Euclidean Norm Minus Research Articles

Seasonal differences in physical activity, sedentary behaviour, and sleep patterns in people with type 1 diabetes in Kuwait

AimsThe main aim of the current study was to measure physical activity, sedentary behaviors and sleep levels across the different seasons in people with type 1 diabetes in Kuwait. MethodsA prospective cross-sectional study was conducted from August 2021 to September 2022. Physical activity and sleep metrics were measured over a 7-day period with a wrist-worn accelerometer (GENEActiv). Overall physical activity was measured as a Euclidean Norm Minus One in milli gravitational units (mg). Accelerometer metrics were compared across the seasons and between the sex. ResultsA total of 784 people with type 1 diabetes participated. Mean daily physical activity was 25.2 mg (SD = 7.3). Seasonal differences were seen in overall physical activity (p = 0.05), inactivity (p = 0.04), light activity (p = 0.001), the intensity gradient (p = 0.001) and sleep efficiency (p = 0.02). Poorer metrics were generally seen in Spring and Summer. Overall physical activity, moderate and vigorous physical activity, and inactivity were significantly higher in males compared to females (p ≤ 0.02). Females had a longer sleeping duration (p = 0.02), and higher sleep efficiency (p = 0.04) and light physical activity (p = 0.01). Overall physical activity and the intensity gradient were negatively associated with HbA1c (both p = 0.01). ConclusionsPhysical activity levels were generally low and sleep poor in people with type 1 diabetes in Kuwait and these varied by sex and season. The current data are useful to target and develop interventions to improve physical activity and glycemic control.

Large-scale assessment of physical activity in a population using high-resolution hip-worn accelerometry: the German National Cohort (NAKO)

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

Joint association of genetic risk and accelerometer-measured physical activity with incident coronary artery disease in the UK biobank cohort.

Previous research demonstrates the joint association of self-reported physical activity and genotype with coronary artery disease. However, an existing research gap is whether accelerometer-measured overall physical activity or physical activity intensity can offset genetic predisposition to coronary artery disease. This study explores the independent and joint associations of accelerometer-measured physical activity and genetic predisposition with incident coronary artery disease. Incident coronary artery disease based on hospital inpatient records and death register data serves as the outcome of this study. Polygenic risk score and overall physical activity, measured as Euclidean Norm Minus One, and intensity, measured as minutes per day of moderate-to-vigorous intensity physical activity (MVPA), are examined both linearly and by decile. The UK Biobank population-based cohort recruited over 500,000 individuals aged 40 to 69 between 2006 and 2010, with 103,712 volunteers participating in a weeklong wrist-worn accelerometer study from 2013 to 2015. Individuals of White British ancestry (n = 65,079) meeting the genotyping and accelerometer-based inclusion criteria and with no missing covariates were included in the analytic sample. In the sample of 65,079 individuals, the mean (SD) age was 62.51 (7.76) and 61% were female. During a median follow-up of 6.8 years, 1,382 cases of coronary artery disease developed. At the same genetic risk, physical activity intensity had a hazard ratio (HR) of 0.41 (95% CI: 0.29-0.60) at the 90th compared to 10th percentile, equivalent to 31.68 and 120.96 minutes of moderate-to-vigorous physical activity per day, respectively, versus an HR of 0.61 (95% CI: 0.52-0.72) for overall physical activity. The combination of high genetic risk and low physical activity intensity showed the greatest risk, with an individual at the 10th percentile of genetic risk and 90th percentile of intensity facing an HR of 0.14 (95% CI: 0.09-0.21) compared to an individual at the 90th percentile of genetic risk and 10th percentile of intensity. Physical activity, especially physical activity intensity, is associated with an attenuation of some of the risk of coronary artery disease but this pattern does not vary by genetic risk. This accelerometer-based study provides the clearest evidence to date regarding the joint influence of genetics, overall physical activity, and physical activity intensity on coronary artery disease.

Influence of Accelerometer Calibration on the Estimation of Objectively Measured Physical Activity: The Tromsø Study

Accelerometers are increasingly used to observe human behavior such as physical activity under free-living conditions. An important prerequisite to obtain reliable results is the correct calibration of the sensors. However, accurate calibration is often neglected, leading to potentially biased results. Here, we demonstrate and quantify the effect of accelerometer miscalibration on the estimation of objectively measured physical activity under free-living conditions. The total volume of moderate to vigorous physical activity (MVPA) was significantly reduced after post hoc auto-calibration for uniaxial and triaxial count data, as well as for Euclidean Norm Minus One and mean amplitude deviation raw data. Weekly estimates of MVPA were reduced on average by 5.5, 9.2, 45.8, and 4.8 min, respectively, when compared to the original uncalibrated estimates. Our results indicate a general trend of overestimating physical activity when using factory-calibrated sensors. In particular, the accuracy of estimates derived from the Euclidean Norm Minus One feature suffered from uncalibrated sensors. For all modalities, the more uncalibrated the sensor was, the more MVPA was overestimated. This might especially affect studies with lower sample sizes.

Comparing Accelerometer Processing Metrics and Hyperparameter Optimization for Physical Activity Classification Accuracy Using Machine Learning Methods

Background: Physical activity (PA) is a crucial factor in maintaining good health and preventing chronic diseases. However, accurately measuring PA is challenging. Euclidean Norm Minus One (ENMO), ActiGraph Counts, and Monitor-Independent Movement Summary (MIMS) units are processing metrics used to classify PA through accelerometry, but they employ different methods to calculate activity levels. This study aimed to compare ENMO, ActiGraph Counts, and MIMS accelerometer metrics using machine learning algorithms. Methods: Data from a smartphone accelerometer were collected from 50 participants who held the smartphone in their right hand while completing six activities. The data were used to generate ENMO, ActiGraph Counts, and MIMS acceleration metrics. Random Forest, K-Nearest Neighbor, and Support Vector Machine algorithms were applied to the data to classify PA into different levels of activity intensity and types. The algorithms’ performance was evaluated using various metrics such as accuracy, precision, and recall. Results: The results showed that both the Random Forest and K-Nearest Neighbor algorithms performed well, achieving above 80% accuracy in classifying PA into different intensity levels and types. Both the ENMO and MIMS metrics proved more accurate than ActiGraph Counts in classifying moderate to vigorous PA. Conclusions: This study provides evidence that both ENMO and MIMS metrics can accurately measure PA with accelerometry, and machine learning algorithms can classify the activity into different intensity levels. These metrics and methods are valuable tools for monitoring PA and understanding the relationship between PA and health outcomes.

The Intrinsic Properties of ActiGraph Counts and Alternatives

There are currently several methods available to generate summary measures from acceleration, while ActiGraph (AG) counts as the first method to be used at large scale. The recent disclosure of the AG counts method exposes its intrinsic properties, which has not been accessible before. The intrinsic properties are the raw acceleration processing elements like filtering, rectification, or dead-band elimination, which are used to estimate physical activity intensity. The aim of this technical note is to compare the intrinsic properties of AG counts method with five alternatives (Euclidean Norm Minus One, mean average deviation, Activity Index, Rate of Change Accelerometry Movement, and Monitor-Independent Movement Summary) and how rescaling of AG counts and Monitor-Independent Movement Summary/minute into the International System of Units can be used to harmonize all summary measures and facilitate direct comparison. A total of 12 intrinsic properties are compared, and the overview demonstrates that there is large diversity regarding the specific intrinsic property elements being included, and with Monitor-Independent Movement Summary to be the only summary measure, which has been developed considering all elements. The harmonized output generated from all summary methods is highly comparable within common activities, but to obtain a robust summary measure recorded in subjects during free-living conditions, more research is warranted to evaluate the effect of the different intrinsic properties.

Comparison of Sleep and Physical Activity Metrics From Wrist-Worn ActiGraph wGT3X-BT and GT9X Accelerometers During Free-Living in Adults

Background: ActiGraph accelerometers can monitor sleep and physical activity (PA) during free-living, but there is a need to confirm agreement in outcomes between different models. Methods: Sleep and PA metrics from two ActiGraphs were compared after participants (N = 30) wore a GT9X and wGT3X-BT on their nondominant wrist for 7 days during free-living. PA metrics including total steps, counts, average acceleration—Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation, intensity gradient, the minimum acceleration value of the most active 10 and 30 min (M10, M30), time spent in activity intensities from vector magnitude (VM) counts, and ENMO cut points and sleep metrics (sleep period time window, sleep duration, sleep onset, and waking time) were compared. Results: Excellent agreement was evident for average acceleration-Mean Amplitude Deviation, counts, total steps, M10, and light PA (VM counts) with good agreement evident from the remaining PA metrics apart from moderate–vigorous PA (VM counts) which demonstrated moderate agreement. Mean bias for all PA metrics were low, as were the limits of agreement for the intensity gradient, average acceleration-Mean Amplitude Deviation, and inactive time (ENMO and VM counts). The limits of agreement for all other PA metrics were >10%. Excellent agreement, low mean bias, and narrow limits of agreement were evident for all sleep metrics. All sleep and PA metrics demonstrated equivalence (equivalence zone of ≤10%) apart from moderate–vigorous PA (ENMO) which needed an equivalence zone of 16%. Conclusions: Equivalent estimates of almost all PA and sleep metrics are provided from the GT9X and wGT3X-BT worn on the nondominant wrist.

Associations of physical activity dose and movement quality with executive functions in socioeconomically disadvantaged children aged 5–6 years

BackgroundGrowing up in areas of high deprivation can negatively impact children's movement behaviours and cognitive development. Enhancing the quantity and quality of children's movement experiences is believed to enhance cognitive development. This study investigated the association of three different modes of movement assessment, movement proficiency and divergent movement ability (collectively understood as motor competence) and PA dose with executive function in a low socio-economic demographic. Demographics, motor competence, and a combination of motor competence and physical activity were hypothesized to be significantly predictor of executive functions. MethodIn this cross-sectional study, 360 children aged 5–6 years from deprived areas were assessed using three movement assessments: wrist-worn accelerometery for physical activity dose, Test of Gross Motor Development-3 for movement proficiency, and divergent movement assessment. Executive function, including inhibitory control, working memory, and cognitive flexibility, was measured using the NIH Toolbox on an iPad. Multiple linear regression models were designed to evaluate the independent and combined association of demographics, movement competence and physical activity variables with executive function. ResultsThe regression analysis, with demographic factors only, explained 12% of EF variance (r2 = 0.12 95%CI 0.06–0.18). In addition to this demographics the model with divergent movement explained 19% of EF variance (r2 = 0.19 95% CI = 0.12–0.28), the model with movement proficiency explained 16% of EF variance (r2 = 0.16 95% CI = 0.08–0.26) and the model with PA dose explained 13% of EF variance (r2 = 0.13 95% CI = 0.07–0.20). In these models both divergent movement and proficiency were significant predictors, whilst physical activity variables were not. The final models, combining motor competence and physical activity variables, explained 24% and 23% of EF variance (r2 = 0.24 CI = 0.14–0.33 and r2 = 0.23 CI = 0.14–0.32). In these models, motor competence variables were significant predictors, and only vigorous physical activity and Euclidean Norm Minus One emerged as significant PA dose predictors. DiscussionThese findings emphasise that motor competence and physical activity variables better predict executive functions when they are combined. When considered individually both motor competence variables were significant predictors of executive function whilst physical activity variables were not. Importantly, among the two movement competence facets, divergent movement assessment exhibited the strongest association with executive function. Future interventions should consider how to facilitate both movement and cognitive development in children. Future interventions should consider both the interplay of movement quality and quantity and the importance of environments that invite children’s exploratory movement behavior.

Reference values for wrist-worn accelerometer physical activity metrics in England children and adolescents

BackgroundOver the last decade use of raw acceleration metrics to assess physical activity has increased. Metrics such as Euclidean Norm Minus One (ENMO), and Mean Amplitude Deviation (MAD) can be used to generate metrics which describe physical activity volume (average acceleration), intensity distribution (intensity gradient), and intensity of the most active periods (MX metrics) of the day. Presently, relatively little comparative data for these metrics exists in youth. To address this need, this study presents age- and sex-specific reference percentile values in England youth and compares physical activity volume and intensity profiles by age and sex.MethodsWrist-worn accelerometer data from 10 studies involving youth aged 5 to 15 y were pooled. Weekday and weekend waking hours were first calculated for youth in school Years (Y) 1&2, Y4&5, Y6&7, and Y8&9 to determine waking hours durations by age-groups and day types. A valid waking hours day was defined as accelerometer wear for ≥ 600 min·d−1 and participants with ≥ 3 valid weekdays and ≥ 1 valid weekend day were included. Mean ENMO- and MAD-generated average acceleration, intensity gradient, and MX metrics were calculated and summarised as weighted week averages. Sex-specific smoothed percentile curves were generated for each metric using Generalized Additive Models for Location Scale and Shape. Linear mixed models examined age and sex differences.ResultsThe analytical sample included 1250 participants. Physical activity peaked between ages 6.5–10.5 y, depending on metric. For all metrics the highest activity levels occurred in less active participants (3rd-50th percentile) and girls, 0.5 to 1.5 y earlier than more active peers, and boys, respectively. Irrespective of metric, boys were more active than girls (p < .001) and physical activity was lowest in the Y8&9 group, particularly when compared to the Y1&2 group (p < .001).ConclusionsPercentile reference values for average acceleration, intensity gradient, and MX metrics have utility in describing age- and sex-specific values for physical activity volume and intensity in youth. There is a need to generate nationally-representative wrist-acceleration population-referenced norms for these metrics to further facilitate health-related physical activity research and promotion.

Development and Evaluation of Sedentary Time Cut-Points for the activPAL in Adults Using the GGIR R-Package.

The purpose of this study was to develop sedentary cut-points for the activPAL and evaluate their performance against a criterion measure (i.e., activPAL processed by PALbatch). Part 1: Thirty-five adults (23.4 ± 3.6 years) completed 12 laboratory activities (6 sedentary and 6 non-sedentary activities). Receiver operator characteristic (ROC) curves proposed optimal Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) cut-points of 26.4 mg (ENMO) and 30.1 mg (MAD). Part 2: Thirty-eight adults (22.6 ± 4.1 years) wore an activPAL during free-living. Estimates from PALbatch and MAD revealed a mean percent error (MPE) of 2.2%, mean absolute percent error (MAPE) of 6.5%, limits of agreement (LoA) of 19% with absolute and relative equivalence zones of 5% and 0.3 SD. Estimates from PALbatch and ENMO revealed an MPE of -10.6%, MAPE of 14.4%, LoA of 31% and 16% and 1 SD equivalence zones. After standing was isolated from sedentary behaviours, ROC analysis proposed an optimal cut-off of 21.9 mg (herein ENMOs). Estimates from PALbatch and ENMOs revealed an MPE of 3.1%, MAPE of 7.5%, LoA of 25% and 9% and 0.5 SD equivalence zones. The MAD and ENMOs cut-points performed best in discriminating between sedentary and non-sedentary activity during free-living.

Agreement and equivalence of estimated physical activity behaviours, using ENMO- and counts-based processing methods, for wrist-worn accelerometers in adolescents

ABSTRACT The present study examined the agreement and equivalence between two physical activity processing methods. Data were obtained from 161 Hong-Kong adolescents (74 girls, age: 12.6 ± 1.7y). Participants wore an Actigraph GT3XBT on their non-dominant wrist for 7d. Time spent sedentary, and in light-(LPA), moderate-(MPA), vigorous-(VPA), and moderate-to-vigorous physical activity (MVPA) were calculated using different processing methods (proprietary counts and Euclidean Norm Minus One (ENMO)). Intraclass correlation coefficients (ICC) were used to examine absolute agreement (ICC2) and consistency (ICC3), and equivalence was assessed using pairwise equivalence tests. Using ENMO, sedentary time and VPA were higher, whereas all other behaviours were lower (compared to counts processing). Agreement ranged from poor (ICC2:0.42(Sedentary)) to moderate (ICC2:0.86(LPA)) and consistency ranged from moderate (ICC3:0.71(sedentary)) to good (ICC3:0.91(LPA)). Methods were not considered equivalent (all p > 0.05). Due to differences in the wear-time validation of processing methods, a sensitivity analyses (sub-sample with the same valid wear time for both methods (n = 56)), resulted in minimal change. Lack of agreement and equivalence between ENMO and counts processing methods suggests that the processing method significantly affects youth physical activity estimates.

How to identify patients with low muscle mass linked to physical capacity and physical activity in heart failure

Abstract Introduction Skeletal muscle wasting is frequent in heart failure (HF) patients, and low muscle mass (MM) is associated with increased morbidity and mortality. Current recommendations suggest the usage of dual-energy X-ray absorptiometry (DXA)-derived appendicular lean mass (ALM) adjusted for height squared to identify low MM. However, it has recently been suggested that ALM adjusted for body mass index (BMI) has a higher predictive value. The relationships between low MM, muscle strength, exercise capacity and daily physical activity (PA) are poorly understood in heart failure with reduced ejection fraction (HFrEF). Purpose This study examined the relationships between low MM defined by two different indices and muscle strength, physical capacity tests and accelerometer measurements. Methods A total of 104 HFrEF patients underwent DXA, maximal isometric knee-extensor strength test, cardiopulmonary exercise testing, 6-minute walking test and accelerometry. Two adjusted indices for low MM were assessed: ALM/m2 and ALM/BMI. We performed Pearson correlations to examine the relationships between low MM and muscle strength, exercise capacity and daily PA. Furthermore, we identified patients with low MM based on the two indices. For ALM/m2 we used guideline-recommended cut-offs and for ALM/BMI we used the mean value since no cut-offs has been validated. Results The ALM/m2-index showed no significant correlation to peak oxygen uptake (VO2peak), 6-minute walking distance (6MWD), average PA or daily time spent in moderate-to-vigorous physical activity (MVPA). A moderate correlation was found to maximal muscle strength was found (r=0.61, p&amp;lt;0.001). The ALM/BMI-index was moderately correlated to maximal muscle strength (r=0.65, p&amp;lt;0.001), VO2peak (r=0.40, p&amp;lt;0.001), 6MWD (r=0.41, p&amp;lt;0.001), average daily PA (r=0.27, p=0.012) and daily time spent in MVPA (r=0.29, p=0.007). Patients with low MM identified by ALM/m2 did not differ from patients with preserved MM with regards to VO2peak, 6MWD or accelerometer measurements. A difference in maximal muscle strength was found (95.7±35.6 Nm vs 116±44.8, p=0.02). Patients with low MM identified by ALM/BMI had lower maximal muscle strength (94.8±36.1 Nm vs 121.9±44.4 Nm, p=0.001), lower VO2peak (13.2±3.5 mL/kg/min vs 16.4±3.7 mL/kg/min, p&amp;lt;0.001), shorter 6MWD (370.5±99.4 m vs 467.3±93.9 m, p&amp;lt;0.001), average daily PA (15.1±4.4 ENMO* vs 17.3±5.3 ENMO*, p=0.04) and daily time spent in MVPA (14.2±16.7 min vs 24.7±21.6 min, p=0.01) compared to patients with preserved MM. Conclusion In HFrEF patients low MM identified by ALM/BMI seems to correlate better to well-established markers for physical capacity (VO2peak and 6MWD), muscle strength and daily PA compared to ALM/m2. No cut-offs have been established for ALM/BMI and there is a need for longitudinal studies to determine the association with mortality in HFrEF patients. *ENMO = Euclidean Norm Minus One, an acceleration metric. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The Karen Elise Jensens Foundation

Calibration and Cross-validation of Accelerometry in Children and Adolescents with Cystic Fibrosis

ABSTRACT Commonly used cut-points may misclassify physical activity (PA) in people with cystic fibrosis (CF). The aim of this study was to develop and cross-validate condition-specific cut-points in children and adolescents with CF. Thirty-five children and adolescents with CF (15 girls; 11.6 ± 2.8 years) and 28 controls (16 girls; 12.2 ± 2.7 years), had their energy expenditure and triaxial acceleration measured during six daily activities of varying intensities. Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) were extracted using both GENEActiv (both wrists) and ActiGraph GT9X (both wrists and right waist) accelerometers. ROC curves were used to determine healthy and CF-specific raw acceleration cut-points for sedentary time (SED), moderate physical activity (MPA) and vigorous physical activity (VPA). The PA cut-points were generally lower in CF compared to controls for both ENMO (60.2–73.1 vs. 63.5–86.8 mg) and MAD (58.9–85.2 vs. 75.9–93.7 mg). These substantial inter-cut-point differences support the need for disease-specific cut-points.

Physical Activity Behaviors of a Middle-Age South African Cohort as Determined by Integrated Hip and Thigh Accelerometry.

ABSTRACTPurposeDescriptive studies of objectively measured physical activity behaviors in African populations are rare. We developed a method of combining hip and thigh accelerometery signals to quantify and describe physical behaviors in middle-age South African men and women.MethodsWe integrated signals from two triaxial accelerometers worn simultaneously during free-living, in a subsample of the Middle-age Soweto Cohort (n = 794; mean (SD) age, 53.7 (6.3) yr). Acceleration time series from the accelerometers were combined and movement-related acceleration was derived using Euclidean Norm Minus One (in milligrams), to determine total movement volume (mean Euclidean Norm Minus One) and nonmovement time (<28 mg), light-intensity physical activity (LPA; 28–85 mg), and moderate- to vigorous-intensity physical activity (MVPA; >85 mg); thigh pitch angle and a sleep diary were used to divide nonmovement time (in minutes per day) into sleep, awake sitting/lying, and standing. Sociodemographic factors were self-reported, and weight and height were measured.ResultsMean (SD) wear time was 128 (48) h. Movement volume was 15.0 (6.5) mg for men and 12.2 (3.4) mg for women. Men spent more time in MVPA and sitting/lying, whereas women spent more time standing. Age was inversely associated with movement volume, MVPA, and LPA. When compared with their normal-weight counterparts, men who were overweight or obese spent less time in MVPA, whereas women who were overweight or obese spent less time in LPA and more time sitting/lying. Socioeconomic status was inversely associated with total movement volume, MVPA, and time spent sleeping, and positively associated with time spent sitting/lying, in both men and women.ConclusionsIntegrating signals from hip and thigh accelerometers enables characterization of physical behaviors that can be applied in an African population.

Comparison of Different Signal Processing Methodologies and Their Impact on the Range of Acceleration Amplitudes Experienced by Preschool-Aged Children

ABSTRACT This study explored 1) the effect of applying an autocalibration algorithm on accelerometer data analyzed using different signal processing techniques and 2) how these techniques changed the signal composition. Using a dataset of preschoolers (n = 137; 4.31 ± 0.87 years; Actigraph accelerometers (100 Hz) on right hip 24 hours/7 days) the vector magnitude (VM) was calculated as a reference; highpass, bandpass and lowpass filters were applied and the “Euclidean Norm Minus One” (ENMO) was calculated. Amplitude probability distribution functions were created to compare the 1) uncalibrated versus calibrated data and, 2) amplitude distribution across the techniques. Results showed that the calibration correction significantly impacted ENMO, VM lowpass and VM data, but the impact on VM highpass and bandpass data was minimal. Comparing across the techniques, results showed the lowest outputs were derived according to ENMO, followed by the VM highpass/VM bandpass and the VM lowpass/VM which yielded very similar results.

Impact of ActiGraph Sampling Rate and Intermonitor Comparability on Measures of Physical Activity in Adults

Background: ActiGraph is a commonly used, research-grade accelerometer brand, but there is little information regarding intermonitor comparability of newer models. In addition, while sampling rate has been shown to influence accelerometer metrics, its influence on measures of free-living physical activity has not been directly studied. Purpose: To examine differences in physical activity metrics due to intermonitor variability and chosen sampling rate. Methods: Adults (n = 20) wore two hip-worn ActiGraph wGT3X-BT monitors for 1 week, with one accelerometer sampling at 30 Hz and the other at 100 Hz, which was downsampled to 30 Hz. Activity intensity was classified using vector magnitude, Euclidean Norm Minus One (ENMO), and mean amplitude deviation (MAD) cut points. Equivalence testing compared outcomes. Results: There was a lack of intermonitor equivalence for ENMO, time in sedentary/light- or moderate-intensity activity according to ENMO cut points, and time in moderate-intensity activity according to MAD cut points. Between sampling rates, differences existed for time in moderate-intensity activity according to vector magnitude, ENMO, and MAD cut points, and time in sedentary/light-intensity activity according to ENMO cut points. While mean differences were small (0.1–1.7 percentage points), this would equate to differences in moderate-to vigorous-intensity activity over a 10-hr wear day of 3.6 (MAD) to 10.8 (ENMO) min/day for intermonitor comparisons or 3.6 (vector magnitude) to 5.4 (ENMO) min/day for sampling rate. Conclusions: Epoch-level intermonitor differences were larger than differences due to sampling rate, but both may impact outcomes such as time spent in each activity intensity. ENMO was the least comparable metric between monitors or sampling rates.

Wrist-worn accelerometers: recommending ~1.0 mg as the minimum clinically important difference (MCID) in daily average acceleration for inactive adults

Physical activity is increasingly assessed using wrist-worn accelerometry.1 The primary unit of measurement is acceleration which lacks an obvious concrete meaning in the clinical and public health settings. If the...

Comparison of Sedentary Time Between Thigh-Worn and Wrist-Worn Accelerometers

Background: The accuracy of wrist-worn accelerometers in identifying sedentary time has been scarcely studied in free-living conditions. The aim of this study was to compare daily sedentary time estimates between a thigh-worn accelerometer, which measured sitting and lying postures, and a wrist-worn accelerometer, which measured low levels of movement. Methods: The study population consisted of 259 participants (Mage = 62.8 years, SD = 0.9) from the Finnish Retirement and Aging Study (FIREA). Participants wore an Axivity AX3 accelerometer on their mid-thigh and an Actigraph wActiSleep-BT accelerometer on their non-dominant wrist simultaneously for a minimum of 4 days in free-living conditions. Two definitions to estimate daily sedentary time were used for data from the wrist-worn accelerometer: 1) the count cutpoint, ≤1853 counts per minute; and 2) the Euclidean Norm Minus One (ENMO) cutpoint, &lt;30 mg. Results: Compared to the thigh-worn accelerometer, daily sedentary time estimate was 63 min (95% confidence interval [CI] = −53 to −73) lower by the count cutpoint and 50 min (95% CI = 34 to 67) lower by the ENMO cutpoint. The limits of agreement in daily sedentary time estimates between the thigh- and cutpoint methods for wrist-worn accelerometers were wide (the count cutpoint: −117 to 243, the ENMO cutpoint: −212 to 313 min). Conclusions: Currently established cutpoint-based methods to estimate sedentary time from wrist-worn accelerometers result in underestimation of daily sedentary time compared to posture-based estimates of thigh-worn accelerometers. Thus, sedentary time estimates obtained from wrist-worn accelerometers using currently available cutpoint-based methods should be interpreted with caution and future work is needed to improve their accuracy.

Cross-generational comparability of hip- and wrist-worn ActiGraph GT3X+, wGT3X-BT, and GT9X accelerometers during free-living in adults

ABSTRACT ActiGraph accelerometers are frequently used to characterize physical activity, but free-living cross-generational comparability of newer models has not been verified. Participants (N = 70) wore GT9X and wGT3X-BT accelerometers at the hip and a sub-sample (n = 54) wore GT9X and either wGT3X-BT or GT3X+ monitors at each wrist for 4 days. Vector magnitude (VM) counts, VM acceleration, Mean Amplitude Deviation (MAD), and Euclidean Norm Minus One (ENMO) were calculated (60-s epoch), and cut-points were used to determine percent of time spent in each intensity (sedentary/light, moderate, vigorous). Epoch-level correlation coefficients (r) were ≥0.73, and weighted kappa for intensity classifications ranged from 0.71 (ENMO, hip) to 0.98 (VM counts, non-dominant wrist). Monitors were equivalent for all outcomes, except ENMO (all locations/monitors), percent of time spent in sedentary/light (hip) and moderate (hip and non-dominant wrist) activity as classified by ENMO-based cut-points, and vigorous activity as classified by VM count cut-points (non-dominant wrist; p > 0.05). While epoch-level data were not identical, most outcomes were strongly related between models (e.g., MAD, VM) and equivalent once reduced to percent of time spent in each intensity. However, monitor output was not equivalent for the acceleration-based metric ENMO, suggesting that caution should be exercised when comparing this outcome among ActiGraph models.

Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults

ABSTRACT Despite recent popularity of wrist-worn accelerometers for assessing free-living physical behaviours, there is a lack of user-friendly methods to characterize physical activity from a wrist-worn ActiGraph accelerometer. Participants in this study completed a laboratory protocol and/or 3–8 hours of directly observed free-living (criterion measure of activity intensity) while wearing ActiGraph GT9X Link accelerometers on the right hip and non-dominant wrist. All laboratory data (n = 36) and 11 participants’ free-living data were used to develop vector magnitude count cut-points (counts/min) for activity intensity for the wrist-worn accelerometer, and 12 participants’ free-living data were used to cross-validate cut-point accuracy. The cut-points were: <2,860 counts/min (sedentary); 2,860–3,940 counts/min (light); and ≥3,941counts/min (moderate-to-vigorous (MVPA)). These cut-points had an accuracy of 70.8% for assessing free-living activity intensity, whereas Sasaki/Freedson cut-points for the hip accelerometer had an accuracy of 77.1%, and Hildebrand Euclidean Norm Minus One (ENMO) cut-points for the wrist accelerometer had an accuracy of 75.2%. While accuracy was higher for a hip-worn accelerometer and for ENMO wrist cut-points, the high wear compliance of wrist accelerometers shown in past work and the ease of use of count-based analysis methods may justify use of these developed cut-points until more accurate, equally usable methods can be developed.