Effect of blue-blocking lenses on colour contrast sensitivity

Knowledge, perception and practice towards blue-blocking lenses among optometrists

CLINICAL RELEVANCE

There is a lack of clinical guidelines in India for the prescription of blue-blocking lenses. Therefore, the practice trends will depend on practitioners' knowledge, attitude, and perception.

BACKGROUND

Exposure to blue light with increased use of light-emitting diode (LED) lights and digital devices along with the commercial availability of blue blocking lenses has warranted the need to understand the factors that influence the prescription of blue blocking lenses among eye care practitioners. Hence, we aim to assess knowledge, perception, and practice pattern of blue blocking lenses among Indian optometrists.

METHODS

This cross-sectional online survey was conducted among Indian Optometrists. The survey was distributed through various social groups of optometrists and state associations. The questionnaire had four main domains with 29 items in total. The four major domains were knowledge, practice, perception and demographic details on education. Descriptive analysis and logistic regression were performed to study the impact of these domains on the prescription of blue block lenses.

RESULTS

Out of 341 responses, 247 were included for analysis as per study criteria. About 50% (n = 123) of the participants had appropriate knowledge about blue light. Blue-blocking lenses were prescribed always or most of the time by 52% (n = 130) of the participants. The odds of prescribing blue blocking lenses were higher among practitioners who considered blue light as an important factor in causing computer vision syndrome (OR 3.77, 95% CI: 1.33-10.69, P = 0.01) or if they considered there is adequate published evidence (OR 3.95, 95% CI: 1.58-9.87, P = 0.003).

CONCLUSIONS

The source of evidence for prescribing blue-blocking lenses for our participants was mainly from advertisements rather than from scientific studies. Factors such as awareness, knowledge, education, and nature of practice did not play a significant role in prescribing blue-blocking lenses. This raises the need for evidence-based practice and the development of practice guidelines for prescribing blue-blocking lenses.

Screen Time at 6 Years Old and Visual Function in Early Adolescence

Excessive screen time has been linked to adverse health outcomes in children, including vision-related problems such as myopia. However, very few studies have evaluated the effect of moderate screen exposure on the development of visual functions. This study aimed to examine the association between screen time during middle childhood and color discrimination, contrast sensitivity, and short-range visual acuity in 12-year-old children (n = 305) from the mother-child PELAGIE cohort (France) for the whole sample and for boys and girls separately. Visual functions were assessed using the Freiburg Acuity and Contrast Test and an adapted version of the Cambridge Color Test. Screen exposure was documented using a parent self-report questionnaire. Regression models showed that screen exposure at 6 years of age was significantly associated with higher contrast sensitivity across the entire sample at 12 years of age. However, when controlling for covariates, this association remained statistically significant in girls only. Sex-stratified analyses also showed that moderate screen exposure was linked to improved tritan-axis color vision in boys only. These findings suggest that moderate screen exposure in middle childhood is not harmful to visual function development and as such, provide new insights into the impact of digital technology on children's visual health and development.

Blue-light filtering spectacle lenses for visual performance, sleep, and macular health in adults

BACKGROUND

'Blue-light filtering', or 'blue-light blocking', spectacle lenses filter ultraviolet radiation and varying portions of short-wavelength visible light from reaching the eye. Various blue-light filtering lenses are commercially available. Some claims exist that they can improve visual performance with digital device use, provide retinal protection, and promote sleep quality. We investigated clinical trial evidence for these suggested effects, and considered any potential adverse effects.

OBJECTIVES

To assess the effects of blue-light filtering lenses compared with non-blue-light filtering lenses, for improving visual performance, providing macular protection, and improving sleep quality in adults.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL; containing the Cochrane Eyes and Vision Trials Register; 2022, Issue 3); Ovid MEDLINE; Ovid Embase; LILACS; the ISRCTN registry; ClinicalTrials.gov and WHO ICTRP, with no date or language restrictions. We last searched the electronic databases on 22 March 2022.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), involving adult participants, where blue-light filtering spectacle lenses were compared with non-blue-light filtering spectacle lenses.

DATA COLLECTION AND ANALYSIS

Primary outcomes were the change in visual fatigue score and critical flicker-fusion frequency (CFF), as continuous outcomes, between baseline and one month of follow-up. Secondary outcomes included best-corrected visual acuity (BCVA), contrast sensitivity, discomfort glare, proportion of eyes with a pathological macular finding, colour discrimination, proportion of participants with reduced daytime alertness, serum melatonin levels, subjective sleep quality, and patient satisfaction with their visual performance. We evaluated findings related to ocular and systemic adverse effects. We followed standard Cochrane methods for data extraction and assessed risk of bias using the Cochrane Risk of Bias 1 (RoB 1) tool. We used GRADE to assess the certainty of the evidence for each outcome.

MAIN RESULTS

We included 17 RCTs, with sample sizes ranging from five to 156 participants, and intervention follow-up periods from less than one day to five weeks. About half of included trials used a parallel-arm design; the rest adopted a cross-over design. A variety of participant characteristics was represented across the studies, ranging from healthy adults to individuals with mental health and sleep disorders. None of the studies had a low risk of bias in all seven Cochrane RoB 1 domains. We judged 65% of studies to have a high risk of bias due to outcome assessors not being masked (detection bias) and 59% to be at high risk of bias of performance bias as participants and personnel were not masked. Thirty-five per cent of studies were pre-registered on a trial registry. We did not perform meta-analyses for any of the outcome measures, due to lack of available quantitative data, heterogenous study populations, and differences in intervention follow-up periods. There may be no difference in subjective visual fatigue scores with blue-light filtering lenses compared to non-blue-light filtering lenses, at less than one week of follow-up (low-certainty evidence). One RCT reported no difference between intervention arms (mean difference (MD) 9.76 units (indicating worse symptoms), 95% confidence interval (CI) -33.95 to 53.47; 120 participants). Further, two studies (46 participants, combined) that measured visual fatigue scores reported no significant difference between intervention arms. There may be little to no difference in CFF with blue-light filtering lenses compared to non-blue-light filtering lenses, measured at less than one day of follow-up (low-certainty evidence). One study reported no significant difference between intervention arms (MD - 1.13 Hz lower (indicating poorer performance), 95% CI - 3.00 to 0.74; 120 participants). Another study reported a less negative change in CFF (indicating less visual fatigue) with high- compared to low-blue-light filtering and no blue-light filtering lenses. Compared to non-blue-light filtering lenses, there is probably little or no effect with blue-light filtering lenses on visual performance (BCVA) (MD 0.00 logMAR units, 95% CI -0.02 to 0.02; 1 study, 156 participants; moderate-certainty evidence), and unknown effects on daytime alertness (2 RCTs, 42 participants; very low-certainty evidence); uncertainty in these effects was due to lack of available data and the small number of studies reporting these outcomes. We do not know if blue-light filtering spectacle lenses are equivalent or superior to non-blue-light filtering spectacle lenses with respect to sleep quality (very low-certainty evidence). Inconsistent findings were evident across six RCTs (148 participants); three studies reported a significant improvement in sleep scores with blue-light filtering lenses compared to non-blue-light filtering lenses, and the other three studies reported no significant difference between intervention arms. We noted differences in the populations across studies and a lack of quantitative data. Device-related adverse effects were not consistently reported (9 RCTs, 333 participants; low-certainty evidence). Nine studies reported on adverse events related to study interventions; three studies described the occurrence of such events. Reported adverse events related to blue-light filtering lenses were infrequent, but included increased depressive symptoms, headache, discomfort wearing the glasses, and lower mood. Adverse events associated with non-blue-light filtering lenses were occasional hyperthymia, and discomfort wearing the spectacles. We were unable to determine whether blue-light filtering lenses affect contrast sensitivity, colour discrimination, discomfort glare, macular health, serum melatonin levels or overall patient visual satisfaction, compared to non-blue-light filtering lenses, as none of the studies evaluated these outcomes.

AUTHORS' CONCLUSIONS

This systematic review found that blue-light filtering spectacle lenses may not attenuate symptoms of eye strain with computer use, over a short-term follow-up period, compared to non-blue-light filtering lenses. Further, this review found no clinically meaningful difference in changes to CFF with blue-light filtering lenses compared to non-blue-light filtering lenses. Based on the current best available evidence, there is probably little or no effect of blue-light filtering lenses on BCVA compared with non-blue-light filtering lenses. Potential effects on sleep quality were also indeterminate, with included trials reporting mixed outcomes among heterogeneous study populations. There was no evidence from RCT publications relating to the outcomes of contrast sensitivity, colour discrimination, discomfort glare, macular health, serum melatonin levels, or overall patient visual satisfaction. Future high-quality randomised trials are required to define more clearly the effects of blue-light filtering lenses on visual performance, macular health and sleep, in adult populations.

A review of the current state of research on artificial blue light safety as it applies to digital devices

Light is necessary for human health and well-being. As we spend more time indoors, we are being increasingly exposed to artificial light. The development of artificial lighting has allowed us to control the brightness, colour, and timing of our light exposure. Yet, the widespread use of artificial light has raised concerns about the impact of altering our light environment on our health. The widespread adoption of personal digital devices over the past decade has exposed us to yet another source of artificial light. We spend a significant amount of time using digital devices with light-emitting screens, including smartphones and tablets, at close range. The light emitted from these devices, while appearing white, has an emission spectrum with a peak in the blue range. Blue light is often characterised as hazardous as its photon energy is higher than that of other wavelengths of visible light. Under certain conditions, visible blue light can cause harm to the retina and other ocular structures. Blue light can also influence the circadian rhythm and processes mediated by melanopsin-expressing intrinsically photosensitive retinal ganglion cells. While the blue component of sunlight is necessary for various physiological processes, whether the low-illuminance artificial blue light emitted from digital devices presents a risk to our health remains an ongoing area of debate. As technological advancements continue, it is relevant to understand how new devices may influence our well-being. This review examines the existing research on artificial blue light safety and the eye, visual performance, and circadian functions.

The Effects of a Blue-Light Filtering Versus Clear Intraocular Implant on Color Appearance

Purpose

More than a dozen studies have investigated whether blue-light filtering (BLF) intraocular lens (IOL) implants influence color vision, generally finding they do not. These studies have not tested color vision per se; rather, they have measured color vision deficiencies or chromatic discrimination. Here, we used additive trichromatic colorimetry to assess color appearance in participants with BLF and clear IOL.

Methods

Seventy-six participants were recruited from two populations: older participants (n = 52) with BLF and clear IOL (n = 98 eyes; M = 67.33 ± 7.48 years; 58.8% female; 25.5% non-White), and young adult control participants (n = 24; M = 21.0 ± 5.13 years; 70.8% female; 41.5% non-White). Participants used a custom-built tricolorimeter to mix three primaries until a perceived perfect neutral white was achieved. Color appearance, expressed as chromaticity coordinates, was measured with a spectral radiometer (ILS950).

Results

Between subjects, the BLF IOL chromaticity coordinates (x = 0.34, y = 0.35, u′ = 0.21, v′ = 0.48) were not significantly different from the clear IOL (x = 0.34, y = 0.33, u′ = 0.22, v′ = 0.48). BLF and clear IOL were also not different within-contralateral subjects (n = 21; BLF x = 0.34, y = 0.33, u′ = 0.22, v′ = 0.47; clear x = 0.34, y = 0.33, u′ = 0.21, v′ = 0.48). Both IOL groups differed from young adults (v′[0.45; P = 0.001], x[0.31; P = 0.008], and y[ 0.30, P < 0.000], but not u′[0.21]).

Conclusions

One advantage of geometric representation of color space is the ability to specify the appearance (rather than spectral composition) of any light mixture by specific coordinates. Using this system, only minor differences in color appearance were found between a BLF, clear IOL, and young natural lens.

Translational Relevance

When color perception is directly measured, the BLF and clear IOL are not meaningfully different.