ArticleFrontiers in nutrition2017
Meal Microstructure Characterization from Sensor-Based Food Intake Detection.
Article in Frontiers in nutrition, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.Sensors (Basel, Switzerland) · 2025Pooled it
- Pooled it
- Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study.JMIR mHealth and uHealth · 2024Article
- A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring.Scientific reports · 2024Article
- Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error.International journal of obesity (2005) · 2022Article
- Improved Wearable Devices for Dietary Assessment Using a New Camera System.Sensors (Basel, Switzerland) · 2022Article
- Glassware design and drinking behaviours: a review of impact and mechanisms using a new typology of drinking behaviours.Health psychology review · 2022Review
- Enhancing Nutrition Care Through Real-Time, Sensor-Based Capture of Eating Occasions: A Scoping Review.Frontiers in nutrition · 2022Article
- "Automatic Ingestion Monitor Version 2" - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images.IEEE journal of biomedical and health informatics · 2021Article
- Vagally Mediated Gut-Brain Relationships in Appetite Control-Insights from Porcine Studies.Nutrients · 2021Review
- Retrieval and Timing Performance of Chewing-Based Eating Event Detection in Wearable Sensors.Sensors (Basel, Switzerland) · 2020Article
- Automatic Count of Bites and Chews From Videos of Eating Episodes.IEEE access : practical innovations, open solutions · 2020Article
- Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review.NPJ digital medicine · 2020Article
- Counting Bites With Bits: Expert Workshop Addressing Calorie and Macronutrient Intake Monitoring.Journal of medical Internet research · 2019Article
- Issues in Measuring and Interpreting Diet and Its Contribution to Obesity.Current obesity reports · 2019Review
- Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor.Scientific reports · 2019Article
- A Systematic Review of Technology-Driven Methodologies for Estimation of Energy Intake.IEEE access : practical innovations, open solutions · 2019Article
- The SPLENDID Eating Detection Sensor: Development and Feasibility Study.JMIR mHealth and uHealth · 2018Article
- Accelerometer-Based Detection of Food Intake in Free-living Individuals.IEEE sensors journal · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
To avoid the pitfalls of self-reported dietary intake, wearable sensors can be used. Many food ingestion sensors offer the ability to automatically detect food intake using time resolutions that range from 23 ms to 8 min. There is no defined standard time resolution to accurately measure ingestive behavior or a meal microstructure. This paper aims to estimate the time resolution needed to accurately represent the microstructure of meals such as duration of eating episode, the duration of actual ingestion, and number of eating events. Twelve participants wore the automatic ingestion monitor (AIM) and kept a standard diet diary to report their food intake in free-living conditions for 24 h. As a reference, participants were also asked to mark food intake with a push button sampled every 0.1 s. The duration of eating episodes, duration of ingestion, and number of eating events were computed from the food diary, AIM, and the push button resampled at different time resolutions (0.1-30s). ANOVA and multiple comparison tests showed that the duration of eating episodes estimated from the diary differed significantly from that estimated by the AIM and the push button (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.