Injury Risk Factors Integrated Into Self-Guided Real-Time Biofeedback Improves High-Risk Biomechanics
Open accessAbstract
Context: Existing anterior cruciate ligament (ACL) injury prevention programs have failed to reverse the high rate of ACL injuries in adolescent female athletes. Objective: This investigation attempts to overcome factors that limit efficacy with existing injury prevention programs through the use of a novel, objective, and real-time interactive visual feedback system designed to reduce the biomechanical risk factors associated with ACL injuries. Design: Crossover study. Setting: Medical center laboratory. Participants: Twenty female participants (age = 19.7 ± 1.34 y; height = 1.74 ± 0.09 m; weight = 72.16 ± 12.45 kg). Methods: Participants performed sets of 10 bodyweight squats in each of eight training blocks (i.e., four real-time and four control blocks) and three testing blocks for a total of 110 squats. Feedback conditions were blocked and counterbalanced with half of participants randomly assigned to receive the real-time feedback block first and half receiving the control (sham) feedback first. Results: Heat map analysis revealed that during interaction with the real-time feedback, squat performance measured in terms of key biomechanical parameters was improved compared to when participants squatted with the sham stimulus. Conclusion: This study demonstrates that the interactive feedback system guided participants to significantly improve movement biomechanics during performance of a body weight squat, which is a fundamental exercise for a longer-term ACL injury risk reduction intervention. A longer training and testing period is necessary to investigate the efficacy of this feedback approach to effect long-term adaptations in the biomechanical risk profile of athletes.
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Cites
Statistics
Claims the paper makes
8 of 25 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Existing ACL injury prevention programs have not reduced the high rate of ACL injuries in adolescent female athletes.
“Existing anterior cruciate ligament (ACL) injury prevention programs have failed to reverse the high rate of ACL injuries in adolescent female athletes.”
- Also stated in: § Introduction · plain
“while these interventions can reduce injury risk factors in the lab their effects have not shown transfer to the actual sport environment, as injury rates have not declined despite the introduction of these programs20–24.”
Quote in the paperSubstantial claim2 of 5 sources checked- cites [20] source
- cites [21] source
The Conclusions (page unavailable) discuss how attentional focus might improve motor learning and potentially reduce injuries, but do not evaluate ACL injury prevention programs or injury rates. Searches for ACL prevention and anterior cruciate ligament injuries in adolescent female athletes found no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [22] source
- cites [23] source
The Methods and Results passages describe a hand-movement targeting task, not ACL injuries or prevention programs. Searches for ACL injury prevention and female athletes found no passage in our copy addressing whether existing programs reduced injury rates. (LLM, conf=0.78)
Experimental — accuracy not yet measured - cites [24] source
- Also stated in: § Introduction · plain
- Premisecitation
Five biomechanical variables-lateral trunk flexion, knee-to-hip joint moment ratio, knee abduction moment, vertical ground reaction force ratio, and foot center of pressure location-have been identified as ACL injury risk factors.
“five kinematic and kinetic variables previously identified as ACL-injury risk factors (for justification and references see Table 1)”
- Also stated in: § Materials, Apparatus, and Stimulus Design · plain
“(1) lateral trunk flexion 21, (2) knee-to-hip joint moment of force ratio (KHMr), (3) knee abduction moment of force (KAM) 21, (4) vertical ground reaction force ratio (vGRF), and (5) foot center of pressure (foot COP) location.”
Quote in the paperSubstantial claim2 of 4 sources checked- cites [51] source
- cites [52] source
The Clinical relevance passage links lateral trunk motion and knee abduction load to ACL injury risk in female athletes, and the Discussion describes a possible role for ground reaction force. The paper does not identify a knee-to-hip joint moment ratio, a vertical ground reaction force ratio, or foot center-of-pressure location as risk factors. Its discussion of flat-footed landing is not the specified center-of-pressure variable. Page numbers were not available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites [53] source
- cites [21] source
The 'ATTENTIONAL FOCUS AND MOTOR CONTROL' passages (page unavailable) discuss balance, movement control, and attentional focus, not ACL injury risk factors. Searches for the named biomechanical variables and for anterior cruciate ligament injury returned no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
- Also stated in: § Materials, Apparatus, and Stimulus Design · plain
- Findingown data
During acquisition, squatting performance (heat map percentage) was significantly better with real-time biofeedback than with sham feedback (61.82% vs 57.56%, p=.006, d=0.57).
“Performance during the real-time biofeedback trials (M = 61.82%, SD = 6.42%) was better than during the sham feedback trials (M = 57.56%, SD = 8.40%), t(19) = 3.06, p = .006, d = 0.57.”
- Also stated in: § Results: · plain
“Heat map analysis revealed that during interaction with the real-time feedback, squat performance measured in terms of key biomechanical parameters was improved compared to when participants squatted with the sham stimulus.”
- Also stated in: § Discussion · plain
“Squatting performance, as measured through heat map analysis, was significantly better when participants interacted with the real-time biofeedback system compared to the sham.”
Quote in the paperSubstantial claim - Also stated in: § Results: · plain
- Findingown data
There were no significant differences in the number of squats performed between the real-first and sham-first feedback groups.
“There were no significant differences in the number of squats performed between the real-first feedback group (M = 111.80, SD = 7.15) and the sham-first feedback group (M = 114.30, SD = 5.76), t(18) = 0.86, p = .40, d = 0.39.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The brief practice period (only 40 total squat repetitions in a single session lasting about 10 minutes) is a limitation that may explain the lack of learning effects.
“Most importantly, and one limitation of this study, was the brief practice period—only 40 total squat repetitions were performed in a single session lasting only about 10 minutes for each participant.”
Quote in the paperSubstantial claim
- Interpretationown data
The design and implementation demonstrate that it is feasible to map multiple variables associated with ACL injury risk in real-time.
“Our design and implementation of the current stimuli, while overcoming the aforementioned technical difficulties, also demonstrate that it is feasible to map multiple variables associated with ACL injury risk in real-time.”
Quote in the paperSubstantial claim - Interpretationown data
A single session with real-time biofeedback was not effective for eliciting stable motor skill changes.
“Non-significant differences between the groups in the absence of the stimulus during both retention tests suggests that a single session with real-time biofeedback was not effective for eliciting stable motor skill changes.”
Quote in the paperSubstantial claim - Interpretationreasoning
The inability to determine how changes in squat mechanics transferred to more dynamic tasks requires cautious interpretation of the real-time biofeedback's ability to reduce ACL injury rates.
“Finally, our inability to determine how changes in squat mechanics transferred to more dynamic tasks, such as the drop vertical jump, require cautious interpretation in our real-time biofeedback’s ability to reduce ACL injury rates.”
Quote in the paperSubstantial claim - Interpretationown data
In a single testing session, the study demonstrated an immediate change in biomechanics associated with ACL injury risk when participants' movements were guided by the interactive stimulus.
“In a single testing session we demonstrated an immediate change in biomechanics associated with ACL injury risk when participants’ movements were guided by the interactive stimulus.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 6
- May combine two claims. 11
- The quote only partly states this claim. 1
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe double-leg squat is commonly used in ACL prevention programs.
“A common exercise, the double-leg squat, is commonly used in ACL prevention programs.14, 25.”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [14] source
- cites [25] source
Section 4.1, “Clinical Examples of Real-Time Feedback” (page unavailable), says a bilateral squat is often used for feedback in patients with side-to-side asymmetries after ACL injury. It does not establish that double-leg squats are commonly used in ACL prevention programs generally. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationExternal perceptual control engages automatic and implicit motor strategies that are more efficient than deliberate conscious control, leading to fast, robust learning with high retention and transfer.
“This mode of control engages automatic and implicit motor strategies that operate more efficiently and effectively than deliberate, explicitly conscious control of movement28–37, which yields fast, robust learning, leading to high retention and transfer38–43.”
Quote in the paperSubstantial claim3 of 10 sources checked- cites [28] source
- cites [29] source
- cites [30] source
The Introduction and Practical Applications sections describe a field-based algorithm for identifying ACL injury risk and guiding targeted neuromuscular training. Searches for automatic or implicit motor control and for fast learning, retention, and transfer found no passage addressing those claims in our copy; page numbers were not available. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [31]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [32] source
- cites [33] source
The Discussion reports that reduced loading after gait retraining persisted at the one-month follow-up, supporting retention of the trained change. Neither the Discussion nor the Introduction establishes fast learning, transfer to other tasks, or that automatic and implicit control is more efficient than deliberate conscious control. The paper attributes the persistence to faded feedback only as a postulate. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [34] source
- cites [35] source
- cites [36] source
The Methods (Intervention; page not available) describe a neuromuscular warm-up, and the Discussion (Prevention of knee injury; page not available) addresses injury prevention. Neither addresses external perceptual control, implicit versus conscious motor strategies, or learning speed, retention, and transfer. Searches for those concepts found no relevant passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [37] source
May combine two claims.
Findingown dataDuring acquisition, there were no significant differences in overall squatting performance for condition, trial block, or their interaction in either acquisition phase.
“There were no significant differences in overall squatting performance as measured by the heat map percentage scores for condition (F(1, 18) = 2.03, p = .17, ƞp2 = .10), trial block (F(3, 54) = 0.86, p = .47, ƞp2= .05), and a condition × trial block interaction (F(3, 54) = 0.23, p = .88, ƞp2 = .01) during acquisition phase 1.”
- Also stated in: § Acquisition Phase Squatting Performance · plain
“The same pattern of results held for acquisition phase 2: condition (F(1, 18) = 1.15, p = .30, ƞp2= .06), trial block (F(3, 54) = 1.90, p = .14, ƞp2 = .10), and condition × trial block interaction (F(3, 54) = .45, p = .72, ƞp2 = .02).”
Quote in the paperNot a substantial claim- Also stated in: § Acquisition Phase Squatting Performance · plain
May combine two claims.
Findingown dataDuring the three test phases assessing learning, there were no significant differences in squatting performance for order, test phase, or their interaction.
“There were no significant differences in squatting performance for order, (F(1, 18) = 0.56, p = .46, ƞp2 = 0.03), test phase (F(2, 36) = 0.19, p = .83, ƞp2 = .01), or test phase × trial block interaction (F(2, 36) = 0.43, p = .66, ƞp2 = .02) during the three test phases that assessed learning.”
Quote in the paperSubstantial claimMay combine two claims. The quote only partly states this claim.
Study design and conductreasoningThe sham feedback provided a comparable visual appearance to the real feedback but limited useful information about squat biomechanics, making it an ineffective guide.
“the sham feedback was designed to limit the amount of useful feedback information available to participants during the squat movements.”
- Also stated in: § Materials, Apparatus, and Stimulus Design · plain
“it provided little to no actual feedback”
Quote in the paperSubstantial claim- Also stated in: § Materials, Apparatus, and Stimulus Design · plain
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationMassed practice is effective for acquisition but typically less successful for learning than distributed practice.
“Relatedly, a second limitation is that the practice schedule used in the current study had minimal breaks (i.e., massed practice) which is effective for acquisition, but is typically less successful for learning than when breaks are administered (i.e., distributed practice)55, 56.”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe sample size (n=20) could be considered small.
“Third, although we used a crossover design to increase statistical power, our sample size (n =20) could nevertheless be considered small.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationPrevious real-time biofeedback studies for ACL prevention were limited because control conditions used traditional feedback or biofeedback was localized to a single factor such as knee abduction/adduction and did not incorporate the full biomechanical profile.
“However, limitations of these previous studies are that control conditions were compared with traditional feedback50, or that biofeedback was localized to a single factor such as knee abduction/adduction45 and did not incorporate the full biomechanical profile associated with ACL injury risk.”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
Interpretationown dataSquatting performance improved only when the real feedback shape was present during acquisition and not when the real feedback was absent for learning assessment.
“Thus, squatting performance only improved when the real feedback shape was present during acquisition and not when the real feedback was absent for learning assessment.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe study was approved by the Institutional Review Board and all participants gave written informed consent.
“The study was approved by the Institutional Review Board and all participants gave written informed consent prior to participation.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningThe study used a crossover design to increase statistical power.
“Third, although we used a crossover design to increase statistical power”
- Also stated in: § Procedure · plain
“In order to compare the effects of the real-time and sham feedback displays, an AB/BA or two-treatment crossover design was utilized.”
Quote in the paperSubstantial claim- Also stated in: § Procedure · plain
May combine two claims.
Interpretationown dataThe interactive feedback system guided participants to significantly improve movement biomechanics during a body weight squat, which is a fundamental exercise for a longer-term ACL injury risk reduction intervention.
“This study demonstrates that the interactive feedback system guided participants to significantly improve movement biomechanics during performance of a body weight squat, which is a fundamental exercise for a longer-term ACL injury risk reduction intervention.”
- Also stated in: § Conclusion · boosted
“In conclusion, this study demonstrates the efficacy of novel real-time, interactive biofeedback system for improving squat performance.”
Quote in the paperSubstantial claim- Rests on an assumption: Improving squat biomechanics reduces the risk of ACL injury.
- Also stated in: § Conclusion · boosted
May combine two claims.
Interpretationown dataInteracting with the stimulus alone was not sufficient to improve performance, but accurate mapping from kinematics to feedback is necessary for improved performance.
“Third, our inclusion of the sham feedback demonstrated that interacting with the stimulus alone was not sufficient to improve performance, but an accurate mapping from kinematics to the feedback is necessary for improved performance.”
- Also stated in: § Conclusion · hedged
“Further, it appears that real-time biofeedback that is not properly mapped on to biomechanics (i.e., our sham condition) is less effective, highlighting the importance of accurate and appropriate mapping of key variables.”
Quote in the paperSubstantial claim- Rests on an assumption: The sham feedback condition provides no useful information for improving squat biomechanics.
- Also stated in: § Conclusion · hedged
Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataParticipants were randomly assigned to receive real-time feedback first or sham feedback first, and feedback conditions were blocked and counterbalanced.
“Feedback conditions were blocked and counterbalanced with half of participants randomly assigned to receive the real-time feedback block first and half receiving the control (sham) feedback first.”
- Also stated in: § Procedure · plain
“We randomly assigned participants to one of two groups: real-time biofeedback first (i.e., ‘A’) or sham feedback first (i.e., ‘B’).”
Quote in the paperSubstantial claim- Also stated in: § Procedure · plain
The paper relies on this without support.
Improving squat biomechanics reduces the risk of ACL injury.
“designed to reduce the biomechanical risk factors associated with ACL injuries”
“which is a fundamental exercise for a longer-term ACL injury risk reduction intervention”
“Prevention programs could use this stimulus to improve performance of prophylactic exercises, which may lead to decreased ACL injury risk.”
The paper relies on this without support.
The sham feedback condition provides no useful information for improving squat biomechanics.
“the sham feedback was designed to limit the amount of useful feedback information available to participants during the squat movements.”
“it provided little to no actual feedback”
The paper relies on this without support.
The heat map percentage score is a valid measure of overall squat biomechanics associated with ACL injury risk.
“The heat maps provided a global assessment of squatting performance by indicating how the movement patterns of the biomechanical variables associated with ACL injury related to the target feedback shape (i.e., a rectangle).”
“which meant that the relevant biomechanical parameters were achieving the desired optimal values associated with lower injury risk.”
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentt(18) = 0.86reported p = .4 · recomputed p = .401
“t(18) = 0.86, p = .40”
- ConsistentF(1, 18) = 2.03reported p = .17 · recomputed p = .171
“F(1, 18) = 2.03, p = .17”
- ConsistentF(3, 54) = 0.86reported p = .47 · recomputed p = .468
“F(3, 54) = 0.86, p = .47”
- ConsistentF(3, 54) = 0.23reported p = .88 · recomputed p = .875
“F(3, 54) = 0.23, p = .88”
- ConsistentF(1, 18) = 1.15reported p = .3 · recomputed p = .298
“F(1, 18) = 1.15, p = .30”
- ConsistentF(3, 54) = 1.9reported p = .14 · recomputed p = .141
“F(3, 54) = 1.90, p = .14”
- ConsistentF(3, 54) = 0.45reported p = .72 · recomputed p = .718
“F(3, 54) = .45, p = .72”
- Consistentt(19) = 3.06reported p = .006 · recomputed p = .006
“t(19) = 3.06, p = .006”
- ConsistentF(1, 18) = 0.56reported p = .46 · recomputed p = .464
“F(1, 18) = 0.56, p = .46”
- ConsistentF(2, 36) = 0.19reported p = .83 · recomputed p = .828
“F(2, 36) = 0.19, p = .83”
- ConsistentF(2, 36) = 0.43reported p = .66 · recomputed p = .654
“F(2, 36) = 0.43, p = .66”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Learning-phase interaction names the wrong factor
MinorThe learning-phase results report an interaction involving trial block, but the specified learning analysis has order and test phase as its two factors. Trial block belongs to the separate acquisition analyses.
“test phase × trial block interaction (F(2, 36) = 0.43, p = .66, ƞp2 = .02)”
“To assess learning (i.e., absent visual feedback), a 2 (order; sham-first and real-first feedback) × 3 (test phase; pre-test, mid-test, post-test) mixed ANOVA with repeated measure on the last factor was conducted.”
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Cited by
Sorted by citations
Diekfuss, J. A., Grooms, D. R., Bonnette, S., DiCesare, C. A., Thomas, S. M., MacPherson, R. P., Ellis, J. D., Kiefer, A. W., Riley, M. A., Schneider, D. K., et al. (2020) · Psychophysiology · doi:10.1111/psyp.13545 · Cited by 50 · see citing works
Self-citationDiekfuss, J. A., Grooms, D. R., Nissen, K. S., Schneider, D. K., Foss, K. D. B., Thomas, S. M., Bonnette, S., Dudley, J. A., Yuan, W., Reddington, D. L., et al. (2019) · Brazilian Journal of Physical Therapy · doi:10.1016/j.bjpt.2019.07.004 · Cited by 45 · see citing works
Self-citationBonnette, S., Diekfuss, J. A., Grooms, D. R., Kiefer, A. W., Riley, M. A., Riehm, C. D., Moore, C., Foss, K. D. B., DiCesare, C. A., Baumeister, J., et al. (2020) · Psychophysiology · doi:10.1111/psyp.13530 · Cited by 33 · see citing works
“Conceptually similar paradigms can be used to implicitly improve sensorimotor control related to ACL injury risk, as prior work has demonstrated (Bonnette et al., 2019).”
BackgroundMethodSelf-citationDiekfuss, J. A., Bonnette, S., Hogg, J. A., Riehm, C. D., Grooms, D. R., Singh, H., Anand, M., Slutsky‐Ganesh, A. B., Wilkerson, G. B., & Myer, G. D. (2020) · Journal of Science in Sport and Exercise · doi:10.1007/s42978-020-00083-0 · Cited by 31 · see citing works
Self-citationA Design Space of Sports Interaction Technology
Postma, D. B. W., van Delden, R. W., Koekoek, J., Walinga, W. W., van Hilvoorde, I. M., van Beijnum, B.-J. F., Salim, F. A., & Reidsma, D. (2022) · Foundations and Trends® in Human–Computer Interaction · doi:10.1561/1100000087 · Cited by 31 · see citing works
Diekfuss, J. A., Grooms, D. R., Hogg, J. A., Singh, H., Slutsky‐Ganesh, A. B., Bonnette, S., Riehm, C. D., Anand, M., Nissen, K. S., Wilkerson, G. B., et al. (2021) · Journal of Science in Sport and Exercise · doi:10.1007/s42978-020-00085-y · Cited by 27 · see citing works
Self-citationvan Delden, R. W., Bergsma, S., Vogel, K., et al. (2020) · Annual Symposium on Computer-Human Interaction in Play · doi:10.1145/3383668.3419865 · Cited by 22 · see citing works
“Inspired by [4], we used a simplified real-time depiction to indicate timing and order of movements during the recovery of rowing, similar to the right side of Figure 2, but we overlayed the performance of the last stroke over this goal timing.”
MethodBates, N. A., Huffman, A., Goodyear, E. G., Nagai, T., Rigamonti, L., Breuer, L., Holmes, B. D., & Schilaty, N. D. (2023) · Clinical Biomechanics · doi:10.1016/j.clinbiomech.2023.105902 · Cited by 23 · see citing works
Kernozek, T. W., Schiller, M., Rutherford, D. N., Smith, A., Durall, C. J., & Almonroeder, T. G. (2020) · Clinical Biomechanics · doi:10.1016/j.clinbiomech.2020.105050 · Cited by 21 · see citing works
Diekfuss, J. A., Hogg, J. A., Grooms, D. R., Slutsky‐Ganesh, A. B., Singh, H., Bonnette, S., Anand, M., Wilkerson, G. B., & Myer, G. D. (2020) · Journal of Science in Sport and Exercise · doi:10.1007/s42978-020-00080-3 · Cited by 17 · see citing works
Self-citationFoss, K. D. B., Slutsky‐Ganesh, A. B., Diekfuss, J. A., Grooms, D. R., Simon, J. E., Schneider, D. K., Jayanthi, N. A., Lamplot, J. D., Hill, D., Pombo, M. W., et al. (2022) · Journal of Sport Rehabilitation · doi:10.1123/jsr.2021-0236 · Cited by 15 · see citing works
Self-citationComplexity in Science Learning: Measuring the Underlying Dynamics of Persistent Mistakes
Fleuchaus, E., Kloos, H., Kiefer, A. W., & da Silva, P. L. P. (2019) · The Journal of Experimental Education · doi:10.1080/00220973.2019.1660603 · Cited by 11 · see citing works
Self-citationKal, E. C., Ellmers, T. J., Hogg, J. A., et al. (2022) · Journal of Athletic Training · doi:10.4085/1062-6050-0166.22 · Cited by 10 · see citing works
Self-citationBonnette, S., DiCesare, C. A., Diekfuss, J. A., Grooms, D. R., MacPherson, R. P., Riley, M. A., & Myer, G. D. (2019) · Journal of Athletic Training · doi:10.4085/1062-6050-54.083 · Cited by 9 · see citing works
Self-citationAnand, M., Diekfuss, J. A., Bonnette, S., Short, I., Hurn, M., Grooms, D. R., & Myer, G. D. (2020) · International Journal of Sports Physical Therapy · doi:10.26603/ijspt20200936 · Cited by 8 · see citing works
Self-citationEditorial: The role of biomechanics in anterior cruciate ligament injuries prevention
Saxby, D. J., Catelli, D. S., Lloyd, D. G., & SAWACHA, Z. (2023) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2023.1134969 · Cited by 5 · see citing works
“approaches such as artificial intelligence platforms and machine learning (27, 28), virtual reality for injury risk screening (29), return to sports assessment (30), and interactive augmentedreality-based neuromuscular training methods (31) have great potential to lead to a better understanding of the role of biomecha…”
BackgroundHogg, J. A., Riehm, C. D., Diekfuss, J. A., Simon, J. E., Acocello, S. N., Liang, Y., Wu, D., Myer, G. D., & Wilkerson, G. B. (2022) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000004203 · Cited by 2 · see citing works
“Given that injury prevention programs have largely been developed using female-dominant movement patterns and that injurymechanisms, biomechanics, and learning (4) are purported to be different between male subjects and female subjects, it is reasonable that male subjects would respond differently than female subjects…”
BackgroundSelf-citationHogg, J. A., Wilkerson, G. B., Acocello, S. N., et al. (2024) · Perceptual and Motor Skills · doi:10.1177/00315125241246361 · Cited by 2 · see citing works
“Spe-ci fi cally, employing real-time visual biofeedback mitigated high-risk biomechanics (Bonnette et al., 2019), and delivering simple EE and AS instructions improved postural balance measures (Chua et al., 2020).”
BackgroundMethodSelf-citationLiu, Z. (2024) · Molecular & cellular biomechanics · doi:10.62617/mcb.v21.197 · Cited by 1 · see citing works
“In order to reduce the biomechanical risk factors related to injury, he used a unique, objective and real-time method to avoid the existing injury problem, which was multi feature medical image fusion [4].”
MethodPrävention von Knieverletzungen
Stoffels, T., & Praetorius, A. (2021) · Knie Journal · doi:10.1007/s43205-020-00091-8
McPherson, A. L., Hogg, J. A., Foreman, E., Riehm, C. D., Howell, D. R., Grooms, D. R., & Myer, G. D. (2025) · Strength and conditioning journal · doi:10.1519/ssc.0000000000000902
Self-citationYang, B., Zhang, X., He, X. H., et al. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3770692
Slutsky‐Ganesh, A. B., Chaput, M., Hogg, J. A., Zuleger, T. M., Kim, H., Diekfuss, J. A., Anand, M., Schille, A., DiCesare, C. A., Warren, S. M., et al. (2026) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003960
Self-citationHogg, J. A., Bonnette, S., Riehm, C. D., Thomas, S. M., Anand, M., DiCesare, C. A., Foss, K. D. B., Diekfuss, J. A., Beasley, K., Schille, A., et al. (2026) · Sports Health A Multidisciplinary Approach · doi:10.1177/19417381261476462
Self-citation
- Authors
- Bonnette, S., DiCesare, C. A., Kiefer, A. W., Riley, M. A., Barber Foss, K. D., Thomas, S., Kitchen, K., Diekfuss, J. A., & Myer, G. D.
- Affiliations
- Cincinnati Children's Hospital Medical Center (US)
- University of Cincinnati (US)
- Corresponding author
- Bonnette, Scott
- ORCID
- Venue
- Journal of sport rehabilitation
- Publisher
- Human Kinetics
- Published
- 2019-11-01
- Volume
- 28
- Issue
- 8
- Pages
- 831-839
- ISSN
- 1056-6716 (ISSN-L)
- 1543-3072
- PMC
- PMC6667313
- OpenAlex
- W2809977892
- Cited by
- 25
- References
- 68
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 2 · 2020: 7 · 2019: 3
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Sport Rehabilitation (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (U01AR067997, R21AR065068)
- Topics
- Health Sciences › Medicine › Surgery › Knee injuries and reconstruction techniques
- Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports injuries and prevention
- Keywords
- anterior cruciate ligament injury, injury prevention, real-time biofeedback, squat biomechanics, female athletes, youth athletes, injury risk factors, self-guided training
- MeSH terms
- Anterior Cruciate Ligament Injuries, Adolescent, Adult, Athletic Injuries, Biomechanical Phenomena, Female, Humans, Kinetics, Risk Factors, Cross-Over Studies, Exercise Movement Techniques, Young Adult, Feedback, Sensory
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read under definition version 3 (extract-v3) on 2026-09-28.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-09-28.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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