An external focus of attention has been shown to improve balance measures during static postural tasks. The purpose of this study was to evaluate the effects of different attentional focus strategies in response to a perturbation while performing a dynamic balance task. Participants (n = 29) performed a dynamic balance task that consisted of stepping onto an uneven surface while attempting to continuously walk in a straight line. The orientation of the surface was unknown to the participants. During the external focus conditions, participants were instructed to focus on the surface they walked on. During the internal focus conditions, participants were instructed to focus on keeping their body over their feet. Analyses revealed that the external focus condition exhibited significantly less lateral displacement from the intended walking line following the perturbation (4.56 ± 2.56 cm) than the internal (5.61 ± 2.89 cm, p = .019) and baseline (6.07 ± 2.6 cm, p = .004) conditions. These data indicate that participants were more resilient to the perturbing surface when their attention was focused on external information. Thus, participants were able to respond to a perturbation more effectively when utilizing an external focus of attention.
Scott W. Ducharme and Will F.W. Wu
Scott W. Ducharme and Richard E.A. van Emmerik
In human locomotion, the magnitude of gait variability is a strong predictor of fall risk and frailty due to aging and disease. Beyond variability magnitude, the past two decades have provided emerging alternative methodologies for studying biological variability. Specifically, coordination variability has been found to be critically important within a healthy, adaptive system. While many activities aim to minimize end-point variability, greater coordination variability indicates a more flexible system, and is greater in experts compared to novices, or healthy compared to diseased individuals. Finally, variability structure (i.e., fractal dynamics) may describe the overall adaptive capacity of the locomotor system. We provide empirical support that fractal dynamics are associated with step length symmetry during challenging split-belt treadmill walking. Individuals whose fractal scaling approached 1/f fractal scaling during constrained walking also exhibited the best gait adaptability performance. Importantly, this relation between fractality and gait adaptability was not observed in unperturbed preferred speed walking.
Christopher C. Moore, Aston K. McCullough, Elroy J. Aguiar, Scott W. Ducharme, and Catrine Tudor-Locke
Background: The authors conducted a scoping review as a first step toward establishing harmonized (ie, consistent and compatible), empirically based best practices for validating step-counting wearable technologies. Purpose: To catalog studies validating step-counting wearable technologies during treadmill ambulation. Methods: The authors searched PubMed and SPORTDiscus in August 2019 to identify treadmill-based validation studies that employed the criterion of directly observed (including video recorded) steps and cataloged study sample characteristics, protocol details, and analytical procedures. Where reported, speed- and wear location–specific mean absolute percentage error (MAPE) values were tabulated. Weighted median MAPE values were calculated by wear location and a 0.2-m/s speed increment. Results: Seventy-seven eligible studies were identified: most had samples averaging 54% (SD = 5%) female and 27 (5) years of age, treadmill protocols consisting of 3 to 5 bouts at speeds of 0.8 (0.1) to 1.6 (0.2) m/s, and reported measures of bias. Eleven studies provided MAPE values at treadmill speeds of 1.1 to 1.8 m/s; their weighted median MAPE values were 7% to 11% for wrist-worn, 1% to 4% for waist-worn, and ≤1% for thigh-worn devices. Conclusions: Despite divergent study methodologies, the authors identified common practices and summarized MAPE values representing device step-count accuracy during treadmill walking. These initial empirical findings should be further refined to ultimately establish harmonized best practices for validating wearable technologies.
Elroy J. Aguiar, Zachary R. Gould, Scott W. Ducharme, Chris C. Moore, Aston K. McCullough, and Catrine Tudor-Locke
Background: A walking cadence of ≥100 steps/min corresponds to minimally moderate intensity, absolutely defined as ≥3 metabolic equivalents (METs). This threshold has primarily been calibrated during treadmill walking. There is a need to determine the classification accuracy of this cadence threshold to predict intensity during overground walking. Methods: In this laboratory-based cross-sectional investigation, participants (N = 75, 49.3% women, age 21–40 y) performed a single 5-minute overground (hallway) walking trial at a self-selected preferred pace. Steps accumulated during each trial were hand tallied and converted to cadence (steps/min). Oxygen uptake was measured using indirect calorimetry and converted to METs. The classification accuracy (sensitivity, specificity, overall accuracy, and positive predictive value) of ≥100 steps/min to predict ≥3 METs was calculated. Results: A cadence threshold of ≥100 steps/min yielded an overall accuracy (combined sensitivity and specificity) of 73.3% for predicting minimally moderate intensity. Moreover, for individuals walking at a cadence ≥100 steps/min, the probability (positive predictive value) of achieving minimally moderate intensity was 80.3%. Conclusions: Although primarily developed using treadmill-based protocols, a cadence threshold of ≥100 steps/min for young adults appears to be a valid heuristic value (evidence-based, rounded, practical) associated with minimally moderate intensity during overground walking performed at a self-selected preferred pace.
Dylan C. Perry, Christopher C. Moore, Colleen J. Sands, Elroy J. Aguiar, Zachary R. Gould, Catrine Tudor-Locke, and Scott W. Ducharme
Background: While previous studies indicate an auditory metronome can entrain cadence (in steps per minute), music may also evoke prescribed cadences and metabolic intensities. Purpose: To determine how modulating the tempo of a single commercial song influences adults’ ability to entrain foot strikes while walking and how this entrainment affects metabolic intensity. Methods: Twenty healthy adults (10 men and 10 women; mean [SD]: age 23.7 [2.7] y, height 172.8 [9.0] cm, mass 71.5 [16.2] kg) walked overground on a large circular pathway for six 5-min conditions; 3 self-selected speeds (slow, normal, and fast); and 3 trials listening to a song with its tempo modulated to 80, 100, and 125 beats per minute. During music trials, participants were instructed to synchronize their step timing with the music tempo. Cadence was measured via direct observation, and metabolic intensity (metabolic equivalents) was assessed using indirect calorimetry. Results: Participants entrained their cadences to the music tempos (mean absolute percentage error = 5.3% [5.8%]). Entraining to a music tempo of 100 beats per minute yielded ≥3 metabolic equivalents in 90% of participants. Trials with music entrainment exhibited greater metabolic intensity compared with self-paced trials (repeated-measures analysis of variance, F 1,19 = 8.05, P = .01). Conclusion: This study demonstrates the potential for using music to evoke predictable metabolic intensities.
Scott W. Ducharme, Jongil Lim, Michael A. Busa, Elroy J. Aguiar, Christopher C. Moore, John M. Schuna Jr., Tiago V. Barreira, John Staudenmayer, Stuart R. Chipkin, and Catrine Tudor-Locke
Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply does not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and nonambulatory activities. Seventy-five adults (21–39 years) completed seven simulated activities of daily living (e.g., sitting, vacuuming, folding laundry) and an incremental treadmill protocol from 0.22 to 2.2 m/s. Directly observed steps were hand-tallied. Participants wore GENEActiv and ActiGraph accelerometers, one of each on their waist and on their nondominant wrist. Raw acceleration (g) signals from the anterior–posterior, medial–lateral, vertical, and vector magnitude directions were assessed separately for each device. Signals were demeaned across all activities and band-pass filtered (0.25, 2.5 Hz). Steps were detected via peak picking, with optimal thresholds (i.e., minimized absolute error from accumulated hand counted) determined by iterating minimum acceleration values to detect steps. Step counts were converted into cadence (steps/minute), and k-fold cross-validation quantified error (root mean squared error [RMSE]). We report optimal thresholds for use of either device on the waist (threshold = 0.0267g) and wrist (threshold = 0.0359g) using the vector magnitude signal. These thresholds yielded low error for the waist (RMSE < 173 steps, ≤2.28 steps/min) and wrist (RMSE < 481 steps, ≤6.47 steps/min) across all activities, and outperformed ActiLife’s proprietary algorithm (RMSE = 1,312 and 2,913 steps, 17.29 and 38.06 steps/min for the waist and wrist, respectively). The thresholds reported herein provide a simple, transparent framework for step detection using accelerometers during treadmill ambulation and activities of daily living for waist- and wrist-worn locations.