Advertisement

Joint and Individual Representation of Domains of Physical Activity, Sleep, and Circadian Rhythmicity

  • Junrui DiEmail author
  • Adam Spira
  • Jiawei Bai
  • Jacek Urbanek
  • Andrew Leroux
  • Mark Wu
  • Susan Resnick
  • Eleanor Simonsick
  • Luigi Ferrucci
  • Jennifer Schrack
  • Vadim Zipunnikov
Article

Abstract

Developments in wearable technology have enabled researchers to continuously and objectively monitor various aspects and physiological domains of real life including levels of physical activity, quality of sleep, and strength of circadian rhythm in many epidemiological and clinical studies. Current analytical practice is to summarize each of these three domains individually via a standard inventory of interpretable features, and explore individual associations between the features and clinical variables. However, the features often exhibit significant interaction and correlation both within and between domains. Integration of features across multiple domains remains methodologically challenging. To address this problem, we propose to use joint and individual variation explained, a dimension reduction technique that efficiently deals with multivariate data representing multiple domains. In this paper, we review the most frequently used features to characterize the domains of physical activity, sleep, and circadian rhythmicity and illustrate the approach using wrist-worn actigraphy data from 198 participants of the Baltimore Longitudinal Study of Aging.

Keywords

Multi-domain Physical activity Sleep Circadian rhythmicity JIVE Dimension reduction 

Notes

Funding

This study was supported in part by the Intramural Research Program (IRP), National Institute on Aging (NIA), National Institutes of Health (NIH), and by Research and Development Contract HHSN-260-2004-00012C. Dr. Adam Spira was supported in part by R01AG050507 from the National Institute on Aging. Dr Adam Spira received an honorarium from Springer Nature Switzerland AG for Guest Editing a Special Issue of Current Sleep Medicine Reports. Dr. Mark Wu was supported by R01AG054771. Dr. Jennifer Schrack was supported by U01AG057545 and R21AG053198.

References

  1. 1.
  2. 2.
    Actigraph (2018b) What does the “detect sleep periods” button do and how does it work? https://actigraph.desk.com/customer/en/portal/articles/2515836-what-does-the-%22detect-sleep-periods%22-button-do-and-how-does-it-work-
  3. 3.
    Ancoli-Israel S, Martin JL, Blackwell T, Buenaver L, Liu L, Meltzer LJ, Sadeh A, Spira AP, Taylor DJ (2015) The sbsm guide to actigraphy monitoring: clinical and research applications. Behav Sleep Med 13(sup1):S4–S38Google Scholar
  4. 4.
    Argiropoulou EC, Michalopoulou M, Aggeloussis N, Avgerinos A (2004) Validity and reliability of physical activity measures in greek high school age children. J Sports Sci Med 3(3):147Google Scholar
  5. 5.
    Ayas NT, White DP, Al-Delaimy WK, Manson JE, Stampfer MJ, Speizer FE, Patel S, Hu FB (2003a) A prospective study of self-reported sleep duration and incident diabetes in women. Diabetes Care 26(2):380–384.  https://doi.org/10.2337/diacare.26.2.380 Google Scholar
  6. 6.
    Ayas NT, White DP, Manson JE, Stampfer MJ, Speizer FE, Malhotra A, Hu FB (2003b) A prospective study of sleep duration and coronary heart disease in women. Arch Intern Med 163(2):205.  https://doi.org/10.1001/archinte.163.2.205 Google Scholar
  7. 7.
    Bai J, Di C, Xiao L, Evenson KR, LaCroix AZ, Crainiceanu CM, Buchner DM (2016) An activity index for raw accelerometry data and its comparison with other activity metrics. PLoS ONE 11(8):e0160.  https://doi.org/10.1371/journal.pone.0160644 Google Scholar
  8. 8.
    Bassett DR (2012) Device-based monitoring in physical activity and public health research. Physiol Meas 33(11):1769–1783.  https://doi.org/10.1088/0967-3334/33/11/1769 Google Scholar
  9. 9.
    Bassett DR, Troiano RP, McClain JJ, Wolff DL (2015) Accelerometer-based physical activity: total volume per day and standardized measures. Med Sci Sports Exerc 47(4):833–8Google Scholar
  10. 10.
    Bellettiere J, Winkler EA, Chastin SF, Kerr J, Owen N, Dunstan DW, Healy GN (2017) Associations of sitting accumulation patterns with cardio-metabolic risk biomarkers in Australian adults. PLoS ONE 12(6):e0180,119Google Scholar
  11. 11.
    Buman MP, Winkler EAH, Kurka JM, Hekler EB, Baldwin CM, Owen N, Ainsworth BE, Healy GN, Gardiner PA (2014) Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005–2006. Am J Epidemiol 179(3):323–334.  https://doi.org/10.1093/aje/kwt292 Google Scholar
  12. 12.
    Chau JY, Grunseit AC, Chey T, Stamatakis E, Brown WJ, Matthews CE, Bauman AE, van der Ploeg HP (2013) Daily sitting time and all-cause mortality: a meta-analysis. PLoS ONE 8(11):e80,000.  https://doi.org/10.1371/journal.pone.0080000 Google Scholar
  13. 13.
    Cornelissen G (2014) Cosinor-based rhythmometry. Theor Biol Med Model 11(1):16Google Scholar
  14. 14.
    Di J, Leroux A, Urbanek J, Varadhan R, Spira A, Schrack J, Zipunnikov V (2017) Patterns of sedentary and active time accumulation are associated with mortality in US adults: the NHANES study. bioRxiv http://biorxiv.org/content/early/2017/08/31/182337.abstract
  15. 15.
    Diaz KM, Howard VJ, Hutto B, Colabianchi N, Vena JE, Safford MM, Blair SN, Hooker SP (2017) Patterns of sedentary behavior and mortality in U.S. middle-aged and older adults. Ann Inter Med 167:465–475.  https://doi.org/10.7326/M17-0212 Google Scholar
  16. 16.
    Dunlap JC, Loros JJ (2004) Chronobiology: biological timekeeping. Sinauer Associates, SunderlandGoogle Scholar
  17. 17.
    Dunstan DW, Kingwell BA, Larsen R, Healy GN, Cerin E, Hamilton MT, Shaw JE, Bertovic DA, Zimmet PZ, Salmon J, Owen N (2012) Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care 35(5):976–983.  https://doi.org/10.2337/dc11-1931 Google Scholar
  18. 18.
    Edgar RS, Green EW, Zhao Y, van Ooijen G, Olmedo M, Qin X, Xu Y, Pan M, Valekunja UK, Feeney KA, Maywood ES, Hastings MH, Baliga NS, Merrow M, Millar AJ, Johnson CH, Kyriacou CP, O’Neill JS, Reddy AB (2012) Peroxiredoxins are conserved markers of circadian rhythms. Nature 485(7399):459.  https://doi.org/10.1038/nature11088 Google Scholar
  19. 19.
    Feng Q, Hannig J, Marron J (2015) Non-iterative joint and individual variation explained. arXiv preprint arXiv:1512.04060
  20. 20.
    Ferrucci L (2008) The baltimore longitudinal study of aging (BLSA): a 50-year-long journey and plans for the future. J Gerontol Ser A 63(12):1416–1419.  https://doi.org/10.1093/gerona/63.12.1416 Google Scholar
  21. 21.
    Ferrucci L, Alley D (2007) Obesity, disability, and mortality: a puzzling link. Arch Intern Med 167(8):750–751Google Scholar
  22. 22.
    Gaynanova I, Li G (2017) Structural learning and integrative decomposition of multi-view data. arXiv preprint arXiv:1707.06573
  23. 23.
    Goldsmith J, Zipunnikov V, Schrack J (2015) Generalized multilevel function-on-scalar regression and principal component analysis. Biometrics 71(2):344–353MathSciNetzbMATHGoogle Scholar
  24. 24.
    Gonçalves BS, Cavalcanti PR, Tavares GR, Campos TF, Araujo JF (2014) Nonparametric methods in actigraphy: an update. Sleep Sci 7(3):158–164Google Scholar
  25. 25.
    Hamilton MT, Healy GN, Dunstan DW, Zderic TW, Owen N (2008) Too little exercise and too much sitting: inactivity physiology and the need for new recommendations on sedentary behavior. Curr Cardiovasc Risk Rep 2(4):292–298.  https://doi.org/10.1007/s12170-008-0054-8 Google Scholar
  26. 26.
    Hauri PJ, Wisbey J (1992) Wrist actigraphy in insomnia. Sleep 15(4):293–301.  https://doi.org/10.1093/sleep/15.4.293 Google Scholar
  27. 27.
    Healy GN, Clark BK, Winkler EA, Gardiner PA, Brown WJ, Matthews CE (2011) Measurement of adults’ sedentary time in population-based studies. Am J Prev Med 41(2):216–227.  https://doi.org/10.1016/j.amepre.2011.05.005 Google Scholar
  28. 28.
    Hofstra WA, de Weerd AW (2008) How to assess circadian rhythm in humans: a review of literature. Epilepsy Behav 13(3):438–444.  https://doi.org/10.1016/j.yebeh.2008.06.002 Google Scholar
  29. 29.
    Hotelling H (1936) Relations between two sets of variates. Biometrika 28(3—-4):321–377.  https://doi.org/10.2307/2333955 zbMATHGoogle Scholar
  30. 30.
    Jean-Louis G, von Gizycki H, Zizi F, Fookson J, Spielman A, Nunes J, Fullilove R, Taub H (1996) Determination of sleep and wakefulness with the actigraph data analysis software (ADAS). Sleep 19(9):739–743Google Scholar
  31. 31.
    Jean-Louis G, Kripke DF, Cole RJ, Assmus JD, Langer RD (2001) Sleep detection with an accelerometer actigraph: comparisons with polysomnography. Physiol Behav 72(1—-2):21–28.  https://doi.org/10.1016/S0031-9384(00)00355-3 Google Scholar
  32. 32.
    John D, Freedso P (2012) ActiGraph and actical physical activity monitors: a peek under the hood. Med Sci Sports Exerc 44(1 Suppl 1):S86–89.  https://doi.org/10.1249/MSS.0b013e3182399f5e Google Scholar
  33. 33.
    Jondeau E, Jurczenko E, Rockinger M (2010) Moment component analysis: an illustration with international stock markets. SSRN Electron J 36(4):576–598.  https://doi.org/10.2139/ssrn.1694643 MathSciNetGoogle Scholar
  34. 34.
    Kaplan A, Lock EF (2017) Prediction with dimension reduction of multiple molecular data sources for patient survival. arXiv preprint arXiv:1704.02069
  35. 35.
    Koster A, Caserotti P, Patel KV, Matthews CE, Berrigan D, Van Domelen DR, Brychta RJ, Chen KY, Harris TB (2012) Association of sedentary time with mortality independent of moderate to vigorous physical activity. PLoS ONE 7(6):e37,696.  https://doi.org/10.1371/journal.pone.0037696 Google Scholar
  36. 36.
    Kripke DF, Garfinkel L, Wingard DL, Klauber MR, Marler MR (2002) Mortality associated with sleep duration and insomnia. Arch Gen Psychiatr 59(2):131.  https://doi.org/10.1001/archpsyc.59.2.131 Google Scholar
  37. 37.
    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.  https://doi.org/10.1016/S1389-9457(00)00098-8 Google Scholar
  38. 38.
    Leroux A, Di J, Smirnova E, Mcguffey EJ, Cao Q, Bayatmokhtari E, Tabacu L, Zipunnikov V, Urbanek JK, Crainiceanu C (2019) Organizing and analyzing the activity data in NHANES. Stat Biosci.  https://doi.org/10.1007/s12561-018-09229-9
  39. 39.
    Li G, Gaynanova I (2017) A general framework for association analysis of heterogeneous data. arXiv preprint arXiv:1707.06485
  40. 40.
    Lock EF, Hoadley KA, Marron JS, Nobel AB (2013) Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. Ann Appl Stat 7(1):523–542.  https://doi.org/10.1214/12-AOAS597 MathSciNetzbMATHGoogle Scholar
  41. 41.
    Marler MR, Gehrman P, Martin JL, Ancoli-Israel S (2006) The sigmoidally transformed cosine curve: a mathematical model for circadian rhythms with symmetric non-sinusoidal shapes. Stat Med 25(22):3893–3904.  https://doi.org/10.1002/sim.2466 MathSciNetGoogle Scholar
  42. 42.
    Matthews CE, George SM, Moore SC, Bowles HR, Blair A, Park Y, Troiano RP, Hollenbeck A, Schatzkin A (2012) Amount of time spent in sedentary behaviors and cause-specific mortality in US adults. Am J Clin Nutr 95(2):437–445.  https://doi.org/10.3945/ajcn.111.019620 Google Scholar
  43. 43.
    Miettinen J, Taskinen S, Nordhausen K, Oja H (2015) Fourth moments and independent component analysis. Stat Sci 30(3):372–390.  https://doi.org/10.1214/15-STS520 MathSciNetzbMATHGoogle Scholar
  44. 44.
    Mills JN (1966) Human circadian rhythms. Physiol Rev 46(1):128–171Google Scholar
  45. 45.
    Morton J, Lim LH (2009) Principal cumulant component analysis. preprintGoogle Scholar
  46. 46.
    Nakazaki K, Kitamura S, Motomura Y, Hida A, Kamei Y, Miura N, Mishima K (2014) Validity of an algorithm for determining sleep/wake states using a new actigraph. J Physiol Anthropol 33(1):31.  https://doi.org/10.1186/1880-6805-33-31 Google Scholar
  47. 47.
    Nastasi AJ, Ahuja A, Zipunnikov V, Simonsick EM, Ferrucci L, Schrack JA (2018) Objectively measured physical activity and falls in well-functioning older adults. Am J Phys Med Rehabil 97(4):255–260.  https://doi.org/10.1097/PHM.0000000000000830 Google Scholar
  48. 48.
    Owen N, Healy GN, Matthews CE, Dunstan DW (2010) Too much sitting: the population-health science of sedentary behavior. Exerc Sport Sci Rev 38(3):105–113.  https://doi.org/10.1097/JES.0b013e3181e373a2 Google Scholar
  49. 49.
    Patel SR, Malhotra A, Gao X, Hu FB, Neuman MI, Fawzi WW (2012) A prospective study of sleep duration and pneumonia risk in women. Sleep 35(1):97–101.  https://doi.org/10.5665/sleep.1594 Google Scholar
  50. 50.
    Pyrkov TV, Slipensky K, Barg M, Kondrashin A, Zhurov B, Zenin A, Pyatnitskiy M, Menshikov L, Markov S, Fedichev PO (2018) Extracting biological age from biomedical data via deep learning: too much of a good thing? Sci Rep 8(1):5210Google Scholar
  51. 51.
    Ramsay JO (2006) Functional data analysis. Wiley, New YorkGoogle Scholar
  52. 52.
    Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304.  https://doi.org/10.1038/nbt0308-303 Google Scholar
  53. 53.
    Schrack JA, Zipunnikov V, Goldsmith J, Bai J, Simonsick EM, Crainiceanu C, Ferrucci L (2014) Assessing the “Physical Cliff”: detailed quantification of age-related differences in daily patterns of physical activity. J Gerontol Ser A.  https://doi.org/10.1093/gerona/glt199 Google Scholar
  54. 54.
    Schrack JA, Kuo PL, Wanigatunga AA, Di J, Simonsick EM, Spira AP, Ferrucci L, Zipunnikov V (2018a) Active-to-sedentary behavior transitions, fatigability, and physical functioning in older adults. J Gerontol 74(4):560–567.  https://doi.org/10.1093/gerona/gly243 Google Scholar
  55. 55.
    Schrack JA, Leroux A, Fleg JL, Zipunnikov V, Simonsick EM, Studenski SA, Crainiceanu C, Ferrucci L (2018b) Using heart rate and accelerometry to define quantity and intensity of physical activity in older adults. J Gerontol 73(5):668–675Google Scholar
  56. 56.
    Shephard RJ (2003) Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med 37(3):197–206MathSciNetGoogle Scholar
  57. 57.
    Shou H, Zipunnikov V, Crainiceanu CM, Greven S (2015) Structured functional principal component analysis. Biometrics 71(1):247–257MathSciNetzbMATHGoogle Scholar
  58. 58.
    Spira AP, An Y, Peng Y, Wu MN, Simonsick EM, Ferrucci L, Resnick SM (2017) Apoe genotype and nonrespiratory sleep parameters in cognitively intact older adults. Sleep 40(8):zsx076Google Scholar
  59. 59.
    Stone JL, Norris AH (1966) Activities and attitudes of participants in the baltimore longitudinal study. J Gerontol 21(4):575–580.  https://doi.org/10.1093/geronj/21.4.575 Google Scholar
  60. 60.
    Taheri S, Lin L, Austin D, Young T, Mignot E (2004) Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med 1(3):e62.  https://doi.org/10.1371/journal.pmed.0010062 Google Scholar
  61. 61.
    Tilmanne J, Urbain J, Kothare MV, Wouwer AV, Kothare SV (2009) Algorithms for sleep-wake identification using actigraphy: a comparative study and new results. J Sleep Res 18(1):85–98.  https://doi.org/10.1111/j.1365-2869.2008.00706.x Google Scholar
  62. 62.
    Tran T, Phung D, Venkatesh S (2011) Mixed-variate restricted boltzmann machines. In: Asian conference on machine learning, pp 213–229Google Scholar
  63. 63.
    Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, Chastin SF, Altenburg TM, Chinapaw MJ (2017) Sedentary behavior research network (SBRN)—terminology consensus project process and outcome. Int J Behav Nutr Phys Activity 14(1):75.  https://doi.org/10.1186/s12966-017-0525-8 Google Scholar
  64. 64.
    Troiano R, Berrigan D, Dodd K, Masse L, Tilert T, McDowell M (2008) Physical activity in the united states measured by accelerometer. Med Sci Sports Exerc 40(1):181Google Scholar
  65. 65.
    Troiano RP, Macera CA, Ballard-Barb R (2001) Be physically active each day. How can we know? J Nutr 131(2S–1):451S–460SGoogle Scholar
  66. 66.
    Udell M, Horn C, Zadeh R, Boyd S et al (2016) Generalized low rank models. Found Trends Mach Learn 9(1):1–118zbMATHGoogle Scholar
  67. 67.
    Urbanek JK, Spira AP, Di J, Leroux A, Crainiceanu C, Zipunnikov V (2018) Epidemiology of objectively measured bedtime and chronotype in us adolescents and adults: Nhanes 2003–2006. Chronobiol Int 35(3):416–434.  https://doi.org/10.1080/07420528.2017.1411359 Google Scholar
  68. 68.
    van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN, Kivimäki M, Frayling TM, Pack AI, Bucan M, Trenell M et al (2018) Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep 8(1):12,975Google Scholar
  69. 69.
    van Someren EJ, Hagebeuk EE, Lijzenga C, Scheltens P, de Rooij SE, Jonker C, Pot AM, Mirmiran M, Swaab DF (1996) Circadian rest—activity rhythm disturbances in Alzheimer’s disease. BiolPsychiatr 40(4):259–270.  https://doi.org/10.1016/0006-3223(95)00370-3 Google Scholar
  70. 70.
    Van Someren EJW, Swaab DF, Colenda CC, Cohen W, McCall WV, Rosenquist PB (1999) Bright light therapy: improved sensitivity to its effects on rest-activity rhythms in alzheimer patients by application of nonparametric methods. Chronobiol Int 16(4):505–518.  https://doi.org/10.3109/07420529908998724 Google Scholar
  71. 71.
    Varma VR, Dey D, Leroux A, Di J, Urbanek J, Xiao L, Zipunniko V (2017a) Re-evaluating the effect of age on physical activity over the lifespan. Prev Med 101:102–108.  https://doi.org/10.1016/j.ypmed.2017.10.028 Google Scholar
  72. 72.
    Varma VR, Dey D, Leroux A, Di J, Urbanek J, Xiao L, Zipunnikov V (2017b) Total volume of physical activity: TAC, TLAC or TAC(\(\lambda \)). Prev Med.  https://doi.org/10.1016/j.ypmed.2017.10.028 Google Scholar
  73. 73.
    Varo JJ, Martínez-González MA, de Irala-Estévez J, Kearney J, Gibney M, Martínez JA (2003) Distribution and determinants of sedentary lifestyles in the European Union. Int J Epidemiol 32(1):138–146.  https://doi.org/10.1093/ije/dyg116 Google Scholar
  74. 74.
    Wanigatunga AA, Simonsick EM, Zipunnikov V, Spira AP, Studenski S, Ferrucci L (2018) Perceived fatigability and objective physical activity in mid- to late-life. J Gerontol 73(5):630–635.  https://doi.org/10.1093/gerona/glx181 Google Scholar
  75. 75.
    Witting W, Kwa I, Eikelenboom P, Mirmiran M, Swaab D (1990) Alterations in the circadian rest-activity rhythm in aging and Alzheimer’s disease. Biol Psychiatr 27(6):563–572Google Scholar
  76. 76.
    Wold H (2004) Partial least squares. In: Encyclopedia of statistical sciences, John, Hoboken.  https://doi.org/10.1002/0471667196.ess1914
  77. 77.
    Wolff-Hughes DL, Fitzhugh EC, Bassett DR, Churilla JR (2015) Waist-worn actigraphy: population-referenced percentiles for total activity counts in us adults. J Phys Activity Health 12(4):447–453Google Scholar
  78. 78.
    Xiao L, Zipunnikov V, Ruppert D, Crainiceanu C (2016) Fast covariance estimation for high-dimensional functional data. Stat Comput 26(1–2):409–421MathSciNetzbMATHGoogle Scholar

Copyright information

© International Chinese Statistical Association 2019

Authors and Affiliations

  • Junrui Di
    • 1
    Email author
  • Adam Spira
    • 2
    • 3
    • 4
  • Jiawei Bai
    • 5
  • Jacek Urbanek
    • 6
  • Andrew Leroux
    • 1
  • Mark Wu
    • 7
  • Susan Resnick
    • 8
  • Eleanor Simonsick
    • 8
  • Luigi Ferrucci
    • 8
  • Jennifer Schrack
    • 2
    • 9
  • Vadim Zipunnikov
    • 2
    • 10
  1. 1.Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Johns Hopkins Center on Aging and HealthBaltimoreUSA
  3. 3.Department of Mental HealthJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  4. 4.Department of Psychiatry and Behavioral SciencesJohns Hopkins School of MedicineBaltimoreUSA
  5. 5.Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  6. 6.Division of Geriatric Medicine and Gerontology, Department of MedicineJohns Hopkins University School of MedicineBaltimoreUSA
  7. 7.Department of Neurology and NeuroscienceJohns Hopkins University School of MedicineBaltimoreUSA
  8. 8.Intramural Research ProgramNational Institute on Aging, National Institutes of HealthBaltimoreUSA
  9. 9.Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  10. 10.Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA

Personalised recommendations