Listening to the body for insight: Self-focused attention during exercise predicts exercising over time
Bibliographic record
Abstract
Self-focused attention (SFA) causes self-evaluation. Some studies report that self-evaluation predicts exercise outcomes while others do not. Few studies have applied SFA to predict exercise behavior. We argue that trait self-control may be a critical moderator of SFA and exercise over time. Trait self-control is the ability to control responses and to alter behavior to meet goals. We predicted that trait self-control moderates SFA and exercising, and we tested the hypotheses that: 1) SFA at baseline (t1) predicts exercise at eight weeks post-baseline (t3); 2) SFA and exercise are each moderated by trait self-control; and 3) exercise pattern (consistent, variable, or no exercise pattern in past four weeks) moderates SFA and trait self-control's influence on exercising. METHODS. 79 students (22.7±4.9 yrs) completed the following self-report measures: SFA was created for this study; exercise over time (t1 & t3; Godin LTEQ); and trait self-control (Self-Control Scale). RESULTS. SFA at t1 was positively associated with exercising at t3 (p<.05). As expected, trait self-control moderated SFA and exercise at t3 (while controlling for t1 exercise), 95% CIs [.03, .49], such that SFA predicted exercise only when trait self-control was high, 95% CIs [1.73, 12.31]. Further, when exercise had no established pattern, then high trait self-control and high SFA were associated with increased exercise, 95% CIs [1.64, 21.45], while low trait self-control and high SFA was associated with lower exercise. CONCLUSION. High trait self-control individuals benefited from tuning-in to their body (i.e., SFA), especially if exercising were not yet an established activity for them.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".