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Advanced analytical methods to assess physical activity behavior using accelerometer time series: A scoping review.
Backes, Anne; Gupta, Tripti; Schmitz, Susanne; Fagherazzi, Guy; van Hees, Vincent; Malisoux, Laurent.
Affiliation
  • Backes A; Physical Activity, Sport and Health Research Group, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.
  • Gupta T; Physical Activity, Sport and Health Research Group, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.
  • Schmitz S; Competence Center for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg.
  • Fagherazzi G; Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.
  • van Hees V; Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
  • Malisoux L; Accelting, Almere, The Netherlands.
Scand J Med Sci Sports ; 32(1): 18-44, 2022 Jan.
Article in En | MEDLINE | ID: mdl-34695249
ABSTRACT
Physical activity (PA) is a complex human behavior, which implies that multiple dimensions need to be taken into account in order to reveal a complete picture of the PA behavior profile of an individual. This scoping review aimed to map advanced analytical methods and their summary variables, hereinafter referred to as wearable-specific indicators of PA behavior (WIPAB), used to assess PA behavior. The strengths and limitations of those indicators as well as potential associations with certain health-related factors were also investigated. Three databases (MEDLINE, Embase, and Web of Science) were screened for articles published in English between January 2010 and April 2020. Articles, which assessed the PA behavior, gathered objective measures of PA using tri-axial accelerometers, and investigated WIPAB, were selected. All studies reporting WIPAB in the context of PA monitoring were synthesized and presented in four summary tables study characteristics, details of the WIPAB, strengths, and limitations, and measures of association between those indicators and health-related factors. In total, 7247 records were identified, of which 24 articles were included after assessing titles, abstracts, and full texts. Thirteen WIPAB were identified, which can be classified into three different categories specifically focusing on (1) the activity intensity distribution, (2) activity accumulation, and (3) the temporal correlation and regularity of the acceleration signal. Only five of the thirteen WIPAB identified in this review have been used in the literature so far to investigate the relationship between PA behavior and health, while they may provide useful additional information to the conventional PA variables.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Text Messaging / Motor Activity Type of study: Prognostic_studies / Systematic_reviews Limits: Humans Language: En Journal: Scand J Med Sci Sports Journal subject: MEDICINA ESPORTIVA Year: 2022 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Text Messaging / Motor Activity Type of study: Prognostic_studies / Systematic_reviews Limits: Humans Language: En Journal: Scand J Med Sci Sports Journal subject: MEDICINA ESPORTIVA Year: 2022 Document type: Article Affiliation country:
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