Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 May 28:8:e51916.
doi: 10.2196/51916.

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  1 , Po-Han Chiang  1 , Parag Agnihotri  2 , Sujit Dey  1
Affiliations

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 et al. JMIR Cardio. .

Abstract

Background: Home 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.

Objective: This 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.

Methods: Participants 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.

Results: In 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.

Conclusions: The 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 registration: ClinicalTrials.gov NCT06337734; https://clinicaltrials.gov/study/NCT06337734.

Keywords: AI; artificial intelligence; blood pressure; digital health; hypertension; lifestyle change; lifestyle medicine; mobile phone; remote patient monitoring; wearables.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: JL, PHC, and SD are cofounders of CIPRA.ai Inc, a start-up company formed out of the University of California, San Diego, which has licensed the intervention technology presented in this paper. PA reports no conflict of interest.

Figures

Figure 1
Figure 1
Architecture of data transmission. Participant data were collected from Bluetooth-enabled blood pressure (BP) monitors, wearable devices, and a mobile app–based questionnaire. Data were uploaded through the respective application programming interfaces (APIs) to our app server, where the individualized analysis was carried out before delivering recommendations to participants.
Figure 2
Figure 2
Lifestyle recommendations delivered in the mobile app. Participants received weekly lifestyle recommendations based on their data and personalized analytics. The recommendations encouraged participants to prioritize a single lifestyle modification at a time, focusing on the factor that had the greatest impact on their blood pressure (BP).
Figure 3
Figure 3
Flow of participants through the study. Adults with hypertension were enrolled from the University of California, San Diego Health between November 2021 and February 2023 into a single-arm nonrandomized trial. BP: blood pressure.
Figure 4
Figure 4
Distribution showing the number of unique recommendations sent to each patient. Patients received an average of 9.4 unique recommendations each.
Figure 5
Figure 5
Percentage of active participants measuring their blood pressure (BP) during the 24 weeks.
Figure 6
Figure 6
Percentage of active participants syncing their wearable device during the 24 weeks.
Figure 7
Figure 7
Percentage of active participants answering the questionnaire during the 24 weeks.
See this image and copyright information in PMC

References

    1. Fryar CD, Ostchega Y, Hales CM, Zhang G, Kruszon-Moran D. Hypertension prevalence and control among adults: United States, 2015-2016. NCHS Data Brief. 2017 Oct;(289):1–8. http://www.cdc.gov/nchs/data/databriefs/db289.pdf - PubMed
    1. Hypertension cascade: hypertension prevalence, treatment and control estimates among US adults aged 18 years and older applying the criteria from the American College of Cardiology and American Heart Association’s 2017 hypertension guideline- NHANES 2017–2020. Centers for Disease Control and Prevention. 2017. [2023-05-12]. https://millionhearts.hhs.gov/data-reports/hypertension-prevalence.html .
    1. Lebeau J, Cadwallader J, Aubin-Auger I, Mercier A, Pasquet T, Rusch E, Hendrickx K, Vermeire E. The concept and definition of therapeutic inertia in hypertension in primary care: a qualitative systematic review. BMC Fam Pract. 2014 Jul 02;15:130. doi: 10.1186/1471-2296-15-130. https://bmcfampract.biomedcentral.com/articles/10.1186/1471-2296-15-130 1471-2296-15-130 - DOI - DOI - PMC - PubMed
    1. Kirkland EB, Heincelman M, Bishu KG, Schumann SO, Schreiner A, Axon RN, Mauldin PD, Moran WP. Trends in healthcare expenditures among US adults with hypertension: national estimates, 2003-2014. J Am Heart Assoc. 2018 May 30;7(11):e008731. doi: 10.1161/JAHA.118.008731. https://www.ahajournals.org/doi/10.1161/JAHA.118.008731?url_ver=Z39.88-2... JAHA.118.008731 - DOI - DOI - PMC - PubMed
    1. Whelton PK, Carey RM, Aronow WS, Casey DE, Collins KJ, Dennison HC, DePalma SM, Gidding S, Jamerson KA, Jones DW, MacLaughlin EJ, Muntner P, Ovbiagele B, Smith SC, Spencer CC, Stafford RS, Taler SJ, Thomas RJ, Williams KA, Williamson JD, Wright JT. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: executive summary: a report of the American College Of Cardiology/American Heart Association Task Force on clinical practice guidelines. Hypertension. 2018 Jun;71(6):1269–324. doi: 10.1161/HYP.0000000000000066.HYP.0000000000000066 - DOI - PubMed

Associated data