Abstract
Purpose.:
To design and evaluate an instrument appropriate for assessing vision-related quality of life (VRQoL) in persons with severe vision loss.
Methods.:
A total of 603 legally blind persons (better eye visual acuity of <20/200) were interviewed using an item pool based on the original Impact of Vision Impairment (IVI) questionnaire, augmented by items appropriate for persons with severe vision loss. Refinement and item reduction was done in three steps using factor and Rasch analysis to assess psychometric properties, exploring key indices, such as response category functioning (floor and ceiling effects), instrument unidimensionality, discriminant ability, and targeting of item difficulty to patient ability.
Results.:
A final pool of 28 items was selected that grouped into two subscales of the IVI-VLV: activities of daily living, mobility, and safety (ADLMS; 16 items) and emotional well-being (EWB; 12 items). Both subscales are unidimensional, able to differentiate reliably between at least three different levels of VRQoL, and item difficulty was adequate for the assessed sample. Using generalized linear models and controlling for age, we found that only poor general health (P = 0.005 and P = 0.007) and concurrent depression and anxiety (P = 0.019 and P < 0.001) were associated with a lower ADLMS and EWB subscale score, respectively.
Conclusions.:
The IVI-VLV is a valid and reliable VRQoL measure in persons with severe vision loss, and its measurement is almost unaffected by participants' self-perceived general or mental health. The IVI-VLV can be used as an outcome measure in trials attempting sight restoration.
Introduction
Vision impairment has a significant negative impact on quality of life (QoL). Using various vision-related QoL instruments, this has been demonstrated for a variety of conditions and levels of visual impairment.
1–6 However, to date most vision-related QoL instruments have been developed for people with mild to moderate visual loss, and are not specific for severe impairment. With the promise of sight-restoring treatments such as retinal prostheses and stem cell or gene therapy on the horizon, it is important to develop outcome measures applicable to persons with very poor vision, as neither visual acuity measurement nor measurement of vision-related QoL using currently available instruments is meaningful in this group of patients.
Measurement of QoL involves a person self-rating the impact of their visual impairment (if any) or ocular condition on various components, such as mobility, activity limitation, reading and accessing information, or emotional well-being, using a set of relevant and validated items (questions).
7 Instruments are commonly developed to capture vision-related QoL across various aspects of vision loss, such as distance visual impairment, problems with near vision, or loss of visual field. Thus, the extremes of visual function, very good or very poor, are commonly not well captured by most instruments due to floor and ceiling effects. To ensure high-quality assessments and correct assumptions based on gathered data, psychometrically valid instruments are required.
8
We therefore developed a vision-related QoL measure, the Impact of Vision Impairment–Very Low Vision (IVI-VLV) questionnaire, appropriate for persons with severe vision loss. The IVI-VLV is based on the existing IVI questionnaire,
9 and we have determined its validity, reliability, and measurement characteristics using factor and Rasch analysis. This instrument development is part of the Low Vision Assessment of Daily Activities Protocol (LoVADA), developed as part of the Bionic Vision Australia retinal prosthesis project.
Methods
Participants
All participants were adults (≥18 years) and legally blind according to the Australian definition, which is based on either distance visual acuity or a visual field restriction (distance visual acuity impairment of less than 6/60 [20/200] in the better eye, or a binocular visual field restriction to no more than the central 10 degrees, or both).
The study was conducted between September 2012 and December 2013 at the Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital (RVEEH). Ethical approval was obtained from the Human Research and Ethics Committee at the RVEEH. All patients gave informed consent for study participation. The study adhered to the tenets of the Declaration of Helsinki.
The Impact of Vision Impairment Questionnaire
The IVI is an instrument to measure the impact of vision impairment on VRQoL. The currently used version of the normal IVI contains 28 items with three to four active response options using Likert scaling, ranging from
not at all to
a lot. Items 1 to 15 have an additional response
don't do this for other reasons. Items form three specific subscales: “reading and accessing information,” “mobility and independence,” and “emotional well-being.”
10,11 In the versions piloted in this study, all items of the IVI-VLV are preceded by “How much does your eyesight….” and use the same rating scale with the following four response options: not at all, a little, some of the time, and a lot. In addition, all items have a “Don't do this for other reasons” option. No items have three response categories only.
Psychometric Evaluation
Rasch analysis is a modern psychometric method that mathematically describes the interaction between respondents and test items and applies a strict model that the pattern of participants' responses should satisfy.
12–15 Rasch analysis provides greater insight into the psychometric properties of the instrument compared with traditional methods. Several techniques are available to determine how well items fit the latent trait being measured, that is, vision-related quality of life, how well the items discriminate between the respondents, and how well item difficulty targets person ability (i.e., level of vision).
8 During Rasch analysis, scores that approximate interval-level measurement (person measures, expressed in log of the odds units, or logits) are estimated from raw ordinal data. We used the following criteria to assess the psychometric properties of the IVI-VLV.
Threshold Ordering.
To determine whether the categories used to rate the IVI-VLV items are valid, we assessed the response category threshold ordering. First, over- or underutilization of response categories, as well as ability of respondents to discriminate between response categories, was assessed. Disordered thresholds, if evident, were addressed by collapsing categories. In addition, the number of responses, average measure per response category, and category thresholds were assessed in detail. Although the Rasch model is based on a strict pattern of expected responses, some random variation is always assumed. This amount is represented by the mean square (MnSq) statistic. An infit MnSq value of 1 is ideal, and up to 1.3 is acceptable. Anything above that indicates an excess amount of “noise” in the data. Similarly, a value below 0.7 indicates an unacceptably high degree of uniformity in the responses.
Precision of the Instrument.
The ability of the scale to discriminate between different levels of person ability is assessed using person separation index (PSI) and person reliability (PR) scores. The PR reflects the spread of the tested underlying trait in the sample, and is high if the assessed trait is well represented in the sample in relation to the length of the questionnaire as well as the targeting of items. The PSI is used to classify respondents into different levels of the assessed trait, that is, those with high QoL and those with low QoL, and demonstrates sensitivity of the instrument. Item precision is assessed by item reliability (IR), which represents the spread of item difficulty in accordance to sample size, and is high if the spread of item difficulty is good and the tested sample sufficiently large. Values of greater than 2.0 for separation and greater than 0.8 for reliability, respectively, are considered adequate and represent the capacity of the scale to distinguish at least three levels of person ability (i.e., visual functioning) for PSI.
Unidimensionality.
Whether the scale measured a single latent trait was assessed in two ways. First, by testing item “fits” or “misfits” as described above.
7 Second, the items were tested for local independence using principal components analysis (PCA), which means that they are not related except for the fact that they measure the same trait, with as little overlap between items as possible. The PCA of residuals for the first factor should exceed 50% and the first contrast of residuals should be less than 2.5 eigenvalues, which reflects the number of items relating to an independent second factor or trait within the trait measured.
7 Principal components analysis in Rasch analysis differs from factor analysis in that it assesses the residuals, that is, the unexplained part (or variance) rather than the explained part of the data. Residuals are assessed as to whether they differ from a random normal distribution, which would be expected if they were just measurement noise. Furthermore, the noise associated with one item should be independent of the noise associated with another item. If PCA detects a residual factor in the unexplained data (i.e., a nonrandom distribution or a correlation of residuals between items), the Rasch model assumes multidimensionality and the scale should be reassessed as to whether it needs to be split into subscales.
Targeting.
The targeting of the instrument was determined by inspection of the person-item map and calculation of the difference between item and person means. A difference of greater than 1.0 logits suggests that the difficulty of the items does not adequately target the ability of the sample participants.
Differential Item Functioning (DIF).
Each item was assessed for DIF, which is a statistical method for detecting whether sample subgroups (e.g., sex, age groups) systematically respond differently to certain items, despite having similar underlying ability. A DIF contrast of greater than 1.0 logits for an item indicates that interpretation of the item may be biased for some participant subgroups.
We performed Rasch analysis on the IVI-VLV using Winsteps software (version 3.68; Chicago, IL, USA) and the Andrich single rating scale model was used.
Statistical Analyses
Data analysis was carried out with the SPSS statistical software (version 19.0; SPSS Science, Chicago, IL, USA). We used factor analysis in addition to the above-described PCA to explore the subscales of the IVI-VLV. The performed factor analysis was exploratory, as we did not prespecify any factors or subscales. Descriptive statistical analyses were performed to characterize the participants' sociodemographic, clinical, and vision-related QoL (VRQoL) data. The association between VRQoL scores and participant characteristics were explored using bivariate correlations and χ2 tests. Factors found to be associated in univariate analyses were subsequently entered into a generalized linear model. All tests were considered to be statistically significant at a level of P less than 0.05. As this was a purely exploratory study, we did not adjust for multiple comparisons.
Phases of Instrument Development
The instrument development was structured in three phases. In the first phase, we pooled data from previous studies that have used the IVI-28 items in persons who are legally blind and assessed its measurement properties.
9,16–19 Data for a total of 204 legally blind participants recruited at low-vision clinics and through patient associations were collated and assessed using Rasch analysis. As expected, measurement properties were suboptimal for our desired target population of people with severe vision loss, and most items had a ceiling effect (a lot of problems/too difficult). In addition, some items were inapplicable to this group, as they had either stopped doing this particular task or activity, such as watching television, or, rarely, had adapted to a degree that caused them to have no problems any longer. Thus, we decided to develop a new instrument with different, more appropriate items.
In the second phase, we went back to the original item pool used to develop the current IVI-28 items. In several focus group discussions with vision-impaired patients, healthy controls, and professionals, an initial item pool of 76 items had been created and subsequently reduced to form the currently used IVI-28, which is a well-validated VRQoL measure for persons with varying degrees of impairment.
10,11,18,20 Using a number of items that had been eliminated based mostly on a floor effect (no problems/too easy) in the initial evaluations, a total of 52 items were taken forward for evaluation in phase two of this study. In this phase, we conducted telephone interviews with 198 legally blind persons, collecting sociodemographic characteristics, VRQoL data, and qualitative feedback. Participants who were not currently registered as legally blind in Australia were excluded. Every participant was asked about their eye disease, and whether this disease was the cause of their vision loss.
Following the Rasch analysis of the 52-item version, a number of items were removed based on floor or ceiling effects, or misfit, and 34 items remained. These grouped into an emotional well-being, and a mobility and activities of daily living and safety subscale. However, based on qualitative feedback gathered after each interview, a number of items and response options were rephrased and new items added. A large number of participants commented on a lack of items related to employment, financial issues, and education, including use of computers. Discussions with low-vision experts confirmed that these were frequently encountered issues. Based on this, three items were added and a 37-item version was taken forward into phase three of the study.
In the third phase, 201 legally blind participants were interviewed by telephone, collecting sociodemographic data in addition to the IVI-VLV 37-item interview. Again, participants who were not currently registered as legally blind in Australia were excluded. Participants were asked several questions pertaining to their remaining vision, that is, ability to see light, (hand) movement, be able to read with magnifiers/aids, and whether they knew their last tested visual acuity. Every participant was asked about their eye disease, and whether this disease was the cause of their vision loss, in addition to questions pertaining to socioeconomic status, use of visual aids, health state, and concurrent morbidity and medication.
The results of this final phase of the instrument development are presented in the results section.
Supplementary Table S1 provides an overview of all items tested and the final items retained for the IVI-VLV.
Results
Participant Characteristics
Of the 201 participants, slightly more than half were female (
n = 116, 58%), and the average age was 72 years (±16 years SD,
Table 1). All participants were legally blind. The most common cause of vision loss was AMD (50%), followed by RP (14%) and other retinal dystrophies (12%;
Table 1). On average, participants had been legally blind for 18 years, and were using just under eight different visual aids and devices each. Most participants were married or had a de facto partner (54%) and lived with someone (62%). Younger participants (<65 years) were less severely visually impaired (
P = 0.033), had been blind for longer (
P < 0.001), more often had a higher educational level (
P = 0.001), and reported fewer other health problems (
P < 0.001) and a better general health (
P = 0.015). Just fewer than 30% of participants suffered from anxiety, depression, or both (
Table 1).
Table 1 Characteristics of the Sample, as Mean (±SD) or n (%)
Table 1 Characteristics of the Sample, as Mean (±SD) or n (%)
| Total Sample, n = 201 | Age Groups |
| | <65 y, n = 64 | ≥65 y, n = 137 | P * |
| Age, y | 72 ± 16 | 52 ± 9 | 81 ± 9 | <0.001 |
| Sex | | | | |
| Male | 85 (42.3) | 32 (50.0) | 53 (38.7) | 0.131 |
| Female | 116 (57.7) | 32 (50.0) | 84 (61.3) | |
| Marital status | | | | |
| Single/divorced/widowed | 92 (45.8) | 29 (45.3) | 63 (46.0) | 0.929 |
| Married/partner | 109 (54.2) | 35 (54.7) | 74 (54.0) | |
| Living situation | | | | |
| Alone | 74 (37.9) | 19 (32.8) | 55 (40.1) | 0.332 |
| With someone | 121 (62.1) | 39 (67.2) | 82 (59.9) | |
| Employment | | | | |
| Full-time | 7 (3.5) | 7 (11.3) | 0 (0) | <0.001 |
| Part-time | 30 (15.2) | 25 (40.3) | 5 (3.7) | |
| Retired | 153 (77.3) | 23 (37.1) | 130 (95.6) | |
| Unemployed | 8 (4.0) | 7 (11.3) | 1 (0.7) | |
| Education | | | | |
| Primary/some secondary | 79 (39.3) | 15 (23.4) | 64 (46.7) | 0.001 |
| Secondary completed | 24 (11.9) | 11 (17.2) | 13 (9.5) | |
| Apprenticeship/TAFE | 56 (27.9) | 17 (26.6) | 39 (28.5) | |
| University | 42 (20.9) | 21 (32.8) | 21 (15.3) | |
| General health | | | | |
| Excellent | 32 (15.9) | 12 (18.8) | 20 (14.6) | 0.015 |
| Very good | 59 (29.4) | 26 (40.6) | 33 (24.1) | |
| Good | 70 (34.8) | 18 (28.1) | 52 (38.0) | |
| Fair | 26 (12.9) | 4 (6.3) | 22 (16.1) | |
| Poor | 14 (7.0) | 4 (6.3) | 10 (7.3) | |
| Other health problems | | | | |
| Yes | 156 (77.6) | 39 (60.9) | 117 (85.4) | <0.001 |
| No | 45 (22.4) | 25 (39.1) | 20 (14.6) | |
| Do other health problems interfere? | | | | |
| Not at all | 52 (25.9) | 13 (20.3) | 39 (28.5) | 0.005 |
| A little | 50 (24.9) | 13 (20.3) | 37 (27.0) | |
| A great deal | 57 (28.4) | 14 (21.9) | 43 (31.4) | |
| Not applicable | 42 (20.9) | 24 (37.5) | 18 (13.1) | |
| Anxiety and depression | | | | |
| Depression | 25 (12.4) | 5 (7.8) | 20 (14.6) | 0.245 |
| Anxiety | 17 (8.5) | 7 (10.9) | 10 (7.3) | |
| Both | 12 (6.0) | 6 (9.4) | 6 (4.4) | |
| None | 147 (73.1) | 46 (71.9) | 101 (73.7) | |
| Eye condition | | | | |
| RP | 28 (13.9) | 21 (32.8) | 7 (5.1) | 0.223 |
| AMD | 101 (50.2) | 6 (9.4) | 95 (69.3) | |
| Other retinal dystrophy | 25 (12.4) | 16 (25.0) | 9 (6.6) | |
| Glaucoma | 15 (7.5) | 2 (3.1) | 13 (9.5) | |
| Other | 32 (15.9) | 19 (29.7) | 13 (9.5) | |
| Level of visual impairment | | | | |
| <20/200 to >CF | 45 (22.4) | 25 (39.1) | 20 (14.6) | 0.033 |
| CF to >PL | 127 (63.2) | 27 (42.2) | 100 (73.0) | |
| PL and worse | 29 (14.4) | 12 (18.8) | 17 (12.4) | |
| Duration of vision loss, y | 18 ± 18 | 24 ± 17 | 15 ± 18 | 0.001 |
| No. of visual aids and devices used | 7.73 ± 3.58 | 7.41 ± 3.44 | 7.88 ± 3.65 | 0.388 |
Psychometric Evaluation of the IVI-VLV
Response category thresholds were ordered, indicating that participants were able to differentiate sufficiently between them. Three items displayed misfit, and the PCA indicated multidimensionality (
Table 2).
Table 2 The Fit Parameters of the Complete IVI-VLV and Its Subscales ADLMS and EWB Compared With the Rasch Model
Table 2 The Fit Parameters of the Complete IVI-VLV and Its Subscales ADLMS and EWB Compared With the Rasch Model
| Parameters | Rasch Model | Complete IVI-VLV | ADLMS | EWB |
| Item No. | | 1–37 | 1, 3, 5, 8–20 | 23–27, 29-31, 34–37 |
| No. of misfitting items | 0 | 3 | 0 | 0 |
| PSI | >2.0 | 3.39 | 2.34 | 2.24 |
| PR | >0.8 | 0.92 | 0.85 | 0.83 |
| IR | >0.8 | 0.98 | 0.99 | 0.99 |
| Person mean | 0 | 0.01 | −0.12 | 0.28 |
| PCA, Eigenvalue for first contrast | <2.5 | 3.4 | 2.3 | 1.6 |
| Variance by the measure, % | 50.0–60.0 | 44.9 | 50.6 | 54.1 |
Based on the PCA, confirmed by a factor analysis, the scale was split into two subscales, the Emotional Well-Being (EWB) and Activities of Daily Living, Mobility, and Safety (ADLMS) subscales. The remaining items did not group together in any particular way and we were unable to incorporate them into an additional subscale or add them to either of the other two. Against this background, we dropped all items not contributing any measurement to the two identified subscales (in total nine items, related to operating household appliances, using a computer, social activities, colliding with an obstacle or a car, education, and employment). The ADLMS subscale contains 16 items, and the EWB subscale 12 items.
Assessing the two identified subscales, none of the items were misfitting, and IR was above 0.9 for all subscales. This indicated that all thresholds were ordered and that the number and clarity of the response categories were appropriate. The PR above 0.8 and PSI above 2 indicated acceptable ability to differentiate reliably between at least three different levels of VRQoL for both subscales. The targeting of the instrument was slightly suboptimal, with a difference between person and item means of −0.12 (ADLMS) and 0.28 (EWB) logits. This, however, is still well within acceptable levels and the person-item maps for both subscales demonstrated a good spread of items across the spectrum of participants' VRQoL (
Fig.). There was no evidence of multidimensionality in either of the subscales. The raw variance explained by the PCA of the residuals was adequate for both the ADLMS and EWB subscales (50.6% and 54.1%). None of the items showed any DIF. Taking these parameters together, both IVI-VLV subscales satisfy all requirements of the Rasch model. A final version of the IVI-VLV questionnaire can be found in the
Supplementary Material.
Association of the IVI-VLV Scores With Sample Characteristics
Rasch analysis was used to generate person measures for both subscales, with higher scores indicating better VRQoL. The mean ADLMS subscale score was −0.05 (±1.03 SD) and the mean EWB subscale score was 0.28 (±1.12 SD). The different eye conditions causing vision loss were associated with the ADLMS, but not with the EWB subscale scores (
P = 0.018 and
P = 0.685, respectively;
Table 3). However, both subscale scores demonstrated changes over the three categories of vision loss (
Table 3). Both ADLMS and EWB scores decreased with worsening general health, the presence of other health problems, with increasing interference of these health problems, and with the presence of depression or anxiety (
Table 3). In generalized linear models, controlling for age, only experiencing a lot of interference from comorbid health problems and suffering from both depression and anxiety were associated with a lower ADLMS and EWB subscale score (
Table 4). AMD remained associated with both ADLMS and EWB subscale scores, and males were more likely to report a lower EWB score (
Table 4). Level of vision loss was not associated with the ADLMS subscale score, and only the intermediate category (Count Fingers to better than Perception of Light) was associated with the EWB subscale score.
Table 3 IVI-VLV Subscale Scores by Sample Characteristics, n = 201
Table 3 IVI-VLV Subscale Scores by Sample Characteristics, n = 201
| | ADLMS | EWB |
| Mean ± SD | P * | Mean ± SD | P * |
| Age |
| <65 | 0.01 ± 0.99 | 0.588 | 0.11 ± 1.00 | 0.140 |
| 65+ | −0.08 ± 1.06 | | 0.36 ± 1.17 | |
| Sex |
| Male | −0.01 ± 1.15 | 0.627 | 0.21 ± 1.12 | 0.465 |
| Female | −0.08 ± 0.95 | | 0.33 ± 1.12 | |
| Marital status |
| Single/divorced/ widowed | −0.03 ± 1.04 | 0.819 | 0.34 ± 1.13 | 0.473 |
| Married/partner | −0.07 ± 1.03 | | 0.23 ± 1.11 | |
| Living situation |
| Alone | 0.01 ± 1.08 | 0.518 | 0.36 ± 1.16 | 0.453 |
| With someone | −0.09 ± 1.01 | | 0.24 ± 1.10 | |
| Employment |
| Full-time | 0.46 ± 0.71 | 0.165 | 0.53 ± 0.93 | 0.726 |
| Part-time | 0.18 ± 1.35 | | 0.43 ± 1.30 | |
| Retired | −0.14 ± 1.00 | | 0.25 ± 1.11 | |
| Unemployed | 0.29 ± 0.53 | | 0.05 ± 1.02 | |
| Education |
| Primary/some secondary | −0.02 ± 1.15 | 0.342 | 0.40 ± 1.26 | 0.287 |
| Secondary completed | −0.36 ± 0.80 | | −0.11 ± 0.89 | |
| Apprenticeship/ TAFE | −0.09 ± 0.93 | | 0.30 ± 1.05 | |
| University | 0.12 ± 1.05 | | 0.27 ± 1.03 | |
| General health |
| Excellent | 0.59 ± 1.68 | <0.001 | 0.67 ± 1.49 | 0.008 |
| Very good | 0.08 ± 0.84 | | 0.55 ± 1.08 | |
| Good | −0.23 ± 0.73 | | 0.06 ± 0.98 | |
| Fair | −0.50 ± 0.76 | | 0.01 ± 0.90 | |
| Poor | −0.38 ± 0.92 | | −0.13 ± 0.89 | |
| Other health problems |
| Yes | −0.16 ± 0.95 | 0.005 | 0.19 ± 1.00 | 0.026 |
| No | 0.32 ± 1.22 | | 0.61 ± 1.42 | |
| Do other health problems interfere? |
| Not at all | 0.17 ± 1.20 | <0.001 | 0.44 ± 1.06 | 0.002 |
| A little | −0.05 ± 0.77 | | 0.41 ± 0.88 | |
| A great deal | −0.53 ± 0.70 | | −0.19 ± 0.95 | |
| Not applicable | 0.32 ± 1.23 | | 0.57 ± 1.46 | |
| Anxiety and depression |
| Depression | −0.43 ± 0.88 | 0.008 | −0.31 ± 0.80 | <0.001 |
| Anxiety | −0.38 ± 0.78 | | −0.40 ± 1.04 | |
| None | 0.10 ± 1.06 | | 0.55 ± 1.09 | |
| Both | −0.62 ± 0.93 | | −0.76 ± 0.65 | |
| Eye condition |
| RP | 0.15 ± 0.90 | 0.018 | 0.24 ± 1.02 | 0.685 |
| AMD | −0.27 ± 0.80 | | 0.21 ± 1.02 | |
| Other retinal dystrophy | 0.37 ± 1.41 | | 0.45 ± 1.14 | |
| Glaucoma | −0.19 ± 1.03 | | 0.11 ± 1.21 | |
| Other | 0.19 ± 1.32 | | 0.48 ± 1.44 | |
| Level of visual impairment |
| 6/60 to >CF | 0.08 ± 1.02 | 0.003 | 0.23 ± 1.01 | 0.010 |
| CF to >PL | −0.22 ± 0.99 | | 0.17 ± 1.12 | |
| PL and worse | 0.47 ± 1.09 | | 0.86 ± 1.15 | |
Table 4 Factors Associated With ADLMS and EWB Subscale Scores
Table 4 Factors Associated With ADLMS and EWB Subscale Scores
| Factors | ADLMS | EWB |
| P | OR | 95% CI | P | OR | 95% CI |
| Lower | Upper | Lower | Upper |
| Eye condition |
| RP | 0.884 | 1.04 | 0.64 | 1.68 | 0.854 | 0.95 | 0.58 | 1.58 |
| AMD | 0.022 | 0.56 | 0.34 | 0.92 | 0.048 | 0.59 | 0.36 | 0.99 |
| Retinal dystrophy | 0.501 | 1.19 | 0.72 | 1.98 | 0.856 | 0.95 | 0.56 | 1.62 |
| Glaucoma | 0.107 | 0.61 | 0.34 | 1.11 | 0.073 | 0.57 | 0.30 | 1.05 |
| Other (reference) | | 1.00 | | | | 1.00 | | |
| Level of visual impairment |
| 6/60 to >CF | 0.325 | 0.80 | 0.51 | 1.25 | 0.110 | 0.69 | 0.43 | 1.09 |
| CF to >PL | 0.095 | 0.69 | 0.45 | 1.07 | 0.027 | 0.60 | 0.38 | 0.94 |
| PL and worse (reference) | | 1.00 | | | | 1.00 | | |
| Sex |
| Male | 0.488 | 0.91 | 0.70 | 1.19 | 0.023 | 0.72 | 0.55 | 0.96 |
| Female (reference) | | 1.00 | | | | 1.00 | | |
| General health | | | | | | | | |
| Excellent | 0.559 | 1.22 | 0.63 | 2.37 | 0.885 | 1.05 | 0.53 | 2.11 |
| Very good | 0.429 | 0.78 | 0.43 | 1.43 | 0.962 | 0.98 | 0.52 | 1.85 |
| Good | 0.420 | 0.80 | 0.46 | 1.38 | 0.305 | 0.74 | 0.42 | 1.31 |
| Fair | 0.551 | 0.83 | 0.45 | 1.52 | 0.971 | 1.01 | 0.54 | 1.90 |
| Poor (reference) | | 1.00 | | | | 1.00 | | |
| Do other health problems interfere? |
| Not at all | 0.456 | 0.86 | 0.58 | 1.28 | 0.242 | 0.78 | 0.51 | 1.18 |
| A little | 0.358 | 0.82 | 0.54 | 1.25 | 0.626 | 0.90 | 0.58 | 1.39 |
| A lot | 0.005 | 0.54 | 0.35 | 0.83 | 0.007 | 0.53 | 0.34 | 0.84 |
| No other health problems (reference) | | 1.00 | | | | 1.00 | | |
| Anxiety or depression |
| Depression | 0.243 | 1.48 | 0.77 | 2.85 | 0.097 | 1.79 | 0.90 | 3.54 |
| Anxiety | 0.678 | 1.16 | 0.58 | 2.34 | 0.290 | 1.48 | 0.71 | 3.08 |
| Both | 0.019 | 2.00 | 1.12 | 3.56 | 0.000 | 3.51 | 1.92 | 6.42 |
| None (reference) | | 1.00 | | | | 1.00 | | |
Discussion
Using a large item pool, participant and expert input, as well as Rasch analysis, we designed a valid and reliable measure of VRQoL in persons with very low vision, the IVI-VLV. It can differentiate between different levels of VRQoL in participants, and measurement is unaffected by almost all levels of general or mental health. The questionnaire is the first instrument to measure VRQoL in persons with severe vision loss whose VRQoL is not captured by available instruments. The IVI-VLV meets all requirements of the Rasch model, and proposed quality criteria for health status questionnaires, such as content validity, internal consistency, reliability, no floor or ceiling effects, and good interpretability.
21
Self-perceived general and mental health is known to affect measurement of VRQoL.
22,23 This is reflected in our results. However, only comorbid health problems that greatly impact individuals' lives did affect IVI-VLV measurement. Thus, distortion is unlikely to be significant. Similarly, severe vision impairment is associated with higher rates of depression and anxiety, which may distort measurement of VRQoL.
24,25 Given that we found depression and anxiety only to be associated with measured VRQoL if occurring concurrently, the distortion of the IVI-VLV measurement is likely to be small.
The VRQoL has been shown to be determined mostly by the extent of visual impairment as well as the type (central versus peripheral visual field loss, and so forth) irrespective of the underlying condition.
3 In our sample, VRQoL decreased with decreasing vision, and, based on Rasch analysis, the IVI-VLV could differentiate among at least three different levels of VRQoL. However, neither the cause nor the level of vision impairment showed strong associations with measured VRQoL when controlling for age, general health, depression, and anxiety in this sample, which may be partly due to the commonly observed high correlation between age and general health.
Although we added further items to reflect change in day-to-day lives over time, such as computer use in phase two of the questionnaire development, they did not contribute to overall measurement and were removed from the final selection of items. In our sample, using the final set of items, we did not observe any floor effects. With a different sample including more persons with less or no residual visual function, we would expect to observe floor effects in some of the selected items.
The spread of retained items is good for the ADLMS subscale and slightly suboptimal for the EWB subscale. This is a problem inherent to measuring EWB in severely visually impaired persons, in whom adaptation to visual loss is very important, and, on average, very different between different persons even at the same level of visual loss. This has been shown to influence coping rates of depression, and emotional well-being.
26,27
In our sample, AMD was associated with the ADLMS and the EWB score, which may be due to its visual impairment being distinctly different (central visual field [VF] and visual acuity [VA] loss only) compared with most of the other conditions (retinal dystrophies including RP and glaucoma, with peripheral and/or central VF and VA loss). Further studies will be required to assess associations with functional clinical measures of vision as well as perform further assessment to demonstrate the IVI-VLV's validity and reliability. Doing this, controlling for adaptation and coping skills using available scales,
26 will be important as the level of adaptation as well as individual coping skills further determine a person's reported VRQoL.
28
Strengths of our study include the use of a large item pool based on focus group discussions with affected persons and experts. The Australian definition of blindness has previously been shown to better identify persons with vision loss–related morbidity compared with the United States' or World Health Organization's definition.
16 Thus, legally blind Australians constitute a valid group of respondents to develop the IVI-VLV. Based on Rasch analysis as well as factor analysis, final items were selected and grouped into two subscales, which could be shown to satisfy all requirements of the Rasch model. In addition, Rasch analysis provided several useful indicators of scale category organization, such as the validity and functioning of the rating scale, and the optimal number of response categories.
29,30 A limitation of our study is the use of respondent-reported clinical characteristics (cause and level of vision impairment), a lack of visual field information, and a lack of a measure of adaptation to visual loss, which may have diminished our ability to reveal significant associations with these factors. Further studies are required to assess associations with functional clinical measures of vision, as well as perform further assessment of the IVI-VLV's validity and reliability. As AMD constitutes the largest single cause of blindness in industrialized or developed countries, like Australia, our cohort was broadly representative of severely visually impaired persons.
In conclusion, the IVI-VLV is a valid VRQoL measure in persons with severe vision loss, and its measurement is almost unaffected by participants' self-perceived general or mental health status. With emerging sight-restoring interventions, it will become increasingly important to have standardized outcome measures appropriate for usage in persons with severe vision loss. The IVI-VLV captures VRQoL at the very low end of visual function, and can be used as an outcome measure in trials attempting sight restoration.
Supplementary Materials
Acknowledgments
Supported by the Australian Research Council through its Special Research Initiative in Bionic Vision Science and Technology grant to Bionic Vision Australia. Centre for Eye Research Australia (CERA) receives operational infrastructure support from the Victorian government and is supported by National Health & Medical Research Council (NHMRC) Centre for Clinical Research Excellence Award 529923; RHG is the recipient of an NHMRC practitioner fellowship (529905). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors alone are responsible for the content and writing of the paper.
Disclosure: R.P. Finger, None; B. Tellis, None; J. Crewe, None; J.E. Keeffe, None; L.N. Ayton, None; R.H. Guymer, None
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LNA and RHG contributed equally to the work presented here and should therefore be regarded as equivalent authors.