{Reference Type}: Clinical Trial Protocol
{Title}: The AuTOMATIC trial: a study protocol for a multi-arm Bayesian adaptive randomised controlled trial of text messaging to improve childhood immunisation coverage.
{Author}: Currie GE;Totterdell J;Bowland G;Leeb A;Peters I;Blyth CC;Waddington C;Marsh JA;Snelling TL;
{Journal}: Trials
{Volume}: 24
{Issue}: 1
{Year}: Feb 2023 7
{Factor}: 2.728
{DOI}: 10.1186/s13063-023-07097-3
{Abstract}: <B>BACKGROUND: </B>While most Australian children are vaccinated, delays in vaccination can put them at risk from preventable infections. Widespread mobile phone ownership in Australia could allow automated short message service (SMS) reminders to be used as a low-cost strategy to effectively 'nudge' parents towards vaccinating their children on time.<BR><B>METHODS: </B>AuTOMATIC is an adaptive randomised trial which aims to both evaluate and optimise the use of SMS reminders for improving the timely vaccination of children at primary care clinics across Australia. The trial will utilise high levels of digital automation to effect, including eligibility assessment, randomisation, delivery of intervention, data extraction and analysis, thereby allowing healthcare-embedded trial delivery. Up to 10,000 parents attending participating primary care clinics will be randomised to one of 12 different active SMS vaccine reminder content and timing arms or usual practice only (no SMS reminder). The primary outcome is vaccine receipt within 28 days of the scheduled date for the index vaccine (the first scheduled vaccine after randomisation). Secondary analyses will assess receipt and timeliness for all vaccine occasions in all children. Regular scheduled analyses will be performed using Bayesian inference and pre-specified trial decision rules, enabling response adaptive randomisation, suspension of any poorly performing arms and early stopping if a single best message is identified.<BR><B>CONCLUSIONS: </B>This study will aim to optimise SMS reminders for childhood vaccination in primary care clinics, directly comparing alternative message framing and message timing. We anticipate that the trial will be an exemplar in using Bayesian adaptive methodology to assess a readily implementable strategy in a wide population, capable of delivery due to the levels of digital automation. Methods and findings from this study will help to inform strategies for implementing reminders and embedding analytics in primary health care settings.<BR><B>BACKGROUND: </B>ANZCTR: ACTRN12618000789268 .