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The Influence of a Digital Weight Loss Intervention on Blood Pressure Outcomes

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Date

July 2024

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2026-07-01

Authors

Maloney, Kallie R. Stephens

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East Carolina University

Abstract

Cardiovascular disease remains a highly prevalent disease that is consistently among the leading cause of death for Americans (Ahmad, 2023). Managing one’s blood pressure is a prominent recommendation in preventing heart disease (Olsen et al., 2016), but over 45% of the US population is classified as having hypertension (Benjamin et al. 2019). While the treatment recommendations for hypertension depend on hypertensive stage and ASCVD risk, lifestyle modification is recommended for all. Typical lifestyle changes include weight loss, diet modification, and increased physical activity (Whelton et al., 2018). Lifestyle intervention is not without challenge, however, with physician time constraints consistently emerging as a barrier to implementation. Digital health provides an avenue for assistance with lifestyle modification, something recognized by a majority of physicians (AMA, 2022). Digital health has shown to be effective in managing various physical health outcomes, including weight loss and diet modification. Little research, however, has focused on hypertension specific goals. Digital health also serves as an opportunity to reach underserved populations, such as those from rural or low-income environments. Preliminary research indicates that digital health interventions are feasible in these populations, but little research has assessed blood pressure outcomes. The aims of the present study, therefore, were to assess the influence of a commercially available digital health intervention for weight loss, Noom, on blood pressure related outcomes, determine what factors influence blood pressure, and determine if rurality and income environment influences rate of blood pressure decrease. It was hypothesized that systolic and diastolic blood pressure would decrease over the 16-week intervention. It was also hypothesized that weight, gender, and physical activity (steps) would be associated with blood pressure. Lastly, it was hypothesized that both rural users and users living in environments with a higher proportion of individuals from low income would display slower blood pressure decrease. User’s weekly blood pressure, weight, and steps were utilized for analysis. User zip codes were used to classify users as rural or urban using secondary Rural Urban Commuting Codes. Zip codes were also utilized to estimate the proportion of individuals from low-income households (<$25,000 annually) within a user’s zip code using 2019 IRS tax data. From there, conditional growth curve models were estimated to address all hypotheses. Systolic and diastolic blood pressure significantly decreased over the 16-week intervention. Baseline weight and physical activity were significantly associated with systolic blood pressure. Lastly, rurality status and income environment were not significantly associated with different systolic or diastolic blood pressure trajectories. While many findings were as expected, rurality and user’s environmental income not significantly slowing rates of blood pressure decrease were surprising. It is possible that the present sample of Noom users do not adequately reflect the average rural individual, or individuals from low income environments. Specifically, the cost associated with Noom and the voluntary nature of inputting blood pressure could have contributed to a sample that was more motivated for blood pressure change.

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