Accelerometry calibration in people with class II-III obesity: Energy expenditure prediction and physical activity intensity identification

Using Raw Accelerometer Data to Predict High-Impact Mechanical Loading

The purpose of this study was to develop peak ground reaction force (pGRF) and peak loading rate (pLR) prediction equations for high-impact activities in adult subjects with a broad range of body masses, from normal weight to severe obesity. A total of 78 participants (27 males; 82.4 ± 20.6 kg) completed a series of trials involving jumps of different types and heights on force plates while wearing accelerometers at the ankle, lower back, and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Body mass was a predictor in all models, along with peak acceleration in the pGRF models and peak acceleration rate in the pLR models. The equations to predict pGRF had a coefficient of determination (R²) of at least 0.83, and a mean absolute percentage error (MAPE) below 14.5%, while the R² for the pLR prediction equations was at least 0.87 and the highest MAPE was 24.7%. Jumping pGRF can be accurately predicted through accelerometry data, enabling the continuous assessment of mechanical loading in clinical settings. The pLR prediction equations yielded a lower accuracy when compared to the pGRF equations.

Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines

The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning ('novel methods') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code.

OBJECTIVE

We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work.

APPROACH

Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created.

MAIN RESULTS

Sixty-three methods are hosted in the repository, in preschoolers (n=6), children/adolescents (n=20), and adults (n=42), using hip (n=45), wrist (n=25), thigh (n=4), chest (n=4), ankle (n=6), other (n=4), or a combination of monitor wear locations (n=9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees.

SIGNIFICANCE

The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.

Step Detection Accuracy and Energy Expenditure Estimation at Different Speeds by Three Accelerometers in a Controlled Environment in Overweight/Obese Subjects

Our aim was to compare three research-grade accelerometers for their accuracy in step detection and energy expenditure (EE) estimation in a laboratory setting, at different speeds, especially in overweight/obese participants. Forty-eight overweight/obese subjects participated. Participants performed an exercise routine on a treadmill with six different speeds (1.5, 3, 4.5, 6, 7.5, and 9 km/h) for 4 min each. The exercise was recorded on video and subjects wore three accelerometers during the exercise: Sartorio Xelometer (SX, hip), activPAL (AP, thigh), and ActiGraph GT3X (AG, hip), and energy expenditure (EE) was estimated using indirect calorimetry for comparisons. For step detection, speed-wise mean absolute percentage errors for the SX ranged between 9.73–2.26, 6.39–0.95 for the AP, and 88.69–2.63 for the AG. The activPALs step detection was the most accurate. For EE estimation, the ranges were 21.41–15.15 for the SX, 57.38–12.36 for the AP, and 59.45–28.92 for the AG. All EE estimation errors were due to underestimation. All three devices were accurate in detecting steps when speed exceeded 4 km/h and inaccurate in EE estimation regardless of speed. Our results will guide users to recognize the differences, weaknesses, and strengths of the accelerometer devices and their algorithms.

Comparison of Energy Expenditure Assessed Using Wrist- and Hip-Worn ActiGraph GT3X in Free-Living Conditions in Young and Older Adults

The World Health Organization has presented their recommendations for energy expenditure to improve public health. Activity trackers do represent a modern solution for measuring physical activity, particularly in terms of steps/day and energy expended in physical activity (active energy expenditure). According to the manufacturer's instructions, these activity trackers can be placed on different body locations, mostly at the wrist and the hip, in an undifferentiated manner. The objective of this study was to compare the absolute error rate of active energy expenditure measured by a wrist-worn and hip-worn ActiGraph GT3X+ over a 24-h period in free-living conditions in young and older adults. Over the period of a 24-h period, 22 young adults and 22 older adults were asked to wear two ActiGraph GT3X+ at two different body locations recommended by the manufacturer, namely one around the wrist and one above the hip. Freedson algorithm was applied for data analysis. For both groups, the absolute error rate tended to decrease from 1,252 to 43% for older adults and from 408 to 46% for young participants with higher energy expenditure. Interestingly, for both young and older adults, the wrist-worn ActiGraph provided a significantly higher values of active energy expenditure (943 ± 264 cal/min) than the hip-worn (288 ± 181 cal/min). Taken together, these results suggest that caution is needed when using active energy expenditure as an activity tracker-based metric to quantify physical activity.

Accelerometer-based prediction of skeletal mechanical loading during walking in normal weight to severely obese subjects

There is no objective way to monitor mechanical loading characteristics during exercise for bone health improvement. We developed accelerometry-based equations to predict ground reaction force (GRF) and loading rate (LR) in normal weight to severely obese subjects. Equations developed had a high and moderate accuracy for GRF and LR prediction, respectively, thereby representing an accessible way to determine mechanical loading characteristics in clinical settings.

INTRODUCTION

There is no way to objectively prescribe and monitor exercise for bone health improvement in obese patients based on mechanical loading characteristics. We aimed to develop accelerometry-based equations to predict peak ground reaction forces (pGRFs) and peak loading rate (pLR) on normal weight to severely obese subjects.

METHODS

Sixty-four subjects (45 females; 84.6 ± 21.7 kg) walked at different speeds (2-6 km·h^-1) on a force plate-equipped treadmill while wearing accelerometers at lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Actual and predicted values at different speeds were compared by repeated measures ANOVA.

RESULTS

Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration transient rate for pLR prediction. All pGRF equation coefficients of determination were above 0.89, a good agreement between actual and predicted pGRFs, with a mean absolute percent error (MAPE) below 6.7%. No significant differences were observed between actual and predicted pGRFs at each walking speed. Accuracy indices from our equations were better than previously developed equations for normal weight subjects, namely a MAPE approximately 3 times smaller. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF.

CONCLUSION

Walking pGRF and pLR in normal weight to severely obese subjects can be predicted with moderate to high accuracy by accelerometry-based equations, representing an easy and accessible way to determine mechanical loading characteristics in clinical settings.