A Consensus Method for Estimating Moderate to Vigorous Physical Activity Levels in Adults Using Wrist-Worn Accelerometers

被引:0
作者
Clevenger, Kimberly A. [1 ]
Mcnarry, Melitta A. [2 ]
Mackintosh, Kelly A. [2 ]
Pfeiffer, Karin A. [3 ]
Nelson, M. Benjamin [4 ,5 ]
Bock, Joshua M. [4 ,6 ]
Imboden, Mary T. [4 ,7 ,8 ]
Kaminsky, Leonard A. [4 ,9 ]
Montoye, Alexander H. K. [4 ,10 ]
机构
[1] Utah State Univ, Dept Kinesiol & Hlth Sci, Logan, UT 84322 USA
[2] Swansea Univ, Appl Sports Technol Exercise & Med A STEM Res Ctr, Swansea, Wales
[3] Michigan State Univ, Dept Kinesiol, E Lansing, MI USA
[4] Ball State Univ, Clin Exercise Physiol Program, Muncie, IN USA
[5] Wake Forest Univ, Sch Med, Dept Internal Med, Sect Cardiovasc Med, Winston Salem, NC USA
[6] Mayo Clin, Dept Cardiovasc Dis, Rochester, MN USA
[7] Providence St Joseph Hlth, Providence Heart Inst, Ctr Cardiovasc Analyt Res & Data Sci, Portland, OR USA
[8] Hlth Enhancement Res Org, Waconia, MN USA
[9] Hlth Living Pandem Event Protect Network, Chicago, IL USA
[10] Alma Coll, Integrat Physiol & Hlth Sci Dept, Alma, MI USA
关键词
Keywords : harmonization; surveillance; raw acceleration; cut-points; machine learning; SEDENTARY BEHAVIOR; ENERGY-EXPENDITURE; VALIDATION; CALIBRATION; HIP; MONITORS;
D O I
10.1123/jmpb.2024-0013
中图分类号
G8 [体育];
学科分类号
04 ; 0403 ;
摘要
Inconsistency in the calculation of time spent in moderate to vigorous physical activity (MVPA) limits interstudy comparability and interpretation of surveillance data. This study assesses whether combining multiple individual methods results in a more accurate estimate of MVPA, while considering the influence of device brand and wear location. Participants (n= 30, age= 49.2 +/- 19.5 years) wore two accelerometers (GENEActiv, ActiGraph) on each wrist during two laboratory visits. Individual classification methods (11 for left wrist, eight for right wrist) estimated minutes of MVPA using three approaches (cut-point, two-regression, and machine learning), two types of input (count and raw), and five epoch lengths (1, 5, 15, 30, and 60 s). The consensus estimate was calculated as the mean or median (due to skew) across all individual estimates. No individual or consensus estimates were statistically equivalent to direct observation (mean 38.2 min), with 81%-95% of individual methods overestimating MVPA. The best-performing individual methods were raw acceleration cut-points, with a bias of -3.2 to 2.4 min across devices and wrists. Correlation coefficients between individual methods and the criterion were .35-.71 for the left and .12-.67 for the right wrist, compared to .65-.70 and .58-.66 for consensus methods, respectively. Correlations between device brands were .23-.99 for individual methods and .70-.86 for consensus methods, while correlations between locations were .55-.86 and .73-.87, respectively. Better methods are required for estimating MVPA from wrist-worn accelerometers given the consistent overestimation of MVPA observed. While a consensus method for wrist-worn data was not able to fully resolve these issues, it improves interwrist or interbrand comparability.
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页码:1 / 11
页数:11
相关论文
共 47 条
[1]  
[Anonymous], 2022, Published cut-points and how to use them in GGIR
[2]   Calibration and Validation of Wearable Monitors [J].
Bassett, David R., Jr. ;
Rowlands, Alex ;
Trost, Stewart G. .
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, 2012, 44 :S32-S38
[3]   US Population-referenced Percentiles for Wrist-Worn Accelerometer-derived Activity [J].
Belcher, Britni R. ;
Wolff-Hughes, Dana L. ;
Dooley, Erin E. ;
Staudenmayer, John ;
Berrigan, David ;
Eberhardt, Mark S. ;
Troiano, Richard P. .
MEDICINE & SCIENCE IN SPORTS & EXERCISE, 2021, 53 (11) :2455-2464
[4]   Equating accelerometer estimates among youth: The Rosetta Stone 2 [J].
Brazendale, Keith ;
Beets, Michael W. ;
Bornstein, Daniel B. ;
Moore, Justin B. ;
Pate, Russell R. ;
Weaver, Robert G. ;
Falck, Ryan S. ;
Chandler, Jessica L. ;
Andersen, Lars B. ;
Anderssen, Sigmund A. ;
Cardon, Greet ;
Cooper, Ashley ;
Davey, Rachel ;
Froberg, Karsten ;
Hallal, Pedro C. ;
Janz, Kathleen F. ;
Kordas, Katarzyna ;
Kriemler, Susi ;
Puder, Jardena J. ;
Reilly, John J. ;
Salmon, Jo ;
Sardinha, Luis B. ;
Timperio, Anna ;
van sluijs, Esther M. F. .
JOURNAL OF SCIENCE AND MEDICINE IN SPORT, 2016, 19 (03) :242-249
[5]   Sampling frequency affects the processing of Actigraph raw acceleration data to activity counts [J].
Brond, Jan Christian ;
Arvidsson, Daniel .
JOURNAL OF APPLIED PHYSIOLOGY, 2016, 120 (03) :362-369
[6]   Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry [J].
Chowdhury, Alok Kumar ;
Tjondronegoro, Dian ;
Chandran, Vinod ;
Trost, Stewart G. .
MEDICINE & SCIENCE IN SPORTS & EXERCISE, 2017, 49 (09) :1965-1973
[7]   A consensus method for estimating physical activity levels in adults using accelerometry [J].
Clevenger, Kimberly A. ;
Mackintosh, Kelly A. ;
McNarry, Melitta A. ;
Pfeiffer, Karin A. ;
Nelson, M. Benjamin ;
Bock, Joshua M. ;
Imboden, Mary T. ;
Kaminsky, Leonard A. ;
Montoye, Alexander H. K. .
JOURNAL OF SPORTS SCIENCES, 2022, 40 (21) :2393-2400
[8]   Impact of ActiGraph sampling rate on free-living physical activity measurement in youth [J].
Clevenger, Kimberly A. ;
Brond, Jan C. ;
Mackintosh, Kelly A. ;
Pfeiffer, Karin A. ;
Montoye, Alexander H. K. ;
McNarry, Melitta A. .
PHYSIOLOGICAL MEASUREMENT, 2022, 43 (10)
[9]   Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines [J].
Clevenger, Kimberly A. ;
Montoye, Alexander H. K. ;
van Camp, Cailyn A. ;
Strath, Scott J. ;
Pfeiffer, Karin A. .
PHYSIOLOGICAL MEASUREMENT, 2022, 43 (09)
[10]   Number of Days Required to Estimate Habitual Activity Using Wrist-Worn GENEActiv Accelerometer: A Cross-Sectional Study [J].
Dillon, Christina B. ;
Fitzgerald, Anthony P. ;
Kearney, Patricia M. ;
Perry, Ivan J. ;
Rennie, Kirsten L. ;
Kozarski, Robert ;
Phillips, Catherine M. .
PLOS ONE, 2016, 11 (05)