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Inter-Brand, -Dynamic Range, and -Sampling Rate Comparability of Raw Accelerometer Data as Used in Physical Behavior Research

Annelinde Lettink, Wessel N. van Wieringen, Teatske M. Altenburg, Mai J.M. Chinapaw, and Vincent T. van Hees

Objective: Previous studies that looked at comparability of accelerometer data focused on epoch or recording level comparability. Our study aims to provide insight into the comparability at raw data level. Methods: We performed five experiments with accelerometers attached to a mechanical shaker machine applying movement along a single axis in the horizontal plane. In each experiment, a 1-min no-movement condition was followed by nineteen 2-min shaker frequency conditions (30–250 rpm). We analyzed accelerometer data from Axivity, ActiGraph, GENEActiv, MOX, and activPAL devices. Comparability between commonly used brands and dynamic ranges was assessed in the frequency domain with power spectra and in the time domain with maximum lagged cross-correlation analyses. The influence of sampling rate on magnitude of acceleration across brands was explored visually. All data were published open access. Results: Magnitude of noise in rest was highest in MOX and lowest in ActiGraph. The signal mean power spectral density was equal between brands at low shaker frequency conditions (<3.13 Hz) and between dynamic ranges within the Axivity brand at all shaker frequency conditions. In contrast, the cross-correlation coefficients between time series across brands and dynamic ranges were higher at higher shaking frequencies. Sampling rate affected the magnitude of acceleration most in Axivity and least in GENEActiv. Conclusions: The comparability of raw acceleration signals between brands and/or sampling rates depends on the type of movement. These findings aid a more fundamental understanding and anticipation of differences in behavior estimates between different implementations of raw accelerometry.

Free access

Measuring Sleep Among Cancer Survivors: Accelerometer Measures Across Days and Agreement Between Accelerometer and Self-Reported Measures

Sidney M. Donzella, Alla Sikorski, Kimberly E. Lind, Meghan B. Skiba, Cynthia A. Thomson, and Tracy E. Crane

Background: The associations between subjective (self-reported) and objective (actigraphy) sleep measurements are not well documented among survivors of cancer. The purpose of this study was to examine actigraphy measurements across days and the associations of two self-reported sleep measures with actigraphy-measured sleep measures. Methods: Sleep data were collected using self-reported sleep diary, the Pittsburgh Sleep Quality Index, and hip-worn actigraphy at baseline for a subsample participating in the Lifestyle Intervention for oVarian cancer Enhanced Survival (N = 516) randomized controlled trial. Intraclass correlation coefficients were used to evaluate consistency of actigraphy sleep measures across days of wear and associations of sleep diary with actigraphy for total sleep time (TST), time asleep, and time awake. Bland–Altman plots were used to assess the associations of sleep duration and sleep efficiency derived from Pittsburgh Sleep Quality Index and actigraphy. Results: Participants were aged 60.3 years (SD 9.3 years). For TST, the associations were strongest after 3 weekdays of consecutive actigraphy wear (ICC = .43 95% CI [.35, .51]), and actigraphy-measured daily TST was longest (617, SD 135 min) compared with self-reported measures. Sleep diary versus actigraphy associations for TST, time asleep, and time awake were weak to moderate. Pittsburgh Sleep Quality Index versus actigraphy association was weak for all sleep constructs. Conclusion: The strength of association between self-reported and actigraphy measures of sleep ranged from weak to very strong, depending on the sleep construct. Impact: Results highlight the importance of selecting an appropriate measurement tool for estimating individual sleep constructs among survivors of cancer.

Free access

Reactions From the Experts: Implications of Open-Source ActiGraph Counts for Analyzing Accelerometer Data

Alexander H.K. Montoye, Samuel R. LaMunion, Jan C. Brønd, and Kimberly A. Clevenger

In 2022, it became possible to produce ActiGraph counts from raw accelerometer data without use of ActiLife software. This supports the availability and use of transparent, open-source methods for producing physical behavior outcomes from accelerometer data. However, questions remain regarding the implications of the availability of open-source ActiGraph counts. This Expert Question and Answer paper solicited and summarized feedback from several noted physical behavior measurement experts on five questions related to open-source counts. The experts agreed that open-source, transparent, and translatable methods help with harmonization of accelerometer methods. However, there were mixed views as to the importance of open-source counts and their place in the field moving forward. This Expert Question and Answer provides initial feedback, but more research both within this special issue and to be conducted moving forward will help to inform whether and how open-source counts will be accepted and adopted for use for device-based physical behavior assessments.

Free access

The Intrinsic Properties of ActiGraph Counts and Alternatives

Jan Christian Brønd, Niels Christian Møller, and Anders Grøntved

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.

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Distinguishing Passive and Active Standing Behaviors From Accelerometry

Robert J. Kowalsky, Herman van Werkhoven, Marco Meucci, Tyler D. Quinn, Lee Stoner, Christopher M. Hearon, and Bethany Barone Gibbs

Purpose: To investigate whether active standing can be identified separately from passive standing via accelerometry data and to develop and test the accuracy of a machine-learning model to classify active and passive standing. Methods: Ten participants wore a thigh-mounted activPAL monitor and stood for three 5-min periods in the following order: (a) PASSIVE: standing with no movement; (b) ACTIVE: five structured weight-shifting micromovements in the medial–lateral, superior–inferior, and anterior–poster planes while standing; and (c) FREE: participant’s choice of active standing. Averages of absolute resultant acceleration values in 15-s epochs were compared via analysis of variance (Bonferroni adjustment for pairwise comparisons) to confirm the dichotomization ability of the standing behaviors. Absolute resultant acceleration values and SDs in 2- and 5-s epochs were used to develop a machine-learning model using leave-one-subject-out cross validation. The final accuracy of the model was assessed using the area under the curve from a receiver operating characteristic curve. Results: Comparison of resultant accelerations across the three conditions (PASSIVE, ACTIVE, and FREE) resulted in a significant omnibus difference, F(2, 19) = [116], p < .001, η2 = .86, and in all pairwise post hoc comparisons (all p < .001). The machine-learning model using 5-s epochs resulted in 94% accuracy for the classification of PASSIVE versus ACTIVE standing. Model application to the FREE data resulted in an absolute average difference of 4.8% versus direct observation and an area under the curve value of 0.71. Conclusions: Active standing in three planes of movement can be identified from thigh-worn accelerometry via a machine-learning model, yet model refinement is warranted.

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Criterion Validity of Commonly Used Sedentary Behavior Questionnaires to Measure Total Sedentary Time in Adults

Madeline E. Shivgulam, Derek S. Kimmerly, and Myles W. O’Brien

Background: Self-report questionnaires are a fast and cost-efficient method to determine habitual sedentary time (sitting/lying time while awake), but their accuracy versus thigh-worn accelerometry (criterion), which can distinguish between sitting and standing postures, is unclear. While the validity of sedentary questionnaires has previously been evaluated, they have not been investigated simultaneously in the same sample population. We tested the hypothesis that common sedentary questionnaires underpredict habitual sedentary time compared with an objective, monitor-based assessment. Methods: Ninety-three participants (30 ± 18 years, 59 females) wore the activPAL inclinometer on the midthigh 24 hr per day for 6.9 ± 0.4 days and completed the SIT-Q, Sedentary Behavior Questionnaire (SBQ), International Physical Activity Questionnaire (IPAQ), and Physical Activity and Sedentary Behavior Questionnaire (PASB-Q). Results: In comparison to the activPAL (9.9 ± 1.9 hr/day), the SIT-Q measured more time (12.9 ± 5.4 hr/day), but the SBQ (7.5 ± 3.3 hr/day), IPAQ (7.4 ± 3.0 hr/day), and PASB-Q (6.6 ± 3.0 hr/day) measured less time (all p < .001). The SIT-Q was positively and weakly correlated (ρ = .230 [95% confidence interval: .020, .422], p = .028) with the activPAL, but the SBQ, IPAQ, and PASB-Q were not (all ps > .760). Equivalence testing demonstrated poor equivalence for the SIT-Q (±40%), SBQ (±31%), IPAQ (±36%), and PASB-Q (±29%). The SIT-Q (β = −1.36), SBQ (β = −0.97), and IPAQ (β = −0.78) exhibited a negative proportional bias (all ps < .002). Conclusions: In summary, the SIT-Q, SBQ, IPAQ, and PASB-Q demonstrated poor validity. Researchers and health promoters should be cautious when implementing these self-report sedentary time questionnaires, as they may not reflect the true sedentary activity and negatively impact study results.

Free access

agcounts: An R Package to Calculate ActiGraph Activity Counts From Portable Accelerometers

Brian C. Helsel, Paul R. Hibbing, Robert N. Montgomery, Eric D. Vidoni, Lauren T. Ptomey, Jonathan Clutton, and Richard A. Washburn

Portable accelerometers are used to capture physical activity in free-living individuals with the ActiGraph being one of the most widely used device brands in physical activity and health research. Recently, in February 2022, ActiGraph published their activity count algorithm and released a Python package for generating activity counts from raw acceleration data for five generations of ActiGraph devices. The nonproprietary derivation of the ActiGraph count improved the transparency and interpretation of accelerometer device-measured physical activity, but the Python release of the count algorithm does not integrate with packages developed by the physical activity research community using the R Statistical Programming Language. In this technical note, we describe our efforts to create an R-based translation of ActiGraph’s Python package with additional extensions to make data processing easier and faster for end users. We call the resulting R package agcounts and provide an inside look at its key functionalities and extensions while discussing its prospective impacts on collaborative open-source software development in physical behavior research. We recommend that device manufacturers follow ActiGraph’s lead by providing open-source access to their data processing algorithms and encourage physical activity researchers to contribute to the further development and refinement of agcounts and other open-source software.

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

Sumayyah Bamidele Musa, Arnab Barua, Kevin G. Stanley, Fabien A. Basset, Hiroshi Mamyia, Kevin Mongeon, and Daniel Fuller

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.

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Examining the Agreement Between the activPAL micro4 and ActiGraph GT9X Accelerometers on Daily Movement Behaviors Among Adults With Total Knee Replacement

Katherine E. DeVivo, Chih-Hsiang Yang, and Christine A. Pellegrini

Objective: The primary purpose was to examine the agreement in sedentary, light, and moderate to vigorous minutes and step counts between the activPAL micro4 and ActiGraph GT9X in adults following total knee replacement. A secondary purpose was to examine the agreement between the activPAL micro4 and ActiGraph GT9X accelerometers at two different time points after surgery (∼1 and 3 months). Methods: Participants in a randomized trial wore ActiGraph GT9X and activPAL micro4 monitors simultaneously for 7 days at ∼1 and 3 months after total knee replacement. The intraclass correlations for time spent in sedentary behavior, light, and moderate to vigorous activity in addition to step counts were estimated to determine consistency between the two monitors. Bland–Altman plot demonstrated the 95% limits of agreement between the monitors at both time points. Results: A total of 480 observations (days) were used from 41 participants (64.9 ± 7 years, 32.4 ± 6.5 kg/m2, 75.6% White, 61% female). The intraclass correlations between the ActiGraph GT9X and activPAL micro4 accelerometers was .839 for sedentary behavior, .853 for light activity, .806 for moderate to vigorous activity, and .937 for steps. The 95% confidence intervals of intraclass correlations between time points indicate a significantly higher agreement between the monitors at 3 months as compared with 1 month. Conclusion: The results suggest that either the ActiGraph GT9X or the activPAL micro4 accelerometers may be used for measuring sedentary, light, and moderate to vigorous minutes and step counts in adults after knee replacement.

Free access

From Research to Application of Wearable-Derived Digital Health Measures—A Perspective From ActiGraph

Jeremy Wyatt and Christine C. Guo

ActiGraph counts were first conceptualized in 1996 to provide an accelerometer-derived metric that can quantify physical activity based on intensity. ActiGraph incorporated this metric into its product suite, enabling its wide adoption in research studies. Over the last 20 years, ActiGraph activity counts have become one of the most common metrics and building blocks of health outcome measures used in wearable research, with >24,000 journal articles published (based on Google Scholar search in 2023). Recently, this field of research is increasingly moving toward clinical application where wearable-derived metrics are growing in industry-sponsored clinical trials, including several use cases endorsed by the regulatory authorities. We celebrate this emerging trend as these patient-generated measures help reduce trial burden and enhance the meaningfulness of developed medical products to the patients. However, true adoption of digital measures in industry research is only in its infancy and still faces many challenges. As a digital health technology provider, ActiGraph has launched several strategic initiatives to support the research community to overcome these challenges and accelerate the translation of research to clinical application. The open-source release of the ActiGraph count algorithm was one of those initiatives. In this commentary, we take the opportunity to share our perspective in supporting the research community with this metric over the last 20 years, the motivation for making this open source, and what we are building to accelerate clinical adoption and realize the promise of better patient care.