A Pilot Study to Assess Performance of a New Low-Cost Mobile Health Based Prototype Tympanometer
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 39
- 主要终点
- Classification Agreement Between Prototype mHealth Tympanometer and Commercial Tympanometer
研究概览
简要总结
Children in rural and underserved communities experience a disproportionately high burden of infection-related hearing loss, which is often preventable with timely identification and follow-up care. A tympanometer is a device that evaluates the health of the middle ear but is often not used in screening programs due to barriers of high cost and need for trained professionals such as audiologists to use the device. To address these barriers, a low cost, lay friendly, mobile health (mHealth) prototype tympanometer has been developed. In addition, a machine learning (ML) algorithm has been designed to guide lay users to interpret tympanometer results, overcoming the need for audiologists. Broad implementation of the lay friendly tympanometer and ML algorithm will transform screening in areas where prevalence of infection-related hearing loss is high, and access to specialty care is limited.
In this study the prototype mHealth tympanometer will be evaluated against commercial tympanometry. It will be used by audiologists, an essential step before testing the performance of the device with lay users. Approximately 20 adult patients and 10 pediatric patients with various middle ear pathologies will be enrolled in the study, with the goal of obtaining 60 ears with data from both the prototype and commercial tympanometers. Audiologists will obtain tympanometry measurements on both ears of each participant and interpret the results, first with the prototype device and then with the commercial tympanometer that is typically used during a clinical evaluation. Tympanometry interpretation will include classification into one of 3 categories (Type A, B, or C). Agreement in results between the prototype and commercial devices will be assessed. Agreement between audiologist interpretations and the ML algorithm from the prototype mHealth tympanometer will be assessed.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Screening
- 盲法
- None
入排标准
- 年龄范围
- 1 Year 至 —(Child, Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Presenting to Audiology Clinic for evaluation, where tympanometry is warranted for testing at the discretion of the audiologist
- •Presence of various middle ear health states/pathologies that result in Type A, B, C tympanograms
- •English-speaking
排除标准
- •Children or adults with cognitive disabilities
- •Unable to provide consent/assent
- •Individuals who are unable to sit still
- •Any other condition that in the opinion of the investigator, might interfere with the safe conduct of the study or place the participant at increased risk
研究组 & 干预措施
Tympanometer Comparison
Both ears of each participant will be tested first with the prototype mHealth tympanometer followed by standard of care commercial tympanometer
干预措施: Prototype mHealth tympanometer (Device)
Tympanometer Comparison
Both ears of each participant will be tested first with the prototype mHealth tympanometer followed by standard of care commercial tympanometer
干预措施: Standard of care commercial tympanometer (Device)
结局指标
主要结局
Classification Agreement Between Prototype mHealth Tympanometer and Commercial Tympanometer
时间窗: Same day as participant visit, up to 10 minutes
Audiologist will test each ear of the participant first with the prototype device, interpret the results into one of the three tympanogram categories (Type A/B/C) and record the results. The procedure will be repeated with a commercial device. Agreement between the two devices with respect to the classification categories will be assessed.
Classification Agreement Between Audiologist and ML Algorithm Interpretations Using Prototype mHealth Tympanometer
时间窗: Same day as participant visit, up to 10 minutes
Tympanogram results collected using the prototype mHealth tympanometer will be run through the ML algorithm for classification into Type A/B/C. Agreement between the audiologist and the ML algorithm with respect to the classification categories will be assessed.
次要结局
未报告次要终点
