Diabetes & Metabolism Journal

Review

Technology/Device
Present and Future of Digital Health in Diabetes and Metabolic Disease: Sang Youl Rhee, Chiweon Kim, Dong Wook Shin, Steven R. Steinhubl; Diabetes Metab J. 2020;44(6):819-827. Published online December 23, 2020; DOI: https://doi.org/10.4093/dmj.2020.0088

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The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.

Citations

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Bárbara Luque, Joaquín Villaécija, Rosario Castillo-Mayén, Esther Cuadrado, Sebastián Rubio, Carmen Tabernero
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Original Articles

Obesity and Metabolic Syndrome
Air Pollution Has a Significant Negative Impact on Intentional Efforts to Lose Weight: A Global Scale Analysis: Morena Ustulin, So Young Park, Sang Ouk Chin, Suk Chon, Jeong-taek Woo, Sang Youl Rhee; Diabetes Metab J. 2018;42(4):320-329. Published online April 24, 2018; DOI: https://doi.org/10.4093/dmj.2017.0104

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Background

Air pollution causes many diseases and deaths. It is important to see how air pollution affects obesity, which is common worldwide. Therefore, we analyzed data from a smartphone application for intentional weight loss, and then we validated them.

Methods

Our analysis was structured in two parts. We analyzed data from a cohort registered to a smartphone application in 10 large cities of the world and matched it with the annual pollution values. We validated these results using daily pollution data in United States and matching them with user information. Body mass index (BMI) variation between final and initial login time was considered as outcome in the first part, and daily BMI in the validation. We analyzed: daily calories intake, daily weight, daily physical activity, geographical coordinates, seasons, age, gender. Weather Underground application programming interface provided daily climatic values. Annual and daily values of particulate matter PM10 and PM2.5 were extracted. In the first part of the analysis, we used 2,608 users and then 995 users located in United States.

Results

Air pollution was highest in Seoul and lowest in Detroit. Users decreased BMI by 2.14 kg/m² in average (95% confidence interval, −2.26 to −2.04). From a multilevel model, PM10 (β=0.04, P=0.002) and PM2.5 (β=0.08, P<0.001) had a significant negative effect on weight loss when collected per year. The results were confirmed with the validation (β_AQI*time=1.5×10^–5; P<0.001) by mixed effects model.

Conclusion

This is the first study that shows how air pollution affects intentional weight loss applied on wider area of the world.

Citations

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Clinical Care/Education
Feasibility of a Patient-Centered, Smartphone-Based, Diabetes Care System: A Pilot Study: Eun Ky Kim, Soo Heon Kwak, Seungsu Baek, Seung Lyeol Lee, Hak Chul Jang, Kyong Soo Park, Young Min Cho; Diabetes Metab J. 2016;40(3):192-201. Published online April 8, 2016; DOI: https://doi.org/10.4093/dmj.2016.40.3.192

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Background

We developed a patient-centered, smartphone-based, diabetes care system (PSDCS). This study aims to test the feasibility of glycosylated hemoglobin (HbA1c) reduction with the PSDCS.

Methods

This study was a single-arm pilot study. The participants with type 2 diabetes mellitus were instructed to use the PSDCS, which integrates a Bluetooth-connected glucometer, digital food diary, and wearable physical activity monitoring device. The primary end point was the change in HbA1c from baseline after a 12-week intervention.

Results

Twenty-nine patients aged 53.9±9.1 years completed the study. HbA1c and fasting plasma glucose levels decreased significantly from baseline (7.7%±0.7% to 7.1%±0.6%, P<0.0001; 140.9±39.1 to 120.1±31.0 mg/dL, P=0.0088, respectively). The frequency of glucose monitoring correlated with the magnitude of HbA1c reduction (r=–0.57, P=0.0013). The components of the diabetes self-care activities, including diet, exercise, and glucose monitoring, were significantly improved, particularly in the upper tertile of HbA1c reduction. There were no severe adverse events during the intervention.

Conclusion

A 12-week application of the PSDCS to patients with inadequately controlled type 2 diabetes resulted in a significant HbA1c reduction with tolerable safety profiles; these findings require confirmation in a future randomized controlled trial.

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Efficacy of the Smartphone-Based Glucose Management Application Stratified by User Satisfaction: Hun-Sung Kim, Wona Choi, Eun Kyoung Baek, Yun A Kim, So Jung Yang, In Young Choi, Kun-Ho Yoon, Jae-Hyoung Cho; Diabetes Metab J. 2014;38(3):204-210. Published online June 17, 2014; DOI: https://doi.org/10.4093/dmj.2014.38.3.204

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Abstract PDF PubReader

Background

We aimed to assess the efficacy of the smartphone-based health application for glucose control and patient satisfaction with the mobile network system used for glucose self-monitoring.

Methods

Thirty-five patients were provided with a smartphone device, and self-measured blood glucose data were automatically transferred to the medical staff through the smartphone application over the course of 12 weeks. The smartphone user group was divided into two subgroups (more satisfied group vs. less satisfied group) based on the results of questionnaire surveys regarding satisfaction, comfort, convenience, and functionality, as well as their willingness to use the smartphone application in the future. The control group was set up via a review of electronic medical records by group matching in terms of age, sex, doctor in charge, and glycated hemoglobin (HbA1c).

Results

Both the smartphone group and the control group showed a tendency towards a decrease in the HbA1c level after 3 months (7.7%±0.7% to 7.5%±0.7%, P=0.077). In the more satisfied group (n=27), the HbA1c level decreased from 7.7%±0.8% to 7.3%±0.6% (P=0.001), whereas in the less satisfied group (n=8), the HbA1c result increased from 7.7%±0.4% to 8.1%±0.5% (P=0.062), showing values much worse than that of the no-smartphone control group (from 7.7%±0.5% to 7.7%±0.7%, P=0.093).

Conclusion

In addition to medical feedback, device and network-related patient satisfaction play a crucial role in blood glucose management. Therefore, for the smartphone app-based blood glucose monitoring to be effective, it is essential to provide the patient with a well-functioning high quality tool capable of increasing patient satisfaction and willingness to use.

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