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Open access

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.

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.

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.

Open access

A Self-Paced Walk Test for Individual Calibration of Heart Rate to Energy Expenditure

Kate Westgate, Tomas I. Gonzales, Stefanie Hollidge, Tim Lindsay, Nick Wareham, and Søren Brage

Introduction: Estimating free-living physical activity (PA) with continuous heart rate (HR) monitoring is challenging due to individual variation in the relationship between HR and energy expenditure. This variation can be captured through individual calibration with graded exercise tests, but structured tests with prescribed load require medical screening and are not always feasible in population settings. We present and evaluate an individual calibration method using HR response to a less demanding self-paced walk test. Methods: Six hundred and forty-three participants from the Fenland Study (Cambridgeshire, the United Kingdom) completed a 200-m self-paced walk test, a treadmill test, and 1 week of continuous HR and accelerometry monitoring. Mixed-effects regression was used to derive a walk test calibration model from HR response to the walk using treadmill-based parameters as criterion. Free-living PA estimates from the calibration model were compared with treadmill-calibrated and non-exercise-calibrated estimates. Results: Walk calibration captured 57% of the variance in the HR–energy expenditure relationship determined by the treadmill test. Applying walk calibration to data from free-living yielded similar PA estimates to those using treadmill calibration (52.7 vs. 52.0 kJ·kg−1·day−1; mean difference: 0.7 kJ·kg−1·day−1, 95% confidence interval [−0.0, 1.5]) and high correlation (r = .89). Individual differences were observed (root mean square error: 10.0 kJ·kg−1·day−1; 95% limits of agreement: −20.6, 19.1 kJ·kg−1·day−1). Walk calibration improved precision by 29% compared with nonexercise group calibration (root mean square error: 14.0 kJ·kg−1·day−1; 95% limits of agreement: −30.4, 24.5 kJ·kg−1·day−1). Conclusions: A 200-m self-paced walk test captures between-individual variation in the HR–energy expenditure relationship and facilitates estimation of free-living PA in population settings.

Free access

Erratum. Context Matters: The Importance of Physical Activity Domains for Public Health

Journal for the Measurement of Physical Behaviour

Free access

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

Marc Weitz, Bente Morseth, Laila A. Hopstock, and Alexander Horsch

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.

Free access

Erratum. Semiautomatic Training Load Determination in Endurance Athletes

Journal for the Measurement of Physical Behaviour