Trial reportObesity (Silver Spring, Md.)2017
Reduction of energy intake using just-in-time feedback from a wearable sensor system.
Trial report in Obesity (Silver Spring, Md.), 2017. 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.
- Application of Just-in-Time Adaptive Interventions in Dietary Health Management: Systematic Review.Journal of medical Internet research · 2026Pooled it
- A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.Sensors (Basel, Switzerland) · 2025Pooled it
- Enhancing Nutritional Ingestive Behavior Microstructure Detection: Video Annotation and Passive Sensing Approaches.Nutrients · 2026Article
- Detection of Food Intake Sensor's Wear Compliance in Free-Living.IEEE sensors journal · 2021Article
- Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use?Journal of diabetes science and technology · 2021Article
- NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020Article
- Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment.Nutrients · 2019Article
- 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
- Advances and Controversies in Diet and Physical Activity Measurement in Youth.American journal of preventive medicine · 2018Article
- The SPLENDID Eating Detection Sensor: Development and Feasibility Study.JMIR mHealth and uHealth · 2018Article
- Physical Activity, Sedentary Behavior, and Diet-Related eHealth and mHealth Research: Bibliometric Analysis.Journal of medical Internet research · 2018Review
- Meal Microstructure Characterization from Sensor-Based Food Intake Detection.Frontiers in nutrition · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveThis work explored the potential use of a wearable sensor system for providing just-in-time (JIT) feedback on the progression of a meal and tested its ability to reduce the total food mass intake.
methodsEighteen participants consumed three meals each in a lab while monitored by a wearable sensor system capable of accurately tracking chew counts. The baseline visit was used to establish the self-determined ingested mass and the associated chew counts. Real-time feedback on chew counts was provided in the next two visits, during which the target chew count was either the same as that at baseline or the baseline chew count reduced by 25% (in randomized order). The target was concealed from the participant and from the experimenter. Nonparametric repeated-measures ANOVAs were performed to compare mass of intake, meal duration, and ratings of hunger, appetite, and thirst across three meals.
resultsJIT feedback targeting a 25% reduction in chew counts resulted in a reduction in mass and energy intake without affecting perceived hunger or fullness.
conclusionsJIT feedback on chewing behavior may reduce intake within a meal. This system can be further used to help develop individualized strategies to provide JIT adaptive interventions for reducing energy intake.
Indexed as
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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.