Towards Optimal Health Through Boredom Aversion Based on Experiencing Psychological Flow in a Self-Directed Exercise Regime—A Scoping Review of Recent Research
Bibliographic record
Abstract
BACKGROUND: Optimal health requires self-direction for exercise regime consistency. Boredom may cause abandoning regular exercise. Experiencing psychological flow-a concept psychologist Csikszentmihalyi originated-may avert boredom. METHOD: A search of post-2020 peer-reviewed publications following the PRISMA-ScR guidelines for scoping reviews investigates the range of research on this topic. The databases searched are OVID, ProQuest, PubMed, Scopus, Web of Science, and Google Scholar. The keywords are "Csikszentmihalyi AND flow AND exercise AND boredom". Included returns contain all the keywords. Those excluded are reviews, books, reports missing any keywords, non-English reports, reports not based on research studies, and research published before 2020. RESULTS: Two databases returned the included results: OVID (n = 3) and Google Scholar (n = 8). CONCLUSIONS: (1) Boredom is not evident when experiencing exercise-programme psychological flow. (2) Psychological flow evolves with self-directed changes in an exercise programme. (3) Successful exercise programme modifications during COVID-19 considered the imposed limitations. (4) Exercise regimes that are neither excessive nor extreme promote optimal health. And (5) optimal health accounts for exercise skill level and gender. Additionally, cognitive bias is avertable with a research team. Studies should include the research date and location and how flow reduces boredom, permitting accurate comparisons.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".