World Heart Federation Roadmap for Secondary Prevention of Cardiovascular Disease: 2023 Update.

Background: Secondary prevention lifestyle and pharmacological treatment of atherosclerotic cardiovascular disease (ASCVD) reduce a high proportion of recurrent events and mortality. However, significant gaps exist between guideline recommendations and usual clinical practice. Objectives: Describe the state of the art, the roadblocks, and successful strategies to overcome them in ASCVD secondary prevention management. Methods: A writing group reviewed guidelines and research papers and received inputs from an international committee composed of cardiovascular prevention and health systems experts about the article's structure, content, and draft. Finally, an external expert group reviewed the paper. Results: Smoking cessation, physical activity, diet and weight management, antiplatelets, statins, beta-blockers, renin-angiotensin-aldosterone system inhibitors, and cardiac rehabilitation reduce events and mortality. Potential roadblocks may occur at the individual, healthcare provider, and health system levels and include lack of access to healthcare and medicines, clinical inertia, lack of primary care infrastructure or built environments that support preventive cardiovascular health behaviours. Possible solutions include improving health literacy, self-management strategies, national policies to improve lifestyle and access to secondary prevention medication (including fix-dose combination therapy), implementing rehabilitation programs, and incorporating digital health interventions. Digital tools are being examined in a range of settings from enhancing self-management, risk factor control, and cardiac rehab. Conclusions: Effective strategies for secondary prevention management exist, but there are barriers to their implementation. WHF roadmaps can facilitate the development of a strategic plan to identify and implement local and national level approaches for improving secondary prevention.

Smartphone Applications to Prevent Type 2 Diabetes: A Systematic Review and Meta-Analysis.

INTRODUCTION: Evidence supporting the use of apps for lifestyle behavior change and diabetes prevention in people at high risk of diabetes is lacking. The aim of this systematic review was to determine the acceptability and effectiveness of smartphone applications (apps) for the prevention of type 2 diabetes. METHODS: PubMed, Embase, CINAHL and PsychInfo were searched from 2008 to 2023. Included studies involved adults at high risk of developing diabetes evaluating an app intervention with the aim of preventing type 2 diabetes. Random-effects meta-analyses were conducted for weight loss, body mass index (BMI), glycated hemoglobin, and waist circumference. Narrative synthesis was conducted for all studies, including qualitative studies exploring user perspectives. RESULTS: Twenty-four studies (n=2,378) were included in this systematic review, including 9 randomized controlled trials (RCTs) with an average duration of 6 months, 10 quasi-experimental and 7 qualitative studies. Socially disadvantaged groups were poorly represented. Six RCTs were combined in meta-analyses. Apps were effective at promoting weight loss [mean difference (MD) -1.85; 95% CI -2.90 to -0.80] and decreasing BMI [MD -0.90, 95% CI -1.53 to -0.27], with no effect on glycated hemoglobin and waist circumference. No studies reported on diabetes incidence. Qualitative studies highlighted the need for app personalization. DISCUSSION: Smartphone apps have a promising effect on preventing type 2 diabetes by supporting weight loss. Future robust trials should include diverse populations in co-design and evaluation of apps and explore the role of artificial intelligence in further personalizing interventions for higher engagement and effectiveness.

Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal.

Artificial Intelligence (AI) is increasingly influential across various sectors, including healthcare, with the potential to revolutionize clinical practice. However, risks associated with AI adoption in medicine have also been identified. Despite the general understanding that AI will impact healthcare, studies that assess the perceptions of medical doctors about AI use in medicine are still scarce. We set out to survey the medical doctors licensed to practice medicine in Portugal about the impact, advantages, and disadvantages of AI adoption in clinical practice. We designed an observational, descriptive, cross-sectional study with a quantitative approach and developed an online survey which addressed the following aspects: impact on healthcare quality of the extraction and processing of health data via AI; delegation of clinical procedures on AI tools; perception of the impact of AI in clinical practice; perceived advantages of using AI in clinical practice; perceived disadvantages of using AI in clinical practice and predisposition to adopt AI in professional activity. Our sample was also subject to demographic, professional and digital use and proficiency characterization. We obtained 1013 valid, fully answered questionnaires (sample representativeness of 99%, confidence level (p< 0.01), for the total universe of medical doctors licensed to practice in Portugal). Our results reveal that, in general terms, the medical community surveyed is optimistic about AI use in medicine and are predisposed to adopt it while still aware of some disadvantages and challenges to AI use in healthcare. Most medical doctors surveyed are also convinced that AI should be part of medical formation. These findings contribute to facilitating the professional integration of AI in medical practice in Portugal, aiding the seamless integration of AI into clinical workflows by leveraging its perceived strengths according to healthcare professionals. This study identifies challenges such as gaps in medical curricula, which hinder the adoption of AI applications due to inadequate digital health training. Due to high professional integration in the healthcare sector, particularly within the European Union, our results are also relevant for other jurisdictions and across diverse healthcare systems.

Oral health literacy, knowledge and perceptions in a socially and culturally diverse population: a mixed methods study.

BACKGROUND: Poor oral health literacy has been proposed as a causal factor in disparities in oral health outcomes. This study aims to investigate oral health literacy (OHL) in a socially and culturally diverse population of Australian adults visiting a public dental clinic in Western Sydney. METHODS: A mixed methods study where oral health literacy was assessed using the Health Literacy in Dentistry scale (HeLD-14) questionnaire and semi-structured interviews explored oral health related knowledge, perceptions and attitudes. Interviews were analysed using a thematic approach. RESULTS: A sample of 48 participants attending a public dental clinic in Western Sydney was recruited, with a mean age of 59.9 (SD16.2) years, 48% female, 50% born in Australia, 45% with high school or lower education, and 56% with low-medium OHL. A subgroup of 21 participants with a mean age of 68.1 (SD14.6) years, 40% female, 64% born in Australia, 56% with a high school or lower education, and 45% with low-medium OHL completed the interview. Three themes identified from the interviews included 1) attitudes and perceptions about oral health that highlighted a lack of agency and low prioritisation of oral health, 2) limited knowledge and education about the causes and consequences of poor oral health, including limited access to oral health education and finally 3) barriers and enablers to maintaining good oral health, with financial barriers being the main contributor to low OHL. CONCLUSIONS: Strategies aimed at redressing disparities in oral health status should include improving access to oral health information. The focus should be on the impact poor oral health has on general health with clear messages about prevention and treatment options in order to empower individuals to better manage their oral health.

Neuroticism personality traits are linked to adverse cardiovascular phenotypes in the UK Biobank.

AIMS: To evaluate the relationship between neuroticism personality traits and cardiovascular magnetic resonance (CMR) measures of cardiac morphology and function, considering potential differential associations in men and women. METHODS AND RESULTS: The analysis includes 36 309 UK Biobank participants (average age = 63.9 ± 7.7 years; 47.8% men) with CMR available and neuroticism score assessed by the 12-item Eysenck Personality Questionnaire-Revised Short Form. CMR scans were performed on 1.5 Tesla scanners (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) according to pre-defined protocols and analysed using automated pipelines. We considered measures of left ventricular (LV) and right ventricular (RV) structure and function, and indicators of arterial compliance. Multivariable linear regression was used to estimate association of neuroticism score with individual CMR metrics, with adjustment for age, sex, obesity, deprivation, smoking, diabetes, hypertension, hypercholesterolaemia, alcohol use, exercise, and education. Higher neuroticism scores were associated with smaller LV and RV end-diastolic volumes, lower LV mass, greater concentricity (higher LV mass to volume ratio), and higher native T1. Greater neuroticism was also linked to poorer LV and RV function (lower stroke volumes) and greater arterial stiffness. In sex-stratified analyses, the relationships between neuroticism and LV stroke volume, concentricity, and arterial stiffness were attenuated in women. In men, association (with exception of native T1) remained robust. CONCLUSION: Greater tendency towards neuroticism personality traits is linked to smaller, poorer functioning ventricles with lower LV mass, higher myocardial fibrosis, and higher arterial stiffness. These relationships are independent of traditional vascular risk factors and are more robust in men than women.

Factors influencing blood pressure control in patients with atrial fibrillation and hypertension in Australian primary care.

OBJECTIVE: This study explored factors that may influence blood pressure (BP) control in patients with atrial fibrillation (AF) with hypertension. METHODS: Cross-sectional retrospective analysis of the MedicineInsight database which includes de-identified electronic health records from general practices (GPs) across Australia. BP control was assessed in patients with diagnosed AF and hypertension (controlled BP defined as <140/90 mm Hg). We explored BP control, factors influencing BP control and likelihood of receiving guideline-recommended treatment. RESULTS: 34 815 patients with AF and hypertension were included; mean age was 76.9 (10.2 SD) years and 46.2% were female. 38.0% had uncontrolled BP. Women (OR 0.72; 95% CI 0.68, 0.76; p<0.001) and adults ≥75 years (OR 0.78; 95% CI 0.70, 0.86; p<0.001) were less likely to have controlled BP. Greater continuity of care (CoC; that is, visits with the same clinician) and having frequent GP visits were associated with higher odds of controlled BP (model 1: CoC, OR 1.29; 95% CI 1.20, 1.40, p<0.001; GP visits, OR 1.71; 95% CI 1.58, 1.85, p<0.001) and a greater likelihood of being prescribed ≥2 types of BP-lowering medicines (model 2: CoC, OR 1.12; 95% CI 1.03, 1.23; p=0.011; GP visits, OR 1.80; 95% CI 1.63, 1.98; p<0.001). CONCLUSIONS: Uncontrolled BP was more likely in women and adults ≥75 years. Patients who had frequent GP visits with the same clinician were more likely to have BP controlled and receive guideline-recommended antihypertensive treatment. This suggests that targeting these primary care factors could potentially improve BP control and subsequently reduce stroke risk in patients with AF.

Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.

BACKGROUND: Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication. METHODS AND RESULTS: MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302 515 individuals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740-0.806) and 0.759 (95% CI: 0.726-0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139-0.140), P < 0.0001. CONCLUSION: ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention.This review was registered with PROSPERO (CRD42020220811).

Primary prevention of cardiovascular disease in women with a Mediterranean diet: systematic review and meta-analysis.

BACKGROUND: Dietary modification is a cornerstone of cardiovascular disease (CVD) prevention. A Mediterranean diet has been associated with a lower risk of CVD but no systematic reviews have evaluated this relationship specifically in women. OBJECTIVE: To determine the association between higher versus lower adherence to a Mediterranean diet and incident CVD and total mortality in women. METHODS: A systematic search of Medline, Embase, CINAHL, Scopus, and Web of Science (2003-21) was performed. Randomised controlled trials and prospective cohort studies with participants without previous CVD were included. Studies were eligible if they reported a Mediterranean diet score and comprised either all female participants or stratified outcomes by sex. The primary outcome was CVD and/or total mortality. A random effects meta-analysis was conducted to calculate pooled hazard ratios (HRs) and confidence intervals (CIs). RESULTS: Sixteen prospective cohort studies were included in the meta-analysis (n=7 22 495 female participants). In women, higher adherence to a Mediterranean diet was associated with a lower CVD incidence (HR 0.76, 95% CI 0.72 to 0.81; I2=39%, p test for heterogeneity=0.07), total mortality (HR 0.77, 95% CI 0.74 to 0.80; I2=21%, p test for heterogeneity=0.28), and coronary heart disease (HR 0.75, 95% CI 0.65 to 0.87; I2=21%, p test for heterogeneity=0.28). Stroke incidence was lower in women with higher Mediterranean diet adherence (HR 0.87, 95% CI 0.76 to 1.01; I2=0%, p test for heterogeneity=0.89), but this result was not statistically significant. CONCLUSION: This study supports a beneficial effect of the Mediterranean diet on primary prevention of CVD and death in women, and is an important step in enabling sex specific guidelines.

Stakeholder preferences for attributes of digital health technologies to consider in health service funding.

OBJECTIVES: Health service providers are currently making decisions on the public funding of digital health technologies (DHTs) for managing chronic diseases with limited understanding of stakeholder preferences for DHT attributes. This study aims to understand the community, patient/carer, and health professionals' preferences to help inform a prioritized list of evaluation criteria. METHODS: An online best-worst scaling survey was conducted in Australia, New Zealand, Canada, and the United Kingdom to ascertain the relative importance of twenty-four DHT attributes among stakeholder groups using an efficient incomplete block design. The attributes were identified from a systematic review of DHT evaluation frameworks for consideration in a health technology assessment. Results were analyzed with multinomial models by stakeholder group and latent class. RESULTS: A total of 1,251 participants completed the survey (576 general community members, 543 patients/carers, and 132 health professionals). Twelve attributes achieved a preference score above 50 percent in the stakeholder group model, predominantly related to safety but also covering technical features, effectiveness, ethics, and economics. Results from the latent class model supported this prioritization. Overall, connectedness with the patient's healthcare team seemed the most important; with "Helps health professionals respond quickly when changes in patient care are needed" as the most highly prioritized of all attributes. CONCLUSIONS: It is proposed that these prioritized twelve attributes be considered in all evaluations of DHTs that manage chronic disease, supplemented with a limited number of attributes that reflect the specific perspective of funders, such as equity of access, cost, and system-level implementation considerations.

A healthy lifestyle text message intervention for adolescents: protocol for the Health4Me randomized controlled trial.

BACKGROUND: Adolescence presents a window of opportunity to establish good nutrition and physical activity behaviours to carry throughout the life course. Adolescents are at risk of developing cardiovascular and other chronic diseases due to poor the complex interplay of physical and mental health lifestyle risk factors. Text messaging is adolescents main form of everyday communication and text message programs offer a potential solution for support and improvement of lifestyle health behaviours. The primary aim of this study is to determine effectiveness of the Health4Me text message program to improve adolescent's physical activity or nutrition behaviours among adolescents over 6-months, compared to usual care. METHODS: Health4Me is a virtual, two-arm, single-blind randomised controlled trial, delivering a 6-month healthy lifestyle text message program with optional health counselling. Recruitment will be through digital advertising and primary care services. In total, 330 adolescents will be randomised 1:1 to intervention or control (usual care) groups. The intervention group will receive 4-5 text messages per week for 6-months. All text messages have been co-designed with adolescents. Messages promote a healthy lifestyle by providing practical information, health tips, motivation and support for behaviour change for physical activity, nutrition, mental health, body image, popular digital media and climate and planetary health. Virtual assessments will occur at baseline and 6-months assessing physical health (physical activity, nutrition, body mass index, sleep), mental health (quality of life, self-efficacy, psychological distress, anxiety, depression, eating disorder risk) and lifestyle outcomes (food insecurity and eHealth literacy). DISCUSSION: This study will determine the effectiveness of a 6-month healthy lifestyle text message intervention to improve physical activity and nutrition outcomes in adolescents. TRIAL REGISTRATION: Australia New Zealand Clinical Trials Registry (ANZCTR) ACTRN12622000949785 , Date registered: 05/07/2022.

Healthcare providers' perspectives on using smart home systems to improve self-management and care in people with heart failure: A qualitative study.

BACKGROUND: Smart home systems can potentially improve heart failure (HF) self-management and care. However, evidence is lacking on clinicians' expectations and opinions about these systems. This study aimed to investigate key healthcare providers' perspectives on using smart home systems for self-management and care in people with HF. METHODS: Semi-structured interviews were conducted with purposively selected healthcare providers experienced in caring for people with HF in Australia. Participants were presented with a schematic for a prototype smart home system comprising a central hub connected with medical devices and sensors. An inductive thematic analysis approach was used to establish the perceived benefits and barriers to using smart home systems and enhance the self-management of people with HF. RESULTS: Overall, participants reported that smart home systems could improve the self-management of people with HF with some reservations and suggestions. Four substantive themes emerged from the qualitative findings: role of smart home systems in self-management, suggested features of a smart home system, benefits of smart home system, and factors that may influence smart home system uptake. Participants shared several reservations, such as the potential for increased workload and difficulty of technology use for some patients due to age and HF-related disabilities. Participants highlighted that the abilities and needs of people with HF must be considered when developing any smart home systems. Furthermore, these systems might benefit health institutions and people with HF by allowing remote monitoring and services and allowing patients to live at home independently. CONCLUSION: Healthcare providers considered smart home systems might be potentially helpful to monitor HF patients and deliver interventions to improve their lives, despite several reservations. Future research is needed to address these reservations, identify the needs and abilities of people with HF to use technologies, effects of smart home systems on HF self-management and their impact on clinical practice and health outcomes.

A personalized mobile app for physical activity: An experimental mixed-methods study.

Objectives: To investigate the feasibility of the be.well app and its personalization approach which regularly considers users' preferences, amongst university students. Methods: We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months. Eligibility criteria included: age 18-34 years; owning an iPhone with Internet access; and fluency in English. Usability was assessed by a validated questionnaire; engagement metrics were reported. Changes in physical activity were assessed by comparing the difference in daily step count between baseline and 2 months. Interviews were conducted to assess acceptability; thematic analysis was conducted. Results: Twenty-three participants were enrolled in the study (mean age = 21.9 years, 71.4% women). The mean usability score was 5.6 ± 0.8 out of 7. The median daily engagement time was 2 minutes. Eighteen out of 23 participants used the app in the last month of the study. Qualitative data revealed that people liked the personalized activity suggestion feature as it was actionable and promoted user autonomy. Some users also expressed privacy concerns if they had to provide a lot of personal data to receive highly personalized features. Daily step count increased after 2 months of the intervention (median difference = 1953 steps/day, p-value <.001, 95% CI 782 to 3112). Conclusions: Incorporating users' preferences in personalized advice provided by a physical activity app was considered feasible and acceptable, with preliminary support for its positive effects on daily step count. Future randomized studies with longer follow up are warranted to determine the effectiveness of personalized mobile apps in promoting physical activity.

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries.

Background: Hypertension is the most common modifiable risk factor for cardiovascular diseases in South Asia. Machine learning (ML) models have been shown to outperform clinical risk predictions compared to statistical methods, but studies using ML to predict hypertension at the population level are lacking. This study used ML approaches in a dataset of three South Asian countries to predict hypertension and its associated factors and compared the model's performances. Methods: We conducted a retrospective study using ML analyses to detect hypertension using population-based surveys. We created a single dataset by harmonizing individual-level data from the most recent nationally representative Demographic and Health Survey in Bangladesh, Nepal, and India. The variables included blood pressure (BP), sociodemographic and economic factors, height, weight, hemoglobin, and random blood glucose. Hypertension was defined based on JNC-7 criteria. We applied six common ML-based classifiers: decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), logistic regression (LR), and linear discriminant analysis (LDA) to predict hypertension and its risk factors. Results: Of the 8,18,603 participants, 82,748 (10.11%) had hypertension. ML models showed that significant factors for hypertension were age and BMI. Ever measured BP, education, taking medicine to lower BP, and doctor's perception of high BP was also significant but comparatively lower than age and BMI. XGBoost, GBM, LR, and LDA showed the highest accuracy score of 90%, RF and DT achieved 89 and 83%, respectively, to predict hypertension. DT achieved the precision value of 91%, and the rest performed with 90%. XGBoost, GBM, LR, and LDA achieved a recall value of 100%, RF scored 99%, and DT scored 90%. In F1-score, XGBoost, GBM, LR, and LDA scored 95%, while RF scored 94%, and DT scored 90%. All the algorithms performed with good and small log loss values <6%. Conclusion: ML models performed well to predict hypertension and its associated factors in South Asians. When employed on an open-source platform, these models are scalable to millions of people and might help individuals self-screen for hypertension at an early stage. Future studies incorporating biochemical markers are needed to improve the ML algorithms and evaluate them in real life.

Use of Mobile Apps in Heart Failure Self-management: Qualitative Study Exploring the Patient and Primary Care Clinician Perspective.

BACKGROUND: Mobile apps have the potential to support patients with heart failure and facilitate disease self-management, but this area of research is recent and rapidly evolving, with inconsistent results for efficacy. So far, most of the published studies evaluated the feasibility of a specific app or assessed the quality of apps available in app stores. Research is needed to explore patients' and clinicians' perspectives to guide app development, evaluation, and implementation into models of care. OBJECTIVE: This study aims to explore the patient and primary care clinician perspective on the facilitators and barriers to using mobile apps, as well as desired features, to support heart failure self-management. METHODS: This is a qualitative phenomenological study involving face-to-face semistructured interviews. Interviews were conducted in a general practice clinic in Sydney, Australia. Eligible participants were adult patients with heart failure and health care professionals who provided care to these patients at the clinic. Patients did not need to have previous experience using heart failure mobile apps to be eligible for this study. The interviews were audio-recorded, transcribed, and analyzed using inductive thematic data analysis in NVivo 12. RESULTS: A total of 12 participants were interviewed: 6 patients (mean age 69 [SD 7.9] years) and 6 clinicians. The interviews lasted from 25 to 45 minutes. The main facilitators to the use of apps to support heart failure self-management were communication ability, personalized feedback and education, and automated self-monitoring. Patients mentioned that chat-like features and ability to share audio-visual information can be helpful for getting support outside of clinical appointments. Clinicians considered helpful to send motivational messages to patients and ask them about signs and symptoms of heart failure decompensation. Overall, participants highlighted the importance of personalization, particularly in terms of feedback and educational content. Automated self-monitoring with wireless devices was seen to alleviate the burden of tracking measures such as weight and blood pressure. Other desired features included tools to monitor patient-reported outcomes and support patients' mental health and well-being. The main barriers identified were the patients' unwillingness to engage in a new strategy to manage their condition using an app, particularly in the case of low digital literacy. However, clinicians mentioned this barrier could potentially be overcome by introducing the app soon after an exacerbation, when patients might be more willing to improve their self-management and avoid rehospitalization. CONCLUSIONS: The use of mobile apps to support heart failure self-management may be facilitated by features that increase the usefulness and utility of the app, such as communication ability in-between consultations and personalized feedback. Also important is facilitating ease of use by supporting automated self-monitoring through integration with wireless devices. Future research should consider these features in the co-design and testing of heart failure mobile apps with patients and clinicians.

Coordinating Health Care With Artificial Intelligence-Supported Technology for Patients With Atrial Fibrillation: Protocol for a Randomized Controlled Trial.

BACKGROUND: Atrial fibrillation (AF) is an increasingly common chronic health condition for which integrated care that is multidisciplinary and patient-centric is recommended yet challenging to implement. OBJECTIVE: The aim of Coordinating Health Care With Artificial Intelligence-Supported Technology in AF is to evaluate the feasibility and potential efficacy of a digital intervention (AF-Support) comprising preprogrammed automated telephone calls (artificial intelligence conversational technology), SMS text messages, and emails, as well as an educational website, to support patients with AF in self-managing their condition and coordinate primary and secondary care follow-up. METHODS: Coordinating Health Care With Artificial Intelligence-Supported Technology in AF is a 6-month randomized controlled trial of adult patients with AF (n=385), who will be allocated in a ratio of 4:1 to AF-Support or usual care, with postintervention semistructured interviews. The primary outcome is AF-related quality of life, and the secondary outcomes include cardiovascular risk factors, outcomes, and health care use. The 4:1 allocation design enables a detailed examination of the feasibility, uptake, and process of the implementation of AF-Support. Participants with new or ongoing AF will be recruited from hospitals and specialist-led clinics in Sydney, New South Wales, Australia. AF-Support has been co-designed with clinicians, researchers, information technologists, and patients. Automated telephone calls will occur 7 times, with the first call triggered to commence 24 to 48 hours after enrollment. Calls follow a standard flow but are customized to vary depending on patients' responses. Calls assess AF symptoms, and participants' responses will trigger different system responses based on prespecified protocols, including the identification of red flags requiring escalation. Randomization will be performed electronically, and allocation concealment will be ensured. Because of the nature of this trial, only outcome assessors and data analysts will be blinded. For the primary outcome, groups will be compared using an analysis of covariance adjusted for corresponding baseline values. Randomized trial data analysis will be performed according to the intention-to-treat principle, and qualitative data will be thematically analyzed. RESULTS: Ethics approval was granted by the Western Sydney Local Health District Human Ethics Research Committee, and recruitment started in December 2020. As of December 2021, a total of 103 patients had been recruited. CONCLUSIONS: This study will address the gap in knowledge with respect to the role of postdischarge digital care models for supporting patients with AF. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://www.australianclinicaltrials.gov.au/anzctr/trial/ACTRN12621000174886. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34470.

The Use of Mobile Apps for Heart Failure Self-management: Systematic Review of Experimental and Qualitative Studies.

BACKGROUND: Heart failure self-management is essential to avoid decompensation and readmissions. Mobile apps seem promising in supporting heart failure self-management, and there has been a rapid growth in publications in this area. However, to date, systematic reviews have mostly focused on remote monitoring interventions using nonapp types of mobile technologies to transmit data to health care providers, rarely focusing on supporting patient self-management of heart failure. OBJECTIVE: This study aims to systematically review the evidence on the effect of heart failure self-management apps on health outcomes, patient-reported outcomes, and patient experience. METHODS: Four databases (PubMed, Embase, CINAHL, and PsycINFO) were searched for studies examining interventions that comprised a mobile app targeting heart failure self-management and reported any health-related outcomes or patient-reported outcomes or perspectives published from 2008 to December 2021. The studies were independently screened. The risk of bias was appraised using Cochrane tools. We performed a narrative synthesis of the results. The protocol was registered on PROSPERO (International Prospective Register of Systematic Reviews; CRD42020158041). RESULTS: A total of 28 articles (randomized controlled trials [RCTs]: n=10, 36%), assessing 23 apps, and a total of 1397 participants were included. The most common app features were weight monitoring (19/23, 83%), symptom monitoring (18/23, 78%), and vital sign monitoring (15/23, 65%). Only 26% (6/23) of the apps provided all guideline-defined core components of heart failure self-management programs: education, symptom monitoring, medication support, and physical activity support. RCTs were small, involving altogether 717 participants, had ≤6 months of follow-up, and outcomes were predominantly self-reported. Approximately 20% (2/10) of RCTs reported a significant improvement in their primary outcomes: heart failure knowledge (P=.002) and self-care (P=.004). One of the RCTs found a significant reduction in readmissions (P=.02), and 20% (2/10) of RCTs reported higher unplanned clinic visits. Other experimental studies also found significant improvements in knowledge, self-care, and readmissions, among others. Less than half of the studies involved patients and clinicians in the design of apps. Engagement with the intervention was poorly reported, with only 11% (3/28) of studies quantifying app engagement metrics such as frequency of use over the study duration. The most desirable app features were automated self-monitoring and feedback, personalization, communication with clinicians, and data sharing and integration. CONCLUSIONS: Mobile apps may improve heart failure self-management; however, more robust evaluation studies are needed to analyze key end points for heart failure. On the basis of the results of this review, we provide a road map for future studies in this area.

Social connections influencing e-cigarette use and intentions in Australia: a survey.

Social factors play a role in e-cigarette uptake, but Australian evidence is limited. This study evaluated associations between social factors and e-cigarette intention and use.Australian participants surveyed between March 2019 and July 2019 using a cross-sectional survey design, measuring e-cigarette intentions and use, and factors including smoking status and social acceptability.Of 243 respondents, 185 were included in the final analysis, measuring e-cigarette intention and use, and factors including smoking status and social acceptability. Of 185 participants, daily, occasional, and ex-smokers (123 participants) were more likely to have used e-cigarettes (OR = 9.33; 95% CI 4.63-18.80) or intend to use e-cigarettes (OR = 4.86; 95% CI 2.32-10.21), relative to nonsmokers (62 participants). Participants reporting acceptability among people they study or work with (70 participants) were more likely to have used e-cigarettes relative to the reference group (OR = 16.76; 95% CI 3.70-75.83; p = 0.001) and were more likely report intending to use e-cigarettes relative to the reference group (OR = 3.40; 95%CI 1.58-7.30; p = 0.002).With caveats related to the survey participant composition, the results suggest that places of work or study may be an appropriate place to consider interventions aimed at reducing e-cigarette uptake among nonsmokers.

General practitioners' perceptions of using virtual primary care during the COVID-19 pandemic: An international cross-sectional survey study.

With the onset of COVID-19, general practitioners (GPs) and patients worldwide swiftly transitioned from face-to-face to digital remote consultations. There is a need to evaluate how this global shift has impacted patient care, healthcare providers, patient and carer experience, and health systems. We explored GPs' perspectives on the main benefits and challenges of using digital virtual care. GPs across 20 countries completed an online questionnaire between June-September 2020. GPs' perceptions of main barriers and challenges were explored using free-text questions. Thematic analysis was used to analyse the data. A total of 1,605 respondents participated in our survey. The benefits identified included reducing COVID-19 transmission risks, guaranteeing access and continuity of care, improved efficiency, faster access to care, improved convenience and communication with patients, greater work flexibility for providers, and hastening the digital transformation of primary care and accompanying legal frameworks. Main challenges included patients' preference for face-to-face consultations, digital exclusion, lack of physical examinations, clinical uncertainty, delays in diagnosis and treatment, overuse and misuse of digital virtual care, and unsuitability for certain types of consultations. Other challenges include the lack of formal guidance, higher workloads, remuneration issues, organisational culture, technical difficulties, implementation and financial issues, and regulatory weaknesses. At the frontline of care delivery, GPs can provide important insights on what worked well, why, and how during the pandemic. Lessons learned can be used to inform the adoption of improved virtual care solutions and support the long-term development of platforms that are more technologically robust and secure.