Skip to main content
European Heart Journal. Digital Health logoLink to European Heart Journal. Digital Health
. 2023 Jun 7;4(4):347–356. doi: 10.1093/ehjdh/ztad035

Mobile health for cardiovascular risk management after cardiac surgery: results of a sub-analysis of The Box 2.0 study

Tommas Evan Biersteker 1, Mark J Boogers 2, Martin Jan Schalij 3,✉,2, Jerry Braun 4, Rolf H H Groenwold 5, Douwe E Atsma 6, Roderick Willem Treskes 7
PMCID: PMC10393886  PMID: 37538141

Abstract

Aims

Lowering low-density lipoprotein (LDL-C) and blood pressure (BP) levels to guideline recommended values reduces the risk of major adverse cardiac events in patients who underwent coronary artery bypass grafting (CABG). To improve cardiovascular risk management, this study evaluated the effects of mobile health (mHealth) on BP and cholesterol levels in patients after standalone CABG.

Methods and results

This study is a post hoc analysis of an observational cohort study among 228 adult patients who underwent standalone CABG surgery at a tertiary care hospital in The Netherlands. A total of 117 patients received standard care, and 111 patients underwent an mHealth intervention. This consisted of frequent BP and weight monitoring with regimen adjustment in case of high BP. Primary outcome was difference in systolic BP and LDL-C between baseline and value after three months of follow-up. Mean age in the intervention group was 62.7 years, 98 (88.3%) patients were male. A total of 26 449 mHealth measurements were recorded. At three months, systolic BP decreased by 7.0 mmHg [standard deviation (SD): 15.1] in the intervention group vs. -0.3 mmHg (SD: 17.6; P < 0.00001) in controls; body weight decreased by 1.76 kg (SD: 3.23) in the intervention group vs. -0.31 kg (SD: 2.55; P = 0.002) in controls. Serum LDL-C was significantly lower in the intervention group vs. controls (median: 1.8 vs. 2.0 mmol/L; P = 0.0002).

Conclusion

This study showed an association between home monitoring after CABG and a reduction in systolic BP, body weight, and serum LDL-C. The causality of the association between the observed weight loss and decreased LDL-C in intervention group patients remains to be investigated.

Keywords: Electronic health, eHealth, Mobile health, mHealth, Coronary artery bypass grafting, Controlled BP, Lipid lowering, Coronary artery disease, Tertiary prevention

Introduction

After coronary artery bypass grafting (CABG), patients remain at high risk of adverse events due to coronary artery disease (CAD). All-cause mortality is 6.2% within the first year after isolated CABG, and 30.7% within 10 years.1,1 Of these deaths, 65% have a cardiac cause, with non ST-elevation myocardial infarction to be the leading cause of death, followed by heart failure.2,3 Clinical trials have shown that a 5 mmHg reduction of systolic blood pressure (BP) reduces the risk of major cardiovascular events by about 10%.4 Adequate regulation of serum low-density lipoprotein (LDL-C) levels is also of importance. In a meta-analysis of 49 clinical trials with 312 175 participants, each 1-mmol/L (38.7 mg/dL) reduction in LDL-C was associated with a relative risk of major vascular events of 0.77.5 Therefore, current European guidelines on cardiovascular disease prevention stress the importance of reducing LDL-C and BP levels in patients who underwent CABG.6 However, a study in 16 646 patients in 24 European countries found that only a minority of patients achieved adequate control of these risk factors 6 months after CABG or percutaneous coronary intervention (PCI): 48.6% continued smoking, 42.7% had a BP ≥140/90 mmHg, and 80.5% had an LDL-C of ≥1.8.7 Moreover, only one-third of all patients with CAD attended cardiac rehabilitation after undergoing CABG or PCI.7,8

Interactive mobile health (mHealth) has been shown to be an effective intervention on lifestyle through health education.9–11 Mobile health is defined as the use of mobile phone and wireless technologies to support the achievement of health objectives.12 The electronic health (eHealth) working group of the European Society of Cardiology (ESC) now recommends the use of mHealth to support remote clinical care and improve psychosocial health, diet, and smoking cessation, in the primary, secondary, and tertiary prevention of CAD.6,13,14 However, positive effects of mHealth on cardiovascular risk management has not yet been definitively demonstrated: several randomized controlled trials (RCTs) suggested a beneficial effect of mHealth interventions on patient self-management,15–20 although other studies found no statistically significant improvement.21–25 Moreover, no published results are available on the use of mHealth in patients after CABG.

The use of mHealth devices, such as a BP monitor and weight scale, may be beneficial in the outpatient follow-up of patients with a high (residual) CAD risk. In order to improve cardiovascular risk management, the aim of the present study—The Box 2.0—is to evaluate the effects of mHealth on BP, body weight, and cholesterol levels in patients after standalone CABG surgery.

Methods

Study design, recruitment, and population

As previously described, The Box 2.0 was a non-randomized observational cohort study with a prospective intervention group and a historical control group for comparison.26 This study was conducted at the department of cardiothoracic surgery of the Leiden University Medical Center (LUMC), a tertiary care hospital in The Netherlands, and registered under NCT03690492 (ClinicalTrials.gov) and NL65959.058.18 (ToetsingOnline.nl). The study complied with the Declaration of Helsinki, and was approved by the ethics committee. The current study is a post hoc analysis of The Box 2.0.

As lack of attainment of lipid target levels following CABG is associated with long-term mortality,27 frequent lipid level measurements are performed in patients after CABG. However, not all cardiac surgery patients need this form of cardiovascular risk management. In order to improve comparability, solely patients who underwent CABG were selected for the present sub-study to ensure comparability regarding BP and lipid level outcomes. As a wide variety of concomitant surgical procedures could be performed, affecting outcomes, patients undergoing concomitant procedures were excluded, as well as those with incomplete BP data at the end of follow-up. We deemed BP data to be complete if there was an available BP measurement at the last outpatient clinic visit. The aim of introducing these selection criteria was to optimize comparability between both study groups. Other exclusion criteria were: pregnancy, incapacitation or mechanical support at the moment of inclusion, ventricular septal rupture, implantation of a ventricular assist device, and emergency cardiac surgery defined as a score 1 or 2 at the Interagency Registry for Mechanically Assisted Circulatory Support scale.

Between December 2017 and September 2018, 365 adult patients who underwent cardiac surgery via sternotomy were consecutively screened and included in the control group, 117 of whom underwent standalone CABG surgery. From September 2018 to November 2020, another 365 patients were consecutively screened and included in the intervention group, 111 of whom underwent standalone CABG surgery. Study results of all 730 patients are described separately.28 Eligible patients were recruited at the outpatient clinic before surgery, 4 to 6 weeks before surgery, or on the ward during admission, 1 to 5 days before surgery or between 3 days after surgery and 1 day before discharge. Eligible patients were given oral and written study information, and were given at least 24 h to consider participation. All patients were recruited by a nurse practitioner (NP) and signed the informed consent form before discharge. To ensure all eligible patients were approached with study information and informed consent forms, the study team reviewed the weekly surgery schedule of the thoracic surgery department, and a weekly meeting with this department was held. Discharge from the department of cardiothoracic surgery marked the start of follow-up. The total duration of follow-up was 92 days.

Control group

Control group patients underwent standardized follow-up, defined as two physical outpatient clinic visits; one visit 2 weeks after initial discharge and one visit 3 months after discharge. The 2-week visit consisted of an examination of the sternal wound and, if applicable, the vein harvesting wound, and a 12-lead 10 s electrocardiogram (ECG) was made. At 3 months, another ECG was made, the BP and a laboratory test for cholesterol levels were taken, and a transthoracic echocardiogram was performed. No mHealth was used in these patients.

Intervention group

Intervention group patients received mHealth intervention The Box, consisting of an activity tracker, BP monitor, thermometer, and a weight scale (all from Withings, Issy les Moulineaux, France). These devices are shown in Figure 1. During the first two weeks of follow-up, patients were requested to take daily measurements with the Withings devices. For the remainder of the 3-month follow-up, measurements were taken three times a week.

Figure 1.

Figure 1

The box and its contents.

Furthermore, the standard first outpatient clinic visit, 2 weeks after discharge, was replaced by an electronic visit (eVisit). This eVisit consisted of an identical patient interview compared to the standard outpatient clinic follow-up and was performed by the same NP, who also checked the sternum wound and, if applicable, also the vein harvesting wound via the webcam. During follow-up, the therapeutic regimen could be revised based on the results of mHealth measurements such as BP as well as on symptoms. The outpatient clinic visit, 3 months after discharge, was identical to the outpatient clinic visit of control group patients, and marked the end of follow-up. Importantly, except for the receiving the mHealth intervention, consisting of this eVisit and scheduled measurements, the follow up of intervention and control groups was equal. A flow chart of patient flow has been published previously.26

The NP checked all sent-in data three times per week. An automated alarm was triggered in case of a data irregularity, which made these irregularities stand out from other measurements. In case of an irregularity, the NP contacted the patient within 48 h after the data were received. An overview of data irregularities has been published previously.26 Based on these irregularities, the NP could amend the medication regime if necessary. Importantly, patients were instructed to contact emergency services if needed, as The Box served to support their convalescence.

Medication

Patients were discharged with either metoprolol or sotalol, unless they were on bisoprolol or other beta-blockers before surgery. As internal cardiothoracic guidelines changed in 2019, we expected significantly more intervention group patients to be discharged with sotalol instead of metoprolol. BP medication was based on daily BP readings during the admission period, and updated until the day of discharge. As the NP could act on data irregularities, BP medication could be amended accordingly during follow-up. This was done in case patients registered three consecutive measurements above either 140 mmHg (systolic BP) or 90 mmHg (diastolic BP), unless a reading was deemed to be incorrect. The NP always discussed medication changes with one supervising cardiologist, who was dedicated to this project.

Cholesterol levels were checked before surgery and medication was either started or amended based on these results. As cholesterol levels were only measured before surgery and after follow-up, not during follow-up, cholesterol medication was only changed in case of potential side-effects.

Connectibility and technical assistance

The Box 2.0 was handed out before discharge from the LUMC; required mobile applications were installed by eHealth-technicians if necessary. A helpdesk was available throughout the duration of each patients’ participation in the study, to assist with technical issues. Patients, who did not own a smartphone, were equipped with a loan device free of charge. To warrant the privacy of all study patients, patients were provided with an @hlc.nl email address based on a randomly generated code as the individual’s login name, combined with a randomly generated password. The @hlc.nl domain is owned and maintained by the LUMC, its data are stored on LUMC servers. Online data from the mHealth devices were accessed via the Application Programming Interface (API; Withings). The Withings API allowed all device data to be automatically imported in the electronic medical records of the LUMC, via a protected authentication protocol (OAUTH2). Patients were phoned by eHealth-technicians after two weeks of not receiving any mHealth measurement, reminding them of the importance of these measurements.

Study endpoints

The primary endpoints of this study were the systolic and diastolic BP, as well as body weight and serum LDL-C levels at the end of follow-up. Secondary endpoints were total cholesterol, HDL, LDL-C/cholesterol ratio and triglycerides at the end of follow-up, as well as BP control and the percentage of patients with an adequate LDL-C at the end of follow-up. These parameters were all measured at the end of follow-up. BP control was defined as a BP below the threshold of hypertension—<140/<90 mmHg—as it was defined by the ESC guidelines,29 measured with a manual sphygmomanometer (Welch Allyn 707) at the outpatient clinic. ESC guidelines were also used to define LDL-C adequacy: in patients with a very high cardiovascular risk, the treatment target for LDL-C is <1.8 mmol/L or a reduction of at least 50% from baseline LDL-C.30

Statistical analysis

Demographic and baseline characteristics are summarized for all subjects as mean ± standard deviation (SD), median and interquartile range (IQR), or frequencies for continuous and categorical variables, respectively. Normality was assessed using the Shapiro–Wilk test. Variables with a skewed distribution were compared using a Mann–Whitney U-test. Categorical variables were compared with Fisher exact tests. Blood pressure and cholesterol results were adjusted for age, gender, body mass index, hypertension at baseline, and antihypertensive treatment at baseline, as these were confounding variables, as well as for baseline differences: length of hospital stay, and either systolic BP at baseline for the analyses of systolic BP endpoints, or diastolic BP at baseline for the analyses of diastolic BP endpoints. All analyses were performed with SPSS version 25.0 (released 2017, IBM SPSS Statistics for Windows, IBM Corp, Armonk, NY, USA).

Results

Patient characteristics

A total of 228 patients were enrolled in this substudy; 117 controls and 111 intervention group patients. All baseline characteristics are presented in Table 1. In both groups, 98 patients were male (84% of controls and 88% of intervention group patients, respectively; P = 0.35). Mean age in the intervention group was 62.7 years vs. 65.3 years for controls (P = 0.05) and significantly more controls had a history of hypertension (n = 74/117, 63% vs. n = 51/111, 46%; P = 0.01). Diastolic BP at discharge was higher in intervention patients than in controls (81.2 mmHg vs. 75.6 mmHg; P = 0.0005). As expected, significantly more intervention group patients were discharged with sotalol compared to controls (n = 80/117, 68% vs. n = 96/11, 87%; P = 0.002), and as a result less metoprolol was used (n = 31/117, 27% vs. n = 11/111, 10%; P = 0.002). At baseline, serum cholesterol levels did not differ significantly between both groups, nor did the percentage of patients treated with cholesterol lowering medication. Importantly, there were no cases of familial hypercholesterolemia in the study population. None of the patients had a contra-indication for the use of statins.

Table 1.

Baseline characteristics

Control (n = 117) Intervention (n = 111) P value
Gender, male (%) 98 (83.8%) 98 (88.3%) 0.347
Age, years (SD) 65.3 (9.9) 62.7 (9.3) 0.046
BMI, kg/m2 (SD) 27.8 (4.1) 26.8 (3.9) 0.043
History of smoking (%) 67 (57.3%) 65 (58.6%) 0.894
Hypertension (%) 74 (63.2%) 51 (45.9%) 0.011
Hypercholesterolemia (%) 50 (42.7%) 51 (45.9%) 0.690
Diabetes Mellitus (%) 40 (34.2%) 28 (25.2%) 0.150
History of myocardial infarction (%) 46 (39.3%) 57 (51.4%) 0.084
History of PCI (%) 37 (31.6%) 37 (33.3%) 0.888
History of CABG (%) 3 (2.6%) 0 (0.0%) 0.247
History of CVA/TIA (%) 9 (7.7%) 10 (9.0%) 0.812
Peripheral arterial disease (%) 6 (5.1%) 9 (8.1%) 0.430
Urgent operation (%) 49 (41.9%) 43 (38.7%) 0.686
Resternotomy (%) 9 (7.7%) 6 (5.4%) 0.597
Length of hospital stay, days (IQR) [Range] 6 (4–7) [2–24] 6 (6–7.5) [5–16] <0.0001
Readmission (%) 8 (6.8%) 2 (1.8%) 0.103
MACE before initial discharge (%) 5 (4.3%) 4 (3.6%) 1.000
LVEF, % (SD) (wel of niet?) 54.4 (8.8) 54.9 (8.3) 0.640
Systolic BP, mmHg (SD) 139.2 (20.6) 141.0 (18.3) 0.478
Diastolic BP, mmHg (SD) 75.6 (11.3) 81.2 (12.5) 0.0005
Use of ≥1 antihypertensive drug (%) 99 (84.6%) 86 (77.5%) 0.168
ACE inhibitor 64 (54.7%) 62 (55.9%)
Angiotensin receptor blocker 27 (23.1%) 16 (14.4%)
Calcium antagonist 37 (31.6%) 14 (12.6%)
Diuretic 27 (23.1%) 12 (10.8%)
Antiarrhythmics/betablockers
Amiodarone (%) 2 (1.7%) 3 (2.7%) 0.273
Sotalol (%) 80 (68.4%) 96 (86.5%) 0.002
Metoprolol (%) 31 (26.5%) 11 (9.9%) 0.002
Bisoprolol (%) 3 (2.6%) 1 (1.0%) 0.340
Total cholesterol, mmol/L (IQR) [Range] 4.6 (3.7–5.5) [2.0–7.5] 4.4 (3.8–5.4) [2.4–8.6] 0.867
LDL, mmol/L (IQR) [Range] 2.9 (2.0–3.6) [1.0–5.6] 2.4 (2.0–3.4) [0.9–6.1] 0.297
HDL, mmol/L (IQR) [Range] 1.1 (0.9–1.3) [0.5–2.4] 1.1 (1.0–1.3) [0.7–2.7] 0.307
Cholesterol ratio (IQR) [Range] 4.2 (3.2–5.2) [1.8–9.0] 3.8 (3.2–4.8) [2.0–9.5] 0.288
Triglycerides, mmol/L (IQR) [Range] 1.5 (1.0–2.2) [0.5–5.6] 1.5 (1.0–2.0) [0.4–5.5] 0.521
Use of cholesterol lowering drug(s) (%) 114 (97.4%) 109 (98.2%) 0.694
Statin 97 (82.9%) 96 (86.5%)
Ezetimibe 6 (5.1%) 4 (3.6%)
Statin + ezetimibe 8 (6.8%) 8 (7.2%)
PCSK9 inhibitor + ezetimibe 3 (2.6%) 1 (1.0%)

Significant P values are highlighted in bold text.

No other antiarrhythmics or betablockers were used in the study population.

Protocol adherence

A total of 26 449 mHealth measurements have been recorded by all intervention group patients, on 6295 unique measurement days. Patients registered a median of 222 measurements (IQR: 164–304) on a median of 52 of out 92 days (IQR: 37–84). A summary of all measurement totals is provided in Table A1 of the Appendix. Figure 2 presents the protocol adherence for all intervention group patients. A total of 16 (14.4%) Box patients registered no measurements for ≥21 consecutive days and were considered non-adherent. Data of all non-adherent patients was used for the analyses; no patients dropped out of the study.

Figure 2.

Figure 2

mHealth device use and Kaplan–Meier estimates of non-adherence, defined as ≥21 consecutive days without at least one registered mHealth measurement regarding BP, weight, temperature or ECG. Step count measurements were not included in this analysis.

Medication

During follow-up, BP medication was unchanged in 105 (89.7%) control group patients vs. 72 (34.9%) intervention group patients (P < 0.00001). This is presented in Table 2. In significantly more intervention group patients (26; 23.4%) vs. controls (4; 3.5%; P < 0.00001), BP medication was added or the dose was increased. On the other hand, BP medication was removed or the dose was reduced in 11 (10.0%) of all intervention group patients vs. 6 (5.1%) controls (P = 0.21).

Table 2.

Blood pressure medication regime during follow-up

BP medication during follow-up P value
BP medication added 3 (2.6%) 15 (13.5%) <0.00001
Dose increased 1 (0.9%) 11 (9.9%)
BP medication removed 6 (5.1%) 10 (9.0%) 0.21
Dose reduced 0 (0.0%) 1 (1.0%)
BP medication unchanged 105 (89.7%) 72 (64.9%) <0.00001
Medication switched, comparable dose 2 (1.7%) 2 (1.8%) 1

Significant P values are highlighted in bold text.

Cholesterol medication was amended in 4 (3.6%) intervention group patients and 4 (3.4%) controls (P = 0.96). Reasons were myalgia (n = 3), inadequate initial treatment (n = 3), drug interactions (n = 1), and dizziness on atorvastatin (n = 1). All medication changes are presented in Table A2 of the Appendix.

Endpoint: BP

Results of the BP endpoints are presented in Table 3. The primary endpoints, being systolic and diastolic BP at the end of follow-up, were both lower in the intervention group. The systolic BP was significantly lower in intervention patients than in controls (mean: 129.5 mmHg vs. 137.4 mmHg, respectively; P = 0.02). The diastolic BP showed no significant difference, although it was lower in intervention patients than in controls (mean: 76.8 mmHg vs. 77.9 mmHg, respectively; P = 0.17). Notably, in the intervention group, both systolic and diastolic BP were significantly lower at the end of follow-up than at baseline: -7.0 (SD: 15.1) and -3.5 (SD: 16.8), respectively. In the control group, systolic and diastolic BP were slightly higher at the end of follow-up than at baseline: 0.3 (SD: 17.6) and 4.7 (SD: 17.3), respectively. When comparing both study groups, the systolic BP difference was significant (P = 0.016) while the diastolic BP difference was not (P = 0.30).

Table 3.

Blood pressure outcomes

Control (n = 117) Intervention (n = 111) P value
Systolic BP, mmHg (SD) 137.4 (19.1) 129.5 (17.2) P = 0.02
Diastolic BP, mmHg (SD) 77.9 (10.5) 76.8 (9.6) P = 0.17
Adequate BP (%)a 67 (57.3%) 91 (82.0%) P = 0.0004
Systolic BP difference from baseline, mmHg (SD) 0.3 (17.6) -7.0 (15.1) P = 0.02
Diastolic BP difference from baseline, mmHg (SD) 4.7 (17.3) -3.5 (16.8) P = 0.30

Significant P values are highlighted in bold text.

Adequate BP is defined as a systolic BP <141 and a diastolic BP <91. These results are adjusted for age, gender, BMI, hypertension at baseline, and antihypertensive treatment at baseline.

For the secondary endpoints, 82% of intervention patients had an adequate BP (n = 91/111) vs. 57% of the control group (n = 67/117; P = 0.0004). Antihypertensive treatment was amended in 39 intervention group patients (35%) vs. 11 controls (9%; P < 0.0001). No correlation was found between adherence (measurement days) and systolic or diastolic BP at the end of follow-up (P = 0.24).

Endpoint: body weight and cholesterol levels

Results of the body weight and cholesterol endpoints are presented in Table 4. During follow-up, intervention group patients lost an average of 1.76 kg (SD: 3.23), while controls on average gained 0.31 kg (SD: 2.55; P = 0.002). Serum LDL-C levels at the end of follow-up were significantly lower in the intervention group vs. controls (median: 1.8 vs. 2.0, respectively; P = 0.0002).

Table 4.

Weight and cholesterol outcomes

Control (n = 117) Intervention (n = 111) P value
Weight loss during follow-up, kg (SD) −0.31 (2.55) 1.76 (3.23) 0.002
Total cholesterol, mmol/L (IQR) [Range] 3.7 (3.3–4.4) [2.0–8.1] 3.6 (3.3–4.2) [2.3–6.6] 0.15
LDL, mmol/L (IQR) [Range] 2.0 (1.7–2.7) [1.0–6.0] 1.8 (1.4–2.2) [0.3–4.7] 0.0002
HDL, mmol/L (IQR) [Range] 1.1 (0.9–1.3) [0.5–3.7] 1.1 (0.9–1.3) [0.4–2.1] 0.47
Cholesterol ratio (IQR) [Range] 3.4 (2.8–4.1) [0.9–7.8] 3.4 (2.7–4.0) [1.9–7.2] 0.27
Triglycerides, mmol/L (IQR) [Range] 1.4 (1.0–1.9) [0.5–4.7] 1.4 (1.1–2.4) [0.5–5.6] 0.12
Adequate LDL (%)a 44 (37.6%) 65 (58.6%) 0.002
LDL <1.8 mmol/L (%) 37 (31.6%) 61 (55.0%) 0 .0003
LDL decreased by >50% (%) 20 (17.1%) 24 (21.6%) 0.41
LDL difference from baseline, % (IQR) [Range] -16.7 (46.2–6.3) [-67.3–181.8] -28.0 (-49.6–-4.2) [-90.7–43.3] 0.04
Cholesterol medication amended (%) 4 (3.4%) 4 (3.6%) 0.96

Significant P values are highlighted in bold text.

Adequate LDL is defined as an LDL <1.8 mmol/L or a >50% reduction compared with the previous measurement. These results are adjusted for age, gender, BMI, hypertension at baseline, and antihypertensive treatment at baseline.

For the secondary endpoints, 59% of intervention patients had an adequate LDL-C at the end of follow-up (n = 65/111) vs. 38% (n = 44/117; P = 0.002) of all controls. Both groups saw a decrease in serum LDL-C levels compared to baseline, with a 28.0% reduction (IQR: 4.2%–49.6%) in the intervention group vs. a 16.7% reduction (IQR: -6.3%–46.2%) in controls. This was a significantly greater decrease in the intervention group compared to controls (P = 0.04). No correlation was found between adherence (measurement days) and LDL-C at the end of follow-up (P = 0.57).

Discussion

Main findings

This study reports the effects of an mHealth intervention on cardiovascular risk factors, in which patients made 26 449 measurements over the course of 6295 unique measurement days. A significant decrease of systolic and diastolic BP as well as serum LDL-C was observed in the intervention group. As the mHealth intervention caused BP levels to be available throughout the follow-up period, BP medication could be amended whenever needed. As expected, this was done in significantly more intervention group patients as compared to controls. This is the main explanation for the significant decrease in systolic and diastolic BPs at the end of follow-up. The same cannot be said of the significant decrease in serum cholesterol levels, as these levels were only assessed before and after follow-up. The observed decrease in serum LDL-C levels is, however, hypothesized to be partly related to an educational consequence of the intervention, such as increased patient engagement and empowerment, and partly to the weight loss at the end of follow-up that has been observed in intervention group patients but not in controls. The reason for this significant difference between intervention and control group patients may be related to the frequent confrontation to the intervention group patients’ body weight, as they were requested to weigh themselves multiple times per week.

Protocol adherence

Patients were instructed to take mHealth measurements every day for the first 2 weeks after discharge, followed by three times a week after these initial 2 weeks. This should lead to 47 unique measurement days and 235 total measurements. Our intervention group patients measured a median of 222 total measurements (IQR: 164–304) during a median of 52 unique days (IQR: 37–84); 95 (85.6%) intervention group patients remained adherent over the course of 3 months. As is shown in Figure 2, however, protocol adherence decreased over time as did the number of patients who logged at least one mHealth measurement per week. Disengagement is a known factor in mHealth,31 and has been reported before.32 Consistent feedback may positively impact the patient’s engagement. As the LUMC recently developed its own app for Box patients to use, further increasing the engagement is currently being studied.

Comparison with literature

To our best knowledge, no results of other studies have been published regarding the effect of mHealth on BP, body weight, or cholesterol levels in post-cardiac surgery patients. In other populations with an increased cardiovascular risk, mHealth interventions have been found to significantly decrease both systolic and diastolic BPs.33–35 The differences in systolic BP after an mHealth intervention were found to be -3.9 to -7.5 mmHg, which is in line with the 7.0 mmHg systolic BP reduction of the current study. Importantly, studies have shown that a 3 mmHg reduction in systolic BP can reduce stroke mortality by 8%.36

Although we found a significant impact of The Box on BP outcomes, an earlier RCT that evaluated The Box in myocardial infarction patients found no significant difference in these outcomes between Box users and controls.37 However, The Box became a standard of care in the management of various outpatient groups of the cardiology department of the LUMC due to the appreciation by both patients and care providers.38 Over these years, The Box has been continuously improved on the patients’ side as well as for staff members. Currently, NP’s have an easier overview of patients’ measurements, and measurement alerts have been introduced. This has improved the detection of data irregularities and, as such, may have led to an improvement in BP treatment during follow-up.

While numerous studies have evaluated mHealth interventions for BP management, very few studies have evaluated mHealth for the management of weight or hyperlipidemia. Studies that have been conducted, often used SMS or phone calls as an intervention and have been mostly unable to show significant benefit. More recent studies have shown the effect of gamification on cardiovascular health outcomes: in 2021, two RCTs were published that demonstrated significant effects on medication adherence,39 as well as increased physical activity and reduced HbA1c levels.32 The latter RCT provided patients with a Withings activity tracker and weight scale for the duration of one year, and an app that provided them with points and levels based on patients meeting their weekly goals and measurement frequencies. As is also seen in the current study, adherence was very high at the start of the intervention and then slowly declined. However, the RCT as well as the current study show an indirect educational effect of the mHealth intervention; the selected outcome measures were not influenced by medication changes that could have directly impacted these outcomes. This indirect educational effect is hypothesized to be caused by an increased patient engagement and empowerment; due to taking daily measurements, patients are confronted with their lifestyle and (the management of) their illness on a daily basis. However, modifying cardiovascular risk factors with the use of gamification is a new area and more research is needed to determine the scale of this effect and the psychology behind it.

Strengths and limitations

The main strength of this study is the protocol adherence of the intervention group, with a high mHealth measurement count and a high number of unique measurement days. Although patients were consecutively included and the exclusion criteria were the same for both the intervention and control group, the non-randomized nature and inclusion of a historical control group were a major limitation. Moreover, selection bias may have occurred due to the impact of COVID-19 after March 2020. This is the main reason for some differences at baseline, such as age, history of hypertension, and length of stay. We corrected for these parameters in the statistical analyses.

Another factor to take into consideration is the cost of The Box, which is around €350 ($350) and currently not refunded by the Dutch healthcare system as well as most healthcare systems around the world, making this intervention less accessible to patients. If cardiovascular risk management is the only requirement, these costs can be reduced as in this case, only a BP monitor, activity tracker, and potentially a weight scale would have to be handed out.

Conclusion

This study demonstrated mHealth to be a potentially useful intervention strategy for BP, weight and cholesterol management. However, long-term effects of mHealth on lifestyle and cardiovascular risk management could not yet be assessed and need to be addressed in further research.

Abbreviations

AF

Atrial Fibrillation

BP

Blood Pressure

CABG

Coronary Artery Bypass Grafting

CAD

Coronary Artery Disease

ECG

Electrocardiogram

eHealth

electronic Health

EMR

Electronic Medical Record

eVisit

electronic Visit

mHealth

mobile Health

NP

Nurse Practitioner

LDL-C

Low-Density Lipoprotein

LUMC

Leiden University Medical Center

RCT

Randomized Controlled Trial

Appendix 1

Table A1.

mHealth measurement totals

Total Median IQR Range
Blood pressure 6767 45 29–87 1–307
Weight 5939 47 28–84 0–172
Temperature 4482 32 9–76 0–116
Step count days 7975 90 64–92 0–92
ECG's 1289 11 5–14 0–102
Measurement total 26 449 222 164–304 1–561
Unique measurement days 6295 52 37–84 0–92

Table A2.

BP medication comparison between controls and intervention group patients at baseline and at the end of follow-up

Control (n = 117) Intervention (n = 111) P value
Baseline
No BP medication 18 (15.4%) 25 (22.5%) 0.17
1 BP medicine 53 (45.3%) 68 (61.3%)
2 BP medicines 36 (30.8%) 18 (16.2%)
3 BP medicines 10 (8.5%) 0 (0.0%)
ACE inhibitor 64 (54.7%) 62 (55.9%)
Angiotensin receptor blocker 27 (23.1%) 16 (14.4%)
Calcium antagonist 37 (31.6%) 14 (12.6%)
Diuretic 27 (23.1%) 12 (10.8%)
End of follow-up
No BP medication 19 (16.2%) 26 (23.4%) 0.19
1 BP medicine 55 (47.0%) 64 (57.7%)
2 BP medicines 33 (28.2%) 21 (18.9%)
3 BP medicines 10 (8.5%) 0 (0.0%)
ACE inhibitor 56 (47.9%) 67 (60.4%)
Angiotensin receptor blocker 30 (25.6%) 17 (15.3%)
Calcium antagonist 35 (29.9%) 15 (13.5%)
Diuretic 27 (23.1%) 10 (9.0%)

Appendix: medication changes

Effect Study arm Cholesterol treatment at discharge Cholesterol treatment at the end of follow-up Reason for treatment change
Medication switch Control 1 Atorvastatin 40 mg once daily Ezetimib 10 mg once daily Persistent costo-myalgenous pain during follow-up
Medication switch Control 2 Atorvastatin 40 mg once daily Atorvastatin 40 mg + ezetimib 10 mg once daily During follow-up, a high LDL (4.6 mmol/L) at discharge was noticed
Medication switch Control 3 Atorvastatin 40 mg once daily Rosuvastatin 10 mg once daily Generalized myalgia
Medication switch Control 4 Simvastatin 40 mg once daily Atorvastatin 40 mg once daily Inadequate treatment according to ESC guidelines
Medication switch Intervention 1 Pravastatin 40 mg once daily Rosuvastatin 10 mg once daily Inadequate treatment according to ESC guidelines
Medication switch Intervention 2 Atorvastatin 40 mg once daily Rosuvastatin 20 mg once daily After consultation of a otorhinolaryngologist for dizziness, it was advised to switch from atorvastatin to a different statin
Medication switch Intervention 3 Atorvastatin 40 mg once daily Rosuvastatin 20 mg once daily Generalized myalgia
Medication switch Intervention 4 Atorvastatin 40 mg once daily Rosuvastatin 20 mg once daily Amiodarone was started during follow-up. As a result, atorvastatin was switched as it is known to interactwith amiodarone.
Effect Study arm BP treatment at discharge BP treatment at the end of follow-up Reason for treatment change
Medication switch, comparable dose Control 1 Sotalol 3 × 40 mg + perindopril 2 mg Metoprolol 50 mg + valsartan 2 × 40 mg A bothersome cough during follow-up
Medication switch, comparable dose Control 2 Metoprolol 2 × 25 mg + perindopril 2mg Metoprolol 2 × 25 mg + losartan 25 mg A bothersome cough during follow-up
Medication switch, comparable dose Intervention 1 Sotalol 3 × 40 mg + perindopril 2 mg Sotalol 3 × 40 mg + losartan 50 mg A bothersome cough during follow-up
Medication switch, comparable dose Intervention 2 Sotalol 2 × 80 mg + perindopril 4 mg Sotalol 2 × 80 mg + losartan 50 mg A bothersome cough during follow-up
BP medication added Control 1 Sotalol 3 × 80 mg + perindopril 4 mg Sotalol 3 × 80 mg + perindopril 4 mg + nifedipine 2 × 30 mg Persistent hypertension during follow-up
BP medication added Control 2 Metoprolol 2 × 25 mg + perindopril 2 mg Metoprolol 2 × 25 mg + candesartan 8 mg + hydrochlorothiazide 25 mg Persistent hypertension during follow-up
BP medication added Control 3 Metoprolol 50 mg Metoprolol 25 mg + perindopril 2 mg Persistent hypertension during follow-up
BP medication added Intervention 1 Sotalol 2 × 80 mg + perindopril 4mg Sotalol 2 × 80 mg + perindopril 4 mg + amlodipine 5 mg Persistent hypertension during follow-up
BP medication added Intervention 2 Sotalol 2 × 80 mg + lisinopril 10mg Sotalol 2 × 80 mg + lisinopril 20 mg + amlodipine 10 mg Persistent hypertension during follow-up
BP medication added Intervention 3 Metoprolol 25mg Metoprolol 25 mg + perindopril 2mg Persistent hypertension during follow-up
BP medication added Intervention 4 Propranolol 2 × 10 mg + hydrochlorothiazide 25 mg Propranolol 2 × 10 mg + hydrochlorothiazide 25 mg + perindopril 2 mg Persistent hypertension during follow-up
BP medication added Intervention 5 Sotalol 3 × 40 mg Sotalol 3 × 40 mg + perindopril 2mg Persistent hypertension during follow-up
BP medication added Intervention 6 Sotalol 3 × 80 mg + diltiazem 300mg Sotalol 3 × 80 mg + diltiazem 300 mg + irbesartan 75 mg Persistent hypertension during follow-up
BP medication added Intervention 7 Sotalol 2 × 80mg Sotalol 2 × 80 mg + amlodipine 10 mg Persistent hypertension during follow-up
BP medication added Intervention 8 Sotalol 3 × 80 mg + lisinopril 2.5 mg Sotalol 3 × 80 mg + lisinopril 2 × 10 mg + hydrochlorothiazide 12.5 mg Persistent hypertension during follow-up
BP medication added Intervention 9 Sotalol 2 × 80 mg Sotalol 2 × 80 mg + perindopril 2mg Persistent hypertension during follow-up
BP medication added Intervention 10 Sotalol 2 × 80 mg + perindopril 2 mg Sotalol 2 × 80 mg + amlodipine 5 mg + valsartan 160 mg Persistent hypertension during follow-up
BP medication added Intervention 11 Sotalol 2 × 80 mg Sotalol 2 × 80 mg + perindopril 2 mg Persistent hypertension during follow-up
BP medication added Intervention 12 Sotalol 3 × 80 mg Metoprolol 100 mg + perindopril 4 mg Persistent hypertension during follow-up
BP medication added Intervention 13 Sotalol 2 × 80 mg Sotalol 2 × 80 mg + perindopril 2 mg Persistent hypertension during follow-up
BP medication added Intervention 14 Metoprolol 75 mg + hydrochlorothiazide 25 mg Metoprolol 75 mg + hydrochlorothiazide 25 mg + valsartan 80 mg Persistent hypertension during follow-up
BP medication added Intervention 15 Sotalol 2 × 80 mg + perindopril 4 mg Sotalol 2 × 80 mg + perindopril 8 mg + amlodipine 10 mg Persistent hypertension during follow-up
Dose increased Control 1 Sotalol 3 × 40 mg + losartan 25 mg Sotalol 3 × 40 mg + losartan 50 mg Persistent hypertension during follow-up
Dose increased Intervention 1 Sotalol 3 × 80 mg + perindopril 4 mg Bisoprolol 2.5 mg + perindopril 8 mg Persistent hypertension during follow-up
Dose increased Intervention 2 Sotalol 2 × 80 mg + enalapril 10 mg Sotalol 2 × 80 mg + enalapril 2 × 10 mg Persistent hypertension during follow-up
Dose increased Intervention 3 Sotalol 2 × 80 mg + perindopril 2 mg Sotalol 2 × 80 mg + perindopril 4 mg Persistent hypertension during follow-up
Dose increased Intervention 4 Sotalol 3 × 80 mg + perindopril 1 mg Sotalol 3 × 80 mg + perindopril 2 mg Persistent hypertension during follow-up
Dose increased Intervention 5 Sotalol 2 × 80 mg + enalapril 2.5 mg Sotalol 2 × 80 mg + enalapril 2 0mg Persistent hypertension during follow-up
Dose increased Intervention 6 Sotalol 2 × 80 mg + perindopril 2 mg Sotalol 2 × 80 mg + perindopril 6 mg Persistent hypertension during follow-up
Dose increased Intervention 7 Sotalol 2 × 40 mg + perindopril 6 mg Metoprolol 12.5 mg + perindopril 8 mg Persistent hypertension during follow-up
Dose increased Intervention 8 Sotalol 2 × 80 mg + candesartan 4 mg Sotalol 2 × 80 mg + candesartan 2 × 4 mg Persistent hypertension during follow-up
Dose increased Intervention 9 Sotalol 2 × 40 mg + irbesartan 75 mg Sotalol 2 × 40 mg + irbesartan 150 mg Persistent hypertension during follow-up
Dose increased Intervention 10 Perindopril 4 mg Perindopril 6 mg Persistent hypertension during follow-up
Dose increased Intervention 11 Sotalol 2 × 80 mg + perindopril 2 mg Sotalol 2 × 80 mg + perindopril 4 mg Persistent hypertension during follow-up
BP medication removed Control 1 Sotalol 3 × 40 mg + lisinopril 2 × 5 mg + amlodipine 5 mg Sotalol 3 × 40 mg + lisinopril 10 mg Hypotension during follow-up
BP medication removed Control 2 Metoprolol 25 mg + perindopril 2 mg + diltiazem 200 mg Diltiazem 200 mg Hypotension during follow-up
BP medication removed Control 3 Metoprolol 50 mg + lisinopril 5 mg + nifedipine 30 mg Lisinopril 5 mg Hypotension during follow-up
BP medication removed Control 4 Metoprolol 50 mg + losartan 100 mg + hydrochlorothiazide 25 mg Losartan 100 mg Hypotension during follow-up
BP medication removed Control 5 Metoprolol 25 mg + perindopril 2 mg None Hypotension during follow-up
BP medication removed Control 6 Metoprolol 2 × 25 mg + lisinopril 5 mg + amlodipine 5 mg None Hypotension during follow-up
BP medication removed Intervention 1 Metoprolol 75 mg + losartan 50 mg + hydrochlorothiazide 12.5 mg None Hypotension during follow-up
BP medication removed Intervention 2 Sotalol 2 × 80 mg + perindopril 2 mg Sotalol 2 × 80 mg Hypotension during follow-up
BP medication removed Intervention 3 Sotalol 3 × 80 mg + perindopril 4mg Sotalol 2 × 80 mg Hypotension during follow-up
BP medication removed Intervention 4 Metoprolol 25 mg + enalapril 2 × 20 mg None Hypotension during follow-up
BP medication removed Intervention 5 Sotalol 3 × 80 mg + enalapril 10 mg + amlodipine 5 mg Sotalol 3 × 80 mg + enalapril 10 mg Hypotension during follow-up
BP medication removed Intervention 6 Sotalol 2 × 80 mg + losartan 50 mg + diltiazem 300 mg Sotalol 2 × 80 mg Hypotension during follow-up
BP medication removed Intervention 7 Sotalol 2 × 80 mg + perindopril 2 mg Sotalol 2 × 80 mg Hypotension during follow-up
BP medication removed Intervention 8 Sotalol 3 × 40 mg + amlodipine 5 mg Sotalol 3 × 40 mg Hypotension during follow-up
BP medication removed Intervention 9 Sotalol 2 × 80 mg + hydrochlorothiazide 12.5 mg Sotalol 2 × 80 mg Hypotension during follow-up
BP medication removed Intervention 10 Sotalol 3 × 40 mg + losartan 50 mg Sotalol 3 × 40 mg Hypotension during follow-up
Dose decreased Intervention 1 Metoprolol 150 mg + olmesartan 20 mg + amlodipine 15 mg Metoprolol 150 mg + olmesartan 20 mg + amlodipine 10 mg Hypotension during follow-up

Contributor Information

Tommas Evan Biersteker, Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.

Mark J Boogers, Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.

Martin Jan Schalij, Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.

Jerry Braun, Department of Cardiothoracic Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.

Rolf H H Groenwold, Department of Clinical Epidemiology and Biomedical Data Sciences, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.

Douwe E Atsma, Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.

Roderick Willem Treskes, Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.

Funding

No funding bodies are applicable to this study.

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

References

  • 1. Siregar S, Groenwold RH, de Mol BA, Speekenbrink RG, Versteegh MI, Brandon Bravo Bruinsma GJ, et al. Evaluation of cardiac surgery mortality rates: 30-day mortality or longer follow-up? Eur J Cardiothorac Surg 2013;44:875–883. [DOI] [PubMed] [Google Scholar]
  • 2. Adelborg K, Horvath-Puho E, Schmidt M, Munch T, Pedersen L, Nielsen PH, et al. Thirty-year mortality after coronary artery bypass graft surgery: A Danish nationwide population-based cohort study. Circ Cardiovasc Qual Outcomes 2017;10:e002708. [DOI] [PubMed] [Google Scholar]
  • 3. Shahian DM, O'Brien SM, Sheng S, Grover FL, Mayer JE, Jacobs JP, et al. Predictors of long-term survival after coronary artery bypass grafting surgery: results from the Society of Thoracic Surgeons Adult Cardiac Surgery Database (the ASCERT study). Circulation 2012;125:1491–1500. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Chow SCY, Wong RHL, Yu PSY, Ho JYK, Chan JWY, Kwok MWT, et al. 10-year outcomes post coronary artery bypass grafting in Asian patients with ischemic cardiomyopathy: a comprehensive analysis of survival and cardiac performance. J Thorac Dis 2020;12:803–812. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Blood Pressure Lowering Treatment Trialists’ Collaboration . Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: an individual participant-level data meta-analysis. Lancet 2021;397:1625–1636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Silverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, et al. Association between lowering LDL-C and cardiovascular risk reduction among different therapeutic interventions: a systematic review and meta-analysis. JAMA 2016;316:1289–1297. [DOI] [PubMed] [Google Scholar]
  • 7. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: the Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts): developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J 2016;37:2315–2381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Kotseva K, Wood D, De Bacquer D, De Backer G, Ryden L, Jennings C, et al. EUROASPIRE IV: A European Society of Cardiology survey on the lifestyle, risk factor and therapeutic management of coronary patients from 24 European countries. Eur J Prev Cardiol 2016;23:636–648. [DOI] [PubMed] [Google Scholar]
  • 9. Brors G, Pettersen TR, Hansen TB, Fridlund B, Holvold LB, Lund H, et al. Modes of e-Health delivery in secondary prevention programmes for patients with coronary artery disease: a systematic review. BMC Health Serv Res 2019;19:364. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Tam HL, Wong EML, Cheung K, Chung SF. Effectiveness of text messaging interventions on blood pressure control among patients with hypertension: systematic review of randomized controlled trials. JMIR Mhealth Uhealth 2021;9:e24527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Godinho MA, Jonnagaddala J, Gudi N, Islam R, Narasimhan P, Liaw ST. mHealth for integrated people-centred health services in the Western Pacific: A systematic review. Int J Med Inform 2020;142:104259. [DOI] [PubMed] [Google Scholar]
  • 12. Beleigoli AM, Andrade AQ, Cancado AG, Paulo MN, Diniz MFH, Ribeiro AL. Web-based digital health interventions for weight loss and lifestyle habit changes in overweight and obese adults: systematic review and meta-analysis. J Med Internet Res 2019;21:e298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Rehman HV. Managing Hyperlipidemia On the Go: Using Mobile Technology to Lower Cholesterol Levels. 2017
  • 14. Frederix I, Caiani EG, Dendale P, Anker S, Bax J, Bohm A, et al. ESC e-Cardiology working group position paper: overcoming challenges in digital health implementation in cardiovascular medicine. Eur J Prev Cardiol 2019;26:1166–1177. [DOI] [PubMed] [Google Scholar]
  • 15. Cowie MR, Bax J, Bruining N, Cleland JGF, Koehler F, Malik M, et al. Vardas P. e-Health: a position statement of the European Society of Cardiology. Eur Heart J 2016;37:63–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Artinian NT, Flack JM, Nordstrom CK, Hockman EM, Washington OG, Jen KL, et al. Effects of nurse-managed telemonitoring on blood pressure at 12-month follow-up among urban African Americans. Nurs Res 2007;56:312–322. [DOI] [PubMed] [Google Scholar]
  • 17. Green BB, Cook AJ, Ralston JD, Fishman PA, Catz SL, Carlson J, et al. Effectiveness of home blood pressure monitoring, web communication, and pharmacist care on hypertension control: a randomized controlled trial. JAMA 2008;299:2857–2867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. McKinstry B, Hanley J, Wild S, Pagliari C, Paterson M, Lewis S, et al. Telemonitoring based service redesign for the management of uncontrolled hypertension: multicentre randomised controlled trial. BMJ 2013;346:f3030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Salisbury C, O'Cathain A, Thomas C, Edwards L, Gaunt D, Dixon P, et al. Telehealth for patients at high risk of cardiovascular disease: pragmatic randomised controlled trial. BMJ 2016;353:i2647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Margolis KL, Asche SE, Bergdall AR, Dehmer SP, Groen SE, Kadrmas HM, et al. Effect of home blood pressure telemonitoring and pharmacist management on blood pressure control: a cluster randomized clinical trial. JAMA 2013;310:46–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Frias J, Virdi N, Raja P, Kim Y, Savage G, Osterberg L. Effectiveness of digital medicines to improve clinical outcomes in patients with uncontrolled hypertension and type 2 diabetes: prospective, open-label, cluster-randomized pilot clinical trial. J Med Internet Res 2017;19:e246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Carrasco MP, Salvador CH, Sagredo PG, Marquez-Montes J, de Mingo MAG, Fragua JA, et al. Impact of patient-general practitioner short-messages-based interaction on the control of hypertension in a follow-up service for low-to-medium risk hypertensive patients: a randomized controlled trial. IEEE Trans Inf Technol Biomed 2008;12:780–791. [DOI] [PubMed] [Google Scholar]
  • 23. Bobrow K, Farmer AJ, Springer D, Shanyinde M, Yu LM, Brennan T, et al. Mobile phone text messages to support treatment adherence in adults with high blood pressure (SMS-text adherence support [StAR]): A single-blind, randomized trial. Circulation 2016;133:592–600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Bosworth HB, Powers BJ, Olsen MK, McCant F, Grubber J, Smith V, et al. Home blood pressure management and improved blood pressure control: results from a randomized controlled trial. Arch Intern Med 2011;171:1173–1180. [DOI] [PubMed] [Google Scholar]
  • 25. Piette JD, Datwani H, Gaudioso S, Foster SM, Westphal J, Perry W, et al. Hypertension management using mobile technology and home blood pressure monitoring: results of a randomized trial in two low/middle-income countries. Telemed J E Health 2012;18:613–620. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Rubinstein A, Miranda JJ, Beratarrechea A, Diez-Canseco F, Kanter R, Gutierrez L, et al. Ramirez-Zea M, group G. Effectiveness of an mHealth intervention to improve the cardiometabolic profile of people with prehypertension in low-resource urban settings in Latin America: a randomised controlled trial. Lancet Diabetes Endocrinol 2016;4:52–63. [DOI] [PubMed] [Google Scholar]
  • 27. Biersteker TE, Boogers MJ, de Lind van Wijngaarden RAF, Groenwold RHH, Trines SA, van Alem AP, et al. Use of smart technology for the early diagnosis of complications after cardiac surgery: The Box 2.0 study protocol. JMIR Res Protoc 2020;9:e16326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Zafrir B, Saliba W, Jaffe R, Sliman H, Flugelman MY, Sharoni E. Attainment of lipid goals and long-term mortality after coronary-artery bypass surgery. Eur J Prev Cardiol 2019;26:401–408. [DOI] [PubMed] [Google Scholar]
  • 29. Biersteker TE, Boogers MJ, Schalij MJ, Penning de Vries BBL, Groenwold RHH, van Alem AP, et al. Mobile health vs. standard care after cardiac surgery: results of The Box 2.0 study. Europace 2023;25:49–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Williams B, Mancia G, Spiering W, Rosei EA, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Kardiol Pol 2019;77:71–159. [DOI] [PubMed] [Google Scholar]
  • 31. Catapano AL, Graham I, De Backer G, Wiklund O, Chapman MJ, Drexel H, et al. 2016 ESC/EAS Guidelines for the management of dyslipidaemias. Eur Heart J 2016;37:2999–3058. [DOI] [PubMed] [Google Scholar]
  • 32. Burns K, Nicholas R, Beatson A, Chamorro-Koc M, Blackler A, Gottlieb U. Identifying mobile health engagement stages: interviews and observations for developing brief message content. J Med Internet Res 2020;22:e15307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Patel MS, Small DS, Harrison JD, Hilbert V, Fortunato MP, Oon AL, et al. Effect of behaviorally designed gamification with social incentives on lifestyle modification among adults with uncontrolled diabetes: a randomized clinical trial. JAMA Netw Open 2021;4:e2110255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Lu X, Yang H, Xia X, Lu X, Lin J, Liu F, et al. Interactive mobile health intervention and blood pressure management in adults. Hypertension 2019;74:697–704. [DOI] [PubMed] [Google Scholar]
  • 35. Lv M, Wu T, Jiang S, Chen W, Zhang J. Effects of telemedicine and mHealth on systolic blood pressure management in stroke patients: systematic review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth 2021;9:e24116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Stogios N, Kaur B, Huszti E, Vasanthan J, Nolan RP. Advancing digital health interventions as a clinically applied science for blood pressure reduction: a systematic review and meta-analysis. Can J Cardiol 2020;36:764–774. [DOI] [PubMed] [Google Scholar]
  • 37. Stamler J, Rose G, Stamler R, Elliott P, Dyer A, Marmot M; INTERSALT study findings . Public health and medical care implications. Hypertension 1989;14:570–577. [DOI] [PubMed] [Google Scholar]
  • 38. Treskes RW, van Winden LAM, van Keulen N, van der Velde ET, Beeres S, Atsma DE, et al. Effect of smartphone-enabled health monitoring devices vs regular follow-up on blood pressure control among patients after myocardial infarction: a randomized clinical trial. JAMA Netw Open 2020;3:e202165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Biersteker T, Hilt A, van der Velde E, Schalij MJ, Treskes RW. Real-world experience of mHealth implementation in clinical practice (the Box): design and usability study. JMIR Cardio 2021;5:e26072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Li A, Del Olmo MG, Fong M, Sim K, Lymer SJ, Cunich M, et al. Effect of a smartphone application (Perx) on medication adherence and clinical outcomes: a 12-month randomised controlled trial. BMJ Open 2021;11:e047041. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.


Articles from European Heart Journal. Digital Health are provided here courtesy of Oxford University Press on behalf of the European Society of Cardiology

RESOURCES