Sedentary behaviour of healthy subjects may have a detrimental impact on cerebral blood flow as well as cognitive measures related to mood and alertness. In this study we focus on the impact of leaving the desk to consume a cup of tea at regular intervals during a sedentary working day.
Prolonged desk work has detrimental impact on cerebral blood flow as well as cognitive measures related to mood and alertness caused. These effects might be prevented by taking short breaks with physical activity. Usually, desk workers have short breaks during office times for either a visit to the restroom or to enjoy for a moment a (hot) drink. Consumption of tea has been associated with benefits related to attention, alertness, mood and creativity. This study focuses on the impact of physically leaving the desk to prepare and consume a cup of tea at regular intervals during a sedentary working day.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
OTHER
Masking
SINGLE
Enrollment
20
John Moores University
Liverpool, United Kingdom
Difference in cerebrovascular perfusion of tea versus water
Cerebrovascular perfusion measured as middle cerebral artery velocity
Time frame: Immediately before and immediately after each of the two 6-hour interventions. The 'before' results will be added to the mixed model as a covariate.
Difference in cerebrovascular auto-regulation gain of tea versus water
Dynamic cerebrovascular autoregulation is assessed via squat-stand manoeuvres performed to elicit oscillations in blood pressure within the high-pass filter frequency range (\<0.20 Hz) of the cerebrovascular. Squat-stand cycles are performed at 0.2 Hz (2.5-seconds squatting, followed by 2.5-seconds standing) and at 0.1 Hz (5-seconds squatting, followed by 5-seconds standing) for 5-minutes each, separated by a 5-minute rest. Transfer function analysis is conducted on the beat-to-beat blood pressure and middle cerebral artery blood flow velocity mean signals to produce values of gain (damping effect of Cerebrovascular autoregulation on the magnitude of blood pressure oscillations).
Time frame: Immediately before and immediately after each of the two 6-hour interventions. The 'before' results will be added to the mixed model as a covariate.
Difference in cerebrovascular auto-regulation phase of tea versus water
Dynamic cerebrovascular autoregulation is assessed via squat-stand manoeuvres performed to elicit oscillations in blood pressure within the high-pass filter frequency range (\<0.20 Hz) of the cerebrovascular. Squat-stand cycles are performed at 0.2 Hz (2.5-seconds squatting, followed by 2.5-seconds standing) and at 0.1 Hz (5-seconds squatting, followed by 5-seconds standing) for 5-minutes each, separated by a 5-minute rest. Transfer function analysis is conducted on the beat-to-beat blood pressure and middle cerebral artery blood flow velocity mean signals to produce values of phase (temporal relationship between changes in blood pressure and middle cerebral artery blood flow velocity).
Time frame: Immediately before and immediately after each of the two 6-hour interventions. The 'before' results will be added to the mixed model as a covariate.
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Difference in cerebrovascular auto-regulation coherence of tea versus water
Dynamic cerebrovascular auto-regulation is assessed via squat-stand manoeuvres performed to elicit oscillations in blood pressure within the high-pass filter frequency range (\<0.20 Hz) of the cerebrovascular. Squat-stand cycles are performed at 0.2 Hz (2.5-seconds squatting, followed by 2.5-seconds standing) and at 0.1 Hz (5-seconds squatting, followed by 5-seconds standing) for 5-minutes each, separated by a 5-minute rest. Transfer function analysis is conducted on the beat-to-beat blood pressure and middle cerebral artery blood flow velocity mean signals to produce values of coherence (linearity of the relationship between the changes in middle cerebral artery blood flow velocity and blood pressure).
Time frame: Immediately before and immediately after each of the two 6-hour interventions. The 'before' results will be added to the mixed model as a covariate.