May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
Predictors of Poor Mobility in People With Age-Related Macular Degeneration
Author Affiliations & Notes
  • R. S. Mathew
    School of Optometry and Vision Science, University of New South Wales, Australia
  • . Vaegan
    School of Optometry and Vision Science, University of New South Wales, Australia
  • P. Herse
    Institute of Learning, Luxottica, Australia
  • S. Lord
    Prince of Wales Medical Research Institute, University of New South Wales, Australia
  • P. Beaumont
    Royal College of Ophthalmologist, Eye and Vision Research Institute, Australia
  • Footnotes
    Commercial Relationships  R.S. Mathew, None; . Vaegan, None; P. Herse, None; S. Lord, None; P. Beaumont, None.
  • Footnotes
    Support  None.
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 2253. doi:https://doi.org/
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      R. S. Mathew, . Vaegan, P. Herse, S. Lord, P. Beaumont; Predictors of Poor Mobility in People With Age-Related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2008;49(13):2253. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: : To identify visual factors that would help to predict poor mobility in people with Age-Related Macular Degeneration (AMD).

Methods: : 40 patients with ARMD (Group 1) and 40 normal individuals with no ocular disease as controls (Group 2) were selected during routine clinical examinations. Subjects were assessed fora) Self reported Quality of life (QoL) using the medical outcomes study short form 36 (SF-36), Low luminance questionnaire (LLQ) and Goldberg anxiety and depression scale (GAD)questionnairesb) Psychophysical measures of visual acuity, contrast sensitivity, photostress, visual fields at photopic, mesopic and scotopic adaptation levels and dark adaptation.c) Sensorimotor function was assessed as a measure of knee strength, balance, reaction time and proprioception.d) Mobility performance was assessed by navigation through an obstacle course. Scores were time taken and number of contacts made.

Results: : MANOVA showed AMD patients differed on SF-36 for subscales of general health (p=0.003) and vitality (p<0.005), LLQ (all subscales (p<0.05) and GAD scale (p=0.05). The two groups differed significantly in all the psychophysical and mobility performance measures even though they were similar in all the sensorimotor functions. Logistic regression showed that photostress test (p<0.01, r=0.5) was the strongest predictor for mobility performance at all light levels, although visual field loss, reduction in cone threshold and reduced contrast sensitivity were statistically significant (p< 0.05, r=0.1-0.3) in predicting mobility performance for AMD subjects.

Conclusions: : These findings provide insight to factors that predispose older people with AMD to fall.

Keywords: age-related macular degeneration • aging: visual performance • visual fields 
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