Effects of nurse-led web-based interventions on people with type 2 diabetes mellitus: A systematic review and meta-analysis

Abstract

Introduction

Diabetes mellitus is an expanding global health problem. Currently, the home management of diabetes is mainly led by a multidisciplinary team based on telemedicine. However, the role nurses play in it remains inconclusive. This study aimed to investigate the effectiveness of nurse-led web-based intervention on glycated haemoglobin, blood pressure and lipid profile in patients with type 2 diabetes.

Methods

An exhaustive systematic literature search was undertaken using the following databases: PubMed, Web of Science, Embase, The Cochrane Central Register of Controlled Trials and CINAHL. Two investigators independently extracted data and assessed the quality of the studies by examining the risk of bias and using Modified Jadad Score system. We conducted a meta-analysis of randomized controlled trials that had been published from inception to July 2020, using Review Manager 5.3.

Results

Eleven randomized controlled trials were selected that included 2063 participants. Meta-analyses results indicated significant effects on not only glycated haemoglobin (pooled mean difference (MD) = –0.40, 95% confidence interval (CI): –0.5 to –0.26, p < 0.00001), but also on systolic blood pressure (pooled MD = –1.91, 95% CI: –3.73 to –0.09, p = 0.04) and low density lipoprotein (pooled standardized MD = –0.29, 95% CI: –0.44 to –0.15, p < 0.0001). There were no effects of nurse-led web-based intervention on fasting blood glucose, diastolic blood pressure, high density lipoprotein, body mass index and triglycerides.

Discussion

Nurse-led web-based intervention is a promising way to complement routine clinical care. However, the specific intervention content and intervention media still need to carry out large-scale well-designed randomized controlled trials. Systematic review registration: PROSPERO CRD 42020204565.

Keywords

Type 2 diabetes mellitus nurse-led web-based intervention meta-analysis,telehealth,remote monitoring

Introduction

Diabetes mellitus is an expanding global health problem. According to the International Diabetes Federation there were 451 million people with diabetes worldwide in 2017; these figures were expected to increase to 693 million by 2045.¹ People with diabetes have an increased risk of developing a number of serious life-threatening health problems resulting in higher medical care costs, lower quality of life and increased mortality.² The absolute burden on the global diabetes economy is projected to increase from US$1.3 trillion in 2015 to US$2.2 trillion by 2030, implying that the cost of diabetes as a proportion of global GDP (gross domestic product) will increase from 1.8% in 2015 to 2.2% in 2018.³ As for the mortality rate, about five million people died of diabetes in 2017, accounting for 9.9% of all-cause mortality worldwide.¹

Proper management of diabetes patients can reduce the medical costs of diabetes, with an average total direct outpatient cost reduction of about US$90 per patient, and can reduce complications by 53–63% and mortality by 46%.^4–8 Management of diabetes is complex, and effective type 2 diabetes management is widely recognized as a challenge for patients and their healthcare providers, with current guidelines recommending a combination of approaches to management, including health education on knowledge of glucose management, lifestyle management and drug therapy.^9,10 The long course of diabetes and slow recovery determine that most diabetes patients need long-term rehabilitation training and medical care, and the main rehabilitation site should be the family.

Home care services have been implemented since the 1970s.¹¹ The home care models for diabetes patients mainly include the General Practitioner services model,¹² Chronic care model,¹³ Diabetes shared care model,¹⁴ and Multidisciplinary management model.^15–17 The above four care models have some shortcomings, such as the need for health institutions to arrange professional medical teams and service time, provide self-management education for patients, consume a lot of manpower, material resources and financial resources, and lack of flexibility in home care, resulting in poor patient compliance.¹⁸

With the development of the internet, telemedicine has become an effective means to promote the health management of diabetes patients and provide diabetes education and training for patients and their families. Web-based intervention transcends the limitation of time and space, promotes data exchange and communication between patients and health care professionals, ensures that patients obtain the required information, greatly compensates for the shortcomings of traditional medicine, increases the treatment confidence of diabetes patients, improves the compliance of patients, and reduces the fear and anxiety of diabetes patients.^19,20 In addition, access to services and care delivery is increased and patient health care costs reduced.^21,22

Current telemedicine management of diabetes is currently dominated by physicians.²³ However, many clinicians still believe that type 2 diabetes is a non-serious disease,²⁴ and qualitative analysis shows that clinicians have doubts about the therapeutic effect and their own ability in diabetes, which adversely affects glycaemic control in diabetes patients.²³

Nurses as educators for diabetes patients can provide compliant care for diabetes patients well.²⁵ Studies have shown that nurse-led telemedicine services are an effective intervention, which can reduce unnecessary hospitalizations of patients²⁶ and reduce the burden of disease in patients with hepatitis.²⁷ The study by Chen et al. of the effect of nurse-led tele‐coaching intervention in patients with type 2 diabetes showed that patients had significantly lower glycosylated haemoglobin and systolic blood pressure.²⁸

However, no systematic reviews focusing on the effects of nurse-led web-based intervention on patients with type 2 diabetes have been retrieved. Therefore, this study conducted a meta-analysis to evaluate the effect of nurse-led web-based intervention on glycosylated haemoglobin, fasting blood glucose, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL) and triglycerides in patients with type 2 diabetes to determine the effectiveness of nurse-led web-based intervention.

Methods

The general principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were followed to perform this review.²⁹ Informed consent and ethical approval were deemed exempt because no clinical intervention was conducted on patients. A protocol had been registered on the PROSPERO database (CRD 42020204565).

Literature search

PubMed, Web of Science, Embase, the Cochrane Central Register of Controlled Trials and CINAHL were searched from inception to July 2020. Medical subject headings and free terms were used together in the process of literature search, including (Diabetes Mellitus, Type 2 OR T2DM OR Diabet* type 2) and (Nurses OR Telenursing OR Patient Education as Topic OR nurse-led OR teach*) and (Social Media OR Education OR Distance OR Internet-Based Intervention OR based on the Internet). We also reviewed the bibliographies of relevant review articles to identify any additional publications. A manual search of the bibliographies of relevant review articles was conducted to identify additional eligible studies. We did not restrict publication year. Specific details of search algorithm are available in the Supplementary material online.

Study selection

We considered randomized controlled trials (RCTs) as eligible studies, which are the gold standard of clinical research. Titles and abstracts of the searched records were screened to remove non-original articles (i.e. editorials, commentaries and letters). The full text of each remaining article was then judged against the inclusion and exclusion criteria. Reason(s) for ineligibility were documented for all articles excluded in the second phase of screening.

The major inclusion criteria were: (a) RCTs of nurse-led interventions, (b) studies on patients with type 2 diabetes, (c) the intervention was comparing web-based intervention with usual care, and (d) studies that reported at least one outcome of interest. Non-original articles, abstracts and pre-clinical studies were excluded. Articles not published in English were also excluded. Two independent reviewers identified articles eligible for analysis, extracted data and assessed the risk of bias. Disagreements were resolved through discussion or consultation with a third reviewer.

Data extraction and quality assessment

With a standardized form, two reviewers independently extracted data of study characteristics, including study year, country, sample size and duration of follow-up, patient age, comparisons (web-based intervention vs. usual care) and outcomes (glycosylated haemoglobin, fasting blood glucose, triglycerides, BMI, SBP, DBP, LDL, HDL). The original authors would be contacted for more details if the information was incomplete.

The Cochrane Risk of Bias Assessment Tool was used for quality assessment of RCTs. This is a validated tool for RCTs based on six domains: adequate sequence randomization, concealment of allocation, blinding of outcome assessors, adequately addressed incomplete outcome data, selective outcome reporting and other risks of bias. Each domain was rated as: (i) low risk of bias, (ii) unclear or (iii) high risk of bias. A study was considered of relatively high quality if it had adequate sequence randomization and a blinding of outcome assessors (i.e. low risk of bias in both domains). Therefore, we considered blinding of outcome assessors as adequate. Two reviewers independently assessed the risk of bias and a third reviewer rechecked the discrepancies and made a final decision.

The methodology quality of RCTs was graded by Modified Jadad Score system. Studies with a score of 1–3 were considered as low quality whereas a score of 4–7 was considered as high quality.

Synthesis analysis

All the statistical analyses were performed using Review Manager 5.3. Mean difference (MD) or standardized mean difference (SMD) along with corresponding 95% confidence interval (CI) were pooled for continuous variables appropriately, according to whether the outcomes were measured by the same scales or not. The Cochran Q-statistic and I² statistic were used to evaluate the between-study heterogeneity. p value <0.1 along with I²>50% indicate significant heterogeneity and a random effects model would be chosen to pool results; otherwise, a fixed-effects model would be used. The subgroup analysis was conducted based on intervention frequency, social media and sample size (number of study subjects).

Results

Search results

Figure 1 presents the search process. The literature search identified 696 records. After deleting 293 duplicates, 403 records were screened for content through titles and abstracts. From these, 268 records were excluded from the study. The full text of 135 articles was then assessed for eligibility and 124 articles were excluded. No additional studies were identified. Finally, 11 RCTs^30–40 with 2063 participants (web-based 1030 vs. conventional nursing 1033) were included in this systematic review.

Figure 1.

Flow diagram of the study selection process.

Risks of bias and quality assessment

Figure 2 shows the graph of risk of bias in included studies. In most domains, few studies had a low risk of bias. Five of 11 studies displayed adequate sequence randomization and a blinding of outcome assessors and were thus considered of relatively high quality.

Figure 2.

Risk of bias graph.

Only four trials adopted allocation concealment.³²,³³,³⁶,³⁷ Two trials blinded the subjects or personnel of research to intervention allocation.³⁰,³³ Three trials did not apply blinding of outcome assessors.³⁴,³⁵,⁴⁰ For attrition bias, nine trials clearly reported the use and details of intention-to-treat analysis; two did not. ³²,³³ Four trials did not use selective reporting.^33,36–38 Only one trial had no other possible bias.³⁸

As Table 1 shows, results from Modified Jadad Score system revealed that two of the included trials were low quality, while the other nine trials were high quality.

Table 1.

Information of included studies.

First author (year)	Study country	No. of participants (T/C)	Age	Social media	Control group	Intervention frequency	Outcomes	Score
Pressman (2014)³⁰	USA	225 (118/107)	18-75	SHD	Usual care	One week	a. c. d. f.	3
Tang (2013)³¹	USA	379 (186/193)	≥18	Online disease management system	Usual care	Unclear	a. d. e. f.	4
Wild (2016)³²	UK	321 (160/161)	≥18	Remote secure server	Usual care	One week	a. d. e.	3
Bonnie (2014)³³	USA	88 (47/41)	≥18	Telemonitoring system from Numera	Usual care	1/2 week	a. d.	5
Cho (2011)³⁴	Korea	71 (36/35)	≥40	Internet-based glucose monitoring system	Usual care	One week	a. b. e. g.	4
Dong (2018)³⁵	China	120 (60/60)	23-60	WeChat	Usual care	Two days	a. b.	6
Iljaž (2017)³⁶	Slovenia	120 (58/62)	18-75	e-Diabetes application	Usual care	Two weeks	a. b. c. d. f. i. g.	6
Murray (2017)³⁷	UK	374 (185/189)	≥18	HeLP-Diabetes	Usual care	One month	a. c. d. e. i.	7
Ramadas (2018)³⁸	Malaysia	128 (66/62)	≥18	myDIDeA	Usual care	Three weeks	a. b.	4
Stone (2010)³⁹	USA	137 (64/73)	≥18	Monitor home telemonitoring device	Telephone care	One week	a. d. e. f. i. g.	5
Wang (2016)⁴⁰	China	100 (50/50)	≥18	WeChat	Usual care	One week	a.	6

a. = glycosylated haemoglobin; b. = fasting blood glucose; c. = body mass index; C = control group; d. = systolic blood pressure; e. = diastolic blood pressure; f. = low-density lipoprotein; g. = triglycerides; i. = high-density lipoprotein; SHD = Samsung Health Diary; T = trial group.

Study characteristics

Table 1 shows the information of the included trials, involving 2063 patients with type 2 diabetes. All the articles were published after 2010. Four trials were performed in the USA,³⁰,³¹,³⁴,³⁹ two in China,³⁵,⁴⁰ two in the UK,³²,³⁷ one in Korea,³⁴ one in Malaysia³⁸ and one in Slovenia.³⁶

Among these trials, the variety of social media used for intervention was different. Three trials adopted mobile phone application-based intervention,³⁵,³⁶,⁴⁰ five trials adopted websites-based intervention^{31,34,37–39} and three trials adopted telemetry device-based intervention.³⁰,³³,³⁴ The intervention duration ranged from three months to 12 months. Seven trials had a sample size less than 200.^33–36,^38–40 Seven trials’ intervention frequency was less than two weeks each.^{30,32–35,39,40}

Specific details of intervention are available in Table 2.

Table 2.

Intervention content of intervention group.

First author (year)	Intervention content
Pressman (2014)³⁰	Provide targeted health education according to the patient’s blood glucose control.
Tang (2013)³¹	Nurse care managers independently adjusted medication on the basis of EMPOWER-D protocols.
Wild (2016)³²	Answer questions from patients, give advice about drug or lifestyle change, feedback to patient participants about blood glucose and blood pressure.
Bonnie (2014)³³	Consult the clinical data submitted by the patient and, if necessary, individualize the treatment plan of the patient.
Cho (2011)³⁴	Upload patient information such as diet, exercise and medications. Answer any questions from patients. Send the appropriate prescriptions and education materials.
Dong (2018)³⁵	Send and explain the diabetes related-knowledge including self-monitoring of blood glucose, reasonable diet, exercise prescription, compliance with prescribed medication, and management of low and high blood sugar, as well as weight control for patient with diabetes.
Iljaž (2017)³⁶	Comment about measurements made by patients in the intervention group and give advice about lifestyle.
Murray (2017)³⁷	Explain the use of HeLP-Diabetes to the patient and answer any questions from patients.
Ramadas (2018)³⁸	Staged tailored dietary advice provided through the study website (myDIDeA).
Stone (2010)³⁹	Review SMBG, blood pressure, weight, and risk stratification reports generated by the Viterion and contacted participants as necessary. Provide timely telephone follow-up. Provide individualized self-management counselling tailored to specific issues, based on the status of glucose and blood pressure control from the transmitted data.
Wang (2016)⁴⁰	Evaluate the nursing needs of patients and give individualized guidance on diet, exercise, drugs and adverse reactions.

SMBG = self-monitored blood glucose.

Meta-analysis

Eleven trials^30–40 with a total of 2063 patients were enrolled in this meta-analysis, to calculate the effectiveness of a web-based intervention nurse-led on glycaemic control and physical parameters in patients with type 2 diabetes.

Primary outcomes: glycosylated haemoglobin and fasting blood glucose

Glycosylated haemoglobin

Eleven trials^30–40 with a total of 2063 patients were enrolled in this meta-analysis calculating the glycosylated haemoglobin (Figure 3(a)). There was heterogeneity across these 11 trials (p < 0.00001, I² = 84%), so a random-effects model was selected for analysis. The effect of nurse-led web-based intervention on the control of glycosylated haemoglobin was statistically significant (MD = –0.40, 95% CI: −0.5 to −0.26, p < 0.00001).

Figure 3.

Forest plot for primary outcomes.

Subsequently, the analysis was performed according to the intervention frequency, social media and sample size (number of study subjects). The results showed that the effect of intervention frequency for two weeks each was better than that of intervention frequency for more than two weeks each (MD = –0.57, 95% CI: –0.89 to –0.25, p = 0.0005) versus (MD = –0.24, 95% CI: –0.26 to –0.22, p < 0.00001); the effect of intervention using smartphone applications was better than that of intervention using website (MD = –0.43, 95% CI: –0.81 to –0.06, p < = 0.02) versus (MD = –0.24, 95% CI: –0.26 to –0.22, p < 0.00001); the smaller the sample size, the better the effect (MD = –0.52, 95% CI: –0.87 to –0.17, p = 0.004) versus (MD = –0.24, 95% CI: –0.26 to –0.22, p < 0.00001). Table 3 shows the results of the meta-analysis of glycosylated haemoglobin.

Table 3.

Glycated haemoglobin meta-analysis.

Subgroups	Heterogeneity	Model	Pooled MD	95% CI	p-value
Intervention frequency
≤2 weeks	I² = 89%, p < 0.00001	Random	–0.57	–0.89 to –0.25	0.0005^a
>2 weeks	I² = 40%, p =0.19	Fixed	–0.24	–0.2 to –0.22	<0.00001^a
Social media
Telemetry device	I² =0%, p = 0.67	Fixed	–0.20	–0.28 to –0.12	<0.00001^a
Application	I² = 91%, p < 0.0001	Random	–0.43	–0.81 to –0.06	0.02^a
Website	I² = 62%, p =0.03	Random	–0.31	–0.53 to –0.10	0.004^a
Study size
<200	I² = 90%, p < 0.00001	Random	–0.52	–0.87 to –0.17	0.004^a
>200	I² = 15%, p =0.32	Fixed	–0.24	–0.26 to –0.22	<0.00001^a

^ap<0.05.

CI: confidence interval; MD: mean difference.

Fasting blood glucose

Four trials^34–36,³⁸ with a total of 439 patients were enrolled in this meta-analysis of fasting blood glucose level (Figure 3(b)). There was no heterogeneity across these three trials (p = 0.67, I² = 0%), so a fixed-effects model was selected for analysis. It was found that the effect of nurse-led web-based intervention on the control of fasting blood glucose level was not statistically significant (MD = −0.15, 95% CI: −0.62 to 0.31, p = 0.52).

Secondary outcomes: BMI, SBP, DBP, LDL, HDL and triglycerides level

BMI

Three trials³⁰,³⁶,³⁷ with a total of 719 patients were enrolled in this meta-analysis calculating the BMI (Figure 4(a)). There was no heterogeneity across these three trials (p = 0.72, I² = 0%), so a fixed-effects model was selected for analysis. It was found that the effect of nurse-led web-based intervention on the control of BMI was not statistically significant (MD = 0.26, 95% CI: 0.16 to 0.36, p < 0.00001).

Figure 4.

Forest plot for secondary outcomes.

SPB

Seven trials^{30–33,36,37,39} with a total of 1554 patients were enrolled in this meta-analysis calculating the SPB (Figure 4(b)). There was heterogeneity across these seven trials (p = 0.005, I² = 75%), so a random-effects model was selected for analysis. It was found that the effect of nurse-led web-based intervention on the control of SBP was statistically significant (MD = −1.91, 95% CI: −3.73 to −0.09, p = 0.04).

DBP

Six trials^{32–34,36,37,39} with a total of 1310 patients were enrolled in this meta-analysis calculating the DBP (Figure 4(c)). There was no heterogeneity across these six trials (p = 0.23, I² = 27%), so a fixed-effects model was selected for analysis. It was found that the effect of nurse-led web-based intervention on the control of DBP was not statistically significant (MD = 0.09, 95% CI: −0.05 to 0.22, p = 0.21).

LDL

Four trials³⁰,³¹,³⁶,³⁹ with a total of 734 patients were enrolled in this meta-analysis calculating the LDL (Figure 4(d)). SMD was selected in this analysis because the measurement units were not the same. There was heterogeneity across these four trials (p = 0.004, I² = 77%); through the sensitivity analysis, it was concluded that the study by Iljaž et al.³⁶ was the main source of heterogeneity, and the heterogeneity was 0% after removal, so a fixed-effects model was selected for analysis. It was found that the effect of nurse-led web-based intervention on the control of LDL was statistically significant (SMD = −0.29, 95% CI: −0.44 to −0.15, p < 0.0001).

HDL

Three trials³⁶,³⁷,³⁹ with a total of 631 patients were enrolled in this meta-analysis calculating the HDL (Figure 4(d)). SMD was selected in this analysis because the measurement units were not the same. There was heterogeneity across these four trials (p = 0.08, I² = 61%), so a random-effects model was selected for analysis. It was found that the effect of nurse-led web-based intervention on the control of HDL was not statistically significant (SMD = −0.21, 95% CI: −0.48 to 0.07, p = 0.14).

Triglycerides

Three trials³⁴,³⁶,³⁹ with a total of 328 patients were enrolled in this meta-analysis calculating the triglycerides (Figure 4(d)). SMD was selected in this analysis because the measurement units were not the same. There was heterogeneity across these four trials (p = 0.05, I² = 66%); through the sensitivity analysis, it was concluded that the study by Iljaž et al.³⁶ was the main source of heterogeneity, and the heterogeneity was 0% after removal, so a fixed-effects model was selected for analysis. It was found that the effect of nurse-led web-based intervention on the control of LDL was statistically significant (SMD = −0.16, 95% CI: −0.43 to 0.11, p = 0.24).

Discussion

This review evaluated the effects of a nurse-led web-based intervention for patients with type 2 diabetes through 11 eligible trials, containing a total of 2063 patients. Meta-analysis of the included studies showed that the nurse-led web-based intervention had favourable effects on glycated haemoglobin, SBP and LDL. For other measures, such as BMI, fasting blood glucose, DBP, HDL and triglycerides, the nurse-led web-based measure seems to be ineffective.

Primary outcome measures

Glycosylated haemoglobin is the gold standard of long-term glucose controlling. Meta-analysis showed that a nurse-led web-based intervention could improve glycosylated haemoglobin in patients with type 2 diabetes, which was consistent with the results of previous studies.²⁸,⁴¹ A meta-analysis of 31 RCTs indicated an association between increased patient contact time with a diabetes nurse educator and lower glycosylated haemoglobin levels, with an estimated decrease in glycosylated haemoglobin levels of 1% for every additional 23.6 h of contact.⁴² This effect may have contributed significantly to the more marked reduction in glycosylated haemoglobin in the intervention group versus control group. However, the frequency of the intervention, the intervention medium and the size of the study can all affect the effect of the intervention. In terms of intervention media, mobile phone-based apps were more effective than websites and telemetry device (0.43% vs. 0.31% vs. 0.20%), which may be due to more skilled and convenient operation of mobile phone-based apps. Other intervention media, which have the disadvantages of inconvenience of carrying and high cost, may be outside of the purview of an individual’s healthcare team or insurance company; thus access to or awareness of quality or formally approved technologies could be limited.⁴³

The effect of the intervention was better when the frequency of intervention was less than two weeks each time (0.57% vs. 0.24%). This result was similar to the previous meta‐analysis.⁴⁴ This could be because shorter durations of intervention increase the likelihood of remembering and applying what was learned, thus increasing confidence and motivation in managing the condition.⁴⁵ In terms of study size, small-scale intervention had better effect (0.52% vs. 0.24%), which was consistent with a previous study.⁴⁶ In the future, it is necessary to develop an intervention system that can rely on popular electronic devices (e.g. mobile phone apps) and carry out small-scale and multi-frequency intervention to make the health education management of diabetes richer and more diverse, convenient and efficient, creating favourable conditions for diabetes patients to carry out self-learning any time and anywhere and receive standardized diabetes education management intervention.

This study showed that there was no significant statistical difference in the improvement effect of nurse-led web-based intervention on fasting blood glucose, and previous studies also confirmed that telemedicine did not improve fasting blood glucose significantly in diabetes patients,^47,48 which may be due to the fact that fasting blood glucose is self-measured by patients, there may be some errors in measurement tools and measurement techniques, and fasting blood glucose is less stable than glycosylated haemoglobin. In addition, intensive glycaemic control would have resulted in hypoglycaemia, which is the conundrum of diabetes treatment,⁴⁹ and may also be the reason for the non-significant improvement in fasting blood glucose in the intervention group in this study. But, as judged from the data (MD = –0.15 mmol/L), a nurse-led web-based intervention was still beneficial for fasting blood glucose. However, more longitudinal research is needed to clarify this relationship in the future.

Secondary outcome measures

The results of this study showed that there was no significant statistical difference in BMI between nurse-led web-based intervention in patients with type 2 diabetes. Previous studies also showed that telemedicine had no significant effect in reducing BMI, and the effect of telemedicine on BMI of patients was temporarily unclear.⁵⁰ The dose and/or length of the intervention may not be sufficient to affect the weight of the study subjects. Additionally, only three original studies were included in this study, and the sample size was small, which needs to be further expanded at a later stage to verify.

As far as blood pressure is concerned, the nurse-led web-based intervention can significantly reduce the SBP of patients, while the effect on DBP is not obvious, which was consistent with previous findings.²⁸ For patients with type 2 diabetes, reduction in SBP is associated with improved mortality and other clinical outcomes.⁵¹ Therefore, in terms of reducing all-cause mortality, the reduction of SBP is more meaningful than DBP, which suggests that we should focus on the SBP of patients.⁵² Although both SBP and DBP should be concomitantly managed in individuals with type 2 diabetes, a higher hazard ratio was found for SBP than for DBP in reducing all‐cause mortality, underlining the importance of optimizing SBP control.⁵²

This study confirmed that the nurse-led web-based intervention significantly reduced 0.29 mmol/L LDL compared with usual care, but there was no significant statistical difference in triglycerides and HDL between the two groups. This may be because the design of the interventions was less targeted for these health indexes. For patients with type 2 diabetes, aerobic exercise combined with resistance exercise is the optimal exercise mode to improve fasting blood glucose, total cholesterol level, triglyceride level and HDL, while aerobic exercise alone is the optimal exercise mode to improve LDL.⁵³ In this study, most of the intervention methods were to provide suggestions to meet the self-care needs of patients or provide personalized suggestions for blood glucose control; without in-depth communication on exercise, most of the elderly patients with type 2 diabetes used aerobic exercise (the optimal exercise mode to improve LDL), resulting in significant decrease of LDL. Since the intervention rarely focused on aerobic exercise combined with resistance exercise (the optimal exercise mode to improve triglyceride level and HDL), we failed to find the effect of nurse-led web-based intervention on triglyceride level and HDL. We need to continue to investigate the optimal intervention dose and length, intervention strategies and information content that influence metabolic outcomes.

Limitations of the study

This systematic review has several limitations. First, there was significant heterogeneity in multiple outcome measures in this study, possibly due to different study sites, target populations and intervention media. Second, for chronic diseases like diabetes, good treatment adherence is key to glycaemic control, and few of the studies included in this study addressed treatment adherence. Third, the measurement results, including LDL, HDL and glycated haemoglobin, were completed by the subjects themselves or submitted through web pages. Moreover, these data and units are more complex, so that the final results have a certain bias.

Conclusions

Our study showed that nurse-led web-based intervention may be a promising method to supplement routine clinical care for patients with type 2 diabetes, which can significantly improve the glycosylated haemoglobin of patients, but its improvement effect on BMI, SBP and triglycerides of patients needs more article data for verification, and subsequent individualized intervention to be developed according to different conditions of patients.

Supplemental Material

sj-pdf-1-jtt-10.1177_1357633X211010019 - Supplemental material for Effects of nurse-led web-based interventions on people with type 2 diabetes mellitus: A systematic review and meta-analysis

Supplemental material, sj-pdf-1-jtt-10.1177_1357633X211010019 for Effects of nurse-led web-based interventions on people with type 2 diabetes mellitus: A systematic review and meta-analysis by Xiao-Dan Niu, Jun-Ting Chi, Jing-Bo Guo, Hai-Hui Ruan, Jing Zhang, Hong-Xia Tao, Yan-Hong Wang in Journal of Telemedicine and Telecare

Footnotes

Contributors' statement

NXD contributed to the design of the study. NXD and CJT undertook the searches and screened studies for eligibility, assessed the quality of the papers, and conducted statistical analyses. NXD drafted the manuscript. All authors revised the important contents of the manuscript and approved the final version

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this study was supported by the National Natural Science Foundation of China. Project Name: Study on the construction and effect evaluation of health management model for frail elderly in community based on interRAI HC. Project Approval Number: 71804064.

ORCID iDs

Jun-Ting Chi

Yan-Hong Wang

Supplemental material

Supplemental material for this article is available online.

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