Influence of computer parameters on Spaeth Richman Contrast Sensitivity test values using different computers

Influence of computer parameters on Spaeth Richman Contrast Sensitivity test values using different computers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Influence of computer parameters on Spaeth Richman Contrast Sensitivity test values using different computers Parul Ichhpujani, Drishti Singh, Sahil Thakur, Suresh Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4513914/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To see the influence of various computer parameters on contrast sensitivity values obtained with the web based, Spaeth Richman Contrast Sensitivity (SPARCS) test. Method This prospective, observational study enrolled 80 consecutive healthy individuals free of ocular disease. Each patient underwent SPARCS CS testing on both the laptops, Apple Macbook and the Surface Pro. SPARCS scores were identified as the primary outcome variables and other ocular parameters were considered secondary outcome variables. Results Data of 160 eyes of 80 subjects who fulfilled the inclusion criteria was evaluated. The average age was 26.75 ± 5.75 years (range 18–40 years) and there was a slight female preponderance (61%, n = 49). None of the eyes had history of any ocular pathology. The Bland-Altman (BA) plot highlights a mean difference of 0.694 in measurements between the Macbook Air and Surface Pro indicating a small bias between the two methods. However, the wider limits of agreement (-12.889 to 14.276 ) suggest that the differences between the two can be substantial for some measurements. This variability is supported by the correlation matrix, which shows that while there is a positive relationship between equivalent measurements on the two devices, but they are not close to 1. Conclusion Given the importance of consistency in contrast sensitivity testing, especially for baseline and follow-up assessments, this data suggests that using different devices may introduce a level of variability that could confound results. Thus, it is essential to use the same device, especially in clinical scenarios, to get precise and reproducible results. Health sciences/Medical research/Outcomes research Health sciences/Health care/Diagnosis Computer display Contrast sensitivity Spaeth Richman Contrast Sensitivity Figures Figure 1 Figure 2 Introduction Approximately 2.2 billion people worldwide experience some form of vision impairment. 1 The major causes of vision loss globally include uncorrected refractive errors (671 million), cataract (approximately 100 million) and glaucoma as well age related macular degeneration (ARMD) (8 million each). 2 Unfortunately, a large majority (nearly 89%) reside in low and middle income countries and lack access to comprehensive ophthalmic care. This underscores the pressing importance of tackling the global challenges related to affordability, accessibility, and availability of vision care. 3 The emergence of smartphone applications and portable ophthalmic devices has enabled the performance of various basic vision tests in underserved and remote areas thus bridging the gap in access to care. Most eHealth applications for screening and detecting eye disease just focus on visual acuity, but a comprehensive understanding of visual function necessitates the inclusion of contrast sensitivity (CS). The holistic approach of incorporating CS tests alongside traditional measures such as letter acuity enables a better understanding of the impact of eye diseases on individuals' vision and overall well-being. Individuals with reduced CS may experience difficulties in mundane tasks like reading, recognizing faces and contours, driving, recognizing road signs, detecting pedestrians and navigating unfamiliar environments. Impaired CS is observed in various ophthalmic and neurologic conditions, including myopia, ARMD, amblyopia, dry eye, glaucoma, ocular hypertension and multiple sclerosis. It is important to note that contrast sensitivity deficits can occur even in visual neuropathologies that do not impact acuity. 4 With advancements in technology, various CS tests based on tablets and computers have emerged, making the CS assessment convenient and quick to perform thereby saving time, cost ease of accessibility. One such test is the Spaeth and Richman Contrast Sensitivity Test (SPARCS). 5 This is an internet-based test available on any web browser and uses the technique of bracketing to calculate the threshold of CS. SPARCS, assesses both central as well as peripheral contrast sensitivity and helps individuals to actively participate in monitoring their eye health at home using just their laptops and an internet connection. 6 There is an increasing amount of literature making use of SPARCS on a laptop or a desktop computer. 5 Therefore, it is important to consider the influence of computer display characteristics on CS outcomes. Although there is some information regarding validation of various digital devices like phones and tablets for ophthalmic testing, 7 literature is sparse as regards laptops for online CS tests. Making use of non-validated display parameters in clinical settings in order to evaluate the contrast sensitivity aspect of visual functions may cause one to make erroneous clinical decisions. 7 In the digital age, while software solutions can be deployed across a variety of platforms and device configurations, it is essential to evaluate the performance of these devices to ensure the robustness and reliability of the diagnostic tests. Therefore, in the current study we plan to use two laptops with different operating systems, to assess the influence of display parameters on the SPARCS test outcome. Ultimately, our goal is to enhance the reliability and accuracy of CS testing in clinical settings, enabling more precise diagnoses and better-informed treatment decisions for patients with visual impairments. Material and Methods This observational, cross-sectional study was conducted at a multispecialty tertiary care institute. We enrolled 80 consecutive healthy individuals from the outpatient services of ophthalmology department, who were free of ocular diseases. The study was registered with the Clinical Trials Registry of India (CTRI), available online at https://www.ctri.nic.in , before enrolment of the first participant (CTRI/2023/07/055606). The study was approved by the institutional ethics committee and was in accordance with the tenets of Declaration of Helsinki. Individuals aged more than 18 and less than 40 years of either gender having an uncorrected visual acuity (UCVA) of 6/6 and near acuity of N6 were enrolled. In order to avoid multiple etiologies of decreased CS and other factors (e.g., cognitive impairment, Parkinson’s disease, Alzheimer’s disease and any other neurological or musculoskeletal disease) that could preclude the patient from providing reliable and valid data, were excluded from the study. Thus any patient with history of Contact lens (cosmetic) use or dry eye, history of incisional or laser eye surgery in the past 3 months, any cause for visual impairment (e.g., glaucoma, cataract more than grade 2 or higher using LOCS III grading, diabetic retinopathy, ARMD, etc.) were excluded from the study. Patients with any history of secondary glaucoma, significant media opacities or history of using oral/topical steroids were also excluded. Participants and data collection methodology Eighty participants were recruited. A careful detailed history was taken in all cases. The ocular examination consisted of uncorrected visual acuity (UCVA), best corrected visual acuity (BCVA), IOP measurement using a calibrated Goldmann applanation tonometer (GAT), a slit lamp examination of the anterior segment and a fundus examination using a + 90D lens. Subjects’ current symptoms, health problems, medications, and ocular co-morbidities were also documented. All subjects included in the study underwent VA assessment using the Snellen’s visual acuity charts. The observed values were converted to Logarithm of Minimal angle of resolution (logMAR) scale for statistical analysis. The Humphrey perimeter HVF 750 II (Zeiss Meditec, Dublin, CA), using SITA-Fast 24 − 2 protocol was used to test visual fields to rule out Glaucoma. Each patient underwent SPARCS CS testing on both the laptops, Apple Macbook and the Surface Pro, with internet access. The order of testing was randomised, viz., some on Apple Macbook laptop first while others performed the test on Surface Pro first. Atleast an hour’s gap was given between testing with different laptops. The participant was given a break of 5 to 15 minutes after the practice test. One eye was tested at a time. The non-testing eye was covered with an occluder. Scores for test for both the laptops were noted. Testing was conducted in a room with fluorescent lighting with no windows to minimize glare and reflections. The light level in the room was measured using a Lux Light Meter Pro (Version 2.1.1; By Marina Polyanska) application on smartphone and was kept in the range of 750 to 780 lux. Additionally, settings were adjusted so that the auto-adjust for brightness was switched off and mains power was connected during testing. In the current study, we standardized several key display parameters for both laptops to ensure consistency and comparability of the results. The brightness of both laptops was set to the maximum level of 400 nits accessed via settings. Both laptops shared the same refresh rate of 60Hz, ensuring consistent image updates. The Apple MacBook was set to its native screen resolution of 2560 x 1600 pixels. To change the screen resolution on a MacBook, the following steps were followed: Apple menu clicked in the top-left corner ◊ "System Preferences" ◊ "Displays" ◊In the "Display" tab, preferred screen resolution chosen from the available options. The "Default for display" option was selected for the native resolution. Windows Surface Pro featured a resolution of 2736 x 1824 using the following steps: Right-click on the desktop opened a context menu ◊ "Display settings" ◊ "Scale and layout" section ◊ desired screen resolution from the available options selected ◊ "Apply" button. The aspect ratio of the Apple MacBook was 16:9 (1.7), while that of the Windows Surface Pro was 3:2 (1.5). Apple MacBook exhibited a contrast ratio of 1069:1, and the Surface Pro had a contrast ratio of 1000:1. The period of time from switching on the display to achieving stable luminance was also accounted for. To assess this, both the laptops were turned off overnight and when switched on the next day, SPARCS testing was done after 30 minutes of starting the laptop to stabilize the luminance values, viz., the display was warmed up and there was no fluctuation in color settings. The viewing angle and distance was also standardized, with a viewing distance of 50 centimeters. To achieve a power of 90% and a level of significance of 5% (two sided), for detecting a mean of the difference of 5 between pairs, assuming the standard deviation of the difference to be 10, the study required a sample size of 46, we enrolled 80 individuals. Sample size calculation was done using https://statulator.com/SampleSize/ss2M.html . The data from SPARCS was initially collected within an online database and subsequently imported into a Microsoft Excel workbook for effective data management. The choice of device for SPARCS testing (either MacBook or Surface Pro) was designated as the primary explanatory variable, while SPARCS scores were identified as the primary outcome variables. Additionally, various other ocular parameters were considered secondary outcome variables. For quantitative variables, we computed the mean and standard deviation, whereas for categorical variables, we calculated frequencies and proportions. For examining the association between categorical explanatory variables and quantitative outcome variables, we compared the mean and median values. Specifically, the Wilcoxon signed-rank test was employed to assess any differences in SPARCS scores obtained using the two devices and browsers. To evaluate the impact of gender and laterality we used the Mann Whitney U test. To explore associations between quantitative explanatory and outcome variables, we calculated Spearman’s rank correlation coefficient and presented the data in scatter diagrams. To evaluate the reliability of the two devices, we calculated the Intra-class Correlation Coefficient (ICC) along with its 95% Confidence Interval (CI) and corresponding P-value. In addition, to gauge the robustness of the SPARCS scores, Lin's concordance coefficient was reported. The Bland-Altman (BA) plots were also utilized to assess bias and establish limits of agreement between the two devices and browsers. A significance threshold of P < 0.05 was employed to determine statistical significance. All data analyses were performed using Stata software (StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX: Stata Corp LLC). Results Data of 160 eyes of 80 patients, who fulfilled the inclusion criteria set for the study were evaluated. The average age was 26.75 ± 5.75 years (range 18–40 years) and there was a slight female preponderance (61%, n = 49). Mean age of males was 27.45 ± 5.51 years and of females was 26.31 ± 5.89 years. None of the eyes had history of any ocular pathology or ocular intervention. No patient reported intake of drugs that could affect the CS. The average BCVA (logMAR) in both eyes of the study population was 0.00. All participants had unremarkable anterior as well as posterior segment examination. BCVA in OD and OS was comparable across all age groups with no statistically significant difference between the two (p > 0.05). Table 1 shows the SPARCS scores in the two different devices. The SPARCS scores for MacBook are more than the Surface Pro except in the UL quadrant. However, the difference is not statistically significant (all p > 0.05). Table 1 SPARCS scores in the two device groups (N = 80 subjects, 160 eyes) MacBook Surface Pro P Value* Mean ± SD Median, IQR Mean ± SD Median, IQR SPARCS Total 85.41 ± 4.82 86,5 84.71 ± 6.78 85,10 0.231 SPARCS UL 17.14 ± 1.69 17.43, 1.5 17.31 ± 1.87 17.43,2.79 0.215 SPARCS UR 17.24 ± 1.54 17.43, 1.5 17.13 ± 2.08 17.43,1.5 0.897 SPARCS Central 16.81 ± 1.77 17.43, 1.5 16.71 ± 2.23 17.43,2.56 0.769 SPARCS LL 17.07 ± 1.48 17.43, 1.5 16.65 ± 2.15 15.93,2.03 0.060 SPRCS LR 17.23 ± 1.59 17.43, 1.5 16.87 ± 2.03 17.43,1.5 0.143 *Wilcoxon Signed Rank Test Non-parametric tests (Spearman coefficient) were used to explore the correlation between the two devices, as the data was not normally distributed. From the results we can see that there is moderate to strong correlation between internal components of SPARCS with the total SPARCS score, however between the two devices the correlation coefficient is weak (0.31, p < 0.001). Figure 1 shows this relationship in a scatter plot. For Total SPARCS between the two laptops Lin’s Coefficient of Concordance ρc = 0.3038 (95% CI: 0.1666 to 0.4294), the ICC = 0.3051(95% CI = 0.1585 to 0.4365). The BA plot for the total SPARCS scores is shown in Fig. 2. The mean difference, also known as the bias, is 0.694. This indicates that, on average, the readings from the Mac Air are about 0.694 units higher than those from the Surface Pro. The 95% limits of agreement range from − 12.889 to 14.276. This means that 95% of the differences between the two methods of measurement fall within this range. A smaller range would indicate better agreement between the two methods. Thus, while the mean difference is relatively low, indicating a small bias between the two methods, the wide limits of agreement and the 6.25% of points outside these limits suggest that there is variability in the agreement between the two methods across the range of measurements. The BA plot and correlation matrix (Table 2 ) both suggest that while there's a general agreement between the MacBook and Surface Pro, there are notable differences and variability. The mean difference is small, but the range of differences (limits of agreement) in the BA plot is broad. Similarly, the correlation matrix shows positive correlations between equivalent measures on the two devices, but they are not close to 1. Given these findings, if accuracy and consistency are paramount, it might be advisable to use the same device for all measurements. Table 2 Correlation between the SPARCS scores in the two device groups (N = 80 subjects, 160 eyes) MacBook Total MacBook UL MacBook UR MacBook central MacBook LL MacBook LR Surface Pro Total Surface Pro UL Surface Pro UR Surface Pro central Surface Pro LL Surface Pro LR MacBook Total 1.000 MacBook UL 0.540* 0.000 1.000 MacBook UR 0.634* 0.000 0.156* 0.048 1.000 MacBook central 0.608* 0.000 0.269* 0.000 0.251* 0.001 1.000 MacBook LL 0.676* 0.00 0.302* 0.001 0.392* 0.000 0.346* 0.000 1.000 MacBook LR 0.672* 0.000 0.156* 0.048 0.382* 0.000 0.270* 0.000 0.400* 0.000 1.000 Surface Pro Total 0.318* 0.000 0.185* 0.186 0.229* 0.003 0.217* 0.005 0.278* 0.000 0.234* 0.002 1.000 Surface Pro UL 0.242* 0.000 0.165* 0.036 0.166* 0.035 0.143 0.070 0.302* 0.000 0.140 0.076 0.692* 0.000 1.000 Surface Pro UR 0.223* 0.00 0.214* 0.006 0.156* 0.048 0.180* 0.022 0.171* 0.030 0.099 0.211 0.716* 0.000 0.437* 0.000 1.000 Surface Pro central 0.157 0.057 0.088 0.265 0.138 0.079 0.149 0.059 0.111 0.160 0.063 0.422 0.533* 0.000 0.213* 0.006 0.209* 0.079 1.000 Surface Pro LL 0.319* 0.00 0.172* 0.029 0.206* 0.008 0.163* 0.038 0.297* 0.000 0.301* 0.000 0.729* 0.000 0.483* 0.000 0.441* 0.000 0.233* 0.002 1.000 Surface Pro LR 0.198* 0.012 0.093 0.240 0.165* 0.036 0.060 0.446 0.106 0.180 0.193* 0.014 0.658* 0.000 0.336* 0.000 0.429* 0.000 0.181* 0.021 0.349* 0.000 1.000 (* SPARCS: Spaeth Richman Contrast Sensitivity Test; UL: Upper Left; UR: Upper Right; LL: Lower Left; LR: Lower Right) Discussion SPARCS, is a cost-effective and easily accessible laptop based online application for assessing both central as well as peripheral CS, helping individuals to actively participate in monitoring their eye health at home using just their laptops and an internet connection. 6 Another such test is ClinicCSF wherein Contrast Sensitivity Function (CSF) is measured using iPad. A recent study compared ClinicCSF against the Functional Acuity Contrast Test (FACT) and demonstrated that there was no significant differences between the two tests suggesting applications on the iPads and smartphones can provide accurate measurements of CS, comparable to the established psychophysical tests like FACT. 8 In another study, a test conducted on a tablet-based platform proved to be a valid method for assessing distance and near visual acuity, as well as CS. The results of this study demonstrated that the tablet-based test yielded comparable outcomes to the gold-standard clinical tests, which included the ETDRS distance acuity, Pelli-Robson CS, and MNRead near acuity tests. 9 As technology continues to evolve, it is imperative to validate the use of visual function evaluation applications on different devices to assess any potential impact of new technology. By prioritizing validated apps, healthcare professionals can ensure reliable and comparable results when utilizing these applications in clinical settings. The validation process should adapt to changes in technology to maintain the accuracy and effectiveness of visual function assessment. Our study assessed the performance of SPARCS score on two different laptops, specifically the MacBook Air and Microsoft Surface Pro 7. Aim was to identify any significant variations in the test results when conducted on different laptops with different display characteristics. These laptops were chosen to compare the application on the two most widely used operating systems (OS), iOS and Windows. With desktop operating system worldwide market share as of May 2024 being 73.9% for Windows and 14.91% for MacOS. 10 We found good repeatability and reliability of the SPARCS scores when the test was performed on the same laptop. The BA plot showed that the mean difference between measurements taken on both devices was modest; however, the broader limits of agreement indicate that the differences between the two could be substantial for some measurements. This variability is supported by the correlation matrix, which showed that while there is a positive relationship between equivalent measurements on the two devices, the correlation was not perfect. This deviation highlights the potential variability in measurements and suggested that the devices do not consistently yield identical results. In the clinical context of CS testing using SPARCS, even minor variations in measurements can carry significant implications. This is because CS testing is highly sensitive and designed to detect subtle changes in a patient’s visual capabilities. As such, any external sources of variability, including those introduced by the choice of device or screen technology, can confound the results. Given these findings, it is advisable to stress the importance of maintaining device consistency during SPARCS testing in clinical practice. Switching between the Macbook and the Surface Pro (or any two different laptops), while seemingly minor, could introduce discrepancies that are not reflective of genuine changes in an individual's CS. Instead, such discrepancies may arise from the inherent variability between the two devices. Various display properties that could have led to the differences include pixel density, screen resolution, luminance, color calibration, refresh rate and laptop display. Increased level of detail with higher pixel density (pixel per inch; PPI) can enhance the CS because of better differentiation of fine contrast differences. LCD screens typically use subpixels (red, green, and blue) to create individual pixels. Thus subpixel arrangement can also affect CS. MacBook Air's Retina display technology has a 13-inch display screen with 2560 x 1600 resolution and high PPI (approximately 227 PPI) with high pixel density and color accuracy resulting in better clarity and visual performance during CS testing. On the other hand, the Microsoft Surface Pro's PixelSense display technology on a 12.3-inch with 2736 x 1824 resolution had a higher PPI (approximately 267 PPI) and offered even higher resolution and color reproduction. Despite comparable high pixel density with good resolution both the laptop screens provided different outcomes. MacBook Air's Retina display technology had higher SPARCS scores than Microsoft Surface Pro. Displays with higher resolutions, such as QHD (2560x1440) or 4K UHD (3840x2160), typically provide sharper and more detailed images. This increased level of detail can enhance CS by making it easier to discern subtle differences in contrast.In a study 15 iPad mini Retina display devices were evaluated for visual function assessment and showed that the tablets required approximately 13 minutes to achieve stable luminance after being powered on, while the chromaticity remained constant throughout. Temperature fluctuations had a minimal impact of 1% on luminance, but had no effect on chromaticity. All 15 tablets exhibited gamma functions that closely approximated the standard gamma value of 2.20, and their color gamut sizes were similar, with only slight differences observed in the blue primary. Considering the comparable physical characteristics of these devices, they can be considered suitable for use as visual stimulus displays. 11 In terms of CS, these findings suggest that luminance variations across the screen, whether due to viewing angle, battery level, or temperature, are likely to have some impact. However, the study does not provide specific information on how these variations would affect CS. Further studies would be needed to determine the exact influence of these factors on contrast sensitivity performance. Additionally, the study was done on tablets, so the direct implications for a given laptop may vary depending on its specific characteristics and display technology. Therefore, it is crucial to calibrate and evaluate the display performance of individual devices, especially if they are not of the same make and model. Color calibration refers to the process of adjusting the display's color reproduction for accurate and consistent color representation. If one device is calibrated to display certain shades of grey as darker or lighter than intended, it can affect the perception of contrast between different elements in the test patterns. The MacBook Air (M1, 2020) is equipped with True Tone technology, enabling automatic adjustment of the display's color temperature according to the ambient lighting conditions. Additionally, it features a wide color gamut, allowing for the display of a broader spectrum of colors. On the other hand, the Microsoft Surface Pro 7 is designed with a high contrast ratio to enhance the differentiation between dark and light areas on the screen. It also incorporates an ambient light sensor that automatically adapts the display brightness based on the surrounding lighting conditions. These optimizations may impact the contrast representation and ultimately affect the SPARCS score obtained during testing. To ensure reliable and consistent CS testing, it is important to calibrate both devices appropriately with a color calibration device, such as a colorimeter or spectrophotometer, which is designed to measure and adjust color accuracy. This helps minimize any potential discrepancies in color representation and allows for more reliable assessment of CS across different devices. A higher refresh rate (60 Hz or above) can minimize screen flickering, ensuring stable and smooth test stimuli. MacBook Air and Microsoft Surface Pro 7 have a standard refresh rate of 60 Hz. This means that the display refreshes the image 60 times per second, providing a smooth visual experience during CS testing. Researchers in a study found that higher refresh rates of video displays positively impacted reading speed and reduced disruptions in eye movements during reading. The study also highlighted the importance of oculomotor adaptation, indicating that participants could adjust their eye movements to match the characteristics of the video display, leading to improved reading outcomes. 12 The MacBook Air features a 16:10 aspect ratio ( divine proportion or gold standard; where width is roughly 1.5 times the height), providing a slightly taller display, while the Microsoft Surface Pro features a 3:2 aspect ratio, providing a taller display similar to the MacBook Air but with a slightly different ratio. While aspect ratio itself does not directly affect CS, it can impact the overall visual experience and potentially influence the perception of CS during SPARCS testing. Image processing algorithms or display enhancements, implemented by the manufacturers can also influence the visual performance and CS on the screen. In the clinical context of CS testing using SPARCS, even minor variations in measurements can carry significant implications. SPARCS test on a single laptop with the same browser can be used reliably and consistently to know and compare the CS between individuals but to ensure comparable results using SPARCS on different laptops, it is important to consider the specific display properties and calibrating them to establish accurate measurements across different laptop models. This understanding will help us take a step ahead in developing standardized and reliable methods for CS testing using modern technologies, ensuring accurate and accessible screening for ophthalmic conditions. Declarations Disclosures : The authors have no conflicts of interests and financial disclosures. References World Health Organization. World report on vision. Geneva: World Health Organization; 2019. Available from: https://www.who.int/publications-detail/world-report-on-vision (Accessed on 17th April, 2024). VLEG/GBD 2020 model [Internet]. Accessed via the IAPB Vision Atlas. (Accessed on 17th April, 2024). Ackland P. World blindness and visual impairment: despite many successes, the problem is growing. Community Eye Health. 2017;20(64):60–61. Jindra LF, Zemon V.Contrast sensitivity testing: a more complete assessment of vision. J Cataract Refract Surg. 1989;15(2):141–145. Ichhpujani P, Thakur S, Spaeth GL. Contrast Sensitivity and Glaucoma. J Glaucoma. 2020;29: 71–5. Bariya S, Ichhpujani P, Rehman O, Kumar S. Normative database for Spaeth Richman contrast sensitivity test for Indian eyes. Indian J Ophthalmol. 2022;70(10):2758–2762. Mena-Guevara KJ, Piñero DP, de Fez D. Validation of Digital Applications for Evaluation of Visual Parameters: A Narrative Review. Vision (Basel). 2021;5(4):58. Rodríguez-Vallejo M, Remón L, Monsoriu JA, Furlan WD. Designing a new test for contrast sensitivity function measurement with iPad. J Optom. 2015;8(2):125–131. Varadaraj V, Assi L, Gajwani P, Wahl M, David J, Swenor BK, Ehrlich JR. Evaluation of Tablet-Based Tests of Visual Acuity and Contrast Sensitivity in Older Adults. Ophthalmic Epidemiol. 2021;28(4):316–324. StatCounter Global Stats. Desktop Operating System Market Share Worldwide [Internet]. Available from: https://gs.statcounter.com/os-market-share/desktop/worldwide (Accessed on 31st May 2024). Bodduluri L, Boon M, Dain SJ. Evaluation of tablet computers for visual function assessment. Behav Res 2017; 49: 548–558. Ghodrati M, Morris AP, Price NS. The (un) suitability of modern liquid crystal displays (LCDs) for vision research. Front Psychol. 2015; 6: 303. Additional Declarations There is no conflict of interest Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4513914","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331739697,"identity":"93c2425d-fdd0-4b84-8f5a-fd997b6bc9d7","order_by":0,"name":"Parul Ichhpujani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYLCCh//+ywEpgwMVNgwMbOzEaElgYzYGazmTBtTCTKSWxAagFgaQFgZCWszZzz6TSOBhS9/e3rzxwIGEbfJ8zAyMHz7m4NZi2ZNuJpEgwZM758yxAqCW24ZtzAzMkjO34dZicCCNTSLBQCJ3hkSOweGPP24zArWwMfPi03L+GVBLgkG6hPwbA5At9oS13ADZciAhQUKCB6wlkaAWyxnPmC0SGw4YzuBJA/sluY2ZsRmvX8z50xhvfGw4IC/BfnjzB6AW2/ntzQc/fMTnMCxijA241ePQMgpGwSgYBaMAFQAAxBVRsZKCz8sAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6256-4002","institution":"Government Medical College and Hospital","correspondingAuthor":true,"prefix":"","firstName":"Parul","middleName":"","lastName":"Ichhpujani","suffix":""},{"id":331739698,"identity":"2e322115-bd4d-4cae-92d4-15778bb8651a","order_by":1,"name":"Drishti Singh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Drishti","middleName":"","lastName":"Singh","suffix":""},{"id":331739699,"identity":"4be9a558-bd55-459a-a025-159916ef2cab","order_by":2,"name":"Sahil Thakur","email":"","orcid":"https://orcid.org/0000-0002-7948-6992","institution":"Singapore Eye Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Sahil","middleName":"","lastName":"Thakur","suffix":""},{"id":331739700,"identity":"6ef3a865-a999-4bf0-9eb2-5d493245f3c5","order_by":3,"name":"Suresh Kumar","email":"","orcid":"","institution":"Government Medical College and Hospital","correspondingAuthor":false,"prefix":"","firstName":"Suresh","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-06-01 13:55:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4513914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4513914/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63369863,"identity":"81a11115-a2aa-421c-a426-b23297f52e7b","added_by":"auto","created_at":"2024-08-27 11:46:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45432,"visible":true,"origin":"","legend":"Scatter plot showing relationship between the total SPARCS across two devices","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4513914/v1/b707d06a804008c355207d77.jpg"},{"id":63369865,"identity":"0af04567-c8f6-40e2-8f0b-9ccf7b3bac63","added_by":"auto","created_at":"2024-08-27 11:46:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60859,"visible":true,"origin":"","legend":"Bland Altman plot showing limits of agreement between the two devices","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4513914/v1/abb2c1ce524b3ce91a7c2af4.jpg"},{"id":104781616,"identity":"97666fe3-a1d1-428a-9003-9e37fd96e0d2","added_by":"auto","created_at":"2026-03-17 07:56:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":799953,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4513914/v1/53a9f611-bb72-480e-80b4-5daeb27ffe66.pdf"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Influence of computer parameters on Spaeth Richman Contrast Sensitivity test values using different computers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately 2.2\u0026nbsp;billion people worldwide experience some form of vision impairment.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eThe major causes of vision loss globally include uncorrected refractive errors (671\u0026nbsp;million), cataract (approximately 100\u0026nbsp;million) and glaucoma as well age related macular degeneration (ARMD) (8\u0026nbsp;million each).\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Unfortunately, a large majority (nearly 89%) reside in low and middle income countries and lack access to comprehensive ophthalmic care. This underscores the pressing importance of tackling the global challenges related to affordability, accessibility, and availability of vision care.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe emergence of smartphone applications and portable ophthalmic devices has enabled the performance of various basic vision tests in underserved and remote areas thus bridging the gap in access to care. Most eHealth applications for screening and detecting eye disease just focus on visual acuity, but a comprehensive understanding of visual function necessitates the inclusion of contrast sensitivity (CS). The holistic approach of incorporating CS tests alongside traditional measures such as letter acuity enables a better understanding of the impact of eye diseases on individuals' vision and overall well-being.\u003c/p\u003e \u003cp\u003eIndividuals with reduced CS may experience difficulties in mundane tasks like reading, recognizing faces and contours, driving, recognizing road signs, detecting pedestrians and navigating unfamiliar environments. Impaired CS is observed in various ophthalmic and neurologic conditions, including myopia, ARMD, amblyopia, dry eye, glaucoma, ocular hypertension and multiple sclerosis. It is important to note that contrast sensitivity deficits can occur even in visual neuropathologies that do not impact acuity.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWith advancements in technology, various CS tests based on tablets and computers have emerged, making the CS assessment convenient and quick to perform thereby saving time, cost ease of accessibility. One such test is the Spaeth and Richman Contrast Sensitivity Test (SPARCS).\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e This is an internet-based test available on any web browser and uses the technique of bracketing to calculate the threshold of CS. SPARCS, assesses both central as well as peripheral contrast sensitivity and helps individuals to actively participate in monitoring their eye health at home using just their laptops and an internet connection.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThere is an increasing amount of literature making use of SPARCS on a laptop or a desktop computer.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Therefore, it is important to consider the influence of computer display characteristics on CS outcomes. Although there is some information regarding validation of various digital devices like phones and tablets for ophthalmic testing,\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e literature is sparse as regards laptops for online CS tests. Making use of non-validated display parameters in clinical settings in order to evaluate the contrast sensitivity aspect of visual functions may cause one to make erroneous clinical decisions.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the digital age, while software solutions can be deployed across a variety of platforms and device configurations, it is essential to evaluate the performance of these devices to ensure the robustness and reliability of the diagnostic tests. Therefore, in the current study we plan to use two laptops with different operating systems, to assess the influence of display parameters on the SPARCS test outcome. Ultimately, our goal is to enhance the reliability and accuracy of CS testing in clinical settings, enabling more precise diagnoses and better-informed treatment decisions for patients with visual impairments.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eThis observational, cross-sectional study was conducted at a multispecialty tertiary care institute. We enrolled 80 consecutive healthy individuals from the outpatient services of ophthalmology department, who were free of ocular diseases. The study was registered with the Clinical Trials Registry of India (CTRI), available online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ctri.nic.in\u003c/span\u003e\u003cspan address=\"https://www.ctri.nic.in\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, before enrolment of the first participant (CTRI/2023/07/055606). The study was approved by the institutional ethics committee and was in accordance with the tenets of Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eIndividuals aged more than 18 and less than 40 years of either gender having an uncorrected visual acuity (UCVA) of 6/6 and near acuity of N6 were enrolled.\u003c/p\u003e \u003cp\u003eIn order to avoid multiple etiologies of decreased CS and other factors (e.g., cognitive impairment, Parkinson\u0026rsquo;s disease, Alzheimer\u0026rsquo;s disease and any other neurological or musculoskeletal disease) that could preclude the patient from providing reliable and valid data, were excluded from the study. Thus any patient with history of Contact lens (cosmetic) use or dry eye, history of incisional or laser eye surgery in the past 3 months, any cause for visual impairment (e.g., glaucoma, cataract more than grade 2 or higher using LOCS III grading, diabetic retinopathy, ARMD, etc.) were excluded from the study. Patients with any history of secondary glaucoma, significant media opacities or history of using oral/topical steroids were also excluded.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and data collection methodology\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEighty participants were recruited. A careful detailed history was taken in all cases. The ocular examination consisted of uncorrected visual acuity (UCVA), best corrected visual acuity (BCVA), IOP measurement using a calibrated Goldmann applanation tonometer (GAT), a slit lamp examination of the anterior segment and a fundus examination using a\u0026thinsp;+\u0026thinsp;90D lens. Subjects\u0026rsquo; current symptoms, health problems, medications, and ocular co-morbidities were also documented. All subjects included in the study underwent VA assessment using the Snellen\u0026rsquo;s visual acuity charts. The observed values were converted to Logarithm of Minimal angle of resolution (logMAR) scale for statistical analysis. The Humphrey perimeter HVF 750 II (Zeiss Meditec, Dublin, CA), using SITA-Fast 24\u0026thinsp;\u0026minus;\u0026thinsp;2 protocol was used to test visual fields to rule out Glaucoma.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eEach patient underwent SPARCS CS testing on both the laptops, Apple Macbook and the Surface Pro, with internet access. The order of testing was randomised, viz., some on Apple Macbook laptop first while others performed the test on Surface Pro first. Atleast an hour\u0026rsquo;s gap was given between testing with different laptops. The participant was given a break of 5 to 15 minutes after the practice test. One eye was tested at a time. The non-testing eye was covered with an occluder. Scores for test for both the laptops were noted.\u003c/p\u003e \u003cp\u003eTesting was conducted in a room with fluorescent lighting with no windows to minimize glare and reflections. The light level in the room was measured using a Lux Light Meter Pro (Version 2.1.1; By Marina Polyanska) application on smartphone and was kept in the range of 750 to 780 lux. Additionally, settings were adjusted so that the auto-adjust for brightness was switched off and mains power was connected during testing.\u003c/p\u003e \u003cp\u003eIn the current study, we standardized several key display parameters for both laptops to ensure consistency and comparability of the results. The brightness of both laptops was set to the maximum level of 400 nits accessed via settings. Both laptops shared the same refresh rate of 60Hz, ensuring consistent image updates. The Apple MacBook was set to its native screen resolution of 2560 x 1600 pixels.\u003c/p\u003e \u003cp\u003eTo change the screen resolution on a MacBook, the following steps were followed: Apple menu clicked in the top-left corner \u0026loz; \"System Preferences\" \u0026loz; \"Displays\" \u0026loz;In the \"Display\" tab, preferred screen resolution chosen from the available options. The \"Default for display\" option was selected for the native resolution. Windows Surface Pro featured a resolution of 2736 x 1824 using the following steps: \u003cem\u003eRight-click on the desktop opened a context menu\u003c/em\u003e \u003cb\u003e\u0026loz;\u003c/b\u003e \u003cem\u003e\"Display settings\"\u003c/em\u003e\u003cb\u003e\u0026loz;\u003c/b\u003e\u003cem\u003e\"Scale and layout\" section\u003c/em\u003e \u003cb\u003e\u0026loz;\u003c/b\u003e \u003cem\u003edesired screen resolution\u003c/em\u003e from the available options selected \u0026loz; \"Apply\" button. The aspect ratio of the Apple MacBook was 16:9 (1.7), while that of the Windows Surface Pro was 3:2 (1.5). Apple MacBook exhibited a contrast ratio of 1069:1, and the Surface Pro had a contrast ratio of 1000:1.\u003c/p\u003e \u003cp\u003eThe period of time from switching on the display to achieving stable luminance was also accounted for. To assess this, both the laptops were turned off overnight and when switched on the next day, SPARCS testing was done after 30 minutes of starting the laptop to stabilize the luminance values, viz., the display was warmed up and there was no fluctuation in color settings. The viewing angle and distance was also standardized, with a viewing distance of 50 centimeters.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo achieve a power of 90% and a level of significance of 5% (two sided), for detecting a mean of the difference of 5 between pairs, assuming the standard deviation of the difference to be 10, the study required a sample size of 46, we enrolled 80 individuals. Sample size calculation was done using \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://statulator.com/SampleSize/ss2M.html\u003c/span\u003e\u003cspan address=\"https://statulator.com/SampleSize/ss2M.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe data from SPARCS was initially collected within an online database and subsequently imported into a Microsoft Excel workbook for effective data management. The choice of device for SPARCS testing (either MacBook or Surface Pro) was designated as the primary explanatory variable, while SPARCS scores were identified as the primary outcome variables. Additionally, various other ocular parameters were considered secondary outcome variables.\u003c/p\u003e \u003cp\u003eFor quantitative variables, we computed the mean and standard deviation, whereas for categorical variables, we calculated frequencies and proportions. For examining the association between categorical explanatory variables and quantitative outcome variables, we compared the mean and median values. Specifically, the Wilcoxon signed-rank test was employed to assess any differences in SPARCS scores obtained using the two devices and browsers. To evaluate the impact of gender and laterality we used the Mann Whitney U test. To explore associations between quantitative explanatory and outcome variables, we calculated Spearman\u0026rsquo;s rank correlation coefficient and presented the data in scatter diagrams.\u003c/p\u003e \u003cp\u003eTo evaluate the reliability of the two devices, we calculated the Intra-class Correlation Coefficient (ICC) along with its 95% Confidence Interval (CI) and corresponding P-value. In addition, to gauge the robustness of the SPARCS scores, Lin's concordance coefficient was reported. The Bland-Altman (BA) plots were also utilized to assess bias and establish limits of agreement between the two devices and browsers. A significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was employed to determine statistical significance. All data analyses were performed using Stata software (StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX: Stata Corp LLC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eData of 160 eyes of 80 patients, who fulfilled the inclusion criteria set for the study were evaluated. The average age was 26.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75 years (range 18\u0026ndash;40 years) and there was a slight female preponderance (61%, n\u0026thinsp;=\u0026thinsp;49). Mean age of males was 27.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51 years and of females was 26.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.89 years. None of the eyes had history of any ocular pathology or ocular intervention. No patient reported intake of drugs that could affect the CS. The average BCVA (logMAR) in both eyes of the study population was 0.00. All participants had unremarkable anterior as well as posterior segment examination. BCVA in OD and OS was comparable across all age groups with no statistically significant difference between the two (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the SPARCS scores in the two different devices. The SPARCS scores for MacBook are more than the Surface Pro except in the UL quadrant. However, the difference is not statistically significant (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSPARCS scores in the two device groups (N\u0026thinsp;=\u0026thinsp;80 subjects, 160 eyes)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMacBook\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSurface Pro\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP Value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian, IQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian, IQR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPARCS Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.71\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPARCS UL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.43, 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.43,2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPARCS UR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.43, 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.43,1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPARCS Central\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.43, 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.43,2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPARCS LL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.43, 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.93,2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPRCS LR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.43, 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.43,1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e*Wilcoxon Signed Rank Test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNon-parametric tests (Spearman coefficient) were used to explore the correlation between the two devices, as the data was not normally distributed. From the results we can see that there is moderate to strong correlation between internal components of SPARCS with the total SPARCS score, however between the two devices the correlation coefficient is weak (0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;1 shows this relationship in a scatter plot.\u003c/p\u003e \u003cp\u003eFor Total SPARCS between the two laptops Lin\u0026rsquo;s Coefficient of Concordance ρc\u0026thinsp;=\u0026thinsp;0.3038 (95% CI: 0.1666 to 0.4294), the ICC\u0026thinsp;=\u0026thinsp;0.3051(95% CI\u0026thinsp;=\u0026thinsp;0.1585 to 0.4365). The BA plot for the total SPARCS scores is shown in Fig.\u0026nbsp;2. The mean difference, also known as the bias, is 0.694. This indicates that, on average, the readings from the Mac Air are about 0.694 units higher than those from the Surface Pro. The 95% limits of agreement range from \u0026minus;\u0026thinsp;12.889 to 14.276. This means that 95% of the differences between the two methods of measurement fall within this range. A smaller range would indicate better agreement between the two methods. Thus, while the mean difference is relatively low, indicating a small bias between the two methods, the wide limits of agreement and the 6.25% of points outside these limits suggest that there is variability in the agreement between the two methods across the range of measurements.\u003c/p\u003e \u003cp\u003eThe BA plot and correlation matrix (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) both suggest that while there's a general agreement between the MacBook and Surface Pro, there are notable differences and variability. The mean difference is small, but the range of differences (limits of agreement) in the BA plot is broad. Similarly, the correlation matrix shows positive correlations between equivalent measures on the two devices, but they are not close to 1. Given these findings, if accuracy and consistency are paramount, it might be advisable to use the same device for all measurements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCorrelation between the SPARCS scores in the two device groups\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;80 subjects, 160 eyes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacBook Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMacBook UL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMacBook UR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMacBook central\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMacBook LL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMacBook LR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSurface Pro Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSurface Pro UL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSurface Pro UR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSurface Pro central\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSurface Pro LL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSurface Pro LR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMacBook Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMacBook UL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.540*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMacBook UR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.634*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156*\u003c/p\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMacBook central\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.608*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.251*\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMacBook LL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.676*\u003c/p\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.302*\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.392*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.346*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMacBook LR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.672*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156*\u003c/p\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.382*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.270*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.400*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurface Pro Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.318*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.185*\u003c/p\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229*\u003c/p\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.217*\u003c/p\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.278*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.234*\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurface Pro UL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.242*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.165*\u003c/p\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.166*\u003c/p\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.302*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.692*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurface Pro UR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.223*\u003c/p\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214*\u003c/p\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.156*\u003c/p\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.180*\u003c/p\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.171*\u003c/p\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.716*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.437*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurface Pro central\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.533*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.213*\u003c/p\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.209*\u003c/p\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurface Pro LL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.319*\u003c/p\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172*\u003c/p\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.206*\u003c/p\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.163*\u003c/p\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.297*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.301*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.729*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.483*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.441*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.233*\u003c/p\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurface Pro LR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.198*\u003c/p\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.165*\u003c/p\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.193*\u003c/p\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.658*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.336*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.429*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.181*\u003c/p\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.349*\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e(* SPARCS: Spaeth Richman Contrast Sensitivity Test; UL: Upper Left; UR: Upper Right; LL: Lower Left; LR: Lower Right)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSPARCS, is a cost-effective and easily accessible laptop based online application for assessing both central as well as peripheral CS, helping individuals to actively participate in monitoring their eye health at home using just their laptops and an internet connection. \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAnother such test is ClinicCSF wherein Contrast Sensitivity Function (CSF) is measured using iPad. A recent study compared ClinicCSF against the Functional Acuity Contrast Test (FACT) and demonstrated that there was no significant differences between the two tests suggesting applications on the iPads and smartphones can provide accurate measurements of CS, comparable to the established psychophysical tests like FACT.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn another study, a test conducted on a tablet-based platform proved to be a valid method for assessing distance and near visual acuity, as well as CS. The results of this study demonstrated that the tablet-based test yielded comparable outcomes to the gold-standard clinical tests, which included the ETDRS distance acuity, Pelli-Robson CS, and MNRead near acuity tests.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAs technology continues to evolve, it is imperative to validate the use of visual function evaluation applications on different devices to assess any potential impact of new technology. By prioritizing validated apps, healthcare professionals can ensure reliable and comparable results when utilizing these applications in clinical settings. The validation process should adapt to changes in technology to maintain the accuracy and effectiveness of visual function assessment.\u003c/p\u003e \u003cp\u003eOur study assessed the performance of SPARCS score on two different laptops, specifically the MacBook Air and Microsoft Surface Pro 7. Aim was to identify any significant variations in the test results when conducted on different laptops with different display characteristics. These laptops were chosen to compare the application on the two most widely used operating systems (OS), iOS and Windows. With desktop operating system worldwide market share as of May 2024 being 73.9% for Windows and 14.91% for MacOS.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe found good repeatability and reliability of the SPARCS scores when the test was performed on the same laptop. The BA plot showed that the mean difference between measurements taken on both devices was modest; however, the broader limits of agreement indicate that the differences between the two could be substantial for some measurements. This variability is supported by the correlation matrix, which showed that while there is a positive relationship between equivalent measurements on the two devices, the correlation was not perfect. This deviation highlights the potential variability in measurements and suggested that the devices do not consistently yield identical results.\u003c/p\u003e \u003cp\u003eIn the clinical context of CS testing using SPARCS, even minor variations in measurements can carry significant implications. This is because CS testing is highly sensitive and designed to detect subtle changes in a patient\u0026rsquo;s visual capabilities. As such, any external sources of variability, including those introduced by the choice of device or screen technology, can confound the results.\u003c/p\u003e \u003cp\u003eGiven these findings, it is advisable to stress the importance of maintaining device consistency during SPARCS testing in clinical practice. Switching between the Macbook and the Surface Pro (or any two different laptops), while seemingly minor, could introduce discrepancies that are not reflective of genuine changes in an individual's CS. Instead, such discrepancies may arise from the inherent variability between the two devices.\u003c/p\u003e \u003cp\u003eVarious display properties that could have led to the differences include pixel density, screen resolution, luminance, color calibration, refresh rate and laptop display.\u003c/p\u003e \u003cp\u003eIncreased level of detail with higher pixel density (pixel per inch; PPI) can enhance the CS because of better differentiation of fine contrast differences. LCD screens typically use subpixels (red, green, and blue) to create individual pixels. Thus subpixel arrangement can also affect CS.\u003c/p\u003e \u003cp\u003eMacBook Air's Retina display technology has a 13-inch display screen with 2560 x 1600 resolution and high PPI (approximately 227 PPI) with high pixel density and color accuracy resulting in better clarity and visual performance during CS testing. On the other hand, the Microsoft Surface Pro's PixelSense display technology on a 12.3-inch with 2736 x 1824 resolution had a higher PPI (approximately 267 PPI) and offered even higher resolution and color reproduction. Despite comparable high pixel density with good resolution both the laptop screens provided different outcomes. MacBook Air's Retina display technology had higher SPARCS scores than Microsoft Surface Pro.\u003c/p\u003e \u003cp\u003eDisplays with higher resolutions, such as QHD (2560x1440) or 4K UHD (3840x2160), typically provide sharper and more detailed images. This increased level of detail can enhance CS by making it easier to discern subtle differences in contrast.In a study 15 iPad mini Retina display devices were evaluated for visual function assessment and showed that the tablets required approximately 13 minutes to achieve stable luminance after being powered on, while the chromaticity remained constant throughout. Temperature fluctuations had a minimal impact of 1% on luminance, but had no effect on chromaticity. All 15 tablets exhibited gamma functions that closely approximated the standard gamma value of 2.20, and their color gamut sizes were similar, with only slight differences observed in the blue primary. Considering the comparable physical characteristics of these devices, they can be considered suitable for use as visual stimulus displays.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e In terms of CS, these findings suggest that luminance variations across the screen, whether due to viewing angle, battery level, or temperature, are likely to have some impact. However, the study does not provide specific information on how these variations would affect CS. Further studies would be needed to determine the exact influence of these factors on contrast sensitivity performance. Additionally, the study was done on tablets, so the direct implications for a given laptop may vary depending on its specific characteristics and display technology. Therefore, it is crucial to calibrate and evaluate the display performance of individual devices, especially if they are not of the same make and model.\u003c/p\u003e \u003cp\u003eColor calibration refers to the process of adjusting the display's color reproduction for accurate and consistent color representation. If one device is calibrated to display certain shades of grey as darker or lighter than intended, it can affect the perception of contrast between different elements in the test patterns. The MacBook Air (M1, 2020) is equipped with True Tone technology, enabling automatic adjustment of the display's color temperature according to the ambient lighting conditions. Additionally, it features a wide color gamut, allowing for the display of a broader spectrum of colors. On the other hand, the Microsoft Surface Pro 7 is designed with a high contrast ratio to enhance the differentiation between dark and light areas on the screen. It also incorporates an ambient light sensor that automatically adapts the display brightness based on the surrounding lighting conditions.\u003c/p\u003e \u003cp\u003eThese optimizations may impact the contrast representation and ultimately affect the SPARCS score obtained during testing. To ensure reliable and consistent CS testing, it is important to calibrate both devices appropriately with a color calibration device, such as a colorimeter or spectrophotometer, which is designed to measure and adjust color accuracy. This helps minimize any potential discrepancies in color representation and allows for more reliable assessment of CS across different devices.\u003c/p\u003e \u003cp\u003eA higher refresh rate (60 Hz or above) can minimize screen flickering, ensuring stable and smooth test stimuli. MacBook Air and Microsoft Surface Pro 7 have a standard refresh rate of 60 Hz. This means that the display refreshes the image 60 times per second, providing a smooth visual experience during CS testing. Researchers in a study found that higher refresh rates of video displays positively impacted reading speed and reduced disruptions in eye movements during reading. The study also highlighted the importance of oculomotor adaptation, indicating that participants could adjust their eye movements to match the characteristics of the video display, leading to improved reading outcomes.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe MacBook Air features a 16:10 aspect ratio (\u003cem\u003edivine proportion\u003c/em\u003e or gold standard; where width is roughly 1.5 times the height), providing a slightly taller display, while the Microsoft Surface Pro features a 3:2 aspect ratio, providing a taller display similar to the MacBook Air but with a slightly different ratio. While aspect ratio itself does not directly affect CS, it can impact the overall visual experience and potentially influence the perception of CS during SPARCS testing. Image processing algorithms or display enhancements, implemented by the manufacturers can also influence the visual performance and CS on the screen. In the clinical context of CS testing using SPARCS, even minor variations in measurements can carry significant implications.\u003c/p\u003e \u003cp\u003eSPARCS test on a single laptop with the same browser can be used reliably and consistently to know and compare the CS between individuals but to ensure comparable results using SPARCS on different laptops, it is important to consider the specific display properties and calibrating them to establish accurate measurements across different laptop models. This understanding will help us take a step ahead in developing standardized and reliable methods for CS testing using modern technologies, ensuring accurate and accessible screening for ophthalmic conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interests and financial disclosures.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. World report on vision. Geneva: World Health Organization; 2019. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications-detail/world-report-on-vision\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications-detail/world-report-on-vision\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on 17th April, 2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVLEG/GBD 2020 model [Internet]. Accessed via the IAPB Vision Atlas. (Accessed on 17th April, 2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAckland P. World blindness and visual impairment: despite many successes, the problem is growing. Community Eye Health. 2017;20(64):60\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJindra LF, Zemon V.Contrast sensitivity testing: a more complete assessment of vision. J Cataract Refract Surg. 1989;15(2):141\u0026ndash;145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIchhpujani P, Thakur S, Spaeth GL. Contrast Sensitivity and Glaucoma. J Glaucoma. 2020;29: 71\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBariya S, Ichhpujani P, Rehman O, Kumar S. Normative database for Spaeth Richman contrast sensitivity test for Indian eyes. Indian J Ophthalmol. 2022;70(10):2758\u0026ndash;2762.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMena-Guevara KJ, Pi\u0026ntilde;ero DP, de Fez D. Validation of Digital Applications for Evaluation of Visual Parameters: A Narrative Review. Vision (Basel). 2021;5(4):58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Vallejo M, Rem\u0026oacute;n L, Monsoriu JA, Furlan WD. Designing a new test for contrast sensitivity function measurement with iPad. J Optom. 2015;8(2):125\u0026ndash;131.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaradaraj V, Assi L, Gajwani P, Wahl M, David J, Swenor BK, Ehrlich JR. Evaluation of Tablet-Based Tests of Visual Acuity and Contrast Sensitivity in Older Adults. Ophthalmic Epidemiol. 2021;28(4):316\u0026ndash;324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatCounter Global Stats. Desktop Operating System Market Share Worldwide [Internet]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gs.statcounter.com/os-market-share/desktop/worldwide\u003c/span\u003e\u003cspan address=\"https://gs.statcounter.com/os-market-share/desktop/worldwide\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on 31st May 2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodduluri L, Boon M, Dain SJ. Evaluation of tablet computers for visual function assessment. Behav Res 2017; 49: 548\u0026ndash;558.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhodrati M, Morris AP, Price NS. The (un) suitability of modern liquid crystal displays (LCDs) for vision research. Front Psychol. 2015; 6: 303.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Computer display, Contrast sensitivity, Spaeth Richman Contrast Sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-4513914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4513914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo see the influence of various computer parameters on contrast sensitivity values obtained with the web based, Spaeth Richman Contrast Sensitivity (SPARCS) test.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis prospective, observational study enrolled 80 consecutive healthy individuals free of ocular disease. Each patient underwent SPARCS CS testing on both the laptops, Apple Macbook and the Surface Pro. SPARCS scores were identified as the primary outcome variables and other ocular parameters were considered secondary outcome variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eData of 160 eyes of 80 subjects who fulfilled the inclusion criteria was evaluated. The average age was 26.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75 years (range 18\u0026ndash;40 years) and there was a slight female preponderance (61%, n\u0026thinsp;=\u0026thinsp;49). None of the eyes had history of any ocular pathology. The Bland-Altman (BA) plot highlights a mean difference of 0.694 in measurements between the Macbook Air and Surface Pro indicating a small bias between the two methods. However, the wider limits of agreement (-12.889 to 14.276 ) suggest that the differences between the two can be substantial for some measurements. This variability is supported by the correlation matrix, which shows that while there is a positive relationship between equivalent measurements on the two devices, but they are not close to 1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven the importance of consistency in contrast sensitivity testing, especially for baseline and follow-up assessments, this data suggests that using different devices may introduce a level of variability that could confound results. Thus, it is essential to use the same device, especially in clinical scenarios, to get precise and reproducible results.\u003c/p\u003e","manuscriptTitle":"Influence of computer parameters on Spaeth Richman Contrast Sensitivity test values using different computers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 11:46:27","doi":"10.21203/rs.3.rs-4513914/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ea4e3b9-bbd0-4b24-be55-771be0bfed54","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35124416,"name":"Health sciences/Medical research/Outcomes research"},{"id":35124417,"name":"Health sciences/Health care/Diagnosis"}],"tags":[],"updatedAt":"2026-03-14T07:35:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 11:46:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4513914","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4513914","identity":"rs-4513914","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}