Analysis of Development Status and Function Features of Diabetes Health Management Apps

doi:10.21203/rs.3.rs-2085430/v1

Background: Using Diabetes-related Apps can effectively reduce the patients’ level of glucose and rehospitalization rate. However, due to the serious homogeneity of diabetes apps content and uneven function quality, medical staffs and patients do not know how to choose. This study aimed to understand the development status of diabetes health management Apps, analyze their functions and characteristics, and provide recommendations for further improvement or development of diabetes-related Apps.

Methods: In May 2022, diabetes-related apps were screened in ten major App markets, including Android and iOS system by using the keywords either Chinese or English. The apps that met the criteria were downloaded and their functions and characteristics were analyzed. Silberg scale was used to assess their information accountability.

Results: 90 diabetes-related apps were included, including 69 Chinese apps and 21 English apps. Almost all (97.8%) of which were developed by companies and commercial teams. Most of the apps (96.7%, 87/90) were targeted at people with diabetes, and only 3.3% were for type 1 and gestational diabetes. The total mean score of Silberg scale was 3.67±0.83. Among 28 functions, individual customized function and social support function are rare.

Conclusion: At present, the overall accountability quality of diabetes-related apps is low. The functions are insufficient, and the individual customized functions need to be further explored. In the future, it should be strengthen the diversity and individualization of diabetes-related apps, increase the development of special types of diabetes apps, and encourage medical staffs and patients to participate in the designment and development of Apps.

Diabetes

Telemedicine

Smart-phone apps

Functional analysis

Information quality

According to the data from The Global Diabetes Overview (Tenth Edition) of the International Diabetes Federation (IDF), up to December 2021, there were about 537 million diabetics in the global population aged 20-79, and the number of diabetics is expected to reach 643 million by 2030, direct medical costs from diabetes are already approaching 1 trillion [1]. China has the largest number of diabetics in the world, and the total number of diabetics has exceeded 116.4 million, equivalent to 1 in 9 diabetics [2].Due to the characteristics of insidious onset, long courses and incurable, patients need long-term treatment and management to control the disease, preventing and delaying acute or chronic complications [3].Poor management can easily lead to diabetic microangiopathy and complications of diabetes, seriously affecting the patients’ quality of life and life span, and bringing great economic and psychological burden to families and society [4].

Mobile Health applications (mHealth Apps), as one of the main forms of mHealth management, refers to smartphones, tablets or other mobile devices as terminals and mobile internet technology as the support, providing services for patients and medical staffs [5]. With the popularity of mobile devices, more than 500 million smartphone users worldwide use mobile health apps for health management [6]. Medical staffs can develop individualized health guidance plans for users through mobile health apps, give instant feedback through the Apps, and provide online consultation for patients, which bringing great convenience to users [7].

With the rapid increase of diabetics and the shortage of medical resources, it is difficult to meet the individual needs of the patients [8,9]. A number of studies have shown that the use of mobile apps for self-management of diabetes patients can improve their cognitive level and self-efficacy of the disease, effectively reduce the level of postprandial blood glucose, glycated hemoglobin (HbA1C) and rehospitalization rate [10-13].Medical staffs can use apps to provide immediate support and help patients improving their compliance with self-management, so as to improve management efficiency [14,15]. At present, mobile health apps have been widely used in the prevention and health management of patients with diabetes [16].

With the development of mobile medical care, a large number of chronic disease management apps have been put into the App market. Recently, more than 320,000 health apps available in major App stores, among which diabetes apps account for about 30% [17]. However, due to the serious homogeneity of diabetes apps content and uneven function quality, medical staffs and patients do not know how to choose [18-20]. Therefore, the purpose of this study is to understand the development status of diabetes-related apps available in China mobile application market, analyze their functions and characteristics, provide help for medical staffs to reasonably select apps, as well as provide reference for the subsequent of diabetes-related apps developing.

1.1 Searching Platform The iOS Apple APP Store and Android Apps market were searched in May 2022, diabetes-related apps were identified using the keywords "diabetes、blood glucose、blood sugar、diabetic、type 1 diabetes、type 2 diabetes、hyperglyceamia、gestational diabetes、GDM" written either in English or Chinese. Apps were searched on the leading platforms from the top 10 most widely used apps store in China, including "Apple App Store (Chinese version) ", "iPad", "Wandoujia", "360 Mobile assistant", "Huawei App store", "Anzhi Market", "Tencent APP Store", "91 assistant", "Xiaomi App Store", " Baidu mobile assistant". Each app was adopted and analyzed independently by 2 researchers using smartphone with Huawei Mate 20 Pro for android platform or iPhone 5s and iPad 14.4.2 for iOS platform.

1.2 Screening Criteria The inclusion criteria of app are as follows: ①The user’s interface is either Chinese or English; ②All type of diabetes management apps can be used normally after registration. Exclusion criteria: ①Only Diet or Complication management apps; ②Medical Version apps; ③Medical consultation apps.

1.3 Screening Methods Two researchers use different mobile phones searched by entering keywords in the two systems respectively. Recording the search results in Excel for deduplication. Download and register the included apps one by one, testing and using the corresponding functions. Finally, 90 apps related to diabetes management were included.

1.4 Extracting Information Extract the functions from the incorporated app. Eventually extraction " Risk prediction and assessment function, Personalized customized function, Blood glucose recording, Trend analysis, Diet recording, Calorie calculation, Food storage, Exercise recording, Reminding function, Weight recoding, Blood pressure recording, Sleep monitoring, Heart rate monitoring, Blood oxygen monitoring, Cholesterol recording, Uric-acid recording, HbA1c recording, Health information, Moments, Rewarding, Sharing data, Consulting services, Live function, Medication recording, Insulin recording, Book store, Mall service, Upload medical records ", a total of 28 functions.

1.5 Evaluation of information accountability Silberg Scale was used to evaluate the accountability of the included Apps. Silberg scale is a validated scale originally designed to determine online health information quality，which was initially developed by Griffiths and Christensen [21]. This scale has recently been used in evaluating the accountability aspect of apps information. The scale includes 4 dimensions, Authorship, Attribution, Disclosure and Currency, with 9 items in total. One point for each item, a total of 9 points, the higher the score, the better the quality of the APP. This scale has been applied to evaluate the information quality of cardiovascular, postpartum depression and other disease applications [22].

1.6 Statistical Analysis SPSS 25.0 software was used to analysis the data. Counting data by frequency and percentages, and measurement data by mean ± standard deviation.

2.1 Screening process By May 2022, a total of 721 apps have been found in Android system and 268 apps in Apple system, 989 apps in total. Through browsing the tabs, exclude apps that are unrelated to the theme, duplicate, or can't be used properly after downloading. Finally, 90 diabetes-related apps were included. Among them, there are 69 apps in Chinese,21 apps in English. See Figure 1 for the specific process.

2.2 General Characteristics of diabetes health management Apps See Table 1. Among them, the average cost of the paid APP is 13.5 Yuan, and the cost of the free APP is 60 to 800 Yuan, which include some charges such as consultation and physical examination package.

Table 1 General characteristics of diabetes management apps (n/%)

Items		Chinese apps n=69	English apps n=21	Total n=90
Applicable population	Type 1 diabetes	1(1.4)	0(0)	1(1.1)
	Gestational diabetes	2(2.9)	0(0)	2(2.2)
	All types of diabetes	66(95.7)	21(100)	87(96.7)
Research and development organization	The institute which collaborated with medical institutions	2(2.9)	0(0)	2(2.2)
Research and development organization	Company or business group	67(97.1)	21(100)	88(97.8)
Cost	Paid	4(5.8)	0(0)	4(4.4)
Cost	Free	65(94.2)	21(100)	86(95.6)

2.3 Analysis of the function characteristics of diabetes management Apps

Number of Diabetes Health Management Apps Functions A total of 28 functions were abstracted in the apps in this study. The numbers of Chinese and English diabetes management apps as shown in Table 2.

Table 2 Classification of the number of diabetes management APP functions (n/%)

numbers of functions	Chinese apps n=69	English apps n=21	Total n=90
Level 1	13（18.8)	0(0.0)	13(14.4)
Level 2	17(24.6)	2(9.5)	21(23.3)
Level 3	29(42.0)	10(47.6)	39(43.3)
Level 4	8(11.6)	9(42.9)	17(18.9)

Note: the number of functions >15 is level 1; The number of functions between 11 and 15 is level 2; The number of functions between 6-10 is level 3; Less than 5 is level 4

2.4 Ranking of Functions about the Diabetes Management Apps Ranked by the number of diabetes management apps feature from most to least, the top five functions are Blood-glucose recording and Trend Analysis, Blood Pressure recording, Exercise recording, Health information and Consulting services. The last five functions are Heart Rate monitoring, Live function, Sleep monitoring, Book store and blood Oxygen monitoring. As shown in Figure 2.

2.5 Function Comparison of Diabetes Management Apps Diabetes related apps features can be divided into six functions, including Prevention Function (Risk prediction and assessment Function), Auxiliary Self-management Functions (Diet recording, Exercise recording, Medication recording, Insulin recording, Blood Glucose recording, HbA1c recording, Blood pressure recording, Weight recoding, Sleep monitoring, Heart rate monitoring, Blood oxygen monitoring, Uric-acid recording, Cholesterol recording, Health information, Live function, Book store), Developing Individual Blood Glucose Program; Social support function (Consulting services, Moments, Sharing data), Reminding function; Service function (Calorie calculation, Food storage, Trend analysis, Mall services, Rewarding, Upload medical records). The comparison of the number of diabetes management apps in different mobile phone systems is shown in Table 3.

Table 3 Comparison of functions of diabetes health management apps (n/%)

Function	The secondary function	Chinese apps n=69	English apps n=21	Total n=90
Auxiliary self-management functions	Blood Glucose Recording	68(98.6)	21(100.0)	87(98.9)
	Diet Recording	33(47.8)	9(42.9)	42(46.7)
	Exercise Recording	39(56.5)	9(42.9)	48(53.3)
	Medication Recording	33(47.8)	9(42.9)	42(46.7)
	Insulin recording	26(37.7)	10(47.6)	36(40.0)
	Health Information	41(59.4)	6(28.6)	47(52.2)
	Living	9(13.0)	0(0.0)	9(10.0)
	Book Storage	2(2.9)	3(14.3)	5(5.6)
	Blood Pressure Recording	49(71.0)	7(33.3)	56(62.2)
	Weight Recording	37(53.6)	5(23.8)	42(46.7)
	HbAlc Recording	21(30.4)	7(33.3)	28(31.1)
	Heart Rate Recording	14(20.3)	0(0.0)	14(15.6)
	Blood Oxygen Recording	5(7.2)	0(0.0)	5(5.6)
	Uric-acid Recording	16(23.2)	0(0.0)	16(17.8)
	Cholesterol Recording	21(30.4)	4(19.0)	25(27.8)
	Sleep Monitoring	7(10.1)	1(4.8)	8(8.9)
Individual blood sugar program	Developing Individual Blood Sugar Program	14(20.3)	1(4.8)	15(16.7)
Prevention	Risk Prediction and Assessment	18(26.1)	0(0.0)	18(20.0)
Reminding	Reminding	30(43.5)	1(4.8)	31(34.4)
Social Support Function	Consulting Services	43(62.3)	2(9.5)	45(50.0)
	Moments	18(26.1)	2(9.5)	20(22.2)
	Sharing Data	24(34.8)	3(14.3)	27(30.0)
Service Function	Trend Analysis	68(98.6)	18(85.7)	86(95.6)
	Mall Service	31(44.9)	2(9.5)	33(36.7)
	Calorie Calculation	28(40.6)	5(23.8)	33(36.7)
	Food Storage	27(39.1)	2(9.5)	29(32.2)
	Upload Medical Records	22(31.9)	1(4.8)	23(25.6)
	Rewarding Points	15(21.7)	0(0.0)	15(16.7)

2.6 Information Quality Analysis Among the 90 apps included in this study, the highest score was 6 and the lowest score was 2. The majority of apps（86，74%）scored no more than 4, and just 3.3% of apps reached above 5. For the authorship, 8.9% Apps identified the authors, although all the applications (100%) identified the author’s affiliations, none of them furnished the credentials of authors. In respect of attribution, 35.6% applications given the source, 3.3% had the references or hyperlinked. As for disclosure, most (95.6%) of the apps have disclosed the ownership of the applications, but none of them disclosed the sponsorship. With regard to currency,97.8% of apps included the last modification date, 22.2% of the apps were updated within the past month. The mean score of each of four domains was very low，ranging from 0.41 out of 2 for attribution and 1.18 out of 2 for currency. The mean score of each dimension of the Silberg scale for diabetes-related apps is presented in Figure 3.

Among all the diabetes management apps in the Chinese mobile App market, only 46.2 percent English or Chinese apps are available for use. The vast majority of apps were developed by commercial teams or companies, with little involvement from medical staffs. Apps related to type 1 diabetes mellitus or gestational diabetes mellitus were rare. The overall information quality of diabetes health management apps is low. More than half of the apps have a small number of functions, and lack of personalized customization functions and social support functions, which are difficult to effectively assist patients with diabetes self-management.

Previous studies have pointed out that technical and quality problems are the main problems existing in health management apps [23]. Currently, there are 195 apps related to diabetes health management in China's mobile App market, but only 46.2% (90 apps) can be used normally. Most apps have technical problems such as failure to register, login and flash back. Therefore, the operation and technical maintenance of mobile health applications should be strengthened in the future to ensure the usability of App market. From the perspective of development teams, the vast majority of apps are developed by commercial teams or companies, with little participation of the medical staff, and uncertain accuracy of push content, which is consistent with the research of Jimenez et al [24]. Secondly, apps fee will affect patients' intention to use apps [25]. This study shows that 95.6% of apps are free software, but nearly half (36.7%-50%) of apps have built-in charging items, such as consultation and mall functions, which will affect the use of apps by patients. Finally, at present, most apps are suitable for all types of diabetic patients, while few apps are designed for the characteristics of type 1 diabetes and gestational diabetes. Due to the different characteristics of various types of diabetes, their health management is also different. It is suggested that medical staff should participate in the design and development of the APP and push evidence-based content to ensure the accuracy and authority of the push content.

In this study, the average information quality score of the 90 apps was 3.67±0.83 (the highest score was 9), which indicates that the overall quality of diabetes-related apps is low and has not yet reached the average level. This result is basically consistent with that of Zhang et al [26], but slightly higher than that of Zhang et al [27] and Xiao et al [28]. This may be related to the fact that 22.2% of diabetes health management apps updated their content within the last month, which is the reason why the average score of information quality of diabetes health management apps was higher than that of other studies. However, this study showed that in all 90 apps, the author's certificate was not provided, and the sponsor of the apps was not disclosed. This is the direction for future improvement.

Research shows that the more problems apps can help users solve, the higher the stickiness of users to medical apps, and the stronger their willingness to use apps in the future [29]. This study shows that more than half of the apps have less than 10 functions, and some of them are only designed for one function, such as blood sugar recording app, diet recording app and exercise recording app, etc. These apps can solve limited problems for patients, so their use is limited. In addition, only about half of all diabetes management apps can provide the function of diet and exercise recording and monitoring, and 40% of them provide the function of Insulin Recording, indicating that most apps lack the core function of assisting self-management and monitoring, which is difficult to meet the needs of assisting patients in self-management [30].Therefore, it is suggested that when developing diabetes management apps in the future, sufficient research should be carried out to understand the needs of patients, and the comprehensiveness and diversity of functions should be considered on the basis of the development of core functions to assist self-management, so as to meet the diversified needs of patients as much as possible.

Research shows that 20% of diabetes management apps have the risk prediction function of diabetes risk or diabetes complications. However, it is uncertain whether the risk prediction function of these apps is developed based on the diabetes risk prediction model. Therefore, the accuracy of their prediction is uncertain. In addition, only 16.7% of diabetes management apps have the function of developing personalized management plans for patients, and most apps cannot develop personalized management plans for patients based on their characteristics. Therefore, these apps are difficult to effectively assist patients to improve their self-management ability. It is suggested that a risk prediction module should be developed based on the diabetes risk prediction model in the future to identify people with different risk levels and give hierarchical management, so as to improve the quality and efficiency of diabetes prevention management. At the same time, personalized management plans should be formulated based on the patient-centered risk factors and evidence-based evidence, combined with the characteristics and needs of patients, so as to better prevent and delay the occurrence and development of the disease.

The function of social support is one of the most important factors affecting patients' behavior change and health status. Professional support from medical staff plays an important role in patients' self-management and blood glucose control [31]. This study shows that half of apps (50%) have expert consultation function. Diabetics can communicate with doctors anytime and anywhere through mobile apps, which can better help patients to manage themselves; Studies have shown that peer support has a good effect on improving patients' behavioral compliance and psychological and emotional management, and patients' real-time sharing and communication in the app can strengthen the role of peer support [32]. However, in this study, apps with communication functions between diabetics such as Moments function account for only 22.2%, and there is no core function for peer support such as group interaction and regular sharing of experience. This suggests that peer support for these apps is limited. As an important part of social support, family support plays an important role in promoting patients' self-management. This study found that no app has the interconnection function with family members. It is suggested that when developing diabetes management apps in the future, attention should be paid to the development of family and social support functions, so as to comprehensively improve patients' self-efficacy and self-management ability.

Timely reminding can help users improve the compliance of self-management and develop good living habits, when the user's laboratory value exceeds the normal limit, it can give an early warning and reminding to help patients better control their blood sugar. In this study, less than half of the apps have reminders (only 34.4%), indicating that existing apps have insufficient auxiliary functions for patient self-management. Studies show that if the push frequency is too low, users will not be able to arouse the activity of the APP, while if the push frequency is too high, users may feel antipathy and even uninstall the apps [33]. Therefore, it is suggested that in the future development of APP, customized reminder functions should be set so that patients can choose the time, frequency and form of reminder to help users improve compliance.

At present, diabetes-related apps have achieved rapid development in the field of mobile health worldwide, but there is still some room for improvement. For example, few apps are designed by medical staffs, and the core function of assisting diabetes self-management behavior monitoring is not enough. It is suggested that the future development of Apps should strengthen the participation of medical staff and users, and develop apps for different types of diabetes. In addition, the development of diabetes-related apps should fully consider the needs of patients, focus on the behavioral changes of patients with diabetes, and provide them with adequate family and social support supplemented by behavioral monitoring to better help patients improve their self-management ability.

Apps

Applications

GDM

Gestational Diabetes Mellitus

HbA1c

Glycated Hemoglobin

Ethical Approval

Not applicable.

Competing interests

No competing financial interests exist

Authors' contributions

Beibei Duan: Conceptualization(equal); Investigation (lead); Methodology (equal), and Original draft (lead); Mengdi Liu: Investigation(supporting) and Original draft(supporting); Cunhao Ma: Investigation (supporting); Weiwei Liu: Conceptualization (equal), Methodology (equal), Supervision, and Review and editing (lead)

Funding

This publication was supported by the National Natural Science Foundation of China, project number was 71904133.

International Diabetes Federation(IDF), Global diabetes Map (IDF Diabetes Atlas)(Tenth Version).Available at https://diabetesatlas.org/data/en/country/42/cn.html [last accessed December 16,2021]
Diabetes Society of Chinese Medical Association. Chinese guidelines for the prevention and treatment of type 2 diabetes mellitus (2020 edition). Chinese Journal of Diabetes,2021,41(05):482–548. https://doi.org/10.3760/cma.j.cn115791-20210221-00095.
Årsand E, Skrøvseth SO, Hejlesen O, et al. Mobile patient applications within diabetes - from few and easy to advanced functionalities. Stud Health Technol Inform, 2013, 192: 1010.
Li S, Zhang E, Li M, et al. Research on the management value and Application of Diabetes APP. China Medical Equipment 2015,30(08):144–146. https://doi.org/10.3969/j.issn.1674-1633.2015.08.048.
Bateman DR, Srinivas B, Emmett TW, et al. Categorizing Health Outcomes and Efficacy of mHealth Apps for Persons With Cognitive Impairment: A Systematic Review. J Med Internet Res. 2017;19(8):e301. https://doi.org/10.2196/jmir.7814
Dicianno BE, Parmanto B, Fairman AD, et al. Perspectives on the evolution of mobile (mHealth) technologies and application to rehabilitation. Phys Ther. 2015;95(3):397–405. https://doi.org/10.2522/ptj.20130534
Research 2 Guidance. MHealth App Economics Report. Available at https://research2guidance.com/product/connectivity-in-digital-health/[last accessed October 28,2019]
Wang Y, Xue H, Huang Y, et al. A Systematic Review of Application and Effectiveness of mHealth Interventions for Obesity and Diabetes Treatment and Self-Management. Adv Nutr, 2017, 8(3): 449–462. https://doi.org/10.3945/an.116.014100
Ajay VS, Prabhakaran D. The scope of cell phones in diabetes management in developing country health care settings. J Diabetes Sci Technol, 2011, 5(3): 778–783. https://doi.org/10.1177/193229681100500332
Wang W, Seah B, Jiang Y, et al. A randomized controlled trial on a nurse-led smartphone-based self-management programme for people with poorly controlled type 2 diabetes: A study protocol. J Adv Nurs, 2018, 74(1): 190–200. https://doi.org/10.1111/jan.13394
Sun C, Sun L, Xi S, et al. Mobile Phone-Based Telemedicine Practice in Older Chinese Patients with Type 2 Diabetes Mellitus: Randomized Controlled Trial. JMIR Mhealth Uhealth, 2019, 7(1): e10664. https://doi.org/10.2196/10664
Wang Y, Li M, Zhao X, et al. Effects of continuous care for patients with type 2 diabetes using mobile health application: A randomized controlled trial. Int J Health Plann Manage, 2019, 34(3): 1025–1035. https://doi.org/10.1002/hpm.2872
Cui M, Wu X, Mao J, et al. T2DM Self-Management via Smartphone Applications: A Systematic Review and Meta-Analysis. PLoS One, 2016, 11(11): e0166718. https://doi.org/10.1371/journal.pone.0166718
Lou G, Li H, Zeng X, et al. Application of mobile health technology in chronic disease management. Chinses Journal of Prevention and Control of Chronic Diseases, 2020, 28(11): 856–861. https://doi.org/10.16386/j.cjpccd.issn.1004-6194.2020.11.014.
Zhao X, Jin J. Application status of modern information technology in self-management of diabetes patients. Military Nursing,2015,32(16):46–47 + 53. https://doi.org/10.3969/j.issn.1008-9993.2015.16.012.
Liu K, Xie Z, Or CK. Effectiveness of Mobile App-Assisted Self-Care Interventions for Improving Patient Outcomes in Type 2 Diabetes and/or Hypertension: Systematic Review and Meta-Analysis of Randomized Controlled Trials. JMIR Mhealth Uhealth. 2020;8(8):e15779. https://doi.org/10.2196/15779.
Lv X, Xia JH, Cao Y. Quality status of diabetes mobile medical APP and research progress of its evaluation tool. Chinese Nursing Management,2019,19(12):1881–1886. https://doi.org/10.3969/j.issn.1672-1756.2019.12.024.
Agarwal P, Gordon D, Griffith J, et al. Assessing the quality of mobile applications in chronic disease management: a scoping review. NPJ Digit Med, 2021, 4(1): 46. https://doi.org/10.1038/s41746-021-00410-x
Eiland L, McLarney M, Thangavelu T, et al. App-Based Insulin Calculators: Current and Future State. Curr Diab Rep, 2018, 18(11): 123. https://doi.org/10.1007/s11892-018-1097-y
Zhang H, Wang T, Wang A, et al. Usability evaluation and User review analysis of Domestic diabetes mobile apps. Military Nursing, 2020, 37(10): 26–30. https://doi.org/10.3969/j.issn.1008-9993.2020.10.007.
Griffiths KM, Christensen H. Quality of web based information on treatment of depression: cross sectional survey. BMJ. 2000 Dec 16;321(7275):1511-5. https://doi.org/10.1136/bmj.321.7275.1511.
Aslani A, Pournik O, Abu-Hanna A, et al. Web-site evaluation tools: a case study in reproductive health information. Stud Health Technol Inform. 2014; 205:895–899.
Li F, She R, Gao Y, et al. Analysis of mobile health APP development status and problems based on public health perspective. Journal of Medical Intelligence, 2020, 41(12): 36–40. https://doi.org/10.3969/j.issn.1673-6036.2020.12.008.
Jimenez G, Lum E, Car J. Examining Diabetes Management Apps Recommended From a Google Search: Content Analysis. JMIR Mhealth Uhealth, 2019, 7(1): e11848. https://doi.org/10.2196/11848
Wang Y, Dai T, Zheng Y, et al. Application status, Problems and Suggestions of Diabetes management App in China. China Digital Medicine, 2018,13(05):57–59. https://doi.org/10.3969/j.issn.1673-7571.2018.05.017.
Zhang H, Wang T, Zhang Z, et al. The current status of stroke-related smartphone applications available to adopt in China: A systematic review study. Medicine (Baltimore). 2020 Jul 2;99(27):e20656. https://doi.org/10.1097/MD.0000000000020656.
Zhang M, Ho R, Loh A, et al. Current status of postnatal depression smartphone applications available on application stores: an information quality analysis. BMJ Open, 2017, 7(11): e015655.
Xiao Q, Lu S, Wang Y, et al. Current Status of Cardiovascular Disease-Related Smartphone Apps Downloadable in China. Telemed J E Health, 2017, 23(3): 219–25.
Zhang N. Research on the Influence of User cognition on the intention of continuous use of Mobile health management APP [D]; Harbin Institute of Technology, 2021.
Donevant SB, Estrada RD, Culley JM, et al. Exploring app features with outcomes in mHealth studies involving chronic respiratory diseases, diabetes, and hypertension: a targeted exploration of the literature.J Am Med Inform Assoc. 2018;25(10):1407–1418. https://doi.org/10.1093/jamia/ocy104
Wang N, Ding Y. Qualitative study on the experience of disease self-management in pregnant women with gestational diabetes mellitus. Journal of Nursing Science, 2014, 29(14): 16–19. https://doi.org/10.3870/hlxzz.2014.14.016.
Azmiardi A, Murti B, Febrinasari RP, et al. The Effect of Peer Support in Diabetes Self-Management Education on Glycemic Control in Patients with Type 2 Diabetes: A Meta-Analysis. Epidemiol Health, 2021: e2021090. https://doi.org/10.4178/epih.e2021090
Fu X, Zheng J. Research on the push function of Foreign Mobile News APP. Southeast Communication, 2016, (12): 6–8.

No competing interests reported.

Analysis of Development Status and Function Features of Diabetes Health Management Apps

Status:

Version 1

Abstract

Figures

Introduction

Data And Methods

Result

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1