Evidence map›Paper›PMID 40226495›Full record

ReviewFrontiers in psychology2025

Dynamic representations of theory testing in physical activity using ecological momentary assessment: an example guide utilizing multi-process action control.

Ryan E Rhodes, Matthew Y W Kwan

Abstract readReview
In one paragraph

Review in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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.

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Ryan E RhodesSchool of Exercise Science, Physical and Health Education, University of Victoria, Victoria, BC, Canada.
Matthew Y W KwanChild and Youth Studies, Brock University, St. Catharines, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Behavioral theories are essential in understanding physical activity (PA) and developing effective intervention strategies, yet most theories have been developed alongside common research methods available at their inception. Contemporary data collection methods such as intensive longitudinal designs (e.g., Ecological Momentary Assessment; EMA) are beginning to facilitate more advanced approaches to theorizing. One of the primary challenges in applying traditional behavior change theories, however, relates to measurement, as traditional multi-item measures are not practical nor may they accurately capture the dynamic elements of the construct sought in intensive longitudinal sampling. The purpose of this paper was to provide a user's guide of measures of the Multi-Process Action Control (M-PAC) Framework for use in EMA, followed by preliminary working examples. EMA offers opportunities to sample and obtain real-time (or near real-time) information that include processes that are more automatically or immediately activated in response to environmental stimuli or informational cues. As a result, we propose a slight re-operationalization of M-PAC as it relates to the interacting psychological systems in determining PA. We outline some of the measurement challenges with M-PAC using EMA, and the opportunities to blend more traditional and contemporary real-time approaches to advance theory and our understanding of PA. Together, this paper is intended to be a starting point, acknowledging the need to adapt traditional behavioral theories to incorporate the dynamic factors in determining PA. By doing so, we can advance our understanding of PA and develop more effective, and theory-based, interventions tailored to individual needs and contexts.

Indexed as

behavioral regulationexercisehabithedonic motivationidentityintention-behavior gapopportunity

Identifiers

PMID40226495
PMCPMC11985800

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.