mHealth Tympanometer: A Digital Innovation to Address Preventable Childhood Hearing Loss in Low- and Middle-Income Countries

mHealth 鼓室压力计：解决中低收入国家可预防的儿童听力损失问题的数字创新

• 批准号: 10468986
• 负责人: Susan Davis Emmett
• 金额: $ 18.55万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10468986
• 关键词: Achievement; Address; Advocacy; Affect; Alaska; Algorithms; Audiology; Award; Caring; Cellular Phone; Child; Child Care; Clinical; Cluster randomized trial; Community Health Aides; Computer software; Country; Data; Data Science; Data Scientist; Database Management Systems; Development; Device Designs; Device or Instrument Development; Devices; Diagnostic; Ear; Ecosystem; Employment Opportunities; Engineering; Environment; Evaluation; Feasibility Studies; Feedback; Funding; Future; Goals; Hearing; High Prevalence; Impairment; Individual; Infection; Information Systems; Infrastructure; International; Language Development; Machine Learning; Masks; Methodology; Methods; Neonatal Screening; Nursery Schools; Performance; Periodicity; Persons; Phase; Population; Preschool Child; Prevalence; Preventive healthcare; Public Health; Research; Resource-limited setting; Risk; Schools; Secure; Site; Software Engineering; South Africa; South African; Tablets; Technology; Testing; Training; Triage; Tympanometry; Universities; Validation; Widespread Disease; World Health Organization; childhood hearing loss; cloud based; cost; design; digital; ear infection; early childhood; empowered; engineering design; health information technology; hearing impairment; hearing screening; improved; innovation; large datasets; low and middle-income countries; mHealth; machine learning algorithm; member; middle ear disorder; programs; prototype; public health relevance; recruit; rural Alaska; school health; screening; screening program; software development; teacher community; technology development; tool; user centered design; validation studies

ABSTRACT Hearing loss is the second leading impairment worldwide. Childhood hearing loss has lifelong implications and disproportionately affects individuals in low- and middle-income countries (LMICs). Up to 75% of childhood hearing loss in LMICs is preventable due to the high prevalence of infection-related hearing loss. School hearing screening is critical for identification of childhood hearing loss in low resource settings, where newborn screening is unavailable. However, most screening programs only use pure-tone screening that does not identify middle ear disease widespread in populations with a high prevalence of infection-related hearing loss. This is because tympanometry, used to clinically identify middle ear disease, is expensive and designed for trained professionals. Our goal is to develop and validate an mHealth tympanometer with machine learning diagnostic support to transform this technology into a low-cost tool that could be broadly disseminated in LMICs, where the burden of hearing loss is greatest and is not addressed by current hearing screening methodology. Our study team is comprised of international leaders in hearing loss, audiology, data science, engineering, user- centered design, and device development in LMICs. We have also partnered with hearX, a University of Pretoria spinout company that developed the only validated mHealth pure-tone screening device. To test this new device in an appropriate LMIC setting, we have partnered with the South African site from the Global HEAR Collaborative, our consortium of collaborators from 28 countries that is the only international research network dedicated to hearing loss. We documented the need for this device in a large cluster randomized trial recently completed in rural Alaska, where tympanometry significantly improved the accuracy of school hearing screening in a population with a high prevalence of infection-related hearing loss. Using data from this trial and pilot funding, we are developing a machine learning tympanometry algorithm for lay screeners, and early hardware prototype fabrication is underway. In Aim 1, we will refine the hardware prototype using a user-centered design approach, cyclically incorporating feedback from South African team members during testing in a lab environment. In Aim 2, we will develop software through user-centered design that integrates the machine learning algorithm and refined hardware prototype. The resulting mHealth tympanometer will advance to the R33 phase. Technology development will be completed in Aims 3 and 4 through integration of the mHealth tympanometer with existing health information technology and an early feasibility study in 15 preschool children in South Africa to optimize device design for lay users. In Aim 5, we will validate the mHealth tympanometer with lay screeners through a clinical performance study in 500 preschool children in South Africa. This technology, developed through partnership and testing in an LMIC setting, will empower teachers and community health workers to identify children at risk for preventable hearing loss. The Global HEAR Collaborative will provide infrastructure for future studies with the proposed device across LMICs, directly addressing disparities in childhood hearing loss globally.

摘要