The normative measures for Chinese Reading Acuity Test (CRAT) in Cantonese-speaking children

Abstract

The Chinese Reading Acuity Test (CRAT), a version of the MNRead Acuity Chart adapted for traditional Chinese, has previously been developed. This study aimed to establish normative values of the CRAT among Cantonese-speaking Hong Kong primary school children. We recruited 188 Cantonese-speaking Chinese children from Grades 1 to 6 without developmental delays or reading difficulties. The CRAT includes three charts, each with 17 sentences in Chinese characters sized from 1.3 to − 0.3 logMAR. After a practice session with Chart 1, participants read Charts 2 and 3 aloud as fast as possible at 40 cm under normal room lighting, continuing until the print size was too small to be resolved. We recorded reading time and errors and calculated maximum reading speed (CMRS), critical print size (CCPS), and reading acuity (CRA) using a non-linear mixed-effects model. Average values from the results in Charts 2 and 3 were used. The study found significant associations between age and grade level with CMRS (r = 0.72, p ≤ 0.001) and CRA (r = -0.47, p < 0.001), indicating improvements in reading speed and acuity with increasing age and grade. Additionally, Chinese near visual acuity (CNVA) showed significant correlations with CMRS (r = -0.24, p = 0.001), CRA (r = 0.41, p < 0.001), and CCPS (r = 0.35, p < 0.001). Normative data were established for each grade level and age group. The findings revealed that as children grow and advance through school grades, their ability to read faster and discern smaller print sizes improves. The CRAT is a valuable tool for clinicians and educators to assess reading performance and identify potential reading difficulties among Cantonese-speaking children. The normative data from this study will serve as a benchmark for evaluating and supporting the enhancement of reading skills in Cantonese-speaking children.

Introduction

Reading is a foundational skill that underpins childhood learning. The ability to read can be affected by the visual system’s integrity¹, cognitive development of literacy skills, visual span, and educational experiences^2–4. The MNRead Acuity Charts measures reading speed and accuracy across different print sizes. This assessment quantifies reading performance using three parameters: maximum reading speed (MRS), critical print size (CPS), and reading acuity (RA)⁵. MRS is the fastest reading speed regardless of print size. It is considered a strong predictor of visual ability and vision-related quality of life for visually impaired patients⁶. CPS is the smallest print size readable at that maximum speed and understanding this value is essential for evaluating functional vision because an individual’s reading ability cannot be reliably predicted by letter acuity alone⁷. RA is the smallest readable print size, indicating the lower limit of font size that human eyes can resolve for reading continuous text. Thus, poor RA in a child may indicate reading disabilities or visual impairment⁸. Due to their clinical utility, MNRead charts are used to test reading abilities in both the general populations⁵ and visually impaired patients, such as those with albinism⁹, glaucoma¹⁰, retinitis pigmentosa¹¹, branch retinal vein occlusion¹² and macular degeneration¹³.

In clinical practice, the most direct approach to differentiate individuals with reading disabilities from typical readers is to compare their reading performance against normative data. MNRead charts norms are given for different age groups^5,14, but are specific to English speakers. To address cultural and linguistic diversity, MNRead charts have been translated into multiple languages, including Korean, Persian, Portuguese, Greek, African and Simplified Chinese^15–20. Unlike alphabetic scripts, traditional Chinese characters are visually complex logograms consisting of multiple strokes and radicals within a uniform square space. This high internal density necessitates larger print sizes for visual resolution, consequently resulting in elevated (poorer) RA and CPS thresholds²¹. Hence, a traditional Chinese version, the Chinese Reading Acuity Test (CRAT), has been developed and validated²².

To minimize recall bias and reliably assess functional reading, the CRAT uses three sets of reading charts featuring different traditional Chinese sentences. Following expert review and pilot testing to eliminate items with high variability, 51 consistent sentences were selected from neutral, child-friendly texts²². To optimize legibility for young readers, the test uses the uniform Heiti font with 10% inter‑character spacing. While the charts reflect real-world visual complexity (1–24 strokes per character), the vocabulary is restricted to a Grade 2 level. This use of simple, high-frequency words ensures that reading performance is constrained visual limits, such as RA and CPS, rather than cognitive or linguistic proficiency, making the test suitable for higher grade students. Additionally, no gender effects have been observed.

The CRAT is administered binocularly at a reading distance of 40 cm. Each chart contains 17 flip cards with print sizes ranging from 1.3 to −0.3 log Minimal Angle Resolution (logMAR) in 0.1 logMAR steps. Each card displays a Chinese sentence with 18 characters and three punctuation marks, splitting it into three lines (Fig. 1). The examiner records the reading time and errors. Reading speed, calculated as the number of correctly read characters divided by reading time, is measured across tested print sizes to determine the Chinese Maximum Reading Speed (CMRS), Chinese Critical Print Size (CCPS), and Chinese Reading Acuity (CRA). Although the CRAT demonstrates good test-retest reliability in Cantonese-speaking children²², the current lack of age- and grade- specific normative data limits its clinical utility. Therefore, this study aimed to establish CRAT normative values for Cantonese-speaking primary school students in Hong Kong from Grades 1 to 6.

Fig. 1.

An example of a reading card from the CRAT chart.

Each card displays a sentence composed of 18 Chinese characters, which are divided into three lines by three punctuation marks. At the top of the card, the font size is indicated using the Metric Bolt size notation and the corresponding Snellen fraction and logMAR acuity, all of which are calibrated for a reading distance of 40 cm.

Methods

Participants

The required sample size was calculated based on the average CMRS of 2.25 characters per second (cps), with a standard deviation of 0.09 cps, as reported in Cheung et al. (2015). To detect an effect size of 0.71 with a statistical power of 0.95% using the F-test in G*power²³ for 6 grade levels, a minimum of 144 Cantonese-speaking students would be needed. To account for a potential 10% of loss of data, 160 Hong Kong primary school children from Grades 1 to 6 were recruited.

Participants were recruited from the Optometry Clinic or Optometry Research Clinic at The Hong Kong Polytechnic University. Before commencing the study, participants’ habitual distance visual acuity (DVA) was measured using the Early Treatment Diabetic Retinopathy Study (ETDRS) chart at 6 m and their Chinese near visual acuity (CNVA) was measured by the Hong Kong Polytechnic University Chinese near visual acuity chart at 40 cm²⁴. Because data collection spanned the entire academic year (September to August), participants were assessed at different stages of their reading development. Therefore, these norms reflect a generalized, year-round average for each grade level rather than a specific baseline taken at a single time point. The majority of children from the optometry clinic presented primarily with refractive errors. To be included in this study, participants were required to meet the following criteria:

1. Proficient in speaking Cantonese, with Cantonese Chinese as their native language.

2. Capable of accurately reading aloud the first CRAT flip chart (letter size of 1.3 logMAR).

3. No known developmental delay or reading difficulty.

4. Habitual distance visual acuity (DVA) of 0.10 logMAR (equivalent to Snellen 20/25) or better in both eyes.

Reading Test Procedures

The CRAT consists of three charts (Charts 1, 2, and 3). In this study, Chart 1 was used as a practice tool for familiarization. Participants who did not understand the procedures or were unable to read the sentence in the largest print size were excluded. The assessment of reading performance was conducted using Charts 2 and 3. The reading test was carried out in a quiet room with controlled standard room lighting conditions, measured at 400 to 500 lx. The lighting illumination was calibrated by using a Lux meter. Each chart was positioned at a fixed distance of 40 cm from the participant’s eyes, and the distance was measured by a meter rule. All participants were required to read both Charts 2 and 3, with a one-minute break between the two charts to minimize fatigue. The sequence for Charts 2 and 3 was randomly assigned using a random number generator function in Microsoft Excel (Microsoft Corp., USA). The reading test began with the sentence in the largest print size, and the participant was instructed to read characters aloud as fast as possible. The time taken to read each sentence and the number of errors made were recorded. The participants continued to read sentences with progressively smaller font sizes until unable to read any character on a card.

During scoring, an error was defined as any character that was skipped, mispronounced, or substituted. Immediate self-corrections were not recorded as errors, but the additional time required to self-correct inherently reduced the participant’s calculated reading speed.

Data Analysis

To account for the impact of reading errors on both reading speed and reading acuity, reading parameters were corrected using the following formulas adopted from the CRAT manual:

Nonlinear mixed-effects (NLME) modeling, implemented in R (RStudio Team, Boston, USA), was employed to determine Maximum Reading Speed (MRS) and Critical Print Size (CPS). This model simultaneously analyzes population-level trends and individual variations through an iterative process. By integrating fixed effects (representing the group mean) and random effects (accounting for individual deviations) the model estimates individual parameters even when clinical datasets are sparse, noisy, or incomplete.

By fitting the Chinese Reading Speed data to the exponential-decay function, the model characterizes the functional relationship between print size and performance. This identifies the plateau where reading speed is maximized (CMRS) and the threshold print size where that speed can no longer be maintained (CCPS). The general NLME formula used to derive these measures is defined as follows:

where:

is the reading speed for the i-th observer at the j-th print size;

is the specific print size tested (logMAR);

is the individual parameter vector for the i-th observer, which incorporates both the Maximum Reading Speed (MRS) and Critical Print Size (CPS);

represents the population means (fixed effects);

represents the individual deviations from the mean (random effects);

is the residual error.

Since there was no significant difference in the reading performance measured by Charts 2 and 3 (paired t-tests, all p > 0.05), the data from these two charts were combined by averaging them for subsequent statistical analysis. Analysis of variance (ANOVA) was used to examine the effect of grade on demographical and CRAT reading parameters. The associations between the CRAT reading parameters, age, and CNVA were examined using Pearson correlation analysis. All statistical analyses were conducted using SPSS software, version 26 (IBM Corporation).

Results

Demographic Information and Reading Parameters Across Grade Levels

A total of 188 children aged from 5.5 to 12.6 years (mean: 9.3 years) from 6 different primary school grades participated in this study. Table 1 summarizes the demographic and reading parameters for each grade level. Participants, consisting of 93 boys and 95 girls, had averaged DVA of −0.03 logMAR (SD 0.09) and CNVA of 0.11 logMAR (SD 0.05). Both DVA and CNVA showed no significant differences across grade levels (F(5) = 0.49, p = 0.77).

Table 1.

Mean (SD) of demographic and CRAT reading parameters across grade levels.

Grade	N	Gender (% of F)	Age (years)	DVA (logMAR)	CNVA (logMAR)	CMRS (cpm)	CCPS (logMAR)	CRA (logMAR)
1	32	53	6.9 (0.6)	–0.02 (0.05)	0.13 (0.05)	106 (30)	0.47 (0.10)	0.45 (0.09)
2	30	63	7.9 (0.5)	–0.03 (0.07)	0.11 (0.04)	146 (45)	0.44 (0.10)	0.39 (0.11)
3	32	41	8.7 (0.8)	–0.02 (0.05)	0.10 (0.07)	162 (35)	0.44 (0.08)	0.36 (0.08)
4	33	55	9.9 (0.5)	–0.05 (0.18)	0.10 (0.04)	179 (29)	0.44 (0.06)	0.33 (0.06)
5	32	63	10.8 (0.6)	–0.04 (0.09)	0.11 (0.03)	198 (38)	0.45 (0.10)	0.35 (0.06)
6	29	28	11.8 (0.5)	–0.04 (0.06)	0.08 (0.05)	221 (34)	0.43 (0.09)	0.31 (0.08)

DVA: distance visual acuity, CNVA: Chinese near visual acuity, CMRS: Chinese maximum reading speed, CCPS: Chinese critical print size, CRA: Chinese reading acuity, cpm: characters per minute, logMAR: log minimal angle of resolution.

The effects of participants’ grade levels on both CMRS (F(5) = 40.73, p < 0.001) and CRA (F(5) = 12.77, p < 0.001) were statistically significant. Post hoc tests using the Hom-Bonferroni correction indicated that higher grade levels were associated with faster CMRS and better CRA.

Specifically, Grade 1 children demonstrated significantly slower reading speeds compared to children in Grades 2 through 6 (p < 0.01). Although no significant differences in CMRS were observed between Grade 2 and Grade 3 children (p = 0.98), Grade 2 children read significantly slower than those in Grades 4 through 6 (p ≤ 0.01). Similarly, Grade 3 children did not differ significantly from Grade 4 children in reading speed (p = 0.81), yet they read slower than Grade 5 and Grade 6 children (p ≤ 0.001). The reading speeds of both Grade 4 and Grade 5 children were slower than those of Grade 6 children (p < 0.001); however, there was no significant difference in reading speed between Grades 4 and 5 (p = 0.55) as well as Grades 5 and 6 (p = 0.14).

Regarding CRA, Grade 1 children exhibited the poorest acuity when compared to children from higher grades (p < 0.05). Grade 2 children also showed worse CRA than those in Grade 6 (p ≤ 0.04), but their performance was not significantly different from those in Grades 3 to 5 (p ≥ 0.07). No significant differences in CRA were found among the other grade levels (p ≥ 0.31).

Conversely, the effect of grade levels on CPS was not statistically significant (F(5) = 1.09, p = 0.37).

Relationships of Reading Parameters with Age, Grade Levels, and Chinese Near Visual Acuity

Age was significantly correlated with CMRS (r = 0.72, p ≤ 0.001) and CRA (r = −0.47, p < 0.001) but not with CCPS (r = −0.14, p = 0.06). Similar results were observed for grade levels (CMRS: r = 0.72, p < 0.001; CRA: r = −0.47 p < 0.001; CCPS; r = −0.13, p = 0.08). In general, with each year of increase in age, children’s reading speed increased by 20.87 characters per minute (cpm), while reading acuity improved by 0.024 logMAR.

Significant correlations were also found between CNVA and reading parameters: CMRS (r = −0.24, p = 0.001), CRA (r = 0.41, p < 0.001), and CCPS (r = 0.35, p < 0.001). These results indicated that children with better CNVA read faster and resolve smaller character sizes. Figures 2 and 3 illustrate the relationships between CRAT outcomes and age, and CNVA, respectively.

Fig. 2.

The relationship between CRAT outcomes and age. Data points are color-coded: green for Chinese Maximum Reading Speed (CMRS), blue for Chinese Critical Print Size (CCPS), and red for Chinese Reading Acuity (CRA). Solid lines represent linear regression lines, with the equations presented in the top right corner of the figure. The equation for CCPS is not shown as the relationship was not statistically significant.

Fig. 3.

The relationship between CRAT outcomes and Chinese Near Visual Acuity (CNVA). Data points are color-coded: green for Chinese Maximum Reading Speed (CMRS), blue for Chinese Critical Print Size (CCPS), and red for Chinese Reading Acuity (CRA). Solid lines represent linear regression lines, with the equations presented in the top left corner of the figure.

Normative Tables for CRAT

Normative percentile tables for each CRAT outcome were computed and organized by school grade levels and age groups. These percentiles were calculated using the statistical formula²⁵ described by:

where  p is the fraction of the percentile ();  r represents the integer part, and  f represents the fractional part of ; is the data at rank  r, and is the subsequent data point.

The resulting tables are included in Appendix 1 and 2. These tables facilitate the comparison of a child’s CRAT outcome with peers in the same grade level or age group, enabling clinicians to quickly assess a child’s reading performance.

Discussion

Our study revealed that as children advanced through school grades and grew older, they exhibited improvements in reading speed (CMRS) and the capacity to read smaller font sizes (CRA). The faster reading speeds observed in older children or those in higher grades are likely attributed to the increased reading practice in school²⁶. These age-related improvements in reading abilities aligned with the findings reported by Cheung et al. (2015)²². Moreover, during the CRAT assessments, we observed that older children exhibited better attention and concentration, which are essential for fluent reading²⁷. Enhanced visual attention is particularly important when reading text in very small font sizes²⁸, which may explain why older children were capable of reading smaller font sizes compared to their younger peers. It is noted that no significant association was found between CCPS and age, which aligned with findings of CPS in the English and Portuguese MNREAD tests^8,14. This lack of a significant association between the critical print size (CCPS) and grade level or age suggests that older children require a greater acuity reserve – the ratio between CCPS and CRA necessary for highly fluent reading¹. Given the significant effect of age on children’s reading performance, our results highlight the importance of developing age-specific normative data for the CRAT to ensure its effective clinical application.

By comparing our findings with data from the MNRead test¹⁴, our results highlight a significant discrepancy in reading performance between English and Chinese readers. To gain a more comprehensive understanding of these performance discrepancies across the two languages, we compared with the unpublished data from Dr Aurélie Calabrèse, which assessed reading performance using MNRead chart among 645 normally sighted American participants aged from 8 to 81 years. Our comparison focused on a subset of data with matched ages from both studies that pertained to children aged 8 to 12 years. Table 2 summarizes the mean (SD) of reading parameters obtained from both studies, including MRS, RA, and CPS.

Table 2.

Mean (SD) of reading parameters from MNRead test (Calabrese et al., 2016) and CRAT (Current study).

Age group (Years)		n	MRS (wpm/cpm)	CPS (logMAR)	RA (logMAR)
8	MNRead	9	138 (26)	0.11 (0.15)	−0.08 (0.08)
	CRAT	34	153 (40)	0.45 (0.09)	0.36 (0.08)
9	MNRead	14	148 (23)	0.13 (0.13)	−0.11 (0.10)
	CRAT	31	177 (35)	0.45 (0.08)	0.34 (0.06)
10	MNRead	10	146 (27)	0.04 (0.06)	−0.15 (0.06)
	CRAT	32	192 (34)	0.44 (0.08)	0.34 (0.06)
11	MNRead	17	158 (34)	0.09 (0.08)	−0.15 (0.09)
	CRAT	29	213 (37)	0.43 (0.09)	0.33 (0.08)
12	MNRead	10	169 (11)	0.15 (0.22)	−0.16 (0.21)
	CRAT	12	222 (33)	0.42 (0.07)	0.29 (0.06)

Results from a two-way ANOVA analysis revealed significant effects of language on all three reading parameters (all p < 0.001) and of age on MRS (F = 52.31, p < 0.001) and RA (F = 1029.17, p < 0.001), but not on CPS (F = 1.29, p = 0.26). Overall, Chinese children exhibited higher MRS but had larger CPS and poorer RA than their Caucasian counterparts. Across both languages, older children displayed higher MRS and better RA compared to younger children. No significant interactions between language and age were found for any reading parameters in the ANOVA analysis (MRS: F = 2.05, p = 0.09; CPS: F = 1.55, p = 0.19; RA: F = 0.44 p = 0.78). As shown in Fig. 4, Chinese children read faster (by an average of 39.62 cpm) but required larger print sizes for reading, as indicated by their larger RA (average of 0.46 logMAR) and CPS (average of 0.33 logMAR), compared to their Caucasian peers.

Fig. 4.

Reading speed (A), critical print size (B), and reading acuity (C) as a function of age. Red and blue colours represent data obtained from CRAT (current study) and MNRead tests¹⁴, respectively. Reading speeds for Chinese text (CRAT) are presented in characters per minute (cpm), and for English text (MNRead), in words per minute (wpm). The shaded areas refer to the 95% confidence interval for each data set.

A potential factor contributing to the discrepancy in reading speeds could be the fundamental differences in language systems. For example, a study by Castro et al. (2005) demonstrated that Portuguese-translated MNRead charts yielded results comparable to the original English version, suggesting similarities in reading performance between these two alphabetic languages. In alphabetic languages like English, word lengths can vary significantly, ranging from a single letter to as many as 45 letters²⁹, with many words comprising multiple syllables. This variability in word length can significantly affect the pronunciation and reading time. In contrast, Chinese (Cantonese was used in this study), is characterized as a monosyllabic language, where each character represents a single syllable³⁰. Consequently, compared to multisyllabic languages like English and Portuguese, Chinese readers may exhibit faster reading speeds due to the fewer syllables and reduced articulatory load. Furthermore, Chinese logograms exhibit high orthographic density and internal complexity. For instance, the characters used in the CRAT contain up to 24 strokes within a uniform square space. This intricate structure potentially reduces the visual span, i.e., the number of characters recognizable in a single glimpse, and increases the visual demand for character resolution. Consequently, traditional Chinese readers require larger print sizes for fluent reading, leading to poorer Reading Acuity (RA) and larger Critical Print Sizes (CPS) compared to readers of alphabetic scripts.

Han et al. (2017) developed a logarithmic Chinese Reading Acuity Chart (C-READ) composed of three sets of 16 simplified-Chinese sentences, each containing 12 characters. Their study recruited college students with a mean age of 22 years. During testing, participants began reading from the largest print size, with only one sentence exposed at a time using a paper mask to ensure controlled presentation²⁰.

The key differences in reading outcomes (from Table 3.) between the CRAT and C-READ are primarily driven by linguistic complexity, chart design, and the age of the participants. Linguistically, the CRAT utilizes traditional Chinese characters, which possess higher orthographic density and stroke counts (up to 24 strokes) compared to the simplified characters in the C-READ, which are visually simpler and have fewer strokes. This increased visual complexity in traditional Chinese leads to the poorer Reading Acuity (RA) and larger Critical Print Size (CPS) observed in CRAT results. Structurally, the CRAT uses 18-character sentences split into three lines, while the C-READ employs 12-character sentences on a single line to prevent unwanted pauses and better reflect simplified Chinese reading habits. Additionally, the studies targeted different demographics: the CRAT focused on primary school children (ages 5.5 to 12.6), whose Maximum Reading Speed (MRS) and RA improve significantly with grade level, whereas the C-READ established norms using college students (mean age 22.1), who demonstrate the superior fluency and faster MRS associated with adult literacy.

Table 3.

Mean (SD) of reading parameters from C-READ (Han et al., 2017) and CRAT (Current study).

Parameter	CRAT study	C-READ study
Language	Traditional Chinese	Simplified Chinese
Sample size	188	118
Mean age (years)	9.3	22.1
Specific Group Age (Mean ± SD years)	Grade 6: 11.8 ± 0.5 (n = 29)	Adults: 22.1 ± 2.1 (n = 30)
CRA (logMAR)	0.31 ± 0.08 (Grade 6)	0.16 ± 0.05
CMRS (characters/min)	221 ± 34 (Grade 6)	273.44 ± 34.37
CCPS (logMAR)	0.43 ± 0.09 (Grade 6)	0.24 ± 0.06

Another possible explanation for the observed differences in reading speeds could be the educational systems. Hong Kong, for instance, is known for its highly competitive educational environment, where there is a strong emphasis on academic performance from an early age³¹. This competitive atmosphere may lead to more rapid improvements in reading speed during school age. As suggested by the data in Fig. 4A, while reading speeds between Chinese and Caucasian children were comparable at age 8 years, the improvement of reading speeds in older age was noticeably more pronounced in Chinese children.

Moreover, the visually complex nature of Chinese characters might explain the poorer CPS and RA observed in Chinese children compared to Caucasian children (see Fig. 4B and C). Chinese characters are logograms, where each character typically represents a word or a morpheme. Unlike English words, which are formed from 26 relatively simple letters, Chinese characters comprise various numbers of strokes within a confined space, leading to higher visual complexity. This complexity can influence the visual span, defined as the number of letters that can be recognized reliably without moving the eyes²¹. Due to the intricate design of Chinese characters, the visual span for Chinese readers is smaller than for English readers, necessitating larger print sizes for comfortable reading³. The greater visual complexity and smaller visual span make Chinese characters more challenging to resolve, explaining why participants required larger print sizes (i.e., higher RA and CPS) when reading Chinese characters on the CRAT charts compared with English words on the MNRead charts³².

The absence of interaction between language and age suggested that the cognitive processes involved in reading and visual resolution of characters in different languages might operate independently of age-related changes. Hence, practitioners can consider ‘age factor’ and ‘language factor’ independently when interpreting the test results. However, it is important to note that while these factors might not interact significantly, they can still independently influence reading performance and should be taken into account during assessment. To explore the combined effect of language and age on reading performance, conducting a study with bilingual readers who are proficient in reading both Chinese and English is warranted. This approach allows for a nuanced analysis of how reading in two distinct languages may vary with age.

While this study provides a valuable cross-linguistic comparison with English readers, a direct comparison with MNRead norms established for the broader Chinese-speaking population (such as Mandarin speakers or readers of simplified Chinese) was not conducted due to the current lack of comprehensive, standardized normative data for those specific groups. Future studies should aim to establish and compare these norms to provide deeper insights into dialectal, regional, and orthographic variability across different Chinese-speaking populations.

This study is the first to establish a link between Chinese near visual acuity (CNVA) as measured by the Hong Kong Polytechnic University CNVA test^24,33,34 and reading parameters from the CRAT. The findings, illustrated in Figs. 2 and 3, indicate that children with better CNVA tend to read more quickly, achieving higher CMRS, and are capable of discerning smaller font sizes, as evidenced by lower CCPS and better CRA. However, the CRA reported in this study was higher than the CNVA. For instance, in Grade 6, the average CNVA was 0.08 logMAR, while the CRA was 0.31 logMAR. This discrepancy contrasts with findings from English reading tasks in previous research. Gupta and colleagues found a close match (within ± 0.16 logMAR) between English NVA and RA when assessing adults using an English NVA chart and the MNRead test³⁵. The notable differences between CNVA and CRA from the CRAT chart is likely attributed to the complexity of Chinese characters used and the specific design of the chart. The CRAT includes a broad spectrum of Chinese characters, ranging from very simple with a single stroke to highly complex one up to 24 strokes³⁶. Conversely, the CNVA test typically employs with only two to four strokes to maintain consistent legibility across all characters, as shown in Fig. 5. Moreover, the spacing between characters on the CRAT is about 10% of the font size, which is similar to the spacing used in standard Chinese texts. In contrast, the CNVA chart is designed with character-to-character space equal to the font size itself, which enhances the legibility of individual character.

Fig. 5.

The Hong Kong Polytechnic University Chinese near visual acuity test.

In the study, we did not measure or control for accommodation, which could affect near focus. While the likelihood of insufficient amplitude of accommodation is low given the good CNVA observed, the potential for an accommodation deficit that might influence our results cannot be entirely dismissed. To address this in future studies using the CRAT, we recommend incorporating the assessment of accommodation functions to account for this possible impact on reading performance. Additionally, this study did not control for the specific semester during which the children’s grade level was reported. It is important to recognize that children’s reading skills and vocabulary typically improve as they progress through the school year. Therefore, the time of year when the data is collected could introduce a learning effect that might influence the outcomes. Future studies should consider documenting the exact semester to refine the understanding of grade-related progress in reading skills. Practitioners assessing reading performance should also be aware of its potential influence on the results, especially when interpreting data from children in transitional educational phases.

While the CRAT aims to isolate visual performance from cognitive proficiency, natural reading is a multifaceted process demanding the integration of visual sensory input, oculomotor control, and high-level cognitive comprehension¹⁵. Incorporating a comprehension component would significantly elevate the CRAT to a more comprehensive reading assessment tool, ensuring that the maximum reading speed reflects genuine fluency rather than mere rapid articulation or “skimming” ²⁵. This addition is particularly vital for Chinese, as the high visual complexity of logograms and the absence of inter-word spacing place substantial cognitive demands on lexical segmentation and character recognition. By measuring meaning construction alongside visual resolution, a modified CRAT could offer a more holistic assessment of a reader’s functional ability in real-world environments.

Conclusion

This study has established normative clinical data for the CRAT among Cantonese-speaking primary school children in Grades 1 to 6 and Ages 6 to 12 years. This dataset provides clinicians with a reference for accurately assessing Chinese reading performance in Cantonese-speaking children, focusing on maximum reading speed, reading acuity, and critical print size. Compared with the normative data, clinicians can evaluate a child’s reading performance effectively, leading to targeted intervention to enhance reading skills.

Appendix 1: Percentile Distribution of CRAT Parameters by Grade for Cantonese-speaking Primary School Children in Hong Kong

Grade 1.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	106 (30)	0.47 (0.10)	0.45 (0.09)
Percentile
1st	60	0.32	0.30
5th	65	0.33	0.32
10th	73	0.36	0.35
15th	81	0.40	0.36
20th	86	0.41	0.39
25th	86	0.41	0.40
30th	88	0.42	0.41
35th	90	0.42	0.42
40th	94	0.43	0.42
45th	98	0.44	0.42
50th	101	0.45	0.42
55th	103	0.48	0.45
60th	109	0.50	0.48
65th	112	0.51	0.49
70th	114	0.52	0.51
75th	117	0.53	0.52
80th	125	0.55	0.53
85th	137	0.56	0.53
90th	140	0.57	0.55
96th	163	0.63	0.59
99th	188	0.72	0.68

Grade 2.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	146 (45)	0.44 (0.10)	0.39 (0.11)
Percentile
1st	84	0.25	0.23
5th	88	0.28	0.30
10th	91	0.31	0.31
15th	100	0.32	0.31
20th	112	0.34	0.31
25th	116	0.37	0.31
30th	120	0.38	0.32
35th	129	0.41	0.32
40th	133	0.43	0.33
45th	135	0.44	0.35
50th	141	0.45	0.36
55th	151	0.47	0.37
60th	152	0.48	0.41
65th	154	0.49	0.43
70th	163	0.51	0.44
75th	165	0.51	0.45
80th	166	0.52	0.47
85th	171	0.53	0.49
90th	185	0.55	0.51
96th	241	0.57	0.59
99th	269	0.62	0.68

Grade 3.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	162 (35)	0.44 (0.08)	0.36 (0.08)
Percentile
1st	105	0.28	0.20
5th	109	0.33	0.26
10th	120	0.35	0.30
15th	131	0.38	0.30
20th	138	0.39	0.31
25th	141	0.40	0.31
30th	146	0.42	0.32
35th	150	0.42	0.32
40th	157	0.43	0.32
45th	158	0.43	0.33
50th	159	0.43	0.35
55th	163	0.44	0.35
60th	168	0.44	0.36
65th	170	0.46	0.36
70th	174	0.49	0.37
75th	176	0.50	0.38
80th	185	0.50	0.42
85th	189	0.53	0.45
90th	192	0.54	0.49
96th	239	0.58	0.50
99th	254	0.59	0.51

Grade 4.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	179 (29)	0.44 (0.06)	0.33 (0.06)
Percentile
1st	115	0.34	0.23
5th	142	0.34	0.27
10th	149	0.35	0.30
15th	151	0.36	0.30
20th	159	0.37	0.30
25th	160	0.37	0.31
30th	164	0.40	0.31
35th	167	0.41	0.31
40th	171	0.43	0.31
45th	175	0.44	0.31
50th	177	0.45	0.31
55th	182	0.46	0.32
60th	185	0.47	0.34
65th	191	0.47	0.35
70th	193	0.47	0.35
75th	198	0.48	0.36
80th	200	0.48	0.36
85th	204	0.49	0.40
90th	217	0.51	0.40
96th	231	0.53	0.44
99th	233	0.53	0.49

Grade 5.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	198 (38)	0.45 (0.10)	0.35 (0.06)
Percentile
1st	123	0.29	0.30
5th	142	0.31	0.30
10th	147	0.35	0.30
15th	150	0.37	0.30
20th	165	0.37	0.30
25th	175	0.39	0.30
30th	180	0.39	0.30
35th	183	0.39	0.30
40th	186	0.41	0.30
45th	189	0.42	0.31
50th	197	0.43	0.32
55th	202	0.44	0.32
60th	204	0.45	0.34
65th	215	0.46	0.35
70th	227	0.48	0.39
75th	229	0.54	0.40
80th	233	0.55	0.41
85th	238	0.56	0.42
90th	249	0.58	0.45
96th	257	0.61	0.45
99th	258	0.63	0.46

Grade 6.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	221 (34)	0.43 (0.09)	0.31 (0.08)
Percentile
1st	174	0.27	0.11
5th	183	0.31	0.22
10th	189	0.34	0.24
15th	190	0.35	0.26
20th	191	0.36	0.27
25th	195	0.37	0.30
30th	197	0.37	0.30
35th	203	0.38	0.30
40th	208	0.38	0.30
45th	214	0.40	0.30
50th	216	0.41	0.30
55th	217	0.42	0.31
60th	220	0.44	0.31
65th	225	0.46	0.31
70th	231	0.47	0.31
75th	242	0.48	0.31
80th	246	0.50	0.33
85th	258	0.53	0.35
90th	279	0.54	0.42
96th	286	0.58	0.46
99th	294	0.59	0.50

Appendix 2: Percentile Distribution of CRAT Parameters by Age for Cantonese-speaking Primary School Children in Hong Kong

6 Years Old.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	106 (21)	0.46 (0.06)	0.43 (0.08)
Percentile
1st	74	0.40	0.28
5th	80	0.40	0.33
10th	85	0.40	0.34
15th	87	0.40	0.36
20th	88	0.41	0.37
25th	89	0.42	0.39
30th	90	0.42	0.39
35th	93	0.42	0.40
40th	97	0.42	0.41
45th	99	0.43	0.42
50th	101	0.43	0.42
55th	104	0.44	0.43
60th	109	0.46	0.46
65th	113	0.51	0.49
70th	115	0.51	0.49
75th	120	0.52	0.50
80th	124	0.53	0.51
85th	131	0.55	0.52
90th	138	0.56	0.53
96th	140	0.57	0.53
99th	141	0.57	0.54

7 Years Old.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	126 (41)	0.46 (0.11)	0.45 (0.12)
Percentile
1st	60	0.29	0.30
5th	65	0.32	0.30
10th	81	0.33	0.31
15th	85	0.34	0.31
20th	87	0.35	0.33
25th	91	0.37	0.35
30th	97	0.38	0.36
35th	107	0.41	0.40
40th	109	0.42	0.42
45th	111	0.43	0.42
50th	125	0.44	0.42
55th	131	0.46	0.45
60th	136	0.48	0.46
65th	146	0.50	0.47
70th	151	0.51	0.51
75th	157	0.51	0.51
80th	164	0.52	0.53
85th	168	0.56	0.56
90th	171	0.60	0.60
96th	192	0.64	0.69
99th	212	0.72	0.72

8 Years Old.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	153 (40)	0.45 (0.09)	0.36 (0.08)
Percentile
1st	89	0.25	0.18
5th	100	0.28	0.27
10th	110	0.31	0.30
15th	115	0.37	0.31
20th	117	0.42	0.31
25th	122	0.43	0.31
30th	132	0.43	0.32
35th	135	0.44	0.32
40th	141	0.44	0.32
45th	150	0.44	0.32
50th	155	0.44	0.34
55th	158	0.47	0.36
60th	160	0.49	0.36
65th	164	0.49	0.37
70th	165	0.50	0.38
75th	173	0.52	0.41
80th	179	0.53	0.43
85th	182	0.53	0.43
90th	188	0.54	0.47
96th	229	0.56	0.50
99th	268	0.58	0.51

9 Years Old.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	177 (35)	0.45 (0.08)	0.34 (0.06)
Percentile
1st	108	0.31	0.26
5th	126	0.35	0.28
10th	137	0.35	0.30
15th	148	0.36	0.30
20th	150	0.37	0.30
25th	151	0.38	0.31
30th	156	0.39	0.31
35th	165	0.42	0.31
40th	168	0.43	0.31
45th	170	0.44	0.32
50th	172	0.46	0.32
55th	175	0.47	0.32
60th	188	0.47	0.33
65th	190	0.48	0.35
70th	193	0.48	0.36
75th	199	0.50	0.36
80th	203	0.52	0.40
85th	204	0.53	0.41
90th	220	0.55	0.41
96th	246	0.58	0.49
99th	250	0.59	0.50

10 Years Old.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	192 (34)	0.44 (0.08)	0.34 (0.06)
Percentile
1st	145	0.33	0.23
5th	148	0.34	0.28
10th	152	0.35	0.30
15th	160	0.36	0.30
20th	161	0.37	0.30
25th	165	0.37	0.30
30th	171	0.38	0.30
35th	176	0.39	0.30
40th	181	0.40	0.31
45th	182	0.41	0.31
50th	185	0.42	0.33
55th	189	0.44	0.35
60th	196	0.45	0.35
65th	199	0.46	0.35
70th	204	0.47	0.36
75th	217	0.48	0.40
80th	229	0.49	0.40
85th	231	0.52	0.41
90th	233	0.55	0.42
96th	256	0.59	0.45
99th	258	0.62	0.46

11 Years Old.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	213 (37)	0.43 (0.09)	0.33 (0.08)
Percentile
1st	142	0.27	0.14
5th	157	0.29	0.30
10th	179	0.30	0.30
15th	184	0.35	0.30
20th	187	0.37	0.30
25th	189	0.37	0.30
30th	190	0.38	0.30
35th	192	0.39	0.30
40th	198	0.41	0.30
45th	203	0.41	0.30
50th	207	0.42	0.31
55th	216	0.45	0.31
60th	220	0.46	0.31
65th	228	0.47	0.31
70th	231	0.47	0.33
75th	234	0.48	0.35
80th	243	0.50	0.36
85th	246	0.53	0.43
90th	261	0.57	0.45
96th	281	0.59	0.46
99th	285	0.61	0.50

12 Years Old.

Mean (SD)	CMRS (wpm)	CCPS (logMAR)	CRA (logMAR)
	222 (33)	0.42 (0.07)	0.29 (0.05)
Percentile
1st	181	0.33	0.21
5th	188	0.34	0.22
10th	195	0.34	0.23
15th	196	0.35	0.24
20th	197	0.35	0.24
25th	197	0.37	0.25
30th	202	0.38	0.26
35th	211	0.38	0.27
40th	214	0.38	0.28
45th	214	0.39	0.30
50th	216	0.40	0.30
55th	217	0.41	0.30
60th	221	0.42	0.30
65th	224	0.44	0.31
70th	224	0.44	0.31
75th	230	0.46	0.31
80th	242	0.48	0.31
85th	252	0.50	0.32
90th	260	0.53	0.35
96th	281	0.53	0.38
99th	293	0.54	0.40

Author contributions

CKPP and MYAC designed this study. CKPP, TWJL and HYHW collected data. All authors contributed to the data analysis and read and approved the final manuscript.

Funding

The study was supported by University Grants Committee - Research Matching Grant Scheme (Project ID: P0040910) and the Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University. The contents of this study were not influenced by the funding bodies.

Data availability

Data are available upon request. Enquiry can be sent to the corresponding author.

Declarations

Ethics approval and consent to participate

This study followed the tenets of the Declaration of Helsinki and was approved by the ethics committee for human subjects of The Hong Kong Polytechnic University (reference number: HSEARS20190625002). The experiments were performed in accordance to the protocols approved by the ethics committee.

Competing interests

Dr. Ming-Yan Allen CHEONG is one of the inventors of CRAT.

Informed consent

Written informed consent was obtained from parents or guardians.