July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Dark adaptation measurement using a smartphone
Author Affiliations & Notes
  • Shrinivas Pundlik
    Schepens Eye Research Institute, Mass Eye and Ear, Boston, Massachusetts, United States
    Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Gang Luo
    Schepens Eye Research Institute, Mass Eye and Ear, Boston, Massachusetts, United States
    Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Shrinivas Pundlik, None; Gang Luo, None
  • Footnotes
    Support  none
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 3425. doi:
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      Shrinivas Pundlik, Gang Luo; Dark adaptation measurement using a smartphone. Invest. Ophthalmol. Vis. Sci. 2018;59(9):3425.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Dark adaptation (DA) measurement is important for detecting retinal diseases. We developed a smartphone app for DA measurement and performed its preliminary evaluation in normal vision (NV) and visually impaired subjects.

Methods : Testing was done by placing a Samsung Galaxy S8 smartphone in front of the subject sitting in a dark room (40 cm from the test eye). After bleaching, a series of blue stimuli of size 1° between luminance range of -1.5 to -4 log Cd/m2 were presented 8° inferior to the fixation on the smartphone display. The subject tapped the screen whenever the stimulus was visible, and the time and stimulus threshold were logged. Test duration was capped at 15 minutes. Evaluation of the app was primarily based on examination of age effect on DA characteristics in NV subjects (n=10), between 24 to 82 years of age (mean 45, std. 20) with visual acuity (VA) 20/25 or better, and without diagnosis of any vitreoretinal conditions. Additionally, we tested 1 patient with retinal damage due to myopic degeneration (MRD) (VA 20/100, age 62) and 1 patient with optic nerve atrophy (ONA) (VA: 20/500, age 40) to verify whether the effect of pathology can be detected by the app. Outcome measures were time to reach the minimum test threshold luminance, TMAX (15 minutes if the test ended before that), and the area under the time-luminance threshold curve (AUC). AUC was normalized using the preset bounds on test duration and luminance thresholds of the device. Regression analysis was used for determining age effect on outcome measures.

Results : TMAX and AUC increased with age in NV subjects (TMAX: R2 = 0.48, p = 0.016; AUC: R2 = 0.51, p = 0.012). The 95% confidence interval (CI) for within-subject differences for TMAX and AUC was ±2 min. and 6%, respectively. As expected, DA was greatly prolonged in the MRD subject with the final luminance threshold 1.2 log Cd/m2 higher than the minimum test threshold at the termination of the test. The AUC was outside the 95% CI of regression of age and 52% greater than the NV mean. On the other hand, for the ONA subject, TMAX (12.8 min.) was not significantly different than the NV subjects (within the interquartile range of the regression of age), and the AUC was 21% larger than the NV mean, but still within the 95% CI of regression of age.

Conclusions : The smartphone app might be able to detect the effects of age and retinal pathology on DA characteristics. Further investigation is warranted.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

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