Gait training in individuals with incomplete SCI: How are they different from healthy subjects?

E. Delikanaki-Skaribas, H. K. Lim, H. Qureshy, S. A. Holmes, E. Protas, A. Sherwood

Research output: Contribution to journalConference articlepeer-review


The purpose of this study was to examine the effects of 3-month gait training on mean gait oxygen consumption (VO2), gait speed (GS), distance covered in a 5-minutes walk, and gait cost (GC). In order to better explain the results, the data were compared to healthy subjects. Ten incomplete SCI patients with mean age 40± 14 years participated to the study. The results showed significant increases in mean walking VO2 (p<0.01), GS (p<0.01), and distance covered (p<0.01) during the 5-minuite walking test. While GC did not improve significantly after training, there was a definite trend of lower VO2 consumption per meter walked. The relation between VO2 and GS was compared to data of a 5-minute walking test of healthy individuals. There was a significant positive correlation between VO2 and GS in healthy (r=0.68; p<0.01) and there was no correlation (r=-0.10) between those variables in SCI individuals. There was no correlation between GC and GS in healthy (r=0.128) and there was a significant negative correlation in SCI individuals. There is evidence that SCI individuals benefit from gait training. While physiological responses during walking are different in individuals with SCI than healthy, gait training reduces that difference.

Original languageEnglish (US)
Pages (from-to)2416-2417
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
StatePublished - 2002
Externally publishedYes
EventProceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States
Duration: Oct 23 2002Oct 26 2002


  • Gait cost
  • Gait speed
  • Incomplete SCI
  • Oxygen consumption

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics


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