Antimicrobial resistance (AMR) is a global public health threat with significant impact on treatment outcomes. The World Health Organization's Global Action Plan on AMR recommended strengthening the evidence base through surveillance programs and research. Comprehensive, timely data on AMR for organisms isolated from ocular infections are needed to guide treatment decisions and inform researchers and microbiologists of emerging trends. This article aims to provide an update on the development of AMR in ocular organisms, AMR in bacterial ocular infections and on AMR stewardship programs globally. The most common ocular pathogens are Pseudomonas aeruginosa, Staphylococcus spp., Streptococcus pneumoniae, and Haemophilus influenzae in ocular infections. A variety of studies and a few surveillance programs worldwide have reported on AMR in these infections over time. Fluoroquinolone resistance has increased particularly in Asia and North America. For conjunctivitis, the ARMOR cumulative study in the USA reported a slight decrease in resistance to ciprofloxacin. For keratitis, resistance to methicillin has remained stable for S. aureus and CoNS, while resistance to ciprofloxacin has decreased for MRSA globally. Methicillin‐resistance and multidrug resistance are also emerging, requiring ongoing monitoring. Antimicrobial stewardship (AMS) programmes have a critical role in reducing the threat of AMR and improving treatment outcomes. To be successful AMS must be informed by up‐to‐date AMR surveillance data. As a profession it is timely for ophthalmology to act to prevent AMR leading to greater visual loss through supporting surveillance programmes and establishing AMS.

Purpose: Smartphone use by children is rising rapidly, but its ocular surface impact is unknown. This study examined the effect of smartphone use on blinking, symptoms, and tear function in children. Methods: Prospective intervention study where 36 children aged 6–15years (14 M:22 F) played games on a smartphone continuously for one hour. Symptoms (SANDE, IOSS, NRS) and tear film (lipid layer thickness, tear secretion, stability) were assessed before and after gaming. Blink rate and interblink interval were measured in situ using an eye tracking headset, before (during conversation) and continuously throughout gaming. Symptoms and tear film changes were examined using paired t-tests. Changes in blinking throughout one hour were examined using repeated measures ANOVA, post-hoc comparisons with Bonferroni correction. Associations examined using Pearson bivariate correlation. Significance level was 0.05. Results: Symptoms worsened following one hour smartphone gaming (SANDE + 8.2units, p = 0.01; IOSS + 1.3units, p < 0.001; NRS-average +6.3units, p = 0.03; NRS-comfort +7.6units, p = 0.04; NRS-tiredness +10.1units, p = 0.01), but tear film remained unchanged. Blink rate reduced from 20.8 blinks/min to 8.9 blinks/min (p < 0.001) and interblink interval increased from 2.9 s to 8.7 s (p = 0.002) within the first minute of gaming relative to baseline conversation, and this effect remained unchanged throughout one hour of gaming. Conclusions: Smartphone use in children results in dry eye symptoms and immediate and sustained slowing of blinking, with no change in tear function evident up to one hour. Given the ubiquitous use of smartphones by children, future work should examine whether effects reported herein persist or get worse over a longer term causing cumulative damage to the ocular surface.

Purpose: To compare blinking measured in situ during various tasks and examine relationships with ocular surface symptoms. The day-to-day repeatability of the blink rate and interblink interval was assessed. Methods: Twenty-four students (28.6 ± 6.3 years; 8 male and 16 female) completed six reading tasks (printed text, laptop, TV, smartphone, smartphone at 50% brightness, smartphone with complex text), and two nonreading tasks (conversation, walking) in a randomized cross-over study. Ocular surface symptoms and clinical signs were assessed. The blink rate and interblink interval were measured using a wearable eye tracking headset. Blink parameters were compared across tasks and time (linear mixed model and post hoc comparisons with Bonferroni correction). Associations between blinking, symptoms, ocular surface, and clinical signs were assessed (Spearman's correlation). The smartphone reading task was completed twice to determine the coefficient of repeatability. Results: The blink rate was lower (mean 10.7 ± 9.7 blinks/min) and the interblink interval longer (mean 9.6 ± 8.7 seconds) during all reading tasks compared with conversation (mean 32.4 ± 12.4 blinks/min; 1.5 ± 0.6 seconds) and walking (mean 31.3 ± 15.5 blinks/min; 1.9 ± 1.3s) (P < 0.001). There were no significant differences in blink parameters between any of the reading tasks or between conversation and walking. Changes in blinking occurred within 1 minute of starting the task. No associations were evident between blink rate or interblink interval and ocular surface symptoms or signs. The coefficient of repeatability was ±12.4 blinks/min for blink rate and ±18.8 seconds for interblink interval. Conclusions: Spontaneous blinking can be measured reliably in situ. The blink rate was decreased and the interblink interval increased during reading compared with conversation and walking. Changes in blinking were immediate, sustained, and not associated with ocular surface symptoms or signs.

Purpose: Baseline ocular surface characteristics in children require investigation. This study characterised blinking and relationships with ocular symptoms, tear film and digital device use.Methods: 45 children aged 6–15 years (56% female) participated in a cross-sectional study. Ocular surface symptoms (Instant Ocular Symptoms Survey, Dry Eye Questionnaire 5, Symptoms Assessment in Dry Eye, Ocular Surface Disease Index, Ocular Comfort Index and Numerical Rating Scale) and clinical indices (lipid layer thickness, tear secretion and stability, meibomian gland) were assessed. Blink rate and interblink interval were measuredin situusing a wearable eye-tracking headset (Pupil Labs GmbH, Germany). Associations between blinking, ocular surface, age, and digital device use (bivariate and partial correlations) and between automated and manually counted blink rate (Bland & Altman) were examined.Results: Mean blink rate and interblink interval were 20.5±10.5 blinks/min and 2.9±1.9 s during conversation. There was no difference between automated and manual blink rate (p=0.78) and no relationship between blinking and digital device use, age or sex. Mean group symptoms were within normal range and not associated with clinical measurements including blinking. Greater tear volume was associated with a faster blink rate (r=0.46, p=0.001) and shorter interblink interval (r=−0.36, p=0.02). Older age was associated with improved tear volume (r=0.37, p=0.01) and stability (r=0.38, p=0.01).Conclusions: Blinking characterisedin situwas not impacted by age or habitual digital device use. A faster blink rate was associated with greater tear volume but not symptoms. Improved tear function was found with age suggesting that the ocular surface continues to develop through childhood.