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      Getting the most out of intensive longitudinal data: a methodological review of workload–injury studies

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          Abstract

          Objectives

          To systematically identify and qualitatively review the statistical approaches used in prospective cohort studies of team sports that reported intensive longitudinal data (ILD) (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Since longitudinal research can be improved by aligning the (1) theoretical model, (2) temporal design and (3) statistical approach, we reviewed the statistical approaches used in these studies to evaluate how closely they aligned these three components.

          Design

          Methodological review.

          Methods

          After finding 6 systematic reviews and 1 consensus statement in our systematic search, we extracted 34 original prospective cohort studies of team sports that reported ILD (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Using Professor Linda Collins’ three-part framework of aligning the theoretical model, temporal design and statistical approach, we qualitatively assessed how well the statistical approaches aligned with the intensive longitudinal nature of the data, and with the underlying theoretical model. Finally, we discussed the implications of each statistical approach and provide recommendations for future research.

          Results

          Statistical methods such as correlations, t-tests and simple linear/logistic regression were commonly used. However, these methods did not adequately address the (1) themes of theoretical models underlying workloads and injury, nor the (2) temporal design challenges (ILD). Although time-to-event analyses (eg, Cox proportional hazards and frailty models) and multilevel modelling are better-suited for ILD, these were used in fewer than a 10% of the studies (n=3).

          Conclusions

          Rapidly accelerating availability of ILD is the norm in many fields of healthcare delivery and thus health research. These data present an opportunity to better address research questions, especially when appropriate statistical analyses are chosen.

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          Most cited references 93

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          Monitoring Training Load to Understand Fatigue in Athletes

          Many athletes, coaches, and support staff are taking an increasingly scientific approach to both designing and monitoring training programs. Appropriate load monitoring can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing non-functional overreaching, illness, and/or injury. In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available for use. However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker described in the literature. Research has investigated a number of external load quantifying and monitoring tools, such as power output measuring devices, time-motion analysis, as well as internal load unit measures, including perception of effort, heart rate, blood lactate, and training impulse. Dissociation between external and internal load units may reveal the state of fatigue of an athlete. Other monitoring tools used by high-performance programs include heart rate recovery, neuromuscular function, biochemical/hormonal/immunological assessments, questionnaires and diaries, psychomotor speed, and sleep quality and quantity. The monitoring approach taken with athletes may depend on whether the athlete is engaging in individual or team sport activity; however, the importance of individualization of load monitoring cannot be over emphasized. Detecting meaningful changes with scientific and statistical approaches can provide confidence and certainty when implementing change. Appropriate monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback.
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            Causation and causal inference in epidemiology.

            Concepts of cause and causal inference are largely self-taught from early learning experiences. A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multi-causality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component causes. Philosophers agree that causal propositions cannot be proved, and find flaws or practical limitations in all philosophies of causal inference. Hence, the role of logic, belief, and observation in evaluating causal propositions is not settled. Causal inference in epidemiology is better viewed as an exercise in measurement of an effect rather than as a criterion-guided process for deciding whether an effect is present or not.
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              Understanding injury mechanisms: a key component of preventing injuries in sport.

              Anterior cruciate ligament (ACL) injuries are a growing cause of concern, as these injuries can have serious consequences for the athlete with a greatly increased risk of early osteoarthrosis. Using specific training programmes, it may be possible to reduce the incidence of knee and ankle injuries. However, it is not known which programme components are the key to preventing knee and ankle injuries or how the exercises work to reduce injury risk. Our ability to design specific prevention programmes, whether through training or other preventive measures, is currently limited by an incomplete understanding of the causes of injuries. A multifactorial approach should be used to account for all the factors involved-that is, the internal and external risk factors as well as the inciting event (the injury mechanism). Although such models have been presented previously, we emphasise the need to use a comprehensive model, which accounts for the events leading to the injury situation (playing situation, player and opponent behaviour), as well as to include a description of whole body and joint biomechanics at the time of injury.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2018
                2 October 2018
                : 8
                : 10
                Affiliations
                [1 ] departmentExperimental Medicine Program , University of British Columbia , Vancouver, British Columbia, Canada
                [2 ] United States Olympic Committee , Colorado Springs, Colorado, USA
                [3 ] United States Coalition for the Prevention of Illness and Injury in Sport , Colorado Springs, Colorado, USA
                [4 ] departmentDivision of Physiotherapy , Linköping University , Linköping, Sweden
                [5 ] departmentSchool of Allied Health , La Trobe University , Melbourne, Victoria, Australia
                [6 ] Gabbett Performance Solutions , Brisbane, Queensland, Australia
                [7 ] departmentInstitute for Resilient Regions , University of Southern Queensland , Ipswich, Queensland, Australia
                [8 ] departmentDepartment of Family Practice , University of British Columbia , Vancouver, British Columbia, Canada
                [9 ] departmentDepartment of Orthopaedics , Duke University , Durham, North Carolina, USA
                [10 ] Vancouver Whitecaps Football Club , Vancouver, British Columbia, Canada
                [11 ] departmentMeasurement, Evaluation, and Research Methodology Program , University of British Columbia , Vancouver, British Columbia, Canada
                Author notes
                [Correspondence to ] Johann Windt; JohannWindt@ 123456gmail.com
                Article
                bmjopen-2018-022626
                10.1136/bmjopen-2018-022626
                6169745
                30282683
                © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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                Sports and Exercise Medicine
                Research
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                1736
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                Medicine

                methodology, workloads, training load, athletic injury, statistics

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