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

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.

Mobile health vs. standard care after cardiac surgery: results of The Box 2.0 study.

AIMS: Postoperative atrial fibrillation (POAF) is a common complication of cardiac surgery, yet difficult to detect in ambulatory patients. The primary aim of this study is to investigate the effect of a mobile health (mHealth) intervention on POAF detection after cardiac surgery. METHODS AND RESULTS: We performed an observational cohort study among 730 adult patients who underwent cardiac surgery at a tertiary care hospital in The Netherlands. Of these patients, 365 patients received standard care and were included as a historical control group, undergoing surgery between December 2017 and September 2018, and 365 patients were prospectively included from November 2018 and November 2020, undergoing an mHealth intervention which consisted of blood pressure, temperature, weight, and electrocardiogram (ECG) monitoring. One physical outpatient follow-up moment was replaced by an electronic visit. All patients were requested to fill out a satisfaction and quality of life questionnaire. Mean age in the intervention group was 62 years, 275 (70.4%) patients were males. A total of 4136 12-lead ECGs were registered. In the intervention group, 61 (16.7%) patients were diagnosed with POAF vs. 25 (6.8%) patients in the control group [adjusted risk ratio (RR) of POAF detection: 2.15; 95% confidence interval (CI): 1.55-3.97]. De novo atrial fibrillation was found in 13 patients using mHealth (6.5%) vs. 4 control group patients (1.8%; adjusted RR 3.94, 95% CI: 1.50-11.27). CONCLUSION: Scheduled self-measurements with mHealth devices could increase the probability of detecting POAF within 3 months after cardiac surgery. The effect of an increase in POAF detection on clinical outcomes needs to be addressed in future research.

The Box-eHealth in the Outpatient Clinic Follow-up of Patients With Acute Myocardial Infarction: Cost-Utility Analysis.

BACKGROUND: Smartphone compatible wearables have been released on the consumers market, enabling remote monitoring. Remote monitoring is often named as a tool to reduce the cost of care. OBJECTIVE: The primary purpose of this paper is to describe a cost-utility analysis of an eHealth intervention compared to regular follow-up in patients with acute myocardial infarction (AMI). METHODS: In this trial, of which clinical results have been published previously, patients with an AMI were randomized in a 1:1 fashion between an eHealth intervention and regular follow-up. The remote monitoring intervention consisted of a blood pressure monitor, weight scale, electrocardiogram device, and step counter. Furthermore, two in-office outpatient clinic visits were replaced by e-visits. The control group received regular care. The differences in mean costs and quality of life per patient between both groups during one-year follow-up were calculated. RESULTS: Mean costs per patient were €2417±2043 (US $2657±2246) for the intervention and €2888±2961 (US $3175±3255) for the control group. This yielded a cost reduction of €471 (US $518) per patient. This difference was not statistically significant (95% CI -€275 to €1217; P=.22, US $-302 to $1338). The average quality-adjusted life years in the first year of follow-up was 0.74 for the intervention group and 0.69 for the control (difference -0.05, 95% CI -0.09 to -0.01; P=.01). CONCLUSIONS: eHealth in the outpatient clinic setting for patients who suffered from AMI is likely to be cost-effective compared to regular follow-up. Further research should be done to corroborate these findings in other patient populations and different care settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT02976376; https://clinicaltrials.gov/ct2/show/NCT02976376. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.8038.

Real-World Experience of mHealth Implementation in Clinical Practice (the Box): Design and Usability Study.

BACKGROUND: Mobile health (mHealth) is an emerging field of scientific interest worldwide. Potential benefits include increased patient engagement, improved clinical outcomes, and reduced health care costs. However, mHealth is often studied in projects or trials, and structural implantation in clinical practice is less common. OBJECTIVE: The purpose of this paper is to outline the design of the Box and its implementation and use in an outpatient clinic setting. The impact on logistical outcomes and patient and provider satisfaction is discussed. METHODS: In 2016, an mHealth care track including smartphone-compatible devices, named the Box, was implemented in the cardiology department of a tertiary medical center in the Netherlands. Patients with myocardial infarction, rhythm disorders, cardiac surgery, heart failure, and congenital heart disease received devices to measure daily weight, blood pressure, heart rate, temperature, and oxygen saturation. In addition, professional and patient user comments on the experience with the care track were obtained via structured interviews. RESULTS: From 2016 to April 2020, a total of 1140 patients were connected to the mHealth care track. On average, a Box cost €350 (US $375), not including extra staff costs. The median patient age was 60.8 (IQR 52.9-69.3) years, and 73.59% (839/1140) were male. A median of 260 (IQR 105-641) measurements was taken on a median of 189 (IQR 98-372) days. Patients praised the ease of use of the devices and felt more involved with their illness and care. Professionals reported more productive outpatient consultations as well as improved insight into health parameters such as blood pressure and weight. A feedback loop from the hospital to patient to focus on measurements was commented as an important improvement by both patients and professionals. CONCLUSIONS: In this study, the design and implementation of an mHealth care track for outpatient follow-up of patients with various cardiovascular diseases is described. Data from these 4 years indicate that mHealth is feasible to incorporate in outpatient management and is generally well-accepted by patients and providers. Limitations include the need for manual measurement data checks and the risk of data overload. Moreover, the tertiary care setting in which the Box was introduced may limit the external validity of logistical and financial end points to other medical centers. More evidence is needed to show the effects of mHealth on clinical outcomes and on cost-effectiveness.

Effect of Smartphone-Enabled Health Monitoring Devices vs Regular Follow-up on Blood Pressure Control Among Patients After Myocardial Infarction: A Randomized Clinical Trial.

Importance: Smart technology via smartphone-compatible devices might improve blood pressure (BP) regulation in patients after myocardial infarction. Objectives: To investigate whether smart technology in clinical practice can improve BP regulation and to evaluate the feasibility of such an intervention. Design, Setting, and Participants: This study was an investigator-initiated, single-center, nonblinded, feasibility, randomized clinical trial conducted at the Department of Cardiology of the Leiden University Medical Center between May 2016 and December 2018. Two hundred patients, who were admitted with either ST-segment elevation myocardial infarction or non-ST-segment acute coronary syndrome, were randomized in a 1:1 fashion between follow-up groups using smart technology and regular care. Statistical analysis was performed from January 2019 to March 2019. Interventions: For patients randomized to regular care, 4 physical outpatient clinic visits were scheduled in the year following the initial event. In the intervention group, patients were given 4 smartphone-compatible devices (weight scale, BP monitor, rhythm monitor, and step counter). In addition, 2 in-person outpatient clinic visits were replaced by electronic visits. Main Outcomes and Measures: The primary outcome was BP control. Secondary outcomes, as a parameter of feasibility, included patient satisfaction (general questionnaire and smart technology-specific questionnaire), measurement adherence, all-cause mortality, and hospitalizations for nonfatal adverse cardiac events. Results: In total, 200 patients (median age, 59.7 years [interquartile range, 52.9-65.6 years]; 156 men [78%]) were included, of whom 100 were randomized to the intervention group and 100 to the control group. After 1 year, 79% of patients in the intervention group had controlled BP vs 76% of patients in the control group (P = .64). General satisfaction with care was the same between groups (mean [SD] scores, 82.6 [14.1] vs 82.0 [15.1]; P = .88). The all-cause mortality rate was 2% in both groups (P > .99). A total of 20 hospitalizations for nonfatal adverse cardiac events occurred (8 in the intervention group and 12 in the control group). Of all patients, 32% sent in measurements each week, with 63% sending data for more than 80% of the weeks they participated in the trial. In the intervention group only, 90.3% of patients were satisfied with the smart technology intervention. Conclusions and Relevance: These findings suggest that smart technology yields similar percentages of patients with regulated BP compared with the standard of care. Such an intervention is feasible in clinical practice and is accepted by patients. More research is mandatory to improve patient selection of such an intervention. Trial Registration: ClinicalTrials.gov Identifier: NCT02976376.

Mobile Health for Central Sleep Apnea Screening Among Patients With Stable Heart Failure: Single-Cohort, Open, Prospective Trial.

BACKGROUND: Polysomnography is the gold standard for detection of central sleep apnea in patients with stable heart failure. However, this procedure is costly, time consuming, and a burden to the patient and therefore unsuitable as a screening method. An electronic health (eHealth) app to measure overnight oximetry may be an acceptable screening alternative, as it can be automatically analyzed and is less burdensome to patients. OBJECTIVE: This study aimed to assess whether overnight pulse oximetry using a smartphone-compatible oximeter can be used to detect central sleep apnea in a population with stable heart failure. METHODS: A total of 26 patients with stable heart failure underwent one night of both a polygraph examination and overnight saturation using a smartphone-compatible oximeter. The primary endpoint was agreement between the oxygen desaturation index (ODI) above or below 15 on the smartphone-compatible oximeter and the diagnosis of the polygraph. RESULTS: The median age of patients was 66.4 (interquartile range, 62-71) years and 92% were men. The median body mass index was 27.1 (interquartile range, 24.4-30.8) kg/m2. Two patients were excluded due to incomplete data, and two other patients were excluded because they could not use a smartphone. Seven patients had central sleep apnea, and 6 patients had obstructive sleep apnea. Of the 7 (of 22, 32%) patients with central sleep apnea that were included in the analysis, 3 (13%) had an ODI≥15. Of all patients without central sleep apnea, 8 (36%) had an ODI<15. The McNemar test yielded a P value of .55. CONCLUSIONS: Oxygen desaturation measured by this smartphone-compatible oximeter is a weak predictor of central sleep apnea in patients with stable heart failure.

Using Smart Technology to Improve Outcomes in Myocardial Infarction Patients: Rationale and Design of a Protocol for a Randomized Controlled Trial, The Box.

BACKGROUND: Recent evidence suggests that frequent monitoring using smartphone-compatible wearable technologies might improve clinical effectiveness and patient satisfaction of care. OBJECTIVE: The aim of this study is to investigate the clinical effectiveness and patient satisfaction of a smart technology intervention in patients admitted with a ST elevation myocardial infarction (STEMI) or non-ST acute coronary syndrome (NST-ACS). METHODS: In this single center, open, randomized controlled trial patients who suffered from STEMI or NST-ACS will be randomized 1:1 to an intervention group or control group. Both groups will be followed up to one year after the index event. The intervention group will take daily measurements with a smartphone-compatible electrocardiogram device, blood pressure (BP) monitor, weight scale, and activity tracker. Furthermore, two of four outpatient clinic visits will be replaced by electronic visits (1 and 6 months after index event). The control group will receive regular care, consisting of four outpatient clinic visits (1, 3, 6, and 12 months after index event). All patients will be asked to fill in validated questionnaires about patient satisfaction, quality of life, propensity of medication adherence, and physical activity. RESULTS: The primary outcome of this trial will be percentage of patients with controlled BP. Secondary outcomes include patient satisfaction, health care utilization, major adverse cardiac events, medication adherence, physical activity, quality of life, and percentage of patients in which a sustained arrhythmia is detected. CONCLUSIONS: Smart technology could potentially improve care in postmyocardial infarction patients. This trial will investigate whether usage of smart technology can improve clinical- and cost-effectiveness of care. TRIAL REGISTRATION: Clinicaltrials.gov NCT02976376; https://clinicaltrials.gov/ct2/show/NCT02976376 (Archived by WebCite at http://www.webcitation.org/6tcvAdbdH).

Quaking, an RNA-binding protein, is a critical regulator of vascular smooth muscle cell phenotype.

RATIONALE: RNA-binding proteins are critical post-transcriptional regulators of RNA and can influence pre-mRNA splicing, RNA localization, and stability. The RNA-binding protein Quaking (QKI) is essential for embryonic blood vessel development. However, the role of QKI in the adult vasculature, and in particular in vascular smooth muscle cells (VSMCs), is currently unknown. OBJECTIVE: We sought to determine the role of QKI in regulating adult VSMC function and plasticity. METHODS AND RESULTS: We identified that QKI is highly expressed by neointimal VSMCs of human coronary restenotic lesions, but not in healthy vessels. In a mouse model of vascular injury, we observed reduced neointima hyperplasia in Quaking viable mice, which have decreased QKI expression. Concordantly, abrogation of QKI attenuated fibroproliferative properties of VSMCs, while potently inducing contractile apparatus protein expression, rendering noncontractile VSMCs with the capacity to contract. We identified that QKI localizes to the spliceosome, where it interacts with the myocardin pre-mRNA and regulates the splicing of alternative exon 2a. This post-transcriptional event impacts the Myocd_v3/Myocd_v1 mRNA balance and can be modulated by mutating the quaking response element in exon 2a of myocardin. Furthermore, we identified that arterial damage triggers myocardin alternative splicing and is tightly coupled with changes in the expression levels of distinct QKI isoforms. CONCLUSIONS: We propose that QKI is a central regulator of VSMC phenotypic plasticity and that intervention in QKI activity can ameliorate pathogenic, fibroproliferative responses to vascular injury.