SynthesisNutrients2022
Capturing Eating Behavior from Video Analysis: A Systematic Review.
Synthesis in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.Sensors (Basel, Switzerland) · 2025Pooled it
- Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Trial
- MediaPipe-Based Activity Analysis by Healthcare Professionals: Method Development and Technology Acceptance-A Pilot Study.Sensors (Basel, Switzerland) · 2026Article
- A Video-Based Measurement Framework for Chewing-Event Detection Using 3D Facial Landmark Dynamics and sEMG-Based Reference Annotation.Sensors (Basel, Switzerland) · 2026Article
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Video-based Intake Gesture Recognition Using Meal-length Context.ACM transactions on computing for healthcare · 2025Article
- Editorial: Challenges in eating behavior.Frontiers in psychology · 2025Article
- Disordered eating behavior and dietary intake prior to metabolic and bariatric surgery.Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery · 2024Article
- Rule-based systems to automatically count bites from meal videos.Frontiers in nutrition · 2024Article
- Exploring the Applicability of General Dietary Recommendations for People Affected by Obesity.Nutrients · 2023Article
- Validation of computational models to characterize cumulative intake curves from video-coded meals.Frontiers in nutrition · 2023Article
- The spectrum of eating environments encountered in free living adults documented using a passive capture food intake wearable device.Frontiers in nutrition · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Current methods to detect eating behavior events (i.e., bites, chews, and swallows) lack objective measurements, standard procedures, and automation. The video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we reviewed the current methods to automatically detect eating behavior events from video recordings. According to PRISMA guidelines, publications from 2010-2021 in PubMed, Scopus, ScienceDirect, and Google Scholar were screened through title and abstract, leading to the identification of 277 publications. We screened the full text of 52 publications and included 13 for analysis. We classified the methods in five distinct categories based on their similarities and analyzed their accuracy. Facial landmarks can count bites, chews, and food liking automatically (accuracy: 90%, 60%, 25%). Deep neural networks can detect bites and gesture intake (accuracy: 91%, 86%). The active appearance model can detect chewing (accuracy: 93%), and optical flow can count chews (accuracy: 88%). Video fluoroscopy can track swallows but is currently not suitable beyond clinical settings. The optimal method for automated counts of bites and chews is facial landmarks, although further improvements are required. Future methods should accurately predict bites, chews, and swallows using inexpensive hardware and limited computational capacity. Automatic eating behavior analysis will allow the study of eating behavior and real-time interventions to promote healthy eating behaviors.
Indexed as
Identifiers
What OpenQuestion holds
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.