Evidence map›Paper›PMID 42252402›Full record

Observational studyBMC cardiovascular disorders2026

Smartwatch-derived exercise metrics as predictors of early hypertension: a prospective observational study.

Esma Selva Ateş Ceyhun, Gökhan Ceyhun

Abstract readObservational Study
In one paragraph

Observational study in BMC cardiovascular disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Esma Selva Ateş CeyhunDepartment of Cardiology, Erzurum City Hospital, Erzurum, Türkiye.
Gökhan CeyhunDepartment of Cardiology, Faculty of Medicine, Ataturk University, Erzurum, Türkiye. gokhanceyhun@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly identification of individuals at risk for hypertension is essential for effective cardiovascular disease. Physiological and activity metrics derived from consumer smartwatches may offer a practical, noninvasive approach to identify individuals at increased risk before the clinical onset of hypertension.

methodsIn this 12-month prospective observational study, 230 normotensive adults aged 30-60 years were followed using consumer smartwatches. Baseline wearable predictors were calculated as the mean of the first 30 days of valid data after enrollment and included heart rate variability, resting heart rate, and time spent in moderate-to-vigorous physical activity. Incident hypertension was defined according to current European guidelines using standardized office blood pressure measurements obtained at follow-up.

resultsDuring follow-up, 28 participants (12.2%) developed hypertension. Individuals who developed hypertension exhibited lower baseline heart rate variability and spent less time in moderate-to-vigorous physical activity compared with those who remained normotensive. In multivariable logistic regression analysis, lower heart rate variability, lower levels of physical activity, and higher body mass index were independently associated with incident hypertension. An interaction between autonomic variability and physical activity was observed, indicating that individuals with both reduced autonomic regulation and low physical activity had the highest predicted risk. Machine-learning models showed improved statistical discrimination compared with clinical variables alone and were used as complementary exploratory analyses.

conclusionsSmartwatch-derived autonomic and physical activity metrics were independently associated with the development of hypertension over a 12-month period. These findings from an observational study suggest a potential role for wearable-derived physiological parameters as digital biomarkers for early hypertension risk stratification, although further validation in larger and externally replicated cohorts is required.

Indexed as

ActigraphyBlood PressureExerciseFitness TrackersHeart RateHypertensionWearable Electronic DevicesAdultDigital HealthEarly DiagnosisFemaleHumansIncidenceMaleMiddle AgedPredictive Learning ModelsHeart rate variabilityHypertensionPhysical activitySmartwatchWearable technology

Identifiers

PMID42252402
PMCPMC13471549

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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.