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Age-Related Macular Degeneration (AMD) is the leading cause of central vision loss among older adults worldwide. Patients with AMD often require frequent monitoring of visual function and disease progression, typically through multiple follow-up visits involving optical coherence tomography (OCT), visual acuity testing (VA), and clinical consultations.
While digital self-monitoring tools have emerged as promising solutions to reduce the burden of in-clinic visits, many elderly patients face challenges in engaging with these platforms due to visual impairment, limited digital literacy, and poorly optimised user interfaces. These barriers may reduce patient willingness to adopt such systems and undermine their long-term effectiveness.
To address this gap, the study team has developed the web-based AVIGA (Automated Vision Impairment Gaze-tracking Analysis) system, which is a portable, self-administered, home-based digital monitoring system designed to minimise cognitive load and maximise usability for elderly AMD patients. The platform integrates patient-centred design principles such as simplified navigation, optimised text, and multimodal feedback (visual and audio) to empower users to independently track their visual health.
A prospective, single-site usability trial to evaluate the AVIGA platform using validated human factors measures: the System Usability Scale (SUS), the Technology Acceptance Model (TAM) will be conducted. By examining the relationship between usability, cognitive load, and perceived empowerment, this study aims to identify critical user interface and user experience (UI/UX) design factors that influence willingness to adopt and sustain use of digital health tools among elderly AMD patients.
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| Measure | Description | Time Frame |
|---|---|---|
| Assess System Usability | Measure and evaluate overall system usability across three checkpoints (Baseline, Mid-point, and End-point) using the System Usability Scale (SUS), a 10-item questionnaire rated on a 5-point Likert scale. | Baseline, Mid-point (6 months from baseline ±3 months), End-point (12 months from baseline ±3 months) |
| Assess System Acceptance | Measure and evaluate overall acceptance towards system adoption across three checkpoints (Baseline, Mid-point, and End-point) using 8 items from the Technology Acceptance Model (TAM), rated on a 5-point Likert scale. | Baseline, Mid-point (6 months from baseline ±3 months), End-point (12 months from baseline ±3 months) |
| Measure | Description | Time Frame |
|---|---|---|
| Track Change Over Time | Compare the changes in expectation (Baseline) versus lived experience (Mid-point, End-point) scores taken from the combined SUS and TAM to identify how usability perceptions and adoption intentions evolve with continued use. | Baseline, Mid-point (6 months from baseline ±3 months), End-point (12 months from baseline ±3 months) |
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Inclusion Criteria:
Exclusion Criteria:
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Patients previously recruited with recurrent vision problems diagnosed with active Wet-AMD at the TTSH Specialist Outpatient Clinic.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Zheng Kuang Noel Soh, BSc | Contact | +65 6256 6011 | zheng.kuang.soh@nhghealth.com.sg |
| Name | Affiliation | Role |
|---|---|---|
| Augustinus Laude, MBChB | Tan Tock Seng Hospital | Principal Investigator |
| Zheng Kuang Noel Soh, BSc | Tan Tock Seng Hospital | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tan Tock Seng Hospital | Singapore | 308433 | Singapore |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28268575 | Background | Huiying Liu, Wong D, Ai Ping Yow, Yanwu Xu, Fengshou Yin, Laude A, Tock Han Lim. Determining the difference in eyegaze measurements in individuals with age related macular degeneration. Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1348-1351. doi: 10.1109/EMBC.2016.7590957. | |
| 29060564 | Background | Huiying Liu, Yanwu Xu, Damon Wong, Ai Ping Yow, Laude A, Tock Han Lim. Detecting impaired vision caused by AMD from gaze data. Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:3142-3145. doi: 10.1109/EMBC.2017.8037523. |
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| ID | Term |
|---|---|
| D008268 | Macular Degeneration |
| ID | Term |
|---|---|
| D012162 | Retinal Degeneration |
| D012164 | Retinal Diseases |
| D005128 | Eye Diseases |
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| Background | Liu, H., et al., Vision assessment based on gaze. 2019, AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH [SG/SG]; 1 Fusionopolis Way, #20-10 Connexis North Tower, Singapore 138632, SG: Singapore. |
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| Background | Goldberg, J.H. and A.M. Wichansky, Eye tracking in usability evaluation: A practitioner's guide, in the Mind's Eye. 2003, Elsevier. p. 493-516. |
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| 29060394 | Background | Ai Ping Yow, Damon Wong, Huiying Liu, Hongyuan Zhu, Ivy Jing-Wen Ong, Laude A, Tock Han Lim. Automatic visual impairment detection system for age-related eye diseases through gaze analysis. Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:2450-2453. doi: 10.1109/EMBC.2017.8037352. |
| 18577497 | Background | Trevino R, Kynn MG. Macular function surveillance revisited. Optometry. 2008 Jul;79(7):397-403. doi: 10.1016/j.optm.2007.09.017. |
| 18439679 | Background | Kawasaki R, Wang JJ, Aung T, Tan DT, Mitchell P, Sandar M, Saw SM, Wong TY; Singapore Malay Eye Study Group. Prevalence of age-related macular degeneration in a Malay population: the Singapore Malay Eye Study. Ophthalmology. 2008 Oct;115(10):1735-41. doi: 10.1016/j.ophtha.2008.02.012. Epub 2008 Apr 25. |
| Background | Brooke, John. |
| Background | Davis, Fred D. |