Proof-of-concept and concurrent validity of a prototype headset to assess peak oxygen uptake without a face mask

Objective Portable gas exchange instruments allow the assessment of peak oxygen uptake (V̇O2peak) but are often bulky, expensive and require wearing a face mask thereby limiting their routine application. A newly developed miniaturized headset (VitaScale, Nuremberg, Germany) may overcome these barriers and allow measuring V̇O2peak without applying a face mask. Here we aimed (i) to disclose the technical setup of a headset incorporating a gas and volume sensor to measure volume flow and expired oxygen concentration and (ii) to assess the concurrent criterion-validity of the headset to measure V̇O2peak in 44 individuals exercising on a stationary cycle ergometer in consideration of the test–retest reliability of the criterion measure. Results The coefficient of variation (CV%) while measuring V̇O2peak during incremental cycling with the headset was 6.8%. The CV% for reliability of the criterion measure was 4.0% for V̇O2peak. Based on the present data, the headset might offer a new technology for V̇O2peak measurement due to its low-cost and mask-free design.

Accuracy and precision of energy expenditure, heart rate, and steps measured by combined-sensing Fitbits against reference measures: Systematic Review and Meta-Analyses (Preprint)

BACKGROUND Although it is widely recognized that physical activity is an important determinant of health there is considerable challenge in assessing this complex behavior. Tools for the objective assessment of the frequency, intensity, and duration of physical activity in adults and children have largely been developed for short-term use within research or public health surveillance environments. However, recent advances in microtechnology, data processing, wireless communication, and battery capacity have resulted in the proliferation of low-cost, non-invasive, wrist-worn devices with attractive designs that can easily be used by consumers to track their physical activity over long periods of time. OBJECTIVE The purpose of the present systematic-review and meta-analyses is to examine, quantify, and report on the current state of evidence for the analytical validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. METHODS Systematic-review and Bland-Altman meta-analyses of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate and steps. RESULTS A total of 52 studies were included in the systematic review. Among them, 41 were included in the meta-analyses, representing 203 individual comparisons between Fitbit devices and a criterion measure (i.e., 117 for heart rate, 49 for energy expenditure, and 37 for steps). Overall, the majority of authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared to criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of, respectively, -2,99 bpm, -2,77 kcal/min and -3,11 steps/min of the Fitbit compared to criterion measure (results obtained after removing high risk of bias studies). CONCLUSIONS Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by quality of study, age of the participants, type of activities, and by model of Fitbit. The qualitative conclusions of the majority of studies aligned with the results of meta-analyses. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. Information about energy expenditure however are likely to be too unprecise.

Assessing Heart Rate Using Consumer Technology Association Standards

It is difficult for developers, researchers, and consumers to compare results among emerging wearable technology without using a uniform set of standards. This study evaluated the accuracy of commercially available wearable technology heart rate (HR) monitors using the Consumer Technology Association (CTA) standards. Participants (N = 23) simultaneously wore a Polar chest strap (criterion measure), Jabra Elite earbuds, Scosche Rhythm 24 armband, Apple Watch 4, and Garmin Forerunner 735 XT during sitting, activities of daily living, walking, jogging, running, and cycling, totaling 57 min of monitored activity. The Apple Watch mean bias was within ±1 bpm, and mean absolute percent error (MAPE) was <3% in all six conditions. Garmin underestimated HR in all conditions, except cycling and MAPE was >10% during sedentary, lifestyle, walk-jog, and running. The Jabra mean bias was within ±5 bpm for each condition, and MAPE exceeded 10% for walk-jog. The Scosche mean bias was within ±1 bpm and MAPE was <5% for all conditions. In conclusion, only the Apple Watch Series 4 and the Scosche Rhythm 24 displayed acceptable agreement across all conditions. By employing CTA standards, future developers, researchers, and consumers will be able to make true comparisons of accuracy among wearable devices.

Accelerometry-Based Step Count Validation for Horse Movement Analysis During Stall Confinement

Quantitative tracking of equine movement during stall confinement has the potential to detect subtle changes in mobility due to injury. These changes may warn of potential complications, providing vital information to direct rehabilitation protocols. Inertial measurement units (IMUs) are readily available and easily attached to a limb or surcingle to objectively record step count in horses. The objectives of this study were: (1) to compare IMU-based step counts to a visually-based criterion measure (video) for three different types of movements in a stall environment, and (2) to compare three different sensor positions to determine the ideal location on the horse to assess movement. An IMU was attached at the withers, right forelimb and hindlimb of six horses to assess free-movement, circles, and figure-eights recorded in 5 min intervals and to determine the best location, through analysis of all three axes of the triaxial accelerometer, for step count during stall confinement. Mean step count difference, absolute error (%) and intraclass correlation coefficients (ICCs) were determined to assess the sensor's ability to track steps compared to the criterion measure. When comparing sensor location for all movement conditions, the right-forelimb vertical-axis produced the best results (ICC = 1.0, % error = 6.8, mean step count difference = 1.3) followed closely by the right-hindlimb (ICC = 0.999, % error = 15.2, mean step count difference = 1.8). Limitations included the small number of horse participants and the lack of random selection due to limited availability and accessibility. Overall, the findings demonstrate excellent levels of agreement between the IMU's vertical axis and the video-based criterion at the forelimb and hindlimb locations for all movement conditions.

Evaluating the Performance of Sensor-Based Bout Detection Algorithms: The Transition Pairing Method

Background: Bout detection algorithms are used to segment data from wearable sensors, but it is challenging to assess segmentation correctness. The purpose of this study was to present and demonstrate the Transition Pairing Method (TPM), a new method for evaluating the performance of bout detection algorithms. Methods: The TPM compares predicted transitions to a criterion measure in terms of number and timing. A true positive is defined as a predicted transition that corresponds with one criterion transition in a mutually exclusive pair. The pairs are established using an extended Gale-Shapley algorithm, and the user specifies a maximum allowable within-pair time lag, above which pairs cannot be formed. Unpaired predictions and criteria are false positives and false negatives, respectively. The demonstration used raw acceleration data from 88 youth who wore ActiGraph GT9X monitors (right hip and non-dominant wrist) during simulated free-living. Youth Sojourn bout detection algorithms were applied (one for each attachment site), and the TPM was used to compare predicted bout transitions to the criterion measure (direct observation). Performance metrics were calculated for each participant, and hip-versus-wrist means were compared using paired t-tests (α = 0.05). Results: When the maximum allowable lag was 1-s, both algorithms had recall <20% (2.4% difference from one another, p < .01) and precision <10% (1.4% difference from one another, p < .001). That is, >80% of criterion transitions were undetected, and >90% of predicted transitions were false positives. Conclusion: The TPM improves on conventional analyses by providing specific information about bout detection in a standardized way that applies to any bout detection algorithm.

Trust and performance in business teams: a meta-analysis

Purpose The purpose of this study is two-fold. First, the nature of the relationship between team trust and team performance in the business context is determined. Second, both team design (team size and team type) and methodological moderators (source of criterion measure and study date) of the relationship are assessed. Design/methodology/approach A random-effects meta-analysis was performed on published and unpublished empirical studies. Subgroup moderator analyses were conducted using Cochran’s Q. Continuous moderator analyses were conducted using meta-regression. Findings Data from 55 independent studies (3,671 teams) were pooled. Results indicated a large, positive relationship between team trust and team performance in real business teams. Further analyses indicated that the relationship was significantly moderated by business team type, team size and source of criterion measure. Research limitations/implications Results indicate that different team types, sizes and performance criteria should not be treated as equivalent. Results are based on cross-sectional research and can only be generalized to business teams. Practical implications Managers should be attentive to trust issues in work teams, as they may portend future performance problems or mirror other organizational issues that affect team performance. Team function and size predict how team trust is related to team performance. Originality/value The present study answers a call by Costa et al. (2018) for additional investigation of moderators of the trust-performance relationship in teams using a quantitative review of studies.

A Novel Video-Based Direct Observation System for Assessing Physical Activity and Sedentary Behavior in Children and Young Adults

Purpose: Develop a direct observation (DO) system to serve as a criterion measure for the calibration of models applied to free-living (FL) accelerometer data. Methods: Ten participants (19.4 ± 0.8 years) were video-recorded during four, one-hour FL sessions in different settings: 1) school, 2) home, 3) community, and 4) physical activity. For each setting, 10-minute clips from three randomly selected sessions were extracted and coded by one expert coder and up to 20 trained coders using the Observer XT software (Noldus, Wageningen, the Netherlands). The coder defines each whole-body movement which was further described with three modifiers: 1) locomotion, 2) activity type, and 3) MET value (used to categorize intensity level). Percent agreement was calculated for intra- and inter-rater reliability. For intra-rater reliability, the criterion coder coded all 12 clips twice, separated by at least one week between coding sessions. For inter-rater reliability, coded clips by trained coders were compared to the expert coder. Intraclass correlations (ICCs) were calculated to assess the agreement of intensity category for intra- and inter-rater comparisons described above. Results: For intra-rater reliability, mean percent agreement ranged from 91.9 ± 3.9% to 100.0 ± 0.0% across all variables in all settings. For inter-rater reliability, mean percent agreement ranged from 88.2 ± 3.5% to 100.0 ± 0.0% across all variables in all settings. ICCs for intensity category ranged from 0.74–1.00 and 0.81–1.00 for intra- and inter-rater comparisons, respectively. Conclusion: The DO system is reliable and feasible to serve as a criterion measure of FL physical activity in young adults to calibrate accelerometers, subsequently improving interpretation of surveillance and intervention research.

Concurrent Validity of ActiGraph GT3X+ and Axivity AX3 Accelerometers for Estimating Physical Activity and Sedentary Behavior

Introduction: Accelerometers are commonly used to assess time-use behaviors related to physical activity, sedentary behavior, and sleep; however, as new accelerometer technologies emerge, it is important to ensure consistency with previous devices. This study aimed to evaluate the concurrent validity of the commonly used accelerometer, ActiGraph GT3X+, and the relatively new Axivity AX3 (fastened to the lower back) for detecting physical activity intensity and body postures when using direct observation as the criterion measure. Methods: A total of 41 children (aged 6–16 years) and 33 adults (aged 28–59 years) wore both monitors concurrently while performing 10 prescribed activities under laboratory conditions. The GT3X+ data were categorized into different physical activity intensity and posture categories using intensity-based cut points and ActiGraph proprietary inclinometer algorithms, respectively. The AX3 data were first converted to ActiGraph counts before being categorized into different physical activity intensity categories, while activity recognition models were used to detect the target postures. Sensitivity, specificity, and the balanced accuracy for intensity and posture category classification were calculated for each accelerometer. Differences in balanced accuracy between the devices and between children and adults were also calculated. Results: Both accelerometers obtained 74–96% balanced accuracy, with the AX3 performing slightly better (∼4% higher, p < .01) for detecting postures and physical activity intensity. Error in both devices was greatest when contrasting sitting/standing, sedentary/light intensity, and moderate/light intensity. Conclusion: In comparison with the GT3X+ accelerometer, AX3 was able to detect various postures and activity intensities with slightly higher balanced accuracy in children and adults.

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

Wearable devices are increasingly prevalent in research environments for the estimation of energy expenditure (EE) and heart rate (HR). The aim of this study was to validate the HR and EE estimates of the Fitbit charge 2 (FC2), and the EE estimates of the Sensewear armband mini (SWA). We recruited 59 healthy adults to participate in walking, running, cycling, sedentary and household tasks. Estimates of HR from the FC2 were compared to a HR chest strap (Polar) and EE to a stationary metabolic cart (Vyntus CPX). The SWA overestimated overall EE by 0.03 kcal/min−1 and was statistically equivalent to the criterion measure, with a mean absolute percentage error (MAPE) of 29%. In contrast, the FC2 was not equivalent overall (MAPE = 44%). In household tasks, MAPE values of 93% and 83% were observed for the FC2 and SWA, respectively. The FC2 HR estimates were equivalent to the criterion measure overall. The SWA is more accurate than the commercial-grade FC2. Neither device is consistently accurate across the range of activities used in this study. The HR data obtained from the FC2 is more accurate than its EE estimates and future research may focus more on this variable.