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Digital technology is an opportunity for public health interventions to reach a large part of the population.
This systematic literature review aimed to assess the effectiveness of mobile health–based interventions in reducing the risk of cardiovascular disease and type 2 diabetes mellitus.
We conducted the systematic search in 7 electronic databases using a predefined search strategy. We included articles published between inception of the databases and March 2019 if they reported on the effectiveness of an intervention for prevention of cardiovascular disease or type 2 diabetes via mobile technology. One researcher performed the search, study selection, data extraction, and methodological quality assessment. The steps were validated by the other members of the research team
The search yielded 941 articles for cardiovascular disease, of which 3 met the inclusion criteria, and 732 for type 2 diabetes, of which 6 met the inclusion criteria. The methodological quality of the studies was low, with the main issue being nonblinding of participants. Of the selected studies, 4 used SMS text messaging, 1 used WhatsApp, and the remaining ones used specific smartphone apps. Weight loss and reduction in BMI were the most reported successful outcomes (reported in 4 studies).
Evidence on the effectiveness of mobile health-based interventions in reducing the risk for cardiovascular disease and type 2 diabetes is low due to the quality of the studies and the small effects that were measured. This highlights the need for further high-quality research to investigate the potential of mobile health interventions.
International Prospective Register of Systematic Reviews (PROSPERO) CRD42019135405; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=135405
Worldwide, chronic diseases are the main cause of death and years lived with disability [
To stop noncommunicable diseases from rising further, the World Health Organization (WHO) developed the Global Action Plan 2013-2020 [
The aim of this systematic literature review was to assess the current evidence regarding the effectiveness of mobile health–based interventions in reducing the risk for CVD and T2DM. The focus was on multiple behavioral risk–factor interventions, rather than single risk–factor interventions, because of the lack of evidence on their combined effectiveness compared with substantial evidence on single risk–factor interventions [
We conducted this systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [
We searched the following medical and bioengineering databases to retrieve all relevant articles regarding preventive mobile health intervention for CVD and T2DM: EMBASE (via Ovid), Scopus, ScienceDirect, CINAHL (via EBSCOhost), MEDLINE (via Ovid), ProQuest science and technology databases, and Ei Compendex and Inspec (both via Engineering Village 2). The search strategy (
The study selection followed predefined inclusion criteria according to the PICOS system (
Inclusion criteria according to the PICOS system.
Criteria | Description of inclusion criteria |
Participants | Adults who are free of CVDa or T2DMb. |
Intervention | Health promotion interventions that use mobile health technology (ie, mobile app or SMS text messaging) aiming to change more than 1 risk factor for 1 of the 2 chronic conditions under study. |
Comparator | No intervention (ie, standard care), or waitlist control, or intervention delivered in person. |
Outcome | Onset of disease (CVD or T2DM) or relative risk reduction, which can be in the form of surrogate parameters. |
Study design | Randomized controlled trial, case-control study, or interrupted time series. |
aCVD: cardiovascular disease.
bT2DM: type 2 diabetes mellitus.
Participants could either be healthy or have an increased disease risk. We excluded interventions targeting adults who were already diagnosed with CVD or T2DM (depending on the aim of the intervention, eg, for CVD prevention, people diagnosed with CVD) at baseline. Further, we excluded studies intended for minors (<18 years of age). The conditions under study were CVD and T2DM, for which we applied the following WHO definitions: CVD is a “group of disorders of heart and blood vessels,” including coronary heart disease, cerebrovascular disease, peripheral vascular disease, heart failure, rheumatic heart disease, congenital heart disease, and cardiomyopathies [
We included primary studies if they evaluated the effectiveness of a mobile phone–based intervention for primary prevention of 1 of the conditions under study. The intervention had to be delivered, at least partially, via mobile health technology (ie, mobile app or SMS text messaging) with the aim of changing more than 1 risk factor for 1 or more of the chronic conditions under study. We defined a mobile app as a software program that can run on mobile devices such as smartphones, and a text message as a written message sent to a mobile phone. The type of interventions that we included needed to be aimed at health promotion using behavior change strategies, including counselling or education regarding disease-related knowledge, healthy diet, physical activity, smoking cessation, motivational messages, and goal setting. We excluded from the review studies that exclusively targeted 1 behavioral risk factor (eg, smoking only, diet only, or step count only).
The comparison group could consist of either no intervention (ie, standard care), or a waitlist control, or an intervention delivered in person. Studies were eligible if they included adults who were free of CVD or T2DM at study baseline, depending on the condition targeted in the study.
Studies were only eligible for inclusion if their main outcomes were disease incidence (either CVD or T2DM) or a reduction in disease risk, which could be measured using a risk prediction tool (such as the Framingham score for CVD [
We restricted the study design to randomized controlled trials (RCTs), case-control studies, and interrupted time series in order to have a measurement against which the effectiveness of the intervention could be compared.
Relevant data (study objective, study design, study population, comparator, description of the intervention, duration of the intervention or follow-up, outcomes, main results, and methodology for the assessment of the study’s quality) were extracted by 1 researcher (VHB) using a standardized form in Excel 365 (Microsoft Corporation). This was reviewed by all the other researchers. We synthesized the main results of the included studies in a narrative manner focusing on the intervention delivery and reported outcomes. A meta-analysis was not possible due to the small number of identified studies and the heterogeneity in interventions and outcomes.
One researcher (VHB) assessed the risk of bias using the following assessment tools: for RCTs, the Cochrane Collaboration’s tool for assessing risk of bias [
In total, we identified 941 articles using the search strategy for CVD and 732 articles using the search strategy for T2DM. In the validation of the 10% random sample of all retrieved articles, there was a 100% agreement (after initial disagreements were resolved by a third investigator) with the selection conducted by the researcher who screened all articles. Finally, 3 CVD articles [
Full article selection process for cardiovascular disease.
Full article selection process for type 2 diabetes.
There were 3 CVD [
Data extraction from cardiovascular disease (CVD) studies.
First author, date, reference | Study design and duration; objectives | Study population | Intervention and comparator | Outcomes | Main results |
Gore, 2019 [ |
Non-RCTa for 12 months; effectiveness of an SMS text message intervention to reduce CVD risk | Adults from the United States at high risk of CVD without preexisting coronary artery disease, cerebrovascular disease, and diabetes; intervention n=204, usual care n=408 | Create action plan with community health workers and return 6-12 months after initial screening for retesting; intervention: text messages once/day on advice on healthy eating, PAb, weight loss, contacting community health worker; control: usual care | Engagement, program retention, changes in risk factors (smoking, fat and fiber intake, PA, weight, BMI, BPc, low-density lipoprotein), Framingham risk score | Only statistically significant decrease in fat intake (intervention −26.3% vs control −10.6%; |
Muntaner-Mas, 2017 [ |
Non-RCT for 10 weeks; effectiveness of a WhatsApp-based PA intervention to reduce CVD risk factors | Spanish adults aged 53-73 years without medical conditions or other physical problems requiring special medical attention and who were able to perform rigorous PA; mobile group n=7, training group n=16, control n=9 | Intervention: twice/week functional fitness for training and mobile group; for training group face-to-face sessions, for mobile group training videos for download via WhatsApp, chat function plus motivational messages from study coordinator; control: no intervention | BP, WCd, waist to height ratio, weight, BMI, fat mass index, fat-free mass index, heart rate after exercise, balance, handgrip strength, aerobic capacity | No statistically significant differences between mobile group and control; statistically significant differences between training group and control group (systolic BP |
Rubinstein, 2016 [ |
RCT for 12 months; effectiveness of preventive mobile health intervention in adults with prehypertension | Adults aged 30-60 years with prehypertension from poor urban settings in Argentina, Guatemala, and Peru, free of hypertension, diabetes, and CVD; intervention n=316, usual care n=321 | Intervention: monthly motivational counselling calls (healthy diet and PA) followed by weekly text messages related to behavior goals and readiness to change; control: usual care | Changes in BP, weight, BMI, WC, PA, diet | Mean differences, baseline-adjusted (95% CI): weight −0.66 kg (−1.24 to −0.07), BMI −0.30 kg/m2 (−0.54 to −0.06), daily intake of high-sugar and -fat servings −0.75 (−1.30 to −0.20); change in BP not significant |
aRCT: randomized controlled trial.
bPA: physical activity.
cBP: blood pressure.
dWC: waist circumference.
Data extraction from type 2 diabetes mellitus (T2DM) studies.
First author, date, reference | Study design and duration; objectives | Study population | Intervention and comparator | Outcomes | Main results |
Arens, 2018 [ |
Non-RCTa for 12 months; effectiveness of app-based weight reduction program for people with metabolic syndrome | German adults aged 30-65 years treated for metabolic syndrome in 23 medical practices; intervention n=148, usual care n=85 | Health goals regarding weight and PAb; app for feedback; physicians with access to app data could give feedback, initiate messages, or modify goals; ≤9 free classes on diet and PA; control: usual care | 5% weight reduction; change in BMI | 5% weight reduction (adjusted for time in study) (95% CI): 44.8% (34.1 to 57.1) in intervention vs 11.5% (4.6 to 27.0) in control; Cox proportional hazard model for time to 5% weight reduction hazard ratio 6.2 (2.4 to 16.2; |
Bender, 2018 [ |
RCT for 3 months plus 3 months follow-up (no control for follow-up); effectiveness of mobile phone-based weight loss intervention to reduce T2DM risk | Filipino-American overweight or obese adults from United States at increased risk for T2DM, able to walk 20 min; intervention n=33, control n=34 | 5 in-person sessions, daily step count via wearable device, daily food intake and weekly weight logged in app, weekly information on weight loss, PA, and diet via private Facebook page; control: waitlist | Recruitment (goal n=50), retention, 5% weight loss, changes in weight, BMI, WCc, FBGd, HbA1ce | Weight loss ≥5%: intervention 36% vs control 6%; between-group cross-level interaction (95% CI): weight −1.1%/month (−1.7 to −0.53) and −0.85 kg/month (−1.4 to −0.35), BMI −0.93 kg/m2 (−1.5 to −0.40), WC −4.9 cm (−7.5 to −2.6), FBG −1.4 mg/dL (−5.9 to 3.6), HbA1c −0.10% (−0.21 to 0.002) |
Block, 2015 [ |
RCT for 6 months plus 6 months follow-up (no control for follow-up); effectiveness of digital health intervention for T2DM risk reduction in prediabetics | Prediabetics aged 30-69 years from United States with BMI ≥27 kg/m2, without diabetes medication; intervention n=163, control n=176 | Tailored behavioral support for PA, diet, weight loss, stress, sleep; weekly emails with goals linked to website (tracking tools, coaching, social support, competition, health advice), app and automated phone calls; control: waitlist | Decreased HbA1c, FBG, weight, BMI, WC, triglyceride to HDLf ratio, metabolic syndrome, Framingham diabetes risk score | Mean (95% CI) HbA1c −0.26% (−0.27 to −0.24) in intervention vs control −0.18% (−0.19 to −0.16), FBG −0.41 mmol/L (−0.44 to, −0.12) in intervention vs −0.21 mmol/L (−0.15 to −0.10) in control, all outcomes significantly greater in intervention than control ( |
Fischer, 2016 [ |
RCT for 12 months; effectiveness of text message–supported T2DM prevention program | Obese and overweight adults from United States without prediabetes, English or Spanish speaking; intervention n=82, control n=81 | 6 text messages per week: skills, problem solving, motivation, stress reduction, recipes, web links to additional resources, PA promotion; weekly self-reported weight; eligible for individual motivational phone health coaching; control: usual care | Change in weight; percentage of participants with ≥3% or 5% weight loss, changes in HbA1c and systolic BPg, costs per participant | Weight (95% CI) in intervention −1.2 kg (−2.5 to 0.1) vs control −0.3 kg (−1.2 to 0.7), |
Fukuoka, 2015 [ |
RCT for 5 months; effectiveness of mobile app-based intervention for T2DM prevention | Overweight adults aged ≥35 years from United States at high risk of diabetes; intervention n=30; control n=31 | 2-week run-in period before randomizing; all daily step count via pedometer; intervention: mobile version of Diabetes Prevention Program, 6 in-person sessions, app: diaries for self-monitoring of weight, PA, and caloric intake, daily reminders and messages; control: pedometer only | % change in weight and BMI; hip circumference, BP, lipid profile, glucose levels, step count, PA, caloric and fat intake | Weight (95% CI) −6.8% (−12.2 to −1.4) in intervention vs 0.3% (−2.7 to 3.3) in control; BMI −6.6% (−12.3 to −0.9) in intervention vs 0.3% (−2.7 to 3.3) in control; both |
Ramachandran, 2013 [ |
RCT for 2 years; effectiveness of SMS text messaging to reduce incidence of T2DM in men with impaired glucose tolerance | Indian men aged 35-55 years with impaired glucose tolerance; intervention n=271, control n=266 | All at baseline: healthy lifestyle education and written information on diet and PA, lifestyle changes prescribed; intervention: frequent reinforcing text messages, content tailored to baseline behavior; control: usual care | Incidence of T2DM; BMI, WC, BP, lipid profile, energy intake, PA | Cumulative T2DM incidence: intervention 18%, control 27%; differences in mean change (95% CI): BMI −0.05 kg/m2 (−0.46 to 0.37); WC 0.04 cm (−0.56 to 0.64); systolic BP 0.04 mmHg (−0.96 to 1.03); diastolic BP −0.07 mmHg (−0.64 to 0.49); total cholesterol 0.01 mmol/L (−0.08 to 0.10); HDL 0.033 mmol/L (0.011 to 0.054); triglycerides −0.08 mmol/L (−0.17 to −0.06); energy intake –43.7 kcal (−65.5 to −22.0); PA score −1.0 (−2.0 to 0.0) |
aRCT: randomized controlled trial.
bPA: physical activity.
cWC: waist circumference.
dFBG: fasting blood glucose.
eHbA1c: glycated hemoglobin.
fHDL: high-density lipoprotein.
gBP: blood pressure.
We synthesized the results of the data extraction according to the PICOS system.
Synthesis of findings.
Finding | Cardiovascular disease | Type 2 diabetes | Total (n) | |||
No. of studies | Reference | No. of studies | Reference | |||
|
||||||
|
General population | 1 | [ |
—a | — | 1 |
|
At risk of the disease | 2 | [ |
6 | [ |
8 |
|
||||||
|
Spain | 1 | [ |
— | — | 1 |
|
United States | 1 | [ |
4 | [ |
5 |
|
Germany | — | — | 1 | [ |
1 |
|
Latin America | 1 | [ |
— | — | 1 |
|
India | — | — | 1 | [ |
1 |
|
||||||
|
SMS text messaging | 2 | [ |
2 | [ |
4 |
|
1 | [ |
— | — | 1 | |
|
Mobile app | — | — | 4 | [ |
4 |
|
||||||
|
Usual care | 3 | [ |
3 | [ |
6 |
|
Waitlist | — | — | 2 | [ |
2 |
|
Pedometer only | — | — | 1 | [ |
1 |
|
Face-to-face training | 1 | [ |
— | — | 1 |
|
||||||
|
Weight loss | 1 | [ |
3 | [ |
4 |
|
Reduced BMI | 1 | [ |
3 | [ |
4 |
|
Reduced waist circumference | — | — | 2 | [ |
2 |
|
Lower fasting blood glucose/glycated hemoglobin | N/Ac | — | 1 | [ |
1 |
|
Improved diet | 2 | [ |
1 | [ |
3 |
|
Improved physical activity | — | — | 2 | [ |
2 |
|
Improved blood pressure | — | — | 1 | [ |
1 |
|
||||||
|
Randomized controlled trial | 1 | [ |
5 | [ |
6 |
|
Nonrandomized controlled trial | 2 | [ |
1 | [ |
3 |
a—: data not available.
bStatistically significant compared with control group.
cN/A: not applicable.
The CVD studies were conducted in Spain, the United States, and Latin America. For the T2DM studies, 1 was conducted in Germany, 1 in India, and 4 in the United States. All studies had small to medium samples, ranging from 32 to 637 participants. For CVD, 2 of the 3 studies targeted populations at higher risk of developing CVD [
The duration of the interventions varied from 10 weeks to 2 years. In 4 studies the participants received text messages [
Of the studies, 6 used usual care as the control group. In 1 trial, the control group received pedometers only [
The mobile phone interventions led to statistically significant weight loss compared with the control group in 4 studies [
A total of 6 studies were RCTs [
All RCTs used acceptable methods for randomization [
Of the 3 non-RCTs [
Risk-of-bias summary table for the randomized controlled trials. The upper 1 is a cardiovascular disease study and the remainder are type 2 diabetes studies.
Risk-of-bias summary table for the nonrandomized controlled trials. The upper 2 are cardiovascular disease studies and the lower 1 is a type 2 diabetes study.
We identified only a small number (n=9) of articles that fulfilled the preset inclusion and exclusion criteria. We assessed most of the studies to be at high risk of bias. Additionally, 3 studies were underpowered (sample size <100), and 2 studies had short follow-up times (<6 months). Ideally, to show the effectiveness in reducing the risk of CVD or T2DM, the studies should have reported disease incidence rates. The only study that did this was that by Ramachandran et al [
The strength of this literature review was that it followed the PRISMA statement. We systematically searched several databases to identify all relevant published articles. Further, we conducted a manual search through the snowballing technique. For the title and abstract screening, a 10% random sample of all retrieved articles was validated by a second researcher, and 2 reviewers independently performed the full article selection. However, only 1 researcher conducted the database search, the data extraction, and the risk-of-bias assessment. Although we a priori restricted the search to English- and German-language articles, we did not exclude any articles because they were not available in these 2 languages. We did not perform a meta-analysis due to the small number of publications that met the inclusion criteria and the differences in their interventions and outcome measures.
Previous mobile health research has focused more on self-management of chronic diseases than on prevention. In their systematic review and meta-analysis, Wu et al [
Palmer et al [
Most studies that were conducted according to the review’s inclusion criteria were at high risk of bias. This review only considered studies of multirisk factor interventions, which resulted in only 9 studies being included. There is a lack of research evaluating interventions that address the 4 common behavioral risk factors (ie, tobacco smoking, excessive alcohol consumption, physical inactivity, and poor diet) in a single mobile health intervention. Researchers may have preferred to focus on 1 risk factor at a time due to simplicity for participants and clarity of intervention-outcome relationships. Hence, future studies should further explore the use of mobile technology for primary disease prevention, by applying a rigorous study design.
According to the findings of this systematic review, evidence for the effectiveness of mobile health-based interventions in reducing the risk of CVD and T2DM is scarce due to the quality of the included studies and the small effects that were measured. This highlights the need for further high-quality research to investigate the potential of mobile health interventions.
Search strategy.
cardiovascular disease
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
International Prospective Register of Systematic Reviews
randomized controlled trial
type 2 diabetes mellitus
World Health Organization
This research was supported by a joint stipend from the University of New South Wales and the Commonwealth Scientific and Industrial Research Organisation.
VHB participated in research design, data collection, data analysis, and writing of the manuscript. SL participated in data collection, data analysis, and revision of the manuscript. MV, MB, and MH contributed to research design, data collection, data analysis, and revision of the manuscript. All authors provided final approval of the version to be published.
None declared.