Association of a Lifestyle Intervention with Blood Pressure Trajectories During Pregnancy and Postpartum in Women with Pre-pregnancy Overweight and Obesity.

OBJECTIVES: Healthy lifestyle, including healthy diet and physical activity, has been associated with lower blood pressure (BP). We hypothesized that overweight/obese women randomized to a lifestyle intervention beginning in early pregnancy would be less likely to have a higher BP trajectory. METHODS: The Health in Pregnancy and Postpartum study promoted a healthy lifestyle in pregnant and postpartum women with pre-pregnancy overweight or obesity. BP was measured at study visits at ≤ 16 and 32 weeks of pregnancy and 6 and 12 months postpartum. Latent class trajectories were constructed to identify groups of participants with similar BP patterns throughout the study period. Odds of being assigned to the higher BP trajectories between the intervention and control group were determined with logistic regression, adjusted for age, race, pre-pregnancy BMI, gestational weight gain, smoking during the study period, and household income. RESULTS: Of the 219 participants enrolled (44.3% Black participants, 55.7% White participants, mean age = 29.7 ± 0.3 years), 112 (51.1%) were randomized to the intervention. Two systolic and diastolic BP trajectory groups were identified (Low-Stable, Moderate-Increasing). Randomization to the lifestyle intervention was not associated with odds of Moderate-Increasing systolic (OR 0.83, 95%CI 0.4, 1.6) or diastolic (OR 0.67, 95%CI 0.3, 1.3) BP trajectory assignment. Those in the Moderate-Increasing BP groups had higher BMI and were more likely to have an adverse pregnancy outcome. CONCLUSIONS: A lifestyle intervention initiated in early pregnancy to help prevent excessive gestational weight gain was not associated with odds of specific BP patterns through pregnancy and postpartum.

A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study.

Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement. METHODS: We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE). RESULTS: ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG. CONCLUSION: This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.