Evidence map›Paper›PMID 41789401›Full record

ArticleThe Lancet regional health. Western Pacific2026

The AuTOMATIC trial: a multicentre digitally-automated, Bayesian, adaptive, parallel, factorial randomised controlled trial of SMS reminders for childhood vaccination.

Grace Currie, James Totterdell, Claire S Waddington, Ian Peters, Alan Leeb, Gary Browne, Grahame Bowland, Katie Attwell, Catherine Hughes, Christopher C Blyth and 3 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Grace CurrieSchool of Public Health, University of Sydney, Sydney, NSW, Australia.
James TotterdellSchool of Public Health, University of Sydney, Sydney, NSW, Australia.
Claire S WaddingtonDepartment of Clinical Sciences, Liverpool School Tropical Medicine, Liverpool, UK.
Ian PetersSmartVax, Illawarra Medical Centre, Perth, Australia.
Alan LeebSmartVax, Illawarra Medical Centre, Perth, Australia.
Gary BrowneSchool of Public Health, University of Sydney, Sydney, NSW, Australia.
Grahame BowlandWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Australia.
Katie AttwellDepartment of Social Sciences, University of Western Australia, Perth, Australia.
Catherine HughesWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Australia.
Christopher C BlythWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Australia.
Julie MarshWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Australia.
Mark JonesSchool of Public Health, University of Sydney, Sydney, NSW, Australia.
Tom SnellingSchool of Public Health, University of Sydney, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The estimated effectiveness of SMS (short message service) reminders for improving childhood vaccine coverage and timeliness has varied in previous studies. The observed heterogeneity in effectiveness may be explained in part by variation in reminder content or timing of the reminder relative to the vaccine schedule date. We sought to evaluate the effectiveness of a range of SMS reminders of varied content and timing for improving on-time childhood vaccination. Methods: AuTOMATIC was a multi-centre Bayesian adaptive factorial randomised trial comparing four alternative SMS message framings at three alternative message timings versus a no reminder control strategy. Participants were parents of children registered with one of 20 primary care clinics Australia-wide and randomly assigned to one of 12 SMS reminder arms or to control. Reminders varied by framing of content (neutral, positive, risk-based or social benefit) and timing (14 days prior to the due date, on the due date, or 7 days afterwards). The primary endpoint was on-time vaccination, i.e. within 28 days of its scheduled date. Allocation probabilities were updated and stopping rules implemented over the trial according to pre-specified rules based on the posterior probability of effectiveness of each arm evaluated at interim analyses. Trial procedures were largely digitally automated. This trial was registered on Australian New Zealand Clinical Trials Registry (ACTRN12618000789268). Findings: Between January 14, 2021 and February 26, 2024, 9993 parents were randomised and all were included in the primary analysis; between 380 and 1110 were assigned to each of the 12 SMS reminder arms and 637 to control. The adjusted odds ratio (aOR) of on-time vaccination for each of the 12 SMS arms compared to control ranged from 1.02 [95% CrI 0.76-1.34] to 1.53 [1.22, 1.92] with a pooled effect aOR of 1.29 [1.06, 1.55]. This pooled effect corresponded to a standardised difference of 6% [2%, 11%] in the proportion of on-time vaccinations. Interpretation: On average, SMS reminders were associated with a modest increase in on-time vaccination compared to no reminder. There was evidence that neutral SMS reminders were less effective than persuasive reminders, but we were unable to identify a single best combination of reminder content framing and timing. Funding: Ramaciotti Foundations, Royal Australasian College of Physicians, and the Western Australia Department of Health.

Indexed as

Childhood vaccinationLearning health systemsNudge interventionsSMS remindersTrial automation

Identifiers

PMID41789401
PMCPMC12958076

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.