Literature DB >> 23584403

Classification accuracy of the wrist-worn gravity estimator of normal everyday activity accelerometer.

Whitney A Welch1, David R Bassett, Dixie L Thompson, Patty S Freedson, John W Staudenmayer, Dinesh John, Jeremy A Steeves, Scott A Conger, Tyrone Ceaser, Cheryl A Howe, Jeffer E Sasaki, Eugene C Fitzhugh.   

Abstract

PURPOSE: The purpose of this study was to determine whether the published left-wrist cut points for the triaxial Gravity Estimator of Normal Everyday Activity (GENEA) accelerometer are accurate for predicting intensity categories during structured activity bouts.
METHODS: A convenience sample of 130 adults wore a GENEA accelerometer on their left wrist while performing 14 different lifestyle activities. During each activity, oxygen consumption was continuously measured using the Oxycon mobile. Statistical analysis used Spearman's rank correlations to determine the relationship between measured and estimated intensity classifications. Cross tabulations were constructed to show the under- or overestimation of misclassified intensities. One-way χ2 tests were used to determine whether the intensity classification accuracy for each activity differed from 80%.
RESULTS: For all activities, the GENEA accelerometer-based physical activity monitor explained 41.1% of the variance in energy expenditure. The intensity classification accuracy was 69.8% for sedentary activities, 44.9% for light activities, 46.2% for moderate activities, and 77.7% for vigorous activities. The GENEA correctly classified intensity for 52.9% of observations when all activities were examined; this increased to 61.5% with stationary cycling removed.
CONCLUSIONS: A wrist-worn triaxial accelerometer has modest-intensity classification accuracy across a broad range of activities when using the cut points of Esliger et al. Although the sensitivity and the specificity are less than those reported by Esliger et al., they are generally in the same range as those reported for waist-worn, uniaxial accelerometer cut points.

Entities:  

Mesh:

Year:  2013        PMID: 23584403      PMCID: PMC3778030          DOI: 10.1249/MSS.0b013e3182965249

Source DB:  PubMed          Journal:  Med Sci Sports Exerc        ISSN: 0195-9131            Impact factor:   5.411


  28 in total

Review 1.  Validity and reliability issues in objective monitoring of physical activity.

Authors:  D R Bassett
Journal:  Res Q Exerc Sport       Date:  2000-06       Impact factor: 2.500

2.  Estimation of energy expenditure using CSA accelerometers at hip and wrist sites.

Authors:  A M Swartz; S J Strath; D R Bassett; W L O'Brien; G A King; B E Ainsworth
Journal:  Med Sci Sports Exerc       Date:  2000-09       Impact factor: 5.411

3.  Validity of accelerometry for the assessment of moderate intensity physical activity in the field.

Authors:  D Hendelman; K Miller; C Baggett; E Debold; P Freedson
Journal:  Med Sci Sports Exerc       Date:  2000-09       Impact factor: 5.411

4.  Comparison of MTI accelerometer cut-points for predicting time spent in physical activity.

Authors:  S J Strath; D R Bassett; A M Swartz
Journal:  Int J Sports Med       Date:  2003-05       Impact factor: 3.118

Review 5.  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

6.  Physical activity classification using the GENEA wrist-worn accelerometer.

Authors:  Shaoyan Zhang; Alex V Rowlands; Peter Murray; Tina L Hurst
Journal:  Med Sci Sports Exerc       Date:  2012-04       Impact factor: 5.411

Review 7.  Assessment of physical activity in epidemiologic research: problems and prospects.

Authors:  R E LaPorte; H J Montoye; C J Caspersen
Journal:  Public Health Rep       Date:  1985 Mar-Apr       Impact factor: 2.792

8.  Assessment of energy expenditure for physical activity using a triaxial accelerometer.

Authors:  C V Bouten; K R Westerterp; M Verduin; J D Janssen
Journal:  Med Sci Sports Exerc       Date:  1994-12       Impact factor: 5.411

9.  Estimation of energy expenditure by a portable accelerometer.

Authors:  H J Montoye; R Washburn; S Servais; A Ertl; J G Webster; F J Nagle
Journal:  Med Sci Sports Exerc       Date:  1983       Impact factor: 5.411

Review 10.  Best practices for using physical activity monitors in population-based research.

Authors:  Charles E Matthews; Maria Hagströmer; David M Pober; Heather R Bowles
Journal:  Med Sci Sports Exerc       Date:  2012-01       Impact factor: 5.411

View more
  21 in total

1.  Sit-to-Stand Transition Reveals Acute Fall Risk in Activities of Daily Living.

Authors:  Tomislav Pozaic; Ulrich Lindemann; Anna-Karina Grebe; Wilhelm Stork
Journal:  IEEE J Transl Eng Health Med       Date:  2016-12-01       Impact factor: 3.316

2.  Physical Activity Following Positive Airway Pressure Treatment in Adults With and Without Obesity and With Moderate-Severe Obstructive Sleep Apnea.

Authors:  Yuan Feng; David Maislin; Brendan T Keenan; Thorarinn Gislason; Erna S Arnardottir; Bryndis Benediktsdottir; Julio A Chirinos; Raymond R Townsend; Bethany Staley; Francis M Pack; Andrea Sifferman; Allan I Pack; Samuel T Kuna
Journal:  J Clin Sleep Med       Date:  2018-10-15       Impact factor: 4.062

3.  Cross-validation of waist-worn GENEA accelerometer cut-points.

Authors:  Whitney A Welch; David R Bassett; Patty S Freedson; Dinesh John; Jeremy A Steeves; Scott A Conger; Tyrone G Ceaser; Cheryl A Howe; Jeffer E Sasaki
Journal:  Med Sci Sports Exerc       Date:  2014-09       Impact factor: 5.411

4.  Performance of Activity Classification Algorithms in Free-Living Older Adults.

Authors:  Jeffer Eidi Sasaki; Amanda M Hickey; John W Staudenmayer; Dinesh John; Jane A Kent; Patty S Freedson
Journal:  Med Sci Sports Exerc       Date:  2016-05       Impact factor: 5.411

Review 5.  Wearable motion sensors to continuously measure real-world physical activities.

Authors:  Bruce H Dobkin
Journal:  Curr Opin Neurol       Date:  2013-12       Impact factor: 5.710

Review 6.  Assessing Daily Physical Activity in Older Adults: Unraveling the Complexity of Monitors, Measures, and Methods.

Authors:  Jennifer A Schrack; Rachel Cooper; Annemarie Koster; Eric J Shiroma; Joanne M Murabito; W Jack Rejeski; Luigi Ferrucci; Tamara B Harris
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2016-03-08       Impact factor: 6.053

7.  Accelerometer-derived physical activity and risk of atrial fibrillation.

Authors:  Shaan Khurshid; Lu-Chen Weng; Mostafa A Al-Alusi; Jennifer L Halford; Julian S Haimovich; Emelia J Benjamin; Ludovic Trinquart; Patrick T Ellinor; David D McManus; Steven A Lubitz
Journal:  Eur Heart J       Date:  2021-07-01       Impact factor: 35.855

Review 8.  Assessment of Physical Activity in Adults Using Wrist Accelerometers.

Authors:  Fangyu Liu; Amal A Wanigatunga; Jennifer A Schrack
Journal:  Epidemiol Rev       Date:  2022-01-14       Impact factor: 4.280

9.  A Validation Study of the Web-Based Physical Activity Questionnaire Active-Q Against the GENEA Accelerometer.

Authors:  Stephanie Erika Bonn; Patrick Bergman; Ylva Trolle Lagerros; Arvid Sjölander; Katarina Bälter
Journal:  JMIR Res Protoc       Date:  2015-07-16

10.  Association between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors.

Authors:  Séverine Sabia; Vincent T van Hees; Martin J Shipley; Michael I Trenell; Gareth Hagger-Johnson; Alexis Elbaz; Mika Kivimaki; Archana Singh-Manoux
Journal:  Am J Epidemiol       Date:  2014-02-04       Impact factor: 4.897

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.