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A doctor-nurse-patient mobile health management system effectively controls blood glucose in chinese patients with type 2 diabetes mellitus: a prospective study

BMC Health Services Research volume 22, Article number: 1564 (2022) Cite this article

Abstract

Background

Coronavirus-2019 pandemic in China aroused increasing interest in telemedicine-supported glycaemic control. We hypothesize that age might influence usage and efficacy of telemedicine-supported glycaemic control. This study aims to measure the effects of a doctor-nurse-patient Mobile Health Management System (MHMS) for fasting plasma glucose (FPG) control in patients with type 2 diabetes mellitus (T2DM).

Methods

Four hundred sixty four patients with T2DM were recruited. A one-hour diabetes education provided to each patient and subsequent follow-ups arranged in the 1st, 2nd, 4th, 8th, and 12th week after enrollment were recorded in MHMS. The effectiveness of MHMS was defined as the proportion of patients achieving FPG target (below 126 mg/dL or 7.0mml/L).

Results

Among the enrolled 464 patients (age: 55.0 ± 13.7 years) who were divided into three groups: young (18–40 years), middle-aged (41–65 years) and elderly (> 65 years), 424 ones completed all follow-ups of 12 weeks. FPG decreased from 178.38 ± 95.04 to 117.90 ± 14.22 mg/dL in the young group, from 180.00 ± 91.08 to 122.94 ± 37.95 mg/dL in the middle-aged group, and from 174.24 ± 80.64 to 128.88 ± 23.4 mg/dL in the elderly group. The proportion of FPG-target-achieved patients increased from 46.2 to 90.4% in the young group, from 32.6 to 82.8% in the middle-aged group, and from 29.5 to 73.3% in the elderly group. The proportion of FPG-target-achieved patients between three age groups were statistically significant (P < 0.001). And the changes of proportion of FPG-target-achieved patients at different follow-up times were statistically significant (P = 0.037). Compared with the young group, the elderly group achieved poorer FPG level (P = 0.032).

Conclusion

MHMS can help patients with T2DM lower FPG and improve proportion of FPG-target-achieved patients. Younger patients may achieve better glycaemic control than older patients. MHMS may serve multitudinous patients with T2DM to achieve adequate FPG self-management.

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Background

Type 2 diabetes mellitus (T2DM) is a leading cause of death among adults. It is estimated that almost 500 million people have been diagnosed with T2DM worldwide [1]. In 2019, there were 116 million diabetic patients living in China, and this number is expected to rise [2]. Poor glycaemic control is associated with high proportion of complications including neuropathy, stroke, and congestive heart failure [3,4,5]. These complications not only increase morbidity and mortality, but also financially burden patients, families, and society [6]. In the era of the coronavirus-2019 pandemic, telemedicine has become an essential tool for glycaemic control [7], hence it is imperative to evaluate and optimize newly developed telemedicine management systems.

Telemedicine is defined as the remote exchange of medical information and/or services between patients and clinicians through electronic information communication technologies [8, 9]. It has already been used to transmit weekly blood glucose data from patients to clinicians and facilitate follow-up [10, 11]. It has also been used to provide education, support, and cellphone text message reminders to improve the self-management of patients with T2DM.

In 2017, 13,000 medical institutions across 22 Chinese provinces developed a telemedicine system which provided teleconsultation, telediagnosis, and remote medical education. China has a universal health coverage through the social insurance scheme [8]. However, China still has fewer telemedicine users as compared with the United States, Canada, and European countries [9]. Additionally, technological advancements and the coronavirus-2019 pandemic have led to increasing interest in telemedicine for glycaemic monitoring and control [10].

Age is an important factor which may influence both the usage and efficacy of telemedicine-based health management and glycaemic control in patients with T2DM. Others have shown that older patients may be less willing and reluctant to utilize remote health management platforms [11, 12]. Barrot-de la Puente reported that the percentage of patients with fair glycaemic control (HbA1c ≤ 7%) was significantly higher among older aged groups (≥ 65) [13], while Chiu reported that age was negatively correlated with HbA1c [14]. Additionally, there is no independent analysis on the relationship between age, as a single factor, and the management of fasting plasma glucose (FPG) through telemedicine.

The mobile health management system (MHMS) was jointly developed by the Chinese Diabetes Society (CDS) and endocrinologists of Shenzhen Qianhai Free Trade Zone Hospital for T2DM management. It is a telemedicine platform that provides patients with disease education, an endocrinologist hotline, and a repository of patient records including their FPG records .The aim of this study was to examine whether the doctor-nurse-patient MHMS effectively controls FPG in young, middle-aged, and elderly patients with T2DM.

Methods

Study design and subjects

Patients with T2DM, who presented to the department of endocrinology of Shenzhen Qianhai Free Trade Zone Hospital between April 2014 and January 2016, were recruited to participate in this prospective study.

The inclusion criteria were: (1) diagnosed as T2DM [15]; (2) age ≥ 18 years; (3) current treatments of basal insulin (involving treatments combining with oral hypoglycemic agents or pre-prandial insulin). The exclusion criteria included: (1) presence of acute diabetic complications, such as ketoacidosis, severe infection, and late-stage heart, liver, or kidney diseases; (2) concurrent administration of pharmaceuticals that interfere with glycaemic control, such as glucocorticoids; and (3) presence of disabilities that prevent self-management.

This study was approved by the Ethical Committee of Shenzhen Qianhai Free Trade Zone Hospital (2022 K-W001). All subjects signed the informed consent.

Patient and public involvement

All the patients were prescribed basal insulin at discharge. They were informed of the plan to manage their T2DM through MHMS. This management involved scheduled follow-up duration and frequency, self-monitoring glucose during follow-up period, an individualized diet and exercise prescription, reporting FPG records in MHMS, and possible influence deriving from their participation.

Procedures

Patients with T2DM underwent face-to-face interviews and education before enrollment. Seven telephone follow-ups were scheduled over a 12-week period. And prior to each follow-up, MHMS sent a text message to remind the patients to monitor their blood glucose. MHMS included three modules: (1) diabetes mellitus education, (2) an endocrinologist hotline, and (3) patient records. The diabetes mellitus education module informed patients of both knowledge about insulin and methods of insulin injection, self-monitoring of blood glucose (SMBG), and the follow-up plan of this project. The endocrinologist hotline provided a platform to the involved doctors or nurses to do online follow-ups. The patient records section stored patients’ medical archive, including gender, age, body mass index (BMI), diabetes duration, education and recorded patients’ FPG.

Follow-up

This project was developed and run by a diabetes specialist team. This team involved two endocrinologists and three advanced practice nurses specializing in diabetes. One of the nurses served as the project leader. The endocrinologists set FPG target and designed the blood glucose management plan. Nurses carried out a one-hour diabetes education for each patient. The contents of education included basic information about insulin and insulin injection methods, methods of SMBG, and an individualized diet and exercise plan. Each patient was offered one blood glucose monitor (Baiankang, Bayer Medical Care Co., LTD, Germany) and adequate blood glucose test strips. Further, the nurses arranged follow-ups in the 1st, 2nd, 4th, 8th, and 12th weeks after enrollment. An endocrinologist gave patients online medication guidance in the 4th and 8th weeks. Patients were instructed to independently monitor their FPG. The data of their FPG and insulin injection doses were input into MHMS by nurses. Table 1 displays the details of the implementation plan.

Table 1 Description of the mobile health management system
Full size table

Outcome

The primary outcome was measured FPG. Nurses called patients to do follow-up according to the MHMS timeline and recorded each patient’s updated FPG data into MHMS. The calls were automatically recorded by MHMS in the 1st, 2nd, 4th, 8th, and 12th weeks, and only the calls lasting longer than 120s were recorded as valid data.

The effectiveness of MHMS was defined as the proportion of patients achieving FPG below 126 mg/dL (7.0mml/L) which is indicative of adequate glycaemic control.

Data collection and definition

The baseline characteristics of patients, including gender, age, educational level, diabetes duration, BMI, reported complications, pre-enrollment treatment plan, and initial FPG and HbA1c data, were recorded in MHMS.

Statistical analysis

Statistical analysis was conducted using IBM SPSS Statistics for Windows, version 22.0 (IBM, Armonk, NY). Descriptive statistics were used to describe the demographic characteristics of the enrolled patients. Pearson’s chi-squared test was used to compare the demographic characteristics of the enrolled patients. Graph Pad Prism 7.0 (Graph Pad Software, San Diego, CA) was used to plot average FPG level of follow-ups. FPG level of different age groups were analyzed through using two-way repeated measures analyses of variance (ANOVA). The least significant difference test was used for pair-wise comparison between groups (LSD). Generalized linear mixed model (GLMM) was used to compare the proportions of FPG-target-achieved patients in different age groups during different follow-ups. Two-tailed P value < 0.05 was considered as statistically significant.

Four hundred sixty four patients’ FPG at baseline were all analyzed. Patients with 5 missed FPG records in follow-ups were excluded. The missed data were abandoned and excluded from the subsequent analysis. Missing data were replaced by using the serial mean. The latter is the most frequently used method to account for arbitrary missing data when less than 5% of data is missing.

Results

A total of 464 patients were enrolled (mean age: 55.0 ± 13.7 years), and 424 of them completed the study. The 424 patients were divided into a young (18–40 years), a middle-aged (41–65 years) and elderly (> 65 years) groups. Diabetes duration, gender, education level, complication proportion, and treatment plan varied significantly between age groups (p > 0.05). Notably, the average diabetes duration was 12.1 ± 6.5 years in the elderly group, 6.4 ± 5.6 years in the middle-aged group, and 2.9 ± 3.5 years in the young group. A total of 24.3% of the elderly patients experienced cardiovascular, cerebrovascular, or other complications, while 8.9% of the middle-aged and 5.0% of the young patients experienced these complications. Moreover, the elderly group was more likely to use oral hypoglycemic agents alone to control blood glucose. Table 2 displays the baseline characteristics of the patients.

Table 2 Baseline patient characteristics
Full size table

The three main treatment regimens prescribed were 1) basal insulin, 2) basal insulin and oral hypoglycemic agents, and 3) basal insulin and pre-prandial insulin. There were no significant differences in the treatment regimens taken by patients among different age groups after enrollment. Endocrinologists called to follow up the patients twice during the study period. Comparisons of the times of patients’ receiving endocrinologists’ calls between different groups were of no statistical significance (Table S1).

During the 1st follow-up, 15 patients did not answer calling and 25 left wrong numbers. Among the 40 patients, 8 were from the young group, 26 from the middle-aged group, and 6 from the elderly group; 21 are male(52.5%), 19 female (47.5%), and mean age 51.68 + 13.54. There was no significant difference in the ratios of follow-up loss among different age groups (P = 0.205). The patients with less than 5 times of FPG data were excluded. Finally 424 patients were included in the final analysis (Figure S1).

The enrolled patients’ FPG level overall decreased with increase of follow-up visit. FPG of patients in three groups decreased rapidly in the first week of the study and plateaued during later stages (Fig. 1). Decrease of patients’ FPG level in each group was respectively significant (P < 0.001), and difference of patients’ FPG level between age groups was significant (P < 0.001) (Table 3). Further pair-wise comparison showed that during the study the elderly experienced smaller decrease of blood glucose than the young (P = 0.001) and the middle-aged (P < 0.001). The young and the middle-aged groups experienced comparable decrease of FPG level (P = 0.359) (Table 4).

Fig. 1
figure 1

Mean FPG by age group over the course of the study. All age groups experienced a reduction in mean FPG over the course of the study, but young and middle-aged patients responded better than elderly ones

Full size image
Table 3 Relationship between age and FPG at each follow-up visit
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Table 4 Comparison of the change in FPG over time among young, middle-aged, and elderly patients
Full size table

The proportions of patients with adequate FPG (≤ 126 mg/dL, or ≤ 7.0 mmol/L) during each follow-up visit are shown in Table 5. The proportion of patients with FPG level below 126 mg/dL increased from 46.2 to 90.4% in the young group, from 32.6 to 82.8% in the middle-aged group, and from 29.5 to 73.3% in the elderly group (Table 5).

Table 5 Comparison of the proportion of patients with eligible FPG at different follow-up times
Full size table

A generalized mixed linear model was used to compare the proportion of patients achieving adequate FPG between age groups, and the results showed that there were significant differences between three age groups (P < 0.001). As the management progressed, the proportion of patients with adequate FPG increased, and this change was statistically significant (P < 0.001). Age was a statistically significant factor related to FPG control in this model. There were significantly different proportions of patients achieving adequate FPG in each age group (P = 0.037). Compared with the young group, the elderly group had a significant lower percentage of patients with FPG level below 126 mg/dL (P = 0.032) (Table 5).

Discussion

This study demonstrated that MHMS can help all patients with T2DM improve FPG control. In the first week of the study, FPG level decreased dramatically among all patients irrespective of age, and FPG control was improved throughout the intervention via MHMS. However, there was a significantly greater proportion of young and middle-aged patients achieving adequate FPG control as compared with elderly patients.

Two reasons could contribute to patients’ adequate FPG. Firstly, injecting basal insulin after enrollment may be related with patients’ achieving adequate FPG. Secondly, better FPG control may also have been related with patients’ increased self-management ability derived from the education session and regular support through frequent follow-ups. Compared with others’ reports that arranged follow-ups and suggested interventions in 3 or 6 months after patients’ discharge [16], this study exhibits that a better management strategy requires to arrange a follow-up in the first week immediately after patients’ discharge. This follow-up aims to address any problems that can occur during the implementation of the prescribed treatment plan. Then, the subsequent follow-up visits can be conducted in 3 and 6 months to track the patient’s self-management and evaluate their health condition.

This study found that elderly patients with T2DM experienced a less improvement in FPG control from telemedicine management than the young. The elderly are one of the primary target groups for telemedicine program because more than 50% of the elderly are afflicted with multiple chronic conditions which require long-term case management [17]. A Nielson Norman Group study which examined technological capabilities across different age groups found that the elderly had a significantly lower success ratio and a six-fold higher error ratio when completing an assigned task on a computer than young adults [18]. Additionally, most elderly patients prefer face-to-face interactions with family physicians, specialists, and nurses [19].

These findings indicate that patients’ demographic characteristics, learning perspectives, and technological access should be considered when designing and performing chronic disease self-management education programs [20]. It also confirms that age and educational level influence FPG self-management [21, 22]. Consistent with the results of a cross-sectional study [23] and contradictory to those of two retrospective studies [13, 14], there was no significant difference in the proportion of middle-aged and young patients achieving adequate glycaemic control. Although age could lead to worsened glycaemic control among elderly patients [24, 25], this study found that the proportion of elderly patients achieving adequate FPG can significantly increase when the patients received regular follow-ups via MHMS (Table 5).

As a chronic disease, T2DM requires long-term self-management to achieve adequate FPG. The self-management abilities of patients with T2DM can be improved through standardized education and regular follow-up [26,27,28]. Different from other telemedicine management systems which use text messages or various online applications [29, 30], our endocrinologists and nurses contacted patients directly via MHMS to address their problems and improve their FPG self-management. Moreover, this study demonstrated that MHMS could work as a platform for 2 doctors and 3 nurses to help 424 patients with T2DM control their FPG during the same period. This indicates that MHMS may be applied to serve multitudinous patients with T2DM.

As a prospective study, there are some limitations in this study. First, the recorded FPG depended on patients’ self-monitoring, which might cause some measured deviation. To improve the reliability of data, we offered each enrolled patient a same brand-name blood glucose detector, adequate blood glucose test strips, and an education session. This offer could help patients report valid data of their FPG self-management during the study. Second, this study used FPG rather than HbA1c as evaluating indicator, which is against conventional procedure of favoring HbA1c. However, HbA1c in China is only measured once every three months during treatment initiation and once every 6 months when achieving planned treatment target. However, because of short study duration (3 months) and short intervals between follow-up visits, this study fixed FPG as evaluating indicator. Thirdly, although this study showed that using MHMS could help patients achieve better FPG management in short term, yet the effects of MHMS in long term remained to be further proved.

The study has several strengths. First, we constructed a doctor-nurse-patient team for glycaemic control, emphasizing cooperation between endocrinologists and nursing specialists. So this study was different from conventional follow-up in China which was typically done only by nurses. Second, this study developed a telemedicine-based follow-up system for patients with T2DM. This design provides patients with more comprehensive and systematic instructions of glycaemic control to achieve effective glycaemic control and so can bring greater convenience to patients with T2DM.

Conclusion

MHMS with regular follow-up and guidance can help patients with T2DM at different ages achieve adequate glycaemic control and lower FPG. Younger patients can achieve better glycemic control than older patients.

This study demonstrates that MHMS may be applied to serve multitudinous patients with T2DM to achieve adequate FPG self-management.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

MHMS:

Mobile Health Management System

FPG:

fasting plasma glucose

T2DM:

Type 2 diabetes mellitus

CDS:

Chinese Diabetes Society

SMBG:

self-monitoring of blood glucose

BMI:

body mass index

ANOVA:

analyses of variance

LSD:

least significant difference

GLMM:

Generalized linear mixed model

References

  1. Shamloul R, Ghanem H. Erectile dysfunction. Lancet. 2013;381:153–65.

    Article  CAS  Google Scholar 

  2. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843.

    Article  Google Scholar 

  3. Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321:405–12.

    Article  CAS  Google Scholar 

  4. Stevens RJ, Coleman RL, Adler AI, Stratton IM, Matthews DR, Holman RR. Risk factors for myocardial infarction case fatality and stroke case fatality in type 2 diabetes: UKPDS 66. Diabetes Care. 2004;27:201–7.

    Article  Google Scholar 

  5. Erqou S, Lee CT, Suffoletto M, Echouffo-Tcheugui JB, de Boer RA, van Melle JP, et al. Association between glycated haemoglobin and the risk of congestive heart failure in diabetes mellitus: systematic review and meta-analysis. Eur J Heart Fail. 2013;15:185–93.

    Article  CAS  Google Scholar 

  6. von Korn P, Sydow H, Neubauer S, Duvinage A, Mocek A, Dinges S, et al. Lifestyle intervention in chronic ischaemic heart disease and type 2 diabetes (the LeIKD study): study protocol of a prospective, multicentre, randomised, controlled trial. BMJ Open. 2021;11:e042818.

    Article  Google Scholar 

  7. Ghosh A, Gupta R, Misra A. Telemedicine for diabetes care in India during COVID19 pandemic and national lockdown period: guidelines for physicians. Diabetes Metab Syndr. 2020;14:273–6.

    Article  Google Scholar 

  8. Yip W, Fu H, Chen AT, Zhai T, Jian W, Xu R, et al. 10 years of health-care reform in China: progress and gaps in Universal Health Coverage. Lancet. 2019;394:1192–204.

    Article  Google Scholar 

  9. Altharthi MS. Telehealth practice in eight countries: New Zealand, Australia, the USA, Canada, UK, Malaysia, China and India: a thesis presented in partial fulfillment of the requirements of degree of Master in Information Science at Massey University, Albany campus, Auckland, New Zealand. 2012. Published online.

  10. Welsh JB, Hu G, Walker TC, Sharma N, Cherñavvsky D. Glucose monitoring and diabetes management in the time of Coronavirus Disease 2019. J Diabetes Sci Technol. 2020;14:809–10.

    Article  CAS  Google Scholar 

  11. Lam K, Lu AD, Shi Y, Covinsky KE. Assessing Telemedicine Unreadiness among older adults in the United States during the COVID-19 pandemic. JAMA Intern Med. 2020;180:1389–91.

    Article  CAS  Google Scholar 

  12. Miyawaki A, Tabuchi T, Ong MK, Tsugawa Y. Age and Social Disparities in the Use of Telemedicine during the COVID-19 pandemic in Japan: cross-sectional study. J Med Internet Res. 2021;23:e27982.

    Article  Google Scholar 

  13. Barrot-de la Puente J, Mata-Cases M, Franch-Nadal J, Mundet-Tudurí X, Casellas A, Fernandez-Real JM, et al. Older type 2 diabetic patients are more likely to achieve glycaemic and cardiovascular risk factors targets than younger patients: analysis of a primary care database. Int J Clin Pract. 2015;69:1486–95.

    Article  CAS  Google Scholar 

  14. Chiu CJ, Wray LA. Factors predicting glycemic control in middle-aged and older adults with type 2 diabetes. Prev Chronic Dis. 2010;7:A08.

    Google Scholar 

  15. American Diabetes Association, Standards of Medical Care in Diabetes-2013. Diabetes Care. 2013;36(Supplement_1):S11–S66.

  16. Blonde L, Meneghini L, Peng XV, Boss A, Rhee K, Shaunik A, et al. Probability of achieving Glycemic Control with basal insulin in patients with type 2 diabetes in real-world practice in the USA. Diabetes Ther. 2018;9:1347–58.

    Article  CAS  Google Scholar 

  17. Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev. 2011;10:430–9.

    Article  Google Scholar 

  18. Horan TC, Gaynes RP, Martone WJ, Jarvis WR, Emori TG. CDC definitions of nosocomial surgical site infections, 1992: a modification of CDC definitions of surgical wound infections. Am J Infect Control. 1992;20:271–4.

    Article  CAS  Google Scholar 

  19. Bujnowska-Fedak MM, Mastalerz-Migas A. Usage of medical internet and e-health services by the elderly. Adv Exp Med Biol. 2015;834:75–80.

    Article  Google Scholar 

  20. Sangrar R, Docherty-Skippen SM, Beattie K. Blended face-to-face and online/ computer-based education approaches in chronic disease self-management: a critical interpretive synthesis. Patient Educ Couns. 2019;102:1822–32.

    Article  Google Scholar 

  21. Kim Yeary KH, Long CR, Bursac Z, McElfish PA. Design of a randomized, controlled, comparative-effectiveness trial testing a family model of diabetes self-management education (DSME) vs. Standard DSME for Marshallese in the United States. Contemp Clin Trials Commun. 2017;6:97–104.

    Article  Google Scholar 

  22. Pérez-Escamilla R, Damio G, Chhabra J, Fernandez ML, Segura-Pérez S, Vega-López S, et al. Impact of a community health workers-led structured program on blood glucose control among latinos with type 2 diabetes: the DIALBEST trial. Diabetes Care. 2015;38:197–205.

    Article  Google Scholar 

  23. Anioke IC, Ezedigboh AN, Dozie-Nwakile OC, Chukwu IJ, Kalu PN. Predictors of poor glycemic control in adult with type 2 diabetes in South-Eastern Nigeria. Afr Health Sci. 2019;19:2819–28.

    Article  Google Scholar 

  24. Alqudah S, Jarab AS, Alefishat EA, Mayyas F, Khdour M, Pinto S. Factors Associated with poor hemoglobin A1c control in patients with type 2 diabetes. Curr Diabetes Rev. 2019;15:164–70.

    Article  CAS  Google Scholar 

  25. McInnes N, Smith A, Otto R, Vandermey J, Punthakee Z, Sherifali D, et al. Piloting a Remission Strategy in Type 2 diabetes: results of a Randomized Controlled Trial. J Clin Endocrinol Metab. 2017;102:1596–605.

    Article  Google Scholar 

  26. Jiang XJ, Jiang H, Chen Y, Wu XA, Yu XL, Liu L, et al. The effectiveness of a self-efficacy-focused Structured Education Program (SSEP) in improving metabolic control and psychological outcomes of type 2 diabetes patients: a 12-Month Follow-Up of a Multicenter Randomized Controlled Trial. Diabetes Metab Syndr Obes. 2021;14:305–13.

    Article  Google Scholar 

  27. McElfish PA, Long CR, Kohler PO, Yeary KHK, Bursac Z, Narcisse MR, et al. Comparative effectiveness and maintenance of diabetes self-management education interventions for marshallese patients with type 2 diabetes: a Randomized Controlled Trial. Diabetes Care. 2019;42:849–58.

    Article  CAS  Google Scholar 

  28. Korcegez EI, Sancar M, Demirkan K. Effect of a pharmacist-led program on improving outcomes in patients with type 2 diabetes Mellitus from Northern Cyprus: a Randomized Controlled Trial. J Manag Care Spec Pharm. 2017;23:573–82.

    Google Scholar 

  29. Siddiqui MA, Ali L, Fawwad A, Banu B, Basit AJCE, Health G. Mobile SMS. A tool for management of diabetes via patients-relative’s knowledge and belief. 2020;8:455–60.

    Google Scholar 

  30. Cai X, Zhang F, Lin C, Zhang X, Wang Z, Xing H, et al. Achieving effective and efficient basal insulin Optimal Management by using Mobile Health Application (APP) for type 2 diabetes patients in China. Diabetes Metab Syndr Obes. 2020;13:1327–38.

    Article  CAS  Google Scholar 

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Acknowledgements

Authors of this manuscript would like to thank all study investigators.

Funding

This work was supported by Chinese Diabetes Society (CDS). The publication of this study was funded by Science and Technology Planning Project of Guangdong Province of China (NO2017A020215118), and “Sanming Project of Medicine in ShenZhen” (SZSM201812047).

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Authors and Affiliations

  1. Department of Neurology, Shenzhen Hospital, Southern Medical University, NO.1333, Xinhu Road, Baoan District, Shenzhen, Guangdong, 518000, China

    Xiaoqing Tan, Zonghai Qi & Yajie Liu

  2. School of Foreign Studies, Southern Medical University, Guangzhou, Guangdong, 510515, China

    Xiaoqing Tan, Zonghai Qi & Yajie Liu

  3. Nursing Department, Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, 518000, China

    Ling Chen

  4. Out-patient Department, Shenzhen Sami International Medical Center, Shenzhen, Guangdong, 518118, China

    Dongmin Li

  5. Out-patient Department, Shenzhen Qianhai Free Trade Zone Hospital, Shenzhen, Guangdong, 518038, China

    Xiangyin Cai

  6. Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, 518037, China

    Yi Song

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Contributions

Xiaoqing Tan, Dongmin Li and Xiangyin Cai carried out the studies, participated in collecting data, follow-ups, and diabetes education. Ling Chen performed the statistical analysis. Yajie Liu was responsible for the study design. Zonghai Qi and Xiaoqing Tan took charge of acquisition, analysis or interpretation of data and drafting the manuscript. Yi Song participated in follow-ups and recording patients’ history in MHMS. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yajie Liu.

Ethics declarations

Ethics approval and consent to participate

This study was accredited by the Chinese Diabetes Society (CDS) and is listed among the CDS diabetes education and management projects. The Mobile Health Management System (MHMS) was provided by the CDS. The processing of patient data and the publication of this study were approved by the Ethical Committee of Shenzhen Qianhai Free Trade Zone Hospital (2022 K-W001). All subjects signed the informed consent. The study was conducted in accordance with the Declaration of Helsinki.

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The authors declare that they have no competing interests.

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Tan, X., Qi, Z., Chen, L. et al. A doctor-nurse-patient mobile health management system effectively controls blood glucose in chinese patients with type 2 diabetes mellitus: a prospective study. BMC Health Serv Res 22, 1564 (2022). https://doi.org/10.1186/s12913-022-08949-5

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