Dynamic representations of theory testing in physical activity using ecological momentary assessment: an example guide utilizing multi-process action control
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".