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Validity of the Modified SIT-Q 7d for Estimating Sedentary Break Frequency and Duration in Home-Based Office Workers During the COVID-19 Global Pandemic: A Secondary Analysis

Kirsten Dillon-Rossiter, Madison Hiemstra, Nina Bartmann, Wuyou Sui, Marc Mitchell, Scott Rollo, Paul A. Gardiner, and Harry Prapavessis

Office workers who transitioned to working from home are spending an even higher percentage of their workday sitting compared with being “in-office” and this is an emerging health concern. With many office workers continuing to work from home since the onset of the COVID-19 pandemic, it is imperative to have a validated self-report questionnaire to assess sedentary behavior, break frequency, and duration, to reduce the cost and burden of using device-based assessments. This secondary analysis study aimed to validate the modified Last 7-Day Sedentary Behavior Questionnaire (SIT-Q 7d) against an activPAL4™ device in full-time home-based “office” workers (n = 148; mean age = 44.90). Participants completed the modified SIT-Q 7d and wore an activPAL4 for a full work week. The findings showed that the modified SIT-Q 7d had low (ρ = .35–.37) and weak (ρ = .27–.28) criterion validity for accurate estimates of break frequency and break duration, respectively. The 95% limits of agreement were large for break frequency (26.85–29.01) and medium for break duration (5.81–8.47), indicating that the modified SIT-Q 7d may not be appropriate for measuring occupational sedentary behavior patterns at the individual level. Further validation is still required before confidently recommending this self-report questionnaire to be used among this population to assess breaks in sedentary time.

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The Stryd Foot Pod Is a Valid Measure of Stepping Cadence During Treadmill Walking and Running

Madeline E. Shivgulam, Jennifer L. Petterson, Liam P. Pellerine, Derek S. Kimmerly, and Myles W. O’Brien

Stepping cadence is an important determinant of activity intensity, with faster stepping associated with the most health benefits. The Stryd monitor provides real-time feedback on stepping cadence. The limited existing literature has neither validated the Stryd across slow walking to fast running speeds nor strictly followed statistical guidelines for monitor validation studies. We assessed the criterion validity of the Stryd monitor to detect stepping cadence across multiple walking and jogging/running speeds. It was hypothesized that the Stryd monitor would be an accurate measure of stepping cadence across all measured speeds. Forty-six participants (23 ± 5 years, 26 females) wore the Stryd monitor on their shoelaces during a 10-stage progressive treadmill walking (Speeds 1–5) and jogging/running (Speeds 6–10) protocol (criterion: manually counted video-recorded cadence; total stages: 438). Standardized guidelines for physical activity monitor statistical analyses were followed. A two-way repeated-measure analysis of variance revealed the Stryd monitor recorded a slightly higher cadence (<1 steps/min difference, all p < .001) at 2 miles/hr (92.1 ± 6.2 steps/min vs. 91.5 ± 6.4 steps/min, p < .001), 2.5 miles/hr (101.3 ± 6.1 steps/min vs. 100.7 ± 6.4 steps/min), and 3.5 miles/hr (117.4 ± 5.9 steps/min vs. 117.0 ± 6.0 steps/min). However, equivalence testing demonstrated high equivalence of the Stryd and manually counted cadence (equivalence zone required: ≤± 2.6%) across all speeds. The Stryd activity monitor is a valid measure of stepping cadence across walking, jogging, and running speeds. By providing real-time cadence feedback, the Stryd monitor has strong potential to help guide the general public monitor their stepping intensity to promote more habitual activity at faster cadences.

Free access

Maximizing the Utility and Comparability of Accelerometer Data From Large-Scale Epidemiologic Studies

I-Min Lee, Christopher C. Moore, and Kelly R. Evenson

There is much evidence showing that physical activity is related to optimal health, including physical and mental function, and quality of life. Additionally, data are accumulating with regard to the detrimental health impacts of sedentary behavior. Much of the evidence related to long-term health outcomes, such as cardiovascular disease and cancer—the two leading causes of death in the United States and worldwide—comes from observational epidemiologic studies and, in particular, prospective cohort studies. Few data on these outcomes are derived from randomized controlled trials, conventionally regarded as the “gold standard” of research designs. Why is there a paucity of data from randomized trials on physical activity or sedentary behavior and long-term health outcomes? A further issue to consider is that prospective cohort studies investigating these outcomes can take a long time to accrue sufficient numbers of endpoints for robust and meaningful findings. This contrasts with the rapid pace at which technology advances. Thus, while the use of devices for measuring physical behaviors has been an important development in large-scale epidemiologic studies over the past decade, cohorts that are now publishing results on health outcomes related to accelerometer-assessed physical activity and sedentary behavior may have been initiated years ago, using “dated” technology. This paper, based on a keynote presentation at 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement 2022, discusses the issues of study design and slow pace of discovery in prospective cohort studies and suggests some possible ways to maximize the utility and comparability of “dated” device data from prospective cohort studies for research investigations using the Women’s Health Study as an example.

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Measurement of Physical Activity Using Accelerometry in Persons With Multiple Sclerosis

Robert W. Motl

The consequences of multiple sclerosis (MS), particularly gait and walking dysfunction, may obfuscate (i.e., make unclear in meaning) the measurement of physical activity using body-worn motion sensors, notably accelerometers. This paper is based on an invited keynote lecture given at the 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement, June 2022, and provides an overview of studies applying accelerometers for the measurement of physical activity behavior in MS. The overview includes initial research uncovering a conundrum with the interpretation of activity counts from accelerometers as a measure of physical activity. It then reviews research on calibration of accelerometer output based on its association with energy expenditure in yielding a biologically based metric for studying physical activity in MS. The paper concludes with other applications and lessons learned for guiding future research on physical activity measurement using accelerometry in MS and other populations with neurological diseases and conditions.

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Processing of Accelerometry Data with GGIR in Motor Activity Research Consortium for Health

Wei Guo, Andrew Leroux, Haochang Shou, Lihong Cui, Sun Jung Kang, Marie-Pierre Françoise Strippoli, Martin Preisig, Vadim Zipunnikov, and Kathleen Ries Merikangas

The Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of clinical and community studies that employ common digital mobile protocols and collect common clinical and biological measures across participating studies. At a high level, a key scientific goal which spans mMARCH studies is to develop a better understanding of the interrelationships between physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. mMARCH studies employ wrist-worn accelerometry to obtain objective measures of PA/SL/CR. However, there is currently no consensus on a standard data processing pipeline for raw accelerometry data and few open-source tools which facilitate their development. The R package GGIR is the most prominent open-source software package for processing raw accelerometry data, offering great functionality and substantial user flexibility. However, even with GGIR, processing done in a harmonized and reproducible fashion across multiple analytical centers requires a nontrivial amount of expertise combined with a careful implementation. In addition, there are many statistical methods useful for analyzing PA/SL/CR patterns using accelerometry data which are implemented in non-GGIR R packages, including methods from multivariate statistics, functional data analysis, distributional data analysis, and time series analyses. To address the issues of multisite harmonization and additional feature creation, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data via GGIR, merging GGIR, and non-GGIR features of PA/SL/CR together, implementing several additional data and feature quality checks, and performing multiple analyses including Joint and Individual Variation Explained, an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. The pipeline is easily modified to calculate additional features of interest, and allows for studies not affiliated with mMARCH to apply a pipeline which facilitates direct comparisons of scientific results in published work by mMARCH studies. This manuscript describes the pipeline and illustrates the use of combined GGIR and non-GGIR features by applying Joint and Individual Variation Explained to the accelerometry component of CoLaus|PsyCoLaus, one of mMARCH sites. The pipeline is publicly available via open-source R package mMARCH.AC.

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Estimating Running Speed From Wrist- or Waist-Worn Wearable Accelerometer Data: A Machine Learning Approach

John J. Davis IV, Blaise E. Oeding, and Allison H. Gruber

Background: Running is a popular form of exercise, and its physiological effects are strongly modulated by speed. Accelerometry-based activity monitors are commonly used to measure physical activity in research, but no method exists to estimate running speed from only accelerometer data. Methods: Using three cohorts totaling 72 subjects performing treadmill and outdoor running, we developed linear, ridge, and gradient-boosted tree regression models to estimate running speed from raw accelerometer data from waist- or wrist-worn devices. To assess model performance in a real-world scenario, we deployed the best-performing model to data from 16 additional runners completing a 13-week training program while equipped with waist-worn accelerometers and commercially available foot pods. Results: Linear, ridge, and boosted tree models estimated speed with 12.0%, 11.6%, and 11.2% mean absolute percentage error, respectively, using waist-worn accelerometer data. Errors were greater using wrist-worn data, with linear, ridge, and boosted tree models achieving 13.8%, 14.0%, and 12.8% error. Across 663 free-living runs, speed was significantly associated with run duration (p = .009) and perceived run intensity (p = .008). Speed was nonsignificantly associated with fatigue (p = .07). Estimated speeds differed from foot pod measurements by 7.25%; associations and statistical significance were similar when speed was assessed via accelerometry versus via foot pod. Conclusion: Raw accelerometry data can be used to estimate running speed in free-living data with sufficient accuracy to detect associations with important measures of health and performance. Our approach is most useful in studies where research grade accelerometry is preferable to traditional global positioning system or foot pod-based measurements, such as in large-scale observational studies on physical activity.

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Volume 5 (2022): Issue 4 (Dec 2022)

Open access

The 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement

Open access

Integrity and Performance of Four Tape Solutions for Mounting Accelerometry Devices: Lolland-Falster Health Study

Therese Lockenwitz Petersen, Jan C. Brønd, Eva Benfeldt, and Randi Jepsen

Background: Tape-mounted Axivity AX3 accelerometers are increasingly being used to monitor physical activity of individuals, but studies on the integrity and performance of diffe1rent attachment protocols are missing. Purpose: The purpose of this paper was to evaluate four attachment protocols with respect to skin reactions, adhesion, and wear time in children and adults using tape-mounted Axivity AX3 accelerometers and to evaluate the associated ease of handling. Methods: We used data from the Danish household-based population study, the Lolland-Falster Health Study. Participants were instructed to wear accelerometers for seven consecutive days and to complete a questionnaire on skin reactions and issues relating to adhesion. A one-way analysis of variance was used to examine differences in skin reactions and adhesion between the protocols. A Tukey post hoc test compared group means. Ease of handling was assessed throughout the data collection. Results: In total, 5,389 individuals were included (1,289 children and 4,100 adults). For both children and adults, skin reactions were most frequent in Protocols 1 and 2. Adhesion problems were most frequent in Protocol 3. Wear time was longest in Protocol 4. Skin reactions and adhesion problems were more frequent in children compared to adults. Adults achieved longest wear time. Discussion: Covering the skin completely with adhesive tape seemed to cause skin reactions. Too short pieces of fixation tape caused accelerometers to fall off. Protocols necessitating removal of remains of glue on the accelerometers required a lot of work. Conclusion: The last of the four protocols was superior in respect to skin reactions, adhesion, wear time, and ease of handling.

Open access

Missing Step Count Data? Step Away From the Expectation–Maximization Algorithm

Mia S. Tackney, Daniel Stahl, Elizabeth Williamson, and James Carpenter

In studies that compare physical activity between groups of individuals, it is common for physical activity to be quantified by step count, which is measured by accelerometers or other wearable devices. Missing step count data often arise in these settings and can lead to bias or imprecision in the estimated effect if handled inappropriately. Replacing each missing value in accelerometer data with a single value using the Expectation–Maximization (EM) algorithm has been advocated in the literature, but it can lead to underestimation of variances and could seriously compromise study conclusions. We compare the performance in terms of bias and variance of two missing data methods, the EM algorithm and Multiple Imputation (MI), through a simulation study where data are generated from a parametric model to reflect characteristics of a trial on physical activity. We also conduct a reanalysis of the 2019 MOVE-IT trial. The EM algorithm leads to an underestimate of the variance of effects of interest, in both the simulation study and the reanalysis of the MOVE-IT trial. MI should be the preferred approach to handling missing data in accelerometer, which provides valid point and variance estimates.