ArticleJournal of health & medical informatics2018
A Mobile-Based Diet Monitoring System for Obesity Management.
Article in Journal of health & medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation.Sensors (Basel, Switzerland) · 2024Pooled it
- Integrating Artificial Intelligence-Driven Wearable Technology in Oncology Decision-Making: A Narrative Review.Oncology · 2025Review
- A comprehensive analysis of vitamin a deficiency burden and trends: insights from the global burden of disease study 2021 and future predictions to 2050.Frontiers in nutrition · 2025Article
- mid-DeepLabv3+: A Novel Approach for Image Semantic Segmentation Applied to African Food Dietary Assessments.Sensors (Basel, Switzerland) · 2023Article
- Multimedia Data-Based Mobile Applications for Dietary Assessment.Journal of diabetes science and technology · 2023Review
- Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Innovations in research and clinical care using patient-generated health data.CA: a cancer journal for clinicians · 2020Review
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
Personal diet management is key to fighting the obesity epidemic. Recent advances in smartphones and wearable sensor technologies have empowered automated food monitoring through food image processing and eating episode detection, with the goal to conquer drawbacks of traditional food journaling that is labour intensive, inaccurate, and low adherent. In this paper, we present a new interactive mobile system that enables automated food recognition and assessment based on user food images and provides dietary intervention while tracking users' dietary and physical activities. In addition to using techniques in computer vision and machine learning, one unique feature of this system is the realization of real-time energy balance monitoring through metabolic network simulation. As a proof of concept, we have demonstrated the use of this system through an Android application.
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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.