Motion and Muscle Activity Are Affected by Instability Location During a Squat Exercise
Bibliographic record
Abstract
Nairn, BC, Sutherland, CA, and Drake, JDM. Motion and muscle activity are affected by instability location during a squat exercise. J Strength Cond Res 31(3): 677-685, 2017-Squat exercise training using instability devices has become increasingly popular for a multitude of reasons. Many devices generate instability at the feet and provide a bottom-up perturbation; however, the effect of a top-down instability device during a squat remains unclear. To induce instability at the upper body, a water-filled cylinder called the Attitube was used. This study analyzed the effects of instability location (top-down, bottom-up, and no instability) during a squat exercise in terms of kinematics and muscle activation. Ten male participants were instrumented with 75 reflective markers to track kinematics of the ankle, knee, hip, trunk, and the Bar/Attitube, and electromyography was recorded from 12 muscles bilaterally. Squats were performed with an Olympic bar on a stable surface, an Olympic bar on a BOSU ball (BALL, bottom-up), and the Attitube on solid ground (TUBE, top-down). The TUBE showed up to 1.5 times reduction in erector spinae activation and up to 1.5 times less trunk flexion while being performed at a slower velocity. There was also higher abdominal activation in the TUBE, with up to 2.8 times greater oblique activation compared with the stable condition. The BALL increased ankle eversion and knee flexion with higher muscle activation in gastrocnemius, biceps femoris, and quadriceps. Overall, changing the location of instability during a squat changed the motion and muscle activation patterns of the trunk and lower extremities. This provides information for future research into rehabilitation, learning proper squat technique, and for specific training scenarios.
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 | 0.001 | 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 | 0.000 |
| 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".