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16 March 2020 Blue light filtering glasses and computer vision syndrome: a pilot study
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Abstract
Computer Vision Syndrome (CVS) is an umbrella term for a pattern of symptoms associated with prolonged digital screen exposure such as eyestrain, headaches, blurred vision, dry eyes, and neck/shoulder pain. Commercially available blue light filtering lenses (BLFL) are advertised as improving CVS. This pilot study evaluated the effectiveness of BLFL on reducing CVS symptoms and fatigue in a cohort of radiology trainees. In this Institutional Review Board approved prospective crossover study, 10 radiology residents were randomized into two cohorts: one wearing BLFL first then a sham pair (non-BLFL), and the other wearing a sham pair first then the BLFL over the course of a typical clinical work day for 5 days. Every evening, participants filled out a questionnaire based on a previously validated CVS questionnaire (CVS-Q:16 questions, Likert scale 1-5) and the Swedish Occupational Fatigue Index (SOFI: 16 questions, Likert scale 0- 10). 10 radiology residents (8 PGY-2, 1 PGY-3, and 1 PGY-4): 4 males, 6 females, participated. Although none of the 32 symptoms demonstrated statistically significant differences, 11/16 (68.8%) symptoms measured on the CVS-Q and 13/16 (81.3%) symptoms measured on the SOFI were reduced with the BLFL compared to the sham glasses. Two symptoms, “drowsy” and “lack of concern,” decreased in the BLFL cohort nearing statistical significance, p = 0.057 and p = 0.075, respectively. Use of BLFL may ameliorate CVS symptoms. Future studies with larger sample sizes and participants of different ages are required to verify the potential of BLFL.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexander Dabrowiecki, Alexander Villalobos, and Elizabeth A. Krupinski "Blue light filtering glasses and computer vision syndrome: a pilot study", Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 1131609 (16 March 2020); https://doi.org/10.1117/12.2547776
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