Many consumer-based monitors are marketed to provide personal information on the levels of physical activity and daily energy expenditure (EE), but little or no information is available to substantiate their validity. This study aimed to examine the validity of EE estimates from a variety of consumer-based, physical activity monitors under free-living conditions. Sixty (26.4 ± 5.7 yr) healthy males (n = 30) and females (n = 30) wore eight different types of activity monitors simultaneously while completing a 69-min protocol. The monitors included the BodyMedia FIT armband worn on the left arm, the DirectLife monitor around the neck, the Fitbit One, the Fitbit Zip, and the ActiGraph worn on the belt, as well as the Jawbone Up and Basis B1 Band monitor on the wrist. The validity of the EE estimates from each monitor was evaluated relative to criterion values concurrently obtained from a portable metabolic system (i.e., Oxycon Mobile). Differences from criterion measures were expressed as a mean absolute percent error and were evaluated using 95% equivalence testing. For overall group comparisons, the mean absolute percent error values (computed as the average absolute value of the group-level errors) were 9.3%, 10.1%, 10.4%, 12.2%, 12.6%, 12.8%, 13.0%, and 23.5% for the BodyMedia FIT, Fitbit Zip, Fitbit One, Jawbone Up, ActiGraph, DirectLife, NikeFuel Band, and Basis B1 Band, respectively. The results from the equivalence testing showed that the estimates from the BodyMedia FIT, Fitbit Zip, and NikeFuel Band (90% confidence interval = 341.1-359.4) were each within the 10% equivalence zone around the indirect calorimetry estimate. The indicators of the agreement clearly favored the BodyMedia FIT armband, but promising preliminary findings were also observed with the Fitbit Zip.

Background Heart rate (HR) monitors are valuable devices for fitness-orientated individuals. There has been a vast influx of optical sensing blood flow monitors claiming to provide accurate HR during physical activities. These monitors are worn on the arm and wrist to detect HR with photoplethysmography (PPG) techniques. Little is known about the validity of these wearable activity trackers. Aim Validate the Scosche Rhythm (SR), Mio Alpha (MA), Fitbit Charge HR (FH), Basis Peak (BP), Microsoft Band (MB), and TomTom Runner Cardio (TT) wireless HR monitors. Methods 50 volunteers (males: n=32, age 19–43 years; females: n=18, age 19–38 years) participated. All monitors were worn simultaneously in a randomised configuration. The Polar RS400 HR chest strap was the criterion measure. A treadmill protocol of one 30 min bout of continuous walking and running at 3.2, 4.8, 6.4, 8.0, and 9.6 km/h (5 min at each protocol speed) with HR manually recorded every minute was completed. Results For group comparisons, the mean absolute percentage error values were: 3.3%, 3.6%, 4.0%, 4.6%, 4.8% and 6.2% for TT, BP, RH, MA, MB and FH, respectively. Pearson product-moment correlation coefficient (r) was observed: r=0.959 (TT), r=0.956 (MB), r=0.954 (BP), r=0.933 (FH), r=0.930 (RH) and r=0.929 (MA). Results from 95% equivalency testing showed monitors were found to be equivalent to those of the criterion HR (±10% equivalence zone: 98.15–119.96). Conclusions The results demonstrate that the wearable activity trackers provide an accurate measurement of HR during walking and running activities.

This study evaluated the relative validity of different consumer and research activity monitors during semistructured periods of sedentary activity, aerobic exercise, and resistance exercise. Fifty-two (28 male and 24 female) participants age 18-65 yr performed 20 min of self-selected sedentary activity, 25 min of aerobic exercise, and 25 min of resistance exercise, with 5 min of rest between each activity. Each participant wore five wrist-worn consumer monitors [Fitbit Flex, Jawbone Up24, Misfit Shine (MS), Nike+ Fuelband SE (NFS), and Polar Loop] and two research monitors [ActiGraph GT3X+ on the waist and BodyMedia Core (BMC) on the arm] while being concurrently monitored with Oxycon Mobile (OM), a portable metabolic measuring system. Energy expenditure (EE) on different activity sessions was measured by OM and estimated by all monitors. Mean absolute percent error (MAPE) values for the full 80-min protocol ranged from 15.3% (BMC) to 30.4% (MS). EE estimates from ActiGraph GT3X+ were found to be equivalent to those from OM (± 10% equivalence zone, 285.1-348.5). Correlations between OM and the various monitors were generally high (ranged between 0.71 and 0.90). Three monitors had MAPE values lower than 20% for sedentary activity: BMC (15.7%), MS (18.2%), and NFS (20.0%). Two monitors had MAPE values lower than 20% for aerobic exercise: BMC (17.2%) and NFS (18.5%). None of the monitors had MAPE values lower than 25% for resistance exercise. Overall, the research monitors and Fitbit Flex, Jawbone Up24, and NFS provided reasonably accurate total EE estimates at the individual level. However, larger error was evident for individual activities, especially resistance exercise. Further research is needed to examine these monitors across various activities and intensities as well as under real-world conditions.

Wearable activity trackers have become popular for tracking individual's daily physical activity, but little information is available to substantiate the validity of these devices in step counts. Thirty‐five healthy individuals completed three conditions of activity tracker measurement: walking/jogging on a treadmill, walking over‐ground on an indoor track, and a 24‐hour free‐living condition. Participants wore 10 activity trackers at the same time for both treadmill and over‐ground protocol. Of these 10 activity trackers three were randomly given for 24‐hour free‐living condition. Correlations of steps measured to steps observed were r = 0.84 and r = 0.67 on a treadmill and over‐ground protocol, respectively. The mean MAPE (mean absolute percentage error) score for all devices and speeds on a treadmill was 8.2% against manually counted steps. The MAPE value was higher for over‐ground walking (9.9%) and even higher for the 24‐hour free‐living period (18.48%) on step counts. Equivalence testing for step count measurement resulted in a significant level within ±5% for the Fitbit Zip, Withings Pulse, and Jawbone UP24 and within ±10% for the Basis B1 band, Garmin VivoFit, and SenseWear Armband Mini. The results show that the Fitbit Zip and Withings Pulse provided the most accurate measures of step count under all three different conditions (i.e. treadmill, over‐ground, and 24‐hour condition), and considerable variability in accuracy across monitors and also by speeds and conditions.

This study examined the effects of resistance training on muscle quality, muscle growth factors, and functional fitness in older adult women with sarcopenia. Twenty-two older adult women aged over 65 with sarcopenia were randomly assigned to either resistance training (RT, n = 12) or non-exercise control group (CG, n = 10). The body weight-based and elastic band RT were performed three times a week, 60 min per session, for 16 weeks. Body composition and thigh muscle quality were estimated by dual-energy X-ray absorptiometry (DEXA) and computed tomography (CT), respectively. The muscle growth factors, including growth differentiation factor-8 (GDF-8), growth differentiation factor-15 (GDF-15), activin A, and follistatin, were analyzed via blood samples. Statistical analyses were performed using repeated measures multivariate analysis of variance (MANOVA), analysis of variance (ANOVA), and effect size (i.e., cohen’s d, partial eta square), and the significance level was set at 0.05. The RT group improved their functional fitness, grip strength, gait speed, and isometric muscle strength (p < 0.01, d > 0.99; large), while these variables did not change in the CG. An increase in intramuscular fat was only observed in the CG (p < 0.01, 1.06; large). Muscle growth factors such as follistatin were significantly increased in the RT (p < 0.05, 0.81; large), but other variables did not change following resistance training. Sixteen weeks of resistance training improved functional fitness and prevented age-related increases in intramuscular fat in the thigh area. However, there were only some changes in muscle growth factors, such as follistatin, suggesting that the effectiveness of resistance training on muscle growth factors is limited. Body weight-based and elastic band resistance training is an alternative training method for sarcopenia to minimize the age-related adverse effects on muscle function and quality.

Introduction: A lack of standardization with accelerometry-based monitors has made it hard to advance applications for both research and practice. Resolving these challenges is essential for developing methods for consistent, agnostic reporting of physical activity outcomes from wearable monitors in clinical applications.Methods: This article reviewed the literature on the methods used to evaluate the validity of contemporary consumer activity monitors. A rationale for focusing on energy expenditure as a key outcome measure in validation studies was provided followed by a summary of the strengths and limitations of different analytical methods. The primary review included 23 recent validation studies that collectively reported energy expenditure estimates from 58 monitors relative to values from appropriate criterion measures.Results: The majority of studies reported weak indicators such as correlation coefficients (87%), but only half (52%) reported the recommended summary statistic of mean absolute percent error needed to evaluate actual individual error. Fewer used appropriate tests of agreement such as equivalence testing (22%).Conclusions: The use of inappropriate analytic methods and incomplete reporting of outcomes is a major limitation for systematically advancing research with both research grade and consumer-grade activity monitors. Guidelines are provided to standardize analytic methods and reporting in these types of studies to enhance the utility of the devices for clinical mHealth applications.