Evidence map›Paper›PMID 38805253›Full record

ArticleJMIR cardio2024

The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial.

Jared Leitner, Po-Han Chiang, Parag Agnihotri, Sujit Dey

Registry-linked trialAbstract read
In one paragraph

Article in JMIR cardio, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06337734 (The Effect of an Artificial Intelligence-based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension), which is not on this map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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.

NCT06337734 nacompletednot on this map

The Effect of an Artificial Intelligence-based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial

TypeinterventionalSponsorUniversity of California, San DiegoRan2021 to 2023Enrolled141ConditionsHypertensionArmsAI-Driven Lifestyle Coaching Program
3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Review
  16. Review
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

4 authors.

Jared LeitnerElectrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA, United States.ORCID https://orcid.org/0000-0001-6652-3240
Po-Han ChiangElectrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA, United States.ORCID https://orcid.org/0000-0002-1584-7496
Parag AgnihotriDepartment of Medicine, University of California, San Diego, La Jolla, CA, United States.ORCID https://orcid.org/0000-0002-6798-5671
Sujit DeyElectrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA, United States.ORCID https://orcid.org/0000-0001-9671-3950

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHome blood pressure (BP) monitoring with lifestyle coaching is effective in managing hypertension and reducing cardiovascular risk. However, traditional manual lifestyle coaching models significantly limit availability due to high operating costs and personnel requirements. Furthermore, the lack of patient lifestyle monitoring and clinician time constraints can prevent personalized coaching on lifestyle modifications.

objectiveThis study assesses the effectiveness of a fully digital, autonomous, and artificial intelligence (AI)-based lifestyle coaching program on achieving BP control among adults with hypertension.

methodsParticipants were enrolled in a single-arm nonrandomized trial in which they received a BP monitor and wearable activity tracker. Data were collected from these devices and a questionnaire mobile app, which were used to train personalized machine learning models that enabled precision lifestyle coaching delivered to participants via SMS text messaging and a mobile app. The primary outcomes included (1) the changes in systolic and diastolic BP from baseline to 12 and 24 weeks and (2) the percentage change of participants in the controlled, stage-1, and stage-2 hypertension categories from baseline to 12 and 24 weeks. Secondary outcomes included (1) the participant engagement rate as measured by data collection consistency and (2) the number of manual clinician outreaches.

resultsIn total, 141 participants were monitored over 24 weeks. At 12 weeks, systolic and diastolic BP decreased by 5.6 mm Hg (95% CI -7.1 to -4.2; P<.001) and 3.8 mm Hg (95% CI -4.7 to -2.8; P<.001), respectively. Particularly, for participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 9.6 mm Hg (95% CI -12.2 to -6.9; P<.001) and 5.7 mm Hg (95% CI -7.6 to -3.9; P<.001), respectively. At 24 weeks, systolic and diastolic BP decreased by 8.1 mm Hg (95% CI -10.1 to -6.1; P<.001) and 5.1 mm Hg (95% CI -6.2 to -3.9; P<.001), respectively. For participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 14.2 mm Hg (95% CI -17.7 to -10.7; P<.001) and 8.1 mm Hg (95% CI -10.4 to -5.7; P<.001), respectively, at 24 weeks. The percentage of participants with controlled BP increased by 17.2% (22/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The percentage of participants with stage-2 hypertension decreased by 25% (32/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The average weekly participant engagement rate was 92% (SD 3.9%), and only 5.9% (6/102) of the participants required manual outreach over 24 weeks.

conclusionsThe study demonstrates the potential of fully digital, autonomous, and AI-based lifestyle coaching to achieve meaningful BP improvements and high engagement for patients with hypertension while substantially reducing clinician workloads.

trial registrationClinicalTrials.gov NCT06337734; https://clinicaltrials.gov/study/NCT06337734.

Indexed as

AIartificial intelligenceblood pressuredigital healthhypertensionlifestyle changelifestyle medicinemobile phoneremote patient monitoringwearables

Identifiers

PMID38805253
PMCPMC11167324

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.