Predictors for Blood Pressure Reduction in American Latinos: Secondary Analysis of the Adelgaza Program Data.
Wen-Wen LiEric VittinghoffYoshimi FukuokaPublished in: Hispanic health care international : the official journal of the National Association of Hispanic Nurses (2019)
Little is known about factors that predict blood pressure (BP) reduction in overweight American Latinos. The aim of this secondary analysis was to explore predictors of changes in mean systolic and diastolic BPs over an 8-week weight loss intervention period in a sample of 54 overweight American Latinos using data collected during the Adelgaza trial. Baseline BP, exercise energy use (in units of metabolic equivalent of task), weight change, average daily intake of calories from beverages, average daily intake of calories from fat, age, and gender were considered as potential predictors of reductions in BP, as measured at baseline, 3, and 8 weeks. Baseline characteristics were as follows: mean age 45.3 (SD = 10.8) years, 31.5% male, 61.1% born in the United States. Mean baseline systolic and diastolic BPs were 122.1 (SD = 14.4) mmHg and 76.6 (SD = 9.8) mmHg, respectively. Both baseline systolic and diastolic BPs predicted reductions in systolic BP after adjusting for other factors (p < .001). None of the nine variables predicted reductions in diastolic BP (p > .05). This finding suggests that overweight American Latinos with higher baseline systolic or diastolic BP should be identified and provided with early intervention education to achieve better hypertension management or prevention.
Keyphrases
- blood pressure
- weight loss
- left ventricular
- physical activity
- hypertensive patients
- weight gain
- heart rate
- bariatric surgery
- randomized controlled trial
- roux en y gastric bypass
- healthcare
- heart failure
- electronic health record
- gastric bypass
- quality improvement
- clinical trial
- body mass index
- blood glucose
- mental health
- type diabetes
- high intensity
- metabolic syndrome
- glycemic control
- risk assessment
- gestational age
- machine learning
- data analysis