Multifactorial e- and mHealth interventions for cardiovascular disease primary prevention: Protocol for a systematic review and meta-analysis of randomised controlled trials

Effective Behavior Change Techniques in Digital Health Interventions for the Prevention or Management of Noncommunicable Diseases: An Umbrella Review

BACKGROUND

Despite an abundance of digital health interventions (DHIs) targeting the prevention and management of noncommunicable diseases (NCDs), it is unclear what specific components make a DHI effective.

PURPOSE

This narrative umbrella review aimed to identify the most effective behavior change techniques (BCTs) in DHIs that address the prevention or management of NCDs.

METHODS

Five electronic databases were searched for articles published in English between January 2007 and December 2022. Studies were included if they were systematic reviews or meta-analyses of DHIs targeting the modification of one or more NCD-related risk factors in adults. BCTs were coded using the Behavior Change Technique Taxonomy v1. Study quality was assessed using AMSTAR 2.

RESULTS

Eighty-five articles, spanning 12 health domains and comprising over 865,000 individual participants, were included in the review. We found evidence that DHIs are effective in improving health outcomes for patients with cardiovascular disease, cancer, type 2 diabetes, and asthma, and health-related behaviors including physical activity, sedentary behavior, diet, weight management, medication adherence, and abstinence from substance use. There was strong evidence to suggest that credible source, social support, prompts and cues, graded tasks, goals and planning, feedback and monitoring, human coaching and personalization components increase the effectiveness of DHIs targeting the prevention and management of NCDs.

CONCLUSIONS

This review identifies the most common and effective BCTs used in DHIs, which warrant prioritization for integration into future interventions. These findings are critical for the future development and upscaling of DHIs and should inform best practice guidelines.

Association of a Community Population and Clinic Education Intervention Program With Guideline-Based Aspirin Use for Primary Prevention of Cardiovascular Disease: A Nonrandomized Controlled Trial

Importance

Low-dose aspirin is used for primary prevention of cardiovascular disease in approximately one-third of the US adult population. Overuse and underuse are common and not concordant with guidelines.

Objective

To test a community and clinic education intervention to improve guideline-based aspirin use for the primary prevention of cardiovascular disease.

Design, Setting, and Participants

The Ask About Aspirin project was a nonrandomized controlled trial conducted from, July 1, 2015, to March 31, 2020, using professional education, traditional media, and digital media to improve guideline-based aspirin use. The adult population (aged 45-79 years for men and 55-79 years for women) and primary care clinics in Minnesota were the education targets. The 4 adjacent states were controls.

Interventions

The statewide campaign distributed billboards, newspaper articles and other print material, and radio announcements. An Ask About Aspirin website was heavily promoted. Primary care clinics identified appropriate aspirin candidates, and clinicians received continuing education about aspirin.

Main Outcomes and Measures

Guideline-based aspirin use by the target population.

Results

Cross-sectional random telephone surveys of 8342 men aged 45 to 79 years and women aged 55 to 79 years were conducted at baseline, 2 years, and 4 years after the intervention. Participation was similar between men and women (baseline: 973 [49%] vs 1001 [51%]; year 4: 912 [50%] vs 930 [50%]). Age during the study also was similar (baseline: 64.7 [IQR, 64.4-65.1] years; year 4: 66.2 [IQR, 65.8-66.5] years). A validated questionnaire evaluated aspirin use. The Ask About Aspirin website had more than 1 million visits; 124 primary care clinics with more than 1000 participating clinicians were part of the education program. Small, nonsignificant increases in discussions with clinicians regarding aspirin resulted (baseline: 341 of 1001 [34%]; year 4: 339 of 930 [36%]; P = .27). Overall aspirin use decreased after the release of new US Preventive Services Task Force guidelines in 2016 and 3 aspirin randomized clinical trials in 2018 suggested reduced aspirin use (baseline: 816 of 1974 [41%]; year 4: 629 of 1842 [34%]; P < .001). Decreases were also noted from year 2 to year 4 in appropriate use (year 2: 597 of 1208 [49%]; year 4: 478 of 1191 [40%]; P < .001) and overuse (year 2: 170 of 602 [28%]; year 4: 151 of 651 [23%]; P = .04). There were no significant differences between Minnesota and the control states.

Conclusions and Relevance

In this nonrandomized controlled trial, a multiyear statewide campaign was not associated with increased appropriate aspirin use for cardiovascular disease prevention. Contextual factors during the project, including guideline changes and media controversy following the new trials, undermined study goals. These findings suggest that although education programs using social media for cardiovascular disease prevention can result in millions of hits, the use of this strategy to encourage behavior change is problematic, even with supportive clinical sites.

Trial Registration

ClinicalTrials.gov Identifier: NCT02607917.

Big Data in Cardiology: State-of-Art and Future Prospects

Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.

Digital health RCT interventions for cardiovascular disease risk reduction: a systematic review and meta-analysis

Heart disease is a leading cause of UK mortality. Evidence suggests digital health interventions (DHIs), such as smartphone applications, may reduce cardiovascular risk, but no recent reviews are available. This review examined the effect of DHIs on cardiovascular disease (CVD) risk scores in patients with increased CVD risk, compared to usual care alone. PubMed, Cochrane Database, Medline, and Google Scholar were searched for eligible trials published after 01/01/2010, involving populations with at least one CVD risk factor. Primary outcome was change in CVD risk score (e.g. QRISK3) between baseline and follow-up. Meta-analysis was undertaken using Revman5/STATA using random-effects modelling. Cochrane RoB-2 tool determined risk-of-bias. 6 randomised controlled trials from 36 retrieved articles (16.7%) met inclusion criteria, involving 1,157 patients treated with DHIs alongside usual care, and 1,127 patients offered usual care only (control group). Meta-analysis using random-effects model in STATA showed an inconclusive effect for DHIs as effective compared to usual care (Mean Difference, MD -0.76, 95% CI -1.72, 0.20), with moderate certainty (GRADEpro). Sensitivity analysis by DHI modality suggested automated email messaging was the most effective DHI (MD -1.09, 95% Cl -2.15, -0.03), with moderate certainty (GRADEpro). However, substantial study heterogeneity was noted in main and sensitivity analyses (I2 = 66% and 64% respectively). Quality assessment identified risk-of-bias concerns, particularly for outcome measurement. Findings suggest specific DHIs such as automated email messaging may improve CVD risk outcomes, but were inconclusive for DHIs overall. Further research into specific DHI modalities is required, with longer follow-up.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12553-022-00651-0.

Classification of cardiac arrhythmia using a convolutional neural network and bi-directional long short-term memory

Cardiac arrhythmia is a leading cause of cardiovascular disease, with a high fatality rate worldwide. The timely diagnosis of cardiac arrhythmias, determined by irregular and fast heart rate, may help lower the risk of strokes. Electrocardiogram signals have been widely used to identify arrhythmias due to their non-invasive approach. However, the manual process is error-prone and time-consuming. A better alternative is to utilize deep learning models for early automatic identification of cardiac arrhythmia, thereby enhancing diagnosis and treatment. In this article, a novel deep learning model, combining convolutional neural network and bi-directional long short-term memory, is proposed for arrhythmia classification. Specifically, the classification comprises five different classes: non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F), and unknown (Q) beats. The proposed model is trained, validated, and tested using MIT-BIH and St-Petersburg data sets separately. Also, the performance was measured in terms of precision, accuracy, recall, specificity, and f1-score. The results show that the proposed model achieves training, validation, and testing accuracies of 100%, 98%, and 98%, respectively with the MIT-BIH data set. Lower accuracies were shown for the St-Petersburg data set. The performance of the proposed model based on the MIT-BIH data set is also compared with the performance of existing models based on the MIT-BIH data set.

A community-based group randomized trial to increase aspirin use for primary prevention of cardiovascular disease: Study protocol and baseline results for the "Ask About Aspirin" initiative

Background

USPSTF evidence-based recommendations for the use of low-dose aspirin for primary prevention of cardiovascular disease were published in 2009. We describe a statewide campaign using innovative methods to educate the public and health communities about appropriate aspirin use.

Methods/design

The "Ask About Aspirin" initiative is designed to lower the number of first heart attacks and strokes in the State of Minnesota by promoting the appropriate use of low dose aspirin. A health system intervention combined with an aspirin awareness media campaign will be evaluated in a pragmatic group randomized controlled trial including 267 primary care clinics within 84 health systems over a four year period. Matched pairs of geographic territories will be randomized to intervention (12 territories) or control (12 territories). The primary outcome of appropriate aspirin use will be measured at the individual level, by community-based telephone surveys of 100 participants in each of the 24 geographically determined clusters.

Discussion

We briefly describe the rationale for the interventions being studied, as well as the major design choices. Rigorous research designs such as the one described here are necessary to determine whether evidence-based recommendations can be effectively disseminated in multiple health systems.

Trial registration

ClinicalTrials.gov: NCT02607917.