Digital Health Programs to Reduce Readmissions in Coronary Artery Disease

Background The use of mobile health (mHealth, wireless communication devices, and/or software technologies) in health care delivery has increased rapidly in recent years. Their integration into disease management programs (DMPs) has tremendous potential to improve outcomes for patients with coronary artery disease (CAD), yet a more robust evaluation of the evidence is required. Objectives The purpose of this study was to undertake a systematic review and meta-analysis of mHealth-enabled DMPs to determine their effectiveness in reducing readmissions and mortality in patients with CAD. Methods We systematically searched English language studies from January 1, 2007, to August 3, 2021, in multiple databases. Studies comparing mHealth-enabled DMPs with standard DMPs without mHealth were included if they had a minimum 30-day follow-up for at least one of all-cause or cardiovascular-related mortality, readmissions, or major adverse cardiovascular events. Results Of the 3,411 references from our search, 155 full-text studies were assessed for eligibility, and data were extracted from 18 publications. Pooled findings for all-cause readmissions (10 studies, n = 1,514) and cardiac-related readmissions (9 studies, n = 1,009) indicated that mHealth-enabled DMPs reduced all-cause (RR: 0.68; 95% CI: 0.50-0.91) and cardiac-related hospitalizations (RR: 0.55; 95% CI: 0.44-0.68) and emergency department visits (RR: 0.37; 95% CI: 0.26-0.54) compared to DMPs without mHealth. There was no significant reduction for mortality outcomes (RR: 1.72; 95% CI: 0.64-4.64) or major adverse cardiovascular events (RR: 0.68; 95% CI: 0.40-1.15). Conclusions DMPs integrated with mHealth should be considered an effective intervention for better outcomes in patients with CAD.

A concerning proportion of patients with coronary artery disease (CAD) have major risk factors, 1 such that the residual lifetime risk for cardiovascular events and death could decrease if risk factor control and treatment improved. 24][5][6][7][8][9] However, despite unequivocal evidence for their effectiveness, CR programs are still underutilized with <50% of eligible patients referred worldwide. 1,10Consequently, cardiac readmission rates remain high and result in substantial costs.A major driver of these costs is hospitalization expenditure 11,12 with the average cost of a 30-day readmission postacute myocardial infarction (AMI) costing approximately USD $15,000, with a cumulative cost of over USD $1 billion per year. 13e rapid use of mobile health (mHealth) technologies has produced strategies and modalities to overcome the historical challenges associated with traditional delivery of CR and DMPs.mHealthdelivered DMP interventions are newly recommended in guidelines, 14 albeit based on lower-level quality evidence derived from a limited number of studies.An in-depth synthesis of the literature is required to keep abreast with the rapid boom in mHealth delivered secondary prevention cardiovascular disease (CVD) care. 151][22] Telephone delivery is resourceintensive, time-consuming, and limits scalability.
Less attention has been paid to the most up-to-date digital technologies, which enable a scalable and personalized service to numerous individuals.
Further, the few systematic reviews that have attempted to address newer technologies 23,24 included only a limited number of studies in their meta-analyses with mixed results.Therefore, the aim of this systematic review and meta-analysis was to develop evidence for the effectiveness of mHealth-enabled DMPs, excluding telephone only, on hospital readmissions and mortality in patients diagnosed with CAD.

METHODS
We conducted this systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 25 and registered it with International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022306749).The specific keywords, Medical Subject Heading terms, and search strategy are provided in Supplemental Table 2.
STUDY SELECTION.We used Covidence software for this systematic review. 26Two independent reviewers scanned the titles and abstracts of publications while a third reviewer adjudicated discrepancies.The full texts of selected studies were read in detail, and reasons for exclusion were recorded.ASSESSMENT OF RISK BIAS AND QUALITY OF THE EVIDENCE.Risk of bias was assessed using the Cochrane Collaboration's tool 27 for randomized controlled trials and the ROBINS-I assessment tool 28 for observational studies.Risk of bias plots were generated using ROBIS. 29GRADEpro GDT software 30 was used to assess the quality of evidence for each outcome reported.
DATA SYNTHESIS AND ANALYSIS.Analysis was performed using Review Manager (RevMan) version 5.3 software.We measured heterogeneity for each outcome across studies qualitatively by comparing study characteristics and quantitatively using the I 2 test statistic.A meta-regression was performed to account for baseline differences between comparator groups for each outcome.Dichotomous variables were converted to log odds differences between comparator groups.Mean differences were used for continuous variables.We undertook subgroup analysis of: duration of DMP, length of follow-up, year of publication, patient characteristics, and intervention components (outlined in Inclusion criteria) to assess the effect of benefit from mHealth DMPs compared to standard DMPs.
We generated estimates of treatment effect using pooled RR with 95% CIs and random-effects models utilizing Mantel-Haenszel methods for combining results across studies.Data were pooled and displayed in forest plots.Hypothesis testing was set at the 2-tailed 0.05 level.The funnel plot and Egger test were used to examine publication bias (Supplemental Figure 1). 31f these, we assessed 155 full-text studies to include a total of 18 publications in the systematic review.
PRIMARY OUTCOME ANALYSIS.The results for dichotomous primary outcome data are shown in separate forest plots for hospital encounters (Figure 3), MACE (Figure 4), and mortality (Figure 5).

R e a d m i s s i o n s .
][43][44][45]47,48 Pooled analysis showed that risk for all-cause readmission (n ¼ 1,514) (Figure 3A) was reduced by 32% (RR: 0.68; 95% CI: 0.50-0.91)and cardiovascular readmissions (n ¼ 1,009) (Figure 3B) by 45% (RR: 0.55; 95% CI: 0.44-0.68) in the mHealth-enabled DMP group compared to the DMP alone group. There was no evidenc of competing risk analysis whereby mortality may lead to a reduction in readmission given there were a total of 4 deaths from 1,514 patients included in all-cause readmission analysis and 5 deaths from 1,009 patients included in cardiac-related readmission analysis.
M o r t a l i t y .Eight studies 34,36,37,40,42,44,46,47 (n ¼ 2,711) assessed all-cause mortality.As shown in Figure 5, there was no risk reduction for all-cause mortality (RR: 1.72; 95% CI: 0.64-4.64) in the mHealth-enabled DMP group compared with the traditional DMP alone group.There were no included studies reporting cardiac-related deaths.
Braver et al (I 2 ¼ 23%).Stratified meta-regression revealed no baseline differences between comparator groups for any primary outcome (Supplemental Table 5).Subgroup analysis using pooled data revealed no significant group differences (Supplemental Table 6).
There were no group differences after removing the 2 observational studies.
RISK OF BIAS AND GRADE ASSESSMENT.The overall risk of bias across domains for each study was judged to be low or unclear (Supplemental Figure 2).The GRADE quality of evidence for each outcome was assessed as moderate for all-cause readmissions, high for cardiac-related readmissions and ED visits, low for MACE and very low for all-cause mortality (Table 2, Digital Health Programs to Reduce Cardiac Readmissions Supplemental Table 7).There was no evidence of funnel plot asymmetry or significant Egger tests (Supplemental Figure 1), and thus no evidence of publication bias.

DISCUSSION
In this systematic review and meta-analysis, mHealth-enabled DMPs for patients with CAD were effective interventions for reducing hospital readmissions and visits to ED.However, there was no greater benefit for mHealth-enabled DMPs on mortality or MACE outcomes (Central Illustration).care, enhance patient motivation and adherence, and achieve effective results. 50They also create cost efficiencies for health care delivery by reducing clinician and health system burden. 51,52Hence, rather than replacing the entire traditional model of care with a digital solution, digitally integrated models may provide disease management strategies in a more engaging, accessible, and scalable manner. 53 appears that the beneficial effects of novel mHealth DMPs are due to the sum of their parts.The evidence suggests that there is no one specific component that is the key but rather a combination of factors working together to improve provider-patient communication and enhance patient-centered care.
These factors combined enhance engagement, adherence, and subsequent outcomes. 24,50,54r study provides evidence for the effectiveness (Supplemental Table 1).There is heterogeneity between DMP interventions such that more tangible benefits might be realized from improved self-care/ behavior change strategies and symptom awareness.
These patient-focused behaviors may result in effective risk factor reduction and minimize exacerbation of CVD (including the onset of other events) rather than reduce mortality.
While our results provide evidence for mHealth interventions in lowering readmission risk, a consistent finding is that there is no evidence for reducing mortality. 17,20,23,24This may be due to comparator groups 20 (either standard care, traditional DMP or cardiac rehabilitation) receiving close to optimal care (Supplemental Tables 8 and 9) or study populations being at low risk of mortality. 17Given the large heterogeneity between DMP interventions, there is also difficulty in assessing the overall impact on survival rates and health outcomes.Importantly, many studies include relatively short follow-up periods, which may be too short to detect longer-term impacts on mortality.
The results of this systematic review support wider implementation of mHealth-enabled DMPs in secondary prevention settings and should be made accessible to all CAD patients to choose their preferred DMP type and setting.In doing so, one needs to consider the implication for vulnerable or disadvantaged patients.We must ensure to continue to innovate and drive rapid translational research in GRADE Working Group grades of evidence.High certainty: we are very confident that the true effect lies close to that of the estimate of the effect.Moderate certainty: we are moderately confident in the effect estimate; the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.Low certainty: our confidence in the effect estimate is limited; the true effect may be substantially different from the estimate of the effect.Very low certainty: we have very little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of effect.GRADEpro GDT software 30 was used to assess the quality of evidence for each outcome reported.The GRADE quality of evidence for each outcome was assessed as moderate for all-cause readmissions, high for cardiac-related readmissions and ED visits, low for MACE, and very low for all-cause mortality.a The risk in the intervention group (and its 95% CI) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).
ED ¼ emergency department; MACE ¼ major adverse cardiac event; RR ¼ risk ratio.Digital Health Programs to Reduce Cardiac Readmissions digital health, but at the same time, consideration must be placed not to exacerbate health inequalities. 580][61] This is notable because many of these populations have greater rates of CVD compounded by less access to care. 62Additional research is needed to strengthen equitable   Digital Health Programs to Reduce Cardiac Readmissions DATA SOURCES AND SEARCHES.MEDLINE, Embase database, the Cochrane Central Register of Controlled Trials, CINAHL, the Web of Science, and Scopus electronic databases were systematically searched for English language studies from January 1, 2007, to August 3, 2021.Grey literature was searched for additional papers.This start date was selected to coincide with the release of the Apple iPhone (the first internet-accessible smartphone with apps).

I
n c l u s i o n c r i t e r i a .Studies of patients who were discharged from hospital with CAD with a minimum of 30-days follow-up and at least 50 patients in the total sample that evaluated a DMP using mHealth compared with a standard DMP without mHealth were included.mHealth was defined as the use of wireless communication devices (mobile phones, smartphones, electronic tablets, and laptops) and/or software technology (apps, video and teleconferencing, email, telemonitoring, social media, and SMS communication), excluding telephone-only interventions.A DMP is defined as a coordinated health care plan to help people manage their disease better.A DMP is the sum of activities that include some if not all of the following: health professional/ nurse consultations, care coordination, regular follow-up, optimization of efficacious medications, education, psychological support, physical activity prescription, self-monitoring strategies (eg, blood pressure measurement), goal setting, and lifestyle/ behavioral self-management strategies (eg, medication adherence and dietary intake).Studies were included if they contained at least one DMP component and reported outcomes for at least one of all-cause or cardiovascular mortality, all-cause or A B B R E V I A T I O N S A N D A C R O N Y M S AMI = acute myocardial infarction CAD = coronary artery disease CR = cardiac rehabilitation CVD = cardiovascular disease DMP = disease management program ED = emergency department MACE = major adverse cardiovascular events mHealth = mobile health ICT = information communication technology Braver et al J A C C : A D V A N C E S , V O L . 2 , N O .8 , 2 0 2 3 Digital Health Programs to Reduce Cardiac Readmissions O C T O B E R 2 0 2 3 : 1 0 0 5 9 1 cardiovascular readmissions, or major adverse cardiovascular events (MACE).E x c l u s i o n c r i t e r i a .Studies were excluded if participants were not diagnosed with CAD or if they had heart failure.Interventions that did not involve mHealth, used the telephone only, or focused on a single behavior (eg, smoking cessation) were excluded.DATA EXTRACTION AND MANAGEMENT.One reviewer extracted information about the study population, intervention and control/comparison group characteristics, and outcome data from each study using a predeveloped data extraction form.Ambiguities were resolved by discussion and consensus.Multiple publications of the same study were assessed for the provision of endpoint data and the most recent publication was chosen for inclusion.

Figure 1 ,
our initial search yielded 3,411 references.After the removal of 1,384 duplicates, 2,016 were reviewed for title and abstract eligibility.

FIGURE 1
FIGURE 1 Study Selection

J 3
A C C : A D V A N C E S , V O L . 2 , N O .8 , 2 0 2 Braver et al O C T O B E R 2 0 2 3 : 1 0 0 5 9 1 Digital Health Programs to Reduce Cardiac Readmissions Digital Health Programs to Reduce Cardiac Readmissions in 3 papers34,39,42 as ST-segment elevation myocardial infarction (34% intervention and 28% control) and non-ST-segment elevation myocardial infarction (mean 34% intervention and 46% control).Overall, 3,818 patients were included ranging from 62 to 879 patients per study.The weighted average age of the intervention and control groups was 60.3 AE 1.3 years and 62.6 AE 1.15 years, respectively, and the majority were men (82% intervention and 80% control).

J
A C C : A D V A N C E S , V O L . 2 , N O .8 , 2 0 2 3 Digital Health Programs to Reduce Cardiac Readmissions O C T O B E R 2 0 2 3 : 1 0 0 5 9 1

FIGURE 3
FIGURE 3 Primary Outcome Analysis

Findings
did not vary across any patient, intervention, or study characteristics.Our results update the evidence for the effectiveness of mHealth-enabled secondary prevention DMPs by including more studies that assessed impact outcomes (hospitalizations, ED visits, MACE, and mortality) and using only the latest digital technologies over and above telephone communication.Our findings indicated a 32% reduction in the relative risk of rehospitalization for any cause and a 45% relative risk reduction in cardiovascular-related rehospitalizations in mHealth-enabled DMP patients compared with patients who undertook a traditional DMP.This contrasts with a prior systematic review that used text messaging or mobile phone app interventions 23 but aligns with others incorporating telephone call interventions, which showed a reduction of between 38% and 44% in all-cause rehospitalizations compared with standard postdischarge secondary prevention care. 20,22Overall, mHealth DMPs are effective and complement existing telephone-based interventions.mHealth-enabled DMPs support the scalability of existing models of

FIGURE 4
FIGURE 4 Primary Outcome Analysis: MACE

FIGURE 5
FIGURE 5 Primary Outcome Analysis: All-Cause Mortality of mHealth interventions (incorporating digital technologies) for reducing readmissions and ED visits in patients with CAD.These tech-integrated models of DMPs provide unique opportunities for providers and health systems to interact directly with patients' contemporary lifestyles, delivering more personalized patient-centered care.Rapid technological advancement, improved user experience, and positive consumer acceptance and adoption (from patients and providers) 55,56 have enhanced engagement and adherence 16,57 to prevention programs and may explain the added benefit of mHealth-enabled DMPs over and above traditional DMPs without mHealth.Despite almost all earlier systemic reviews showing significant improvements in clinical, behavioral and lifestyle risk factors when comparing digital technology interventions with traditional DMPs or usual care, 16-19 previous studies have not investigated the impact of mHealth interventions on readmission and mortality outcomes using emerging digital technologies and devoid of telephone only interventions access to digital health-based DMPs for these key populations58 and investigate the factors that are important for implementation of mHealth-enabled DMPs in real-world settings, particularly in low-and middle-income countries.STRENGTHS AND LIMITATIONS.This systematic review and meta-analysis provides evidence for the effectiveness of the most contemporary mHealthenabled DMPs on readmission outcomes.There are a few limitations to our study.Firstly, the limited CENTRAL ILLUSTRATION mHealth-Enabled DMPs Reduced All-Cause and Cardiac-Related Hospitalizations and Emergency Department Visits Compared to DMPs Without mHealth Braver J, et al.JACC Adv.2023;2(8):100591.There was no significant reduction for mortality outcomes or MACE.DMP ¼ disease management program; MACE ¼ major adverse cardiac event; mHealth mobile health.Braver et al J A C C : A D V A N C E S , V O L . 2 , N O .8 , 2 0 2 3 Digital Health Programs to Reduce Cardiac Readmissions O C T O B E R 2 0 2 3 : 1 0 0 5 9 1availability of mortality outcomes with a relatively short follow-up period made it challenging to assess the intervention's effect on mortality.Secondly, while we extracted all available data in each publication, adjudication of cardiovascular events that constitute a cardiovascular readmission may vary between studies, and similarly, noncardiovascularrelated readmissions may not have been included among all studies.Finally, most studies included were conducted in high-income countries, yet more than 75% of CVD deaths take place in low-and middle-income countries.63Hence, caution is required with regards to generalizability of the findings in these less represented populations.CONCLUSIONSIn this contemporary systematic review and metaanalysis, mHealth-integration into DMPs was an effective intervention for reducing hospital readmissions and visits to ED. DMPs supported by mHealth should be considered for improving outcomes in patients with CAD.ACKNOWLEDGMENTS The authors thank Tania Celeste and Dr Jocasta Ball for their support with the search and selection process.The authors are grateful to Dr Dulari Hakamuwa Lekamlage for undertaking the statistical analysis.The authors also thank Dr Chris Lynch for his support with the risk of bias assessment.

TABLE 1
Publication and mHealth Intervention Characteristics Continued on the next page

TABLE 1 Continued
DMP ¼ disease management program; RCT ¼ randomized controlled trial.

TABLE 2
Summary Findings of Grade Quality Assessment 60. Kotseva K, Wood D, De Bacquer D, EURO-ASPIRE Investigators.Determinants of participation and risk factor control according to attendance in cardiac rehabilitation programmes in coronary patients in Europe: EUROASPIRE IV survey.Eur J Prev Cardiol.2020;25:1242-1251.61.Chindhy S, Taub PR, Lavie CJ, Shen J. Current challenges in cardiac rehabilitation: strategies to overcome social factors and attendance barriers.Expert Rev Cardiovasc Ther.2020;18:777-789.62. Troy A, Xu J, Wadhera R. Abstract 10423: US counties with low broadband internet access have a high burden of cardiovascular risk factors, disease, and mortality.Circulation.2022;146: A10423.63.WHO.Cardiovascular diseases (CVDs) fact sheets.In: Organisation WH, editor.2021.Accessed March 10, 2022.https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) KEY WORDS cardiac rehabilitation, coronary artery disease, digital health, disease management, health technology, mHealth APPENDIX For supplemental tables and figures, please see the online version of this paper.