Multi-Device Nutrition Control

Cunha, Carlos; Duarte, Rui P.

doi:10.3390/s22072617

Cited by 7 publications

(3 citation statements)

References 45 publications

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“…For AD caregivers (either FC or IC), there are still few apps that focus on improving the quality of life of an IC [14], and those that are available focus on the monitoring and caring of AD patients. These can be subdivided into tips for caring, monitoring, tracking, and caring for health [24][25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

“…The appointment refers to managing energy expenditure and its distribution throughout macro-and micro-nutrients. To support user monitoring between appointments, it presents historical data related to previous food plans, physical conditions (weight, body fat, visceral fat, fat-free mass, muscle mass, and body mass index), and other parameters for the control and analysis of user goals (bowel function, sleep quality, race, food preferences, lifestyle, clinical conditions, and water intake) [24]. It was developed as a Progressive Web Application (PWA) in LitElement [52].…”

Section: System Architecturementioning

confidence: 99%

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Mobile Application for Real-Time Food Plan Management for Alzheimer Patients through Design-Based Research

2023

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Alzheimer’s disease is a type of dementia that affects many individuals, mainly in an older age group. Over time, it leads to other diseases that affect their autonomy and independence. The quality of food ingestion is a way to mitigate the disease and preserve the patient’s well-being, which substantially impacts their health. Many existing applications for food plan management focus on the prescription of food plans but do not provide feedback to the nutritionist on the real amount of ingested calories. It makes these applications inadequate for these diseases, where monitoring and control are most important. This paper proposed the design and development of a mobile application to monitor and control the food plans of Alzheimer’s patients, focused on informal caregivers and respective patients. It allows both the realistic visualization of the food plans and users to adjust their consumption and register extra meals and water consumption. The interface design process comprises a two-level approach: the user centered design methodology that accounts for users’ needs and requirements and the user experience questionnaire to measure user satisfaction. The results show that the interface is intuitive, visually appealing, and easy to use, adjusted for users that require a particular level of understanding regarding specific subjects.

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Section: Related Workmentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

Mobile Application for Real-Time Food Plan Management for Alzheimer Patients through Design-Based Research

2023

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“…For personalized precision nutrition, adequate intakes of macro-and micro-nutrients could reduce disease risks of obesity, metabolic disease, cardiovascular disease, and cancer across an individual’s the life span [ 1 , 2 , 3 , 4 ]. Hence, accurate measurements of essential micro-nutrients, in addition to macro-nutrients, are vital in achieving precision nutrition [ 5 , 6 ]. Methyl donors including micro-nutrients such as vitamins (folate B9, cobalamin B12) and amino acids are crucial for DNA methylation in nutrigenomics pathways for personalized precision nutrition [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Validating Accuracy of an Internet-Based Application against USDA Computerized Nutrition Data System for Research on Essential Nutrients among Social-Ethnic Diets for the E-Health Era

Yang

Kusuma

et al. 2022

Nutrients

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Internet-based applications (apps) are rapidly developing in the e-Health era to assess the dietary intake of essential macro-and micro-nutrients for precision nutrition. We, therefore, validated the accuracy of an internet-based app against the Nutrition Data System for Research (NDSR), assessing these essential nutrients among various social-ethnic diet types. The agreement between the two measures using intraclass correlation coefficients was good (0.85) for total calories, but moderate for caloric ranges outside of <1000 (0.75) and >2000 (0.57); and good (>0.75) for most macro- (average: 0.85) and micro-nutrients (average: 0.83) except cobalamin (0.73) and calcium (0.51). The app underestimated nutrients that are associated with protein and fat (protein: −5.82%, fat: −12.78%, vitamin B12: −13.59%, methionine: −8.76%, zinc: −12.49%), while overestimated nutrients that are associated with carbohydrate (fiber: 6.7%, B9: 9.06%). Using artificial intelligence analytics, we confirmed the factors that could contribute to the differences between the two measures for various essential nutrients, and they included caloric ranges; the differences between the two measures for carbohydrates, protein, and fat; and diet types. For total calories, as an example, the source factors that contributed to the differences between the two measures included caloric range (<1000 versus others), fat, and protein; for cobalamin: protein, American, and Japanese diets; and for folate: caloric range (<1000 versus others), carbohydrate, and Italian diets. In the e-Health era, the internet-based app has the capacity to enhance precision nutrition. By identifying and integrating the effects of potential contributing factors in the algorithm of output readings, the accuracy of new app measures could be improved.

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