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.
Alex V. Rowlands
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
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
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, Pedro F. Saint-Maurice, and Philippa M. Dall
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.
Andrea Ramirez Varela, Robert Sallis, Alex V. Rowlands, and James F. Sallis
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.