Associations of exercise snacks with sarcopenia and all-cause mortality: A national cohort study
College of Physical Education, Yangzhou University, Yangzhou, Jiangsu, 225127, China
Changzhou Vocational Institute of Textile and Garment, Changzhou, Jiangsu, 213164, China
Biology of Sport, Vol. 43, 2026: 1615-1639
Data publikacji online: 2026/07/27
Article file
- Nakayama JY, Mouhanna F, Whitfield GP, et al. Accelerometer-derived total activity and cardiometabolic health factors among U.S. adults, NHANES 2011–2014. Med Sci Sports Exerc. 2025; 57(12):2787–2794. doi:10.1249 /MSS.0000000000003827
- Aguiar EJ, Turner DT, Pleuss JD, Zheng P, Benitez CJ, Ducharme SW. Daily and peak monitor independent movement summary (MIMS) values associated with metabolic syndrome: NHANES 2011–12 and 2013–14. Scand J Med Sci Sports. 2024; 34(11):e14762. doi: 10.1111/sms.14762.
- Rodríguez MÁ, Quintana-Cepedal M, Cheval B, Thøgersen-Ntoumani C, Crespo I, Olmedillas H. Effect of exercise snacks on fitness and cardiometabolic health in physically inactive individuals: systematic review and meta-analysis. Br J Sports Med. 2026; 60(2):133–141. doi: 10.1136/bjsports-2025-110027.
- Islam H, Gibala MJ, Little JP. Exercise snacks: A novel strategy to improve cardiometabolic health. Exerc Sport Sci Rev. 2022; 50(1):31–37. doi: 10.1249 /JES.0000000000000275.
- Leroux A, Cui E, Smirnova E, Muschelli J, Schrack JA, Crainiceanu CM. NHANES 2011–2014: Objective physical activity is the strongest predictor of all-cause mortality. Med Sci Sports Exerc. 2024; 56(10):1926–1934. doi: 10.1249/ MSS.0000000000003497.
- Zheng P, Pleuss JD, Turner DS, Ducharme SW, Aguiar EJ. Dose–response association between physical activity (daily MIMS, peak 30-minute MIMS) and cognitive function among older adults: NHANES 2011–2014. J Gerontol A Biol Sci Med Sci. 2023; 78(2):286–291. doi: 10.1093/gerona/glac076.
- Ducharme SW, Pleuss JD, Turner DS, Zheng P, Adandom II, Aguiar EJ. Normative peak physical activity values for monitor-independent movement summary units: National health and nutrition examination survey 2011–2014. J Phys Act Health. 2025; 22(10):1297–1306. doi: 10.1123/jpah.2025-0182.
- Welk GJ, Lamoureux NR, Zeng C, et al. Equating NHANES monitor-based physical activity to self-reported methods to enhance ongoing surveillance efforts. Med Sci Sports Exerc. 2023; 55(6):1034–1043. doi: 10.1249 /MSS.0000000000003123.
- John D, Tang Q, Albinali F, Intille S. An open-source monitor-independent movement summary for accelerometer data processing. J Meas Phys Behav. 2019; 2(4):268–281. doi: 10.1123 /jmpb.2018-0068.
- Doncaster P, Spake R. Correction for bias in meta-analysis of little-replicated studies. Methods Ecol Evol. 2018; 9(3):634–644. doi: 10.1111/2041 -210X.12927.
- Cukier RI. Variance of a weighted mean force obtained using the weighted histogram analysis method. J Phys Chem B. 2013; 117(47):14785–14796. doi: 10.1021/jp407956c.
- Beretta L, Tetek J. Better sum estimation via weighted sampling. ACM Trans Algorithms. 2021; 20:1–33. doi: 10.1145/3650030.
- Markatou M. Mixture models, robustness, and the weighted likelihood methodology. Biometrics. 2000; 56(2):483–486. doi: 10.1111 /j.0006-341X.2000.00483.x.
- Cheah J, Roldán J, Ciavolino E, Ting H, Ramayah T. Sampling weight adjustments in partial least squares structural equation modeling: Guidelines and illustrations. Total Qual Manag Bus Excell. 2020; 32:1594–1613. doi: 10 .1080/14783363.2020.1754 125.
- Slade E, Naylor M. A fair comparison of tree-based and parametric methods in multiple imputation by chained equations. Stat Med. 2020; 39:1156–1166. doi: 10.1002 /sim.8468.
- Deng Y, Chang C, Ido M, Long Q. Multiple imputation for general missing data patterns in the presence of high-dimensional data. Sci Rep. 2016; 6:21689. doi: 10.1038/srep21689.
- Resche-Rigon M, White I. Multiple imputation by chained equations for systematically and sporadically missing multilevel data. Stat Methods Med Res. 2018; 27:1634–1649. doi: 10.1177/0962280216666564.
- Badhiwala J, Karmur B, Wilson J. Propensity score matching: A powerful tool for analyzing observational nonrandomized data. Clin Spine Surg. 2021; 34(1):22–24. doi: 10.1097 /BSD.0000000000001055.
- Schober P, Vetter T. Propensity score matching in observational research. Anesth Analg. 2020; 130(6):1616–1617. doi: 10.1213 /ANE.0000000000004770.
- Elze MC, Gregson J, Baber U, et al. Comparison of propensity score methods and covariate adjustment: evaluation in 4 cardiovascular studies. J Am Coll Cardiol. 2017 Jan 24; 69(3):345–357. doi: 10.1016/j.jacc.2016.10.060.
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