Literature DB >> 28164455

Smart approaches for assessing free-living energy expenditure following identification of types of physical activity.

G Plasqui1.   

Abstract

Accurate assessment of physical activity and energy expenditure has been a research focus for many decades. A variety of wearable sensors have been developed to objectively capture physical activity patterns in daily life. These sensors have evolved from simple pedometers to tri-axial accelerometers, and multi sensor devices measuring different physiological constructs. The current review focuses on how activity recognition may help to improve daily life energy expenditure assessment. A brief overview is given about how different sensors have evolved over time to pave the way for recognition of different activity types. Once the activity is recognized together with the intensity of the activity, an energetic value can be attributed. This concept can then be tested in daily life using the independent reference technique doubly labeled water. So far, many studies have been performed to accurately identify activity types, and some of those studies have also successfully translated this into energy expenditure estimates. Most of these studies have been performed under standardized conditions, and the true applicability in daily life has rarely been addressed. The results so far however are highly promising, and technological advancements together with newly developed algorithms based on physiological constructs will further expand this field of research.
© 2017 World Obesity Federation.

Entities:  

Keywords:  Accelerometer; obesity; sedentary behavior

Mesh:

Year:  2017        PMID: 28164455     DOI: 10.1111/obr.12506

Source DB:  PubMed          Journal:  Obes Rev        ISSN: 1467-7881            Impact factor:   9.213


  7 in total

1.  Bidirectional Day-to-Day Associations of Reported Sleep Duration With Accelerometer Measured Physical Activity and Sedentary Time Among Dutch Adolescents: An Observational Study.

Authors:  Nathalie Berninger; Gregory Knell; Kelley Pettee Gabriel; Guy Plasqui; Rik Crutzen; Gill Ten Hoor
Journal:  J Meas Phys Behav       Date:  2020-10-13

Review 2.  Validity of Accelerometers for the Evaluation of Energy Expenditure in Obese and Overweight Individuals: A Systematic Review.

Authors:  Silvia Pisanu; Andrea Deledda; Andrea Loviselli; Inge Huybrechts; Fernanda Velluzzi
Journal:  J Nutr Metab       Date:  2020-08-04

Review 3.  Working against the biological clock: a review for the Occupational Physician.

Authors:  Alfredo Copertaro; Massimo Bracci
Journal:  Ind Health       Date:  2019-02-22       Impact factor: 2.179

4.  Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.

Authors:  Eduardo Gomes; Luciano Bertini; Wagner Rangel Campos; Ana Paula Sobral; Izabela Mocaiber; Alessandro Copetti
Journal:  Sensors (Basel)       Date:  2021-02-09       Impact factor: 3.576

5.  Strength exercises during physical education classes in secondary schools improve body composition: a cluster randomized controlled trial.

Authors:  G A Ten Hoor; G M Rutten; G J P Van Breukelen; G Kok; R A C Ruiter; K Meijer; S P J Kremers; F J M Feron; R Crutzen; A M J W Schols; G Plasqui
Journal:  Int J Behav Nutr Phys Act       Date:  2018-09-25       Impact factor: 6.457

6.  Deep learning-based classification with improved time resolution for physical activities of children.

Authors:  Yongwon Jang; Seunghwan Kim; Kiseong Kim; Doheon Lee
Journal:  PeerJ       Date:  2018-10-19       Impact factor: 2.984

Review 7.  Use of accelerometer-based activity monitoring in orthopaedics: benefits, impact and practical considerations.

Authors:  Maik Sliepen; Matthijs Lipperts; Marianne Tjur; Inger Mechlenburg
Journal:  EFORT Open Rev       Date:  2020-01-28
  7 in total

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