Applying time series analyses on continuous accelerometry data-A clinical example in older adults with and without cognitive impairment

Torsten Rackoll, Konrad Neumann, Sven Passmann, Ulrike Grittner, Nadine Külzow, Julia Ladenbauer, Agnes Flöel, Torsten Rackoll, Konrad Neumann, Sven Passmann, Ulrike Grittner, Nadine Külzow, Julia Ladenbauer, Agnes Flöel

Abstract

Introduction: Many clinical studies reporting accelerometry data use sum score measures such as percentage of time spent in moderate to vigorous activity which do not provide insight into differences in activity patterns over 24 hours, and thus do not adequately depict circadian activity patterns. Here, we present an improved functional data analysis approach to model activity patterns and circadian rhythms from accelerometer data. As a use case, we demonstrated its application in patients with mild cognitive impairment (MCI) and age-matched healthy older volunteers (HOV).

Methods: Data of two studies were pooled for this analysis. Following baseline cognitive assessment participants were provided with accelerometers for seven consecutive days. A function on scalar regression (FoSR) approach was used to analyze 24 hours accelerometer data.

Results: Information on 48 HOV (mean age 65 SD 6 years) and 18 patients with MCI (mean age 70, SD 8 years) were available for this analysis. MCI patients displayed slightly lower activity in the morning hours (minimum relative activity at 6:05 am: -41.3%, 95% CI -64.7 to -2.5%, p = 0.031) and in the evening (minimum relative activity at 21:40 am: -48.4%, 95% CI -68.5 to 15.4%, p = 0.001) as compared to HOV after adjusting for age and sex.

Discussion: Using a novel approach of FoSR, we found timeframes with lower activity levels in MCI patients compared to HOV which were not evident if sum scores of amount of activity were used, possibly indicating that changes in circadian rhythmicity in neurodegenerative disease are detectable using easy-to-administer accelerometry.

Clinical trials: Effects of Brain Stimulation During Nocturnal Sleep on Memory Consolidation in Patients With Mild Cognitive Impairments, ClinicalTrial.gov identifier: NCT01782391. Effects of Brain Stimulation During a Daytime Nap on Memory Consolidation in Patients With Mild Cognitive Impairment, ClinicalTrial.gov identifier: NCT01782365.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Comparison of absolute and relative…
Fig 1. Comparison of absolute and relative activity distribution between MCI and HOV.
Distribution of a) mean absolute activity between MCI and HOV over time and b) mean relative activity between MCI and HOV over time with 95% Confidence Interval (CI) adjusted for age. Horizontal lines in Fig 1A) displays the average daily activity of log-transformed data from fitted curves of respective groups. The black line in Fig 1B) displays a relative activity. A value of one indicates no difference of activity between the two groups.
Fig 2. Activity level by time for…
Fig 2. Activity level by time for two sample participants with fitted curve from FoSR.
Distribution of absolute activity over a five day period for A) one HOV sample participant and B) one MCI sample participant. Red lines denote the wavelet smoothing.

References

    1. Perry B, Herrington W, Goldsack JC, Grandinetti CA, Vasisht KP, Landray MJ, et al.. Use of Mobile Devices to Measure Outcomes in Clinical Research, 2010–2016: A Systematic Literature Review. Digit Biomarkers. 2018;2(1):11–30. 10.1159/000486347
    1. García-Hermoso A, Notario-Pacheco B, Recio-Rodríguez JI, Martínez-Vizcaíno V, Rodrigo de Pablo E, Magdalena Belio JF, et al.. Sedentary behaviour patterns and arterial stiffness in a Spanish adult population—The EVIDENT trial. Atherosclerosis. 2015;243(2):516–22. 10.1016/j.atherosclerosis.2015.10.004
    1. Hamer M, Coombs N, Stamatakis E. Associations between objectively assessed and self-reported sedentary time with mental health in adults: An analysis of data from the health survey for England. BMJ Open. 2014;4(3):1–7. 10.1136/bmjopen-2013-004580
    1. Diaz KM, Howard VJ, Hutto B, Colabianchi N, Vena JE, Safford MM, et al.. Patterns of sedentary behavior and mortality in U.S. middle-aged and older adults a national cohort study. Ann Intern Med. 2017;167(7):465–75. 10.7326/M17-0212
    1. Quante M, Kaplan ER, Rueschman M, Cailler M, Buxton OM, Redline S. Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep. Sleep Heal. 2015;1(4):275–84.
    1. Rosenberger ME, Fulton JE, Buman MP, Troiano RP, Grandner MA, Buchner DM, et al.. The 24-Hour Activity Cycle: A New Paradigm for Physical Activity. Vol. 51, Medicine and Science in Sports and Exercise. 2019. 454–464 p.
    1. Leise TL. Analysis of Nonstationary Time Series for Biological Rhythms Research. J Biol Rhythms. 2017;32(3):187–94. 10.1177/0748730417709105
    1. Refinetti R, Cornélissen G, Halberg F. Procedures for numerical analysis of circadian rhythms. Vol. 38, Biological Rhythm Research. 2007. 275–325 p. 10.1080/09291010600903692
    1. Bai J, Di C, Xiao L, Evenson KR, LaCroix AZ, Crainiceanu CM, et al.. An activity index for raw accelerometry data and its comparison with other activity metrics. PLoS One. 2016;11(8):1–14. 10.1371/journal.pone.0160644
    1. Xiao L, Huang L, Schrack JA, Ferrucci L, Zipunnikov V, Crainiceanu CM. Quantifying the lifetime circadian rhythm of physical activity: A covariate-dependent functional approach. Biostatistics. 2015;16(2):352–67. 10.1093/biostatistics/kxu045
    1. Goldsmith J, Liu X, Jacobson JS, Rundle A. New Insights into Activity Patterns in Children, Found Using Functional Data Analyses. Med Sci Sports Exerc. 2016;48(9):1723–9. 10.1249/MSS.0000000000000968
    1. Reisa A. Sperling, Paul S. Aisen, Laurel A. Beckett, David A. Bennet SC. Toward defining the preclinical stages of alzheimer’s disease: Recommendations from the national institute on aging. Alzheimer’s Dement. 2011;7(3):280–92.
    1. Brasure M, Desai P, Davila H, Nelson VA, Calvert C, Jutkowitz E, et al.. Physical activity interventions in preventing cognitive decline and alzheimer-type dementia a systematic review. Vol. 168, Annals of Internal Medicine. American College of Physicians; 2018. p. 30–8. 10.7326/M17-1528
    1. Bahar-Fuchs A, Martyr A, Goh AMY, Sabates J, Clare L. Cognitive training for people with mild to moderate dementia. Vol. 2019, Cochrane Database of Systematic Reviews. John Wiley and Sons Ltd; 2019.
    1. Ngandu T, Lehtisalo J, Solomon A, Levälahti E, Ahtiluoto S, Antikainen R, et al.. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet. 2015;385(9984):2255–63. 10.1016/S0140-6736(15)60461-5
    1. Mander BA, Winer J, Jagust WJ, Walker MP. Sleep: A Novel Mechanistic Pathway, Biomarker, and Treatment Target in the Pathology of Alzheimer’s Disease? Trends Neurosci. 2016;xx:1–15.
    1. Leng Y, Musiek ES, Hu K, Cappuccio FP, Yaffe K. Association between circadian rhythms and neurodegenerative diseases. Lancet Neurol. 2019;18(3):307–18. 10.1016/S1474-4422(18)30461-7
    1. Schmidt C, Peigneux P, Cajochen C. Age-related changes in sleep and circadian rhythms: Impact on cognitive performance and underlying neuroanatomical networks. Front Neurol. 2012;3:1–11. 10.3389/fneur.2012.00001
    1. Coogan AN, Schutová B, Husung S, Furczyk K, Baune BT, Kropp P, et al.. The circadian system in Alzheimer’s disease: Disturbances, mechanisms, and opportunities. Biol Psychiatry. 2013;74(5):333–9. 10.1016/j.biopsych.2012.11.021
    1. Kondratova AA, Kondratova R V. Circadian clock and pathology of the ageing brain. Nat Rev Neurosci. 2012;13(5):325–35. 10.1038/nrn3208
    1. Lim ASP, Kowgier M, Yu L, Buchman AS, Bennett DA. Sleep Fragmentation and the Risk of Incident Alzheimer’s Disease and Cognitive Decline in Older Persons. Sleep. 2013;36(7). 10.5665/sleep.2802
    1. Musiek ES, Bhimasani M, Zangrilli MA, Morris JC, Holtzman DM, Ju Y-ES. Circadian Rest-Activity Pattern Changes in Aging and Preclinical Alzheimer Disease. JAMA Neurol. 2018. May;75(5):582–90. 10.1001/jamaneurol.2017.4719
    1. Satlin A, Volicer L, Stopa EG, Harper D. Circadian locomotor activity and core-body temperature rhythms in Alzheimer’s disease. Neurobiol Aging. 1995;16(5):765–71. 10.1016/0197-4580(95)00059-n
    1. Oyegbami O, Collins HM, Pardon M-C, Ebling FJP, Heery DM, Moran PM. Abnormal Clock Gene Expression and Locomotor Activity Rhythms in Two Month-Old Female APPSwe/PS1dE9 Mice. Curr Alzheimer Res. 2017;14(8):850–60. 10.2174/1567205014666170317113159
    1. Ladenbauer J, Külzow N, Passmann S, Antonenko D, Grittner U, Tamm S, et al.. Brain stimulation during an afternoon nap boosts slow oscillatory activity and memory consolidation in older adults. Neuroimage. 2016. November 15;142:311–23. 10.1016/j.neuroimage.2016.06.057
    1. Paßmann S, Külzow N, Ladenbauer J, Antonenko D, Grittner U, Tamm S, et al.. Boosting Slow Oscillatory Activity Using tDCS during Early Nocturnal Slow Wave Sleep Does Not Improve Memory Consolidation in Healthy Older Adults. Brain Stimul. 2016;9(5):730–9. 10.1016/j.brs.2016.04.016
    1. Kühner C, Bürger C, Keller F, Hautzinger M. Reliabilität und Validität des revidierten Beck-Depressionsinventars (BDI-II). Nervenarzt. 2007. June 7;78(6):651–6. 10.1007/s00115-006-2098-7
    1. Petersen RC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol. 2004;62(7):1160–3; discussion 1167.
    1. Jarret H, Fitzgerald L, Routen AC. Inter-instrument reliability of the Actigraph GT3X+ Ambulatory Activity Monitor during free-living conditions in adults. J Phys Act Health. 2015;12(3):382–7. 10.1123/jpah.2013-0070
    1. Marino M, Li Y, Rueschman MN, Winkelman JW, Ellenbogen JM, Solet JM, et al.. Measuring Sleep: Accuracy, Sensitivity, and Specificity of Wrist Actigraphy Compared to Polysomnography. Sleep. 2013;36(11):1747–55. 10.5665/sleep.3142
    1. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, Mcdowell M. Physical Activity in the United States measured by Accelerometer. Med Sci Sport Exerc. 2008;40(1):181–8. 10.1249/mss.0b013e31815a51b3
    1. Statistics C for DC and PNC for H. NHANES Questionnaires, Datasets, and Related Documentation [Internet]. [cited 2021 Feb 20]. Available from: .
    1. Quante M, Mariani S, Weng J, Marinac CR, Kaplan ER, Rueschman M, et al.. Zeitgebers and their association with rest-activity patterns. Chronobiol Int. 2019;36(2):203–13. 10.1080/07420528.2018.1527347
    1. Crainiceanu CM, Staicu AM, Ray S, Punjabi N. Bootstrap-based inference on the difference in the means of two correlated functional processes. Stat Med. 2012;31(26):3223–40. 10.1002/sim.5439
    1. Aldrich E. wavelets: Functions for Computing Wavelet Filters, Wavelet Transforms and Multiresolution Analyses. 2019; Available from:
    1. Xiao L, Zipunnikov V, Ruppert D, Crainiceanu C. Fast covariance estimation for high-dimensional functional data. Stat Comput. 2016;26(1–2):409–21. 10.1007/s11222-014-9485-x
    1. Percival DB, Walden AT. Wavelet Methods for Time Series Analysis [Internet]. Cambridge: Cambridge University Press; 2000. Available from: .
    1. Hastie T, Tibshirani R, Friedman JH (Jerome H. The elements of statistical learning: data mining, inference, and prediction. Second Edi. Springer Series in Statistics; 2009. 745 p.
    1. Matthews CE, Hagströmer M, M PD, Bowles HR. Best Practices for Using Physical Activity Monitors. Med Sci Sports Exerc. 2013;44(18):1–17.
    1. Ortiz-Tudela E, Martinez-Nicolas A, Díaz-Mardomingo C, García-Herranz S, Pereda-Pérez I, Valencia A, et al.. The Characterization of Biological Rhythms in Mild Cognitive Impairment. Biomed Res Int. 2014;2014:1–7. 10.1155/2014/524971
    1. Cochrane A, Robertson IH, Coogan AN. Association between circadian rhythms, sleep and cognitive impairment in healthy older adults: An actigraphic study. J Neural Transm. 2012. October 10;119(10):1233–9. 10.1007/s00702-012-0802-2
    1. Stevanovic K, Yunus A, Joly-Amado A, Gordon M, Morgan D, Gulick D, et al.. Disruption of normal circadian clock function in a mouse model of tauopathy. Exp Neurol. 2017;294:58–67. 10.1016/j.expneurol.2017.04.015
    1. Lu Z, Harris TB, Shiroma EJ, Leung J, Kwok T. Patterns of Physical Activity and Sedentary Behavior for Older Adults with Alzheimer’s Disease, Mild Cognitive Impairment, and Cognitively Normal in Hong Kong. J Alzheimer’s Dis. 2018;66(4):1453–62. 10.3233/JAD-180805
    1. Weissová K, Bartoš A, Sládek M, Nováková M, Sumová A. Moderate Changes in the Circadian System of Alzheimer’s Disease Patients Detected in Their Home Environment. Cermakian N, editor. PLoS One. 2016;11(1):e0146200. 10.1371/journal.pone.0146200

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir