A precision approach to diabetes care will require that the relevant laboratory methods and assays are carefully standardized and comparable. Assessments that need to be standardized include:.

A challenge is that the frequency of various diabetes phenotypes and risk genotypes may vary by regions of the world and between ethnicities within a region.

For example, T2D often manifests very differently in Native Americans than in people of European ancestry, with Native Americans tending to develop diabetes at a much younger age and experience loss of β-cell function earlier in the life course of the disease Recent insights following the ADA Precision Diabetes Medicine meeting in Madrid held in October confirm that case-based interactive learning is an excellent way to support this type of postgraduate education for clinicians at all levels of training.

Advances in science allow for generation of large-scale biological and physiological data that can be harnessed for precision diagnostic Fig. Programs are needed to train, foster, and retain individuals with biological and data science expertise who will contribute to precision diabetes medicine efforts.

Worldwide differences in prevalence of the forms of diabetes necessitates inclusion of currently understudied populations for the development of precision diagnostics and therapeutics.

As a result, the precise subtype of diabetes a particular individual is diagnosed with may vary in different populations based on subtype frequency or genetic or dietary or lifestyle differences.

Both personal and societal barriers may exist to the implementation of precision prevention across geographic regions and countries. Discussions with global and regional regulatory agencies will be needed to determine the level of evidence needed for approval and adoption of precision diagnostics and therapeutics.

The development of tools and strategies to synthesize patient data and facilitate shared decision making will be needed to translate evidence for precision diabetes medicine into individualized diabetes care, accounting for patient preferences and behaviors, health literacy, and socioeconomic considerations.

Pragmatic studies of decision-support systems utilizing rich information in these health care systems, particularly those with biobank-linked electronic health care records, are needed to guide implementation of precision diabetes medicine into clinical practice and to generate the much needed cost-efficacy data for broader adoption.

Partnerships must be established between the scientific community, patients, health care systems, providers, payors, industry, and regulatory bodies involved in the development, evaluation, approval, adoption, and implementation of precision diagnostics, monitoring, and therapeutics that are deemed acceptable for safe, efficacious, and cost-effective use in precision diabetes care.

Making the most of the opportunities offered by precision diabetes medicine will require many different stakeholders to form highly effective partnerships. Without networks of partnerships that span academic institutions, corporations, payors, regulators, and medical and public interest groups with shared understanding and vision Fig.

Partners in making precision diabetes medicine a reality include:. People with diabetes. People with diabetes are the most important stakeholders. In Western countries, between 1 in 10 and 1 in 20 people have diabetes, while in other parts of the world, diabetes is more prevalent 1 in 3 in some Middle Eastern populations , and 1 in 2 in some Native American tribes [ ].

The precision approach to diabetes will require effective patient-facing, bidirectional communication strategies that explain what precision medicine is and how it works. People with diabetes should be invited to contribute to research through advisory and advocacy positions, to contribute to postgraduate educational programs for clinicians and to play a central role in discussions with politicians, regulators, and payors.

Regulatory agencies. The transition from current diabetes clinical practice to a precision medicine approach will have important implications for the development, prescription, and regulation of diagnostics and therapeutics.

Involvement of regulators at the earliest stages of the precision diabetes medicine workflow will be critical to the successful implementation of the precision approach. Recognizing these challenges, the FDA and the European Medicines Agency have initiated discussions relating to standards for evidence and the design of future clinical trials for precision diabetes medicine Payment for medical care related to diabetes varies greatly, including between regions within countries, with costs for diabetes often hidden in other areas of medical care.

Fragmentation of sites of delivery for diabetes care and its costs directly impact payment policies. There is evidence in the case of monogenic diabetes that a precision medicine approach is cost-effective The delay, or prevention, of complications the major contributor to diabetes costs through precision diabetes medicine may be the strongest driver for adoption.

Product manufacturers. Diabetes technology, including the development of wearable devices for glucose monitoring and for regulating insulin infusions i.

Technology and pharmaceutical implementation is currently at a pre-precision level, and treatment guidelines are quite generic. The European Federation of Pharmaceutical Industries and Associations EFPIA Diabetes Platform, in which six leading pharmaceutical companies are developing shared policy goals focused on improving diabetes clinical outcomes, has initiated multiple projects with strong precision diabetes medicine agendas, with other public-private partnerships focused on precision diabetes medicine underway Private and public supporters of research.

Support for diabetes research funding has struggled as its priority has fallen among the general public and some political decision makers, where cancer and cardiovascular disease rank consistently higher than diabetes on the public agenda. For precision diabetes medicine to meaningfully improve the lives of patients, it will be necessary to build highly effective networks of key stakeholders, such that common agendas are agreed to and funding for research and implementation is made available.

This, in turn, requires that the evidence justifying a precision diabetes medicine approach is clearly articulated to all major decision makers, including funders.

Clinicians and professional organizations. Medical care for the person with diabetes involves a wide spectrum of health care providers, including tertiary and secondary specialists, general internists, primary care doctors, nurses, dietitians, podiatrists, pharmacists, and other paramedical professionals.

Several organizations are engaged in the PMDI ADA, EASD, NIDDK and representatives of professional bodies in Asia, Africa, and elsewhere are being engaged by the PMDI to ensure global impact. Tailoring educational modules and content to different professional and cultural settings is ideally suited to these partner organizations.

General public. The enormous burden that diabetes places on many health care systems is usually shouldered by the general public, owing to the high costs of treating the disease and loss of public revenue through decreased productivity.

The effective implementation of precision prevention will require that the general public embraces the approach and that those in greatest need can access precision prevention programs. Diabetes messaging for the general public can be modeled on precision oncology, for which public advocacy and engagement have been successful, effectively utilizing social media as well as traditional media to communicate not only its strengths and weaknesses but also its benefits and risks.

The path to precision diabetes medicine. HEA, health economic assessment. Adapted from Fitipaldi et al. Precision diabetes medicine has found a firm foothold in the diagnosis and treatment of monogenic diabetes, while the application of precision medicine to other types of diabetes is at this time aspirational, rather than standard of care.

The ability to integrate the diagnosis of monogenic diabetes into routine clinical care is one example where diagnostics are essential and meet many of the characteristics of the ideal test. Despite an excellent diagnostic paradigm, there are no known avenues for prevention in monogenic diabetes, although careful monitoring in presymptomatic variant carriers may lead to early detection of diabetes and rapid treatment.

Future precision diabetes medicine approaches are likely to include diagnostic algorithms for defining diabetes subtypes in order to decide the best interventional and therapeutic approaches. The scope and potential for precision treatment in diabetes is vast, yet deep understanding is lacking.

It will be imperative to determine when and how the application of therapeutics in precision diabetes medicine improves outcomes in a cost-effective fashion.

There are many important stakeholders whose engagement will be necessary for the implementation of precision diabetes medicine to succeed Fig.

Progress in translating advances in biology and technology will be governed by the identification, accurate measurement, and scalable deployment of agents for diagnosis and therapy, so broad stakeholder engagement is essential.

It is crucial that precision approaches are available to the full diversity of human populations and societal contexts, such that precision diabetes medicine does not widen health disparity but achieves the greatest benefits to all individuals and society as a whole.

Highly functional partnerships with patient representatives and public organizations will be required to reap the benefits of precision diabetes medicine. This article is being simultaneously published in Diabetologia DOI: and P. F contributed equally to this Consensus Report and are co-chairs of the Precision Medicine in Diabetes Initiative.

The authors thank P. Siming Lund University for editorial assistance, H. Fitipalidi Lund University for assistance with the design of the figures and Prof. Mulder Lund University for technical critique.

The authors acknowledge the invited peer reviewers who provided comments on an earlier draft of this report: Helen Colhoun University of Edinburgh , Boris Draznin University of Colorado School of Medicine , Torben Hansen University of Copenhagen , Pål Njølstad University of Bergen , and Matthew C.

Riddle Oregon Health and Science University. Funding for the PMDI is from the American Diabetes Association. In-kind support has been provided by the academic institutions of each Task Force member.

The ideas and opinions expressed in this report were derived in part from work undertaken by the coauthors, for which they report the following support: W. NIH: R01 DK, P30 DK, U54 TR, and U54DK ; A. Wellcome Trust Senior Investigator: ; National Institute for Health Research [NIHR] Senior Investigator and support of Exeter NIHR Clinical Research Facility; Medical Research Council [MRC]: MR-K ; M.

ADA ACE, NIH 5R01HD ; M. Wellcome Trust Senior Investigator and NIHR Senior Investigator: , , ; NIDDK: UDK ; J. NIH: R01 DK, R21 AI ; E. NIH: R01DK, P30 DK, U54DK ; S. NIH: DP3 DK, R01 DK; University of Virginia Strategic Investment Fund SIF88 ; P.

Duality of Interest. is on the scientific advisory board of the Regeneron Genetics Center. has received a speaking honorarium from Novo Nordisk and consulting fees from Janssen Pharmaceuticals. As of June , M. is an employee of Genentech and a holder of Roche stock.

has received research funding from Janssen and Provention Bio. No other potential conflicts of interest relevant to this article were reported. Sign In or Create an Account. Search Dropdown Menu.

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Rationale for Precision Medicine in Diabetes. The Precision Medicine in Diabetes Initiative. Precision Diabetes Medicine: What it is and What it is Not.

Precision Diagnostics. Precision Therapeutics. Precision Approaches to Diabetes in Pregnancy. Patient-Centered Mental Health and Quality-of-Life Outcomes. Equity in Precision Diabetes Medicine.

The Road to Implementation. Building Partnerships. Summary and Future Perspectives. Article Information. Article Navigation. Consensus Reports June 11 Precision Medicine in Diabetes: A Consensus Report From the American Diabetes Association ADA and the European Association for the Study of Diabetes EASD Wendy K.

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Diabetes Care ;43 7 — Connected Content. A reference has been published: In This Issue of Diabetes Care. Get Permissions. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. Figure 1.

View large Download slide. PMDI activities. PM, precision medicine; RFA, research funding announcement. Text Box 1 Definitions. View Large. Text Box 2 Precision diagnostics: background, barriers to implementation, and research gaps.

Text Box 3 Precision prevention: background, barriers to implementation, and research gaps. Text Box 4 Precision medicine approaches to treat diabetes: background, barriers to implementation, and research gaps.

Figure 2. Figure 3. Figure 4. One approach for implementing precision medicine in the case of monogenic diabetes would be to:. Text Box 5 Precision medicine approaches to lessen treatment burden and improve quality of life.

Figure 5. M is currently affiliated with Genentech, South San Francisco, CA. Search ADS. De Franco. The effect of early, comprehensive genomic testing on clinical care in neonatal diabetes: an international cohort study.

A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. A type 1 diabetes genetic risk score can identify patients with GAD65 autoantibody-positive type 2 diabetes who rapidly progress to insulin therapy.

Development and standardization of an improved type 1 diabetes genetic risk score for use in newborn screening and incident diagnosis. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data.

Diabetes digital app technology: benefits, challenges, and recommendations. A consensus report by the European Association for the Study of Diabetes EASD and the American Diabetes Association ADA Diabetes Technology Working Group.

American Diabetes Association. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes— Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank.

Type 1 diabetes defined by severe insulin deficiency occurs after 30 years of age and is commonly treated as type 2 diabetes. Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6. Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.

Permanent neonatal diabetes due to mutations in KCNJ11 encoding Kir6. Effectiveness and safety of long-term treatment with sulfonylureas in patients with neonatal diabetes due to KCNJ11 mutations: an international cohort study.

Prevalence of vascular complications among patients with glucokinase mutations and prolonged, mild hyperglycemia. Cross-sectional and longitudinal studies suggest pharmacological treatment used in patients with glucokinase mutations does not alter glycaemia.

Molecular genetics and phenotypic characteristics of MODY caused by hepatocyte nuclear factor 4alpha mutations in a large European collection. The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes. Absence of islet autoantibodies and modestly raised glucose values at diabetes diagnosis should lead to testing for MODY: lessons from a 5-year pediatric Swedish national cohort study.

Prediction algorithms: pitfalls in interpreting genetic variants of autosomal dominant monogenic diabetes. HNF1B-associated renal and extra-renal disease-an expanding clinical spectrum. Seattle, WA, University of Washington, Seattle, [Internet].

Accessed 13 May Antibodies to glutamic acid decarboxylase reveal latent autoimmune diabetes mellitus in adults with a non-insulin-dependent onset of disease. Time to insulin initiation cannot be used in defining latent autoimmune diabetes in adults.

Adult-onset autoimmune diabetes in Europe is prevalent with a broad clinical phenotype: Action LADA 7. Introducing the endotype concept to address the challenge of disease heterogeneity in type 1 diabetes.

Type 1 diabetes risk in African-Ancestry participants and utility of an ancestry-specific genetic risk score. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Patients also benefit from best practices for holistic care at the Kovler Diabetes Center.

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Study provides preliminary evidence in favor of a new type 1 diabetes treatment. The Future: Diabetes Youth and Parent Group. In the iCareD system, specific content for each message was developed based on the clinical practice guidelines of the Korean Diabetes Association and multidisciplinary expert opinions from our diabetes care team endocrinologist, certified dietician, and diabetes educator.

In both the MC and MPC groups, the mobile app was integrated with the EMR in each hospital; therefore, HCPs also evaluated participants at every 3-month visit based on the data obtained from the mobile app. Participants were instructed to upload their diet photos through the app.

These data were also transferred to the iCareD system, which was integrated with the EMR system for HCPs in the hospital. For the MPC group, based on our previous study, an HCP sent additional personalized recommendations and bidirectional feedback to each participant every 2 weeks through the iCareD system during the intervention period; the feedback was mainly related to diabetes self-care, the SMBG, or lifestyle modification [ 16 ].

In the offline system, patients visited the outpatient clinic every 3 months, and at these visits, physicians conducted face-to-face interviews with their patients, reviewed their uploaded data linked to the EMR, and provided individualized interventions based on these data.

All participants were allowed to contact educator nurses over telephone but were encouraged to use the app.

In addition, lifestyle changes based on PA and diet records; cardiometabolic risk factors such as body weight, blood pressure, and lipid profile; program satisfaction and compliance or adherence ; frequency of hypoglycemia; and changes in homeostasis model assessment of insulin resistance and β cell function were assessed at 26 weeks.

Adherence was defined as the proportion of intervention participation using the iCareD app, including blood glucose measurement and feedback confirmation, over a week period.

Exploratory assessment variables included changes in diabetes prescriptions, SMBG frequency, and BMI. Participant satisfaction was assessed in the 2 intervention groups by using a locally developed satisfaction survey at 26 weeks.

The survey included 5-level Likert-type questions evaluating self-care efficacy and various opinions on the iCareD system, such as the ease and frequency of text messages, perceived efficacy, and willingness to continue with or recommend the iCareD program to family or friends.

A score of 5 indicated very satisfied or strongly agree. Higher scores on the satisfaction scale reflect better results.

Demographic and clinical information collected at baseline and follow-up has been described previously [ 13 ]. PA was tracked using a Google Fit mobile app and assessed as the total step count per day [ 17 ].

Body composition data were obtained using a bioimpedance analyzer InBody and , InBody Co, Ltd at baseline and every 26 weeks. Laboratory parameters, including fasting glucose, HbA 1c level, and lipid profile, were collected at every visit. C-peptide and urinary albumin to creatinine ratios were measured at baseline and every 26 weeks.

We used the updated homeostasis model assessment calculator to evaluate the homeostasis model assessment of insulin resistance and β cell function [ 18 - 20 ]. Diabetes management behaviors such as SMBG frequency, PA, and diet records were obtained at every visit.

SMBG frequency was defined as the average number of tests performed per day, calculated for each patient based on the records in the web system. User satisfaction with mobile app was surveyed in the MC and MPC groups.

Continuous variables were presented as mean SD , whereas categorical data were presented as frequencies with percentages. Analysis of covariance was used to compare the mean week HbA 1c levels among the 3 groups.

Post hoc analysis was performed using the Bonferroni method. The number of hypoglycemic events among the groups was compared using the chi-square test or Fisher exact test. Missing data were replaced by the last-observation-carried-forward method for all participants who were followed up at least once after enrollment.

Both per-protocol and ITT analyses were conducted. Unless otherwise specified, analyses were performed based on the results of the ITT analysis.

The analysis was performed using SAS version 9. The study protocol was approved by the ethics committee of St. All participants provided written informed consent before enrollment in the study. All data and information were anonymized according to the International Conference on Harmonization Good Clinical Practice guidelines.

During the recruitment period from August to August in the outpatient clinics of 2 separate university-affiliated diabetes centers, a total of participants were assessed for eligibility and A total of 10 participants withdrew consent, leaving participants to be included in this study Figure 2.

After the week follow-up, the total retention rate was The baseline analysis revealed no significant differences between those who completed the study and those who were lost to follow-up data not shown.

The mean age of the participants was The mean baseline HbA 1c level and duration of diabetes were 8. The mean BMI was None of the other baseline characteristics or variables differed significantly among the 3 study groups.

c MPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians. The change in HbA 1c levels did not differ significantly at 26 weeks among the 3 groups Figure 3. In the post hoc analysis, only the MPC group showed a significant decrease in HbA 1c levels compared with the UC group.

Table 2. Adjusting for age, sex, and baseline HbA 1c level did not affect the HbA 1c level change results. Other changes in clinical and behavioral outcomes from baseline to follow-up are shown in Table 3. However, the change in fasting glucose levels did not differ among the 3 groups during the week intervention period Table 3.

Changes in body weight and BMI from baseline to 26 weeks also showed no differences among the study groups. The frequency of the SMBG did not show any significant differences among the 3 groups at the week follow-up.

However, compared with patients in the UC group, those in the 2 intervention groups iCareD system users tended to have more frequent SMBG recordings at 12 weeks UC vs iCareD system users: 1. PA, defined as step counts per day, was not significantly different among the study groups 26 weeks after the intervention.

The goal achievement rate for PA was higher in the MC and MPC groups than that in the UC group at 26 weeks, but the difference was not significant UC vs MC vs MPC: Low-density lipoprotein—cholesterol levels increased in the UC group and decreased in the MC and MPC groups during the follow-up period.

A total of out of the No differences were observed between the 2 groups Table 4. b MPC: mobile diabetes self-care with personalized, bidirectional feedback from physicians. There were no statistically significant differences between the MC and MPC groups in skill and technique acquisition, health service navigation, or manipulation of app content.

An end-of-intervention usability survey demonstrated that participants were comfortable with using the iCareD system. With regard to adherence, compared with participants in the MC group, those in the MPC group checked the automated text messages from the iCareD system for 26 weeks.

No serious adverse events were reported from enrollment until the completion of this study. Hypoglycemic events were infrequent and showed no differences among the groups at 26 weeks Table 5.

No deaths, direct study-related adverse events, or severe hypoglycemic episodes were reported or detected. This study was an RCT investigating a hospital-based, EMR-integrated mobile app—based diabetes self-care intervention over a week period in patients with T2DM.

Although the HbA 1c level decreased from the baseline value in all 3 groups, the HbA 1c changes did not show any significant difference between the control and 2 intervention groups at 26 weeks.

Owing to the global growth in the use of mobile phones with powerful platforms to help health care, many types of apps have been developed. In , more than diabetes-related apps were reported to be available to users.

Mobile apps related to diabetes management generally deal with information about diabetes, healthy diet, PA, weight loss, the SMBG, adherence, and motivation [ 6 ]. mHealth interventions support self-care and diabetes education and encourage lifestyle modification.

These data may be used to tailor feedback messages or advice on specific behavior changes to implement; these messages are usually sent automatically according to an algorithm [ 5 , 9 , 23 ]. Compared with conventional mobile apps that collect only patient-driven data, our EMR-integrated mobile app could provide important clues to the future direction of mobile app development for diabetes management in 2 respects.

Given the high rates of comorbidity and concurrent medications in patients with T2DM [ 24 ], this integrated provision of medical information may allow HCPs to provide accurate guidance to patients on diet, exercise, and management of comorbid diseases rather than simply focusing on the message to lower blood glucose levels.

In particular, our systems adopted visualization of glucose levels by color to improve awareness or alertness of hyperglycemia red or hypoglycemia black [ 13 ]. Using the EMR-integrated mobile app intervention, we demonstrated a significant reduction in HbA 1c levels after 12 weeks of intervention.

Consistent with our results, a 3-month RCT using DialBetics, a smartphone-based self-management support system for Japanese patients with T2DM, demonstrated that HbA 1c levels decreased by an average of 0.

A systematic review also revealed limited robust evidence of the promising short-term effectiveness of mHealth interventions for diabetes, such as the improvement of HbA 1c levels [ 14 , 15 , 26 , 27 ].

However, a caveat of these RCT analyses is that most of them included only studies conducted under highly controlled conditions with a small number of patients [ 14 , 15 , 26 , 27 ].

It is noteworthy that our study showed significant differences in HbA 1c levels among groups at 3 months in real-world practice, with a relatively large number of patients at 2 different clinical sites. This finding suggests the potential usefulness of EMR-integrated mobile app interventions in diabetes management.

In addition, we found that the intervention effects in the MPC group were prominent in patients with younger age, obesity, higher C-peptide levels, and no insulin treatment. This finding implies that mobile-based interventions, such as other diabetes treatments, may be more effective when β cell function is preserved.

This also highlights the importance of early intervention. This indicates that although early intervention may be important, such interventions may also be effective in long-standing diabetes. Mobile phone apps that receive blood glucose data from a connected glucometer are available and have the capacity to make data upload and review less burdensome [ 30 ].

The internet-based SMBG system, which augments the SMBG by giving patients the means to communicate their blood glucose levels to their HCP for actional feedback, has been shown to reduce HbA 1c levels in some RCTs involving patients with T2DM [ 31 ].

The inverse correlation between reporting frequency and HbA 1c levels, as well as the significant difference in HbA 1c levels only for frequent testers defined as those who test on average twice or more per day , suggests that frequent SMBG has an effect on reducing HbA 1c levels only when combined with regular, frequent communication of SMBG with an HCP [ 32 ].

The recording of a food diary using a smartphone app is a well-known simple tool, and technology to use images to quantify the composition and calorie content of food has been developed.

However, it is difficult and cumbersome for users to constantly record data based on their eating habits [ 33 , 34 ]. However, automated integration of glucose and lifelog data in the EMR between scheduled clinic visits improves the HCP workflow for reviewing data and improves communication with patients, eventually leading to better care [ 35 ].

In this study, There are possible explanations for the lack of improvement in HbA 1c levels in mHealth app users. First, age is a barrier to digital health care adoption and may influence the adoption of new technologies [ 36 ].

The mean age of the participants in this study was Second, the iCareD system was developed with a focus on lifestyle changes rather than strict glucose control or active medication adjustment, such as whirlwind dosage escalation of antidiabetic medications.

In the case of the TExT-MED study, a unidirectional text message intervention for diabetes self-care providing text message triggers to encourage individuals to engage in self-care behaviors, the TExT-MED program also did not result in a significant improvement in HbA 1c levels.

However, trends toward improvement in the primary outcome of HbA 1c levels and other secondary outcomes, including quality of life, were observed. Patient engagement was highest for more medical topics, such as glucose monitoring and medications, and lower for lifestyle topics, such as PA and healthy coping [ 6 ].

Therefore, we suggest that interventions for diabetes self-care should include improving HbA 1c levels through modification of lifestyle, glucose monitoring, and adherence and dosage adjustment for antidiabetic medications [ 37 ].

Third, our patients had a long duration of diabetes and were insulin users [ 32 ]. In general, the effects of education and lifestyle changes decrease with the duration of diabetes [ 38 ]. Fourth, there was no evidence of the most effective frequency of the intervention messages.

We sent personalized intervention messages from HCPs every 2 weeks and automated general informative messages every other day. Patient satisfaction and accessibility are important for improving self-management efficiency, and the clinical course can be improved through personalized intervention [ 4 ].

More frequent, bidirectional, real-time communication with HCPs and patients would lead to more effective improvement in HbA 1c levels. Although we did not observe remarkable improvement in HbA 1c levels over the long term, it is encouraging that the goal achievement rates for PA were higher in the intervention group at 26 weeks.

When the target of steps per day was applied [ 39 - 41 ], the difference in goal achievement rates among the groups further increased UC vs MC vs MPC: