
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12384-5
10.1016/j.heliyon.2024.e36353
e36353
Research Article
Structural validity of the impact of vision impairment questionnaire among patients with visual impairment in Thailand
Tantirattanakulchai Pankaew a
Hounnaklang Nuchanad nuchanad.h@chula.ac.th
a⁎
Win Nanda a
Khambhiphant Bharkbhum b
Pongsachareonnont Pear Ferreira cd
a College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand
b Department of Ophthalmology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
c Center of Excellence in Retina, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
d King Chulalongkorn Memorial Hospital, Bangkok, Thailand
⁎ Corresponding author. nuchanad.h@chula.ac.th
18 8 2024
30 8 2024
18 8 2024
10 16 e3635327 12 2023
6 8 2024
14 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Background

The Impact of Vision Impairment (IVI) questionnaire is used to assess vision-related quality of life (VRQOL) among patients with visual impairment. This study aims to evaluate the factor structure of the IVI in the context of Thai culture to assess psychometric properties.

Methods

This cross-sectional study was conducted between February and July 2022. Purposive sampling was used to include 502 patients with visual impairment who received treatment at a tertiary eye center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand. The data were collected using the interviewer-administered questionnaire. The participants were randomly divided into two groups: one employed for exploratory factor analysis (EFA; n = 251) and the other for confirmatory factor analysis (CFA; n = 251).

Results

EFA yielded 28 items that supported a four-factor structure: behaviors related to mobility and independence (8 items), reading (3 items), self-dependence (7 items), and emotional impact of vision loss (10 items), with a total variance of 64.9 %. The model fit was good: χ2/df = 1.66, CFI = 0.949, TLI = 0.940, RMSEA = 0.052, and SRMR = 0.041. The AIC and BIC for the bifactor four-factor model (17,484.86 and 17,879.41, respectively) were lower than those for the bifactor three-factor model (17,566.44 and 17,961.29, respectively), indicating that the former showed the best fit.

Conclusions

Given its good reliability and validity, the IVI scale has been extensively used to explore the impact of visual impairment on the VRQOL of patients in the clinical setting, thus greatly benefitting researchers, healthcare providers, and ophthalmologists.

Keywords

Visual impairment
Quality of life
Factor analysis
Measurement
Validity
==== Body
pmc1 Introduction

In 2020, the World Health Organization reported that at least 2.2 billion people have near or distant vision impairment globally. The leading causes of visual impairment and blindness are uncorrected refractive errors and cataracts. A previous study found that visual impairment and blindness were more prevalent in patients aged ≥50 years [1]. Visual impairment prevents patients from obtaining required information because vision is critical for gathering sensory information from surrounding environments. For instance, they may have dysfunctional object recognition and experience difficulty in figuring out three-dimensional relationships of objects and identifying their locations relative to the surrounding objects (egocentric).

The detrimental impacts of impaired vision on vision-related quality of life (VRQOL) have been well documented using several psychometric instruments [2,3]. Vision impairment substantially impacts social functioning, mobility, physical and emotional wellbeing, and quality of life [4,5]. Furthermore, it affects nearly all aspects of a patient's life, presenting difficulties in performing routine activities, reduced mobility and social participation, and stigma and discrimination, possibly leading to a reduced quality of life [[6], [7], [8]].

VRQOL represents the degree to which visual impairment affects the quality of daily life activities as well as social, emotional, and economic wellbeing. Previous studies had identified that patients with high degrees of visual impairment are associated with poor quality of life [[9], [10], [11]]. This can be attributed to complex factors such as visual function, symptoms, emotional wellbeing, social relationships, concerns, and the inconvenience caused by visual impairment [12].

The Impact of Vision Impairment (IVI) questionnaire was used to evaluate limitations in performing daily life activities among patients with vision impairment. Developed by the Center for Eye Research Australia, the original 32-item IVI questionnaire was validated using classical test theory, measuring limitations across five domains—leisure and work, consumer interaction and social engagement, household tasks and personal care, mobility, and emotional response to vision loss [3]. The original 32-item IVI questionnaire can either be self-reported or administered as an interview [3,13]. Subsequent revalidation using CFA and Rasch analysis resulted in a more robust and psychometrically sound 28-item version of the IVI. Results suggested a three-factor model supported by the CFA, encompassing subscales for reading and accessing information, mobility and independence, and emotional wellbeing subscales [14]. Currently, the IVI is widely used in many countries. Furthermore, it has been translated to various languages such as Melanesian [15], German [9], Telugu, Hindi [16], Mandarin Chinese [17], Turkish [18], Greek [19], and Thai [10].

Rasch analysis and EFA were used to evaluate the measurement structural validity. Rasch analysis visualizes the tests as measuring unidimensional abilities. Moreover, EFA and CFA were utilized to investigate the underlying structure of the IVI findings and validate the factor structure proposed by EFA [20,21].

Therefore, this present study aims to analyze the factorial structure using EFA, and CFA evaluating the factor structure of the 28-item IVI questionnaires when applied in a Thai context or cultural background to assess psychometric properties. Additionally, our study aims to cross-validate with previous model over the unidimensional, correlated, and bifactor model. Moreover, we conducted the measurement invariance of gender among the Thai clinical sample.

2 Material and methods

2.1 Participants and procedure

This cross-sectional study was conducted between February 2022 and July 2022. Participants were selected using purposive sampling. All participants were chosen by the researcher based on specific inclusion criteria such as visual impairment who had Snellen visual acuities worse than 6/12 (20/40) in the best eye, were aged ≥18 years, and were receiving treatment at a tertiary eye center, King Chulalongkorn Memorial Hospital in Bangkok, Thailand, where patients were recruited from the outpatient clinic. Patients with severe psychological illness, auditory deficits, and cognitive impairment were excluded. According to the patient's visual problems, the sociodemographic data and the IVI questionnaire were collected using an interviewer-administered questionnaire by a trained interviewer. Only completed questionnaires were used for further analysis.

2.2 Measures

2.2.1 Sociodemographic questionnaire

Interviewer-administered questionnaire was employed to gather personal data such as age, religion, level of education, monthly income, occupation, marital status, living arrangement, eye disease, and duration of eye and underlying diseases.

2.2.1.1 The impact of vision impairment (IVI) questionnaire

The IVI was designed to measure the impact of vision impairment on VRQOL [9,22]. The Thai version of the IVI questionnaire used in this study included 28 items that were categorized into three specific subscales: (1) reading and accessing information, (2) mobility and independence, and (3) emotional wellbeing. The response of each item was rated on a 4-point Likert scale from 0 (not at all) to 3 (a lot) [10]. In this study, the value of Cronbach's alpha was 0.95.

2.3 Data analyses

The participants (502) were divided into two subsamples using random sampling of 50 % of all cases. One subsample was used for EFA (n = 251; 132 females, 48.9 %; 119 males, 51.3 %; mean age = 61.1 years, SD = 17.2), and another subsample was used for CFA (n = 251; 138 females, 51.1 %; 113 males, 48.7 %; mean age = 61.7 years, SD = 14.9). The subsamples did not significantly differ with respect to age (t = 0.432 and p = > 0.05), gender (χ2 = 0.289, df = 1, and p = >0.05), marital status (χ2 = 3.201, df = 3, and p = >0.05), education level (χ2 = 0.173, df = 1, and p = >0.05), visual impairment level (χ2 = 5.545, df = 3, and p = >0.05).

EFA was conducted using principal component analysis factoring. Factors with eigenvalues exceeding one were extracted [23]. Items with loading values of >0.32 were retained as specific factors [[24], [25], [26], [27]]. The varimax rotation was the preferred rotation method. Before conducting the EFA, the Bartlett's test of sphericity and Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy were used to verify the suitability of using factor models. Furthermore, internal consistency reliability was assessed using Cronbach's alpha (α) (accepted value ≥ 0.70). Moreover, convergent validity was evaluated using average variance extracted and composite reliability with values of >0.40 and > 0.70, respectively.

The maximum likelihood estimation was used for the unidimensional, correlated, and bifactor models. We used the indexes and cutoff scores of χ2/df to assess the goodness of the model fit; a good fit was indicated by a value of <2 [28]. An indicated model fit was defined as CFI and TLI ≥0.90 [29], RMSEA and SRMR ≤0.08 [30]. The smallest AIC and BIC were used to determine the best-fit model [31].

We examined the measurement invariance of the IVI across genders using CFI, RMSEA, and SRMR [32]. The χ2 test was performed, revealing a non-significant Δχ2; therefore, the metric or scalar invariance was uniform. We considered ΔCFI <0.001, ΔRMSEA <0.015, and ΔSRMR <0.03 to assess model similarity and support the conclusion of invariance [33]. Analyses were conducted using SPSS version 28 and Mplus software version 8.7.

2.4 Ethical consideration

This study was approved by the institutional review board of Human Research, Faculty of Medicine, Chulalongkorn University (COA No. 1638/2021). The participants provided written consent before the interview. Those who could not read owing to visual problems were required to provide verbal consent to the interviewers who would explain the research rationale and consent details. This research followed the tenets of the Declaration of Helsinki.

3 Results

3.1 Demographic variables

Table 1 presents the following characteristics of 502 participants. The participants were aged 18–96 years with a mean age of (±SD) of 61.4 (±16.1) years, and females accounted for 53.8 %. Most of the participants were married (49.4 %), living with family (65.5 %), had an educational level lower than a bachelor's degree (75.7 %), had mild visual impairment (43.4 %), had cataract (53.4 %), had used eyeglasses (51.8 %), and had two eye diseases (34.5 %).Table 1 Demographic characteristics of the two samples (n = 502).

Table 1Variables	EFA (n = 251) n (%)	CFA (n = 251) n (%)	p-value	
Age (years)			0.666	
 Mean (SD)	61.1 (17.2)	61.7 (14.9)		
 Range	18–90	18–96		
Total IVI			0.433	
 Mean (SD)	37.8 (21.0)	37.5 (21.8)		
Gender			0.591	
 Female	132 (48.9)	138 (51.1)		
 Male	119 (51.3)	113 (48.7)		
Marital Status			0.362	
 Single	69 (54.8)	57 (45.2)		
 Married	123 (49.6)	125 (50.4)		
 Widowed	47 (49.0)	49 (51.0)		
 Divorced/separated	12 (37.5)	20 (62.5)		
Living status			0.631	
 Live alone	21 (48.8)	22 (51.2)		
 With family	163 (49.5)	166 (50.5)		
 With spouse	57 (49.6)	58 (50.4)		
 With others	10 (66.7)	5 (33.3)		
Education level			0.677	
 Lower than Bachelor's degree	188 (49.5)	192 (50.5)		
 Bachelor's degree and higher	63 (51.6)	59 (48.4)		
Visual impairment level			0.136	
 Mild	97 (44.5)	121 (55.5)		
 Moderate	78 (51.7)	73 (48.3)		
 Severe	31 (56.4)	24 (43.6)		
 Blindness	45 (57.7)	33 (42.3)		
Degeneration of macular			0.092	
 No	154 (47.2)	172 (52.8)		
 Yes	97 (55.1)	79 (44.9)		
Cataract			0.858	
 No	118 (50.4)	116 (49.6)		
 Yes	133 (49.6)	135 (50.4)		
Retinal Detachment			0.902	
 No	212 (50.1)	211 (49.9)		
 Yes	39 (49.4)	40 (50.6)		
Number of eye diseases			0.855	
 One	55 (47.0)	62 (53.0)		
 Two	90 (52.0)	83 (48.0)		
 Three	59 (49.2)	61 (50.8)		
 Higher than three	47 (51.1)	45 (48.9)		
Vision aid equipment			0.520	
 Did not use any visual aid equipment	94 (53.4)	82 (46.6)		
 Eyeglasses	126 (48.5)	134 (51.5)		
 Magnifying glass	31 (47.0)	35 (53.0)		
EFA, Exploratory factor analysis; CFA, Confirmatory factor analysis; SD, standard deviation.

3.2 Exploratory factor analysis

Based on the IVI factor structure, the EFA result has shown that the 28-item IVI had a factor loading of >0.32. The four factors explained the variance of 64.9 %. The first principal factor was the most dominant, with an eigenvalue of 13.456, which explained 20.336 % of the variation in the data. Factors 2, 3, and 4 explained 18.104 %, 16.063 %, and 10.460 % of the variation, respectively.

The complete pattern loading matrix is presented, revealing the loadings of each item on the identified factors. All items exhibited acceptable loadings, exceeding a cutoff value of 0.32 for interpretability [[24], [25], [26], [27]]. Four items exhibited high cross-loadings (Items 6, 16, 17, and 18). Considering the original 32-item IVI three-factor model, we allocated items with cross-loadings to factors that were contextually appropriate for the corresponding item content. These four items were also in the same group as the original IVI three-factor model.

The four factors were labeled as follows: (1) behaviors related to mobility and independence, (2) reading, (3) self-dependence, and (4) emotional impact of vision loss. The behaviors related to mobility and independence factor are Items 1, 2, 3, 4, 5, 6, 7, and 9. The reading factor comprised Items 8, 14, and 15. The self-dependence factor comprised Items 10, 11, 12, 13, 16, 17, and 18. The emotional impact of vision loss factor comprised Items 19, 20, 21, 22, 23, 24, 25, 26, 27, and 28.

The 28-item IVI four-factor model showed an excelled KMO of 0.951 [34]. Additionally, Bartlett's test of sphericity was statistically significant (χ2 = 4939.201, p < 0.01).

The internal reliability and item-total correlations were determined for EFA; behaviors related to mobility and independence factor had a Cronbach's alpha value of 0.86 and an item-total correlation range of 0.53–0.75. The reading factor had a Cronbach's alpha value of 0.81 and an item-total correlation range of 0.61–0.77. The independence factor had a Cronbach's alpha value of 0.90 and an item-total correlation range of 0.60–0.79. The emotional impact of vision loss, instead, had a Cronbach's alpha value of 0.92 and an item-total correlation range of 0.59–0.77.

The average variances extracted from Factors 1–4 were 0.406, 0.617, 0.434, and 0.436, respectively. Additionally, composite reliability scores for Factors 1–4 were 0.843, 0.825, 0.839, and 0.880, respectively, indicating acceptable convergent validity (Table 2).Table 2 Exploratory factor analysis of the impact of vision impairment (IVI) (n = 251).

Table 2IVI items	Factor loading	
1	2	3	4	
Behaviors related to mobility and independence	Reading	Self-dependence	Emotional impact of vision loss	
1. Your ability to see and enjoy T.V.?	0.618	0.374	0.273	0.193	
2. Taking part in recreational activities such as walking, jogging, patong, or aerobics?	0.760	0.203	0.176	0.243	
3. Shopping? (finding what you want and paying for it)	0.670	0.320	0.283	0.181	
4. Visiting friends or family?	0.712	0.099	0.316	0.260	
5. Recognising or meeting people?	0.565	0.251	0.367	0.254	
6. Generally looking after your appearance? (face, hair, clothing, etc.)	0.587	0.097	0.149	0.412	
7. Opening packaging? (for example, around food, medicines)	0.663	0.194	0.160	0.248	
8. Reading labels or instructions on medicines?	0.361	0.614	0.308	0.112	
9. Operating household appliances and the telephone?	0.484	0.202	0.215	0.246	
10. How much has your eyesight interfered with getting about outdoors? (on the pavement or crossing the street)	0.431	0.267	0.686	0.214	
11. In the past month, how often has your eyesight made you go carefully to avoid falling or tripping?	0.181	0.268	0.816	0.163	
12. In general, how much has your eyesight interfered with traveling or using transport? (bus & train)	0.338	0.214	0.698	0.235	
13. Going down steps, stairs, or curbs?	0.183	0.226	0.808	0.201	
14. Reading ordinary size print? (for example, newspapers)	0.223	0.854	0.133	0.168	
15. Getting information that you need?	0.188	0.863	0.155	0.137	
16. Your general safety at home?	0.396	−0.105	0.433	0.426	
17. Spilling or breaking things?	0.420	−0.116	0.492	0.427	
18. Your general safety when out of your home?	0.334	0.068	0.598	0.428	
19. In the past month, how often has your eyesight stopped you doing the things you want to do?	0.366	0.144	0.384	0.521	
20. In the past month, how often have you needed help from other people because of your eyesight?	0.331	0.377	0.307	0.383	
21. Have you felt embarrassed because of your eyesight?	0.248	0.037	0.087	0.743	
22. Have you felt frustrated or annoyed because of your eyesight?	0.200	0.203	0.248	0.757	
23. Have you felt lonely or isolated because of your eyesight?	0.162	0.071	0.113	0.813	
24. Have you felt sad or low because of your eyesight?	0.161	0.165	0.070	0.848	
25. In the past month, how often have you worried about your eyesight getting worse?	0.204	0.176	0.328	0.601	
26. In the past month how often has your eyesight made you concerned or worried about coping with everyday life?	0.308	0.129	0.318	0.664	
27. Have you felt like a nuisance or a burden because of your eyesight?	0.312	0.196	0.323	0.632	
28. In the past month, how much has your eyesight interfered with your life in general?	0.397	0.204	0.379	0.490	
AVE	0.406	0.617	0.437	0.436	
CR	0.843	0.825	0.839	0.880	

Table 3 presents the correlations among IVI factors. The results show that all IVI factors correlate significantly (p < 0.001). Total IVI scores, behaviors related to mobility and independence, and emotional impact of vision loss are strongly positively correlated. All IVI factors are positively significant and show moderate to strong correlations. The correlation ranged between 0.586 and 0.910.Table 3 Correlation between IVI factors.

Table 3Factors	1	2	3	4	5	
1. Behaviors related to mobility and independence	−					
2. Reading	0.630**	–				
3. Self-dependence	0.745**	0.606**	–			
4. Emotional impact of vision loss	0.730**	0.586**	0.716**	–		
5. Total IVI scores	0.910**	0.730**	0.887**	0.909**	−	

3.3 Confirmatory factor analysis

Fig. 1 demonstrates all models tested. Table 4 presents the goodness-of-fit indices for different factor models. The fit indices of the unidimensional (Supplementary Fig. 3) and correlated three-factor model (Supplementary Fig. 4) showed an unsatisfactory fit to the data.Fig. 1 Descriptions of the presented models. IVI, Impact of vision impairment; RA, Reading and accessing information; MI, Mobility and independence; EW, Emotional well-being; BM, Behaviors related to mobility and independence; RE, Reading; SD, Self-dependence; EI, Emotional impact of vision loss.

Fig. 1

Table 4 Model fit indicators.

Table 4Model	df	χ2	CFI	TLI	RMSEA	SRMR	AIC	BIC	
Model 1: Unidimensional	350	1222.21	0.791	0.774	0.100	0.066	18114.60	18410.74	
Model 2:
Correlated 3 factors	347	1003.26	0.843	0.828	0.087	0.061	17901.65	18208.37	
Model 3:
Correlated 4 factors	344	731.62	0.907	0.898	0.067	0.062	17636.01	17953.30	
Model 4:
Bifactor 3 factors	322	618.04	0.929	0.917	0.061	0.045	17566.44	17961.29	
Model 5:
Bifactor 4 factors	322	536.46	0.949	0.940	0.052	0.041	17484.86	17879.41	
df, degrees of freedom; χ2, chi-squared; CFI, Comparative Fit Index; TLI, Tucker-Lewis Index; RMSEA, Root Mean Square Error of Approximation; SRMR, Standardized Root Mean Square Residual; AIC, Akaike information criterion; BIC, Bayesian information criterion.

The AIC and BIC for the correlated four-factor model (Supplementary Fig. 5) (17,636.01 and 17,953.30, respectively) were smaller than those for the correlated three-factor model (17,901.65 and 18,208.37, respectively), indicating that the correlated four-factor model was a better model. Likewise, the AIC and BIC for the bifactor four-factor model (Fig. 2) (17,484.86 and 17,879.41, respectively) were lower than those bifactor three-factor model (Supplementary Fig. 6) (17,566.44 and 17,961.29, respectively), indicating that the bifactor four-factor model had the best fit (Table 4).Fig. 2 Bifactor four-factor model. IVI, Impact of vision impairment; BM, Behaviors related to mobility and independence; RE, Reading; SD, Self-dependence; EI, Emotional impact of vision loss.

Fig. 2

Model were conducted to test measurement invariance across genders, by comparing configural, metric, and scalar invariance metrics. The model indicated configural and metric invariance. Metric against configural was not statistically significant (Δχ2 = 24.43, p = 0.437; ΔCFI = 0.000, ΔRMSEA = 0.001, and ΔSRMR = 0.002), whereas scalar against metric was statistically significant (Δχ2 = 46.84, p = 0.003; ΔCFI = 0.003, ΔRMSEA = 0.001, and ΔSRMR = 0.001) (Table 5).Table 5 Comparing configural, metric, and scalar invariance of gender.

Table 5Invariance	χ2	df	Δχ2	p	CFI	RMSEA	SRMR	ΔCFI	ΔRMSEA	ΔSRMR	
Configural	1582.56	688			0.899	0.072	0.066				
Metric	1606.99	712	24.43a	0.437	0.899	0.071	0.068	0.000	0.001	0.002	
Scalar	1653.83	736	46.84b	0.003	0.896	0.070	0.069	0.003	0.001	0.001	
df, degrees of freedom; χ2, chi-squared; Δχ2, the change in chi-squared; p, p-value; CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of Approximation; SRMR, Standardized Root Mean Square Residual; ΔCFI, the change in CFI; ΔRMSEA, the change in RMSEA; ΔSRMR, the change in SRMR.

a Metric against Configural.

b Scalar against Metric.

4 Discussion

The IVI questionnaire was used as a vision-specific instrument for data collection to measure the IVI on different aspects of quality of life [35]. Originally, the IVI questionnaire included 32 items under five domains of participation—leisure and work, consumer and social interaction, household and personal care, mobility, and emotional wellbeing [3]. However, the 28-item IVI comprises three subscales: reading and accessing information, mobility and independence, and emotional wellbeing [14].

Based on our findings, the IVI had a good internal consistency. The study results revealed that the four factors explained 64.9 % of the total variance. Furthermore, another solution accounting for 60 % of the total variance should be considered [3]. Our findings indicated four factors, namely, behaviors related to mobility and independence, reading, self-dependence, and emotional impact of vision loss, in which the number of subscales is inconsistent from those in the original 28-item IVI and previous studies [9,14,18,36,37].

The study conducted in the low-vision rehabilitation center in Australia assessed and validated the factor structure of the 28-item IVI scale using CFA and Rasch analysis, and the fit indices showed that the three-factor model was a good fit. A three-factor model included items for mobility and independence, emotional wellbeing, and reading and accessing information subscales [14].

There were similarities and differences in our study findings. One factor of the three-factor model (reading and accessing information) consists of Items 1, 3, 5, 6, 7, 8, 9, 14, and 15. However, in our study, we found that this factor can have another subdivision, namely, reading, which consists of Items 8, 14, and 15. The three items were only related to reading activity, while the remaining six items in our study were involved with behaviors related to mobility and independence factors.

Moreover, there was a clinic-based cross-sectional study that validated the German-translated IVI questionnaire with two factors extracted in functional and emotional IVI [9]. Similarly, studies conducted in Australia, Ireland, and Turkey used the three factors with the same subscales and followed the original 28-item IVI [18,37].

The factor structure of the IVI was tested, and the bifactor four-factor model showed the best fit (RMSEA = 0.052, SRMR = 0.041, CFI = 0.949, and TLI = 0.940). Furthermore, the increments in AIC and BIC of five models, namely, unidimensional, correlated three-factor, correlated four-factor, bifactor three-factor, and bifactor four-factor models, were compared in the study. The bifactor four-factor model had the smallest AIC and BIC values (17,484.86 and 17,879.41, respectively), indicating that it is the best model.

The correlated four-factor and bifactor four-factor models showed acceptable fit indices without modification. Factor loading was reduced when the correlated four-factor model without the general factor was changed to the bifactor four-factor model, indicating that factor loading was shared with the general factor.

The measurement invariance across genders was tested in this study; it was found that invariance is comparable between males and females. The next step is to determine items that may be biased. Further studies could test the measurement invariance of IVI by age and VI level.

Besides the original 28-item IVI, a computerized adaptive test for the 28-item IVI (IVI-CAT) [36], and a 15-item short version of the scale have been developed [38], which are high-quality instruments [39]. The brief IVI and IVI-CAT have crucial distinctive features that reduce burden on participants, reduce selection bias, and enhance the data quality. Owing to the fact that the number of ophthalmologists is small than the number of patients in Thailand, using the brief IVI will help stakeholders reduce the burden on participants, reduce choice bias, and improve data quality. These advantages will help ophthalmologists to prevent and help patients promptly and efficiently.

Similarly, paper-and-pencil questionnaires are generally a burden to participants, often necessitating subsequent data entry and calculations. These processes require substantial resources and expertise and are susceptible to human error. However, the IVI-CAT can provide precise measurements of the overall and specific aspects of VRQOL using far fewer items than the paper-and-pencil versions. Additionally, it enables automated scoring and reporting, thus simplifying the assessment process.

To suit the context of people with visual impairments, chatbot-based CAT-IVI can be used to communicate with visually impaired people via real-time speech with the aid of artificial intelligence technology; these advantages will help ophthalmologists to help patients promptly and efficiently. However, Thailand has limited resources and accessibility. Therefore, the use of the original paper-and-pencil 28-item IVI is still essential and crucial.

The limitations of this study are mainly the use of purposive sampling, and the sample was not randomly selected from all potential participants. Additionally, the study was not population based, and the generalizability of our results may be limited. Furthermore, related psychometric properties such as concurrent validity, predictive validity, known-group validity, and test–retest reliability were not investigated in this study. To address these, further research should be conducted.

5 Conclusion

Considering all the aforementioned limitations and discrepancies, this study has, to a greater extent, provided data to support the psychometric properties of the IVI. The Thai version of the 28-item IVI-28 exhibited an appropriate construct, validity, and reliability. It has four factors that explained 64.9 % of the variance in the results of patients with visual impairment in a clinical setting. This scale may be beneficial to future studies and researchers, ophthalmologists, and healthcare providers to assess the challenges experienced by patients with visual impairment.

Ethics statement

This study adhered to the principles outlined in the Declaration of Helsinki and received approval from the institutional review board of Human Research, Faculty of Medicine, Chulalongkorn University (COA No. 1638/2021). We confirm that written informed consent was obtained from all participants before the interviews.

Data availability statement

The data supporting the findings of this study are available. For inquiries or assistance regarding data, please contact nuchanad.h@chula.ac.th.

CRediT authorship contribution statement

Pankaew Tantirattanakulchai: Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Formal analysis, Data curation, Conceptualization. Nuchanad Hounnaklang: Writing – review & editing, Writing – original draft, Supervision, Methodology, Formal analysis, Conceptualization. Nanda Win: Writing – review & editing, Writing – original draft. Bharkbhum Khambhiphant: Data curation. Pear Ferreira Pongsachareonnont: Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is/are the supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Multimedia component 3

Multimedia component 3

Acknowledgements

The authors acknowledge the 100th Anniversary 10.13039/501100002873 Chulalongkorn University Fund for Doctoral Scholarship for supporting this study. Their appreciation also goes to National University of Singapore (NUS), Center for Eye Research Australia Limited (CERA), and Prof. Mansing Ratanasukon for their kindness related to IVI questionnaire.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36353.
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