Evidence map›Paper›PMID 41721367›Full record

ArticleTrials2026

Using artificially intelligent text messaging to improve health behaviors to reduce cardiovascular risk: study protocol for a pragmatic randomized control trial.

Amy D Lu, Catia Chavez, Stacie L Daugherty, Pradeep Dhar, Catherine G Derington, Rachel Everhart, Erica F Morse, Sylvie Novins-Montague, Katy E Trinkley, Joy M Waughtal and 5 more

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06324981 (Using Artificially Intelligent Text Messaging Technology to Improve American Heart Association's Life's Essential 8 Health Behaviors), which is not on this 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.

NCT06324981 narecruitingnot on this map

Using Artificially Intelligent Text Messaging Technology to Improve American Heart Association's Life's Essential 8 Health Behaviors: LE8 Bot + Backup

TypeinterventionalSponsorUniversity of Colorado, DenverRan2024 to 2028Enrolled2,100ConditionsCardiovascular Diseases, Lifestyle FactorsArmsGeneric text messages, Interactive AI chatbot text messaging, Proactive pharmacist support
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

15 authors.

Amy D LuDepartment of General Internal Medicine, Denver Health and Hospital Authority, Denver, CO, USA. Amy.Lu@dhha.org.
Catia ChavezDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
Stacie L DaughertyDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
Pradeep DharSalud Family Health, Fort Lupton, CO, USA.
Catherine G DeringtonDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
Rachel EverhartDepartment of General Internal Medicine, Denver Health and Hospital Authority, Denver, CO, USA.
Erica F MorseInstitute for Health Research, Kaiser Permanente Colorado, Aurora, CO, USA.
Sylvie Novins-MontagueACCORDS, Adult & Child Center for Outcomes Research & Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Katy E TrinkleyACCORDS, Adult & Child Center for Outcomes Research & Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Joy M WaughtalDepartment of Community and Behavioral Health, Colorado School of Public Health, Aurora, CO, USA.
Rachel ZuckerInstitute for Health Research, Kaiser Permanente Colorado, Aurora, CO, USA.
Evan CareyDepartment of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.
Sheana BullClinic Chat, LLC, Denver, CO, USA.
Pamela N PetersonDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
P Michael HoDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO, USA.

Funding

NIH Health Care Systems Research Collaboratory-Coordinating Center (U24)U24AT009676 · NCCIH · DUKE UNIVERSITY · PI LESLEY H CURTIS, Adrian Hernandez · 2017 to 2026
$21.5M
Using artificially intelligent text messaging technology to improve American Heart Association’s Life’s Simple 7 Health Behaviors: LS7 Bot + BackupUH3HL168504 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI P. MICHAEL HO, PAMELA N PETERSON · 2024 to 2026
$2.6M
Personalizing Clinical Decision Support for Heart Failure Treatment to Clinicians' NeedsK23HL161352 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI Katy E Trinkley · 2022 to 2026
$869k
NCCIH NIH HHS U24 AT009676NHLBI NIH HHS 4UH3HL168504-02NHLBI NIH HHS K23 HL161352NHLBI NIH HHS UH3 HL168504
6 · The paper itself

Abstract

backgroundCardiovascular (CV) disease and its risk factors such as hypertension, diabetes, and hyperlipidemia account for most chronic diseases experienced by adults in the US. The use of text-based "behavioral nudges" supports behavior change and self-management of chronic disease. Building upon our previous trial utilizing an artificially intelligent (AI) chatbot for medication adherence, the Chat for Heart Health randomized controlled trial aims to test the comparative effectiveness of 3 text-based methods of delivering "nudges" to change health behaviors to reduce cardiovascular disease risk in patients across 3 safety-net healthcare systems.

methodsAdult patients ages 18-89 with CV risk factors will be recruited from 3 health systems. A target of 2097 participants will be randomized to 3 study arms: generic text messaging, AI interactive chatbot messaging, and AI interactive chatbot messaging plus pharmacist support. Evaluation of the intervention and the program will be carried out using the RE-AIM and PRISM frameworks. The primary effectiveness outcome is the change in CV risk reduction behaviors as defined by the American Heart Association's Life's Essential 8 score. Secondary outcomes including patient self-efficacy scores, clinical events, healthcare utilization, and facilitators and barriers to implementation and adoption will also be assessed. DISCUSSION: Our large-scale pragmatic trial engages with patients and health systems who have traditionally not engaged in research, which presented substantial challenges yet will make our results more generalizable to diverse populations. Additionally, we engaged a diverse advisory panel made up of patients, community members, providers, and health systems leaders throughout the study to ensure sociocultural, linguistic, and community relevance. The results of this trial (if the intervention is effective) could lead to broader dissemination of a low-cost intervention to support behavior change to reduce CV risk.

trial registrationNCT06324981 (3/14/2024), https://clinicaltrials.gov/study/NCT06324981.

Indexed as

Cardiovascular DiseasesHealth BehaviorRisk Reduction BehaviorText MessagingAdherence InterventionsAdolescentAdultAgedAged, 80 and overDigital HealthFemaleHeart Disease Risk FactorsHumansMaleMedication AdherenceMiddle AgedCardiovascularmHealthPragmatic trialRE-AIM

Identifiers

PMID41721367
PMCPMC13032664

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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.