Advertisement

Toward Improving the Prediction of Functional Ambulation After Spinal Cord Injury Through the Inclusion of Limb Accelerations During Sleep and Personal Factors

  • Stephanie K. Rigot
    Affiliations
    Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA

    Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
  • Michael L. Boninger
    Affiliations
    Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA

    Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA

    Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA

    Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
  • Dan Ding
    Affiliations
    Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA

    Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
  • Gina McKernan
    Affiliations
    Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

    Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
  • Edelle C. Field-Fote
    Affiliations
    Crawford Research Institute, Shepherd Center, Atlanta, GA

    Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA

    Program in Applied Physiology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA
    Search for articles by this author
  • Jeanne Hoffman
    Affiliations
    Department of Rehabilitation Medicine, University of Washington School of Medicine, Seattle, WA
    Search for articles by this author
  • Rachel Hibbs
    Affiliations
    Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA

    Physical Therapy, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
  • Lynn A. Worobey
    Correspondence
    Corresponding author Lynn A. Worobey, PhD, DPT, ATP, Department of Physical Medicine & Rehabilitation, University of Pittsburgh, 3520 Fifth Ave, Suite 300, Pittsburgh, PA 15213.
    Affiliations
    Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA

    Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA

    Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA

    Physical Therapy, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
Published:April 07, 2021DOI:https://doi.org/10.1016/j.apmr.2021.02.029

      Abstract

      Objective

      To determine if functional measures of ambulation can be accurately classified using clinical measures; demographics; personal, psychosocial, and environmental factors; and limb accelerations (LAs) obtained during sleep among individuals with chronic, motor incomplete spinal cord injury (SCI) in an effort to guide future, longitudinal predictions models.

      Design

      Cross-sectional, 1-5 days of data collection.

      Setting

      Community-based data collection.

      Participants

      Adults with chronic (>1 year), motor incomplete SCI (N=27).

      Interventions

      Not applicable.

      Main Outcome Measures

      Ambulatory ability based on the 10-m walk test (10MWT) or 6-minute walk test (6MWT) categorized as nonambulatory, household ambulator (0.01-0.44 m/s, 1-204 m), or community ambulator (>0.44 m/s, >204 m). A random forest model classified ambulatory ability using input features including clinical measures of strength, sensation, and spasticity; demographics; personal, psychosocial, and environmental factors including pain, environmental factors, health, social support, self-efficacy, resilience, and sleep quality; and LAs measured during sleep. Machine learning methods were used explicitly to avoid overfitting and minimize the possibility of biased results.

      Results

      The combination of LA, clinical, and demographic features resulted in the highest classification accuracies for both functional ambulation outcomes (10MWT=70.4%, 6MWT=81.5%). Adding LAs, personal, psychosocial, and environmental factors, or both increased the accuracy of classification compared with the clinical/demographic features alone. Clinical measures of strength and sensation (especially knee flexion strength), LA measures of movement smoothness, and presence of pain and comorbidities were among the most important features selected for the models.

      Conclusions

      The addition of LA and personal, psychosocial, and environmental features increased functional ambulation classification accuracy in a population with incomplete SCI for whom improved prognosis for mobility outcomes is needed. These findings provide support for future longitudinal studies that use LA; personal, psychosocial, and environmental factors; and advanced analyses to improve clinical prediction rules for functional mobility outcomes.

      Keywords

      List of abbreviations:

      CPR (clinical prediction rule), LA (limb acceleration), OCA (overall classification accuracy), SCI (spinal cord injury), 6MWT (6-minute walk test), 10MWT (10-m walk test.)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Archives of Physical Medicine and Rehabilitation
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • National Spinal Cord Injury Statistical Center
        Annual Statistical Report for the Spinal Cord Injury Model Systems.
        University of Alabama at Birmingham, Birmingham2019
        • National Spinal Cord Injury Statistical Center
        Facts and Figures at a Glance.
        University of Alabama at Birmingham, Birmingham2020
        • Ditunno PL
        • Patrick M
        • Stineman M
        • Ditunno JF.
        Who wants to walk? Preferences for recovery after SCI: a longitudinal and cross-sectional study.
        Spinal Cord. 2008; 46: 500-506
        • Simpson LA
        • Eng JJ
        • Hsieh JT
        • Wolfe DL.
        The health and life priorities of individuals with spinal cord injury: a systematic review.
        J Neurotrauma. 2012; 29: 1548-1555
        • Hiremath SV
        • Hogaboom NS
        • Roscher MR
        • Worobey LA
        • Oyster ML
        • Boninger ML.
        Longitudinal prediction of quality-of-life scores and locomotion in individuals with traumatic spinal cord injury.
        Arch Phys Med Rehabil. 2017; 98: 2385-2392
        • Riggins MS
        • Kankipati P
        • Oyster ML
        • Cooper RA
        • Boninger ML.
        The relationship between quality of life and change in mobility 1 year postinjury in individuals with spinal cord injury.
        Arch Phys Med Rehabil. 2011; 92: 1027-1033
        • Rigot SK
        • Worobey L
        • Boninger ML.
        Gait training in acute spinal cord injury rehabilitation-utilization and outcomes among nonambulatory individuals: findings from the SCIRehab Project.
        Arch Phys Med Rehabil. 2018; 99: 1591-1598
        • Hylin MJ
        • Kerr AL
        • Holden R.
        Understanding the mechanisms of recovery and/or compensation following injury.
        Neural Plast. 2017; 20177125057
        • Barbeau H
        • Nadeau S
        • Garneau C.
        Physical determinants, emerging concepts, and training approaches in gait of individuals with spinal cord injury.
        J Neurotrauma. 2006; 23: 571-585
        • Burns AS
        • Marino RJ
        • Kalsi-Ryan S
        • et al.
        Type and timing of rehabilitation following acute and subacute spinal cord injury: a systematic review.
        Global Spine J. 2017; 7: 175S-194S
        • Fouad K
        • Tetzlaff W.
        Rehabilitative training and plasticity following spinal cord injury.
        Exp Neurol. 2012; 235: 91-99
        • Boggenpoel B
        • Madasa V
        • Jeftha T
        • Joseph C.
        Systematic scoping review protocol for clinical prediction rules (CPRs) in the management of patients with spinal cord injuries.
        BMJ Open. 2019; 9e025076
        • Tetreault L
        • Le D
        • Côté P
        • Fehlings M.
        The practical application of clinical prediction rules: a commentary using case examples in surgical patients with degenerative cervical myelopathy.
        Global Spine J. 2015; 5: 457-465
        • Hicks KE
        • Zhao Y
        • Fallah N
        • et al.
        A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry.
        Spine J. 2017; 17: 1383-1392
        • van Middendorp JJ
        • Hosman AJ
        • Donders AR
        • et al.
        A clinical prediction rule for ambulation outcomes after traumatic spinal cord injury: a longitudinal cohort study.
        Lancet. 2011; 377: 1004-1010
        • Malla R.
        External validation study of a clinical prediction rule for ambulation outcomes after traumatic spinal cord injury.
        Texas Medical Center Dissertations; Dallas, TX. 2013;
        • van Silfhout L
        • Peters AE
        • Graco M
        • Schembri R
        • Nunn AK
        • Berlowitz DJ.
        Validation of the Dutch clinical prediction rule for ambulation outcomes in an inpatient setting following traumatic spinal cord injury.
        Spinal Cord. 2016; 54: 614-618
        • Sturt R
        • Hill B
        • Holland A
        • New PW
        • Bevans C.
        Correction: validation of a clinical prediction rule for ambulation outcome after non-traumatic spinal cord injury.
        Spinal Cord. 2020; 58: 631
        • Everhart J
        • Somers M
        • Hibbs R
        • Worobey L.
        Clinical utility during inpatient rehabilitation of a clinical prediction rule for ambulation prognosis following spinal cord injury.
        J Spinal Cord Med. 2021 Mar 11; ([Epub ahead of print])
        • 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; 20: 1666-1675
        • Phan P
        • Budhram B
        • Zhang Q
        • et al.
        Highlighting discrepancies in walking prediction accuracy for patients with traumatic spinal cord injury: an evaluation of validated prediction models using a Canadian Multicenter Spinal Cord Injury Registry.
        Spine J. 2019; 19: 703-710
        • Chay W
        • Kirshblum S.
        Predicting outcomes after spinal cord injury.
        Phys Med Rehabil Clin N Am. 2020; 31: 331-343
        • Sharif S
        • Jazaib Ali MY
        Outcome prediction in spinal cord injury: myth or reality.
        World Neurosurg. 2020; 140: 574-590
        • Belliveau T
        • Jette AM
        • Seetharama S
        • et al.
        Developing artificial neural network models to predict functioning one year after traumatic spinal cord injury.
        Arch Phys Med Rehabil. 2016; 97: 1663-1668
        • DeVries Z
        • Hoda M
        • Rivers CS
        • et al.
        Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients.
        Spine J. 2020; 20: 213-224
        • Bolliger M
        • Blight AR
        • Field-Fote EC
        • et al.
        Lower extremity outcome measures: considerations for clinical trials in spinal cord injury.
        Spinal Cord. 2018; 56: 628-642
        • Jackson AB
        • Carnel CT
        • Ditunno JF
        • et al.
        Outcome measures for gait and ambulation in the spinal cord injury population.
        J Spinal Cord Med. 2008; 31: 487-499
        • Khan O
        • Badhiwala JH
        • Wilson JRF
        • Jiang F
        • Martin AR
        • Fehlings MG.
        Predictive modeling of outcomes after traumatic and nontraumatic spinal cord injury using machine learning: review of current progress and future directions.
        Neurospine. 2019; 16: 678-685
        • Peter C
        • Muller R
        • Cieza A
        • Geyh S.
        Psychological resources in spinal cord injury: a systematic literature review.
        Spinal Cord. 2012; 50: 188-201
        • Waldron B
        • Benson C
        • O'Connell A
        • Byrne P
        • Dooley B
        • Burke T
        Health locus of control and attributions of cause and blame in adjustment to spinal cord injury.
        Spinal Cord. 2010; 48: 598-602
        • Chevalier Z
        • Kennedy P
        • Sherlock O.
        Spinal cord injury, coping and psychological adjustment: a literature review.
        Spinal Cord. 2009; 47: 778-782
        • Horn SD
        • Smout RJ
        • DeJong G
        • et al.
        Association of various comorbidity measures with spinal cord injury rehabilitation outcomes.
        Arch Phys Med Rehabil. 2013; 94: S75-S86
        • Tian W
        • Hsieh CH
        • DeJong G
        • Backus D
        • Groah S
        • Ballard PH.
        Role of body weight in therapy participation and rehabilitation outcomes among individuals with traumatic spinal cord injury.
        Arch Phys Med Rehabil. 2013; 94: S125-S136
        • Dvir Z.
        Grade 4 in manual muscle testing: the problem with submaximal strength assessment.
        Clin Rehabil. 1997; 11: 36-41
        • Bohannon RW.
        Considerations and practical options for measuring muscle strength: a narrative review.
        Biomed Res Int. 2019; 20198194537
        • Hales M
        • Biros E
        • Reznik JE.
        Reliability and validity of the sensory component of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI): a systematic review.
        Top Spinal Cord Inj Rehabil. 2015; 21: 241-249
        • Scholtes VA
        • Becher JG
        • Beelen A
        • Lankhorst GJ.
        Clinical assessment of spasticity in children with cerebral palsy: a critical review of available instruments.
        Dev Med Child Neurol. 2006; 48: 64-73
        • Haas BM
        • Bergstrom E
        • Jamous A
        • Bennie A.
        The inter rater reliability of the original and of the Modified Ashworth Scale for the assessment of spasticity in patients with spinal cord injury.
        Spinal Cord. 1996; 34: 560-564
        • 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: 702-708
        • Waters RL
        • Adkins RH
        • Yakura JS
        • Sie I.
        Motor and sensory recovery following incomplete paraplegia.
        Arch Phys Med Rehabil. 1994; 75: 67-72
        • Basso DM
        • Beattie MS
        • Bresnahan JC.
        Graded histological and locomotor outcomes after spinal cord contusion using the NYU weight-drop device versus transection.
        Exp Neurol. 1996; 139: 244-256
        • Onifer SM
        • Smith GM
        • Fouad K.
        Plasticity after spinal cord injury: relevance to recovery and approaches to facilitate it.
        Neurotherapeutics. 2011; 8: 283-293
        • 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: 748-752
        • Ferri R
        • Proserpio P
        • Rundo F
        • et al.
        Neurophysiological correlates of sleep leg movements in acute spinal cord injury.
        Clin Neurophysiol. 2015; 126: 333-338
        • Lee MS
        • Choi YC
        • Lee SH
        • Lee SB.
        Sleep-related periodic leg movements associated with spinal cord lesions.
        Mov Disord. 1996; 11: 719-722
        • Proserpio P
        • Lanza A
        • Sambusida K
        • et al.
        Sleep apnea and periodic leg movements in the first year after spinal cord injury.
        Sleep Med. 2015; 16: 59-66
        • McCall WV
        • Boggs N
        • Letton A.
        Changes in sleep and wake in response to different sleeping surfaces: a pilot study.
        Appl Ergon. 2012; 43: 386-391
        • Giganti F
        • Ficca G
        • Gori S
        • Salzarulo P.
        Body movements during night sleep and their relationship with sleep stages are further modified in very old subjects.
        Brain Res Bull. 2008; 75: 66-69
        • Oleson CV
        • Marino RJ
        • Leiby BE
        • Ditunno JF.
        Influence of age alone, and age combined with pinprick, on recovery of walking function in motor complete, sensory incomplete spinal cord injury.
        Arch Phys Med Rehabil. 2016; 97: 1635-1641
        • Kirshblum SC
        • Botticello AL
        • Dyson-Hudson TA
        • Byrne R
        • Marino RJ
        • Lammertse DP.
        Patterns of sacral sparing components on neurologic recovery in newly injured persons with traumatic spinal cord injury.
        Arch Phys Med Rehabil. 2016; 97: 1647-1655
        • Ditunno Jr, JF
        • Barbeau H
        • Dobkin BH
        • et al.
        Validity of the walking scale for spinal cord injury and other domains of function in a multicenter clinical trial.
        Neurorehabil Neural Repair. 2007; 21: 539-550
        • Bohannon RW
        • Smith MB.
        Interrater reliability of a Modified Ashworth Scale of muscle spasticity.
        Phys Ther. 1987; 67: 206-207
        • Kirshblum S
        • Waring 3rd, W
        Updates for the International Standards for Neurological Classification of Spinal Cord Injury.
        Phys Med Rehabil Clin N Am. 2014; 25: 505-517
      1. Centers for Disease Control and Prevention; National Center for Health Statistics. NCHS urban-rural classification scheme for counties. Available at: https://www.cdc.gov/nchs/data_access/urban_rural.htm. Accessed August 1, 2020.

        • Centers for Disease Control and Prevention
        • National Center for Health Statistics
        • Office of Analysis and Epidemiology
        2013 NCHS urban-rural classification scheme for counties.
        US Department of Health and Human Services, Hyattsville2014
        • Botticello AL
        • Chen Y
        • Cao Y
        • Tulsky DS.
        Do communities matter after rehabilitation? The effect of socioeconomic and urban stratification on well-being after spinal cord injury.
        Arch Phys Med Rehabil. 2011; 92: 464-471
        • Myaskovsky L
        • Gao S
        • Hausmann LRM
        • et al.
        How are race, cultural, and psychosocial factors associated with outcomes in veterans with spinal cord injury?.
        Arch Phys Med Rehabil. 2017; 98: 1812-1820
      2. Cleeland CS, Ryan K. The brief pain inventory. Pain Research Group; Available at: http://www.npcrc.org/files/news/briefpain_long.pdf. Accessed March 1, 2020

        • Widerstrom-Noga E
        • Biering-Sorensen F
        • Bryce T
        • et al.
        The international spinal cord injury pain basic data set.
        Spinal Cord. 2008; 46: 818-823
        • Craig Hospital
        Craig Hospital inventory of environmental factors, version 3.0.
        Craig Hospital Research Department, Englewood CO2001
        • Ware Jr, JE
        • Sherbourne CD.
        The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection.
        Med Care. 1992; 30: 473-483
        • Sherbourne CD
        • Stewart AL.
        The MOS social support survey.
        Soc Sci Med. 1991; 32: 705-714
        • Middleton JW
        • Tran Y
        • Lo C
        • Craig A.
        Reexamining the validity and dimensionality of the Moorong Self-Efficacy Scale: improving its clinical utility.
        Arch Phys Med Rehabil. 2016; 97: 2130-2136
        • 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
        • 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: 682-685
        • 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, Washington, DC2008
        • Hammell KW
        • Miller WC
        • Forwell SJ
        • Forman BE
        • Jacobsen BA.
        Fatigue and spinal cord injury: a qualitative analysis.
        Spinal Cord. 2009; 47: 44-49
        • Ahn SH
        • Park HW
        • Lee BS
        • 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 ([discussion 6-7]): 341-346
        • Usui A
        • Ishizuka Y
        • Obinata I
        • Okado T
        • Fukuzawa H
        • Kanba S.
        Validity of sleep log compared with actigraphic sleep-wake state II.
        Psychiatry Clin Neurosci. 1999; 53: 183-184
        • Victorson D
        • Tulsky DS
        • Kisala PA
        • Kalpakjian CZ
        • Weiland B
        • Choi SW.
        Measuring resilience after spinal cord injury: development, validation and psychometric characteristics of the SCI-QOL Resilience item bank and short form.
        J Spinal Cord Med. 2015; 38: 366-376
        • Middleton J
        • Tran Y
        • Craig A.
        Relationship between quality of life and self-efficacy in persons with spinal cord injuries.
        Arch Phys Med Rehabil. 2007; 88: 1643-1648
        • Whiteneck G
        • Meade MA
        • Dijkers M
        • Tate DG
        • Bushnik T
        • Forchheimer MB.
        Environmental factors and their role in participation and life satisfaction after spinal cord injury.
        Arch Phys Med Rehabil. 2004; 85: 1793-1803
        • Whiteneck GG
        • Harrison-Felix CL
        • Mellick DC
        • Brooks CA
        • Charlifue SB
        • Gerhart KA.
        Quantifying environmental factors: a measure of physical, attitudinal, service, productivity, and policy barriers.
        Arch Phys Med Rehabil. 2004; 85: 1324-1335
        • Post MWM
        • van Leeuwen CMC.
        Psychosocial issues in spinal cord injury: a review.
        Spinal Cord. 2012; 50: 382-389
        • Kennedy P
        • Lude P
        • Elfström ML
        • Smithson EF.
        Psychological contributions to functional independence: a longitudinal investigation of spinal cord injury rehabilitation.
        Arch Phys Med Rehabil. 2011; 92: 597-602
        • Migueles JH
        • Cadenas-Sanchez C
        • Ekelund U
        • et al.
        Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations.
        Sports Med. 2017; 47: 1821-1845
        • Cole RJ
        • Kripke DF
        • Gruen W
        • Mullaney DJ
        • Gillin JC.
        Automatic sleep/wake identification from wrist activity.
        Sleep. 1992; 15: 461-469
        • 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: 898-903
        • Warms CA
        • Belza BL.
        Actigraphy as a measure of physical activity for wheelchair users with spinal cord injury.
        Nurs Res. 2004; 53: 136-143
        • Spivak E
        • Oksenberg A
        • Catz A.
        The feasibility of sleep assessment by actigraph in patients with tetraplegia.
        Spinal Cord. 2007; 45: 765-770
        • Albaum E
        • Quinn E
        • Sedaghatkish S
        • et al.
        Accuracy of the Actigraph wGT3x-BT for step counting during inpatient spinal cord rehabilitation.
        Spinal Cord. 2019; 57: 571-578
        • 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: 10
        • 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: 2193-2203
        • Moore 4th, H
        • Leary E
        • Lee SY
        • et al.
        Correction: design and validation of a periodic leg movement detector.
        PLoS One. 2015; 10e0138205
        • Sforza E
        • Johannes M
        • Claudio B
        The PAM-RL ambulatory device for detection of periodic leg movements: a validation study.
        Sleep Med. 2005; 6: 407-413
        • Bayat A
        • Pomplun M
        • Tran DA.
        A study on human activity recognition using accelerometer data from smartphones.
        Procedia Comput Sci. 2014; 34: 450-457
        • Mathie MJ
        • Coster ACF
        • Lovell NH
        • Celler BG.
        Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement.
        Physiol Meas. 2004; 25: R1-20
        • Mannini A
        • Sabatini AM.
        Machine learning methods for classifying human physical activity from on-body accelerometers.
        Sensors (Basel). 2010; 10: 1154-1175
        • 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: 603-612
        • van Hedel HJ
        • Dietz V.
        Walking during daily life can be validly and responsively assessed in subjects with a spinal cord injury.
        Neurorehabil Neural Repair. 2009; 23: 117-124
        • Altenburger PA
        • Dierks TA
        • Miller KK
        • Combs SA
        • Van Puymbroeck M
        • Schmid AA.
        Examination of sustained gait speed during extended walking in individuals with chronic stroke.
        Arch Phys Med Rehabil. 2013; 94: 2471-2477
        • Forrest GF
        • Hutchinson K
        • Lorenz DJ
        • et al.
        Are the 10 meter and 6 minute walk tests redundant in patients with spinal cord injury?.
        PLoS One. 2014; 9: e94108
        • van Hedel HJ
        • Group Emsci Study
        Gait speed in relation to categories of functional ambulation after spinal cord injury.
        Neurorehabil Neural Repair. 2009; 23: 343-350
        • Fulk GD
        • He Y
        • Boyne P
        • Dunning K.
        Predicting home and community walking activity poststroke.
        Stroke. 2017; 48: 406-411
        • Pedregosa F
        • Varoquaux G
        • Gramfort A
        • et al.
        Scikit-learn: machine learning in Python.
        J Mach Learn Res. 2011; 12: 2825-2830
        • Lebedev AV
        • Westman E
        • Van Westen GJP
        • et al.
        Random forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness.
        Neuroimage Clin. 2014; 6: 115-125
        • Hajjem A.
        Mixed effects trees and forests for clustered data.
        Montreal, Canada: HEC Montreal, Department of Management Sciences, 2010
        • Cawley GC
        • Talbot NL.
        On over-fitting in model selection and subsequent selection bias in performance evaluation.
        J Mach Learn Res. 2010; 11: 2079-2107
        • Varma S
        • Simon R.
        Bias in error estimation when using cross-validation for model selection.
        BMC Bioinformatics. 2006; 7: 91
        • Vabalas A
        • Gowen E
        • Poliakoff E
        • Casson AJ.
        Machine learning algorithm validation with a limited sample size.
        PLoS One. 2019; 14e0224365
        • Hossin M
        • Sulaiman M.
        A review on evaluation metrics for data classification evaluations.
        Int J Data Min Knowl Manage Process. 2015; 5: 1
        • Krause J
        • Carter RE
        • Brotherton S.
        Association of mode of locomotion and independence in locomotion with long-term outcomes after spinal cord injury.
        J Spinal Cord Med. 2009; 32: 237-248
        • Gaspar R
        • Padula N
        • Freitas TB
        • de Oliveira JPJ
        • Torriani-Pasin C.
        Physical exercise for individuals with spinal cord injury: systematic review based on the international classification of functioning, disability, and health.
        J Sport Rehabil. 2019; 28: 505-516
        • Liu H
        • Li J
        • Du L
        • et al.
        Short-term effects of core stability training on the balance and ambulation function of individuals with chronic spinal cord injury: a pilot randomized controlled trial.
        Minerva Med. 2019; 110: 216-223
        • DiPiro ND
        • Embry AE
        • Fritz SL
        • Middleton A
        • Krause JS
        • Gregory CM.
        Effects of aerobic exercise training on fitness and walking-related outcomes in ambulatory individuals with chronic incomplete spinal cord injury.
        Spinal Cord. 2016; 54: 675-681
        • Martin Ginis KA
        • Papathomas A
        • Perrier MJ
        • Smith B
        • SHAPE-SCI Research Group
        Psychosocial factors associated with physical activity in ambulatory and manual wheelchair users with spinal cord injury: a mixed-methods study.
        Disabil Rehabil. 2017; 39: 187-192
        • Chasens ER
        • Sereika SM
        • Weaver TE
        • Umlauf MG.
        Daytime sleepiness, exercise, and physical function in older adults.
        J Sleep Res. 2007; 16: 60-65
        • Goldman SE
        • Stone KL
        • Ancoli-Israel S
        • et al.
        Poor sleep is associated with poorer physical performance and greater functional limitations in older women.
        Sleep. 2007; 30: 1317-1324
        • Alessi CA
        • Martin JL
        • Webber AP
        • et al.
        More daytime sleeping predicts less functional recovery among older people undergoing inpatient post-acute rehabilitation.
        Sleep. 2008; 31: 1291-1300
        • Beveridge C
        • Knutson K
        • Spampinato L
        • et al.
        Daytime physical activity and sleep in hospitalized older adults: association with demographic characteristics and disease severity.
        J Am Geriatr Soc. 2015; 63: 1391-1400
        • Qi Y.
        Random forest for bioinformatics.
        in: Zhang C Ma Y Ensemble machine learning. Springer US, Boston2012: 307-323