Estimating Sedentary Time from a Hip- and Wrist-Worn Accelerometer

Abstract

Purpose This study aimed to determine the validity of existing methods to estimate sedentary behavior (SB) under free-living conditions using ActiGraph GT3X+ accelerometers (AG). Methods Forty-eight young (18–25 yr) adults wore an AG on the right hip and nondominant wrist and were video recorded during four 1-h sessions in free-living settings (home, community, school, and exercise). Direct observation videos were coded for postural orientation, activity type (e.g., walking), and METs derived from the Compendium of Physical Activities, which served as the criterion measure of SB (sitting or lying posture, <1.5 METs). Thirteen methods using cut points from vertical counts per minute (CPM), counts per 15-s (CP15s), and vector magnitude (VM) counts (e.g., CPM1853VM), raw acceleration and arm angle (sedentary sphere), Euclidean norm minus one (ENMO) corrected for gravity (mg) thresholds, uni- or triaxial sojourn hybrid machine learning models (Soj1x and Soj3x), random forest (RF), and decision tree (TR) models were used to estimate SB minutes from AG data. Method bias, mean absolute percent error, and their 95% confidence intervals were estimated using repeated-measures linear mixed models. Results On average, participants spent 34.1 min per session in SB. CPM100, CPM150, Soj1x, and Soj3x were the only methods to accurately estimate SB from the hip. Sedentary sphere and ENMO44.8 overestimated SB by 3.9 and 6.1 min, respectively, whereas the remaining wrist methods underestimated SB (range, 9.5–2.5 min). In general, mean absolute percent error was lower using hip methods compared with wrist methods. Conclusion Accurate group-level estimates of SB from a hip-worn AG can be achieved using either simpler count-based approaches (CPM100 and CPM150) or machine learning models (Soj1x and Soj3x). Wrist methods did not provide accurate or precise estimates of SB. The development of large open-source free-living calibration data sets may lead to improvements in SB estimates.

Advances in accelerometry for cardiovascular patients: a systematic review with practical recommendations

“…85 However, the traditional hip/waist placement is still preferred by many researchers, as it seems to have better validity for some physical behaviours. 82,89 When the study focus is on sedentary behaviour, thigh placement is currently considered the gold standard. 90 In populations with decreased functional capacity, ankle placement is also gaining popularity as it performs well even with slow gait where other placements usually fail.…”

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review

“…The fourth group of works is dedicated to the evaluation of EE-related features (14 studies) [ 6 , 7 , 17 , 27 , 38 , 66 , 69 ]. Some of these studies intend to detect the amount of time spent in activities that require a greater or lesser expenditure of energy when carried out (sedentary activities for instance that is to say activities with a Metabolic Equivalent of Task (MET) is below 1.5.…”

“…In several articles, protocols include the use of several sensors positioned on different locations on the body [ 40 , 44 , 62 ]. Figure 4 details the distribution of the studies according to the number of inertial sensors associated with them [ 17 , 38 , 62 , 66 , 73 , 78 , 80 , 84 , 85 ]. It shows that the majority of studies in FLEs or semi FLEs—even when using several IMUs, accelerometers, or gyroscopes—limit the number of the latter.…”

“…The last case observed mostly in FLEs situations is “complete freedom”. Participants are thus equipped with one or more IMUs and continue their daily and usual activities at home (including going to work, hobbies, and home activities) without any instruction or suggestion [ 6 , 7 , 17 , 27 , 38 , 39 , 44 , 60 , 69 , 72 , 73 , 74 , 75 , 80 , 89 , 91 , 100 , 104 , 104 ]. In the latter case, the participant is in the most natural conditions possible (both environment and activity).…”

“…In addition, some studies have highlighted the difficulties of transposing high-performance algorithms intended for controlled environments to free-living conditions, in particular because of the greater complexity of movements and their associations [ 33 ]. Nevertheless, these challenges have been, and are still being, met today through studies allowing interesting and reliable quantitative feedback using inertial sensors of the movements carried out in free-living settings [ 37 , 38 , 39 , 40 ]. The increasing use of machine learning and efficient algorithms also contributed to this effort, by allowing the processing of complex data and allowing to transpose protocols from laboratory to natural conditions.…”