4.3 Article

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

出版社

MDPI
DOI: 10.3390/ijerph18115782

关键词

ActiGraph; activPAL; calibration; free-living behaviour; machine learning; objective measurement; office worker; physical activity; Posture and Physical Activity Index (POPAI); validation

资金

  1. Swiss National Science Foundation [187637]
  2. KK-Stiftelsen [20160040]
  3. Swedish Knowledge Foundation (KK-Stiftelsen) [20160040]
  4. ICA-gruppen
  5. Intrum
  6. SATS Elixia
  7. Monark Exercise
  8. Itrim Sweden

向作者/读者索取更多资源

This study found that combining proprietary data with algorithms using sensors can improve the accuracy and precision of sedentary time estimates, reducing the overestimation of sedentary time.
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.

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