Comparison and agreement between device-estimated and self-reported sleep periods in adults

References

• Grgic J, Dumuid D, Bengoechea EG, et al. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies. Int J Behav Nutr Phys Act. 2018;15(1):1244–1255.
   Google Scholar
• Rosenberger ME, Fulton JE, Buman MP, et al. The 24-Hour activity cycle: a new paradigm for physical activity. Med Sci Sports Exer. 2019;51(3):454–464.
   PubMed Web of Science ®Google Scholar
• Chastin SFM, Palarea-Albaladejo J, Dontje ML, et al. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PLoS One. 2015;10(10):e0139984.
   PubMed Web of Science ®Google Scholar
• Farrahi V, Kangas M, Walmsley R, et al. Compositional associations of sleep and activities within the 24-h cycle with cardiometabolic health markers in adults. Med Sci Sports Exer. 2021;53(2):324–332.
   PubMed Web of Science ®Google Scholar
• Dumuid D, Stanford TE, Martin-Fernández JA, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726–3738.
   PubMed Web of Science ®Google Scholar
• de Almeida Mendes M, da Silva ICM, Ramires V. V, et al. Calibration of raw accelerometer data to measure physical activity: a systematic review. Gait & Posture. 2018;61:98–110.
   PubMed Web of Science ®Google Scholar
• Farrahi V, Niemelä M, Kangas M, et al. Calibration and validation of accelerometer-based activity monitors: a systematic review of machine-learning approaches. Gait Posture. 2019;68:285–299.
   PubMed Web of Science ®Google Scholar
• Borazio M, Berlin E, Kucukyildiz N, et al. Towards Benchmarked Sleep Detection with Wrist-Worn Sensing Units. 2014;125–134
   Google Scholar
• Te Lindert BHW, van Someren EJW. Sleep estimates using microelectromechanical systems (MEMS). Sleep. 2013;36(5):781–789.
   PubMed Web of Science ®Google Scholar
• Gorzelitz J, Farber C, Gangnon R, et al. Accuracy of wearable trackers for measuring moderate- to Vigorous-Intensity physical activity: a systematic review and Meta-Analysis. Journal for the Measurement of Physical Behaviour. 2020;3(4):346–357.
   Google Scholar
• Prince SA, Cardilli L, Reed JL, et al. A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2020;17(1):31.
   PubMed Web of Science ®Google Scholar
• Smith MT, McCrae CS, Cheung J, et al. Use of actigraphy for the evaluation of sleep disorders and circadian rhythm Sleep-Wake disorders. An American Academy of Sleep Medicine Systematic Review, Meta-Analysis, and GRADE Assessment. J Clin Sleep Med. 2021;14(07):1209–1230.
   Google Scholar
• Migueles JH, Aadland E, Andersen LB, et al. External review group, et alGRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies. Br J Sports Med. 2022;56(7):376–384.
   PubMed Web of Science ®Google Scholar
• Cespedes EM, Hu FB, Redline S, et al. Comparison of self-reported sleep duration with actigraphy: results From the Hispanic community health study/study of Latinos sueño ancillary study. Am J Epidemiol. 2016;183(6):561–573.
   PubMed Web of Science ®Google Scholar
• Lauderdale DS, Knutson KL, Yan LL, et al. Self-reported and measured sleep duration: how similar are they? Epidemiology (Cambridge, Mass.). 2008;19(6):838–845.
   PubMed Web of Science ®Google Scholar
• Leinonen AM, Ahola R, Kulmala J, et al. Measuring physical activity in free-living conditions-comparison of three accelerometry-based methods. Front Physiol. 2016;J7:681.
   PubMed Web of Science ®Google Scholar
• Yano S, Koohsari MJ, Shibata A, et al. Assessing physical activity and sedentary behavior under Free-Living conditions: comparison of active style pro HJA-350IT and ActiGraphTM GT3X+. IJERPH. 2019;16(17):3065.
   Web of Science ®Google Scholar
• Chong J, Tjurin P, Niemelä M, et al. Machine-learning models for activity class prediction: a comparative study of feature selection and classification algorithms. Gait Posture. 2021 ;Sep89:45–53.
   PubMed Web of Science ®Google Scholar
• Chaput J-P, Dutil C, Featherstone R, et al. Sleep timing, sleep consistency, and health in adults: a systematic review. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2):S232–S247.
   PubMed Web of Science ®Google Scholar
• University of Oulu: Northern Finland Birth Cohort 1986. University of Oulu. 1986. http://urn.fi/urn:nbn:fi:att:f5c10eef-3d25-4bd0-beb8-f2d59df95b8e.
   Google Scholar
• Hautala A, Martinmaki K, Kiviniemi A, et al. Effects of habitual physical activity on response to endurance training. J Sports Sci. 2012;30(6):563–569.
   PubMed Web of Science ®Google Scholar
• Kinnunen H, Häkkinen K, Schumann M, et al. Training-induced changes in daily energy expenditure: methodological evaluation using wrist-worn accelerometer, heart rate monitor, and doubly labeled water technique. PLoS ONE. 2019;14(7):e0219563.
   PubMed Web of Science ®Google Scholar
• Ōura Health Oy. n.d. API documentation: sleep. https://cloud.ouraring.com/docs/sleep
   Google Scholar
• Getting started with the Oura Cloud API, Sleep. 2020. https://cloud.ouraring.com/docs/sleep
   Google Scholar
• de Zambotti M, Rosas L, Colrain IM, et al. The sleep of the ring: comparison of the ŌURA sleep tracker Against polysomnography. Behav Sleep Med. 2019;17(2):124–136.
   PubMed Web of Science ®Google Scholar
• Mehrabadi A, Azimi M, Sarhaddi I, et al. Sleep tracking of a commercially available smart ring and smartwatch Against Medical-Grade actigraphy in everyday settings: instrument validation study. JMIR Mhealth Uhealth. 2020;8(10):e20465.
   PubMedGoogle Scholar
• Helakari H, Korhonen V, Holst SC, et al. Human NREM sleep promotes Brain-Wide vasomotor and respiratory pulsations. J Neurosci. 2022;42(12):2503–2515.
   PubMed Web of Science ®Google Scholar
• Charles AC. Glossary of standardized terminology for sleep biological rhythm research. Sleep. 1980;2(3):287–288.
   Web of Science ®Google Scholar
• Robbins R, Quan SF, Barger LK, et al. Self-reported sleep duration and timing: a methodological review of event definitions, context, and timeframe of related questions. Sleep Epidemiol. 2021;1:100016.
   PubMedGoogle Scholar
• Leister KR, Garay J, Barreira TV. Validity of a novel algorithm to detect bedtime, wake time, and sleep time in adults. J Measurement Phys Behav. 2022;5(2):76–84.
   Google Scholar
• van der Berg JD, Willems PJ, van der Velde JH, et al. Identifying waking time in 24-h accelerometry data in adults using an automated algorithm. J Sports Sci. 2016;34(19):1867–1873.
   PubMed Web of Science ®Google Scholar
• van Hees VT, Sabia S, Jones SE, et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep. 2018;8:12975. https://doi.org/10.1038/s41598-018-31266-z.
   Web of Science ®Google Scholar
• Ōura Health Oy. n.d. Product safety & use. https://support.ouraring.com/hc/en-us/articles/360025428394-Product-Safety-Use
   Google Scholar
• Urbanek JK, Spira AP, Di J, et al. Epidemiology of objectively measured bedtime and chronotype in US adolescents and adults: NHANES 2003–2006. Chronobiol Int. 2018;35(3):416–434.
   PubMed Web of Science ®Google Scholar
• Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine. 2016;15(2):155–163.
   PubMed Web of Science ®Google Scholar
• Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud. 2010;47(8):931–936.
   Web of Science ®Google Scholar
• Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–310.
   PubMed Web of Science ®Google Scholar
• Benjamin DJ, Berger JO, Johannesson M, et al. Redefine statistical significance. Nat Hum Behav. 2018;2(1):6–10.
   PubMed Web of Science ®Google Scholar
• de Zambotti M, Cellini N, Goldstone A, et al. Wearable sleep technology in clinical and research settings. Med Sci Sports Exerc. 2019;51(7):1538–1557.
   PubMed Web of Science ®Google Scholar
• Lujan MR, Perez-Pozuelo I, Grandner MA. Past, present, and future of multisensory wearable technology to monitor sleep and circadian rhythms. Front Digit Health. 2021;3:721919.
   PubMedGoogle Scholar
• Jackson CL, Ward JB, Johnson DA, et al. Concordance between self-reported and actigraphy-assessed sleep duration among African-American adults: findings from the jackson heart sleep study. Sleep. 2020;43(3): zsz246.
   PubMed Web of Science ®Google Scholar
• O’Donnell J, Hollowell S, Salimi-Khorshidi G, et al. Automated detection of sleep-boundary times using wrist-worn accelerometry. BioRxiv. 2017;225516.
   Google Scholar
• Quante M, Kaplan ER, Rueschman M, et al. Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep. Sleep Health. 2015;1(4):275–284.
   PubMed Web of Science ®Google Scholar
• Grandner MA, Lujan MR, Ghani SB. Sleep-tracking technology in scientific research: looking to the future. Sleep. 2021;44(5):zsab071.
   Web of Science ®Google Scholar
• Shelgikar AV, Anderson PF, Stephens MR. Sleep ­tracking, wearable technology, and opportunities for research and clinical care. Chest. 2016;150(3):732–743.
   PubMed Web of Science ®Google Scholar
• Khosla S, Deak MC, Gault D, American Academy of Sleep Medicine Board of Directors, et al. Consumer sleep technology: an American academy of sleep medicine position statement. J Clin Sleep Med. 2018;2018;14(5):877–880.
   PubMed Web of Science ®Google Scholar
• Shei RJ, Holder IG, Oumsang AS, et al. Wearable activity trackers-advanced technology or advanced marketing?. Eur J Appl Physiol.2022;122(9):1–16.
   PubMed Web of Science ®Google Scholar