Systematic Literature Review of Food-Intake Monitoring in an Aging Population

Abstract

The dietary habits of people directly impact their health conditions. Especially in elder populations (in 2017, 6.7% of the world’s population was over 65 years of age), these habits could lead to important-nutrient losses that could seriously affect their cognitive and functional state. Recently, a great research effort has been devoted to using different technologies and proposing different techniques for monitoring food-intake. Nevertheless, these techniques are usually generic but make use of the most innovative technologies and methodologies to obtain the best possible monitoring results. However, a large percentage of elderly people live in depopulated rural areas (in Spain, 28.1% of the elderly population lives in this type of area) with a fragile cultural and socioeconomic context. The use of these techniques in these environments is crucial to improving this group’s quality of life (and even reducing their healthcare expenses). At the same time, it is especially challenging since they have very specific and strict requirements regarding the use and application of technology. In this Systematic Literature Review (SLR), we analyze the most important proposed technologies and techniques in order to identify whether they can be applied in this context and if they can be used to improve the quality of life of this fragile collective. In this SLR, we have analyzed 326 papers. From those, 29 proposals have been completely analyzed, taking into account the characteristics and requirements of this population.

Smart Nursing Homes: Self‐Management Architecture Based on IoT and Machine Learning for Rural Areas

“…Also, other initiatives facilitate the work of caregivers and medical staff, such as devices that assist in the early intervention of diseases such as diabetes [5], dementia [28], or other diseases [29]. The authors of this paper are working on different lines to improve the life and health of the elderly; among the many works we can highlight are an extensible environment for monitoring and detecting symptoms of depression [30], different systems for food and beverage monitoring [31][32][33], or a voice assistant to remind the pharmacological treatment [34]. Although in this work we will only focus on the self-management architecture based on IoT and machine learning for rural areas, these approaches are interesting to be aware of the common diseases and how the caregivers can be supported.…”

Predicting Infections Using Computational Intelligence – A Systematic Review

“…In this paper, we conduct an analysis of the state of the art in infection prediction, by means of a well-described procedure known as a systematic literature review (SLR). SLRs hold a good prestige when it comes to analyze the body of academic knowledge in complex domains, such as recommender systems [3], Internet of things [79], food-intake monitoring [68], penetration testing in mobile applications [2], big data in healthcare applications [77], or computational intelligence in sports [11].…”

Sensory Interactive Table (SIT)—Development of a Measurement Instrument to Support Healthy Eating in a Social Dining Setting

“…Traditionally, this form of support is done by dietitians that provide personal dietary advice and create awareness at the side of the individual by letting them collect insights about their eating behavior and habits [7,8]. Recently, this process of gathering insights is supported by technological developments such as smartphone apps [9,10], on-body wearables [11], instrumented dining trays [12], and cutlery [13] by collecting quantitative data about, for example, the exact amounts of micro and macro nutrients in the food, the quantity of the consumed food (per meal or per bite), or the speed with which a meal was eaten. Although these forms of support are all relevant when considering the individual aspects of eating, they do not take into account the social aspects of eating [14,15].…”