[en] The possibility to continuously record locomotor movements using accelerometers (actigraphy) has allowed field studies of sleep and rest-activity patterns. It has also enabled large-scale data collections, opening new avenues for research. However, each brand of actigraph devices encodes recordings in its own format and closed-source proprietary softwares are typically used to read and analyse actigraphy data. In order to provide an alternative to these softwares, we developed a comprehensive open-source toolbox for actigraphy data analysis, pyActigraphy. It allows researchers to read actigraphy data from 7 different file formats and gives access to a variety of rest-activity rhythm variables, automatic sleep detection algorithms and more advanced signal processing techniques. Besides, in order to empower researchers and clinicians with respect to their analyses, we created a series of interactive tutorials that illustrate how to implement the key steps of typical actigraphy data analyses. As an open-source project, all kind of user’s contributions to our toolbox are welcome. As increasing evidence points to the predicting value of rest-activity patterns derived from actigraphy for brain integrity, we believe that the development of the pyActigraphy package will not only benefit the sleep and chronobiology research, but also the neuroscientific community at large.
Research Center/Unit :
GIGA CRC (Cyclotron Research Center) In vivo Imaging-Aging & Memory - ULiège
Disciplines :
Neurosciences & behavior
Author, co-author :
Hammad, Grégory ; Université de Liège - ULiège > GIGA CRC In vivo Imaging - Sleep and chronobiology
Reyt, Mathilde ; Université de Liège - ULiège > GIGA CRC In vivo Imaging - Sleep and chronobiology
Beliy, Nikita ; Université de Liège - ULiège > GIGA CRC In vivo Imaging - Aging & Memory
Baillet, Marion ; Université de Liège - ULiège > GIGA CRC In vivo Imaging
Deantoni, Michele ; Université de Liège - ULiège > GIGA CRC In vivo Imaging - Sleep and chronobiology
Lesoinne, Alexia ; Université de Liège - ULiège > GIGA CRC In vivo Imaging - Sleep and chronobiology
Muto, Vincenzo ; Université de Liège - ULiège > GIGA CRC In vivo Imaging
Schmidt, Christina ; Université de Liège - ULiège > GIGA CRC In vivo Imaging - Sleep and chronobiology
Language :
English
Title :
pyActigraphy: Open-source python package for actigraphy data visualization and analysis
Publication date :
19 October 2021
Journal title :
PLoS Computational Biology
ISSN :
1553-734X
eISSN :
1553-7358
Publisher :
Public Library of Science, United States - California
Volume :
17
Issue :
10
Pages :
e1009514
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
H2020 - 757763 - COGNAP - To nap or not to nap? Why napping habits interfere with cognitive fitness in ageing
Name of the research project :
COGNAP
Funders :
ERC - European Research Council EC - European Commission
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Bibliography
Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak CP. The Role of Actigraphy in the Study of Sleep and Circadian Rhythms. Sleep. 2003; 26(3):342–392. https://doi.org/10.1093/sleep/26.3.342 PMID: 12749557
Oosterman JM, Van Someren EJW, Vogels RLCC, Van Harten B, Scherder EJAA. Fragmentation of the rest-activity rhythm correlates with age-related cognitive deficits. Journal of sleep research. 2009; 18(1):129–35. https://doi.org/10.1111/j.1365-2869.2008.00704.x PMID: 19250179
Van Someren EJW, Oosterman JM, Van Harten B, Vogels RL, Gouw AA, Weinstein HC, et al. Medial temporal lobe atrophy relates more strongly to sleep-wake rhythm fragmentation than to age or any other known risk. Neurobiology of Learning and Memory. 2018; p. 0–1. PMID: 29864525
Lim ASP, Yu L, Costa MD, Leurgans SE, Buchman AS, Bennett DA, et al. Increased Fragmentation of Rest-Activity Patterns Is Associated With a Characteristic Pattern of Cognitive Impairment in Older Individuals. Sleep. 2012; 35(5):633–640. https://doi.org/10.5665/sleep.1820 PMID: 22547889
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):1027–1032. https://doi.org/10.5665/sleep.2802 PMID: 23814339
Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, et al. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study. PLOS ONE. 2017; 12(2):e0169649. https://doi.org/10.1371/journal.pone.0169649 PMID: 28146576
Zhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, et al. The National Sleep Research Resource: towards a sleep data commons. Journal of the American Medical Informatics Association. 2018; 25:1351–1358. https://doi.org/10.1093/jamia/ocy064 PMID: 29860441
Ferguson A, Lyall LM, Ward J, Strawbridge RJ, Cullen B, Graham N, et al. Genome-Wide Association Study of Circadian Rhythmicity in 71,500 UK Biobank Participants and Polygenic Association with Mood Instability. EBioMedicine. 2018; 35:279–287. https://doi.org/10.1016/j.ebiom.2018.08.004 PMID: 30120083
Jones SE, van Hees VT, Mazzotti DR, Marques-Vidal P, Sabia S, van der Spek A, et al. Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour. Nature Communications. 2019; 10(1):1585. https://doi.org/10.1038/s41467-019-09576-1 PMID: 30952852
Erickson KI, Leckie RL, Weinstein AM. Physical activity, fitness, and gray matter volume. Neurobiology of Aging. 2014; 35(2 II):S20–S28. https://doi.org/10.1016/j.neurobiolaging.2014.03.034 PMID: 24952993
Hamer M, Sharma N, Batty GD. Association of objectively measured physical activity with brain structure: UK Biobank study. Journal of Internal Medicine. 2018; 284(4):439–443. https://doi.org/10.1111/joim.12772 PMID: 29776014
Lim ASP, Fleischman DA, Dawe RJ, Yu L, Arfanakis K, Buchman AS, et al. Regional Neocortical Gray Matter Structure and Sleep Fragmentation in Older Adults. Sleep. 2016; 39(1):227–235. https://doi.org/10.5665/sleep.5354 PMID: 26350471
Baillet M, Dilharreguy B, Pérès K, Dartigues JF, Mayo W, Catheline G. Activity/rest cycle and disturbances of structural backbone of cerebral networks in aging. NeuroImage. 2017; 146:814–820. https://doi.org/10.1016/j.neuroimage.2016.09.051 PMID: 27664829
Meyer C, Muto V, Jaspar M, Kussé C, Lambot E, Chellappa SL, et al. Seasonality in human cognitive brain responses. Proceedings of the National Academy of Sciences. 2016; 113(11):3066–3071. https://doi.org/10.1073/pnas.1518129113 PMID: 26858432
Muto V, Jaspar M, Meyer C, Kusse C, Chellappa SL, Degueldre C, et al. Local modulation of human brain responses by circadian rhythmicity and sleep debt. Science. 2016; 353(6300):687–690. https://doi.org/10.1126/science.aad2993 PMID: 27516598
Borazio M, Berlin E, Kucukyildiz N, Scholl P, Laerhoven KV. Towards benchmarked sleep detection with wrist-worn sensing units. Proceedings—2014 IEEE International Conference on Healthcare Informatics, ICHI 2014. 2014; p. 125–134.
van Hees VT, Sabia S, Anderson KN, Denton SJ, Oliver J, Catt M, et al. A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer. PLOS ONE. 2015; 10:e0142533. https://doi.org/10.1371/journal.pone.0142533 PMID: 26569414
te Lindert BHW, Van Someren EJW. Sleep Estimates Using Microelectromechanical Systems (MEMS). Sleep. 2013; 36(5):781–789. https://doi.org/10.5665/sleep.2648 PMID: 23633761
Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Medicine and science in sports and exercise. 2011; 43(2):357–64. https://doi.org/10.1249/MSS.0b013e3181ed61a3 PMID: 20581716
Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Medicine and science in sports and exercise. 2008; 40(1):181–8. https://doi.org/10.1249/mss.0b013e31815a51b3 PMID: 18091006
Zeitzer JM, Blackwell T, Hoffman AR, Cummings S, Ancoli-Israel S, Stone K. Daily Patterns of Accelerometer Activity Predict Changes in Sleep, Cognition, and Mortality in Older Men. Journals of Gerontology—Series A Biological Sciences and Medical Sciences. 2018; 73(5):682–687. https://doi.org/10.1093/gerona/glw250 PMID: 28158467
Gershon A, Ram N, Johnson SL, Harvey AG, Zeitzer JM. Daily Actigraphy Profiles Distinguish Depressive and Interepisode States in Bipolar Disorder. Clinical Psychological Science. 2016; 4(4):641–650. https://doi.org/10.1177/2167702615604613 PMID: 27642544
Inc PT. Collaborative data science; 2015. Available from: https://plot.ly.
Witting W, Kwa IH, Eikelenboom P, Mirmiran M, Swaab DF. Alterations in the circadian rest-activity rhythm in aging and Alzheimer’s disease. Biological Psychiatry. 1990; 27(6):563–572. https://doi.org/10.1016/0006-3223(90)90523-5 PMID: 2322616
Van Someren EJW, Lijzenga C, Mirmiran M, Swaab DF. Long-Term Fitness Training Improves the Circadian Rest-Activity Rhythm in Healthy Elderly Males. Journal of Biological Rhythms. 1997; 12(2):146–156. https://doi.org/10.1177/074873049701200206 PMID: 9090568
Lim ASP, Yu L, Costa MD, Buchman AS, Bennett DA, Leurgans SE, et al. Quantification of the Fragmentation of Rest-Activity Patterns in Elderly Individuals Using a State Transition Analysis. Sleep. 2011; 34(11):1569–1581. https://doi.org/10.5665/sleep.1400 PMID: 22043128
Oakley N. Validation with polysomnography of the Sleepwatch sleep/wake scoring algorithm used by the Actiwatch activity monitoring system. Bend: Mini Mitter, Cambridge Neurotechnology. 1997;.
Sadeh A, Sharkey M, Carskadon MA. Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues. Sleep. 1994; 17(3):201–207. https://doi.org/10.1093/sleep/17.3.201 PMID: 7939118
Crespo C, Aboy M, Fernández JR, Mojón A. Automatic identification of activity-rest periods based on actigraphy. Medical & Biological Engineering & Computing. 2012; 50(4):329–340. https://doi.org/10.1007/s11517-012-0875-y
Roenneberg T, Keller LK, Fischer D, Matera JL, Vetter C, Winnebeck EC. Human activity and rest in situ. Methods in enzymology. 2015; 552:257–283. https://doi.org/10.1016/bs.mie.2014.11.028 PMID: 25707281
Phillips AJK, Clerx WM, O’Brien CS, Sano A, Barger LK, Picard RW, et al. Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Scientific Reports. 2017; 7(1):3216. https://doi.org/10.1038/s41598-017-03171-4 PMID: 28607474
Lunsford-Avery JR, Engelhard MM, Navar AM, Kollins SH. Validation of the Sleep Regularity Index in Older Adults and Associations with Cardiometabolic Risk. Scientific Reports. 2018; 8(1):14158. https://doi.org/10.1038/s41598-018-32402-5 PMID: 30242174
Refinetti R, Cornélissen G, Halberg F. Procedures for numerical analysis of circadian rhythms. Biological Rhythm Research. 2007; 38(4):275–325. https://doi.org/10.1080/09291010600903692 PMID: 23710111
Peng CK, Buldyrev SV, Havlin S, Simons M, Stanley HE, Goldberger AL. Mosaic organization of DNA nucleotides. Physical Review E. 1994; 49(2):1685–1689. https://doi.org/10.1103/PhysRevE.49.1685 PMID: 9961383
Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science. 1995; 5(1):82–87. https://doi.org/10.1063/1.166141 PMID: 11538314
Hu K, Van Someren EJW, Shea SA, Scheer FAJL. Reduction of scale invariance of activity fluctuations with aging and Alzheimer’s disease: Involvement of the circadian pacemaker. Proceedings of the National Academy of Sciences. 2009; 106(8):2490–2494. https://doi.org/10.1073/pnas.0806087106 PMID: 19202078
Ramsay JO, Silverman BW. Applied Functional Data Analysis: Methods and Case Studies. Springer series in statistics ed. New York: Springer-Verlag; 2002.
Wang J, Xian H, Licis A, Deych E, Ding J, McLeland J, et al. Measuring the impact of apnea and obesity on circadian activity patterns using functional linear modeling of actigraphy data. Journal of Circadian Rhythms. 2011; 9(1):11. https://doi.org/10.1186/1740-3391-9-11 PMID: 21995417
Winnebeck EC, Fischer D, Leise T, Roenneberg T. Dynamics and Ultradian Structure of Human Sleep in Real Life. Current Biology. 2018; 28(1):49–59.e5. https://doi.org/10.1016/j.cub.2017.11.063 PMID: 29290561
Vautard R, Yiou P, Ghil M. Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena. 1992; 58(1-4):95–126. https://doi.org/10.1016/0167-2789(92)90103-T
Golyandina N, Zhigljavsky A. Singular Spectrum Analysis for Time Series. No. January 2013 in SpringerBriefs in Statistics. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013. Available from: http://link.springer.com/10.1007/978-3-642-34913-3.
Fossion R, Rivera AL, Toledo-Roy JC, Ellis J, Angelova M. Multiscale adaptive analysis of circadian rhythms and intradaily variability: Application to actigraphy time series in acute insomnia subjects. PLOS ONE. 2017; 12(7):e0181762. https://doi.org/10.1371/journal.pone.0181762 PMID: 28753669
Lam SK, Pitrou A, Seibert S. Numba: A LLVM-Based Python JIT Compiler. In: Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC. LLVM’15. New York, NY, USA: Association for Computing Machinery; 2015. Available from: https://doi.org/10.1145/2833157.2833162.
Kluyver T, Ragan-Kelley B, Pérez F, Granger BE, Bussonnier M, Frederic J, et al. Jupyter Notebooks—a publishing format for reproducible computational workflows. In: ELPUB; 2016.
Silver A. Collaborative software development made easy. Nature. 2017; 550(7674):143–144. https://doi.org/10.1038/550143a PMID: 28980652
Blume C, Santhi N, Schabus M. ‘nparACT’ package for R: A free software tool for the non-parametric analysis of actigraphy data. MethodsX. 2016; 3:430–435. https://doi.org/10.1016/j.mex.2016.05.006 PMID: 27294030
Doherty A, Smith-Byrne K, Ferreira T, Holmes MV, Holmes C, Pulit SL, et al. GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nature Communications. 2018; 9:5257. https://doi.org/10.1038/s41467-018-07743-4 PMID: 30531941
Walmsley R, Chan S, Smith-Byrne K, Ramakrishnan R, Woodward M, Rahimi K, et al. Reallocating time from device-measured sleep, sedentary behaviour or light physical activity to moderate-to-vigorous physical activity is associated with lower cardiovascular disease risk. medRxiv. 2020;.
Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports. 2018; 8:7961. https://doi.org/10.1038/s41598-018-26174-1 PMID: 29784928
Migueles JH, Rowlands AV, Huber F, Sabia S, van Hees VT. GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data. Journal for the Measurement of Physical Behaviour. 2019; 2(3):188–196. https://doi.org/10.1123/jmpb.2018-0063
Jackson D. OMGUI; 2019. Available from: https://github.com/digitalinteraction/openmovement/wiki/ AX3-GUI.
Eglen SJ, Marwick B, Halchenko YO, Hanke M, Sufi S, Gleeson P, et al. Toward standard practices for sharing computer code and programs in neuroscience. Nature Neuroscience. 2017; 20(6):770–773. https://doi.org/10.1038/nn.4550 PMID: 28542156
Narbutas J, Egroo MV, Chylinski D, González PV, Jimenez CG, Besson G, et al. Cognitive efficiency in late midlife is linked to lifestyle characteristics and allostatic load. Aging. 2019; 11(17):7169–7186. https://doi.org/10.18632/aging.102243 PMID: 31503006
Spitschan M, Garbazza C, Kohl S, Cajochen C. Sleep and circadian phenotype in people without cone-mediated vision: case series of five CNGB3 and two CNGA3 patients. Brain Communications. 2021; p. 1–31. https://doi.org/10.1093/braincomms/fcab159 PMID: 34447932
Loock A, Khan Sullivan A, Reis C, Paiva T, Ghotbi N, Pilz LK, et al. Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep–wake patterns from activity recordings. Journal of Sleep Research. 2021;(April):1–12. PMID: 33960551
Sundararajan K, Georgievska S, te Lindert BHW, Gehrman PR, Ramautar J, Mazzotti DR, et al. Sleep classification from wrist-worn accelerometer data using random forests. Scientific Reports. 2021; 11 (1):1–10. https://doi.org/10.1038/s41598-020-79217-x PMID: 33420133
Muto V, Koshmanova E, Ghaemmaghami P, Jaspar M, Meyer C, Elansary M, et al. Alzheimer’s disease genetic risk and sleep phenotypes in healthy young men: association with more slow waves and daytime sleepiness. Sleep. 2021; 44(1):1–12. https://doi.org/10.1093/sleep/zsaa137
Hammad G, Reyt M, Schmidt C. pyActigraphy: Open-source python package for actigraphy data visualization and analysis; 2019. Available from: https://doi.org/10.5281/zenodo.3379063.
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