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. Author manuscript; available in PMC: 2016 Apr 1.
Published in final edited form as: Mayo Clin Proc. 2015 Apr;90(4):469–480. doi: 10.1016/j.mayocp.2014.12.026

Digital Health Interventions for the Prevention of Cardiovascular Disease: A Systematic Review and Meta-Analysis

R Jay Widmer 1, Nerissa M Collins 2, C Scott Collins 2, Colin P West 2,3, Lilach O Lerman 4, Amir Lerman 1
PMCID: PMC4551455  NIHMSID: NIHMS715520  PMID: 25841251

Abstract

Objective

To assess the potential benefit of digital health interventions (DHI) on cardiovascular disease outcomes (CVD events, all-cause mortality, hospitalizations) and risk factors compared to non-DHI interventions.

Patients and Methods

We conducted a systematic search of PubMed, MEDLINE, EMBASE, Web of Science, OVID, CINHAL, ERIC, PsychInfo, Cochrane, and CENTRAL from January 1, 1990 and January 21, 2014. Included studies examined any element of DHI (telemedicine, web-based strategies, email, mobile phones, mobile applications, text messaging, and monitoring sensors) and CVD outcomes or risk factors. Two reviewers independently evaluated study quality utilizing a modified version of the Cochrane Collaboration risk assessment tool. Authors extracted CVD outcomes and risk factors for CVD such as weight, BMI, blood pressure, and lipids from 51 full-text articles that met validity and inclusion criteria.

Results

DHI significantly reduced CVD outcomes (RR=0.61, (95% CI, 0.45–0.83), P=.002; I2=22%). Concomitant reductions in weight (−3.35 lbs, (95% CI, −6.08 lbs, −1.01 lbs); P=.006; I2=96%) and BMI (−0.59 kg/m2, (95% CI, −1.15 kg/m2, −0.03 kg/m2); P=.04; I2=94%) but not blood pressure (+4.95 mmHg, (95% CI, −4.5 mmHg, 14.4 mmHg); P=.30; I2=100%) were found in these DHI trials compared to usual care. Framingham 10 year risk percentages were also significantly improved (−1.24%; 95% CI −1.73%, −0.76%; n=6; P<0.001; I2=94%). Results were limited by heterogeneity not fully explained by study population (primary or secondary prevention) or DHI modality.

Conclusions

Overall, these aggregations of data provide evidence that DHI can reduce CVD outcomes and have a positive impact on risk factors for CVD.

Keywords: cardiovascular disease, outcomes, digital health, mobile health, prevention, weight loss, MACE

Introduction

Cardiovascular disease (CVD) is the primary cause for morbidity and mortality, and is associated with markedly rising health care costs in the United States 1. Approximately one in three deaths can be attributed to CVD 1,2, and over 90% of CVD morbidity and mortality to preventable risk factors 3. According to 2012 statistics, poor diet, smoking, and lack of physical activity continue to account for an overwhelming majority of CVD and death 4 with the cost of CVD to the US approaching $200 billion per year 1. What is more, the average hospitalization for acute coronary syndrome (ACS) is estimated to cost roughly $20,000 with repeat events costing up to two and three times the original amount 5. Clearly, better interventions to improve CVD prevention, both primary and secondary, are needed.

Internet and smart phone use has grown exponentially in the past decade, opening up the possibility that these increasingly prevalent technological tools could improve health. Digital health interventions (DHI), including such modalities as telemedicine, web-based strategies, email, mobile phones, mobile applications, text messaging, and monitoring sensors, are the most recent iteration of an effort to shift health care burden outside of the walls of medical institutions, and improve individualized care through positive behavior change theory 6. Although prior studies have suggested benefits of DHI in focused areas such as smoking cessation 7, behavior patterns 8, physical activity 9, HbA1c 10, blood pressure 11, and weight loss 12, evidence concerning the benefit of DHI on CVD risk factors, let alone CVD outcomes such as CVD events, hospitalizations, and all-cause mortality, is lacking. With nearly 50,000 healthcare related apps now available for download 13, and numerous internet-based DHI solutions available, the benefit of DHI on CVD prevention and outcomes, both primary and secondary, merits reexamination.

The purpose of this systematic review and meta-analysis was to inclusively review randomized controlled trials (RCTs) and cohort studies incorporating DHI for the prevention of CVD outcomes (CVD events including myocardial infarction, stroke, revascularization, hospitalizations, and all-cause mortality) and modification of risk factors for CVD such as weight, BMI, blood pressure, cholesterol, glucose, and Framingham Risk Scores (FRS). We aim to establish the potential benefit of DHI on both primary and secondary CVD prevention, and identify future needs in DHI and CVD research.

Methods

Data Sources and Searches

This systematic review was conducted in accordance with PRISMA guidelines 14. We included all RCTs and observational/cohort studies published between January 1, 1990 and January 21, 2014 that examined any element of DHI (telemedicine, web-based strategies, email, mobile phones, mobile applications, text messaging, and monitoring sensors) and impact on CVD. We intentionally and broadly included any studies of adult patients seeking CVD prevention to present a comprehensive overview of DHI studies analyzing CVD outcomes (CVD events, hospitalizations, or all-cause mortality) and modification of risk factors for CVD such as weight, BMI, blood pressure, cholesterol, glucose, and FRS regardless of type of healthcare provider or healthcare setting. Control interventions included usual care following standard guidelines, and could involve non-DHI intervention (such as paper instructions or telephone calls) or no active intervention beyond usual care. We excluded studies in which the intervention lasted less than a month in order to assess long-term impact and sustainability, studies that did not report any CVD risk factors, redundant studies which were repeated in the literature without new data presented, protocol manuscripts, reviews, studies only including usability or adherence data, pediatric studies, and studies where the intervention involved the healthcare provider, rather than the patient.

Our search strategy was performed with the assistance of a medical librarian, and included the databases PubMed, MEDLINE, EMBASE, Web of Science, OVID, CINAHL, ERIC, PsychInfo, Cochrane, and CENTRAL over the specified dates. We included the search terms mobile health, mobile, mhealth, digital health, eHealth, internet, telemedicine, web, smartphone, cardiovascular, cardiac, prevention, outcomes, mortality, morbidity, event, Framingham, blood pressure, weight, BMI, waist circumference, glucose, lipids, cholesterol, smoking, tobacco, quality of life, emergency department, visits, hospitalizations, rehospitalizations, office visits, phone calls, cost, cost of care, and ROI. This strategy identified 574 relevant abstracts with an additional 14 references identified through bibliography searches and personal contacts (Figure 1). Most articles were in English, and those in Spanish, Polish, and German were translated for review.

Figure 1.

Figure 1

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PRISMA schematic for study selection.

Study Selection

Two reviewers (RJW and NMC) assessed each of the identified abstracts. Full text versions of potentially eligible studies, categorized for inclusion by either reviewer, were requested (n=73). The two reviewers worked independently to evaluate the full text reports for study inclusion and disagreements were reconciled by consensus. Agreement on study inclusion was high, with kappa = 0.92.

Data Extraction and Quality Assessment

Extracted data included study participant demographics (age, gender, prior internet use, education level, socioeconomic status, race, comorbidities, and baseline markers of CVD), the DHI they received (frequency, type, and duration), and the control intervention. DHIs were identified as involving telemedicine, web-based strategies, email, mobile phones, mobile applications, SMS text messaging, and monitoring sensors. Control comparisons were heterogeneous and could include a non-DHI intervention or usual care. CVD outcomes included CVD events including myocardial infarction, stroke, or revascularization, hospitalizations, and all-cause mortality. Risk factors for CVD included weight, BMI, blood pressure, cholesterol, (total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides), glucose, and FRS.

Risk of bias and methodological quality was assessed independently by two authors (RJW and CSC) using a modified version of the Cochrane Collaboration risk assessment tool 15 (Supplementary Figure 1). To evaluate the quality of non-randomized studies, we assessed blinding of the outcome assessors to arm assignment in relation to the outcomes of CVD outcomes and CVD surrogates, comparability of outcome assessment, and completeness of follow-up. The latter criteria followed a revised Newscastle–Ottawa quality assessment tool for observational studies16 (Supplementary Figure 1) which emphasized proper definition of the CVD pertinent to the study, legitimate DHI intervention, and reasonable follow up. One study (Nolan, 2012) was considered an observational study as the randomization scheme was compromised due to unintentional cross-over of the participants forcing the authors to report the data in separate, non-randomized cohorts. Finally, a study by Wister et al 17 allowed separation of studies for primary and secondary prevention.

Data Synthesis and Analysis

When possible, we generated meta-analytic estimates of treatment effect using pooled relative risks and random-effects models. Analyses were performed using RevMan v.5.2 (The Cochrane Collaboration; Oxford, UK). We measured heterogeneity for each outcome across studies using the I2 test 18. When standard deviations were missing for a study, imputation of the mean standard deviation of the group for that particular variable was utilized in no more than two values per variable. Imputation of more than two standard deviations was not required for any analysis.

To explore causes of inconsistency in study findings and subgroup-treatment interactions, we planned subgroup analyses comparing results by patient population (primary prevention versus secondary prevention) and DHI subtype (telemedicine, web-based, email reminders, SMS texting, mobile application, and data monitoring). Random effects methods utilizing Mantel-Haenszel methods for combining results across studies were undertaken as part of the RevMan 5.2 software package 18. Sensitivity analyses controlling for workplace versus healthcare delivered DHI were performed as were sensitivity analyses removing the two observational, non-randomized studies.

We contacted all authors with a prepopulated form including data for verification and missing data for their completion. Of the original 49 authors contacted, 28 returned correspondence with either verification of reported data, or the addition of missing or incomplete data. There was no impact of the funding source on the design, execution, or analysis of the study.

Results

Fifty-one studies met criteria for full-text review and were included in the systematic review with nine studies providing analyzable CVD outcome data. A summary table of studies reporting CVD outcomes is presented in Table 1. Risk of bias among studies reporting CVD outcomes was predominantly low apart from a consistent lack of participant blinding (Table 2) with a funnel plot included (Supplementary Figure 2).

Table 1.

RCTs reporting CVD outcomes with DHI (n=9)

Study ID Duration (mo) Total N DHI N Study Population DHI Findings
Appel, 201126 24 415 139 Primary Prevention, Hypertension Web-based Larger, healthcare site obesity intervention delivered remotely or in person significantly reduced weight (−4.6 kg and −5.1 kg, respectively) vs. controls. No impact on CVD events, rehospitalizations, or all-cause mortality.
Blasco 201227 12 203 102 Secondary Prevention SMS text, Smart Phone Healthcare secondary prevention trial showing improved secondary prevention outcomes (repeat CVD events, rehospitalizations, or all-cause mortality; RR = 1.4; 95% CI = 1.1–1.7) with telemonitoring and SMS text.
Dendale, 201228 6 160 80 Secondary Prevention, Heart Failure Telephone, Data Monitoring Healthcare-delivered telemonitoring service in HF patients showed significantly reduced all-cause mortality (P=.01) but did not reduce hospitalizations per patient (0.24 vs. 0.42, P=.06).
Frederix, 201329 4.5 80 40 Secondary Prevention Email, SMS text, Data Monitoring Body sensor data-monitoring in CR patients improved exercise capacity (26.88+220.33 ml/min vs. 285.89+385.44 ml/min, P=.014) and improvements in rehospitalizations.
Green, 200930 12 778 520 Primary Prevention Telephone, Web-based Hypertensive patients assigned to usual care vs. a web-based or telephone-based intervention showed those using the web-based platform had a greater percentage of achieving target BP (55% vs. 39%; 95% CI, 49%–62%; P < .001). Increased adverse events in intervention group.
Reid, 201231 12 223 115 Secondary Prevention Web-based Internet-based data monitoring for physical activity in post-MI patients showed significant improvements in physical activity and QOL compared to usual care. The intervention had a small, non-significant effect on hard CVD outcomes.
Scherr, 200920 6 120 54 Secondary Prevention, Heart Failure Telephone, SMS text, Data Monitoring Data monitoring in patients with recent decompensated HF showed a high attrition rate; yet a 50% reduction in CVD endpoints and hospitalizations with a mean improvement in NYHA class by one category in the treatment group.
Southard, 200332 6 104 53 Secondary Prevention Web-based Internet-based secondary prevention tool reduced CVD endpoints (15.7% vs. 4.6%) and provided a significant cost savings. The intervention group had a more robust weight loss (−3.68 lbs. vs. 0.47 pounds, P =.003), with no other surrogate markers of CVD achieving statistical significance.
Vernooij, 201233 12 330 164 Secondary Prevention Web-based Clinic-based online risk factor improvement tool showed a significant reduction in Framingham scores (−14%; −25% to − 2%) after 12 months in patients randomized to the intervention. No significant reduction in CVD events, death, and hospitalizations in DHI group.
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Table 2.

Risk of bias for outcomes studies:

Assessment of risk of bias based validity assessment tool used by authors (Supplementary Figure 1) for the nine studies with CVD outcomes analyzed. The x-axis represents the percentage of studies which were found to be of low (green), unclear (yellow), or high (red) risk of bias.

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Thirty-nine studies focused on primary CVD prevention (Supplementary Table 1A) and 13 studies primarily involved secondary CVD prevention (Supplementary Table 1B) (one study fit into both categories separately). The total number of patients included was 23,962, with 13,618 assigned to DHI and 10,344 to control groups. Mean age (SD) for all of the participants in the studies was 54.0 (9.4) years with a majority of the participants being Caucasian and 54% male. Five studies evaluated a solely female population, and two focused only on male participants. Socioeconomic status, geographical information, and prior internet usage were not universally reported. Additionally, the timeframe of a majority of studies was between 6 and 12 months, and most studies were published within the past decade. RCTs were blinded with specific mention of study personnel blinded to allocation and grouping during the study and to data analysis, with the exception of three studies 19–21.

CVD outcomes including myocardial infarction, stroke, revascularization, hospitalizations, and all-cause mortality were abstracted from 9 RCTs (2 primary prevention studies, 2 involving patients with heart failure (HF), and 5 secondary prevention studies). The 1267 participants in the DHI arms had 104 events, and the 996 participants in the usual care arms had 162 combined events. Overall, DHI significantly reduced CVD outcomes (RR=0.61, (95% CI, 0.46–0.80); P<0.001; I2=22%; Figure 2). Subgroup analyses showed no interaction between the primary prevention (no prior CVD diagnosis), secondary prevention (known prior CVD diagnosis), and HF groups (P=.11). When the outcome “hospitalizations” was removed from the combined endpoint there remained a 52% reduction in CVD events/deaths that was not statistically significant (RR=0.48, (95% CI, 0.21–1.11); p=0.09). In addition, DHI was associated with a significant reduction in Framingham 10 year risk percentages in the 6 studies reporting FRS data (−1.24%; 95% CI −1.73%, −0.76%; P<0.001; I2=94%).

Figure 2.

Figure 2

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CVD Outcomes and DHI.

The effect of DHI in Primary Prevention Studies

Separate subgroup analyses of primary prevention studies (n=2) were unable to provide statistical evidence of a positive effect on CVD outcomes (RR=1.21, (95% CI, 0.58–2.54); P=.61; I2=15%; Figure 2). Eleven primary prevention studies showed a significant reduction in weight (−3.35 lbs (95%CI −5.22 lbs, −1.48 lbs), P<0.001, I2=96%; Figure 3a), but not BMI (n=15) (mean difference = −0.11 kg/m2, (95% CI, −0.30 kg/m2, 0.08 kg/m2); P=.26; I2=98%; Figure 3b). When the three workplace intervention studies were removed from the pooled analysis, there was a significant reduction in BMI in primary prevention populations (n=12), (mean difference = −0.29 kg/m2, (95% CI, −0.5 kg/m2, −0.09 kg/m2); P=.006; I2=98%). We found a significant reduction in systolic blood pressure (SBP) among primary prevention studies (n=23), (mean difference = −2.12 mmHg, (95% CI, −4.15 mmHg, −0.09 mmHg); P=.04; I2=100%; Supplementary Figure 3) which failed to maintain a statistically significant reduction when two observational studies were removed in sensitivity analysis (mean difference = −1.31 mmHg, (95% CI, −3.43 mmHg, 0.80 mmHg); P=.22; I2=100%).

Figure 3.

Figure 3

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Figure 3a: Weight and DHI.

Figure 3b: BMI and DHI.

There was insufficient evidence to show a positive impact on triglyceride levels (n=7) (mean difference = −9.06 mg/dL, (95% CI, −22.7 mg/dL, 4.6 mg/dL); P=.19; I2=99%); however, we found significant reductions in total cholesterol (n=13) (mean difference = −5.39 mg/dL, (95% CI, −9.80 mg/dL, −0.99 mg/dL); P=.02; I2=98%; Supplementary Figure 4a), LDL cholesterol (n=8) (mean difference = −4.96 mg/dL, (95% CI, −8.54 mg/dL, −1.38 mg/dL); P=.007; I2=95%; Supplementary Figure 4b), and glucose (n=6) (mean difference = −1.38 mg/dL, (95% CI, −2.13 mg/dL, −0.63 mg/dL); P<0.001; I2=81%) in primary prevention populations.

The effect of DHI in Secondary Prevention Studies

Subgroup analyses of secondary prevention studies showed significant impact of DHI on CVD outcomes (RR=0.60, (95% CI, 0.43–0.83); P=.002; I2=0%; Figure 2). Pooled data from four secondary prevention trials demonstrated no improvement in weight (−0.93 lbs (95%CI −7.74 lbs, 5.88 lbs), P=.79, I2=97%; Figure 3a), but did show significant reductions in BMI (n=6) (mean difference = −0.31 kg/m2, (95% CI, −0.60 kg/m2, −0.03 kg/m2); P=.03; I2=67%; Figure 3b). We found no improvement in SBP in secondary prevention DHI trials (mean difference = 1.98 mmHg, (95% CI, −1.05 mmHg, 5.01 mmHg); P=.20; I2=94%; Supplementary Figure 3).

Similarly, there was no positive impact on triglyceride levels (n=5) (mean difference = −17.19 mg/dL, (95% CI, −49.45 mg/dL, 15.07 mg/dL); P=.30; I2=99%), total cholesterol (n=6) (mean difference = −1.80 mg/dL, (95% CI, −6.23 mg/dL, 2.64 mg/dL); P=.43; I2=94%; Supplementary Figure 4a), LDL cholesterol (n=5) (mean difference = −10.43 mg/dL, (95% CI, −21.69 mg/dL, 0.83 mg/dL); P=.07; I2=100%; Supplementary Figure 4b), or glucose (n=4) (mean difference = 0.45 mg/dL, (95% CI, −9.68 mg/dL, 10.58 mg/dL); P=.93; I2=100%) in secondary prevention populations.

The impact of various DHI modalities on risk factors for CVD

When we evaluated individual DHI modalities and their effects on risk factors for CVD, we found significant reductions in weight in studies which incorporated three modalities including web-based (−3.18 lbs (95%CI −5.61 lbs, −0.75 lbs), P=.01; I2=98%; Figure 4A), telemedicine (−2.30 lbs (95%CI −2.47 lbs, −2.14 lbs), P<0.001; I2=0%; Figure 4B), and SMS text (−3.85 lbs (95%CI −5.54 lbs, −2.17 lbs), P<0.001; I2=83%; Figure 4C) with email interventions showing no significant reduction in weight (0.74 lbs (95%CI −1.19 lbs, 2.68 lbs), P=.45; I2=0%; Figure 4D). Web-based modalities also had a beneficial impact on SBP (−2.63 mmHg, 95% CI −5.04 mmHg, −0.23 mmHg; p=0.03 I2=100%). Studies that incorporated data monitoring (n=5) reported no weight outcomes, and showed a significant benefit only in reducing diastolic blood pressure (−3.08 mmHg, 95% CI −4.8 mmHg, −1.36 mmHg; P<0.001; I2=0%).

Figure 4.

Figure 4

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Figure 4a: Web-based DHI and weight loss:

Figure 4b: Telehealth-based DHI and weight loss:

Figure 4c: SMS Text-based DHI and weight loss:

Figure 4d: Email-based DHI and weight loss:

Discussion

This systematic review and meta-analysis demonstrates that digital health has a beneficial effect on CVD risk factors and outcomes. Applying an inclusive definition of DHI broadly applied to studies ranging from two to 36 months, we found a CVD morbidity and all-cause mortality benefit for secondary CVD prevention and heart failure groups, with primary prevention populations showing benefit with regard to weight loss, BMI, SBP, total cholesterol, and LDL cholesterol. However, there was no clear benefit of DHI in primary prevention populations for CVD outcomes, although a reduction in Framingham risk scores was seen in our pooled analyses. In subgroup analysis by DHI subtype, there was particular benefit seen for web-based, telemedicine, and SMS texting DHI approaches, with insufficient data to support a benefit for email DHI.

As noted previously, prior literature on DHI and CVD-related outcomes has been limited. A recent systematic review of PubMed for mobile health and secondary CVD prevention over the prior ten years identified three studies without any quantitative results 22. Other systematic reviews have shown the efficacy of DHI on certain specific risk factors for CVD. Whittaker et al 7 showed improvements in smoking cessation across a wide variety of studies. Furthermore, additional work has shown DHI to positively affect behavior patterns 8 and physical activity 9. Liang et al 10 showed reductions of nearly 0.5% in HbA1c in 22 studies evaluating mobile phone program or text messaging tactics on participants with diabetes. Uhlig et al showed a favorable change in blood pressure at six months in 26 separate studies 11, yet they noted a lack of improvement in blood pressure at 12 months. A separate meta-analysis of 36 weight loss studies found that 71% of the studies reported some form of weight loss, although participant and intervention heterogeneity precluded a summary estimate of weight loss achieved through DHI 12.

In this systematic review and meta-analysis, we note a nearly 40% relative risk reduction in CVD outcomes with DHI, with particular impact on secondary CVD prevention and in patients with heart failure. This level of risk reduction surpasses other prevalent, guideline-based preventative measures such as statins 23, aspirin 24, or blood pressure reduction with beta-blockade 25. Furthermore, the absolute risk reduction in events was 6.5% in our pooled analysis and 7.5% in secondary prevention populations. This translates into a number needed to treat of 14 and 16 patients, respectively, also surpassing reported absolute benefits of other guideline-based measures. As DHI use does not directly reduce CVD risk, these observed benefits likely reflect increased adherence to evidence-based preventative therapies such as statins, aspirin, or beta-blockers.

We found significant improvements in the risk factors of weight loss, BMI, blood pressure, and LDL-cholesterol in patients seeking primary prevention of CVD. These improvements in risk factors did not translate into an improvement in CVD outcomes in primary prevention studies, at least partly owing to lower risk populations and lack of long-term follow up. Conversely, we found significant reductions in these events in secondary prevention studies despite a lack of consistent reductions in CVD risk factors in secondary prevention studies. This heterogeneity in results is not readily explained by existing studies, and should prompt future DHI research focusing on furthering our understanding of the variables determining success of specific DHI in specific populations.

Limitations

In an attempt to be inclusive in assessing the impact of DHI on CVD, we collected data utilizing multiple DHI modalities applied in multiple populations. Therefore, as noted previously heterogeneity in study results was present secondary to variation in study populations, DHI types, comparator groups, and lengths of follow up. Heterogeneity in these analyses was not explained by DHI modality or study design. Despite this heterogeneity, the data demonstrate an overall benefit of DHI for CVD prevention. However, the observed level of heterogeneity precludes definitive conclusions regarding specific DHIs that should be clinically applied to CVD prevention at the present time.

In addition, this analysis was unable to assess behavior change and motivational techniques, either of which could impact the outcomes of trials or be a contributor to DHI efficacy. Research attempting to better assess these issues will be vital in future work. Despite these limitations, the existing studies confirm that technological advances such as DHI can have a positive impact on preventative cardiovascular medicine.

Conclusion

The data synthesized and analyzed in this systematic review show a net benefit of DHI on overall CVD outcomes (CVD events, hospitalizations, and all-cause mortality) compared to usual care. These gains are largely driven by improvements in CVD outcomes among higher risk populations such as patients with HF or those targeting secondary CVD prevention. DHI were also associated with improvement in risk factors for CVD in primary studies, suggesting the potential for positive impact of DHI in a wide variety of participants and settings. Further research is needed to determine the most effective DHI modalities and to better understand the determinants of their success in specific cardiovascular risk populations.

Supplementary Material

supplement

Supplementary Table 1

A) Primary CVD Prevention (39):

B) Secondary CVD Prevention (13):

Supplementary Figure 1: Validity Assessment Tools:

Supplementary Figure 2: Funnel plot for CVD outcomes among primary and secondary prevention along with heart failure.

Supplementary Figure 3: Systolic Blood Pressure and DHI.

Supplementary Figure 4a: Total-Cholesterol and DHI.

Supplementary Figure 4b: LDL-Cholesterol and DHI.

Acknowledgments

Funding for this project was provided by National Institute of Health (NIH Grants HL-92954 and AG-31750 and the Mayo Foundation

This work was supported by funding from the BIRD foundation as well as the National Institute of Health (NIH Grants HL-92954 and AG-31750) and the Mayo Foundation. There was no direct role of the funding agencies in this study or manuscript.

The authors would like to thank the CTSA program of Mayo Clinic including the faculty and staff of CTSC 5740 (Drs. Murad, and Montori as well as Ms. Welsh) for their guidance. We would also like to show great appreciation toward the 28 authors who returned contact in an effort to improve the validity of our data extraction and assessment.

Abbreviations

ACS

Acute Coronary Syndrome

BMI

Body Mass Index

CVD

Cardiovascular Disease

DHI

Digital Health Intervention

FRS

Framingham Risk Score

HF

Heart Failure

RCT

Randomized Controlled Trial

ROI

Return on Investment

Footnotes

All authors contributed to the work, and have no conflicts of interest to disclose.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supplement

Supplementary Table 1

A) Primary CVD Prevention (39):

B) Secondary CVD Prevention (13):

Supplementary Figure 1: Validity Assessment Tools:

Supplementary Figure 2: Funnel plot for CVD outcomes among primary and secondary prevention along with heart failure.

Supplementary Figure 3: Systolic Blood Pressure and DHI.

Supplementary Figure 4a: Total-Cholesterol and DHI.

Supplementary Figure 4b: LDL-Cholesterol and DHI.

NIHMS715520-supplement.pdf (780.5KB, pdf)

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