Predicting physical activity intensity using raw accelerometer signals in manual wheelchair users with spinal cord injury

Shwetar, Yousif; Huang, Zijian; Veerubhotla, Akhila; Knezevic, Steven; Hong, Eunkyoung; Spungen, Ann M.

doi:10.1038/s41393-021-00728-z

Cited by 4 publications

(4 citation statements)

References 42 publications

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“…In model 3, BMR1 refers to the basal metabolic rate by World Health Organization [24]. BMR2 refers to the basal metabolic rate by Mi in et al (1990) [27].…”

Section: Ee Prediction Modelsmentioning

confidence: 99%

“…In addition, a random forest (RF) model using raw acceleration signals from ActiGraph activity monitors was developed by aggregating data from two published studies that shared similar inclusion/exclusion criteria and study protocols [27].…”

Section: Rmr -Resting Metabolic Ratementioning

confidence: 99%

“…A total of 7,640 minutes of data from 78 participants was used to develop the RF model [27]. Table 2 depicts the demographics of the 78 participants.…”

Section: Rf Model Developmentmentioning

confidence: 99%

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Preliminary Field Validity of Free-Living Energy Expenditure Estimation in Wheelchair Users with Spinal Cord Injury via Wearable Device-based Models

Ding,

Huang,

Veerubhotla

et al. 2024

Preprint

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Study Design Cross-sectional validation study. Objectives To develop a raw acceleration signal-based random forest (RF) model for predicting total energy expenditure (TEE) in manual wheelchair users (MWUs) and evaluate the preliminary field validity of this new model along with four existing models published in prior literature using the Doubly Labeled Water (DLW) method. Setting General community and research institution in Pittsburgh, USA. Methods A total of 78 participants’ data from two previous studies were used to develop the new RF model. A seven-day cross-sectional study was conducted to collect participants’ free-living physical activity and TEE data, resting metabolic rate, demographics, and anthropometrics. Ten MWUs with spinal cord injury (SCI) completed the study, with seven participants having valid data for evaluating the preliminary field validity of the five models. Results The RF model achieved a mean absolute error (MAE) of 0.59 ± 0.60 kcal/min and a mean absolute percentage error (MAPE) of 23.6%±24.3% on the validation set. For preliminary field validation, the five assessed models yielded MAE from 136 kcal/day to 1141 kcal/day, and MAPE from 6.1–50.2%. The model developed by Nightingale et al. in 2015 achieved the best performance (MAE: 136 ± 96 kcal/day, MAPE: 6.1%±4.7%), while the RF model achieved comparable performance (MAE: 167 ± 99 kcal/day, MAPE: 7.4%±5.1%). Conclusions: Two existing models and our newly developed RF model showed good preliminary field validity for assessing TEE in MWUs with SCI and the potential to detect lifestyle change in this population. Future large-scale field validation study and model iteration is recommended.

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“…In model 3, BMR1 refers to the basal metabolic rate by World Health Organization [24]. BMR2 refers to the basal metabolic rate by Mi in et al (1990) [27].…”

Section: Ee Prediction Modelsmentioning

confidence: 99%

Section: Rmr -Resting Metabolic Ratementioning

confidence: 99%

See 1 more Smart Citation

Preliminary Field Validity of Free-Living Energy Expenditure Estimation in Wheelchair Users with Spinal Cord Injury via Wearable Device-based Models

Ding,

Huang,

Veerubhotla

et al. 2024

Preprint

Self Cite

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“…We have devised the mean amplitude deviation (MAD) of the resultant acceleration signal as a metric for comparable classification of PA intensity irrespective of substantial differences in measurement ranges and sampling rates of different accelerometer brands [7]. Several other researchers have evaluated the performance of the MAD metric and found it at least satisfactory [8][9][10][11][12][13][14][15]. The initial MAD method provides a valid and accurate estimate of incident VO 2 within a wide range of walking and running speeds on track locomotion [16].…”

Section: Introductionmentioning

confidence: 99%

Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range

Vähä-Ypyä,

Bretterhofer,

Husu

et al. 2023

Sensors

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Accelerometer data can be used to estimate incident oxygen consumption (VO2) during physical activity. Relationships between the accelerometer metrics and VO2 are typically determined using specific walking or running protocols on a track or treadmill. In this study, we compared the predictive performance of three different metrics based on the mean amplitude deviation (MAD) of the raw three-dimensional acceleration signal during maximal tests performed on a track or treadmill. A total of 53 healthy adult volunteers participated in the study, 29 performed the track test and 24 the treadmill test. During the tests, the data were collected using hip-worn triaxial accelerometers and metabolic gas analyzers. Data from both tests were pooled for primary statistical analysis. For typical walking speeds at VO2 less than 25 mL/kg/min, accelerometer metrics accounted for 71–86% of the variation in VO2. For typical running speeds starting from VO2 of 25 mL/kg/min up to over 60 mL/kg/min, 32–69% of the variation in VO2 could be explained, while the test type had an independent effect on the results, except for the conventional MAD metrics. The MAD metric is the best predictor of VO2 during walking, but the poorest during running. Depending on the intensity of locomotion, the choice of proper accelerometer metrics and test type may affect the validity of the prediction of incident VO2.

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Enhancing Spinal Cord Injury Care: Using Wearable Technologies for Physical Activity, Sleep, and Cardiovascular Health

Zhao,

Balthazaar,

Hiremath

et al. 2024

Archives of Physical Medicine and Rehabilitation

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Predicting physical activity intensity using raw accelerometer signals in manual wheelchair users with spinal cord injury

Cited by 4 publications

References 42 publications

Preliminary Field Validity of Free-Living Energy Expenditure Estimation in Wheelchair Users with Spinal Cord Injury via Wearable Device-based Models

Preliminary Field Validity of Free-Living Energy Expenditure Estimation in Wheelchair Users with Spinal Cord Injury via Wearable Device-based Models

Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range

Enhancing Spinal Cord Injury Care: Using Wearable Technologies for Physical Activity, Sleep, and Cardiovascular Health

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