The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders: A randomized, controlled, open-label study

Effects of digital self-care intervention for Korean older adults with type 2 diabetes: A randomized controlled trial over 12 weeks

This study developed and assessed the impact of a digitally enabled self-care intervention program tailored for older adults with type 2 diabetes led by nursing professionals. A randomized controlled trial of a 12-week digital self-care intervention was conducted with 105 older Korean adults with type 2 diabetes. The intervention involved self-recording in the DiaNote application, newly developed for the study and a phone visit. Participants were randomly allocated to DiaNote or traditional logbook groups. Outcomes were collected at baseline and again after 12 weeks. Generalized estimating equations indicated that HbA1c level changes over time significantly in DiaNote group. Diabetes self-care activities and quality of life changed over time in both groups. Self-efficacy did not significantly differ between groups or over time. The digital self-care intervention was beneficial for blood sugar control, being equivalent to using a traditional diabetes logbook for quality of life and diabetic self-care.

Evaluation of Mobile Applications for Patients with Diabetes Mellitus: A Scoping Review

There has been increasing interest in mobile healthcare for diabetes management. However, there remains limited evidence regarding the effectiveness of these mobile applications (apps). This scoping review aimed to evaluate the clinical effectiveness of mobile diabetes management apps. We used the following search terms: "mobile app", "mobile application", and "diabetes". We included only articles written in English and published between January 2016 and August 2021. We identified two, six, and four articles focused on type 1 diabetes, type 2 diabetes, and both diabetes types, respectively. Five, four, and three of these studies reported on the apps' functionality, usability, and both, respectively. Our findings indicated that diabetes mobile apps allowed for convenient user experience and improved blood sugar levels in patients with diabetes. Considering these findings, usability must be comprehensively evaluated by using definitions such as the ISO9241-11 usability definition or the mobile application rating scale (MARS) when developing diabetes-related apps. For the feasibility of diabetes mobile apps, we recommend that HbA1C and self-management be included as evaluation variables. Given the increasing importance of continuous management for patients with diabetes, interventions using mobile apps are bound to become effective tools for patient-led self-management.

Real-World Evidence of a Hospital-Linked Digital Health App for the Control of Hypertension and Diabetes Mellitus in South Korea: Nationwide Multicenter Study

BACKGROUND

Digital health care apps have been widely used for managing chronic conditions such as diabetes mellitus and hypertension, providing promising prospects for enhanced health care delivery, increased patient engagement, and improved self-management. However, the impact of integrating these apps within hospital systems for managing such conditions still lacks conclusive evidence.

OBJECTIVE

We aimed to investigate the real-world effectiveness of using hospital-linked digital health care apps in lowering blood pressure (BP) and blood glucose levels in patients with hypertension and diabetes mellitus.

METHODS

Nationwide multicenter data on demographic characteristics and the use of a digital health care app from 233 hospitals were collected for participants aged 20 to 80 years in South Korea between August 2021 and June 2022. We divided the participants into 2 groups: 1 group consisted of individuals who exclusively used the digital health app (control) and the other group used the hospital-linked digital health app. All the patients participated in a 12-week digital health care intervention. We conducted a comparative analysis to assess the real-world effectiveness of the hospital-linked digital health app. The primary outcome was the differences in the systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG) level, and postprandial glucose (PPG) level between baseline and 12 weeks.

RESULTS

A total of 1029 participants were analyzed for the FBG level, 527 participants were analyzed for the PPG level, and 2029 participants for the SBP and DBP were enrolled. After 12 weeks, a hospital-linked digital health app was found to reduce SBP (-5.4 mm Hg, 95% CI -7.0 to -3.9) and DBP (-2.4 mm Hg, 95% CI -3.4 to -1.4) in participants without hypertension and FBG level in all participants (those without diabetes, -4.4 mg/dL, 95% CI -7.9 to -1.0 and those with diabetes, -3.2 mg/dL, 95% CI -5.4 to -1.0); however, there was no statistically significant difference compared to the control group (using only digital health app). Specifically, participants with diabetes using a hospital-linked digital health app demonstrated a significant decrease in PPG after 12 weeks (-10.9 mg/dL, 95% CI -31.1 to -5.3) compared to those using only a digital health app (P=.006).

CONCLUSIONS

Hospital-linked digital interventions have greatly improved glucose control for diabetes compared with using digital health technology only. These hospital-linked digital health apps have the potential to offer consumers and health care professionals cost-effective support in decreasing glucose levels when used in conjunction with self-monitoring.

Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes

BACKGROUND

There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision-makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations.

OBJECTIVES

To update existing systematic reviews of diabetes QI programmes and apply novel meta-analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care.

SEARCH METHODS

We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top-up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section.

SELECTION CRITERIA

We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system- or provider-targeted QI strategy alone or in combination with a patient-targeted strategy. - System-targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). - Provider-targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). - Patient-targeted: patient education (PE); promotion of self-management (PSM); patient reminders (PR). Patient-targeted QI strategies needed to occur with a minimum of one provider or system-targeted strategy.

DATA COLLECTION AND ANALYSIS

We dual-screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta-regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outcomes. To explore this further, we extended the main additive model for continuous outcomes (HbA1c, SBP and LDL-C) to include an interaction term between each strategy and average baseline risk for each study (baseline thresholds were based on a data-driven approach; we used the median of all baseline values reported in the trials). Based on model diagnostics, the baseline interaction models for HbA1c, SBP and LDL-C performed better than the main model and are therefore presented as the primary analyses for these outcomes. Based on the model results, we qualitatively ordered each QI strategy within three tiers (Top, Middle, Bottom) based on its magnitude of effect relative to the other QI strategies, where 'Top' indicates that the QI strategy was likely one of the most effective strategies for that specific outcome. Secondary analyses explored the sensitivity of results to choices in model specification and priors.  Additional information about the methods and results of the review are available as Appendices in an online repository. This review will be maintained as a living systematic review; we will update our syntheses as more data become available.

MAIN RESULTS

We identified 553 trials (428 patient-randomised and 125 cluster-randomised trials), including a total of 412,161 participants. Of the included studies, 66% involved people living with type 2 diabetes only. Participants were 50% female and the median age of participants was 58.4 years. The mean duration of follow-up was 12.5 months. HbA1c was the commonest reported outcome; screening outcomes and outcomes related to cardiovascular medications, smoking and harms were reported infrequently. The most frequently evaluated QI strategies across all study arms were PE, PSM and CM, while the least frequently evaluated QI strategies included AF, FI and CQI. Our confidence in the evidence is limited due to a lack of information on how studies were conducted.  Four QI strategies (CM, TC, PE, PSM) were consistently identified as 'Top' across the majority of outcomes. All QI strategies were ranked as 'Top' for at least one key outcome. The majority of effects of individual QI strategies were modest, but when used in combination could result in meaningful population-level improvements across the majority of outcomes. The median number of QI strategies in multicomponent QI programmes was three.  Combinations of the three most effective QI strategies were estimated to lead to the below effects:  - PR + PSM + CE: decrease in HbA1c by 0.41% (credibility interval (CrI) -0.61 to -0.22) when baseline HbA1c < 8.3%; - CM + PE + EPR: decrease in HbA1c by 0.62% (CrI -0.84 to -0.39) when baseline HbA1c > 8.3%;  - PE + TC + PSM: reduction in SBP by 2.14 mmHg (CrI -3.80 to -0.52) when baseline SBP < 136 mmHg; - CM + TC + PSM: reduction in SBP by 4.39 mmHg (CrI -6.20 to -2.56) when baseline SBP > 136 mmHg;  - TC + PE + CM: LDL-C lowering of 5.73 mg/dL (CrI -7.93 to -3.61) when baseline LDL < 107 mg/dL; - TC + CM + CR: LDL-C lowering by 5.52 mg/dL (CrI -9.24 to -1.89) when baseline LDL > 107 mg/dL. Assuming a baseline screening rate of 50%, the three most effective QI strategies were estimated to lead to an absolute improvement of 33% in retinopathy screening (PE + PR + TC) and 38% absolute increase in foot screening (PE + TC + Other).

AUTHORS' CONCLUSIONS

There is a significant body of evidence about QI programmes to improve the management of diabetes. Multicomponent QI programmes for diabetes care (comprised of effective QI strategies) may achieve meaningful population-level improvements across the majority of outcomes. For health system decision-makers, the evidence summarised in this review can be used to identify strategies to include in QI programmes. For researchers, this synthesis identifies higher-priority QI strategies to examine in further research regarding how to optimise their evaluation and effects. We will maintain this as a living systematic review.

The Clinical Effects of Type 2 Diabetes Patient Management Using Digital Healthcare Technology: A Systematic Review and Meta-Analysis

The disease control rate is very low (at less than 30%) for diabetes. The use of digital healthcare technology is increasing recently for continuous management in daily life. In this study, a meta-analysis was conducted to evaluate the clinical effects of digital healthcare technology for patients with type 2 diabetes management. For a review of the literature, databases such as PubMed, Embase, and Cochrane Library were searched using Medical Subject Heading (MeSH) terms published up to 9 August 2021. As a result, 2354 articles were identified, and 12 randomized controlled trial articles were finally included. Digital healthcare technology combined management for type 2 diabetes significantly decreased HbA1c (p < 0.00001, standardized mean difference (SMD) = −0.49) and marginally decreased triglyceride, compared with usual care (p = 0.06, SMD = −0.18). However, it did not significantly affect BMI (p = 0.20, SMD = −0.47), total cholesterol (p = 0.13, SMD = −0.19), HLD-C (p = 0.89, SMD = −0.01), LDL-C (p = 0.95, SMD = −0.01), systolic BP (p = 0.83, SMD = 0.03), or diastolic BP (p = 0.23, SMD = 0.65), compared with usual care. These results indicate that digital healthcare technology can improve HbA1c and triglyceride levels of type 2 diabetes patients. Further well-designed randomized controlled clinical trials are needed to confirm the clinical effect of digital healthcare technology.

Effect of mHealth Interventions on Glycemic Control and HbA1c Improvement among Type II Diabetes Patients in Asian Population: A Systematic Review and Meta-Analysis

INTRODUCTION

Due to the high prevalence of diabetes mellitus, it is pertinent to educate and inform diabetes patients about their self-management. It can be done effectively using innovative methods like mobile health (mHealth), which includes mobile applications, phone calls, and text messages. Thus, this meta-analysis was conducted to summarize the effectiveness of mHealth interventions for the management of diabetes compared with usual care in the Asian population.

MATERIALS AND METHODS

Searches were performed in electronic databases, namely PubMed, Scopus, Embase, and Cochrane Library, in August and September 2020. Search terms used were "Diabetes Mellitus," "mHealth," "glycemic control", "HbA1c levels," and "Blood glucose levels." The primary outcome was glycated hemoglobin and blood glucose levels. Trials were pooled, and heterogeneity was quantified using the I² statistic.

RESULTS

The search yielded 3980 abstracts, of which 18 trials met the inclusion criteria. Lowering of Hba1c levels was reported in the majority of trials, which aided in Glycemic control. For post prandial blood glucose (PPBG) levels, a statistically significant reduction of value -20.13 (95%CI -35.16 to -5.10, P = 0.009, I² = 59%) was seen in the mean in the intervention group, whereas for HbA1c levels the mean reduction in the intervention group was -0.44 (95%CI, -0.79 to 0.10, P = 0.01, I² = 87%). Although these interventions proved beneficial for these outcomes, there was a difference in the amount of effects caused by different mHealth interventions.

CONCLUSION

This study acknowledged the effects of different mHealth interventions as per their accessibility and availability in recent years. There is a need to include more studies in future reviews to generate a larger body of evidence for the reported outcomes. The researchers should give the utmost priority to the transparency while reporting the interventions for effective interpretation of the retrieved data.

A Novel User Utility Score for Diabetes Management Using Tailored Mobile Coaching: Secondary Analysis of a Randomized Controlled Trial

Background

Mobile health applications have been developed to support diabetes self-management, but their effectiveness could depend on patient engagement. Therefore, patient engagement must be examined through multifactorial tailored behavioral interventions from an individual perspective.

Objective

This study aims to evaluate the usefulness of a novel user utility score (UUS) as a tool to measure patient engagement by using a mobile health application for diabetes management.

Methods

We conducted a subanalysis of results from a 12-month randomized controlled trial of a tailored mobile coaching (TMC) system among insurance policyholders with type 2 diabetes. UUS was calculated as the sum of the scores for 4 major core components (range 0-8): frequency of self-monitoring blood glucose testing, dietary and exercise records, and message reading rate. We explored the association between UUS for the first 3 months and glycemic control over 12 months. In addition, we investigated the relationship of UUS with blood pressure, lipid profile, and self-report scales assessing diabetes self-management.

Results

We divided 72 participants into 2 groups based on UUS for the first 3 months: UUS:0-4 (n=38) and UUS:5-8 (n=34). There was a significant between-group difference in glycated hemoglobin test (HbA_1c) levels for the 12-months study period (P=.011). The HbA_1c decrement at 12 months in the UUS:5-8 group was greater than that of the UUS:0-4 group [–0.92 (SD 1.24%) vs –0.33 (SD 0.80%); P=.049]. After adjusting for confounding factors, UUS was significantly associated with changes in HbA_1c at 3, 6, and 12 months; the regression coefficients were –0.113 (SD 0.040; P=.006), –0.143 (SD 0.045; P=.002), and –0.136 (SD 0.052; P=.011), respectively. Change differences in other health outcomes between the 2 groups were not observed throughout a 12-month follow-up.

Conclusions

UUS as a measure of patient engagement was associated with changes in HbA_1c over the study period of the TMC system and could be used to predict improved glycemic control in diabetes self-management through mobile health interventions.

Trial Registration

ClinicalTrial.gov NCT03033407; https://clinicaltrials.gov/ct2/show/NCT03033407

The difference in knowledge and attitudes of using mobile health applications between actual user and non-user among adults aged 50 and older

Background

Despite the great benefits of mobile health applications (mHAs) in managing non-communicable diseases (NCDs) internationally, studies have documented general challenges to broad adoption of mHAs among older age groups. By focusing on broad adoption, these studies have been limited in their evaluation of adults aged 50 and older who have high risk of NCDs and can benefit the most from the functionalities provided by mHAs.

Objective

This study aims to evaluate the knowledge, self-confidence, perceived benefits, and barriers of using mHAs depending on experience with mHAs among adults aged 50 and older. Furthermore, we aim to identify the factors associated with the actual use of mHAs.

Methods

We conducted a cross-sectional survey at a single tertiary hospital in Seoul, Korea, between May 1 and May 31, 2018. Of the 625 participants who were contacted, 323 participants were granted full inclusion to the study. We compared demographics, knowledge, self-confidence, and perceived benefits and barriers by experience with using mHAs, then performed logistic regression to identify the factors associated with mHA use.

Results

Among the participants, 64.1% (N = 207) had experience using mHAs. Those in the experienced group were more likely to have more than college education (55.1% vs. 27.5%, P < 0.001) and to report a higher monthly income (≥ $7,000, 22.7% vs. 18.1%, P = 0.05) than their less-experienced counterparts. Although the experienced group was more likely to have higher self-confidence in using mHAs, about half of the study participants, including people with experience using mHAs, did not have appropriate knowledge of mobile technology. With adjusted logistic model, higher educated (adjusted PR (aPR) = 1.53, 95% CI, 1.26–1.80), higher perceived benefits of mHAs (aPR = 1.43, 95% CI, 1.04–1.83), and higher self-confidence using mHAs (aPR = 1.41, 95% CI, 1.12–1.70) were significant factors associated with mHA use.

Conclusions

The use of mHAs among adults aged 50 and older is becoming more common globally; nevertheless, there are still people unable to use mHAs properly because of lack of experience and knowledge. Strategies are needed to encourage the reliable usage of mHAs among those who may need it the most by improving self-confidence and better articulating benefits.

Effect of Voluntary Participation on Mobile Health Care in Diabetes Management: Randomized Controlled Open-Label Trial

Background

The role of mobile health care (mHealth) in glycemic control has been investigated, but its impact on self-management skills and its psychological aspects have not been studied.

Objective

We evaluated the efficacy of mHealth-based diabetes self-management education and the effect of voluntary participation on its effects.

Methods

This study was a randomized controlled open-label trial conducted for 6 months at Kangbuk Samsung Hospital. Participants in the control group (n=31) maintained their previous diabetes management strategies. Participants in the intervention group (n=41) additionally received mHealth-based diabetes self-management education through a mobile app and regular individualized feedback from health care professionals. The primary outcome was change in glycated hemoglobin (HbA_1c) level over 6 months between the 2 groups (intervention versus control) and within each group (at 6 months versus baseline). The secondary outcomes were changes in body mass index, blood pressure, lipid profile, and questionnaire scores (the Korean version of the Summary of Diabetes Self-Care Activities Questionnaire, an Audit of Diabetes Dependent Quality of Life, the Appraisal of Diabetes Scale, and Problem Areas in Diabetes) over 6 months between groups and within each group.

Results

A total of 66 participants completed this study. HbA_1c (P=.04), total cholesterol level (P=.04), and Problem Areas in Diabetes scores (P=.02) significantly decreased; total diet (P=.03) and self-monitoring of blood glucose level scores (P=.01), based on the Summary of Diabetes Self-Care Activities Questionnaire, markedly increased within the intervention group. These significant changes were observed in self-motivated participants who were recruited voluntarily via advertisements.

Conclusions

mHealth-based diabetes self-management education was effective at improving glycemic control and diabetes self-management skills and lowering diabetes-related distress in voluntary participants.

Trial Registration

ClinicalTrials.gov NCT03468283; http://clinicaltrials.gov/ct2/show/NCT03468283

The Use of Mobile Personal Health Records for Hemoglobin A1c Regulation in Patients With Diabetes: Retrospective Observational Study

BACKGROUND

The effectiveness of personal health records (PHRs) in diabetes management has already been verified in several clinical trials; however, evidence of their effectiveness in real-world scenarios is also necessary. To provide solid real-world evidence, an analysis that is more accurate than the analyses solely based on patient-generated health data should be conducted.

OBJECTIVE

This study aimed to conduct a more accurate analysis of the effectiveness of using PHRs within electronic medical records (EMRs). The results of this study will provide precise real-world evidence of PHRs as a feasible diabetes management tool.

METHODS

We collected log data of the sugar function in the My Chart in My Hand version 2.0 (MCMH 2.0) app from Asan Medical Center (AMC), Seoul, Republic of Korea, between December 2015 and April 2018. The EMR data of MCMH 2.0 users from AMC were collected and integrated with the PHR data. We classified users according to whether they were continuous app users. We analyzed and compared their characteristics, patterns of hemoglobin A_1c (HbA_1c) levels, and the proportion of successful HbA_1c control. The following confounders were adjusted for HbA_1c pattern analysis and HbA_1c regulation proportion comparison: age, sex, first HbA_1c measurement, diabetes complications severity index score, sugar function data generation weeks, HbA_1c measurement weeks before MCMH 2.0 start, and generated sugar function data count.

RESULTS

The total number of MCMH 2.0 users was 64,932, with 7453 users having appropriate PHRs and diabetes criteria. The number of continuous and noncontinuous users was 133 and 7320, respectively. Compared with noncontinuous users, continuous users were younger (P<.001) and had a higher male proportion (P<.001). Furthermore, continuous users had more frequent HbA_1c measurements (P=.007), shorter HbA_1c measurement days (P=.04), and a shorter period between the first HbA_1c measurement and MCMH 2.0 start (P<.001). Diabetes severity-related factors were not statistically significantly different between the two groups. Continuous users had a higher decrease in HbA_1c (P=.02) and a higher proportion of regulation of HbA_1c levels to the target level (P=.01). After adjusting the confounders, continuous users had more decline in HbA_1c levels than noncontinuous users (P=.047). Of the users who had a first HbA_1c measurement higher than 6.5% (111 continuous users and 5716 noncontinuous users), continuous users had better regulation of HbA_1c levels with regard to the target level, 6.5%, which was statistically significant (P=.04).

CONCLUSIONS

By integrating and analyzing patient- and clinically generated data, we demonstrated that the continuous use of PHRs improved diabetes management outcomes. In addition, the HbA_1c reduction pattern was prominent in the PHR continuous user group. Although the continued use of PHRs has proven to be effective in managing diabetes, further evaluation of its effectiveness for various diseases and a study on PHR adherence are also required.

Development and Assessment of a Mobile Health Application for Monitoring the Linkage Among Treatment Factors of Type 1 Diabetes Mellitus

Background: In the daily routine of type 1 diabetes mellitus (T1DM), the patients deal with many data and consider many variables to perform actions, decisions, and regimen adjustments. There is a need to apply filtering techniques to extract relevant information and provide appropriate data visualization methods to assist in clinical tasks and decision making. Objective: To present Soins DM, a mobile health tool, for monitoring the linkage among treatment factors of T1DM with an interactive data visualization approach. Methods: First, we performed a literature review, a commercial search, and ideation. Next, we created a prototype and an online survey for its feedback, with participation of 76 individuals. Afterward, the mobile app and its website version were built. Eventually, we conducted a pilot experiment with 4 patients, an online experiment for satisfaction assessment with 97 patients, and an online assessment by 9 health professionals. Results: Prototyping and feedback facilitated the design refinement. Soins DM enables the recording of data from routines of glycemia, insulin applications, meals, and physical exercises. From these logs, the app builds two different ways of interactive data visualization, a timeline and an integrated chart, providing personalized feedback on bad glycemia with its possible causes. The assessments revealed overall satisfaction with the app's characteristics. Conclusions: Soins DM is a novel application with interactive visualization and personalized feedback for easy identification of the linkage among treatment factors of T1DM. The test scenario with patients and health professionals indicates Soins DM as a useful and reliable tool.

Popular Diabetes Apps and the Impact of Diabetes App Use on Self-Care Behaviour: A Survey Among the Digital Community of Persons With Diabetes on Social Media

Introduction: This study aimed to identify popular diabetes applications (apps) and to investigate the association of diabetes app use and other factors with cumulative self-care behaviour. Methods: From November 2017 to March 2018, we conducted a web-based survey with persons 18 years of age and above. We recruited respondents via diabetes Facebook groups, online patient-forums and targeted Facebook advertisements (ads). Data on participants' demographic, clinical, and self-management characteristics, as well as on self-care behaviour and characteristics of the diabetes apps use were collected. Self-care behaviour was measured using a licensed version of the Summary of Diabetes Self-care Activities (SDSCA) questionnaire. The cumulative self-care score was calculated by summing up scores for "general diet," "specific diet," "exercise," "blood glucose testing," "foot care" and "smoking." To identify popular diabetes apps, users were requested to list all apps they use for diabetes self-management. Two sample t-test and multiple linear regression stratified by type of diabetes were performed to examine associations between app use and self-care behaviour, by controlling for key confounders. Results: One thousand fifty two respondents with type 1 and 630 respondents with type 2 diabetes mellitus (DM) entered the survey. More than half, 549 (52.2%), and one third, 210 (33.3%), of respondents with type 1 and 2 DM, respectively, reported using diabetes apps for self-management. "mySugr" and continuous glucose monitoring apps, such as "Dexcom," "Freestyle Libre," and "Xdrip+" were some of the most popular diabetes apps. In both respondent groups, the cumulative self-care behaviour score was significantly higher among diabetes app users (compared to non-users) and scores for three individual self-care components, namely "blood glucose monitoring," "general diet," and "physical activity" were significantly higher among diabetes app users than among non-users. After adjusting for confounding factors, diabetes app use increased the cumulative self-care score by 1.08 (95%CI: 0.46-1.7) units among persons with type 1 DM and by 1.18 (95%CI: 0.26-2.09) units among persons with type 2 DM, respectively. Conclusion: For both, persons with type 1 and type 2 diabetes, using diabetes apps for self-management was positively associated with self-care behaviour. Our findings suggest that apps can support changes in lifestyle and glucose monitoring in these populations.