Literature DB >> 33670507

The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings.

Sunku Kwon1, Neng Wan2, Ryan D Burns1, Timothy A Brusseau1, Youngwon Kim3,4, Santosh Kumar5, Emre Ertin6, David W Wetter7, Cho Y Lam7, Ming Wen8, Wonwoo Byun1.   

Abstract

MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland-Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes.

Entities:  

Keywords:  MotionSense HRV; accelerometer; mobile health; physical activity; sedentary behavior; validity

Mesh:

Year:  2021        PMID: 33670507      PMCID: PMC7922785          DOI: 10.3390/s21041411

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  66 in total

Review 1.  Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement.

Authors:  Merryn J Mathie; Adelle C F Coster; Nigel H Lovell; Branko G Celler
Journal:  Physiol Meas       Date:  2004-04       Impact factor: 2.833

2.  The effect of social desirability and social approval on self-reports of physical activity.

Authors:  Swann Arp Adams; Charles E Matthews; Cara B Ebbeling; Charity G Moore; Joan E Cunningham; Jeanette Fulton; James R Hebert
Journal:  Am J Epidemiol       Date:  2005-02-15       Impact factor: 4.897

3.  Estimating Energy Expenditure with ActiGraph GT9X Inertial Measurement Unit.

Authors:  Paul R Hibbing; Samuel R Lamunion; Andrew S Kaplan; Scott E Crouter
Journal:  Med Sci Sports Exerc       Date:  2018-05       Impact factor: 5.411

4.  Moving Forward with Backward Compatibility: Translating Wrist Accelerometer Data.

Authors:  Alex V Rowlands; Dylan P Cliff; Stuart J Fairclough; Lynne M Boddy; Tim S Olds; Gaynor Parfitt; Rob J Noonan; Samantha J Downs; Zoe R Knowles; Michael W Beets
Journal:  Med Sci Sports Exerc       Date:  2016-11       Impact factor: 5.411

5.  A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations.

Authors:  Kate Lyden; Sarah L Kozey; John W Staudenmeyer; Patty S Freedson
Journal:  Eur J Appl Physiol       Date:  2010-09-15       Impact factor: 3.078

6.  More than black and white: differences in predictors of obesity among Native Hawaiian/Pacific Islanders and European Americans.

Authors:  Alok Madan; Olga G Archambeau; Vanessa A Milsom; Rachel L Goldman; Jeffery J Borckardt; Anouk L Grubaugh; Peter W Tuerk; B Christopher Frueh
Journal:  Obesity (Silver Spring)       Date:  2012-01-28       Impact factor: 5.002

7.  Are wearable heart rate measurements accurate to estimate aerobic energy cost during low-intensity resistance exercise?

Authors:  Victor M Reis; Jeferson M Vianna; Tiago M Barbosa; Nuno Garrido; Jose Vilaça Alves; André L Carneiro; Felipe J Aidar; Jefferson Novaes
Journal:  PLoS One       Date:  2019-08-22       Impact factor: 3.240

8.  Feasibility, Acceptability, and Clinical Effectiveness of a Technology-Enabled Cardiac Rehabilitation Platform (Physical Activity Toward Health-I): Randomized Controlled Trial.

Authors:  Jomme Claes; Véronique Cornelissen; Clare McDermott; Niall Moyna; Nele Pattyn; Nils Cornelis; Anne Gallagher; Ciara McCormack; Helen Newton; Alexandra Gillain; Werner Budts; Kaatje Goetschalckx; Catherine Woods; Kieran Moran; Roselien Buys
Journal:  J Med Internet Res       Date:  2020-02-04       Impact factor: 5.428

9.  Age group comparability of raw accelerometer output from wrist- and hip-worn monitors.

Authors:  Maria Hildebrand; Vincent T VAN Hees; Bjorge Hermann Hansen; Ulf Ekelund
Journal:  Med Sci Sports Exerc       Date:  2014-09       Impact factor: 5.411

10.  Big data analytics for preventive medicine.

Authors:  Muhammad Imran Razzak; Muhammad Imran; Guandong Xu
Journal:  Neural Comput Appl       Date:  2019-03-16       Impact factor: 5.102

View more
  2 in total

Review 1.  Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review.

Authors:  Marco Giurgiu; Irina Timm; Marlissa Becker; Steffen Schmidt; Kathrin Wunsch; Rebecca Nissen; Denis Davidovski; Johannes B J Bussmann; Claudio R Nigg; Markus Reichert; Ulrich W Ebner-Priemer; Alexander Woll; Birte von Haaren-Mack
Journal:  JMIR Mhealth Uhealth       Date:  2022-06-09       Impact factor: 4.947

2.  An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches.

Authors:  Emre Ertin; Nithin Sugavanam; August F Holtyn; Kenzie L Preston; Jeremiah W Bertz; Lisa A Marsch; Bethany McLeman; Dikla Shmueli-Blumberg; Julia Collins; Jacqueline S King; Jennifer McCormack; Udi E Ghitza
Journal:  Front Psychiatry       Date:  2021-06-24       Impact factor: 4.157

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.