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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

Free access

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

Tyler D. Quinn and Bethany Barone Gibbs

Physical activity can be performed across several domains, including leisure, occupation, household, and transportation, but physical activity research, measurement, and surveillance have historically been focused on leisure-time physical activity. Emerging evidence suggests differential health effects across these domains. In particular, occupational physical activity may be associated with adverse health outcomes. We argue that to adequately consider and evaluate such impacts, physical activity researchers and public health practitioners engaging in measurement, surveillance, and guideline creation should measure and consider all relevant physical activity domains where possible. We describe why physical activity science is often limited to the leisure-time domain and provide a rationale for expanding research and public health efforts to include all physical activity domains.

Free access

Evaluation of Physical Activity Assessment Using a Triaxial Activity Monitor in Community-Dwelling Older Japanese Adults With and Without Lifestyle-Related Diseases

Sho Nagayoshi, Harukaze Yatsugi, Xin Liu, Takafumi Saito, Koji Yamatsu, and Hiro Kishimoto

Background: Several previous studies investigated physical activity of older adults using wearable devices, but more studies need to develop normative values for chronic disease conditions. This study aimed to investigate physical activity using a triaxial activity monitor in community-dwelling older Japanese adults with and without lifestyle-related diseases. Methods: Data from a total of 732 community-dwelling older Japanese men and women were collected and analyzed in a cross-sectional study. The participants’ physical activity was assessed for seven consecutive days by a triaxial accelerometer. Physical activity was assessed by number of lifestyle-related diseases and six lifestyle-related diseases categories by gender. Physical activity was assessed separately for total, locomotive, and nonlocomotive physical activity. Results: Participants with multiple (two or more) diseases had significantly lower total light-intensity physical activity (LPA; 278.5 ± 8.4 min/day) and nonlocomotive LPA (226.4 ± 7.0 min/day) versus without diseases in men. Compared in each disease category, total LPA and nonlocomotive LPA was significantly lower in men with hypertension and diabetes. Total sedentary time was significantly higher in men with hypertension, diabetes, and heart disease. Locomotive LPA was significantly lower in men with diabetes. In women, locomotive moderate- to vigorous-intensity physical activity was significantly higher in women with diabetes, and nonlocomotive moderate- to vigorous-intensity physical activity was significantly lower in women with heart disease. Conclusion: This study demonstrated that older Japanese men with multiple lifestyle-related diseases had lower physical activity. In each disease category, hypertension, diabetes, and heart disease affected lower physical activity, especially in men.

Open access

Semiautomatic Training Load Determination in Endurance Athletes

Christophe Dausin, Sergio Ruiz-Carmona, Ruben De Bosscher, Kristel Janssens, Lieven Herbots, Hein Heidbuchel, Peter Hespel, Véronique Cornelissen, Rik Willems, André La Gerche, Guido Claessen, and on behalf of the Pro@Heart Consortium*

Background: Despite endurance athletes recording their training data electronically, researchers in sports cardiology rely on questionnaires to quantify training load. This is due to the complexity of quantifying large numbers of training files. We aimed to develop a semiautomatic postprocessing tool to quantify training load in clinical studies. Methods: Training data were collected from two prospective athlete’s heart studies (Master Athlete’s Heart study and Prospective Athlete Heart study). Using in-house developed software, maximal heart rate (MaxHR) and training load were calculated from heart rate monitored during cumulative training sessions. The MaxHR in the lab was compared with the MaxHR in the field. Lucia training impulse score, based on individually based exercise intensity zones, and Edwards training impulse, based on MaxHR in the field, were compared. A questionnaire was used to determine the number of training sessions and training hours per week. Results: Forty-three athletes recorded their training sessions using a chest-worn heart rate monitor and were selected for this analysis. MaxHR in the lab was significantly lower compared with MaxHR in the field (183 ± 12 bpm vs. 188 ± 13 bpm, p < .01), but correlated strongly (r = .81, p < .01) with acceptable limits of agreement (±15.4 bpm). An excellent correlation was found between Lucia training impulse score and Edwards training impulse (r = .92, p < .0001). The quantified number of training sessions and training hours did not correlate with the number of training sessions (r = .20) and training hours (r = −.12) reported by questionnaires. Conclusion: Semiautomatic measurement of training load is feasible in a wide age group. Standard exercise questionnaires are insufficiently accurate in comparison to objective training load quantification.

Open access

Daily Activity of Individuals With an Amputation Above the Knee as Recorded From the Nonamputated Limb and the Prosthetic Limb

Kerstin Hagberg, Roland Zügner, Peter Thomsen, and Roy Tranberg

Introduction: Mobility restriction following limb loss might lead to a sedentary lifestyle, impacting health. Daily activity monitoring of amputees has focused on prosthetic steps, neglecting overall activity. Purpose: To assess daily activity in individuals with an established amputation and to explore the amount of activity recorded from the prosthesis as compared to the overall activity. Methods: Individuals with a unilateral transfemoral amputation or knee disarticulation who had used a prosthesis in daily life for >1 year and could walk 100 m (unsupported or single aided) were recruited. Descriptive information and prosthetic mobility were collected. Two activPAL™ accelerometers were attached to the nonamputated thigh and the prosthesis, respectively. The mean daily activity over 7 days was compared between the nonamputated limb and the prosthesis. Results: Thirty-nine participants (22 men/17 women; mean age 54 [14.5] years) with amputation mainly due to trauma (59%) or tumor (28%) were included. Overall, participants took 6,125 steps and spent 10.2 hr sedentary, 5.0 hr upright, and 8.7 hr laying per day. Compared to recordings from the nonamputated limb, 85% of sit-to-stand transitions (32/38), 73% of steps (4,449/6,125), and 68% of walking time (1.0/1.5 hr) were recorded from the prosthesis. Recordings seemed to be less adequate for incidental prosthetic steps than for walks. Conclusions: Sedentary behavior accounted for most of the day demonstrating the importance to encourage physical activity among established prosthetic users. The prosthesis is used for daily activity to a great extent. However, noted pitfalls in the recordings call for further refinement of the measurements.