mHealth Apps for Self-Management of Cardiovascular Diseases: A Scoping Review

Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry

Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest predictive value. Machine learning (ML) may achieve greater results when predicting adverse outcomes in patients with recently diagnosed AF. Several ML models were tested and compared with current clinical risk scores on a cohort of 26,183 patients (mean age 70.13 (standard deviation 10.13); 44.8% female) with non-valvular AF. Inputted into the ML models were 23 demographic variables alongside comorbidities and current treatments. For one-year stroke prediction, ML achieved an area under the curve (AUC) of 0.653 (95% confidence interval 0.576-0.730), compared to the CHADS2 and CHA2DS2-VASc scores performance of 0.587 (95% CI 0.559-0.615) and 0.535 (95% CI 0.521-0.550), respectively. Using ML for one-year major bleed prediction increased the AUC from 0.537 (95% CI 0.518-0.557) generated by the HAS-BLED score to 0.677 (95% CI 0.619-0.724). ML was able to predict one-year and three-year all-cause mortality with an AUC of 0.734 (95% CI 0.696-0.771) and 0.742 (95% CI 0.718-0.766). In this study a significant improvement in performance was observed when transitioning from clinical risk scores to machine learning-based approaches across all applications tested. Obtaining precise prediction tools is desirable for increased interventions to reduce event rates.Trial Registry https://www.clinicaltrials.gov ; Unique identifier: NCT01468701, NCT01671007, NCT01937377.

Development of a Digital Health Intervention for the Secondary Prevention of Cardiovascular Disease (INTERCEPT): Co-Design and Usability Testing Study

BACKGROUND

Secondary prevention is an important strategy to reduce the burden of cardiovascular disease (CVD), a leading cause of death worldwide. Despite the growing evidence for the effectiveness of digital health interventions (DHIs) for the secondary prevention of CVD, the majority are designed with minimal input from target end users, resulting in poor uptake and usage.

OBJECTIVE

This study aimed to optimize the acceptance and effectiveness of a DHI for the secondary prevention of CVD through co-design, integrating end users' perspectives throughout.

METHODS

A theory-driven, person-based approach using co-design was adopted for the development of the DHI, known as INTERCEPT. This involved a 4-phase iterative process using online workshops. In phase 1, a stakeholder team of health care professionals, software developers, and public and patient involvement members was established. Phase 2 involved identification of the guiding principles, content, and design features of the DHI. In phase 3, DHI prototypes were reviewed for clarity of language, ease of navigation, and functionality. To anticipate and interpret DHI usage, phase 4 involved usability testing with participants who had a recent cardiac event (<2 years). To assess the potential impact of usability testing, the System Usability Scale was administered before and after testing. The GUIDED (Guidance for Reporting Intervention Development Studies in Health Research) checklist was used to report the development process.

RESULTS

Five key design principles were identified: simplicity and ease of use, behavioral change through goal setting and self-monitoring, personalization, system credibility, and social support. Usability testing resulted in 64 recommendations for the app, of which 51 were implemented. Improvements in System Usability Scale scores were observed when comparing the results before and after implementing the recommendations (61 vs 83; P=.02).

CONCLUSIONS

Combining behavior change theory with a person-based, co-design approach facilitated the development of a DHI for the secondary prevention of CVD that optimized responsiveness to end users' needs and preferences, thereby potentially improving future engagement.

Development and validation of a mobile application prototype for postoperative cardiac surgery

OBJECTIVES

to develop and validate the content, appearance, and semantics of a prototype application for monitoring patients in the postoperative period of cardiac surgery.

METHODS

this is a technological development study based on Contextualized Instructional Design. The content and appearance evaluation was conducted by a committee of specialists, and semantic validation was carried out by patients from a cardiac surgery outpatient clinic.

RESULTS

the application prototype consisted of 43 screens, validated by 17 health specialists, with content validity ratio and appearance validity index results of 0.86 and 0.99, respectively. For semantic validation, 10 patients participated in data collection, with a total content validity index of 0.978.

CONCLUSIONS

the prototype of the "VivaCor PósOp" application demonstrated evidence of content, appearance, and semantic validity, with the potential to stimulate self-care in patients in the postoperative period of cardiac surgery.

Cardiovascular Risk Factors in Socioeconomically Disadvantaged Populations in a Suburb of the Largest City in Western Romania

Background and Objectives: Cardiovascular disease (CVD) remains a major public health issue worldwide, disproportionately affecting socioeconomically disadvantaged populations due to the social determinants of health (SDOHs). In Western Romania, these populations are particularly vulnerable to CVD. This study aims to investigate the prevalence and impact of cardiovascular risk factors (CVRFs) among socioeconomically disadvantaged individuals in Western Romania and identify the primary CVRFs contributing to the high incidence of CVD within this population. Materials and Methods: A retrospective observational design was employed, utilizing data from the medical records of 1433 eligible individuals. The inclusion criteria were based on Eurostat's EU-SILC benchmarks, focusing on severe material deprivation, at-risk-of-poverty rates, and low work intensity. Data on demographics, familial and personal medical history, smoking status, blood pressure, glucose, cholesterol, triglycerides, and HbA1c levels were collected. Results: Of the 1433 subjects, 34.75% were male, with a median age of 52 years. Significant conditions included diabetes (7.39%), coronary disease (3.83%), arterial hypertension (35.58%), and dyslipidemia (21.28%). Median ages were higher for those with diabetes (65 vs. 51 years, p < 0.0001), coronary disease (64 vs. 51 years, p < 0.0001), arterial hypertension (65 vs. 43 years, p < 0.0001), and dyslipidemia (66 vs. 47 years, p < 0.0001). BMI (Body Mass Index) classifications showed 33.77% were overweight, 21.21% obese, and 15.07% morbidly obese. Smokers were younger than non-smokers (48 vs. 54 years, p < 0.0001). Conclusions: The findings highlight the significant prevalence of CVRFs among socioeconomically disadvantaged populations in Western Romania. Socioeconomically disadvantaged populations exhibit a significantly higher prevalence of cardiovascular risk factors such as diabetes, impaired glucose regulation, hypertension, and dyslipidemia compared to their before known status.

Exploring the Feasibility and Initial Impact of an mHealth-Based Disease Management Program for Chronic Ischemic Heart Disease: Formative Study

BACKGROUND

Ischemic heart disease (IHD) is a leading cause of morbidity and mortality worldwide, requiring innovative management strategies. Traditional disease management programs often struggle to maintain patient engagement and ensure long-term adherence to lifestyle modifications and treatment plans. Mobile health (mHealth) technologies have emerged as a promising approach to address these challenges by providing continuous, personalized support and monitoring. However, the reported use and effectiveness of mHealth in the management of chronic diseases, such as IHD, have not been fully explored.

OBJECTIVE

The primary aim of this study was to evaluate the feasibility and initial impact of an mHealth-based disease management program on coronary risk factors, specifically focusing on low-density lipoprotein cholesterol (LDL-C) levels, in individuals with chronic IHD. This formative study assessed changes in LDL-C and other metabolic health indicators over a 6-month period to determine the initial impact of the program on promoting cardiovascular health and lifestyle modification.

METHODS

This study was conducted using data from 266 individuals enrolled in an mHealth-based disease management program between December 2018 and October 2022. Eligibility was based on a documented history of IHD, with participants undergoing a comprehensive cardiac risk assessment before enrollment. The program included biweekly telephone sessions, health tracking via a smartphone app, and regular progress reports to physicians. The study measured change in LDL-C levels as the primary outcome, with secondary outcomes including body weight, triglyceride levels, and other metabolic health indicators. Statistical analysis used paired 2-tailed t tests and stratified analyses to assess the impact of the program.

RESULTS

Participants experienced a significant reduction in LDL-C, with LDL-C levels decreasing from a mean of 98.82 (SD 40.92) mg/dL to 86.62 (SD 39.86) mg/dL (P<.001). The intervention was particularly effective in individuals with high baseline LDL-C levels. Additional improvements were seen in body weight and triglyceride levels, suggesting a broader impact on metabolic health. Program adherence and engagement metrics suggested high participant satisfaction and compliance.

CONCLUSIONS

The results of this study suggest that the mHealth-based disease management program is feasible and has an initial positive impact on reducing LDL-C levels and improving metabolic health in individuals with chronic IHD. However, the study design does not allow for a definitive conclusion regarding whether mHealth-based disease management programs are more effective than traditional face-to-face care. Future studies are needed to further validate these findings and to examine the comparative effectiveness of these interventions in more detail.

Optimizing Cardiovascular Health: A Comprehensive Review of Risk Assessment Strategies for Primary Prevention

Cardiovascular disease (CVD) is a leading global health concern, and effective primary prevention strategies are essential to mitigate its impact. This comprehensive review examines current risk assessment strategies for primary prevention of CVD, emphasizing the importance of early identification and intervention to reduce disease incidence. Traditional risk factors such as hypertension, hyperlipidemia, smoking, and lifestyle choices are discussed alongside emerging factors, including genetic predispositions and biomarkers. The review evaluates various risk assessment tools and models, such as the Framingham risk score, atherosclerotic CVD risk calculator, QRISK, and Reynolds risk score, highlighting their methodologies, strengths, and limitations. Additionally, the review explores lifestyle modifications, including dietary changes, physical activity, weight management, smoking cessation, and pharmacological interventions like statins and antihypertensives. Special considerations for different populations, including the elderly, women, and those with a family history of CVD, are addressed. Future directions in cardiovascular risk assessment are also discussed, focusing on technological advancements and personalized medicine. This review aims to enhance the implementation of effective primary prevention measures and improve cardiovascular health outcomes by providing a thorough analysis of risk assessment strategies.

Effectiveness of a Smartphone App-Based Intervention With Bluetooth-Connected Monitoring Devices and a Feedback System in Heart Failure (SMART-HF Trial): Randomized Controlled Trial

BACKGROUND

Current heart failure (HF) guidelines recommend a multidisciplinary approach, discharge education, and self-management for HF. However, the recommendations are challenging to implement in real-world clinical settings.

OBJECTIVE

We developed a mobile health (mHealth) platform for HF self-care to evaluate whether a smartphone app-based intervention with Bluetooth-connected monitoring devices and a feedback system can help improve HF symptoms.

METHODS

In this prospective, randomized, multicenter study, we enrolled patients 20 years of age and older, hospitalized for acute HF, and who could use a smartphone from 7 tertiary hospitals in South Korea. In the intervention group (n=39), the apps were automatically paired with Bluetooth-connected monitoring devices. The patients could enter information on vital signs, HF symptoms, diet, medications, and exercise regimen into the app daily and receive feedback or alerts on their input. In the control group (n=38), patients could only enter their blood pressure, heart rate, and weight using conventional, non-Bluetooth devices and could not receive any feedback or alerts from the app. The primary end point was the change in dyspnea symptom scores from baseline to 4 weeks, assessed using a questionnaire.

RESULTS

At 4 weeks, the change in dyspnea symptom score from baseline was significantly greater in the intervention group than in the control group (mean -1.3, SD 2.1 vs mean -0.3, SD 2.3; P=.048). A significant reduction was found in body water composition from baseline to the final measurement in the intervention group (baseline level mean 7.4, SD 2.5 vs final level mean 6.6, SD 2.5; P=.003). App adherence, which was assessed based on log-in or the percentage of days when symptoms were first observed, was higher in the intervention group than in the control group. Composite end points, including death, rehospitalization, and urgent HF visits, were not significantly different between the 2 groups.

CONCLUSIONS

The mobile-based health platform with Bluetooth-connected monitoring devices and a feedback system demonstrated improvement in dyspnea symptoms in patients with HF. This study provides evidence and rationale for implementing mobile app-based self-care strategies and feedback for patients with HF.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05668000; https://clinicaltrials.gov/study/NCT05668000.

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10-3 comparing the DigiPen's computed Δt (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.

Mobile Apps for Common Noncommunicable Disease Management: Systematic Search in App Stores and Evaluation Using the Mobile App Rating Scale

Background

The success of mobile apps in improving the lifestyle of patients with noncommunicable diseases through self-management interventions is contingent upon the emerging growth in this field. While users of mobile health (mHealth) apps continue to grow in number, little is known about the quality of available apps that provide self-management for common noncommunicable diseases such as diabetes, hypertension, and obesity.

Objective

We aimed to investigate the availability, characteristics, and quality of mHealth apps for common noncommunicable disease health management that included dietary aspects (based on the developer's description), as well as their features for promoting health outcomes and self-monitoring.

Methods

A systematic search of English-language apps on the Google Play Store (Google LLC) and Apple App Store (Apple Inc) was conducted between August 7, 2022, and September 13, 2022. The search terms used included weight management, obesity, diabetes, hypertension, cardiovascular diseases, stroke, and diet. The selected mHealth apps' titles and content were screened based on the description that was provided. Apps that were not designed with self-management features were excluded. We analyzed the mHealth apps by category and whether they involved health care professionals, were based on scientific testing, and had self-monitoring features. A validated and multidimensional tool, the Mobile App Rating Scale (MARS), was used to evaluate each mHealth app's quality based on a 5-point Likert scale from 1 (inadequate) to 5 (excellent).

Results

Overall, 42 apps were identified. Diabetes-specific mHealth apps accounted for 7% (n=3) of the market, hypertension apps for 12% (n=5), and general noncommunicable disease management apps for 21% (n=9). About 38% (n=16) of the apps were for managing chronic diseases, while 74% (n=31) were for weight management. Self-management features such as weight tracking, BMI calculators, diet tracking, and fluid intake tracking were seen in 86% (n=36) of the apps. Most mHealth apps (n=37, 88%) did not indicate whether there was involvement of health professionals in app development. Additionally, none of the apps reported scientific evidence demonstrating their efficacy in managing health. The overall mean MARS score was 3.2 of 5, with a range of 2.0 to 4.1. Functionality was the best-rated category (mean score 3.9, SD 0.5), followed by aesthetics (mean score 3.2, SD 0.9), information (mean score 3.1, SD 0.7), and engagement (mean score 2.9, SD 0.6).

Conclusions

The quality of mHealth apps for managing chronic diseases was heterogeneous, with roughly half of them falling short of acceptable standards for both quality and content. The majority of apps contained scant information about scientific evidence and the developer's history. To increase user confidence and accomplish desired health outcomes, mHealth apps should be optimized with the help of health care professionals. Future studies on mHealth content analysis should focus on other diseases as well.

Mobile Health Intervention Contents and Their Effects on the Healthcare of Patients with Left Ventricular Assist Devices: An Integrative Review

Self-care in daily life is important for patients with a left ventricular assist device. Mobile health interventions that use an application, the cloud, or telemonitoring enable active health management. This study reviewed the literature on the contents of mobile health interventions for patients with left ventricular assist devices and their effects. We searched four electronic databases (CINAHL, Cochrane Library, EMBASE, and MEDLINE) and reference lists in May 2022. The search terms consisted of "heart-assist devices" and "residence characteristics," combined with "mobile applications," "telemonitoring," "medical informatics applications," "cell*," "app*," "smartphone," and "cloud." In total, seven studies were included in the review. Mobile health interventions included (1) self-management contents and (2) interactions between patient and healthcare providers. The mobile health device and patient's health management were evaluated as a measure of the effect. This review provides a unique understanding for leveraging mobile health interventions as an effective approach to improve healthcare among patients with left ventricular assist devices. Future mobile health intervention strategies targeting these patients should fully consider the patient's perspective. Furthermore, they should be designed and applied to help with long-term health management, accompanied by an evaluation of their effectiveness on self-care improvement.

Diabetes and hypertension MobileHealth systems: a review of general challenges and advancements

Mobile health (mHealth) systems are sipping into more and more healthcare functions with self-management being the foremost modus operandi. However, there has been challenges. This study explores challenges with mHealth self-management of diabetes and hypertension, two of the most comorbid chronic diseases. Existing literature present the challenges in fragments, certain subsets of the challenges at a time. Nevertheless, feedback from patient/users in extant literature depict very variegated concerns that are also interdependent. This work pursues provision of an encyclopedic, but not redundant, view of the challenges with mHealth systems for self-management of diabetes and hypertension.

Furthermore, the work identifies machine learning (ML) and self-management approaches as potential drivers of potency of diabetes and hypertension mobile health systems. The nexus between ML and diabetes and hypertension mHealth systems was found to be under-explored. For ML contributions to management of diabetes, we found that machine learning has been applied most to diabetes prediction followed by diagnosis, with therapy in distant third. For diabetes therapy research, only physical and dietary therapy were emphasized in reviewed literature. The four most considered performance metrics were accuracy, ROC-AUC, sensitivity, and specificity. Random forest was the best performing algorithm across all metrics, for all purposes covered in the literature. For hypertension, in descending order, hypertension prediction, prediction of risk factors, and prediction of prehypertension were most considered areas of hypertension management witnessing application of machine learning. SVM averaged best ML algorithm in accuracy and sensitivity, while random forest averaged best performing in specificity and ROC-AUC.

E-Health: A Game Changer in Fetal and Neonatal Cardiology?

Technological advancements have greatly impacted the healthcare industry, including the integration of e-health in pediatric cardiology. The use of telemedicine, mobile health applications, and electronic health records have demonstrated a significant potential to improve patient outcomes, reduce healthcare costs, and enhance the quality of care. Telemedicine provides a useful tool for remote clinics, follow-up visits, and monitoring for infants with congenital heart disease, while mobile health applications enhance patient and parents' education, medication compliance, and in some instances, remote monitoring of vital signs. Despite the benefits of e-health, there are potential limitations and challenges, such as issues related to availability, cost-effectiveness, data privacy and security, and the potential ethical, legal, and social implications of e-health interventions. In this review, we aim to highlight the current application and perspectives of e-health in the field of fetal and neonatal cardiology, including expert parents' opinions.

mHealth Self-Monitoring Model for Medicine Adherence of Patients With Diabetes in Resource-Limited Countries: Structural Equation Modeling Approach

BACKGROUND

The COVID-19 pandemic has led to serious challenges and emphasized the importance of using technology for health care operational transformation. Consequently, the need for technological innovations has increased, thus empowering patients with chronic conditions to tighten their adherence to medical prescriptions.

OBJECTIVE

This study aimed to develop a model for a mobile health (mHealth) self-monitoring system for patients with diabetes in rural communities within resource-limited countries. The developed model could be based on the implementation of a system for the self-monitoring of patients with diabetes to increase medical adherence.

METHODS

This study followed a quantitative approach, in which data were collected from health care providers using a questionnaire with close-ended questions. Data were collected from district hospitals in 3 South African provinces that were selected based on the prevalence rates of diabetes and the number of patients with diabetes treated. The collected data were analyzed using smart partial least squares to validate the model and test the suggested hypotheses.

RESULTS

Using variance-based structural equation modeling that leverages smart partial least squares, the analysis indicated that environmental factors significantly influence all the independent constructs that inform patients' change of behavior toward the use of mHealth for self-monitoring of medication adherence. Technology characteristics such as effort expectancy, self-efficacy, and performance expectancy were equally significant; hence, their hypotheses were accepted. In contrast, the contributions of culture and social aspects were found to be insignificant, and their hypotheses were rejected. In addition, an analysis was conducted to determine the interaction effects of the moderating variables on the independent constructs. The results indicated that with the exception of cultural and social influences, there were significant interacting effects on other independent constructs influencing mHealth use for self-monitoring.

CONCLUSIONS

On the basis of the findings of this study, we conclude that behavioral changes are essential for the self-monitoring of chronic diseases. Therefore, it is important to enhance those effects that stimulate the behavior to change toward the use of mHealth for self-monitoring. Motivational aspects were also found to be highly significant as they triggered changes in behavior. The developed model can be used to extend the research on the self-monitoring of patients with chronic conditions. Moreover, the model will be used as a basic architecture for the implementation of fully fledged systems for self-monitoring of patients with diabetes.

Needs and demands for mHealth cardiac health promotion among individuals with cardiac diseases: a patient-centred design approach

Aims

Cardiovascular diseases are one of the main contributors to disability and mortality worldwide. Meanwhile, risk factors can be modified by lifestyle changes. mHealth is an innovative and effective way to deliver cardiac health promotion. This study aims to examine the needs and demands regarding the design and contents of an mHealth intervention for cardiac health promotion among individuals with cardiac diseases. Different clusters were determined and analysed in terms of the intention to use an mHealth intervention.

Methods and results

A cross-sectional study was conducted via a web-based survey. Three hundred and four individuals with coronary artery diseases (CADs) and/or congestive heart failure (CHF) were included in the data analysis. Descriptive statistics were applied to evaluate needs and demands regarding an mHealth intervention. A k-medoids cluster analysis was performed. Individuals with CAD and CHF favoured an mHealth intervention that supports its users permanently and is easily integrated into everyday life. Handheld devices and content formats that involve active user participation and regular updates were preferred. Three clusters were observed and labelled high, moderate, and low burden, according to their psychometric properties. The high burden cluster indicated higher behavioural intention towards use of an mHealth intervention than the other clusters.

Conclusion

The results of the study are a valuable foundation for the development of an mHealth intervention for cardiac health promotion following a user-centred design approach. Individuals with cardiac diseases report positive attitudes in the form of high usage intention regarding mHealth. Highly burdened individuals report a high intention to use such interventions.

Positivity and Health Locus of Control: Key Variables to Intervene on Well-Being of Cardiovascular Disease Patients

Psychological well-being is a good predictor of several health outcomes in cardiovascular disease patients (adherence, quality of life, and healthy behaviors). The perception of health control and a positive orientation seem to have a beneficial effect on health and well-being. Therefore, the aim of this study was to investigate the role of the health locus of control and positivity in the psychological well-being and quality of life of cardiovascular patients. A total of 593 cardiac outpatients completed the Multidimensional Health Locus of Control Scale, the Positivity Scale and the Hospital Anxiety and Depression Scale at baseline (January 2017) and 9 m later (follow-up; n = 323). A Spearman rank correlation coefficient and a structural equation modeling approach were determined to explore the relationships between those variables both cross-sectionally and longitudinally. A cross-sectional correlation analysis at baseline revealed that the internal health locus of control and positivity were negatively associated with anxiety (rs = -0.15 and -0.44, ps < 0.01) and depression (rs = -0.22 and -0.55, ps < 0.01) and positively associated with health-related quality of life (rs = 0.16 and 0.46, ps < 0.01). Similar outcomes were found at follow-up and in longitudinal correlations. According to the path analysis, positivity was found to be negatively associated with anxiety and depression level at baseline (β = -0.42 and -0.45, ps < 0.001). Longitudinally, positivity was negatively associated with depression (β = 0.15, p < 0.01) and together with the internal health locus of control, was positively associated with health-related quality of life (β = 0.16 and 0.10, respectively, ps < 0.05). These findings suggest that focusing on the health locus of control and especially positivity may be crucial in enhancing the psychological well-being of patients in the context of cardiac care. The potential impact of these results on future interventions is discussed.

Sensor-Controlled Digital Game for Heart Failure Self-management: Protocol for a Randomized Controlled Trial

BACKGROUND

Heart failure (HF) is the leading cause of hospitalization among older adults in the United States. There are substantial racial and geographic disparities in HF outcomes, with patients living in southern US states having a mortality rate 69% higher than the national average. Self-management behaviors, particularly daily weight monitoring and physical activity, are extremely important in improving HF outcomes; however, patients typically have particularly low adherence to these behaviors. With the rise of digital technologies to improve health outcomes and motivate health behaviors, sensor-controlled digital games (SCDGs) have become a promising approach. SCDGs, which leverage sensor-connected technologies, offer the benefits of being portable and scalable and allowing for continuous observation and motivation of health behaviors in their real-world contexts. They are also becoming increasingly popular among older adults and offer an immersive and accessible way to measure self-management behaviors and improve adherence. No SCDGs have been designed for older adults or evaluated to test their outcomes.

OBJECTIVE

This randomized clinical trial aims to assess the efficacy of a SCDG in integrating the behavioral data of participants with HF from weight scale and activity tracker sensors to activate game progress, rewards, and feedback and, ultimately, to improve adherence to important self-management behaviors.

METHODS

A total of 200 participants with HF, aged ≥45 years, will be recruited and randomized into 2 groups: the SCDG playing group (intervention group) and sensor-only group (control group). Both groups will receive a weight scale, physical activity tracker, and accompanying app, whereas only the intervention group will play the SCDG. This design, thereby, assesses the contributions of the game. All participants will complete a baseline survey as well as posttests at 6 and 12 weeks to assess the immediate effect of the intervention. They will also complete a third posttest at 24 weeks to assess the maintenance of behavioral changes. Efficacy and benefits will be assessed by measuring improvements in HF-related proximal outcomes (self-management behaviors of daily weight monitoring and physical activity) and distal outcomes (HF hospitalization, quality of life, and functional status) between baseline and weeks 6, 12, and 24. The primary outcome measured will be days with weight monitoring, for which this design provides at least 80% power to detect differences between the 2 groups.

RESULTS

Recruitment began in the fall of 2022, and the first patient was enrolled in the study on November 7, 2022. Recruitment of the last participant is expected in quarter 1 of 2025. Publication of complete results and data from this study is expected in 2026.

CONCLUSIONS

This project will generate insight and guidance for scalable and easy-to-use digital gaming solutions to motivate persistent adherence to HF self-management behaviors and improve health outcomes among individuals with HF.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05056129; https://clinicaltrials.gov/ct2/show/NCT05056129.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45801.

A Digital Infrastructure for Cardiovascular Patient Care Based on Mobile Health Data and Patient-Reported Outcomes: Concept Details of the Helios TeleWear Project Including Preliminary Experiences

BACKGROUND

Mobile health (mHealth) approaches are already having a fundamental impact on clinical practice in cardiovascular medicine. A variety of different health apps and wearable devices for capturing health data such as electrocardiograms (ECGs) exist. However, most mHealth technologies focus on distinct variables without integrating patients' quality of life, and the impact on clinical outcome measures of implementing those digital solutions into cardiovascular health care is still to be determined.

OBJECTIVE

Within this document, we describe the TeleWear project, which was recently initiated as an approach for contemporary patient management integrating mobile-collected health data and the standardized mHealth-guided measurement of patient-reported outcomes (PROs) in patients with cardiovascular disease.

METHODS

The specifically designed mobile app and clinical frontend form the central elements of our TeleWear infrastructure. Because of its flexible framework, the platform allows far-reaching customization with the possibility to add different mHealth data sources and respective questionnaires (patient-reported outcome measures).

RESULTS

With initial focus on patients with cardiac arrhythmias, a feasibility study is currently carried out to assess wearable-recorded ECG and PRO transmission and its evaluation by physicians using the TeleWear app and clinical frontend. First experiences made during the feasibility study yielded positive results and confirmed the platform's functionality and usability.

CONCLUSIONS

TeleWear represents a unique mHealth approach comprising PRO and mHealth data capturing. With the currently running TeleWear feasibility study, we aim to test and further develop the platform in a real-world setting. A randomized controlled trial including patients with atrial fibrillation that investigates PRO- and ECG-based clinical management based on the established TeleWear infrastructure will evaluate its clinical benefits. Widening the spectrum of health data collection and interpretation beyond the ECG and use of the TeleWear infrastructure in different patient subcohorts with focus on cardiovascular diseases are further milestones of the project with the ultimate goal to establish a comprehensive telemedical center entrenched by mHealth.

Patient and Healthcare Professional Priorities for a Mobile Phone Application for Patients With Peripheral Arterial Disease

Introduction Supervised exercise therapy (SET) is the first-line treatment for the peripheral arterial disease (PAD), however, access and compliance are low. An alternative method of delivering this therapy is through mobile health applications, which can be more accessible and convenient for patients. The aim of this study is to evaluate patient, public and healthcare professional (HCP) priorities with regard to a dedicated mobile phone application to deliver remote SET. Methods Bespoke questionnaires were designed for patients and HCPs to assess app functionality and prioritisations for development. These were distributed through social media and the Norfolk and Norwich University Hospital. Results Functionality questionnaires were completed by 62 patients and 44 HCPs. Eighty-four per cent of patients wanted their therapy to be monitored by their vascular team with the majority (78%) interested in measuring walking distances. Most patients (76%) were interested in watching exercise videos. These views were shared by HCPs. A communication platform was prioritised for messaging and pictures by the patient (74% and 68% respectively), but not so by HCPs (40%). Documenting other forms of physical activity and the use of wearable technology was less valuable to patients but favoured by HCPs (50%). The ability to interact with other users was not prioritised by either group. Conclusion Delivery of a mobile phone application to deliver health programmes for SET in patients with PAD is an acceptable method for patients and HCPs. This data will enable the next stages of mobile phone application development to be appropriately prioritised, focusing on building exercise videos, a communication platform and further walking tests.

ECG Standards and Formats for Interoperability between mHealth and Healthcare Information Systems: A Scoping Review

Interoperability is defined as the ability of a system or device to communicate between different technologies and software applications. This allows the exchange and use of data in an efficient, precise, and robust way. The present article gives researchers and healthcare information systems developers a qualitative and quantitative synthesis of the state of knowledge related to data formats and data standards proposed for mHealth devices interoperability in healthcare information systems that retrieve and store ECG data. We carry out a scoping review to answer to following questions: (1) What digital data formats or data standards have been proposed for the interoperability of electrocardiograph data between traditional healthcare information systems and mobile healthcare information systems? (2) What are the advantages and disadvantages of these data formats or data standards? The scoping review was conducted in four databases in accordance with the JBI methodology for scoping reviews, and in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A total of 4018 studies were identified of which 30 studies met the inclusion criteria. Based on our findings, we identify four standards and nine formats for capturing and storing streaming ECG data in mobile health applications. The standards used were HL7, SCP-ECG, x73-PHD, and PDF/A. Formats include CSV, PDF-ECG, and seven XML-based formats. These are ECG-XML, HL7-XML, mPCG-XML, mECGML, JSON, SaECG, and CDA R2.

A Brief mHealth-Based Psychological Intervention in Emotion Regulation to Promote Positive Subjective Well-Being in Cardiovascular Disease Patients: A Non-Randomized Controlled Trial

The emotional impact that a cardiovascular disease may have on a person’s life can affect the prognosis and comorbidity of the disease. Therefore, emotion regulation is most important for the management of the disease. The aim of this study was to analyze the effectiveness of a brief mHealth psychological intervention in emotion regulation to promote positive subjective well-being in cardiovascular disease patients. The study sample (N = 69, 63.7 ± 11.5 years) was allocated to either the experimental group (n = 34) or control group (n = 35). The intervention consisted of a psychoeducational session in emotion regulation and an mHealth-based intervention for 2 weeks. Positive subjective well-being as a primary outcome and self-efficacy to manage the disease as a secondary outcome were assessed at five time points evaluated over a period of 6 weeks. The experimental group showed higher improvement in positive subjective well-being and self-efficacy for managing the disease compared to the control group over time. The experimental group also improved after the intervention on all outcome measures. Brief mHealth interventions in emotion regulation might be effective for improving positive subjective well-being and self-efficacy to manage the disease in cardiovascular patients.

Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset.

Wearable Devices for Physical Monitoring of Heart: A Review

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients' biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient's medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient's health.

Patient and physician perspectives on the use and outcome measures of mHealth apps: Exploratory survey and focus group study

Objective

Factors that physicians and patients consider when making decisions about using or recommending health apps are not well understood. We explored these factors to better assess how to support such decision making.

Methods

We conducted an exploratory cross-sectional study in Ontario using qualitative focus groups and quantitative surveys. 133 physicians and 94 community dwelling adults completed online surveys and we held two focus groups of nine community dwelling participants who had cardiovascular risk factors and an interest in using mHealth apps. Quantitative survey data was analyzed descriptively. Focus groups were audio-recorded and transcribed verbatim prior to inductive thematic content analysis. We integrated the results from the surveys and focus groups to understand factors that influence physicians' and patients' selection and use of such apps.

Results

Physicians recommend apps to patients but the level of evidence they prefer to use to guide selection did not align with what they were currently using. Patients trusted recommendations and reviews from medical organizations and healthcare professionals when selecting apps and were motivated to continue using apps when they supported goal setting and tracking, data sharing, decision making, and empowerment.

Conclusions

The findings highlight the significance of evaluating mHealth apps based on metrics that patients and physicians value beyond usage and clinical outcome data. Patients engage with apps that support them in confidently managing their health. Increased training and awareness of apps and creating a more rigorous evidence base showing the value of apps to supporting health goals will support greater adoption and acceptance of mHealth apps.