SynthesisNutrients2019
Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review.
Synthesis in Nutrients, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Standardized Methods for Evaluating Physical and Eating Behaviors: The WEALTH Cross-Sectional Study Protocol.JMIR research protocols · 2026Article
- Wearable sensing in eating episode monitoring: an updated systematic review protocol.BMJ open · 2025Article
- Boosting Immunity Through Nutrition and Gut Health: A Narrative Review on Managing Allergies and Multimorbidity.Nutrients · 2025Review
- Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis.IEEE journal of biomedical and health informatics · 2024Article
- E-Health Interventions for Older Adults With Frailty: A Systematic Review.Annals of rehabilitation medicine · 2023Article
- Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study.JMIR mHealth and uHealth · 2022Observational
- Top-Down Detection of Eating Episodes by Analyzing Large Windows of Wrist Motion Using a Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2022Article
- Review
- Deep Learning-Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors.JMIR mHealth and uHealth · 2021Article
- Artificial Intelligence in Nutrients Science Research: A Review.Nutrients · 2021Review
- A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.JMIR mHealth and uHealth · 2020Article
- Future Directions for Integrative Objective Assessment of Eating Using Wearable Sensing Technology.Frontiers in nutrition · 2020Review
- Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review.NPJ digital medicine · 2020Article
- Article
- Article
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
No grant is acknowledged in the PubMed record.
Abstract
Wearable motion tracking sensors are now widely used to monitor physical activity, and have recently gained more attention in dietary monitoring research. The aim of this review is to synthesise research to date that utilises upper limb motion tracking sensors, either individually or in combination with other technologies (e.g., cameras, microphones), to objectively assess eating behaviour. Eleven electronic databases were searched in January 2019, and 653 distinct records were obtained. Including 10 studies found in backward and forward searches, a total of 69 studies met the inclusion criteria, with 28 published since 2017. Fifty studies were conducted exclusively in laboratory settings, 13 exclusively in free-living settings, and three in both settings. The most commonly used motion sensor was an accelerometer (64) worn on the wrist (60) or lower arm (5), while in most studies (45), accelerometers were used in combination with gyroscopes. Twenty-six studies used commercial-grade smartwatches or fitness bands, 11 used professional grade devices, and 32 used standalone sensor chipsets. The most used machine learning approaches were Support Vector Machine (SVM,
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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.