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OBJECTIVE: Identifying which types of athletes have increased injury risk (ie, predictive risk factors) should help develop cost-effective selective injury prevention strategies. Our objective was to compare a theoretical injury risk classification system developed by coaches and rehabilitation therapists, with observed injury rates in human circus acts across dimensions of physical stressors, acrobatic complexity, qualifications, and residual risks. DESIGN: Descriptive epidemiological study. SETTING: professional circus company. PATIENTS OR OTHER PARTICIPANTS: Human circus artists performing in routine roles between 2007 and 2017. ASSESSMENT OF RISK FACTORS: Characteristics of circus acts categorized according to 4 different dimensions. MAIN OUTCOME MEASURES: Medical attention injury rates (injury requiring a visit to the therapist), time-loss injury rates (TL-1; injury resulting in at least one missed performance), and time-loss 15 injury rates (TL-15; injury resulting in at least 15 missed performances). RESULTS: Among 962 artists with 1 373 572 performances, 89.4% (860/962) incurred at least one medical attention injury, 74.2% (714/962) incurred at least one TL-1 injury, and 50.8% (489/962) incurred at least one TL-15 injury. There were important inconsistencies between theoretical and observed injury risk patterns in each of the 4 dimensions for all injury definitions (medical attention, TL-1, and TL-15). CONCLUSIONS: Although theoretical classifications are the only option when no data are available, observed risk patterns based on injury surveillance programs can help identify artists who have a high (or low) theoretical risk but are nonetheless actually at low (or high) risk of injury, given their current roles. This will help develop more cost-effective selective injury prevention programs.

Various terms used in sport and exercise science, and medicine, are derived from other fields such as epidemiology, pharmacology and causal inference. Conceptual and nomological frameworks have described training load as a multidimensional construct manifested by two causally related subdimensions: external and internal training load. In this article, we explain how the concepts of training load and its subdimensions can be aligned to classifications used in occupational medicine and epidemiology, where exposure can also be differentiated into external and internal dose. The meanings of terms used in epidemiology such as exposure, external dose, internal dose and dose–response are therefore explored from a causal perspective and their underlying concepts are contextualised to the physical training process. We also explain how these concepts can assist in the validation process of training load measures. Specifically, to optimise training (i.e. within a causal context), a measure of exposure should be reflective of the mediating mechanisms of the primary outcome. Additionally, understanding the difference between intermediate and surrogate outcomes allows for the correct investigation of the effects of exposure measures and their interpretation in research and applied settings. Finally, whilst the dose–response relationship can provide evidence of the validity of a measure, conceptual and computational differentiation between causal (explanatory) and non-causal (descriptive and predictive) dose–response relationships is needed. Regardless of how sophisticated or “advanced” a training load measure (and metric) appears, in a causal context, if it cannot be connected to a plausible mediator of a relevant response (outcome), it is likely of little use in practice to support and optimise the training process.

This study aimed to investigate the trajectories of spinal pain frequency from 6 to 17 years of age and describe the prevalence and frequency of spinal pain and related diagnoses in children following different pain trajectories. First through fifth-grade students from 13 primary schools were followed for 5.5 years. Occurrences of spinal pain were reported weekly via text messages. Children reporting spinal pain were physically evaluated and classified using International Classification of Disease criteria. Trajectories of spinal pain frequency were modeled from age 6 to 17 years with latent class growth analysis. We included data from 1556 children (52.4% female, mean (SD) baseline age = 9.1 (1.9) years) and identified 10,554 weeks of spinal pain in 329,756 weeks of observation. Sixty-three percent of children reported one or more occurrences of spinal pain. We identified five trajectories of spinal pain frequency. Half the children (49.8%) were classified as members of a "no pain" trajectory. The remaining children followed "rare" (27.9%), "rare, increasing" (14.5%), "moderate, increasing" (6.5%), or "early-onset, decreasing" (1.3%) spinal pain trajectories. The most common diagnoses in all trajectory groups were non-specific (e.g., "back pain"). Tissue-specific diagnoses (e.g., muscle strain) were less common and pathologies (e.g., fracture) were rare. Conclusion: From childhood through adolescence, spinal pain was common and followed heterogeneous courses comprising stable, increasing, and early-onset trajectories. These findings accord with recommendations from adult back pain guidelines that most children with spinal pain can be reassured that they do not have a serious disease and encouraged to stay active. What is Known: • Spinal pain imposes a large burden on individuals and society. • Although many people first experience the condition in childhood, little is known about the developmental trajectories of spinal pain from childhood to adolescence. What is New: • Data from 1556 children and 329,756 participant weeks showed five unique spinal pain trajectories from 6 to 17 years: most children rarely reported spinal pain, while one in five followed increasing or early-onset trajectories. • Most pain occurrences were non-specific; pathological diagnoses were rare.

Contains fulltext : 231157.pdf (Publisher’s version ) (Closed access) Objective: To support physical activity among people with systemic sclerosis (SSc; scleroderma), we sought to determine the (1) prevalence and importance of barriers and (2) likelihood of using possible facilitators. Methods: We invited 1,707 participants from an international SSc cohort to rate the (1) importance of 20 barriers (14 medical; 4 social or personal; 1 lifestyle; 1 environmental); and (2) likelihood of using 91 corresponding barrier-specific and 12 general facilitators. Results: Among 721 respondents, 13 barriers were experienced by ≥25% of participants, including 2 (fatigue, Raynaud's) rated 'important' or 'very important' by ≥50% of participants, 7 (joint stiffness and contractures, shortness of breath, gastrointestinal problems, difficulty grasping, pain, muscle weakness and mobility limitations, low motivation) by 26-50%, and 4 by 50% as 'likely' or 'very likely'. Conclusion: Medical-related physical activity barriers were common and considered important. Facilitators considered as most likely to be used involved adapting exercise, taking care of one’s body, keeping warm, and protecting skin. 11 p.

We sought to compare the ability of the prehospital Canadian C-Spine Rule to selectively recommend immobilization in sport-related versus non-sport-related injuries and describe sport-related mechanisms of injury.We reviewed data from the prospective paramedic Canadian C-Spine Rule validation and implementation studies in 7 Canadian cities. A trained reviewer further categorized sport-related mechanisms of injury collaboratively with a sport medicine physician using a pilot-tested standardized form. We compared the Canadian C-Spine Rule's recommendation to immobilize sport-related versus non-sport-related patients using Chi-square and relative risk statistics with 95% confidence intervals.There were 201 sport-related patients among the 5,978 included. Sport-related injured patients were younger (mean age 36.2 vs. 42.4) and more predominantly male (60.5% vs. 46.8%) than non-sport-related patients. Paramedics did not miss any C-Spine injury when using the Canadian C-Spine Rule. C-Spine injury rates were similar between sport (2/201; 1.0%) and non-sport-injured patients (47/5,777; 0.8%). The Canadian C-Spine Rule recommended immobilization equally between groups (46.4% vs. 42.5%; RR 1.09 95%CI 0.93-1.28), most commonly resulting from a dangerous mechanism among sport-injured (68.7% vs. 54.5%; RR 1.26 95%CI 1.08-1.47). The most common dangerous mechanism responsible for immobilization in sport was axial load.Although equal proportions of sport and non-sport-related injuries were immobilized, a dangerous mechanism was most often responsible for immobilization in sport-related cases. These findings do not address the potential impact of using the Canadian C-Spine Rule to evaluate collegiate or pro athletes assessed by sport medicine physicians. It does support using the Canadian C-Spine Rule as a tool in sport-injured patients assessed by paramedics.RéSUMé: OBJECTIFS: Nous avons cherché à comparer la capacité préhospitalière de la Canadian C-spine Rule à recommander de façon sélective l’immobilisation dans les blessures liées au sport par rapport aux blessures non liées au sport et à décrire les mécanismes des blessures liés au sport. LES MéTHODES: Nous avons examiné les données des études prospectives de validation et de mise en œuvre de la règle canadienne de la colonne vertébrale dans sept villes canadiennes. Un examinateur qualifié a ensuite classé les mécanismes de blessure liés au sport, en collaboration avec un médecin du sport, à l'aide d'un formulaire standardisé testé dans le cadre d'un projet pilote. Nous avons comparé la recommandation de la Canadian C-Spine Rule d'immobiliser les patients liés au sport par rapport aux patients non liés au sport en utilisant les statistiques du chi carré et du risque relatif avec un intervalle de confiance de 95 %. RéSULTATS: Parmi les 5 978 patients inclus il y avait 201 patients liés au sport. Les patients blessés liés au sport étaient plus jeunes (âge moyen 36,2 ans contre 42,4 ans) et plus majoritairement de sexe masculin (60,5 % contre 46,8 %) que les patients non liés au sport. Les ambulanciers paramédicaux n’ont manqué aucune blessure au rachis cervical lorsqu’ils ont utilisé la Canadian C-spine Rule. Les taux de blessures au rachis cervical étaient semblables chez les patients sportifs (2/201 ; 1,0 %) et non sportifs (47/5 777 ; 0,8 %). La Canadian C-spine Rule recommande l'immobilisation de manière égale entre les groupes (46,4 % contre 42,5 % ; RR 1,09 95 % IC 0,93-1,28), le plus souvent en raison d'un mécanisme dangereux chez les sportifs blessés (68,7 % contre 54,5 % ; RR 1,26 95 % IC 1,08-1,47). Le mécanisme dangereux le plus souvent responsable de l'immobilisation dans le sport était la charge axiale. CONCLUSION: Bien que des proportions égales de blessures sportives et non sportives aient été immobilisées, un mécanisme dangereux était le plus souvent responsable de l'immobilisation dans les cas liés au sport. Ces conclusions n'abordent pas l'impact potentiel de l'utilisation de la Canadian C-spine Rule pour évaluer les athlètes collégiaux ou professionnels évalués par les médecins du sport. Elle est favorable à l'utilisation de la Canadian C-spine Rule comme outil pour les patients blessés par le sport et évalués par les ambulanciers.

BackgroundPrevious research on the depression scale of the Patient Health Questionnaire (PHQ-9) has found that different latent factor models have maximized empirical measures of goodness-of-fit. The clinical relevance of these differences is unclear. We aimed to investigate whether depression screening accuracy may be improved by employing latent factor model-based scoring rather than sum scores.MethodsWe used an individual participant data meta-analysis (IPDMA) database compiled to assess the screening accuracy of the PHQ-9. We included studies that used the Structured Clinical Interview for DSM (SCID) as a reference standard and split those into calibration and validation datasets. In the calibration dataset, we estimated unidimensional, two-dimensional (separating cognitive/affective and somatic symptoms of depression), and bi-factor models, and the respective cut-offs to maximize combined sensitivity and specificity. In the validation dataset, we assessed the differences in (combined) sensitivity and specificity between the latent variable approaches and the optimal sum score (⩾10), using bootstrapping to estimate 95% confidence intervals for the differences.ResultsThe calibration dataset included 24 studies (4378 participants, 652 major depression cases); the validation dataset 17 studies (4252 participants, 568 cases). In the validation dataset, optimal cut-offs of the unidimensional, two-dimensional, and bi-factor models had higher sensitivity (by 0.036, 0.050, 0.049 points, respectively) but lower specificity (0.017, 0.026, 0.019, respectively) compared to the sum score cut-off of ⩾10.ConclusionsIn a comprehensive dataset of diagnostic studies, scoring using complex latent variable models do not improve screening accuracy of the PHQ-9 meaningfully as compared to the simple sum score approach.

Objective To describe injury rates and injury patterns in the Canadian Football League (CFL) according to time during the season, player position, injury type, and injury location. Design Prospective, cohort study. Setting and participants Eight seasons from CFL injury surveillance database. Independent variables Depending on the analysis, time of season (preseason, regular, and playoffs), player position, injury type, and injury location. Main outcome measures Medical attention and time-loss injury rates per 100 athletes at risk (AAR), and prevalence of time-loss injuries per week. Results The average game injury rate was 45.2/100 AAR medical attention injuries and 30.7/100 AAR time-loss injuries. Injury rates declined by 1% per week over the season for both medical attention (rate ratio = 0.99) and time-loss (rate ratio = 0.99) injuries, with a substantial decline during the playoffs compared with preseason (rate ratio = 0.70-0.77). The number of ongoing time-loss injuries increased over the course of the regular season. Quarterbacks, offensive backs, and linebackers had the highest game injury rates. Joint/ligament and muscle/tendon injuries were the most common injury types for games and practices, respectively. The lower extremity was the most commonly affected area, specifically the lower leg/ankle/foot and hip/groin/thigh. Conclusions There was a 1% decline in injury rate per week during the season and a 30% decline during the playoffs. The number of ongoing time-loss injuries increased over the regular season. Current results can aid league officials and medical staff in making evidence-based decisions concerning player safety and health.

Chinchin Wang,1,2 Tyrel Stokes,3 Russell J Steele,3 Niels Wedderkopp,4 Ian Shrier1 1Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2, Canada; 2Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, H3A 1A2, Canada; 3Department of Mathematics and Statistics, McGill University, Montreal, Quebec, H3A 0B9, Canada; 4Orthopedic Department University Hospital of South West Denmark, Department of Regional Health Research, University of Southern Denmark, Odense, DenmarkCorrespondence: Ian Shrier, Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, 3755 Côte Ste-Catherine Road, Montreal, Quebec, H3T 1E2, Canada, Tel +1-514-229-0114, Email ian.shrier@mcgill.caPurpose: Researchers often use model-based multiple imputation to handle missing at random data to minimize bias. However, constraints within the data may sometimes result in implausible values, making model-based imputation infeasible. In these contexts, we illustrate how random hot deck imputation can allow for plausible multiple imputation in longitudinal studies.Patients and Methods: Our motivating example is the Childhood Health, Activity, and Motor Performance School Study Denmark (CHAMPS-DK), a prospective cohort study that measured weekly sports participation for 1700 Danish schoolchildren. Using observed data on 4 variables (pain, activity frequency, sport, sport counts), we created a gold-standard data set without missing data. We then created a synthetic data set by setting some variable values to missing based on a prediction model that mimicked real-data missingness patterns. To create 5 imputed data sets, we matched each record with missing data to several fully observed records, generated probabilities from matched records, and sampled from these records based on the probability of each occurring. We assessed variability and agreement (kappa) between the imputed data sets and the gold-standard data set. We compare results to common model-based imputation methods.Results: Variability across data sets appeared reasonable. The range of kappa for the random hot deck approach was moderate for activity frequency (0.65 to 0.71) and sport (0.59 to 0.85), and poor for common model-based approaches (range 0.00 to 0.11). The range of kappas for sport count was strong (0.87 to 0.97) for random hot deck imputation and weak to moderate (0.55 to 0.71) for common model-based imputation. Agreement was higher when more information was present, and when prevalence was higher for our binary variable sport.Conclusion: Random hot deck imputation should be considered as an alternative method when model-based approaches are infeasible, specifically where there are constraints within and between covariates.Keywords: multiple imputation, missing data, missing at random, hot deck imputation, random hot deck imputation, longitudinal studies

We take steps towards causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target population, one-trial-at-a-time and pooling all trials. We discuss identifiability conditions for average treatment effects in the target population and provide identification results. We show that assuming inferences are transportable from all trials in the collection to the same target population has implications for the law underlying the observed data. We propose average treatment effect estimators that rely on different working models and provide code for their implementation in statistical software. We discuss how to use the data to examine whether transported inferences are homogeneous across the collection of trials, sketch approaches for sensitivity analysis to violations of the identifiability conditions, and describe extensions to address non-adherence in the trials. Last, we illustrate the proposed methods using data from the HALT-C multi-center trial.

Summary Background Global migration from regions where strongyloidiasis and schistosomiasis are endemic to non-endemic countries has increased the potential individual and public health effect of these parasitic diseases. We aimed to estimate the prevalence of these infections among migrants to establish which groups are at highest risk and who could benefit from screening. Methods We did a systematic review and meta-analysis of strongyloidiasis and schistosomiasis prevalence among migrants born in endemic countries. Original studies that included data for the prevalence of Strongyloides or Schistosoma antibodies in serum or the prevalence of larvae or eggs in stool or urine samples among migrants originating from countries endemic for these parasites and arriving or living in host countries with low endemicity—specifically the USA, Canada, Australia, New Zealand, Israel, and 23 western European countries—were eligible for inclusion. Pooled estimates of the prevalence of strongyloidiasis and schistosomiasis by stool or urine microscopy for larvae or eggs or serum antibodies were calculated with a random-effects model. Heterogeneity was explored by stratification by age, region of origin, migrant class, period of study, and type of serological antigen used. Findings 88 studies were included. Pooled strongyloidiasis seroprevalence was 12·2% (95% CI 9·0–15·9%; I2 96%) and stool-based prevalence was 1·8% (1·2–2·6%; 98%). Migrants from east Asia and the Pacific (17·3% [95% CI 4·1–37·0]), sub-Saharan Africa (14·6% [7·1–24·2]), and Latin America and the Caribbean (11·4% [7·8–15·7]) had the highest seroprevalence. Pooled schistosomiasis seroprevalence was 18·4% (95% CI 13·1–24·5; I2 97%) and stool-based prevalence was 0·9% (0·2–1·9; 99%). Sub-Saharan African migrants had the highest seroprevalence (24·1·% [95% CI 16·4–32·7]). Interpretation Strongyloidiasis affects migrants from all global regions, whereas schistosomiasis is focused in specific regions and most common among sub-Saharan African migrants. Serological prevalence estimates were several times higher than stool estimates for both parasites. These data can be used to inform screening decisions for migrants and support the use of serological screening, which is more sensitive and easier than stool testing. Funding None.

Background: Overdiagnosis is thought to be common in some mental disorders, but it has not been defined or examined systematically. Assessing overdiagnosis in mental health requires a consistently applied definition that differentiates overdiagnosis from other problems (e.g., misdiagnosis), as well as methods for quantification. Objectives: Our objectives were to (1) describe how the term “overdiagnosis” has been defined explicitly or implicitly in published articles on mental disorders, including usages consistent (overdefinition, overdetection) and inconsistent (misdiagnosis, false-positive test results, overtreatment, overtesting) with accepted definitions of overdiagnosis; and (2) identify examples of attempts to quantify overdiagnosis. Method: We searchedPubMed through January 5, 2019. Articles on mental disorders, excluding neurocognitive disorders, were eligible if they usedthe term “overdiagnosis” in the title, abstract, or text. Results: We identified 164 eligible articles with 193 total explicit or implicit uses of the term “overdiagnosis.” Of 9 articles with an explicit definition, only one provided a definition that was partially consistent with accepted definitions. Of all uses, 11.4% were consistent, and 76.7% were related to misdiagnosis and thus inconsistent. No attempts to quantify the proportion of patients who were overdiagnosed based on overdetection or overdefinition were identified. Conclusions: There are few examples of mental health articles that describe overdiagnosis consistent with accepted definitions and no examples of quantifying overdiagnosis based on these definitions. A definition of overdiagnosis based on diagnostic criteria that include people with transient or mild symptoms not amenable to treatment (overdefinition) could be used to quantify the extent of overdiagnosis in mental disorders.

A major objective of sport medicine research is to assess causes of injuries. Studying causes of subsequent injuries, including the effects of rehabilitation programs, presents unique challenges to provide appropriate guidance for treatment. In this commentary, we highlight the issue of “collider stratification bias”. Collider stratification bias is a major concern whenever we restrict populations based on variables that have multiple causes, which is common in surveillance programs studying subsequent injuries.

To illustrate why the research question determines whether and how sport medicine investigators should adjust for workload when interested in interventions or causal risk factors for injury.Theoretical conceptualization.We use current concepts of causal inference to demonstrate the advantages and disadvantages of adjusting for workload through different analytic approaches when evaluating causal effects on injury risk.When a risk factor of interest changes workload, including workload in the regression will cause bias. When workload represents time-at-risk (e.g. games played, minutes run), including workload as an offset in Poisson regression provides a comparison of injury rates (injuries per unit time). This is equivalent to including log(workload) as an independent variable with the coefficient fixed to 1. If workload is included as an independent variable instead of an offset, using log(workload) rather than workload is more consistent with theory. This practice is similar to the principles of allometric scaling. When workload represents a combination of both time-at-risk and intensity, such as with session ratings of perceived exertion, the optimal analytical strategy may require modeling time-at-risk and intensity separately rather than as one factor.Whether to account for recent workload or not, and how to account for recent workload, depends on the research question and the causal assumptions, both of which should be explicitly stated.

Around the world, people are wondering, ‘Do I have SARS-CoV-2?’. Determining a person’s probability of illness is central to the practice and teaching of evidence-based medicine (EBM). Probabilities can be calculated through mathematical formulas, but studies find that many clinicians have difficulty with the calculations.1–4 For many conditions in medicine, clinicians can rely on experience and/or established algorithms. However, with SARS-CoV-2 infection, clinicians lack experience and, given the rapidly changing prevalence of the illness, algorithms are difficult to establish. In this manuscript, we use the example of SARS-CoV-2 as an educational reminder of how clinicians can roughly gauge the probability of illness in different contexts and to highlight some conceptual issues in the estimates needed for the formulas. Broadly speaking, the diagnostic process involves a sequence of estimating the probability of an illness and then adjusting this estimate with each new piece of information. Clinicians are generally taught to calculate the probability of illness given new information through either the use of Bayes probability theorem or the use of a 2×2 contingency table. Mathematically, Bayes theorem and the 2×2 table are the same calculation. The 2×2 table calculation appears less complex than Bayes theorem, because it converts conditional probabilities into numbers that are entered into the cells of the table.2 ### Bayes probability theorem In writing formulas, the term ‘given’ is symbolised as a straight vertical line. Letting ‘P’=probability, ‘A’=SARS-CoV-2 and ‘B’=new information, Bayes probability theorem is: P(A I B)=[P(A) × P(B I A)] / P(B).5 Imagine that B, the new information, is the symptom of a cough. Then the probability of SARS-CoV-2 for a patient given the presence of cough, P(A I B), is equal to the probability of SARS-CoV-2 in the patient’s community, P(A), multiplied by the probability of cough given that the patient has SARS-CoV-2, P(B I A), …

Objectives: Estimates of depression prevalence in pregnancy and postpartum are based on the Edinburgh Postnatal Depression Scale (EPDS) more than on any other method. We aimed to determine if any EPDS cutoff can accurately and consistently estimate depression prevalence in individual studies. Methods: We analyzed datasets that compared EPDS scores to Structured Clinical Interview for DSM (SCID) major depression status. Random‐effects meta‐analysis was used to compare prevalence with EPDS cutoffs versus the SCID. Results: Seven thousand three hundred and fifteen participants (1017 SCID major depression) from 29 primary studies were included. For EPDS cutoffs used to estimate prevalence in recent studies (≥9 to ≥14), pooled prevalence estimates ranged from 27.8% (95% CI: 22.0%–34.5%) for EPDS ≥ 9 to 9.0% (95% CI: 6.8%–11.9%) for EPDS ≥ 14; pooled SCID major depression prevalence was 9.0% (95% CI: 6.5%–12.3%). EPDS ≥14 provided pooled prevalence closest to SCID‐based prevalence but differed from SCID prevalence in individual studies by a mean absolute difference of 5.1% (95% prediction interval: 13.7%, 12.3%). Conclusion: EPDS ≥14 approximated SCID‐based prevalence overall, but considerable heterogeneity in individual studies is a barrier to using it for prevalence estimation., This study was funded by the Canadian Institutes of Health Research (CIHR, KRS‐140994). Ms. Lyubenova was supported by the Mitacs Globalink Research Internship Program. Ms. Neupane was supported by G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University. Drs. Levis and Wu were supported by Fonds de recherche du Québec‐Santé (FRQS) Postdoctoral Training Fellowships. Mr. Bhandari was supported by a studentship from the Research Institute of the McGill University Health Centre. Ms. Rice was supported by a Vanier Canada Graduate Scholarship. Ms. Azar was supported by a FRQS Masters Training Award. The primary study by Barnes et al. was supported by a grant from the Health Foundation (1665/608). The primary study by Beck et al. was supported by the Patrick and Catherine Weldon Donaghue Medical Research Foundation and the University of Connecticut Research Foundation. The primary study by Helle et al. was supported by the Werner Otto Foundation, the Kroschke Foundation, and the Feindt Foundation. Prof. Robertas Bunevicius, MD, PhD (1958‐2016) was Principal Investigator of the primary study by Bunevicius et al., but passed away and was unable to participate in this project. The primary study by Chaudron et al. was supported by a grant from the National Institute of Mental Health (grant K23 MH64476). The primary study by Tissot et al. was supported by the Swiss National Science Foundation (grant 32003B 125493). The primary study by Tendais et al. was supported under the project POCI/SAU‐ESP/56397/2004 by the Operational Program Science and Innovation 2010 (POCI 2010) of the Community Support Board III and by the European Community Fund FEDER. The primary study by Garcia‐Esteve et al. was supported by grant 7/98 from the Ministerio de Trabajo y Asuntos Sociales, Women's Institute, Spain. The primary study by Howard et al. was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Numbers RP‐PG‐1210‐12002 and RP‐DG‐1108‐10012) and by the South London Clinical Research Network. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The primary study by Phillips et al. was supported by a scholarship from the National Health and Medical and Research Council (NHMRC). The primary study by Nakić Radoš et al. was supported by the Croatian Ministry of Science, Education, and Sports (134‐0000000‐2421). The primary study by Navarro et al. was supported by grant 13/00 from the Ministry of Work and Social Affairs, Institute of Women, Spain. The primary study by Pawlby et al. was supported by a Medical Research Council UK Project Grant (number G89292999N). The primary study by Quispel et al. was supported by Stichting Achmea Gezondheid (grant number z‐282). Dr. Robertson‐Blackmore was supported by a Young Investigator Award from the Brain and Behavior Research Foundation and NIMH grant K23MH080290. The primary study by Rochat et al. was supported by grants from the University of Oxford (HQ5035), the Tuixen Foundation (9940), the Wellcome Trust (082384/Z/07/Z and 071571), and the American Psychological Association. Dr. Rochat receives salary support from a Wellcome Trust Intermediate Fellowship (211374/Z/18/Z). The primary study by Prenoveau et al. was supported by The Wellcome Trust (grant number 071571). The primary study by Stewart et al. was supported by Professor Francis Creed's Journal of Psychosomatic Research Editorship fund (BA00457) administered through University of Manchester. The primary study by Tandon et al. was funded by the Thomas Wilson Sanitarium. The primary study by Tran et al. was supported by the Myer Foundation who funded the study under its Beyond Australia scheme. Dr. Tran was supported by an early career fellowship from the Australian National Health and Medical Research Council. The primary study by Vega‐Dienstmaier et al. was supported by Tejada Family Foundation, Inc, and Peruvian‐American Endowment, Inc. Drs. Benedetti and Thombs were supported by FRQS researcher salary awards.

Objective To conduct a scoping review that identifies different nominal group technique (NGT) methods used to elicit items for health surveys, and their advantages and disadvantages. Study design and setting We conducted a comprehensive search process from database inception to July 22, 2019 in Medline, EMBASE, PsychInfo, CINAHL, Cochrane Central and Scopus without language restriction. We screened titles and abstracts. Data from potentially relevant articles were extracted by one reviewer and verified by a second reviewer, with disagreements resolved by consensus or a third reviewer. Results We included 57 studies, which used between 1 and 41 nominal groups that included between 2 and 30 participants per group. We grouped the 30 identified decision points for the NGT process into two stages common to most qualitative group methods [Research objectives; Group characteristics] and three stages related to the nominal groups themselves [Eliciting survey items; Refining survey elicited items from stage 3; Evaluating and selecting final survey items]. We discuss the advantages and disadvantages of each option in relation to specific study contexts. Conclusion Investigators should carefully consider their options for each of the identified decision points and document the reasons for their choices in their protocol to maximize validity and transparency.

Limited research exists on the relationship between changes in physical activity levels and injury in children. In this study, we investigated the prognostic relationship between changes in activity, measured by the acute:chronic workload ratio (ACWR), and injury in children. We used data from the Childhood Health, Activity, and Motor Performance School Study Denmark (2008–2014), a prospective cohort study of 1,660 children aged 6–17 years. We modeled the relationship between the uncoupled 5-week ACWR and injury, defined as patient-reported musculoskeletal pain, using generalized additive mixed models. These methods accounted for repeated measures, and they improved model fit and precision compared with previous studies that used logistic models. The prognostic model predicted an injury risk of approximately 3% between decreases in activity level of up to 60% and increases of up to 30%. Predicted risk was lower when activity decreased by more than 60% (minimum of 0.5% with no recreational activity). Predicted risk was higher when activity increased by more than 30% (4.5% with a 3-fold increase in activity). Girls were at significantly higher risk of injury than boys. We observed similar patterns but lower absolute risks when we restricted the outcome to clinician-diagnosed injury. Predicted increases in injury risk with increasing activity were much lower than those of previous studies carried out in adults.