Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study.

ABSTRACT:

Background

Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist.

Objective

We investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data co

SUBMITTER: Slade C 

PROVIDER: S-EPMC11632896 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature