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RehabMeasures

(Hauser) Ambulation Index

Last Updated

Purpose

An ordinal scale designed to quantify changes in gait.

Acronym AI

Assessment Type

Performance Measure

Administration Mode

Paper & Pencil

Cost

Free

Diagnosis/Conditions

  • Multiple Sclerosis

Key Descriptions

  • Has also been referred to as the Hauser Deambulation Index.
  • Score range 0 = no symptoms to 9 = restricted to wheelchair, and unable to transfer independently.

Number of Items

1

Equipment Required

  • Stop watch
  • Patient's self-selected assistive device

Time to Administer

1-5 minutes

Required Training

No Training

Age Ranges

Adult

18 - 64

years

Elderly Adult

65 +

years

Instrument Reviewers

Initially reviewed by Susan E. Bennett, PT, DPT, EdD, NCS, MSCS and the MS EDGE task force of the neurology section of the APTA in 2011.

ICF Domain

Activity

Multiple Sclerosis

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Test/Retest Reliability

Multiple Sclerosis:

 

(National MS Society webpage )

  • Test-retest reliability reported to be good

(Amato et al, 1999)

  • AI might have questionable test-retest reliability, especially for patients with EDSS scores between 2.0 – 3.0 and 4.0 – 5.0, as scores in these ranges may change by one point in a short time period, but no data provided

(Syndulko et al, 1996)

  • Excellent test-retest reliability (ICC = 0.91)

Interrater/Intrarater Reliability

Multiple Sclerosis:

 

(National MS Society webpage)

  • Interrater is good but no references cited

(Amato et al, 1999)

  • Reported that AI is more precise and has better interrater reliability as compared to the EDSS, but no data provided

 

(Sharrack et al, 1999; = 64 individuals with MS; mean EDSS = 4.5; range of EDSS = 0.0 – 7.5)

  • Excellent intrarater reliability (ICC = 0.93; = 0.59; repeatability coefficient = 1.5 points
  • Intrarater agreement = 66, 94, 97 and 100% when agreement was defined as no difference, <1 point, <2 points and <3 points
  • Excellent interrater reliability (ICC = 0.96; K = 0.73; repeatability coefficient = 1 point)
  • Interrater agreement = 77 and 100% when agreement was defined as no difference and <1 point

Criterion Validity (Predictive/Concurrent)

Multiple Sclerosis:

(Cattaneo et al, 2006; = 63 individuals with MS; able to stand independently for >3 seconds and walk 6 meters with or without an assistive device)

  • Excellent correlation between AI and Berg’s Balance Scale (rho = 0.74)
  • Excellent correlation between AI and Dynamic Gait Index (rho = 0.80)
  • Excellent concurrent correlation between AI and Timed Up and Go (rho = 0.74)
  • Adequate correlation between AI and Activities-specific Balance Confidence Scale (rho = 0.45)
  • Adequate correlation between AI and Dizziness Handicap Inventory (rho = 0.32)

(Provinciali et al, 1999)

  • AI is unable to predict handicap as measured by London Handicap Scale and quality of life impairment as measured by Functional Assessment of MS

(Sharrack et al, 1999; mean EDSS = 4.5; range of EDSS = 0.0 – 7.5; AI correlated significantly with EDSS = 0.68; all p < 0.001 – 0.008)

  • Excellent correlation between AI and the Scripps Neurological Rating Scale (r = 0.67)
  • Excellent correlation with Functional Independence Measure (r = 0.73)
  • Adequate correlation with Cambridge MS Basic Score disability (r = 0.54) and handicap (r = 0.55)
  • Excellent correlation with Barthel Index and London Handicap Scale (r = 0.72)
  • Excellent correlation with the EuroQoLVAS (r = 0.73)
  • Excellent correlation with the SF-36 physical functioning (r = 0.87)
  • Adequate correlation with the physical role limitation (r = 0.52), social functioning (r = 0.52), vitality (r = 0.39) and general health perception (r = 0.38)
  • Did not correlate significantly to SF-36-emotional role limitation, and social functioning (mental health bodily pain and health change)
  • Adequate correlation to patients’ ability to work (r = 0.59); do housework (r = 0.55
  • Excellent correlation with the disability rank (r = 0.88) at p < 0.001
  • Adequate correlation for patients to look after themselves (r = 0.35) at p < 0.01 – 0.02

(Vaney et al, 1996; mean EDSS = 6.6(1.7) for inpatients with MS compared to groups of MS subjects with various walking capabilities of normal, slow and unable)

  • Adequate correlation of the Hauser’s AI for inpatients with MS to the Rivermead Mobility Index with multiple groups of MS (rho = 0.45, < 0.01 for the normal walk group; rho = 0.96, = 0.001 for all groups

Construct Validity

Multiple Sclerosis:

(Cattaneo et al, 2006; n = 63 individuals with MS and able to stand independently for > 3 seconds and walk 6 meters with or without as assistive device)

  • AI unable to discriminate between non-fallers and fallers

(Schwartz et al, 1999; individuals with MS)

  • Excellent correlation for EDSS levels of 1 – 2.5 (mean = 0.8 (0.7))
  • Excellent correlation for EDSS levels of 3.0 – 6.0 (mean = 3.1 (1.3))
  • Excellent correlation for EDSS levels above 6.0 (mean = 7.0 (1.5))

(Vaney et al, 1996; discriminate among inpatients with MS who have normal walking capability vs. slow walking vs. unable to walk)

  • Mean AI scores for 3 groups = 2.2(0.9), 5.1(1.0), and 8.5( 0.8), p < 0.001

Floor/Ceiling Effects

Multiple Sclerosis:

(Cattaneo et al, 2006; n = 63 individuals with MS and able to stand independently for > 3 seconds and walk 6 meters with or without as assistive device)

  • Adequate ceiling effect with 7.8% of subjects reaching maximum AI score

(Vaney et al, 1996; subjects with MS had mean EDSS = 6.6( 1.7))

  • Poor ceiling effect for 28% of subjects reaching maximum score of 9 on the AI
  • Excellent floor effect with 0% of subjects reaching minimum score

Responsiveness

Multiple Sclerosis:

(Sharrack et al, 1999; individuals with MS; mean EDSS = 4.5, range = 0.0 – 7.5, p = 0.039)

  • Moderate effect size in individuals with MS indicating limited responsiveness to change

(Syndulko et al, 1996)

  • Using a signal-to-noise ratio, determined that the AI has responsiveness values (R1 = 2.37) for all patients, 2.65 for patients with EDSS < 5.5, and 2.14 for patients with EDSS > 5.5; indicating better sensitivity to change as compared to the EDSS and two components of the Incapacity Status Scale composites, but not as responsive as neuro-performance composites (global and lower and upper extremity)

(Vaney et al, 1996)

  • The AI is reported to be more able to detect change as compared to 10 meter walk test and EDSS but less responsive than the Rivermead Mobiliity Index; the AI was able to detect changes in 18.5% of patients with MS (RMI was able to detect changes in 39%, 10 meter walk test 16.5% and EDSS 7.5%

Bibliography

Amato, M. P. and Ponziani, G. (1999). "Quantification of impairment in MS: discussion of the scales in use." Multiple Sclerosis 5(4): 216-219.

Cattaneo, D., Regola, A., et al. (2006). "Validity of six balance disorders scales in persons with multiple sclerosis." Disability & Rehabilitation 28(12): 789-795.

Hauser, S. L., Dawson, D. M., et al. (1983). "Intensive immunosuppression in progressive multiple sclerosis." New England Journal of Medicine 308(4): 173-180.

Provinciali, L., Ceravolo, M., et al. (1999). "A multidimensional assessment of multiple sclerosis: relationships between disability domains." Acta neurologica scandinavica 100(3): 156-162.

Schwartz, C. E., Vollmer, T., et al. (1999). "Reliability and validity of two self-report measures of impairment and disability for MS." Neurology 52(1): 63-63.

Sharrack, B., Hughes, R. A. C., et al. (1999). "The psychometric properties of clinical rating scales used in multiple sclerosis." Brain 122(1): 141-159.

Syndulko, K., Ke, D., et al. (1996). "Comparative evaluations of neuroperformance and clinical outcome assessments in chronic progressive multiple sclerosis: I. Reliability, validity and sensitivity to disease progression." Multiple Sclerosis 2(3): 142-156.

Vaney, C., Blaurock, H., et al. (1996). "Assessing mobility in multiple sclerosis using the Rivermead Mobility Index and gait speed." Clinical rehabilitation 10(3): 216-226.