ArticleJMIR formative research2022
Relationships Between Blood Pressure Reduction, Weight Loss, and Engagement in a Digital App-Based Hypertension Care Program: Observational Study.
Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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.
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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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Smartphone application-based interventions for cardiometabolic risk factor management: A systematic review and meta-analysis.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Pooled it
- Smartphone application-based intervention to lower blood pressure: a systematic review and meta-analysis.Hypertension research : official journal of the Japanese Society of Hypertension · 2025Pooled it
- Does clinical practice supported by artificial intelligence improve hypertension care management? A pilot systematic review.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Pooled it
- The Japanese Society of Hypertension Guidelines for blood pressure control using digital technologies.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Mobile Health Application for the Prevention of Stroke (MAPS): a pilot, single-arm study on an innovative health application designed for primary stroke prevention.BMC neurology · 2026Article
- Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.Journal of medical Internet research · 2025Review
- Cardiac Self-Efficacy Improvement in a Digital Heart Health Program: Secondary Analysis From a Feasibility and Acceptability Pilot Study.JMIR formative research · 2025Article
- Advances in digital technology in healthcare.Hypertension research : official journal of the Japanese Society of Hypertension · 2025Article
- The Role of Intermittent Fasting on Metabolic Syndrome: A Systematic Review and Meta-Analysis.Cureus · 2024Review
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Article
- Evaluating a New Digital App-Based Program for Heart Health: Feasibility and Acceptability Pilot Study.JMIR formative research · 2024Article
- Comprehensive Cardiovascular Risk Factor Control With a Mobile Health Cardiovascular Risk Self-Management Program.Journal of the American Heart Association · 2024Article
- Business vs After-Hours Use of an Artificial Intelligence-Powered Digital Health Platform Among Insured Patients.JAMA network open · 2023Article
- Predictors of program interest in a digital health pilot study for heart health.PLOS digital health · 2023Article
- Review
- The Effects of Providing a Connected Scale in an App-Based Digital Health Program: Cross-sectional Examination.JMIR mHealth and uHealth · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHome blood pressure (BP) monitoring is recommended for people with hypertension; however, meta-analyses have demonstrated that BP improvements are related to additional coaching support in combination with self-monitoring, with little or no effect of self-monitoring alone. High-contact coaching requires substantial resources and may be difficult to deliver via human coaching models.
objectiveThis observational study assessed changes in BP and body weight following participation in a fully digital program called Lark Hypertension Care with coaching powered by artificial intelligence (AI).
methodsParticipants (N=864) had a baseline systolic BP (SBP) ≥120 mm Hg, provided their baseline body weight, and had reached at least their third month in the program. The primary outcome was the change in SBP at 3 and 6 months, with secondary outcomes of change in body weight and associations of changes in SBP and body weight with participant demographics, characteristics, and program engagement.
resultsBy month 3, there was a significant drop of -5.4 mm Hg (95% CI -6.5 to -4.3; P<.001) in mean SBP from baseline. BP did not change significantly (ie, the SBP drop maintained) from 3 to 6 months for participants who provided readings at both time points (P=.49). Half of the participants achieved a clinically meaningful drop of ≥5 mm Hg by month 3 (178/349, 51.0%) and month 6 (98/199, 49.2%). The magnitude of the drop depended on starting SBP. Participants classified as hypertension stage 2 had the largest mean drop in SBP of -12.4 mm Hg (SE 1.2 mm Hg) by month 3 and -13.0 mm Hg (SE 1.6 mm Hg) by month 6; participants classified as hypertension stage 1 lowered by -5.2 mm Hg (SE 0.8) mm Hg by month 3 and -7.3 mm Hg (SE 1.3 mm Hg) by month 6; participants classified as elevated lowered by -1.1 mm Hg (SE 0.7 mm Hg) by month 3 but did not drop by month 6. Starting SBP (β=.11; P<.001), percent weight change (β=-.36; P=.02), and initial BMI (β=-.56; P<.001) were significantly associated with the likelihood of lowering SBP ≥5 mm Hg by month 3. Percent weight change acted as a mediator of the relationship between program engagement and drop in SBP. The bootstrapped unstandardized indirect effect was -0.0024 (95% CI -0.0052 to 0; P=.002).
conclusionsA hypertension care program with coaching powered by AI was associated with a clinically meaningful reduction in SBP following 3 and 6 months of program participation. Percent weight change was significantly associated with the likelihood of achieving a ≥5 mm Hg drop in SBP. An AI-powered solution may offer a scalable approach to helping individuals with hypertension achieve clinically meaningful reductions in their BP and associated risk of cardiovascular disease and other serious adverse outcomes via healthy lifestyle changes such as weight loss.
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Registered trials
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