How Accurate and Precise Can We Measure the Posture and the Energy Expenditure Component of Sedentary Behaviour with One Sensor?

Roman P. Kuster, Wilhelmus J. A. Grooten, Victoria Blom, Daniel Baumgartner, Maria Hagströmer and Örjan Ekblom
Additional contact information
Roman P. Kuster: Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 141 83 Stockholm, Sweden
Wilhelmus J. A. Grooten: Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 141 83 Stockholm, Sweden
Victoria Blom: Department of Physical Activity and Health, The Swedish School of Sport and Health Sciences, 114 86 Stockholm, Sweden
Daniel Baumgartner: IMES Institute of Mechanical Systems, School of Engineering, ZHAW Zurich University of Applied Sciences, 8401 Winterthur, Switzerland
Maria Hagströmer: Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 141 83 Stockholm, Sweden
Örjan Ekblom: Department of Physical Activity and Health, The Swedish School of Sport and Health Sciences, 114 86 Stockholm, Sweden

IJERPH, 2021, vol. 18, issue 11, 1-13

Abstract: Sedentary behaviour is an emergent public health topic, but there is still no method to simultaneously measure both components of sedentary behaviour—posture and energy expenditure—with one sensor. This study investigated the accuracy and precision of measuring sedentary time when combining the proprietary processing of a posture sensor (activPAL) with a new energy expenditure algorithm and the proprietary processing of a movement sensor (ActiGraph) with a published posture algorithm. One hundred office workers wore both sensors for an average of 7 days. The activPAL algorithm development used 38 and the subsequent independent method comparison 62 participants. The single sensor sedentary estimates were compared with Bland–Atman statistics to the Posture and Physical Activity Index, a combined measurement with both sensors. All single-sensor methods overestimated sedentary time. However, adding the algorithms reduced the overestimation from 129 to 21 (activPAL) and from 84 to 7 min a day (ActiGraph), with far narrower 95% limits of agreements. Thus, combining the proprietary data with the algorithms is an easy way to increase the accuracy and precision of the single sensor sedentary estimates and leads to sedentary estimates that are more precise at the individual level than those of the proprietary processing are at the group level.

Keywords: ActiGraph; activPAL; calibration; free-living behaviour; machine learning; objective measurement; office worker; physical activity; Posture and Physical Activity Index (POPAI); validation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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