Here, it may also be difficult to isolate the effects of visual impairment from the impact of coexisting impairments (e.g., cognitive decline Wood et al., 2010). In addition, interactive experiments can burden the participants, since visually impaired populations are more likely to have multiple physical and mental comorbidities ( Court et al., 2014). Testing visually impaired patients for research purposes can sometimes be challenging because of safety, practical, or availability reasons. However, directly measuring task performance (e.g., reading, writing, collecting groceries) may be more useful than self-reports because it offers clinicians and researchers an objective assessment of the impact of the visual disability ( Culham et al., 2004 Varadaraj et al., 2018 Wittich et al., 2018). Previous research has identified difficulties in ADL based upon self-reports from visually impaired patients ( Scilley et al., 2002 Walker et al., 2006 Desrosiers et al., 2009). Characterizing these practical difficulties is an important step in adopting intervention strategies and facilitating positive change for visually impaired individuals. Recently, there has been increasing interest into the extent to which AMD affects ADL and quality of life ( Jelin et al., 2019 Broadhead et al., 2020 Zult et al., 2020). As vision declines, those with AMD report increasing difficulties engaging in activities of daily living (ADL), such as reading, cleaning, and cooking ( Bennion et al., 2012 Taylor et al., 2016). Vision loss due to non-neovascular AMD can be managed with the support of rehabilitation ( Hooper et al., 2008), visual aids ( Morrice et al., 2017), or environmental adaptions ( Brunnström et al., 2004), but in severe cases of exudative AMD there may be irreversible central vision loss ( Jonas et al., 2017). The vision loss experienced by AMD patients can manifest as a blur, distortion, different colors, or darkness ( Taylor et al., 2018a). Researchers could also come to a consensus regarding the length and form of adaptation by exploring what is an adequate amount of time and type of training required to acclimatize participants to vision impairment simulations.Īge-related macular degeneration (AMD) is a leading cause of visual impairments, that affects ~200 million people globally ( Wong et al., 2014 Jonas et al., 2017), and continues to rise due to the aging population ( Velez-Montoya et al., 2014). While simulations may never completely replicate vision loss experienced during AMD, consistency in simulation methodology is critical for generating realistic behavioral responses under vision impairment simulation and limiting the influence of confounding factors. The use of validation and adaptation procedures were reported in approximately two-thirds and half of studies, respectively.Ĭonclusions: Synthesis of the methodology demonstrated that the choice of simulation has been, and should continue to be, guided by the nature of the study. Contact lenses, computer manipulations, gaze contingent displays, and simulation glasses were the main forms of AMD simulation identified. Results: Nineteen studies met the criteria for inclusion. The review focuses on the suitability of each method for investigating activities of daily living, an assessment of clinical validation procedures, and an evaluation of the adaptation periods for participants. Methods: We conducted a systematic literature search in five databases and a critical analysis of the advantages and disadvantages of various AMD simulation methods (following PRISMA guidelines). The aim of this review is to synthesize and assess the types of simulation methods that have been used to simulate age-related macular degeneration (AMD) in normally sighted participants, during activities of daily living (e.g., reading, cleaning, and cooking). One way to do this is through visual impairment simulations. Purpose: Investigating difficulties during activities of daily living is a fundamental first step for the development of vision-related intervention and rehabilitation strategies. 4Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.
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