom meetings and continuous updates were conducted until all three reached an agreement. Data collection by reviewers Three authors (ESC, MJ, CD) initially conducted data extraction using the newly developed BEST taxonomy. The coding of the BCTs based on the BEST taxonomy for each article were double coded, and they were interpreted based on the contextual use of selected behavioral change techniques based on the theoretical constructs, strategies, etc. described in the intervention activities. The final data extraction spreadsheet included the following 12 items: 1. Authors, title, journal, year of publication, issue, volume 2. Aims of study 3. Study design 4. Theories or models of basis of intervention 5. Technology types and modalities of DSME/S (e.g., website, apps, text messaging) 6. Behavioral Education and Support Techniques (BESTs) used 7. Study location (place and country), recruitment time, period, and method 8. Eligibility (inclusion & exclusion criteria) 9. Intervention vs. control group descriptions, intervention duration, measurement method and tools (i.e.,data from survey, qualitative research and clinical lab results) 10. Behavioral and clinical targets 11. Baseline socio-demographics 12. Findings on behavioral and clinical targets Each reviewer completed an initial extraction and then forwarded the form to a second reviewer, who cross-checked the completed extraction and re-identified the applicable BEST categories to ensure completeness and accuracy. Following this preliminary phase, MSF joined the research team to provide independent verification and enhance the robustness of the taxonomy. MSF reviewed the classification of BEST categories and conducted an additional cross-check to ensure the quality and consistency of the data previously extracted by ESC, MJ, and CD. In cases of disagreement, one of the authors independently re-coded the article for additional verification. The results were then discussed by all four authors during Zoom meetings until consensus was reached. Stage 5. Collating, summarizing and reporting the results After extracting the relevant data from the selected articles into separate Excel or Word files, ESC, MJ, CD, and MSF discussed data presentation methods. In addition to answering RQs 1–4, the research team examined the settings, target audience, and duration in previous digital DSME/S to obtain lessons for future digital DSME/S intervention. At this stage, XQ and HJ were asked to collaborate with MJ and ESC, respectively, to ensure accurate data extraction and enhance the quality of reporting. Results The results of the scoping review are described based upon the final selection of 41 studies. The findings were organized according to the research questions. Figure 2 presents the general characteristics of the included studies and summarizes the target populations, intervention duration, technology modalities, and clinical outcomes. Figure 2. Open in a new tab Summary of the studies included. Characteristics of studies included A total of 7,051 participants with sample sizes ranging from n=20 to n=723 were included in the 41 articles. The majority were conducted in the United States (n=18), followed by Europe (n=11: United Kingdom [n=3], Netherland [n=2], Sweden [n=2], Denmark [n=1], Greece [n=1], Ireland [n=1], Belgium [n=1]), Australia (n=5), Asia (n= 7: Malaysia [n=2], Saudi Arabia [n=1], Taiwan [n=1], Singapore [n=1], Iran [n=1], and Thailand[n=1]). The most common sample size range was 100–250 (n=16), followed by 50–100 (n=9), more than 250 (n=8), and less than 50 (n=8). Theories and models used Among the 41 studies, a total of 27 different theories, models, or frameworks were applied. Thirteen studies incorporated more than one theory into their intervention development, 43 – 55 and Boels et al. 45 employed four theories, the highest number. The Diabetes Self-management Education and Support framework, also known as ADCES7 self-care behaviors outlined by the Association of Diabetes Care and Education Specialists, was the most common and utilized by 14 studies. 46 , 48 , 56 – 67 The second most common was the Social Cognitive Theory, used by seven studies. 43 , 48 , 50 , 52 , 54 , 68 , 69 The Integrated Theory of Health Behavior Change was the third most commonly used theory, with six studies employing it. 44 , 49 , 51 , 70 – 72 Well-known approaches, such as Cognitive Behavior Therapy 52 , 73 and the Transtheoretical Model of Behavior Change, 45 , 48 , 74 were used in two and three studies, respectively (See Table 2 ). Table 2. Final BEST taxonomy (9 domains, 58 techniques) and frequencies of BEST in the studies selected. Domain Techniques # of studies using the corresponding behavioral techniques I. Goals and planning I.1. Goal setting 25 I.2.Goal review 16 I.3. Action planning 21 ​ ​ Total = 62 II. Monitoring with/without feedback II.1. Behavioral monitoring without feedback or self-reflection 3 ​ II.2. Behavioral monitoring with feedback (other, IT etc.) 17 ​ II.3. Behavioral monitoring with self-reflection 13 ​ II.4. Clinical outcome monitoring without feedback or self-reflection 1 ​ II.5. Clinical outcome self-monitoring without emotional response and/or biofeedback 0 ​ II.6. Clinical outcome self-monitoring with biofeedback 1 ​ II. 7. Clinical outcome monitoring with feedback by other (other, IT) 10 ​ ​ Total = 45 III. Social support III.1. Material support by HCP (education material, recipe etc.) 16 ​ III.2. Emotional support by HCP 18 ​ III.3. Social support by family vs. friends 8 ​ III.4. Social support by peers 8 ​ III.5. Support by social system 1 ​ ​ Total = 51 IV. Shaping knowledge IV.1. Information, health related causes 19 IV.2. Information, health related consequences 18 IV.3. Information, pharmacology related 11 IV.4. Information, social/environmental related consequences or social norms 0 IV.5. Information, emotional consequences 3 IV.6. Skill training knowledge, self-care related 31 IV.7. Skill training knowledge, communication related 5 IV.8. Skill training knowledge, emotional control (coping, Stress management) 10 ​ ​ Total = 97 V. Comparison of behavior V.1. Past experience 1 V.2. Social comparison 0 V.3. Vicarious experiences observing others or role model 0 V.4. Social norms/expectations 0 ​ ​ Total = 1 VI. Making operant condition VI.1. Prompt/cues 11 VI.2. Fading including time-out 1 VI.3. Remove access to the reward/aversive stimulus 0 VI.4. Satiation, expose to excessive stimulus 0 VI.5. Exposure to pleasant/unpleasant stimulus 4 VI.6. Associative learning (e.g., Skinner’s box) 0 VI.7. Verbal/self persuasion 2 VI.8. Cognitive appraisal 2 VI.9. Distraction 0 VI.10. Mental rehearsal of (un)successful performance 0 Total = 20 VII. Skill training VII.1. Behavioral practice/rehearsal/habit formation 2 VII.2. Behavioral substitution, habit reversal 0 VII.3. Overcorrection 0 VII.4. Translation (Generalization of target behavior) 0 VII.5. Graded (up, down) tasks 2 ​ ​ Total = 4 VIII. Reward and threat VIII.1. Material incentive and reward 3 VIII.2. Emotional reward by other 2 VIII.3. Self-reward/incentive including emotional reward 1 VIII.4. Social incentive/reward 1 VIII.5. Material threat/punishment 0 VIII.6. Emotional threat/Punishment 0 VIII.7. Emotional regret by self 0 VIII.8. Social threat/punishment 0 VIII.9. Future Punishment 0 VIII.10. Other reward, incentive and threat 1 ​ ​ Total = 8 IX. Environmental context and resources IX.1. Restructuring the physical environment 1 IX.2. Restructuring the social environment 3 IX.3. Restructuring mind-set 0 IX.4. Body change 0 IX.5. Avoidance/reducing exposure to cues for behaviors 0 IX.6. Distraction 0 ​ ​ Total = 4 Total number of BESTs used 292 Open in a new tab Study design, target populations, and duration The majority of the studies utilized a randomized controlled trial (RCT) design (n=32) 45 – 57 , 59 – 62 , 64 , 65 , 68 – 80 while five were pre-post single-arm studies, 43 , 44 , 58 , 63 , 81 and four were two-group comparative studies without randomization. 66 , 67 , 82 , 83 No study specifically targeted adults under 40 years old with T2D, although five studies did include participants under 40. 43 , 47 , 59 , 70 , 73 One study did not report participants' ages at all, 46 while three studies 56 , 59 , 70 provided only age-group proportions, making it impossible to calculate the mean age. Except for Haste et al. 62 and Alanzi et al., 70 that focused specifically on either men or women, respectively, all studies included both men and women, with a nearly even gender distribution (male: 51.29%, female:48.81 %). Study durations ranged from ≤4 weeks to ≥12 months. Seventeen studies were between 1-3 months long, 44 , 46 , 47 , 51 , 53 , 54 , 63 , 64 , 66 – 68 , 70 , 72 , 73 , 77 , 81 , 83 ten studies were 6 months long, 45 , 49 , 56 , 57 , 60 , 61 , 65 , 71 , 75 , 79 four were between 3 and 6 months, 52 , 55 , 69 , 82 six were 12 months long, 48 , 50 , 62 , 74 , 76 , 78 two were 4 weeks or less, 43 , 59 one was longer than 1 year, 80 and one study did not specify duration. 58 Generally, randomized controlled trials (RCTs) featured longer intervention periods compared to studies with quasi-experimental designs. Digital technology modalities for intervention Various digital technologies were used in the intervention. These encompassed a broad spectrum of approaches, ranging from basic methods such as phone calls, emails, or text messaging to specialized smartphone-based applications designed for diabetes management, social media, or continuous glucose monitoring. Two most frequent delivery methods were smartphone apps (n=15) 43 – 45 , 48 , 52 – 54 , 56 , 63 – 65 , 69 – 71 , 80 and websites (n=14). 48 – 50 , 53 , 59 , 60 , 62 , 64 , 66 , 73 , 74 , 76 , 77 , 83 Twelve studies utilized text messaging to deliver care with various levels of interactions, 46 , 47 , 51 , 54 , 57 , 61 , 68 , 72 , 75 , 79 , 80 , 82 five studies used the phone (n=5) 55 , 72 , 79 , 81 , 83 and three studies used telehealth. 58 , 67 , 78 Nine studies were classified as using multiple technological modalities in Figure 2 since some studies are counted in more than one category. Behavioral techniques by study group Control groups Twenty-five control group participants in the forty-one studies received usual care compared to the intervention group. 45 , 47 – 49 , 51 , 53 , 56 , 57 , 59 – 62 , 64 – 72 , 74 – 77 Usual care, including the terms traditional care and standard care, is defined as diabetes education, medication, health care provider (HCP) visits, lab work, and self-monitoring according to clinical judgment. Since DSME/S based on the ADCES 7 Self-Care Behaviors framework is recognized as the national standard for DSME/S by the ADA, it was used as the standard care for the control group. Eleven studies did not have a control arm or provided no description of care for the control group. 43 , 52 , 54 , 58 , 62 , 63 , 65 , 68 , 72 , 81 , 82 Four studies offered usual care and “waitlist” status for the eHealth intervention. 45 , 53 , 69 , 77 Details about control group approaches are presented in Supplemental Table 2 . Intervention groups As shown in Supplemental Table 2 , all interventions employed behavioral techniques designed to provide education, promote health behaviors through reminders, offer support, raise awareness, and/or enhance self-efficacy. However, the descriptions of these techniques were primarily goal-oriented (e.g., increasing physical activity) 70 rather than explicitly grounded in behavioral theory rationales (e.g., goal setting guided by predefined strategies). Additionally, the level of interaction varied; many studies employed automated messaging for education, reminders, and updates as an intervention. 46 , 47 , 51 , 54 , 57 , 61 , 68 , 72 , 75 , 79 , 80 , 82 The app-based interventions (n=15) incorporated multiple functionalities, including in-app coaching, group support, and reminders via text messages or gamified features to improve glucose, nutrition, and activity. 43 – 45 , 48 , 52 – 54 , 56 , 63 – 65 , 69 , 71 , 80 They also provided interactive education modules, appointment scheduling with health care professionals (HCPs), and feedback from HCPs. Seventeen studies used web-based (websites) platforms as part of, or as the sole mode of delivery, some of which allowed interaction with HCPs and/or fellow participants and support people (e.g., friends and family). 48 – 50 , 53 , 59 , 60 , 62 , 64 , 66 , 67 , 71 , 73 , 74 , 76 , 77 , 83 , 84 BEST taxonomy frequently used vs. never used techniques in the research selected Table 2 presents the final BEST taxonomy, comprising nine domains and 58 BEST techniques, along with the frequency of their use. Across the 41 reviewed studies, 14 BEST techniques were applied a total of 292 times, whereas 22 techniques spanning seven domains were not utilized at all. Consistent with findings by Kebede et al., 27 the most frequently applied domains were Shaping Knowledge (Category IV, 92 occurrences), Goals and Planning (Category I, 62 occurrences), Social Support (Category III, 51 occurrences), and Monitoring With/Without Feedback (Category II, 45 occurrences). All techniques within domains of “Goals and Planning” and “Social Support” were represented. Conversely, negatively framed techniques (e.g., “threat,” “punishment”) from Category VIII (Reward and Threat), techniques potentially undermining patient autonomy such as “controlling stimulus” from Category VI (Operant Conditioning), and techniques involving interpersonal comparisons from Category V (Comparison of Behaviors) including role modeling, a comparatively mild form of behavioral comparison were entirely absent. Behavioral targets Table 3 presents the types of behavioral education and support target outcomes used in the studies included in this review. The most commonly examined measures were self-efficacy, physical activity engagement, self-monitoring, and consistent medication adherence. Two-thirds of the studies used more than one behavioral outcome. 43 – 46 , 48 – 50 , 53 – 55 , 59 , 62 – 66 , 68 , 70 , 71 , 73 – 76 , 79 – 81 , 83 Twelve studies measured self-efficacy, 44 , 46 , 54 , 55 , 59 , 63 , 66 , 68 , 70 , 75 , 79 , 83 and seven measured knowledge change, 46 , 54 , 59 , 64 , 70 , 74 , 76 while four directly measured behavior change. 46 , 53 , 59 , 74 Nutrition changes were used in seven studies, 43 , 45 , 50 , 54 , 65 , 71 , 74 and health or diabetes-related quality of life was measured in seven studies. 45 , 46 , 48 , 66 , 71 , 76 , 77 Table 3. Study design, duration, technology (Tech) modality, theory, behavioral and clinical targets and the findings. Study Study design Duration Tech modality Theory Behavioral target Clinical target Primary outcomes of interventions Findings* 1. Adu, M.D., et al./2020 43 Mixed-methods design: qualitative and quantitative; Quantitative: Single arm-pre and post design 3 weeks App Social Cognitive Theory; Behavioral Skills (IMBS model) Nutrition changes; Physical Activity changes; Consistent Self-monitoring Undefined Diabetes self-management behavior as determined by change in skills (self-monitoring scores, etc.) and self-efficacy from baseline to post-intervention SS improved self-monitoring score from baseline compared to control 2. Alanzi, T., et al./2018 70 Parallel Randomized clinical trial 8 weeks Social media Behavioral Change Model/Integrated Theory of Health Behavior Change Knowledge change; Self-efficacy change; awareness of DSM None Change in knowledge, self-efficacy and awareness about diabetes management from baseline to post-intervention SS improved knowledge & self-efficacy from baseline compared to control 3. Baldwin, P.A., et al./2020 73 RCT 8 weeks Website Cognitive Behavior Theory (CBT) Work/social function; Depression/anxiety screens HbA1c; Health care utilization; Days out of role Improved self-reported work and social functioning, depression & anxiety symptoms from baseline to post-intervention Unexpectedly SS Control > intervention in social/work function; Both groups improved depression/anxiety from baseline 4. Bartlett, Y.K., et al./2022 75 Parallel-group, randomized controlled feasibility trial 6-months Text msg Health Action Process Approach Theory Self-efficacy; medication adherence Undefined Improvement in psychological construct and medication adherence from baseline to post-intervention SS intervention group> control group self-efficacy which was positively associated with change in med adherence 5. Batch, B.C., et al./2021 44 Non-randomized comparative study. 12 weeks App Behavioral Change Model/Integrated Theory of Health Behavior Change; Gamification Medication adherence; self-care; self-efficacy; diabetes distress HbA1C Improved HbA1c and change in self-reported health behavior from baseline to post-intervention NSS in HbA1c; however, SS in improved self-efficacy & diabetes distress scores between groups 6. Boels, A.M., et al./2019 45 Non-blinded two-arm multi-center randomized controlled trial with parallel-groups and equal randomization 6 months App (with push notification) From 2018 paper: Health Belief Model; Leventhal’s Self-Regulation Model; Transtheoretical Model of Behavior Change; Fogg Behavioral Model Self-management; Nutrition; Physical activity; Diabetes-related QoL HbA1c; BMI; Lipids; Blood pressure; Waist circumference; Medication dose change; hypoglycemia events; glucose variability Improved HbA1c level and proportion of patients who achieve an level ≤7%, along with changes in health behaviors and health status from baseline to post-intervention NSS between two groups 7. Buendia, R., et al./2021 56 RCT 6 months App Diabetes Self-Management(ADCES7 guidelines) App use HbA1c, Weight Improvement in HbA1c and weight from baseline to post-intervention SS , HbA1c reduced only in “high users” of app 8. Burner, E., et al./2018 46 Non-blinded, parallel randomized controlled trial with a 1:1 allocation 3 months. Text msg Diabetes Self-Management(ADCES7 guidelines); Hinson-Langford Module of Social Support Knowledge change; self-efficacy; QoL; change in health behaviors; social support HbA1c Change in diabetes-specific outcomes (HbA1c, self-efficacy, health behaviors, etc.) and social support from baseline to post-intervention NSS in HbA1c, knowledge change, self-efficacy, QoL, or health behaviors between intervention & control groups 9. Christensen, J.R., et al./2022; 71 RCT 6months Face to face w/coach first, then App or website w/coach Behavior Change Model Physical Activity, Nutrition, QoL Weight; HbA1c; Blood pressure; Lipids Change in weight, HbA1c from baseline to post-intervention SS , weight & HbA1c change compared to control; however NSS change in BP, lipids, physical activity, nutrition, or QoL 10. Chrysi, M.S 76 /2023 RCT 6 months Website Health Promotion Model Knowledge change; anxiety/depression; QoL Measured at baseline only: BMI; HbA1c; Blood pressure; Lipids; Waist Circumference Change in knowledge, behavior and quality of life from baseline to post-intervention SS Knowledge increase, reduced anxiety/depression, & improved QoL 11. Clark, T.L. 57 /2020 Parent study is a randomized, nonblinded clinical trial; 6 months Text msg Diabetes Self-Management (ADCES7 guidelines) Diabetes Distress HbA1c Improved diabetes distress and HbA1c from baseline to post-intervention SS , decreased HbA1c in intervention only if high diabetes distress; NSS in diabetes distress between group 12. Cross, S., et al./2021 58 Retrospective study 6 hours curriculum (frequency/timing not specified) Telehealth Diabetes Self-Management(ADCES7 guidelines) None HbA1c Change in HbA1c from baseline to post-intervention NSS between groups 13. DeBarthe Sadler, M., et al./2017 59 2-group pretest-posttest comparative study 4 weeks Website Diabetes Self-Management (ADCES7 guidelines) Adoption of health behaviors; Knowledge change; Self-efficacy change Undefined BMI collected at baseline Changes in self-reported knowledge, self-efficacy, and behaviors from baseline to post-intervention SS , intervention group w/increased knowledge, self-efficacy, & adoption of health behaviors, in pre- to post-test scores 14. Du Pon et al./2020 60 RCT with non-stratified block randomization. 6 months Website (“Online care platform”) Diabetes Self-Management(ADCES7 guidelines) Participation in intervention None Change in usage (#onf log-ons, time spent per session) of the platform from baseline to post-intervention NSS between intervention and control groups 15. Dwibedi et al./2022 77 RCT 12 weeks Website Self-Affirmation Theory Quality of life (QoL) HbA1c Change n HbA1c and QoL from baseline to post-intervention SS improvement in HbA1c and QoL in TDM if the intervention tool was sued 16. Egede et al./2018 78 RCT 12 months Telemed: telephone and Videoconferencing with built-incamera and color screen display CBT IMB Skills Model Depression HbA1c Change in HbA1c and depression from baseline to post-intervention SS , HbA1c stable in intervention, increased in control; no change in depression scores 17. Fortmann et al./2017 61 RCT. A randomized, nonblinded, parallel-groups clinical trial design 6 months Text msg Diabetes self-management (ADCES7 guidelines) Participation in Intervention (feasibility) Weight; HbA1c; Fasting glucose; Blood pressure; Lipids Change in weight, HbA1c, blood pressure, lipid levels from baseline to post-intervention SS improved HbA1c, however NSS in weight, fasting blood glucose, blood pressure or lipids. The intervention was found to be acceptable and feasible. 18. Gatwood et al./2016 47 RCT. 3 months Text msg Health Belief Model; Self-Determination Theory Medication use None Change in medication adherence from baseline to post-intervention NSS , med use decreased in both intervention & control 19 Gong, E., et al./2020 48 RCT. 12 months App + website Social Cognitive Theory; Transtheoretical Model of Behavior Change; Diabetes self-management (ADCES7 guidelines); Gamification Participation in Intervention HbA1c; Health-related QoL Changes in HbA1c and health related QoL from baseline to post-intervention SS QoL increased in intervention, NSS change in HbA1c 20. Haste, A., et al./2017 62 RCT One to one ratio: 12 months. Website Diabetes Self-Management (ADCES7 guidelines) Participation in Intervention (feasibility); Self-monitoring consistently Weight; BMI; waist circumference Changes in weight, BMI, waist circumference from baseline to post-intervention, despite recruitment and retention SS , Weight, BMI, and waist circumference reduction in intervention group 21. Hidrus, A., et al./2021 69 RCT. 4 months Social media via WhatsApp Transtheoretical Model Exercise self-efficacy None Change in exercise self-efficacy from baseline to post-intervention SS improved exercise self-efficacy score in intervention 22. Holmes-Truscott, E., et al./2023 49 RCT 1:1 ratio; 6 months Website Behavioral Change Model/Integrated Theory of Health Behavior Change; Theoretical Domain Framework Participation in Intervention; Attitude changes None Change in attitudes towards insulin from baseline to post-intervention NSS in attitudes towards insulin between groups; Participation was lower than pilot study 23. Johnson, C.M., et al./2023 50 RCT. 12 months Web + synchronous interaction, education, and support Social Cognitive Theory; Social-ecological Model of Social Support; Diabetes self-management (ADCES7 guidelines); Diet, Physical Activity; foot care; glucose monitoring; medication adherence changes, social support HbA1c; Lipids; Blood pressure; BMI; Waist circumference Change in diet, physical activity, health behavior (medication, foot care, self-monitoring), HbA1c, lipid levels from baseline to post-intervention NSS between groups; attrition in both groups a challenge 24. Kassavou, A., et al./2020 51 RCT. 12 weeks Text + voice msg: Interactive text and voice recognition messages Behavioral Change Model/Integrated Theory of Health Behavior Change; Intentional & Nonintentional Nonadherence Framework Medication adherence HbA1c; Blood pressure Change in medication adherence, HbA1c and blood pressure from baseline to post-intervention SS improvement in medication adherence. NSS in change in BP or HbA1c 25. Kumar, S., et al./2018 63 Single arm Pre-post test 3 months App + coaching Diabetes Self-Management (ADCES7 guidelines) Diabetes distress screen; self-efficacy HbA1c Change in HbA1c, diabetes distress and self-efficacy from baseline to post-intervention SS improvement in A1C, diabetes distress, and self-efficacy 26. Leong et al./2022 64 RCT 3 months Social media (LINE) Web + App with education videos Diabetes Self-Management (ADCES7 guidelines) Knowledge, attitudes, self-care HbA1c Change in HbA1c, knowledge, attitudes and self-care from baseline to post-intervention NSS HbA1c; SS improved attitudes in intervention group 27. Lim et al./2021 65 RCT; A part of D'LITE study that 6 months App functioning with tracking, videos, sending messages and feedback etc Diabetes Self-Management (ADCES7 guidelines) Nutrition, Physical Activity; Medication adherence Weight; HbA1c; fasting glucose, blood pressure, Lipids, creatinine Change in weight, metabolic profiles (HbA1c, fasting, lipids, etc.), diet, physical activity and medication adherence from baseline to post-intervention SS weight, nutrition, and physical activity improvement in intervention group 28. Litchman et al./2022 66 A mixed method feasibility study (pilot intervention + individual interviews) with single arm pre-posttest design 12 weeks Website (Spanish-language) + CGM Diabetes Self-Management (ADCES7 guidelines) Self-efficacy, QoL CGM Time in range; HbA1c Change in HbA1c, self-efficacy, QoL from baseline to post-intervention NSS time in range, A1C, or QoL. SS improvement in self-efficacy 29. Mayberry et al./2021 79 A mixed method to evaluate FAMS in a randomized controlled trial (RCT). 6 months Phone + Text msg Family System Theory Family/friend involvement, Self-efficacy, Self-care None Change in family involvement, self-efficacy and self-care from baseline to post-intervention SS improvement in family/friend involvement and self-efficacy compared to control 30. McGloin et al./2020 83 Single arm pre-post test design; 12 weeks Telemonitoring Website + phone Empowerment Philosophy Theory Self-efficacy, Diabetes distress BMI; HbA1c Change in self-efficacy, diabetes, distress, HbA1c, BMI from baseline to post-intervention SS improved HbA1c, self-efficacy, and diabetes distress, but not BMI. 31. Nelson et al./2021 80 RCT: Three arms: Control, REACH only, and REACH with FAMS for a 2:1:1 design. 6 months (daily text) + 6 months (fewer texts). Text + App (In-App coaching) Theory of Health Behavior -Information-Motivation-Behavioral skills model Self-care; Medication adherence HbA1c Change in HbA1c, self-care and medication adherence from baseline to post-intervention NSS HbA1c, however SS improvement in medication adherence and self-care 32. Olomu et al./2022 82 Quasi-experimental design; Office-GAP plus mHealth vs mHealth only. 4 months Text msg Health Literacy Care Model Guidelines of the American College of Cardiology - Smith EBP-centered method for patient-provider partnership -ADCES7 guidelines Medication adherence 10-year ASCVD risk score Change in medication adherence and ASCVD 10-year risk score from baseline to post-intervention SS in medication adherence and 10-year ASCVD risk score from baseline but not between groups 33. Peimani et al./2016 68 3 arm RCT; Tailored SMS group, non-tailored SMS group, and the control group. 12 weeks Text msg Social Cognitive Theory Self-care and self-efficacy BMI; HbA1c; Fasting glucose; Lipids Change in HbA1c, fasting glucose, lipid, self-care and self-efficacy from baseline to post-intervention SS improvement BMI, FBG, self-care, and self-efficacy compared to control 34. Pichayapinyo et al.,/2019 81 Single-arm pre-post trial. 12 weeks Phone (Interactive Voice Response) + email PRECEDE-PROCEED Model Physical Activity changes; Medication adherence; Sleep; Foot care HbA1c; Fasting glucose; Medication dose reduction Change in HbA1c, self-management behaviors from baseline to post-intervention SS improvements in A1C, fasting glucose, physical activity, medication adherence, sleep, and foot care 35. Plotnikoff et al./2017 52 RCT 20 weeks App Social Cognitive Theory; Cognitive Behavior Theory; Health Action Process Approach (HAPA) Model none Aerobic & muscular fitness; BMI; Blood pressure; Waist Circumference Change in Aerobic (VO 2 max) and muscular fitness from baseline to post-intervention SS improvement in aerobic fitness, muscular fitness, functional mobility, and systolic blood pressure. NSS for waist circumference, BMI, diastolic blood pressure 36. Poppe et al./2019 53 2 RCTs 1st RCT consisted of adults with T2D; 2nd RCT consisted of adults aged 50 years or older. 5-weeks Web + App Leventhal’s Self-Regulation Model; Health Action Process Approach Physical Activity changes; sedentary behavior change None Change in physical activity and sedentary behavior from baseline to post-intervention NSS improvement in intervention 37. Ramadas et al./2018 74 RCT 12 months Website Transtheoretical Model of Behavior Change Dietary Knowledge, Attitude, and Behavior (DKAB), Dietary Stages of Change (DSOC) HbA1C; Fasting blood glucose(FBG) Change in DKAB, DSOC, HbA1c, FBG from baseline to post-intervention SS in DKAB and DSOC scores, but not A1c or fasting glucose 38. Ramirez et al./2017 72 RCT (three arms) 12 weeks Phone (Voice) + text Social Cognitive Theory Self-Regulation Theory Physical Activity Changes, Social Support None Change in physical activity and social support from baseline to post-intervention SS improvement in physical activity in phone message intervention group 39. Sittig et al./2020 54 The pilot randomized unblinded study comprised two cohorts. 9 weeks App + text Social Cognitive Theory; Fogg Behavioral Model; Persuasive technology; Interactive Health Communication Applications (ICHA) framework Physical Activity; nutrition; Knowledge; Self-efficacy; foot care changes Blood glucose Improvement in physical activity, blood glucose, as well as behavioral and knowledge changes from baseline to post-intervention SS in nutrition and blood glucose compared to control; and SS improvement in self-efficacy for mid to high App users 40. Threatt et al./2017 67 Pilot RCT telehealth study 2 to 3 months Telehealth Website Diabetes Self-Management (ADCES7 guidelines) None BMI; HbA1c; Blood pressure; Lipids Change in HbA1c, blood pressure, lipids, and BMI from baseline to post-intervention SS reduction in HbA1c in both groups, but not between groups 41. Trief et al./2016 55 3 arms, multicenter, randomized clinical trial. 4 months Phone Social Ecological Theory, Social Learning Theory, Interdependence Theory Self-efficacy and diabetes distress, depressive symptoms BMI; HbA1c; Blood pressure; Waist Circumference Change in HbA1c, BMI, blood pressure, diabetes distress, depressive symptoms, self-efficacy from baseline to post-intervention SS improvement HbA1c in intervention; SS improvement in diabetes distress for CC intervention arm Open in a new tab Note. *SS = statistically significant; NSS = Not statistically significant. Clinical targets Nearly two-thirds of the studies which the research team evaluated used HbA1c as the primary clinical outcome. 44 – 46 , 48 , 50 , 51 , 55 – 58 , 61 , 63 – 68 , 71 , 73 , 74 , 77 , 78 , 80 , 81 , 83 Weight or body mass index (BMI) was the second most used outcome, followed by blood pressure, lipids, glucose, and waist circumference. Twelve studies did not have a clinical outcome. 43 , 47 , 49 , 53 , 59 , 60 , 69 , 70 , 72 , 75 , 76 , 79 Seventeen studies used multiple clinical markers as a primary or secondary outcome. 45 , 50 – 52 , 55 , 56 , 61 , 62 , 65 – 68 , 71 , 73 , 74 , 81 , 83 Only two of the 41 studies used medication dose reduction as a measure. 45 , 81 Findings of the interventions While some outcome targets were behavioral and others clinical, not all studies yielded statistical significance. Factors such as study duration (e.g., ≥ 6 months), 45 , 60 the intensity of app utilization, 56 and the severity of diabetes distress 57 appeared to influence the study findings. Batch et al., 44 Boels et al., 45 Burner et al., 46 Cross et al., 58 Gatwood et al., 47 Holmes-Truscott et al., 49 Johnson et al., 50 Sitting et al., 54 and Trief et al. 55 reported outcomes that did not achieve statistical significance across various measures, including HbA1c, weight, blood pressure, lipid levels, medication adherence, physical activity, dietary intake, and/or self-efficacy. Christensen et al., 71 Kassavou et al., 51 Peimani et al., 68 and Plotnikoff et al. 52 reported statistically significant improvements in behavioral targets, notably medication adherence. However, clinical measures such as blood pressure and HbA1c improvement did not achieve statistical significance (See Table 3 ). Discussion This scoping review examined behavioral education and support techniques using digital technologies to address self-management in adults with T2D as reflected in the new BEST taxonomy that was revised from the one established by Michie et al. 33 This review extended the existing research provided by Kebede et al. 2017 27 that included published studies from 1990 to 2016. Published studies on behavioral change techniques from June 2016 to June 2023 were carefully examined in this current scoping review to summarize the most common techniques and outcomes in diabetes self-management education and support (DSME/S) interventions. DSME/S delivered through digital technologies continues to increase. Compared with the review by Kebede et al., 27 we identified 41 eligible studies, whereas their review included 32 studies. Similar to Kebede et al., most studies were conducted in the United States, Europe, Australia, or Asia. 27 Web-based interventions remained the most common delivery modality, with 14 studies identified in our review compared to 18 in Kebede et al. However, smartphone applications have become increasingly prominent (n = 15), and multiple technologies were frequently combined within a single intervention (n = 9). Text messaging was also commonly used as a delivery component in both reviews (12 studies in our review vs. 6 in Kebede et al.). Nevertheless, frequency of use does not necessarily indicate effectiveness. Moreover, as many mobile applications incorporate messaging functions, it is challenging to clearly isolate the independent contribution of text messaging. With continued digital advancement, additional modalities such as video conferencing, 58 , 67 , 78 interactive voice communication, 50 , 51 , 81 and continuous glucose monitoring are increasingly being integrated into DSME/S interventions. 66 Of particular mention is the rapid development of artificial intelligence that suggests digitally delivered DSME/S using multiple modalities may become a mainstream approach in the future. 85 , 86 Approximately one-third (31 out of 93, 33.3%) of the behavior techniques in BCTTv1 taxonomy were noted by Kebede et al. compared to 36 out of 58 behavior techniques (62.07%) addressed in the new BEST taxonomy. Of importance is that 9 of the 36 techniques in our review were used only once, though. The most common strategies in the Kebede et al. review included how to perform specific behaviors, information about health consequences and feedback and self-monitoring of outcomes. In comparison, goal setting and planning, monitoring with feedback, social support and skill training were the techniques used with greatest frequencies in our review. Most behavioral targets were self-efficacy, physical activity, medication adherence, and self-monitoring. Clearly, both reviews emphasized the importance of strengthening individual-level capabilities that enable individuals to evaluate their own performance and outcomes, rather than relying on contextual or system-level changes intended to promote desirable behaviors without restricting freedom of choice. According to the current scoping review, the use of theory-based interventions in clinical settings has notably increased. Kebede et al. identified only 8 studies that applied a behavioral change model or theory for intervention development, whereas the current review found 41 theory-based DSME/S publications between 2016 and 2023. However, it remains unclear which behavioral techniques were selected based on specific behavioral insights targeting particular behaviors, rather than on behavioral theories generally intended for designing DSME/S. 17 , 25 , 26 One significant difference between the two reviews may be the target populations. While Kebede et al. focused exclusively on individuals with poorly controlled type 2 diabetes (HbA1c≥7.0%), our review included broader studies of individuals with type 2 diabetes, as one of our primary aims was to examine the feasibility of digital DSME/S interventions across different age groups. Consequently, Kebede et al. reported statistically significant improvements in the primary outcome (HbA1c) in 25 of their 32 studies (78.1%), whereas only 11 of 25 studies (44.0%) in our review showed statistical improvement in HbA1c. However, diabetes self-management behavioral interventions primarily aim to induce behavioral change with the secondary aim of targeting clinical outcomes, although these two aspects are closely interrelated. Additionally, studies included in both reviews lacked detailed descriptions of the behavioral interventions, making it difficult to identify specific reasons for the observed differences. A clear gap, however, was identified in that available interventions did not adequately address age-specific unmet needs. 12 , 29 , 41 In particular, there was a notable scarcity of clinical trials and support services explicitly targeting individuals under 40 years old, a gap previously highlighted in the literature. 14 , 18 Additionally, person-first and non-judgmental language was not consistently applied across studies, despite the possibility that prioritizing such language might be more critical than employing other behavioral techniques. 2 Future scoping reviews need to address this area. Despite the valuable contribution of this review in developing theory-driven, age-specific digital DSME/S programs designed to promote positive health behaviors, maintain fidelity, and ensure replicability to achieve desired outcomes, 17 , 26 , 28 several limitations remain. First, the reviewed studies had highly varied sample sizes, highlighting the need for future systematic reviews and meta-analyses to obtain more accurate and reliable conclusions. Second, the restriction to English-language publications and the narrow review period (2016–2023) may limit the generalizability of the findings. Given the rapid evolution of digital DSME/S, particularly in the emerging era of generative AI, more recent developments may not have been fully captured. Therefore, an updated scoping review conducted beyond 2023, when generative AI gained widespread prominence, is warranted. Third, effective DSME/S interventions require the selection of tailored educational and support techniques aligned with individuals’ psycho-behavioral characteristics. 87 In addition, DSME/S should address critical diabetes-related complications, including foot ulcer prevention, amputation prevention, and neuropathy screening, which highlights an important gap in the existing literature that should be addressed in future research. Fourth, although digital DSME/S interventions show considerable potential, patient readiness including digital literacy and individual needs in specific areas is critical for successful implementation. 87 However, these factors were insufficiently addressed in the reviewed studies. Therefore, future digital DSME/S interventions should systematically incorporate assessments of patient readiness and individualized needs. Finally, although the development of the BEST taxonomy followed careful review and sophisticated validation among research team members, it did not go through external validation (such as a Delphi process), which warrants further examination of the taxonomy. The BEST taxonomy is deemed appropriate for DSME/S interventions, rather than for other health education programs, such as smoking cessation, that employ negative behavioral techniques involving threats, penalties, or punishments. 88 Therefore, careful consideration is needed when applying the BEST framework beyond the context of DSME/S. Future studies As we are currently in an era of generative artificial intelligence (AI), 6 , 85 , 89 AI-powered DSME/S interventions that support decision-making based on behavioral and psychological phenotypes, 4 , 85 , 89 or utilize AI-driven chatbots, 6 hold significant potential. Therefore, future research should investigate the efficacy of digital DSME/S interventions leveraging AI technology. Future studies can benefit from stricter control over sample selection criteria when addressing goal setting, monitoring and feedback, and skill development, as well as utilizing easily accessible modalities such as text messaging, mobile applications, and web-based platforms. Conclusions This scoping review identified critical gaps in the evidence regarding which behavioral techniques are most effective in achieving desired behavioral and clinical outcomes across specific subpopulations, including individuals with early-onset type 2 diabetes and older adults. It also highlighted a notable lack of focus on complication-related outcomes within DSME/S, such as foot ulcer prevention, neuropathy screening, and amputation prevention. These outcomes should be recognized as core components aligned with ADCES7 self-care behaviors and prioritized in future DSME/S interventions. Furthermore, this review underscores the importance of patient-centered approaches, particularly those that account for population-specific preferences in engaging with digital technologies for diabetes self-management, alongside contextual and system-level strategies that enhance motivation without constraining individual autonomy. Finally, greater attention is required to identify and tailor implementation strategies that facilitate the translation of effective interventions into practice across diverse subpopulations. Supplemental material Supplemental material - Characteristics of behavior change techniques in theory-based self-management education and support interventions for adults with type 2 diabetes using digital technology: A scoping review sj-pdf-1-dhj-10.1177_20552076261443759.pdf (403.3KB, pdf) Supplemental material for Characteristics of behavior change techniques in theory-based self-management education and support interventions for adults with type 2 diabetes using digital technology: A scoping review by EunSeok Cha, PhD, MPH, CDCES, RN, Meihua Ji, PhD, MSN, RN, Colleen Dawkins, PhDc, FNP, RN, RDN, CSOWM, Xiaoyan Qi, BSN, RN, Hyesun Jang, PhDc, RN, Melissa Spezia Faulkner, PhD, RN, FAAN in DIGITAL HEALTH Acknowledgements The authors sincerely thank Ms. Sue-Mi Shin and Ms. Hyejin Jeong for their contributions to data extraction across seven databases and to the preliminary screening of titles and abstracts during their time as undergraduate research assistants. ChatGPT (GPT-5.2, OpenAI) was used to improve the clarity and readability of the manuscript. Author contributions: Study conception: ESC, MJ; Data collection/formulation: ESC, MJ, SS, HJ, XQ; Data analysis: ESC, MJ, CD, MSF; Data interpretation: ESC, MJ, CD, MSF, HS, XQ; Drafting of the manuscript: ESC, MJ, CD, MSF, HS, XQ; Reviewing and revising the manuscript: ESC, MJ, CD, MSF, HS, XQ. Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported by the R&D Program of Beijing Municipal Education Commission (SZ202310025009), and Chungnam National University Research Fund. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Guarantor: Since this is an analysis of published studies, and there are no original data generated, therefore no data guarantor is needed. Supplemental material: Supplemental material for this article is available online. ORCID iDs EunSeok Cha https://orcid.org/0000-0001-6115-567X Meihua Ji https://orcid.org/0000-0002-9421-6077 Colleen Dawkins https://orcid.org/0009-0009-7663-9875 Xiaoyan Qi https://orcid.org/0009-0000-5839-6712 Hyesun Jang https://orcid.org/0009-0005-8315-0809 Melissa Spezia Faulkner https://orcid.org/0000-0003-4483-670X Ethical considerations As this is a scoping review, this study did not require ethic approval and consent from participants. Consent for publication All authors have approved the final version of the manuscript and associated materials, and consented for publication. Data Availability Statement Related materials (protocol, BEST taxonomy, etc.) regarding the scoping review are available upon request. * References 1. Chowdhury HA, Harrison CL, Siddiquea BN, et al. 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Supplementary Materials Supplemental material - Characteristics of behavior change techniques in theory-based self-management education and support interventions for adults with type 2 diabetes using digital technology: A scoping review sj-pdf-1-dhj-10.1177_20552076261443759.pdf (403.3KB, pdf) Supplemental material for Characteristics of behavior change techniques in theory-based self-management education and support interventions for adults with type 2 diabetes using digital technology: A scoping review by EunSeok Cha, PhD, MPH, CDCES, RN, Meihua Ji, PhD, MSN, RN, Colleen Dawkins, PhDc, FNP, RN, RDN, CSOWM, Xiaoyan Qi, BSN, RN, Hyesun Jang, PhDc, RN, Melissa Spezia Faulkner, PhD, RN, FAAN in DIGITAL HEALTH Data Availability Statement Related materials (protocol, BEST taxonomy, etc.) regarding the scoping review are available upon request. * Articles from Digital Health are provided here courtesy of SAGE Publications ACTIONS View on publisher site PDF (3.4 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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