Introduction

According to the International Diabetes Federation (IDF), 425 million people were living with diabetes in 2017 and by 2045 this figure will rise to 628 million people, more than 80% in low and middle income countries.¹ According to recent projections of global mortality world-wide, diabetes will become the 7^th leading cause of death by 2030.² The prospects in terms of solutions to fight diabetes remain a topical issue for all stakeholders (scientists, professionals, decision makers and patients). For several experts, new technologies are now presenting themselves as a credible solution.³ The WHO is on the front line and some countries such as France have taken the lead by leg islating.^4,5 Today, the Internet is a widely accessible means of promoting health and managing chronic diseases. The number of scientific publications on digital health has increased considerably over the last decade, based on the use of the internet, mobile phones and computers to support lifestyle changes for people living with diabetes.^6-10

A systematic review of Internet focused interventions aimed to identify Internet based lifestyle modification interventions in adults with type 2 diabetes demonstrated an improvement in diet and metabolic indicators in diabetic patients.¹¹ Two meta-analyses, coupled with a narrative report of randomized controlled trials, examined the use of digital health to improve self-management skills and evaluated the effect of mobile phone interventions on glycaemic control in people with diabetes.^12,13 They found an improvement in self-management skills and documented an average decrease in fly coated haemoglobin of 0.14 mil and blood glucose of 0.33 mmol. Two other systematic reviews have been identified, one based on online programs and the use of technology to promote physical activity in T2DM.^14,15 These reviews sought to identify behaviour change techniques (BCTs) that are applied in web based self-management and physical activity promotion programs and their effectiveness in relation to predefined health outcomes. Based on their results, it appears that it is still difficult to know which BCTs are effective in improving diabetes self-management and physical activity. Other studies have focused on specific technologies (e.g. telemedicine, decision support tool, electronic medical record) to support diabetes care. However, evaluation of these interventions to improve the quality of diabetes care in low resource countries is largely lacking.

Over the past 30 years, the effectiveness of digital health interventions in the field of diabetes has been assessed. Our aim is to synthesize this large body of evidence to demonstrate the importance of digital health in the prevention and management of diabetes. Conducting a systematic review of systematic reviews will allow summarizing and analysing the scientific evidence regarding the surveillance, treatment and prevention of diabetes complications.

Methods

Epidemiological studies have been conducted to describe the characteristics of Digital health based interventions and identify those associated with diabetes. Several of these studies used data from pilot interventions. However, most of them sought factors positively associated with the management and prevention of diabetes related complications through cross sectional analyses, i.e. comparing users and Digital health solutions at inclusion or over a given period of time. Although a few longitudinal analyses have focused on factors preventing complications or glycaemic control in people with diabetes.

Sources of information

As a first step, we conducted bibliographic searches on PubMed^® and Google Scholar. Several terms related to the effectiveness of digital health interventions for diabetes were searched. Papers published between January 1990 and December 2019 was considered. In a second step, we searched the references listed in the articles collected through previous searches.

Eligibility criteria

We included systematic reviews that evaluated and described the effects of digital health on diabetes management indicators, regardless of the type of diabetes and the type of interventions. Those published in English or French over the last 30 years (1991-2019), since we considered that the Internet was not widely available prior to 1991. Reviews clearly describing the method used to select studies were selected. We included reviews that used systematic search strategies to identify the selected studies, had a systematic presentation, and provided a summary of the results and main characteristics of the included studies.

Selection process

Following the final decision to include the studies in the review, data extraction was performed by two independent individuals. We used a data collection form (AMSTAR) that allowed us to collect all of the data needed to analyse the studies, such as eligibility criteria, methods and participants, interventions, judging criteria or outcomes. Risks of bias were also extracted in order to assess the level of evidence for each study. Once all the data was extracted and collected in the collection form, we used Microsoft Excel data management software to prepare the data analysis.

Data extraction and analyses

We produced a synthesis table for each included systematic review. It indicated the references of the articles (author, year, name of the journal), the objective of the study, the study population, the methods used, the definition of the variables of interest, the exposure variables, the main and secondary results, the limitations of the study as well as any comments related to the study (adjustments, definitions, classifications used). Given the length of the table and by way of example, an extract concerning two selected articles is available in supplementary data. A first table presenting the authors, the types of intervention, technologies and the number of participants in each retention were produced Figure 1.

Results

The first query had yielded 7761 results. The same date query at Google Scholar after excluding duplicates yielded 4230 results. After reading titles and abstracts, 117 publications were retained as potentially relevant to this study and were read in full. A total of 10 full text articles were selected.

Teen systematic reviews were included in our review. Nearly 74% of the reviews showed an additional form of benefit linked to the use of digital health. Conversely, 13% of the reviews were unable to clearly define the additional benefit linked to digital health.¹⁶ Information technologies used in the studies included the Internet (47%), cellular telephones (32%), telemedicine (12%) and decision support systems (9%).¹² There are also notable advantages i.e. user friendliness, real time feed-back, integration of Digital health with the medical system, improved analytical and decision support capabilities. According to the review authors, these studies have limitations including the heterogeneity of methods, disparity of results, failure to take into account specificities such as minorities, and methodological difficulties related to the study of particular cases.

Section 3.01 clinical outcomes and ICT use in health care among included reviews, there were 22 trials that made reference to clinical data and digital health: Internet, telephone, computer assisted integration of clinical data information. ^12,17

The results of the Meta analyses of 14 interventions revealed that digital health use was associated with significant reductions in HbA1c levels, blood pressure, total cholesterol, and triglycerides compared with usual care. A meta-analysis involving 1657 participants showed that mobile phone diabetes self-management interventions reduced HbA1c values by a mean of 0.5% [6 mmol⁄l; 95% Confidence Interval, 0.3%-0.7% (4-8 mmol⁄l)] over a median follow up time of 6 months.¹³

In subgroup analysis, 10 reviews targeting patients with type 2 diabetes reported significantly greater reductions in HbA1c than studies in patients with type 1 diabetes [0.8 (9 mmol⁄l) vs. 0.3% (3 mmol⁄l); P=0.02]. The effect of the mobile phone intervention did not differ significantly from other participant characteristics or intervention strategies. Another review of 40 studies, in which glycated haemoglobin, and blood glucose levels were reported, showed significant improvement in 7 and 6 trials, respectively.¹⁸

A significant improvement in compliance was found in 6 of the 8 computerized incentive studies. Meta-analysis of studies using home blood glucose records in insulin dose adjustment documented a mean decrease in glycated haemoglobin of 0.14 mmol/L (95% CI, 0.11-1.16), and a decrease in blood glucose of 0.33 mmol/L (95% CI, 0.28- 0.39).¹⁹ several computerized educational programs improved metabolic indicators.

One of the included reviews had retained only 9 studies based on its inclusion criteria. Of the 9, two studies demonstrated improvements in glycaemic control.

Dietary outcomes and behavioral changes

Several computer based educational programs have been shown to significantly improve the diets and behaviours of people living with diabetes, particularly type 2 diabetes. ^11,17We identified a review of 37 included studies, including 13 trials, based on the link between ICT and diet and behaviour change.15 In the same review cited above, which focused on randomized trials, improvements in diet and/or physical activity were found in comparison with a control group (Table 1). Successful studies were interactive components based on theory, with personalized follow up and feedback, and provided opportunities for peer support. Use of the site decreased over time in all studies. Few studies focused on high risk and underserved populations.¹⁴

In another review, which included 15 eligible studies, interventions were delivered through Internet, mobile phone, CD-ROM and computer.²⁰ These studies showed an increase in physical activity, but only nine were significant (Tables 2 and 3).

Discussion

This review of systematic reviews provides a state of the art on the issue of the effectiveness of digital health in the monitoring and prevention of diabetes. To date, we find many reviews and studies on the use of digital health in diabetes around the world, but few studies seem to highlight the use of Digital health in Africa on non-communicable diseases, particularly diabetes.

The data were synthesized in a qualitative and descriptive manner. We did not conduct a meta-analysis, as the set of studies was not homogeneous. For this reason, we opted for a descriptive synthesis. One of the limitations of the chosen methodology could be completeness, as it is quite possible that published or unpublished evidence may not have been uncovered either due to lack of access.

The combined results of the included trials have provided strong evidence that a mobile phone intervention can lead to statistical analysis significantly improving glycaemic control and self-management in the treatment of diabetes, particularly in patients with type 2 diabetes.6 This review reinforces the need for well-designed surveys to assess the role and feasibility of interventional technologies in clinical decision making for diabetes care.

The results of this review consolidate the growing body of knowledge that emerging Digital health can improve the prevention and management of diabetes related diseases. Digital health is a rapidly developing tool in improving the care process for patients with type 2 diabetes, as these significant healthcare outcomes indicate.¹²

These results must be put into perspective with respect to the decrease in HbA1c levels and other clinical outcomes (i.e. lipids).²¹ Although these findings are consistent with reviews of type 1 diabetes in terms of psychological and biological outcomes, a formal meta-analysis conducted primarily in type 1 diabetes supports the hypothesis that computer systems can effectively improve metabolic control. ²²

Internet assisted diabetic patients, usually focused on education, should assess their HbA1c, health care utilization and costs associated with the intervention.¹¹ by mode of delivery, Internet based interventions have a positive impact on patient centred outcomes. Unfortunately, however, they have not been evaluated in health care utilization and clinical outcomes. Telephone interventions had a positive impact on primary care visits. Computer assisted integration of clinical management also had a positive impact on most health care utilization outcomes.^10,23 while several interventions resulted in significant reductions in HbA1c, there was no clear pattern by mode, and many studies reported no significant change in HbA1c.

A review by Farmer et al. evaluated the feasibility, acceptability and effectiveness of telemedicine used to support self-monitoring of blood glucose levels by transmitting results from a server with automated or clinician feedback.⁶ The results judged telemedicine to be feasible and acceptable, but the evidence for its effectiveness in improving diabetes management was not considered strong. Although the main objective of the review and the inclusion/exclusion criteria led to a set of studies, which differed from this review, the results are consistent with our findings related to telemedicine interventions and HbA1c.

Computerized knowledge management is becoming a vital component of quality diabetes care. It promotes procedure monitoring, computerized adjustment of home insulin therapy, blood glucose records, remote feedback and documented advice benefits of improved diabetes outcomes.^11,24,25 There is growing evidence that information technology can improve diabetes care. Future research should characterize the long term benefits, establish methods to evaluate clinical outcomes and determine the cost-effectiveness of ICT use.

There is a clear need for more comprehensive interventions, in which multiple technologies are integrated, to manage chronic diseases such as diabetes. These digital interventions should be theoretically based, and based on user centred socio technical design principles, in their planning, design, and implementation. The effectiveness of self-management systems should be evaluated according to several main criteria: motivation for self-management, long term adherence, cost, uptake, satisfaction, and outcomes as an end result. The use of digital health in supporting diabetes self-management appears to have potential benefits for patient self-management of diabetes. However, the effective ness of technologies to improve patient outcomes is still awaiting confirmation in future studies.

In a context of limited resources, individual economic difficulties, lack of structured systems or skilled human capital in Africa, digital health can be a credible alternative for improving diabetes management. However, future research is needed on the use of digital health interventions in under-served communities, examining patterns of use and user engagement over time. Today, digital technologies are increasingly present in the health field. In Africa, the adoption of new technologies has had undeniable positive effects on the socio-economic development of the continent, the most striking example being mobile telephony. According to a 2013 study by the World Association of Telecom Operators (GSMA), the number of mobile subscribers in Africa grew by almost 20% per year between 2007 and 2012.

Digital health has the potential to improve care for chronic diseases, provide better health outcomes and reduce direct and indirect costs. However, realizing these gains requires new forms of care delivery through the adoption of new technologies. Achieving these new modes of delivery requires appropriate payment policies. Future authors should review the literature on health care payment and Digital health in chronic care and then apply a specific theoretical economic analysis of how Digital health is changing health care payment policy recommendations.²⁶

Primary and secondary prevention is a major focus in the fight against diabetes, in line with the over-all objective of promoting good behaviour in terms of diet and physical activity among people at risk or with diabetes. Digital health offers interesting perspectives with the benefit of reaching the greatest number of people and on a continuous basis. A relatively small number of online self-management support programs for WD2 have been reported using only a selected number of behaviours (physical activity, diet). Technology based interventions to promote physical activity are effective. Methods to promote participant adherence is associated with greater benefit. The development of future online self-management interventions should be based on the use of theories, and behaviour changes should be accurately reported. Future research should focus on strategies to improve adherence and sustainability to increase the effectiveness of technology based physical activity interventions in diabetes treatment.^27-37

Conclusion

This review allowed us to synthesize and update information on the use of digital health in diabetes management and monitoring, and to provide several implications for future research. Our review suggests that digital health could be an important tool and should be further evaluated for its potential to improve diabetes care. On the other hand, studies should consider participants’ perceptions of diabetes self-management, as digital health can likely be a component of care that depends on patient acceptance. There is also a need to improve the quality of studies and the communication of results.

Based on this review, we strongly recommend that future studies, particularly in Africa, should:

Favour designs that allow comparisons with a concurrent control group with a less biased assessment of the effectiveness of these interventions.

Study the long term effects of these interventions.

Address and report on the costs associated with the intervention and the resulting cost effectiveness.

Include a representative number of minorities and underserved populations and determine whether these interventions are equally effective in these groups.

Acknowledgement

None

Conflict of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

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