Living for Today or Tomorrow? Self‐Regulation amidst Proximal or Distal Exercise Outcomes
Bibliographic record
Abstract
BACKGROUND: Although health promotion efforts to increase exercise behavior often emphasise long-term outcomes, sustained action in service of a distal reward is challenging. These studies examined how focusing on the proximal benefits of exercise, compared to distal outcomes or more general outcomes, may strengthen individuals' self-regulatory self-efficacy and support physical activity or exercise behavior. METHODS: Participants in Study 1 (N = 1057 community members) completed an online survey. Participants in Study 2 (N = 69 students) and Study 3 (N = 107 students) experienced experimental manipulations related to proximal or distal outcomes of exercise, and then completed survey measures. In Study 4, new members at a commercial gym (N = 210) completed a survey and had check-ins recorded over 17 weeks. RESULTS: In Study 1, participants who ranked proximal outcomes of exercise as relatively more important than distal outcomes reported more frequent physical activity. In Studies 2 and 3, participants induced to focus on proximal outcomes reported increased self-regulatory self-efficacy. In Study 4, valuing proximal benefits predicted sustained exercise behavior (i.e. check-ins), particularly when fitness goal adherence felt difficult. CONCLUSIONS: Those holding increased proximal outcome beliefs reported more activity and greater efficacy to overcome the barriers that derail exercise.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".