References
- Ancoli-Israel S, Cole RD, Alessi CA, Chambers ML, Moorcroft WH, Pollak CP. 2003. The role of actigraphy in the study of sleep and circadian rhythms. Sleep. 26(3):342–392. doi:https://doi.org/10.1093/sleep/26.3.342
- Baron KG, Duffecy J, Berendsen MA, Cheung Mason I, Lattie EG, Manalo NC. 2018. Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep Med Rev. 40:151–159. doi:https://doi.org/10.1016/j.smrv.2017.12.002
- Berry RB, Brooks R, Gamaldo C, Harding SM, Lloyd RM, Quan SF, Troester MT, Vaughn BV. 2017. AASM scoring manual updates for 2017 (Version 2.4). J Clin Sleep Med. 13(5):665–666. doi:https://doi.org/10.5664/jcsm.6576
- Blackwell T, Redline S, Ancoli-Israel S, Schneider JL, Surovec S, Johnson NL, Cauley JA, Stone KL. 2008. Comparison of sleep parameters from actigraphy and polysomnography in older women: the SOF study. Sleep. 31(2):283–291. doi:https://doi.org/10.1093/sleep/31.2.283
- Blood MA, Sack R. 1997. A comparison of sleep detection by wrist actigraphy, behavioral response, and polysomnography. Sleep. 20:388–395. doi:https://doi.org/10.1093/sleep/20.6.388
- Bonnet MH, Arand DL. 2003. Clinical effects of sleep fragmentation versus sleep deprivation. Sleep Med Rev. 7(4):297–310. doi:https://doi.org/10.1053/smrv.2001.0245
- Bonnet MH, Arand DL. 2007. EEG arousal norms by age. J Clin Sleep Med JCSM Off Publ Am Acad Sleep Med. 3(3):271–274. doi:https://doi.org/10.5664/jcsm.26796
- Chae KY, Kripke DF, Poceta JS, Shadan F, Jamil SM, Cronin JW, Kline LE. 2009. Evaluation of immobility time for sleep latency in actigraphy. Sleep Med. 10(6):621–625. doi:https://doi.org/10.1016/j.sleep.2008.07.009
- Cheng P, Luik AI, Fellman-Couture C, Peterson E, Joseph CLM, Tallent G, Tran KM, Ahmedani BK, Roehrs T, Roth T, et al. 2019. Efficacy of digital CBT for insomnia to reduce depression across demographic groups: a randomized trial. Psychol Med. 49(3):491–500. doi:https://doi.org/10.1017/S0033291718001113
- Chow CM, Wong SN, Shin M, Maddox RG, Feilds K-L, Paxton K, Hawke C, Hazell P, Steinbeck K. 2016. Defining the rest interval associated with the main sleep period in actigraph scoring. Nat Sci Sleep. 8:321–328. doi:https://doi.org/10.2147/NSS.S114969
- Cole RJ. 1992. Automatic sleep/wake identification from wrist activity. Cole RJ. 15(5):461–469. doi:https://doi.org/10.1093/sleep/15.5.461
- Conley S, Knies A, Batten J, Ash G, Miner B, Hwang Y, Jeon S, Redeker NS. 2019. Agreement between actigraphic and polysomnographic measures of sleep in adults with and without chronic conditions: A systematic review and meta-analysis. Sleep Med Rev. 46:151–160. doi:https://doi.org/10.1016/j.smrv.2019.05.001
- Danzig R, Wang M, Shah A, Trotti LM. 2020. The wrist is not the brain: estimation of sleep by clinical and consumer wearable actigraphy devices is impacted by multiple patient- and device-specific factors. J Sleep Res. 29(1):e12926. doi:https://doi.org/10.1111/jsr.12926
- Darwent D, Dawson D, Paterson JL, Roach GD, Ferguson SA. 2015. Managing fatigue: it really is about sleep. Accid Anal Prev. 82:20–26. doi:https://doi.org/10.1016/j.aap.2015.05.009
- de Arriba-pérez F, Caeiro-Rodríguez M, Santos-Gago JM. 2018. How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators. J Ambient Intell Human Comput. 9(4):897–917. doi:https://doi.org/10.1007/s12652-017-0477-5
- de Souza L, Benedito-Silva AA, Pires MLN, Poyares D, Tufik S, Calil HM. 2003. Further validation of actigraphy for sleep studies. Sleep. 26(1):81–85. doi:https://doi.org/10.1093/sleep/26.1.81
- de Zambotti M, Goldstone A, Claudatos S, Colrain IM, Baker FC. 2018. A validation study of Fitbit Charge 2TM compared with polysomnography in adults. Chronobiol Int. 35(4):465–476. doi:https://doi.org/10.1080/07420528.2017.1413578
- de Zambotti M, Rosas L, Colrain IM, Baker FC. 2019. The sleep of the ring: comparison of the ŌURA sleep tracker against polysomnography. Behav Sleep Med. 17(2):124–136. doi:https://doi.org/10.1080/15402002.2017.1300587
- Deutsch PA. 2006. Cost-effectiveness of split-night polysomnography and home studies in the evaluation of obstructive sleep apnea syndrome. J Clin Sleep Med. 2(2):9. doi:https://doi.org/10.5664/jcsm.26508
- Dunn J, Runge R, Snyder M. 2018. Wearables and the medical revolution. Pers Med. 15(5):429–448. doi:https://doi.org/10.2217/pme-2018-0044
- Fonseca P, Weysen T, Goelema MS, EIS M, Radha M, Lunsingh Scheurleer C, van den Heuvel L, Aarts RM. 2017. Validation of photoplethysmography-based sleep staging compared with polysomnography in healthy middle-aged adults. Sleep. 40(7). doi:https://doi.org/10.1093/sleep/zsx097
- Grandner M, Mullington JM, Hashmi SD, Redeker NS, Watson NF, Morgenthaler TI. 2018. Sleep duration and hypertension: analysis of > 700,000 adults by age and sex. J Clin Sleep Med. 14(6):1031–1039. doi:https://doi.org/10.5664/jcsm.7176
- Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. 2019. Accuracy of wristband fitbit models in assessing sleep: systematic review and meta-analysis. J Med Internet Res. 21(11):e16273. doi:https://doi.org/10.2196/16273
- Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. 2020. Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages. Chronobiol Int. 37(1):47–59. doi:https://doi.org/10.1080/07420528.2019.1682006
- Herzig D, Eser P, Omlin X, Riener R, Wilhelm M, Achermann P. 2018. Reproducibility of heart rate variability is parameter and sleep stage dependent. Front Physiol. 8:1100. doi:https://doi.org/10.3389/fphys.2017.01100
- Ho KM. 2018. Using linear regression to assess dose-dependent bias on a Bland-Altman plot. J Emerg Crit Care Med. 2(2):68. doi:https://doi.org/10.21037/jeccm.2018.08.02
- Hwang SH, Seo S, Yoon HN, Jung DW, Baek HJ, Cho J, Choi JW, Lee YJ, Jeong D-U, Park KS. 2017. Sleep period time estimation based on electrodermal activity. IEEE J Biomed Health Inform. 21(1):115–122. doi:https://doi.org/10.1109/JBHI.2015.2490480
- Kushida CA, Chang A, Gadkary C, Guilleminault C, Carrillo O, Dement WC. 2001. Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients. Sleep Med. 2(5):389–396. doi:https://doi.org/10.1016/S1389-9457(00)00098-8
- Kushida CA, Littner MR, Morgenthaler T, Alessi CA, Bailey D, Coleman J, Friedman L, Hirshkowitz M, Kapen S, Kramer M, et al. 2005. Practice parameters for the indications for polysomnography and related procedures: an update for 2005. Sleep. 28(4):499–523. doi:https://doi.org/10.1093/sleep/20.6.406
- Landis JR, Koch GG. 1977. The measurement of observer agreement for categorical data. Biometrics. 33(1):159. doi:https://doi.org/10.2307/2529310
- Lee J-M, Byun W, Keill A, Dinkel D, Seo Y. 2018. Comparison of wearable trackers’ ability to estimate sleep. Int J Environ Res Public Health. 15:6. doi:https://doi.org/10.1016/j.smrv.2016.02.001
- Lee SWH, Ng KY, Chin WK. 2017. The impact of sleep amount and sleep quality on glycemic control in type 2 diabetes: A systematic review and meta-analysis. Sleep Med Rev. 31:91–101. doi:https://doi.org/10.1016/j.smrv.2016.02.001
- Li X, Zhang Y, Jiang F, Zhao H. 2020. A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy. Chronobiol Int. 37:1–14. doi:https://doi.org/10.1080/07420528.2020.1754848
- Martin JL, Hakim AD. 2011. Wrist actigraphy. Chest. 139(6):1514–1527. doi:https://doi.org/10.1378/chest.10-1872
- Mate App. Seizure diary | activity and rest monitoring. Empatica [Internet]. https://www.empatica.com/mate
- Meltzer LJ, Walsh CM, Traylor J, Westin AML. 2012. Direct comparison of two new actigraphs and polysomnography in children and adolescents. Sleep. 35(1):159–166. doi:https://doi.org/10.5665/sleep.1608
- Miner B, Kryger MH. 2017. Sleep in the Aging Population. Sleep Med Clin. 12(1):31–38. doi:https://doi.org/10.1016/j.jsmc.2016.10.008
- Morgenthaler TI, Lee-Chiong T, Alessi C, Friedman L, Aurora RN, Boehlecke B, Brown T, Chesson AL, Kapur V, Maganti R, et al. 2007. Practice parameters for the clinical evaluation and treatment of circadian rhythm sleep disorders. Sleep. 30(11):1445–1459. doi:https://doi.org/10.1093/sleep/30.11.1445
- Morin CM, Gramling SE. 1989. Sleep patterns and aging: comparison of older adults with and without insomnia complaints. Psychol Aging. 4(3):290–294. doi:https://doi.org/10.1037/0882-7974.4.3.290
- Mullington JM, Haack M, Toth M, Serrador J, Meier-Ewert H. 2009. Cardiovascular, inflammatory and metabolic consequences of sleep deprivation. Prog Cardiovasc Dis. 51(4):294–302. doi:https://doi.org/10.1016/j.pcad.2008.10.003
- Newell J, Mairesse O, Verbanck P, Neu D. 2012. Is a one-night stay in the lab really enough to conclude? First-night effect and night-to-night variability in polysomnographic recordings among different clinical population samples. Psychiatry Res. 200(2):795–801. doi:https://doi.org/10.1016/j.psychres.2012.07.045
- O’Donnell J, Hollowell S. 2017. Automated detection of sleep-boundary times using wrist-worn accelerometry. bioRxiv. 225516. doi:https://doi.org/10.1101/225516
- Palotti J, Mall R, Aupetit M, Rueschman M, Singh M, Sathyanarayana A, Taheri S, Fernandez-Luque L. 2019. Benchmark on a large cohort for sleep-wake classification with machine learning techniques. Npj Digit Med. 2(1):50. doi:https://doi.org/10.1038/s41746-019-0126-9
- Pan J, Tompkins W. 1985. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 32:1396–1398. doi:https://doi.org/10.1109/TBME.1985.325532
- Paquet J, Kawinska A, Carrier J. 2007. Wake detection capacity of actigraphy during sleep. Sleep. 30(10):1362–1369. doi:https://doi.org/10.1093/sleep/30.10.1362
- Portaluppi F, Smolensky MH, Touitou Y. 2010. Ethics and methods for biological rhythm research on animals and human beings. Chronobiol Int. 27(9–10):1911–1929. doi:https://doi.org/10.3109/07420528.2010.516381
- Quante M, Kaplan ER, Cailler M, Rueschman M, Wang R, Weng J, Taveras EM, Redline S. 2018. Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms. Nat Sci Sleep. 10:13–20. doi:https://doi.org/10.2147/nss.s151085
- Radha M, Fonseca P, Moreau A, Ross M, Cerny A, Anderer P, Long X, Aarts RM. 2019. Sleep stage classification from heart-rate variability using long short-term memory neural networks. Sci Rep. 9(1):14149. doi:https://doi.org/10.1038/s41598-019-49703-y
- Sadeh A. 2011. The role and validity of actigraphy in sleep medicine: an update. Sleep Med Rev. 15:31–34. doi:https://doi.org/10.1016/j.smrv.2010.10.001
- Sadeh A, Sharkey KM, Carskadon MA. 1994. Activity-based sleep—wake identification: an empirical test of methodological issues. Sleep. 17(3):201–207. doi:https://doi.org/10.1093/sleep/17.3.201
- Sano A, Chen W, Lopez-Martinez D, Taylor S, Picard RW. 2019. Multimodal ambulatory sleep detection using LSTM recurrent neural networks. IEEE J Biomed Health Inform. 23(4):1607–1617. doi:https://doi.org/10.1109/JBHI.2018.2867619
- Sano A, Taylor S, McHill AW, Phillips AJ, Barger LK, Klerman E, Picard R. 2018. Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study. J Med Internet Res. 20(6):e210. doi:https://doi.org/10.2196/jmir.9410
- Sarkis RA, Alam J, Pavlova MK, Dworetzky BA, Pennell PB, Stickgold R, Bubrick EJ. 2016. Sleep-dependent memory consolidation in the epilepsy monitoring unit: A pilot study. Clin Neurophysiol Off J Int Fed Clin Neurophysiol. 127(8):2785–2790. doi:https://doi.org/10.1016/j.clinph.2016.05.275
- Shrivastava D, Jung S, Saadat M, Sirohi R, Crewson K. 2014. How to interpret the results of a sleep study. J Community Hosp Intern Med Perspect. 4:5. doi:https://doi.org/10.3402/jchimp.v4.24983
- Sivertsen B, Omvik S, Havik OE, Pallesen S, Bjorvatn B, Nielsen GH, Straume S, Nordhus IH. 2006. A comparison of actigraphy and polysomnography in older adults treated for chronic primary insomnia. Sleep. 29(10):1353–1358. doi:https://doi.org/10.1093/sleep/29.10.1353
- Smith MT, McCrae CS, Cheung J, Martin JL, Harrod CG, Heald JL, Carden KA. 2018. 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. 14(7):1209–1230. doi:https://doi.org/10.5664/jcsm.7228
- Spruyt K, Gozal D, Dayyat E, Roman A, Molfese DL. 2011. Sleep assessments in healthy school-aged children using actigraphy: concordance with polysomnography. J Sleep Res. 20(1pt2):223–232. doi:https://doi.org/10.1111/j.1365-2869.2010.00857.x
- Stickgold R. 2005. Sleep-dependent memory consolidation. Nature. 437(7063):1272–1278. doi:https://doi.org/10.1038/nature04286
- Taibi DM, Landis CA, Vitiello MV. 2013. Concordance of polysomnographic and actigraphic measurement of sleep and wake in older women with insomnia. J Clin Sleep Med. 9(3):217–225. doi:https://doi.org/10.5664/jcsm.2482
- Tracy DJ, Xu Z, Choi L, Acra S, Chen KY, Buchowski MS. 2014. Separating bedtime rest from activity using waist or wrist-worn accelerometers in youth. PLoS ONE. 9:4. doi:https://doi.org/10.1371/journal.pone.0092512
- Tracy JD, Acra S, Chen KY, Buchowski MS. 2018. Identifying bedrest using 24-h waist or wrist accelerometry in adults. PloS One. 13(3):e0194461. doi:https://doi.org/10.1371/journal.pone.0194461
- van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN, Kivimäki M, Frayling TM, Pack AI, Bucan M, Trenell MI, et al. 2018. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep. 8. doi:https://doi.org/10.1101/257972
- Walch O, Huang Y, Forger D, Goldstein C. 2019. Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device. Sleep. 42(12):19. doi:https://doi.org/10.1093/sleep/zsz067.325
- Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, Dinges DF, Gangwisch J, Grandner MA, Kushida C, et al. 2015. Recommended amount of sleep for a healthy adult: a joint consensus statement of the American academy of sleep medicine and sleep research society. Sleep. 38(6):843–844. doi:https://doi.org/10.5665/sleep.4716
- Webster JB, Kripke DF, Messin S, Mullaney DJ, Wyborney G. 1982. An activity-based sleep monitor system for ambulatory use. Sleep. 5(4):389–399. doi:https://doi.org/10.1093/sleep/5.4.389
- Zhang L, Fabbri D, Upender R, Kent D. 2019. Automated sleep stage scoring of the sleep heart health study using deep neural networks. Sleep. 42:11. doi:https://doi.org/10.1093/sleep/zsz159