Significant advances have been made in the measurement of physical activity in youth over the past decade. Monitors and protocols promote very high compliance, both night and day, and raw measures are available rather than “black box” counts. Consequently, many surveys and studies worldwide now assess children’s physical behaviors (physical activity, sedentary behavior, and sleep) objectively 24 hours a day, 7 days a week using accelerometers. The availability of raw acceleration data in many of these studies is both an opportunity and a challenge. The richness of the data lends itself to the continued development of innovative metrics, whereas the removal of proprietary outcomes offers considerable potential for comparability between data sets and harmonizing data. Using comparable physical activity outcomes could lead to improved precision and generalizability of recommendations for children’s present and future health. The author will discuss 2 strategies that he believes may help ensure comparability between studies and maximize the potential for data harmonization, thereby helping to capitalize on the growing body of accelerometer data describing children’s physical behaviors.
Alex V. Rowlands
Alex V. Rowlands
The total activity volume performed is an overall measure that takes into account the frequency, intensity, and duration of activities performed. The importance of considering total activity volume is shown by recent studies indicating that light physical activity (LPA) and intermittent moderate-to-vigorous physical activity (MVPA) have health benefits. Accelerometer-derived total activity counts (TAC) per day from a waist-worn accelerometer can serve as a proxy for an individual’s total activity volume. The purpose of this study was to develop age- and gender-specific percentiles for daily TAC, minutes of MVPA, and minutes of LPA in U.S. youth ages 6-19 y.
Data from the 2003-2006 NHANES waist-worn accelerometer component were used in this analysis. The sample was composed of youth aged 6-19 years with at least 4 d of ≥10 hr of accelerometer wear time (N = 3698). MVPA was defined using age specific cutpoints as the total number of minutes at ≥4 metabolic equivalents (METs) for youth 6-17 y or minutes with ≥2020 counts for youth 18-19 y. LPA was defined as the total number of minutes between 100 counts and the MVPA threshold. TAC/d, MVPA, and LPA were averaged across all valid days.
For males in the 50th percentile, the median activity level was 441,431 TAC/d, with 53 min/d of MVPA and 368 min/d of LPA. The median level of activity for females was 234,322 TAC/d, with 32 min/d of MVPA and 355 min/d of LPA.
Population referenced TAC/d percentiles for U.S. youth ages 6-19 y provide a novel means of characterizing the total activity volume performed by children and adolescents.
Alex V. Rowlands
2016 has been an exciting year for research in physical activity, inactivity and health. Recognition of the importance of all physical behaviors (physical activity, sedentary time and sleep) across the 24-hr day continues to grow. Notable advances have included: applications of recent methodological innovations that account for the codependence of the behaviors in the finite 24-hr period showing that the balance of these behaviors is associated with health; methodological innovations focusing on the classification of behaviors and/or quantification of the 24-hr diurnal activity pattern; and a series of systematic reviews that helped provide the evidence base for the release of the innovative 24-hr movement guidelines earlier this year. This commentary focuses on just two of these papers: the first by Goldsmith and colleagues who demonstrate a new statistical method that exploits the time series nature of accelerometer data facilitating new insights into time-specific determinants of children’s activity patterns and associations with health; the second by Tremblay and colleagues who describe the evidence base for associations between each physical behavior and children’s health, the emerging evidence base for associations between the balance of behaviors and health, and development of the world’s first 24-hr movement guidelines.
Alex V. Rowlands
It is well known that physical activity is important for children’s current and future mental and physical health. Despite this, there appears to be a secular decline in children’s physical activity (Cameron et al. ; Dalene et al. ). Furthermore, (frustratingly) interventions aiming to increase children’s physical activity have limited success (Metcalf et al. ), demonstrating a need for more information on the amenability of activity levels to change.
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.
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.
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.
Tatiana Plekhanova, Alex V. Rowlands, Tom Yates, Andrew Hall, Emer M. Brady, Melanie Davies, Kamlesh Khunti and Charlotte L. Edwardson
Introduction: This study examined the equivalency of sleep estimates from Axivity, GENEActiv, and ActiGraph accelerometers worn on the nondominant and dominant wrists and with and without using a sleep log to guide the algorithm. Methods: 47 young adults wore an Axivity, GENEActiv, and ActiGraph accelerometer continuously on both wrists for 4–7 days. Sleep time, sleep window, sleep efficiency, sleep onset, and wake time were produced using the open-source software (GGIR). For each outcome, agreement between accelerometer brands, dominant and nondominant wrists, and with and without use of a sleep log, was examined using pairwise 95% equivalence tests (±10% equivalence zone) and intraclass correlation coefficients (ICCs), with 95% confidence intervals and limits of agreement. Results: All sleep outcomes were within a 10% equivalence zone irrespective of brand, wrist, or use of a sleep log. ICCs were poor to good for sleep time (ICCs ≥ .66) and sleep window (ICCs ≥ .56). Most ICCs were good to excellent for sleep efficiency (ICCs ≥ .73), sleep onset (ICCs ≥ .88), and wake time (ICCs ≥ .87). There were low levels of mean bias; however, there were wide 95% limits of agreement for sleep time, sleep window, sleep onset, and wake time outcomes. Sleep time (up to 25 min) and sleep window (up to 29 min) outcomes were higher when use of the sleep log was not used. Conclusion: The present findings suggest that sleep outcomes from the Axivity, GENEActiv, and ActiGraph, when analyzed identically, are comparable across studies with different accelerometer brands and wear protocols at a group level. However, caution is advised when comparing studies that differ on sleep log availability.
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.