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Using Health Forum Data to Assess User Needs for mHealth App Development: A Feasibility Study on Alzheimer's Disease.

Danny T Y Wu^1,2, Shwetha Bindhu¹, Hoang Vu¹

¹College of Medicine, University of Cincinnati, Cincinnati, OH.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|June 23, 2023

Summary

Online health forums provide valuable insights for designing digital health apps, particularly for Alzheimer's disease management. Automated analysis of patient and caregiver posts can identify user needs, though further refinement is needed for broader application.

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Area of Science:

Digital Health
Human-Computer Interaction
Health Informatics
Neurodegenerative Diseases

Background:

Online health forums serve as crucial community and information hubs for patients and caregivers, particularly in managing chronic conditions.
These platforms offer a potential data source for understanding user needs in digital health application design.

Purpose of the Study:

To evaluate the feasibility of using online forum discussions about Alzheimer's disease to inform the design of mobile health applications.
To determine if text clustering methods can automate the identification of user needs from these online discussions.

Main Methods:

Manual thematic coding of 413 online forum posts related to Alzheimer's disease to identify patient and caregiver needs.
Application of text clustering algorithms to automate the analysis of forum data.
External evaluation comparing automated and manual coding results, and persona development to validate forum-generated needs.

Main Results:

Manual analysis identified three main themes and nine subthemes reflecting patient and caregiver needs for Alzheimer's disease management.
Automated text clustering demonstrated fair to substantial similarity with manually derived labels.
Developed personas confirmed the validity of needs identified from forum data.

Conclusions:

Online health forum data is a viable source for understanding user needs in digital health design.
Automated analysis using text clustering shows promise but requires further refinement for generalizability across different diseases and data types.