Validation of the Clouclip and utility in measuring viewing distance in adults

Digital device viewing behaviour in children

INTRODUCTION

Habitual viewing behaviour is widely believed to be an important contributing factor to the onset and progression of myopia and may be task dependent. The purpose of this study was to quantify the habitual viewing distance of children performing five different tasks on a smartphone digital device.

METHODS

The real-time viewing distance in 38 children with their habitual correction was measured using software (MyopiaApp) on a handheld (Google Pixel 3) device. Five tasks were performed in a randomised sequence: playing a game, watching video in a light (680 lux) and dark (5.5 lux) environment and reading small (8 pt) and large (16 pt) text. ANCOVA statistical analysis was used to evaluate the effect of task, group (myope vs. non-myope) and arm length on the median relative viewing distance.

RESULTS

Arm length was not correlated with viewing distance in any of the tasks, and there was no significant difference in viewing distance between any of the tasks. Specifically, a two-way mixed ANCOVA indicated that task, refractive group (myopic vs. non-myopic), age and arm length, as well as all two-way interactions were not significantly associated with viewing distance. Overall, 60% of the total variance in viewing distance was accounted for by individual differences.

CONCLUSIONS

The average handheld viewing distance was similar across a variety of everyday tasks in a representative sample of myopic and emmetropic children. Neither arm length, age nor refractive group were associated with viewing distance in any of the tasks. Importantly, myopic children of a given size did not hold the smartphone digital device at a different distance for any task than their equally sized non-myopic peers. However, both groups exhibited high inter-individual variability in mean viewing distance, indicating some subjects performed all tasks at further distances while other subjects used at nearer distances.

Insights into artificial intelligence in myopia management: from a data perspective

Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.

Development of the University of Houston near work, environment, activity, and refraction (UH NEAR) survey for myopia

CLINICAL RELEVANCE

There is a need to better elucidate demographic and behavioural factors that are contributing to the rising prevalence of myopia. Doing so will aid in developing evidence-based recommendations for behavioural modifications to prevent onset and slow progression of myopia in children.

BACKGROUND

The contributions of environmental and behavioural factors in myopia remain unclear. The goal of this work was to provide a standardised survey to better understand risk factors for myopia.

METHODS

Development of the survey was carried out in 4 phases. In phase 1, three methods (direct, lay terms, and indirect) of parental reporting for the presence of myopia in their child were investigated through a questionnaire (N = 109) to determine sensitivity and specificity. The best method determined from phase 1 was used in phase 2, where questions regarding demographics, ocular history, and visual behaviour were compiled and refined. In phase 3, the survey was administered to focus groups of parents (N = 9). In phase 4, a scoring system was developed.

RESULTS

The highest sensitivity for parental reporting for myopia of their child was the indirect method (0.84), and the lowest sensitivity was the direct method (0.41). The highest specificity was the direct method (0.86), once excluding the 'do not know' responses, and the lowest specificity was the indirect method (0.53). The direct method yielded a 53.2% 'do not know' response rate, 50.5% for the lay method, and 1.8% for the indirect method. Time to complete the survey was 10:09 ± 2:45 minutes.

CONCLUSION

This study provides a comprehensive and up-to-date myopia risk factor survey that can be utilised by researchers and clinicians. Parents found the survey to be easy to understand and relatively quick to answer, and the scoring system allows quantification of behaviours across different categories using provided equations.

Evaluating the optimised font size and viewing time of online learning in young children: a multicentre cross-sectional study

OBJECTIVES

Near viewing distance (VD) and longer viewing times are associated with myopia. This study aimed to identify the font size and viewing time that guarantee the appropriate VD and pixels per degree (PPD) for children's online learning.

DESIGN

This cross-sectional study comprised two experiments. In experiment A, participants read text in five font sizes on three backlit displays (a personal computer, a smartphone and a tablet), an E-ink display and paper for 5 min per font size. In experiment B, participants watched videos for 30 min on three backlit displays.

SETTING

The Peking University People's Hospital in Beijing (China) and the School of Ophthalmology and Optometry, Wenzhou Medical University (Zhejiang Province, China).

PARTICIPANTS

Thirty-five participants completed experiment A. Ten of them participated in experiment B.

PRIMARY AND SECONDARY OUTCOME MEASURES

VDs were measured by Clouclip. The corresponding PPD was calculated.

RESULTS

In experiment A, font size and display type significantly affected VD (F₍₄₈₄₀₎=149.44, p<0.001, ES (Effect size)=0.77; F₍₄₈₄₀₎, p<0.001, ES=0.37). VDs were >33 cm for all five font sizes on the PC, the tablet and paper and for 18-pt on the smartphone and 16-pt on E-ink. PPD for 16-pt on the PC, 14-pt on the tablet and all five font sizes on the phone were >60. In experiment B, VD increased over the four previous 5 min periods but decreased slightly on tablets and PCs in the fifth 5 min period. PPD was >60.

CONCLUSION

Children demonstrated different VDs and PPDs based on font size and display type. To ensure a 33 cm VD and 60 PPD, the minimum font size for online reading should be 18-pt on smartphones, 16-pt on PCs and E-ink, 10.5-pt on tablets and 9-pt on paper. More attention should be given to children's VD with continuous video viewing of more than 25 min.

TRIAL REGISTRATION NUMBER

ChiCTR2100049584.

Development of smart spectacles to monitor and modify myopia-related health behaviour in children

PURPOSE

To develop a wearable, spectacle-mounted, sensor-based device to monitor and modify myopia risk factors in children, namely near-working distance, light levels and spectral composition.

METHODS

A wearable, spectacle-mounted device has been developed, which consists of inbuilt sensors, namely: (i) light sensor to detect ambient light intensity; (ii) proximity sensor to measure near-work distance; (iii) microspectrograph to measure spectral power for six visible channels, namely red, green, blue, yellow, orange and violet and (iv) a global positioning system tracker to track the location and the device. The sensors were programmed by Arduino Nano, and the circuit was fixed onto a printed circuit board fitted onto a spectacle frame for pilot testing. Laboratory testing of the prototype was performed using a mannequin. An alert will be set when exceeding the predetermined threshold to help control myopia risk factors.

RESULTS

The indoor and outdoor light levels measured using the prototype were less than and greater than 1000 lux, respectively. The actual target distance and the corresponding distance measured by the prototype were highly correlated (R²  = 0.99). The mean distance measured by the prototype was within 1.5 cm of the actual target distance for distances between 30 and 95 cm. The spectral energy of the indoor location was highest for the orange channel at about 100-160 counts/μW/cm² , whereas it was highest for the blue channel in outdoor daylight (10,000-19,000 counts/μW/cm² ).

CONCLUSION

A working prototype that can simultaneously measure the viewing distance, light intensity and spectral composition has been developed.

Objective measures of viewing behaviour in children during near tasks

CLINICAL RELEVANCE

Objective assessment of near viewing behaviours performed in a laboratory setting showed that children demonstrate differing viewing distances and angles based on the type of task. Findings will contribute to our understanding of how near work influences myopia.

BACKGROUND

Evidence suggests that near working distance and viewing breaks are associated with myopia. The purpose of this study was to use an objective, continuously measuring range finding device to examine these viewing behaviours in children.

METHODS

Viewing distance, number of breaks, and head and eye angles were assessed in 16 non-myopic and 19 myopic children (ages 13.38 ± 4.14 years) using the Clouclip, an objective rangefinder, during five 15-minute near tasks, including (a) passive reading and (b) active writing on printed material, (c) passive viewing and (d) active engagement on an iPad, and (e) active engagement on a cell phone. Height and Harmon distance were measured. Viewing behaviours were analysed by task, refractive error group, and gender.

RESULTS

Mean viewing distances significantly differed by task (P < 0.001) and were highly correlated with children's Harmon distance and height for all near tasks (P < 0.05), except for the active printed task (P > 0.05). Viewing distances did not differ by gender or refractive error group. During each task, mean number of viewing breaks was 2.6 ± 4.1 and did not vary between task (P = 0.92) or refractive error group (P = 0.65). Head declination and total viewing angle varied by type of near task (P < 0.001 for both).

CONCLUSION

Children demonstrated differing viewing distances and viewing angles based on the type of near task they were performing. Viewing behaviours did not vary between myopic and non-myopic children. Findings will contribute to a better understanding of how near viewing behaviours can be quantified objectively and relationships with myopia.

A Latent Class Analysis of Student Eye Care Behavior: Evidence From a Sample of 6–17 Years Old in China

Purpose

To understand the latent classes and distribution of an adolescent eye care behavior, and to provide a basis for the formulation of appropriate adolescent vision health management interventions.

Methods

Information on eye behavior and eye health of primary and secondary school students in Wuhan was collected by multistage stratified cluster sampling. The latent class analysis (LCA) method was used to analyze the students' eye care behavior, and the latent class model (LCM) was built.

Results

A total of 6,130 students were enrolled in this study, of which 53.56% were males, aged from 6 to 17 years old, with an average age of 10.33 ± 2.60. The latent class results classified the adolescents' eye care behaviors into bad behaviors, moderate behaviors, and healthy behaviors. The model fitting results were as follows: Akaike Information Criterion (AIC) was 36,698.216, Bayesian Information Criterion (BIC) was 36,906.565, Adjusted Bayesian Information Criterion (aBIC) was 36,808.056, and entropy was 0.838.Compared with the healthy behaviors class, the bad behaviors class was more prevalent in high schools (p = 0.003), non-demonstration schools (p = 0.001), and most of this group had astigmatism (p = 0.002). The moderate behaviors class predominately consisted of females (p = 0.001), 15–17 years old (p = 0.005, 6~8 years old as the reference), from non-demonstration schools (p < 0.001), and most had myopia (p = 0.009).

Conclusion

There were differences in basic demographic characteristics, visual acuity development level, and family visual environment among different classes. In the management and intervention of an adolescent vision health, we should continue to promote the visual health management of adolescents based on visual monitoring and realize the early intervention and guidance of individuals in bad behaviors class.

A systematic review of near work and myopia: measurement, relationships, mechanisms and clinical corollaries

After decades of investigation, the role of near work in myopia remains unresolved, with some studies reporting no relationship and others finding the opposite. This systematic review is intended to summarize classic and recent literature investigating near work and the onset and progression of myopia, potential mechanisms and pertinent clinical recommendations. The impact of electronic device use is considered. PubMed and Medline were used to find peer-reviewed cross-sectional and longitudinal studies related to near work and myopia from 1980 to July 2020 using the PRISMA checklist. Studies were chosen using the Joanna Briggs Institute checklist, with a focus on studies with a sample size greater than 50. Studies were independently evaluated; conclusions were drawn per these evaluations. Numerous cross-sectional studies found increased odds ratio of myopia with increased near work. While early longitudinal studies failed to find this relationship, more recent longitudinal studies have found a relationship between myopia and near work. Rather than daily duration of near work, interest has increased regarding absolute working distance and duration of continuous near viewing. Several reports have found that shorter working distances (<30 cm) and continuous near-work activity (>30 min) are risk factors for myopia onset and progression. Novel objective continuously measuring rangefinding devices have been developed to better address these questions. The literature is conflicting, likely due to the subjective and variable nature in which near work has been quantified and a paucity of longitudinal studies. We conclude that more precise objective measures of near viewing behaviour are necessary to make definitive conclusions regarding the relationship between myopia and near work. Focus should shift to utilizing objective and continuously measuring instruments to quantify near-work behaviours in children, followed longitudinally, to understand the complex factors related to near work. A better understanding of the roles of absolute working distance, temporal properties, viewing breaks and electronic device use on myopia development and progression will aid in the development of evidence-based clinical recommendations for behavioural modifications to prevent and slow myopia.

A Study on the Design of Vision Protection Products Based on Children’s Visual Fatigue under Online Learning Scenarios

The rate of myopia in children is increasing rapidly under online learning scenarios. One of the important reasons for this is incorrect reading and writing posture. Three screen view parameters (viewing angle, viewing height, and viewing distance) are selected as significant influencing factors and blink rating is used as a sign of visual fatigue through literature analysis to study the influence factors of myopia in children, and their correlation. Children’s visual fatigue is evaluated by subjective evaluation and is recording using an eye tracker for changes in the three factors through online learning scenario simulation experiment. An optimal regression model is constructed that illustrates the relationship between the three variables and the visual fatigue levels. The aim of this study is to confirm the quantitative relationship between the screen view parameters and visual fatigue, and to design a child vision protection product on this basis. The test results show there is a linear positive correlation between the viewing angle, viewing height, and viewing distance. A vision protection device has been designed based on this model and was verified through function prototype testing. The result of this study quantified the relationship among screen view parameters and children’s visual fatigue, which provides a theoretical basis for the design of a children’s visual protection device.

Objective Measures of Near Viewing and Light Exposure in Schoolchildren during COVID-19

SIGNIFICANCE

Wearable sensors provide the opportunity for continuous objective measurement of the visual environment with high resolution. Our findings show that absolute and temporal properties of near viewing and time outdoors vary between myopic and nonmyopic schoolchildren, which are important considerations when studying refractive error pathogenesis.

PURPOSE

Numerous behavioral factors, including near work, time outdoors, electronic device use, and sleep, have been linked to myopia. The purpose of this study was to assess behaviors using subjective and objective methods in myopic and nonmyopic schoolchildren in the United States.

METHODS

Forty children (aged 14.6 ± 0.4 years) simultaneously wore two sensors for 1 week, a Clouclip for objective measurement of near viewing and light exposure and an Actiwatch for objective measurement of activity and sleep. Parents completed an activity questionnaire for their child. Near-viewing distance, daily duration, short-duration (>1 minute) and long-duration (>30 minutes) near-viewing episodes, light exposure, time outdoors, electronic device use, and sleep duration were analyzed by refractive error group and day of the week.

RESULTS

Objectively measured daily near-viewing duration was 6.9 ± 0.3 hours. Myopes spent more time in near + intermediate viewing than nonmyopes (P = .008) and had higher diopter hours (P = .03). Short- and long-duration near-viewing episodes were similar between groups (P < .05 for both). Daily light exposure and time outdoors were significantly lower for myopes (P < .05 for both). Electronic device use (12.0 ± 0.7 hours per day) and sleep duration (8.2 ± 0.2 hours per night) were similar between groups (P > .05 for both).

CONCLUSIONS

Objective and subjective measures confirm that myopic and nonmyopic schoolchildren exhibit different behaviors. Combining wearable sensors with questionnaires provides a comprehensive description of children's visual environment to better understand factors that contribute to myopia.

Wearable Sensors for Measurement of Viewing Behavior, Light Exposure, and Sleep

The purpose of this study was to compare two wearable sensors to each other and to a questionnaire in an adult population. For one week, participants aged 29.2 ± 5.5 years (n = 25) simultaneously wore a Clouclip, a spectacle-mounted device that records viewing distance and illuminance, and an Actiwatch, a wrist-worn device that measures illuminance and activity. Participants maintained a daily log of activities and completed an activity questionnaire. Objective measures of time outdoors, near (10–< 60 cm) and intermediate (60–100 cm) viewing, and sleep duration were assessed with respect to the daily log and questionnaire. Findings showed that time outdoors per day from the questionnaire (3.2 ± 0.3 h) was significantly greater than the Clouclip (0.9 ± 0.8 h) and Actiwatch (0.7 ± 0.1 h, p < 0.001 for both). Illuminance from the Actiwatch was systematically lower than the Clouclip. Daily near viewing duration was similar between the questionnaire (5.7 ± 0.6 h) and Clouclip (6.1 ± 0.4 h, p = 0.76), while duration of intermediate viewing was significantly different between methods (p < 0.001). In conclusion, self-reported time outdoors and viewing behaviors were different than objective measures. The Actiwatch and Clouclip are valuable tools for studying temporal patterns of behavioral factors such as near work, light exposure, and sleep.

Reliability and validity of the Actiwatch and Clouclip for measuring illumination in real-world conditions

PURPOSE

To compare real-world measures of illumination obtained with the Actiwatch-2 and Clouclip-M2 with 'gold standard' photometry measures and to evaluate the ability of Actiwatch-2 to correctly identify photometer-defined conditions: scotopic (≤0.01 lux), mesopic (0.02-3 lux), indoor photopic (>3-1,000 lux) and outdoor photopic (>1,000 lux); and Clouclip to correctly identify photometer-defined conditions within its operating range (>1 lux). Inter-device reliability of Clouclip for illumination and viewing distance measures was also investigated.

METHODS

A Hagner-S2 photometer was used as reference. Measures of illumination were obtained from a range of real-world conditions. To investigate inter-device reliability, five Clouclips were simultaneously exposed to varied light conditions and object distances.

RESULTS

Strong correlations existed between illumination measured with the photometer and both Actiwatch-2 (ρ = 0.99, p < 0.0001) and Clouclip (ρ = 0.99, p < 0.0001). However, both devices underestimated illumination compared to the photometer; disparity increased with increasing illumination and was greater for Actiwatch-2 than Clouclip measures. Actiwatch-2 successfully categorised illumination level (scotopic, mesopic, indoor and outdoor photopic) in 71.2% of cases. Clouclip successfully categorised illumination levels as scotopic/mesopic (≤3 lux) and indoor and outdoor photopic in 100% of cases. Mean differences and limits of agreement (LOA) were 430.92 ± 1,828.74 and 79.35 ± 407.33 lux, between the photometer and Actiwatch-2 and photometer and Clouclip, respectively. The Intra-class Correlation Coefficients for illumination and viewing distance measured with five Clouclips were 0.85 and 0.96, respectively.

CONCLUSION

These data illustrate that different Clouclip devices produce comparable measures of viewing distance and illumination in real-world settings. Both Actiwatch-2 and Clouclip underestimate illumination in the field compared to gold standard photometer measures. The disparity increases at higher levels of illumination and the discrepancy was greater for Actiwatch-2 measures. For researchers interested in categorising light exposure, Clouclip classifies illumination levels >2 lux more accurately than Actiwatch-2 but cannot discriminate between scotopic and low mesopic light.

IMI Risk Factors for Myopia

Risk factor analysis provides an important basis for developing interventions for any condition. In the case of myopia, evidence for a large number of risk factors has been presented, but they have not been systematically tested for confounding. To be useful for designing preventive interventions, risk factor analysis ideally needs to be carried through to demonstration of a causal connection, with a defined mechanism. Statistical analysis is often complicated by covariation of variables, and demonstration of a causal relationship between a factor and myopia using Mendelian randomization or in a randomized clinical trial should be aimed for. When strict analysis of this kind is applied, associations between various measures of educational pressure and myopia are consistently observed. However, associations between more nearwork and more myopia are generally weak and inconsistent, but have been supported by meta-analysis. Associations between time outdoors and less myopia are stronger and more consistently observed, including by meta-analysis. Measurement of nearwork and time outdoors has traditionally been performed with questionnaires, but is increasingly being pursued with wearable objective devices. A causal link between increased years of education and more myopia has been confirmed by Mendelian randomization, whereas the protective effect of increased time outdoors from the development of myopia has been confirmed in randomized clinical trials. Other proposed risk factors need to be tested to see if they modulate these variables. The evidence linking increased screen time to myopia is weak and inconsistent, although limitations on screen time are increasingly under consideration as interventions to control the epidemic of myopia.