Cardiovascular disease (CVD) remains one of the leading contributors to premature mortality and long-term disability worldwide. India carries a substantial burden, characterized by relatively early onset of disease and a high prevalence of cardiometabolic risk factors.[1,2] Current cardiovascular prevention therefore depends strongly on the early identification of individuals with increased risk before clinically evident disease develops.

Conventional cardiovascular-risk assessment incorporates age, sex, smoking, diabetes, blood pressure and lipid measurements into validated risk models.[3] Although these approaches are clinically valuable, they predominantly represent measurements obtained at a single point in time. Cardiovascular physiology, however, is dynamic and varies with sleep, activity, emotional stress, circadian rhythm and autonomic regulation. Wearable devices offer the possibility of measuring such physiological changes repeatedly under real-world conditions.[4]

Resting heart rate is a simple but biologically meaningful cardiovascular variable. Increased resting heart rate has been associated with cardiovascular events and mortality in population studies.[5,6] It reflects multiple interacting processes, including autonomic tone, physical fitness, metabolic status, inflammation and haemodynamic demand. Wearable devices may improve the characterization of resting heart rate by obtaining repeated measurements during periods of inactivity and sleep rather than relying on an isolated clinic pulse measurement.

Heart-rate variability represents beat-to-beat variation in cardiac cycle length and provides a non-invasive marker of autonomic cardiovascular regulation. Common time-domain measures include SDNN and RMSSD. SDNN reflects overall variability, while RMSSD is particularly influenced by short-term parasympathetic modulation.[7] Reduced HRV has been associated with cardiovascular morbidity, mortality and adverse outcomes in both apparently healthy populations and patients with cardiovascular disease.[8–10]

Consumer and medical-grade wearable devices increasingly estimate inter-beat intervals using photoplethysmography or wearable electrocardiography. This permits longitudinal assessment of HRV outside conventional laboratories.[4,11] However, wearable HRV measurements may be influenced by motion, posture, sleep stage, respiration, skin-device contact and proprietary signal-processing algorithms.[12] Therefore, their usefulness for cardiovascular-risk assessment requires evaluation in appropriate clinical populations.

In the present study, wearable-derived heart rate and HRV were assessed in adults without established cardiovascular disease and compared across conventional cardiovascular-risk categories. The study hypothesized that individuals with greater conventional cardiovascular risk would demonstrate higher resting heart rates, lower HRV and a pattern compatible with increased sympathetic predominance. The study additionally examined correlations with conventional cardiovascular-risk scores and the ability of individual wearable-derived autonomic biomarkers to discriminate high-risk participants.

The parent thesis specifically identified a progressive increase in resting heart rate and decreases in SDNN and RMSSD across cardiovascular-risk categories.

This was a hospital-based prospective observational analytical study with a cross-sectional correlation component and a short longitudinal wearable-monitoring period. Participants were recruited through the Department of Physiology in collaboration with general medicine and cardiology outpatient services at Index Medical College and Hospital.

 Study population

Adults aged 30–70 years of either sex without previously established cardiovascular disease were eligible. Participants were required to be willing to wear the study wearable device continuously for the monitoring period and to provide written informed consent.

Participants with known coronary artery disease, myocardial infarction, stroke or transient ischaemic attack, heart failure, cardiomyopathy, significant valvular disease, previously documented atrial fibrillation or another sustained arrhythmia were excluded. Additional exclusions included implanted cardiac electronic devices, conditions preventing adequate wrist-sensor contact, acute illness, pregnancy, severe non-cardiac disease likely to influence biomarker assessment and inability to comply with wearable monitoring.

 Sample size

The primary sample-size calculation was based on detecting a correlation between a wearable-derived biomarker and cardiovascular-risk status. Assuming a minimum correlation coefficient of 0.25, two-sided α=0.05 and 80% power, approximately 123 participants were required. Allowing for data loss related to inadequate wear time or signal quality, the sample was increased to 150 participants.

 Clinical assessment

A structured clinical assessment was performed at baseline. Recorded variables included age, sex, smoking history, comorbidities and medication history. Anthropometric measurements included height, weight and body mass index. Blood pressure was measured according to standard clinical technique after adequate rest.

Laboratory assessment included fasting plasma glucose and/or HbA1c and fasting lipid profile, including total cholesterol, LDL cholesterol, HDL cholesterol and triglycerides. Resting 12-lead ECG was performed.

Participants were classified into low-, moderate- or high-cardiovascular-risk groups using an established conventional cardiovascular-risk assessment approach.

 Wearable monitoring

Participants were fitted with a validated regulatory-cleared wrist-worn wearable capable of photoplethysmography-based heart-rate assessment, HRV measurement, accelerometry and sleep monitoring. Participants were instructed to wear the device continuously, including during sleep, for a minimum of seven consecutive days, with 14 days as the target period.

For the present analysis, the principal wearable-derived variables were mean resting heart rate, night-time nadir heart rate, SDNN, RMSSD and LF/HF ratio.

 Data quality

Wear adherence and completeness were evaluated. A minimum valid-wear period was specified, and low-quality signal segments were excluded according to device signal-quality indicators.

 Statistical analysis

Continuous variables were summarized as mean ± standard deviation and categorical variables as frequency and percentage. Comparisons across the three cardiovascular-risk groups were performed using appropriate parametric or non-parametric tests. Pearson or Spearman correlation was used to assess associations between wearable biomarkers and conventional cardiovascular-risk scores.

Receiver-operating-characteristic analysis was used to assess the ability of resting heart rate, RMSSD and SDNN to identify participants in the high cardiovascular-risk category. A two-sided p-value <0.05 was considered statistically significant.

A total of 150 adults were included: 68 in the low-risk group, 52 in the moderate-risk group and 30 in the high-risk group. Conventional risk factors increased progressively across cardiovascular-risk categories.

 Table 1. Baseline characteristics according to cardiovascular-risk category

Table 2. Wearable-derived heart rate and HRV according to cardiovascular-risk category

There was a clear autonomic gradient with increasing cardiovascular risk. Resting and nocturnal heart rates increased, while both HRV indices decreased.

 Table 3. Correlation of autonomic biomarkers with cardiovascular-risk score

RMSSD showed the strongest relationship with conventional cardiovascular-risk score.

 Table 4. Diagnostic performance for identifying high cardiovascular risk

Among the autonomic measures, RMSSD demonstrated the largest AUC.

The present study demonstrated a consistent relationship between wearable-derived autonomic biomarkers and conventional cardiovascular-risk status. Individuals categorized as having higher cardiovascular risk showed progressively higher resting and night-time heart rates, lower SDNN and RMSSD, and higher LF/HF ratios.

Resting heart rate increased from 67.3 bpm in low-risk participants to 78.1 bpm among high-risk participants. This finding is consistent with earlier epidemiological evidence associating increased resting heart rate with cardiovascular morbidity and mortality. Zhang et al. reported a dose-response relationship between resting heart rate and all-cause and cardiovascular mortality.[5] Similarly, Aune et al. found that elevated resting heart rate was associated with increased risk of cardiovascular disease and mortality.[6]

Repeated wearable measurement may offer an important advantage over clinic-based pulse assessment. A single clinic heart rate may be influenced by anxiety, recent activity or transient physiological conditions. Wearable devices can estimate habitual resting heart rate repeatedly during free-living conditions, potentially providing a more stable physiological phenotype.[11]

The most notable finding was the progressive decline in HRV. Mean SDNN decreased from 52.8 ms among low-risk participants to 34.9 ms in the high-risk group, while RMSSD fell from 45.3 to 27.4 ms. These observations are consistent with evidence linking reduced HRV to cardiovascular risk. Maheshwari et al. demonstrated an association between HRV and lifetime cardiovascular risk in the Atherosclerosis Risk in Communities study.[8] Hillebrand et al. reported that lower HRV was associated with a greater risk of a first cardiovascular event among individuals without known cardiovascular disease.[9]

A meta-analysis by Jarczok et al. further supported the prognostic relevance of HRV across healthy and patient populations.[10] These associations are biologically plausible because impaired parasympathetic modulation and increased sympathetic activation are involved in hypertension, arrhythmogenesis, metabolic dysregulation and cardiovascular disease progression.

RMSSD demonstrated the strongest correlation with conventional risk score in the current study (r=−0.49). Its AUC of 0.78 was also greater than that observed for resting heart rate. RMSSD is particularly influenced by short-term vagal modulation, and its reduction may reflect impaired autonomic adaptability among higher-risk individuals.

The increase in LF/HF ratio from 1.41 in low-risk participants to 2.08 among high-risk participants was compatible with a shift toward sympathetic predominance. However, LF/HF ratio should be interpreted cautiously because autonomic physiology is complex and spectral HRV measures may not represent a simple direct measure of sympathovagal balance.[7]

Wearable-derived HRV also has important methodological limitations. Optical HRV may be influenced by movement, skin contact, peripheral perfusion and pulse-transit effects.[12] HRV additionally varies with age, sex, respiration, sleep, medication, fitness and metabolic conditions. Consequently, wearable HRV should not be interpreted as a standalone diagnostic test.

Wearable-derived resting heart rate and HRV demonstrated significant associations with conventional cardiovascular-risk status. Increasing cardiovascular risk was characterized by elevated resting and night-time heart rates, reduced SDNN and RMSSD, and higher LF/HF ratios. RMSSD demonstrated the strongest correlation and discriminatory ability among the autonomic biomarkers evaluated. Wearable HRV may therefore provide useful complementary information for early cardiovascular-risk assessment, but it should be integrated with established clinical risk factors rather than used as an independent diagnostic measure. Long-term prospective studies are needed to establish whether wearable-derived HRV predicts subsequent cardiovascular events.

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