ArticleJournal of medical systems2017
A Novel Smartphone Accelerometer Application for Low-Intensity Activity and Energy Expenditure Estimations in Overweight and Obese Adults.
Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
- Associations Between Stress Level, Environment, and Emotional and Behavioral Characteristics in Service Sector Employees.International journal of environmental research and public health · 2025Article
- Weight Status Prediction Using a Neuron Network Based on Individual and Behavioral Data.Healthcare (Basel, Switzerland) · 2023Article
- Objective evaluation of the first post-lockdown on physical activity, sedentary behavior and food choice in a sample of French young adult students.Preventive medicine reports · 2022Article
- Behavioral and Emotional Changes One Year after the First Lockdown Induced by COVID-19 in a French Adult Population.Healthcare (Basel, Switzerland) · 2022Article
- Energy Intake Evaluation by a Learning Approach Using the Number of Food Portions and Body Weight.Foods (Basel, Switzerland) · 2021Article
- Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury.Sensors (Basel, Switzerland) · 2021Article
- How to Measure Sedentary Behavior at Work?Frontiers in public health · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physical inactivity and sedentary behaviors are on the rise worldwide and contribute to the current overweight and obesity scourge. The loss of healthy life style benchmarks and the lack of the need to move make it necessary to provide feedback about physical and sedentary activities in order to promote active ways of life. The aim of this study was to develop a specific function adapted to overweight and obese people to identify four physical activity (PA) categories and to estimate the associated total energy expenditure (TEE). This function used accelerometry data collected from a smartphone to evaluate activity intensity and length, and TEE. The performance of the proposed function was estimated according to two references (Armband® and FitmatePro®) under controlled conditions (CC) for a 1.5-h scenario, and to the Armband® device in free-living conditions (FLC) over a 12-h monitoring period. The experiments were carried out with overweight and obese volunteers: 13 in CC and 27 in FLC. The evaluation differences in time spent in each category were lower than 7% in CC and 6% in FLC, in comparison to the Armband® and FitmatePro® references. The TEE mean gap in absolute value between the function and the two references was 9.3% and 11.5% in CC, and 8.5% according to Armband® in FLC.
Indexed as
Identifiers
28674841What 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.