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

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 Slow³⁴ 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 Behavior⁶” and “Comparison of Outcomes⁹” 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	Changes of domain	Reasons for developing 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	-	-

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.

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.

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

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./2020⁴³	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./2018⁷⁰	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./2020⁷³	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./2022⁷⁵	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./2021⁴⁴	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./2019⁴⁵	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./2021⁵⁶	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./2018⁴⁶	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.S⁷⁶/2023	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./2021⁵⁸	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./2017⁵⁹	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./2020⁶⁰	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./2022⁷⁷	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./2018⁷⁸	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./2017⁶¹	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./2016⁴⁷	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./2020⁴⁸	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./2017⁶²	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./2021⁶⁹	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./2023⁴⁹	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./2023⁵⁰	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./2020⁵¹	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./2018⁶³	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./2022⁶⁴	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./2021⁶⁵	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./2022⁶⁶	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./2021⁷⁹	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./2020⁸³	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./2021⁸⁰	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./2022⁸²	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./2016⁶⁸	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.,/2019⁸¹	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./2017⁵²	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 (VO₂max) 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./2019⁵³	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./2018⁷⁴	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./2017⁷²	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./2020⁵⁴	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./2017⁶⁷	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./2016⁵⁵	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

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 distress⁵⁷ 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. 2017²⁷ 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

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

Footnotes

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.

ORCID iDs

EunSeok Cha

Meihua Ji

Colleen Dawkins

Xiaoyan Qi

Hyesun Jang

Melissa Spezia Faulkner

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.

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.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Related materials (protocol, BEST taxonomy, etc.) regarding the scoping review are available upon request.*

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.

References

Chowdhury

Harrison

Siddiquea

, et al. The effectiveness of diabetes self-management education intervention on glycaemic control and cardiometabolic risk in adults with type 2 diabetes in low- and middle-income countries: A systematic review and meta-analysis. PLOS ONE 2024; 19(2): e0297328. https://doi.org/10.1371/journal.pone.0297328

American Diabetes Association Professional Practice Committee for Diabetes* . 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes—2026. Diabetes Care 2025; 49(Supplement_1): S89–S131. https://doi.org/10.2337/dc26-S005

Ernawati

Wihastuti

Utami

. Effectiveness of diabetes self-management education (DSME) in type 2 diabetes mellitus (T2DM) patients: Systematic literature review. J Public Health Res 2021; 10(2): 2240. https://doi.org/10.4081/jphr.2021.2240

Cha

Son

N-H

Huh

, et al. AI-powered hybrid self-management coaching using inter-disciplinary approach for adults with early-onset type 2diabetes: A randomized clinical trial protocol. Contemporary Clinical Trials. In press.

Deegan

Riain

Martin

, et al. A scoping review of frameworks evaluating digital health applications. DIGITAL HEALTH 2025; 11: 20552076251315297. https://doi.org/10.1177/20552076251315297

Aggarwal

Tam

, et al. Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. J Med Internet Res 2023; 25: e40789. https://doi.org/10.2196/40789

American Diabetes Association Professional Practice Committee for Diabetes* . 7. Diabetes Technology: Standards of Care in Diabetes—2026. Diabetes Care 2025; 49(Supplement_1): S150–S165. https://doi.org/10.2337/dc26-S007

Nkhoma

Soko

Bowrin

, et al. Digital interventions self-management education for type 1 and 2 diabetes: A systematic review and meta-analysis. Computer Methods and Programs in Biomedicine 2021; 210: 106370. https://doi.org/10.1016/j.cmpb.2021.106370

Shan

Sarkar

Martin

. Digital health technology and mobile devices for the management of diabetes mellitus: State of the art. Diabetologia 2019; 62(6): 877–887. https://doi.org/10.1007/s00125-019-4864-7

10.

Longoni

Bonezzi

Morewedge

. Resistance to Medical Artificial Intelligence. Journal of Consumer Research 2019; 46(4): 629–650. https://doi.org/10.1093/jcr/ucz013

11.

American Diabetes Association Professional Practice Committee for Diabetes* . 13. Older Adults: Standards of Care in Diabetes—2026. Diabetes Care 2025; 49(Supplement_1): S277–S296. https://doi.org/10.2337/dc26-S013

12.

Cha

Son

Joung

, et al. Differences in patient-reported and clinical characteristics by age group in adults with type 2 diabetes. Worldviews Evid Based Nurs 2024; 21(4): 467–476. https://doi.org/10.1111/wvn.12715

13.

Strati

Moustaki

Psaltopoulou

, et al. Early onset type 2 diabetes mellitus: An update. Endocrine 2024; 85(3): 965–978. https://doi.org/10.1007/s12020-024-03772-w

14.

Misra

Gable

Khunti

, et al. Developing services to support the delivery of care to people with early-onset type 2 diabetes. Diabet Med 2022; 39(10): e14927. https://doi.org/10.1111/dme.14927

15.

Misra

Khunti

Goyal

, et al. Managing early-onset type 2 diabetes in the individual and at the population level. The Lancet 2025; 405(10497): 2341–2354. https://doi.org/10.1016/S0140-6736(25)01067-0

16.

Ariely

. Predictably Irrational. Expanded edition ed. Harper Perennial, 2010.

17.

Wendel

. Designing for behaviroal change: Applying Psychology and Behavioral Economics. O’Reilly Media, Inc, 2020, p. 355.

18.

Sargeant

Brady

Zaccardi

, et al. Adults with early-onset type 2 diabetes (aged 18-39 years) are severely underrepresented in diabetes clinical research trials. Diabetologia 2020; 63(8): 1516–1520. https://doi.org/10.1007/s00125-020-05174-9

19.

Atkins

Francis

Islam

, et al. A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems. Implement Sci 2017; 12(1): 77. https://doi.org/10.1186/s13012-017-0605-9

20.

Cane

O’Connor

Michie

. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implementation science 2012; 7(1): 37. https://doi.org/10.1186/1748-5908-7-37

21.

Rejeski

Fanning

. Models and theories of health behavior and clinical interventions in aging: A contemporary, integrative approach. Clin Interv Aging 2019; 14: 1007–1019. https://doi.org/10.2147/CIA.S206974

22.

Loewenstein

Asch

Friedman

, et al.

Can behavioural economics make us healthier?

BMJ: British Medical Journal 2012; 344: e3482. https://doi.org/10.1136/bmj.e3482

23.

Skivington

Matthews

Simpson

, et al. A new framework for developing and evaluating complex interventions: Update of Medical Research Council guidance. BMJ 2021; 374: n2061. https://doi.org/10.1136/bmj.n2061

24.

Loewenstein

Hagmann

Schwartz

, et al. Behavioral insights for health care policy. Behavioral Science & Policy 2017; 3(1): 53–66. https://doi.org/10.1177/237946151700300106

25.

Michie

van Stralen

West

. The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science 2011; 6(1): 42. https://doi.org/10.1186/1748-5908-6-42

26.

Perry

Chhatralia

Damesick

, et al. Behaviorul insights in health care: Nudging to reduce inefficiency and waste. The Health Foundation, 2015, pp. 1–73.

27.

Kebede

Liedtke

Möllers

, et al. Characterizing Active Ingredients of eHealth Interventions Targeting Persons With Poorly Controlled Type 2 Diabetes Mellitus Using the Behavior Change Techniques Taxonomy: Scoping Review. J Med Internet Res 2017; 19(10): e348. https://doi.org/10.2196/jmir.7135

28.

American Diabetes Association Professional Practice Committee . 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes—2025. Diabetes Care 2024; 48(Supplement_1): S86–S127. https://doi.org/10.2337/dc25-S005

29.

American Diabetes Association Professional Practice Committee . 13. Older Adults: Standards of Care in Diabetes—2025. Diabetes Care 2024; 48(Supplement_1): S266–S282. https://doi.org/10.2337/dc25-S013

30.

Munn

Peters

MDJ

Stern

, et al. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol 2018; 18(1): 143. https://doi.org/10.1186/s12874-018-0611-x

31.

Arksey

O’Malley

. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 2005; 8(1): 19–32. https://doi.org/10.1080/1364557032000119616

32.

Levac

Colquhoun

O’Brien

. Scoping studies: Advancing the methodology. Implement Sci 2010; 5: 69. https://doi.org/10.1186/1748-5908-5-69

33.

Michie

Richardson

Johnston

, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013; 46(1): 81–95. https://doi.org/10.1007/s12160-013-9486-6

34.

Kahneman

. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011.

35.

Black

Johnston

Michie

, et al. Behaviour change techniques associated with smoking cessation in intervention and comparator groups of randomized controlled trials: A systematic review and meta‐regression. Addiction 2020; 115(11): 2008–2020. https://doi.org/10.1111/add.15056

36.

Carraça

Encantado

Battista

, et al. Effective behavior change techniques to promote physical activity in adults with overweight or obesity: A systematic review and meta‐analysis. Obesity Reviews 2021; 22(S4): e13258. https://doi.org/10.1111/obr.13258

37.

Turner

LaMonica

Flood

. Behaviour change techniques used in mediterranean diet interventions for older adults: A systematic scoping review. Nutrients 2023; 15: 1–15. https://doi.org/10.3390/nu15051189

38.

Corker

Marques

Johnston

, et al. Behaviour change techniques taxonomy v1: Feedback to inform the development of an ontology. Wellcome Open Res 2022; 7: 211. https://doi.org/10.12688/wellcomeopenres.18002.2

39.

Scott

de Barra

Johnston

, et al. Using the behaviour change technique taxonomy v1 (BCTTv1) to identify the active ingredients of pharmacist interventions to improve non-hospitalised patient health outcomes. BMJ Open 2020; 10(9): e036500. https://doi.org/10.1136/bmjopen-2019-036500

40.

Eysenbach

. What is e-health? J Med Internet Res 2001; 3(2): e20. https://doi.org/10.2196/jmir.3.2.e20

41.

Richardson

Lawrence

Schoenthaler

, et al. A framework for digital health equity. NPJ Digit Med 2022; 5(1): 119. https://doi.org/10.1038/s41746-022-00663-0

42.

Dening

George

Ball

, et al. Randomised controlled trial of a web-based low carbohydrate diet intervention for adults with type 2 diabetes: The T2Diet study protocol. BMJ Open 2022; 12(2): e054594. https://doi.org/10.1136/bmjopen-2021-054594

43.

Adu

Malabu

Malau-Aduli

AEO

, et al. Efficacy and acceptability of My Care Hub mobile app to support self-management in Australians with type 1 or type 2 diabetes. International Journal of Environmental Research and Public Health 2020; 17(7): 2573. https://doi.org/10.3390/ijerph17072573

44.

Batch

Spratt

Blalock

, et al. General Behavioral Engagement and Changes in Clinical and Cognitive Outcomes of Patients with Type 2 Diabetes Using the Time2Focus Mobile App for Diabetes Education: Pilot Evaluation. Journal of Medical Internet Research 2021; 23(1): e17537. https://doi.org/10.2196/17537

45.

Boels

Vos

Dijkhorst-Oei

, et al. Effectiveness of diabetes self-management education and support via a smartphone application in insulin-treated patients with type 2 diabetes: Results of a randomized controlled trial (TRIGGER study). BMJ Open Diabetes Research and Care 2019; 7(1): e000981. https://doi.org/10.1136/bmjdrc-2019-000981

46.

Burner

Lam

DeRoss

, et al. Using Mobile Health to Improve Social Support for Low-Income Latino Patients with Diabetes: A Mixed-Methods Analysis of the Feasibility Trial of TExT-MED + FANS. Diabetes Technol Ther 2018; 20(1): 39–48. https://doi.org/10.1089/dia.2017.0198

47.

Gatwood

Balkrishnan

Erickson

, et al. The impact of tailored text messages on health beliefs and medication adherence in adults with diabetes: A randomized pilot study. Res Social Adm Pharm 2016; 12(1): 130–140. https://doi.org/10.1016/j.sapharm.2015.04.007

48.

Gong

Baptista

Russell

, et al. My Diabetes Coach, a Mobile App-Based Interactive Conversational Agent to Support Type 2 Diabetes Self-Management: Randomized Effectiveness-Implementation Trial. J Med Internet Res 2020; 22(11): e20322. https://doi.org/10.2196/20322

49.

Holmes-Truscott

Holloway

Lam

, et al. ‘Is Insulin Right for Me?’: Web-based intervention to reduce psychological barriers to insulin therapy among adults with non-insulin-treated type 2 diabetes-A randomised controlled trial. Diabet Med 2023; 40(7): e15117. https://doi.org/10.1111/dme.15117

50.

Johnson

D'Eramo Melkus

Reagan

, et al. Learning in a Virtual Environment to Improve Type 2 Diabetes Outcomes: Randomized Controlled Trial. JMIR Form Res 2023; 7: e40359. https://doi.org/10.2196/40359

51.

Kassavou

Mirzaei

Brimicombe

, et al. A Highly Tailored Text and Voice Messaging Intervention to Improve Medication Adherence in Patients With Either or Both Hypertension and Type 2 Diabetes in a UK Primary Care Setting: Feasibility Randomized Controlled Trial of Clinical Effectiveness. J Med Internet Res 2020; 22(5): e16629. https://doi.org/10.2196/16629

52.

Plotnikoff

Wilczynska

Cohen

, et al. Integrating smartphone technology, social support and the outdoor physical environment to improve fitness among adults at risk of, or diagnosed with, Type 2 Diabetes: Findings from the 'eCoFit' randomized controlled trial. Prev Med 2017; 105: 404–411. https://doi.org/10.1016/j.ypmed.2017.08.027

53.

Poppe

De Bourdeaudhuij

Verloigne

, et al. Efficacy of a Self-Regulation-Based Electronic and Mobile Health Intervention Targeting an Active Lifestyle in Adults Having Type 2 Diabetes and in Adults Aged 50 Years or Older: Two Randomized Controlled Trials. J Med Internet Res 2019; 21(8): e13363. https://doi.org/10.2196/13363

54.

Sittig

Wang

Iyengar

, et al. Incorporating Behavioral Trigger Messages Into a Mobile Health App for Chronic Disease Management: Randomized Clinical Feasibility Trial in Diabetes. JMIR Mhealth Uhealth 2020; 8(3): e15927. https://doi.org/10.2196/15927

55.

Trief

Fisher

Sandberg

, et al. Health and Psychosocial Outcomes of a Telephonic Couples Behavior Change Intervention in Patients With Poorly Controlled Type 2 Diabetes: A Randomized Clinical Trial. Diabetes Care 2016; 39(12): 2165–2173. https://doi.org/10.2337/dc16-0035

56.

Buendia

Havsol

Lundberg

, et al. (eds) Analysis of Use and Outcomes of the Balance Digital Disease Management Tool for Patients with Type 2 Diabetes. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBS, 2021.

57.

Clark

Gallo

Euyoque

, et al.

Does Diabetes Distress Influence Clinical Response to an mHealth Diabetes Self-Management Education and Support Intervention?

Diabetes Educ 2020; 46(3): 289–296. https://doi.org/10.1177/0145721720913276

58.

Cross

Kurmas

Byrne

, et al. Diabetes Self-Management Education Support Healthy Teaching Kitchen Cooking Classes Delivered Using a Telehealth Technology. Science of Diabetes Self-Management and Care 2021; 47(3): 199–206. https://doi.org/10.1177/26350106211004887

59.

DeBarthe Sadler

Saperstein

Carpenter

, et al. Community evaluation of the national diabetes education program’s diabetes healthsense website. The Diabetes Educator 2017; 43(5): 476–485. https://doi.org/10.1177/0145721717721183

60.

du Pon

Kleefstra

Cleveringa

, et al. Effects of the Proactive Interdisciplinary Self-Management (PRISMA) Program on Online Care Platform Usage in Patients with Type 2 Diabetes in Primary Care: A Randomized Controlled Trial. J Diabetes Res 2020; 2020: 5013142. https://doi.org/10.1155/2020/5013142

61.

Fortmann

Gallo

Garcia

, et al. Dulce Digital: An mHealth SMS-Based Intervention Improves Glycemic Control in Hispanics With Type 2 Diabetes. Diabetes Care 2017; 40(10): 1349–1355. https://doi.org/10.2337/dc17-0230

62.

Haste

Adamson

McColl

, et al. Web-Based Weight Loss Intervention for Men With Type 2 Diabetes: Pilot Randomized Controlled Trial. JMIR Diabetes 2017; 2(2): e14. https://doi.org/10.2196/diabetes.7430

63.

Kumar

Moseson

Uppal

, et al. A Diabetes Mobile App With In-App Coaching From a Certified Diabetes Educator Reduces A1C for Individuals With Type 2 Diabetes. Diabetes Educ 2018; 44(3): 226–236. https://doi.org/10.1177/0145721718765650

64.

Leong

Lee

Chien

, et al. Social Media-Delivered Patient Education to Enhance Self-management and Attitudes of Patients with Type 2 Diabetes During the COVID-19 Pandemic: Randomized Controlled Trial. Journal of Medical Internet Research 2022; 24(3): e31449. https://doi.org/10.2196/31449

65.

Lim

Ong

Johal

, et al. Effect of a Smartphone App on Weight Change and Metabolic Outcomes in Asian Adults With Type 2 Diabetes: A Randomized Clinical Trial. JAMA Netw Open 2021; 4(6): e2112417. https://doi.org/10.1001/jamanetworkopen.2021.12417

66.

Litchman

Sanchez-Birkhead

, et al. Combining CGM and an Online Peer Support Community for Hispanic Adults with T2D: A Feasibility Study. Journal of Diabetes Science and Technology 2022; 16(4): 866–873. https://doi.org/10.1177/19322968211032278

67.

Threatt

Ward

. Telehealth for diabetes self-management education and support in an underserved, free clinic population: A pilot study. J Am Pharm Assoc 2017; 57(3): 402–406. https://doi.org/10.1016/j.japh.2017.01.019

68.

Peimani

Rambod

Omidvar

, et al. Effectiveness of short message service-based intervention (SMS) on self-care in type 2 diabetes: A feasibility study. Prim Care Diabetes 2016; 10(4): 251–258. https://doi.org/10.1016/j.pcd.2015.11.001

69.

Hidrus

Kueh

Norsa'adah

, et al. Effects of Brain Breaks Video Intervention of Decisional Balance among Malaysians with Type 2 Diabetes Mellitus: A Randomised Controlled Trial. Int J Environ Res Public Health 2021; 18(17): 8972.

70.

Alanzi

Bah

Alzahrani

, et al. Evaluation of a mobile social networking application for improving diabetes Type 2 knowledge: An intervention study using WhatsApp. J Comp Eff Res 2018; 7(9): 891–899. https://doi.org/10.2217/cer-2018-0028

71.

Christensen

Laursen

Lauridsen

, et al. Reversing Type 2 Diabetes in a Primary Care-Anchored eHealth Lifestyle Coaching Programme in Denmark: A Randomised Controlled Trial. Nutrients 2022; 14(16): 3424. https://doi.org/10.3390/nu14163424

72.

Ramirez

. Phone Messaging to Prompt Physical Activity and Social Support Among Low-Income Latino Patients With Type 2 Diabetes: A Randomized Pilot Study. JMIR Diabetes 2017; 2(1): e8. https://doi.org/10.2196/diabetes.7063

73.

Baldwin

Sanatkar

Clarke

, et al. A Web-Based Mental Health Intervention to Improve Social and Occupational Functioning in Adults With Type 2 Diabetes (The Springboard Trial): 12-Month Outcomes of a Randomized Controlled Trial. J Med Internet Res 2020; 22(12): e16729. https://doi.org/10.2196/16729

74.

Ramadas

Chan

CKY

Oldenburg

, et al. Randomised-controlled trial of a web-based dietary intervention for patients with type 2 diabetes: Changes in health cognitions and glycemic control. BMC Public Health 2018; 18(1): 716. https://doi.org/10.1186/s12889-018-5640-1

75.

Bartlett

Farmer

Newhouse

, et al. Effects of Using a Text Message Intervention on Psychological Constructs and the Association Between Changes to Psychological Constructs and Medication Adherence in People With Type 2 Diabetes: Results From a Randomized Controlled Feasibility Study. JMIR Formative Research 2022; 6(4): e30058. https://doi.org/10.2196/30058

76.

Chrysi

Michopoulos

Dimitriadis

, et al. A modern web-based health promotion program for patients in Greece with diabetes 2 and obesity: An interventional study. BMC Public Health 2023; 23(1): 639. https://doi.org/10.1186/s12889-023-15557-3

77.

Dwibedi

Abrahamsson

Rosengren

. Effect of Digital Lifestyle Management on Metabolic Control and Quality of Life in Patients with Well-Controlled Type 2 Diabetes. Diabetes Therapy 2022; 13(3): 423–439. https://doi.org/10.1007/s13300-022-01214-2

78.

Egede

Walker

Payne

, et al. Effect of psychotherapy for depression via home telehealth on glycemic control in adults with type 2 diabetes: Subgroup analysis of a randomized clinical trial. J Telemed Telecare 2018; 24(9): 596–602. https://doi.org/10.1177/1357633X17730419

79.

Mayberry

Berg

Greevy

, et al. Mixed-Methods Randomized Evaluation of FAMS: A Mobile Phone-Delivered Intervention to Improve Family/Friend Involvement in Adults’ Type 2 Diabetes Self-Care. Ann Behav Med 2021; 55(2): 165–178. https://doi.org/10.1093/abm/kaaa041

80.

Nelson

Greevy

Spieker

, et al. Effects of a Tailored Text Messaging Intervention Among Diverse Adults With Type 2 Diabetes: Evidence From the 15-Month REACH Randomized Controlled Trial. Diabetes Care 2021; 44(1): 26–34. https://doi.org/10.2337/dc20-0961

81.

Pichayapinyo

Saslow

Aikens

, et al. Feasibility study of automated interactive voice response telephone calls with community health nurse follow-up to improve glycaemic control in patients with type 2 diabetes. Int J Nurs Pract 2019; 25(6): e12781. https://doi.org/10.1111/ijn.12781

82.

Olomu

Tikaria

Kelly-Blake

, et al. Type 2 diabetes patient activation and mHealth interventions decreased cardiovascular disease risk. Am J Manag Care 2022; 28(11): e392–e398. https://doi.org/10.37765/ajmc.2022.89263

83.

McGloin

O'Connell

Glacken

, et al. Patient empowerment using electronic telemonitoring with telephone support in the transition to insulin therapy in adults with type 2 diabetes: Observational, pre-post, mixed methods study. Journal of Medical Internet Research 2020; 22(5): e16161. https://doi.org/10.2196/16161

84.

Holmes-Truscott

Holloway

Husin

, et al. ‘Is insulin right for me?’: Feasibility of a pilot randomised controlled trial and acceptability of a web-based intervention to reduce psychological barriers to insulin therapy among adults with type 2 diabetes. Diabet Med 2022; 39(3): e14759. https://doi.org/10.1111/dme.14759

85.

Arslan

. Exploring the Potential of Chat GPT in Personalized Obesity Treatment. Ann Biomed Eng 2023; 51(9): 1887–1888. https://doi.org/10.1007/s10439-023-03227-9

86.

Cha

Son

Huh

, et al. AI-powered hybrid self-management coaching for adults with early-onset Type 2 diabetes in Korea: A randomized clinical trial protocol. Contemp Clin Trials 2025; 159: 108132. https://doi.org/10.1016/j.cct.2025.108132

87.

Cha

Hwan Shin

Lee

, et al. Precision Diabetes Education and Support Considering Patients' Behavioral and Psychological Phenotype: A Q-Methodology Study. West J Nurs Res 2024; 46(8): 602–610. https://doi.org/10.1177/01939459241258139

88.

Levy

Huang

Havumaki

, et al. The role of public policies in reducing smoking prevalence: results from the Michigan SimSmoke tobacco policy simulation model. Cancer Causes Control 2016; 27(5): 615–625. https://doi.org/10.1007/s10552-016-0735-4

89.

Hassan

Slight

Bimpong

, et al. Systematic review to understand users perspectives on AI-enabled decision aids to inform shared decision making. npj Digital Medicine 2024; 7(1): 332. https://doi.org/10.1038/s41746-024-01326-y

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