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Background: Several health organizations recommend lowering the consumption of sweet-tasting foods. The rationale behind this recommendation is that a lower exposure to sweet foods may reduce preferences for sweet tasting foods, thus lowering sugar and energy intake, and in turn aiding in obesity prevention. However, empirical data supporting this narrative are lacking. In fact, relatively little is known about the contribution of long-term sweet taste exposure on one's sweetness preferences. Methods: The primary objective of this randomized controlled trial is to assess the effect of low, regular and high dietary sweetness exposure on preference for sweet foods and beverages, and to compare these effects between intervention groups. One hundred and eighty adults aged 18–65 years with a BMI of 18.5–30.0 kg/m2 will be recruited and randomly allocated to either: low dietary sweetness exposure (LSE) (10–15% daily energy from sweet tasting foods), regular dietary sweetness exposure (RSE) (25–30% daily energy from sweet tasting foods), or high dietary sweetness exposure (HSE) (40–45% daily energy from sweet tasting foods), for 6 months, followed by a 4-month follow up. Intervention foods are provided ad libitum, covering approximately 50% of the daily number of food items, to include sugar-sweetened, low-calorie-sweetener-sweetened and non-sweet foods. The primary outcome measure is the difference in change in sweetness preference from baseline to 6 months between intervention groups. Secondary outcomes include: change in sweet taste preferences at different time-points; taste intensity perception; behavioral outcomes: food choice and intake, sweet-liker type, food cravings, dietary taste preferences and dietary taste patterns; anthropometric outcomes: body composition, waist-hip circumference, body weight; and biochemical outcomes: glucose variability and biomarkers related to CVD and diabetes. Discussion: This study will generate important data on the effect of dietary sweetness exposure on sweetness preferences in terms of effect size and change, duration of change and its impact on food intake, body weight status and associated health outcomes. Trial Registration: The study protocol has been registered on ClinicalTrials.gov (ID no. NCT04497974, Registered 4 August 2020, https://clinicaltrials.gov/ct2/show/NCT04497974) and approved by Wageningen's Medical Ethical Committee (ABR no. NL72134). [ABSTRACT FROM AUTHOR]

• We tested a standardised method to measure differences in consumer oral processing behaviour and eating rate. • We compared eating rate of a test food to self-reported eating rate (SRER) and subsequent intake of a lunchtime-meal. • SRER was effective at measuring differences in eating rate but did not predict energy intake at a lunch-time meal. • The standardised test can be used to characterise differences in eating rate and was a significant predictor of eating rate and energy intake for a later test meal. Eating rate is a modifiable risk factor for obesity and efficient methods to objectively characterise an individual's oral processing behaviours could help better identify people at risk of increased energy consumption. Many previous approaches to characterise oral processing and eating rate have relied on specialised equipment or wearable devices that are time consuming, expensive or require expertise to administer. The current trial used video-coding of the consumption of a standardised test food (the 'carrot test') to measure oral processing. We sought (i) to test whether self-reported eating rate (SRER) is predictive of food oral processing derived from coded eating behaviours captured in the laboratory with a standardised test food, and (ii) to test whether differences in SRER are predictive of oral processing behaviours, eating rate and intake of a test meal. Two hundred and fifty-three volunteers (86 male and 167 female, mean age 39.5 ± 13.6 years, mean BMI 22.2 ± 3.4 kg/m2) provided their SRER and anthropometric measurements of height, weight and dual-energy X-ray absorptiometry (DEXA) percentage fat mass. Participants were also video recorded eating a fixed 50 g portion of carrot and an ad libitum lunch meal of fried rice. Average eating rate (g/min), bite size (g) and number of chews per bite for the carrot and lunch were derived through behavioural coding of the videos. Energy intake (kcal) was recorded at lunch and a later afternoon snack. Faster SRER significantly predicted faster eating rate, larger bite size and more chews per bite observed during intake of the carrot (ß = −0.26–0.21, p ≤ 0.001) and the lunch (ß = −0.26–0.35, p ≤ 0.014). SRER did not significantly predict intake at lunch or during the afternoon snack (ß = 0.05–0.07, p ≥ 0.265). Participants' oral processing of the carrot significantly predicted oral processing of the lunch (ß = −0.25–0.40, p ≤ 0.047) and faster eating rate of the carrot significantly predicted increased lunch intake (ß = 0.119, p = 0.045). None of the oral processing behaviours predicted afternoon snack intake (ß = −0.01–0.05, p ≥ 0.496). None of these associations were moderated by BMI or body composition. We confirm that SRER is a valid measure of group level differences in individual oral processing behaviours, but did not predict an individual's energy intake at a lunch-time meal. With this approach, it is possible to characterise differences in eating rate by coding eating behaviours for a standardized test food (in this case, a fixed portion of raw carrot). This approach could be used to provide an objective measure of a person's habitual oral processing behaviour, and was shown to be a significant predictor of eating rate and energy intake for a later test meal. [ABSTRACT FROM AUTHOR]