ArticleSensors (Basel, Switzerland)2020
Health Promotion for Childhood Obesity: An Approach Based on Self-Tracking of Data.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 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
7 citing papers in PubMed, 3 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Gamified Health Promotion in Schools: The Integration of Neuropsychological Aspects and CBT-A Systematic Review.Medicina (Kaunas, Lithuania) · 2024Pooled it
- Pooled it
- Trust in AI Agent: A Systematic Review of Facial Anthropomorphic Trustworthiness for Social Robot Design.Sensors (Basel, Switzerland) · 2020Pooled it
- Mapping Digital Nudges and Recommender Systems for Obesity Prevention: Scoping Review.Interactive journal of medical research · 2026Review
- Recommender systems use in weight management mHealth interventions: A scoping review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Article
- Active Game-Based Solutions for the Treatment of Childhood Obesity.Sensors (Basel, Switzerland) · 2021Article
- Distal and proximal factors of wearable users' quantified-self dependence: A cognitive-behavioral model.Digital healthArticle
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 at 2 institutions in 1 country.
Funding
Abstract
At present, obesity and overweight are a global health epidemic. Traditional interventions for promoting healthy habits do not appear to be effective. However, emerging technological solutions based on wearables and mobile devices can be useful in promoting healthy habits. These applications generate a considerable amount of tracked activity data. Consequently, our approach is based on the quantified-self model for recommending healthy activities. Gamification can also be used as a mechanism to enhance personalization, increasing user motivation. This paper describes the quantified-self model and its data sources, the activity recommender system, and the PROVITAO App user experience model. Furthermore, it presents the results of a gamified program applied for three years in children with obesity and the process of evaluating the quantified-self model with experts. Positive outcomes were obtained in children's medical parameters and health habits.
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.