Basketball Activity Research Articles

The reliability and discriminant validity of physical, technical, and perceptual-physiological measures during a game-specific basketball activity simulation protocol.

Activity simulation protocols offer useful applications in research and practice; however, the specificity of such protocols to basketball game-play is currently lacking. Consequently, this study aimed to develop a game-specific basketball activity simulation protocol representative of typical playing durations and assess its reliability and discriminant validity. The simulation protocol was modified from an original version (i.e., Basketball Exercise Simulation Test) to incorporate regular breaks indicative of time-outs, free-throws, and substitutions. Twelve competitive male and female adult basketball players competing in the fourth or fifth Spanish basketball division underwent repeated trials of the simulation protocol (min. 4 to max. 14 days apart) for reliability analyses. In turn, 13 competitive male (fifth division), 9 competitive female (fourth division), and 13 recreational male adult basketball players completed the simulation protocol to assess discriminant validity via comparisons between sexes (competitive players) and playing levels (males). A range of physical, technical, and perceptual-physiological variables were collected during and following the simulation protocol. Several physical and heart rate variables displayed the strongest reliability (intraclass correlation coefficient [ICC] = 0.72-0.96; coefficient of variation [CV] = 1.78-6.75%), with physical decrement, technical, blood lactate concentration, and rating of perceived exertion (RPE) variables having the weakest (ICC = 0.52-0.75; CV = 10.34-30.85%). Regarding discriminant analyses between sexes, males demonstrated significantly greater physical outputs in several variables and lower RPE compared to females (p < 0.05, moderate-to-large effects). Comparisons between playing levels revealed competitive males had significantly greater physical outputs across many variables, alongside higher mean heart rate and lower RPE than recreational males (p < 0.05, moderate-to-large effects). This study presents a novel game-specific basketball activity simulation protocol replicating actual playing durations and game configurations that might be successfully applied for both training and research purposes. Reliability statistics are provided for several variables to inform end-users on potential measurement error when implementing the simulation protocol. Discriminant validity of the simulation protocol was supported for several variables, suggesting it may hold practical utility in benchmarking or selecting players. Future research on this topic is encouraged examining wider samples of male and female basketball players at different levels as well as additional forms of validity for the protocol.

The Effect of Basketball Activity on Developing the Mental Health of University Students in Shenzhen

The Effect of Basketball Activity on Developing the Mental Health of University Students in Shenzhen

Psychology-Guided Environment Aware Network for Discovering Social Interaction Groups from Videos

Social interaction is a common phenomenon in human societies. Different from discovering groups based on the similarity of individuals’ actions, social interaction focuses more on the mutual influence between people. Although people can easily judge whether or not there are social interactions in a real-world scene, it is difficult for an intelligent system to discover social interactions. Initiating and concluding social interactions are greatly influenced by an individual’s social cognition and the surrounding environment, which are closely related to psychology. Thus, converting the psychological factors that impact social interactions into quantifiable visual representations and creating a model for interaction relationships poses a significant challenge. To this end, we propose a Psychology-Guided Environment Aware Network (PEAN) that models social interaction among people in videos using supervised learning. Specifically, we divide the surrounding environment into scene-aware visual-based and human-aware visual-based descriptions. For the scene-aware visual clue, we utilize 3D features as global visual representations. For the human-aware visual clue, we consider instance-based location and behaviour-related visual representations to map human-centred interaction elements in social psychology: distance, openness, and orientation. In addition, we design an environment aware mechanism to integrate features from visual clues, with a Transformer to explore the relation between individuals and construct pairwise interaction strength features. The interaction intensity matrix reflecting the mutual nature of the interaction is obtained by processing the interaction strength features with the interaction discovery module. An interaction constrained loss function composed of interaction critical loss function and smooth F β loss function is proposed to optimize the whole framework to improve the distinction of the interaction matrix and alleviate class imbalance caused by pairwise interaction sparsity. Given the diversity of real-world interactions, we collect a new dataset named Social Basketball Activity Dataset (Soical-BAD), covering complex social interactions. Our method achieves the best performance among social-CAD, social-BAD, and their combined dataset named Video Social Interaction Dataset (VSID).

A Self-Supervised Human Activity Recognition Approach via Body Sensor Networks in Smart City

In smart cities, pervasive sensing and wearable computing techniques are increasingly being employed to monitor and recognize human activities through body sensor networks, which have been widely used in urban safety, healthcare, and manufacturing. However, most researchers regard human activity recognition (HAR) as a high-cost research task requiring a large amount of labeled data, which is often unrealistic in real-world applications. To address this problem, we propose a self-supervised learning framework for HAR (SS-HAR). SS-HAR initially takes the unlabeled data generated by data augmentation as the input of the network, mines the supervised information of the unlabeled data under the effect of self-supervised learning, and uses the obtained backbone network as a feature extractor to extract activity features for subsequent classification. After that, we use part of the labeled data as the training set and extract the activity features using the backbone network for training and fitting the classifier. Then we utilize the rest of the data to verify the feasibility and effectiveness of the proposed self-supervised learning method. We have conducted multiple experiments on three publicly available datasets and one self-collected basketball activity dataset SZU_HAD_Basketball. The experimental results show that the SS-HAR method is able to achieve higher classification accuracy and stability than supervised and semi-supervised methods. Specifically, on the UCI dataset, SS-HAR achieves better classification performance compared to other approaches which improve the classification accuracy by 1% over the supervised method and by 5%-6% over the semi-supervised method respectively.

Basketball Activity Recognition Using Supervised Machine Learning Implemented on Tizen OS Smartwatch

Basketball Activity Recognition (BAR) in sports teams, especially in basketball, to make statistical analysis of player activity data is currently a very important thing. BAR is one part of sports science that recognizes the movement of players in each activity, such as dribbling, passing, etc. Sport science in the sports business is used as one of the factors of coaches and management to determine strategy, starter line-up, check the condition of players after injury, etc. the current technology to recognize player activity only depends on the object detection method of players' through video recordings of players is considered lacking because it only sees the perspective of the coach to reduce players as starter line-up and there is no logical calculation of why players are not installed as starter line-up. One method for recognizing player activity is using a wearable device that has an accelerometer and gyroscope sensor with high accuracy. The values from those sensors will be classified and recognize their activity, i.e., Dribbling, Passing, and Shooting. Smartwatch is one of those wearable devices that meet those criteria. For the activity classification process, the use of the K-NN classification method is the most appropriate because it has a low computational level that is in accordance with the smartwatch specifications. The results of the classification using accelerometer sensor data and gyroscopes with K-NN as an activity recognition method have an accuracy of 81.62%, and player activity recognition applications using accelerometer and gyroscope sensors can also record the results of player movements for further analysis by management and coaches. This is the advantage of this BAR application compared to the recognition of player activity using object detection on video recordings.

Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training.

Nowadays, China's sports industry has attained effective development, but the athlete's efficiency in the training process is too complex to have a scientific guarantee. Machine learning technology's help in guiding the sports training process has become a hot spot. In this work, we investigate the use of deep learning in real-time analysis of basketball sports data, utilizing research approaches such as scientific reporting, audio/video analysis, experimental research, and mathematical statistics. The suggested basketball stance action recognition and analysis system are made up of two pieces that are sequentially connected. The bottom-up stance estimate approach is utilized to locate the joint locations in the first segment, which is then used to extract the target's posture sequence from the video. The analyses are needed for a Support Vector Machine (SVM) algorithm based on the deep learning method of the space-time graph. The basketball activity of the set classification is recognized and extracted from the segmented stance sequence. The study used an auxiliary method, which is contrasted to standard training, in order to get higher accuracy and also correct player errors in a timely manner. The approach can help players rectify technical errors, develop muscle memory, and increase their abilities. The results revealed that the algorithm generated 97.7% accuracy in evaluating data from basketball training.

Structural Relationship between Motivation, Involvement, Satisfaction and Loyalty of University Basketball Participants in China

PURPOSE The main purpose of this study is to test the structural relationship between motivation, involvement, satisfaction and loyalty of university basketball participants in China. METHOD The study took the participants of basketball clubs from five universities in Hunan Province as the subject. 846 questionnaires were collected and 800 were used in this study. SPSS24 and AMOS were used for this research. RESULT The results show that the motivation of basketball club’s participants has a significant positive impact on involvement, satisfaction and loyalty; Involvement has a significant positive effect on satisfaction and loyalty; Satisfaction has significant positive influence to loyalty. Finally, involvement and satisfaction can play a partial mediating role between motivation and loyalty. CONCLUSION This study verifies the structural relationship between motivation, involvement, satisfaction and loyalty of Chinese college basketball club participants. Motivation is the most important factor affecting participants’ loyalty, while involvement has the least impact on participants’ satisfaction and loyalty. Consequently, the PE management staff in university should attach more importance the motivation of participants in order to attract more students to participate in basketball activity and maintain their interests.

Features of game activity of basketball students

The article covers the issue of playing activities in basketball. The value of speed and accuracy of basketball players' game actions is revealed. The dependence of basketball players' game efficiency on indicators of their psychophysical development of their organism and the need to study the influence of morphofunctional and psychophysiological indicators of basketball players on the speed and accuracy of game actions are substantiated.&#x0D; In sports games, technical training is of great importance, which includes a large number of different techniques. Basketball activity is based on individual and group technical actions of athletes, the main of which are throws to the basket, passes, driving, interception, selection, selection of the ball, etc. The ability to perform these actions quickly and accurately is the main condition for a successful game of basketball players.&#x0D; Game efficiency depends on the spatio-temporal characteristics of game actions, which, in turn, are determined by the peculiarities of the psychophysical development of the body of athletes.&#x0D; One of the main ways to achieve high sportsmanship is the constant improvement of the technique of the chosen sport, the study of its structure, components and psychophysiological basis.&#x0D; Therefore, the study of the parameters of the game activities of students who play basketball, which includes analysis of the impact of morphofunctional and psychophysiological indicators of the body on competitive efficiency, is relevant in today's conditions of intensification of sports training.

Analysis of knee joint injury and prevention strategy of college basketball students

Basketball in college physical education occupies an critical position, but during basketball teaching, in the standard course for many students have developed knee joint injuries which most are caused by inadequate warm-up exercise. In advance of basketball activity, it’s necessary to prepare a scientific training plan and do enough warm-up exercises to reduce the risk of knee injuries.

The Effects of Music on Student Step Counts and Time in Activity in College Basketball Activity Classes

Music is a powerful influence in life. We hear music at work, in the car, at the mall, and in our homes. Music has also been found to have an affect during physical activity. The purpose of this study was to examine the effects of music on physical activity rates, via pedometers, of college-aged students in basketball class. For this study 106 college-aged students participated in this study. Two classes played basketball with no music playing during game play. Two other classes played basketball with music playing during game play. It was found that the two classes that played basketball with music took more steps and were in more time in activity.

Basketball Activity Classification Based on Upper Body Kinematics and Dynamic Time Warping.

Basketball activity classification can help document players' statistics, allow coaches, trainers and the medical team to quantitatively supervise players' physical exertion and optimize training strategy, and further help prevent potential injuries. Traditionally, sports activity classification was done by manual notational, or through multi-camera systems or motion sensing technology. These methods were often erroneous and limited by space. This study presents a basketball activity classification model based on Dynamic Time Warping (DTW) and body kinematic measures. Twenty participants, including 10 experienced players and 10 novice players, were involved in an experimental study. The experienced and novice players differed in their years of playing basketball. Four basketball movements, including shooting, passing, dribbling, and lay-up were classified by kinematic measures. The results indicate that the proposed model can successfully classify different basketball movements with high accuracy and efficiency. Specifically, with the resultant acceleration of the hand, this model can achieve classification precision, recall, and specificity up to 0.984, 0.983 and 0.994, respectively. Findings from this study supported the feasibility of using DTW in real-time sports activity classification and provided insights into the optimal sensor placement for basketball activity classification applications.

REACH Harlem: young urban boys’ experiences in an after-school PA positive youth development program

ABSTRACTBackground: After-school approaches offer a viable solution to the current challenge of physical inactivity among many adolescents; however, availability, cost, transportation, and interest limit many youths’ choices during non-school hours [Roth, Brooks-Gunn, Murray, and Foster 2003. “Youth Development Programs: Risk, Prevention and Policy.” Journal of Adolescent Health 32 (3): 170–182]. In addition to these limitations, safety concerns in urban communities act as barriers to participation in both after-school programs [Sanderson and Richards 2010. “The After-School Needs and Resources of a Low-income Urban Community: Surveying Youth and Parents for Community Change.” American Journal of Community Psychology 45: 430–440] and physical activity [Moore, Roux, Evenson, McGinn, and Brines 2010. “A Qualitative Examination of Perceived Barriers and Facilitators of Physical Activity for Urban and Rural Youth.” Health Education Research 25 (2): 355–367].Purpose: This year-long qualitative case study sought to explore young urban boys’ experiences in an after-school program that aimed to empower young men through the sport of basketball.Method: Two coaches facilitated weekly sessions for 5–20 young men of color in an urban, public school after-school program in Harlem, NY. The program recruited young men to an after-school program that offered basketball instruction in a safe space. During the program, students worked on basketball skills as well as reflective reading and writing through weekly journals and immersive conversations that revolved around weekly themes such as sportsmanship, culture, power, and communication. At the conclusion of the study seven boys aged 10–14 agreed to be interviewed about their experiences in the REACH program. Sources of data include journals, interviews, and observations. These data were coded by two independent coders, peer reviewed by a third researcher and analyzed using the constant comparative method.Findings: Theme one: Shooting guns (violence) is a barrier to shooting hoops at local courts. Analysis of the data highlighted violence as an always-present barrier to playing basketball, specifically, and physical activity, generally speaking. Theme two: Future plans, the NBA. The vast majority of boys discussed joining the NBA, either directly from high school or after a year at a college. The NBA dream operated as a perceived pathway to a safer future and higher education. Theme three: Having and being role models. Analysis of the data also pointed to participants both being and/or having a role model. These relationships impacted them in a variety of ways and not only helps us better understand their experiences in the program but also elucidates how barriers to physical activity were alleviated. The young men who participated in this after-school program were able to engage in interest-based physical activity and academic learning tasks, thus creating a peaceful space to develop positive associations between school, physical activity, and community.Conclusion: Closely examining the lived experiences of the REACH participants is significant for understanding their barriers to physical activity and how REACH, as a PYD sport program, alleviated those barriers. Examining the data around these issues also expands on the need for PYD programs [Wright and Li 2009. “Exploring the Relevance of Positive Youth Development in Urban Physical Education.” Physical Education and Sport Pedagogy 14 (3): 241–251] and extends understandings of the PYD framework put forth by Holt et al. [2017. “A Grounded Theory of Positive Youth Development Through Sport Based on Results From a Qualitative Meta-Study.” International Review of Sport and Exercise Psychology 10: 1–49]. Specifically, our study demonstrates the reciprocal and iterative nature of PYD climate, life skills focus, and PYD outcomes. Our study also expands on the understandings of PYD climate, extending the focus beyond relationships to also including participants’ experiences and perceptions.

Decrements in knee extensor and flexor strength are associated with performance fatigue during simulated basketball game-play in adolescent, male players

ABSTRACTThis study quantified lower-limb strength decrements and assessed the relationships between strength decrements and performance fatigue during simulated basketball. Ten adolescent, male basketball players completed a circuit-based, basketball simulation. Sprint and jump performance were assessed during each circuit, with knee flexion and extension peak concentric torques measured at baseline, half-time, and full-time. Decrement scores were calculated for all measures. Mean knee flexor strength decrement was significantly (P < 0.05) related to sprint fatigue in the first half (R = 0.65), with dominant knee flexor strength (R = 0.67) and dominant flexor:extensor strength ratio (R = 0.77) decrement significantly (P < 0.05) associated with sprint decrement across the entire game. Mean knee extensor strength (R = 0.71), dominant knee flexor strength (R = 0.80), non-dominant knee flexor strength (R = 0.75), mean knee flexor strength (R = 0.81), non-dominant flexor:extensor strength ratio (R = 0.71), and mean flexor:extensor strength ratio (R = 0.70) decrement measures significantly (P < 0.05) influenced jump fatigue during the entire game. Lower-limb strength decrements may exert an important influence on performance fatigue during basketball activity in adolescent, male players. Consequently, training plans should aim to mitigate lower-limb fatigue to optimise sprint and jump performance during game-play.

Influences of load carriage and physical activity history on tibia bone strain

BackgroundMilitary recruits are often afflicted with stress fractures. The military's strenuous training programs involving load carriage may contribute to the high incidence of tibia stress fractures in the army. The purpose of this study was to assess the influences of incremented load carriage and history of physical activity on tibia bone strain and strain rate during walking. MethodsTwenty recreational basketball players and 20 recreational runners performed 4 walking tasks while carrying 0 kg, 15 kg, 25 kg, and 35 kg loads, respectively. Tibia bone strain and strain rate were obtained through subject-specific multibody dynamic simulations and finite element analyses. Mixed model repeated-measures analyses of variance were conducted. ResultsThe mean ± SE of the runners' bone strain (μs) during load carriages (0 kg, 15 kg, 25 kg, and 35 kg) were 658.11 ± 1.61, 804.41 ± 1.96, 924.49 ± 2.23, and 1011.15 ± 2.71, respectively, in compression and 458.33 ± 1.45, 562.11 ± 1.81, 669.82 ± 2.05, and 733.40 ± 2.52, respectively, in tension. For the basketball players, the incremented load carriages resulted in compressive strain of 634.30 ± 1.56, 746.87 ± 1.90, 842.18 ± 2.16, and 958.24 ± 2.63, respectively, and tensile strain of 440.04 ± 1.41, 518.86 ± 1.75, 597.63 ± 1.99, and 700.15 ± 2.47, respectively. A dose–response relationship exists between incremented load carriage and bone strain and strain rate. A history of regular basketball activity could result in reduced bone strain and reduced strain rate. ConclusionLoad carriage is a risk factor for tibia stress fracture during basic training. Preventative exercise programs, such as basketball, that involved multidirectional mechanical loading to the tibia bones can be implemented for military recruits before basic training commences.

Cumulative Training Dose's Effects on Interrelationships Between Common Training-Load Models During Basketball Activity.

The influence of various factors on training-load (TL) responses in basketball has received limited attention. This study aimed to examine the temporal changes and influence of cumulative training dose on TL responses and interrelationships during basketball activity. Ten state-level Australian male junior basketball players completed 4 × 10-min standardized bouts of simulated basketball activity using a circuit-based protocol. Internal TL was quantified using the session rating of perceived exertion (sRPE), summated heart-rate zones (SHRZ), Banister training impulse (TRIMP), and Lucia TRIMP models. External TL was assessed via measurement of mean sprint and circuit speeds. Temporal TL comparisons were performed between 10-min bouts, while Pearson correlation analyses were conducted across cumulative training doses (0-10, 0-20, 0-30, and 0-40 min). sRPE TL increased (P < .05) after the first 10-min bout of basketball activity. sRPE TL was only significantly related to Lucia TRIMP (r = .66-.69; P < .05) across 0-10 and 0-20 min. Similarly, mean sprint and circuit speed were significantly correlated across 0-20 min (r = .67; P < .05). In contrast, SHRZ and Banister TRIMP were significantly related across all training doses (r = .84-.89; P < .05). Limited convergence exists between common TL approaches across basketball training doses lasting beyond 20 min. Thus, the interchangeability of commonly used internal and external TL approaches appears dose-dependent during basketball activity, with various psychophysiological mediators likely underpinning temporal changes.

The Commonality Between Approaches to Determine Jump Fatigue During Basketball Activity in Junior Players: In-Game Versus Across-Game Decrements.

Declines in high-intensity activity during game play (in-game approach) and performance tests measured pre- and postgame (across-game approach) have been used to assess player fatigue in basketball. However, a direct comparison of these approaches is not available. Consequently, this study examined the commonality between in- and across-game jump fatigue during simulated basketball game play. Australian, state-level, junior male basketball players (n = 10; 16.6 ± 1.1 y, 182.4 ± 4.3 cm, 68.3 ± 10.2 kg) completed 4 × 10-min standardized quarters of simulated basketball game play. In-game jump height during game play was measured using video analysis, while across-game jump height was determined pre-, mid-, and postgame play using an in-ground force platform. Jump height was determined using the flight-time method, with jump decrement calculated for each approach across the first half, second half, and entire game. A greater jump decrement was apparent for the in-game approach than for the across-game approach in the first half (37.1% ± 11.6% vs 1.7% ± 6.2%; P = .005; d = 3.81, large), while nonsignificant, large differences were evident between approaches in the second half (d = 1.14) and entire game (d = 1.83). Nonsignificant associations were evident between in-game and across-game jump decrement, with shared variances of 3-26%. Large differences and a low commonality were observed between in- and across-game jump fatigue during basketball game play, suggesting that these approaches measure different constructs. Based on our findings, it is not recommended that basketball coaches use these approaches interchangeably to monitor player fatigue across the season.

Temporal changes in physiological and performance responses across game-specific simulated basketball activity

PurposeThe aims of this study were to: (1) provide a comprehensive physiological profile of simulated basketball activity and (2) identify temporal changes in player responses in controlled settings. MethodsState-level male basketball players (n = 10) completed 4 × 10 min simulated quarters of basketball activity using a reliable and valid court-based test. A range of physiological (ratings of perceived exertion, blood lactate concentration ([BLa−]), blood glucose concentration ([BGlu]), heart rate (HR), and hydration) and physical (performance and fatigue indicators for sprint, circuit, and jump activity) measures were collected across testing. ResultsSignificantly reduced [BLa−] (6.19 ± 2.30 vs. 4.57 ± 2.33 mmol/L; p = 0.016) and [BGlu] (6.91 ± 1.57 vs. 5.25 ± 0.81 mmol/L; p = 0.009) were evident in the second half. A mean HR of 180.1 ± 5.7 beats/min (90.8% ± 4.0% HRmax) was observed, with a significant increase in vigorous activity (77%–95% HRmax) (11.31 ± 6.91 vs. 13.50 ± 6.75 min; p = 0.024) and moderate decrease in near-maximal activity (>95% HRmax) (7.24 ± 7.45 vs. 5.01 ± 7.20 min) in the second half. Small increases in performance times accompanied by a significantly lower circuit decrement (11.67% ± 5.55% vs. 7.30% ± 2.16%; p = 0.032) were apparent in the second half. ConclusionThese data indicate basketball activity imposes higher physiological demands than previously thought and temporal changes in responses might be due to adapted pacing strategies as well as fatigue-mediated mechanisms.

Cumulative Training Dose Augments The Interrelationships Between Common Training Load Models During Basketball Activity

Cumulative Training Dose Augments The Interrelationships Between Common Training Load Models During Basketball Activity

Accelerometer-determined Step Counts In Simulated Free-living Activities In Children And Adolescents

PURPOSE: To evaluate step counting accuracy of the ActiGraph GT3X+ accelerometer in waist- and wrist-worn locations during simulated free-living activities among children and adolescents. METHODS: 123 children and adolescents (6-20 years) performed simulated free-living activities (i.e., seated watching a movie and basketball skill development activity) while concurrently wearing accelerometers on their waist and non-dominant wrist. Each activity was performed for 5 minutes and visually tallied steps served as the criterion (steps/min). Accelerometer data were processed using two filters: 1) normal (NF), and 2) low-frequency extension (LFE). Confidence intervals (95%) for the difference (criterion-test method) were generated using the non-parametric bootstrap (n = 5000 replicates) and used within an equivalence testing framework to evaluate accuracy relative to the pre-defined equivalence margins of ± 3 and ± 10 steps/min for movie watching and basketball, respectively. RESULTS: Compared to visually tallied steps (steps/min), both waist and wrist-worn accelerometers over-counted steps taken during movie watching (waist: NF = 0.02 [0, 0.05], LFE = 0.37 [0.23, 0.54]; wrist: NF = 0.50 [0.37, 0.64], LFE = 2.2 [1.64, 2.81]); however, all confidence intervals were completely contained within the pre-defined equivalence margin. Conversely, waist and wrist-worn accelerometers under-counted steps while performing a simulated basketball skill development activity (waist: NF = -36.51 [-40.31, -33.23], LFE = -11.87 [-15.37, -8.95]; wrist: NF = -28.85 [-32.64, -25.51], LFE = -17.43 [-21.19, -14.11]) with none of the associated confidence intervals falling completely within the pre-defined equivalence margin. CONCLUSION: In agreement with direct observation, the ActiGraph GT3X+ detected few false steps during movie watching with the waist-worn location and NF combination performing best. All combinations of wear location and filter choice underestimated step counts during a simulated basketball activity. Given these limited data, applying the LFE filter to a waist-worn device may provide the best overall free-living step count estimates for children and adolescents. Supported by NIH NCHID 1R21HD073807-01A1

Psychosocial effects of reverse-integrated basketball activity compared to separate and no physical activity in young people with physical disability

The purpose of this study was to examine the impact of participation in different sport modalities on quality of life (QOL) and perceived social competence (PSC) in young people with physical disability. Ninety participants (33 females and 57 males) were monitored across four conditions: competitive separate physical activity (COSPA), recreational separate physical activity (RESPA), reverse-integrated basketball activity (RIBA), and no physical activity (NOPA). QOL and PSC questionnaires were administered at the beginning and the end of the study's duration of six months. ANCOVA corrected for functional independence and gender revealed significant group effects for pre to post change values of QOL and PSC, with greater positive change in the RIBA compared to all other groups. In addition, one-way ANOVA on pre to post change values with LSD post hoc revealed significant differences. RIBA change values for QOL with 8.77%, and for PSC with 9.98% change were significantly higher (p<.001) than in all other groups (ranges -0.18 through 1.36% for QOL, and -2.31 through 2.34% for PSC). These outcomes demonstrate a favorable outcome of the RIBA on participants. Low functional ability did not constrain the effects of sport participation.