Dietary Assessment of Shared Plate Eating: A Missing Link

Abstract

Shared plate eating is a defining feature of the way food is consumed in some countries and cultures. Food may be portioned to another serving vessel or directly consumed into the mouth from a centralised dish rather than served individually onto a discrete plate for each person. Shared plate eating is common in some low- and lower-middle income countries (LLMIC). The aim of this narrative review was to synthesise research that has reported on the assessment of dietary intake from shared plate eating, investigate specific aspects such as individual portion size or consumption from shared plates and use of technology in order to guide future development work in this area. Variations of shared plate eating that were identified in this review included foods consumed directly from a central dish or shared plate food, served onto additional plates shared by two or more people. In some settings, a hierarchical sharing structure was reported whereby different family members eat in turn from the shared plate. A range of dietary assessment methods have been used in studies assessing shared plate eating with the most common being 24-h recalls. The tools reported as being used to assist in the quantification of food intake from shared plate eating included food photographs, portion size images, line drawings, and the carrying capacity of bread, which is often used rather than utensils. Overall few studies were identified that have assessed and reported on methods to assess shared plate eating, highlighting the identified gap in an area of research that is important in improving understanding of, and redressing dietary inadequacies in LLMIC.

Adaptation, acceptability and feasibility of a Short Food Survey to assess the dietary intake of children during attendance at childcare

“…It is also suspected that this distress is related to the overall workload experienced by some educators, with the completion of the tool representing an additional, cognitively challenging task to be completed within a short timeframe. The task of reporting the frequency and portion size for individual children may be difficult for educators in services that encourage children to serve themselves from shared plates or 'family style' (27) . Serving styles, in addition to daily variation in the servings of meals and snacks, the frequency of staff working and the frequency of child attendance per week were noted by five educators as challenges to tool completion.…”

OREBA: A Dataset for Objectively Recognizing Eating Behavior and Associated Intake

“…The sampling rates are 24 fps for OREBA-DIS, and 30 fps for OREBA-SHA. Each participant wore two IMUs, one on each wrist (Movisens Move 3+ 6 ). The IMU included an accelerometer and a gyroscope with a sampling rate of 64 Hz.…”

“…At the same time, current research on dietary monitoring identified a gap in research on shared plate eating [6]. Communal eating (i.e., eating occasions involving more than one person) is not yet well understood, let alone the impact it has on accuracy of automatic dietary monitoring.…”

“…At the same time, it reduces the time-consuming burden for researchers to collect, annotate and cross-check their own data. 3) Communal eating: While existing research has provided important insights into automatically detecting human intake gestures in individual settings, research on communal eating is scant [13] [6]. To the best of our knowledge, this is the first dataset capturing communal eating from both discrete and shared dishes.…”

Deep Learning for Intake Gesture Detection From Wrist-Worn Inertial Sensors: The Effects of Data Preprocessing, Sensor Modalities, and Sensor Positions

“…Understanding the impact of sensor modalities and configurations is important in settings where there can be constraints on (1) the number of sensors and devices, (2) energy consumption in data collection over extended periods of time, particularly in low-income countries [7], and 3users' acceptance towards wearing sensors on both hands. Given the different approaches in data preprocessing, it is currently not clear which data preprocessing steps achieve high model performance.…”