Comparison of Consumer and Research Monitors under Semistructured Settings
In recent years, there has been an enormous increase in the number of commercially available activity monitors designed to enhance self-monitoring and behavior change. Market research predicted that more than 42 million of these wearable devices would be sold in 2014. Additionally, these devices are appearing in more research centered on promotion of physical activity and behavior change. Authors in the January 2016 issue of Medicine & Science in Sports & Exercise® sought to evaluate the relative validity of different consumer and research activity monitors during semistructured periods of sedentary activity, aerobic exercise, and resistance exercise (1).
The study involved 52 participants between the ages of 18 and 65 years. There were 28 men and 24 women. Each participant performed 20 min of self-selected sedentary activities, 25 min of aerobic exercise, and 25 min of resistance exercise while wearing five wrist-worn devices. The consumer monitors were the Fitbit Flex (FBF), Jawbone UP24 (JU24), Misfit Shine (MS), Nike + Fuelband SE (NFS), and Polar Loop. In addition, they also wore two research monitors: ActiGraph GT3X+ (GT3X+) on the waist and Body Media Core (BMC) on the arm. All the devices were monitored for accuracy by having the participants wear the Oxycon Mobile (OM) portable metabolic system. The energy expenditure (EE) from each of the different activity sessions was recorded by the OM and estimated by all the monitors being studied.
The EE estimates from the GT3X+ were essentially equivalent to the values recorded from the OM. Correlations between the OM and the various monitors were generally high with the research monitors and the FBF, JU24, and NFS providing reasonably accurate total EE estimates at the individual level. The results also looked at the mean absolute percent error (MAPE) values for the full protocol. The MAPE ranged from 15.3% (BMC) to 30.4% (MS).
Three of the monitors had MAPE values less than 20% for sedentary activity: BMC (15.7%), MS (18.2%), and NFS (20.0%). Two monitors had MAPE values less than 20% for aerobic exercise: BMC (17.2%) and NFS (18.5%). None of the monitors had MAPE values less than 25% for the resistance exercise component of the study.
Bottom Line: Overall, one’s total EE for a given day was fairly accurate with the use of either of the research devices (GT3X+ and BMC) or the commercially available FBF, JU24, and NFS monitors. A larger amount of error was seen during individual activities, especially resistance exercise, with all of the devices indicating the need for more research. It would be helpful to examine these devices in settings that are more “real-world” at a variety of intensities to better determine their accuracy in monitoring individual activities.
Biomechanical Deficit Profiles Associated with Anterior Cruciate Ligament Injury Risk in Female Athletes
Approximately 250,000 anterior cruciate ligament (ACL) injuries occur per year in the United States with rising associated health care costs, loss of athletic scholarships, and decreases in physical activity. Much research has been done over the years in an attempt to define risk factors for ACL injury as a means to develop screening tools as well as injury prevention programs.
Four theories have been developed in an attempt to explain ACL injury risk. The ligament dominance theory suggests excessive knee valgus, hip adduction, and hip internal rotation during high-risk athletic maneuvers. The trunk dominance theory suggests poor trunk control during athletic maneuvers. The quadriceps dominance theory proposes excessive relative quadriceps forces or reduced hamstring recruitment, whereas the leg dominance theory suggests large leg-to-leg asymmetries as what predisposes athletes to ACL injury.
To date, there have been no large studies that attempt to quantify the prevalence of all four of these biomechanical deficit patterns and their interconnections in a large cohort of female athletes during an unanticipated cutting task. This knowledge could help to advance the science on the etiology of ACL injury by identifying the most prevalent biomechanical profiles and using that information to develop injury prevention programs that target these profiles.
The purpose of this study also in the January 2016 edition of Medicine & Science in Sports & Exercise® was to identify the prevalence and overlap of the most common biomechanical deficit profiles associated with ACL injury in a large cohort of adolescent female athletes during an unanticipated cutting task (2).
The study tested 721 high school female athletes from the sports of basketball, volleyball, and soccer with no history of knee ligament injury or knee surgery. Testing was performed before the start of their competitive seasons, and the subjects represented more than 95% of the athletes in those sports of an entire county, thus eliminating selection bias. They performed an unanticipated cutting task in the biomechanics laboratory using trunk and lower extremity 3D kinetics and kinematics.
The results revealed that approximately 40% of the female athletes demonstrated no biomechanical deficits. These athletes were classified as the low-risk group. The second most prevalent profile (24%) demonstrated a combination of high quadriceps and leg dominance deficits and was labeled as quadriceps-leg. The third most prevalent profile (22%) demonstrated a combination of trunk and leg dominance deficits and, to a lesser extent, ligamentous deficits, which was labeled as trunk–leg–ligament. Lastly, the fourth profile (14%) demonstrated very high ligament dominance deficits only and was labeled as the ligament dominance profile. These results demonstrated that approximately 60% of the female athletes tested belonged to one of the high-risk groups.
Bottom Line: This study indicated that the majority of risk profiles for ACL injury consisted of a combination of biomechanical deficits. It also demonstrated that 40% of this large cohort of female athletes was at low risk for ACL injury. This information is essential in helping to guide the development of both easy screening tests as well as more effective injury prevention programs aimed at those athletes who are determined to be at risk.
1. Bai Y, Welk GJ, Nam YH, et al. Comparison of consumer and research monitors under semistructured settings. Med. Sci. Sports Exerc.
2016; 48: 151–58.
2. Pappas E, Shiyko MP, Ford KR, Myer GD, Hewett TE. Biomechanical deficit profiles associated with ACL injury risk in female athletes. Med. Sci. Sports Exerc.
2016; 48: 107–13.