Wearable and Mobile Sensors for Personalized Nutrition
Abstract
While wearable and mobile chemical sensors have experienced tremendous growth over the past decade, their potential for tracking and guiding nutrition has emerged only over the past three years. Currently, guidelines from doctors and dietitians represent the most common approach for maintaining optimal nutrition status. However, such recommendations rely on population averages and do not take into account individual variability in responding to nutrients. Precision nutrition has recently emerged to address the large heterogeneity in individuals' responses to diet, by tailoring nutrition based on the specific requirements of each person. It aims at preventing and managing diseases by formulating personalized dietary interventions to individuals on the basis of their metabolic profile, background, and environmental exposure. Recent advances in digital nutrition technology, including calories-counting mobile apps and wearable motion tracking devices, lack the ability of monitoring nutrition at the molecular level. The realization of effective precision nutrition requires synergy from different sensor modalities in order to make timely reliable predictions and efficient feedback. This work reviews key opportunities and challenges toward the successful realization of effective wearable and mobile nutrition monitoring platforms. Non-invasive wearable and mobile electrochemical sensors, capable of monitoring temporal chemical variations upon the intake of food and supplements, are excellent candidates to bridge the gap between digital and biochemical analyses for a successful personalized nutrition approach. By providing timely (previously unavailable) dietary information, such wearable and mobile sensors offer the guidance necessary for supporting dietary behavior change toward a managed nutritional balance. Coupling of the rapidly emerging wearable chemical sensing devices-generating enormous dynamic analytical data-with efficient data-fusion and data-mining methods that identify patterns and make predictions is expected to revolutionize dietary decision-making toward effective precision nutrition.
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In CGM systems, another form of feedback can be nutrient levels by employing wearable sensors systems and advanced AI-powered data-fusion algorithms to manage nutritional imbalance toward maintaining desirable nutrient levels.
“For instance, in CGM systems, besides feedback at the biomarker level, another form of feedback can be nutrient levels by employing wearable sensors systems and advanced AI-powered data-fusion algorithms to manage nutritional imbalance toward maintaining desirable nutrient levels [221].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [221]
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End-to-end design of wearable sensors
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- Authors
- Sempionatto, J. R., Montiel, V. R.-V., Vargas, E., Teymourian, H., & Wang, J.
- Affiliations
- University of California San Diego (US)
- Department of Nanoengineering, University of California San Diego, La Jolla, California 92093, United States
- Corresponding author
- Wang, Joseph
- Venue
- ACS Sensors
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-05-19
- Volume
- 6
- Issue
- 5
- Pages
- 1745-1760
- ISSN
- 2379-3694 (ISSN-L)
- OpenAlex
- W3163274300
- Cited by
- 237
- References
- 160
- Citations by year
- 2026: 31 · 2025: 66 · 2024: 43 · 2023: 53 · 2022: 33 · 2021: 11
- FWCI
- 10.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Sensors (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Center for Wearable Sensors, University of California, San Diego
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Chemical Engineering › Bioengineering › Analytical Chemistry and Sensors
- Keywords
- wearable sensors, mobile sensors, personalized nutrition, electrochemical sensor, dietary monitoring, dietary supplements, data fusion, data mining, dietary behavior change
- MeSH terms
- Wearable Electronic Devices, Humans, Nutritional Status, Mobile Applications
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