Research Article
Lower-body Wingate Anaerobic Performance of Indian Adolescent Athletes Across Sport Categories:
A Cross-sectional Study
Rangan Pan,
Sudeep Satpathy,
Swapan Kumar Dey,
Arnab Das*
Issue:
Volume 11, Issue 3, September 2026
Pages:
45-54
Received:
8 August 2026
Accepted:
20 August 2026
Published:
9 September 2026
Abstract: Optimum lower-body anaerobic power is critical for athletic performances, yet there is limited information of the power profile of Indian adolescent athletes across various sports, age and sexes. This study aims to develop a power profile for this demographic and also examine the effects of sex, chronological age and sport type on anaerobic power. The study included 391 Indian adolescent athletes divided into different groups based on sex, age group, and sport type. Participants completed a 30-second Wingate anaerobic power test on a peak bike and the relative peak (PP), average (AP) and minimum power (MP) along with fatigue index (FI) were measured. A three-way MANOVA (sports-age -sex) was used to find power differences between the groups. Male athletes of all developmental stages and sport types showed significantly higher PP, AP, and MP than females. Middle and late adolescents athletes had higher power output than the early adolescents. Athletes involved in power sports achieved the highest PP and AP, whereas endurance athletes had greater MP and lowest FI. MANOVA revealed significant effects of sex (p<0.001, ηp2=0.373), age group (p=0.013, ηp2=0.027) and sport type (p<0.001, ηp2=0.084) on all anaerobic power variables, but none had three-way interaction effect. Two way interaction of sex and sport type was found only for MP (p<0.001, ηp2=0.080). These anaerobic power profiling and regular assessment can help coaches and sport science professionals to identify talents, monitor performance progress, and develop age-, sex-, and sport-specific training strategies accordingly.
Abstract: Optimum lower-body anaerobic power is critical for athletic performances, yet there is limited information of the power profile of Indian adolescent athletes across various sports, age and sexes. This study aims to develop a power profile for this demographic and also examine the effects of sex, chronological age and sport type on anaerobic power. ...
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Research Article
Wind-Normalized Southeast Asian 100-m Sprint Performances and a Conceptual Framework for AI-Powered Exoskeletons
Edgar Jeevan Danaraj*
Issue:
Volume 11, Issue 3, September 2026
Pages:
55-63
Received:
6 October 2026
Accepted:
15 October 2026
Published:
28 October 2026
Abstract: This study has two distinct components. First, an empirical secondary analysis applies a quadratic wind-normalization model to 20 recorded Southeast Asian (SEA) men's 100-m performances for which official times and wind readings were available in the compiled dataset. The mean official time was 10.399 ± 0.177 s and the mean model-adjusted time was 10.379 ± 0.185 s. The adjustment changes the relative ordering of some performances, illustrating how wind conditions can affect historical comparisons. Second, the paper presents a conceptual framework for an AI-powered lower-limb exoskeleton using inertial sensing, temporal-convolutional and recurrent neural-network components, athlete-specific digital twins, phase-specific assistance, and independent safety supervision. No exoskeleton intervention or athlete trial was conducted in this study; therefore, its proposed effects on sprint kinematics, performance, neuromuscular function, and injury risk remain hypotheses requiring controlled validation. The combined contribution is a wind-adjusted regional performance analysis and a technology proposal motivated by the training and performance questions that such comparisons raise.
Abstract: This study has two distinct components. First, an empirical secondary analysis applies a quadratic wind-normalization model to 20 recorded Southeast Asian (SEA) men's 100-m performances for which official times and wind readings were available in the compiled dataset. The mean official time was 10.399 ± 0.177 s and the mean model-adjusted time was ...
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