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Standing Still or Standing Out: Distinguishing Passive and Active Standing Is a Step in the Right Direction

Madeline E. Shivgulam, Emily E. MacDonald, Jocelyn Waghorn, and Myles W. O’Brien

Standing is a solution to reduce or break-up sedentary time (sitting/reclining/lying while awake); however, the measurable health benefits of standing are conflicting. A recent article in the Journal for the Measurement of Physical Behaviour has demonstrated that the thigh-worn activPAL inclinometer can distinguish between passive (no movement) and active (structured micromovements) standing using a machine learning model in lab-based and free-living environments. The predictive model extends beyond previous research by considering three-dimensional aspects of movement into the decision tree model. The ability to characterize these distinct postures is increasingly important to understand the physiological difference between passive and active standing. Notably, active standing, when stepping is not feasible, may be superior to passive standing for improving metabolic activity, reducing fatigue, and enhancing blood flow. Applied to free-living settings, active standing could help mitigate or attenuate some adverse cardiometabolic effects of stationary activity, thereby yielding positive cardiovascular outcomes. As standing gains recognition as a potentially important health behavior, distinguishing between passive and active standing offers a unique opportunity to clarify the health impacts of standing time, contributing to the evidence base. This evidence may contribute to more detailed activity guidelines and support public health initiatives to promote active standing. These advancements have the potential to enhance our understanding of standing behaviors’ health impacts and the possible divergent physiological effects of active versus passive standing.

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Identifying Multicomponent Patterns and Correlates of Accelerometry-Assessed Physical Behaviors Among Postmenopausal Women: The Women’s Health Accelerometry Collaboration

Kelly R. Evenson, Annie Green Howard, Fang Wen, Chongzhi Di, and I-Min Lee

Understanding the simultaneous patterning of accelerometer-measured physical activity and sedentary behavior (physical behaviors) can inform targeted interventions. This cross-sectional study described multicomponent patterns and correlates of physical behaviors using accelerometry among diverse postmenopausal women. The Women’s Health Accelerometry Collaboration combined two United States-based cohorts of postmenopausal women with similar accelerometry protocols and measures. Women (n = 22,612) aged 62–97 years enrolled in the Women’s Health Study (n = 16,742) and the Women’s Health Initiative Objective Physical Activity and Cardiovascular Health Study (n = 5,870) wore an ActiGraph GT3X+ accelerometer on their hip for 1 week. Awake-time accelerometry data were summarized using the accelerometer activity index into sedentary behavior, light (low, high), and moderate to vigorous physical activity. Latent class analysis was used to classify physical behavior hour-by-hour. Five unique patterns were identified with higher total volume of physical activity and lower sedentary behavior with each successively higher class number based on percentage of the day in physical activity/sedentary behavior per hour over 7 days. The percentage assignment was 16.3% Class 1, 33.9% Class 2, 20.2% Class 3, 18.0% Class 4, and 11.7% Class 5. The median posterior probabilities ranged from 0.99 to 1.00. Younger age, higher education and general health, normal weight, never smokers, weekly drinking, and faster self-reported walking speed generally had higher-class assignments compared with their counterparts. History of diabetes and cardiovascular disease generally had lower-class assignments compared with those without these conditions. These results can inform targeted interventions based on common patterns of physical behaviors by time of day among postmenopausal women.

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Device-Based Measurement of Office-Based Physical Activity and Sedentary Time: A Systematic Review

Noah Bongers, Genevieve N. Healy, George Thomas, and Bronwyn K. Clark

Background: The aim of this study was to systematically review the findings for validity, reliability, and acceptability of device-based measures of office-based physical activity and/or sedentary time in an office context to evaluate workplace interventions. Methods: The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Analysis guidelines. Five electronic databases (PubMed, EMBASE, CINAHL, Cochrane, and Web of Science) were searched (inception to December 2023). Keywords included population (e.g., workers), type of measure (e.g., device-based), measurement constructs (e.g., validity), context (e.g., office), and behavior (e.g., sitting). Two authors screened titles, abstracts, and full texts independently with disagreements resolved by a third author. Findings were reported using narrative synthesis, and COnsensus-based standards for the Selection of health status Measurement INstruments was used for quality assessment. Results: In total, 2,299 articles were identified, with 16 articles retained. These reported 21 measurement protocols (nine in free-living settings) assessing eight worn, four remote, and one combined method. Sixteen protocols assessed office sitting, with standing (n = 8), moving (n = 11), postural transitions (n = 7), and location (n = 2) also assessed. Participant sample sizes ranged from one to 42 (median = 13). Criterion validity was assessed in all 21 protocols, with lower limb–worn measures of sitting, and worn and remote measures of location reporting the highest validity/accuracy compared with the ground truth (good to excellent). Only two articles reported acceptability (good acceptability), with none reporting reliability. Conclusions: There is evidence of valid device-based measures of office behavior (particularly sitting and location of workers), but this has largely been obtained in laboratory settings and/or with small samples. Larger studies in more varied free-living settings, potentially using multiples sources of data and assessing acceptability, are required.

Free access

Effect of Accelerometer Cut-Points on Preschoolers’ Physical Activity and Sedentary Time: A Systematic Review and Meta-Analysis

Sophie M. Phillips, Kimberly A. Clevenger, Brianne A. Bruijns, Patricia Tucker, Leigh M. Vanderloo, Aidan Loh, Manahil Naveed, and Matthew Bourke

This systematic review and meta-analysis aimed to compare estimated levels of physical activity (PA) and sedentary time (ST) of preschool-aged children (3–5 years old) based on different published accelerometer cut-points used in this age group. Four electronic databases were searched to identify studies estimating levels of PA or ST (ST, light PA [LPA], and moderate to vigorous PA [MVPA]) using multiple accelerometer cut-points, in a sample of preschool-aged children. Data were extracted and risk of bias assessed for all included studies. Random-effects meta-analysis was used to estimate pooled effects for unique combinations of accelerometer cut-points for each outcome. Twenty-four studies, reporting on 18 unique samples, were included. Results demonstrated substantial variability in estimates of PA and ST across different cut-points, with significant differences in estimates of the behaviors between most cut-points. Few cut-points showed similarity; Evenson and Pate were some of the most similar for the assessment of PA and ST of young children. However, when calculating the differences in ST, LPA, and MVPA between the cut-points, the Evenson cut-point estimates approximately 60 min more LPA per day and the Pate 148CPM cut-points estimates 23 and 37 min more ST and MVPA each day, respectively. Given that these were the most similar estimates, this highlights the magnitude of differences between the accelerometer cut-points when estimating preschool-aged children’s movement behavior. This review provides an illustration on the limitations of accelerometer cut-points used to determine PA and ST of preschool-aged children; in that they often produce substantially different estimates. This review provides a compelling rationale as to why further research moving toward alternative data processing methodologies is required, including to identify an optimal approach to estimating movement behavior outcomes in young children that considers congruence with past and future research.

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Methodology for Assessing Infant (0–2 Years) Movement Using Accelerometers: A Scoping Review

Danae Dinkel, John P. Rech, Priyanka Chaudhary, Rama Krishna Thelagothoti, Jon Youn, Hesham Ali, Michaela Schenkelberg, and Brian Knarr

Measuring infants’ (0–2 years) physical activity is a growing area of research globally. Accelerometers have been widely used to measure older children’s and adults’ physical activity. An increasing number of studies have used accelerometers as a way to measure infant physical activity, which has resulted in the application of a variety of methods. The purpose of this scoping review is to synthesize the published literature on accelerometer methodology to measure daytime physical activity among infants (0–2 years). A systematic search of five online databases using carefully selected key terms was conducted to compile relevant literature. The results of the online database searches were screened for inclusion in the scoping review. In total, 105 articles met the inclusion criteria of using accelerometers to measure infants’ physical activity. The methodologies used in the included studies were categorized by age groups: <1 month, 1–6 months, >6–12 months, >12–18 months, >18–24 months, and longitudinal (i.e., multiple measurements taken across the previously mentioned age groups). Accelerometry methodologies (e.g., wear location, number of devices, device initialization) and study design qualities (e.g., outcome of interest and location of data collection) varied widely between and within the various age groups. Accelerometer brand or type of device demonstrated greatest variation across included studies. However, ActiGraph devices to measure physical activity within free-living environments were the most common. This review provides evidence of the need for researchers to ensure the methodology used is reported in detail in order to help develop methodology that can accurately assess infant daytime movement.

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Shaking Up Activity Counts: Assessing the Comparability of Accelerometers and Activity Count Computation

Hannah J. Coyle-Asbil, Bernadette Murphy, and Lori Ann Vallis

Accelerometers have been at the forefront of free-living activity capture for decades, and accordingly ActiGraph the largest distributor. Historically, limitations in data storage and battery power led to the use of summary metrics, which have been termed activity counts. Recently, ActiGraph publicly released their count-based algorithm, marking a notable development in the field. This study aimed to assess and compare activity counts generated through different processing techniques (ActiLife and open-source), filters that are available through ActiGraph count generation (normal- and low-frequency extension), and data from various ActiGraph models and GENEActiv devices. We evaluated ActiGraph GT3X+ (n = 8), ActiGraph wGT3X-BT (n = 10), ActiGraph GT9X (n = 8; primary and secondary sensors), OPAL (n = 6), and GENEActiv (n = 5), subjected to oscillations across their full dynamic range (0.005–8 G) using a multiaxis shaker table. Results indicated that the low-frequency extension produced significantly higher counts compared to the normal frequency across the devices and processing techniques. Notably, open-source counts (R and Python) were statistically equivalent to ActiLife-generated counts (p < .05) for the GT9X, wGT3X-BT, and the GT3X+. Overall, many of the counts generated by different ActiGraph models were statistically equivalent or had mean differences <5.03 counts. Conversely, the GENEActiv, OPAL, and GT9X secondary monitor exhibited significantly higher responses than the other ActiGraph models at higher frequencies with mean differences ranging from 55.50 to 104.91 counts. This study provides insights into accelerometer data processing methods and highlights the comparability of counts across different devices and techniques.

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A Walkthrough of ActiGraph Counts

Ali Neishabouri, Joe Nguyen, Matthew R. Patterson, Rakesh Pilkar, and Christine C. Guo

Activity counts have been used for over two decades with over 22,000 published scientific papers in public health and clinical research. ActiGraph recently released the algorithm for computing counts from raw accelerometer data as an open-source Python library, which is now ported by researchers to other languages, notably R. The current commentary presents historical overview of ActiGraph counts, and its development and evolution as a measure of physical activity. Further, we provide general recommendations on extracting counts from raw accelerometer data and discuss specific considerations with respect to device types, resampling, nonwear, axes orientations, and epoch length that may influence counts. Last, we provide a tutorial on how to use ActiGraph’s open-source Python library, agcounts, for consistent, accurate, and reproducible count. We expect this commentary will provide familiarity and transparency needed to adopt and produce activity counts in a consistent manner, allowing researchers to conduct statistical comparisons across multiple data sets and studies.

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Evaluating Step Counting Algorithms on Subsecond Wrist-Worn Accelerometry: A Comparison Using Publicly Available Data Sets

Lily Koffman and John Muschelli III

Background: Walking-based metrics, including step count and total time walking, are easily interpretable measures of physical activity. Algorithms can estimate steps from accelerometry, which increasingly is measured with accelerometers located on the wrist. However, many existing step counting algorithms have not been validated in free-living settings, exhibit high error rates, or cannot be used without proprietary software. We compare the performance of several existing open-source step counting algorithms on three publicly available data sets, including one with free-living data. Methods: We applied five open-source algorithms: Adaptive Empirical Pattern Transformation, Oak, Step Detection Threshold, Verisense, and stepcount, and one proprietary algorithm (ActiLife) to three publicly available data sets with ground truth step counts: Clemson Ped-Eval, Movement Analysis in Real-World Environments Using Accelerometers, and OxWalk. We evaluate F1 score, precision, recall, mean absolute percent error (MAPE), and mean bias for each algorithm and setting. Results: The machine learning-based stepcount algorithm exhibited the highest F1 score (0.89 ± 0.11) and lowest MAPE (8.6 ± 9%) across all data sets and had the best, or comparable, F1 scores and MAPE in each individual data set. All algorithms performed worse with respect to both F1 score and MAPE in free-living compared with regular walking scenarios, and stepcount and Verisense were most sensitive to sampling frequency of input data. Conclusion: Machine learning-based algorithms, including stepcount, are a promising avenue for step counting. More free-living accelerometry data sets with ground truth step counts are needed for testing, validation, and continued refinement of algorithms.

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.