Background: Running-related overuse injuries can result from the combination of extrinsic(e.g., running mileage) and intrinsic risk factors(e.g.,biomechanics and gender), but the relationship between these factors is ...Background: Running-related overuse injuries can result from the combination of extrinsic(e.g., running mileage) and intrinsic risk factors(e.g.,biomechanics and gender), but the relationship between these factors is not folly understood. Therefore, the first purpose of this study was to determine whether we could classify higher-and lower-mileage runners according to differences in lower extremity kinematics during the stance and swing phases of running gait. I he second purpose was to subgroup the runners by gender and determine whether we could classify higherand lower-mileage runners in male and female subgroups.Methods: Participants were allocated to the "higher-mileage" group(≥32 km/week; n= 41(30 females)) or to the "lower-mileage" group(≤25 km;n= 40(29 females)). Three-dimensional kinematic data were collected during 60 s of treadmill running at a self-selected speed(2.61 ± 0.23 m/s).A support vector machine classifier identified kinematic differences between higher-and lower-mileage groups based on principal component scores.Results: Higher-and lower-mileage runners(both genders) could be separated with 92.59% classification accuracy. When subgrouping by gender,higher-and lower-mileage female runners could be separated with 89.83% classification accuracy, and higher-and lower-mileage male runners could be separated with 100% classification accuracy.Conclusion: These results demonstrate there are distinct kinematic differences between subgroups related to both mileage and gender, and that these factors need to be considered in future research.展开更多
Background:Advances in artificial intelligence have enabled the simulation of human-like behaviors,raising the possibility of using large language models(LLMs)to generate synthetic population samples for research purp...Background:Advances in artificial intelligence have enabled the simulation of human-like behaviors,raising the possibility of using large language models(LLMs)to generate synthetic population samples for research purposes,which may be particularly useful in health and social sciences.Methods:This paper explores the potential of LLMs to simulate population samples mirroring real ones,as well as the feasibility of using personality questionnaires to assess the personality of LLMs.To advance in that direction,2 experiments were conducted with GPT-4o using the Eysenck Personality Questionnaire Revised-Abbreviated(EPQR-A)in 6 languages:Spanish,English,Slovak,Hebrew,Portuguese,and Turkish.Results:We find that GPT-4o exhibits distinct personality traits,which vary based on parameter settings and the language of the questionnaire.While the model shows promising trends in reflecting certain personality traits and differences across gender and academic fields,discrepancies between the synthetic populations’responses and those from real populations remain.Conclusions:These inconsistencies suggest that creating fully reliable synthetic population samples for questionnaire testing is still an open challenge.Further research is required to better align synthetic and real population behaviors.展开更多
基金partially provided by a Discovery Grant (No.1028495) and Accelerator Award (No.1030390) through the Natural Sciences and Engineering Research Council of Canada (NSERC)the Faculty of Kinesiology Dean's Doctoral Studentship Program at the University of Calgary
文摘Background: Running-related overuse injuries can result from the combination of extrinsic(e.g., running mileage) and intrinsic risk factors(e.g.,biomechanics and gender), but the relationship between these factors is not folly understood. Therefore, the first purpose of this study was to determine whether we could classify higher-and lower-mileage runners according to differences in lower extremity kinematics during the stance and swing phases of running gait. I he second purpose was to subgroup the runners by gender and determine whether we could classify higherand lower-mileage runners in male and female subgroups.Methods: Participants were allocated to the "higher-mileage" group(≥32 km/week; n= 41(30 females)) or to the "lower-mileage" group(≤25 km;n= 40(29 females)). Three-dimensional kinematic data were collected during 60 s of treadmill running at a self-selected speed(2.61 ± 0.23 m/s).A support vector machine classifier identified kinematic differences between higher-and lower-mileage groups based on principal component scores.Results: Higher-and lower-mileage runners(both genders) could be separated with 92.59% classification accuracy. When subgrouping by gender,higher-and lower-mileage female runners could be separated with 89.83% classification accuracy, and higher-and lower-mileage male runners could be separated with 100% classification accuracy.Conclusion: These results demonstrate there are distinct kinematic differences between subgroups related to both mileage and gender, and that these factors need to be considered in future research.
文摘Background:Advances in artificial intelligence have enabled the simulation of human-like behaviors,raising the possibility of using large language models(LLMs)to generate synthetic population samples for research purposes,which may be particularly useful in health and social sciences.Methods:This paper explores the potential of LLMs to simulate population samples mirroring real ones,as well as the feasibility of using personality questionnaires to assess the personality of LLMs.To advance in that direction,2 experiments were conducted with GPT-4o using the Eysenck Personality Questionnaire Revised-Abbreviated(EPQR-A)in 6 languages:Spanish,English,Slovak,Hebrew,Portuguese,and Turkish.Results:We find that GPT-4o exhibits distinct personality traits,which vary based on parameter settings and the language of the questionnaire.While the model shows promising trends in reflecting certain personality traits and differences across gender and academic fields,discrepancies between the synthetic populations’responses and those from real populations remain.Conclusions:These inconsistencies suggest that creating fully reliable synthetic population samples for questionnaire testing is still an open challenge.Further research is required to better align synthetic and real population behaviors.
