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Diabetes and obesity have become epidemics and costly chronic diseases. The impact of mobile health (mHealth) interventions on diabetes and obesity management is promising; however, studies showed varied results in the efficacy of mHealth interventions.
This review aimed to evaluate the effectiveness of mHealth interventions for diabetes and obesity treatment and management on the basis of evidence reported in reviews and meta-analyses and to provide recommendations for future interventions and research.
We systematically searched the PubMed, IEEE Xplore Digital Library, and Cochrane databases for systematic reviews published between January 1, 2005, and October 1, 2019. We analyzed 17 reviews, which assessed 55,604 original intervention studies, that met the inclusion criteria. Of those, 6 reviews were included in our meta-analysis.
The reviews primarily focused on the use of mobile apps and text messaging and the self-monitoring and management function of mHealth programs in patients with diabetes and obesity. All reviews examined changes in biomarkers, and some reviews assessed treatment adherence (n=7) and health behaviors (n=9). Although the effectiveness of mHealth interventions varied widely by study, all reviews concluded that mHealth was a feasible option and had the potential for improving patient health when compared with standard care, especially for glycemic control (−0.3% to −0.5% greater reduction in hemoglobin A1c) and weight reduction (−1.0 kg to −2.4 kg body weight). Overall, the existing 6 meta-analysis studies showed pooled favorable effects of these mHealth interventions (−0.79, 95% CI −1.17 to −0.42; I2=90.5).
mHealth interventions are promising, but there is limited evidence about their effectiveness in glycemic control and weight reduction. Future research to develop evidence-based mHealth strategies should use valid measures and rigorous study designs. To enhance the effectiveness of mHealth interventions, future studies are warranted for the optimal formats and the frequency of contacting patients, better tailoring of messages, and enhancing usability, which places a greater emphasis on maintaining effectiveness over time.
Diabetes and obesity have become global epidemics [
Self-management practices, such as maintaining a healthy diet and weight, engaging in adequate physical activity (PA), using prescribed medications consistently, frequently checking body weight and blood sugar levels, and maintaining good mental health habits, help patients control diabetes and obesity efficiently [
Emerging mobile health (mHealth) approaches may help meet these needs. In both developed and developing countries, mobile technology and device usage has been rapidly increasing and plays a vital role in people’s daily life [
The availability of commercial chronic disease self-management apps has been increasing rapidly [
Some previous reviews, including ours, have described the development of app technologies and their utility for patients with obesity, diabetes, and other chronic conditions [
This study evaluated the effectiveness of mHealth interventions for diabetes and obesity treatment/management by examining published systematic reviews and meta-analyses and provided recommendations for future research and interventions.
We searched the PubMed, IEEE Xplore Digital Library, and Cochrane databases to identify systematic reviews and meta-analyses published in English between January 1, 2005, and October 1, 2019, that evaluated the effectiveness of mHealth interventions for obesity and/or diabetes treatment/management. For the search, combinations of key terms were used in the PubMed, for example, “mhealth[Title/Abstract] AND (obesity[Title/Abstract] OR diabetes*[Title/Abstract]) AND review[Title/Abstract].” Search results were further screened manually by study title, abstract, and full text on the basis of inclusion and exclusion criteria.
The initial search yielded 95 articles. After eliminating duplicates and studies that did not fit the inclusion criteria, 17 reviews meeting the inclusion criteria remained; 6 of the 17 reviews were meta-analyses with randomized controlled trials (RCTs;
A flow chart of the literature search and study selection procedures. mHealth: mobile health.
Studies were included if they (1) reviewed intervention studies on patients with obesity or/and diabetes; (2) were a systematic review and/or a meta-analysis; (3) tested an mHealth intervention (eg, use of mobile devices, apps, and text message) for managing or treating obesity/diabetes while measuring clinical biomarkers, treatment adherence, or health-related behaviors (eg, healthy eating and exercise); and (4) provided quantitative results examining the effectiveness of the intervention (or use of the mHealth devices/programs).
Studies were excluded if they (1) did not explicitly target diabetes or obesity; (2) were diabetes or obesity prevention studies, not using an mHealth-based intervention program; and (3) did not report quantitative outcomes of mHealth intervention effects in managing obesity or diabetes.
We used the Assessment of Multiple Systematic Reviews (AMSTAR 2) to assess the quality of selected studies by 16 criteria (eg, study selection, data extraction, assessing risk of bias, study description, and statistical methods) according to the study characteristics [
Data were reviewed and extracted by 2 coauthors following the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines [
Using mixed effect models, we conducted a meta-analysis to evaluate the overall effectiveness of mHealth interventions on the basis of other published meta-analysis results with RCTs. The STATA (StataCorp LLC) metan command was used to calculate pooled estimates of mean differences in changes in clinical outcomes such as HbA1c, body weight, and BMI between intervention and control groups [
We conducted the meta-analysis of the
The mean difference was calculated by subtracting the level of clinical outcomes at the end of follow-up from the baseline, comparing the intervention and control groups. This allowed for a comparison of clinical improvement because of the mHealth interventions vs the control group.
We categorized the mHealth interventions studied into 5 types (
Mobile apps were the most widely studied intervention type (15 reviews), followed by text messaging (11 reviews) and PDAs (5 reviews). Regarding the major targeted functions of the mHealth interventions reviewed, self-monitoring and management was most common (15 reviews), followed by education or health promotion (8 reviews), reminders or alerts (5 reviews), feedback (3 reviews), social or peer support (2 reviews), and counseling or entertainment (1 review; see
All 17 reviews examined changes in clinical biomarkers as outcomes, whereas 9 evaluated health-related behaviors and 7 assessed treatment adherence (
We found much heterogeneity in the effectiveness of mHealth interventions for clinical biomarkers (
Regarding the meta-analyses, 3 reviews reported on the effect of mobile apps on HbA1c levels in diabetes [
A meta-analysis on RCTs consistently found that app use was associated with significant improvements in body weight and BMI [
Summary of clinical outcomes and behavioral changes from 18 meta-analyses reported in 6 reviews of diabetes and obesity mobile health interventions.
Outcomes | Referencesa | Tested interventions/target patient | Intervention vs control groups | Estimated effect of intervention: meta-analysis results of the mean difference between intervention and control groups | Conclusions |
HbA1cb | Wang et al [ |
Self-management of patients with T1DMc | Mobile app or text messaging intervention vs standard care |
−0.25% (95% CI −0.41 to −0.09; I2=12%) Subgroup analysis—age: teenagers −0.05% (95% CI −0.43 to 0.33; I2=0%); adults −0.29% (95% CI −0.47 to −0.11; I2=48%) Subgroup analysis—intervention: text message −0.20% (95% CI −0.73 to 0.32; I2=0%); mobile apps −0.25% (95% CI −0.42 to −0.08; I2=49%) Subgroup analysis—duration: ≥6 months −0.29% (95% CI −0.46 to −0.11; I2=32%); <6 months −0.01% (95% CI −0.44 to 0.41; I2=0%) |
mHealthd favors |
HbA1c | Wu et al [ |
Self-management of patients with diabetes | Mobile app intervention vs standard care alone |
−0.48% (95% CI −0.78 to −0.19; I2=76%) Subgroup analysis: patients with T2DMe −0.67% (95% CI −1.03 to −0.30; I2=47%); patients with T1DM −0.37% (95% CI −0.86 to −0.12; I2=86%) |
mHealth favors |
HbA1c | Cui et al [ |
Self-management of patients with T2DM | Smartphone app strategies vs standard diabetes care |
−0.40% (95% CI −0.69 to −0.11; I2=77%) Subgroup analysis: baseline HbA1c<8% −0.33% (95% CI −0.59 to −0.06; I2=70%) |
mHealth favors |
Body weight | Park et al [ |
Weight loss interventions on patients with OWBf | Mobile app/text messaging intervention vs nonmobile device care (standard) |
−2.35 kg (95% CI −2.84 to −1.87; I2=94%) Subgroup analysis—duration: at 6 months −2.66 kg (95% CI −3.94 to −1.38; I2=95%); at ≥12 months −1.23 kg (95% CI −2.25 to −0.21; I2=0%) |
mHealth favors |
Body weight | Mateo et al [ |
Weight loss and PAg promotion on patients with OWB | Mobile app intervention vs the control diet |
−1.04 kg (95% CI −1.75 to −0.34; I2=41%) |
mHealth favors |
Body weight | Khokhar et al [ |
Weight loss interventions on patients with OWB | Mobile electronic device intervention vs the control |
−1.09 kg (95% CI −2.12 to −0.05; I2=50%) Subgroup analysis—duration: ≤6 months −0.97 kg (95% CI −2.23 to 0.30; I2=47%); >6 months −1.20 kg (95% CI −3.34 to 0.94; I2=62%) Subgroup analysis—intervention: mobile phone −1.78 kg (95% CI −2.92 to −0.63; I2=16%); personal digital assistant −0.23 kg (95% CI −0.87 to 0.41; I2=0.0%) |
mHealth favors |
BMI | Park et al [ |
Weight loss interventions on patients with OWB | Mobile app/text messaging intervention vs nonmobile device care (standard) |
–0.77 kg/m2 (95% CI −1.01 to −0.52; I2=0%) Subgroup analysis—duration: at 3 months −1.10 kg/m2 (95% CI −2.79 to 0.59; I2=95%); at 6 months −0.67 kg/m2 (95% CI −0.71 to −0.63; I2=0%) |
mHealth favors |
BMI | Mateo et al [ |
Weight loss and PA promotion on patients with OWB | Mobile app intervention vs the control diet |
−0.43 kg/m2 (95% CI −0.74 to −0.13; I2=50%) |
mHealth favors |
Physical activity | Mateo et al [ |
Weight loss and PA promotion on patients with OWB | Mobile app intervention vs control intervention |
Standardized mean difference in net change 0.40 (95% CI −0.07 to 0.87; I2=93%) |
No significant difference |
aWe selected 6 meta-analyses on randomized controlled trial studies. Please see our pooled meta-analysis presented in
bHbA1c: hemoglobin A1c (glycated hemoglobin).
cT1DM: type 1 diabetes mellitus.
dmHealth: mobile health.
eT2DM: type 2 diabetes mellitus.
fOWB: overweight and obesity.
gPA: physical activity.
A meta-analysis of mean differences in changes in clinical outcomes after an intervention, mobile health versus control groups. HbA1c: hemoglobin A1c.
Relatively few reviews examined the treatment effect of mHealth interventions and reported inconsistent results. Out of the 7 reviews that investigated mHealth intervention effects on treatment adherence, 4 reviews found a moderate improvement in glycemic control [
Results for behavioral changes were not consistent, and 3 diabetes reviews [
Our meta-analysis (see
Although there is a strong interest among researchers, health care workers, and patients in mHealth interventions for the treatment of diabetes and obesity, overall, very little is known about its effectiveness. Moreover, at present, the use of mHealth interventions for these conditions is limited. To our knowledge, this is the first study that provides a comprehensive summary of research assessing the effectiveness of mHealth interventions for these conditions.
Published research has yielded mixed results. Examining evidence reported in 17 reviews that assessed a total of 55,604 original studies, this systematic review found that, overall, the impact of mHealth interventions on diabetes and obesity management is promising, especially in the areas of glycemic control and weight management. The majority of the 17 reviews focused on the self-monitoring functions of mHealth. Text messaging and apps were the primary types of mHealth interventions utilized to date. There was heterogeneity in the effectiveness of mHealth as diverse health outcomes (eg, blood pressure, weight, lipids, HbA1c, clinical biomarkers, treatment adherence, and health-related behavior changes) were tested in the original studies, but only a few studies with various study designs and populations (eg, clinical trials, nonclinical trials, and diverse patient subgroups by severity and disease type) and study focus (eg, incentive-driven technology) were available in the review. Nevertheless, all the 17 reviews concluded that mHealth was feasible and potentially can improve health outcomes among patients suffering from diabetes and/or obesity.
Clinical biomarkers such as glycemic control and weight change were the primary focus in evaluating the effect of mHealth interventions in the reviews assessed. For example, the change in HbA1c pre- and postintervention was evaluated in 10 reviews. Of these, 7 reviews reported statistically significant/large improvements, but 3 reviews did not; 2 meta-analyses showed 0.25% to 0.48% greater changes in HbA1c following an mHealth intervention compared with standard diabetes care. In contrast, only 4 reviews found some improvement in treatment adherence in all 7 reviews that assessed it. Furthermore, small or insignificant improvements in health-related behaviors were reported in 9 reviews.
Several factors could have caused substantial heterogeneity among the assessment of clinical biomarkers, treatment adherence, and health-related behaviors. First, a small number of original studies examining treatment adherence and behavioral changes might have underpowered the systematic approach in the literature review. Second, the inclusion criteria for the study design (eg, clinical/nonclinical trial and quasi-experimental study), study subjects (eg, mixture of patients with T1DM and T2DM, patients with T1DM only, or poorly controlled patients with diabetes), and application type were not controlled efficiently in the previous reviews. In addition, patient health–related behaviors may require more time to change than was generally allowed in the studies compared with the typically more rapid change in biomarkers, possibly because of the influence of cognitive biases, habits, and social behavioral norms [
In recent years, there has been explosive growth in the number of mobile apps [
As education and health promotion can favorably influence clinical outcomes, app developers need to fully consider the needs of users in designing features for patients suffering from diabetes/obesity. For example, self-management should be promoted as a key feature in apps targeting patients with T1DM who may need to check their blood glucose level more frequently than those with T2DM. In addition, new mobile messaging services, such as Facebook Messenger, WhatsApp, Snapchat, and Instagram, now exceed the functionality of traditional text messaging. Relevantly, social media features are increasingly popular, particularly among young people. Social networks can help patients achieve behavioral changes by, for instance, providing peer support among patients with similar conditions [
The 17 reviews and the included intervention studies share some limitations. First, some reviews only included a small number of studies but examined a relatively large number of outcomes [
First, we examined the results reported in the 17 identified reviews without analyzing the findings from the original studies. Second, there was a high level of heterogeneity in the characteristics and findings of the 17 reviews. Thus, it was challenging to adequately interpret the effectiveness of mHealth interventions across reviews because of different study designs, objectives, and settings. Despite these limitations, this study provided a higher level of analysis and a comprehensive summary of the findings in the growing mHealth field. Compared with previous studies, our study has a number of unique contributions, including the following: (1) our study added quantitative evidence specifically on the applications of mHealth in diabetic and obesity care research and studied objective changes in biomarkers, treatment adherence, and health behaviors after an mHealth intervention, whereas previous studies were general and narratively described mHealth effects on diverse diseases using a small number of articles with low quality; (2) we conducted a meta-analysis on the intervention effects of clinical outcomes, which was lacking in the existing reviews; and (3) our review included newly published reviews that were not included in other studies. This helps identify best practices for fighting the epidemics of diabetes and obesity. In addition, we found a fairly consistent reduction in HbA1c and body weight from mHealth interventions across multiple reviews.
Regarding future evaluations of mHealth interventions, more rigorous study designs and strategies are needed to enable us to draw more precise and specific conclusions regarding their effectiveness for diabetes and obesity management. To enhance app design, including user ratings and experiences may be useful in developing evidence-based strategies. The level to which users truly engage with these mHealth apps is not yet clear. Patient-centered self-monitoring with personalized feedback is important in behavioral change and has been shown to improve user engagement and adherence [
To promote an evidence-based approach in mHealth use for diabetes and obesity management, multiple validation tests and, when appropriate, regulations will be needed. Objective and validated measures should be used, in particular, when studying behavioral changes following mHealth interventions. Furthermore, there is a need to identify and focus on high-risk groups (eg, low socioeconomic status populations), as most previous reviews did not include studies conducted in these populations.
In conclusion, findings from the 17 reviews, including 6 meta-analyses published since 2005, suggested promising but limited evidence on the effectiveness of mHealth interventions for diabetes and obesity management. Self-management, monitoring, and use of text messaging and apps are the primary target functions and application types of mHealth investigated in the field. More rigorous study designs should be applied in future studies for assessing the impact of mHealth interventions on diabetes and obesity management. To enhance the effectiveness of mHealth interventions, studies are warranted for the optimal formats and the frequency of contacting patients, using theory-based interventions; for the better tailoring of messages to the specific needs and communication style of recipients; and for enhancing the usability by adapting approaches to recipients with varying degrees of technological and health literacy, thus placing a greater emphasis on maintaining effectiveness over time.
Methodological quality of 17 studies based on AMSTAR2 criteria.
Characteristics of 17 review studies on the effectiveness of mobile health interventions for diabetes and obesity management.
Summary of findings from the 17 reviews on the effectiveness of mobile health interventions for diabetes and obesity management.
hemoglobin A1c
mobile health
physical activity
personal digital assistant
randomized controlled trial
type 1 diabetes mellitus
type 2 diabetes mellitus
This work was supported by a research grant from the US National Institutes of Health, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD070725), which YW received while he worked at the Johns Hopkins University and established a global center of excellence. The content of the paper is solely the responsibility of the authors and does not necessarily represent the official views of the funder.
None declared.