Effect of Using Myopia Prediction Algorithm

Study Overview
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Brief Summary
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In this study, the investigators aim to perform a prospective, randomized controlled study to compare the myopia development between children using myopia prediction algorithm and children without using myopia prediction algorithm.

Detailed Description
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We propose to enroll Grade 3 children from primary schools in China. Children will be given examinations of visual acuity, eye refraction and biometrics, and be assigned to two groups: participants in group A use myopia prediction algorithm to predict myopia development, while in Group B, the participants do not use myopia prediction algorithm to predict myopia development. The visual acuity, eye refraction and biometrics will be investigated over the one-year follow-up period, aiming at comparison of actual myopia development between the two groups.

Official Title
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Effect of Using Myopia Prediction Algorithm on Children's Eye Refraction in China: a Multi-center Randomized Control Trial

Conditions
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Eye--Refractive Errors

Intervention / Treatment
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* Other: Myopia prediction algorithm


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Children at Grade 3 (aged 8-9) Has the record of eye refraction examined in the past year Written informed consents provided Exclusion Criteria: Definitive diagnosis of other ocular abnormalities except for refractive error Previous eye surgery Unwilling to participate in this trial

Ages Eligible for Study
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Minimum Age: 8 Years
Maximum Age: 9 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Using myopia prediction algorithm<br>After examination of visual acuity, eye refraction and biometrics, the examination results are shown to the children. Meanwhile, myopia prediction algorithm will be used to predict SE and presence of high myopia in the subsequent 10 years. | Other: Myopia prediction algorithm<br>* Candidate predictors included age at examination, SE, and annual progression rate. Using these predictors, the algorithm will be used to predict SE and whether patients will progress to high myopia in the subsequent 10 years.<br>|
| No Intervention: Not using myopia prediction algorithm<br>After examination of visual acuity, eye refraction and biometrics, the examination results are shown to the children. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Incidence of SER development of at least -0.5 dioptres (D) | SER: sphere + ½ 360 cylinder; SER development of at least -0.5 dioptres: SER2 - SER1≤-0.5D; SER1: SER at baseline; SER2: SER up to 1 year | Up to 1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Changes in SER \and AL | Changes in SER \and AL will be calculated; AL: axial length | Up to 1 year | 
| Changes in proportion of children using atropine | Changes in proportion of children using atropine will be calculated | Up to 1 year | 
| Changes in proportion of children using orthokeratology lenses | Changes in proportion of children using orthokeratology lenses will be calculated | Up to 1 year | 
| Changes in proportion of children using spectacles | Changes in proportion of children using spectacles will be calculated | Up to 1 year | 
| Changes in child's average outdoor activity time per day | Changes in child's average outdoor activity time per day will be calculated | Up to 1 year | 
| Changes in child's average screen time per day | Changes in child's average screen time per day will be calculated | Up to 1 year |