September 2011
Volume 52, Issue 10
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Clinical and Epidemiologic Research  |   September 2011
Age-Related Eye Disease and Mobility Limitations in Older Adults
Author Affiliations & Notes
  • Mihaela L. Popescu
    From the Centre de Recherche, Hôpital Maisonneuve-Rosemont, Montréal, Canada;
  • Hélène Boisjoly
    From the Centre de Recherche, Hôpital Maisonneuve-Rosemont, Montréal, Canada;
    the Département d'ophtalmologie, Université de Montréal, Montréal, Québec, Canada;
  • Heidi Schmaltz
    the Department of Geriatric Medicine, University of Calgary, Calgary, Alberta, Canada; and
  • Marie-Jeanne Kergoat
    Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada.
  • Jacqueline Rousseau
    Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada.
  • Solmaz Moghadaszadeh
    From the Centre de Recherche, Hôpital Maisonneuve-Rosemont, Montréal, Canada;
  • Fawzia Djafari
    From the Centre de Recherche, Hôpital Maisonneuve-Rosemont, Montréal, Canada;
    the Département d'ophtalmologie, Université de Montréal, Montréal, Québec, Canada;
  • Ellen E. Freeman
    From the Centre de Recherche, Hôpital Maisonneuve-Rosemont, Montréal, Canada;
    the Département d'ophtalmologie, Université de Montréal, Montréal, Québec, Canada;
  • Corresponding author: Ellen E. Freeman, Hôpital Maisonneuve-Rosemont, Recherche Ophtalmologie, CSA, RC, F131, 5415 boulevard de l'Assomption, Montréal (QC) H1T 2M4, Canada; eefreeman@gmail.com
Investigative Ophthalmology & Visual Science September 2011, Vol.52, 7168-7174. doi:10.1167/iovs.11-7564
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      Mihaela L. Popescu, Hélène Boisjoly, Heidi Schmaltz, Marie-Jeanne Kergoat, Jacqueline Rousseau, Solmaz Moghadaszadeh, Fawzia Djafari, Ellen E. Freeman; Age-Related Eye Disease and Mobility Limitations in Older Adults. Invest. Ophthalmol. Vis. Sci. 2011;52(10):7168-7174. doi: 10.1167/iovs.11-7564.

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      © 2016 Association for Research in Vision and Ophthalmology.

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Abstract

Purpose.: To examine the extent of mobility limitations in patients with age-related macular degeneration (AMD), glaucoma, or Fuchs' corneal dystrophy compared with that in a control group of older adults with good vision.

Methods.: Two hundred seventy-two patients (68 with AMD, 49 with Fuchs' dystrophy, 82 with glaucoma, and 73 controls) from the ophthalmology clinics of Maisonneuve-Rosemont Hospital (Montreal, Canada) participated in a cross-sectional study from September 2009 until February 2011. Control patients who had normal visual acuity and visual fields were recruited from the same clinics. Questionnaire (life space, falls, and driving) and performance-based (one-legged balance test, Timed Up and Go [TUG] test) mobility data were collected; visual acuity, contrast sensitivity, and visual field were measured; and the medical record was reviewed.

Results.: The three eye diseases were associated with different patterns of mobility limitations. Patients with glaucoma had the most types of mobility limitations, as they had reduced life-space scores, had worse TUG scores, were less likely to drive, and were more likely to have poor balance than the control group (P < 0.05). Compared with the controls, patients with AMD and Fuchs' corneal dystrophy had reduced life-space scores and were less likely to drive (P < 0.05).

Conclusions.: The results suggest that eye diseases, especially glaucoma, restrain the mobility of older people in many different ways. It is important to further explore the impact of eye disease on mobility in this population, to develop interventions that could help affected older adults maintain their independence.

The impact of age-related eye disease on mobility is an important area of research, given the aging of the population and the importance of mobility in the prevention of disability 1 and mortality. 2 Yet, we are only beginning to understand the mobility limitations of people with eye disease. Friedman et al. 3 found that people with bilateral glaucoma were slower and bumped into more objects on an artificial mobility course. These tendencies may translate into a greater falls risk, although the real-world implications of these findings are not known. Other research has examined glaucoma and driving. 4 6 One study found that people with glaucoma were more likely to stop driving, 4 whereas conflicting results were found for an association with motor vehicle crashes. 5,6 Some small studies have been done on patients with age-related macular degeneration (AMD) and have mainly focused on falls, postural stability, and driving. 7 10 A null relationship was reported between AMD and gait time in the Beaver Dam Eye study. 11 We are unaware of any studies examining the mobility problems of people with Fuchs' corneal dystrophy. More research is needed on how different eye diseases are associated with a range of mobility limitations. 
Our first objective was to comprehensively examine several measures of mobility performance (such as life space, balance, timed up-and-go test, driving status, falls) in people with one of three common age-related conditions (AMD, Fuchs' corneal dystrophy, and glaucoma) compared to a control group. The late stage of AMD is estimated to affect 890,000 Americans, 12 and open-angle glaucoma is estimated to affect 2.22 million Americans. 13 A reliable estimate for Fuchs' corneal dystrophy could not be found. We chose these three age-related diseases because they have very different profiles of vision loss that might impact mobility in different patterns. Our hypothesis was that all three eye diseases would be associated with the five mobility limitations, but that diseases with a greater impact on central vision (AMD and Fuchs') would have stronger associations with driving cessation and life space, whereas diseases with a greater impact on peripheral vision (glaucoma) would have stronger associations with falls, balance, and Timed Up-and-Go (TUG) test results. The rationale for this hypothesis comes from research that has shown visual field to be more relevant than visual acuity or contrast sensitivity for falls and balance. 14,15 Also, we expected that the AMD and Fuchs' patients in our study would have difficulty passing the visual acuity test required for unrestricted driving in Quebec. Our second objective was to determine whether any relationships between eye disease and mobility were primarily explained by visual acuity, contrast sensitivity, or visual field. 
Methods
Study Population
All participants were recruited from the ophthalmology clinics at Maisonneuve-Rosemont Hospital in Montreal, Quebec, Canada, from September 2009 through February 2011. Three members of the research team (MLP, SM, FD) reviewed patient files for eligibility each day. 
All patients had to be 65 years of age or older. Furthermore, the patients with a clinical diagnosis of AMD, Fuchs' corneal dystrophy, or glaucoma had to have at least some vision loss. Specifically, the AMD and Fuchs' patients had to have disease in both eyes and visual acuity worse than 20/40 in the better eye. Glaucoma patients had to have the disease in both eyes and a visual field mean deviation worse than or equal to −4 dB in the worse eye. These criteria would be considered “early” visual field loss, according to the literature. 16 All glaucoma types were recruited. The three groups with eye disease were allowed to have other eye diseases, which may have also impaired vision. However, a person was not included if he/she met the visual inclusion criteria for multiple groups (i.e., AMD and Fuchs'). Finally, the controls had to have visual acuity of 20/40 or better in the better eye and a visual field in the worse eye better than −4 dB. Controls either had no current eye disease (66%) or they had non–vision-impairing conditions such as early cataract (12%), early AMD (4%), ocular hypertension (5%), blepharitis (4%), or other (7%). People who had undergone eye surgery, laser surgery, or an intravitreal injection in the past 3 months were enrolled after a 2- to 3-month delay so that their mobility would not be affected by their recovery. Patients also had to score 10 or better on the Mini-Mental State Examination (MMSE), Blind Version, to optimize the reliability of the collection of the self-reported data. The blind version of the MMSE omits eight items that rely on vision and has been validated against the original version. 17 A score of 10 on the blind version corresponds to a score of 18 on the original version, which was used in previous vision research on older adults. 18  
There were 483 patients who appeared to meet eligibility criteria from a review of the medical records. Of the 483 patients, 335 accepted our invitation to be in the study (69%), 127 refused (26%), and 21 (4%) were not capable of responding for themselves. Of the 335 who accepted, 272 people met final eligibility criteria, including 68 with age-related macular degeneration (AMD), 82 with glaucoma, 49 with Fuchs' corneal dystrophy, and 73 without significant eye disease. The project was approved by the Ethics Committee at the Hospital, and the research conformed to the tenets of the Declaration of Helsinki. 
Data Collection
Data were collected in a 1- to 1.5-hour session by one of three trained research personnel. Participants first answered questions on demographics (age, sex), mobility, and health and then performed brief mobility, clinical, and vision tests. The medical chart was also reviewed. 
Mobility.
Questionnaires and performance-based tasks were performed to assess five different mobility outcomes. The Life Space Assessment (LSA) was used to measure the spatial extent of participants in a given month. 19,20 The LSA takes into account the frequency of going to different life-space levels (bedroom, driveway, within neighborhood, outside neighborhood but within town, and out of town) and whether assistance was necessary to get to those levels. A composite life-space score (range, 0–120) is calculated that combines information on the life-space level, the degree of independence, and the frequency. Higher scores indicate greater life space. The reliability and construct/criterion validity of this questionnaire have been published. 19 We inquired about current driving status and use of public transportation. People were asked whether they had fallen or been injured by a fall in the past year, a fall being defined as unintentionally coming to rest on the ground or on some other level. 21 Balance was assessed using the one-legged balance test in which the person is asked to stand on the leg of choice with eyes open for up to 30 seconds. 22 People who cannot stand for 5 seconds on one leg are at an increased risk of having an injurious fall. 22 Finally, the TUG test was performed, in which a person is asked to rise from a seated position, walk 3 m as quickly and as safely as possible, and return to the seat while being timed. 23 The TUG test was found to have good reliability and correlated with gait speed, activities of daily living limitations, and balance in previous research. 23 Reasons for not doing either the balance test or the TUG test were noted (e.g., safety concern of participant or researcher or refusal for nonsafety reasons such as pain). 
Health and Anthropometric Measures.
Participants were asked to self-report a physician diagnosis of 13 chronic comorbid conditions (e.g., stroke, Parkinson's, heart disease, and diabetes) and whether they were currently taking benzodiazepines, a potential risk factor for falls. 24 26 A sum of the total number of chronic conditions was used to indicate comorbidity, as has been done in previous research. 3 Depression was assessed using the Geriatric Depression 15-Item Scale. 27,28 A score of 5 or greater indicated depression. 29 Cognitive status, as mentioned earlier, was measured using the MMSE, Blind Version, which excludes eight items that rely heavily on vision, for a total maximum score of 22. 17 Height and weight were measured without shoes using a balance scale with height rod (Detecto Medic, Brooklyn, NY). Body mass index was calculated as kilograms per meters squared. Obesity was defined as a body mass index of 30 kg/m2 or greater. 30  
Vision and Eye Disease.
Binocular habitual visual acuity was measured using the ETDRS chart with illuminated light box at 2 m or at 1 m if the participant could not read any letters at 2 m. 31,32 Letter-by-letter scoring was performed with scores at 2 m converted to scores at 1 m by adding 15. Scores were converted to logMAR. Contrast sensitivity was measured using the Pelli-Robson chart at 1 m for each eye. 33 Forced choice letter-by-letter scoring procedures were used until a participant read all three letters of a triplet incorrectly. Log contrast sensitivity was then calculated. Visual fields were measured with full threshold N-30 testing in each eye (Humphrey FDT; Carl Zeiss Meditec, Inc., Dublin, CA). 34 The FDT measures 30° horizontally and 24° vertically. The medical record was reviewed and further detail on the patient's eye disease, and any coexisting eye disease (such as lens opacity) was recorded. For those who could not perform the FDT test because of advanced eye disease the last visual field examination results from the medical record were used in the analyses. 
Outcomes
The life-space scores and TUG times were examined as continuous variables given their approximately normal distributions. The other outcomes were dichotomized. The balance time was dichotomized at 5 seconds due to the truncated nature of the measurement at 30 seconds and to the previous finding that a time of 5 seconds or less was indicative of a recent fall. 22 Driving status was examined as either current driver or not (people who formerly or never drove were combined). Falls were examined as having reported a fall in the past year or not. 
Statistical Analyses
Descriptive statistics were calculated including means, standard deviations, and percentages. Vision, demographic, health, and mobility variables were compared for the three eye disease groups and the control group, using Student's t-test, χ2 test, or Fisher's exact test. Next, to determine whether eye disease was independently associated with any of the mobility outcomes, regression was used to adjust for potential confounding. Linear or logistic regression was used to determine whether the mobility outcomes differed in any of the eye disease groups compared with the control group after adjustment for demographic and health variables including age, sex, obesity, depression, number of comorbidities, benzodiazepine use, and cognitive status. Ethnicity was not included in the regression models due to the absence of patients of African descent in certain eye disease groups. 
To determine the measures of visual function primarily explaining the relationships between eye disease and mobility, the measures of visual function (visual acuity, contrast sensitivity, and visual field) were entered one at a time into the model with the eye disease variable, and changes in the regression coefficients for the eye disease variables were noted (Stata Ver. 11.0; Stata Corp., College Station, TX). 
Results
We recruited 272 patients who resided in the community (80%), in assisted living (11%), or in a retirement home (9%). In Table 1, the demographic, visual, and health characteristics of the four groups are compared. The groups with eye disease were older than the control group (P < 0.05). The Fuchs' group had a higher percentage of women than the control group (P < 0.05). The glaucoma group had a greater percentage of patients of African descent than the other groups, which had one or no patients of African descent (P < 0.05). The groups with eye disease had worse cognitive, depression, and comorbidity scores and were more likely to have a lens opacity than those in the control group (P < 0.01). AMD and Fuchs' patients were more likely to take benzodiazepines (P < 0.05) than the control group, whereas there was no significant difference in obesity between the groups. 
Table 1.
 
Description of Four Study Groups
Table 1.
 
Description of Four Study Groups
AMD (n = 68) Fuchs' (n = 49) Glaucoma (n = 82) Controls (n = 73)
Age 82.6 (5.8)* 79.4 (7.3)* 76.5 (7.4)* 72.8 (4.6)
Female 76 84* 56 62
Caucasian 100 100 87 99
African descent 0 0 12* 1
Obese 17 19 22 15
Binocular visual acuity, LogMAR 0.75 (0.40)* 0.65 (0.33)* 0.33 (0.32)* 0.04 (0.06)
Log contrast sensitivity in worse eye 0.61 (0.53)* 0.69 (0.48)* 0.85 (0.56)* 1.72 (0.17)
Visual field in better eye, MD −2.9 (4.1)* −3.0 (3.8)* −9.6 (6.6)* 0.5 (2.0)
MMSE, Blind Version (max score, 22) 18.8 (2.8)* 19.5 (2.5)* 19.0 (3.0)* 20.7 (1.4)
Depressive symptoms 3.9 (3.1)* 3.4 (2.7)* 2.8 (2.7)* 1.3 (1.8)
Comorbidity score 3.1 (2.0)* 2.7 (1.7)* 2.7 (1.6)* 2.0 (1.5)
Benzodiazepine use 35* 36* 19 14
Lens opacity 32* 24* 20* 12
As expected, visual acuity and contrast sensitivity were most impaired in the AMD and Fuchs' groups, whereas visual field was the most impaired in the glaucoma group (Table 1). The binocular visual acuity in the AMD group was 0.75 logMAR (∼20/90 Snellen), in the Fuchs' group was 0.65 logMAR (∼20/80 Snellen), in the glaucoma group was 0.33 logMAR (∼20/45 Snellen), and in the control group was 0.04 logMAR (∼20/20 Snellen). The glaucoma patients mainly had primary open-angle glaucoma (85%), 5% had secondary glaucoma, and the remainder had other forms or the medical record did not specify (10%). The mean pachymetry value in the worse eye of the Fuchs' patients was 693 μm (SD 109). 
In unadjusted analyses, the three groups with eye disease had worse average life-space scores and TUG results. They were also more likely to have poor balance and not to drive (P < 0.05; Table 2). Those in the glaucoma group were more likely to report a fall in the past year than were the controls (P < 0.05). 
Table 2.
 
Unadjusted Mobility Scores of Four Groups
Table 2.
 
Unadjusted Mobility Scores of Four Groups
AMD (n = 68) Fuchs' (n = 49) Glaucoma (n = 82) Controls (n = 73)
Life space 37.5 (17.3)* 45.4 (24.6)* 54.0 (25.5)* 73.8 (18.5)
Poor balance† 67* 45* 54* 22
TUG time‡ 13.7 (4.7)* 13.2 (5.7)* 13.0 (6.2)* 9.6 (2.1)
No driving in past year 88* 67* 61* 19
Fallen in past year 32 31 37* 22
In linear or logistic regression models adjusted for demographic and health variables, all three groups with eye disease had worse life-space scores (Table 3). Patients with AMD had life-space scores that were 16 points lower on average (95% CI, −24 to −8) than those of the control patients, whereas patients with Fuchs' dystrophy and glaucoma had life-space scores that were 13 (95% CI, −21 to −5) and 12 points (95% CI, −18 to −5) lower, respectively (Table 3). Those in all three groups with eye disease were much more likely to have stopped driving than were the controls (P < 0.05; Table 3). The groups with eye disease were similarly likely to take public transportation, as between 54% and 62% of each group had taken public transportation at least sometime in the past year (P = 0.52, data not shown). 
Table 3.
 
Multiple Linear or Logistic Regression Results on Adjusted Relationship between Eye Disease and Mobility Outcomes
Table 3.
 
Multiple Linear or Logistic Regression Results on Adjusted Relationship between Eye Disease and Mobility Outcomes
Life Space (n = 234) TUG Time (n = 221) Poor Balance (n = 198) Nondriver (n = 235) Fallen in Last Year n = 235)
β 95% CI* β 95% CI* OR 95% CI† OR 95% CI† OR 95% CI†
Control 0.00 0.00 1.00 1.00 1.00
AMD −15.96 −24.02 to −7.90 0.18 −1.61 to 1.98 1.76 0.56 to 5.48 9.28 2.70 to 31.83 1.22 0.46 to 3.26
Fuchs' dystrophy −12.73 −20.60 to −4.86 1.46 −0.32 to 3.24 1.54 0.50 to 4.79 2.90 1.02 to 8.29 1.13 0.42 to 3.03
Glaucoma −11.52 −18.08 to −4.96 1.86 0.42 to 3.29 3.51 1.42 to 8.67 5.23 2.06 to 13.29 1.77 0.79 to 3.94
Age −0.92 −1.33 to −0.50 0.22 0.13 to 0.32 1.19 1.11 to 1.28 1.11 1.05 to 1.19 1.01 0.96 to 1.06
Female −11.61 −16.98 to −6.24 1.44 0.25 to 2.63 2.23 1.02 to 4.85 9.66 4.00 to 23.31 1.27 0.66 to 2.45
Obese −1.73 −8.19 to 4.72 1.61 0.14 to 3.07 0.72 0.27 to 1.88 0.74 0.30 to 1.85 0.75 0.33 to 1.69
MMSE, Blind Version 1.20 0.19 to 2.21 −0.26 −0.50 to −0.02 1.05 0.90 to 1.21 0.80 0.67 to 0.95 1.01 0.90 to 1.14
Depression −11.35 −17.16 to −5.54 0.70 −0.63 to 2.04 3.42 1.40 to 8.34 1.43 0.59 to 3.48 2.09 1.08 to 4.04
Comorbidity score −1.95 −3.61 to −0.29 0.36 −0.02 to 0.73 1.03 0.81 to 1.33 1.15 0.90 to 1.47 0.96 0.78 to 1.17
Benzodiazepine use 1.50 −4.76 to 7.76 −0.12 −1.52 to 1.28 0.80 0.33 to 1.99 1.03 0.42 to 2.55 1.22 0.59 to 2.53
Patients with glaucoma performed worse on the TUG test and the one-legged balance test than did the controls (Table 3). Glaucoma patients took 1.9 seconds longer (95% CI, 0.4 to 3.3) on average to complete the TUG test and had 3.5 times the odds (95% CI, 1.4 to 8.7) of being unable to hold their balance for at least 5 seconds compared with the control patients. Patients with AMD or Fuchs' did not perform significantly worse than the controls on these tests after adjustment. 
The relationship between glaucoma and falls was no longer statistically significant after adjustment (OR = 1.77, 95% CI, 0.79 to 3.94; Table 3). Secondary analyses examining two or more falls, injurious falls, or number of falls also did not indicate significant associations (data not shown). 
Twenty-two percent of the study population had a lens opacity. Therefore, in sensitivity analyses, we included lens opacity as a variable, since it was associated with AMD and glaucoma status. However, lens opacity was not associated with mobility, and its inclusion in the models did not affect the results. We also ran models excluding the four people with secondary glaucoma and 11 people in wheelchairs from the analyses, and our results were unchanged. Finally, we ran models excluding the 12 people of African ethnicity, since we were unable to adjust for ethnicity because of the small number. Again, the results were unchanged. 
To determine which of the three measures of visual function explained most of the relationship between each eye disease and each mobility outcome, each measure of visual function was entered one at a time into the regression models while keeping the number of observations constant between the models (Table 4). For life space, contrast sensitivity in the worse eye explained the biggest parts of the relationships for AMD, Fuchs', and glaucoma. For the TUG score, visual field in the better eye explained the biggest part of the relationship with glaucoma. For the balance test, contrast sensitivity in the worse eye explained most of the relationship with glaucoma, although visual field in the better eye explained almost the same amount. For driving, the results were more difficult to interpret. The relationship between AMD and not driving was best explained by binocular visual acuity, and the relationship with Fuchs' was best explained by contrast sensitivity, whereas inconclusive results were seen for glaucoma. 
Table 4.
 
Multiple Linear or Logistic Regression Models Showing How Three Measures of Visual Function Explain Relationships between Eye Diseases and Mobility Outcomes
Table 4.
 
Multiple Linear or Logistic Regression Models Showing How Three Measures of Visual Function Explain Relationships between Eye Diseases and Mobility Outcomes
Outcome Model 1 Model 2 Model 3 Model 4
β 95% CI β 95% CI β 95% CI β 95% CI
Life space (n = 201 for all models)*
    Control 0.00 0.00 0.00 0.00
    AMD −15.24 −23.93 to −6.54 −13.11 −24.51 to −1.71 −6.51 −16.54 to 3.52 −14.01 −22.64 to −5.37
    Fuchs' dystrophy −11.28 −19.73 to −2.83 −9.35 −20.12 to 1.42 −3.08 −12.74 to 6.57 −9.36 −17.83 to −0.89
    Glaucoma −13.15 −19.86 to −6.44 −12.40 −19.60 to −5.24 −5.70 −13.69 to 2.28 −5.92 −14.96 to 2.82
    Visual acuity, binocular logMAR −3.43 −15.31 to 8.44
    Log contrast sensitivity, worse eye 10.12 3.92 to 16.31
    Visual field, better eye 0.72 0.15 to 1.29
TUG (n = 198 for all models)*
    Control 0.00 0.00 0.00 0.00
    AMD 0.44 −1.51 to 2.39 −0.23 −2.74 to 2.27 −0.22 −2.50 to 2.07 −0.02 −2.00 to 1.96
    Fuchs' dystrophy 1.44 −0.49 to 3.37 0.81 −1.61 to 3.23 0.79 −1.48 to 3.05 0.86 −1.13 to 2.84
    Glaucoma 1.88 0.38 to 3.37 1.64 0.03 to 3.23 1.29 −0.54 to 3.12 −0.09 −2.41 to 2.24
    Visual acuity, binocular 1.11 −1.46 to 3.69
    Log contrast sensitivity, worse eye −0.79 −2.21 to 0.63
    Visual field, better eye −0.13 −0.25 to −0.01
Poor balance (n = 178 for all models)*
    Control 1.00 1.00 1.00 1.00
    AMD 1.69 0.51 to 5.65 1.76 0.35 to 8.89 0.75 0.18 to 3.17 1.40 0.40 to 4.88
    Fuchs' dystrophy 0.96 0.28 to 3.36 1.00 0.21 to 4.76 0.44 0.10 to 1.89 0.81 0.23 to 2.89
    Glaucoma 3.27 1.30 to 8.22 3.32 1.23 to 8.93 1.64 0.53 to 5.10 1.77 0.51 to 6.21
    Visual acuity, binocular logMAR 0.94 0.17 to 5.16
    Log contrast sensitivity, worse eye 0.38 0.15 to 0.95
    Visual field, better eye 0.94 0.85 to 1.03
No driving (n = 202 for all models)*
    Control 1.00 1.00 1.00 1.00
    AMD 7.09 1.89 to 26.62 0.69 0.12 to 3.92 2.59 0.56 to 12.00 4.42 1.02 to 19.09
    Fuchs' dystrophy 2.70 0.84 to 8.69 0.25 0.04 to 1.44 1.05 0.26 to 4.25 0.91 0.24 to 3.49
    Glaucoma 5.65 2.11 to 15.13 2.81 0.95 to 8.32 2.48 0.75 to 8.18 0.37 0.09 to 1.64
    Visual acuity, binocular logMAR 72.39 8.14 to 644.06
    Log contrast sensitivity, worse eye 0.30 0.11 to 0.81
    Visual field, better eye 0.70 0.60 to 0.82
Discussion
All patients with vision loss had reduced mobility, but the pattern of mobility impairment differed by diagnosis. Our results indicated that patients with glaucoma had the highest number of mobility limitations with reduced life-space scores, slower TUG times, poorer balance, and more nondrivers compared with the controls. Patients with AMD had the lowest life-space scores and were the most likely to not drive. The measures of visual function that explained these relationships differed depending on the mobility task. For example, the relationship between glaucoma and balance was explained almost equally as well by contrast sensitivity and visual field, whereas the relationship between glaucoma and the TUG time was explained mainly by visual field. Contrast sensitivity and visual field may be helpful in maintaining balance by helping to detect small movements to allow rapid postural response to maintain balance. The visual contribution to balance is thought to be especially important for more difficult balance tasks like standing on one leg. 14,35  
We were correct that AMD would have the strongest association with life space and driving, but glaucoma also showed strong associations with life space and driving. Glaucoma was related to the TUG time and to poor balance, but it was not related to falls after adjustment. It was hypothesized that diseases affecting peripheral vision would have more of an effect on balance and falls because of prior research showing the importance of visual field on postural stability, falls, and balance. 14,15,36 It was hypothesized that the Fuchs' patients would be more similar to the AMD patients. While Fuchs' can affect both central and peripheral vision, our Fuchs' participants had decreased visual acuity and contrast sensitivity, but only modestly decreased visual fields. Fuchs' patients and AMD patients may also struggle with glare sensitivity, which may be important for driving. 37 The Fuchs' participants in our study had reduced life-space scores and were more likely to have stopped driving, similar to the AMD participants, as we expected. 
Our study is novel in its ability to examine a range of mobility outcomes across patients with different eye diseases and compare the results to those of controls without significant vision loss. We used validated measures of mobility that have been found to be associated with a range of adverse health outcomes. 19,22,23 To our knowledge, no previous studies have examined the relationships between the eye diseases of interest and life-space scores, TUG time, or the one-legged balance test. (Haymes et al. 5 did find a significant difference in TUG times between glaucoma patients and controls but they did not present adjusted analyses). Previous research has been done examining postural sway in glaucoma patients or AMD patients. 9,36,38 Our results for one-legged balance support the findings of these studies. 
Our results are in agreement with those in other studies that found that patients with AMD and glaucoma are less likely to drive. 4,39,40 The Province of Quebec currently requires a medical examination including an eye examination with visual acuity and visual field testing at ages 75 and 80 and then every 2 years thereafter, to renew the driver's license. However, very few licenses are actually suspended under the current policy, as 964 licenses were suspended for all medical reasons in 2009 out of a province of 7 million people. 41 It is likely that many patients with eye disease decide to stop driving on their own through self-regulation rather than having their license revoked by the Société de l'assurance automobile du Québec. 
The lack of association between eye disease and falls was surprising to us, given that various measures of visual function have been associated with falls in other research. 15,42 46 Few studies have examined falls in a population of patients with eye disease. One study found that glaucoma patients had more than three times the odds of a self-reported fall than did controls (OR = 3.71; 95% CI, 1.14, 12.05). 5 We were not able to replicate this finding, possibly because of differences in the control subject selection. Controls in the previous study were recruited by public notices within a health sciences center, whereas controls in this study were patients without significant vision loss from the same ophthalmology clinic, to be as similar as possible to the cases. In a study of AMD and falls, a study found that neovascular AMD patients had twice the odds of falling than did those without AMD. 47 Controls were recruited from general practitioner offices, and patients came from retinal clinics. Selection bias is always a concern when cases and controls come from different source populations and any hospital or clinic-based study can be prone to this bias. Our study attempted to capture patients and controls from the same clinic to minimize this problem. Despite the lack of an association between eye disease and falls in our data, we did find that glaucoma was associated with worse TUG times and poor balance—two mobility outcomes that are themselves related to falls or disability. 22,23 It is possible that patients with eye disease have developed compensatory strategies (such as reduced life space) or gait adaptations to avoid falling. 48 Another possibility is that recall bias resulted in significant misclassification of the self-report of falls in our population. We suspect any misclassification would be nondifferential, which would tend to bias our results toward the null. 
Not surprisingly, other known risk factors for mobility limitations also played a role in the mobility of our patients with eye disease including having depression and being female. A large percentage (27%) of the study population met the criteria for depression using a cutoff of 5 or greater. We suggested that people whose scores indicated depression should consider consulting with their primary care physician about treatment options. Our data fit with prior research indicating that women have a greater prevalence of mobility disability than men. 49 Our study was not designed to examine interaction between eye disease and other factors like depression or sex, but future research should examine whether there is a synergistic effect on mobility limitations. 
Strengths of this study include the use of multiple questionnaire and performance-based measures of mobility; the examination and comparison of people with different eye diseases representing different patterns of vision loss; the measurement of visual acuity, contrast sensitivity, and visual field; and the inclusion of many potential confounding factors in the analysis. 
A limitation of this study is the use of self-reported data to measure falls. The validity of the retrospective falling question was found by Cumming et al. 50 to have a correlation of 0.6 with the prospective reporting of falls, with a 12-month recall being better than a shorter recall of 3 or 6 months. We designed the study to have 80% power to detect odds ratios of 2.6 or greater as being statistically significant for falls. An odds ratio larger than this was reported by Haymes et al. 5 for glaucoma and falls. Our odds ratios for falls were all less than 1.8, which our study was not designed to detect. 
To conclude, we found that different eye diseases were associated with different patterns of mobility limitations. This knowledge is relevant to older patients with bilateral eye disease and their families, to physicians caring for older patients with eye disease, and to those providing low-vision rehabilitation services. Keeping older adults with eye disease as safely mobile as possible may help prevent morbidity associated with a sedentary lifestyle, mobility disability, and ultimately mortality 2 in this vulnerable population. 
Footnotes
 Supported by a CNIB New Investigator Grant, Toronto, ONT, Canada; Canadian Institutes of Health Research Grant IAP-98996, Ottawa, ONT, Canada; Fonds de Recherche en Santé du Québec salary award (EEF); and a Fonds de recherche en ophtalmologie de l'Université de Montréal salary award (MLP).
Footnotes
 Disclosure: M.L. Popescu, None; H. Boisjoly, None; H. Schmaltz, None; M.-J. Kergoat, None; J. Rousseau, None; S. Moghadaszadeh, None; F. Djafari, None; E.E. Freeman, None
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Table 1.
 
Description of Four Study Groups
Table 1.
 
Description of Four Study Groups
AMD (n = 68) Fuchs' (n = 49) Glaucoma (n = 82) Controls (n = 73)
Age 82.6 (5.8)* 79.4 (7.3)* 76.5 (7.4)* 72.8 (4.6)
Female 76 84* 56 62
Caucasian 100 100 87 99
African descent 0 0 12* 1
Obese 17 19 22 15
Binocular visual acuity, LogMAR 0.75 (0.40)* 0.65 (0.33)* 0.33 (0.32)* 0.04 (0.06)
Log contrast sensitivity in worse eye 0.61 (0.53)* 0.69 (0.48)* 0.85 (0.56)* 1.72 (0.17)
Visual field in better eye, MD −2.9 (4.1)* −3.0 (3.8)* −9.6 (6.6)* 0.5 (2.0)
MMSE, Blind Version (max score, 22) 18.8 (2.8)* 19.5 (2.5)* 19.0 (3.0)* 20.7 (1.4)
Depressive symptoms 3.9 (3.1)* 3.4 (2.7)* 2.8 (2.7)* 1.3 (1.8)
Comorbidity score 3.1 (2.0)* 2.7 (1.7)* 2.7 (1.6)* 2.0 (1.5)
Benzodiazepine use 35* 36* 19 14
Lens opacity 32* 24* 20* 12
Table 2.
 
Unadjusted Mobility Scores of Four Groups
Table 2.
 
Unadjusted Mobility Scores of Four Groups
AMD (n = 68) Fuchs' (n = 49) Glaucoma (n = 82) Controls (n = 73)
Life space 37.5 (17.3)* 45.4 (24.6)* 54.0 (25.5)* 73.8 (18.5)
Poor balance† 67* 45* 54* 22
TUG time‡ 13.7 (4.7)* 13.2 (5.7)* 13.0 (6.2)* 9.6 (2.1)
No driving in past year 88* 67* 61* 19
Fallen in past year 32 31 37* 22
Table 3.
 
Multiple Linear or Logistic Regression Results on Adjusted Relationship between Eye Disease and Mobility Outcomes
Table 3.
 
Multiple Linear or Logistic Regression Results on Adjusted Relationship between Eye Disease and Mobility Outcomes
Life Space (n = 234) TUG Time (n = 221) Poor Balance (n = 198) Nondriver (n = 235) Fallen in Last Year n = 235)
β 95% CI* β 95% CI* OR 95% CI† OR 95% CI† OR 95% CI†
Control 0.00 0.00 1.00 1.00 1.00
AMD −15.96 −24.02 to −7.90 0.18 −1.61 to 1.98 1.76 0.56 to 5.48 9.28 2.70 to 31.83 1.22 0.46 to 3.26
Fuchs' dystrophy −12.73 −20.60 to −4.86 1.46 −0.32 to 3.24 1.54 0.50 to 4.79 2.90 1.02 to 8.29 1.13 0.42 to 3.03
Glaucoma −11.52 −18.08 to −4.96 1.86 0.42 to 3.29 3.51 1.42 to 8.67 5.23 2.06 to 13.29 1.77 0.79 to 3.94
Age −0.92 −1.33 to −0.50 0.22 0.13 to 0.32 1.19 1.11 to 1.28 1.11 1.05 to 1.19 1.01 0.96 to 1.06
Female −11.61 −16.98 to −6.24 1.44 0.25 to 2.63 2.23 1.02 to 4.85 9.66 4.00 to 23.31 1.27 0.66 to 2.45
Obese −1.73 −8.19 to 4.72 1.61 0.14 to 3.07 0.72 0.27 to 1.88 0.74 0.30 to 1.85 0.75 0.33 to 1.69
MMSE, Blind Version 1.20 0.19 to 2.21 −0.26 −0.50 to −0.02 1.05 0.90 to 1.21 0.80 0.67 to 0.95 1.01 0.90 to 1.14
Depression −11.35 −17.16 to −5.54 0.70 −0.63 to 2.04 3.42 1.40 to 8.34 1.43 0.59 to 3.48 2.09 1.08 to 4.04
Comorbidity score −1.95 −3.61 to −0.29 0.36 −0.02 to 0.73 1.03 0.81 to 1.33 1.15 0.90 to 1.47 0.96 0.78 to 1.17
Benzodiazepine use 1.50 −4.76 to 7.76 −0.12 −1.52 to 1.28 0.80 0.33 to 1.99 1.03 0.42 to 2.55 1.22 0.59 to 2.53
Table 4.
 
Multiple Linear or Logistic Regression Models Showing How Three Measures of Visual Function Explain Relationships between Eye Diseases and Mobility Outcomes
Table 4.
 
Multiple Linear or Logistic Regression Models Showing How Three Measures of Visual Function Explain Relationships between Eye Diseases and Mobility Outcomes
Outcome Model 1 Model 2 Model 3 Model 4
β 95% CI β 95% CI β 95% CI β 95% CI
Life space (n = 201 for all models)*
    Control 0.00 0.00 0.00 0.00
    AMD −15.24 −23.93 to −6.54 −13.11 −24.51 to −1.71 −6.51 −16.54 to 3.52 −14.01 −22.64 to −5.37
    Fuchs' dystrophy −11.28 −19.73 to −2.83 −9.35 −20.12 to 1.42 −3.08 −12.74 to 6.57 −9.36 −17.83 to −0.89
    Glaucoma −13.15 −19.86 to −6.44 −12.40 −19.60 to −5.24 −5.70 −13.69 to 2.28 −5.92 −14.96 to 2.82
    Visual acuity, binocular logMAR −3.43 −15.31 to 8.44
    Log contrast sensitivity, worse eye 10.12 3.92 to 16.31
    Visual field, better eye 0.72 0.15 to 1.29
TUG (n = 198 for all models)*
    Control 0.00 0.00 0.00 0.00
    AMD 0.44 −1.51 to 2.39 −0.23 −2.74 to 2.27 −0.22 −2.50 to 2.07 −0.02 −2.00 to 1.96
    Fuchs' dystrophy 1.44 −0.49 to 3.37 0.81 −1.61 to 3.23 0.79 −1.48 to 3.05 0.86 −1.13 to 2.84
    Glaucoma 1.88 0.38 to 3.37 1.64 0.03 to 3.23 1.29 −0.54 to 3.12 −0.09 −2.41 to 2.24
    Visual acuity, binocular 1.11 −1.46 to 3.69
    Log contrast sensitivity, worse eye −0.79 −2.21 to 0.63
    Visual field, better eye −0.13 −0.25 to −0.01
Poor balance (n = 178 for all models)*
    Control 1.00 1.00 1.00 1.00
    AMD 1.69 0.51 to 5.65 1.76 0.35 to 8.89 0.75 0.18 to 3.17 1.40 0.40 to 4.88
    Fuchs' dystrophy 0.96 0.28 to 3.36 1.00 0.21 to 4.76 0.44 0.10 to 1.89 0.81 0.23 to 2.89
    Glaucoma 3.27 1.30 to 8.22 3.32 1.23 to 8.93 1.64 0.53 to 5.10 1.77 0.51 to 6.21
    Visual acuity, binocular logMAR 0.94 0.17 to 5.16
    Log contrast sensitivity, worse eye 0.38 0.15 to 0.95
    Visual field, better eye 0.94 0.85 to 1.03
No driving (n = 202 for all models)*
    Control 1.00 1.00 1.00 1.00
    AMD 7.09 1.89 to 26.62 0.69 0.12 to 3.92 2.59 0.56 to 12.00 4.42 1.02 to 19.09
    Fuchs' dystrophy 2.70 0.84 to 8.69 0.25 0.04 to 1.44 1.05 0.26 to 4.25 0.91 0.24 to 3.49
    Glaucoma 5.65 2.11 to 15.13 2.81 0.95 to 8.32 2.48 0.75 to 8.18 0.37 0.09 to 1.64
    Visual acuity, binocular logMAR 72.39 8.14 to 644.06
    Log contrast sensitivity, worse eye 0.30 0.11 to 0.81
    Visual field, better eye 0.70 0.60 to 0.82
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