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Letts E, Jakubowski JS, King-Dowling S, Clevenger K, Kobsar D, Obeid J. Accelerometer techniques for capturing human movement validated against direct observation: a scoping review. Physiol Meas 2024; 45:07TR01. [PMID: 38688297 DOI: 10.1088/1361-6579/ad45aa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 04/29/2024] [Indexed: 05/02/2024]
Abstract
Objective.Accelerometers are devices commonly used to measure human physical activity and sedentary time. Accelerometer capabilities and analytical techniques have evolved rapidly, making it difficult for researchers to keep track of advances and best practices for data processing and analysis. The objective of this scoping review is to determine the existing methods for analyzing accelerometer data for capturing human movement which have been validated against the criterion measure of direct observation.Approach.This scoping review searched 14 academic and 5 grey databases. Two independent raters screened by title and abstract, then full text. Data were extracted using Microsoft Excel and checked by an independent reviewer.Mainresults.The search yielded 1039 papers and the final analysis included 115 papers. A total of 71 unique accelerometer models were used across a total of 4217 participants. While all studies underwent validation from direct observation, most direct observation occurred live (55%) or using recordings (42%). Analysis techniques included machine learning (ML) approaches (22%), the use of existing cut-points (18%), receiver operating characteristic curves to determine cut-points (14%), and other strategies including regressions and non-ML algorithms (8%).Significance.ML techniques are becoming more prevalent and are often used for activity identification. Cut-point methods are still frequently used. Activity intensity is the most assessed activity outcome; however, both the analyses and outcomes assessed vary by wear location. This scoping review provides a comprehensive overview of accelerometer analysis and validation techniques using direct observation and is a useful tool for researchers using accelerometers.
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Affiliation(s)
- Elyse Letts
- Child Health & Exercise Medicine Program, Department of Pediatrics, McMaster University, Hamilton, Canada
| | - Josephine S Jakubowski
- Child Health & Exercise Medicine Program, Department of Pediatrics, McMaster University, Hamilton, Canada
- School of Medicine, Queen's University, Kingston, Canada
| | - Sara King-Dowling
- Division of Oncology, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America
| | - Kimberly Clevenger
- Department of Kinesiology and Health Science, Utah State University, Logan, UT, United States of America
| | - Dylan Kobsar
- Department of Kinesiology, McMaster University, Hamilton, Canada
| | - Joyce Obeid
- Child Health & Exercise Medicine Program, Department of Pediatrics, McMaster University, Hamilton, Canada
- Department of Kinesiology, McMaster University, Hamilton, Canada
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Petersen BA, Erickson KI, Kurowski BG, Boninger ML, Treble-Barna A. Emerging methods for measuring physical activity using accelerometry in children and adolescents with neuromotor disorders: a narrative review. J Neuroeng Rehabil 2024; 21:31. [PMID: 38419099 PMCID: PMC10903036 DOI: 10.1186/s12984-024-01327-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 02/21/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Children and adolescents with neuromotor disorders need regular physical activity to maintain optimal health and functional independence throughout their development. To this end, reliable measures of physical activity are integral to both assessing habitual physical activity and testing the efficacy of the many interventions designed to increase physical activity in these children. Wearable accelerometers have been used for children with neuromotor disorders for decades; however, studies most often use disorder-specific cut points to categorize physical activity intensity, which lack generalizability to a free-living environment. No reviews of accelerometer data processing methods have discussed the novel use of machine learning techniques for monitoring physical activity in children with neuromotor disorders. METHODS In this narrative review, we discuss traditional measures of physical activity (including questionnaires and objective accelerometry measures), the limitations of standard analysis for accelerometry in this unique population, and the potential benefits of applying machine learning approaches. We also provide recommendations for using machine learning approaches to monitor physical activity. CONCLUSIONS While wearable accelerometers provided a much-needed method to quantify physical activity, standard cut point analyses have limitations in children with neuromotor disorders. Machine learning models are a more robust method of analyzing accelerometer data in pediatric neuromotor disorders and using these methods over disorder-specific cut points is likely to improve accuracy of classifying both type and intensity of physical activity. Notably, there remains a critical need for further development of classifiers for children with more severe motor impairments, preschool aged children, and children in hospital settings.
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Affiliation(s)
- Bailey A Petersen
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Kirk I Erickson
- AdventHealth Research Institute Department of Neuroscience, AdventHealth, Orlando, FL, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brad G Kurowski
- Division of Pediatric Rehabilitation Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - M L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - A Treble-Barna
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
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Hendry D, Rohl AL, Rasmussen CL, Zabatiero J, Cliff DP, Smith SS, Mackenzie J, Pattinson CL, Straker L, Campbell A. Objective Measurement of Posture and Movement in Young Children Using Wearable Sensors and Customised Mathematical Approaches: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:9661. [PMID: 38139507 PMCID: PMC10747033 DOI: 10.3390/s23249661] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
Given the importance of young children's postures and movements to health and development, robust objective measures are required to provide high-quality evidence. This study aimed to systematically review the available evidence for objective measurement of young (0-5 years) children's posture and movement using machine learning and other algorithm methods on accelerometer data. From 1663 papers, a total of 20 papers reporting on 18 studies met the inclusion criteria. Papers were quality-assessed and data extracted and synthesised on sample, postures and movements identified, sensors used, model development, and accuracy. A common limitation of studies was a poor description of their sample data, yet over half scored adequate/good on their overall study design quality assessment. There was great diversity in all aspects examined, with evidence of increasing sophistication in approaches used over time. Model accuracy varied greatly, but for a range of postures and movements, models developed on a reasonable-sized (n > 25) sample were able to achieve an accuracy of >80%. Issues related to model development are discussed and implications for future research outlined. The current evidence suggests the rapidly developing field of machine learning has clear potential to enable the collection of high-quality evidence on the postures and movements of young children.
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Affiliation(s)
- Danica Hendry
- School of Allied Health, Curtin University, Perth, WA 6102, Australia; (D.H.); (C.L.R.); (J.Z.); (L.S.)
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
| | - Andrew L. Rohl
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
- School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia
| | - Charlotte Lund Rasmussen
- School of Allied Health, Curtin University, Perth, WA 6102, Australia; (D.H.); (C.L.R.); (J.Z.); (L.S.)
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
| | - Juliana Zabatiero
- School of Allied Health, Curtin University, Perth, WA 6102, Australia; (D.H.); (C.L.R.); (J.Z.); (L.S.)
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
| | - Dylan P. Cliff
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
- Early Start, School of Education, University of Wollongong, Keiraville, NSW 2522, Australia
| | - Simon S. Smith
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
- Institute for Social Science Research, The University of Queensland, Brisbane, QLD 4006, Australia
| | - Janelle Mackenzie
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Cassandra L. Pattinson
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
- Institute for Social Science Research, The University of Queensland, Brisbane, QLD 4006, Australia
| | - Leon Straker
- School of Allied Health, Curtin University, Perth, WA 6102, Australia; (D.H.); (C.L.R.); (J.Z.); (L.S.)
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
| | - Amity Campbell
- School of Allied Health, Curtin University, Perth, WA 6102, Australia; (D.H.); (C.L.R.); (J.Z.); (L.S.)
- ARC Centre of Excellence for the Digital Child, Brisbane, ACT 2609, Australia; (A.L.R.); (D.P.C.); (S.S.S.); (J.M.); (C.L.P.)
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Bourke M, Haddara A, Loh A, Carson V, Breau B, Tucker P. Adherence to the World Health Organization's physical activity recommendation in preschool-aged children: a systematic review and meta-analysis of accelerometer studies. Int J Behav Nutr Phys Act 2023; 20:52. [PMID: 37101226 PMCID: PMC10132436 DOI: 10.1186/s12966-023-01450-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/06/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND The World Health Organization (WHO) recommend that preschool-aged children should engage in 180 min of total physical activity (TPA) including 60 min of moderate-to-vigorous physical activity (MVPA) each day. No systematic reviews or meta-analyses have pooled adherence to the recommendation across multiple studies. This study aimed to estimate the prevalence of preschool-aged children achieving the WHO's physical activity recommendation for young children, and determine if the prevalence differed between boys and girls. METHODS Primary literature searches were conducted on six online databases and a machine learning assisted systematic review was used to identify relevant studies. Studies written in English reporting on the prevalence of children aged 3-5 years achieving overall WHO physical activity recommendation or the individual TPA or MVPA aspects of the recommendation measured using accelerometers were eligible for inclusion. Random effects meta-analysis was used to determine the prevalence of preschools achieving the overall WHO recommendation and the individual TPA and MVPA aspect of the recommendation, and to determine difference in prevalence between boys and girls. RESULTS Forty-eight studies reporting on 20,078 preschool-aged children met the inclusion criteria. Based on the most commonly employed accelerometer cut-points across all aspects of the recommendation, 60% (95% Confidence Interval [CI] = 37%, 79%) of preschool-aged children adhered to the overall physical activity recommendation, 78% (95% CI = 38%, 95%) adhered to the TPA aspect of the recommendation, and 90% (95% CI = 81%, 95%) adhered to the MVPA aspect of the recommendation. There was substantial variability is prevalence estimates between different accelerometer cut-points. Girls were significantly less likely to achieve the overall recommendation and the MVPA aspect of the recommendation than boys were. CONCLUSIONS Although there was substantial variability in estimated prevalence of preschool-aged children adhering the WHO physical activity recommendation between various accelerometer cut-points, the weight of available evidence suggests that the majority of young children are adhering to the overall recommendation and the individual TPA and MVPA aspects of the recommendation. Large-scale, intercontinental surveillance studies are needed to further strengthen the evidence regarding the prevalence of preschool-aged children achieving physical activity recommendation globally.
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Affiliation(s)
- Matthew Bourke
- School of Occupational Therapy, Faculty of Health Sciences, Western University, London, ON, Canada.
| | - Ameena Haddara
- School of Occupational Therapy, Faculty of Health Sciences, Western University, London, ON, Canada
| | - Aidan Loh
- School of Occupational Therapy, Faculty of Health Sciences, Western University, London, ON, Canada
| | - Valerie Carson
- Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, AB, Canada
| | - Becky Breau
- Fowler Kennedy Sport Medicine Clinic, Western University, London, ON, Canada
| | - Patricia Tucker
- School of Occupational Therapy, Faculty of Health Sciences, Western University, London, ON, Canada
- Children's Health Research Institute, London, ON, Canada
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Welch SB, Honegger K, O'Brien M, Capan S, Kwon S. Examination of physical activity development in early childhood: protocol for a longitudinal cohort study of mother-toddler dyads. BMC Pediatr 2023; 23:129. [PMID: 36941567 PMCID: PMC10026417 DOI: 10.1186/s12887-023-03910-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 02/15/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Physical activity (PA) development in toddlers (age 1 and 2 years) is not well understood, partly because of a lack of analytic tools for accelerometer-based data processing that can accurately evaluate PA among toddlers. This has led to a knowledge gap regarding how parenting practices around PA, mothers' PA level, mothers' parenting stress, and child developmental and behavioral problems influence PA development in early childhood. METHODS The Child and Mother Physical Activity Study is a longitudinal study to observe PA development in toddlerhood and examine the influence of personal and parental characteristics on PA development. The study is designed to refine and validate an accelerometer-based machine learning algorithm for toddler activity recognition (Aim 1), apply the algorithm to compare the trajectories of toddler PA levels in males and females age 1-3 years (Aim 2), and explore the association between gross motor development and PA development in toddlerhood, as well as how parenting practices around PA, mothers' PA, mothers' parenting stress, and child developmental and behavioral problems are associated with toddlerhood PA development (Exploratory Aims 3a-c). DISCUSSION This study will be one of the first to use longitudinal data to validate a machine learning activity recognition algorithm and apply the algorithm to quantify free-living ambulatory movement in toddlers. The study findings will help fill a significant methodological gap in toddler PA measurement and expand the body of knowledge on the factors influencing early childhood PA development.
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Affiliation(s)
- Sarah B Welch
- Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Arthur J. Rubloff Building, 420 E. Superior St, IL, 60611, Chicago, USA.
- Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, USA.
| | - Kyle Honegger
- Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Megan O'Brien
- Department of Physical Medicine & Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, USA
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, USA
| | - Selin Capan
- Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Arthur J. Rubloff Building, 420 E. Superior St, IL, 60611, Chicago, USA
- Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Soyang Kwon
- Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
- Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, USA
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Kirk D, Kok E, Tufano M, Tekinerdogan B, Feskens EJM, Camps G. Machine Learning in Nutrition Research. Adv Nutr 2022; 13:2573-2589. [PMID: 36166846 PMCID: PMC9776646 DOI: 10.1093/advances/nmac103] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/02/2022] [Accepted: 09/22/2022] [Indexed: 01/29/2023] Open
Abstract
Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.
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Affiliation(s)
- Daniel Kirk
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Esther Kok
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Michele Tufano
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Bedir Tekinerdogan
- Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands
| | - Edith J M Feskens
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Guido Camps
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands.,OnePlanet Research Center, Wageningen, The Netherlands
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Physical Activity among U.S. Preschool-Aged Children: Application of Machine Learning Physical Activity Classification to the 2012 National Health and Nutrition Examination Survey National Youth Fitness Survey. CHILDREN (BASEL, SWITZERLAND) 2022; 9:children9101433. [PMID: 36291373 PMCID: PMC9600221 DOI: 10.3390/children9101433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/19/2022] [Accepted: 09/19/2022] [Indexed: 01/21/2023]
Abstract
Early childhood is an important development period for establishing healthy physical activity (PA) habits. The objective of this study was to evaluate PA levels in a representative sample of U.S. preschool-aged children. The study sample included 301 participants (149 girls, 3-5 years of age) in the 2012 U.S. National Health and Examination Survey National Youth Fitness Survey. Participants were asked to wear an ActiGraph accelerometer on their wrist for 7 days. A machine learning random forest classification algorithm was applied to accelerometer data to estimate daily time spent in moderate- and vigorous-intensity PA (MVPA; the sum of minutes spent in running, walking, and other moderate- and vigorous-intensity PA) and total PA (the sum of MVPA and light-intensity PA). We estimated that U.S. preschool-aged children engaged in 28 min/day of MVPA and 361 min/day of total PA, on average. MVPA and total PA levels were not significantly different between males and females. This study revealed that U.S. preschool-aged children engage in lower levels of MVPA and higher levels of total PA than the minimum recommended by the World Health Organization.
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Lettink A, Altenburg TM, Arts J, van Hees VT, Chinapaw MJM. Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old). Int J Behav Nutr Phys Act 2022; 19:116. [PMID: 36076221 PMCID: PMC9461103 DOI: 10.1186/s12966-022-01296-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 05/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accurate accelerometer-based methods are required for assessment of 24-h physical behavior in young children. We aimed to summarize evidence on measurement properties of accelerometer-based methods for assessing 24-h physical behavior in young children. METHODS We searched PubMed (MEDLINE) up to June 2021 for studies evaluating reliability or validity of accelerometer-based methods for assessing physical activity (PA), sedentary behavior (SB), or sleep in 0-5-year-olds. Studies using a subjective comparison measure or an accelerometer-based device that did not directly output time series data were excluded. We developed a Checklist for Assessing the Methodological Quality of studies using Accelerometer-based Methods (CAMQAM) inspired by COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). RESULTS Sixty-two studies were included, examining conventional cut-point-based methods or multi-parameter methods. For infants (0-12 months), several multi-parameter methods proved valid for classifying SB and PA. From three months of age, methods were valid for identifying sleep. In toddlers (1-3 years), cut-points appeared valid for distinguishing SB and light PA (LPA) from moderate-to-vigorous PA (MVPA). One multi-parameter method distinguished toddler specific SB. For sleep, no studies were found in toddlers. In preschoolers (3-5 years), valid hip and wrist cut-points for assessing SB, LPA, MVPA, and wrist cut-points for sleep were identified. Several multi-parameter methods proved valid for identifying SB, LPA, and MVPA, and sleep. Despite promising results of multi-parameter methods, few models were open-source. While most studies used a single device or axis to measure physical behavior, more promising results were found when combining data derived from different sensor placements or multiple axes. CONCLUSIONS Up to age three, valid cut-points to assess 24-h physical behavior were lacking, while multi-parameter methods proved valid for distinguishing some waking behaviors. For preschoolers, valid cut-points and algorithms were identified for all physical behaviors. Overall, we recommend more high-quality studies evaluating 24-h accelerometer data from multiple sensor placements and axes for physical behavior assessment. Standardized protocols focusing on including well-defined physical behaviors in different settings representative for children's developmental stage are required. Using our CAMQAM checklist may further improve methodological study quality. PROSPERO REGISTRATION NUMBER CRD42020184751.
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Affiliation(s)
- Annelinde Lettink
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Public and Occupational Health, De Boelelaan 1117, Amsterdam, The Netherlands. .,Amsterdam Public Health, Methodology, Amsterdam, The Netherlands. .,Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands.
| | - Teatske M Altenburg
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Public and Occupational Health, De Boelelaan 1117, Amsterdam, The Netherlands.,Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.,Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands
| | - Jelle Arts
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Public and Occupational Health, De Boelelaan 1117, Amsterdam, The Netherlands.,Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands
| | - Vincent T van Hees
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Public and Occupational Health, De Boelelaan 1117, Amsterdam, The Netherlands.,, Accelting, Almere, The Netherlands
| | - Mai J M Chinapaw
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Public and Occupational Health, De Boelelaan 1117, Amsterdam, The Netherlands.,Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.,Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands
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9
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Huang W, Luo J, Chen Y. Effects of Kindergarten, Family Environment, and Physical Activity on Children's Physical Fitness. Front Public Health 2022; 10:904903. [PMID: 35757641 PMCID: PMC9226412 DOI: 10.3389/fpubh.2022.904903] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
To explore the relationship between kindergarten environmental factors, children's physical activity, and physical fitness, this study uses the stratified random sampling method to obtain 4,600 children in relevant kindergartens. The questionnaire survey and children's physical fitness test were completed with the help of parents and kindergarten staff. The exploratory (EFA) and confirmatory (CFA) factor analysis is used to process the obtained database and set the significance level of all indicators α = 0.05. The results show that kindergarten environmental factors significantly affect children's physical activity and healthy physical fitness. Children with large play areas in these kindergartens, more sports equipment items, who participate in more than three games per week, of no <40 min of each class, with an appropriate number of classes, and excellent teachers' teaching ability have better physical fitness. Family environmental factors significantly affect children's physical activity and fitness. Children with more family sports equipment items, more peers living nearby, safer playing places, more hands and feet, and parents who are good at sports have better performance in health fitness. Children's physical activity not only directly affects their performance of physical fitness, but also plays a dual intermediary role between kindergarten environment and physical fitness, family environment, and healthy physical fitness.
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Affiliation(s)
- Wenyan Huang
- College of Physical Education, Southwest University, Chongqing, China
| | - Jiong Luo
- College of Physical Education, Southwest University, Chongqing, China
| | - Yanmei Chen
- College of Physical Education, Southwest University, Chongqing, China
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Trost SG, Brookes DSK, Ahmadi MN. Evaluation of Wrist Accelerometer Cut-Points for Classifying Physical Activity Intensity in Youth. Front Digit Health 2022; 4:884307. [PMID: 35585912 PMCID: PMC9108175 DOI: 10.3389/fdgth.2022.884307] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Wrist worn accelerometers are convenient to wear and provide greater compliance. However, methods to transform the resultant output into predictions of physical activity (PA) intensity have been slow to evolve, with most investigators continuing the practice of applying intensity-based thresholds or cut-points. The current study evaluated the classification accuracy of seven sets of previously published youth-specific cut-points for wrist worn ActiGraph accelerometer data. Methods Eighteen children and adolescents [mean age (± SD) 14.6 ± 2.4 years, 10 boys, 8 girls] completed 12 standardized activity trials. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the wrist and energy expenditure (Youth METs) was measured directly using the Oxycon Mobile portable calorimetry system. Seven previously published sets of ActiGraph cut-points were evaluated: Crouter regression vertical axis, Crouter regression vector magnitude, Crouter ROC curve vertical axis, Crouter ROC curve vector magnitude, Chandler ROC curve vertical axis, Chandler ROC curve vector magnitude, and Hildebrand ENMO. Classification accuracy was evaluated via weighted Kappa. Confusion matrices were generated to summarize classification accuracy and identify patterns of misclassification. Results The cut-points exhibited only moderate agreement with directly measured PA intensity, with Kappa ranging from 0.45 to 0.58. Although the cut-points classified sedentary behavior accurately (> 95%), classification accuracy for the light (3-51%), moderate (12-45%), and vigorous-intensity trials (30-88%) was generally poor. All cut-points underestimated the true intensity of the walking trials, with error rates ranging from 35 to 100%, while the intensity of activity trials requiring significant upper body and/or arm movements was consistently overestimated. The Hildebrand cut-points which serve as the default option in the popular GGIR software package misclassified 30% of the light intensity trials as sedentary and underestimated the intensity of moderate and vigorous intensity trials 75% of the time. Conclusion Published ActiGraph cut-points for the wrist, developed specifically for school-aged youth, do not provide acceptable classification accuracy for estimating daily time spent in light, moderate, and vigorous intensity physical activity. The development and deployment of more robust accelerometer data reduction methods such as functional data analysis and machine learning approaches continues to be a research priority.
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Affiliation(s)
- Stewart G. Trost
- School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, QLD, Australia
- School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Denise S. K. Brookes
- School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Matthew N. Ahmadi
- Charles Perkins Centre, Faculty of Medicine and Health, School of Health Sciences, The University of Sydney, NSW, Australia
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Ahmadi MN, Trost SG. Device-based measurement of physical activity in pre-schoolers: Comparison of machine learning and cut point methods. PLoS One 2022; 17:e0266970. [PMID: 35417492 PMCID: PMC9007358 DOI: 10.1371/journal.pone.0266970] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/30/2022] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION Machine learning (ML) accelerometer data processing methods have potential to improve the accuracy of device-based assessments of physical activity (PA) in young children. Yet the uptake of ML methods by health researchers has been minimal and the use of cut-points (CP) continues to be the norm, despite evidence of significant misclassification error. The lack of studies demonstrating a relative advantage for ML approaches over CP methods maybe a key contributing factor. PURPOSE The current study compared the accuracy of PA intensity predictions provided by ML classification models and previously published CPs for preschool-aged children. METHODS In a free-living study, 31 preschool-aged children (mean age = 4.0 ± 0.9 y) wore wrist and hip ActiGraph GT3X+ accelerometers while completing a video recorded 20-minute free play session. Ground truth PA intensity was coded continuously using the Children's Activity Rating Scale (CARS). Accelerometer data was classified as sedentary (SED), light intensity (LPA), or moderate-to-vigorous intensity (MVPA) using ML random forest PA classifiers and published CPs for preschool-aged children. Performance differences were evaluated in a hold-out sample by comparing weighted kappa statistics, classification accuracy for each intensity band, and equivalence testing. RESULTS ML classification models (hip: κ = 0.76; wrist: κ = 0.72) exhibited significantly higher agreement with ground truth PA intensity than CP methods (hip: κ = 0.38-0.49; wrist: κ = 0.31-0.44). For the ML models, classification accuracy for SED and LPA ranged from 83% - 88%, while classification accuracy for MVPA ranged from 68% - 78%. For the CP's, classification accuracy ranged from 50% - 94% for SED, 19% - 75% for LPA, and 44% - 76.1% for MVPA. ML classification models showed equivalence (within ± 0.5 SD) with directly observed time in SED, LPA, and MVPA. None of the CP's exhibited evidence of equivalence. CONCLUSIONS Under free living conditions, ML classification models for hip or wrist accelerometer data provide more accurate assessments of PA intensity in young children than CP methods. The results demonstrate the relative advantage of ML methods over threshold-based approaches and adds to a growing evidence base supporting the feasibility and accuracy of ML accelerometer data processing methods.
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Affiliation(s)
- Matthew N. Ahmadi
- Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Stewart G. Trost
- School of Human Movement and Nutrition Sciences, University of Queensland, St Lucia, QLD, Australia
- School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- * E-mail:
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Logacjov A, Bach K, Kongsvold A, Bårdstu HB, Mork PJ. HARTH: A Human Activity Recognition Dataset for Machine Learning. SENSORS (BASEL, SWITZERLAND) 2021; 21:7853. [PMID: 34883863 PMCID: PMC8659926 DOI: 10.3390/s21237853] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/29/2022]
Abstract
Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera's video signal and achieved high inter-rater agreement (Fleiss' Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.
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Affiliation(s)
- Aleksej Logacjov
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway;
| | - Kerstin Bach
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway;
| | - Atle Kongsvold
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway; (A.K.); (P.J.M.)
| | - Hilde Bremseth Bårdstu
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway;
- Department of Sport, Food and Natural Sciences, Faculty of Education, Arts and Sports, Western Norway University of Applied Sciences, 6851 Sogndal, Norway
| | - Paul Jarle Mork
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway; (A.K.); (P.J.M.)
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Joschtel B, Gomersall SR, Tweedy S, Petsky H, Chang AB, Trost SG. Fundamental movement skill proficiency and objectively measured physical activity in children with bronchiectasis: a cross-sectional study. BMC Pulm Med 2021; 21:269. [PMID: 34404362 PMCID: PMC8371810 DOI: 10.1186/s12890-021-01637-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 08/06/2021] [Indexed: 12/04/2022] Open
Abstract
Background Bronchiectasis is a major contributor to respiratory morbidity and healthcare utilization in children. Children with bronchiectasis exhibit low levels of physical activity (PA) and poor fundamental movement skills (FMS) may be a contributing factor. However, there are no data on FMS’s in this population. The current study assessed FMS proficiency in children with bronchiectasis and examined associations with objectively measured PA. Methods Forty-six children with bronchiectasis (mean age 7.5 ± 2.6 year, 63% Male) were recruited from the Queensland Children’s Hospital, Brisbane. PA was measured using the ActiGraph GT3X + accelerometer. Raw accelerometer data were processed into daily time spent in sedentary activities, light-intensity activities and games, walking, running, and moderate-to-vigorous activities and games using a random forest (RF) PA classification algorithm specifically developed for children. Daily MVPA was calculated by summing time spent in walking, running, and moderate-to-vigorous activities and games. FMS were assessed using the Test of Gross Motor Development 2nd Edition (TGMD-2). Results Fewer than 5% of children demonstrated mastery in the run, gallop, hop, and leap; while fewer than 10% demonstrated mastery for the two-handed strike, overarm throw, and underarm throw. Only eight of the 46 children (17.4%) achieved their age equivalency for locomotor skills, while just four (8.7%) achieved their age equivalency for object control skills. One-way ANCOVA revealed that children achieving their age equivalency for FMS had significantly higher levels of MVPA than children not achieving their age equivalency (51.7 vs 36.7 min/day). When examined by the five activity classes predicted by the RF algorithm, children achieving their age equivalency exhibited significantly greater participation moderate-to-vigorous intensity activities and games (22.1 vs 10.7 min/day). No significant differences were observed for sedentary activities, light-intensity activities and games, walking, and running. Conclusion Children with bronchiectasis exhibit significant delays in their FMS development. However, those who meet their age equivalency for FMS proficiency participate in significantly more daily MVPA than children who do not meet their age-equivalency. Therapeutic exercise programs designed to improve FMS proficiency are thus likely to be beneficial in this population.
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Affiliation(s)
- Barbara Joschtel
- School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia
| | - Sjaan R Gomersall
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia
| | - Sean Tweedy
- School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia
| | - Helen Petsky
- School of Nursing and Midwifery, Griffith University, Brisbane, Australia
| | - Anne B Chang
- Child Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, Australia.,Department of Respiratory and Sleep Medicine, Queensland Children's Hospital, Brisbane, Australia
| | - Stewart G Trost
- School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australia. .,QLD Centre for Children's Health Research (CCHR), Level 6, 62 Graham Street, South Brisbane, QLD, 4101, Australia.
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Kirk D, Catal C, Tekinerdogan B. Precision nutrition: A systematic literature review. Comput Biol Med 2021; 133:104365. [PMID: 33866251 DOI: 10.1016/j.compbiomed.2021.104365] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/04/2021] [Accepted: 03/28/2021] [Indexed: 12/12/2022]
Abstract
Precision Nutrition research aims to use personal information about individuals or groups of individuals to deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch of Artificial Intelligence, has promise to aid in the development of predictive models that are suitable for Precision Nutrition. As such, recent research has applied machine learning algorithms, tools, and techniques in precision nutrition for different purposes. However, a systematic overview of the state-of-the-art on the use of machine learning in Precision Nutrition is lacking. Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. Nine research questions were defined in this study. We retrieved 4930 papers from electronic databases and 60 primary studies were selected to respond to the research questions. All of the selected primary studies were also briefly discussed in this article. Our results show that fifteen problems spread across seven domains of nutrition and health are present. Four machine learning tasks are seen in the form of regression, classification, recommendation and clustering, with most of these utilizing a supervised approach. In total, 30 algorithms were used, with 19 appearing more than once. Models were through the use of four groups of approaches and 23 evaluation metrics. Personalized approaches are promising to reduce the burden of these current problems in nutrition research, and the current review shows Machine Learning can be incorporated into Precision Nutrition research with high performance. Precision Nutrition researchers should consider incorporating Machine Learning into their methods to facilitate the integration of many complex features, allowing for the development of high-performance Precision Nutrition approaches.
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Affiliation(s)
- Daniel Kirk
- Information Technology Group, Wageningen University and Research, Wageningen, the Netherlands.
| | - Cagatay Catal
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar.
| | - Bedir Tekinerdogan
- Information Technology Group, Wageningen University and Research, Wageningen, the Netherlands.
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Ahmadi MN, Pavey TG, Trost SG. Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children. SENSORS 2020; 20:s20164364. [PMID: 32764316 PMCID: PMC7472058 DOI: 10.3390/s20164364] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/23/2020] [Accepted: 08/04/2020] [Indexed: 01/14/2023]
Abstract
Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children’s movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children’s movement behaviors under real-world conditions.
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Affiliation(s)
- Matthew N. Ahmadi
- Institute of Health and Biomedical Innovation at Queensland Centre for Children’s Health Research, Queensland University of Technology, South Brisbane 4101, Australia;
- Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove 4059, Australia;
| | - Toby G. Pavey
- Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove 4059, Australia;
| | - Stewart G. Trost
- Institute of Health and Biomedical Innovation at Queensland Centre for Children’s Health Research, Queensland University of Technology, South Brisbane 4101, Australia;
- Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove 4059, Australia;
- Correspondence: ; Tel.: +61-7-3069-7301
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Ahmadi MN, O’Neil ME, Baque E, Boyd RN, Trost SG. Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models. SENSORS 2020; 20:s20143976. [PMID: 32708963 PMCID: PMC7411900 DOI: 10.3390/s20143976] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 07/09/2020] [Accepted: 07/16/2020] [Indexed: 11/16/2022]
Abstract
Pattern recognition methodologies, such as those utilizing machine learning (ML) approaches, have the potential to improve the accuracy and versatility of accelerometer-based assessments of physical activity (PA). Children with cerebral palsy (CP) exhibit significant heterogeneity in relation to impairment and activity limitations; however, studies conducted to date have implemented “one-size fits all” group (G) models. Group-personalized (GP) models specific to the Gross Motor Function Classification (GMFCS) level and fully-personalized (FP) models trained on individual data may provide more accurate assessments of PA; however, these approaches have not been investigated in children with CP. In this study, 38 children classified at GMFCS I to III completed laboratory trials and a simulated free-living protocol while wearing an ActiGraph GT3X+ on the wrist, hip, and ankle. Activities were classified as sedentary, standing utilitarian movements, or walking. In the cross-validation, FP random forest classifiers (99.0–99.3%) exhibited a significantly higher accuracy than G (80.9–94.7%) and GP classifiers (78.7–94.1%), with the largest differential observed in children at GMFCS III. When evaluated under free-living conditions, all model types exhibited significant declines in accuracy, with FP models outperforming G and GP models in GMFCS levels I and II, but not III. Future studies should evaluate the comparative accuracy of personalized models trained on free-living accelerometer data.
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Affiliation(s)
- Matthew N. Ahmadi
- Institute of Health and Biomedical Innovation at Queensland Centre for Children’s Health Research, Queensland University of Technology, South Brisbane 4101, Australia; (M.N.A.); (E.B.)
- Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove 4059, Australia
| | - Margaret E. O’Neil
- Department of Rehabilitation and Regenerative Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - Emmah Baque
- Institute of Health and Biomedical Innovation at Queensland Centre for Children’s Health Research, Queensland University of Technology, South Brisbane 4101, Australia; (M.N.A.); (E.B.)
- School of Allied Health Sciences, Griffith University, Gold Coast 4215, Queensland, Australia
| | - Roslyn N. Boyd
- Queensland Cerebral Palsy and Rehabilitation Research Centre, UQ Child Health Research Centre, Faculty of Medicine, The University of Queensland, South Brisbane 4101, Australia;
| | - Stewart G. Trost
- Institute of Health and Biomedical Innovation at Queensland Centre for Children’s Health Research, Queensland University of Technology, South Brisbane 4101, Australia; (M.N.A.); (E.B.)
- Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove 4059, Australia
- Correspondence: ; Tel.: +61-7-3069-7301
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Trost SG. Population-level physical activity surveillance in young people: are accelerometer-based measures ready for prime time? Int J Behav Nutr Phys Act 2020; 17:28. [PMID: 32183807 PMCID: PMC7079381 DOI: 10.1186/s12966-020-00929-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Stewart G Trost
- Institute of Health and Biomedical Innovation at QLD Centre for Children's Health Research, Queensland University of Technology, Level 6, 62 Graham Street, South Brisbane, QLD, 4101, Australia.
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