Is there an association between external workload and lower-back injuries in cricket fast bowlers? A systematic review

The Relationship between Training Load and Injury Risk in Basketball: A Systematic Review

The relationship between training load and injury risk in basketball is an important area in sports injury prevention and performance enhancement; however, there is limited conclusive evidence of their associations. The aim of this systematic review was to examine the evidence of the relationship between training load and injury risk in basketball, which is one of the most common sports worldwide. This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive literature search was conducted on the PubMed, SCOPUS, and Web of Science databases up until March 2024. The search aimed to identify studies that prospectively and/or retrospectively examined the relationship between training load and injury risk in basketball. Inclusion criteria were limited to studies published before February 2024. The quality of each study was assessed using the Newcastle-Ottawa Quality Assessment Scale and Oxford Centre for Evidence-Based Medicine levels of evidence. A narrative synthesis of the findings was performed. A total of 14 articles met the inclusion criteria and were included in the review. Of these, 11 studies reported at least partially statistically significant results, providing evidence of a relationship between training load and injury risk. In conclusion, the findings of this review suggest a clear relationship between training load and injury risk in basketball.

Can an inertial measurement unit, combined with machine learning, accurately measure ground reaction forces in cricket fast bowling?

This study examined whether an inertial measurement unit (IMU) could measure ground reaction force (GRF) during a cricket fast bowling delivery. Eighteen male fast bowlers had IMUs attached to their upper back and bowling wrist. Each participant bowled 36 deliveries, split into three different intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. A force plate was embedded into the bowling crease to measure the ground truth GRF. Three machine learning models were used to estimate GRF from the IMU data. The best results from all models showed a mean absolute percentage error of 22.1% body weights (BW) for vertical and horizontal peak force, 24.1% for vertical impulse, 32.6% and 33.6% for vertical and horizontal loading rates, respectively. The linear support vector machine model had the most consistent results. Although results were similar to other papers that have estimated GRF, the error would likely prevent its use in individual monitoring. However, due to the large differences in raw GRFs between participants, researchers may be able to help identify links among GRF, injury, and performance by categorising values into levels (i.e., low and high).

Intrinsic variables associated with low back pain and lumbar spine injury in fast bowlers in cricket: a systematic review

BACKGROUND

Lumbar spine injuries in fast bowlers account for the greatest missed playing time in cricket. A range of extrinsic and intrinsic variables are hypothesised to be associated with low back pain and lumbar spine injury in fast bowlers, and an improved understanding of intrinsic variables is necessary as these may alter load tolerance and injury risk associated with fast bowling. This review critically evaluated studies reporting intrinsic variables associated with low back pain and lumbar spine injury in fast bowlers and identified areas for future investigation.

METHODS

OVID Medline, EMBASE, SPORTDiscus, CINAHL, Web of Science and SCOPUS databases were last searched on 3 June 2022 to identify studies investigating intrinsic variables associated with low back pain and lumbar spine injury in cricket fast bowlers. Terms relevant to cricket fast bowling, and intrinsic variables associated with lumbar spine injury and low back pain in fast bowlers were searched. 1,503 abstracts were screened, and 118 full-text articles were appraised to determine whether they met inclusion criteria. Two authors independently screened search results and assessed risk of bias using a modified version of the Quality in Prognostic Studies tool.

RESULTS

Twenty-five studies met the inclusion criteria. Overall, no included studies demonstrated a low risk of bias, two studies were identified as moderate risk, and twenty-three studies were identified as high risk. Conflicting results were reported amongst studies investigating associations of fast bowling kinematics and kinetics, trunk and lumbar anatomical features, anthropometric traits, age, and neuromuscular characteristics with low back pain and lumbar spine injury.

CONCLUSION

Inconsistencies in results may be related to differences in study design, injury definitions, participant characteristics, measurement parameters, and statistical analyses. Low back pain and lumbar spine injury occurrence in fast bowlers remain high, and this may be due to an absence of low bias studies that have informed recommendations for their prevention. Future research should employ clearly defined injury outcomes, analyse continuous datasets, utilise models that better represent lumbar kinematics and kinetics during fast bowling, and better quantify previous injury, lumbar anatomical features and lumbar maturation.

TRIAL REGISTRATION

Open Science Framework https://doi.org/10.17605/OSF.IO/ERKZ2 .

Management of Spine-Related Problems Following Sport Injuries: A Review Article

Context: Athletes have routine activity programs, and better physical health is expected among them. There is limited information about the connection between athletes' injuries during sports activity and their future health status. Objectives: The prevalence of spine changes related to sports activities and what risk factors were associated with these events in athletes. Method: SCOPUS, EMBASE, and Web of Science were used for article searching. Results: Sports injuries in sports happen frequently. Our data is limited to introducing sports with the highest risk of injury. A history of sports injury, an intense training program, and years of sports activities are factors predicting injury. It has been seen that former top male athletes have more activity than age-matched control individuals. Conclusions: Spinal injury management in athletes needs a proper diagnosis, work-up, and complex rehabilitation plans; otherwise, it can lead to spinal complaints in the future. Rest, appropriate analgesia, and rehabilitation are the three main primary treatments. The usage of orthoses is not recommended for these injuries in general.

The relationship between bowling intensity and ground reaction force in cricket pace bowlers

This study examined the relationship between perceived bowling intensity, ball release speed and ground reaction force (measured by peak force, impulse and loading rate) in male pace bowlers. Twenty participants each bowled 36 deliveries, split evenly across three perceived intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. Peak force and loading rate were significantly different across the three perceived intensity zones in the horizontal and vertical directions (Cohen's d range = 0.14-0.45, p < 0.01). When ball release speed increased, peak force and loading rate also increased in the horizontal and vertical directions (η_p² = 0.04-0.18, p < 0.01). Lastly, bowling at submaximal intensities (i.e., low - medium) was associated with larger decreases in peak horizontal force (7.9-12.3% decrease), impulse (15.8-21.4%) and loading rate (7.4-12.7%) compared to decreases in ball release speed (5.4-8.3%). This may have implications for bowling strategies implemented during training and matches, particularly for preserving energy and reducing injury risk.

Quantifying cricket fast bowling volume, speed and perceived intensity zone using an Apple Watch and machine learning

This study examined whether an inertial measurement unit (IMU) and machine learning models could accurately measure bowling volume (BV), ball release speed (BRS), and perceived intensity zone (PIZ). Forty-four male pace bowlers wore a high measurement range, research-grade IMU (SABELSense) and a consumer-grade IMU (Apple Watch) on both wrists. Each participant bowled 36 deliveries, split into two different PIZs (Zone 1 = 70-85% of maximum bowling effort, Zone 2 = 100% of maximum bowling effort). BRS was measured using a radar gun. Four machine learning models were compared. Gradient boosting models had the best results across all measures (BV: F-score = 1.0; BRS: Mean absolute error = 2.76 km/h; PIZ: F-score = 0.92). There was no significant difference between the SABELSense and Apple Watch on the same hand when measuring BV, BRS, and PIZ. A significant improvement in classifying PIZ was observed for IMUs located on the dominant wrist. For all measures, there was no added benefit of combining IMUs on the dominant and non-dominant wrists.

Quantification of the demands of cricket bowling and the relationship to injury risk: a systematic review

BACKGROUND

Bowling in cricket is a complex sporting movement which, despite being well characterised, still produces a significant number of injuries each year. Fast bowlers are more likely to be injured than any other playing role. Frequency, duration, intensity and volume of bowling, which have been generalised as measurements of workload, are thought to be risk factors for injuries. Injury rates of fast bowlers have not reduced in recent years despite the implementation of various workload monitoring practices.

OBJECTIVE

To identify the variables used to quantify frequency, intensity, time and volume of bowling; and evaluate relationships between these variables and injury risk.

METHODS

Six online databases were systematically searched for studies on fast bowling that included terms related to workload. Population characteristics, variables relating to demand and their relationship to standardised definitions of physical activity were extracted from all included studies.

RESULTS

Bowling workload is typically quantified through measures of frequency, duration, or indirect intensity, with few studies reporting on bowling volume.

CONCLUSIONS

When reported on, volume was often described using imprecise or insufficient measures of intensity. There is a need to develop more appropriate measures of intensity during bowling and improve the quality of evidence to inform on bowling programme management practices.

Associations between sleep and academic performance in US adolescents: a systematic review and meta-analysis

This systematic review and meta-analysis aim to investigate the relationship between sleep and academic performance in students enrolled in secondary education programs in the United States. The study team conducted a literature search of 4 databases-PubMed, Embase, CINAHL, and ERIC-on September 19 and repeated December 17, 2020. Studies were included if they were observational, published in a peer-reviewed, non-predatory journal, available in full-text, written in English, included adolescents enrolled in an organized academic program, took place in the US, and evaluated the effect of sleep duration and/or sleep quality on academic performance. After excluding reviews, editorials, interventions, and those targeting diagnostic groups, 14 studies met inclusion criteria. Risk of bias was assessed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies; 12 studies were found to be good or high quality, 2 were adequate/fair or poor quality. A meta-analysis of 11 of the included studies revealed that sleep duration (r = 0.03; 95%CI -0.027, 0.087; p = 0.087) and sleep quality (r = 0.089; 95%CI 0.027, 0.151; p = 0.005) had negligible correlations with academic performance (non-significant and significant, respectively). Inconsistencies in definitions, methods, and measures utilized to assess sleep duration, sleep quality, and academic performance constructs may offer insight into seemingly conflicting findings. Given the pivotal role sleep plays in development, future investigations utilizing validated and objective sleep and academic performance measures are needed in adolescents.

Can an inertial measurement unit (IMU) in combination with machine learning measure fast bowling speed and perceived intensity in cricket?

This study examined whether an inertial measurement unit (IMU), in combination with machine learning, could accurately predict two indirect measures of bowling intensity through ball release speed (BRS) and perceived intensity zone (PIZ). One IMU was attached to the thoracic back of 44 fast bowlers. Each participant bowled 36 deliveries at two different PIZ zones (Zone 1 = 24 deliveries at 70% to 85% of maximum perceived bowling effort; Zone 2 = 12 deliveries at 100% of maximum perceived bowling effort) in a random order. IMU data (sampling rate = 250 Hz) were downsampled to 125 Hz, 50 Hz, and 25 Hz to determine if model accuracy was affected by the sampling frequency. Data were analysed using four machine learning models. A two-way repeated-measures ANOVA was used to compare the mean absolute error (MAE) and accuracy scores (separately) across the four models and four sampling frequencies. Gradient boosting models were shown to be the most consistent at measuring BRS (MAE = 3.61 km/h) and PIZ (F-score = 88%) across all sampling frequencies. This method could be used to measure BRS and PIZ which may contribute to a better understanding of overall bowling load which may help to reduce injuries.