Comparison of wrist accelerometer cut-points for classifying physical activity intensity in youth

The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities

Physical behaviors (e.g., physical activity and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. The recent shift from hip- to wrist-worn accelerometers in these fields has signaled the need to develop novel approaches to process raw acceleration data of physical activity and sedentary behavior. However, there is currently no consensus regarding the best practices for analyzing wrist-worn accelerometer data to accurately predict individuals' energy expenditure and the times spent in different intensities of free-living physical activity and sedentary behavior. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many clinicians and researchers. In response, this paper attempts to review different methodologies for analyzing wrist-worn accelerometer data and offer cutting edge, yet appropriate analysis plans for wrist-worn accelerometer data in the assessment of physical behavior. In this paper, we first discuss the fundamentals of wrist-worn accelerometer data, followed by various methods of processing these data (e.g., cut points, steps per minute, machine learning), and then we discuss the opportunities, challenges, and directions for future studies in this area of inquiry. This is the most comprehensive review paper to date regarding the analysis and interpretation of free-living physical activity data derived from wrist-worn accelerometers, aiming to help establish a blueprint for processing wrist-derived accelerometer data.

Segmented sedentary time and physical activity patterns throughout the week from wrist-worn ActiGraph GT3X+ accelerometers among children 7-12 years old

BACKGROUND

This study examined the volume and patterns of physical activity (PA) and sedentary time (ST) across different segments of the week among boys and girls.

METHODS

A total of 188 children aged 7-12 years wore a wrist-mounted ActiGraph GT3X+ accelerometer for 7 days. Time spent in PA and ST was calculated using ActiLife software. The mean number of minutes of light PA, moderate PA, vigorous PA, moderate-to-vigorous PA (MVPA), and ST were calculated per weekday (before school, during school, and after school) and per weekend day (morning and afternoon-evening).

RESULTS

After school represented the greatest accumulation of ST compared with before school and during school segments. Boys engaged in 225.4 min/day of ST (95% confidence interval (CI): 216-235), and girls engaged in 222.2 min/day of ST (95%CI: 213-231). During school, boys engaged in significantly more MVPA than girls (46.1 min/day (95%CI: 44-48) vs. 40.7 min/day (95%CI: 39-43)). Across the whole weekday, boys participated in significantly more MVPA than girls (103.9 min/day (95%CI: 99-109) vs. 95.7 min/day (95%CI: 90-101)). The weekend afternoon-evening segment represented the larger accumulation of ST, where boys were significantly more sedentary than girls (367.5 min/day (95%CI: 353-382) vs. 339.8 min/day (95%CI: 325-355), respectively).

CONCLUSION

Our findings suggest that children are highly sedentary and spend little of their time in school in MVPA, especially girls. Routine breaks in school elicit increases in light PA and MVPA. Future work should consider the use of more active breaks within school time to encourage PA and reduce ST.

Comparing physical activity estimates in children from hip-worn Actigraph GT3X+ accelerometers using raw and counts based processing methods

This study examined differences in physical activity (PA) estimates provided from raw and counts processing methods. One hundred and sixty-five children (87 girls) wore a hip-mounted ActiGraph GT3X+ accelerometer for 7 days. Data were available for 129 participants. Time in moderate PA (MPA), vigorous PA (VPA) and moderate-vigorous PA (MVPA) were calculated using R-package GGIR and ActiLife. Participants meeting the wear time criteria for both processing methods were included in the analysis. Time spent in MPA (-21.4 min.d^-1, 95%CI -21 to -20) and VPA (-36 min.d^-1, 95%CI -40 to -33) from count data were higher (P < 0.001) than raw data. Time spent in MVPA between the two processing methods revealed significant differences (All P < 0.001). Bland-Altman plots suggest that the mean bias for time spent in MPA, VPA and MVPA were large when comparing raw and count methods. Equivalence tests showed that estimates from raw and count processing methods across all activity intensities lacked equivalence. Lack of equivalence and poor agreement between raw and count processing methods suggest the two approaches to estimate PA are not comparable. Further work to facilitate the comparison of findings between studies that process and report raw and count physical activity data may be necessary.

Wear compliance, sedentary behaviour and activity in free-living children from hip-and wrist-mounted ActiGraph GT3X+ accelerometers

The GT3X+ worn at the wrist promotes greater compliance than at the hip. Minutes in SB and PA calculated from raw accelerations at the hip and wrist provide contrasting estimates and cannot be directly compared. Wear-time for the wrist (15.6 to 17.4 h.d^-1) was greater than the hip (15.2 to 16.8 h.d^-1) across several wear-time criteria (all P < 0.05). Moderate-strong associations were found between time spent in SB (r = 0.39), LPA (r = 0.33), MPA (r = 0.99), VPA (r = 0.82) and MVPA (r = 0.81) between the two device placements (All P < 0.001). The wrist device detected more minutes in LPA, MPA, VPA and MVPA whereas the hip detected more SB (all P = 0.001). Estimates of time in SB and all activity outcomes from the wrist and hip lacked equivalence. One hundred and eighty-eight 9-12-year-old children wore a wrist- and hip-mounted accelerometer for 7 days. Data were available for 160 (hip) and 161 (wrist) participants. Time spent in SB and PA was calculated using GGIR. This study examined the compliance of children wearing wrist- and hip-mounted ActiGraph GT3X+ accelerometers and compared estimates of sedentary behaviour (SB) and physical activity (PA) between devices.