Background Approximately 50% of cardiovascular disease (CVD) cases are attributable to lifestyle risk factors. Despite widespread education, personal knowledge, and efficacy, many individuals fail to adequately modify these risk factors, even after a cardiovascular event. Digital technology interventions have been suggested as a viable equivalent and potential alternative to conventional cardiac rehabilitation care centers. However, little is known about the clinical effectiveness of these technologies in bringing about behavioral changes in patients with CVD at an individual level. Objective The aim of this study is to identify and measure the effectiveness of digital technology (eg, mobile phones, the internet, software applications, wearables, etc) interventions in randomized controlled trials (RCTs) and determine which behavior change constructs are effective at achieving risk factor modification in patients with CVD. Methods This study is a systematic review and meta-analysis of RCTs designed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement standard. Mixed data from studies extracted from selected research databases and filtered for RCTs only were analyzed using quantitative methods. Outcome hypothesis testing was set at 95% CI and P=.05 for statistical significance. Results Digital interventions were delivered using devices such as cell phones, smartphones, personal computers, and wearables coupled with technologies such as the internet, SMS, software applications, and mobile sensors. Behavioral change constructs such as cognition, follow-up, goal setting, record keeping, perceived benefit, persuasion, socialization, personalization, rewards and incentives, support, and self-management were used. The meta-analyzed effect estimates (mean difference [MD]; standard mean difference [SMD]; and risk ratio [RR]) calculated for outcomes showed benefits in total cholesterol SMD at −0.29 [−0.44, −0.15], P<.001; high-density lipoprotein SMD at –0.09 [–0.19, 0.00], P=.05; low-density lipoprotein SMD at −0.18 [−0.33, −0.04], P=.01; physical activity (PA) SMD at 0.23 [0.11, 0.36], P<.001; physical inactivity (sedentary) RR at 0.54 [0.39, 0.75], P<.001; and diet (food intake) RR at 0.79 [0.66, 0.94], P=.007. Initial effect estimates showed no significant benefit in body mass index (BMI) MD at −0.37 [−1.20, 0.46], P=.38; diastolic blood pressure (BP) SMD at −0.06 [−0.20, 0.08], P=.43; systolic BP SMD at −0.03 [−0.18, 0.13], P=.74; Hemoglobin A1C blood sugar (HbA1c) RR at 1.04 [0.40, 2.70], P=.94; alcohol intake SMD at −0.16 [−1.43, 1.10], P=.80; smoking RR at 0.87 [0.67, 1.13], P=.30; and medication adherence RR at 1.10 [1.00, 1.22], P=.06. Conclusions Digital interventions may improve healthy behavioral factors (PA, healthy diet, and medication adherence) and are even more potent when used to treat multiple behavioral outcomes (eg, medication adherence plus). However, they did not appear to reduce unhealthy behavioral factors (smoking, alcohol intake, and unhealthy diet) and clinical outcomes (BMI, triglycerides, diastolic and systolic BP, and HbA1c).

An ageing population and chronic disease are putting pressure on the Irish health system. The field of eHealth is rapidly evolving and has the potential to become an important component of healthcare, but there appears to be a gap currently between research in this field and the integration of eHealth technology into clinical practice. During the eHealth Ireland Ecosystem Conference held in April 2018, a workshop was conducted to explore the barriers and facilitators to the adoption of eHealth technology, particularly remote monitoring systems in community and home cardiac care. Participants included clinicians, academic researchers, technologists, patient advocates, policy makers, and representatives from the health service. The conversations in the workshop pivoted around why technology systems in cardiac care rarely moved beyond the research project stage and what can be done to address this issue. The discussions in the workshop focused around the lack of funding available, the need for reimbursement models, the lack of awareness about remote monitoring, the angst about who is responsible for the data generated, the design of systems, regulatory standards, and the increasing demand on services, education, and patient empowerment.

Background Managing the care of older adults with heart failure (HF) largely centers on medication management. Because of frequent medication or dosing changes, an app that supports these older adults in keeping an up-to-date list of medications could be advantageous. During the COVID-19 pandemic, HF outpatient consultations are taking place virtually or by telephone. An app with the capability to share a patient’s medication list with health care professionals before consultation could support clinical efficiency, for example, by reducing consultation time. However, the influence of apps on maintaining an up-to-date medication history for older adults with HF in Ireland remains largely unexplored. Objective The aims of this review are twofold: to review apps with a medication list functionality and to assess the quality of the apps included in the review using the Mobile App Rating Scale (MARS) and the IMS Institute for Healthcare Informatics functionality scale. Methods A systematic search of apps was conducted in June 2019 using the Google Play Store and iTunes App Store. The MARS was used independently by 4 researchers to assess the quality of the apps using an Android phone and an iPad. Apps were also evaluated using the IMS Institute for Healthcare Informatics functionality score. Results Google Play and iTunes App store searches identified 483 potential apps (292 from Google Play and 191 from iTunes App stores). A total of 6 apps (3 across both stores) met the inclusion criteria. Of the 6 apps, 4 achieved an acceptable MARS score (3/5). The Medisafe app had the highest overall MARS score (4/5), and the Medication List & Medical Records app had the lowest overall score (2.5/5). On average, the apps had 8 functions based on the IMS functionality criteria (range 5-11). A total of 2 apps achieved the maximum score for number of features (11 features) according to the IMS Institute for Healthcare Informatics functionality score, and 2 scored the lowest (5 features). Peer-reviewed publications were identified for 3 of the apps. Conclusions The quality of current apps with medication list functionality varies according to their technical aspects. Most of the apps reviewed have an acceptable MARS objective quality (ie, the overall quality of an app). However, subjective quality (ie, satisfaction with the apps) was poor. Only 3 apps are based on scientific evidence and have been tested previously. A total of 2 apps featured all the IMS Institute for Healthcare Informatics functionalities, and half did not provide clear instructions on how to enter medication data, did not display vital parameter data in an easy-to-understand format, and did not guide users on how or when to take their medication.

BACKGROUND Cardiovascular diseases (CVDs) remain one of the commonest causes of early death and disability worldwide with 17.9 million deaths and 422.7 million cases annually. There are approximately 1.7 million inpatient episodes in the UK per year. Approximately 50% of CVD is attributable to lifestyle risk factors. Despite widespread education, personal knowledge and efficacy, many individuals fail to adequately modify these risk factors, even after a cardiovascular event. Digital technologies have been suggested as a viable equivalent and potential alternative to conventional cardiac rehabilitation centre care. However, little is known about the clinical effectiveness of these technologies in bringing about behaviour change in CVD patients at individual level. OBJECTIVE This systematic review seeks to 1) identify digital technologies and measure effectiveness of their interventions that have been tested in randomized control trials (RCTs) and 2) summarize their behavioural change and clinical outcome applications, and demographic qualities; for risk factor modification among CVD patients. METHODS Mixed data from studies, extracted from selected research databases and filtered to RCTs only, were analysed using qualitative and quantitative methods. RESULTS The use of digital technologies in cardiac patients was associated with improvements in total cholesterol, high density lipoprotein, low density lipoprotein, physical activity, physical inactivity (sedentary), healthy diet and medication adherence (at P≤0.05). However, there were no differences seen in body mass index, triglycerides, blood pressures (diastolic and systolic), blood sugar, alcohol intake and smoking (at P=0.05). CONCLUSIONS This systematic review concludes that digital technology interventions may have benefit in improving protective behavioural factors (physical activity, healthy diet and medication adherence) and more potent when engaged in multiple behavioural outcome treatment (e.g. medication adherence plus…), but did not appear to reduce risky behavioural factors (smoking, alcohol intake and unhealthy diet) and clinical outcomes (body mass index, diastolic blood pressure, systolic blood pressure and blood sugar, HbA1c).

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life and users’ inability to remember to charge and wear the devices are often the challenges for their usage. In addition, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. Therefore, this paper proposes the use and fusion of privacy-friendly and Unobtrusive Sensing Solutions (USSs) for data collection and processing during SPAREs in home environments. The present work aims to monitor SPAREs such as dorsiflexion, plantarflexion, inversion, and eversion using radar and thermal sensors. The main contributions of this paper include (i) privacy-friendly monitoring of SPAREs in a home environment, (ii) fusion of SPAREs data from homogeneous and heterogeneous USSs, and (iii) analysis and comparison of results from single, homogeneous, and heterogeneous USSs. Experimental results indicated the advantages of using heterogeneous USSs and data fusion. Cluster-based analysis of data gleaned from the sensors indicated an average classification accuracy of 96.9% with Neural Network, AdaBoost, and Support Vector Machine, amongst others.

BACKGROUND Managing the care of older patients with heart failure (HF) largely centres on medication management. Due to their frequent medication or dosing changes, an app supporting them to keep an up-to-date list of medication could be advantageous. During COVID-19 times, HF outpatients’ consultations are taking place virtually or by phone. An app with the capability to share the medication list with healthcare professionals before consultation could support the clinic efficiency, for example, reducing consultation time. However, the influence of apps on maintaining an up to date medication history for older adults with HF in Ireland remains largely unexplored. OBJECTIVE The objectives of this review are twofold: to review apps with a medication list functionality and to evaluate the quality of the apps included in the review using the Mobile Application Rating Scale (MARS) and the IMS Institute for Healthcare Informatics functionality scale. METHODS A systematic search of apps was conducted in June 2019 using the Google Play StoreTM and iTunes App StoreTM. The MARS was used independently by four researchers to assess the quality of the apps using an Android phone and an iPad. Apps were also evaluated using the IMS Institute for Healthcare Informatics functionality score. RESULTS Google play and iOS app stores searches identified 483 potential apps (292 from Android stores and 191 from Apple stores). Six apps met the inclusion criteria. Medisafe app had the highest overall MARS score (4/5) and the medication list & medical records app had the lowest overall score (2.5/5). Five out of the six apps achieved an acceptable quality MARS score (>3.0). Two apps scored the maximum number of features (n=11) according to the IMS Institute for Healthcare Informatics functionality score and two scored the lowest (n=5). The apps had on average 8 functions based on the IMS functionality criteria (range 5 to 11). CONCLUSIONS The quality of current apps with a medication list functionality varies regarding their technical aspects. Most of the apps reviewed have an acceptable MARS objective quality. However, the subjective quality or satisfaction with the apps was poor. Only three apps are based on scientific evidence and have been previously tested. Two apps featured all the IMS Institute for Healthcare Informatics functionalities and half do not provide clear instructions on how to enter medication data, do not display vital parameters data in an easy to understand format and do not guide users on how or when to take their medication. CLINICALTRIAL N/A

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