Abstract
Keywords
Lifestyle improvement is effective for glycemic control in people with diabetes. 1 Several studies have shown that the use of the internet of things (IoT), the network of various things that enables the connection of data over the internet, facilitates an improvement in the glycemic control. These include automated messages promoting lifestyle changes, 2 automated feedback messages conveying health parameters,3,4 and a smartphone-based self-management support system that can interact with patients’ inputs in real time. 5 However, it should be noted that the control group was not provided with measurement devices in any of these previous studies, and therefore it is possible that the achieved improvement in glycemic control was due to measurement of health parameters per se but not to the IoT system.
We have developed an IoT automated system that demonstrates a summary of lifelogging data (body weight, blood pressure, and daily activities) from each measurement device on one screen (Figure 1a). As shown in Figure 1b, the system also sends messages with characters of “Shichifukujin,” the seven deities of good luck who are believed to grant good luck in Japanese mythology, via a smartphone application encouraging patients to increase their physical activity and monitor body weight and blood pressure. In this prospective multicenter randomized controlled study, we examined whether the IoT system benefits glycemic control in people with diabetes of two independent models, the health guidance model and hospitalization model. We provided the control group with the same devices not equipped with the IoT system.
An overview of the IoT system. Bluetooth-enabled body weight (BW) meter, Bluetooth-enabled blood pressure (BP) meter, and Bluetooth-enabled activity tracker (Omron Healthcare, Japan) —all of which could transmit measurement data over the internet to the Cloud Server—were provided to patients (measurement devices). Patients’ data on BW, BP, and daily activity were wirelessly transmitted to their smartphone, which then sent these data to the Cloud Server via the internet. The data were summarized and presented to patients as well as their primary care physicians to review their lifelogging data on the smartphone or web. The data were also sent to the automated data analysis system, which evaluated collected data and generated advice automatically (database and analysis system). This advice was presented to the patients via the smartphone application “Shichifukujin-app” (Feedback system). The “Shichifukujin app” was newly developed for this study and presented supportive advice or warning messages together with facial expressions of “Shichifukujin” twice a week based on their analyzed lifelogging data.
The seven deities of good luck (role and support attention message). Each Shichifukujin character sent supportive advice or warning messages according to the patients’ data transmission status, the total number of steps per day, exercise above a certain intensity, BP, weight change, and comprehensive evaluation. For example, Jurojin smiled and encouraged patients to maintain their BP control when their BP management state was favorable, whereas he appeared to be sad with increased BP or sent alert messages when the patient displayed extremely high BP levels such as systolic BP > 180 mmHg and/or diastolic BP > 110 mmHg.
In the health guidance model, both the IoT and control groups (n = 50, respectively) showed a significant HbA1c reduction at 3 months as compared with the baseline (from 7.0 to 6.7% in the IoT group [P < .005] and from 7.1 to 6.8% in the control group [P < .005]), while only the IoT group maintained a significant reduction at 6 months (P < .05) along with BMI decreases. It was suggested that our IoT system was effective for the maintenance of lifestyle modification after the initial education at the health guidance.
In the hospitalization model, both the IoT (n = 42) and the control groups (n = 39) showed significant HbA1c reductions at 3 and 6 months as compared with the baseline, while there were no differences in the values between groups. Previous studies have shown that the effects of intensive lifestyle intervention during hospitalization lasted for 12 months after discharge. 6 Thus, the 6-month period after discharge might not be sufficiently long to observe the effects of the IoT system in this model.
Our IoT system has several advantages over conventional devices. First, it could collect large amounts of lifelogging data and provide summaries to patients as well as physicians. Second, effective messages that help maintain lifestyle modification could be analyzed based on the integrated data, which is useful for establishing more effective systems. On the other hand, the limitations of the study are relatively small sample size and short observation period.
In conclusion, the newly developed IoT system was beneficial in maintaining glycemic control in people with diabetes.
Footnotes
Acknowledgements
The authors acknowledge Kunikazu Kondo, Masayuki Hayashi, Tetsuji Okawa, Koichi Adachi, Yoshio Nomura, Yoh Ariyoshi, Nobuaki Ozaki, Akemi Inagaki, Etsuko Yamamori, Hiromitsu Sasaki, Hideki Okamoto, Yoko Eguchi, Masanori Yoshida, Hiroshi Shimizu, Minemori Watanabe, Shuko Yoshioka, Ikuo Yamamori, Junji Shinoda, Yuka Muraoka, Shoko Nakajima.
Abbreviations
app, application; BMI, body mass index; BP, blood pressure; BW, body weight; HbA1c, glycosylated hemoglobin; IoT, internet of things.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Ministry of Economy, Trade and Industry of Japan.