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Recent metascience suggests that motor behavior research may be underpowered, on average. Researchers can perform a priori power analyses to ensure adequately powered studies. However, there are common pitfalls that can result in underestimating the required sample size for a given design and effect size of interest. Critical evaluation of power analyses requires successful analysis reproduction, which is conditional on the reporting of sufficient information. Here, we attempted to reproduce every power analysis reported in articles (k = 84/635) in three motor behavior journals between January 2019 and June 2021. We reproduced 7% of analyses using the reported information, which increased to 43% when we assumed plausible values for missing parameters. Among studies that reported sufficient information to evaluate, 63% reported using the same statistical test in the power analysis as in the study itself, and in 77%, the test addressed at least one of the identified hypotheses. Overall, power analyses were not commonly reported with sufficient information to ensure reproducibility. A nontrivial number of power analyses were also affected by common pitfalls. There is substantial opportunity to address the issue of underpowered research in motor behavior by increasing adoption of power analyses and ensuring reproducible reporting practices.

American student-athletes (SAs) are at heightened risk for hazardous alcohol consumption compared with their nonathlete peers. However, little is known about this risk or the influence of psychosocial predictors on drinking behavior among Canadian SAs. This study compared rates of alcohol use across Canadian SAs and nonathletes and investigated whether the use of athlete-specific psychosocial predictors can improve the prediction of alcohol use outcomes in SAs. Participants (179 varsity athletes and 366 nonathletes) completed anonymous self-report questionnaires. Results suggest that Canadian athletes are at a heightened risk for experiencing alcohol-related problems compared with nonathletes, with general psychosocial predictors explaining the majority of variance in SA alcohol use. However, and quite notably, athlete-specific positive reinforcement motives predicted SA binge drinking. This research provides some of the first evidence of drinking-related problems among Canadian SAs and supports the potential use of preventative efforts to help SAs develop safe strategies for alcohol use.

Background: Incomplete data due to nonparticipation and noncompliance with the study protocol can bias the results of studies. We investigated how a nationwide accelerometer sample of adolescents and young adults is affected by such incomplete data. Methods: We analyzed cross-sectional data from 6465 participants (11–31 y old) who participated in a national health survey in Germany (KiGGS Wave 2; 2014–2017). The data included information about the participation in the measurement of physical activity using accelerometers, compliance with the wear-time protocol, and sociodemographic and health-related variables. Multivariable regression analyses were conducted to detect factors associated with incomplete data. Results: Of the total sample, 78.0% participated in the accelerometer part of the study, and 83.5% of the participants with data available complied with the wear-time protocol. In 11- to 17-year-olds, the likelihood of having incomplete accelerometer data was higher in boys, older adolescents, adolescents with a lower sociodemographic status, adolescents with overweight, adolescents not participating in organized sport, adolescents not speaking only German at home, current smokers, and adolescents having a higher soft drink consumption. In 18- to 31-year-olds, the likelihood of having incomplete accelerometer data was higher in men, adults with a lower educational level, adults not speaking only German at home, and adults who smoke. Conclusions: Our results suggest that accelerometer samples are biased such that participants with more beneficial health behaviors provide complete accelerometer data more often. This knowledge should be used to design effective recruitment strategies and should be considered when interpreting results of accelerometer studies.

The onset of COVID-19 and cancellation of collegiate sports may have exacerbated student-athletes’ psychological distress. Within a national sample of collegiate athletes (N = 5,755; 66.7% women), we determined how gender and race related to rates of depression, stress, and counseling use at the beginning of the pandemic (April/May 2020). Overall, 26.5% (n = 1,526) and 10.6% (n = 612) endorsed clinical levels of depression and stress, respectively; 25.1% (n = 1,443) and 69.7% (n = 4,014) reported subclinical levels. Few athletes (2.3%–17.1%) reported counseling use before or after the onset of COVID-19; those who did reported higher levels of depression and stress than those who never sought services. The female athletes reported higher rates of depression, stress, and counseling use than the male athletes. There were no race effects. Athletic departments must address their student-athletes’ psychological distress by facilitating a higher use of mental health services.

Background: Running is a popular form of exercise, and its physiological effects are strongly modulated by speed. Accelerometry-based activity monitors are commonly used to measure physical activity in research, but no method exists to estimate running speed from only accelerometer data. Methods: Using three cohorts totaling 72 subjects performing treadmill and outdoor running, we developed linear, ridge, and gradient-boosted tree regression models to estimate running speed from raw accelerometer data from waist- or wrist-worn devices. To assess model performance in a real-world scenario, we deployed the best-performing model to data from 16 additional runners completing a 13-week training program while equipped with waist-worn accelerometers and commercially available foot pods. Results: Linear, ridge, and boosted tree models estimated speed with 12.0%, 11.6%, and 11.2% mean absolute percentage error, respectively, using waist-worn accelerometer data. Errors were greater using wrist-worn data, with linear, ridge, and boosted tree models achieving 13.8%, 14.0%, and 12.8% error. Across 663 free-living runs, speed was significantly associated with run duration (p = .009) and perceived run intensity (p = .008). Speed was nonsignificantly associated with fatigue (p = .07). Estimated speeds differed from foot pod measurements by 7.25%; associations and statistical significance were similar when speed was assessed via accelerometry versus via foot pod. Conclusion: Raw accelerometry data can be used to estimate running speed in free-living data with sufficient accuracy to detect associations with important measures of health and performance. Our approach is most useful in studies where research grade accelerometry is preferable to traditional global positioning system or foot pod-based measurements, such as in large-scale observational studies on physical activity.