Cluster of Excellence Hearing4All, Oldenburg, Germany.
Psychological Methods and Statistics Lab, Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
JMIR Hum Factors. 2024 Aug 12;11:e52310. doi: 10.2196/52310.
Mobile health (mHealth) solutions can improve the quality, accessibility, and equity of health services, fostering early rehabilitation. For individuals with hearing loss, mHealth apps might be designed to support the decision-making processes in auditory diagnostics and provide treatment recommendations to the user (eg, hearing aid need). For some individuals, such an mHealth app might be the first contact with a hearing diagnostic service and should motivate users with hearing loss to seek professional help in a targeted manner. However, personalizing treatment recommendations is only possible by knowing the individual's profile regarding the outcome of interest.
This study aims to characterize individuals who are more or less prone to seeking professional help after the repeated use of an app-based hearing test. The goal was to derive relevant hearing-related traits and personality characteristics for personalized treatment recommendations for users of mHealth hearing solutions.
In total, 185 (n=106, 57.3% female) nonaided older individuals (mean age 63.8, SD 6.6 y) with subjective hearing loss participated in a mobile study. We collected cross-sectional and longitudinal data on a comprehensive set of 83 hearing-related and psychological measures among those previously found to predict hearing help seeking. Readiness to seek help was assessed as the outcome variable at study end and after 2 months. Participants were classified into help seekers and nonseekers using several supervised machine learning algorithms (random forest, naïve Bayes, and support vector machine). The most relevant features for prediction were identified using feature importance analysis.
The algorithms correctly predicted action to seek help at study end in 65.9% (122/185) to 70.3% (130/185) of cases, reaching 74.8% (98/131) classification accuracy at follow-up. Among the most important features for classification beyond hearing performance were the perceived consequences of hearing loss in daily life, attitude toward hearing aids, motivation to seek help, physical health, sensory sensitivity personality trait, neuroticism, and income.
This study contributes to the identification of individual characteristics that predict help seeking in older individuals with self-reported hearing loss. Suggestions are made for their implementation in an individual-profiling algorithm and for deriving targeted recommendations in mHealth hearing apps.
移动健康 (mHealth) 解决方案可以提高医疗服务的质量、可及性和公平性，促进早期康复。对于听力损失的个体，mHealth 应用程序可能旨在支持听觉诊断过程中的决策，并向用户提供治疗建议（例如，助听器需求）。对于某些人来说，这样的 mHealth 应用程序可能是与听力诊断服务的首次接触，应该以有针对性的方式激励听力损失患者寻求专业帮助。然而，只有了解个体在感兴趣的结果方面的个人资料，才能实现个性化治疗建议。
本研究旨在描述在反复使用基于应用程序的听力测试后更倾向于寻求专业帮助或不太倾向于寻求专业帮助的个体。目标是为 mHealth 听力解决方案的用户得出相关的听力相关特征和个性特征，以进行个性化治疗建议。
共有 185 名（n=106，57.3% 为女性）未佩戴助听器的老年个体（平均年龄 63.8，SD 6.6 y）参与了一项移动研究。我们收集了 83 项与听力相关和心理测量的横断面和纵向数据，这些数据先前被发现可预测听力求助。在研究结束时和 2 个月后，将寻求帮助的意愿作为结果变量进行评估。使用几种监督机器学习算法（随机森林、朴素贝叶斯和支持向量机）将参与者分类为求助者和非求助者。使用特征重要性分析确定预测最相关的特征。
算法在 65.9%（122/185）至 70.3%（130/185）的情况下正确预测了研究结束时的求助行为，在随访时达到了 74.8%（98/131）的分类准确率。在分类中除听力表现外最重要的特征包括听力损失对日常生活的感知后果、对助听器的态度、寻求帮助的动机、身体健康、感官敏感性人格特质、神经质和收入。
本研究有助于确定预测老年个体自我报告听力损失寻求帮助的个体特征。建议将其应用于个人档案算法，并在 mHealth 听力应用程序中提供有针对性的建议。
