METHODS: This study was a cross-sectional study using multi-stage stratified sampling method. Data collection was carried out via face-to-face interview at the respondent's home from October 2017 until March 2018. A total of 1047 respondents aged 18 years and above completed the questionnaires and blood pressure measurement. A person who reported diagnosis of hypertension by a physician and had systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg on three readings was categorised as hypertensive. Risk factors of hypertension were analysed using multiple logistic regression.

RESULTS: The prevalence of hypertension in the present study was 49.39% (95% CI 44.27-54.51). There was no statistically significant difference in gender. Age, household income, BMI, and diabetes were significantly associated with hypertension. Hypertension found had inverse association with the level of education. Age was the strongest predictor of hypertension (35-44 years old; OR=2.39, 95% CI=1.39-4.09, 45-54 years old; OR=5.50, 95% CI=3.23-9.38, 55-64 years old OR=13.56, 95% CI=7.77-23.64 and 65 years old and above; OR=25.28, 95% CI=13.33-48.66). Those who had higher BMI more likely to be hypertensive as compared to respondents with normal weight (overweight, OR=1.84; 95% CI=1.18-2.86; obese, OR=4.29% CI=2.56-7.29).

METHODS: Data of participants in the MyBFF@home study (intervention and control groups) were analysed. Participants in the intervention group received personalised dietary counselling consisted of reduced calorie diet 1200-1500 kcal/day, while the control group was assigned to receive women's health seminars. The dietary assessment was done during the intervention phase at baseline, 1 month (m), 2 m, 3 m and 6 m using a 3-day food diary. Body fat was measured using a bioelectrical impedance analyser (In-body 720) at baseline and at the end of the intervention phase. The mean differences of nutrient intake and body compositions during the intervention phase were measured with paired t-test. The changes in body fat and nutrients intake were calculated by subtracting baseline measurements from those taken at 6 months. Multiple linear regression analysis was conducted to determine the extent to which the changes in each gram of nutrients per 1000 kcal were predictive of changes in body fat mass.

METHODS: A cross-sectional study was conducted from October 2017 to March 2018 using a multi-stage stratified sampling method among Malaysian adults aged 18 years old and above. Sodium intake was determined by 24-h urinary sodium excretion, estimated from the respondents' 24-h urinary sample. Height was obtained based on standard protocol. Weight and WC were measured twice using validated anthropometric equipment and BMI was calculated according to World Health Organization (WHO) 1998 classification. Descriptive analysis was done to describe socio-demographic characteristics. A simple linear regression and multiple linear regression tests were done to assess the relationship of 24-h urinary excretion and anthropometric measurement. All statistical analysis was done using SPSS version 22.0.

RESULTS: Of 1047 interviewed respondents, 798 respondents had done the 24-h urine collection (76.0% response rate). Majority was between 40 and 59 years old (43.5%) and married (77.7%). Simple linear regression showed a significant positive linear association between 24-h urinary excretion and household income, WC, and obese group. In the multivariate analysis, it was indicated that, an increase of 1 unit of BMI will significantly increase the sodium intake by 129.20 mg/dl and an increase of 1 cm of WC will significantly increase the sodium intake by 376.45 mg/dl.