The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Identifying Children’s Nocturnal Sleep Using 24-h Waist Accelerometry
Open accessAbstract
PURPOSE: The purposes of this study were 1) to add layers and features to a previously published fully automated algorithm designed to identify children's nocturnal sleep and to exclude episodes of nighttime nonwear/wakefulness and potentially misclassified daytime sleep episodes and 2) to validate this refined sleep algorithm (RSA) against sleep logs. METHODS: Forty-five fourth-grade school children (51% female) participants were asked to log evening bedtime and morning wake time and wear an ActiGraph GT3X+ (ActiGraph LLC, Pensacola, FL) accelerometer at their waist for seven consecutive days. Accelerometers were distributed through a single school participating in the Baton Rouge, USA, site of the International Study of Childhood Obesity, Lifestyle, and the Environment. We compared log-based variables of sleep period time (SPT), bedtime, and wake time to corresponding accelerometer-determined variables of total sleep episode time, sleep onset, and sleep offset estimated with the RSA. In addition, SPT and sleep onset estimated using standard procedures combining sleep logs and accelerometry (Log + Accel) were compared to the RSA-derived values. RESULTS: RSA total sleep episode time (540 ± 36 min) was significantly different from Log SPT (560 ± 24 min), P = 0.003, but not different from Log + Accel SPT (549 ± 24 min), P = 0.15. Significant and moderately high correlations were apparent between RSA-determined variables and those using the other methods (r = 0.61 to 0.74). There were no differences between RSA and Log + Accel estimates of sleep onset (P = 0.15) or RSA sleep offset and log wake time (P = 0.16). CONCLUSIONS: The RSA is a refinement of our previous algorithm, allowing researchers who use a 24-h waist-worn accelerometry protocol to distinguish children's nocturnal sleep (including night time wake episodes) from daytime activities.
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In the citing paper
“Nocturnal sleep duration was estimated using 60-s epochs and using a recently validated algorithm for 24-h waist-worn accelerometers (9).”
From Are children like werewolves? Full moon and its association with sleep and activity behaviors in an international sample of children (2016) · cites this paper as [9]
In the citing paper
“This new algorithm captures total sleep time from sleep onset to the end of sleep, including all epochs and wakefulness after onset, and provides more accurate sleep duration estimates than previous algorithms (9, 10).”
From Are children like werewolves? Full moon and its association with sleep and activity behaviors in an international sample of children (2016) · cites this paper as [9]
In the citing paper
“Nocturnal sleep duration was estimated from the accelerometry data using 60-s epochs and a fully automated algorithm for 24-h waist-worn accelerometers that was recently validated for ISCOLE [24].”
From Proportion of children meeting recommendations for 24-hour movement guidelines and associations with adiposity in a 12-country study (2016) · cites this paper as [24]
In the citing paper
“This new algorithm produces more precise estimates of sleep duration than previous algorithms and captures total sleep time from sleep onset to the end of sleep, including all epochs and wakefulness after onset [24, 25].”
From Proportion of children meeting recommendations for 24-hour movement guidelines and associations with adiposity in a 12-country study (2016) · cites this paper as [24]
Citations
Cites
Statistical Power Analysis for the Behavioral Sciences (2nd ed.).
Lachenbruch, P. A., & Cohen, J. (1989) · Journal of the American Statistical Association · doi:10.2307/2290095 · Cited by 37,421
Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues
Sadeh, A., Sharkey, M., & Carskadon, M. A. (1994) · Sleep · doi:10.1093/sleep/17.3.201 · Cited by 1,487
Using Accelerometers in Youth Physical Activity Studies: A Review of Methods
Cain, K. L., Sallis, J. F., Conway, T. L., Dyck, D. V., & Calhoon, L. (2013) · Journal of Physical Activity and Health · doi:10.1123/jpah.10.3.437 · Cited by 735
Use of actigraphy for assessment in pediatric sleep research
Meltzer, L. J., Montgomery‐Downs, H. E., Insana, S., & Walsh, C. M. (2012) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2011.10.002 · Cited by 679
Accelerometer Data Reduction: A Comparison of Four Reduction Algorithms on Select Outcome Variables
Mâsse, L. C., Fuemmeler, B. F., Anderson, C. B., Matthews, C. E., Trost, S. G., Catellier, D., & Treuth, M. S. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185674.09066.8a · Cited by 676
Katzmarzyk, P. T., Barreira, T. V., Broyles, S. T., Champagne, C. M., Chaput, J.-P., Fogelholm, M., Hu, G., Johnson, W. D., Kuriyan, R., Kurpad, A., et al. (2013) · BMC Public Health · doi:10.1186/1471-2458-13-900 · Cited by 357
Self-citationAccelerometer Assessment of Physical Activity in Children: An Update
Rowlands, A. V. (2007) · Pediatric Exercise Science · doi:10.1123/pes.19.3.252 · Cited by 342
Calibration of the GENEA accelerometer for assessment of physical activity intensity in children
Phillips, L. R. S., Parfitt, G., & Rowlands, A. V. (2012) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2012.05.013 · Cited by 332
Tudor-Locke, C., Barreira, T. V., Schuna, J. M., Mire, E. F., & Katzmarzyk, P. T. (2014) · Applied Physiology Nutrition and Metabolism · doi:10.1139/apnm-2013-0173 · Cited by 285
Self-citationEstimating Activity and Sedentary Behavior from an Accelerometer on the Hip or Wrist
Rosenberger, M. E., Haskell, W. L., Albinali, F., Mota, S., Nawyn, J., & Intille, S. S. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31827f0d9c · Cited by 258
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Migueles, J. H., Cadenas‐Sánchez, C., Ekelund, U., Nyström, C. D., Mora-González, J., Löf, M., Labayen, I., Ruiz, J. R., & Ortega, F. B. (2017) · Sports Medicine · doi:10.1007/s40279-017-0716-0 · Cited by 2,022
Compositional data analysis for physical activity, sedentary time and sleep research
Dumuid, D., Stanford, T., Fernández, J. A. M., Pedišić, Ž., Maher, C. A., Lewis, L. K., Hron, K., Katzmarzyk, P. T., Chaput, J., Fogelholm, M., et al. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217710835 · Cited by 548
Self-citationDumuid, D., Pedišić, Ž., Stanford, T., Fernández, J. A. M., Hron, K., Maher, C. A., Lewis, L. K., & Olds, T. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217737805 · Cited by 369
for the ISCOLE Research Group, Roman-Viñas, B., Chaput, J.-P., Katzmarzyk, P. T., Fogelholm, M., Lambert, E. V., Maher, C., Maia, J., Olds, T., Onywera, V., et al. (2016) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-016-0449-8 · Cited by 368
“This new algorithm produces more precise estimates of sleep duration than previous algorithms and captures total sleep time from sleep onset to the end of sleep, including all epochs and wakefulness after onset [24, 25].”
MethodSelf-citationLeBlanc, A. G., Katzmarzyk, P. T., Barreira, T. V., Broyles, S. T., Chaput, J.-P., Church, T. S., Fogelholm, M., Harrington, D. M., Hu, G., Kuriyan, R., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0129622 · Cited by 335
Self-citation
Metadata
- Authors
- BARREIRA, T. V., SCHUNA, J. M., MIRE, E. F., KATZMARZYK, P. T., CHAPUT, J.-P., LEDUC, G., & TUDOR-LOCKE, C.
- Affiliations
- Pennington Biomedical Research Center (US)
- Children's Hospital of Eastern Ontario (CA)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2015-05
- Volume
- 47
- Issue
- 5
- Pages
- 937-943
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2334259804
- Cited by
- 163
- References
- 16
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 13 · 2021: 7 · 2020: 14 · 2019: 16 · 2018: 20 · 2017: 17 · 2016: 23 · 2015: 23
- FWCI
- 30.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- children, sleep onset, bedtime, wake time
- MeSH terms
- Activity Cycles, Algorithms, Child, Female, Humans, Male, Sleep, Accelerometry
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