Considerations in Processing Accelerometry Data to Explore Physical Activity and Sedentary Time in Older Adults

Cleland, Claire; Ferguson, Stuart; McCrorie, Paul; Schipperijn, Jasper; Ellis, Geraint; Hunter, Ruth F.

doi:10.1123/japa.2019-0244

Cited by 12 publications

(8 citation statements)

References 42 publications

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“…Because of the large number of different monitors and mounting and positioning options, and different modes of data processing and evaluation, recommendations are lacking to guide their optimal use in SB research. 12 In addition, multiple combinations of settings and often-missing information about those settings make it difficult to compare results across studies.…”

Section: Introductionmentioning

confidence: 99%

Measuring sedentary behavior using waist- and thigh-worn accelerometers and inclinometers – are the results comparable?

Kalisch

Theil

Gosheger

et al. 2022

Therapeutic Advances in Musculoskeletal

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Background: Objective sensor-based quantification of sedentary behavior is an important tool for planning and evaluating interventions for excessive sedentary behavior in patients with musculoskeletal diseases. Although waist-worn accelerometers are the standard for physical activity (PA) assessment, only thigh-worn inclinometers can clearly distinguish sedentary behavior from any light PA or standing activity. Methods: In this study, 53 adults (ages 20–85 years) wore two ActiGraph wGT3X-BT monitors, each containing an inclinometer and accelerometer (set for acquisition of slow movements in all three planes), attached to the right waist and thigh for a period of about 4 days. Both monitors recorded total sedentary time and continuous sedentary 10-min bouts by synchronous accelerometry and inclinometry. Differences and correlations between methods and wearing positions were evaluated against participant age, body mass index (BMI), and number of steps taken. Thigh-worn inclinometry was used as reference. Results: Data from thigh-worn inclinometry and waist-worn accelerometry were highly correlated for total sedentary time [rho = 0.888; intraclass correlation coefficient (ICC) = 0.937] and time in sedentary bouts (rho = 0.818; ICC = 0.848). Nevertheless, accelerometry at the waist underestimated sedentary time by ≈17% ( p < 0.001) and time in sedentary bouts by ≈54% ( p < 0.001). A satisfactory concordance thus could be demonstrated only for total sedentary time, based on the Bland–Altmann method (≈96% of data within the limits of agreement). The differences between waist-worn accelerometry and thigh-worn inclinometry did not correlate with age but did correlate with BMI and PA for both sedentary behavior parameters ( r ⩾ 0.240, p ⩽ 0.043). Conclusion: A waist-worn accelerometer can be used to determine total sedentary time under free-living conditions with sufficient accuracy if the correct settings are chosen. Further investigations are necessary to investigate why short sedentary bouts cannot be reliably assessed. Trial registration: DRKS00024060 (German Clinical Trials Register)

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Section: Introductionmentioning

confidence: 99%

Measuring sedentary behavior using waist- and thigh-worn accelerometers and inclinometers – are the results comparable?

Kalisch

Theil

Gosheger

et al. 2022

Therapeutic Advances in Musculoskeletal

View full text Add to dashboard Cite

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“…Numerous studies have attempted to distinguish PA from sedentary behaviors by adopting a cut-point approach with the accelerometry data [ 18 , 19 , 20 , 21 , 22 ]. Cut-points are generated to differentiate moderate-to-vigorous physical activity (MVPA) by finding the optimal accelerometer activity counts that best correspond to the energy expenditure [ 23 , 24 , 25 ]. However, because the cut-point approach has limitations in differentiating the activities of daily living from similar patterns of acceleration, it can lead to a biased estimation of energy expenditure [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on the Influence of Undersampling and Oversampling Techniques for the Classification of Physical Activities Using an Imbalanced Accelerometer Dataset

et al. 2022

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Accelerometer data collected from wearable devices have recently been used to monitor physical activities (PAs) in daily life. While the intensity of PAs can be distinguished with a cut-off approach, it is important to discriminate different behaviors with similar accelerometry patterns to estimate energy expenditure. We aim to overcome the data imbalance problem that negatively affects machine learning-based PA classification by extracting well-defined features and applying undersampling and oversampling methods. We extracted various temporal, spectral, and nonlinear features from wrist-, hip-, and ankle-worn accelerometer data. Then, the influences of undersampilng and oversampling were compared using various ML and DL approaches. Among various ML and DL models, ensemble methods including random forest (RF) and adaptive boosting (AdaBoost) exhibited great performance in differentiating sedentary behavior (driving) and three walking types (walking on level ground, ascending stairs, and descending stairs) even in a cross-subject paradigm. The undersampling approach, which has a low computational cost, exhibited classification results unbiased to the majority class. In addition, we found that RF could automatically select relevant features for PA classification depending on the sensor location by examining the importance of each node in multiple decision trees (DTs). This study proposes that ensemble learning using well-defined feature sets combined with the undersampling approach is robust for imbalanced datasets in PA classification. This approach will be useful for PA classification in the free-living situation, where data imbalance problems between classes are common.

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“…Moreover, most studies with an adult population do not specifically address older adults. Changing gait patterns with age [ 16 ] and a different range of typical activities limit the transferability of these cut-points for older adults [ 17 , 18 ]. In addition, cut-points are specific to the type of accelerometer and placement at the body, and research on wrist- or ankle-worn accelerometry in older adults is scarce.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Considerations in Processing Accelerometry Data to Explore Physical Activity and Sedentary Time in Older Adults

Cited by 12 publications

References 42 publications

Measuring sedentary behavior using waist- and thigh-worn accelerometers and inclinometers – are the results comparable?

Measuring sedentary behavior using waist- and thigh-worn accelerometers and inclinometers – are the results comparable?

A Comparative Study on the Influence of Undersampling and Oversampling Techniques for the Classification of Physical Activities Using an Imbalanced Accelerometer Dataset

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

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