Limb accelerations during sleep are related to measures of strength, sensation, and spasticity among individuals with spinal cord injury

Garcia-Masso X, Serra-Ano P, Garcia-Raffi LM, Sanchez-Perez EA, Lopez-Pascual J, Gonzalez LM. Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheelchair users with spinal cord injury. Spinal Cord. 2013;51(12):898–903. doi:https://doi.org/10.1038/sc.2013.85.

Article  CAS  PubMed  Google Scholar 

Warms CA, Belza BL. Actigraphy as a measure of physical activity for wheelchair users with spinal cord injury. Nurs Res. 2004;53(2):136–43.

Article  PubMed  Google Scholar 

Spivak E, Oksenberg A, Catz A. The feasibility of sleep assessment by actigraph in patients with tetraplegia. Spinal Cord. 2007;45(12):765–70. doi:https://doi.org/10.1038/sj.sc.3102040.

Article  CAS  PubMed  Google Scholar 

Albert MV, Azeze Y, Courtois M, Jayaraman A. In-lab versus at-home activity recognition in ambulatory subjects with incomplete spinal cord injury. J Neuroeng Rehabil. 2017;14(1):10. doi:https://doi.org/10.1186/s12984-017-0222-5.

Article  PubMed  PubMed Central  Google Scholar 

Albaum E, Quinn E, Sedaghatkish S, Singh P, Watkins A, Musselman K, et al. Accuracy of the Actigraph wGT3x-BT for step counting during inpatient spinal cord rehabilitation. Spinal Cord. 2019. doi:https://doi.org/10.1038/s41393-019-0254-8.

Article  PubMed  Google Scholar 

Rigot SK, Boninger ML, Ding D, McKernan G, Field-Fote EC, Hoffman J, et al. Towards Improving the Prediction of Functional Ambulation after Spinal Cord Injury Through the Inclusion of Limb Accelerations During Sleep and Personal Factors. Arch Phys Med Rehab. 2021. doi:https://doi.org/10.1016/j.apmr.2021.02.029.

Article  Google Scholar 

Lavigne G, Zucconi M, Castronovo C, Manzini C, Marchettini P, Smirne S. Sleep arousal response to experimental thermal stimulation during sleep in human subjects free of pain and sleep problems. Pain. 2000;84(2–3):283–90. doi:https://doi.org/10.1016/s0304-3959(99)00213-4.

Article  CAS  PubMed  Google Scholar 

Kato T, Montplaisir JY, Lavigne GJ. Experimentally induced arousals during sleep: a cross-modality matching paradigm. J Sleep Res. 2004;13(3):229–38. doi:https://doi.org/10.1111/j.1365-2869.2004.00409.x.

Article  CAS  PubMed  Google Scholar 

Granat MH, Edmond P. The application of air bag technology: an objective clinical measure of involuntary muscle spasm. Spinal Cord. 1999;37(7):501–7. doi:https://doi.org/10.1038/sj.sc.3100864.

Article  CAS  PubMed  Google Scholar 

Baunsgaard CB, Nissen UV, Christensen KB, Biering-Sorensen F. Modified Ashworth scale and spasm frequency score in spinal cord injury: reliability and correlation. Spinal Cord. 2016;54(9):702–8. doi:https://doi.org/10.1038/sc.2015.230.

Article  CAS  PubMed  Google Scholar 

Rahimi F, Salahshour N, Eyvazpour R, Azghani MR. An accelerometer-based objective assessment of spasticity: A simple pendulum model to evaluate outcome measures. Proceedings of the 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME): IEEE; 2020.

Fowler EG, Nwigwe AI, Ho TW. Sensitivity of the pendulum test for assessing spasticity in persons with cerebral palsy. Dev Med Child Neurol. 2000;42(3):182–9. doi:https://doi.org/10.1017/s0012162200000323.

Article  CAS  PubMed  Google Scholar 

Phadke CP, Balasubramanian CK, Ismail F, Boulias C. Revisiting physiologic and psychologic triggers that increase spasticity. Am J Phys Med Rehabil. 2013;92(4):357–69. doi:https://doi.org/10.1097/PHM.0b013e31827d68a4.

Article  PubMed  Google Scholar 

Fleuren JF, Voerman GE, Snoek GJ, Nene AV, Rietman JS, Hermens HJ. Perception of lower limb spasticity in patients with spinal cord injury. Spinal Cord. 2009;47(5):396–400. doi:https://doi.org/10.1038/sc.2008.153.

Article  CAS  PubMed  Google Scholar 

Gadotti IC, Vieira ER, Magee DJ. Importance and clarification of measurement properties in rehabilitation. Rev Bras Fisioter. 2006;10(2):137–46. doi:https://doi.org/10.1590/S1413-35552006000200002.

Article  Google Scholar 

Engel-Haber E, Zeilig G, Haber S, Worobey L, Kirshblum S. The effect of age and injury severity on clinical prediction rules for ambulation among individuals with spinal cord injury. Spine J 2020. doi: https://doi.org/10.1016/j.spinee.2020.05.551.

Myaskovsky L, Gao S, Hausmann LRM, Bornemann KR, Burkitt KH, Switzer GE, et al. How Are Race, Cultural, and Psychosocial Factors Associated With Outcomes in Veterans With Spinal Cord Injury? Arch Phys Med Rehabil 2017;98(9):1812-20 e3. doi: https://doi.org/10.1016/j.apmr.2016.12.015.

Cleeland CS, Ryan K. The brief pain inventory. Pain Research Group; 1991.

Widerstrom-Noga E, Biering-Sorensen F, Bryce T, Cardenas DD, Finnerup NB, Jensen MP, et al. The international spinal cord injury pain basic data set. Spinal Cord. 2008;46(12):818–23. doi:https://doi.org/10.1038/sc.2008.64.

Article  CAS  PubMed  Google Scholar 

Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30(6):473–83.

Article  PubMed  Google Scholar 

Mollayeva T, Thurairajah P, Burton K, Mollayeva S, Shapiro CM, Colantonio A. The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: A systematic review and meta-analysis. Sleep Med Rev. 2016;25:52–73. doi:https://doi.org/10.1016/j.smrv.2015.01.009.

Article  PubMed  Google Scholar 

LaVela SL, Burns SP, Goldstein B, Miskevics S, Smith B, Weaver FM. Dysfunctional sleep in persons with spinal cord injuries and disorders. Spinal Cord. 2012;50(9):682–5. doi:https://doi.org/10.1038/sc.2012.31.

Article  CAS  PubMed  Google Scholar 

Schoenborn CA, Adams PF. Sleep Duration as a Correlate of Smoking, Alcohol Use, Leisure-Time Physical Inactivity, and Obesity Among Adults: United States, 2004–2006. NCHS Health & Stats; 2008.

Hammell KW, Miller WC, Forwell SJ, Forman BE, Jacobsen BA. Fatigue and spinal cord injury: a qualitative analysis. Spinal Cord. 2009;47(1):44–9. doi:https://doi.org/10.1038/sc.2008.68.

Article  CAS  PubMed  Google Scholar 

Ahn SH, Park HW, Lee BS, Moon HW, Jang SH, Sakong J, et al. Gabapentin effect on neuropathic pain compared among patients with spinal cord injury and different durations of symptoms. Spine (Phila Pa 1976). 2003;28(4):341–6. doi:https://doi.org/10.1097/01.BRS.0000048464.57011.00. discussion 6–7.

Article  Google Scholar 

Usui A, Ishizuka Y, Obinata I, Okado T, Fukuzawa H, Kanba S. Validity of sleep log compared with actigraphic sleep-wake state II. J Neuropsychiatry Clin Neurosci. 1999;53(2):183–4. doi:https://doi.org/10.1046/j.1440-1819.1999.00529.x.

Article  CAS  Google Scholar 

Noor MHM, Salcic Z, Wang KIK. Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer. Pervasive Mob Comput. 2017;38:41–59. doi:https://doi.org/10.1016/j.pmcj.2016.09.009.

Article  Google Scholar 

Choi S, Shin YB, Kim S-Y, Kim J. A novel sensor-based assessment of lower limb spasticity in children with cerebral palsy. J Neuroeng Rehabil. 2018;15(1):45. doi:https://doi.org/10.1186/s12984-018-0388-5.

Article  PubMed  PubMed Central  Google Scholar 

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(11):e0142533. doi:https://doi.org/10.1371/journal.pone.0142533.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lugade V, Fortune E, Morrow M, Kaufman K. Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection. Med Eng Phys. 2014;36(2):169–76. doi:https://doi.org/10.1016/j.medengphy.2013.06.005.

Article  PubMed  Google Scholar 

Twomey N, Diethe T, Fafoutis X, Elsts A, McConville R, Flach P, et al. A comprehensive study of activity recognition using accelerometers. Informatics. 2018;5(2):27. doi:https://doi.org/10.3390/informatics5020027.

Article  Google Scholar 

Wang X, Smith KA, Hyndman RJ. Dimension reduction for clustering time series using global characteristics. In: Sunderam VS, van Albada GD, Sloot PMA, Dongarra J, editors. Computational science – ICCS 2005. Berlin: Springer Berlin Heidelberg; 2005. pp. 792–5.

Chapter  Google Scholar 

Bayat A, Pomplun M, Tran DA. A Study on Human Activity Recognition Using Accelerometer Data from Smartphones. Procedia Comput Sci. 2014;34:450–7. doi:https://doi.org/10.1016/j.procs.2014.07.009.

Article  Google Scholar 

Sejdić E, Lowry KA, Bellanca J, Redfern MS, Brach JS. A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains. IEEE Trans Neural Syst Rehabil Eng. 2014;22(3):603–12. doi:https://doi.org/10.1109/TNSRE.2013.2265887.

Article  PubMed  Google Scholar 

Krishnan S, Athavale Y. Trends in biomedical signal feature extraction. Biomed Signal Process Control. 2018;43:41–63. doi:https://doi.org/10.1016/j.bspc.2018.02.008.

Article  Google Scholar 

Tedesco S, Urru A, O’Flynn B. Spectral and time-frequency domains features for quantitative lower-limb rehabilitation monitoring via wearable inertial sensors. Proceedings of the 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS): IEEE; 2017.

Rosati S, Balestra G, Knaflitz M. Comparison of different sets of features for human activity recognition by wearable sensors. Sens Basel Sens 2018;18(12). doi:https://doi.org/10.3390/s18124189.

Machado IP, Luísa Gomes A, Gamboa H, Paixão V, Costa RM. Human activity data discovery from triaxial accelerometer sensor: Non-supervised learning sensitivity to feature extraction parametrization. Inf Process Manag. 2015;51(2):204–14. doi:https://doi.org/10.1016/j.ipm.2014.07.008.

Article  Google Scholar 

Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W. Activity recognition using a single accelerometer placed at the wrist or ankle. Med Sci Sports Exerc. 2013;45(11):2193–203. doi:https://doi.org/10.1249/MSS.0b013e31829736d6.

Article  PubMed  PubMed Central  Google Scholar 

Attal F, Mohammed S, Dedabrishvili M, Chamroukhi F, Oukhellou L, Amirat Y. Physical human activity recognition using wearable sensors. Sens Basel Sens. 2015;15(12):31314–38. doi:https://doi.org/10.3390/s151229858.

Article  Google Scholar 

Athavale Y, Krishnan S, Dopsa DD, Berneshawi AG, Nouraei H, Raissi A, et al. Advanced signal analysis for the detection of periodic limb movements from bilateral ankle actigraphy. J Sleep Res. 2017;26(1):14–20. doi:https://doi.org/10.1111/jsr.12438.

Article  PubMed  Google Scholar 

Zhang S, Rowlands AV, Murray P, Hurst TL. Physical activity classification using the GENEA wrist-worn accelerometer. Med Sci Sports Exerc. 2012;44(4):742–8. doi:https://doi.org/10.1249/MSS.0b013e31823bf95c.

Article  PubMed  Google Scholar 

Kimura S, Ozasa S, Nomura K, Yoshioka K, Endo F. Estimation of muscle strength from actigraph data in Duchenne muscular dystrophy. Pediatr Int. 2014;56(5):748–52. doi:https://doi.org/10.1111/ped.12348.

Article  PubMed  Google Scholar 

Sforza E, Johannes M, Claudio B. The PAM-RL ambulatory device for detection of periodic leg movements: a validation study. Sleep Med. 2005;6(5):407–13. doi:https://doi.org/10.1016/j.sleep.2005.01.004.

留言 (0)

沒有登入
gif