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      “What Is a Step?” Differences in How a Step Is Detected among Three Popular Activity Monitors That Have Impacted Physical Activity Research

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          Abstract

          (1) Background: This study compared manually-counted treadmill walking steps from the hip-worn DigiwalkerSW200 and OmronHJ720ITC, and hip and wrist-worn ActiGraph GT3X+ and GT9X; determined brand-specific acceleration amplitude (g) and/or frequency (Hz) step-detection thresholds; and quantified key features of the acceleration signal during walking. (2) Methods: Twenty participants (Age: 26.7 ± 4.9 years) performed treadmill walking between 0.89-to-1.79 m/s (2–4 mph) while wearing a hip-worn DigiwalkerSW200, OmronHJ720ITC, GT3X+ and GT9X, and a wrist-worn GT3X+ and GT9X. A DigiwalkerSW200 and OmronHJ720ITC underwent shaker testing to determine device-specific frequency and amplitude step-detection thresholds. Simulated signal testing was used to determine thresholds for the ActiGraph step algorithm. Steps during human testing were compared using bias and confidence intervals. (3) Results: The OmronHJ720ITC was most accurate during treadmill walking. Hip and wrist-worn ActiGraph outputs were significantly different from the criterion. The DigiwalkerSW200 records steps for movements with a total acceleration of ≥1.21 g. The OmronHJ720ITC detects a step when movement has an acceleration ≥0.10 g with a dominant frequency of ≥1 Hz. The step-threshold for the ActiLife algorithm is variable based on signal frequency. Acceleration signals at the hip and wrist have distinctive patterns during treadmill walking. (4) Conclusions: Three common research-grade physical activity monitors employ different step-detection strategies, which causes variability in step output.

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          Effects of aerobic training, resistance training, or both on glycemic control in type 2 diabetes: a randomized trial.

          Previous trials have evaluated the effects of aerobic training alone and of resistance training alone on glycemic control in type 2 diabetes, as assessed by hemoglobin A1c values. However, none could assess incremental effects of combined aerobic and resistance training compared with either type of exercise alone. To determine the effects of aerobic training alone, resistance training alone, and combined exercise training on hemoglobin A1c values in patients with type 2 diabetes. Randomized, controlled trial. 8 community-based facilities. 251 adults age 39 to 70 years with type 2 diabetes. A negative result on a stress test or clearance by a cardiologist, and adherence to exercise during a 4-week run-in period, were required before randomization. Aerobic training, resistance training, or both types of exercise (combined exercise training). A sedentary control group was included. Exercise training was performed 3 times weekly for 22 weeks (weeks 5 to 26 of the study). The primary outcome was the change in hemoglobin A1c value at 6 months. Secondary outcomes were changes in body composition, plasma lipid values, and blood pressure. The absolute change in the hemoglobin A1c value in the combined exercise training group compared with the control group was -0.51 percentage point (95% CI, -0.87 to -0.14) in the aerobic training group and -0.38 percentage point (CI, -0.72 to -0.22) in the resistance training group. Combined exercise training resulted in an additional change in the hemoglobin A1c value of -0.46 percentage point (CI, -0.83 to -0.09) compared with aerobic training alone and -0.59 percentage point (CI, -0.95 to -0.23) compared with resistance training alone. Changes in blood pressure and lipid values did not statistically significantly differ among groups. Adverse events were more common in the exercise groups. The generalizability of the results to patients who are less adherent to exercise programs is uncertain. The participants were not blinded, and the total duration of exercise was greater in the combined exercise training group than in the aerobic and resistance training groups. Either aerobic or resistance training alone improves glycemic control in type 2 diabetes, but the improvements are greatest with combined aerobic and resistance training. ClinicalTrials.gov registration number: NCT00195884.
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            Validity of 10 electronic pedometers for measuring steps, distance, and energy cost.

            This study examined the effects of walking speed on the accuracy and reliability of 10 pedometers: Yamasa Skeletone (SK), Sportline 330 (SL330) and 345 (SL345), Omron (OM), Yamax Digiwalker SW-701 (DW), Kenz Lifecorder (KZ), New Lifestyles 2000 (NL), Oregon Scientific (OR), Freestyle Pacer Pro (FR), and Walk4Life LS 2525 (WL). Ten subjects (33 +/- 12 yr) walked on a treadmill at various speeds (54, 67, 80, 94, and 107 m x min-1) for 5-min stages. Simultaneously, an investigator determined steps by a hand counter and energy expenditure (kcal) by indirect calorimetry. Each brand was measured on the right and left sides. Correlation coefficients between right and left sides exceeded 0.81 for all pedometers except OR (0.76) and SL345 (0.57). Most pedometers underestimated steps at 54 m x min-1, but accuracy for step counting improved at faster speeds. At 80 m x min-1 and above, six models (SK, OM, DW, KZ, NL, and WL) gave mean values that were within +/- 1% of actual steps. Six pedometers displayed the distance traveled. Most of them estimated mean distance to within +/- 10% at 80 m x min-1 but overestimated distance at slower speeds and underestimated distance at faster speeds. Eight pedometers displayed kilocalories, but except for KZ and NL, it is unclear whether this should reflect net or gross kilocalories. If one assumes they display net kilocalories, the general trend was an overestimation of kilocalories at every speed. If one assumes they display gross kilocalorie, then seven of the eight pedometers were accurate to within +/-30% at all speeds. In general, pedometers are most accurate for assessing steps, less accurate for assessing distance, and even less accurate for assessing kilocalories.
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              Pedometer measures of free-living physical activity: comparison of 13 models.

              The purpose of this study was to compare the step values of multiple brands of pedometers over a 24-h period. The following 13 electronic pedometers were assessed in the study: Accusplit Alliance 1510 (AC), Freestyle Pacer Pro (FR), Colorado on the Move (CO), Kenz Lifecorder (KZ), New-Lifestyles NL-2000 (NL), Omron HJ-105 (OM), Oregon Scientific PE316CA (OR), Sportline 330 (SL330) and 345 (SL345), Walk4Life LS 2525 (WL), Yamax Skeletone EM-180 (SK), Yamax Digi-Walker SW-200 (YX200), and the Yamax Digi-Walker SW-701 (YX701). Ten males (39.5 +/- 16.6 yr, mean +/- SD) and 10 females (43.3 +/- 16.6 yr) ranging in BMI from 19.8 to 35.4 kg.m-2 wore two pedometers for a 24-h period. The criterion pedometer (YX200) was worn on the left side of the body, and a comparison pedometer was worn on the right. Steps counted by each device were recorded at the end of the day for each of the thirteen pedometers. Subjects took an average of 9244 steps.d-1. The KZ, YX200, NL, YX701, and SL330 yielded mean values that were not significantly different from the criterion. The FR, AC, SK, CO, and SL345 significantly underestimated steps (P < 0.05) and the WL, OM, and OR significantly overestimated steps (P < 0.05) when compared with the criterion. In addition, some pedometers underestimated by 25% whereas others overestimated by 45%. The KZ, YX200, NL, and YX701 appear to be suitable for most research purposes. Given the potential for pedometers in physical activity research, it is necessary that there be consistency across studies in the measurement of "steps per day."
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                15 April 2018
                April 2018
                : 18
                : 4
                : 1206
                Affiliations
                [1 ]Department of Health Sciences, Northeastern University, Boston, MA 02115, USA; arguello.d@ 123456husky.neu.edu
                [2 ]Department of Kinesiology, Recreation, and Sport Studies, University of Tennessee, Knoxville, TN 37996 USA; amorto16@ 123456vols.utk.edu (A.M.); dbassett@ 123456utk.edu (D.B.)
                [3 ]KAL Research/Consulting, Denver, CO 80206, USA; katelyden6@ 123456gmail.com
                Author notes
                [* ]Correspondence: d.john@ 123456neu.edu ; Tel.: 617-373-5695
                Author information
                https://orcid.org/0000-0003-2177-8148
                Article
                sensors-18-01206
                10.3390/s18041206
                5948774
                29662048
                ac5855c0-d2a5-4783-ac26-2d9465f2ab14
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 February 2018
                : 12 April 2018
                Categories
                Article

                Biomedical engineering
                step-detection,actigraph,pedometer,acceleration,physical activity
                Biomedical engineering
                step-detection, actigraph, pedometer, acceleration, physical activity

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