Cross-cultural adaptation and construct validity of the Chinese Version of Visual Vertigo Analogue Scale by using structural equation modeling

Abstract

BACKGROUND:

Visual vertigo (VV) is a disease characterized by various visual signal-induced discomforts, including dizziness, unsteady balance, activity avoiding, and so forth. Distinguishing it from other kinds of dizziness is important because it needs the combination of visual training and vestibular rehabilitation together. However, there is no appropriate tool to diagnose VV in China, thus we would like to introduce an effective tool to China.

OBJECTIVE:

The aim of this study was to establish the reliability and validity of the Chinese version of visual vertigo analogue scale (VVAS-CH) and to achieve its cross-cultural adaptation in order to promote its further usage in China.

METHODS:

A total of 1681 patients complaining of vertigo or dizziness were enrolled and they were asked to complete the VVAS-CH. The cross-cultural adaptation, reliability and construct validity of the VVAS-CH were determined.

RESULTS:

Split-half reliability was 0.939, showing a good reliability. Factor analysis identified only one common factor for the nine items that explained 64.83% of the total variance. Most fit indices reached acceptable levels, proving the good fit of the VVAS-CH model.

CONCLUSIONS:

The VVAS-CH validated in this study can be used as an effective tool for diagnosing and evaluating VV in patients whose native language is Chinese.

Keywords

Visual vertigo dizziness scale validity common factor

1 Background

Visual vertigo (VV) manifests with one or more symptoms of dizziness, unsteadiness, or non-spinning vertigo that are exacerbated by exposure to moving or complex visual stimuli, e.g., being in fast moving traffic, walking through supermarkets or shopping mall, watching action movies, and so on [5]. As described originally by Bronstein, VV may develop as a complication of acute peripheral or central vestibular syndromes [2]. It is now known to occur transiently during attacks of episodic vestibular syndromes such as vestibular migraine and may persist interictally. When present chronically, it occurs most commonly, though not exclusively, as a core feature of persistent postural perceptual dizziness [20]. This can invoke stress, anxiety, frustration, and overall discomfort, and can potentially limits subject in their day-to-day activities, and which can lead to a vicious circle in which visual dependence and VV continue to worsen [18, 26]. The prevalence of VV might have been underestimated for a long time, because not only patients but also doctors are unaware of the causal relationship of vision and dizziness. According to a report, of patients with peripheral vestibular disorders and referred for vestibular rehabilitation, 77.5% were diagnosed with VV [5].

Distinguishing VV from other kinds of dizziness is meaningful because vestibular rehabilitation training is the key to cure kinds of chronic dizziness caused by acute or chronic vestibular problems [9, 11]. But it is not enough for VV. Alternatively, the combination of visual training and vestibular rehabilitation is more appropriate to treat VV [3, 17].

Visually induced dizziness is difficult to be quantified by machine, as an alternative, self-reported scale has been proved to be good to quantitatively analyze the symptoms. However, there is no suitable tool in China. Several self-report scales focusing on vertigo or dizziness that have been translated into Chinese before have only a few items related to the symptoms of VV, which is ineligible for the clinical usage [6 , 27]. Therefore, we would like to introduce something new. Visual vertigo analogue scale (VVAS), proposed by Dannenbaum in 2011, is a novel nine-item scale for evaluating VV [5]. As a quantitative tool for specifically assessing VV, it has been widely adopted in the English-speaking countries. A VVAS version for the Chinese Mandarin language, however, is still missing. The current work aims to fill this gap. In particular, the current work builds upon a previous result from a pilot study of our group for evaluating the internal consistency reliability, test-retest reliability and criterion validity, and which had been proved good results [28]. And this time we would like to further establish the construct validity and cross-cultural adaptation, and split-half reliability.

2 Methods

It was a retrospective analysis, and the study was approved by the Institutional Review Board of Nan-fang Hospital, Southern Medical University (NFEC-2020-028). Each patient signed an informed consent form before completing the questionnaire.

2.1 Development of the VVAS-CH and cross-cultural adaptation

An integrative translation method was developed based on the method of translation and back-translation after permission from the original author of VVAS, Elizabeth Dannenbaum. First, the VVAS was translated into Chinese by an otolaryngologist who is a native Chinese speaker and proficient in English. After discussion among the translator and three technicians, the translation was reviewed and revised to finalize the first Chinese version. Next, the Chinese version was translated back into English by another otolaryngologist who is fluent in English but had not read the original English version of the scale. The translated English version was then compared with the original English version. Group members then engaged in discussion and introduced modifications based on the differences between the two English versions, after which the second Chinese version was finalized. Subsequently, any translation discrepancies resulting from differences between Chinese and foreign cultures were discussed and addressed through multiple communications with Professor Elizabeth Dannenbaum and her colleague, Professor Joyce Fung, who is proficient in Chinese. The Chinese version of VVAS (VVAS-CH) was finally produced.

According to Table 1, the Chinese localization of the VVAS was not a simple process of literal translation due to economic and cultural differences among different countries. In the original English version of the scale, item 2 refers to “being a passenger in a moving car”; however, at present, most people in China use public transportation. After consulting with the original authors, we added a parenthetical note after item 2 stating that “vehicles include buses, taxis, and private cars”. Item 3 asks about being “being under fluorescent lights”; with respect to this item, the original authors were referring to white fluorescent lights. However, in China, most fluorescent lights are colored, and fluorescent lights are not commonly used in daily life. Instead, energy-saving bulbs and led lamp are commonly used in China. Therefore, a parenthetical note stating “including incandescent bulbs, energy-saving bulbs, and white fluorescent bulbs” was added after item 3. Item 5 refers to “being in busy shopping centers”, which intended to describe a location with a large space that contains many people or stores. However, in China, “shopping centers” include typical malls, various markets, and commercial streets. Additionally, in Chinese, a simple and straightforward literal translation of item 9 would ask about “watching television”, but the original authors of the VVAS pointed out that this item refers to only one specific type of TV program: action movies.

Table 1
VVAS-CH and VVAS

Please select a corresponding number to represent your dizziness degree, with zero (0) representing no dizziness and ten (10) representing extreme dizziness or activity avoided due to dizziness.

Walking in supermarket

Passenger in a car

Under fluorescent lights

At an intersection

In shopping centers

Going on escalators

At movies, at the theatre

On patterned floors

Watching television


	Walking in supermarket
	Passenger in a car
	Under fluorescent lights
	At an intersection
	In shopping centers
	Going on escalators
	At movies, at the theatre
	On patterned floors
	Watching television

VVAS-CH: Chinese version of Visual Vertigo Analogue Scale. VVAS: Visual Vertigo Analogue Scale.

2.2 Completing the VVAS-CH

An electronic version of the questionnaires of VVAS-CH and Dizziness Handicap Inventory (DHI) was created using a free website (https://www.wjx.cn/). The patient scanned the QR code for the scales using his/her mobile phone and completed the questionnaires by himself/herself. If patients had any questions during the process of completing the questionnaire, a technician would attempt to address these questions, but the technician was not allowed to guide patients to choose certain answers.

Inclusion criteria: 1) Outpatients in our department who were experiencing symptoms that included dizziness, vertigo, giddiness, disorientation, light headedness, and/or unsteadiness; 2) they would undergone detailed vestibular function tests in our department in the same day after completing the questionnaire; 3) the patient’s age was 18∼60 years old, no gender limitation; 4) they could understand the questions and fill out the assessment scale using a mobile phone by themselves. An upper age limit at 60 years was set in this experiment after a pilot study, because we had found that some old people could not use the mobile phone without a free WiFi, some could not scan the two-dimensional code without help, some could not read the words clearly without a pair of presbyopic glasses, and so on.

Exclusion criteria: 1) patients without recorded medical history and test report associated with vertigo or dizziness; 2) patients filled out the scale more than once; 3) patients whose questionnaires had more than one missing response for the VVAS-CH section, or more than three in the DHI section were also excluded out of concern for their irresponsible or inpatient attitude in answering questions. Although the data of DHI questionnaire were not statistically analyzed in this manuscript, the action of filling DHI played a role in this research for the sake of reliable conclusion.

The study flow was shown in Fig. 1. The 1040 patients involved should be divided into two groups because exploratory factor analysis and confirmatory factor analysis cannot use the same data. As exploratory factor analysis is comparatively more important in this study, people who have no missing items in DHI were placed in the group of Exploratory factor analysis.

Fig. 1

The study flow.

2.3 Statistical methods

Construct validity and split-half reliability of VVAS-CH were detected by using SPSS 20.0 and AMOS 24.0. Structural equation modeling, consisting of exploratory factor analysis and confirmatory factor analysis, was conducted to analyze the construct validity. Exploratory factor analysis was used to extract common factors by using maximum likelihood estimation, aiming to reveal the structure of the scale and to explain the relationship between common factors and each item. Confirmatory factor analysis was also used to confirm the factor structure by using model fits. A P value <0.05 indicated statistical significance.

3 Results

Subjects were patients treated in our outpatient department from September 2017 to September 2019. The baseline conditions were shown in Table 2.

Table 2
The baseline condition of patients enrolled

Characteristics n(%) VVAS-CH (Mean±SD)

Gender Male 403(38.8%) 17.5±23.4

Female 637(61.3%) 18.5±22.1

Age (years) 40.8±11.6

Course of disease (days) 7(0.5, 7300)

Diagnosis BPPV 383(36.8%) 15.0±20.8

Vestibular migraine 117(11.3%) 22.2±23.3

Meniere’s disease 19(1.8%) 22.9±30.1

Vestibular neuritis 13(1.3%) 21.0±22.2

Persistent postural-perceptual dizziness 20(1.9%) 28.6±18.3

Sudden deafness 42(4.0%) 23.4±27.9

Herpez zoster oticus 5(0.5%) 31.1±23.2

Otitis media 20(1.9%) 20.9±19.2

Unilateral vestibular dysfunction 18(1.7%) 19.3±20.3

Bilateral vestibulopathy 6(0.6%) 16.5±10.6

Central nervous system disease 20(1.9%) 16.7±14.4

Anxiety or depression 18(1.7%) 36.9±30.5

Previous history Hypertension 117(11.3%) 18.7±21.7

Diabetes 41(3.9%) 22.3±26.5

Hyperlipemia 70(6.7%) 20.5±20.6

Obesity 11(1.1%) 20.4±23.6

Head trauma 75(7.2%) 19.2±21.8

Cervical diseases 328(31.5%) 18.3±22.1

Headache 94(9.0%) 19.3±20.8

Insufficiency of cerebral blood supply 67(6.4%) 22.9±22.1

Stroke 6(0.6%) 7.2±9.5

BPPV 94(9.0%) 19.8±22.9

Sleep disorders 12(1.2%) 21.6±24.2

Anxiety 19(1.8%) 25.1±23.6

Depression 21(2.0%) 24.8±22.2

Family history Cardiovascular and cerebrovascular diseases 133(12.8%) 21.1±24.2

Migraine 114(11.0%) 20.1±23.0

Meniere’s disease 12(1.15%) 16.4±23.3

Deafness 27(2.6%) 17.0±24.8

Anxiety or depression 24(2.3%) 17.5±22.2

Characteristics		n(%)	VVAS-CH (Mean±SD)
Gender	Male	403(38.8%)	17.5±23.4
	Female	637(61.3%)	18.5±22.1
Age (years)		40.8±11.6
Course of disease (days)		7(0.5, 7300)
Diagnosis	BPPV	383(36.8%)	15.0±20.8
	Vestibular migraine	117(11.3%)	22.2±23.3
	Meniere’s disease	19(1.8%)	22.9±30.1
	Vestibular neuritis	13(1.3%)	21.0±22.2
	Persistent postural-perceptual dizziness	20(1.9%)	28.6±18.3
	Sudden deafness	42(4.0%)	23.4±27.9
	Herpez zoster oticus	5(0.5%)	31.1±23.2
	Otitis media	20(1.9%)	20.9±19.2
	Unilateral vestibular dysfunction	18(1.7%)	19.3±20.3
	Bilateral vestibulopathy	6(0.6%)	16.5±10.6
	Central nervous system disease	20(1.9%)	16.7±14.4
	Anxiety or depression	18(1.7%)	36.9±30.5
Previous history	Hypertension	117(11.3%)	18.7±21.7
	Diabetes	41(3.9%)	22.3±26.5
	Hyperlipemia	70(6.7%)	20.5±20.6
	Obesity	11(1.1%)	20.4±23.6
	Head trauma	75(7.2%)	19.2±21.8
	Cervical diseases	328(31.5%)	18.3±22.1
	Headache	94(9.0%)	19.3±20.8
	Insufficiency of cerebral blood supply	67(6.4%)	22.9±22.1
	Stroke	6(0.6%)	7.2±9.5
	BPPV	94(9.0%)	19.8±22.9
	Sleep disorders	12(1.2%)	21.6±24.2
	Anxiety	19(1.8%)	25.1±23.6
	Depression	21(2.0%)	24.8±22.2
Family history	Cardiovascular and cerebrovascular diseases	133(12.8%)	21.1±24.2
	Migraine	114(11.0%)	20.1±23.0
	Meniere’s disease	12(1.15%)	16.4±23.3
	Deafness	27(2.6%)	17.0±24.8
	Anxiety or depression	24(2.3%)	17.5±22.2

3.1 Split-half reliability

Guttman Split-Half coefficient was 0.939, indicating a good reliability of the VVAS-CH.

3.2 Extraction of common factors through exploratory factor analysis

The value of Bartlett spherical inspection was 6489.129 and P = 0.000, which indicated that there was relevance among items, and it had possibilities of sharing factors among them. The sample appropriateness measure value, Kaiser-Meyer-Olkin (KMO), was 0.950, which also indicated that it was suitable for factor analysis and common factor sharing.

Only one common factor was identified that accounted for 64.83% of the variance and had an eigenvalue of more than 1; this result indicates that all nine items of the VVAS-CH are focused on the issue of VV. Results of structural equation modeling and path coefficient analysis of a single-factor model are shown in Fig. 2.

Fig. 2

Single-factor structural model of the VVAS-CH. ∘: Latent variable refers to some abstract conceptions that can hardly be observed directly but actually are the key to a scale. □: Observational variable, could be observed and measured directly, are explained by the latent variables. *: Path coefficient, a kind of standard regression coefficient, reflecting the proportion of the variance in the observed variable (Items) explained by the latent variable (common factor). #: Standard error, which accounts for measurement error and any other sources of variance not accounted for by the latent variables. &: The square of the factor loading (the reliability index), which reflects the percentage of variation in the observed variables that is explained by the latent variables.

3.3 Construct validity through confirmatory factor analysis

Most fit indexes of the model in Table 3 reached acceptable levels, indicating that the hypothesized model we built with nine items and one factor fit the actual data well and was good fit of the model. After all the fit coefficients are considered, the single-factor structure obtained from exploratory factor analysis can fit the data well.

Table 3
Fit indexes to prove the good model of the VVAS-CH

Abbreviation Full Name Function Model Value Recommended value Acceptance

x²/df Chi-Square/Degree of Freedom Absolute fit index 94.123/27 = 3.486 Good fit if <3, Reasonable fit if <5 Reasonable

GFI Goodness of Fit Index Absolute fit index 0.952 Reasonable fit if >0.8, Good fit if >0.9 Good

RMR Root Mean Square Residual Absolute fit index 0.081 Good fit if <0.05, Reasonable fit if <0.1 Reasonable

RMSEA Root Mean Square Error of Approximation Absolute fit index 0.074 Good fit if <0.05, Reasonable fit if <0.1 Reasonable

AGFI Adjust Goodness of Fit Index Value-added fit index 0.920 Reasonable fit if >0.8, Good fit if >0.9 Good

NFI Normal Fit Index Value-added fit index 0.983 Reasonable fit if >0.8, Good fit if >0.9 Good

CFI Comparative Fit Index Value-added fit index 0.988 Reasonable fit if >0.8, Good fit if >0.9 Good

IFI Incremental Fit Index Value-added fit index 0.988 Reasonable fit if >0.8, Good fit if >0.9 Good

PGFI Parsimony Goodness of Fit Index Reduced fit index 0.571 Reasonable fit if >0.8, Good fit if >0.9 ^*

NNFI Non-Normed Fit Index Comparative fit index 0.984 Reasonable fit if >0.8, Good fit if >0.9 Good

RFI Relative Fit Index Comparative fit index 0.977 Reasonable fit if >0.8, Good fit if >0.9 Good

Abbreviation	Full Name	Function	Model Value	Recommended value	Acceptance
x²/df	Chi-Square/Degree of Freedom	Absolute fit index	94.123/27 = 3.486	Good fit if <3, Reasonable fit if <5	Reasonable
GFI	Goodness of Fit Index	Absolute fit index	0.952	Reasonable fit if >0.8, Good fit if >0.9	Good
RMR	Root Mean Square Residual	Absolute fit index	0.081	Good fit if <0.05, Reasonable fit if <0.1	Reasonable
RMSEA	Root Mean Square Error of Approximation	Absolute fit index	0.074	Good fit if <0.05, Reasonable fit if <0.1	Reasonable
AGFI	Adjust Goodness of Fit Index	Value-added fit index	0.920	Reasonable fit if >0.8, Good fit if >0.9	Good
NFI	Normal Fit Index	Value-added fit index	0.983	Reasonable fit if >0.8, Good fit if >0.9	Good
CFI	Comparative Fit Index	Value-added fit index	0.988	Reasonable fit if >0.8, Good fit if >0.9	Good
IFI	Incremental Fit Index	Value-added fit index	0.988	Reasonable fit if >0.8, Good fit if >0.9	Good
PGFI	Parsimony Goodness of Fit Index	Reduced fit index	0.571	Reasonable fit if >0.8, Good fit if >0.9	^*
NNFI	Non-Normed Fit Index	Comparative fit index	0.984	Reasonable fit if >0.8, Good fit if >0.9	Good
RFI	Relative Fit Index	Comparative fit index	0.977	Reasonable fit if >0.8, Good fit if >0.9	Good

^*This fit index did not reach acceptable level.

4 Discussion

Visual system and vestibular system play an important role in the maintenance of human balance, supporting and complementing each other [14]. Temporary enhancement of a patient’s visual dependence is necessary during the period of vestibular compensation after acute vestibular injury, but strong visual dependence for a long time will be detrimental [7, 19]. It may also excite neurons in the vestibular nuclei and locus coeruleus and adversely affect the mechanism of visual-vestibular integration, leading to a vicious circle in which visual dependence, dizziness, and unsteady balance continue to worsen [13, 21]. Ignorance of VV or insufficient care about the affection of visual system can aggravate a patient’s nervousness and anxieties, negatively affecting disease recovery. Conversely, empathy from a doctor and a clear diagnosis can help the patient devote additional attention to the connection between his/her sensory symptoms and emotions, thereby forming a positive feedback loop [8].

However, there is no scale specifically for VV in China although there are several international ones. Besides, only a few scales evaluating vertigo or dizziness exist in China. In 2011, a Chinese version of the Activities-specific Balance Confidence Scale (ABC) was introduced to measure self-perceived balance confidence in older adults [27]. Among the 16 items, it has three items concerning about VV, one is about “walking in a crowded mall where people rapidly walk past you” and the other two are about “walking onto or off an escalator”. In 2015, Dizziness Handicap Inventory (DHI) was introduced to evaluate the impact of vertigo on patients in three respects: physical, emotional, and functional influences [25]. Although widely used in China, it has only one item related to VV saying “Does walking down the aisle of a supermarket increase your problem”. Vertigo Symptom Scale (VSS) introduced in 2015 focusing on evaluating the degree of discomfort caused by vertigo doesn’t contain any item associated with VV [6]. The shortened version of vestibular activities and participation measure (VAP) translated in 2019 has two related items, “using transportation (traveling using private or public transportation being a passenger)” and “operating a vehicle (driving a car or riding a bicycle)” [24].

Among the international scales specifically for VV, we prefer to VVAS because it’s an effective tool with the characters of timesaving, detailed, focused, and quantitative, and it has been widely adopted in the English-speaking countries [4 , 10]. It addresses nine scenarios that may provoke or exacerbate VV and uses the 11-level scoring system commonly employed for visual analogue scales, which has responses ranging from 0–10, thereby enabling patients to more accurately assess the severity of their symptoms. A person might be considered VV when 2 or more items score a higher value than zero [5]. In comparison, the other two scales assessing VV have some disadvantages. The Situation Characteristics Questionnaire (SitQ) has 41 items, which is not a small amount, and 4 response options from 0–3 for each [15]. The Longridge and Mallison interview questions (LMIQ) consists of five dizziness provoking situations to which the clients only answer “yes” or “no” [16]. Therefore, we would like to translate, formulate, and validate VVAS-CH to allow for its application in the Chinese Mandarin language for use in China and in other countries with Mandarin speakers.

According to our pilot study in 2019 [28], the internal consistency reliability of VVAS-CH was good with the Cronbach’s alpha of 0.952. The test-retest reliability was also good with a non-significant difference between two time points (t = 0.953, P > 0.05). Scores of VVAS-CH and DHI were compared by using spearman correlation coefficient, showing a good correlation (r = 0.512, P < 0.001). VVAS has also been translated to other languages and the translated scales also showed satisfactory properties for the assessment of self-perceived and severity of VV. In 2022, a Portuguese version had been confirmed valid internally consistent with Cronbach’s of 0.9 and a positive strong correlation with DHI (p < 0.0001) [1]. In the same year, an Argentine version had also been proved acceptable internal consistency (Cronbach’s alpha: 0.91) and reliability (r = 0.764 [CI 95% : 0.7 –0.86]), and also acceptable correlations when compared with DHI (rho: 0.387∼0.578) [23]. Considering the different cultures, some explanation, notation or modification could be seen in the Chinese version (item 2, 3, 5, and 9) and Argentine version (item 2, 3, 4, 5, and 8) for further linguistic adaptation, while no obvious change in the Portuguese version [1, 22].

In this study, the results supported the idea that the one single-factor structure of VVAS-CH with good construct validity and split-half reliability was an efficient analytic tool for patients with VV, along with the important characters of easy to perform and excellent cross-cultural adaptation. Although there was one did not reach acceptable level and three were reasonable other than good, most fit indexes in Table 3 showed good result to conform the structure and validity of VVAS-CH. Sometimes, it is hard to make sure every index reaches a good level. Given the complexity of structural equation modeling, it is not uncommon to find that the fit of a proposed model is poor. Selecting those that indicate good model fit should be desisted at all costs [12].

Lacking of responsiveness test of VVAS-CH is a limitation of the study and it will be the goal of the next one [4].

5 Conclusion

The use of this scale for evaluating VV and assessing the severity is highly feasible, and general application of the VVAS-CH in China should be promoted.

Footnotes

Acknowledgments

We are thankful for the support provided by not only Prof. Elizabeth Dannenbaum, the original creator of the VVAS, but also Prof. Joyce Fung, a member of the original research team. We also thank teacher Qilun Cai for his patient guidance throughout the statistical analysis.

Conflict of interest

The authors have declared no conflicts of interest for this article.

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	Please select a corresponding number to represent your dizziness degree, with zero (0) representing no dizziness and ten (10) representing extreme dizziness or activity avoided due to dizziness.
	Walking in supermarket
	Passenger in a car
	Under fluorescent lights
	At an intersection
	In shopping centers
	Going on escalators
	At movies, at the theatre
	On patterned floors
	Watching television