Transitions in Motivational Profiles among Mid-life Adults in the Context of an RCT Designed to Increase Exercise Self-Regulation
Bibliographic record
Abstract
The association between cognitive training and exercise motivation remains unclear, particularly when the training is not explicitly focused on modifying goal content. Our study investigated the impact of such training on exercise-related decision-making skills among a sample of low-active adults (N = 233; Mage = 46.7 years) who completed the Exercise Motivation Inventory-2 (EMI2) at baseline and one-month follow-up. Latent profile analyses (LPA) were used to identify participants’ motivational profiles at each time point, and longitudinal tests of profile similarity and latent transition analyses (LTA) assessed the replicability of these profiles and the stability of participants’ profile membership over time. Our results revealed four profiles, which were replicated over time: Weakly Motivated, Moderately Motivated with Fitness Orientation, Moderately Motivated with Psychological Well-being Orientation, and Strongly Motivated. Membership to these profiles was not influenced by participants demographic characteristics, and only minimally by baseline Fitbit steps and cardiorespiratory fitness. The first two profiles displayed fewer months (over a period of 12 months post-intervention) meeting physical activity guidelines, whereas the latter two showed higher levels of adherence. The Strongly Motivated profile was the least stable but most influenced by the cognitive training intervention, whereas the Moderately Motivated with Fitness Orientation was more stable but similarly influenced by the intervention. These findings highlight the potential of a multimodal cognitive training in influencing exercise motivation and behavior, providing practical implications for tailoring interventions to specific motivational profiles. Future research should investigate the specific types of cognitive training most effective for enhancing exercise motivation and adherence.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".