Number of steps and systolic blood pressure: Do work and leisure matter?

Abstract

The number of steps per day influences blood pressure and health. However, the association between steps at work and leisure and blood pressure is unknown. Thus, we aimed to investigate the association between the domain-specific number of steps and systolic blood pressure. A thigh-worn accelerometer was used to measure the steps of 694 workers over 1-5 consecutive days, separated into work and leisure domains using a self-reported diary. We linearly regressed steps at work, leisure and total day against systolic blood pressure, adjusting for age, sex, body mass index, smoking, and antihypertensive medication. Additionally, we stratified the analyses on job type (blue-collar or white-collar). The results of this cross-sectional analysis indicated a beneficial association between the number of steps (per 2000-step interval) and systolic blood pressure for the total day (-0.5 mmHg; -1.0 to -0.8, 95% CI, p < 0.05) and work (-0.9 mmHg; -1.5 to -0.4, 95% CI, p < 0.05), but not for leisure (+0.1 mmHg; -0.7 to 0.9, 95% CI, p = 0.75). Blue-collar workers took almost twice as many steps at work (9143 ± SD3837) as white-collar workers (5863 ± SD3565) and, after stratification on job type, we observed a beneficial association between the number of steps at work and systolic blood pressure among blue-collar workers (-1.1 mmHg; -1.7 to -0.4, 95% CI, p < 0.05), but not for white-collar workers (-0.3 mmHg; -1.7 to 1.1, 95% CI, p = 0.7). These findings indicate that the number of steps at work, particularly among blue-collar workers, is beneficially associated with systolic blood pressure. Such findings support the potential of work (re)design to promote walking to improve blood pressure.

Keywords: accelerometer; domain; gait; health; occupation.

FIGURE 1

The flow of workers

FIGURE 2

Fitted values from adjusted models representing the association between: Daily steps (A), steps at work (B), steps at leisure (C), and systolic blood pressure. Adjusted models included smoking status, BMI, sex, and age. Analysis was conducted on the data from 694 workers, of which 560 blue‐collar workers and 134 white‐collar workers. The dashed line indicates the direction of association. The color gradient indicates the density of data points, where light gray is the least dense and black is the densest