TEXT messages to improve MEDication adherence and Secondary prevention (TEXTMEDS) after acute coronary syndrome: a randomised clinical trial protocol

Mobile phone text messaging for medication adherence in secondary prevention of cardiovascular disease

BACKGROUND

Cardiovascular diseases (CVDs) are the leading cause of death globally, accounting for almost 18 million deaths annually. People with CVDs have a five times greater chance of suffering a recurrent cardiovascular event than people without known CVDs. Although drug interventions have been shown to be cost-effective in reducing the risk of recurrent cardiovascular events, adherence to medication remains suboptimal. As a scalable and cost-effective approach, mobile phone text messaging presents an opportunity to convey health information, deliver electronic reminders, and encourage behaviour change. However, it is uncertain whether text messaging can improve medication adherence and clinical outcomes. This is an update of a Cochrane review published in 2017.

OBJECTIVES

To evaluate the benefits and harms of mobile phone text messaging for improving medication adherence in people with CVDs compared to usual care.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, four other databases, and two trial registers. We also checked the reference lists of all primary included studies and relevant systematic reviews and meta-analyses. The date of the latest search was 30 August 2023.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) with participants with established arterial occlusive events. We included trials investigating interventions using short message service (SMS) or multimedia messaging service (MMS) with the aim of improving adherence to medication for the secondary prevention of cardiovascular events. The comparator was usual care. We excluded cluster-RCTs and quasi-RCTs.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our primary outcomes were medication adherence, fatal cardiovascular events, non-fatal cardiovascular events, and combined CVD event. Secondary outcomes were low-density lipoprotein cholesterol for the effect of statins, blood pressure for antihypertensive drugs, heart rate for the effect of beta-blockers, urinary 11-dehydrothromboxane B2 for the antiplatelet effects of aspirin, adverse effects, and patient-reported experience. We used GRADE to assess the certainty of the evidence for each outcome.

MAIN RESULTS

We included 18 RCTs involving a total of 8136 participants with CVDs. We identified 11 new studies in the review update and seven studies in the previous version of the review. Participants had various CVDs including acute coronary syndrome, coronary heart disease, stroke, myocardial infarction, and angina. All studies were conducted in middle- and high-income countries, with no studies conducted in low-income countries. The mean age of participants was 53 to 64 years. Participants were recruited from hospitals or cardiac rehabilitation facilities. Follow-up ranged from one to 12 months. There was variation in the characteristics of text messages amongst studies (e.g. delivery method, frequency, theoretical grounding, content used, personalisation, and directionality). The content of text messages varied across studies, but generally included medication reminders and healthy lifestyle information such as diet, physical activity, and weight loss. Text messages offered advice, motivation, social support, and health education to promote behaviour changes and regular medication-taking. We assessed risk of bias for all studies as high, as all studies had at least one domain at unclear or high risk of bias. Medication adherence Due to different evaluation score systems and inconsistent definitions applied for the measurement of medication adherence, we did not conduct meta-analysis for medication adherence. Ten out of 18 studies showed a beneficial effect of mobile phone text messaging for medication adherence compared to usual care, whereas the other eight studies showed either a reduction or no difference in medication adherence with text messaging compared to usual care. Overall, the evidence is very uncertain about the effects of mobile phone text messaging for medication adherence when compared to usual care. Fatal cardiovascular events Text messaging may have little to no effect on fatal cardiovascular events compared to usual care (odds ratio 0.83, 95% confidence interval (CI) 0.47 to 1.45; 4 studies, 1654 participants; low-certainty evidence). Non-fatal cardiovascular events We found very low-certainty evidence that text messaging may have little to no effect on non-fatal cardiovascular events. Two studies reported non-fatal cardiovascular events, neither of which found evidence of a difference between groups. Combined CVD events We found very low-certainty evidence that text messaging may have little to no effect on combined CVD events. Only one study reported combined CVD events, and did not find evidence of a difference between groups. Low-density lipoprotein cholesterol Text messaging may have little to no effect on low-density lipoprotein cholesterol compared to usual care (mean difference (MD) -1.79 mg/dL, 95% CI -4.71 to 1.12; 8 studies, 4983 participants; very low-certainty evidence). Blood pressure Text messaging may have little to no effect on systolic blood pressure (MD -0.93 mmHg, 95% CI -3.55 to 1.69; 8 studies, 5173 participants; very low-certainty evidence) and diastolic blood pressure (MD -1.00 mmHg, 95% CI -2.49 to 0.50; 5 studies, 3137 participants; very low-certainty evidence) when compared to usual care. Heart rate Text messaging may have little to no effect on heart rate compared to usual care (MD -0.46 beats per minute, 95% CI -1.74 to 0.82; 4 studies, 2946 participants; very low-certainty evidence).

AUTHORS' CONCLUSIONS

Due to limited evidence, we are uncertain if text messaging reduces medication adherence, fatal and non-fatal cardiovascular events, and combined cardiovascular events in people with cardiovascular diseases when compared to usual care. Furthermore, text messaging may result in little or no effect on low-density lipoprotein cholesterol, blood pressure, and heart rate compared to usual care. The included studies were of low methodological quality, and no studies assessed the effects of text messaging in low-income countries or beyond the 12-month follow-up. Long-term and high-quality randomised trials are needed, particularly in low-income countries.

Efficacy of eHealth Technologies on Medication Adherence in Patients With Acute Coronary Syndrome: Systematic Review and Meta-Analysis

BACKGROUND

Suboptimal adherence to cardiac pharmacotherapy, recommended by the guidelines after acute coronary syndrome (ACS) has been recognized and is associated with adverse outcomes. Several randomized controlled trials (RCTs) have shown that eHealth technologies are useful in reducing cardiovascular risk factors. However, little is known about the effect of eHealth interventions on medication adherence in patients following ACS.

OBJECTIVE

The aim of this study is to examine the efficacy of the eHealth interventions on medication adherence to selected 5 cardioprotective medication classes in patients with ACS.

METHODS

A systematic literature search of PubMed, Embase, Scopus, and Web of Science was conducted between May and October 2022, with an update in October 2023 to identify RCTs that evaluated the effectiveness of eHealth technologies, including texting, smartphone apps, or web-based apps, to improve medication adherence in patients after ACS. The risk of bias was evaluated using the modified Cochrane risk-of-bias tool for RCTs. A pooled meta-analysis was performed using a fixed-effect Mantel-Haenszel model and assessed the medication adherence to the medications of statins, aspirin, P2Y12 inhibitors, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and β-blockers.

RESULTS

We identified 5 RCTs, applicable to 4100 participants (2093 intervention vs 2007 control), for inclusion in the meta-analysis. In patients who recently had an ACS, compared to the control group, the use of eHealth intervention was not associated with improved adherence to statins at different time points (risk difference [RD] -0.01, 95% CI -0.03 to 0.03 at 6 months and RD -0.02, 95% CI -0.05 to 0.02 at 12 months), P2Y12 inhibitors (RD -0.01, 95% CI -0.04 to 0.02 and RD -0.01, 95% CI -0.03 to 0.02), aspirin (RD 0.00, 95% CI -0.06 to 0.07 and RD -0.00, 95% CI -0.07 to 0.06), angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (RD -0.01, 95% CI -0.04 to 0.02 and RD 0.01, 95% CI -0.04 to 0.05), and β-blockers (RD 0.00, 95% CI -0.03 to 0.03 and RD -0.01, 95% CI -0.05 to 0.03). The intervention was also not associated with improved adherence irrespective of the adherence assessment method used (self-report or objective).

CONCLUSIONS

This review identified limited evidence on the effectiveness of eHealth interventions on adherence to guideline-recommended medications after ACS. While the pooled analyses suggested a lack of effectiveness of such interventions on adherence improvement, further studies are warranted to better understand the role of different eHealth approaches in the post-ACS context.

The use of the Capability-Opportunity- Motivation Behavior (COM-B) model to identify barriers to medication adherence and the application of mobile health technology in adults with coronary heart disease: A qualitative study

Objective

Among patients with coronary heart disease, we sought to address the research questions of: 1) What is the acceptability of applying a technology-enabled approach to support medication adherence?; and 2) What are barriers to medication adherence using the Capability-Opportunity-Motivation Behavior (COM-B) model as a guiding framework?

Methods

Applying qualitative research methods, we employed a series of 3 focus groups per individual (total 9 sessions). Coded data from thematic analysis were mapped to the COM-B model components for meaningful associations.

Results

Fourteen participants were recruited (median age 69.5 ± 11, 50% female). Barriers to medication adherence were organized along these COM-B domains: psychological capability (forgetfulness, distractions, fear of side effects), physical opportunity (inaccessible medications, inability to renew prescriptions), reflective (burdening family members), and automatic motivation (medication fatigue, health decline).

Conclusions

Tailored text messaging and mobile phone apps were perceived as helpful tools for medication adherence. The COM-B model was useful to provide a comprehensive, theory-driven evaluation of patients' beliefs and motivations on whether to engage in medication adherence.

Innovation

To date, text messaging and mobile applications have not been widely implemented in the clinical setting and provide a major opportunity to innovate on approaches to address medication adherence.

Patient Adherence to Secondary Prevention Therapies After an Acute Coronary Syndrome: A Scoping Review

PURPOSE

Adherence to guideline-recommended, long-term secondary preventative therapies among patients with acute coronary syndrome (ACS) is fundamental to improving long-term outcomes. The purpose of this scoping review was to provide a broad synopsis of pertinent studies in a structured and comprehensive way regarding factors that influence patient adherence to medical therapy after ACS.

METHODS

Relevant articles focusing on adherence to medical therapy after ACS were retrieved from the EMBASE and MEDLINE databases (search date, September 7, 2021). Studies were independently screened, and relevant information was extracted.

FINDINGS

A total of 58 studies were identified by using the EMBASE and MEDLINE databases. Adherence to secondary prevention was moderate to low and steadily decreased over time. Nearly 30% of patients discontinued one or more medications within 90 days of their primary ACS, and adherence decreased to 50% to 60% at 1 year postdischarge. There were no major differences in adherence between drug classes. Factors influencing patient adherence can be broadly divided into 3 categories: patient related, health care system related, and disease related. Patients managed with percutaneous coronary interventions were more adherent to follow-up treatment than medically managed patients. Depression was reported as a major psychological factor that negatively affected adherence. Improved adherence was observed when higher levels of patient education and provider engagement were delivered during postdischarge follow-up, particularly when scheduled early. Notably, the incidence of major adverse cardiovascular events was lower in hospitals with high 90-day medication adherence than those with moderate or low adherence.

IMPLICATIONS

Patient nonadherence to guideline-recommended long-term pharmacologic secondary preventative therapies after ACS is multifactorial. A comprehensive multifaceted approach should be implemented to improve adherence and clinical outcomes. This approach should include key interventions such as early follow-up visits, high medication adherence at 90 days, patient engagement and education, and development of novel interventions that support the 3 broad categories influencing patient adherence as discussed in this review.

Primary care Adherence To Heart Failure guidelines IN Diagnosis, Evaluation and Routine management (PATHFINDER): a randomised controlled trial protocol

INTRODUCTION

General practitioners (GPs) routinely provide care for patients with heart failure (HF); however, adherence to management guidelines, including titrating medication to optimal dose, can be challenging in this setting. This study will evaluate the effectiveness of a multifaceted intervention to support adherence to HF management guidelines in primary care.

METHODS AND ANALYSIS

We will undertake a multicentre, parallel-group, randomised controlled trial of 200 participants with HF with reduced ejection fraction. Participants will be recruited during a hospital admission due to HF. Following hospital discharge, the intervention group will have follow-up with their GP scheduled at 1 week, 4 weeks and 3 months with the provision of a medication titration plan approved by a specialist HF cardiologist. The control group will receive usual care. The primary endpoint, assessed at 6 months, will be the difference between groups in the proportion of participants being prescribed five guideline-recommended treatments; (1) ACE inhibitor/angiotensin receptor blocker/angiotensin receptor neprilysin inhibitor at least 50% of target dose, (2) beta-blocker at least 50% of target dose, (3) mineralocorticoid receptor antagonist at any dose, (4) anticoagulation for patients diagnosed with atrial fibrillation, (5) referral to cardiac rehabilitation. Secondary outcomes will include functional capacity (6-minute walk test); quality of life (Kansas City Cardiomyopathy Questionnaire); depressive symptoms (Patient Health Questionnaire-2); self-care behaviour (Self-Care of Heart Failure Index). Resource utilisation will also be assessed.

ETHICS AND DISSEMINATION

Ethical approval was granted by the South Metropolitan Health Service Ethics Committee (RGS3531), with reciprocal approval at Curtin University (HRE2020-0322). Results will be disseminated via peer-reviewed publications and conferences.

TRIAL REGISTRATION NUMBER

ACTRN12620001069943.

Digital Health Interventions in Patient Management Following Acute Coronary Syndrome: A Meta-Analysis of the Literature

OBJECTIVE

Acute coronary syndrome patients should be closely followed-up to maintain optimal adherence to medical treatments and to reduce adverse events. Digital health interventions might provide improved outcomes for patient care by providing closer follow- up, compared to standard care. Thus, in this meta-analysis, we aimed to evaluate the effect of digital health interventions on follow-up in acute coronary syndrome patients.

METHODS

We searched medical databases to obtain all relevant studies comparing digital health interventions with standard care in acute coronary syndrome patients. After reviewing all eligible studies, a meta-analysis was conducted with the remaining 11 randomized controlled studies and 2 non-randomized controlled studies. A modified Jadad scale and Newcastle-Ottawa scale were used to assess the quality of the publications for randomized controlled studies and non-randomized controlled studies, respectively.

RESULTS

This meta-analysis consisted of 7657 patients. The all-cause mortality rate was 49% lower in the digital health intervention cases, compared to those who received standard care [relative risk (RR) = 0.51 (0.37; 0.70), P <.01]. There was a significant decrease in systolic blood pressure in the digital health interventions group, compared to the standard care group [mean difference = -5.28 (-9.47; -1.08), P =.01]. The rate of nonadherence to anti-aggregant drugs was 69% lower in the digital health interventions than in the standard care group [RR = 0.31 (0.20; 0.46), P <.01]. Also, nonadherence rates for statin and beta-blockers were lower in the digital health interventions group. The risk of rehospitalization was observed to be 55% less in the digital health interventions patients, compared to the standard care group [RR = 0.45 (0.30; 0.67), P <.01].

CONCLUSION

Digital health interventions can be effective in follow-up for secondary prevention in acute coronary syndrome patients.

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%; β-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.

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.

Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text-Based Secondary Prevention Program: Feasibility Study

BACKGROUND

SMS text messaging programs are increasingly being used for secondary prevention, and have been shown to be effective in a number of health conditions including cardiovascular disease. SMS text messaging programs have the potential to increase the reach of an intervention, at a reduced cost, to larger numbers of people who may not access traditional programs. However, patients regularly reply to the SMS text messages, leading to additional staffing requirements to monitor and moderate the patients' SMS text messaging replies. This additional staff requirement directly impacts the cost-effectiveness and scalability of SMS text messaging interventions.

OBJECTIVE

This study aimed to test the feasibility and accuracy of developing a machine learning (ML) program to triage SMS text messaging replies (ie, identify which SMS text messaging replies require a health professional review).

METHODS

SMS text messaging replies received from 2 clinical trials were manually coded (1) into "Is staff review required?" (binary response of yes/no); and then (2) into 12 general categories. Five ML models (Naïve Bayes, OneVsRest, Random Forest Decision Trees, Gradient Boosted Trees, and Multilayer Perceptron) and an ensemble model were tested. For each model run, data were randomly allocated into training set (2183/3118, 70.01%) and test set (935/3118, 29.98%). Accuracy for the yes/no classification was calculated using area under the receiver operating characteristics curve (AUC), false positives, and false negatives. Accuracy for classification into 12 categories was compared using multiclass classification evaluators.

RESULTS

A manual review of 3118 SMS text messaging replies showed that 22.00% (686/3118) required staff review. For determining need for staff review, the Multilayer Perceptron model had highest accuracy (AUC 0.86; 4.85% false negatives; and 4.63% false positives); with addition of heuristics (specified keywords) fewer false negatives were identified (3.19%), with small increase in false positives (7.66%) and AUC 0.79. Application of this model would result in 26.7% of SMS text messaging replies requiring review (true + false positives). The ensemble model produced the lowest false negatives (1.43%) at the expense of higher false positives (16.19%). OneVsRest was the most accurate (72.3%) for the 12-category classification.

CONCLUSIONS

The ML program has high sensitivity for identifying the SMS text messaging replies requiring staff input; however, future research is required to validate the models against larger data sets. Incorporation of an ML program to review SMS text messaging replies could significantly reduce staff workload, as staff would not have to review all incoming SMS text messages. This could lead to substantial improvements in cost-effectiveness, scalability, and capacity of SMS text messaging-based interventions.

Interventions supporting long term adherence and decreasing cardiovascular events after myocardial infarction (ISLAND): pragmatic randomised controlled trial

OBJECTIVE

To test a scalable health system intervention to improve long term adherence to secondary prevention treatments among patients who have had a recent myocardial infarction.

DESIGN

Three arm, pragmatic randomised controlled trial with blinded outcome assessment.

SETTING

Nine cardiac centres in Ontario, Canada.

PARTICIPANTS

2632 patients with obstructive coronary artery disease after a myocardial infarction, identified from a centralised cardiac registry.

INTERVENTIONS

Participants were randomised 1:1:1 to receive usual care, five mail-outs developed through a user centred design process, or mail-outs plus phone calls. The phone calls were delivered first by an interactive automated system to screen for non-adherence to treatment. Trained lay health workers followed up as necessary. Interventions were coordinated centrally but delivered from each patient's hospital site.

MAIN OUTCOME MEASURES

Co-primary outcomes were completion of cardiac rehabilitation and adherence to recommended medication. Data were collected by blinded assessors through patient report and from administrative health databases at 12 months.

RESULTS

2632 patients (mean age 66, 71% male) were randomised: 878 to the full intervention (mail plus phone calls), 878 to mail only, and 876 to usual care. Of the respondents, 174 (27%) of 643 in the usual care group, 200 (32%) of 628 in the mail only group, and 196 (37%) of 531 allocated to the full intervention completed cardiac rehabilitation (adjusted odds ratio 1.55, 95% confidence interval 1.18 to 2.03). In the mail plus phone group, 11.7%, 6.0%, 14.4%, 32.9%, and 35.0% reported adherence to 0, 1, 2, 3, and 4 drug classes after one year, respectively, in comparison with 12.5%, 6.8%, 13.6%, 30.2%, and 36.8% in the mail only group, and 12.2%, 8.4%, 13.1%, 30.3%, and 36.1% in the usual care group, respectively (mail only v usual care, odds ratio 0.98, 95% confidence interval 0.81 to 1.19; full intervention v usual care, 0.99, 0.82 to 1.20).

CONCLUSIONS

Scalable interventions delivered by mail plus phone can increase completion of cardiac rehabilitation after myocardial infarction but not adherence to medication. More intensive interventions should be tested to improve adherence to medication and to evaluate the association between attendance at cardiac rehabilitation and adherence to medication.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02382731, registered 9 March 2015 before any patient enrolment.

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.

Text Message Behavioral Intervention for Teens on Eating, Physical Activity and Social Wellbeing (TEXTBITES): Protocol for a Randomized Controlled Trial

BACKGROUND

Obesity is among the most significant health challenges facing today's adolescents. Weight gain during adolescence is related to cardiovascular disease, type 2 diabetes, and some cancers in later life. Presently, adolescents living in Australia have limited access to age-appropriate obesity prevention services.

OBJECTIVE

This study aims to investigate whether a two-way text message program, with optional telephone health counseling, improves body mass index (BMI) z score and lifestyle outcomes in adolescents who are overweight.

METHODS

This study will be a single-blind randomized controlled trial (N=150) comparing a two-way text message intervention, with optional telephone health counseling, to usual care in adolescents (13-18 years old, inclusive) who are overweight (recruited from a pediatric weight management clinic and the broader community in Sydney, Australia). The intervention group will receive a six-month text message program, which consists of two-way, semipersonalized, lifestyle-focused text messages (four messages/week) in addition to usual care. The control group will be assigned to receive usual care. The study also includes a follow-up at 12-months. The primary outcome is a change in BMI z score at six months. Secondary outcomes are changes in waist-to-height ratio, diet, physical and sedentary activity levels, sleep quality, quality of life, self-esteem, self-efficacy, social support, and eating disorder and depression symptoms. Also, we will examine acceptability, utility, and engagement with the program through a study-specific process evaluation questionnaire, semi-structured telephone interviews, and an analysis of health counselor communication logs. The analyses will be performed by the intention-to-treat principle to assess differences between intervention and control groups.

RESULTS

The study opened for recruitment in December 2019. Data collection is expected to be completed by December 2021, and the results for the primary outcome are expected to be published in early 2022.

CONCLUSIONS

This study will test the effectiveness of an interactive two-way text message program compared to usual care in improving BMI z score and lifestyle outcomes in adolescents with overweight. This interactive, innovative, and scalable project also aims to inform future practice and community initiatives to promote obesity prevention behaviors for adolescents.

TRIAL REGISTRATION

Australia New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619000389101; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377158&amp;isReview=true.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/16481.

Secondary prevention of cardiovascular disease in China

OBJECTIVE

We aimed to estimate the current use of secondary prevention drugs and identify its associated individual characteristics among those with established cardiovascular diseases (CVDs) in the communities of China.

METHODS

We studied 2 613 035 participants aged 35-75 years from 8577 communities in 31 provinces in the China Patient-Centered Evaluative Assessment of Cardiac Events Million Persons Project, a government-funded public health programme conducted from 2014 to 2018. Participants self-reported their history of ischaemic heart disease (IHD) or ischaemic stroke (IS) and medication use in an interview. Multivariable mixed models with a logit link function and community-specific random intercepts were fitted to assess the associations of individual characteristics with the reported use of secondary prevention therapies.

RESULTS

Among 2 613 035 participants, 2.9% (74 830) reported a history of IHD and/or IS, among whom the reported use rate either antiplatelet drugs or statins was 34.2% (31.5% antiplatelet drugs, 11.0% statins and 8.3% both). Among the 1 530 408 population subgroups, which were defined by all possible permutations of 16 individual characteristics, reported use of secondary prevention drugs varied substantially (8.4%-60.6%). In the multivariable analysis, younger people, women, current smokers, current drinkers, people without hypertension or diabetes and those with established CVD for more than 2 years were less likely to report taking antiplatelet drugs or statins.

CONCLUSIONS

The current use of secondary prevention drugs in China is suboptimal and varies substantially across population subgroups. Our study identifies target populations for scalable, tailored interventions to improve secondary prevention of CVD.

The effect of text message reminders on medication adherence among patients with coronary heart disease: A systematic review and meta-analysis

BACKGROUND

To determine the effectiveness of text message reminders (TMR) on medication adherence (MA) and to investigate the effects of TMR on clinical outcomes.

METHODS

The PubMed, Cochrane library, EMbase, and China Biology Medicine databases were searched for randomized-controlled trials with TMR as the intervention for patients with coronary heart disease. Two reviewers independently extracted data and assessed the risk of bias. Meta-analysis was conducted using Stata 15.0 software.

RESULTS

In total, 1678 patients in 6 trials were included. Compared with the control group, the MA was 2.85 times greater among the intervention group (RR [relative risk] 2.85; 95% confidence interval [CI] 1.07-7.58). TMR reduced systolic blood pressure (BP) (weighted mean difference) = -6.51; 95% CI -9.79 to -3.23), cholesterol (standard mean difference = -0.26; 95% CI -0.4 to -0.12) and increased the number of patients with BP <140/90 mm Hg (RR 1.39; 95% CI 1.26-1.54).

CONCLUSION

TMR significantly promoted MA and reduced systolic BP, cholesterol level, and body mass index, but had no effect on mortality, diastolic BP, or lipoproteins. However, substantial heterogeneity existed in our analyses.

Understanding Nonadherence with Hydroxychloroquine Therapy in Systemic Lupus Erythematosus

Objective.

Hydroxychloroquine (HCQ) is a cornerstone to managing systemic lupus erythematosus (SLE), yet adherence to medication is poor. We sought to measure the association of adherence with 5 “dimensions of adherence” as articulated by the World Health Organization for chronic conditions: the patient’s socioeconomic status, and patient-, condition-, therapy-, and healthcare system–related factors. Our longterm goal is to generate evidence to design effective interventions to increase adherence.

Methods.

The retrospective cohort study included Kaiser Permanente Northern California patients ≥ 18 years old during 2006–2014, with SLE and ≥ 2 consecutive prescriptions for HCQ. Adherence was calculated from the medication possession ratio and dichotomized as < 80% versus ≥ 80%. Predictor variables were obtained from the electronic medical record and census data. We used multivariable logistic regression to estimate adjusted OR and 95% CI.

Results.

The study included 1956 patients. Only 58% of patients had adherence ≥ 80%. In adjusted analyses, socioeconomic variables did not predict adherence. Increasing age (65–89 yrs compared with ≤ 39 yrs: OR 1.44, 95% CI 1.07–1.93), white race (p < 0.05), and the number of rheumatology visits in the year before baseline (≥ 3 compared with 0 or 1: OR 1.47, 95% CI 1.18–1.83) were positively associated with adherence. The rheumatologist and medical center providing care were not associated with adherence.

Conclusion.

At our setting, as in other settings, about half of patients with SLE were not adherent to HCQ therapy. Differences in adherence by race/ethnicity suggest the possibility of using tailored interventions to increase adherence. Qualitative research is needed to elucidate patient preferences for adherence support.