ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2024
Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Intensive Longitudinal Data Collection Methods in Health Research: Tutorial for Selecting Measures and Designing a Sampling Protocol.JMIR mHealth and uHealth · 2026Article
- Three-Dimensional Interconnect Technologies for Advanced Flexible Electronics.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Longitudinal User Engagement with Microinteraction Ecological Momentary Assessment (μEMA).Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Assessment of Negative Symptoms in Schizophrenia: From the Consensus Conference-Derived Scales to Remote Digital Phenotyping.Brain sciences · 2025Review
- Feasibility and usability of microinteraction ecological momentary assessment using a smartwatch in military personnel with a history of traumatic brain injury.Frontiers in neurology · 2025Article
- A Multi-Agent LLM Network for Suggesting and Correcting Human Activity and Posture Annotations.Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) · 2025Article
- Feasibility and Utility of Multimodal Micro Ecological Momentary Assessment on a Smartwatch.Proceedings of the SIGCHI conference on human factors in computing systems. CHI ConferenceArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Ecological momentary assessment (EMA) is an approach to collect self-reported data repeatedly on mobile devices in natural settings. EMAs allow for temporally dense, ecologically valid data collection, but frequent interruptions with lengthy surveys on mobile devices can burden users, impacting compliance and data quality. We propose a method that reduces the length of each EMA question set measuring interrelated constructs, with only modest information loss. By estimating the potential information gain of each EMA question using question-answer prediction models, this method can prioritize the presentation of the most informative question in a question-by-question sequence and skip uninformative questions. We evaluated the proposed method by simulating question omission using four real-world datasets from three different EMA studies. When compared against the random question omission approach that skips 50% of the questions, our method reduces imputation errors by 15%-52%. In surveys with five answer options for each question, our method can reduce the mean survey length by 34%-56% with a real-time prediction accuracy of 72%-95% for the skipped questions. The proposed method may either allow more constructs to be surveyed without adding user burden or reduce response burden for more sustainable longitudinal EMA data collection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.