ArticleArchives of orthopaedic and trauma surgery2026
Visual processing and interference performance influences on knee angular impulse in ACLR individuals: a cognitive-biomechanical analysis of drop-jumps.
Article in Archives of orthopaedic and trauma surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionVisual processing speed and cognitive interference control are critical for athletic performance and Anterior Cruciate Ligament (ACL) injury risk. However, how these cognitive functions relate to biomechanical performance after Anterior Cruciate Ligament Reconstruction (ACLR) remains unclear. This gap is clinically relevant given persistent re-injury rates of 20-25% in young athletes returning to sport, suggesting that rehabilitation may insufficiently address cognitive demands. This study examined cognitive differences between ACLR individuals and controls during drop-jump tasks, and their relationship with knee angular impulse. MATERIALS AND
methodsThirty-two females (16 ACLR, 16 controls; 20 ± 1 years) completed cognitive assessments including the Stroop Color and Word Test, Trail Making Test, Digit Span Memory Test, and visual/auditory reaction time tests. Participants performed drop-jumps under four conditions: standard, choice, visual-cued, and audio-cued. Knee angular impulse was calculated for eccentric, concentric, and net phases. Binomial logistic regression identified cognitive predictors distinguishing groups, followed by factorial ANOVA to assess biomechanical differences. Spearman correlations examined relationships between cognition and knee angular impulse.
resultsThree cognitive variables distinguished groups: cognitive interference score, visual simple reaction time, and visual complex reaction time (χ²(3) = 55.090, p < 0.001). The ACLR group demonstrated faster visual reaction times but impaired interference control compared to controls. Biomechanically, ACLR participants showed lower eccentric knee angular impulse (p = 0.003, d=-0.37), indicating persistent protective strategies. Audio-cued conditions elicited higher eccentric impulse than standard and choice conditions. Minimal correlations between cognitive variables and eccentric knee angular impulse suggest independent (parallel) adaptations.
conclusionsACLR individuals exhibit distinct cognitive and biomechanical profiles characterized by enhanced visual reactivity, reduced interference control, and altered landing mechanics. The lack of association between domains indicates parallel adaptations, supporting rehabilitation approaches that independently target both cognitive function and biomechanical performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.