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Record W4404655592 · doi:10.2196/66052

Evaluating the Effectiveness and Scalability of the World Health Organization MyopiaEd Digital Intervention: Mixed Methods Study

2024· article· en· W4404655592 on OpenAlexvenueno aff
Stuart Keel, Sangchul Yoon

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsnot available
FundersWorld Health Organization
KeywordsPreprintComputer scienceScalabilityIntervention (counseling)Data scienceMedicineWorld Wide WebNursingDatabase

Abstract

fetched live from OpenAlex

BACKGROUND: The rapid rise of myopia worldwide, particularly in East and Southeast Asia, has implied environmental influences beyond genetics. To address this growing public health concern, the World Health Organization and International Telecommunication Union launched the MyopiaEd program. South Korea, with its high rates of myopia and smartphone use, presented a suitable context for implementing and evaluating the MyopiaEd program. OBJECTIVE: This is the first study to date to evaluate the effectiveness and scalability of the MyopiaEd program in promoting eye health behavior change among parents of children in South Korea. METHODS: Parents of children aged 7 and 8 years were recruited through an open-access website with a recruitment notice distributed to public elementary schools in Gwangju Metropolitan City. Beginning in September 2022, parents received 42 SMS text messages from the MyopiaEd program over 6 months. This digital trial used a mixed methods approach combining both quantitative and qualitative data collection. Pre- and postintervention surveys were used to assess changes in parental knowledge and behavior regarding myopia prevention. Additionally, semistructured interviews were conducted to explore participants' experiences in depth and receive feedback on program design. Prior to the intervention, the MyopiaEd program design and message libraries were adapted for the Korean context following World Health Organization and International Telecommunication Union guidelines. RESULTS: A total of 133 parents participated in this study, including 60 parents whose children had myopia and 73 parents whose children did not. Both groups reported high engagement and satisfaction with the program. Significant increases in knowledge about myopia were observed in both groups (P<.001). While time spent on near-work activities did not change significantly, parents of children with myopia reported increased outdoor time for their children (P=.048). A substantial increase in eye checkups was observed, with 52 (86.7%) out of 60 children with myopia and 50 (68.5%) out of 73 children without myopia receiving eye examinations following the intervention. Qualitative analysis indicated a shift in parents' attitudes toward outdoor activities, as increased recognition of their benefits prompted positive changes in behavior. However, reducing near-work activities posed challenges due to children's preference for smartphone use during leisure periods and the demands of after-school academies. The credibility of the institution delivering the program enhanced parental engagement and children's adoption of healthy behaviors. Messages that corrected common misconceptions about eye health and provided specific behavioral guidance were regarded as impactful elements of the program. CONCLUSIONS: This study demonstrates the MyopiaEd program's potential as a scalable and innovative digital intervention to reduce myopia risk in children. The program's effectiveness provides support for broader adoption and offers valuable insights to inform future myopia prevention policies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.070
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.499
Teacher spread0.422 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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