An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing

John, Dinesh; Tang, Qu; Albinali, Fahd; Intille, Stephen

doi:10.1123/jmpb.2018-0068

Cited by 117 publications

(105 citation statements)

References 40 publications

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“…In this regard, modern accelerometers provide raw accelerations that can be consistently processed to obtain comparable outputs from different monitors [ 27 ]. In this line, open-source metrics, such as ENMO, are of great value for the field as they ease replicability and comparability across different cohorts using different devices [ 11 , 28 ]. We provided cut-points using both ENMO and counts in this study in order to fit different needs.…”

Section: Discussionmentioning

confidence: 99%

Calibration and Cross-Validation of Accelerometer Cut-Points to Classify Sedentary Time and Physical Activity from Hip and Non-Dominant and Dominant Wrists in Older Adults

Migueles

Cadenas‐Sanchez

Alcantara³

et al. 2021

Sensors

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Accelerometers’ accuracy for sedentary time (ST) and moderate-to-vigorous physical activity (MVPA) classification depends on accelerometer placement, data processing, activities, and sample characteristics. As intensities differ by age, this study sought to determine intensity cut-points at various wear locations people more than 70 years old. Data from 59 older adults were used for calibration and from 21 independent participants for cross-validation purposes. Participants wore accelerometers on their hip and wrists while performing activities and having their energy expenditure measured with portable calorimetry. ST and MVPA were defined as ≤1.5 metabolic equivalents (METs) and ≥3 METs (1 MET = 2.8 mL/kg/min), respectively. Receiver operator characteristic (ROC) analyses showed fair-to-good accuracy (area under the curve [AUC] = 0.62–0.89). ST cut-points were 7 mg (cross-validation: sensitivity = 0.88, specificity = 0.80) and 1 count/5 s (cross-validation: sensitivity = 0.91, specificity = 0.96) for the hip; 18 mg (cross-validation: sensitivity = 0.86, specificity = 0.86) and 102 counts/5 s (cross-validation: sensitivity = 0.91, specificity = 0.92) for the non-dominant wrist; and 22 mg and 175 counts/5 s (not cross-validated) for the dominant wrist. MVPA cut-points were 14 mg (cross-validation: sensitivity = 0.70, specificity = 0.99) and 54 count/5 s (cross-validation: sensitivity = 1.00, specificity = 0.96) for the hip; 60 mg (cross-validation: sensitivity = 0.83, specificity = 0.99) and 182 counts/5 s (cross-validation: sensitivity = 1.00, specificity = 0.89) for the non-dominant wrist; and 64 mg and 268 counts/5 s (not cross-validated) for the dominant wrist. These cut-points can classify ST and MVPA in older adults from hip- and wrist-worn accelerometers.

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Section: Discussionmentioning

confidence: 99%

Calibration and Cross-Validation of Accelerometer Cut-Points to Classify Sedentary Time and Physical Activity from Hip and Non-Dominant and Dominant Wrists in Older Adults

Migueles

Cadenas‐Sanchez

Alcantara³

et al. 2021

Sensors

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“…Data quality was reviewed by staff from the National Center for Health Statistics (NCHS) and contractors at Northeastern University in Boston, MA. Acceleration measurements from all three axes were summed for each minute as Monitor-Independent Movement Summary (MIMS) units, a non-proprietary and device-independent universal summary metric [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Demographic characteristics associated with circadian rest-activity rhythm patterns: a cross-sectional study

Somers

López-Jiménez

et al. 2021

Int J Behav Nutr Phys Act

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Background Rest-activity rhythm (RAR), a manifestation of circadian rhythms, has been associated with morbidity and mortality risk. However, RAR patterns in the general population and specifically the role of demographic characteristics in RAR pattern have not been comprehensively assessed. Therefore, we aimed to describe RAR patterns among non-institutionalized US adults and age, sex, and race/ethnicity variation using accelerometry data from a nationally representative population. Methods This cross-sectional study was conducted using the US National Health and Nutrition Examination Survey (NHANES) 2011–2014. Participants aged ≥20 years who were enrolled in the physical activity monitoring examination and had at least four 24-h periods of valid wrist accelerometer data were included in the present analysis. 24-h RAR metrics were generated using both extended cosinor model (amplitude, mesor, acrophase and pseudo-F statistic) and nonparametric methods (interdaily stability [IS] and intradaily variability [IV]). Multivariable linear regression was used to assess the association between RAR and age, sex, and race/ethnicity. Results Eight thousand two hundred participants (mean [SE] age, 49.1 [0.5] years) were included, of whom 52.2% were women and 67.3% Whites. Women had higher RAR amplitude and mesor, and also more robust (pseudo-F statistic), more stable (higher IS) and less fragmented (lower IV) RAR (all Ptrend < 0.001) than men. Compared with younger adults (20–39 years), older adults (≥ 60 years) exhibited reduced RAR amplitude and mesor, but more stable and less fragmented RAR, and also reached their peak activity earlier (advanced acrophase) (all Ptrend < 0.001). Relative to other racial/ethnic groups, Hispanics had the highest amplitude and mesor level, and most stable (highest IS) and least fragmented (lowest IV) RAR pattern (Ptrend < 0.001). Conversely, non-Hispanic blacks had the lowest peak activity level (lowest amplitude) and least stable (lowest IS) RAR pattern (all Ptrend < 0.001). Conclusions In the general adult population, RAR patterns vary significantly according to sex, age and race/ethnicity. These results may reflect demographic-dependent differences in intrinsic circadian rhythms and may have important implications for understanding racial, ethnic, sex and other disparities in morbidity and mortality risk.

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“…The activPAL tended to record lower acceleration values for the physical activity outputs calculated from direct acceleration relative to the other devices. The lower acceleration values observed with the activPAL device were anticipated due to the lower dynamic range (± 2 g vs ± 8 g) and frequency (20 Hz vs 100 Hz) of the device settings for data collection in comparison to other brands (John et al, 2019). The ActiGraph device also recorded lower accelerations for the physical activity outputs relative to the GENEActiv and Axivity accelerometers.…”

Section: All Sitting/lyingmentioning

confidence: 88%

Comparability of Postural and Physical Activity Metrics from Different Accelerometer Brands Worn on the Thigh: Data Harmonization Possibilities

Edwardson

Maylor

Dawkins

et al. 2021

Measurement in Physical Education and Exercise Science

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The aim was to establish which postural and physical activity outcomes are comparable across different accelerometer brands worn on the thigh when processed using open-source methods. Twenty participants wore four accelerometers (Axivity, ActiGraph, activPAL, GENEActiv) for three free-living days. Postural and physical activity outputs (average acceleration, intensity gradient, intensity of the most active 30 min, 60 min, and 8 h) were generated. Postural outputs: Mean absolute percent errors (MAPEs) were low, reliability excellent, and equivalency within the 5% zone across all monitor pairings for sitting/lying and upright times, but not specific lying postures. Physical activity outputs: MAPEs were higher and reliability lower than for sitting/lying and upright time. However, the majority of the outcomes were within the 10% equivalency zone for Axivity/ GENEActiv and Axivity/ActiGraph pairings. Total sitting/lying and upright times show strong potential for harmonization across studies utilizing different thigh-worn accelerometers. The majority of acceleration outcomes compare well for Axivity, GENEActiv, and ActiGraph.

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An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing

Cited by 117 publications

References 40 publications

Calibration and Cross-Validation of Accelerometer Cut-Points to Classify Sedentary Time and Physical Activity from Hip and Non-Dominant and Dominant Wrists in Older Adults

Calibration and Cross-Validation of Accelerometer Cut-Points to Classify Sedentary Time and Physical Activity from Hip and Non-Dominant and Dominant Wrists in Older Adults

Demographic characteristics associated with circadian rest-activity rhythm patterns: a cross-sectional study

Comparability of Postural and Physical Activity Metrics from Different Accelerometer Brands Worn on the Thigh: Data Harmonization Possibilities

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