Evidence map›Paper›PMID 36840916›Full record

ArticleBehavior research methods2024

Just-in-time adaptive ecological momentary assessment (JITA-EMA).

Stefan Schneider, Doerte U Junghaenel, Joshua M Smyth, Cheng K Fred Wen, Arthur A Stone

Abstract read
In one paragraph

Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Stefan SchneiderDornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA. schneids@usc.edu.
Doerte U JunghaenelDornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA.
Joshua M SmythBiobehavioral Health and Medicine, Pennsylvania State University, State College, PA, USA.
Cheng K Fred WenDornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA.
Arthur A StoneDornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA.

Funding

Advancing the Science and Practice of Ecological Momentary AssessmentR37AG057685 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Arthur A Stone · 2018 to 2026
$9.4M
NIA NIH HHS R37 AG057685
6 · The paper itself

Abstract

Interest in just-in-time adaptive interventions (JITAI) has rapidly increased in recent years. One core challenge for JITAI is the efficient and precise measurement of tailoring variables that are used to inform the timing of momentary intervention delivery. Ecological momentary assessment (EMA) is often used for this purpose, even though EMA in its traditional form was not designed specifically to facilitate momentary interventions. In this article, we introduce just-in-time adaptive EMA (JITA-EMA) as a strategy to reduce participant response burden and decrease measurement error when EMA is used as a tailoring variable in JITAI. JITA-EMA builds on computerized adaptive testing methods developed for purposes of classification (computerized classification testing, CCT), and applies them to the classification of momentary states within individuals. The goal of JITA-EMA is to administer a small and informative selection of EMA questions needed to accurately classify an individual's current state at each measurement occasion. After illustrating the basic components of JITA-EMA (adaptively choosing the initial and subsequent items to administer, adaptively stopping item administration, accommodating dynamically tailored classification cutoffs), we present two simulation studies that explored the performance of JITA-EMA, using the example of momentary fatigue states. Compared with conventional EMA item selection methods that administered a fixed set of questions at each moment, JITA-EMA yielded more accurate momentary classification with fewer questions administered. Our results suggest that JITA-EMA has the potential to enhance some approaches to mobile health interventions by facilitating efficient and precise identification of momentary states that may inform intervention tailoring.

Indexed as

Ecological Momentary AssessmentResearch DesignComputer SimulationFatigueHumansComputerized adaptive testingEcological momentary assessmentJust-in-time adaptive interventionmHealth

Identifiers

PMID36840916
PMCPMC10450096

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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.