Euclidean Norm Minus Research Articles

Cross-Generational Comparability of Raw and Count-Based Metrics from ActiGraph GT9X and wGT3X-BT Accelerometers during Free-Living in Youth

ABSTRACT Free-living comparability of wGT3X-BT and GT9X ActiGraphs has not been verified. Participants (n = 34, 35% females, 7–17 y) wore hip-worn monitors for one week. Vector magnitude (VM; 15-s epoch), Mean Amplitude Deviation (MAD) and Euclidean Norm Minus One (ENMO) (5-s epoch), and percent of time spent in each activity intensity (using cut-points) were calculated. At the epoch-level, correlations were strong (r = 0.822–0.963), mean absolute percent difference (MAPD) was 41.5 ± 62.2% (VM), 40.8 ± 52.7% (MAD), and 110.9 ± 74.0%, with moderate (ENMO κ = 0.664–0.789) to strong (VM κ = 0.915) agreement for activity intensity. When collapsed to the individual level, MAPD was 4.2 ± 3.4% (VM), 6.8 ± 3.7% (MAD), and 35.9 ± 26.1% (ENMO), correlations were moderate (ENMO r = 0.618) to high (MAD and VM r = 0.996), but ENMO, MAD, and time spent in activity intensities using ENMO cut-points were not statistically equivalent (using 2 one-sided tests) between monitors. While time spent in activity intensities was often comparable between models, caution is warranted for epoch-level comparisons.

Physical Activity, Sedentary Behavior, and White Matter Microstructure in Children with Overweight or Obesity.

This study aimed to investigate the associations of objectively measured and self-reported physical activity (PA) and sedentary behavior with white matter microstructure in children with overweight or obesity. In a sample of 103 participants (age, 10.02 ± 1.15 yr; 42 girls) from the ActiveBrains project, we assessed PA and sedentary behavior using accelerometers (GT3X+; ActiGraph, Pensacola, FL), and the Youth Activity Profile-Spain questionnaire. Objectively measured PA and sedentary behavior were classified into different intensities following the hip- and wrist-based cutoff points for the Euclidean Norm Minus One metric by Hildebrand et al., wrist-based cutoff points for counts metric by Chandler et al., and hip-based cutoff points for counts metric for Romanzini et al. and Evenson et al. Magnetic resonance imaging of white matter microstructure, fractional anisotropy (FA), and mean diffusivity (MD) were derived from diffusion tensor imaging. Linear regression models were used to examine the associations of objectively measured and self-reported PA and sedentary behavior with global and tract-specific FA and MD. Self-reported total PA was positively associated with global FA (β = 0.236, P = 0.038), whereas watching television was negatively associated with global FA (β = -0.270, P = 0.014). In regard to the objective measures, using single regression models, light PA (β = 0.273, P = 0.016), moderate-to-vigorous PA (β = 0.257, P = 0.035), and total PA (β = 0.294, P = 0.013) were positively associated with global FA only when Hildebrand-Euclidean Norm Minus One hip cut points were used for analyses. Lastly, no association was found between PA and sedentary behavior and FA and MD within individual tracts. Our results suggest that PA and watching television are related to white matter microstructure in children with overweight or obesity. However, longitudinal large-scale studies are needed to confirm and expand these findings.

Comparability of accelerometer signal aggregation metrics across placements and dominant wrist cut points for the assessment of physical activity in adults

Large epidemiological studies that use accelerometers for physical behavior and sleep assessment differ in the location of the accelerometer attachment and the signal aggregation metric chosen. This study aimed to assess the comparability of acceleration metrics between commonly-used body-attachment locations for 24 hours, waking and sleeping hours, and to test comparability of PA cut points between dominant and non-dominant wrist. Forty-five young adults (23 women, 18–41 years) were included and GT3X + accelerometers (ActiGraph, Pensacola, FL, USA) were placed on their right hip, dominant, and non-dominant wrist for 7 days. We derived Euclidean Norm Minus One g (ENMO), Low-pass filtered ENMO (LFENMO), Mean Amplitude Deviation (MAD) and ActiGraph activity counts over 5-second epochs from the raw accelerations. Metric values were compared using a correlation analysis, and by plotting the differences by time of the day. Cut points for the dominant wrist were derived using Lin’s concordance correlation coefficient optimization in a grid of possible thresholds, using the non-dominant wrist estimates as reference. They were cross-validated in a separate sample (N = 36, 10 women, 22–30 years). Shared variances between pairs of acceleration metrics varied across sites and metric pairs (range in r2: 0.19–0.97, all p < 0.01), suggesting that some sites and metrics are associated, and others are not. We observed higher metric values in dominant vs. non-dominant wrist, thus, we developed cut points for dominant wrist based on ENMO to classify sedentary time (<50 mg), light PA (50–110 mg), moderate PA (110–440 mg) and vigorous PA (≥440 mg). Our findings suggest differences between dominant and non-dominant wrist, and we proposed new cut points to attenuate these differences. ENMO and LFENMO were the most similar metrics, and they showed good comparability with MAD. However, counts were not comparable with ENMO, LFENMO and MAD.

Association between sleep quality and physical activity in postpartum women

ObjectiveTo describe sleep quality and sleep duration in primiparous women 6 months postpartum and to determine whether sleep quality is associated with 24-hour physical activity assessed using triaxial wrist accelerometry and a novel analytic technique, functional data analysis (FDA). MethodsSix months postpartum, participants wore a triaxial wrist accelerometer for 7 days and completed the Pittsburgh Sleep Quality Index (PSQI). We categorized sleep quality as good (score ≤5) or poor (>5). Data were summarized as vector magnitude of accelerations net of local gravity, or “Euclidean Norm Minus One” (ENMO, mg), in 5-second epochs. Using FDA, we modeled ENMO as a continuously changing, functional outcome predicted by sleep quality. We also compared mean total ENMO per 24-hour day, mean minutes of moderate to vigorous physical activity (MVPA) and of light activity/day, and mean minutes of MVPA in 10-minute bouts/day by sleep quality. ResultsOf 365 women that completed the PSQI, 215 (59%) reported poor sleep quality. Two hundred ninety-six women completed both the PSQI and valid accelerometry. With the exception of one brief period around noon, sleep quality was not significantly associated with 24-hour physical activity according to FDA. Women with poor sleep quality had slightly greater total mean activity, minutes of light activity, and minutes of MVPA per day, but no difference in minutes of MVPA in at least 10-minute bouts. ConclusionPoor sleep quality is common 6 months postpartum. We identified no clinically significant differences in physical activity metrics between women with good and poor quality sleep.

Can we use the Apple Watch to measure sleep reliably?

Introduction: Individuals and researchers are increasingly interested in using consumer‐grade wearable devices to gain insight into health and wellbeing, including sleep. Sleep researchers are interested because of the potential to monitor much larger numbers people than polysomnography or clinical‐grade systems devices would permit. This study aims to investigate the feasibility of using an Apple Watch for sleep/wake monitoring and compare its performance to the clinically validated Philips Actiwatch Spectrum Pro, under free‐living conditions. Methods: We recorded 26 nights of sleep was from 13 healthy adults (9 male, 4 female). Each participant was asked to wear both devices on their non‐dominant wrist for two consecutive days. We extracted activity counts from the Actiwatch, and classified 15‐second epochs into sleep or wake using the Actiware Software (version 6.0.9). With the Apple Watch, we extracted raw acceleration data (at 50Hz), calculated Euclidean Norm Minus One (ENMO) for the same epochs, and classified them using a similar algorithm (with a sleep/wake threshold based on data from another participant). We used a range of analyses, including Bland‐Altman plots and linear correlation, to visualize and assess the agreement between Actiwatch and Apple Watch. Results: The Apple Watch had high overall accuracy (97.3%), high sensitivity (99.1%) in detecting sleep, and adequate specificity (75.8%) in detecting wakefulness. The total sleeping period is highly correlated with a Pearson Correlation coefficient of 0.99 between the two devices. On average, the Apple Watch over‐estimated the total sleep time by 4.65 min. Discussion: Popular consumer‐grade devices like the Apple Watch (of which over 30 million have been sold) provide an opportunity to conduct longitudinal studies at very large scales. Our initial results indicate promising performance in comparison to a clinically‐validated device. Optimization of the classification algorithm for high‐resolution acceleration data is likely to further improve the results. Further study is also needed to assess performance over a long period of time in ecological settings, and against polysomnography in a clinical environment.

Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches.

Objectives(1) To develop and internally-validate Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) thresholds for separating sedentary behaviours from common light-intensity physical activities using raw acceleration data collected from both hip- and wrist-worn tri-axial accelerometers; and (2) to compare and evaluate the performances between the ENMO and MAD metrics.MethodsThirty-three adults [mean age (standard deviation (SD)) = 27.4 (5.9) years; mean BMI (SD) = 23.9 (3.7) kg/m2; 20 females (60.6%)] wore four accelerometers; an ActiGraph GT3X+ and a GENEActiv on the right hip; and an ActiGraph GT3X+ and a GENEActiv on the non-dominant wrist. Under laboratory-conditions, participants performed 16 different activities (11 sedentary behaviours and 5 light-intensity physical activities) for 5 minutes each. ENMO and MAD were computed from the raw acceleration data, and logistic regression and receiver-operating-characteristic (ROC) analyses were implemented to derive thresholds for activity discrimination. Areas under ROC curves (AUROC) were calculated to summarise performances and thresholds were assessed via executing leave-one-out-cross-validations.ResultsFor both hip and wrist monitor placements, in comparison to the ActiGraph GT3X+ monitors, the ENMO and MAD values derived from the GENEActiv devices were observed to be slightly higher, particularly for the lower-intensity activities. Monitor-specific hip and wrist ENMO and MAD thresholds showed excellent ability for separating sedentary behaviours from motion-based light-intensity physical activities (in general, AUROCs >0.95), with validation indicating robustness. However, poor classification was experienced when attempting to isolate standing still from sedentary behaviours (in general, AUROCs <0.65). The ENMO and MAD metrics tended to perform similarly across activities and accelerometer brands.ConclusionsResearchers can utilise these robust monitor-specific hip and wrist ENMO and MAD thresholds, in order to accurately separate sedentary behaviours from common motion-based light-intensity physical activities. However, caution should be taken if isolating sedentary behaviours from standing is of particular interest.

An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics.

Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10–100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60–91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI’s largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.