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Systematic Review and Meta-Analysis

Effect of mHealth Interventions on Glycemic Control and HbA1c Improvement among Type II Diabetes Patients in Asian Population: A Systematic Review and Meta-Analysis

Verma, Divya; Bahurupi, Yogesh1,; Kant, Ravi2; Singh, Mahendra1; Aggarwal, Pradeep1; Saxena, Vartika1

Author Information
Indian Journal of Endocrinology and Metabolism: Nov–Dec 2021 - Volume 25 - Issue 6 - p 484-492
doi: 10.4103/ijem.ijem_387_21

Abstract

INTRODUCTION

Diabetes mellitus is a component of “Metabolic Syndrome” usually characterized by hyperglycemia. For a few decades, documented cases of diabetes mellitus have shown an incremental trend.[12] “The Global Diabetes Report” by World Health Organization (WHO) states that about 422 million patients suffered from diabetes mellitus in 2014. An incremental trend is seen in the South-Eastern Asian Region of WHO, with approximately 96 million diabetes patients.[1]

Thus, the hour's need is to educate and aware patients with diabetes about their self-management and self-care to improve their clinical and health outcomes.[1] Glycemic control is associated with complications resulting from diabetes. Hence, a balanced glycemic control is required to avoid chronic complications in T2DM patients.[345]

mHealth is an essential component of electronic health (eHealth). As stated by the “Global Observatory for eHealth (GOe),” mobile health or mHealth can be defined as “a medical and public health practice promoted and supported by mobile devices like mobile phones, personal digital assistants (PDAs), patient monitoring devices and other wireless devices.”[6]

The “International Telecommunication Union (ITU)” reported that the number of wireless subscribers has risen to over 5 billion, and nearly 70% of these users belong to LMIC.[7] With this extensive market penetration of mobile and wireless technologies, it serves as an essential means to enhance the education and support for the patients and prove beneficial for health care professionals.[8] Various components of mHealth include mobile apps, phone calls, and text messages, which help in the fast and instant transmission of the information at a low cost to users and could become an ideal technique for diabetes self-management.[89]

Diabetes Mellitus exhibits disparities in Asia compared to Western countries. The disease biology, etiology, and genetic predilection are different for Asians.[1011] Hence, it became pertinent for a systematic review and meta-analysis of the trials specifically confined to the Asian population to evaluate and assess the effects of mHealth interventions on glycemic control and HbA1c among type II diabetes patients. The review aimed to estimate the mean difference in blood glucose levels measured in mg/dL and mean the difference in glycosylated hemoglobin (HbA1c) measured in % (mmol/mol) levels intervention and control group.

MATERIALS AND METHODS

This Systematic Review with Meta-Analysis was conducted and reported according to the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” guidelines.[12]

The protocol was duly submitted to the Institutional Research Review Board and PROSPERO. It has been registered in PROSPERO under the registration ID- CRD42020194063. Ethical approval for the same was also obtained from the Institutional Ethics Committee as letter no- AIIMS/IEC/20/710 dated 19th October 2020.

Database and search strategies

A comprehensive and thorough search strategy was conducted in August and September 2020 on electronic database searches, namely PubMed, Scopus, Embase, and Cochrane Library. Google Scholar was used to browsing gray literature, and the Trial registry - clinicaltrials.org was searched to track publications not indexed in other databases.

A standard and accepted search strategy was designed for PubMed and other databases to broadly search the publications starting from the month of January in 1990 to the month of August in 2020. It was later modified as per the requirement of other databases.

Various search strategies, according to database scanned, are given in the table below [Table 1]:

T1
Table 1:
Various search strategies according to databases scanned

Criteria for study inclusion and exclusion

Randomized controlled trials (RCTs) or clinical trials that reported the clinical outcomes of the mHealth interventions in T2DM adults compared with conventional care or usual care were included. The mHealth intervention arm was needed to have one or more of the following categories:

  1. Mobile Health applications targeting patients with type II DM
  2. Text messages- SMS (Short Message Service) used to manage type II DM.
  3. Phone calls used for management of type II DM.

The studies which were conducted in the Asian population and published in English were included.

The studies where diabetes patients reported severe diabetic complications such as diabetic foot, diabetic heart disease, etc., were excluded. Also, the studies with mixed population of patients such as type 1 and type 2 diabetics were excluded, along with the studies on pregnant women with type 2 diabetes.

Study selection

After the literature search, the titles and abstracts of the obtained studies were individually scanned by authors, and potentially eligible studies were identified. Consensus was obtained between the two reviewers in case of disagreement and the exclusion reasons were recorded. [Annexure I].

Data extraction

All the obtained records were then collected into the Zotero library for deletion of duplicate studies. The remaining references were then transported to an excel file that contained all the essential information required for screening.

Outcome measures

The primary outcomes assessed were the change in glycated hemoglobinA1c (HbA1c) and blood glucose levels post-intervention in both the arms.

Assessment of risk of bias

The “risk of bias” was assessed in the included studies as per “Cochrane Handbook for Systematic Reviews of Interventions.”[13] Two reviewers independently evaluated the studies and the risk of bias was noted. The risk had a judgment as high risk, low risk, unclear risk, and the reason for every decision was further recorded. [Annexure II].

Data analysis methods

A quantitative synthesis of data was further done to have a pooled estimate of the included studies to estimate mHealth interventions’ effect in glycemic control outcomes and HbA1c Improvement on type 2 diabetes patients.

“Review Manager Software (version 5.3)” was used for statistical analysis. Cochrane's Q statistic and inconsistency index (I²) was used to compute the statistical heterogeneity. Pooled effect size estimates along with a 95% confidence interval were calculated. The mean, standard deviation (SD), and the participant number given in both the groups (intervention and control) for each outcome at last follow-up were collected from each study. Funnel plots were used to assess publication bias.

Subgroup analysis was done based on the type of mHealth intervention used among the RCTs participants.

RESULT

After the combined database search, it resulted in a total number of 3980 records. Out of these, 72 articles were shortlisted based on their eligibility, and after the full-text screening, it resulted in 18 eligible trials for qualitative synthesis and 14 trials for quantitative synthesis (Meta-Analysis). Details of the screening process and results are presented in Supplementary Figure 1.

Characteristics of the studies

The studies included in the systematic review are listed in Table 2.

T2
Table 2:
Characteristics of Included Studies in the Systematic Review

18 trials were obtained for the qualitative synthesis (Systematic Review), whereas only 14 trials were finalized for Quantitative synthesis (Meta-Analysis). A total of 3368 participants were recruited in these trials, whereas only 2931 participants could complete the trials. The majority of trials were conducted in the Southern Asian region, followed by the eastern region, western region, and the southeastern region. The region-wise distribution of included studies is given in Table 3.

T3
Table 3:
Region wise distribution of included studies from Asia

Supplementary Figure 2 demonstrates the distribution of included trials based on the Asian region in which they are conducted.

The intervention duration was 7 months on average and ranged from 3 months to 24 months. Most of the trials were published in the current decade (2011–2020). In 6 trials, the mobile application was used as an intervention. Phone calls were used in 4 trials, and text messages were used in 7 trials. One trial involved both the use of text messages and phone calls in the intervention arm.

In most trials, the number of participants recruited ranged from 100 to 500. Seven trials reported only about HbA1c as an outcome measure. Seven trials reported fasting blood glucose (FBG) levels along with HbA1c levels, whereas one trial reported HbA1c and Post Prandial Blood glucose (PPBG) levels. Four trials reported all the three outcome measures i.e., HbA1c, FBG and PPBG. One trial reported only about FBG levels, whereas another reported only blood glucose levels, including FBG and PPBG levels.

Risk of bias

The risk of bias observed commonly was unclear bias, reported in all the studies due to insufficient evidence as no information was given regarding their protocol registration or publication. High risk of bias was also reported in maximum studies as no blinding of participants and researcher was possible due to the nature and requirement of these trials and thus, the majority of trials were open labeled. Figure 1 (a) demonstrates the risk of bias graph where each risk is given as low risk, unclear risk and high risk and Figure 1 (b) summarizes the risk of bias summary and assessment for every included study.

F1
Figure 1:
(a) Risk of bias graph: review authors’ judgements about each risk of bias item presented as percentages across all included studies (b) Risk of bias summary: review authors’ judgements about each risk of bias item for each selected study

META-ANALYSIS

Part A: Primary objective

A meta-analysis of the effect of mHealth interventions on

  • (i) Glycosylated Hemoglobin (HbA1c)
  • The data from 13 eligible studies, included a total of 1713 type 2 diabetes patients, were pooled to find the effects of diverse mHealth interventions on HbA1c. The impact of mHealth intervention was favoring the intervention group as a statistically significant reduction was seen in the mean in the intervention group as –0.44 (95%CI, –0.79 to 0.10, P = 0.01, I2 = 87%), suggesting that HbA1c levels in the mHealth group were significantly lower than those in the usual care group [Figure 2a].
  • (ii) Fasting Blood Glucose (FBG) levels
  • 8 studies which included a total of 1893 type 2 diabetes patients, were found eligible while reporting the effect of mHealth interventions on FBG levels. The result suggested that the effect of mHealth intervention was inconclusive and doesn’t affect FBG in T2DM patients in the intervention group. The studies sample showed no heterogeneity (I2 = 0%) with fixed-effects model [Figure 3a].
  • (iii) Post-Prandial Blood Glucose (PPBG) Levels
  • While reporting about the effect of mHealth interventions on PPBG levels, 6 studies were found eligible which included a total of 858 type 2 diabetes patients. The forest plot of these studies concluded the results as -20.13 (95%CI –35.16 to –5.10, P = 0.009, I2 = 59%). There was a reduction in PPBG levels in mHealth group as compared to the usual care group. A moderate heterogeneity was seen with random-effects model [Figure 4a].

F2
Figure 2:
(a) Effect of mHealth interventions on HbA1c (b) Effect of mobile applications as an intervention on HbA1c (c) Effect of phone calls as an intervention on HbA1c (d) Effect of SMS as an intervention on HbA1c
F3
Figure 3:
(a) Effect of mHealth interventions on FBG (b) Effect of mobile applications as an intervention on FBG (c) Effect of phone calls as an intervention on FBG (d) Effect of SMS as an intervention on FBG
F4
Figure 4:
(a) Effect of mHealth interventions on PPBG (b) Effect of mobile applications as an intervention on PPBG (c) Effect of phone calls as an intervention on PPBG (d) Effect of SMS as an intervention on PPBG

Part B: Subgroup analysis

Subgroup analyses were done for the different mHealth interventions on all the primary outcome measures- glycated hemoglobin (HbA1c), FBG, and PPBG levels.

The subgroup analysis done to assess the effect of different mHealth intervention on Glycosylated Hemoglobin (HbA1c) showed that when SMSs were used as an intervention, the result showed –0.58 (95%CI, –1.03 to –0.13, P = 0.01, I2 = 84%) suggesting that there was a reduction in HbA1c levels in T2DM patients of SMS group compared to a usual care group. Other interventions didn’t have any effect on HbA1c levels [Figure 2b-d].

To report the effect of different mHealth interventions on FBG levels, the result of subgroup analysis suggested that all three interventions showed an inconclusive result and no effect can be seen in FBG levels in any intervention group than usual care groups [Figure 3b-d].

The result of subgroup analysis on PPBG levels showed that mobile applications were the most effective intervention used to reduce PPBG levels in the intervention group compared with the usual care group. The result showed a reduction in mean of mHealth group as –21.70 (95%CI –35.28 to –8.12, P = 0.002, I2 = 42%). No conclusive result was seen in the use of other interventions. [Figure 4b-d].

Another Subgroup analyses were done based on duration of follow-up on all the primary outcome measures- glycated hemoglobin (HbA1c), FBG, and PPBG levels. There are two subgroups on the basis of follow-up period. One subgroup consists of studies whose follow-up duration was from 3 to 6 months. Second subgroup included the studies with a follow-up duration of 7–24 months.

The subgroup analysis done to assess the effect of follow-up duration on Glycosylated Hemoglobin (HbA1c) showed the result as –0.20 (95%CI, –0.33 to –0.07, P = 0.002, I2 = 85%) in studies with the duration of 3–6 months while in studies with follow-up period of 7–24 months, the result was as –0.85 (95%CI, –1.15 to –0.55, P < 0.00001, I2 = 94%). It suggested that there was a reduction in HbA1c levels in T2DM patients of mHealth group compared to a usual care group in both the subgroups [Supplementary Figure 3a-b].

To report the effect of different follow-up duration on FBG levels, the result of subgroup analysis of follow-up duration of 3–6 months was –4.72 (95%CI, –13.52 to 4.08, P = 0.29, I2 = 0%). The studies with duration of 7–24 months showed 2.72 (95%CI, –3.62 to 9.06, P = 0.40, I2 = 0%). No conclusive result was seen in the FBG levels on the basis of follow-up duration [Supplementary Figure 4a-b].

The result of subgroup analysis on PPBG levels showed that the result was -27.15 (95%CI, -39.33 to -14.08, P < 0.0001, I2 = 50%) in subgroup of 3–6 months follow-up period, whereas the subgroup with 7–24 months showed –5.09 (95%CI, –17.99 to 7.81, P = 0.44, I2 = 0%). The result showed a reduction in mean of mHealth group when follow-up continued for 3–6 months and no conclusive result was seen in the other subgroup [Supplementary Figure 5a-b].

Funnel plots

Publication bias was assessed by a funnel plot for each outcome measure [Figure 5(a), (b) and (c)]. The symmetrical presentation of the funnel plot for HbA1c and PPBG levels indicated slight publication bias. For FBG levels, no significant publication bias was observed. Each study was symmetrically distributed on both sides [Figure 5a-c].

F5
Figure 5:
(a) Funnel plot of comparison: 1 mHealth intervention v/s usual care, outcome: 1.1 HbA1c Outcome (b) Funnel plot of comparison: 1 mHealth intervention v/s usual care, outcome: 1.5 Fasting Blood Glucose levels (c) Funnel plot of comparison: 1 mHealth intervention v/s usual care, outcome: 1.9 Post prandial blood glucose levels

DISCUSSION

This systematic review and meta-analysis gave a vast horizon on the effects of mHealth interventions on managing type 2 diabetes patients in the Asian population. This study acknowledged the effects of different mHealth interventions as per their accessibility and availability in recent years. The effects of various mHealth interventions are very well reported in the Asian population. It is also evident that these interventions can also be utilized to increase the quality of diabetes self-management and serve to collect patients’ clinical data.

In most of the studies, there was an improvement in HbA1c levels and glycemic control. Although these interventions proved beneficial for these outcomes, there was a difference in the effects it caused in specific trials.

When meta-analysis was done based on any mHealth interventions,’ it reduced HbA1c and PPBG levels. No effect can be seen in FBG levels.

After the subgroup analysis, the most effective mHealth intervention was the use of SMSs while reporting their effect on HbA1c levels. No remarkable change in HbA1c levels was reported in mobile applications and phone calls as mHealth intervention. When subgroup analysis was done for FBG levels, no specific mHealth intervention proved to be conclusive about their effect in reducing FBG levels. While reporting about PPBG levels, the most effective intervention was seen in the form of mobile applications. They help reduce the PPBG levels while given in the intervention arm compared to the usual care arm. The other two interventions produce no conclusive result on PPBG levels.

Among the included trials, there is vast difference in sample size and intervention duration. Also, there was a considerable variability in the types of mHealth technology used. This wide variation may have caused the observed heterogeneity. Compared with usual care, the addition of mHealth intervention appeared to have a significant effect on people with type 2 diabetes. Although there was substantial heterogeneity, the pooled analyses showed that mHealth intervention lowered HbA1C levels and Post Prandial blood glucose levels. The effect of intervention on Fasting Blood Glucose levels remains inconclusive.

The difference in effects can be attributed to the different technology which was incorporated for various mHealth interventions. The mixed results can be attributed to having different lengths of intervention periods and a large difference in the number of participants included in separate trials.

Most mobile applications were linked with a glucometer to record the patients’ values of different clinical outcomes, which was later used for person-specific recommendations to all the patients as per their needs. Although these mobile apps are very beneficial, they might have posed difficulty using their technical advancement, specifically in the elderly population. Compared to mobile apps, phone calls and SMSs are considered an easy option to transmit information quickly. But due to various additional features available in mobile applications, we can still consider them as one of the most promising platforms compared to phone calls and SMSs.

These interventions were strong evidence that their effectiveness was based on the users’ awareness and education, and the type of behavior change communication methods used. Hence, these interventions must be designed in a user-friendly manner and should be able to produce similar effects in all the patients. The health care professionals should also take the patients’ economic condition into account while developing a mHealth intervention to obtain full use and services. Also, patients’ needs should be prioritized, and their present situation and complexities should be assessed before any intervention is administered. Our findings suggest that all three mHealth interventions can be a highly effective mechanism for linking providers to patients with diabetes.

Limitations

This review was confined to the Asian population, so it included the studies conducted only in the Asian population. Since there is a remarkable difference in terms of income and education compared to Asians and non-Asians, this review's results may not apply to global studies. As our systematic review included fewer studies, there was a limitation of the inclusion of constituent trials. There is a need to include more studies in future reviews to generate a larger body of evidence and establish their integration with already published research. Some of the trials reported a smaller sample size, insufficient blinding, and shorter trial duration, which is inadequate to determine the effects of mHealth interventions on this population over a long period. We did not report the data regarding the effects of mHealth on cost-effectiveness or amount of care satisfaction. The effectiveness of these interventions on various self-management aspects such as dietary management, more physical activity, or increased medication adherence was not considered in this review.

Implications

Further exploration of the relationships between different intervention strategies and their components is recommended. Patients’ beliefs and attitudes focused on the design aspects and physical features of various interventions- mobile applications, text messages, and phone calls need to be explored further. After exploring the patients’ belief regarding the mHealth usage, the factors regarding its acceptability and utility need to be put forward in future research. The evaluation of these interventions based on their cost-effectiveness aspect should also be assessed, as it is crucial for their impact and applicability in clinical practice. The use of these mHealth interventions can be prioritized in National Health Programs, and their cost-effectiveness can be assessed at larger levels.

CONCLUSION

In conclusion, the current research has assessed mHealth interventions on glycemic control and HbA1c improvement in T2DM patients in the Asian population. Although the evidence that is generated by this review shows a mixed result, mHealth interventions can be seen as a suitable medium to improve the glycemic index among diabetic patients. The available literature about assessing the use of mHealth is limited and inconsistent to draw any robust conclusions.

This review recommends that mHealth researchers give the utmost priority to the transparency in the reporting of interventions based on their contexts, aims, delivery pathway and mechanisms of impact for effective interpretation of the retrieved data. These interventions work on the following aspects: easy transmission of health-related information and timely notifications for various health-related behaviors, including medication adherence, proper dietary intake, and regular exercise and also give the patients a chance to provide their feedback, which can enhance the further development of these interventions. More innovative and robust research work concerning various mHealth intervention strategies is needed in the near future.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Supplementary Figure 1

PRISMA flow Diagram

Supplementary Figure 2

Graph showing the distribution of included trials on the basis of Asian region in which they are conducted. Source: emapsworld.com

Supplementary Figure 3

(a) Effect of follow up duration (3-6 months) on HbA1c (b) Effect of follow up duration (7-24 months) on HbA1c

Supplementary Figure 4

(a) Effect of follow up duration (3-6 months) on FBG (b) Effect of follow up duration (7-24 months) on FBG

Supplementary Figure 5

(a) Effect of follow up duration (3-6 months) on PPBG (b) Effect of follow up duration (7-24 months) on PPBG

Acknowledgements

All the authors contributed to all stages of the review, including conception of the review question, quality appraisal, and synthesis.

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                  Annexure I: Characteristics of Excluded studies

                  Annexure II: Risk of bias of included studies

                  Keywords:

                  Diabetes mellitus; glycemic control; HbA1c; meta-analysis; mHealth

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