In-Depth Analysis of Personalized Training Effects Based on DSI for Basketball Players

1. Research Background and Significance

The high intensity and complexity of basketball require athletes to possess excellent acceleration, jumping, and agility abilities. Previous studies have recognized the critical role of these abilities, but most training interventions have adopted a “one-size-fits-all” strategy without adequately considering individual differences. The emergence of the Dynamic Strength Index (DSI) provides a new perspective for personalized training, reflecting athletes’ strength and explosiveness characteristics through specific strength test ratios. However, there has been a lack of in-depth research on its application effects in basketball training. This study aims to fill this gap by exploring the impact of DSI-based personalized training on key athletic performances of basketball players, which has important guiding significance for optimizing basketball training practices.

2. Research Design and Implementation

• • Research Design: This study employed a rigorous parallel group, randomized controlled trial design, registered in the ClinicalTrails.gov database (ID NCT06094075), enhancing the scientific rigor and transparency of the research. The participants were 43 elite basketball players, with an average age of 19.4 ± 2.9 years, height of 1.97 ± 0.08 m, and weight of 89.1 ± 9.5 kg. They were randomly assigned to an intervention group (IG, 27 individuals) and a control group (CG, 16 individuals), with IG further subdivided into a ballistic group (DSI ≤ 0.90, 11 individuals) and a strength group (DSI > 0.90, 16 individuals). This grouping ensured that the study could deeply explore the effects of different training strategies on athletes with varying DSI characteristics. The entire measurement and intervention process took place over seven weeks during the season, with both groups performing two resistance training sessions each week. IG trained according to a personalized plan based on DSI, while CG followed a standard training program, with comprehensive assessments of key athletic indicators such as the 20-meter acceleration run, 505 agility test, CMJ, and IMTP conducted before and after the intervention.

• • Sample Selection and Characteristics: In sample selection, the research team meticulously calculated the sample size using G*Power 3.1 software, considering potential participant dropouts, ultimately recruiting 43 high-level athletes. These athletes came from three teams of the same competitive level and maintained a regular training rhythm of five basketball training sessions, two matches, and two resistance training sessions per week during the study period. They all had over two years of strength training experience, were free from injuries in the past six months, participated normally in all basketball activities, did not take any medications affecting testing and training, and maintained a consistent daily diet. Strict inclusion criteria ensured the homogeneity and reliability of the sample, while the experiment adhered to the Helsinki Declaration and received approval from the medical ethics committee (Approval No. 0120 – 99/2018/5), ensuring the legality and feasibility of the research from ethical and regulatory perspectives.

• • Training and Testing Process: During the training and testing phases, athletes averaged 10 hours of training per week throughout the intervention period, with the time allocation for basketball training and resistance training aligning with the regular season schedule of high-level basketball players. Pre-tests were conducted one week before the first resistance training session, and post-tests took place one week after the training ended, both scheduled at the same time in the afternoon (±1 hour) to effectively avoid circadian rhythm interference with test results. Additionally, the order of subjects during each test was randomized to reduce order effects. The testing venue was a stable environment basketball gym, with temperature controlled at 20 – 22°C and humidity maintained at 40 – 60%, and tests were conducted the day after rest days to minimize fatigue factors. All assessments were executed by professionals with master’s degrees in sports science and extensive experience in testing elite athletes, ensuring the accuracy and reliability of the test data. The testing process included standardized warm-up activities such as 5 minutes of slow jogging, dynamic stretching, and bodyweight strength exercises, as well as specific testing items. In terms of training, the resistance training program lasted for five weeks, with two sessions per week, totaling 10 training sessions. The strength training group primarily focused on high-load exercises, such as squats and Bulgarian split squats; the ballistic group emphasized dynamic/explosive movements with low-load or bodyweight exercises, such as depth jumps and burpees. Each training session included three exercises, with a total of six exercises per week, covering both multi-joint and single-joint tasks, and training intensity was strictly controlled through precise %RM (percentage of maximum weight) and self-reported repetitions in reserve (RIR), with the coaching team supervising training execution under research team training to ensure the training process was standardized and effective.

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3. Detailed Analysis of Research Results

• • Training Completion and Baseline Data of Groups: All 43 participants successfully completed all 10 training sessions over the five-week period, ensuring the completeness of the research data. In terms of baseline data, the average DSI value for the strength training group was 0.98, ranging from 0.92 to 1.12; the ballistic training group had an average DSI of 0.82, ranging from 0.71 to 0.90; and the control group had an average DSI of 0.86, ranging from 0.70 to 1.12. These baseline data provide an important reference for subsequent analyses of the training effects of different groups, initially reflecting differences in strength and explosiveness characteristics among athletes in each group at baseline.

• • Changes in Athletic Performance Indicators: In terms of core athletic performance indicators, post-intervention assessments showed significant differences between the IG and CG groups. The IG group showed significant reductions in acceleration run times and 505 test times, with p-values ranging from 0.002 to 0.010 for the acceleration run and from 0.008 to 0.009 for the 505 test, and effect sizes (η2) reaching 0.116 to 0.209, indicating a significant enhancement in acceleration and agility abilities for the IG group. Within-group analyses further revealed significant improvements in the IG group for the 5-meter acceleration run and 505 test, with p-values of 0.027 and 0.006 to 0.025, respectively, while the CG group exhibited significant declines in performance over distances of 5, 10, and 20 meters in the acceleration run (p = 0.004 – 0.015). Although there were no statistically significant differences in the CMJ variable and change of direction deficit (CoD deficit) between groups (p = 0.551 – 0.837), both groups showed a downward trend in CoD deficit values (p = 0.184 – 0.480), suggesting that the training may have a positive potential impact on agility, though not reaching significance.

• • Changes in Strength-Related Indicators: Regarding strength-related indicators, ANCOVA analysis indicated significant differences in relative strength for the IG group during post-intervention assessments (p = 0.039, η2 = 0.108). Further post-hoc tests showed a significant increase in peak force for the IMTP in the IG group post-training (p = 0.025), while CMJ force showed no significant changes (p = 0.939), leading to a downward trend in the DSI value for the IG group. In contrast, the CG group showed a slight increase in CMJ force but a decrease in IMTP force, resulting in a significant increase in DSI value (p = 0.049), reflecting the unique impacts of different training strategies on athletes’ strength composition and DSI indicators, further verifying the effectiveness of DSI-based personalized training.

4. Discussion and Insights from the Research

• • In-Depth Interpretation of Training Effects: The core finding of this study is the significant improvement in multiple key athletic performance indicators in the IG group, particularly in jumping, 5-meter acceleration, and 505 agility performance, while the CG group exhibited a general decline in acceleration performance. This result strongly supports the positive role of DSI-based personalized training during the basketball season, effectively enhancing athletes’ physical performance and mitigating the negative impacts of seasonal fatigue and competition pressure on athletic ability. Further subgroup analyses revealed that although both intervention subgroups showed improvement trends overall, only the strength group demonstrated statistically significant enhancements, with the DSI value of the strength group decreasing while that of the ballistic group remained relatively stable. This indicates that under the specific conditions of the basketball season, strength training may be a more critical pathway for improvement for most athletes, particularly for those with higher DSI values, as high-load personalized strength training can yield more pronounced benefits, providing important practical guidance for coaches in developing training plans.

• • Comparison and Advantages Over Traditional Methods: In comparing training methods, DSI shares certain similarities with the traditional force-velocity profile (FVP) analysis method. Previous studies have indicated that FVP is an effective tool for guiding training decisions by assessing athletes’ abilities under varying loads to determine training direction. The training effects based on DSI in this study were comparable to those of personalized training based on FVP over the five-week period; however, the unique advantage of this study lies in its implementation during the basketball season, closely aligning with actual competition scenarios. This means that DSI-based personalized training is not only theoretically sound but also highly feasible and effective in practical seasonal training, better meeting athletes’ training needs during competition cycles and helping them maintain optimal competitive states amid intense competition.

• • Research Limitations and Future Directions: Despite the significant results achieved in this study, there are still some limitations. Firstly, the research was conducted only during a specific period before the playoffs, and the sample came from teams of a specific competitive level, which may limit the generalizability of the findings. Secondly, the study could not control the number and intensity of basketball games, as game schedules were determined by coaches, leading to potential variability among individuals that could affect the precision of the results. Future research could further expand the scope of the study, tracking fluctuations in DSI and its components throughout the entire season, delving into the underlying physiological mechanisms, and determining more precise baseline values for DSI to better guide personalized training for different types of basketball players, further refining the theoretical and practical systems of DSI-based training.

5. Practical Application Recommendations

• • Formulating Personalized Training Plans: In practical training, coaches can utilize the findings of this study to first assess athletes’ DSI values. For athletes with DSI > 0.9, emphasis should be placed on arranging high-load strength training, such as increasing the intensity and weight of traditional strength training exercises like squats and Bulgarian split squats, controlling the number of repetitions per set to 3 – 6, and conducting 3 – 5 sets, training 2 – 3 times per week. For athletes with DSI ≤ 0.9, the focus should be on low-load explosive training, such as drop jumps and burpees, with each exercise repeated 8 – 12 times, also conducting 3 – 5 sets, training 2 – 3 times per week. Adequate rest periods should be provided between exercises to ensure athletes can fully recover and maintain training quality. By designing such personalized training plans, training can be more accurately targeted to meet individual athlete needs, enhancing training effectiveness and promoting improvements in acceleration, jumping, and agility abilities.

• • Dynamic Monitoring and Adjustment of Training Processes: Throughout the training process, coaches need to closely monitor athletes’ training responses and physical conditions, regularly (e.g., every two weeks or monthly) retesting athletes’ DSI and related strength indicators, such as CMJ peak force and IMTP peak force. Based on changes in test results, training plans should be adjusted timely to avoid overtraining or insufficient training. Particularly during the season, as games are frequent, athletes are prone to accumulating fatigue; coaches should rationally arrange training intensity and rest times, such as appropriately reducing training intensity and increasing recovery training content like yoga and stretching during periods of intensive competition, ensuring athletes can maintain good physical conditions and competitive levels during games, fully leveraging training effects to enhance competition performance.