Advancement in Dietary Assessment and Self-Monitoring Using Technology

Although methods to assess or self-monitor intake may be considered similar, the intended function of each is quite distinct. For the assessment of dietary intake, methods aim to measure food and nutrient intake and/or to derive dietary patterns for determining diet-disease relationships, population...

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Year of Publication:2020
Language:English
Physical Description:1 electronic resource (348 p.)
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100 1 |a Burrows, Tracy  |4 edt 
245 1 0 |a Advancement in Dietary Assessment and Self-Monitoring Using Technology 
260 |a Basel, Switzerland  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2020 
300 |a 1 electronic resource (348 p.) 
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520 |a Although methods to assess or self-monitor intake may be considered similar, the intended function of each is quite distinct. For the assessment of dietary intake, methods aim to measure food and nutrient intake and/or to derive dietary patterns for determining diet-disease relationships, population surveillance or the effectiveness of interventions. In comparison, dietary self-monitoring primarily aims to create awareness of and reinforce individual eating behaviours, in addition to tracking foods consumed. Advancements in the capabilities of technologies, such as smartphones and wearable devices, have enhanced the collection, analysis and interpretation of dietary intake data in both contexts. This Special Issue invites submissions on the use of novel technology-based approaches for the assessment of food and/or nutrient intake and for self-monitoring eating behaviours. Submissions may document any part of the development and evaluation of the technology-based approaches. Examples may include: web adaption of existing dietary assessment or self-monitoring tools (e.g., food frequency questionnaires, screeners) image-based or image-assisted methods mobile/smartphone applications for capturing intake for assessment or self-monitoring wearable cameras to record dietary intake or eating behaviours body sensors to measure eating behaviours and/or dietary intake use of technology-based methods to complement aspects of traditional dietary assessment or self-monitoring, such as portion size estimation. 
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653 |a children 
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653 |a nutrients 
653 |a carbohydrate counting 
653 |a protein and fat counting 
653 |a calorie counting 
653 |a automatic bolus calculator 
653 |a voice description of meals 
653 |a insulin dosage 
653 |a glycemic control 
653 |a diabetes mellitus 
653 |a nutrition 
653 |a food measurement 
653 |a nutrient database 
653 |a energy intake 
653 |a validity 
653 |a reliability 
653 |a food frequency questionnaire 
653 |a web 
653 |a under-reporting 
653 |a over-reporting 
653 |a mobile applications 
653 |a adults 
653 |a nutritional science 
653 |a qualitative research 
653 |a mobile food record 
653 |a 24-h recall 
653 |a developmental disabilities 
653 |a spina bifida 
653 |a down syndrome 
653 |a technology 
653 |a pediatrics 
653 |a image-assisted method 
653 |a infant 
653 |a food record 
653 |a doubly labeled water 
653 |a nutritional application 
653 |a smartphone 
653 |a DGA 
653 |a dietary behaviors 
653 |a household food purchase behavior 
653 |a obesity 
653 |a overweight weight control 
653 |a mobile technologies 
653 |a Web-based technologies 
653 |a usability 
653 |a human factors 
653 |a Automated Self-Administered Dietary Assessment Tool (ASA24) 
653 |a 24-h dietary recall 
653 |a low socioeconomic status 
653 |a diet 
653 |a assessment 
653 |a food log 
653 |a recall 
653 |a diet apps 
653 |a recipe calculations 
653 |a nutrient retention 
653 |a dietary intake assessment 
653 |a technological innovations 
653 |a Type 2 diabetes mellitus 
653 |a diabetes management 
653 |a dietary application 
653 |a physical activity 
653 |a blood glucose 
653 |a mHealth 
653 |a sugar intakes 
653 |a dietary record 
653 |a East Asians 
653 |a chewing detection 
653 |a AIM 
653 |a neural networks 
653 |a food intake detection 
653 |a video annotation 
653 |a sensor validation 
653 |a diet assessment 
653 |a relative validity 
653 |a young adults 
653 |a apps 
653 |a mobile app 
653 |a fruits 
653 |a vegetables 
653 |a self-monitoring 
653 |a healthy diet 
653 |a shared plate eating 
653 |a lower middle income countries 
653 |a food energy estimation 
653 |a generative models 
653 |a generative adversarial networks 
653 |a image-to-energy mapping 
653 |a regressions 
653 |a eating activity detection 
653 |a hand-to-mouth movement 
653 |a wrist-mounted motion tracking sensor 
653 |a accelerometer 
653 |a gyroscope 
653 |a text messages 
653 |a type 2 diabetes 
653 |a diabetes self-care activities 
653 |a cardiovascular disease risk awareness 
653 |a food availability 
653 |a food choices 
776 |z 3-03928-058-9 
776 |z 3-03928-059-7 
700 1 |a Rollo, Megan  |4 edt 
700 1 |a Burrows, Tracy  |4 oth 
700 1 |a Rollo, Megan  |4 oth 
906 |a BOOK 
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