ArticleField methods2026
Enrollment and Compliance in an Ecological Momentary Assessment Study of Later Life.
Article in Field methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
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Authors and funding
6 authors.
Funding
Abstract
This study addresses enrollment and compliance in ecological momentary assessment (EMA) research by focusing on a sample of older adults with varying levels of technological familiarity. This is noteworthy because contemporary EMA studies predominantly require participants to use smartphones to record data in real time. We employ a two-stage analysis where we estimate the probability of enrollment based on sociodemographic and research design attributes and next use the output from this model to address potential selection bias in a subsequent model estimating compliance rates. We find that smartphone ownership is a major predictor of enrollment and that offering participants a loaner smartphone is insufficient to overcome this barrier. Ultimately, our findings suggest that while there are some potential design strategies that prospective researchers could implement to improve enrollment and compliance, there will likely always be some people whose attributes decrease their probability of enrollment and compliance regardless of intervention.
Identifiers
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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.