Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

Social Factors and Older Adults' Use of Wearable Activity Trackers: Before and During the First Wave of the COVID-19 Pandemic

Wearable activity trackers (WAT) have shown high potential to improve health in the aging population. Evidence links various social factors with WAT use in older adults, but mainly within small samples and the prevalence of their WAT use during the COVID-19 is unknown. We reported WAT use prevalence before and during the first wave of COVID-19 and examined social factors associated with WAT use frequency using a nationally representative sample of 3302 U.S. older adults. We used Multinomial Logistic Regression to identify social factors associated with WAT use frequency. Only 10.3% of pre-COVID-19 and 10.9% of first-wave subsamples were frequent WAT users. Older adults aged 75 and above and those with low incomes were less likely to frequently use WATs. Our findings suggest socioeconomic and age disparities in WAT use among older Americans. Future studies should focus on enhancing low-income older adults' WAT adoption to enable equal access to WAT-related health benefits.

Technology Innovation and Guardrails in Elite Sport: The Future is Now

A growing number of companies are developing or using wearable sensor technologies that can monitor, analyse and transmit data from humans in real time that can be used by the sporting, biomedical and media industries. To explore this phenomenon, we describe and review two high-profile sporting events where innovations in wearable technologies were trialled: the Tokyo 2020 Summer Olympic Games (Tokyo 2020, Japan) and the 2022 adidas Road to Records (Germany). These two major sporting events were the first time academic and industry partners came together to implement real-time wearable solutions during major competition, to protect the health of athletes competing in hot and humid environments, as well as to better understand how these metrics can be used moving forwards. Despite the undoubted benefits of such wearables, there are well-founded concerns regarding their use including: (1) limited evidence quantifying the potential beneficial effects of analysing specific parameters, (2) the quality of hardware and provided data, (3) information overload, (4) data security and (5) exaggerated marketing claims. Employment and sporting rules and regulations also need to evolve to facilitate the use of wearable devices. There is also the potential to obtain real-time data that will oblige medical personnel to make crucial decisions around whether their athletes should continue competing or withdraw for health reasons. To protect athletes, the urgent need is to overcome these ethical/data protection concerns and develop wearable technologies that are backed by quality science. The fields of sport and exercise science and medicine provide an excellent platform to understand the impact of wearable sensors on performance, wellness, health, and disease.

Biocompatible Material-Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems

Human beings have a greater need to pursue life and manage personal or family health in the context of the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies. The application of micro biosensing devices is crucial in connecting technology and personalized medicine. Here, the progress and current status from biocompatible inorganic materials to organic materials and composites are reviewed and the material-to-device processing is described. Next, the operating principles of pressure, chemical, optical, and temperature sensors are dissected and the application of these flexible biosensors in wearable/implantable devices is discussed. Different biosensing systems acting in vivo and in vitro, including signal communication and energy supply are then illustrated. The potential of in-sensor computing for applications in sensing systems is also discussed. Finally, some essential needs for commercial translation are highlighted and future opportunities for flexible biosensors are considered.

Perspective on "in the wild" movement analysis using machine learning

Recent advances in wearable sensing and machine learning have created ample opportunities for "in the wild" movement analysis in sports, since the combination of both enables real-time feedback to be provided to athletes and coaches, as well as long-term monitoring of movements. The potential for real-time feedback is useful for performance enhancement or technique analysis, and can be achieved by training efficient models and implementing them on dedicated hardware. Long-term monitoring of movement can be used for injury prevention, among others. Such applications are often enabled by training a machine learned model from large datasets that have been collected using wearable sensors. Therefore, in this perspective paper, we provide an overview of approaches for studies that aim to analyze sports movement "in the wild" using wearable sensors and machine learning. First, we discuss how a measurement protocol can be set up by answering six questions. Then, we discuss the benefits and pitfalls and provide recommendations for effective training of machine learning models from movement data, focusing on data pre-processing, feature calculation, and model selection and tuning. Finally, we highlight two application domains where "in the wild" data recording was combined with machine learning for injury prevention and technique analysis, respectively.

Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.

LifeSnaps, a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral and psychological patterns due to challenges in collecting and releasing such datasets, including waning user engagement or privacy considerations. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n = 71 participants. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71 M rows of data. The participants contributed their data through validated surveys, ecological momentary assessments, and a Fitbit Sense smartwatch and consented to make these data available to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data will open novel research opportunities and potential applications in multiple disciplines.

Wearable Inertial Sensor Approach for Postural Adjustment Assessments during Predictable Perturbations in Sport

INTRODUCTION

Evidence supports the importance of efficient postural control to improve performance in sports. This involves the use of strategies such as anticipatory posture adjustments and compensatory adjustments. Technology makes analysis and assessments in sports cheaper, while being valid and reliable compared to the gold-standard assessment equipment.

OBJECTIVES

This article aimed to test the validity and reliability of signals extracted from the sensor's accelerometer (Metamotion C), by comparing it to the data obtained from the gold-standard equipment (a three-dimensional video-motion-capture system).

DESIGN

Observational, cross-sectional study.

METHODS

We exposed 20 healthy young standing people to the pendulum impact paradigm, which consisted of predictable anteroposterior disturbances applied at the shoulder level. In order to measure this, we observed the acceleration of the center of mass in the anticipatory and compensatory phase of the disturbance and compared the signals of the two devices (Metamotion C and a motion-capture system).

RESULTS

The validation results showed the significant linear correlation of all variables with a moderate to large correlation of r ≥ 0.5 between the devices. In contrast, the reliability results between sessions obtained by filming were all significant and above 0.75, indicating excellent reliability. The APAonset variable had a reasonable to high intra-class correlation in the anticipatory phase. In the compensatory phase, the CPA_time variable showed an excellent correlation.

CONCLUSIONS

Metamotion C proved reasonably valid and highly reliable in measuring the center of mass acceleration compared to the camera system in both the anticipatory and compensatory phases.

Wearable Devices in Diving: Scoping Review

Background

Wearables and their benefits for the safety and well-being of users have been widely studied and have had an enormous impact on the general development of these kinds of devices. Yet, the extent of research into the use and impact of wearable devices in the underwater environment is comparatively low. In the past 15 years, there has been an increased interest in research into wearables that are used underwater, as the use of such wearables has steadily grown over time. However, there has so far been no clear indication in the literature about the direction in which efforts for the design and construction of underwater wearable devices are developing. Therefore, the analysis presented in this scoping review establishes a good and powerful basis for the further development and orientation of current underwater wearables within the field.

Objective

In this scoping review, we targeted wearable devices for underwater use to make a comprehensive map of their capabilities and features and discuss the general direction of the development of underwater wearables and the orientation of research into novel prototypes of these kinds of devices.

Methods

In September 2021, we conducted an extensive search for existing literature on 4 databases and for grey literature to identify developed prototypes and early-stage products that were described and tested in water, could be worn and interacted with (eg, displays, buttons, etc), and were fully functional without external equipment. The studies were written in English, came from peer-reviewed academic sources, and were published between 2005 and 2021. We reviewed each title and abstract. The data extraction process was carried out by one author and verified by another author.

Results

In total, 36 relevant studies were included. Among these, 4 different categories were identified; 18 studies dealt primarily with safety devices, 9 dealt with underwater communication devices, 7 dealt with head-up displays, and 2 dealt with underwater human-computer interaction approaches. Although the safety devices seemed to have gained the most interest at the time of this study, a clear trend toward underwater communication wearables was identified.

Conclusions

This review sought to provide a first insight into the possibilities and challenges of the technologies that have been used in and for wearable devices that are meant for use in the underwater environment. Among these, underwater communication technologies have had the most significant influence on future developments. Moreover, a topic that has not received enough attention but should be further addressed is human-computer interaction. By developing underwater wearables that cover 2 or more of the technology categories that we identified, the extent of the benefits of such devices can be significantly increased in the future.

Wearable Devices in Veterinary Health Care

Wearables are an up-and-coming tool in veterinary health care. This article reviews the current and prospective wearable technology for veterinary patients and the future of wearables in veterinary medicine. These devices allow veterinarians to monitor a patient's vital signs remotely, in addition to other variables, and push the profession away from a reactive health-care system toward a proactive culture that is able to identify diseases earlier. Advances in this technology have the potential to profoundly change the way veterinarians obtain and use patient data to practice medicine.

The Effects of Physiological Demands on Visual Search Behaviours During 2 vs. 1 + GK Game Situations in Football: An in-situ Approach

We examined the effect of physiological workload on gaze behaviour during defensive performance in 2 vs. 1 +goalkeeper game situations in football. Twenty-two players were assigned to either a high- or low-performing group based on a validated measure of tactical performance. A total of 12 game sequences (trials) were presented under high- and low-workload conditions. At the end of each sequence, participants were asked to indicate their perceived exertion using the Rating Scale of Mental Effort and the Borg Scale. The low- and high-workload conditions were defined when the players achieved 60 and 90% of their maximal heart rate, respectively, as per their performance in the Yo-Yo Intermittent Recovery Test. Visual search behaviours were recorded using Tobii Pro eye-movement registration glasses. Players reported higher rates of perceived exertion on the high- compared to low-workload condition. Participants in the low-performing group increased their average fixation duration and decreased the number of fixations and number of fixation locations from the low- to high-workload conditions. The low- and high-performing groups displayed different visual search strategies with regards the areas of interest fixated upon. Participants in the high-performing group focused on the SpaceFrontPlayer, followed by Ball, and AnotherOpponent. The low-performing group spent more time focusing on the SpaceFrontPlayer and SpacePlayer than Ball and AnotherOpponent. It appears that physiological workload and tactical expertise interact in constraining visual search behaviours in football players. Coaches and practitioners should consider ways to manipulate individual and task constraints while attending to the close interplay between physiological workload, visual behaviour, and tactical performance during practise.

System Performance and User Feedback Regarding Wearable Bioimpedance System for Multi-Site Knee Tissue Monitoring: Free-Living Pilot Study With Healthy Adults

Knee-focused wearable devices have the potential to support personalized rehabilitation therapies by monitoring localized tissue alterations related to activities that reduce functional symptoms and pain. However, supporting these applications requires reported data to be reliable and accurate which can be challenging in the unsupervised free-living conditions that wearable devices are deployed. This pilot study has assessed a knee-focused wearable sensor system to quantify 1) system performance (operation, rates of data artifacts, environment impacts) to estimate realistic targets for reliable data with this system and 2) user experiences (comfort, fit, usability) to help inform future designs to increase usability and adoption of knee-focused wearables. Study data was collected from five healthy adult participants over 2 days, with 84.5 and 35.9% of artifact free data for longitudinal and transverse electrode configurations. Small to moderate positive correlations were also identified between changes in resistance, temperature, and humidity with respect to acceleration to highlight how this system can be used to explore relationships between knee tissues and environmental/activity context.

Validity of the Training-Load Concept

Training load (TL) is a widely used concept in training prescription and monitoring and is also recognized as as an important tool for avoiding athlete injury, illness, and overtraining. With the widespread adoption of wearable devices, TL metrics are used increasingly by researchers and practitioners worldwide. Conceptually, TL was proposed as a means to quantify a dose of training and used to predict its resulting training effect. However, TL has never been validated as a measure of training dose, and there is a risk that fundamental problems related to its calculation are preventing advances in training prescription and monitoring. Specifically, we highlight recent studies from our research groups where we compare the acute performance decrement measured following a session with its TL metrics. These studies suggest that most TL metrics are not consistent with their notional training dose and that the exercise duration confounds their calculation. These studies also show that total work done is not an appropriate way to compare training interventions that differ in duration and intensity. We encourage scientists and practitioners to critically evaluate the validity of current TL metrics and suggest that new TL metrics need to be developed.

Assessment of Peak Oxygen Uptake with a Smartwatch and its Usefulness for Training of Runners

Peak oxygen uptake (˙VO _2peak ) is an important factor contributing to running performance. Wearable technology may allow the assessment of ˙VO _2peak more frequently and on a larger scale. We aim to i) validate the ˙VO _2peak assessed by a smartwatch (Garmin Forerunner 245), and ii) discuss how this parameter may assist to evaluate and guide training procedures. A total of 23 runners (12 female, 11 male; ˙VO _2peak : 48.6±6.8 ml∙min ⁻¹ ∙kg ⁻¹ ) visited the laboratory twice to determine their ˙VO _2peak during a treadmill ramp test. Between laboratory visits, participants wore a smartwatch and performed three outdoor runs to obtain ˙VO _2peak values provided by the smartwatch. The ˙VO _2peak obtained by the criterion measure ranged from 38 to 61 ml∙min ⁻¹ ∙kg ⁻¹ . The mean absolute percentage error (MAPE) between the smartwatch and the criterion ˙VO _2peak was 5.7%. The criterion measure revealed a coefficient of variation of 4.0% over the VO2peak range from 38–61 ml∙min ⁻¹ ∙kg ⁻¹ . MAPE between the smartwatch and criterion measure was 7.1, 4.1 and −6.2% when analyzing ˙VO _2peak ranging from 39–45 ml∙min ⁻¹ ∙kg ⁻¹ , 45–55 ml∙min ⁻¹ ∙kg ⁻¹ or 55–61 ml∙min ⁻¹ ∙kg ⁻¹ , respectively.

Moving Together While Staying Apart: Practical Recommendations for 24-Hour Home-Based Movement Behaviours for Those With Cardiovascular Disease

The novel coronavirus disease 2019 is a global public health crisis that disproportionately affects those with preexisting conditions. Cardiovascular disease (CVD) is the leading cause of death worldwide and many key CVD risk factors are modifiable (e.g., physical inactivity, sedentary behaviour, obesity). To limit the spread of coronavirus 2019, most governments have implemented restrictions and recommended staying at home, reducing social contact to a select and exclusive few, and limiting large gatherings. Such public health constraints may have unintended, negative health consequences on 24-hour movement behaviours. The primary purpose of this review is to provide practical at-home recommendations for sedentary time, sleep, and physical activity in those living with CVD. Adults with CVD will benefit from practical recommendations to reduce sedentary time, increase purposeful exercise, and maintain optimal sleep patterns while being at home and adhering to public health restrictions. Our recommendations include the following: (i) self-monitoring sitting time; (ii) engaging in 2-3 days per week of purposeful exercise for those with low exercise capacity and > 3 days per week for those with moderate-to-high exercise capacity; (iii) self-monitoring exercise intensity through the use of scales or wearable devices; (iv) maintaining a regular sleep schedule; and (v) moving daily. Clinicians should be aware that clear communication of the importance of limiting prolonged sedentary time, engaging in regular physical activity and exercise, and ensuring optimal sleep in association with the provision of clear, comprehensible, and practical advice is fundamental to ensuring that those living with CVD respond optimally to the challenges posed by the pandemic.

Flexible PCB Failures From Dynamic Activity and Their Impacts on Bioimpedance Measurements: A Wearable Case Study

Wearable health monitoring systems that collect data in free-living environments are becoming increasingly popular. Flexible printed circuits provide a commercially available option that can conform to the shape of a wearable system and support electronic sensing and flexible interconnect. However, repetitive dynamic activity can stress and damage the interconnect of flexible PCBs which degrades data quality. This case study evaluated the performance of flexible PCBs providing interconnect between electrodes and sensing electronics for tissue bioimpedance measurements in a wearable system. Resistance data (1 kHz to 128 kHz) was collected from localized knee tissues of 3 participants using the wearable design with flexible PCBs over 7 days of free-living. From electrical and optical inspection after use trace cracking of the flexible PCBs occurred, degrading tissue resistances reported by the wearable system. Exploration of these results advances understanding of how flexible PCBs perform in free-living conditions for wearable bioimpedance applications.

Monitoring and adapting endurance training on the basis of heart rate variability monitored by wearable technologies: A systematic review with meta-analysis

OBJECTIVES

To systematically perform a meta-analysis of the scientific literature to determine whether the outcomes of endurance training based on heart rate variability (HRV) are more favorable than those of predefined training.

DESIGN

Systematic review and meta-analysis.

METHODS

PubMed and Web of Science were searched systematically in March of 2020 using keywords related to endurance, the ANS, and training. To compare the outcomes of HRV-guided and predefined training, Hedges' g effect size and associated 95% confidence intervals were calculated.

RESULTS

A total of 8 studies (198 participants) were identified comprising 9 interventions involving a variety of approaches. Compared to predefined training, most HRV-guided interventions included fewer moderate- and/or high-intensity training sessions. Fixed effects meta-analysis revealed a significant medium-sized positive effect of HRV-guided training on submaximal physiological parameters (g = 0.296, 95% CI 0.031 to 0.562, p = 0.028), but its effects on performance (g = 0.079, 95% CI -0.050 to 0.393, p = 0.597) and V̇O2peak (g = 0.171, 95% CI -0.213 to 0.371, p = 0.130) were small and not statistically significant. Moreover, with regards to performance, HRV-guided training was associated with fewer non-responders and more positive responders.

CONCLUSIONS

In comparison to predefined training, HRV-guided endurance training had a medium-sized effect on submaximal physiological parameters, but only a small and non-significant influence on performance and V̇O_2peak. There were fewer non-responders regarding performance with HRV-based training.

Self-efficacy and trust in consumers' use of health-technologies devices for sports

The commercial market for wearable health technology is growing but the value these technologies provide for consumers is debatable, as many technologies lack formal validation and are being abandoned at a high rate. The enabling of self-efficacy mechanisms in the design of health technologies, through the factors identified by self-determination theory and the Technology Acceptance Model, could increase the uptake and continued use of these devices. The aim of this research was to investigate how and why people use wearable health technology, and to evaluate their experiences from the perspective of perceptions of autonomy, safety, information security, information accuracy and willingness for continued use. Forty-eight sport enthusiasts or athletes, age range 18–65 and over, completed an online survey with 46 questions. A statistical analysis that included a Mann-Whitney U Test and a Person's Correlation analysis indicated that participants who were confident in their use of a health technology showed satisfaction with previous uses and a sense of autonomy leading to an overall positive experience. Issues around data and personal information security were identified, aligning with extant literature. Findings suggest that: (i) past experience play a role in people's perception of self-efficacy, (ii) the tracking of activities enables of autonomy and confidence, (iii) autonomy influences personal willingness to use health technologies, (iv) strong interest in personal health technologies motivates sustained engagement, and that (v) reliability and validity of data impacts on confidence in health technologies. A conceptual model is proposed for consideration when designing and evaluating health technologies, based on the factors supporting self-efficacy and trust in health technologies. Further research is required to develop this model with the aim of informing designers and developers about how to translate these factors into design features for the development of more effective personal health technology.

Indirect Estimation of Breathing Rate from Heart Rate Monitoring System during Running

Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment.

Exploring the Use of Mobile and Wearable Technology among University Student Athletes in Lebanon: A Cross-Sectional Study

The markets of commercial wearables and health and fitness apps are constantly growing globally, especially among young adults and athletes, to track physical activity, energy expenditure and health. Despite their wide availability, evidence on use comes predominantly from the United States or Global North, with none targeting college student-athletes in low- and middle-income countries. This study was aimed to explore the use of these technologies among student-athletes at the American University of Beirut (AUB). We conducted a cross-sectional survey of 482 participants (average age 20 years) enrolled in 24 teams during Fall 2018; 230 students successfully completed the web-based survey, and 200 provided valid data. Fifty-three (26.5%) have owned a fitness tracker, mostly for self-monitoring. The most popular were Fitbit, Apple Watch, and Garmin. Similarly, 82 students (40%) used apps, primarily MyFitnessPal, Apple Health, and Samsung Health. Nevertheless, many participants discontinued use due to loss of interest or technical issues (breaking, usability, obsolescence, or lack of engagement). Wearable devices were considered superior to mobile phones alone as physical activity monitors. However, forming regular habits made self-monitoring via technology irrelevant. Further research is needed to better understand what motivates continuous use among student-athletes, who could use trackers to improve athletic performance and overall health.

Predicted sweat rates for group water planning in sport: accuracy and application

This study tested the accuracy of a novel, limited-availability web application (H₂Q™) for predicting sweat rates in a variety of sports using estimates of energy expenditure and air temperature only. The application of predictions for group water planning was investigated for soccer match play. Fourteen open literature studies were identified where group sweat rates were reported (n = 20 group means comprising 230 individual observations from 179 athletes) with fidelity. Sports represented included: walking, cycling, swimming, and soccer match play. The accuracy of H₂Q™ sweat rates was tested by comparing to measured group sweat rates using the concordance correlation coefficient (CCC) with 95% confidence interval [CI]. The relative absolute error (RAE) with 95% [CI] was also assessed, whereby the mean absolute error was expressed relative to an acceptance limit of 0.250 L/h. The CCC was 0.98 [0.95, 0.99] and the RAE was 0.449 [0.279, 0.620], indicating that the prediction error was on average 0.112 L/h. The RAE was < 1.0 for 19/20 observations (95%). Drink volumes modeled as a proxy for sweat losses during soccer match play prevented dehydration (< 1% loss of body mass). The H₂Q™ web application demonstrated high group sweat prediction accuracy for the variety of sports activities tested. Water planning for soccer match play suggests the feasibility of easily and accurately predicting sweat rates to plan group water needs and promote optimal hydration in training and/or competition.

A Novel Accelerometry-Based Metric to Improve Estimation of Whole-Body Mechanical Load

While the Player Load is a widely-used parameter for physical demand quantification using wearable accelerometers, its calculation is subjected to potential errors related to rotational changes of the reference frame. The aims of this study were (i) to assess the concurrent validity of accelerometry-based Player Load against force plates; (ii) to validate a novel metric, the Accel’Rate overcoming this theoretical issue. Twenty-one recreational athlete males instrumented with two triaxial accelerometers positioned at the upper and lower back performed running-based locomotor movements at low and high intensity over six in-series force plates. We examined the validity of the Player Load and the Accel’Rate by using force plates. Standard error of the estimate was small to moderate for all tested conditions (Player Load: 0.45 to 0.87; Accel’Rate: 0.25 to 0.95). Accel’Rate displayed trivial to small mean biases (−1.0 to 6.1 a.u.) while the Player Load displayed systematic very large to extremely large mean biases (17.1 to 226.0 a.u.). These findings demonstrate a better concurrent validity of the Accel’Rate compared to the Player Load. This metric could be used to improve the estimation of whole-body mechanical load, easily accessible in sport training and competition settings.

Harnessing Mobile Health Technology for Secondary Cardiovascular Disease Prevention in Older Adults: A Scientific Statement From the American Heart Association

Secondary prevention of cardiovascular disease (CVD), the leading cause of morbidity and mortality, is critical to improving health outcomes and quality of life in our aging population. As mobile health (mHealth) technology gains universal leverage and popularity, it is becoming more user-friendly for older adults and an adjunct to manage CVD risk and improve overall cardiovascular health. With the rapid advances in mHealth technology and increasing technological engagement of older adults, a comprehensive understanding of the current literature and knowledge of gaps and barriers surrounding the impact of mHealth on secondary CVD prevention is essential. After a systematic review of the literature, 26 studies that used mHealth for secondary CVD prevention focusing on lifestyle behavior change and medication adherence in cohorts with a mean age of ≥60 years were identified. Improvements in health behaviors and medication adherence were observed, particularly when there was a short message service (ie, texting) component involved. Although mobile technologies are becoming more mainstream and are starting to blend more seamlessly with standard health care, there are still distinct barriers that limit implementation particularly in older adults, including affordability, usability, privacy, and security issues. Furthermore, studies on the type of mHealth that is the most effective for older adults with longer study duration are essential as the field continues to grow. As our population ages, identifying and implementing effective, widely accepted, cost-effective, and time-efficient mHealth interventions to improve CVD health in a vulnerable demographic group should be a top health priority.

Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis

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.

Effects of Wearable Devices with Biofeedback on Biomechanical Performance of Running-A Systematic Review

This present review includes a systematic search for peer-reviewed articles published between March 2009 and March 2020 that evaluated the effects of wearable devices with biofeedback on the biomechanics of running. The included articles did not focus on physiological and metabolic metrics. Articles with patients, animals, orthoses, exoskeletons and virtual reality were not included. Following the PRISMA guidelines, 417 articles were first identified, and nineteen were selected following the removal of duplicates and articles which did not meet the inclusion criteria. Most reviewed articles reported a significant reduction in positive peak acceleration, which was found to be related to tibial stress fractures in running. Some previous studies provided biofeedback aiming to increase stride frequencies. They produced some positive effects on running, as they reduced vertical load in knee and ankle joints and vertical displacement of the body and increased knee flexion. Some other parameters, including contact ground time and speed, were fed back by wearable devices for running. Such devices reduced running time and increased swing phase time. This article reviews challenges in this area and suggests future studies can evaluate the long-term effects in running biomechanics produced by wearable devices with biofeedback.

Zur Reliabilität von Wearable Devices am Beispiel einer Premium Multisport-Smartwatch

Wearable Devices versprechen durch ihre motivierende Wirkung einen wichtigen Beitrag zur Bindung des Individuums an körperlich-sportliche Aktivitäten und somit zum Aufbau und Erhalt von Gesundheit und Leistungsfähigkeit in Zeiten des digitalen gesellschaftlichen Wandels. Übergeordnetes Ziel der vorliegenden Untersuchung war die Beurteilung der Testgüte von Wearable Devices anhand eines marktrelevanten Gerätes, der Garmin fēnix® 5. Als Forschungsdesiderat wurde der Reliabilitätsaspekt der Methodenkonkordanz identifiziert. Zur Überprüfung der Methodenkonkordanz wurden das Stresslevel bei kognitiver Stressinduktion, der Kalorienverbrauch bei moderater Ausdauerlaufbelastung sowie die maximale Sauerstoffaufnahme bei Laufausbelastung von 30 männlichen Probanden (Alter: 23,13 ± 2,5 Jahre; BMI: 24,95 ± 2,45 kg/m2) mit der Garmin fēnix® 5 bestimmt und die Ergebnisse mit denen im Feld gängigen Referenzmethoden Elektrokardiographie, Indirekte Kalorimetrie bzw. Spiroergometrie verglichen. Zur rechnerischen Überprüfung der Methodenkonkordanz diente Lin’s Konkordanzkorrelationskoeffizient (CCCLin). Die Ergebnisse zeigen eine hohe Präzision der Garmin fēnix® 5 im Vergleich mit der Referenzmethode Elektrokardiographie hinsichtlich der Messung des notwendigerweise z-standardisierten Stressparameters (p = 0,89) sowie eine gerade mittlere exakte intrainidividuelle Konkordanz mit der Referenzmethode Indirekte Kalorimetrie bzw. Spiroergometrie hinsichtlich der Messung des Parameters Kalorienverbrauch (CCCLin = 0,43 [p = 0,52, Cb = 0,82]) bzw. maximale Sauerstoffaufnahme (CCCLin = 0,50 [p = 0,77, Cb = 0,66]). Die Garmin fēnix® 5 kann somit zumindest bei erstmaliger Verwendung nicht als hinreichend konkordante Alternative zu den gängigen aktivitäts- und leistungsbezogenen Referenzmethoden empfohlen werden.

Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?

Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g., blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non-linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behavior and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport-specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a “global” system parameter and proxy for organismic demands.

Predefined vs data-guided training prescription based on autonomic nervous system variation: A systematic review

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 ( V ˙ O 2 peak ) 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 V ˙ O 2 peak . 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.

Laboratory medicine: health evaluation in elite athletes

The need to evaluate the health status of an athlete represents a crucial aim in preventive and protective sports science in order to identify the best diagnostic strategy to improve performance and reduce risks related to physical exercise. In the present review we aim to define the main biochemical and haematological markers that vary significantly during and after sports training to identify risk factors, at competitive and professional levels and to highlight the set up of a specific parameter's panel for elite athletes. Moreover, we also intend to consider additional biomarkers, still under investigation, which could further contribute to laboratory sports medicine and provide reliable data that can be used by athlete's competent staff in order to establish personal attitudes and prevent sports injuries.

Heart Rate Behavior in Speed Climbing

Speed climbing is an Olympic discipline within the combined sport climbing event in 2020 for the first time. Speed climbing is a high-speed and anaerobic exercise against gravity over a few seconds with extreme psychological pressure. Although there is some literature on heart rate (HR) when lead climbing, there is no literature on the behavior of the HR when speed climbing. The HR of seven near-elite participants was measured with a Polar HR monitor while climbing a 10- and 15-m wall, respectively, three times each, with pauses of 5 min between the first and last three climbs and a 20-min pause between the third and fourth climb. The average climbing times on the 10- and 15-m walls were 9.16 ± 3.06 s and 14.95 ± 3.14 s, respectively (data pooled between climbing heights). The peak HR on the 10- and 15-m walls were 164.57 ± 7.45 bpm and 176.43 ± 8.09 bpm. The rates of change in HR were as follows: average HR acceleration before peak HR, 2.53 ± 0.80 bpm/s; peak HR acceleration before peak HR, 4.16 ± 1.08 bpm/s; and average HR deceleration after peak HR, −0.98 ± 0.30 bpm/s. The average HR during the pauses ranged from 105.80 to 117.89 bpm. From the results, in comparison to the literature, we conclude that athletes, trained for sustaining high physical exertion and psychological pressure, have a far smaller HR acceleration than untrained people during light and unstressful exercises. Furthermore, the current rule that athletes shall have a minimum resting time of 5 min between climbing attempts during a speed climbing competition seems justified as sufficient time for HR recovery.

A Discussion on Different Approaches for Prescribing Physical Interventions - Four Roads Lead to Rome, but Which One Should We Choose?

It is well recognized that regular physical exercise has positive effects on physical and mental health. To use the beneficial health effects of physical exercise, there are worldwide movements encouraging health care providers to include physical exercise in their care and treatments strategies. However, a crucial point in administering the "exercise polypill" is the dosing and, in turn, the prescription of the physical intervention (PI). In this perspective article, we discuss the advantages and disadvantages of different approaches to prescribe PI. In this context, we also highlight outstanding questions and potential areas of opportunity for further investigations.

The accurate prediction of sweat rate from energy expenditure and air temperature: a proof-of-concept study

This proof-of-concept study used a web application to predict runner sweat losses using only energy expenditure and air temperature. A field study (FS) of n = 37 runners was completed with n = 40 sweat loss observations measured over 1 h (sweat rate, SR). Predictions were also compared with 10 open literature (OL) studies in which individual runner SR was reported (n = 82; 109 observations). Three prediction accuracy metrics were used: for FS, the mean absolute error (MAE) and concordance correlation coefficient (CCC) were calculated to include a 95% confidence interval [CI]; for OL, the percentage concordance (PC) was examined against calculation of accumulated under- and over-drinking potential. The MAE for FS runners was 0.141 kg [0.105, 0.177], which was less than estimated scale weighing error on 85% of occasions. The CCC was 0.88 [0.82, 0.93]. The PC for OL was 96% for avoidance of both under- and over-drinking and 93% overall. All accuracy metrics and their CIs were below acceptable error tolerance. Input errors of ±10% and ±1 °C for energy expenditure and air temperature dropped the PC to between 84% and 90%. This study demonstrates the feasibility of accurately predicting SR from energy expenditure and air temperature alone. Novelty Results demonstrate that accurate runner SR prediction is possible with knowledge of only energy expenditure and air temperature. SR prediction error was smaller than scale weighing error in 85% of observations. Accurate runner SR prediction could help mitigate the common risks of over- and under-drinking.

Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study

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.

Regulation of Circulatory Muscle-specific MicroRNA during 8 km Run

Acute prolonged endurance running has been shown to alter muscle-specific circulating microRNA (miRNA) levels. Here, eighteen participants completed an 8 km run. We assessed the levels of hsa-miR-1-3p, -133a-3p, -133b, and -206 and their correlation with conventional biomarkers following exercise. Compared to before exercise (Pre), 8 km run significantly increased the lactate level immediately after exercise (0 h). Myoglobin (Mb) level increased at 0 h while creatine kinase (CK) level increased 24 h after exercise (24 h). The levels of creatine kinase MB isoenzyme (CK-MB) and cardiac troponin I (cTnI) were all elevated at 24 h and within the normal physiological range; The levels of hsa-miR-1-3p, -133a-3p, -133b significantly increased at 0 h but only hsa-miR-133a-3p still elevated at 24 h. Only hsa-miR-206 level decreased at 24 h; Additionally, the changes of hsa-miR-1-3p and hsa-miR-133a-3p were correlated with Mb at 24 h. These findings suggest that muscle-specific miRNA elevation in plasma is likely physiological and that these miRNA may be used as potential biomarkers for load monitoring in individuals.

Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

Early detection of physiological deterioration has been shown to improve patient outcomes. Due to recent improvements in technology, comprehensive outpatient vital signs monitoring is now possible. This is the first review to collate information on all wearable devices on the market for outpatient physiological monitoring.

A scoping review was undertaken. The monitors reviewed were limited to those that can function in the outpatient setting with minimal restrictions on the patient’s normal lifestyle, while measuring any or all of the vital signs: heart rate, ECG, oxygen saturation, respiration rate, blood pressure and temperature.

A total of 270 papers were included in the review. Thirty wearable monitors were examined: 6 patches, 3 clothing-based monitors, 4 chest straps, 2 upper arm bands and 15 wristbands. The monitoring of vital signs in the outpatient setting is a developing field with differing levels of evidence for each monitor. The most common clinical application was heart rate monitoring. Blood pressure and oxygen saturation measurements were the least common applications. There is a need for clinical validation studies in the outpatient setting to prove the potential of many of the monitors identified.

Research in this area is in its infancy. Future research should look at aggregating the results of validity and reliability and patient outcome studies for each monitor and between different devices. This would provide a more holistic overview of the potential for the clinical use of each device.

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

BACKGROUND

Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups.

OBJECTIVE

The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression.

METHODS

The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models).

RESULTS

On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960).

CONCLUSIONS

This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

Validity of Wrist-Worn Activity Trackers for Estimating VO2max and Energy Expenditure

Activity trackers are a simple and mostly low-priced method to capture physiological parameters. Despite the high number of wrist-worn devices, there is a lack of scientific validation. The purpose of this study was to assess whether the activity trackers represent a valid alternative to gold-standard methods in terms of estimating energy expenditure (EE) and maximum oxygen uptake (VO_2max). Twenty-four healthy subjects participated in this study. In total, five commercially available wrist-worn devices were tested with regard to their validity of EE and/or VO_2max. Estimated values were compared with indirect calorimetry. Validity of the activity trackers was determined by paired sample t-tests, mean absolute percentage errors (MAPE), Intraclass Correlation Coefficient, and Bland-Altman plots. Within the tested devices, differences in scattering in VO_2max and EE could be observed. This results in a MAPE > 10% for all evaluations, except for the VO_2max-estimation of the Garmin Forerunner 920XT (7.3%). The latter significantly underestimates the VO_2max (t(23) = –2.37, p = 0.027), whereas the Garmin Vivosmart HR significantly overestimates the EE (t(23) = 2.44, p = 0.023). The tested devices did not show valid results concerning the estimation of VO_2max and EE. Hence, the current wrist-worn activity trackers are most likely not accurate enough to be used for neither purposes in sports, nor in health care applications.

Cardio-Respiratory Monitoring in Archery Using a Smart Textile Based on Flexible Fiber Bragg Grating Sensors

In precision sports, the control of breathing and heart rate is crucial to help the body to remain stable in the shooting position. To improve stability, archers try to adopt similar breathing patterns and to have a low heartbeat during each shot. We proposed an easy-to-use and unobtrusive smart textile (ST) which is able to detect chest wall excursions due to breathing and heart beating. The sensing part is based on two FBGs housed into a soft polymer matrix to optimize the adherence to the chest wall and the system robustness. The ST was assessed on volunteers to figure out its performance in the estimation of respiratory frequency (f_R) and heart rate (HR). Then, the system was tested on two archers during four shooting sessions. This is the first study to monitor cardio-respiratory activity on archers during shooting. The good performance of the ST is supported by the low mean absolute percentage error for f_R and HR estimation (≤1.97% and ≤5.74%, respectively), calculated with respect to reference signals (flow sensor for f_R, photopletismography sensor for HR). Moreover, results showed the capability of the ST to estimate f_R and HR during different phases of shooting action. The promising results motivate future investigations to speculate about the influence of f_R and HR on archers' performance.

On-Ice Measures of External Load in Relation to Match Outcome in Elite Female Ice Hockey

The aim of this study is to investigate the differences between select on-ice measures using inertial movement sensors based on match outcome, and to determine changes in player movements across three periods of play. Data were collected during one season of competition in elite female ice hockey players (N = 20). Two-factor mixed effects ANOVAs for each skating position were performed to investigate the differences in match outcome, as well as differences in external load measures during the course of a match. For match outcome, there was a small difference for forwards in explosive ratio (p = 0.02, ES = 0.26) and percentage high force strides (p = 0.04, ES = 0.50). When viewed across three periods of a match, moderate differences were found in skating load (p = 0.01, ES = 0.75), explosive efforts (p = 0.04, ES = 0.63), and explosive ratio (p = 0.002, ES = 0.87) for forwards, and in PlayerLoad (p = 0.01, ES = 0.70), explosive efforts (p = 0.04, ES = 0.63), and explosive ratio (p = 0.01, ES = 0.70) for defense. When examining the relevance to match outcome, external load measures associated with intensity appear to be an important factor among forwards. These results may be helpful for coaches and sport scientists when making decisions pertaining to training and competition strategies.

The Development of a Personalised Training Framework: Implementation of Emerging Technologies for Performance

Over the last decade, there has been considerable interest in the individualisation of athlete training, including the use of genetic information, alongside more advanced data capture and analysis techniques. Here, we explore the evidence for, and practical use of, a number of these emerging technologies, including the measurement and quantification of epigenetic changes, microbiome analysis and the use of cell-free DNA, along with data mining and machine learning. In doing so, we develop a theoretical model for the use of these technologies in an elite sport setting, allowing the coach to better answer six key questions: (1) To what training will my athlete best respond? (2) How well is my athlete adapting to training? (3) When should I change the training stimulus (i.e., has the athlete reached their adaptive ceiling for this training modality)? (4) How long will it take for a certain adaptation to occur? (5) How well is my athlete tolerating the current training load? (6) What load can my athlete handle today? Special consideration is given to whether such an individualised training framework will outperform current methods as well as the challenges in implementing this approach.

Activity modification in heat: critical assessment of guidelines across athletic, occupational, and military settings in the USA

Exertional heat illness (EHI) risk is a serious concern among athletes, laborers, and warfighters. US Governing organizations have established various activity modification guidelines (AMGs) and other risk mitigation plans to help ensure the health and safety of their workers. The extent of metabolic heat production and heat gain that ensue from their work are the core reasons for EHI in the aforementioned population. Therefore, the major focus of AMGs in all settings is to modulate the work intensity and duration with additional modification in adjustable extrinsic risk factors (e.g., clothing, equipment) and intrinsic risk factors (e.g., heat acclimatization, fitness, hydration status). Future studies should continue to integrate more physiological (e.g., valid body fluid balance, internal body temperature) and biometeorological factors (e.g., cumulative heat stress) to the existing heat risk assessment models to reduce the assumptions and limitations in them. Future interagency collaboration to advance heat mitigation plans among physically active population is desired to maximize the existing resources and data to facilitate advancement in AMGs for environmental heat.

Cardiorespiratory Temporal Causal Links and the Differences by Sport or Lack Thereof

Fitness level, fatigue and adaptation are important factors for determining the optimal training schedule and predicting future performance. We think that adding analysis of the mutual relationships between cardiac and respiratory activity enables better athlete profiling and feedback for improving training. Therefore, the main objectives were (1) to apply several methods for temporal causality analysis to cardiorespiratory data; (2) to establish causal links between the signals; and (3) to determine how parameterized connections differed across various subgroups. One hundred elite athletes (31 female) and a control group of 20 healthy students (6 female) took part in the study. All were asked to follow a protocol comprising two 5-min sessions of free breathing - once supine, once standing. The data were collected using Pneumonitor 2. Respiratory-related curves were obtained through impedance pneumography, along with a single-lead ECG. Several signals (e.g., tidal volume, instantaneous respiratory rate, and instantaneous heart rate) were derived and stored as: (1) raw data down-sampled to 25Hz; (2) further down-sampled to 2.5Hz; and (3) beat-by-beat sequences. Granger causality frameworks (pairwise-conditional, spectral or extended), along with Time Series Models with Independent Noise (TiMINo), were studied. The connections enabling the best distinctions were found using recursive feature elimination with a random forest kernel. Temporal causal links are the most evident between tidal volume and instantaneous heart rate signals. Predictions of the “effect” variable were improved by adding preceding “cause” samples, by medians of 20.3% for supine and 14.2% for standing body positions. Parameterized causal link structures and directions distinguish athletes from non-athletes with 83.3% accuracy on average. They may also be used to supplement standard analysis and enable classification into groups exhibiting different static and dynamic components during performance. Physiological markers of training may be extended to include cardiorespiratory data, and causality analysis may improve the resolution of training profiling and the precision of outcome prediction.

Does force myography recorded at the wrist correlate to resistance load levels during bicep curls?

Resistance strength training is a proven method to improve bone density and muscle strength. A solution capable of automatically detecting the resistance force level exerted by a user from a wrist-based device can offer great convenience to the trainee and hence facilitate a better training outcome. In this short communication, we present our investigation aimed at exploring if force myographic (FMG) signals recorded at the wrist can predict the relative resistance levels that are associated with different weights. Specifically, we investigated the Spearman's correlations between the wrist FMG signal features and the dumbbell weights during a bicep curl exercise. 10 volunteers were recruited to perform a total of 100 curl actions, which included both the hammer and regular curls while the wrist FMG signals were being recorded. Three sets of weights ranging from 0.2 lb to 8 lb were used. For the hammer curls, a median correlation coefficient of 0.92 with an interquartile range (IQR) of 0.03 was obtained. For the regular curls, a 0.94 median correlation with a 0.02 IQR was obtained. We also used the data from the first 36 curls to generate a classifier model and applied it onto the rest of the data. An averaged validation accuracy of 88% was obtained. The results of this study showed the potential use of wrist FMG signal to detect different levels of the load during exercises; such information could potentially be used as feedback in fitness, sports, and rehabilitation activities.

Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes

Training of elite athletes requires regular physiological and medical monitoring to plan the schedule, intensity and volume of training, and subsequent recovery. In sports medicine, ECG-based analyses are well-established. However, they rarely consider the correspondence of respiratory and cardiac activity. Given such mutual influence, we hypothesize that athlete monitoring might be developed with causal inference and that detailed, time-related techniques should be preceded by a more general, time-independent approach that considers the whole group of participants and parameters describing whole signals. The aim of this study was to discover general causal paths among cardiac and respiratory variables in elite athletes in two body positions (supine and standing), at rest. ECG and impedance pneumography signals were obtained from 100 elite athletes. The mean heart rate, the root-mean-square difference of successive RR intervals (RMSSD), its natural logarithm (lnRMSSD), the mean respiratory rate (RR), the breathing activity coefficients, and the resulting breathing regularity (BR) were estimated. Several causal discovery frameworks were applied, comprising Generalized Correlations (GC), Causal Additive Modeling (CAM), Fast Greedy Equivalence Search (FGES), Greedy Fast Causal Inference (GFCI), and two score-based Bayesian network learning algorithms: Hill-Climbing (HC) and Tabu Search. The discovery of cardiorespiratory paths appears ambiguous. The main, still mild, rules best supported by data are: for supine - tidal volume causes heart activity variation, which causes average heart activity, which causes respiratory timing; and for standing - normalized respiratory activity variation causes average heart activity. The presented approach allows data-driven and time-independent analysis of elite athletes as a particular population, without considering prior knowledge. However, the results seem to be consistent with the medical background. Causality inference is an interesting mathematical approach to the analysis of biological responses, which are complex. One can use it to profile athletes and plan appropriate training. In the next step, we plan to expand the study using time-related causality analyses.

A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations

The commercial market for technologies to monitor and improve personal health and sports performance is ever expanding. A wide range of smart watches, bands, garments, and patches with embedded sensors, small portable devices and mobile applications now exist to record and provide users with feedback on many different physical performance variables. These variables include cardiorespiratory function, movement patterns, sweat analysis, tissue oxygenation, sleep, emotional state, and changes in cognitive function following concussion. In this review, we have summarized the features and evaluated the characteristics of a cross-section of technologies for health and sports performance according to what the technology is claimed to do, whether it has been validated and is reliable, and if it is suitable for general consumer use. Consumers who are choosing new technology should consider whether it (1) produces desirable (or non-desirable) outcomes, (2) has been developed based on real-world need, and (3) has been tested and proven effective in applied studies in different settings. Among the technologies included in this review, more than half have not been validated through independent research. Only 5% of the technologies have been formally validated. Around 10% of technologies have been developed for and used in research. The value of such technologies for consumer use is debatable, however, because they may require extra time to set up and interpret the data they produce. Looking to the future, the rapidly expanding market of health and sports performance technology has much to offer consumers. To create a competitive advantage, companies producing health and performance technologies should consult with consumers to identify real-world need, and invest in research to prove the effectiveness of their products. To get the best value, consumers should carefully select such products, not only based on their personal needs, but also according to the strength of supporting evidence and effectiveness of the products.