Lifestyle changes and quality of life a year after attending Rapid Access Cardiology Clinics: an observational study.

We examined behavioural risk factors and quality of life (QoL) in women and men, younger and older adults 12 months after a Rapid Access Cardiology Clinic (RACC) visit. Routine clinical care data were collected in person from three Sydney hospitals between 2017 and 2018 and followed up by questionnaire at 365 days. 1491 completed the baseline survey, at 1 year, 1092 provided follow-up data on lifestyle changes, and 811 completed the EQ-5D-5L (QoL) survey. 666 (44.7%) were women, and 416 (27.9%) were older than 60 years of age. Almost 50% of participants reported improving physical activity and diet a year after their RACC visit. These changes were less likely in women and older participants.

The Burden of Uncontrolled Cardiovascular Risk Factors in Men With Prostate Cancer: A RADICAL-PC Analysis.

Background: Cardiovascular disease (CVD) incidence is higher in men with prostate cancer (PC) than without. Objectives: We describe the rate and correlates of poor cardiovascular risk factor control among men with PC. Methods: We prospectively characterized 2,811 consecutive men (mean age 68 ± 8 years) with PC from 24 sites in Canada, Israel, Brazil, and Australia. We defined poor overall risk factor control as ≥3 of the following: suboptimal low-density lipoprotein cholesterol (>2 mmol/L if Framingham Risk Score [FRS] ≥15 and ≥3.5 mmol/L if FRS <15), current smoker, physical inactivity (<600 MET min/wk), suboptimal blood pressure (BP) (≥140/90 mm Hg if no other risk factors, systolic BP ≥120 mm Hg if known CVD or FRS ≥15, and ≥130/80 mm Hg if diabetic), and waist:hip ratio >0.9. Results: Among participants (9% with metastatic PC and 23% with pre-existing CVD), 99% had ≥1 uncontrolled cardiovascular risk factor, and 51% had poor overall risk factor control. Not taking a statin (odds ratio [OR]: 2.55; 95% CI: 2.00-3.26), physical frailty (OR: 2.37; 95% CI: 1.51-3.71), need for BP drugs (OR: 2.36; 95% CI: 1.84-3.03), and age (OR per 10-year increase: 1.34; 95% CI: 1.14-1.59) were associated with poor overall risk factor control after adjustment for education, PC characteristics, androgen deprivation therapy, depression, and Eastern Cooperative Oncology Group functional status. Conclusions: Poor control of modifiable cardiovascular risk factors is common in men with PC, highlighting the large gap in care and the need for improved interventions to optimize cardiovascular risk management in this population.

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).

Text Messages to Improve Medication Adherence and Secondary Prevention After Acute Coronary Syndrome: The TEXTMEDS Randomized Clinical Trial.

BACKGROUND: TEXTMEDS (Text Messages to Improve Medication Adherence and Secondary Prevention After Acute Coronary Syndrome) examined the effects of text message-delivered cardiac education and support on medication adherence after an acute coronary syndrome. METHODS: TEXTMEDS was a single-blind, multicenter, randomized controlled trial of patients after acute coronary syndrome. The control group received usual care (secondary prevention as determined by the treating clinician); the intervention group also received multiple motivational and supportive weekly text messages on medications and healthy lifestyle with the opportunity for 2-way communication (text or telephone). The primary end point of self-reported medication adherence was the percentage of patients who were adherent, defined as >80% adherence to each of up to 5 indicated cardioprotective medications, at both 6 and 12 months. RESULTS: A total of 1424 patients (mean age, 58 years [SD, 11]; 79% male) were randomized from 18 Australian public teaching hospitals. There was no significant difference in the primary end point of self-reported medication adherence between the intervention and control groups (relative risk, 0.93 [95% CI, 0.84-1.03]; P=0.15). There was no difference between intervention and control groups at 12 months in adherence to individual medications (aspirin, 96% vs 96%; ß-blocker, 84% vs 84%; angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 77% vs 80%; statin, 95% vs 95%; second antiplatelet, 84% vs 84% [all P>0.05]), systolic blood pressure (130 vs 129 mm Hg; P=0.26), low-density lipoprotein cholesterol (2.0 vs 1.9 mmol/L; P=0.34), smoking (P=0.59), or exercising regularly (71% vs 68%; P=0.52). There were small differences in lifestyle risk factors in favor of intervention on body mass index <25 kg/m2 (21% vs 18%; P=0.01), eating ≥5 servings per day of vegetables (9% vs 5%; P=0.03), and eating ≥2 servings per day of fruit (44% vs 39%; P=0.01). CONCLUSIONS: A text message-based program had no effect on medical adherence but small effects on lifestyle risk factors. REGISTRATION: URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364448; Unique identifier: ANZCTR ACTRN12613000793718.

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.

Can existing electronic medical records be used to quantify cardiovascular risk at point of care?

BACKGROUND: Using electronic data for cardiovascular risk stratification could help in prioritising healthcare access and optimise cardiovascular prevention. AIMS: To determine whether assessment of absolute cardiovascular risk (Australian absolute cardiovascular disease risk (ACVDR)) and short-term ischaemic risk (History, ECG, Age, Risk factors, and Troponin (HEART) score) is possible from available data in Electronic Medical Record (EMR) and My Health Record (MHR) of patients presenting with acute cardiac symptoms to a Rapid Access Cardiology Clinic (RACC). METHODS: Audit of EMR and MHR on 200 randomly selected adults who presented to RACC between 1 March 2017 and 4 February 2020. The main outcomes were the proportion of patients for which ACVDR score and HEART score could be calculated. RESULTS: Mean age was 55.2 ± 17.8 years and 43% were female. Most (85%) were referred from emergency for chest pain (52%). Forty-six percent had hypertension, 35% obesity, 20% diabetes mellitus, 17% ischaemic heart disease and 18% were current smokers. There was no significant difference in MHR accessibility with age, gender and number of comorbidities. An ACVDR score could be estimated for 17.5% (EMR) and 0% (MHR) of patients. None had complete data to estimate HEART score in either EMR or MHR. Most commonly missing variables for ACVDR score were blood pressure (MHR) and high-density lipoprotein cholesterol (EMR), and for HEART score the missing variables were body mass index and comorbidities (MHR and EMR). CONCLUSIONS: Significant gaps are apparent in electronic medical data capture of key variables to perform cardiovascular risk assessment. Medical data capture should prioritise the collection of clinically important data to help address gaps in cardiovascular management.

Rapid Access Chest Pain Clinics: An Australian Cost-Benefit Study.

OBJECTIVE: Chest pain is a large health care burden in Australia and around the world. Its management requires specialist assessment and diagnostic tests, which can be costly and often lead to unnecessary hospital admissions. There is a growing unmet clinical need to improve the efficiency and management of chest pain. This study aims to show the cost-benefit of rapid access chest pain clinics (RACC) as an alternative to hospital admission. DESIGN: Retrospective cost-benefit analysis for 12 months. SETTING: RACCs in three Sydney tertiary referral hospitals. MAIN OUTCOME MEASURES: Cost per patient. RESULTS: Hospitals A, B and C implemented RACCs but each operating with slightly different staffing, referral patterns, and diagnostic services. All RACCs had similar costs per patient of AUD$455.25, AUD$427.12 and AUD$474.45, hospitals A, B and C respectively, and similar cost benefits per patient of AUD$1,168.75, AUD$1,196.88 and AUD$1,149.55, respectively. At least 28%, 26% and 29% of these RACC patients for hospitals A, B, and C, respectively, would have otherwise had to have been admitted to hospital for the model to be cost-beneficial. CONCLUSION: This study shows that a RACC model of care is cost-beneficial in the state of NSW as an alternative strategy to inpatient care for managing chest pain. Scaling up to a national level could represent an even larger benefit for the Australian health system.

Determinants of Exercise Capacity Following ST-Elevation Myocardial Infarction (STEMI).

BACKGROUND: Abnormal left ventricular systolic and diastolic function and reduced exercise capacity are associated with worse prognosis following ST-elevation myocardial infarction (STEMI). However, evidence is lacking on the determinants of exercise capacity following STEMI. We sought to determine the impact of systolic and diastolic dysfunction on exercise capacity and outcomes following first-ever STEMI. METHODS: In a retrospective analysis of 139 consecutive STEMI patients who had a transthoracic echocardiogram following STEMI and completed exercise treadmill testing, the primary outcome was to identify clinical and echocardiographic determinants of exercise capacity, and the secondary outcome was to identify determinants of major adverse cardiac events (MACEs). RESULTS: Mean number of metabolic equivalents (METs > 8) was used as a cut-off. Age, female sex, anterior infarction, abnormal diastolic function, minimum left atrial indexed volume (LAVImin) ≥ 18 mL/m2, average e', and E/e' were associated with METs ≤ 8, but not left ventricular ejection fraction (LVEF). On multivariate analysis, LAVImin (OR 4.3, 95%CI 1.3-14.2; p = 0.017), anterior infarction (OR 2.6, 95%CI 1.2-5.9; p = 0.022), and abnormal diastolic function (OR 3.73, 95%CI 1.7-8.4; p = 0.001) were independent predictors of METs ≤ 8. On Kaplan-Meier analysis, METs ≤ 8 (p = 0.01) and abnormal diastolic function (p = 0.04) were associated with MACEs (median follow-up 2.3 years). METs ≤ 8 was an independent predictor of MACEs (HR 3.4, 95%CI 1.2-9.8; p = 0.02). CONCLUSIONS: Following first-ever STEMI, increased LAVImin, anterior infarction, and abnormal diastolic function were independent predictors of reduced exercise capacity. Furthermore, reduced exercise capacity was an independent predictor of MACEs. These results highlight important prognostic and therapeutic implications related to abnormal diastolic function in STEMI patients that are distinct from those with LV systolic impairment.

Text Message Analysis Using Machine Learning to Assess Predictors of Engagement With Mobile Health Chronic Disease Prevention Programs: Content Analysis.

BACKGROUND: SMS text messages as a form of mobile health are increasingly being used to support individuals with chronic diseases in novel ways that leverage the mobility and capabilities of mobile phones. However, there are knowledge gaps in mobile health, including how to maximize engagement. OBJECTIVE: This study aims to categorize program SMS text messages and participant replies using machine learning (ML) and to examine whether message characteristics are associated with premature program stopping and engagement. METHODS: We assessed communication logs from SMS text message-based chronic disease prevention studies that encouraged 1-way (SupportMe/ITM) and 2-way (TEXTMEDS [Text Messages to Improve Medication Adherence and Secondary Prevention]) communication. Outgoing messages were manually categorized into 5 message intents (informative, instructional, motivational, supportive, and notification) and replies into 7 groups (stop, thanks, questions, reporting healthy, reporting struggle, general comment, and other). Grid search with 10-fold cross-validation was implemented to identify the best-performing ML models and evaluated using nested cross-validation. Regression models with interaction terms were used to compare the association of message intent with premature program stopping and engagement (replied at least 3 times and did not prematurely stop) in SupportMe/ITM and TEXTMEDS. RESULTS: We analyzed 1550 messages and 4071 participant replies. Approximately 5.49% (145/2642) of participants responded with stop, and 11.7% (309/2642) of participants were engaged. Our optimal ML model correctly classified program message intent with 76.6% (95% CI 63.5%-89.8%) and replies with 77.8% (95% CI 74.1%-81.4%) balanced accuracy (average area under the curve was 0.95 and 0.96, respectively). Overall, supportive (odds ratio [OR] 0.53, 95% CI 0.35-0.81) messages were associated with reduced chance of stopping, as were informative messages in SupportMe/ITM (OR 0.35, 95% CI 0.20-0.60) but not in TEXTMEDS (for interaction, P<.001). Notification messages were associated with a higher chance of stopping in SupportMe/ITM (OR 5.76, 95% CI 3.66-9.06) but not TEXTMEDS (for interaction, P=.01). Overall, informative (OR 1.76, 95% CI 1.46-2.12) and instructional (OR 1.47, 95% CI 1.21-1.80) messages were associated with higher engagement but not motivational messages (OR 1.18, 95% CI 0.82-1.70; P=.37). For supportive messages, the association with engagement was opposite with SupportMe/ITM (OR 1.77, 95% CI 1.21-2.58) compared with TEXTMEDS (OR 0.77, 95% CI 0.60-0.98; for interaction, P<.001). Notification messages were associated with reduced engagement in SupportMe/ITM (OR 0.07, 95% CI 0.05-0.10) and TEXTMEDS (OR 0.28, 95% CI 0.20-0.39); however, the strength of the association was greater in SupportMe/ITM (for interaction P<.001). CONCLUSIONS: ML models enable monitoring and detailed characterization of program messages and participant replies. Outgoing message intent may influence premature program stopping and engagement, although the strength and direction of association appear to vary by program type. Future studies will need to examine whether modifying message characteristics can optimize engagement and whether this leads to behavior change.

Text messages for primary prevention of cardiovascular disease: The TextMe2 randomized clinical trial.

BACKGROUND: Primary prevention guidelines emphasize the importance of lifestyle modification, but many at high-risk have suboptimal cardiovascular risk factor (CVRF) control. Text message support may improve control, but the evidence is sparse. Our objective was to determine the impact of text messages on multiple CVRFs in a moderate-high risk primary prevention cohort. METHODS: This study was a single-blind randomized clinical trial comparing semi-personalized text message-based support to standard care. A random sample of adults with 10-year absolute cardiovascular risk score ≥10% and without coronary heart disease, referred from February 2019 to January 2020, were recruited from an outpatient cardiology clinic in a large tertiary hospital in Sydney, Australia. Patients were randomized 1:1 to intervention or control. Intervention participants received 4 texts per week over 6 months, and standard care, with content covering: diet, physical activity, smoking, general cardiovascular health, and medication adherence. Controls received standard care only. Content was semipersonalized (smoking status, vegetarian or not-vegetarian, physical ability, taking medications or not) and delivered randomly using automated software. The primary outcome was the difference in the proportion of patients who have ≥3 uncontrolled CVRFs (out of: low-density lipoprotein cholesterol >2.0 mmol/L, blood pressure >140/90 mm Hg, body mass index ≥25 kg/m2, physical inactivity, current smoker) at 6 months adjusted for baseline. Secondary outcomes included differences in biomedical and behavioral CVRFs. RESULTS: Among 295 eligible participants, 246 (mean age, 58.6 ± 10.7 years; 39.4% female) were randomized to intervention (n = 124) or control (n = 122). At 6 months, there was no significant difference in the proportion of patients with ≥3 uncontrolled CVRFs (adjusted relative risk [RR] 0.98; 95% confidence interval [CI] 0.75-1.29; P = .88). Intervention participants were less likely to be physically inactive (adjusted RR 0.72; 95% CI 0.57-0.92; P = .01), but there were no significant changes in other single CVRFs. More intervention participants reduced the number of uncontrolled CVRFs at 6-months from baseline than controls (86% vs 75%; RR 1.15; 95% CI 1.00-1.32; P = .04). CONCLUSIONS: In moderate-high cardiovascular risk primary prevention, text message-based support did not significantly reduce the proportion of patients with ≥3 uncontrolled CVRFs. However, the program did motivate behavior change and significantly improved cardiovascular risk factor control overall. Larger multicenter studies are needed.

Education on cardiac risk and CPR in cardiology clinic waiting rooms: a randomised clinical trial.

OBJECTIVE: Waiting time is inevitable during cardiovascular (CV) care. This study examines whether waiting room-based CV education could complement CV care. METHODS: A 2:1 randomised clinical trial of patients in waiting rooms of hospital cardiology clinics. Intervention participants received a series of tablet-delivered CV educational videos and were randomised 1:1 to receive another video on cardiopulmonary resuscitation (CPR) or no extra video. Control received usual care. The primary outcome was the proportion of participants reporting high motivation to improve CV risk-modifying behaviours (physical activity, diet and blood pressure monitoring) post-clinic. SECONDARY OUTCOMES: clinic satisfaction, CV lifestyle risk factors (RFs) and confidence to perform CPR. Assessors were blinded to treatment allocation. RESULTS: Among 514 screened, 330 were randomised (n=220 intervention, n=110 control) between December 2018 and March 2020, mean age 53.8 (SD 15.2), 55.2% male. Post-clinic, more intervention participants reported high motivation to improve CV risk-modifying behaviours: 29.6% (64/216) versus 18.7% (20/107), relative risk (RR) 1.63 (95% CI 1.04 to 2.55). Intervention participants reported higher clinic satisfaction RR: 2.19 (95% CI 1.45 to 3.33). Participants that received the CPR video (n=110) reported greater confidence to perform CPR, RR 1.61 (95% CI 1.20 to 2.16). Overall, the proportion of participants reporting optimal CV RFs increased between baseline and 30-day follow-up (16.1% vs 24.8%, OR=2.44 (95% CI 1.38 to 4.49)), but there was no significant between-group difference at 30 days. CONCLUSION: CV education delivery in the waiting room is a scalable concept and may be beneficial to CV care. Larger studies could explore its impact on clinical outcomes. TRIAL REGISTRATION NUMBER: ANZCTR12618001725257.

Rapid Access Arrhythmia Clinics (RAACs) Versus Usual Care: Improving Efficiency and Safety of Arrhythmia Management.

BACKGROUND: Rapid access cardiology services have been proposed for assessment of acute cardiac conditions via an outpatient model-of-care that potentially could reduce hospitalisations. We describe a new Rapid Access Arrhythmia Clinic (RAAC) and compare major safety endpoints to usual care. METHODS: We matched 312 adult patients with suspected arrhythmia in RAAC to historical age and sex-matched controls discharged from hospital within Western Sydney Local Health District with suspected arrhythmia. The primary endpoint was a composite of time to first unplanned cardiovascular hospitalisation or cardiac death over 12 months. RESULTS: The average age of RAAC patients was 52.2±18.8 years and 51.6±18.8 years for controls, and 48.4% were female in both groups. Mean time from referral to first attended RAAC appointment was 10.5 days. Most were referred from emergency (177, 56.7%) and cardiologists at time of discharge (65, 20.8%). The most common reason for referral was palpitations (180, 57.7%). In total, 155 (49.7%) had a documented arrhythmia, with the most common being atrial fibrillation/flutter (88, 28.2%). The primary endpoint occurred in 35 (11.2%) patients in the RAAC pathway (97.1[95% CI 70-131.3] per 1,000 person-years), compared to 72 (23.1%) patients for usual care controls (229.5[95% CI 180.2-288.1] per 1,000 person-years). Using a propensity score analysis, RAAC pathway significantly reduced the primary endpoint by 59% compared to usual care (HR 0.41, 95% CI 0.27-0.62; p<0.001). CONCLUSIONS: RAACs for the early investigation and management of suspected arrhythmia is superior to usual care in terms of reduction in unplanned cardiovascular hospitalisation and death.

Diagnostic accuracy of handheld electrocardiogram devices in detecting atrial fibrillation in adults in community versus hospital settings: a systematic review and meta-analysis.

With increasing use of handheld ECG devices for atrial fibrillation (AF) screening, it is important to understand their accuracy in community and hospital settings and how it differs among settings and other factors. A systematic review of eligible studies from community or hospital settings reporting the diagnostic accuracy of handheld ECG devices (ie, devices producing a rhythm strip) in detecting AF in adults, compared with a gold standard 12-lead ECG or Holter monitor, was performed. Bivariate hierarchical random-effects meta-analysis and meta-regression were performed using R V.3.6.0. The search identified 858 articles, of which 14 were included. Six studies recruited from community (n=6064 ECGs) and eight studies from hospital (n=2116 ECGs) settings. The pooled sensitivity was 89% (95% CI 81% to 94%) in the community and 92% (95% CI 83% to 97%) in the hospital. The pooled specificity was 99% (95% CI 98% to 99%) in the community and 95% (95% CI 90% to 98%) in the hospital. Accuracy of ECG devices varied: sensitivity ranged from 54.5% to 100% and specificity ranged from 61.9% to 100%. Meta-regression showed that setting (p=0.032) and ECG device type (p=0.022) significantly contributed to variations in sensitivity and specificity. The pooled sensitivity and specificity of single-lead handheld ECG devices were high. Setting and handheld ECG device type were significant factors of variation in sensitivity and specificity. These findings suggest that the setting including user training and handheld ECG device type should be carefully reviewed.

Text messages for primary prevention of cardiovascular disease: the TextMe2 randomised controlled trial protocol.

INTRODUCTION: Mobile health may be an effective means of delivering customised individually directed health promotion interventions for cardiovascular disease (CVD) primary prevention. The aim of this study is to evaluate the effectiveness of a lifestyle-focused text messaging programme for primary CVD prevention. METHODS AND ANALYSIS: Single-blind randomised controlled trial with 6 months' follow-up in 246 patients with moderate-high absolute cardiovascular risk and without coronary heart disease recruited from a rapid access cardiology clinic. Participants will be randomised to receive either usual care or TextMe2 (text message-based prevention programme). The TextMe2 programme provides support, motivation and education on five topics: diet, physical activity, smoking, general cardiovascular health and medication adherence, and is delivered in four text messages per week over 6 months. The primary outcome is change in the proportion of patients who have three or more of five key modifiable risk factors that are uncontrolled (low-density lipoprotein >2.0 mmol/L, systolic blood pressure >140 mm Hg, body mass index >24.9 kg/m2, physical activity (less than the equivalent of 150 min of moderate intensity each week), current smoker). Secondary outcomes are changes in single biomedical risk factors, behavioural risk factors, quality of life, depression/anxiety scores, medication adherence, cardiovascular health literacy and hospital readmissions/representations. Analysis will be according to the intention-to-treat principle and full statistical analysis plan developed prior to data lock. ETHICS AND DISSEMINATION: This study has been approved by the Western Sydney Local Health District Human Research Ethics Committee at Westmead (AU/RED/HREC/17/WMEAD/186). Results will be presented at scientific meetings and published in peer-reviewed publications. TRIAL REGISTRATION NUMBER: ACTRN12618001153202.

Predictors of Smoking Cessation in a Lifestyle-Focused Text-Message Support Programme Delivered to People with Coronary Heart Disease: An Analysis From the Tobacco Exercise and Diet Messages (TEXTME) Randomised Clinical Trial.

BACKGROUND: Studies have demonstrated the effectiveness of text message-based prevention programs on smoking cessation, including our recently published TEXTME randomised controlled trial. However, little is known about the predictors of smoking cessation in this context and if other clinically important factors interact with the program to lead to quitting. Hence, the objective of this study was to first assess the predictors of smoking cessation in TEXTME and then determine if the effect of texting on quitting was modified by interactions with important clinical variables. This will allow us to better understand how text messaging works and thus help optimise future text-message based prevention programs. METHODS: This sub-analysis used data collected as part of the TEXTME trial which recruited 710 participants (377 current smokers at baseline) between September 2011 and November 2013 from a large tertiary hospital in Sydney, Australia. Smokers at baseline were analysed at 6 months and grouped into those who quit and those who did not. Univariate analyses were performed to determine associations between the main outcome and clinically important baseline factors selected a priori. A multiple binominal logistic regression analysis was conducted to develop a predictive model for the dependent variable smoking cessation. A test of interaction between the intervention group and baseline variables selected a priori with the outcome smoking cessation was performed. RESULTS: Univariate analysis identified receiving text-messages, age, and mean number of cigarettes smoked each day as being associated with quitting smoking. After adjusting for age, receiving the text-messaging program (OR 2.34; 95%CI 1.43-3.86; p<0.01) and mean number of cigarettes smoked per day (OR 1.02; 95%CI 1.00-1.04; p=0.03) were independent predictors for smoking cessation. LDL-C showed a significant interaction effect with the intervention (High LDL*Intervention OR 3.77 (95%CI 2.05-6.94); Low LDL*Intervention OR 1.42 (95%CI 0.77-2.60); P=0.03). CONCLUSIONS: Smoking quantity at baseline is independently associated with smoking cessation and higher LDL-C may interact with the intervention to result in quitting smoking. Those who have a higher baseline risk maybe more motivated towards beneficial lifestyle change including quitting smoking, and thus more likely to respond to mHealth smoking cessation programs. The effect of text-messages on smoking cessation was independent of age, gender, psychosocial parameters, education, and baseline control of risk factors in a secondary prevention cohort.