Comparison of accelerometer cut points to estimate physical activity in US adults - PubMed (original) (raw)
Comparative Study
Comparison of accelerometer cut points to estimate physical activity in US adults
Kathleen B Watson et al. J Sports Sci. 2014.
Abstract
The purpose of this study was (1) to describe physical activity prevalence, categorised according to the 2008 Physical Activity Guidelines for Americans (2008 Guidelines), using different accelerometer cut points and (2) to examine physical activity prevalence patterns by reported cut points across selected characteristics. Cut points from 9 studies were used to estimate physical activity prevalence in a national adult sample (n = 6547). Estimates were stratified by validation study activity protocols used to derive cut points--ambulatory (walking/running) and lifestyle activities (e.g. gardening, housework, walking). Results showed that the prevalence of meeting the 2008 Guidelines ranged from 6.3% to 98.3% overall and was lower for cut points derived from ambulatory (median = 11.5%, range = 6.3-27.4%) compared to lifestyle (median = 77.2%, range = 60.6-98.3%) protocols. Prevalence patterns across protocols differed for age, but were similar for other characteristics. In conclusion, prevalence of meeting the 2008 Guidelines varied widely, indicating that choice of cut point had an impact on prevalence. To generate future accelerometer cut points one may consider developing cut points for demographic subgroups using a variety of lifestyle physical activities.
Similar articles
- Validation of Cut-Points for Evaluating the Intensity of Physical Activity with Accelerometry-Based Mean Amplitude Deviation (MAD).
Vähä-Ypyä H, Vasankari T, Husu P, Mänttäri A, Vuorimaa T, Suni J, Sievänen H. Vähä-Ypyä H, et al. PLoS One. 2015 Aug 20;10(8):e0134813. doi: 10.1371/journal.pone.0134813. eCollection 2015. PLoS One. 2015. PMID: 26292225 Free PMC article. - Generation and validation of ActiGraph GT3X+ accelerometer cut-points for assessing physical activity intensity in older adults. The OUTDOOR ACTIVE validation study.
Bammann K, Thomson NK, Albrecht BM, Buchan DS, Easton C. Bammann K, et al. PLoS One. 2021 Jun 3;16(6):e0252615. doi: 10.1371/journal.pone.0252615. eCollection 2021. PLoS One. 2021. PMID: 34081715 Free PMC article. - Measuring moderate-intensity walking in older adults using the ActiGraph accelerometer.
Barnett A, van den Hoek D, Barnett D, Cerin E. Barnett A, et al. BMC Geriatr. 2016 Dec 8;16(1):211. doi: 10.1186/s12877-016-0380-5. BMC Geriatr. 2016. PMID: 27931188 Free PMC article. - Everything you wanted to know about selecting the "right" Actigraph accelerometer cut-points for youth, but…: a systematic review.
Kim Y, Beets MW, Welk GJ. Kim Y, et al. J Sci Med Sport. 2012 Jul;15(4):311-21. doi: 10.1016/j.jsams.2011.12.001. Epub 2012 Feb 4. J Sci Med Sport. 2012. PMID: 22306372 Review. - Adherence to the World Health Organization's physical activity recommendation in preschool-aged children: a systematic review and meta-analysis of accelerometer studies.
Bourke M, Haddara A, Loh A, Carson V, Breau B, Tucker P. Bourke M, et al. Int J Behav Nutr Phys Act. 2023 Apr 26;20(1):52. doi: 10.1186/s12966-023-01450-0. Int J Behav Nutr Phys Act. 2023. PMID: 37101226 Free PMC article. Review.
Cited by
- A comparative analysis of 24-hour movement behaviors features using different accelerometer metrics in adults: Implications for guideline compliance and associations with cardiometabolic health.
Willems I, Verbestel V, Dumuid D, Calders P, Lapauw B, De Craemer M. Willems I, et al. PLoS One. 2024 Sep 17;19(9):e0309931. doi: 10.1371/journal.pone.0309931. eCollection 2024. PLoS One. 2024. PMID: 39288135 Free PMC article. - Foundations of Exercise and Physical Activity Research.
Brellenthin AG, Sirotiak Z. Brellenthin AG, et al. Curr Top Behav Neurosci. 2024;67:3-22. doi: 10.1007/7854_2024_488. Curr Top Behav Neurosci. 2024. PMID: 39080239 - The Role of Wearable Sensors to Monitor Physical Activity and Sleep Patterns in Older Adult Inpatients: A Structured Review.
Bate GL, Kirk C, Rehman RZU, Guan Y, Yarnall AJ, Del Din S, Lawson RA. Bate GL, et al. Sensors (Basel). 2023 May 18;23(10):4881. doi: 10.3390/s23104881. Sensors (Basel). 2023. PMID: 37430796 Free PMC article. Review. - Physical activity and cognitive function: A comparison of rural and urban breast cancer survivors.
Page LL, Kahn CJ, Severson J, Kramer AF, McAuley E, Ehlers DK. Page LL, et al. PLoS One. 2023 Apr 13;18(4):e0284189. doi: 10.1371/journal.pone.0284189. eCollection 2023. PLoS One. 2023. PMID: 37053178 Free PMC article. - Comparison of different software for processing physical activity measurements with accelerometry.
Verhoog S, Gubelmann C, Bano A, Muka T, Franco OH, Marques-Vidal P. Verhoog S, et al. Sci Rep. 2023 Feb 18;13(1):2879. doi: 10.1038/s41598-023-29872-7. Sci Rep. 2023. PMID: 36806337 Free PMC article.
References
- Atienza AA, Moser RP, Perna F, Dodd K, Ballard-Barbash R, Troiano RP, Berrigan D. Self-reported and objectively measured activity related to biomarkers using NHANES. Med Sci Sports Exerc. 2011;43(5):815–821. - PubMed
- Bassett DR, Jr, Ainsworth BE, Swartz AM, Strath SJ, O'Brien WL, King GA. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc. 2000;32(9 Suppl):S471–S480. - PubMed
- Bowles HR. Measurement of active and sedentary behaviors: closing the gaps in self-report methods. J Phys Act Health. 2012;9(Suppl 1):S1–S4. - PubMed
- Bowles HR, Subar AF, Matthews CE, Troiano RP, Dodd K, Midthune D, Park Y. Designing measurement error investigations of self-reported and objectively monitored physical activity: The IDATA Study and MEASURE; Paper presented at the 8th International Conference on Diet and Activity Methods; Rome, Itlay. 2012. May 14–17, 2012.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical