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Drinking microstructure in humans: A proof of concept study of a novel drinkometer in healthy adults

Gero, Daniel, File, Balint, Justiz, Jörn, Steinert, Robert E., Frick, Lukas, Spector, Alan C., Bueter, Marco
Appetite 2019 v.133 pp. 47-60
adults, drinking, energy intake, liquids, liquified foods, microstructure, nutrient content, physiological state, probability distribution, sucrose, ultrasonics
Microstructural analysis of ingestion provides valuable insight into the roles of chemosensory signals, nutritional content, postingestive events, and physiological state. Our aim was to develop a novel drinkometer for humans to measure detailed aspects of ingestion of an entire liquid meal or drinking session. The drinkometer records, in high definition (1 kHz), the weight of a fluid reservoir from which participants drink via a tube. An ultrasonic sensor measures the height of the fluid to derive density. Drinking speed over time can be displayed as a waveform. The smallest units of ingestion are sucks, which are organized in bursts. By applying probability density functions (PDF) on loge-transformed inter-suck intervals (ISI), an optimal burst-pause criterion (PC) can be identified. Information on ingestive volumes, rates, and durations can be then computed for the entire session, as well as for sucks and bursts. We performed a validation study on 12 healthy adults in overnight-fasted and in non-fasted states in 16 drinking sessions with 8 concentrations of sucrose (0–280 mM) presented in a blinded and random fashion. PDF determined PC = 2.9 s as optimal. Two-way RM-ANOVA revealed that total caloric intake during a drinking session depended on sucrose concentration (P < .001) and fasted state (P = .006); total drinking time (P < .001), total consumed volume (P = .003), number of sucks in total (P < .001), number of sucks per burst (P = .03), and burst duration (P = .02) were significantly influenced by fasting. In contrast, volume per suck (P = .002), suck speed (P < .001), and maximal speed per suck (P < .001) depended on sucrose concentration. We conclude that the novel drinkometer is able to detect differences in microstructural parameters of drinking behavior dependent on different motivational states, thus, adds to the technological toolbox used to explore human ingestive behavior.