Andrew Bastawrous1, Hillary K Rono2, Iain A T Livingstone3, Helen A Weiss4, Stewart Jordan1, Hannah Kuper1, Matthew J Burton1. 1. International Centre for Eye Health, Clinical Research Department, London School of Hygiene and Tropical Medicine (LSHTM), London, England. 2. Kitale Hospital and Zonal Ophthalmologist, North Rift, Kenya. 3. Glasgow Centre for Ophthalmic Research, NHS Greater Glasgow and Clyde, Glasgow, Scotland. 4. MRC Tropical Epidemiology Group, Faculty of Epidemiology and Population Health, LSHTM, London, England.
Abstract
IMPORTANCE: Visual acuity is the most frequently performed measure of visual function in clinical practice and most people worldwide living with visual impairment are living in low- and middle-income countries. OBJECTIVE: To design and validate a smartphone-based visual acuity test that is not dependent on familiarity with symbols or letters commonly used in the English language. DESIGN, SETTING, AND PARTICIPANTS: Validation study conducted from December 11, 2013, to March 4, 2014, comparing results from smartphone-based Peek Acuity to Snellen acuity (clinical normal) charts and the Early Treatment Diabetic Retinopathy Study (ETDRS) logMAR chart (reference standard). This study was nested within the 6-year follow-up of the Nakuru Eye Disease Cohort in central Kenya and included 300 adults aged 55 years and older recruited consecutively. MAIN OUTCOMES AND MEASURES: Outcome measures were monocular logMAR visual acuity scores for each test: ETDRS chart logMAR, Snellen acuity, and Peek Acuity. Peek Acuity was compared, in terms of test-retest variability and measurement time, with the Snellen acuity and ETDRS logMAR charts in participants' homes and temporary clinic settings in rural Kenya in 2013 and 2014. RESULTS: The 95% CI limits for test-retest variability of smartphone acuity data were ±0.033 logMAR. The mean differences between the smartphone-based test and the ETDRS chart and the smartphone-based test and Snellen acuity data were 0.07 (95% CI, 0.05-0.09) and 0.08 (95% CI, 0.06-0.10) logMAR, respectively, indicating that smartphone-based test acuities agreed well with those of the ETDRS and Snellen charts. The agreement of Peek Acuity and the ETDRS chart was greater than the Snellen chart with the ETDRS chart (95% CI, 0.05-0.10; P = .08). The local Kenyan community health care workers readily accepted the Peek Acuity smartphone test; it required minimal training and took no longer than the Snellen test (77 seconds vs 82 seconds; 95% CI, 71-84 seconds vs 73-91 seconds, respectively; P = .13). CONCLUSIONS AND RELEVANCE: The study demonstrated that the Peek Acuity smartphone test is capable of accurate and repeatable acuity measurements consistent with published data on the test-retest variability of acuities measured using 5-letter-per-line retroilluminated logMAR charts.
IMPORTANCE: Visual acuity is the most frequently performed measure of visual function in clinical practice and most people worldwide living with visual impairment are living in low- and middle-income countries. OBJECTIVE: To design and validate a smartphone-based visual acuity test that is not dependent on familiarity with symbols or letters commonly used in the English language. DESIGN, SETTING, AND PARTICIPANTS: Validation study conducted from December 11, 2013, to March 4, 2014, comparing results from smartphone-based Peek Acuity to Snellen acuity (clinical normal) charts and the Early Treatment Diabetic Retinopathy Study (ETDRS) logMAR chart (reference standard). This study was nested within the 6-year follow-up of the Nakuru Eye Disease Cohort in central Kenya and included 300 adults aged 55 years and older recruited consecutively. MAIN OUTCOMES AND MEASURES: Outcome measures were monocular logMAR visual acuity scores for each test: ETDRS chart logMAR, Snellen acuity, and Peek Acuity. Peek Acuity was compared, in terms of test-retest variability and measurement time, with the Snellen acuity and ETDRS logMAR charts in participants' homes and temporary clinic settings in rural Kenya in 2013 and 2014. RESULTS: The 95% CI limits for test-retest variability of smartphone acuity data were ±0.033 logMAR. The mean differences between the smartphone-based test and the ETDRS chart and the smartphone-based test and Snellen acuity data were 0.07 (95% CI, 0.05-0.09) and 0.08 (95% CI, 0.06-0.10) logMAR, respectively, indicating that smartphone-based test acuities agreed well with those of the ETDRS and Snellen charts. The agreement of Peek Acuity and the ETDRS chart was greater than the Snellen chart with the ETDRS chart (95% CI, 0.05-0.10; P = .08). The local Kenyan community health care workers readily accepted the Peek Acuity smartphone test; it required minimal training and took no longer than the Snellen test (77 seconds vs 82 seconds; 95% CI, 71-84 seconds vs 73-91 seconds, respectively; P = .13). CONCLUSIONS AND RELEVANCE: The study demonstrated that the Peek Acuity smartphone test is capable of accurate and repeatable acuity measurements consistent with published data on the test-retest variability of acuities measured using 5-letter-per-line retroilluminated logMAR charts.
Authors: Aaron Y Lee; Joanne C Wen; Yue Wu; Ian Luttrell; Shu Feng; Philip P Chen; Ted Spaide Journal: Br J Ophthalmol Date: 2019-12-23 Impact factor: 4.638
Authors: Akhilesh S Pathipati; Edward H Wood; Carson K Lam; Christopher S Sáles; Darius M Moshfeghi Journal: Graefes Arch Clin Exp Ophthalmol Date: 2016-03-01 Impact factor: 3.117
Authors: Pete R Jones; Heiko Philippin; William U Makupa; Matthew J Burton; David P Crabb Journal: Ophthalmic Epidemiol Date: 2019-09-13 Impact factor: 1.648
Authors: Andrew Bastawrous; Wanjiku Mathenge; John Buchan; Fatima Kyari; Tunde Peto; Hillary Rono; Helen A Weiss; David Macleod; Allen Foster; Matthew J Burton; Hannah Kuper Journal: J Glaucoma Date: 2018-05 Impact factor: 2.503
Authors: Andrew Bastawrous; Mario Ettore Giardini; Nigel M Bolster; Tunde Peto; Nisha Shah; Iain A T Livingstone; Helen A Weiss; Sen Hu; Hillary Rono; Hannah Kuper; Matthew Burton Journal: JAMA Ophthalmol Date: 2016-02 Impact factor: 7.389