Clinical validation of a novel web-application for remote assessment of distance visual acuity
Background/Objectives Ophthalmic disorders cause 8% of hospital clinic attendances, the highest of any specialty. The fundamental need for a distance visual acuity (VA) measurement constrains remote consultation. A web-application, DigiVis, facilitates self-assessment of VA using two internet-connec...
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| Veröffentlicht in: | Eye (London) 2022-10, Vol.36 (10), p.2057-2061 |
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| creator | Thirunavukarasu, Arun James Mullinger, Deborah Rufus-Toye, Remi Mohan Farrell, Sarah Allen, Louise E. |
| description | Background/Objectives
Ophthalmic disorders cause 8% of hospital clinic attendances, the highest of any specialty. The fundamental need for a distance visual acuity (VA) measurement constrains remote consultation. A web-application, DigiVis, facilitates self-assessment of VA using two internet-connected devices. This prospective validation study aimed to establish its accuracy, reliability, usability and acceptability.
Subjects/Methods
In total, 120 patients aged 5–87 years (median = 27) self-tested their vision twice using DigiVis in addition to their standard clinical assessment. Eyes with VA worse than +0.80 logMAR were excluded. Accuracy and test-retest (TRT) variability were compared using Bland–Altman analysis and intraclass correlation coefficients (ICC). Patient feedback was analysed.
Results
Bias between VA tests was insignificant at −0.001 (95% CI −0.017 to 0.015) logMAR. The upper limit of agreement (LOA) was 0.173 (95% CI 0.146 to 0.201) and the lower LOA −0.175 (95% CI −0.202 to −0.147) logMAR. The ICC was 0.818 (95% CI 0.748 to 0.869). DigiVis TRT mean bias was similarly insignificant, at 0.001 (95% CI −0.011 to 0.013) logMAR, the upper LOA was 0.124 (95% CI 0.103 to 0.144) and the lower LOA −0.121 (95% CI −0.142 to −0.101) logMAR. The ICC was 0.922 (95% CI 0.887 to 0.946). 95% of subjects were willing to use DigiVis to monitor vision at home.
Conclusions
Self-tested distance VA using DigiVis is accurate, reliable and well accepted by patients. The app has potential to facilitate home monitoring, triage and remote consultation but widescale implementation will require integration with NHS databases and secure patient data storage. |
| doi_str_mv | 10.1038/s41433-021-01760-2 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8403827</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2716792352</sourcerecordid><originalsourceid>FETCH-LOGICAL-c451t-15b74ca1a98d491b838e60a93780fd5d9fa8ecf19b9ae0905d67ec4885d8e5173</originalsourceid><addsrcrecordid>eNp9kU9rFTEUxYNY7Gv1C7gacONmav5Oko0gj9oKBTcVXBnuZO7UlMzkmcw86bc3r68ounB14Z7fOdzkEPKa0QtGhXlXJJNCtJSzljLd0ZY_IxsmddcqqeRzsqFW1SXnX0_JWSn3lFZR0xfkVEjZcaXthnzbxjAHD7HZQwwDLCHNTRobaOa0x9j8xL6F3S5W5FEaU24yTmnBBkrBUiacl4NhCGWB2WOzD2WtceDXsDy8JCcjxIKvnuY5-fLx8nZ73d58vvq0_XDTeqnY0jLVa-mBgTWDtKw3wmBHwQpt6DiowY5g0I_M9haQWqqGTqOXxqjBoGJanJP3x9zd2k84-HpUhuh2OUyQH1yC4P5W5vDd3aW9M7L-JD8EvH0KyOnHimVxUygeY4QZ01ocV522xlhmKvrmH_Q-rXmuz3Ncs4pxoXil-JHyOZWScfx9DKPuUJ871udqfe6xPncwiaOpVHi-w_wn-j-uX9aPnYU</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2716792352</pqid></control><display><type>article</type><title>Clinical validation of a novel web-application for remote assessment of distance visual acuity</title><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Thirunavukarasu, Arun James ; Mullinger, Deborah ; Rufus-Toye, Remi Mohan ; Farrell, Sarah ; Allen, Louise E.</creator><creatorcontrib>Thirunavukarasu, Arun James ; Mullinger, Deborah ; Rufus-Toye, Remi Mohan ; Farrell, Sarah ; Allen, Louise E.</creatorcontrib><description>Background/Objectives
Ophthalmic disorders cause 8% of hospital clinic attendances, the highest of any specialty. The fundamental need for a distance visual acuity (VA) measurement constrains remote consultation. A web-application, DigiVis, facilitates self-assessment of VA using two internet-connected devices. This prospective validation study aimed to establish its accuracy, reliability, usability and acceptability.
Subjects/Methods
In total, 120 patients aged 5–87 years (median = 27) self-tested their vision twice using DigiVis in addition to their standard clinical assessment. Eyes with VA worse than +0.80 logMAR were excluded. Accuracy and test-retest (TRT) variability were compared using Bland–Altman analysis and intraclass correlation coefficients (ICC). Patient feedback was analysed.
Results
Bias between VA tests was insignificant at −0.001 (95% CI −0.017 to 0.015) logMAR. The upper limit of agreement (LOA) was 0.173 (95% CI 0.146 to 0.201) and the lower LOA −0.175 (95% CI −0.202 to −0.147) logMAR. The ICC was 0.818 (95% CI 0.748 to 0.869). DigiVis TRT mean bias was similarly insignificant, at 0.001 (95% CI −0.011 to 0.013) logMAR, the upper LOA was 0.124 (95% CI 0.103 to 0.144) and the lower LOA −0.121 (95% CI −0.142 to −0.101) logMAR. The ICC was 0.922 (95% CI 0.887 to 0.946). 95% of subjects were willing to use DigiVis to monitor vision at home.
Conclusions
Self-tested distance VA using DigiVis is accurate, reliable and well accepted by patients. The app has potential to facilitate home monitoring, triage and remote consultation but widescale implementation will require integration with NHS databases and secure patient data storage.</description><identifier>ISSN: 0950-222X</identifier><identifier>EISSN: 1476-5454</identifier><identifier>DOI: 10.1038/s41433-021-01760-2</identifier><identifier>PMID: 34462579</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>692/1807/1482 ; 692/700/139 ; 692/700/228 ; Accuracy ; Acuity ; Automation ; Bias ; Consent ; Coronaviruses ; COVID-19 ; Data storage ; Diabetic retinopathy ; Hospitals ; Internet ; Laboratory Medicine ; Medicine ; Medicine & Public Health ; Ophthalmology ; Patients ; Pharmaceutical Sciences/Technology ; Questionnaires ; Self-assessment ; Smartphones ; Surgery ; Surgical Oncology ; Usability ; Validation studies ; Vision ; Visual acuity</subject><ispartof>Eye (London), 2022-10, Vol.36 (10), p.2057-2061</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-15b74ca1a98d491b838e60a93780fd5d9fa8ecf19b9ae0905d67ec4885d8e5173</citedby><cites>FETCH-LOGICAL-c451t-15b74ca1a98d491b838e60a93780fd5d9fa8ecf19b9ae0905d67ec4885d8e5173</cites><orcidid>0000-0001-5957-3282 ; 0000-0001-8968-4768 ; 0000-0001-5452-9397</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403827/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403827/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids></links><search><creatorcontrib>Thirunavukarasu, Arun James</creatorcontrib><creatorcontrib>Mullinger, Deborah</creatorcontrib><creatorcontrib>Rufus-Toye, Remi Mohan</creatorcontrib><creatorcontrib>Farrell, Sarah</creatorcontrib><creatorcontrib>Allen, Louise E.</creatorcontrib><title>Clinical validation of a novel web-application for remote assessment of distance visual acuity</title><title>Eye (London)</title><addtitle>Eye</addtitle><description>Background/Objectives
Ophthalmic disorders cause 8% of hospital clinic attendances, the highest of any specialty. The fundamental need for a distance visual acuity (VA) measurement constrains remote consultation. A web-application, DigiVis, facilitates self-assessment of VA using two internet-connected devices. This prospective validation study aimed to establish its accuracy, reliability, usability and acceptability.
Subjects/Methods
In total, 120 patients aged 5–87 years (median = 27) self-tested their vision twice using DigiVis in addition to their standard clinical assessment. Eyes with VA worse than +0.80 logMAR were excluded. Accuracy and test-retest (TRT) variability were compared using Bland–Altman analysis and intraclass correlation coefficients (ICC). Patient feedback was analysed.
Results
Bias between VA tests was insignificant at −0.001 (95% CI −0.017 to 0.015) logMAR. The upper limit of agreement (LOA) was 0.173 (95% CI 0.146 to 0.201) and the lower LOA −0.175 (95% CI −0.202 to −0.147) logMAR. The ICC was 0.818 (95% CI 0.748 to 0.869). DigiVis TRT mean bias was similarly insignificant, at 0.001 (95% CI −0.011 to 0.013) logMAR, the upper LOA was 0.124 (95% CI 0.103 to 0.144) and the lower LOA −0.121 (95% CI −0.142 to −0.101) logMAR. The ICC was 0.922 (95% CI 0.887 to 0.946). 95% of subjects were willing to use DigiVis to monitor vision at home.
Conclusions
Self-tested distance VA using DigiVis is accurate, reliable and well accepted by patients. The app has potential to facilitate home monitoring, triage and remote consultation but widescale implementation will require integration with NHS databases and secure patient data storage.</description><subject>692/1807/1482</subject><subject>692/700/139</subject><subject>692/700/228</subject><subject>Accuracy</subject><subject>Acuity</subject><subject>Automation</subject><subject>Bias</subject><subject>Consent</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Data storage</subject><subject>Diabetic retinopathy</subject><subject>Hospitals</subject><subject>Internet</subject><subject>Laboratory Medicine</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Ophthalmology</subject><subject>Patients</subject><subject>Pharmaceutical Sciences/Technology</subject><subject>Questionnaires</subject><subject>Self-assessment</subject><subject>Smartphones</subject><subject>Surgery</subject><subject>Surgical Oncology</subject><subject>Usability</subject><subject>Validation studies</subject><subject>Vision</subject><subject>Visual acuity</subject><issn>0950-222X</issn><issn>1476-5454</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU9rFTEUxYNY7Gv1C7gacONmav5Oko0gj9oKBTcVXBnuZO7UlMzkmcw86bc3r68ounB14Z7fOdzkEPKa0QtGhXlXJJNCtJSzljLd0ZY_IxsmddcqqeRzsqFW1SXnX0_JWSn3lFZR0xfkVEjZcaXthnzbxjAHD7HZQwwDLCHNTRobaOa0x9j8xL6F3S5W5FEaU24yTmnBBkrBUiacl4NhCGWB2WOzD2WtceDXsDy8JCcjxIKvnuY5-fLx8nZ73d58vvq0_XDTeqnY0jLVa-mBgTWDtKw3wmBHwQpt6DiowY5g0I_M9haQWqqGTqOXxqjBoGJanJP3x9zd2k84-HpUhuh2OUyQH1yC4P5W5vDd3aW9M7L-JD8EvH0KyOnHimVxUygeY4QZ01ocV522xlhmKvrmH_Q-rXmuz3Ncs4pxoXil-JHyOZWScfx9DKPuUJ871udqfe6xPncwiaOpVHi-w_wn-j-uX9aPnYU</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Thirunavukarasu, Arun James</creator><creator>Mullinger, Deborah</creator><creator>Rufus-Toye, Remi Mohan</creator><creator>Farrell, Sarah</creator><creator>Allen, Louise E.</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5957-3282</orcidid><orcidid>https://orcid.org/0000-0001-8968-4768</orcidid><orcidid>https://orcid.org/0000-0001-5452-9397</orcidid></search><sort><creationdate>20221001</creationdate><title>Clinical validation of a novel web-application for remote assessment of distance visual acuity</title><author>Thirunavukarasu, Arun James ; Mullinger, Deborah ; Rufus-Toye, Remi Mohan ; Farrell, Sarah ; Allen, Louise E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-15b74ca1a98d491b838e60a93780fd5d9fa8ecf19b9ae0905d67ec4885d8e5173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>692/1807/1482</topic><topic>692/700/139</topic><topic>692/700/228</topic><topic>Accuracy</topic><topic>Acuity</topic><topic>Automation</topic><topic>Bias</topic><topic>Consent</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Data storage</topic><topic>Diabetic retinopathy</topic><topic>Hospitals</topic><topic>Internet</topic><topic>Laboratory Medicine</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Ophthalmology</topic><topic>Patients</topic><topic>Pharmaceutical Sciences/Technology</topic><topic>Questionnaires</topic><topic>Self-assessment</topic><topic>Smartphones</topic><topic>Surgery</topic><topic>Surgical Oncology</topic><topic>Usability</topic><topic>Validation studies</topic><topic>Vision</topic><topic>Visual acuity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Thirunavukarasu, Arun James</creatorcontrib><creatorcontrib>Mullinger, Deborah</creatorcontrib><creatorcontrib>Rufus-Toye, Remi Mohan</creatorcontrib><creatorcontrib>Farrell, Sarah</creatorcontrib><creatorcontrib>Allen, Louise E.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Eye (London)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Thirunavukarasu, Arun James</au><au>Mullinger, Deborah</au><au>Rufus-Toye, Remi Mohan</au><au>Farrell, Sarah</au><au>Allen, Louise E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical validation of a novel web-application for remote assessment of distance visual acuity</atitle><jtitle>Eye (London)</jtitle><stitle>Eye</stitle><date>2022-10-01</date><risdate>2022</risdate><volume>36</volume><issue>10</issue><spage>2057</spage><epage>2061</epage><pages>2057-2061</pages><issn>0950-222X</issn><eissn>1476-5454</eissn><abstract>Background/Objectives
Ophthalmic disorders cause 8% of hospital clinic attendances, the highest of any specialty. The fundamental need for a distance visual acuity (VA) measurement constrains remote consultation. A web-application, DigiVis, facilitates self-assessment of VA using two internet-connected devices. This prospective validation study aimed to establish its accuracy, reliability, usability and acceptability.
Subjects/Methods
In total, 120 patients aged 5–87 years (median = 27) self-tested their vision twice using DigiVis in addition to their standard clinical assessment. Eyes with VA worse than +0.80 logMAR were excluded. Accuracy and test-retest (TRT) variability were compared using Bland–Altman analysis and intraclass correlation coefficients (ICC). Patient feedback was analysed.
Results
Bias between VA tests was insignificant at −0.001 (95% CI −0.017 to 0.015) logMAR. The upper limit of agreement (LOA) was 0.173 (95% CI 0.146 to 0.201) and the lower LOA −0.175 (95% CI −0.202 to −0.147) logMAR. The ICC was 0.818 (95% CI 0.748 to 0.869). DigiVis TRT mean bias was similarly insignificant, at 0.001 (95% CI −0.011 to 0.013) logMAR, the upper LOA was 0.124 (95% CI 0.103 to 0.144) and the lower LOA −0.121 (95% CI −0.142 to −0.101) logMAR. The ICC was 0.922 (95% CI 0.887 to 0.946). 95% of subjects were willing to use DigiVis to monitor vision at home.
Conclusions
Self-tested distance VA using DigiVis is accurate, reliable and well accepted by patients. The app has potential to facilitate home monitoring, triage and remote consultation but widescale implementation will require integration with NHS databases and secure patient data storage.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>34462579</pmid><doi>10.1038/s41433-021-01760-2</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0001-5957-3282</orcidid><orcidid>https://orcid.org/0000-0001-8968-4768</orcidid><orcidid>https://orcid.org/0000-0001-5452-9397</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 692/1807/1482 692/700/139 692/700/228 Accuracy Acuity Automation Bias Consent Coronaviruses COVID-19 Data storage Diabetic retinopathy Hospitals Internet Laboratory Medicine Medicine Medicine & Public Health Ophthalmology Patients Pharmaceutical Sciences/Technology Questionnaires Self-assessment Smartphones Surgery Surgical Oncology Usability Validation studies Vision Visual acuity |
| title | Clinical validation of a novel web-application for remote assessment of distance visual acuity |
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