Literature DB >> 26451900

An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations.

Femina H A Beevi1, Jorge Miranda2, Christian F Pedersen1, Stefan Wagner1.   

Abstract

BACKGROUND: Pedometers are considered desirable devices for monitoring physical activity. Two population groups of interest include patients having undergone surgery in the lower extremities or who are otherwise weakened through disease, medical treatment, or surgery procedures, as well as the slow walking senior population. For these population groups, pedometers must be able to perform reliably and accurately at slow walking speeds. The objectives of this study were to evaluate the step count accuracy of three commercially available pedometers, the Yamax (Tokyo, Japan) Digi-Walker(®) SW-200 (YM), the Omron (Kyoto, Japan) HJ-720 (OM), and the Fitbit (San Francisco, CA) Zip (FB), at slow walking speeds, specifically at 1, 2, and 3 km/h, and to raise awareness of the necessity of focusing research on step-counting devices and algorithms for slow walking populations.
MATERIALS AND METHODS: Fourteen participants 29.93 ±4.93 years of age were requested to walk on a treadmill at the three specified speeds, in four trials of 100 steps each. The devices were worn by the participants on the waist belt. The pedometer counts were recorded, and the error percentage was calculated.
RESULTS: The error rate of all three evaluated pedometers decreased with the increase of speed: at 1 km/h the error rates varied from 87.11% (YM) to 95.98% (FB), at 2 km/h the error rates varied from 17.27% (FB) to 46.46% (YM), and at 3 km/h the error rates varied from 22.46% (YM) to a slight overcount of 0.70% (FB).
CONCLUSIONS: It was observed that all the evaluated devices have high error rates at 1 km/h and mixed error rates at 2 km/h, and at 3 km/h the error rates are the smallest of the three assessed speeds, with the OM and the FB having a slight overcount. These results show that research on pedometers' software and hardware should focus more on accurate step detection at slow walking speeds.

Entities:  

Keywords:  e-health; home health monitoring; rehabilitation; sensor technology; telehealth

Mesh:

Year:  2015        PMID: 26451900     DOI: 10.1089/tmj.2015.0120

Source DB:  PubMed          Journal:  Telemed J E Health        ISSN: 1530-5627            Impact factor:   3.536


  31 in total

1.  Adding Pedometers to Pulmonary Rehabilitation Did Not Result in Greater Physical Activity. An Important Answer, but What Was the Question?

Authors:  Carlos H Martinez
Journal:  Am J Respir Crit Care Med       Date:  2017-05-15       Impact factor: 21.405

Review 2.  The Digital Outcome Measure.

Authors:  Adam B Cohen; Simon C Mathews
Journal:  Digit Biomark       Date:  2018-09-21

3.  Using Commercial Physical Activity Trackers for Health Promotion Research: Four Case Studies.

Authors:  Gabrielle Turner-McGrievy; Danielle E Jake-Schoffman; Camelia Singletary; Marquivieus Wright; Anthony Crimarco; Michael D Wirth; Nitin Shivappa; Trisha Mandes; Delia Smith West; Sara Wilcox; Clemens Drenowatz; Andrew Hester; Matthew J McGrievy
Journal:  Health Promot Pract       Date:  2018-04-04

Review 4.  Toward Harmonized Treadmill-Based Validation of Step-Counting Wearable Technologies: A Scoping Review.

Authors:  Christopher C Moore; Aston K McCullough; Elroy J Aguiar; Scott W Ducharme; Catrine Tudor-Locke
Journal:  J Phys Act Health       Date:  2020-07-11

5.  Use of a Wearable Activity Device in Rural Older Obese Adults: A Pilot Study.

Authors:  John A Batsis; John A Naslund; Lydia E Gill; Rebecca K Masutani; Nayan Agarwal; Stephen J Bartels
Journal:  Gerontol Geriatr Med       Date:  2016-11-21

6.  Evaluation of Commercial Self-Monitoring Devices for Clinical Purposes: Results from the Future Patient Trial, Phase I.

Authors:  Soren Leth; John Hansen; Olav W Nielsen; Birthe Dinesen
Journal:  Sensors (Basel)       Date:  2017-01-22       Impact factor: 3.576

Review 7.  Smartphones and e-tablets in perioperative medicine.

Authors:  Frederic Michard
Journal:  Korean J Anesthesiol       Date:  2017-09-28

8.  Windows Into Human Health Through Wearables Data Analytics.

Authors:  Daniel Witt; Ryan Kellogg; Michael Snyder; Jessilyn Dunn
Journal:  Curr Opin Biomed Eng       Date:  2019-01-28

9.  Cardiac Patients' Walking Activity Determined by a Step Counter in Cardiac Telerehabilitation: Data From the Intervention Arm of a Randomized Controlled Trial.

Authors:  Charlotte Thorup; John Hansen; Mette Grønkjær; Jan Jesper Andreasen; Gitte Nielsen; Erik Elgaard Sørensen; Birthe Irene Dinesen
Journal:  J Med Internet Res       Date:  2016-04-04       Impact factor: 5.428

10.  Pedometer use and self-determined motivation for walking in a cardiac telerehabilitation program: a qualitative study.

Authors:  Charlotte Brun Thorup; Mette Grønkjær; Helle Spindler; Jan Jesper Andreasen; John Hansen; Birthe Irene Dinesen; Gitte Nielsen; Erik Elgaard Sørensen
Journal:  BMC Sports Sci Med Rehabil       Date:  2016-08-18
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