Purpose: To examine the effect of brief, intermittent stepping bouts on step counts from 10 physical activity monitors (PAMs). Methods: Adults (N = 21; M ± SD, 26 ± 9.0 yr) wore four PAMs on the wrist (Garmin Vivofit 2, Fitbit Charge, Withings Pulse Ox, and ActiGraph wGT3X-BT [AG]), four on the hip (Yamax Digi-Walker SW-200 [YX], Fitbit Zip, Omron HJ-322U, and AG), and two on the ankle (StepWatch [SW] with default and modified settings). AG data were processed with and without the low frequency extension (AGL) and with the Moving Average Vector Magnitude algorithm. In Part 1 (five trials), walking bouts were varied (4–12 steps) and rest intervals were held constant (10 s). In Part 2 (six trials), walking bouts were held constant (4 steps) and rest intervals were varied (1–10 s). Percent of hand-counted steps and mean absolute percentage error were calculated. One sample t-test was used to compare percent of hand-counted steps to 100%. Results: In Parts 1 and 2, the SWdefault, SWmodified, YX, and AGLhip captured within 10% of hand-counted steps across nearly all conditions. In Part 1, estimates of most methods improved as the number of steps per bout increased. In Part 2, estimates of most methods decreased as the rest duration increased. Conclusion: Most methods required stepping bouts of >6–10 consecutive steps to record steps. Rest intervals of 1–2 seconds were sufficient to break up walking bouts in many methods. The requirement for several consecutive steps in some methods causes an underestimation of steps in brief, intermittent bouts.
Lindsay P. Toth, Susan Park, Whitney L. Pittman, Damla Sarisaltik, Paul R. Hibbing, Alvin L. Morton, Cary M. Springer, Scott E. Crouter and David R. Bassett
Monika Uys, Susan Bassett, Catherine E. Draper, Lisa Micklesfield, Andries Monyeki, Anniza de Villiers, Estelle V. Lambert and the HAKSA 2016 Writing Group
We present results of the 2016 Healthy Active Kids South Africa (HAKSA) Report Card on the current status of physical activity (PA) and nutrition in South African youth. The context in which we interpret the findings is that participation in PA is a fundamental human right, along with the right to “attainment of the highest standard of health.”
The HAKSA 2016 Writing Group was comprised of 33 authorities in physical education, exercise science, nutrition, public health, and journalism. The search strategy was based on peer-reviewed manuscripts, dissertations, and ‘gray’ literature. The core PA indicators are Overall Physical Activity Level; Organized Sport Participation; Active and Outdoor Play; Active Transportation; Sedentary Behaviors; Family and Peer Influences; School; Community and the Built Environment; and National Government Policy, Strategies, and Investment. In addition, we reported on Physical Fitness and Motor Proficiency separately. We also reported on nutrition indicators including Overweight and Under-nutrition along with certain key behaviors such as Fruit and Vegetable Intake, and policies and programs including School Nutrition Programs and Tuck Shops. Data were extracted and grades assigned after consensus was reached. Grades were assigned to each indicator ranging from an A, succeeding with a large majority of children and youth (81% to 100%); B, succeeding with well over half of children and youth (61% to 80%); C, succeeding with about half of children and youth (41% to 60%); D, succeeding with less than half but some children and youth (21% to 40%); and F, succeeding with very few children and youth (0% to 20%); INC is inconclusive.
Overall PA levels received a C grade, as we are succeeding with more than 50% of children meeting recommendations. Organized Sports Participation also received a C, and Government Policies remain promising, receiving a B. Screen time and sedentary behavior were a major concern. Under- and over-weight were highlighted and, as overweight is on the rise, received a D grade.
In particular, issues of food security, obesogenic environments, and access to activity-supportive environments should guide social mobilization downstream and policy upstream. There is an urgent need for practice-based evidence based on evaluation of existing, scaled up interventions.
Susan Park, Lindsay P. Toth, Paul R. Hibbing, Cary M. Springer, Andrew S. Kaplan, Mckenzie D. Feyerabend, Scott E. Crouter and David R. Bassett
It has become common to wear physical activity monitors on the wrist to estimate steps per day, but few studies have considered step differences between monitors worn on the dominant and non-dominant wrists. Purpose: The purpose of this study was to compare four step counting methods on the dominant versus non-dominant wrist using the Fitbit Charge (FC) and ActiGraph GT9X (GT9X) across all waking hours of one day. Methods: Twelve participants simultaneously wore two monitors (FC and GT9X) on each wrist during all waking hours for an entire day. GT9X data were analyzed with three step counting methods: ActiLife algorithm with default filter (AG-noLFE), ActiLife algorithm with low-frequency extension (AG-LFE), and the Moving Average Vector Magnitude (AG-MAVM) algorithm. A 2-way repeated measures ANOVA (method × wrist) was used to compare step counts. Results: There was a significant main effect for wrist placement (F(1,11) = 11.81, p = .006), with the dominant wrist estimating an average of 1,253 more steps than the non-dominant wrist. Steps differed between the dominant and non-dominant wrist for three of the step methods: AG-noLFE (1,327 steps), AG-LFE (2,247 steps), AG-MAVM (825 steps), and approached statistical significance for FC (613 steps). No significant method x wrist placement interaction was found (F(3,9) = 2.62, p = .115). Conclusion: Findings suggest that for step counting algorithms, it may be important to consider the placement of wrist-worn monitors since the dominant wrist location tended to yield greater step estimates. Alternatively, standardizing the placement of wrist-worn monitors could help to reduce the differences in daily step counts across studies.