Xiaodong Wang

This study compared cardiorespiratory fitness, bio-motor abilities, and body composition indexes of sprint kayakers categorized into three different age groups and two expertise levels of international- and club-level athletes. Seventy-three male juniors (n = 14, age = 16.2 ± 0.8), under 23 [U23 (n = 15, age = 20.2 ± 1.6)], professionals (n = 16, age = 27.1 ± 4.8), club-level (n = 15, age = 26.9 ± 6.6), and international-level (n = 13, age = 27.3 ± 3.2) sprint kayakers were studied. Cardiorespiratory fitness (assessed using incremental exercise test), 500 and 1,000-m paddling performance (assessed using kayak ergometer), upper-body power (assessed using 30 s all-out Wingate test) and strength (assessed through one repetition tests for bench press, cable row, and prone bench pull exercises), as well as body composition indexes (measured using dual-energy X-ray absorptiometry) were evaluated on four occasions separated by 48 h recovery. U23 and, especially, professionals indicated significantly (p < 0.05) greater outcomes for the majority of the cardiorespiratory fitness parameters [maximal oxygen uptake (VO_2max), velocity corresponding to VO_2max, Oxygen pulse, maximal ventilation, and second ventilatory threshold] and 500 and 1,000-m performance. U23 and professional sprint kayakers significantly (p < 0.05) differed in the first ventilatory threshold and 500, and 1,000-m performance but not in VO_2max or the second ventilatory threshold. Professionals also showed a lower fat mass, higher muscle mass, and higher strength (bench press, prone bench pull, and seated cable row) and power than U23 and junior kayakers. Strength and power indicators had significantly greater values in U23 athletes compared to juniors. International-level athletes also showed superior VO_2max, velocity corresponding to VO_2max, middle (500-m), and long-distance (1,000-m) time trial performance, strength and power, lower fat, and higher muscle mass than club-level sprint kayakers. Cardiorespiratory fitness (particularly ventilatory threshold), body composition, and muscle strength/power are the best differentiating factors for sprint kayakers of different ages and expertise levels. These findings could aid coaches in prescribing training programs focusing on improving determining factors in paddling performance, as well as in predicting performance and identifying talent.

The current study investigated the efficacy of individualizing exercise intensity according to anaerobic power reserve (APR) on hormonal, physiological, and performance adaptations in athletes with different profiles. Sixteen highly-trained male rowers (age = 22 ± 3 years, height = 183 ± 6 cm, weight = 83 ± 7 kg, body fat = 11 ± 2%, experience = 12 ± 5 years) were randomized to a high-intensity interval training consisting of 2 × (6, 6, 8, 8, 10, 10 repetitions from 1st to 6th week, respectively) × 60 s intervals using a rowing ergometer at ∆%30 APR (APR_∆%30) or the same sets and repetitions at 130% maximal aerobic power (MAP_130%). In both groups, relief intervals were set at 1:1 with 3 min of rest between sets. On four occasions separated by 24 h recovery, participants attended the laboratory to assess 2000-m rowing ergometer performance, maximal oxygen uptake (V̇O₂max) and related physiological adaptations, and hormonal parameters. Significant increases were observed in 2000-m performance, V̇O₂max, ventilation at V̇O₂max, first and second ventilatory threshold, MAP and maximal sprinting power (MSP), total testosterone, and testosterone to cortisol ratio in response to 6 weeks of APR_∆%30 and MAP_130% protocols. The coefficient of variation (inter-subject variability) in the adaptive response of cardiorespiratory parameters to HIIT performed using the APR_∆%30 protocol was lower than those of the MAP_130% group. However, this is not the case for hormonal changes. Prescribing HIIT based on an athlete’s APR may help to create a more consistent level of the mechanical and physiological stimulus relative to the athlete’s capacity, potentially leading to more similar adaptations across athletes with varying profiles. Mechanisms influencing total testosterone are multifactorial and are not affected by this approach.

With the rapid progress of network technology and computers, the Internet of Things has slowly entered peopleʼs lives and work. The Internet of Things can bring a lot of convenience to peopleʼs lives and work. People have been living in a networked era, and communications, computers, and network technologies are changing the entire human race and society. The extensive application of databases and computer networks, coupled with the use of advanced automatic data collection tools, has dramatically increased the amount of data that people have. There are many important information hidden behind the surge of data, and people hope to conduct higher-level analysis on it in order to make better use of these data. This article mainly introduces the prediction model algorithm and index optimization analysis of athletesʼ physical fitness under the Internet of things environment. This paper proposes an algorithm and index optimization method for the athletesʼ physical fitness prediction model in the Internet of Things environment, which is used to conduct athletesʼ fitness prediction model algorithm and index optimization experiments in the Internet of Things environment, and designs steps for athletesʼ physical fitness prediction in the Internet of Things environment to lay a solid foundation for related applications of athlete index optimization. The experimental results in this article show that the prediction accuracy rate of the professional group with the athleteʼs physical fitness prediction model and index optimization under the Internet of Things environment is higher than that of the control group, with a difference .