Prevention of fall-related injuries in 7-year-old to 12-year-old children: a cluster randomised controlled trial

Nauta, Joske; Knol, Dirk L.; Adriaensens, L.; Wolt, Karin Klein; Mechelen, Willem van; Verhagen, Evert

doi:10.1136/bjsports-2012-091439

Cited by 18 publications

(8 citation statements)

References 29 publications

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“…For example, one trial stated “sample size calculations showed 382 participants were needed.” [ 35 ] Furthermore, accounting for clustered data in sample size calculations differed among trials. One trial arbitrarily chose to increase the sample size by 30 % to account for clustering [ 36 ]. One trial stated that clustering was not accounted for in the sample size calculation because cluster sizes were expected to be small and within-cluster comparisons were not considered to be clinically meaningful [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Statistical analysis and handling of missing data in cluster randomized trials: a systematic review

Fiero

Huang

Oren

et al. 2016

Trials

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BackgroundCluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs.MethodsWe systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and method(s) used for handling missing data in the primary and sensitivity analyses. We evaluated the primary analysis and determined whether it was at the cluster or individual level.ResultsOf the 86 included CRTs, 80 (93 %) trials reported some missing outcome data. Of those reporting missing data, the median percent of individuals with a missing outcome was 19 % (range 0.5 to 90 %). The most common way to handle missing data in the primary analysis was complete case analysis (44, 55 %), whereas 18 (22 %) used mixed models, six (8 %) used single imputation, four (5 %) used unweighted generalized estimating equations, and two (2 %) used multiple imputation. Fourteen (16 %) trials reported a sensitivity analysis for missing data, but most assumed the same missing data mechanism as in the primary analysis. Overall, 67 (78 %) trials accounted for clustering in the primary analysis.ConclusionsHigh rates of missing outcome data are present in the majority of CRTs, yet handling missing data in practice remains suboptimal. Researchers and applied statisticians should carry out appropriate missing data methods, which are valid under plausible assumptions in order to increase statistical power in trials and reduce the possibility of bias. Sensitivity analysis should be performed, with weakened assumptions regarding the missing data mechanism to explore the robustness of results reported in the primary analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-016-1201-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Statistical analysis and handling of missing data in cluster randomized trials: a systematic review

Fiero

Huang

Oren

et al. 2016

Trials

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“…Furthermore, small effect was reported for changes in the chil dren's attitudes towards their nearest community [18]. In another study a trend was found suggesting that a primary school-based 8-week educational programme to improve falling skills may be more beneficial for the prevention of falling-related injuries in children with low levels of motor activity [19]. In the same way, members of Polish junior national team found beneficial effects of this sport and the values it has on social relations [6].…”

Section: Discussionmentioning

confidence: 99%

Educational judo benefits on the preschool children’s behaviour

Sterkowicz-Przybycień¹,

Kłys²,

Almansba³

2014

Journal of Combat Sports and Martial Arts

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“…17 The estimated injury incidence (ie, the number of injuries divided by the number of participants per season) 20 of 46% (ie, 110 000/240 000) and potential reduction of 50% in the number of injuries informed the sample-size calculation for this study. [21][22][23] Based on an a of .05 and a b of 90%, we estimated a required sample size of 67 field hockey players per study group. Considering a team-cluster effect of 0.1 24 and the fact that youth field hockey teams in the Netherlands generally consist of 17 players, a sample of 336 players distributed over 20 teams (ie, 10 teams per study group) was required.…”

Section: Sample-size Calculationmentioning

confidence: 99%

A Warm-Up Program to Reduce Injuries in Youth Field Hockey Players: A Quasi-Experiment

Barboza

Nauta

Emery

et al. 2019

Journal of Athletic Training

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Context Field hockey is popular worldwide; however, it entails a risk of injury. Injuries hamper players' participation in the sport and impose a burden on public health. Objective To investigate the effectiveness of a structured exercise program among youth field hockey players on the injury rate, severity, and burden. Design Quasi-experimental study. Setting On field during 1 season of field hockey (October 2016 through June 2017). Patients or Other Participants A convenience sample of 22 teams (291 players): 10 teams (135 players, mean age = 11.5 years [95% confidence interval (CI) = 11.2, 11.7 years]) in the intervention group and 12 teams (156 players, mean age = 12.9 years [95% CI = 12.6, 13.2 years]) in the control group. Intervention(s) The Warming-up Hockey program, a sex- and age-specific, structured, evidence-informed warm-up program consisting of a preparation phase (ie, agility and cardiovascular warm-up exercises), movement skills (ie, stability and flexibility exercises), and sport-specific skills (ie, speed and strength exercises in field hockey situations). Participants in the control group performed their usual warm-up routines. Main Outcome Measure(s) Injury rate (ie, the number of injuries per 1000 player-hours of field hockey exposure), severity (ie, days of player time-loss), and burden on athletes' availability to play (ie, days of time loss due to injury per 1000 player-hours of field hockey exposure). Results The injury rate was lower in the intervention group (hazard ratio of 0.64 [95% CI = 0.38, 1.07]); however, this result was not statistically significant. The severity of injuries was similar in both groups (t statistic P = .73). The burden of injuries on players' field hockey participation was lower in the intervention group (difference of 8.42 [95% CI = 4.37, 12.47] days lost per 1000 player-hours of field hockey). Conclusions Exposure to the Warming-up Hockey program was not significantly associated with a lower injury rate. No reduction was observed in the severity of injuries alone; however, the burden of injuries on players' field hockey participation was lower in the intervention group.

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Prevention of fall-related injuries in 7-year-old to 12-year-old children: a cluster randomised controlled trial

Cited by 18 publications

References 29 publications

Statistical analysis and handling of missing data in cluster randomized trials: a systematic review

Statistical analysis and handling of missing data in cluster randomized trials: a systematic review

Educational judo benefits on the preschool children’s behaviour

A Warm-Up Program to Reduce Injuries in Youth Field Hockey Players: A Quasi-Experiment

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