Generation and validation of ActiGraph GT3X+ accelerometer cut-points for assessing physical activity intensity in older adults. The OUTDOOR ACTIVE validation study

Abstract

The study of physical activity in older adults is becoming more and more relevant. For evaluation of physical activity recommendations, intensity-specific accelerometer cut-points are utilized. However, research on accelerometer cut-points for older adults is still scarce. The aim of the study was to generate placement-specific cut-points of ActiGraph GT3X+ activity counts and raw measures of acceleration to determine physical activity intensity in older adults. A further aim was to compare the validity of the generated cut-points for a range of different physical activities. The study was a single experimental trial using a convenience sample. Study participants were 20 adults aged 59 to 73 years. Accelerometers were worn at six different placements (one on each wrist, one on each ankle, and two at the hip) and breath-by-breath indirect calorimetry was used as the reference for energy. The experiment comprised of two parts; a) The first required participants to walk on a treadmill at incremental speeds (3.0–5.0 km·h-1), and b) Five different everyday activities (reading, cleaning, shopping, cycling, aerobics) were staged in the laboratory setting. Accelerometer cut-points (activity counts, raw data) were derived for each of the investigated placements by linear regression using the treadmill part. Performance of the cut-points was assessed by applying the cut-points to the everyday activities. We provide cut-points for six placements and two accelerometer metrics in the specific age group. However, the derived cut-points did not outperform published ones. More research and innovative approaches are needed for improving internal and external validity of research results across populations and age groups.

The effects of a real-life lifestyle program on physical activity and objective and subjective sleep in adults aged 55+ years

“…It is known that hip-worn accelerometers reproduce much smaller acceleration values during walking than wrist-worn accelerometers which in turn could result in a misclassification of LPA as MVPA. Indeed, a recent study by Bammann et al (2021) in which older participants wore Actigraph GT3x + accelerometers on the ankle, hip and wrist, concluded that there was a higher inter-individual variability of arm movement (wrist-worn accelerometer) compared with core body (hip or ankle-worn accelerometer) during activity [ 40 ]. In this study, we used this wrist-worn placement for measures of both MVPA levels and sleep.…”

“…In this study, we used this wrist-worn placement for measures of both MVPA levels and sleep. In order to identify MVPA levels and avoid misclassification of LPA as MVPA, we made use of a tri-axial vector magnitude (VM3) cut-point (≥3268 CPM) for wrist-worn actigraphy in older adults [ 40 ].…”

“…The coefficients of validity of actigraphy are higher compared with medical tests and psychological tests for sleep [ 38 ]. The Actigraph wGT3X-BT has been used in numerous studies to measure MVPA and sleep in older adults [ 21 – 23 , 39 , 40 ]. The Actigraph accelerometer is considered a reliable tool for measuring overall PA level and intensity-specific PA in adults under free-living conditions [ 24 ].…”

“…Accelerometer data were processed using well established validated algorithms available in Actilife software package (Actilife, v6.13.4), namely the Choi algorithm for wear time validation [ 39 ] and the Cole-Kripke algorithm for sleep-wake identification [ 41 ]. The following tri-axial vector magnitude (VM3) cut-points for wrist-worn actigraphy in older adults were used to identify MVPA (≥3268 CPM) [ 40 ].…”

Detailed analysis and comparison of different activity metrics

“…Based on the different preprocessings and activity metrics used, we determined all possible (a total of 148) minutely-sampled activity signals from a single subject’s 10-days-long raw acceleration data. Then we computed the Pearson’s correlation coefficients between these minutely-sampled activity signals to assess their similarity [ 8 , 50 ], which resulted in a 148×148 correlation matrix. It was performed on all the 42 subjects to obtain 42 correlation matrices.…”