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Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity.
Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa).
Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE.
Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively.
Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested."
Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis
(2020)
Background:
Decreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA.
Objective:
The aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users’ PA behavior.
Methods:
The BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin Vívoactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature.
Results:
The most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin Vívoactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA.
Conclusions:
Wearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.
Monitoring variations in the functioning of the autonomic nervous system may help personalize training of runners and provide more pronounced physiological adaptations and performance improvements. We systematically reviewed the scientific literature comparing physiological adaptations and/or improvements in performance following training based on responses of the autonomic nervous system (ie, changes in heart rate variability) and predefined training. PubMed, SPORTDiscus, and Web of Science were searched systematically in July 2019. Keywords related to endurance, running, autonomic nervous system, and training. Studies were included if they (a) involved interventions consisting predominantly of running training; (b) lasted at least 3 weeks; (c) reported pre‐ and post‐intervention assessment of running performance and/or physiological parameters; (d) included an experimental group performing training adjusted continuously on the basis of alterations in HRV and a control group; and (e) involved healthy runners. Five studies involving six interventions and 166 participants fulfilled our inclusion criteria. Four HRV‐based interventions reduced the amount of moderate‐ and/or high‐intensity training significantly. In five interventions, improvements in performance parameters (3000 m, 5000 m, Loadmax, Tlim) were more pronounced following HRV‐based training. Peak oxygen uptake (VO\(_{2peak}\)) and submaximal running parameters (eg, LT1, LT2) improved following both HRV‐based and predefined training, with no clear difference in the extent of improvement in VO\(_{2peak}\). Submaximal running parameters tended to improve more following HRV‐based training. Research findings to date have been limited and inconsistent. Both HRV‐based and predefined training improve running performance and certain submaximal physiological adaptations, with effects of the former training tending to be greater.
3D printing is a rapidly evolving field for biological (bioprinting) and non-biological applications. Due to a high degree of freedom for geometrical parameters in 3D printing, prototype printing of bioreactors is a promising approach in the field of Tissue Engineering. The variety of printers, materials, printing parameters and device settings is difficult to overview both for beginners as well as for most professionals. In order to address this problem, we designed a guidance including test bodies to elucidate the real printing performance for a given printer system. Therefore, performance parameters such as accuracy or mechanical stability of the test bodies are systematically analysed. Moreover, post processing steps such as sterilisation or cleaning are considered in the test procedure. The guidance presented here is also applicable to optimise the printer settings for a given printer device. As proof of concept, we compared fused filament fabrication, stereolithography and selective laser sintering as the three most used printing methods. We determined fused filament fabrication printing as the most economical solution, while stereolithography is most accurate and features the highest surface quality. Finally, we tested the applicability of our guidance by identifying a printer solution to manufacture a complex bioreactor for a perfused tissue construct. Due to its design, the manufacture via subtractive mechanical methods would be 21-fold more expensive than additive manufacturing and therefore, would result in three times the number of parts to be assembled subsequently. Using this bioreactor we showed a successful 14-day-culture of a biofabricated collagen-based tissue construct containing human dermal fibroblasts as the stromal part and a perfusable central channel with human microvascular endothelial cells. Our study indicates how the full potential of biofabrication can be exploited, as most printed tissues exhibit individual shapes and require storage under physiological conditions, after the bioprinting process.