BACKGROUND Mobile health (mHealth) tools may be useful platforms for dietary monitoring and assessment. OBJECTIVE This study aims to evaluate the effectiveness of a mobile dietary self-monitoring application (app) for weight loss versus a paper-based diary among adults with a body mass index (BMI) of 23 kg/m2 or above. METHODS A total of 33 men and 17 women aged 18-39 years participated in a six-week randomized trial. We randomly assigned participants to one of two groups: (A) Smartphone app group (n=25) or (B) paper-based diary group (n=25). The smartphone app group recorded foods and dietary supplements that they consumed and received immediate dietary feedback using ‘Well-D’, a dietary self-monitoring app developed by our team. The paper-based diary group was instructed to record foods or supplements that they consumed using the self-recorded diary. The primary outcomes were weight, BMI, waist circumference, body fat mass and skeletal muscle mass. We also examined changes in nutrient intakes including energy, carbohydrate, protein, fat, dietary fiber, vitamins, and minerals using 3-day 24-hour recalls (24HR). Differences in changes between the two groups were analyzed using independent t-tests or Wilcoxon Mann-Whitney tests. All of the data were analyzed using intent-to-treat analysis. RESULTS The average number of days recorded (mean ± SD) was 18.5 ± 14.1 for the app group and 15.5 ± 10.1 for the paper-based diary group. The differences in changes in weight, BMI and waist circumference were not significantly different between the app group and paper-based diary group (P=0.33, 0.34, and 0.70, respectively). Similarly, changes in body fat mass or skeletal muscle mass did not differ between the two groups (P=0.71 and 0.054, respectively). Although energy intake was reduced in both groups, there was no significant difference in change of energy intake between the two groups (P=0.98). CONCLUSIONS There were no differences in changes in anthropometric measures and nutrient intake between the app group and the paper-based diary group. Both mobile dietary self-monitoring app and paper-based diary may be useful for improving anthropometric measures. CLINICALTRIAL KCT, KCT0003170; https://cris.nih.go.kr/cris/search/search_result_st01_en.jsp?seq=11642<ype=&rtype=
Background: Limited evidence exists on the association between dietary fat intake and lipid profiles in Southeast Asian populations. Objectives: We aimed to examine the cross-sectional associations of dietary intake of total and specific types of fat with dyslipidemia in Filipino immigrant women in Korea. Methods: We included 406 Filipino women married to Korean in the Filipino Women’s Diet and Health Study (FiLWHEL). Dietary fat intake was assessed using 24-hour recalls. Impaired blood lipid profiles were defined as high total cholesterol (TC) (≥200 mg/dL), high triglyceride (TG) (≥150 mg/dL), high LDL Cholesterol (LDL-C) (≥ 130 mg/dL), or low HDL cholesterol (HDL-C) (<50 mg/dL). The genomic DNA samples were genotyped using DNA chip. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using multivariate logistic regression. Results: Substituting carbohydrates with dietary saturated fat (SFA) intake was associated with increased prevalence of dyslipidemia; ORs (95% CIs) for subsequent tertiles compared to the first tertile were 2.28 (1.19–4.35), and 2.88 (1.29–6.39) (P for trend = 0.02). When we examined individual markers, ORs (95% CIs, P for trend) comparing the third to the first tertile were 3.62 (1.53–8.55, 0.01) for high TC, 1.46 (0.42–5.10, 0.72) for high TG, 4.00 (1.48–10.79, 0.02) for high LDL-C, and 0.69 (0.30–1.59, 0.36) for low HDL-C. When we examined the interaction by LDL-C-related polymorphisms, the association with dyslipidemia was more pronounced among participants with CC alleles than among those with T alleles of rs6102059 (P for interaction = 0.01). Conclusions: High dietary SFA intake was significantly associated with a high prevalence of dyslipidemia in Filipino women in Korea. Further prospective cohort studies are warranted to determine risk factors for CVD in Southeast Asian populations.
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