Advertisement

Estimation of Energy Expenditure for Wheelchair Users Using a Physical Activity Monitoring System

  • Shivayogi V. Hiremath
    Correspondence
    Corresponding author Shivayogi V. Hiremath, PhD, Department of Physical Therapy, College of Public Health, Temple University, 3307 N Broad St, Jones Hall, Ste 623, Philadelphia, PA 19140.
    Affiliations
    Department of Veterans Affairs, Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

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

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

    Department of Physical Therapy, Temple University, Philadelphia, PA
    Search for articles by this author
  • Stephen S. Intille
    Affiliations
    College of Computer and Information Science, Northeastern University, Boston, MA

    Department of Health Sciences, Northeastern University, Boston, MA
    Search for articles by this author
  • Annmarie Kelleher
    Affiliations
    Department of Veterans Affairs, Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

    Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
  • Rory A. Cooper
    Affiliations
    Department of Veterans Affairs, Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

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

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
  • Dan Ding
    Affiliations
    Department of Veterans Affairs, Human Engineering Research Laboratories, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA

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

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
    Search for articles by this author
Published:March 11, 2016DOI:https://doi.org/10.1016/j.apmr.2016.02.016

      Abstract

      Objective

      To develop and evaluate energy expenditure (EE) estimation models for a physical activity monitoring system (PAMS) in manual wheelchair users with spinal cord injury (SCI).

      Design

      Cross-sectional study.

      Setting

      University-based laboratory environment, a semistructured environment at the National Veterans Wheelchair Games, and the participants' home environments.

      Participants

      Volunteer sample of manual wheelchair users with SCI (N=45).

      Intervention

      Participants were asked to perform 10 physical activities (PAs) of various intensities from a list. The PAMS consists of a gyroscope-based wheel rotation monitor (G-WRM) and an accelerometer device worn on the upper arm or on the wrist. Criterion EE using a portable metabolic cart and raw sensor data from PAMS were collected during each of these activities.

      Main Outcome Measures

      Estimated EE using custom models for manual wheelchair users based on either the G-WRM and arm accelerometer (PAMS-Arm) or the G-WRM and wrist accelerometer (PAMS-Wrist).

      Results

      EE estimation performance for the PAMS-Arm (average error ± SD: −9.82%±37.03%) and PAMS-Wrist (−5.65%±32.61%) on the validation dataset indicated that both PAMS-Arm and PAMS-Wrist were able to estimate EE for a range of PAs with <10% error. Moderate to high intraclass correlation coefficients (ICCs) indicated that the EE estimated by PAMS-Arm (ICC3,1=.82, P<.05) and PAMS-Wrist (ICC3,1=.89, P<.05) are consistent with the criterion EE.

      Conclusions

      Availability of PA monitors can assist wheelchair users to track PA levels, leading toward a healthier lifestyle. The new models we developed can estimate PA levels in manual wheelchair users with SCI in laboratory and community settings.

      Keywords

      List of abbreviations:

      EE (energy expenditure), G-WRM (gyroscope-based wheel rotation monitor), ICC (intraclass correlation coefficient), MSE (mean signed error), PA (physical activity), PAMS (physical activity monitoring system), SCI (spinal cord injury)
      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

        • Durstine J.L.
        • Painter P.
        • Franklin B.A.
        • Morgan D.
        • Pitetti K.H.
        • Roberts S.O.
        Physical activity for the chronically ill and disabled.
        Sports Med. 2000; 30: 207-219
        • Hoenig H.
        • Landerman L.R.
        • Shipp K.M.
        • George L.
        Activity restriction among wheelchair users.
        J Am Geriatr Soc. 2003; 51: 1244-1251
        • Glaser R.M.
        • Janssen T.W.J.
        • Suryaprasad A.G.
        • Gupta S.C.
        • Mathews T.
        The physiology of exercise.
        in: Apple D.F. Physical fitness: a guide for individuals with spinal cord injury. Dept of Veterans Affairs, Washington (DC)1996
        • Rimmer J.H.
        Use of the ICF in identifying factors that impact participation in physical activity/rehabilitation among people with disabilities.
        Disabil Rehabil. 2006; 28: 1087-1095
        • Jacobs P.L.
        • Nash M.S.
        • Rusinowski J.W.
        Circuit training provides cardiorespiratory and strength benefits in persons with paraplegia.
        Med Sci Sports Exerc. 2001; 33: 711-717
        • Rimmer J.H.
        • Yamaki K.
        • Davis B.M.
        • Wang E.
        • Vogel L.C.
        Obesity and overweight prevalence among adolescents with disabilities.
        Prev Chronic Dis. 2011; 8: 1-6
        • Rimmer J.H.
        • Shenoy S.S.
        Impact of exercise on targeted secondary conditions.
        in: Field M.J. Jette A.M. Martin L. Workshop on Disability in America, a New Look: summary and papers: based on a workshop of the Committee on Disability in America: A New Look, Board on Health Sciences Policy. National Academy of Sciences, Washington (DC)2006
        • Jakicic J.M.
        • Tate D.F.
        • Lang W.
        • et al.
        Effect of a stepped-care intervention approach on weight loss in adults: a randomized clinical trial.
        JAMA. 2012; 307: 2617-2626
        • Coons M.J.
        • DeMott A.
        • Buscemi J.
        • et al.
        Technology interventions to curb obesity: a systematic review of the current literature.
        Curr Cardiovasc Risk Rep. 2012; 6: 120-134
        • Alhassan S.
        • Kim S.
        • Bersamin A.
        • King A.
        • Gardner C.
        Dietary adherence and weight loss success among overweight women: results from the A to Z weight loss study.
        Int J Obes (Lond). 2008; 32: 985-991
        • Baker R.C.
        • Kirschenbaum D.S.
        Self-monitoring may be necessary for successful weight control.
        Behav Ther. 1993; 24: 377-394
        • Wing R.
        • Phelan S.
        Long-term weight loss maintenance.
        Am J Clin Nutr. 2005; 82: 2225-2255
        • Shuger S.L.
        • Barry V.W.
        • Sui X.
        • et al.
        Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial.
        Int J Behav Nutr Phys Act. 2011; 8: 41
        • Spring B.
        • Duncan J.M.
        • Janke E.A.
        • et al.
        Integrating technology into standard weight loss treatment: a randomized controlled trial.
        JAMA Intern Med. 2013; 173: 105-111
        • Tolerico M.L.
        • Ding D.
        • Cooper R.A.
        • et al.
        Assessing mobility characteristics and activity levels of manual wheelchair users.
        J Rehabil Res Dev. 2007; 44: 561-572
        • Coulter E.H.
        • Dall P.M.
        • Rochester L.
        • Hasler J.P.
        • Granat M.H.
        Development and validation of a physical activity monitor for use on a wheelchair.
        Spinal Cord. 2011; 49: 445-450
        • Sonenblum S.E.
        • Sprigle S.
        • Caspall J.
        • Lopez R.
        Validation of an accelerometer-based method to measure the use of manual wheelchairs.
        Med Eng Phys. 2012; 34: 781-786
        • Conger S.A.
        • Scott S.N.
        • Bassett D.R.
        Predicting energy expenditure through hand rim propulsion power output in individuals who use wheelchairs.
        Br J Sports Med. 2014; 48: 1048-1053
        • Hiremath S.V.
        • Ding D.
        • Cooper R.A.
        Development and evaluation of a gyroscope based wheel rotation monitor for manual wheelchair users.
        J Spinal Cord Med. 2013; 36: 347-356
        • Kiuchi K.
        • Inayama T.
        • Muraoka Y.
        • Ikemoto S.
        • Uemura O.
        • Mizuno K.
        Preliminary study for the assessment of physical activity using a triaxial accelerometer with a gyro sensor on the upper limbs of subjects with paraplegia driving a wheelchair on a treadmill.
        Spinal Cord. 2014; 52: 556-563
        • Hiremath S.V.
        • Ding D.
        • Farringdon J.
        • Cooper R.A.
        Predicting energy expenditure of manual wheelchair users with spinal cord injury using a multi-sensor based activity monitor.
        Arch Phys Med Rehabil. 2012; 93: 1937-1943
        • García-Massó X.
        • Serra-Añó P.
        • García-Raffi L.
        • Sánchez-Pérez E.
        • López-Pascual J.
        • Gonzalez L.
        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
        • Nightingale T.E.
        • Walhin J.-P.
        • Thompson D.
        • Bilzon J.L.
        Predicting physical activity energy expenditure in manual wheelchair users.
        Med Sci Sports Exerc. 2014; 46: 1849-1858
      1. Intille SS, Albinali F, Mota S, Kuris B, Botana P, Haskell WL. Design of a wearable physical activity monitoring system using mobile phones and accelerometers. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE; Boston (MA); 2011. p 3636-9.

        • Hiremath S.V.
        • Intille S.S.
        • Kelleher A.
        • Cooper R.A.
        • Ding D.
        Detection of physical activities using a physical activity monitor system for wheelchair users.
        Med Eng Phys. 2015; 37: 68-76
        • Bland J.M.
        • Altman D.G.
        Statistical methods for assessing agreement between two methods of clinical measurement.
        Lancet. 1986; 1: 307-310
        • Collins E.G.
        • Gater D.
        • Kiratli J.
        • Butler J.
        • Hanson K.
        • Langbein W.E.
        Energy cost of physical activities in persons with spinal cord injury.
        Med Sci Sports Exerc. 2010; 42: 691-700
        • American College of Sports Medicine
        ACSM's resource manual for guidelines for exercise testing and prescription.
        7th ed. Wolters Kluwer Health | Lippincott Williams & Wilkins, Philadelphia2005
        • Washburn R.
        • Hedrick B.N.
        Descriptive epidemiology of physical activity in university graduates with locomotor disabilities.
        Int J Rehabil Res. 1997; 20: 275-287
        • Tasiemski T.
        • Kennedy P.
        • Gardner B.P.
        • Taylor N.
        The association of sports and physical recreation with life satisfaction in a community sample of people with spinal cord injuries.
        NeuroRehabilitation. 2005; 20: 253-265