Applying theories and interventions from behavioral medicine to understand and reduce visual field variability in...

Visual field (VF) test results are often unreliable in visually impaired patients, but continue to be a cornerstone of clinical trials and play a vital role in clinical decision making since they are the primary method to determine patients' functional vision loss or progression. Currently, patients are typically asked to perform VF tasks with minimal instruction or consideration of their psychological experience during the test. The gradual loss of vision due to retinal diseases, such as retinitis pigmentosa (RP), age-related macular degeneration (AMD), or glaucoma can contribute to the experience of negative psychosocial states, such as anxiety, stress, and depression, as well as diminished quality of life. We hypothesize that VF testing elicits test performance anxiety and perception of functional losses of vision, which induces distracting negative thoughts that result in increased VF test variability. Resources for processing and responding to vision-related information may be diverted from task-relevant VF stimuli to task-irrelevant ones, such as internal worry and test anxiety, thereby resulting in VF test performance decrements. We present a theoretical model to support the hypothesis that VF variability is linked to patients' negative thoughts during VF testing. This conceptual framework provides a basis for the development of coping strategies and mindfulness-based interventions to be evaluated in future research aimed at improving psychosocial states and VF reliability in visually-impaired patients. It would be highly significant to intervene by modifying negative thoughts during VF testing to reduce test variability in glaucoma patients who are progressively losing vision to a blinding eye disease, but whose vision loss has not been accurately identified and treated early enough due to variable VF results. In clinical trials of potential interventions for RP and non-neovascular AMD, reducing VF variability would effectively increase the precision for detecting treatment effects and allow a reduction in the number of VF tests needed to estimate the treatment responses, thus reducing burden on investigators and patients, as well as saving time and money.