Work

Cognitive Load or Timely Answers? Cognitive Effort Markers in Context-Aware EMA Sampling

Year: 2026

Type: article

Abstract: Mobile self-reports provide subjective and contextual information that passive sensors cannot recover, but frequent prompts compete with everyday activities and are often answered late. Here, we show that situational context and recent response history predict whether an ecological momentary assessment response begins within 15 min. We analyzed 70,375 records from 170 participants in the Italian arm of DiversityOne; 43.19% met the operational timeliness criterion. Participant-clustered generalized estimating equations identified associations with activity, social setting, location, mood, weekday, hour and survey day, with corrected Cramér’s V values of 0.044–0.175. In leakage-resistant evaluation, gc-Forest achieved an accuracy of 0.719 for personalized forward prediction, whereas history-augmented LightGBM achieved an accuracy of 0.704 and area under the receiver operating characteristic curve of 0.779 when participants were held out. Retrospectively ranking ten candidate moments increased the participant-balanced timely-response rate from 44.60% to 51.43%. These results establish prompt timeliness as a learnable scheduling outcome, while distinguishing it from content accuracy or cognitive effort.

Source: Data

DOI: 10.3390/data11090221

Authors: Xiaoyue Li, Haonan Zhao

Institutions: Changchun University of Technology, Changchun University of Science and Technology

Language: English

Topic: Mobile Crowdsensing and Crowdsourcing

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: Changchun University of Technology