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  • Author: Alex Rowlands x
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Charlotte L. Edwardson, Melanie Davies, Kamlesh Khunti, Thomas Yates and Alex V. Rowlands

Purpose: To compare steps counts recorded by consumer activity trackers when worn on the non-dominant and dominant wrist against a waist-worn pedometer during free-living. Methods: 30 participants wore six consumer wrist-worn physical activity trackers and a pedometer. On day 1, three trackers were worn on the non-dominant wrist (ND) and three on the dominant (D) wrist. On day 2 trackers were worn on the opposite wrist. On both days, a pedometer (New-Lifestyles NL-800) was worn at the waist. Mean absolute percent error (MAPE) and the Bland-Altman method assessed tracker agreement with the pedometer. Repeated measures ANOVA examined whether MAPEs were significantly different between wrist trackers (i.e., brand comparison) and between wrist location (i.e., non-dominant vs. dominant). Results: MAPEs were higher for the D wrist trackers. Five out of six trackers on the D wrist over-counted, while four out of six trackers on the ND wrist under-counted. MAPE errors were significant (p ≤ .001) between trackers but not across wrist location (p > .05). Fitbit Flex_ND, Mi Band_ND and D, Garmin Vivofit3_D and Jawbone UP24_D had a mean bias of <500 steps. 95% limits of agreement were narrowest for Mi Band_ND. Conclusions: Tracker agreement with the waist-worn pedometer varied widely but trackers on the ND wrist had better agreement. The Mi Band was the most comparable to the pedometer.

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Alex V. Rowlands, John M. Schuna Jr., Victoria H. Stiles and Catrine Tudor-Locke


Previous research has reported peak vertical acceleration and peak loading rate thresholds beneficial to bone mineral density (BMD). Such thresholds are difficult to translate into meaningful recommendations for physical activity. Cadence (steps/min) is a more readily interpretable measure of ambulatory activity.


To examine relationships between cadence, peak vertical acceleration and peak loading rate during ambulation and identify the cadence associated with previously reported bone-beneficial thresholds for peak vertical acceleration (4.9 g) and peak loading rate (43 BW/s).


Ten participants completed 8 trials each of: slow walking, brisk walking, slow running, and fast running. Acceleration data were captured using a GT3×+ accelerometer worn at the hip. Peak loading rate was collected via a force plate.


Strong relationships were identified between cadence and peak vertical acceleration (r = .96, P < .05) and peak loading rate (r = .98, P < .05). Regression analyses indicated cadences of 157 ± 12 steps/min (2.6 ± 0.2 steps/s) and 122 ± 10 steps/min (2.0 ± 0.2 steps/s) corresponded with the 4.9 g peak vertical acceleration and 43 BW/s peak loading rate thresholds, respectively.


Cadences ≥ 2.0 to 2.6 steps/s equate to acceleration and loading rate thresholds related to bone health. Further research is needed to investigate whether the frequency of daily occurrences of this cadence is associated with BMD.

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Jairo H. Migueles, Alex V. Rowlands, Florian Huber, Séverine Sabia and Vincent T. van Hees

Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.

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Alex V. Rowlands, Tatiana Plekhanova, Tom Yates, Evgeny M. Mirkes, Melanie Davies, Kamlesh Khunti and Charlotte L. Edwardson

Introduction: To capitalize on the increasing availability of accelerometry data for epidemiological research it is desirable to compare and/or pool data from surveys worldwide. This study aimed to establish whether free-living physical activity outcomes can be considered equivalent between three research-grade accelerometer brands worn on the dominant and non-dominant wrist. Of prime interest were the average acceleration (ACC) and the intensity gradient (IG). These two metrics describe the volume and intensity of the complete activity profile; further, they are comparable across populations making them ideal for comparing and/or pooling activity data. Methods: Forty-eight adults wore a GENEActiv, Axivity, and ActiGraph on both wrists for up to 7-days. Data were processed using open-source software (GGIR) to generate physical activity outcomes, including ACC and IG. Agreement was assessed using pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC). Results: ACC was equivalent between brands when measured at the non-dominant wrist (ICC ≥ 0.93), but approximately 10% higher when measured at the dominant wrist (GENEActiv and Axivity only, ICC ≥ 0.83). The IG was equivalent irrespective of monitor brand or wrist (ICC ≥ 0.88). After adjusting ACC measured at the dominant wrist by −10% (GENEActiv and Axivity only), ACC was also within (or marginally outside) the 10% equivalence zone for all monitor pairings. Conclusion: If average acceleration is decreased by 10% for studies deploying monitors on the dominant wrist (GENEActiv and Axivity only), ACC and IG may be suitable for comparing and/or collating physical activity outcomes across accelerometer datasets, regardless of monitor brand and wrist.

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Rawan Hashem, Juan P. Rey-López, Mark Hamer, Anne McMunn, Peter H. Whincup, Christopher G. Owen, Alex Rowlands and Emmanuel Stamatakis

Background: There is only scarce number of studies available describing the lifestyle of adolescents living in Arab countries. Hence, we described physical activity (PA) and sedentary behaviors patterns among Kuwaiti adolescents and the associations with parental education. Methods: Cross-sectional data from 435 adolescents (201 boys and 234 girls) were collected from the Study of Health and Activity among Adolescents in Kuwait conducted between 2012 and 2013. Outcome variables included PA (ActiGraph GT1M accelerometers) and sedentary behaviors. Exposure variable was parental education. Descriptive and multiple logistic regression analyses were used to examine the association between parental education and outcome variables. Results: Total sedentary time (minutes per day) was higher in girls [568.2 (111.6)] than in boys [500.0 (102.0)], whereas boys accumulated more minutes in light, moderate, and vigorous PA (all Ps ≤ .001). In total, 3.4% of adolescents spent ≥60 minutes per day of moderate to vigorous PA (by accelerometry), while only 21% met the screen time guidelines. Low/medium maternal education was associated with a higher odds of exceeding screen time guidelines (odds ratio = 2.09; 95% confidence interval, 1.09–4.02). Conclusions: Most Kuwaiti adolescents in this sample were physically inactive and exceeded screen time guidelines. Objective PA was not socially patterned, yet an inverse association between maternal education and screen time behaviors was found.