Purpose: To examine the effect of activity monitor placement on daily step counts when monitors are worn at different positions on the wrist/forearm and the hip. Methods: Participants (N = 18) wore eight different models (four wrist and four hip models) across four days. Each day, one hip and one wrist model were selected, and four identical monitors of each model were worn on the right hip and the non-dominant wrist/forearm, respectively, during all waking hours. Step counts of each monitor were compared to the same model worn in the referent position (wrist: proximal to ulnar styloid process; hip: midline of thigh). Percent of referent steps and mean difference between observed and referent positions were computed. Significant differences in steps between positions for each method were determined using one-way repeated measures ANOVAs. For significant main effects, pairwise comparisons with Bonferroni corrections were used to determine which positions were significantly different. Results: All wrist methods showed a significant main effect for placement (p < .05) and alternate positions were 1–16% lower than the referent position. For hip methods, only the Omron HJ-325 differed across positions (p < .05), but differences were among non-referent positions and all were within ±2% of steps recorded by the referent position. Conclusions: Researchers should be aware that positions that deviate from the manufacturer’s recommended position at the wrist could influence step counts. Of all hip methods examined, the Omron had a significant placement effect which did not constitute a practical difference.
Susan Park, Lindsay P. Toth, Scott E. Crouter, Cary M. Springer, Robert T. Marcotte and David R. Bassett
Stacy N. Scott, Cary M. Springer, Jennifer F. Oody, Michael S. McClanahan, Brittany D. Wiseman, Tyler J. Kybartas and Dawn P. Coe
Previous progressive aerobic cardiovascular endurance run (PACER) equations were developed to estimate peak oxygen consumption (VO2peak) from data collected during treadmill running. No equation has been developed using VO2peak assessed during the PACER. Purpose: To develop and validate a prediction equation to estimate VO2peak from the PACER in 10- to 15-year-olds. Methods: A sample of 163 youth were recruited to develop (n = 101) and validate (n = 62) a prediction equation. VO2peak was measured using a portable metabolic unit. Regression analysis yielded a prediction equation that included laps, body mass index, and interaction between sex and age. Correlations and repeated-measures analysis of variances were used to compare the measured and estimated VO2peak from the new Scott et al equation and 2 commonly used FitnessGram™ (Mahar et al 2011 and Mahar et al 2018) equations, and the impact of sex on predicted VO2peak. Results: Predicted VO2peak from the Mahar et al 2011 and 2018 equations was significantly lower compared with measured values, and the Scott et al prediction was not different. The Mahar et al 2018 equation tended to overestimate VO2peak in males but worked well for females. The Mahar et al 2011 and Scott et al equations revealed no sex differences. Conclusions: The Scott et al equation resulted in a more accurate estimate of VO2peak, performing equally well for both sexes.
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
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.
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.