Foods consumed at lower eating rates (ER) lead to reductions in energy intake. Previous research has shown that texture-based differences in eating rateER can reduce meal size. The effect size and consistency of these effects across a wide range of composite and complex meals differing considerably in texture and varying in meal occasion have not been reported. We determined how consistently texture-based differences in ER can influence food and energy intake across a wide variety of meals. In a crossover design, healthy participants consumed twelve breakfast and twelve lunch meals that differed in texture to produce a fast or slow ER. A breakfast group (n = 15) and lunch group (n = 15) completed twelve ad libitum meal sessions each (six ‘fast’ and six ‘slow’ meals), where intake was measured and behavioural video annotation was used to characterise eating behaviour. Liking did not differ significantly between fast and slow breakfasts (P = 0·44) or lunches (P = 0·76). The slow meals were consumed on average 39 % ± 9 % (breakfast) and 45 % ± 7 % (lunch) slower than the fast meals (both P < 0·001). Participants consumed on average 22 % ± 5 % less food (84 g) and 13 % ± 6 % less energy (71 kcal) from slow compared with fast meals (mean ± SE; P < 0·001). Consuming meals with a slower ER led to a reduction in food intake, where an average decrease of 20 % in ER produced an 11 % ± 1 % decrease in food intake (mean ± SE). These findings add to the growing body of evidence showing that ER can be manipulated using food texture and that this has aits consistent effect on food and energy intake across a wide variety of Hedonically equivalent meals.

Objective measurement of RMR may be important for optimal nutritional care but is hindered by the price and practicality of the metabolic monitoring device. This study compared two metabolic monitoring devices for measuring RMR and VO2 and compared the measured RMR with the predicted RMR calculated from equations. RMR was measured using QUARK RMR (reference device) and Fitmate GS (COSMED) in a random order for 30 min, each on fasted participants. In total, sixty-eight adults participated (median age 22 years, interquartile range 21–32). Pearson correlation showed that RMR (r 0·86) and VO2 (r 0·86) were highly correlated between the two devices (P < 0·05). Intraclass correlation coefficients (ICC) showed good relative agreements regarding RMR (ICC = 0·84) and VO2 (ICC = 0·84) (P < 0·05). RMR measured by QUARK RMR was significantly higher (649 (sd 753) kJ/d) than Fitmate GS. Equations significantly overpredicted RMR. Accurate RMR (i.e. within ±10 % of the RMR measured by QUARK RMR) was found among 38 % of the participants for Fitmate GS and among 46–68 % depending on the equations. Bland–Altman analysis showed a low absolute agreement with QUARK RMR at an individual level for both Fitmate GS (limits of agreement (LOA): −828 to +2125 kJ/d) and equations (LOA ranged from −1979 to +1879 kJ/d). In conclusion, both Fitmate GS and predictive equations had low absolute agreements with QUARK RMR at an individual level. Therefore, these limitations should be considered when determining RMR using Fitmate GS or equations.

In the present study, we evaluated the reproducibility and validity of dietary patterns among Chinese adult populations. A random subsample of 203 participants (aged 31–80 years) from a community-based nutrition and health survey was enrolled. An eighty-seven-item FFQ was administered twice (FFQ1 and FFQ2) 1 year apart; four 3 consecutive day, 24-h dietary recalls (24-HDR, as a reference method) were performed between the administrations of the two FFQ every 3 months. Dietary patterns from three separate dietary sources were derived using factor analysis based on twenty-eight predefined food groups. Comparisons between dietary pattern scores were made by using Pearson’s or intraclass correlation coefficients (ICC), cross-classification analysis, weighted κ statistic and Bland–Altman plots; the four major dietary patterns identified from FFQ1, FFQ2 and 24-HDR were similar. Regarding reproducibility, ICC for z-scores between FFQ1 and FFQ2 were all >0·6 for dietary patterns. The ‘animal and plant protein’ pattern had the highest ICC of 0·870. For validity, the adjusted Pearson’s correlation coefficients for dietary pattern z-scores between two FFQ and the mean of four 3 consecutive day 24-HDR ranged from 0·387 for the ‘Chinese traditional’ pattern to 0·838 for the ‘animal and plant protein’ pattern. More than 75 % of the participants were classified into the same or adjacent quartile, and <5 % were misclassified into opposite quartiles. The weighted κ ranged from 0·259 to 0·680. Bland–Altman plots indicated that no significant deviation was found between two dietary assessment methods. Our findings indicate a good reasonable reproducibility and a reasonable validity of dietary patterns derived by factor analysis in China.