PDF(Pubmed)
Abstract：
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：移动健康（mHealth）解决方案可以提高质量，可访问性,和卫生服务的公平性，促进早期康复。对于听力损失的人，mHealth应用程序可能旨在支持听觉诊断中的决策过程，并向用户提供治疗建议(例如，助听器需要)。对于一些人来说,这样的mHealth应用程序可能是与听力诊断服务的第一次接触，应该激励听力损失的用户有针对性地寻求专业帮助。然而,个性化的治疗建议是可能的，只有通过了解个人的资料有关的结果的兴趣。
目的：本研究旨在表征重复使用基于应用程序的听力测试后或多或少倾向于寻求专业帮助的个体。目标是得出相关的听力相关特征和个性特征，为mHealth听力解决方案的用户提供个性化治疗建议。
方法：总共，185名（n=106，57.3％的女性）患有主观听力损失的无辅助老年人（平均年龄63.8，SD6.6y）参加了一项移动研究。我们收集了一系列全面的83项听力相关和心理测量的横截面和纵向数据，这些测量先前发现可以预测听力帮助寻求。在研究结束时和2个月后，将寻求帮助的准备度评估为结果变量。参与者使用几种有监督的机器学习算法(随机森林，天真贝叶斯，和支持向量机)。使用特征重要性分析确定了用于预测的最相关特征。
结果：算法正确预测了65.9％（122/185）至70.3％（130/185）的研究结束时寻求帮助的行动，随访时分类准确率达到74.8%(98/131)。除了听力表现之外，最重要的分类特征是日常生活中听力损失的感知后果，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。
结论：这项研究有助于识别个体特征，预测自我报告听力损失的老年人寻求帮助。建议在个人分析算法中实施它们，并在mHealth听力应用程序中得出有针对性的建议。
目的：本研究旨在表征重复使用基于应用程序的听力测试后或多或少倾向于寻求专业帮助的个体。目标是得出相关的听力相关特征和个性特征，为mHealth听力解决方案的用户提供个性化治疗建议。
方法：总共，185名（n=106，57.3％的女性）患有主观听力损失的无辅助老年人（平均年龄63.8，SD6.6y）参加了一项移动研究。我们收集了一系列全面的83项听力相关和心理测量的横截面和纵向数据，这些测量先前发现可以预测听力帮助寻求。在研究结束时和2个月后，将寻求帮助的准备度评估为结果变量。参与者使用几种有监督的机器学习算法(随机森林，天真贝叶斯，和支持向量机)。使用特征重要性分析确定了用于预测的最相关特征。
结果：算法正确预测了65.9％（122/185）至70.3％（130/185）的研究结束时寻求帮助的行动，随访时分类准确率达到74.8%(98/131)。除了听力表现之外，最重要的分类特征是日常生活中听力损失的感知后果，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。
结论：这项研究有助于识别个体特征，预测自我报告听力损失的老年人寻求帮助。建议在个人分析算法中实施它们，并在mHealth听力应用程序中得出有针对性的建议。
