Designing personalised mHealth solutions: An overview

Rivera-Romero, Octavio; Gabarron, Elia; Ropero, Jorge; Denecke, Kerstin

doi:10.1016/j.jbi.2023.104500

Cited by 4 publications

(1 citation statement)

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“…Moreover, it can enhance the empowerment and participation of the individual in their own hearing health care, promoting informed decision-making. Indeed, personalization strategies (such as tailored treatment recommendations) increase the effectiveness of mHealth interventions [82]. To conclude, a hearing mHealth app that provides targeted treatment recommendations could facilitate faster access to hearing-care services and subsequent earlier intervention, where needed, to pursue the long-term goal of achieving "hearing for all".…”

Section: Discussionmentioning

confidence: 99%

Prediction of Hearing Help Seeking to Design a Recommendation Module of an mHealth Hearing App: Intensive Longitudinal Study of Feature Importance Assessment

Angonese,

Buhl,

Kuhlmann

et al. 2024

JMIR Hum Factors

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Background: Mobile health care solutions can improve quality, accessibility and equity of health services, fostering early rehabilitation. For people suffering from hearing loss, mobile applications might be designed to support the decision-making processes in auditory diagnostics and to provide treatment recommendations to the user (e.g., hearing aid need). For some individuals, such mobile app might be the first contact with a hearing diagnostic service and should motivate users with hearing loss to seek professional help.Objective: This study aims at characterizing individuals who are more or less prone to seek professional help after the repeated use of an app-based hearing test. The goal is to develop a profiling module building upon hearing related traits and personality characteristics to secure personalized treatment recommendations in hearing mHealth solutions.Methods: N=185 (106 females) non-aided older individuals (Mage=63.8, SDage=6.6) with subjective hearing loss participated in a comprehensive online study. We collected cross-sectional and longitudinal data on several hearing-related and psychological features that were previously found to predict hearing help-seeking. Readiness to seek help was assessed as outcome variable at study-end and after two months. Participants were classified into help-seekers and non-seekers with several supervised machine learning algorithms (Random Forest, Naïve Bayes and Support Vector Machine). The most relevant features for prediction were identified with feature importance analysis. Results:The algorithms correctly predicted action to seek help at study-end in 66 to 70% of cases, reaching 75% classification accuracy at follow-up. Among the most important features for classifications were the degree of hearing loss and its perceived consequences in daily life, attitude towards hearing aids, physical health and sensory-sensitivity personality.Conclusions: This study contributes to the identification of individual characteristics that predict help-seeking in older individuals with self-perceived hearing loss. Suggestions for the implementation of an individual profiling algorithm and for targeted recommendations in hearing mHealth applications are derived.

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Section: Discussionmentioning

confidence: 99%