Using Daily Stretching to Counteract Performance Decreases as a Result of Reduced Physical Activity—A Controlled Trial
Bibliographic record
Abstract
There are many reasons for reduced physical activity leading to reduced maximal strength and sport-specific performance, such as jumping performance. These include pandemic lockdowns, serious injury, or prolonged sitting in daily work life. Consequently, such circumstances can contribute to increased morbidity and reduced physical performance. Therefore, a demand for space-saving and home-based training routines to counteract decreases in physical performance is suggested in the literature. This study aimed to investigate the possibility of using daily static stretching using a stretching board to counteract inactivity-related decreases in performance. Thirty-five (35) participants were either allocated to an intervention group (IG), performing a daily ten-minute stretch training combined with reduced physical activity or a reduced physical activity-only group (rPA). The effects on maximal voluntary contraction, range of motion using the knee-to-wall test, countermovement jump height (CMJheight), squat jump height (SJheight), drop jump height (DJheight), contact time (DJct) and the reactive strength index (DJRSI) were evaluated using a pre-test-post-test design. The rPA group reported reduced physical activity because of lockdown. Results showed significant decreases in flexibility and jump performance (d = −0.11–−0.36, p = 0.004–0.046) within the six weeks intervention period with the rPA group. In contrast, the IG showed significant increases in MVC90 (d = 0.3, p < 0.001) and ROM (d = 0.44, p < 0.001) with significant improvements in SJheight (d = 0.14, p = 0.002), while no change was measured for CMJheight and DJ performance. Hence, 10 min of daily stretching seems to be sufficient to counteract inactivity-related performance decreases in young and healthy participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".