Trial reportBMJ open2018
TEXT messages to improve MEDication adherence and Secondary prevention (TEXTMEDS) after acute coronary syndrome: a randomised clinical trial protocol.
Trial report in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Mobile phone text messaging for medication adherence in secondary prevention of cardiovascular disease.The Cochrane database of systematic reviews · 2024Pooled it
- Digital Health Interventions in Patient Management Following Acute Coronary Syndrome: A Meta-Analysis of the Literature.Anatolian journal of cardiology · 2023Pooled it
- Pooled it
- Sex differences in risk factor control and medication adherence post-ACS: insights from the TEXTMEDS randomised clinical trial.Open heart · 2026Trial
- The impact of an educational discharge program based on Orem's self-care model on lifestyle, hope, and treatment adherence in coronary angioplasty patients: a randomized controlled clinical trial.BMC research notes · 2025Trial
- Interventions supporting long term adherence and decreasing cardiovascular events after myocardial infarction (ISLAND): pragmatic randomised controlled trial.BMJ (Clinical research ed.) · 2020Trial
- Family history of premature CHD and risk factor control in patients with a recent ACS.NPJ cardiovascular health · 2025Article
- Digital Health Interventions for Atherosclerotic Cardiovascular Disease: The Current Impact and Future Directions for Prevention and Management.Journal of atherosclerosis and thrombosis · 2025Review
- World Heart Federation Roadmap for Secondary Prevention of Cardiovascular Disease: 2023 Update.Global heart · 2024Article
- Efficacy of eHealth Technologies on Medication Adherence in Patients With Acute Coronary Syndrome: Systematic Review and Meta-Analysis.JMIR cardio · 2023Review
- Article
- Article
- Text Message Analysis Using Machine Learning to Assess Predictors of Engagement With Mobile Health Chronic Disease Prevention Programs: Content Analysis.JMIR mHealth and uHealth · 2021Article
- Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text-Based Secondary Prevention Program: Feasibility Study.JMIR mHealth and uHealth · 2020Article
- Text messages for primary prevention of cardiovascular disease: the TextMe2 randomised controlled trial protocol.BMJ open · 2020Article
- Text Message Behavioral Intervention for Teens on Eating, Physical Activity and Social Wellbeing (TEXTBITES): Protocol for a Randomized Controlled Trial.JMIR research protocols · 2020Article
- Development and application of artificial intelligence follow-up system in patients with coronary heart disease.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying simple, low-cost and scalable means of supporting lifestyle change and medication adherence for patients following a cardiovascular (CV) event is important.
objectiveThe TEXTMEDS (TEXT messages to improve MEDication adherence and Secondary prevention) study aims to investigate whether a cardiac education and support programme sent via mobile phone text message improves medication adherence and risk factor levels in patients following an acute coronary syndrome (ACS). STUDY
designA single-blind, multicentre, randomised clinical trial of 1400 patients after an ACS with 12 months follow-up. The intervention group will receive multiple weekly text messages that provide information, motivation, support to adhere to medications, quit smoking (if relevant) and recommendations for healthy diet and exercise. The primary endpoint is the percentage of patients who are adherent to cardioprotective medications and the key secondary outcomes are mean systolic blood pressure (BP) and low-density lipoprotein cholesterol. Secondary outcomes will also include total cholesterol, mean diastolic BP, the percentage of participants who are adherent to each cardioprotective medication class, the percentage of participants who achieve target levels of CV risk factors, major vascular events, hospital readmissions and all-cause mortality. The study will be augmented by formal economic and process evaluations to assess acceptability, utility and cost-effectiveness. SUMMARY: The study will provide multicentre randomised trial evidence of the effects of a text message-based programme on cardioprotective medication adherence and levels of CV risk factors. ETHICS AND DISSEMINATION: Primary ethics approval was received from Western Sydney Local Health District Human Research Ethics Committee (HREC2012/12/4.1 (3648) AU RED HREC/13/WMEAD/15). Results will be disseminated via peer-reviewed publications and presentations at international conferences. TRIAL REGISTRATION NUMBER: ACTRN12613000793718; Pre-results.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.