Self-control strength depletion reduces self-efficacy to exert self-control, task self-efficacy, and impairs resistance exercise performance
Bibliographic record
Abstract
Recent research based on the strength model (Baumeister, 2014) showed self-control depletion led to a reduction in task self-efficacy, which was found to mediate the effect of depletion on physical endurance (Graham & Bray, 2015). However, because self-control strength reflects a generalized capacity, we reasoned that self-control depletion should lead to an overall reduction in self-efficacy to exert self-control (SESC), which then informs task self-efficacy. This study investigated the effects of self-control depletion on perceived SESC, task self-efficacy, and endurance performance of resistance exercise. We tested a sequential mediation model predicting self-control depletion --> SESC --> task self-efficacy --> task performance. Participants (N = 50) completed a baseline measure of SESC and then performed one set of maximum repetitions on bench press (at 60% of 1RM) and leg extension (at 40% of 1RM) followed by either an incongruent (depletion) or congruent (control) Stroop task. They then completed measures of SESC and task self-efficacy, followed by a second set of maximum repetitions. Participants in the depletion condition reported lower SESC and task self-efficacy, and performed fewer repetitions compared to controls (ps < .01). Mediation analyses revealed an indirect (mediation) effect for task self-efficacy in the relationship between self-control depletion and performance for bench press (95% C.I. = 0.28-1.98) and leg extension (95% C.I. = 0.19-1.38). However, the effects for SESC were not significant. Although sequential mediation was not evident, findings supported theorizing that self-control depletion should weaken SESC. Results also extend prior research and are the first to have used resistance exercise as a physical performance task.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".