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Academics in sports medicine as well as other medical fields are generally expected to publish research and opinions in peer-reviewed journals. The peer-review process is intended to protect against the publication of flawed research and unsubstantiated claims. However, both financial and non-financial competing interests may result in sub-optimal results by affecting investigators, editors, peer reviewers, academic institutions, and publishers. In this article, we focus on the non-financial competing interests created in our current academic system. Because these competing interests are embedded in our current scholastic framework, the potential biases are difficult to quantify. To minimize the effect of these competing interests, we review and highlight some underlying incentives for each stakeholder and some potential solutions to mitigate their effects.

BACKGROUND: The optimization of athlete training load is not a new concept; however in recent years, the concept of "load management" is one of the most widely studied and divisive topics in sports science and medicine. PURPOSE: Discuss the challenges faced by sports when utilizing training load monitoring and management, with a specific focus on the use of data to inform load management guidelines and policies/mandates, their consequences, and how we move this field forward. CHALLENGES: While guidelines can theoretically help protect athletes, overzealous and overcautious guidelines may restrict an athlete's preparedness, negatively influence performance, and increase injury risk. Poor methods, wrong interpretation of study findings, and faulty logic do not allow for systematic scientific evaluations to inform guidelines. Practical Solutions: Guidelines and mandates should be developed through a systematic research process with stronger research designs and clear research questions. Collaborating with statistical and epidemiological experts is essential. Implementing open science principles and sharing all sports training load data increase transparency and allow for more rapid and valid advancements in knowledge. Practitioners should incorporate multiple data streams and consider individual athlete responses, rather than applying broad guidelines based on average data. CONCLUSION: Many current training load guidelines and mandates in sports come from good intentions; however, they are arbitrary without sound knowledge of the underlying scientific principles or methods. Common sense guidelines are helpful when there is sparse literature, but they should be careful to avoid arbitrarily choosing findings from weak research. Without precise scientific inquiries, implementing training load interventions or guidelines can have negative implications.

Return-to-play decision making should be based on all the advantages and disadvantages of return to play for athletes, not just the risk of injury. For competitive athletes, this includes the effect of early versus delayed return to sport on performance. In this paper, we address the questions "How can I estimate the effect of injury on the individual's performance at return to play?" and "What is the effect of delaying return to sport on the individual's performance?". To address these questions, we describe (1) some foundational concepts, design and analytical challenges related to estimating the causal effect of return to play timing on performance in the athlete, (2) additional challenges if one is interested in the effects of delaying return to play and (3) differences when the questions relate to the team's performance. Although the analytical strategies described appear complicated, coaches and athletes make these judgements informally every day without explicitly stating their assumptions. Using a formal approach should help analysts provide the most valid answers to the questions asked by athletes and coaches. In brief, the choice of a comparison group depends on the research question and requires that one consider the hypothetical performance trajectory of the athlete had they never been injured. Thus, the optimal comparison group depends on the shape of the expected trajectory and the specific research question being asked.

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.

Item does not contain fulltext, 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 <26%. Overall, 23 (18 medical-related) of 103 facilitators were rated by ≥75% as 'likely' or 'very likely' to use among those who experienced corresponding barriers; these facilitators focused on adapting exercise (e.g., using controlled, slow movement), taking care of one’s body (e.g., stretching), keeping warm (e.g., wearing gloves), and protecting skin (e.g., covering ulcers). Among those who had previously tried the facilitator, all facilitators were rated by ≥50% as 'likely' or 'very likely' to use; among those with the barrier who had not tried the facilitator, only 12 of 103 were rated 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.

02 februari 2021, Item does not contain fulltext, 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 <26%. Overall, 23 (18 medical-related) of 103 facilitators were rated by ≥75% as 'likely' or 'very likely' to use among those who experienced corresponding barriers; these facilitators focused on adapting exercise (e.g., using controlled, slow movement), taking care of one’s body (e.g., stretching), keeping warm (e.g., wearing gloves), and protecting skin (e.g., covering ulcers). Among those who had previously tried the facilitator, all facilitators were rated by ≥50% as 'likely' or 'very likely' to use; among those with the barrier who had not tried the facilitator, only 12 of 103 were rated 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.

Objectives: Selectively reported results from only well-performing cutoffs in diagnostic accuracy studies may bias estimates in meta-analyses. We investigated cutoff reporting patterns for the Patient Health Questionnaire-9 (PHQ-9; standard cutoff 10) and Edinburgh Postnatal Depression Scale (EPDS; no standard cutoff, commonly used 10–13) and compared accuracy estimates based on published cutoffs versus all cutoffs. Methods: We conducted bivariate random effects meta-analyses using individual participant data to compare accuracy from published versus all cutoffs. Results: For the PHQ-9 (30 studies, N = 11,773), published results underestimated sensitivity for cutoffs below 10 (median difference: −0.06) and overestimated for cutoffs above 10 (median difference: 0.07). EPDS (19 studies, N = 3637) sensitivity estimates from published results were similar for cutoffs below 10 (median difference: 0.00) but higher for cutoffs above 13 (median difference: 0.14). Specificity estimates from published and all cutoffs were similar for both tools. The mean cutoff of all reported cutoffs in PHQ-9 studies with optimal cutoff below 10 was 8.8 compared to 11.8 for those with optimal cutoffs above 10. Mean for EPDS studies with optimal cutoffs below 10 was 9.9 compared to 11.8 for those with optimal cutoffs greater than 10. Conclusion: Selective cutoff reporting was more pronounced for the PHQ-9 than EPDS.

OBJECTIVE: To evaluate, across multiple sample sizes, the degree that data-driven methods result in (1) optimal cutoffs different from population optimal cutoff and (2) bias in accuracy estimates. STUDY DESIGN AND SETTING: A total of 1,000 samples of sample size 100, 200, 500 and 1,000 each were randomly drawn to simulate studies of different sample sizes from a database (n = 13,255) synthesized to assess Edinburgh Postnatal Depression Scale (EPDS) screening accuracy. Optimal cutoffs were selected by maximizing Youden's J (sensitivity+specificity-1). Optimal cutoffs and accuracy estimates in simulated samples were compared to population values. RESULTS: Optimal cutoffs in simulated samples ranged from ≥ 5 to ≥ 17 for n = 100, ≥ 6 to ≥ 16 for n = 200, ≥ 6 to ≥ 14 for n = 500, and ≥ 8 to ≥ 13 for n = 1,000. Percentage of simulated samples identifying the population optimal cutoff (≥ 11) was 30% for n = 100, 35% for n = 200, 53% for n = 500, and 71% for n = 1,000. Mean overestimation of sensitivity and underestimation of specificity were 6.5 percentage point (pp) and -1.3 pp for n = 100, 4.2 pp and -1.1 pp for n = 200, 1.8 pp and -1.0 pp for n = 500, and 1.4 pp and -1.0 pp for n = 1,000. CONCLUSIONS: Small accuracy studies may identify inaccurate optimal cutoff and overstate accuracy estimates with data-driven methods.

Introduction: Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results. Objective: To compare the odds of the major depression classification based on the SCID, CIDI, and MINI. Methods: We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis. Results: In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80). Conclusions: Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics. © 2020

OBJECTIVE: Two previous individual participant data meta-analyses (IPDMAs) found that different diagnostic interviews classify different proportions of people as having major depression overall or by symptom levels. We compared the odds of major depression classification across diagnostic interviews among studies that administered the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D). METHODS: Data accrued for an IPDMA on HADS-D diagnostic accuracy were analysed. We fit binomial generalized linear mixed models to compare odds of major depression classification for the Structured Clinical Interview for DSM (SCID), Composite International Diagnostic Interview (CIDI), and Mini International Neuropsychiatric Interview (MINI), controlling for HADS-D scores and participant characteristics with and without an interaction term between interview and HADS-D scores. RESULTS: There were 15,856 participants (1942 [12%] with major depression) from 73 studies, including 15,335 (97%) non-psychiatric medical patients, 164 (1%) partners of medical patients, and 357 (2%) healthy adults. The MINI (27 studies, 7345 participants, 1066 major depression cases) classified participants as having major depression more often than the CIDI (10 studies, 3023 participants, 269 cases) (adjusted odds ratio [aOR] = 1.70 (0.84, 3.43)) and the semi-structured SCID (36 studies, 5488 participants, 607 cases) (aOR = 1.52 (1.01, 2.30)). The odds ratio for major depression classification with the CIDI was less likely to increase as HADS-D scores increased than for the SCID (interaction aOR = 0.92 (0.88, 0.96)). CONCLUSION: Compared to the SCID, the MINI may diagnose more participants as having major depression, and the CIDI may be less responsive to symptom severity.

BACKGROUND: Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9. METHODS: We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy. RESULTS: 16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (-0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01). CONCLUSIONS: PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic approaches assume that the primary studies report the sample mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several methods have been developed to estimate the sample mean and standard deviation from the reported summary data. A major limitation of these widely used methods is that they assume that the outcome distribution is normal, which is unlikely to be tenable for studies reporting medians. We propose two novel approaches to estimate the sample mean and standard deviation when data are suspected to be non-normal. Our simulation results and empirical assessments show that the proposed methods often perform better than the existing methods when applied to non-normal data.

OBJECTIVES: Depression symptom questionnaires are not for diagnostic classification. Patient Health Questionnaire-9 (PHQ-9) scores ≥10 are nonetheless often used to estimate depression prevalence. We compared PHQ-9 ≥10 prevalence to Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) major depression prevalence and assessed whether an alternative PHQ-9 cutoff could more accurately estimate prevalence. STUDY DESIGN AND SETTING: Individual participant data meta-analysis of datasets comparing PHQ-9 scores to SCID major depression status. RESULTS: A total of 9,242 participants (1,389 SCID major depression cases) from 44 primary studies were included. Pooled PHQ-9 ≥10 prevalence was 24.6% (95% confidence interval [CI]: 20.8%, 28.9%); pooled SCID major depression prevalence was 12.1% (95% CI: 9.6%, 15.2%); and pooled difference was 11.9% (95% CI: 9.3%, 14.6%). The mean study-level PHQ-9 ≥10 to SCID-based prevalence ratio was 2.5 times. PHQ-9 ≥14 and the PHQ-9 diagnostic algorithm provided prevalence closest to SCID major depression prevalence, but study-level prevalence differed from SCID-based prevalence by an average absolute difference of 4.8% for PHQ-9 ≥14 (95% prediction interval: -13.6%, 14.5%) and 5.6% for the PHQ-9 diagnostic algorithm (95% prediction interval: -16.4%, 15.0%). CONCLUSION: PHQ-9 ≥10 substantially overestimates depression prevalence. There is too much heterogeneity to correct statistically in individual studies.

Objectives: A previous individual participant data meta-analysis (IPDMA) identified differences in major depression classification rates between different diagnostic interviews, controlling for depressive symptoms on the basis of the Patient Health Questionnaire-9. We aimed to determine whether similar results would be seen in a different population, using studies that administered the Edinburgh Postnatal Depression Scale (EPDS) in pregnancy or postpartum. Methods: Data accrued for an EPDS diagnostic accuracy IPDMA were analysed. Binomial generalised linear mixed models were fit to compare depression classification odds for the Mini International Neuropsychiatric Interview (MINI), Composite International Diagnostic Interview (CIDI), and Structured Clinical Interview for DSM (SCID), controlling for EPDS scores and participant characteristics. Results: Among fully structured interviews, the MINI (15 studies, 2,532 participants, 342 major depression cases) classified depression more often than the CIDI (3 studies, 2,948 participants, 194 major depression cases; adjusted odds ratio [aOR] = 3.72, 95% confidence interval [CI] [1.21, 11.43]). Compared with the semistructured SCID (28 studies, 7,403 participants, 1,027 major depression cases), odds with the CIDI (interaction aOR = 0.88, 95% CI [0.85, 0.92]) and MINI (interaction aOR = 0.95, 95% CI [0.92, 0.99]) increased less as EPDS scores increased. Conclusion: Different interviews may not classify major depression equivalently.