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Cited by 44Ophthalmology and Visual Impairment StudiesDigital Mental Health InterventionsTelemedicine and Telehealth Implementation

Improved adherence and treatment outcomes with an engaging, personalized digital therapeutic in amblyopia

Open access
Xiao, Scott, Gaier, Eric D., Mazow, Malcolm L., Stout, Ann U., Travers, Dean A., Angjeli, Endri, Wu, Hank C., Binenbaum, Gil +1 more·2020·Scientific Reports
DOI 10.1038/s41598-020-65234-3PMC PMC7239850
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Abstract

Given the prevalence of poor adherence to therapy and the biases of self-reporting across healthcare, we hypothesized that an engaging, personalized therapy may improve adherence and treatment outcomes in the home. We tested this hypothesis in the initial indication of amblyopia, a neurodevelopmental disorder for which available treatments are limited by low adherence. We designed a novel digital therapeutic that modifies patient-selected cinematic content in real-time into therapeutic visual input, while objectively monitoring adherence. The therapeutic design integrated a custom-designed headset that delivers precise visual input to each eye, computational algorithms that apply real-time therapeutic modifications to source content, a cloud-based content management system that enables treatment in the home, and a broad library of licensed content. In a proof-of-concept human study on the therapeutic, we found that amblyopic eye vision improved significantly after 12 weeks of treatment, with higher adherence than that of available treatments. These initial results support the utility of personalized therapy in amblyopia and may have broader relevance for improving treatment outcomes in additional indications.

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  • All personalized intervention studies use predictive models based on multi-subject data.

    “Yet, of note, all of these personalized intervention studies use predictive models based on multi-subject data.”

    From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [20]

    The Results – Therapeutic Design, ‘Computational algorithms’ section describes personalization through patient-selected videos and a prescription identifying the amblyopic eye. It says the treatment protocol was the same for all patients, rather than describing a predictive model based on multi-subject data. No page number is available in our copy. (LLM, conf=0.86)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • Personalizing treatment across multiple fields of healthcare leads to higher treatment adherence and improved satisfaction.

    “Studies across multiple fields of healthcare have shown that personalizing treatment leads to higher treatment adherence as well as improved satisfaction [20–23].”

    From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [20]

    The Results report 78% mean adherence and high parent/guardian satisfaction with a personalized digital therapeutic for amblyopia. But the Discussion identifies this as a small proof-of-concept study without a comparison group. It does not establish that personalization caused higher adherence or improved satisfaction, or demonstrate those effects across multiple fields of healthcare. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. [2]Barash CI Pursel M Vogenberg FR Personalized Medicine P&T. 2010 35 560 562 21037908 PMC2957753

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Authors
Xiao, S., Gaier, E. D., Mazow, M. L., Stout, A. U., Travers, D. A., Angjeli, E., Wu, H. C., Binenbaum, G., & Hunter, D. G.
Affiliations
  • Cambridge Intellectual Property (United Kingdom) (GB)
  • Houston Eye Associates (US)
  • Children's Hospital of Philadelphia (US)
Corresponding author
  • Xiao, Scott
  • Hunter, David G.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2020-05-20
Volume
10
Issue
1
Pages
8328-8328
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-020-65234-3
PMC
PMC7239850
OpenAlex
W3026959915
Cited by
44
References
32
Citations by year
2026: 11 · 2025: 8 · 2024: 7 · 2023: 4 · 2022: 6 · 2021: 6 · 2020: 1 · 2019: 1
FWCI
3.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Luminopia, Inc.
Topics
  • Health Sciences › Medicine › Epidemiology › Ophthalmology and Visual Impairment Studies
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
Keywords
amblyopia, digital therapeutics, medication adherence, personalized treatment, home-based therapy, neurodevelopmental disorders
MeSH terms
Wearable Electronic Devices, Algorithms, Amblyopia, Child, Child, Preschool, Female, Humans, Male, Patient Compliance, Visual Acuity, Treatment Outcome, Patient Satisfaction

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