Abstract
Numerous diabetes-management systems and programs for improving glycemic control to meet guideline targets have been proposed, using IT technology. But all of them allow only limited—or no—real-time interaction between patients and the system in terms of system response to patient input; few studies have effectively assessed the systems’ usability and feasibility to determine how well patients understand and can adopt the technology involved. DialBetics is composed of 4 modules: (1) data transmission module, (2) evaluation module, (3) communication module, and (4) dietary evaluation module. A 3-month randomized study was designed to assess the safety and usability of a remote health-data monitoring system, and especially its impact on modifying patient lifestyles to improve diabetes self-management and, thus, clinical outcomes. Fifty-four type 2 diabetes patients were randomly divided into 2 groups, 27 in the DialBetics group and 27 in the non-DialBetics control group. HbA1c and fasting blood sugar (FBS) values declined significantly in the DialBetics group: HbA1c decreased an average of 0.4% (from 7.1 ± 1.0% to 6.7 ± 0.7%) compared with an average increase of 0.1% in the non-DialBetics group (from 7.0 ± 0.9% to 7.1 ± 1.1%) (P = .015); The DialBetics group FBS decreased an average of 5.5 mg/dl compared with a non-DialBetics group average increase of 16.9 mg/dl (P = .019). BMI improvement—although not statistically significant because of the small sample size—was greater in the DialBetics group. DialBetics was shown to be a feasible and an effective tool for improving HbA1c by providing patients with real-time support based on their measurements and inputs.
Keywords: type 2 diabetes, mobile phone, self-management, telemedicine
The worldwide spread of type 2 diabetes certainly includes Japan. A 2007-08 survey reported that 10.5% of the Japanese population had diabetes, and the percentage has been increasing.1 Diabetes can lead to a number of complications—both macrovascular, such as myocardial infarction and stroke, and microvascular, such as neuropathy, nephropathy, and retinopathy—all of them resulting in greater expenditure and reduced productivity.2
A growing body of evidence shows that optimal glycemic control prevents or delays progression of diabetes complications, and allows those with diabetes to lead healthy and productive lives.3-5 So the goal of diabetic care is to optimize glycemic control and minimize complications. However, under 10% of those with diabetes meet the guideline targets for glycemia, lipid, and blood pressure.6,7
To meet the goal of therapy for diabetes, patients must have access to the integral components of diabetes care: health care visits, diabetes supplies and medications, and self-management education.8 It is self-management skill, especially, that ties the components of diabetes therapy together, enabling patients to assess and control the interplay of nutrition, physical activity, emotional/physical stress, and medications critical with diabetes. But there are various barriers to patients’ obtaining self-management skill. These include lack of time for health care professionals to provide patients with the continuous education that is necessary—with counseling on nutrition and physical activity—the high cost of evidence-based lifestyle interventions, and sometimes even patients’ access to health care providers.
Clearly, there is a need for an effective self-management tool that can overcome the barriers by automating and standardizing much of the counseling process.
Telemedicine and technology-based intervention seem the answer to minimizing health care providers’ workload and cost while increasing patient convenience. Numerous diabetes-management systems and programs for improving glycemic control to meet guideline targets have been proposed, using IT technology (eg, Wi-Fi, Bluetooth, and NFC). But all of them allow only limited—or no—real-time interaction between patients and the system, limiting the ability of those systems to provide continuous reinforcement to improve diabetes self-management.9
A number of web- and mobile-based systems and programs also exist to promote compliance with the guidelines.10 However, few studies have effectively assessed their usability and feasibility to determine how well patients understand and can adopt the technology involved.
The objective of the current study was to develop a real-time, partially automated interactive system to interpret patients’ data—biological information, exercise, and dietary content calculated from a message sent by patients—and respond with appropriate actionable findings, helping the patients achieve diabetes self-management. This is the first system—a combination of IT technology and a natural language processing method (NLP)—that performs real-time automated text communication with patients. Following a successful 11-patient pilot study,11 we conducted a larger randomized study designed to assess the usability of a remote health-data monitoring system, and especially its impact on modifying patient lifestyles to improve diabetes self-management and, thus, clinical outcomes.
Research Design and Methods
Design of DialBetics
DialBetics is composed of 4 modules (Figure 1). First is the data transmission module: patients’ data—blood glucose, blood pressure, body weight, and pedometer counts—are measured at home and sent to the server twice a day right after the patients’ measurement, the first 3 upon waking in the morning, then blood glucose, blood pressure, and pedometer readings at bed time. Second is the evaluation module: data are automatically evaluated following the Japan Diabetes Society (JDS) guideline’s targeted values—optimally, blood glucose below 110 mg/dl before breakfast, below 140 mg/dl at bed time; blood pressure below 130/80 mmHg; and pedometer count above 10,000. DialBetics determines if each reading satisfies guideline requirements, then immediately sends those results to each patient’s smartphone. Readings defined as abnormal—blood glucose above 400 mg/dl or below 40 mg/dl, and systolic blood pressure above 220 mmHg—are reported to a doctor as “Dr Call,” meaning a physician will check the data and interact with the patient if necessary. Third is the communication module: (a) the patient’s voice/text messages about meals—main dish of a meal—and exercise that is not counted by a pedometer—the type of exercise and its duration—are sent to the server; (b) message processing, if by voice input, is converted to text and matched with text in the DialBetics database; (c) advice on lifestyle modification, matched to the patient’s input about food and exercise, is sent back to each patient immediately after the patient’s input. For example, if the patient’s input showed that a patient had consumed too many calories, the patient will immediately see appropriate advice such as “It appears that your meal has over 640 calories, the recommended maximum per meal for the average adult. To reduce calorie intake you should try to leave some of the food unfinished if the portions are large. If you don’t feel full, try to eat more vegetables, which are high in fiber, and are filling yet low-calorie.” Fourth is dietary evaluation: patients’ photos of meals are sent to the server; the nutritional value of those meals is calculated by dieticians, then sent back to each patient. This process usually takes 1 or 2 days. This service was partially assisted by IMD, Inc, Tokyo, Japan.
Figure 1.
An overview of DialBetics.
An NLP method was used with message processing and lifestyle modification advice. Because patients’ voice/text messages may contain various orthographical and terminological ambiguities, patient input sometimes does not match the words in the DialBetics database. For example, patients may call seafood spaghetti “sea food pasta,” “pasta and sea food,” or “squid and shrimp pasta.” It is extremely hard for the database to anticipate all synonyms for all foods. To reduce the burden, we employed the NLP-based disambiguate system. That allows our system to choose the word in the database with the highest agreement rate for the patients’ input. As a result, the system can search for food and exercise input by patients among 3000 foods and 20 exercises in the database. The success rate for matching was 81.6%. The method is detailed elsewhere.12
Patients can view their measurement data as well as graphic outputs of their measurements with diet and exercise history.
Study Design
A 3-month, nonblinded randomized controlled study was designed and approved by the Institutional Review Board. To be eligible, patients could not have any severe complications—serum creatinine below 1.5 mg/dl, or proliferative retinopathy—and had to be able to exercise. No limit for HbA1c was defined. Sixty-six patients diagnosed with type 2 diabetes more than 5 years ago were initially recruited through posters at the University of Tokyo Hospital. Interested candidates, who provided informed consent, were screened for inclusion and exclusion criteria and asked to try the system for 2 weeks to ascertain that they had no problems using the system and its devices. Twelve candidates were excluded because they could not use the system and the devices properly, leaving 54 patients enrolled in the study. These 54 were then randomly divided into 2 groups, 27 in the DialBetics group and 27 in the non-DialBetics control group.
The research team included an endocrinologist as principal investigator, technology implementation and system administration specialists, experts in technical and database application, a diabetes nurse, and a dietitian. For baseline, all patients completed the Japanese version of the questionnaire summary of diabetes self-care activities, and the research team obtained their hemoglobin A1c (HbA1c) and complete medical and demographic history.
Study Methods
Participants visited us twice, at week 0 and week 12. At week 0, participants in the DialBetics group received a smartphone (NEC, Tokyo, Japan: MEDIAS WP N-06C), NFC-enabled glucometer (Terumo, Tokyo, Japan: MS-FR201B) and Bluetooth-enabled BP monitor (Omron, Kyoto, Japan: HEM-7081-IT), pedometer (Omron HJ-720IT) with adapter (Omron HHX-IT1), and scale (Omron HBF-206IT), all devices paired with a unique communicator that transmitted the readings by wireless network to the DialBetics server. The research team trained each participant to take measurements and transmit them properly, and to understand the readings. To ensure accuracy, the researchers had permission to monitor the data that patients measured at home. Following each new measurement, the patient profile was updated on the server, which controlled access to the patients’ data and recorded access history. Participants in the non-DialBetics group would continue their self-care regimen, but they did not receive or use any devices to monitor their health data; they did not record their diet and exercise.
Predetermined thresholds and safety parameters for blood glucose and blood pressure were programmed in the database with readings outside threshold limits triggering an email automatically sent to the principal investigator and the diabetes nurse. The specialists provided technical troubleshooting and monitoring for devices, data portal, database, and alerts. The database triggered alerts for missed or late readings, the alerts sent to the nurse and the participants, with the nurse emailing the participants (after 1 week missed) or phoning (with 2 weeks missed), encouraging them to measure their data, and involving the specialists and experts if their help was required.
The participants contacted the nurse by smartphone or email only for equipment failures, technical questions, or in response to alerts. For questions related to their health status, they were asked to consult their primary physicians.
Questionnaires rating the participants’ assessment of DialBetics’s usability, their satisfaction with the system, and their adherence to it were collected when the study ended.
Statistical Analysis
As noted, participants were randomly divided into a DialBetics group and non-DialBetics group using a computer-generated list of random numbers. Sample size was based on the primary outcome, change in HbA1c. Descriptive statistics at baseline and at completion of the study were calculated for all study variables—HbA1c, FBS, blood pressure (BP), body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG). A 1-sided 1-way analysis of the variance t-test on paired readings was used to evaluate the difference between the 2 groups in the change in HbA1c from baseline to 3-month follow-up for each patient with an intention-to-treat analysis. Because the number of participants was only 54, the effect of adjusting for confounding factors in the study group was not evaluated. Three patients from the DialBetics group and 2 from the non-DialBetics group dropped out of the study.
Results
Demographic characteristics of the 54 participants in the randomized control trial are shown in Table 1 by study group. Demographic characteristics were similar for both groups except for smoking, which was more prevalent in the non-DialBetics group (10 vs 4). Characteristics of drop-out patients were not different from those of the remaining subjects. The overall study population was predominantly male (66%), average age was 57.3 years (SD = 9.7), and average HbAc1 7.1% (SD = 0.9). Prior to study enrollment, all participants had owned and daily used a cell phone.
Table 1.
Baseline Characteristics of the Clinical Trial Population by Study Group.
| DialBetics group (n = 27) | Non-DialBetics group (n = 27) | |
|---|---|---|
| HbA1c | ||
| 8.6-9.5% | 3 | 2 |
| 7.6-8.5% | 4 | 6 |
| 7.1-7.5% | 4 | 2 |
| ≤7.0% | 16 | 17 |
| Age (years) | 57.1 ± 10.2 | 57.4 ± 9.4 |
| Sex | ||
| Female | 7 | 6 |
| Male | 20 | 21 |
| Smoking | ||
| Yes | 4 | 10 |
| No | 23 | 17 |
| Years with diabetes | 9.6 ± 7.0 | 8.5 ± 8.0 |
| Body mass index (kg/m2) | ||
| ≥30 | 6 | 5 |
| 25.0-29.9 | 7 | 9 |
| ≤24.9 | 14 | 13 |
| Comorbid conditions | ||
| Hypertension | 13 | 15 |
| Lipid disorders | 12 | 19 |
| Atherosclerotic diseases | 8 | 5 |
| Microvascular diseases | 4 | 8 |
| Medication treatment regimen | ||
| No medication | 7 | 6 |
| Oral hypoglycemic alone | 13 | 20 |
| Injectable noninsulin alone | 4 | 0 |
| Injectable noninsulin and oral hypoglycemic | 3 | 1 |
Change in Clinical and Behavioral Outcomes
Analysis of the change in clinical and behavioral outcomes from baseline to follow-up is shown in Table 2. HbA1c and FBS values declined significantly in the DialBetics group: HbA1c decreased an average of 0.4% compared with an average increase of 0.1% in the non-DialBetics group (P = .015); DialBetics group FBS decreased an average of 5.5 mg/dl compared with a non-DialBetics group average increase of 16.9 mg/dl (P = .019). BMI improvement—although not statistically significant because of the small sample size—was greater in the DialBetics group. Other clinical variables—LDL-C, HDL-C, TG, and BP—remained similar between the 2 groups. Change from the baseline in medication remained similar between the 2 groups. So did self-care of diabetes (in terms of diet and exercise), possibly for reasons discussed in the conclusion section.
Table 2.
Comparison of Changes in Clinical and Behavioral Outcomes in DialBetics and Non-DialBetics Groups.
| DialBetics Group (n = 27) |
Non-DialBetics Group (n = 27) |
||||
|---|---|---|---|---|---|
| Baseline | Follow-up | Baseline | Follow-up | P value | |
| HbA1c (%) | 7.1 ± 1.0 | 6.7 ± 0.7 | 7.0 ± 0.9 | 7.1 ± 1.1 | .015 |
| Fasting blood sugar | 140.2 ± 33.5 | 134.7 ± 24.6 | 127.4 ± 26.9 | 144.3 ± 46.5 | .019 |
| Low-density lipoprotein cholesterol | 115.4 ± 25.6 | 114.0 ± 24.4 | 106.9 ± 26.8 | 104.5 ± 24.4 | .43 |
| High-density lipoprotein cholesterol | 61.6 ± 14.8 | 63.0 ± 16.4 | 54.9 ± 14.1 | 55.5 ± 12.6 | .36 |
| Triglyceride | 140.2 ± 33.5 | 128.4 ± 85.7 | 129.3 ± 59.0 | 146.0 ± 94.8 | .24 |
| Body mass index (kg/m2) | 26.2 ± 6.1 | 25.9 ± 5.9 | 27.1 ± 7.6 | 27.1 ± 7.5 | .062 |
| Systolic blood pressure | 132.3 ± 18.7 | 130.1 ± 17.3 | 123.5 ± 15.0 | 122.1 ± 16.8 | .40 |
| Diastolic blood pressure | 76.4 ± 14.7 | 76.1 ± 9.7 | 72.5 ± 8.8 | 71.1 ± 9.0 | .35 |
| Changes in medication (%) | |||||
| Medication increased | 3 | 3 | |||
| Medication reduced | 1 | 2 | |||
| No change | 23 | 22 | |||
| Diabetes self-management | |||||
| Diet | 15.7 ± 7.2 | 16.6 ± 7.1 | 13.4 ± 6.5 | 14.5 ± 7.7 | .48 |
| Exercise | 6.0 ± 4.0 | 5.9 ± 4.3 | 5.0 ± 4.0 | 4.9 ± 3.7 | .34 |
Usability
An end-of-study usability survey demonstrated that participants were comfortable with the use of the equipment (Table 3). A majority of the 24 DialBetics patients who stayed the course easily incorporated the system into their daily routine (n = 14); the remainder could not maintain daily use due to the time required (though they continued when they could), but even 2 of those patients agreed that, objectively, using the system did not require too much time—22.5 minutes a day on average (n = 16). All 24 participants were motivated by the sense of security derived from using the system, and reported being able to better control their diabetes. The advice the system provided helped them modify their lifestyle.
Table 3.
Usability Survey Results.
| Statement or question | Yes (%) |
|---|---|
| 1. I could use a smartphone with no problem. | 19 (79) |
| 2. I could use a blood sugar monitor with no problem. | 22 (92) |
| 3. I could use a sphygmomanometer with no problem. | 24 (100) |
| 4. I could use a pedometer count with no problem. | 21 (88) |
| 5. The interface of the system was easy to use. | 17 (71) |
| 6. The instructions were easy to understand. | 16 (66) |
| 7. The devices caused me physical discomfort. | 5 (21) |
| 8. I easily incorporate using the system into daily practice. | 14 (58) |
| 9. Any technical problems were resolved within 24 hours. | 15 (62) |
| 10. Using the system gave me a sense of security. | 24 (100) |
| 11. I found the advice from the system useful. | 18 (75) |
| 12. Participation in the study helped me to improve lifestyle and diabetes self-management. | 23 (96) |
| 13. Participation in the study took too much of my time. | 8 (33) |
| 14. Using the system caused me some problems. | 2 (8) |
| 15. How much time did you spend using the system? (minutes)a | 22.5 |
| 16. Is the system worth using for the time you spent? | 24 (100) |
Three participants who dropped out of the DialBetics groups were not included.
The average time required was calculated.
Compliance
When measurement rates for the first and the last 2 weeks were compared (Table 4), morning measurements stayed over 70%, but bedtime measurements, except for pedometer count, declined to around 50%. Although input of photo of a meal was higher than that of diet and exercise, it declined to 51.2% by the end.
Table 4.
Compliance Data of DialBetics Group by Measurements and Input.
| Measurement | Total (%) | The first 2 weeks (%) | The last 2 weeks (%) |
|---|---|---|---|
| Morning blood glucose | 84.1 ± 21.2 | 88.7 ± 25.3 | 70.8 ± 29.1 |
| Morning blood pressure | 82.1 ± 25.6 | 85.7 ± 25.1 | 74.7 ± 28.9 |
| Morning body weight | 83.7 ± 20.9 | 90.8 ± 20.8 | 71.7 ± 31.0 |
| Bedtime blood glucose | 69.0 ± 26.5 | 82.1 ± 23.7 | 49.1 ± 32.2 |
| Bedtime blood pressure | 68.9 ± 27.8 | 80.6 ± 23.4 | 52.4 ± 34.1 |
| Bedtime pedometer count | 77.6 ± 30.8 | 78.0 ± 35.9 | 67.2 ± 34.2 |
| Input | |||
| Diet | 40.1 ± 35.6 | 53.5 ± 35.7 | 26.5 ± 37.3 |
| Exercise | 30.3 ± 32.8 | 37.5 ± 35.2 | 24.1 ± 28.8 |
| Photo of a meal | 68.8 ± 32.6 | 77.1 ± 35.1 | 51.2 ± 42.0 |
Conclusions
This randomized study showed that remote health data monitoring, plus real-time communication with patients, supported self-management of diabetes, which resulted in improved HbA1c—even in just a 3-month period. Furthermore, most patients found DialBetics easy to use and not unduly time-consuming. The improvement in diabetes control is partially explained by the system’s timely evaluation of whether measured data achieved the Japan Diabetes Society’s diabetes treatment goal, with (if the goals were not met) advice sent automatically to help patients modify their diet and exercise to meet those goals. Because no readings were defined as “Dr Call,” a health care provider’s time was not required.
Our results were consistent with previous reports showing the usefulness of diabetes-management by mobile phone.13,14 The DialBetics group, which received evaluation of their health data when measured, and timely diet and exercise advice, had significantly improved HbA1c compared with that of the non-DialBetics patients, who received their usual care. Since the study lasted just 3 months and medication increased for only 11%—meaning that improved blood glucose (BG) control with DialBetics could not reasonably be attributed to medication—the average 0.4% HbA1c decrease and BMI reduction were particularly significant. DialBetics evaluated patients’ diets in greater detail than previously reported systems because patients could input pictures of their meals, whose nutritional balance and value were calculated by a dietician each time.13-15
In the usability survey, all participants reported thinking that using the system helped improve self-management skills. By examining BG patterns resulting from their diet and exercise, they could see the need for changes to achieve treatment goals. With their own data at hand, the system made it easier for them to think about their diet in terms of calories and nutritional balance and the effect on BG. That increase in attention to diet and exercise in conjunction with BG may be the major explanation for improved diabetes control in the DialBetics group. However, we did not see significant difference between the DialBetics and non-DialBetics group in patient-reported self-care of diet and exercise or in other laboratory values like lipids. That may be partially because of the study’s small sample size, and the residual effects from all study candidates having used the system for 2 weeks before enrolment and randomization. Many in the non-DialBetics group said that this exposure to DialBetics impelled them to think more about their lifestyles.
One especially notable finding was that the morning BP of 7 patients (26.0%), bedtime BP of 3 (12.5%), and both BPs of 8 (33.3%)—75% total—did not meet the JDS goal for BP. Yet only 3 patients (12.5%) received the indicated increased antihypertensive medication. This suggests that home monitoring of both BG and BP might improve diabetes control because uncontrolled BP was frequently overlooked in regular hospital visits. A recent article reported that home BP telemonitoring and pharmacist case management achieved better BP control than did usual care.16 DialBetics, which focused on diabetes self-management, did not include case management by a pharmacist or physician to control hypertension, but we are upgrading the system to alert a patient’s primary physician when monitoring shows a BP problem.
Although this was a randomized study, its size and duration limit the generalizability of the results. First, the study was based at a university hospital, its participants recruited there, and hospital outpatients have greater access to resources like specialists, clinical practice guidelines, and education programs. Second, although we planned to have participants input all their meals (including photos) for dietician evaluation, most patients input only 1 meal per day—2 at best—feeling that the procedure took too much time, and, for the photos, lacking the positive reinforcement of real-time evaluation since it took 1 or 2 days after input to hear back from the dietician because this part of the system was not automated. As a result, we could not objectively analyze how their dietary habits improved over the study period, and how those habits affected diabetes control. Third, the usability survey was developed specifically for this study and was not a validated questionnaire. Fourth, the study’s dropout rate was relatively high (9.3%): 3 DialBetics and 2 non-DialBetics group members. One in the DialBetics group (and both in the non-DialBetics group) dropped out for hospitalization. The other 2 DialBetics dropouts cited unwillingness to continue constant measurements as their main reason. Although interpretation should be cautious given the small sample size, dropout rate is a potential source of bias in the current study. Finally, BG monitoring, itself, has been shown to improve diabetes control,17 so improved HbA1c, here, might be mainly attributed to the measurement of BG. We could not quantify the contribution of each feature to improvement of HbA1c. In addition, we were unable to exclude a potential bias associated with daily use of devices, which could have improved HbA1c. We were unable to evaluate the real impact of the application usage. Nevertheless, given the fact that all participants reported being motivated by the sense of security utilizing the system imparted, there is a strong possibility that using the system encouragingly raised their awareness of newer methods of diabetes self-management based on information and communication technology that they may not have been aware existed.
In conclusion, DialBetics was shown to be a feasible and effective tool for improving HbA1c by providing patients with real-time support based on their measurements and inputs.
Footnotes
Abbreviations: BG, blood glucose; BMI, body mass index; BP, blood pressure; FBS, fasting blood sugar; HDL-C, high-density lipoprotein cholesterol; JDS, Japan Diabetes Society; LDL-C, low-density lipoprotein cholesterol; NFC, near field communication; NLP, natural language processing method; SD, standard deviation; TG, triglyceride.
Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: KW, HF, SK, and HL are members of the Department of Ubiquitous Health Informatics, which is engaged in a cooperative program between the University of Tokyo and NTT DOCOMO.
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 NTT DOCOMO and Japan Society for Promotion of Science Grant-in-Aid for Young Scientist Research (B) 23790559.
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