This systematic review was conducted and reported following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines of 2020 [27]. Details of the PRISMA 2020 checklist are shown in Multimedia Appendix 1 [27]. The protocol of our systematic review was prospectively registered at PROSPERO (International Prospective Register of Systematic Reviews; registration number CRD42024512690). Minor changes were made to the registration information: the title was updated, the quadruple aims were added to the objectives, and WHO’s DHI classification was included in the data synthesis.

Our study was a systematic review that used nonidentifiable, secondary data from published studies. According to institutional policies, no ethics review was required.

The PubMed, Embase, CINAHL, and Web of Science databases were searched with keywords in both Medical Subject Headings and title/abstract formats: “digital health interventions,” “type 2 diabetes,” and “prevention.” The review included only studies conducted in the past 10 years, from January 2014 to March 2024 (due to the emerging nature of the digital transformation in health care). Building, testing, and finalizing the search approach were performed by the research team, in consultation with 2 research librarians from the University of Queensland (Multimedia Appendix 2).

Inclusion and exclusion criteria are listed in Table 1. The intervention of interest was defined as the use of DHIs in support of the prevention of T2DM in individuals with monitored blood glucose or HbA_1c. Given our focus on assessing the effects of DHIs on preventing or delaying the onset of T2DM, and considering that the diagnostic criteria for T2DM include HbA_1c, blood glucose, and clinical criteria, our primary outcomes were the development of T2DM, HbA_1c change, or blood glucose change (dysglycemia changes). Secondary outcomes were consumer experience, HCP experience, and health care costs. Quantitative and qualitative data were included in our review. Studies were excluded if individuals or populations had known diabetes (T2DM, type 1 diabetes, or gestational diabetes, as described by authors of specific studies) or outcomes that did not report the development of T2DM, HbA_1c change, or blood glucose change.

Five reviewers participated in the selection and data extraction processes (authors TD, WW, QO, and LW as primary reviewers and author CS as a senior reviewer). All papers retrieved from the database were collected and imported to EndNote version 20 (Clarivate) before being uploaded to Covidence version 2. Titles and abstracts of identified studies were screened twice (by TD, WW, QO, and LW) for potential eligibility using the inclusion and exclusion criteria. Full texts that met the inclusion criteria were retrieved and independently evaluated for their eligibility by the reviewers. Duplicates identified either automatically by Covidence or manually were excluded. Reasons for excluding full-text papers were reported. Any disagreements were solved via discussion and consensus.

A data extraction form was developed by the research team and uploaded to Covidence (Multimedia Appendix 3). Data from the selected papers were extracted and then checked (TD and QO). Any discordance was resolved via discussion and consensus of the reviewers.

Primary and secondary outcomes were mapped to the quadruple aims, which included improving population health, enhancing consumer experience, enhancing HCP experience, and reducing health costs. The population health aspect was measured by the development of T2DM and dysglycemia changes (changes in HbA_1c or blood glucose).

Using WHO’s DHI classification (2023 version) [7], DHIs were classified into different categories, such as targeted communication with persons (TCP) for targeted individuals, untargeted communication with persons for undefined groups, person-to-person communication (PPC) in networks or forums, personal health tracking (PHT) for self-monitoring, on-demand communication with persons (DCP) for accessing health information, person-centered health records (PHR), HCP decision support (DS), and TM [7]. Details of the classification are shown in Multimedia Appendix 4.

For each study, DHI categories were assessed for their effects on each outcome, categorizing the effects as positive, negative, or neutral:

The overall effects of each DHI category were determined by synthesizing all reported positive, neutral, or negative effects regardless of the number of studies supporting each effect.

A meta-analysis was not performed because of the numerous heterogeneous study designs with different interventions or outcomes.

The Cochrane risk-of-bias version 2 tool (RoB 2) for randomized trials was used to assess the quality of RCTs [32], while the ROBINS-I (risk of bias in nonrandomized studies of interventions) tool was applied for nonrandomized studies [33] by TD, QO, and LJ. Any disagreements were solved via discussion and consensus.

In total, 3373 citations were retrieved from the database search, of which 53 (1.6%) met the inclusion criteria, encompassing a total of 34,488 participants. The number of papers at each stage and the reasons for exclusion are detailed in Figure 1.

The characteristics of the included studies are shown in Multimedia Appendix 5 [5,10,16,34-83]. Most studies were conducted in health care settings (n=35, 66%) and in high-income countries (n=49, 92.5%) [84]. RCTs were the most common research design (n=31, 58.5%). All studies had a duration of at least 3 months. The DHI duration fluctuated from 1 to 48 months. DHIs were mostly applied in combination with HCP interactions (n=43, 81.1%).

The nonrandomized studies showed a disproportionately high number of moderate and serious risks of bias (n=17, 77.3%), predominantly due to not accurately recording and analyzing confounders. In the RCTs, the risk of bias in population health outcomes ranged from low to high, while the risks of bias in HbA_1c and blood glucose change outcomes exhibited some similarities, with approximately 12 of 22 (54.5%) and 10 of 23 (43.5%) studies, respectively, presenting either some concerns or high risks. However, a few studies (n=7, 77.8%) had a high risk of bias or some concerns in the T2DM development outcome. This was mainly due to discrepancies observed in the measurement of the T2DM development outcome between groups and bias resulting from missing outcome data. There were no high risks of bias in consumer experience and health care cost outcomes, with the majority having low risks (n=37, 70%, and n=35, 66.7%, respectively). The HCP experience outcome was not assessed in RCTs. There were 24 (45.3%) studies with a low risk of bias in all outcomes and 10 (41.7%) studies with both a low risk of bias and an intervention duration of at least 1 year. Details are shown in Multimedia Appendix 6 [5,10,16,34-83].

Five outcomes were reported: consumer experience (n=13, 24.5%), health care costs (n=3, 5.7%), HCP experience (n=1, 1.9%), development of T2DM (n=14, 26.4%), and dysglycemia changes (n=52, 98.1%). Many studies reported multiple outcomes.

In total, 15 DHIs were investigated. The prevalence of each DHI is visualized in Figure 2 [5,10,16,34-44,46-83,94].

Using WHO’s DHI classification, 7 DHI categories were identified: TCP, PPC, PHT, DCP, PHR, HCP DS, and TM.

For the T2DM development outcome, the effects of DHIs were positive in 1 (7.1%) study and neutral in 9 (64.3%) studies, and there were insufficient data to assess in 4 (28.6%) studies. For the dysglycemia outcome, the effects were positive in 23 (43.4%) studies and neutral in 24 (45.3%) studies, and there were insufficient data in 6 (11.3%) studies. Among the 10 (18.9%) studies with a low risk of bias in all outcomes and an intervention duration of at least 1 year, none assessed the development of T2DM; the effects of DHIs on dysglycemia were positive in 7 (70%) studies and neutral in 2 (20%) studies, and there were insufficient data in 1 (10%) study.

The effects of DHIs in all studies on consumer experience were mixed, with most being positive (n=7, 53.8%). The effects of DHIs on costs, assessed in 2 (3.8%) studies, were found to be negative. The only study reporting HCP experience indicated a positive effect.

Table 2 highlights the effect of each DHI category on each outcome. The effects of DHI categories and subcategories on the outcomes in each study are illustrated in Multimedia Appendix 7 [5,10,16,34-83].

This is the first systematic review to evaluate the effects of DHIs on the quadruple aims in T2DM prevention. Our findings enhance other recent studies, such as Nguyen et al [19], by offering a more comprehensive insight into the outcomes that are measured (and not measured), and the effects of DHIs on each outcome, in alignment with the quadruple aims of health care. This contributes to an evidence-based foundation for the future successful customization and implementation of DHIs for T2DM prevention. This is also the first systematic review of T2DM prevention using WHO’s DHI classification, which significantly aids in a mutually comprehensible language for various communities involved in digital health for T2DM prevention, such as technologists, researchers, clinicians, and consumers.

Our review highlights several important findings. First, there is emerging evidence supporting the effectiveness of DHIs in preventing T2DM; however, the evidence remains limited. Although only 1 study [39] reported positive effects, 9 studies indicated neutral effects and no studies reported negative effects. This is consistent with the findings by Nguyen et al [19]. Despite this, most of these neutral studies still demonstrated clinical improvements in T2DM development, though these improvements did not reach statistical significance. The evidence is clearer on dysglycemia, where the effects were positive in nearly half of the studies and neutral in the other half. Further research will need to conclusively determine the effectiveness of DHIs in preventing T2DM.

Second, the duration of DHI may play an important role in the effects on the population health outcome. All studies successful in improving dysglycemia had a DHI duration of at least 3 months, while the study successful in T2DM prevention had a duration of 12 months [39]. Among the 10 studies with a low risk of bias in all outcomes and an intervention duration of at least 1 year, a high percentage of studies (70%) demonstrated positive effects of DHIs on dysglycemia. Comparable results about dysglycemia were reported by Van Rhoon et al [18] and Donevant et al [85]. This can be because habit formation usually takes 2-3 months [86], and significant HbA_1c changes require at least 3 months [87]. However, recent evidence from DPPs that have proven to be successful shows that long intervention durations are required for delaying T2DM [88], with the National Health Service recommending at least 9 months and the Centers for Disease Control and Prevention suggesting 12 months. For effective T2DM prevention, a minimum DHI duration of 9-12 months may be ideal. Future research should validate these findings.

Third, there was no evidence that DHIs are effective in preventing T2DM without HCP interaction, and most studies (69.6%) successful in improving dysglycemia involved HCP interaction (75% remote, 12.5% in person, and 12.5% both). Comparable results were reported in the review by Grock et al [89], which highlighted the importance of social interaction for successful diabetes prevention interventions. The meta-analysis by Schippers et al [90] reported that mobile apps with personal interaction tools (messages, calls, email, or in-person meetings) are more effective for weight loss than automated interaction. This underscores the critical role of HCP interaction in preventing T2DM, while also revealing the promising potential of replacing in-person HCP interactions with remote interactions for effective DHIs for T2DM prevention.

Next, all studies that reported positive population health outcomes used a minimum of 2 distinct categories of DHIs. These findings share similarities with the systematic review by Van Rhoon et al [18], which suggested that interventions with a larger number of passive and interactive digital features are more effective. In our review, TCP, which involves transmitting health information or health alerts and reminders to patients, was adopted in all included studies, demonstrating its widespread use and simplicity. TCP was used in combination with other DHI categories, such as PHT, TM, PCP, or DCP.

PHT showed evidence as a potentially useful tool for T2DM prevention and dysglycemia improvement in our review. Similarly, many studies showed that PHT successfully increases physical activity and decreases a sedentary lifestyle [91-93]. The effect of PHT on consumer experience was mixed. Automatic data capture in wearables and medical devices received more positive feedback than manual data capture, which had only neutral or negative effects. This is likely because data capture is more aligned with manual tasks, whereas wearables and medical devices are designed for automatic data collection. This result aligns with the study by Kim et al [94] on self-tracking via a web-based platform. Participants using devices with automatic data entry engaged with the platform 4 times longer than those who manually entered data [94]. This evidence strongly suggests that PHT, particularly automated tracking devices, could play a pivotal role in the prevention strategies for T2DM.

Our review suggests that TM may be effective in preventing T2DM and managing dysglycemia. This aligns with the review by Nguyen et al [19]. TM consists of remote consultations through calls and messages, and remote health monitoring. This monitoring can be achieved either automatically or manually via PHT tools, such as wearables, medical devices, or web-based apps. Consequently, there is a significant correlation between TM and PHT. Our review also shows evidence that the combination of TM, PHT, and TCP is effective in T2DM prevention. This suggests that such DHIs should not only be embraced but also be integrated with other DHIs in T2DM prevention.

Although PPC or DCP did not prove effective in preventing T2DM, they showed evidence of improving dysglycemia. PPC may be beneficial in glycemic control for diabetes management [95,96].

There is evidence that using DCP (look-up tools, human-like conversations) is helpful in diabetes prevention and management. In our review, AI used with wearables showed positive effects on glycemic status [46,56]. Similarly, evidence indicates that using AI technology in diabetes management is effective when combined with wearable technologies [97]. Advanced algorithms and data from everyday participants’ activities allowed AI to provide lifestyle recommendations, which were real time, personalized, and contextual for each participant, contributing to delivering patient-centered care [98]. VR was used in 1 study, providing an immersive experience that increased participant engagement and enjoyment [99]. This highlights an advantage of VR technology. Further studies are needed to explore the potential of PPC, AI, and VR in T2DM prevention.

Health records and HCP DS were the least common in our review, with only 1 included study showing neutral effects. These DHIs target HCPs rather than consumers. Since preventing T2DM requires participants to modify lifestyles for a long period, DHIs that motivate and engage consumers may be more beneficial than DHIs targeting HCPs alone. Further studies should explore combining these DHIs with consumer-targeted interventions.

Finally, there were insufficient studies assessing HCP experience (1 study) and health care costs (3 studies). For other topics, there were several studies focusing on the effects of DHIs on HCP experience. Lampickienė et al [100] concluded that HCPs mostly report positive experiences with digital consultations, which have advantages for HCPs and patients. Studies that reported health care costs of DHIs were sparse; all 3 studies in our review indicated no positive cost outcomes in DHIs. According to a systematic review of DHIs by Gentili et al [101], there is convincing evidence of the cost-effectiveness of DHI in health care. They indicated that several types of DHIs, such as videoconferencing systems, messaging, calls, mobile apps, and web-based platforms, help reduce health care costs. Although the findings in our review were different, the small number of studies suggests that it may still be feasible to implement DHIs that are cost-effective in T2DM prevention. Further studies implementing DHIs should assess not only population health outcomes and consumer experiences but also HCP experiences and health care costs.

There are some limitations of our review. The diagnosis criteria for T2DM varied slightly across studies. This is because of different guidelines, resources, and clinical considerations. Although our study reflected a real-world scenario, the variation in diagnosis criteria could potentially lead to data inconsistencies, because different diagnosis criteria may classify the same participant differently.

The inclusion criteria of our study permitted a wide range of variability in aspects, such as study design, study population, DHIs, and duration. DHIs are intended for real-life implementation, and the condition of RCTs is unlikely to match those routine settings, while before-and-after studies or cohort studies can provide valuable insights into special populations, such as patients with chronic liver disease [57] or renal transplant [58], groups that are often difficult to enroll in RCTs. This diversity, while inclusive, posed challenges in drawing direct comparisons and made it unfeasible to conduct a meta-analysis or certainty-of-evidence assessment.

The intervention duration fluctuated between 1 month and 48 months. Chronic diseases, such as T2DM, develop over a long period [102]. Short-term studies may not allow for the comprehensive effects of DHIs to show a clear impact on disease progression. More studies with longer durations are needed. Our study did not summarize the intensity and frequency of the DHI used in each included study. To provide a more comprehensive understanding of the effects of DHIs, further reviews and studies should examine the intensity and frequency of DHIs, rather than solely focusing on their duration.

Finally, our inclusion criteria included papers in English only. This may have potentially excluded some relevant studies.

The findings from this systematic review demonstrate that the effects of DHIs on the quadruple aims in T2DM prevention have proven benefits for population health, mixed results for consumer experience, and insufficient studies on HCP experience and health care costs. Further studies should prioritize improving consumer experience, while also addressing HCP experience and health care costs.

Although evidence supporting the effectiveness of DHIs in reducing the burden of T2DM remains limited, it is clear DHIs are effective in improving dysglycemia. To maximize their effectiveness in preventing T2DM and managing dysglycemia, DHIs should be strategically integrated with in-person or remote HCP interaction. The incorporation of health information transmission, alerts, and reminders for targeted individuals, along with TM and PHT strategies, is paramount. Peer group support, look-up tools, AI, and VR hold promising potential for future exploration in this field. We anticipate the advancement in these technologies will significantly influence the prevention of T2DM in the future.