Skip Navigation
Skip to contents

Diabetes Metab J : Diabetes & Metabolism Journal

Search
OPEN ACCESS

Articles

Page Path
HOME > Diabetes Metab J > Volume 39(3); 2015 > Article
Original Article
Clinical Care/Education A Smartphone Application Significantly Improved Diabetes Self-Care Activities with High User Satisfaction
Yu Jin Kim1, Sang Youl Rhee1, Jong Kyu Byun1, So Young Park1, Soo Min Hong1, Sang Ouk Chin2, Suk Chon1, Seungjoon Oh1, Jeong-taek Woo1, Sung Woon Kim1, Young Seol Kim1
Diabetes & Metabolism Journal 2015;39(3):207-217.
DOI: https://doi.org/10.4093/dmj.2015.39.3.207
Published online: April 22, 2015
  • 11,162 Views
  • 74 Download
  • 39 Web of Science
  • 49 Crossref
  • 47 Scopus

1Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea.

2Division of Endocrinology and Metabolism, Department of Internal Medicine, Jeju National University Hospital, Jeju National University College of Medicine, Jeju, Korea.

corresp_icon Corresponding author: Sang Youl Rhee. Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 130-872, Korea. bard95@hanmail.net
• Received: May 13, 2014   • Accepted: October 15, 2014

Copyright © 2015 Korean Diabetes Association

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

prev next
  • Background
    We developed for the first time a smartphone application designed for diabetes self-management in Korea and registered a patent for the relevant algorithm. We also investigated the user satisfaction with the application and the change in diabetes related self-care activities after using the application.
  • Methods
    We conducted a questionnaire survey on volunteers with diabetes who were using the application. Ninety subjects responded to the questionnaire between June 2012 and March 2013. A modified version of the Summary of Diabetes Self-Care Activities (SDSCA) was used in this study.
  • Results
    The survey results exhibited a mean subject age of 44.0 years old, and males accounted for 78.9% of the subjects. Fifty percent of the subjects had diabetes for less than 3 years. The majority of respondents experienced positive changes in their clinical course after using the application (83.1%) and were satisfied with the structure and completeness of the application (86.7%). Additionally, the respondents' answers indicated that the application was easy to use (96.7%) and recommendable to others (97.7%) and that they would continue using the application to manage their diabetes (96.7%). After using the Diabetes Notepad application, diabetes related self-care activities assessed by SDSCA displayed statistically significant improvements (P<0.05), except for the number of days of drinking.
  • Conclusion
    This smartphone-based application can be a useful tool leading to positive changes in diabetes related self-care activities and increase user satisfaction.
Diabetes is a chronic, progressive disease that is difficult to cure completely and requires the management of blood glucose and comorbidities over one's lifetime. The self-measurement of blood glucose, diabetes journaling, exercise, maintaining the diet for diabetes, and regular medication are generally recommended for the self-management of diabetes [123]. However, due to cumbersomeness, these self-management techniques are not practiced by many patients, and some patients even experienced exhaustion of diabetes management [45].
Recently, attempts to develop new management models using information technology (IT) for chronic diseases including diabetes occurred [6]. In particular, mobile health intervention for the management of chronic diseases has positive effects [789]. Among the latest IT technologies for such mobile health interventions, a smartphone is one of the most promising tools with a wide user base and easy utility [10].
Korea has a smartphone use rate as high as 85% [11] and a high preference for mobile application use [12]. Therefore, inconveniences related to diabetes self-management such as blood glucose measurement and diabetes journaling can be effectively improved by utilizing smartphone applications [13].
However, few studies exist on the effect of a smartphone-based diabetes self-management system in Korea, and thus, more evidence is needed. In previous studies on the efficacy of smartphone-based applications for controlling blood glucose levels, glycosylated hemoglobin levels decreased more in subjects who displayed higher satisfaction with the use of the smartphone applications than in subjects with lower satisfaction. Therefore, satisfaction with the application was an important factor for controlling blood glucose [14]. In the current study, the investigators developed the 'Diabetes Notepad' application designed to enable self-management of diabetes using a smartphone. This study was conducted to obtain basic data pertaining to the usefulness of the application, including whether diabetes self-management activities improved and to what extent the diabetes self-management activities improved according to the satisfaction with the smartphone application.
'Diabetes Notepad' application
The 'Diabetes Notepad' application used in the present study is a diabetes self-management system that records personal information, blood glucose levels and test records for comorbidities. The application also includes additional features such as diabetes education material and a cardiovascular risk calculator (Fig. 1A). Additionally, the application allows easy access to the saved blood glucose history through the blood glucose record sheet and a graph (Fig. 1B). The algorithm of the 'Diabetes Notepad' application has been patented in recognition of its originality (10-1075596). The application is free of charge, available for anyone with a smartphone and is renowned as the most widely distributed diabetes self-management application in Korea with approximately 50,000 downloads (as of March 30, 2013).
Subjects
Subjects included those who were using the 'Diabetes Notepad' application and willing to give consent to participate in the research study. A final analysis was performed on data from 90 patients who completed the questionnaire.
Methods
A questionnaire on the satisfaction with the Korea National Diabetes Program Diabetes Notepad application used in this study consisted of personal information of the subjects and the diabetes management status before and after application use. The study was designed to identify user satisfaction and changes in the Summary of Diabetes Self-Care Activities (SDSCA) after the use of the application. For the management status of diabetes, the questionnaire asked subjects to state the number of days (out of a week) that the patient measured blood glucose levels, had regular meals, drank alcohol, smoked, exercised, took regular medication, weighed themselves, and kept regular hours. These correspond to items that SDSCA measures. SDSCA is the most widely used tool to measure the degree of diabetes self-management, and its usefulness has been verified through various studies involving Korean subjects [1516].
Subjects who agreed to participate in the survey sent their agreement via email to the investigators. Next, the investigators emailed the subject the SDSCA questionnaire and received the survey results from the subjects via email. In addition, patients wanting an additional consultation were asked to email the clinical information recorded in the application to the investigators.
Ethics statement
This study was carried out with subjects who agreed to participate in the research among those using the Diabetes Notepad application. The study subjects were recruited using the notice board window on the smartphone without direct interviews, and email was used as the primary communication method to send the questionnaire and receive its answers. Institutional Review Board (IRB) of Kyung Hee University Hospital approval for the exemption of a written agreement of study subjects and investigators was obtained before starting the study (KMC IRB 1217-01). The study design was registered in the national clinical trial registry (CRIS, No. KCT0000714).
Statistical analysis
The SPSS version 18.0 (SPSS Inc., Chicago, IL, USA) was used for the statistical analysis of the survey results. Subjects' characteristics and survey results were represented through descriptive statistics, and outcomes are shown as the mean±standard deviation. In addition, the internal consistency of questions of the questionnaire or the reliability was represented as a Cronbach's α value.
The Wilcoxon signed rank test was used for the comparison of SDSCA items before and after the use of the diabetes application, and a Mann-Whitney U test was performed for the comparison of changes in SDSCA between the group with high satisfaction and the group with low satisfaction with using the application. Additionally, the Mann-Whitney U test and Kruskal-Wallis test were conducted to identify the difference in the satisfaction with the application and the changes in SDSCA according to the demographic characteristics of the subjects. The significance of all statistical analyses was verified at P<0.05.
General characteristics of the survey population
The survey results showed that 90 patients, corresponding to 30.8% of those who had originally agreed to participate in the survey, completed the final questionnaire as of March 30, 2013. The clinical characteristics of the subjects who completed the survey are shown in Table 1. The mean age of the subjects was 44.0 years old, and 71 subjects were male, which accounted for 78.9% of the total subjects. At least 50% of the subjects had diabetes for less than 3 years. Eighty-two percent of the subjects had used the Diabetes Notepad application for less than 6 months, 14.6% for 6 months to 1 year, and only 3.4% for more than 1 year. Eighty percent of the subjects graduated from a university or had a higher degree, and 51.7% were high-income earners with a monthly income of 3 million won or more. Subjects without other comorbidities besides diabetes accounted for 56.7% of the respondents. Because each subject used the Diabetes Notepad application through his/her own will, data from the questionnaire were obtained from subjects living all over the country, which was a clear advantage. The nationwide distribution of users of this application is shown in Fig. 2.
User satisfaction
As shown in Table 2, survey results of the user satisfaction with the application showed that 83.1% of subjects indicated positive changes in the clinical course of diabetes after using the application, and 86.7% were satisfied with the structure and completeness of the application. Furthermore, 96.7% of respondents stated that the application was easy to use. Almost all (97.7%) of the subjects were willing to actively recommend the application to other people, and 96.7% would continue to manage their diabetes by using the application. The internal consistency of the survey questions about satisfaction before and after the use of the application was 0.791 as estimated with Cronbach's α.
Subjects responding to questions pertaining to satisfaction selected their response from a 4-point scale including (1) strongly agree, (2) agree, (3) disagree, and (4) strongly disagree. Then, in the statistical analysis, scores were given to each response as follows: (1) 4 points to strongly agree, (2) 3 points to agree, (3) 2 points to disagree, and (4) 1 point to strongly disagree. Subsequently, the scores for all the questions were added up, and the high rank and low rank groups were classified relative to the 50% point of the maximum total score of all the questions pertaining to satisfaction. This resulted in 53 subjects in the high satisfaction group and 37 in the low satisfaction group. In the next paragraph, the difference in the changes in SDSCA items between these two groups will be discussed.
Table 3 shows the difference in satisfaction with the application according to the demographic characteristics, and the male patients (3.54±0.50) found it easier to use the application than the female patients (3.16±0.69), which was statistically significant (P=0.026). In other questions pertaining to satisfaction, gender, age, the duration of diabetes, the duration of using the application, the smartphone model, education level, and the presence of a comorbidity did not impact the satisfaction with the application.
Changes in diabetes self-care activities after the use of the application
Table 4 shows the difference in the management status of diabetes before and after the use of the Diabetes Notepad application. The mean number of days the subject measured their blood glucose, had regular meals, drank alcohol, smoked, exercised, took their regular medication, weighed themselves and kept regular hours during a week before and after the use of the application were compared using the Wilcoxon signed rank test for 90 patients who completed the questionnaire.
All items displayed statistically significant differences with a P=0.000 except for the number of drinking days. Additionally, the frequencies of measuring blood glucose levels, having regular meals, exercising, taking regular medication, weighing oneself, and keeping regular hours increased, while of the frequency of smoking decreased after the use of the application.
Tables 5 and 6 show the differences in the changes in SDSCA items according to the demographic characteristics and the level of satisfaction, respectively, after defining the difference between the numbers of SDSCA items before and after the use of the application as the changes in SDSCA items.
Tables 5 shows the changes in SDSCA items according to gender, age, the duration of diabetes, the duration of using the application, education level, income, and comorbidities. The changes were analyzed, and the significance of the analysis was verified at P<0.05 using the Mann-Whitney U test and Kruskal-Wallis test. The changes in the number of days the subject measured their blood glucose and kept regular hours were significantly different between males and females (P=0.020). The changes in SDSCA items other than those above were not affected by gender, age, the duration of diabetes, the duration of using the application, education level, and the presence of comorbidities.
Table 6 shows the difference in the change in SDSCA items according to the groups with high or low satisfaction with the application. The changes in the number of days the patient measured their blood glucose (P=0.020), exercised (P=0.000), weighed themselves (P=0.003), and kept regular hours (P= 0.000) displayed statistically significant difference between the high satisfaction and low satisfaction groups.
Several recent studies show the positive effects of mobile health intervention on the management of chronic diseases [789]. Other studies report that data input through a personal digital assistant (PDA) and handheld computer was more effective than hand written diabetes journaling [1718]. A smartphone is a device that can be used for internet access, data storage, setting an alarm, email, and running many applications [19], and a smartphone is much more powerful than an existing mobile phone or PDA for applying mobile technology to clinical practice. Applications run on smartphones helped patients monitor meals and medication for the management of chronic diseases including diabetes and also improved communication with medical staff as well as the patient's compliance [20].
This study demonstrates positive changes in the clinical course of diabetes when using a smartphone application as investigated by a user satisfaction survey and changes in the SDSCA. The survey was conducted with subjects who use the Diabetes Notepad application, which is the most widely available diabetes smartphone application in Korea. In particular, this study showed significant changes in the SDSCA, which is the most frequently used tool for the evaluation of diabetes self-management in Koreans [1516].
Previous studies on diabetes self-management using a smartphone showed that a specific application tailored to the respective study design was often used within a limited region and for limited patients. Additionally, only one study has investigated the actual environment for an application that was more common and widespread than 'Diabetes Notepad' [10]. In the current study, 90 users of 'Diabetes Notepad,' the most widely available application in Korea, responded to the survey, and significant changes in diabetes self-management occurred before and after using the application.
However, like other studies [910], this study only confirmed positive changes with the short term use of the application because 82% of the subjects had only used the application for less than 6 months. So speculating the persistence and compliance of diabetes self-management with the use of a smartphone application as well as the future role of the application in the entire healthcare system is difficult. The self-care and monitoring of diabetes is considered necessary for life, as opposed to patients fully recovering from diabetes [2]. Additionally, longterm studies on the effects of a smartphone-based self-management system are needed.
Therefore, the investigators of this study are planning to conduct a follow-up study for patients who have been using the Diabetes Notepad application for more than 1 year. The investigators recently released a new version of the application that allows synchronizing measurements obtained from a blood glucose tester with the Diabetes Notepad application. This synchronization would enable patients who are not familiar with smartphones to store their blood glucose information in the application by connecting their smartphone to the blood glucose tester rather than typing their own blood glucose levels into the smartphone application. One study suggests improved blood glucose control and a decreased hospital admission rate when patients and medical staff share and manage blood glucose information effectively [21]. Subsequently, the use of smartphone applications that can synchronize with a blood glucose tester will open a new horizon for future diabetes management. In particular, coupling a blood glucose tester with the Diabetes Notepad application is more likely to be advantageous to the elderly who usually find it difficult to use smartphone applications [13]. Therefore, we expect that patients older than 44.0 years old, which was the average age in this study, and those with difficulties using smartphones could more easily use this application.
As mentioned in the results, the majority of subjects in this study were males in their 40s without specific comorbidities who were highly educated and earn a high-income. Moreover, the subjects voluntarily participated in the study after reading the application notice. Subsequently, these subjects likely have high accessibility to smartphones and do not have difficulties in using them due to their economic means and high level of education. In addition, as these subjects were more active in managing their disease as voluntary participants and thus are less likely to represent the entire population of diabetes patients, selection bias should be considered.
Therefore, limitations of this study include not analyzing the long-term effects of the use of the application and the selection bias. However, this study still has great significance in that the statistically significant positive changes in the clinical course of diabetes were displayed among users of the widely available application.
Emerging evidence suggests that smartphones induce positive changes on the management of chronic diseases including diabetes. This study shows that the 'Diabetes Notepad' application developed by the investigators also showed a high level of user satisfaction and had a positive effect on diabetes self-management. In addition, this study demonstrated that improvement in diabetes self-management activities is greater when user satisfaction is high. After confirming the long-term effects of the use of smartphone applications, one may hypothesize that smartphone applications could play a pivotal role in the management of diabetes if the application can easily be used by more people and at lower prices.
Acknowledgements
This study was supported by a grant from the Korea Healthcare Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HI10C2020) and a grant (SYR, 2009, 2010) from the Korean Diabetes Association.

CONFLICTS OF INTEREST: No potential conflict of interest relevant to this article was reported.

  • 1. The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1993;329:977-986. ArticlePubMed
  • 2. Hill-Briggs F. Problem solving in diabetes self-management: a model of chronic illness self-management behavior. Ann Behav Med 2003;25:182-193. ArticlePubMed
  • 3. American Diabetes Association. Standards of medical care in diabetes: 2012. Diabetes Care 2012;35(Suppl 1):S11-S63. ArticlePubMedPDF
  • 4. Zgibor JC, Simmons D. Barriers to blood glucose monitoring in a multiethnic community. Diabetes Care 2002;25:1772-1777. ArticlePubMedPDF
  • 5. Ahola AJ, Groop PH. Barriers to self-management of diabetes. Diabet Med 2013;30:413-420. ArticlePubMed
  • 6. Siriwardena LS, Wickramasinghe WA, Perera KL, Marasinghe RB, Katulanda P, Hewapathirana R. A review of telemedicine interventions in diabetes care. J Telemed Telecare 2012;18:164-168. ArticlePubMedPDF
  • 7. Beratarrechea A, Lee AG, Willner JM, Jahangir E, Ciapponi A, Rubinstein A. The impact of mobile health interventions on chronic disease outcomes in developing countries: a systematic review. Telemed J E Health 2014;20:75-82. ArticlePubMedPMC
  • 8. de Jongh T, Gurol-Urganci I, Vodopivec-Jamsek V, Car J, Atun R. Mobile phone messaging for facilitating self-management of long-term illnesses. Cochrane Database Syst Rev 2012;12:CD007459PubMed
  • 9. Liang X, Wang Q, Yang X, Cao J, Chen J, Mo X, Huang J, Wang L, Gu D. Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis. Diabet Med 2011;28:455-463. ArticlePubMed
  • 10. Holtz B, Lauckner C. Diabetes management via mobile phones: a systematic review. Telemed J E Health 2012;18:175-184. ArticlePubMed
  • 11. 2013 Internet Trends. Kleiner Perkins Caufield & Byers (KPCB); updated 2013 May 29. Available from: http://www.kpcb.com/blog/2013-internet-trends.
  • 12. 2012 The second half of survey result on the use of smartphones. Korea Internet & Security Agency; updated 2013 Jan 29. Available from: http://isis.kisa.or.kr/board/index.jsp?pageId=060200&bbsId=3&itemId=799&pageIndex=1.
  • 13. Rao A, Hou P, Golnik T, Flaherty J, Vu S. Evolution of data management tools for managing self-monitoring of blood glucose results: a survey of iPhone applications. J Diabetes Sci Technol 2010;4:949-957. ArticlePubMedPMCPDF
  • 14. Kim HS, Choi W, Baek EK, Kim YA, Yang SJ, Choi IY, Yoon KH, Cho JH. Efficacy of the smartphone-based glucose management application stratified by user satisfaction. Diabetes Metab J 2014;38:204-210. ArticlePubMedPMC
  • 15. Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care 2000;23:943-950. ArticlePubMedPDF
  • 16. Choi EJ, Nam M, Kim SH, Park CG, Toobert DJ, Yoo JS, Chu SH. Psychometric properties of a Korean version of the summary of diabetes self-care activities measure. Int J Nurs Stud 2011;48:333-337. ArticlePubMed
  • 17. Dale O, Hagen KB. Despite technical problems personal digital assistants outperform pen and paper when collecting patient diary data. J Clin Epidemiol 2007;60:8-17. ArticlePubMed
  • 18. Lane SJ, Heddle NM, Arnold E, Walker I. A review of randomized controlled trials comparing the effectiveness of hand held computers with paper methods for data collection. BMC Med Inform Decis Mak 2006;6:23ArticlePubMedPMCPDF
  • 19. Demidowich AP, Lu K, Tamler R, Bloomgarden Z. An evaluation of diabetes self-management applications for Android smartphones. J Telemed Telecare 2012;18:235-238. ArticlePubMedPDF
  • 20. Bloomgarden Z, Dagogo-Jack S. Five Ms of adherence. J Diabetes 2011;3:169-171. ArticlePubMed
  • 21. Polisena J, Tran K, Cimon K, Hutton B, McGill S, Palmer K. Home telehealth for diabetes management: a systematic review and meta-analysis. Diabetes Obes Metab 2009;11:913-930. ArticlePubMed
Fig. 1

Smart phone application outline. (A) The "Diabetes Notepad" application records personal information, blood glucose levels and test records for comorbidities and includes additional features such as diabetes education materials and a cardiovascular risk calculator. (B) The application allows easy access to the saved blood glucose history through the blood glucose record sheet and a graph.

dmj-39-207-g001.jpg
Fig. 2

Users of the application were widely distributed across the nation. Users were from Seoul (28.9%), Gyeonggi (24.4%), Incheon (5.6%), Gangwon (0.0%), Chungcheong (5.6%), Jeolla (5.6%), Daejeon (3.3%), Daegu (8.9%), Gwangju (2.2%), Gyeongsang (7.8%), Ulsan (2.2%), Busan (5.6%), and Jeju (0.0%).

dmj-39-207-g002.jpg
Table 1

Demographic characteristics of the study subjects

dmj-39-207-i001.jpg
Characteristic Value
Sex, male/female 71 (78.9)/19 (21.1)
Age, yr
 Mean±SD 43.5±10.5
 Median (range) 44.0 (14-74)
Duration of diabetes, yr
 <1 33 (36.7)
 1 to <3 18 (20.0)
 3 to <5 12 (13.3)
 5 to <10 11 (12.2)
 ≥10 16 (17.8)
Period of 'Diabetes Notepad' application usage
 <6 mo 73 (82.0)
 6 mo to <1 yr 13 (14.6)
 ≥1 yr 3 (3.4)
Smartphone model
 iPhone 12 (13.3)
 Android phone 78 (86.7)
Education level
 Primary school 2 (2.2)
 Junior high school 1 (1.1)
 High school 15 (16.7)
 University or higher 72 (80.0)
Income/mo (Korean won)
 ≤1,000,000 15 (16.9)
 1,010,000-2,000,000 13 (14.6)
 2,010,000-3,000,000 15 (16.9)
 3,010,000-5,000,000 25 (28.1)
 ≥5,010,000 21 (23.6)
Comorbidities
 None 38 (56.7)
 Hypertension 13 (19.4)
 Dyslipidemia 12 (17.9)
 Other 4 (6.0)

Values are presented as the number (%).

Table 2

Results for user satisfaction

dmj-39-207-i002.jpg
Strongly agree Agree Disagree Strongly disagree
Have there been positive changes in the clinical course of diabetes after the use of the application? 22 (24.7) 52 (58.4) 14 (15.7) 1 (1.1)
Are you satisfied with the structure and completeness of the application? 28 (31.1) 50 (55.6) 12 (13.3) 0
Is the application easy to use? 44 (48.9) 43 (47.8) 3 (3.3) 0
Are you willing to actively recommend the application to others? 48 (53.3) 40 (44.4) 2 (2.2) 0
Will you keep using the application to manage diabetes? 62 (69.7) 24 (27.0) 2 (2.2) 1 (1.1)

Values are presented as the number (%). Cronbach's α=0.791.

Table 3

User satisfaction with the application according to demographic characteristics

dmj-39-207-i003.jpg
Characteristic Have there been positive changes in the clinical course of diabetes after the use of the application? Are you satisfied with the structure and completeness of the application? Is the application easy to use? Are you willing to actively recommend the application to others? Will you keep using the application to manage diabetes?
Sex
 Male (n=71) 3.08±0.69 3.20±0.62 3.54±0.50 3.55±0.56 3.70±0.55
 Female (n=19) 3.00±0.59 3.11±0.74 3.16±0.69 3.37±0.50 3.44±0.71
P value 0.556 0.642 0.026a 0.144 0.106
Age, yr
 <40 (n=28) 3.00±0.68 3.18±0.72 3.46±0.58 3.61±0.50 3.63±0.63
 ≥40 (n=62) 3.10±0.67 3.18±0.62 3.45±0.56 3.47±0.56 3.66±0.57
P value 0.477 0.914 0.905 0.299 0.951
Duration of diabetes, yr
 <1 (n=33) 3.06±0.72 3.24±0.66 3.61±0.50 3.58±0.50 3.63±0.55
 1 to <3 (n=18) 3.11±0.76 3.11±0.68 3.28±0.58 3.50±0.62 3.67±0.77
 3 to <5 (n=12) 3.00±0.74 2.92±0.29 3.25±0.45 3.33±0.65 3.58±0.52
 5 to <10 (n=11) 3.00±0.63 3.18±0.60 3.55±0.52 3.36±0.50 3.55±0.69
 ≥10 (n=16) 3.13±0.50 3.31±0.79 3.44±0.73 3.63±0.50 3.81±0.40
P value 0.961 0.411 0.177 0.564 0.634
Period of 'Diabetes Notepad' application usage
 <6 mo (n=73) 3.07±0.65 3.16±0.66 3.46±0.55 3.50±0.56 3.60±0.62
 6 mo to <1 yr (n=13) 3.15±0.80 3.23±0.60 3.46±0.66 3.62±0.51 3.92±0.28
 ≥1 yr (n=3) 2.67±0.58 3.33±0.58 3.33±0.58 3.33±0.58 3.67±0.58
P value 0.484 0.89 0.888 0.658 0.158
Smartphone model
 iPhone (n=12) 3.00±0.85 2.92±0.52 3.33±0.49 3.50±0.67 3.67±0.65
 Android phone (n=78) 3.08±0.64 3.22±0.66 3.47±0.58 3.51±0.53 3.65±0.58
P value 0.724 0.113 0.331 0.881 0.764
Education level
 High school or lower (n=18) 3.06±0.54 3.39±0.61 3.22±0.65 3.39±0.50 3.78±0.43
 University or higher (n=72) 3.07±0.70 3.13±0.65 3.51±0.53 3.54±0.56 3.62±0.62
P value 0.835 0.121 0.077 0.22 0.366
Income/mo (Korean won)
 ≤1,000,000 (n=15) 3.07±0.62 3.33±0.62 3.40±0.63 3.40±0.51 3.67±0.49
 1,010,000-2,000,000 (n=13) 2.92±0.50 2.77±0.73 3.15±0.69 3.46±0.52 3.46±0.66
 2,010,000-3,000,000 (n=15) 3.07±0.70 3.27±0.70 3.60±0.51 3.67±0.49 3.79±0.58
 3,010,000-5,000,000 (n=25) 3.08±0.69 3.19±0.69 3.50±0.51 3.50±0.65 3.46±0.71
 ≥5,010,000 (n=21) 3.14±0.79 3.24±0.43 3.52±0.51 3.52±0.51 3.90±0.30
P value 0.883 0.206 0.399 0.697 0.020a
Comorbidities
 None (n=38) 3.12±0.67 3.18±0.62 3.48±0.57 3.54±0.57 3.63±0.64
 Hypertension (n=13) 3.15±0.69 3.23±0.73 3.31±0.63 3.54±0.52 3.69±0.48
 Dyslipidemia (n=12) 2.67±0.65 3.17±0.72 3.58±0.52 3.42±0.52 3.67±0.49
 Other (n=4) 3.25±0.50 3.00±0.82 3.25±0.50 3.25±0.50 3.75±0.50
P value 0.141 0.943 0.556 0.591 0.993

Values are presented as mean±standard deviation. Statistics were analyzed by the Mann-Whitney U test and Kruskal-Wallis test.

aStatistically significant differences (P<0.05).

Table 4

Changes in diabetes related self-care activities as assessed by the Summary of Diabetes Self-Care Activities

dmj-39-207-i004.jpg
Variable Before, day After, day P value
Blood sugar testing 3.60±2.67 5.53±1.78 0.000a
Diet 4.97±2.05 5.70±1.52 0.000a
Alcohol 1.26±1.69 1.07±1.71 0.054
Smoking 2.31±3.16 1.80±2.83 0.002a
Exercise 2.67±2.14 3.78±2.23 0.000a
Medication 4.64±2.83 5.55±2.31 0.000a
Weight measurement 1.80±2.04 2.66±2.29 0.000a
Comply with regular lifestyle 3.61±2.15 5.00±1.94 0.000a

Values are presented as mean±standard deviation. Statistics were analyzed by the Wilcoxon signed rank test.

aStatistically significant differences (P<0.05).

Table 5

Changes in the Summary of Diabetes Self-Care Activities items according to demographic characteristics

dmj-39-207-i005.jpg
ΔVariable ΔBlood sugar testing ΔDiet ΔAlcohol ΔSmoking ΔExercise ΔMedication Δweight measurement ΔComply with regular lifestyle
Sex
 Male (n=71) 2.26±2.89 0.73±1.58 -0.20±1.77 -0.66±1.80 1.33±2.21 0.86±2.23 0.99±1.49 1.68±1.96
 Female (n=19) 0.74±1.94 0.74±1.05 -0.16±0.38 0.00±0.00 0.26±2.28 1.11±2.18 0.37±1.64 0.32±0.75
P value 0.020a 0.709 0.578 0.088 0.170 0.728 0.076 0.006a
Age, yr
 <40 (n=28) 1.39±2.49 0.82±1.66 0.21±2.52 -0.78±2.24 0.96±2.74 1.04±2.15 0.82±2.04 1.11±1.91
 ≥40 (n=62) 2.18±2.89 0.69±1.40 -0.37±0.85 -0.40±1.26 1.16±2.03 0.85±2.25 0.87±1.26 1.52±1.84
P value 0.113 0.936 0.285 0.948 0.600 0.892 0.271 0.198
Duration of diabetes, yr
 <1 (n=33) 2.30±3.32 0.76±1.64 -0.15±2.46 -1.00±2.22 1.94±2.72 1.61±2.57 0.85±1.87 1.58±2.09
 1 to <3 (n=18) 1.22±2.92 0.72±1.13 -0.17±0.71 -0.71±1.83 0.33±2.09 1.12±2.50 0.83±1.92 1.06±1.59
 3 to <5 (n=12) 1.91±2.39 0.00±1.13 -0.50±1.00 -0.08±0.29 0.64±1.36 0.17±0.94 1.00±1.13 1.17±1.64
 5 to <10 (n=11) 2.36±2.46 0.73±1.35 -0.09±0.54 0.00±0.00 0.45±0.82 0.18±2.27 1.00±1.00 1.45±1.75
 ≥10 (n=16) 1.69±1.78 1.25±1.69 1.25±1.69 0.00±0.00 1.00±2.19 0.27±1.03 0.69±0.79 1.50±2.00
P value 0.723 0.492 0.875 0.195 0.585 0.206 0.860 0.945
Period of 'Diabetes Notepad' application usage
 <6 mo (n=73) 2.22±2.88 0.80±1.54 -0.31±1.41 -0.51±1.68 1.12±2.33 0.97±2.27 0.78±1.42 1.50±1.93
 6 mo to <1 yr (n=13) 0.42±1.83 0.38±1.21 0.31±2.39 -0.69±1.44 1.08±2.02 0.69±2.10 1.38±2.18 0.92±1.32
 ≥1 yr (n=3) 1.00±1.00 0.67±1.16 0.67±0.58 0.00±0.00 0.67±2.08 0.33±0.58 0.33±0.58 0.67±2.08
P value 0.196 0.713 0.065 0.467 0.810 0.926 0.702 0.475
Smartphone model
 iPhone (n=12) 2.83±2.86 0.25±1.42 -0.83±2.13 -0.33±1.16 1.08±2.28 1.00±2.13 1.25±2.30 1.33±1.88
 Android phone (n=78) 1.79±2.76 0.81±1.48 -0.09±1.47 -0.55±1.68 1.10±2.27 0.89±2.23 0.79±1.39 1.40±1.87
P value 0.242 0.286 0.161 0.681 0.576 0.935 0.670 0.775
Education level
 High school or lower (n=18) 1.17±1.98 0.61±1.09 0.06±2.10 0.00±0.00 0.83±1.76 0.29±1.05 0.28±1.45 1.00±1.85
 University or higher (n=72) 2.13±2.93 0.76±1.56 -0.25±1.43 -0.65±1.79 1.17±2.37 1.06±2.38 1.00±1.53 1.49±1.86
P value 0.186 0.716 0.239 0.100 0.474 0.315 0.302 0.150
Income/mo (Korean won)
 ≤1,000,000 (n=15) 0.67±1.95 0.87±1.25 0.47±1.81 0.00±0.00 0.07±2.69 0.93±1.91 0.47±1.64 0.40±1.21
 1,010,000-2,000,000 (n=13) 1.62±2.53 1.00±2.04 -0.77±1.54 -0.38±0.96 1.67±2.64 1.23±2.65 0.77±1.74 1.77±2.09
 2,010,000-3,000,000 (n=15) 3.13±2.85 1.47±1.89 -0.07±2.02 -1.67±2.94 2.53±3.16 1.93±2.71 1.47±1.92 2.20±2.78
 3,010,000-5,000,000 (n=25) 1.04±2.41 0.08±0.85 -0.08±1.52 -0.38±1.39 0.58±1.21 0.92±1.98 0.96±1.37 1.04±1.43
 ≥5,010,000 (n=21) 3.35±3.10 0.76±1.30 -0.52±0.98 -0.30±1.13 1.14±1.35 -0.10±1.68 0.62±1.61 1.71±1.52
P value 0.003a 0.049a 0.146 0.225 0.185 0.127 0.411 0.067
Comorbidities
 None (n=38) 1.50±2.69 0.87±1.54 -0.08±1.44 -0.38±1.45 1.07±2.32 0.63±2.03 0.70±1.54 1.28±1.86
 Hypertension (n=13) 3.08±2.66 0.38±1.76 -0.46±2.57 -0.77±1.96 1.46±2.18 1.00±2.13 1.46±1.56 2.15±2.30
 Dyslipidemia (n=12) 2.75±2.99 0.50±0.67 -0.42±1.17 -0.83±2.13 1.17±2.48 2.00±2.76 1.08±1.62 0.75±0.75
 Other (n=4) 2.25±3.30 0.50±1.29 -0.25±0.50 -0.75±1.50 0.25±0.50 1.50±3.00 0.50±0.58 2.50±1.92
P value 0.165 0.936 0.674 0.500 0.766 0.430 0.420 0.280

Values are presented as mean±standard deviation. Statistics were analyzed by the Mann-Whitney U test and Kruskal-Wallis test. ΔVariables (day/week)=after use of application (day/wk)-before use of application (day/wk).

aStatistically significant differences (P<0.05).

Table 6

Changes in the Summary of Diabetes Self-Care Activities items according to the satisfaction with the application

dmj-39-207-i006.jpg
ΔVariable Two groups according to the satisfaction with the application P value
Groups with high satisfaction with the application (n=53) Groups with low satisfaction with the application (n=37)
ΔBlood sugar testing 2.43±2.77 1.19±2.66 0.024a
ΔDiet 0.85±1.66 0.57±1.17 0.781
ΔAlcohol -0.23±1.67 -0.14±1.46 0.249
ΔSmoking -0.85±2.05 -0.05±0.23 0.092
ΔExercise 1.58±2.43 0.43±1.82 0.000a
ΔMedication 0.92±2.42 0.89±1.89 0.516
ΔWeight measurement 1.21±1.83 0.35±0.72 0.003a
ΔComply with regular lifestyle 1.91±1.94 0.65±1.46 0.000a

Values are presented as mean±standard deviation. Statistics were analyzed by the Mann-Whitney U test. ΔMeans the difference between the numbers of SDSCA items before and after the use of the application. ΔVariables (day/wk)=after use of application (day/wk)-before use of application (day/wk).

aStatistically significant differences (P<0.05).

Figure & Data

References

    Citations

    Citations to this article as recorded by  
    • Time Efficiency, Reliability, and User Satisfaction of the Tooth Memo App for Recording Oral Health Information: Cross-Sectional Questionnaire Study
      Palinee Detsomboonrat, Pagaporn Pantuwadee Pisarnturakit
      JMIR Formative Research.2024; 8: e56143.     CrossRef
    • Weight Management Health Note, a Mobile Health Platform for Obesity Management Developed by the Korean Society for the Study of Obesity
      Yujung Lee, Hyunji Sang, Sunyoung Kim, Doo Ah Choi, Sang Youl Rhee
      Journal of Obesity & Metabolic Syndrome.2024; 33(1): 1.     CrossRef
    • Relationship between Diabetes Self-Management and the Use of Health Care Apps: A Cross-Sectional Study
      Satoshi Inagaki, Kenji Kato, Kozue Abe, Hiroaki Takahashi, Tomokazu Matsuda
      ACI Open.2023; 07(01): e23.     CrossRef
    • The effect of mHealth program on behavior modification and health outcomes among patients with diabetes: A randomized controlled trial study
      Mohd Khairul Zul Hasymi Firdaus, Piyanuch Jittanoon, Umaporn Boonyasopun, Muhammad Kamil Che Hasan
      Belitung Nursing Journal.2023; 9(5): 437.     CrossRef
    • Clinical Effects of a Home Care Pilot Program for Patients with Type 1 Diabetes Mellitus: A Retrospective Cohort Study
      Sejeong Lee, KyungYi Kim, Ji Eun Kim, Yura Hyun, Minyoung Lee, Myung-Il Hahm, Sang Gyu Lee, Eun Seok Kang
      Diabetes & Metabolism Journal.2023; 47(5): 693.     CrossRef
    • Mobile-Based Application Interventions to Enhance Cancer Control and Care in Low- and Middle-Income Countries: A Systematic Review
      Andrew Donkor, Jennifer Akyen Ayitey, Prince Nyansah Adotey, Esther Oparebea Ofori, Doris Kitson-Mills, Verna Vanderpuye, Samuel Yaw Opoku, Tim Luckett, Meera R. Agar, Penelope Engel-Hills
      International Journal of Public Health.2023;[Epub]     CrossRef
    • Effect of an Integrative Mobile Health Intervention in Patients With Hypertension and Diabetes: Crossover Study
      Sang Woo Oh, Kyoung-Kon Kim, Sung Soo Kim, Su Kyung Park, Sangshin Park
      JMIR mHealth and uHealth.2022; 10(1): e27192.     CrossRef
    • Evaluation of a Smart After-Care Program for Patients with Lung Cancer: A Prospective, Single-Arm Pilot Study
      Hee Chul Yang, Seung Hyun Chung, Ji Sung Yoo, Boram Park, Moon Soo Kim, Jong Mog Lee
      Journal of Chest Surgery.2022; 55(2): 108.     CrossRef
    • Personalized Nutrition for the Prevention and Treatment of Metabolic Diseases: Opportunities and Perspectives
      I. N. Napolsky, P. V. Popova
      Russian Journal for Personalized Medicine.2022; 2(1): 15.     CrossRef
    • Effectiveness of Mobile Application for Promotion of Physical Activity Among Newly Diagnosed Patients of Type II Diabetes – A Randomized Controlled Trial
      Lipilekha Patnaik, Sandeep Kumar Panigrahi, Abhay Kumar Sahoo, Debahuti Mishra, Anil Kumar Muduli, Saswatika Beura
      International Journal of Preventive Medicine.2022; 13(1): 54.     CrossRef
    • The effect of a mobile application on the foot care of individuals with type 2 diabetes: A randomised controlled study
      Berna Dincer, Nefise Bahçecik
      Health Education Journal.2021; 80(4): 425.     CrossRef
    • Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review
      Claudia Eberle, Maxine Löhnert, Stefanie Stichling
      JMIR mHealth and uHealth.2021; 9(2): e23477.     CrossRef
    • Diabetology 4.0: Scoping Review of Novel Insights and Possibilities Offered by Digitalization
      Claudia Eberle, Stefanie Stichling, Maxine Löhnert
      Journal of Medical Internet Research.2021; 23(3): e23475.     CrossRef
    • Mobile Healthcare System Provided by Primary Care Physicians Improves Quality of Diabetes Care
      Tae Jung Oh, Jie-Eun Lee, Seok Kim, Sooyoung Yoo, Hak Chul Jang
      CardioMetabolic Syndrome Journal.2021; 1(1): 88.     CrossRef
    • A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study
      Ian Yi Han Ang, Kyle Xin Quan Tan, Clive Tan, Chiew Hoon Tan, James Wei Ming Kwek, Joanne Tay, Sue Anne Toh
      JMIR Diabetes.2021; 6(3): e25820.     CrossRef
    • The use of mobile health interventions for gestational diabetes mellitus: a descriptive literature review
      Maryam Zahmatkeshan, Somayyeh Zakerabasali, Mojtaba Farjam, Yousef Gholampour, Maryam Seraji, Azita Yazdani
      Journal of Medicine and Life.2021; 14(2): 131.     CrossRef
    • Methods and Measures Used to Evaluate Patient-Operated Mobile Health Interventions: Scoping Literature Review
      Meghan Bradway, Elia Gabarron, Monika Johansen, Paolo Zanaboni, Patricia Jardim, Ragnar Joakimsen, Louise Pape-Haugaard, Eirik Årsand
      JMIR mHealth and uHealth.2020; 8(4): e16814.     CrossRef
    • Effect of a Mobile Phone–Based Glucose-Monitoring and Feedback System for Type 2 Diabetes Management in Multiple Primary Care Clinic Settings: Cluster Randomized Controlled Trial
      Yeoree Yang, Eun Young Lee, Hun-Sung Kim, Seung-Hwan Lee, Kun-Ho Yoon, Jae-Hyoung Cho
      JMIR mHealth and uHealth.2020; 8(2): e16266.     CrossRef
    • Methods and Evaluation Criteria for Apps and Digital Interventions for Diabetes Self-Management: Systematic Review
      Dillys Larbi, Pietro Randine, Eirik Årsand, Konstantinos Antypas, Meghan Bradway, Elia Gabarron
      Journal of Medical Internet Research.2020; 22(7): e18480.     CrossRef
    • Evaluating the effect of a smartphone app-based self-management program for people with COPD: A randomized controlled trial
      Soo Kyung Park, Cho Hee Bang, Seung Hyeun Lee
      Applied Nursing Research.2020; 52: 151231.     CrossRef
    • Development of an mHealth application for family carers of people with dementia: A study protocol
      Sarath Rathnayake, Wendy Moyle, Cindy Jingwen Jones, Pauline Calleja
      Collegian.2019; 26(2): 295.     CrossRef
    • Use of Weight-Management Mobile Phone Apps in Saudi Arabia: A Web-Based Survey
      Ghadeer S Aljuraiban
      JMIR mHealth and uHealth.2019; 7(2): e12692.     CrossRef
    • Appropriate Medical Technology in the Era of the 4th Industrial Revolution
      Sang Youl Rhee
      The Korean Journal of Medicine.2019; 94(5): 387.     CrossRef
    • 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
      Mihiretu M. Kebede, Claudia R. Pischke
      Frontiers in Endocrinology.2019;[Epub]     CrossRef
    • Development and Evaluation: The Satisfaction of Using an Oral Health Survey Mobile Application
      Palinee Detsomboonrat, Pagaporn Pantuwadee Pisarnturakit
      Telemedicine and e-Health.2019; 25(1): 55.     CrossRef
    • Usability of a Disease Management Mobile Application as Perceived by Patients With Diabetes
      Roqaieh Janatkhah, Rasoul Tabari-Khomeiran, Attaolah Asadi-Louyeh, Ehsan Kazemnejad
      CIN: Computers, Informatics, Nursing.2019; 37(8): 413.     CrossRef
    • Structure and Characteristics of Diabetes Self-management Applications
      Sara Angelini, Gregorio Marco Alicastro, Sara Dionisi, Marco Di Muzio
      CIN: Computers, Informatics, Nursing.2019; 37(7): 340.     CrossRef
    • Digital Health Transformation of Integrated Care in Europe: Overarching Analysis of 17 Integrated Care Programs
      Erik Baltaxe, Thomas Czypionka, Markus Kraus, Miriam Reiss, Jan Erik Askildsen, Renata Grenkovic, Tord Skogedal Lindén, János György Pitter, Maureen Rutten-van Molken, Oscar Solans, Jonathan Stokes, Verena Struckmann, Josep Roca, Isaac Cano
      Journal of Medical Internet Research.2019; 21(9): e14956.     CrossRef
    • A Mobile Prenatal Care App to Reduce In-Person Visits: Prospective Controlled Trial
      Kathryn I Marko, Nihar Ganju, Jill M Krapf, Nancy D Gaba, James A Brown, Joshua J Benham, Julia Oh, Lorna M Richards, Andrew C Meltzer
      JMIR mHealth and uHealth.2019; 7(5): e10520.     CrossRef
    • Users’ preferences and design recommendations to promote engagements with mobile apps for diabetes self-management: Multi-national perspectives
      Mary D. Adu, Usman H. Malabu, Aduli E. O. Malau-Aduli, Bunmi S. Malau-Aduli, Simone Borsci
      PLOS ONE.2018; 13(12): e0208942.     CrossRef
    • Developing and Clinical Application of a Smartphone Mobile Mood Chart Application in Korean for Patients with Bipolar Disorder
      Kayoung Song, Saejeong Lee, Woon Yoon, Changyoon Kim, Yeonho Joo, Jungsun Lee, Myong-Wuk Chon
      Journal of Korean Neuropsychiatric Association.2018; 57(3): 244.     CrossRef
    • mHealth Tools for the Self-Management of Patients With Multimorbidity in Primary Care Settings: Pilot Study to Explore User Experience
      Anum Irfan Khan, Ashlinder Gill, Cheryl Cott, Parminder Kaur Hans, Carolyn Steele Gray
      JMIR mHealth and uHealth.2018; 6(8): e171.     CrossRef
    • Rules of engagement in mobile health: what does mobile health bring to research and theory?
      Cigdem Sahin
      Contemporary Nurse.2018; 54(4-5): 374.     CrossRef
    • Diabetes Mellitus m-Health Applications: A Systematic Review of Features and Fundamentals
      Ericles Andrei Bellei, Daiana Biduski, Nathália Pinto Cechetti, Ana Carolina Bertoletti De Marchi
      Telemedicine and e-Health.2018; 24(11): 839.     CrossRef
    • Current Status and Effects of Nutrition Education Programs for Diabetic Patients in Korea
      Hae Jin Kang
      The Journal of Korean Diabetes.2018; 19(2): 106.     CrossRef
    • Mobile direct observation of therapy (MDOT) - A rapid systematic review and pilot study in children with asthma
      Michael D. Shields, Fahad ALQahtani, Michael P. Rivey, James C. McElnay, Jacobus P. van Wouwe
      PLOS ONE.2018; 13(2): e0190031.     CrossRef
    • App-basiertes Selbstmonitoring bei Typ-2-Diabetes
      A. Steinert, M. Haesner, E. Steinhagen-Thiessen
      Zeitschrift für Gerontologie und Geriatrie.2017; 50(6): 516.     CrossRef
    • Smartphone App Use for Diabetes Management: Evaluating Patient Perspectives
      Kirstie Lithgow, Alun Edwards, Doreen Rabi
      JMIR Diabetes.2017; 2(1): e2.     CrossRef
    • Needs for Development of IT-based Nutritional Management Program for Women with Gestational Diabetes Mellitus
      Chan-Jung Han, Sun-Young Lim, Eunsuk Oh, Yoon-Hee Choi, Kun-Ho Yoon, Jin-Hee Lee
      Korean Journal of Community Nutrition.2017; 22(3): 207.     CrossRef
    • Semantic representation and processing of hypoglycemic events derived from wearable sensor data
      Jean-Paul Calbimonte, Jean-Eudes Ranvier, Fabien Dubosson, Karl Aberer, Antonis Bikakis, Thanos G. Stavropoulos, Georgios Meditskos
      Journal of Ambient Intelligence and Smart Environments.2017; 9(1): 97.     CrossRef
    • Acceptability and Feasibility of a Smartphone Application for 5th, 6th Grade Elementary Students to Prevent Childhood Obesity; a Qualitative Study
      Ji-Hye Jung, Young-Gyu Cho, Da-Ye Ji, Jae-Heon Kang
      Korean Journal of Health Promotion.2016; 16(4): 251.     CrossRef
    • m-santé francophone et diabète : mise au point
      M. Halbron, M. Joubert, E. Sonnet
      Médecine des Maladies Métaboliques.2016; 10(3): 243.     CrossRef
    • Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity
      Sang Ouk Chin, Changwon Keum, Junghoon Woo, Jehwan Park, Hyung Jin Choi, Jeong-taek Woo, Sang Youl Rhee
      Scientific Reports.2016;[Epub]     CrossRef
    • The Wills Eye Glaucoma App: Interest of Patients and Their Caregivers in a Smartphone-based and Tablet-based Glaucoma Application
      Michael Waisbourd, Hermandeep Dhami, Chen Zhou, Michael Hsieh, Pramod Abichandani, Michael J. Pro, Marlene R. Moster, L. Jay Katz, Lisa A. Hark, Jonathan S. Myers
      Journal of Glaucoma.2016; 25(9): e787.     CrossRef
    • Testing the Feasibility of Remote Patient Monitoring in Prenatal Care Using a Mobile App and Connected Devices: A Prospective Observational Trial
      Kathryn I Marko, Jill M Krapf, Andrew C Meltzer, Julia Oh, Nihar Ganju, Anjali G Martinez, Sheetal G Sheth, Nancy D Gaba
      JMIR Research Protocols.2016; 5(4): e200.     CrossRef
    • Social Networking Services-Based Communicative Care for Patients with Diabetes Mellitus in Korea
      Hun-Sung Kim, Yoo Jeong, Sun Baik, So Yang, Tong Kim, Hyunah Kim, Hyunyong Lee, Seung-Hwan Lee, Jae Cho, In-Young Choi, Kun-Ho Yoon
      Applied Clinical Informatics.2016; 07(03): 899.     CrossRef
    • Efficacy of a New Medical Information system, Ubiquitous Healthcare Service with Voice Inception Technique in Elderly Diabetic Patients
      Kyoung Min Kim, Kyeong Seon Park, Hyun Ju Lee, Yun Hee Lee, Ji Seon Bae, Young Joon Lee, Sung Hee Choi, Hak Chul Jang, Soo Lim
      Scientific Reports.2015;[Epub]     CrossRef
    • Six-month Outcomes of Mobile Phone Application-based Self-management in a Patient with Type 2 Diabetes
      Mi Kyeong Hong, Young Yun Cho, Mi Yong Rha, Jae Hyeon Kim, Moon-Kyu Lee
      Clinical Nutrition Research.2015; 4(3): 201.     CrossRef
    • Physical Activity Capture Technology With Potential for Incorporation Into Closed-Loop Control for Type 1 Diabetes
      Vikash Dadlani, James A. Levine, Shelly K. McCrady-Spitzer, Eyal Dassau, Yogish C. Kudva
      Journal of Diabetes Science and Technology.2015; 9(6): 1208.     CrossRef

    • PubReader PubReader
    • Cite this Article
      Cite this Article
      export Copy Download
      Close
      Download Citation
      Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

      Format:
      • RIS — For EndNote, ProCite, RefWorks, and most other reference management software
      • BibTeX — For JabRef, BibDesk, and other BibTeX-specific software
      Include:
      • Citation for the content below
      A Smartphone Application Significantly Improved Diabetes Self-Care Activities with High User Satisfaction
      Diabetes Metab J. 2015;39(3):207-217.   Published online April 22, 2015
      Close
    • XML DownloadXML Download
    Figure
    • 0
    • 1
    A Smartphone Application Significantly Improved Diabetes Self-Care Activities with High User Satisfaction
    Image Image
    Fig. 1 Smart phone application outline. (A) The "Diabetes Notepad" application records personal information, blood glucose levels and test records for comorbidities and includes additional features such as diabetes education materials and a cardiovascular risk calculator. (B) The application allows easy access to the saved blood glucose history through the blood glucose record sheet and a graph.
    Fig. 2 Users of the application were widely distributed across the nation. Users were from Seoul (28.9%), Gyeonggi (24.4%), Incheon (5.6%), Gangwon (0.0%), Chungcheong (5.6%), Jeolla (5.6%), Daejeon (3.3%), Daegu (8.9%), Gwangju (2.2%), Gyeongsang (7.8%), Ulsan (2.2%), Busan (5.6%), and Jeju (0.0%).
    A Smartphone Application Significantly Improved Diabetes Self-Care Activities with High User Satisfaction
    CharacteristicValue
    Sex, male/female71 (78.9)/19 (21.1)
    Age, yr
     Mean±SD43.5±10.5
     Median (range)44.0 (14-74)
    Duration of diabetes, yr
     <133 (36.7)
     1 to <318 (20.0)
     3 to <512 (13.3)
     5 to <1011 (12.2)
     ≥1016 (17.8)
    Period of 'Diabetes Notepad' application usage
     <6 mo73 (82.0)
     6 mo to <1 yr13 (14.6)
     ≥1 yr3 (3.4)
    Smartphone model
     iPhone12 (13.3)
     Android phone78 (86.7)
    Education level
     Primary school2 (2.2)
     Junior high school1 (1.1)
     High school15 (16.7)
     University or higher72 (80.0)
    Income/mo (Korean won)
     ≤1,000,00015 (16.9)
     1,010,000-2,000,00013 (14.6)
     2,010,000-3,000,00015 (16.9)
     3,010,000-5,000,00025 (28.1)
     ≥5,010,00021 (23.6)
    Comorbidities
     None38 (56.7)
     Hypertension13 (19.4)
     Dyslipidemia12 (17.9)
     Other4 (6.0)
    Strongly agreeAgreeDisagreeStrongly disagree
    Have there been positive changes in the clinical course of diabetes after the use of the application?22 (24.7)52 (58.4)14 (15.7)1 (1.1)
    Are you satisfied with the structure and completeness of the application?28 (31.1)50 (55.6)12 (13.3)0
    Is the application easy to use?44 (48.9)43 (47.8)3 (3.3)0
    Are you willing to actively recommend the application to others?48 (53.3)40 (44.4)2 (2.2)0
    Will you keep using the application to manage diabetes?62 (69.7)24 (27.0)2 (2.2)1 (1.1)
    CharacteristicHave there been positive changes in the clinical course of diabetes after the use of the application?Are you satisfied with the structure and completeness of the application?Is the application easy to use?Are you willing to actively recommend the application to others?Will you keep using the application to manage diabetes?
    Sex
     Male (n=71)3.08±0.693.20±0.623.54±0.503.55±0.563.70±0.55
     Female (n=19)3.00±0.593.11±0.743.16±0.693.37±0.503.44±0.71
    P value0.5560.6420.026a0.1440.106
    Age, yr
     <40 (n=28)3.00±0.683.18±0.723.46±0.583.61±0.503.63±0.63
     ≥40 (n=62)3.10±0.673.18±0.623.45±0.563.47±0.563.66±0.57
    P value0.4770.9140.9050.2990.951
    Duration of diabetes, yr
     <1 (n=33)3.06±0.723.24±0.663.61±0.503.58±0.503.63±0.55
     1 to <3 (n=18)3.11±0.763.11±0.683.28±0.583.50±0.623.67±0.77
     3 to <5 (n=12)3.00±0.742.92±0.293.25±0.453.33±0.653.58±0.52
     5 to <10 (n=11)3.00±0.633.18±0.603.55±0.523.36±0.503.55±0.69
     ≥10 (n=16)3.13±0.503.31±0.793.44±0.733.63±0.503.81±0.40
    P value0.9610.4110.1770.5640.634
    Period of 'Diabetes Notepad' application usage
     <6 mo (n=73)3.07±0.653.16±0.663.46±0.553.50±0.563.60±0.62
     6 mo to <1 yr (n=13)3.15±0.803.23±0.603.46±0.663.62±0.513.92±0.28
     ≥1 yr (n=3)2.67±0.583.33±0.583.33±0.583.33±0.583.67±0.58
    P value0.4840.890.8880.6580.158
    Smartphone model
     iPhone (n=12)3.00±0.852.92±0.523.33±0.493.50±0.673.67±0.65
     Android phone (n=78)3.08±0.643.22±0.663.47±0.583.51±0.533.65±0.58
    P value0.7240.1130.3310.8810.764
    Education level
     High school or lower (n=18)3.06±0.543.39±0.613.22±0.653.39±0.503.78±0.43
     University or higher (n=72)3.07±0.703.13±0.653.51±0.533.54±0.563.62±0.62
    P value0.8350.1210.0770.220.366
    Income/mo (Korean won)
     ≤1,000,000 (n=15)3.07±0.623.33±0.623.40±0.633.40±0.513.67±0.49
     1,010,000-2,000,000 (n=13)2.92±0.502.77±0.733.15±0.693.46±0.523.46±0.66
     2,010,000-3,000,000 (n=15)3.07±0.703.27±0.703.60±0.513.67±0.493.79±0.58
     3,010,000-5,000,000 (n=25)3.08±0.693.19±0.693.50±0.513.50±0.653.46±0.71
     ≥5,010,000 (n=21)3.14±0.793.24±0.433.52±0.513.52±0.513.90±0.30
    P value0.8830.2060.3990.6970.020a
    Comorbidities
     None (n=38)3.12±0.673.18±0.623.48±0.573.54±0.573.63±0.64
     Hypertension (n=13)3.15±0.693.23±0.733.31±0.633.54±0.523.69±0.48
     Dyslipidemia (n=12)2.67±0.653.17±0.723.58±0.523.42±0.523.67±0.49
     Other (n=4)3.25±0.503.00±0.823.25±0.503.25±0.503.75±0.50
    P value0.1410.9430.5560.5910.993
    VariableBefore, dayAfter, dayP value
    Blood sugar testing3.60±2.675.53±1.780.000a
    Diet4.97±2.055.70±1.520.000a
    Alcohol1.26±1.691.07±1.710.054
    Smoking2.31±3.161.80±2.830.002a
    Exercise2.67±2.143.78±2.230.000a
    Medication4.64±2.835.55±2.310.000a
    Weight measurement1.80±2.042.66±2.290.000a
    Comply with regular lifestyle3.61±2.155.00±1.940.000a
    ΔVariableΔBlood sugar testingΔDietΔAlcoholΔSmokingΔExerciseΔMedicationΔweight measurementΔComply with regular lifestyle
    Sex
     Male (n=71)2.26±2.890.73±1.58-0.20±1.77-0.66±1.801.33±2.210.86±2.230.99±1.491.68±1.96
     Female (n=19)0.74±1.940.74±1.05-0.16±0.380.00±0.000.26±2.281.11±2.180.37±1.640.32±0.75
    P value0.020a0.7090.5780.0880.1700.7280.0760.006a
    Age, yr
     <40 (n=28)1.39±2.490.82±1.660.21±2.52-0.78±2.240.96±2.741.04±2.150.82±2.041.11±1.91
     ≥40 (n=62)2.18±2.890.69±1.40-0.37±0.85-0.40±1.261.16±2.030.85±2.250.87±1.261.52±1.84
    P value0.1130.9360.2850.9480.6000.8920.2710.198
    Duration of diabetes, yr
     <1 (n=33)2.30±3.320.76±1.64-0.15±2.46-1.00±2.221.94±2.721.61±2.570.85±1.871.58±2.09
     1 to <3 (n=18)1.22±2.920.72±1.13-0.17±0.71-0.71±1.830.33±2.091.12±2.500.83±1.921.06±1.59
     3 to <5 (n=12)1.91±2.390.00±1.13-0.50±1.00-0.08±0.290.64±1.360.17±0.941.00±1.131.17±1.64
     5 to <10 (n=11)2.36±2.460.73±1.35-0.09±0.540.00±0.000.45±0.820.18±2.271.00±1.001.45±1.75
     ≥10 (n=16)1.69±1.781.25±1.691.25±1.690.00±0.001.00±2.190.27±1.030.69±0.791.50±2.00
    P value0.7230.4920.8750.1950.5850.2060.8600.945
    Period of 'Diabetes Notepad' application usage
     <6 mo (n=73)2.22±2.880.80±1.54-0.31±1.41-0.51±1.681.12±2.330.97±2.270.78±1.421.50±1.93
     6 mo to <1 yr (n=13)0.42±1.830.38±1.210.31±2.39-0.69±1.441.08±2.020.69±2.101.38±2.180.92±1.32
     ≥1 yr (n=3)1.00±1.000.67±1.160.67±0.580.00±0.000.67±2.080.33±0.580.33±0.580.67±2.08
    P value0.1960.7130.0650.4670.8100.9260.7020.475
    Smartphone model
     iPhone (n=12)2.83±2.860.25±1.42-0.83±2.13-0.33±1.161.08±2.281.00±2.131.25±2.301.33±1.88
     Android phone (n=78)1.79±2.760.81±1.48-0.09±1.47-0.55±1.681.10±2.270.89±2.230.79±1.391.40±1.87
    P value0.2420.2860.1610.6810.5760.9350.6700.775
    Education level
     High school or lower (n=18)1.17±1.980.61±1.090.06±2.100.00±0.000.83±1.760.29±1.050.28±1.451.00±1.85
     University or higher (n=72)2.13±2.930.76±1.56-0.25±1.43-0.65±1.791.17±2.371.06±2.381.00±1.531.49±1.86
    P value0.1860.7160.2390.1000.4740.3150.3020.150
    Income/mo (Korean won)
     ≤1,000,000 (n=15)0.67±1.950.87±1.250.47±1.810.00±0.000.07±2.690.93±1.910.47±1.640.40±1.21
     1,010,000-2,000,000 (n=13)1.62±2.531.00±2.04-0.77±1.54-0.38±0.961.67±2.641.23±2.650.77±1.741.77±2.09
     2,010,000-3,000,000 (n=15)3.13±2.851.47±1.89-0.07±2.02-1.67±2.942.53±3.161.93±2.711.47±1.922.20±2.78
     3,010,000-5,000,000 (n=25)1.04±2.410.08±0.85-0.08±1.52-0.38±1.390.58±1.210.92±1.980.96±1.371.04±1.43
     ≥5,010,000 (n=21)3.35±3.100.76±1.30-0.52±0.98-0.30±1.131.14±1.35-0.10±1.680.62±1.611.71±1.52
    P value0.003a0.049a0.1460.2250.1850.1270.4110.067
    Comorbidities
     None (n=38)1.50±2.690.87±1.54-0.08±1.44-0.38±1.451.07±2.320.63±2.030.70±1.541.28±1.86
     Hypertension (n=13)3.08±2.660.38±1.76-0.46±2.57-0.77±1.961.46±2.181.00±2.131.46±1.562.15±2.30
     Dyslipidemia (n=12)2.75±2.990.50±0.67-0.42±1.17-0.83±2.131.17±2.482.00±2.761.08±1.620.75±0.75
     Other (n=4)2.25±3.300.50±1.29-0.25±0.50-0.75±1.500.25±0.501.50±3.000.50±0.582.50±1.92
    P value0.1650.9360.6740.5000.7660.4300.4200.280
    ΔVariableTwo groups according to the satisfaction with the applicationP value
    Groups with high satisfaction with the application (n=53)Groups with low satisfaction with the application (n=37)
    ΔBlood sugar testing2.43±2.771.19±2.660.024a
    ΔDiet0.85±1.660.57±1.170.781
    ΔAlcohol-0.23±1.67-0.14±1.460.249
    ΔSmoking-0.85±2.05-0.05±0.230.092
    ΔExercise1.58±2.430.43±1.820.000a
    ΔMedication0.92±2.420.89±1.890.516
    ΔWeight measurement1.21±1.830.35±0.720.003a
    ΔComply with regular lifestyle1.91±1.940.65±1.460.000a
    Table 1 Demographic characteristics of the study subjects

    Values are presented as the number (%).

    Table 2 Results for user satisfaction

    Values are presented as the number (%). Cronbach's α=0.791.

    Table 3 User satisfaction with the application according to demographic characteristics

    Values are presented as mean±standard deviation. Statistics were analyzed by the Mann-Whitney U test and Kruskal-Wallis test.

    aStatistically significant differences (P<0.05).

    Table 4 Changes in diabetes related self-care activities as assessed by the Summary of Diabetes Self-Care Activities

    Values are presented as mean±standard deviation. Statistics were analyzed by the Wilcoxon signed rank test.

    aStatistically significant differences (P<0.05).

    Table 5 Changes in the Summary of Diabetes Self-Care Activities items according to demographic characteristics

    Values are presented as mean±standard deviation. Statistics were analyzed by the Mann-Whitney U test and Kruskal-Wallis test. ΔVariables (day/week)=after use of application (day/wk)-before use of application (day/wk).

    aStatistically significant differences (P<0.05).

    Table 6 Changes in the Summary of Diabetes Self-Care Activities items according to the satisfaction with the application

    Values are presented as mean±standard deviation. Statistics were analyzed by the Mann-Whitney U test. ΔMeans the difference between the numbers of SDSCA items before and after the use of the application. ΔVariables (day/wk)=after use of application (day/wk)-before use of application (day/wk).

    aStatistically significant differences (P<0.05).

    Kim YJ, Rhee SY, Byun JK, Park SY, Hong SM, Chin SO, Chon S, Oh S, Woo Jt, Kim SW, Kim YS. A Smartphone Application Significantly Improved Diabetes Self-Care Activities with High User Satisfaction. Diabetes Metab J. 2015;39(3):207-217.
    Received: May 13, 2014; Accepted: Oct 15, 2014
    DOI: https://doi.org/10.4093/dmj.2015.39.3.207.

    Diabetes Metab J : Diabetes & Metabolism Journal
    Close layer
    TOP