The aim of the research study is to determine the wearable technology in sports. The research determines that monitoring performance also health metrics. Wearable technology has become a common sight ...in sports, transforming how players prepare, compete, and care for their health. This research investigates the multidimensional influence of wearables on sports performance monitoring and health measures. For measuring, the research study used smart PLS software and AMOS software: the descriptive statistic, correlation coefficient analysis, and the smart PLS Algorithm between them. Wearables have transformed sporting ecosystems, from precision training and injury prevention to data-driven coaching tactics. The ramifications go beyond sports science, impacting long-term athlete development and encouraging a culture of holistic health monitoring. Overall, the research found that wearable technology in sports shows a positive and significant link between them. However, ethical concerns about privacy and equitable access emerge as substantial hurdles. As wearable technology advances, its incorporation with sports promises a future in which human potential is constantly challenged, and the quest for athletic greatness has no bounds.
Abstract
Introduction
Polysomnography (PSG) is the gold standard for measuring sleep, but this method is cumbersome, costly, and sometimes does not reflect naturalistic sleep patterns. Leading ...technology companies have developed non-wearable sleep tracking devices that have attracted public interest. However, the accuracy of these devices has either been shown to be poor or the validation tests have not been conducted by independent laboratories without potential conflicts of interest. Relative to PSG and actigraphy, and under conditions of both normal and restricted sleep, we assessed the accuracy of early and newer versions of a non-wearable sleep tracking device (Beddit, Apple Inc.).
Methods
Participants were 35 healthy young adults (Mage=18.97, SD=0.95 years; 77.14% female; 42.86% Caucasian). We randomly assigned them to go to bed at 10:30pm (normal sleep) or 1:30am (restricted sleep) in a controlled sleep laboratory environment. Lights-on was 7:00am for all participants. Sleep was measured by the early version (3.0) or newer version (3.5) of a non-wearable device that uses a sensor strip to measure movement, heart rate, and breathing. We also measured PSG, wristband actigraphy, and self-report. For each device, we tested accuracy against PSG for total sleep time (TST), sleep efficiency (SE%), sleep onset latency (SOL), and wake after sleep onset (WASO).
Results
While the early version displayed poor reliability (ICCs<0.30), the newer version of the non-wearable device yielded excellent reliability with PSG under both normal and restricted sleep conditions. Not only was agreement excellent for TST (ICC=0.96) and SE% (ICC=0.98), but agreement was also excellent for the notoriously difficult metrics of SOL (ICC=0.92) and WASO (ICC=0.92). This newer version significantly outperformed clinical grade actigraphy (ICCs often in the 0.40 to 0.75 range), and self-reported sleep (ICCs often below 0.40).
Conclusion
Surprisingly, a non-wearable device demonstrated greater agreement with PSG than clinical grade actigraphy. Though the field has generally been skeptical of commercial non-wearable devices, this independent validation provides optimism that some such devices would be efficacious for research in healthy adults. Future work is needed to test the validity of this device in older adults and clinical populations.
Support (if any)
National Science Foundation (1920730 and 1943323)
Background: The use of smart phone applications (apps) and wearable devices for patients with chronic diseases is attractive since it supports behavior changes by providing personalized guidance, ...timely suggestions, interactive responses, and social support. Smart phone apps paired with wearable devices facilitate monitoring and feedback, which are often emphasized in health interventions and research. This research has two aims: 1) to assess the extent to which researchers are evaluating or comparing commercial or proprietary apps for weight loss or weight management, and 2) to identify the most commonly utilized apps for weight management research, as well as the most popular among consumers. Methods: We searched databases from Obesity Week abstracts, ISRCTN registry, clincaltrials.gov, and NIH Reporter from 2013-2020 using search terms (smartphone or mobile app) AND (weight loss or weight management). We also searched Google for the most popular weight loss apps in consumer reports, professional opinion pieces, and health journals. Results were cross-referenced with the Google Play Store and Apple App Store to determine the top ten apps based on highest average rating, number of ratings, and number of downloads. To be included, apps must have at least two features supporting diet guidance or tracking, exercise, and weight trackers. Results: In research, 5 commercial apps were cited > 1x: Lose It! (n=5), Fitbit (n=6), MyFitnessPal(n=3), Noom(n=3), and WW (n=4); 4 proprietary apps were cited > 1x: Daily24 (n=2), SMART (n=2), POWeR(n=2), and LIITah(n=2); and 17 proprietary apps were cited only 1x. The top 5 most used commercial apps in research were present in the top 6 most popular apps by user base. Conclusions: Through this review, we found the amount of information on app utilization in research being reported to be relatively sparse given the burgeoning climate for use of mobile app technology in weight management and lifestyle medicine. Commercial app developers are driven by consumer preferences and incremental monetization. Apps can be updated and changed without warning. This shifting landscape is a great challenge of research in this area and might explain why some researchers opt to develop custom apps for their purposes. These findings reflect a lack of cohesiveness and maturity in the features and recent technologies being evaluated.
Harvesting biomechanical energy is an important route for providing electricity to sustainably drive wearable electronics, which currently still use batteries and therefore need to be charged or ...replaced/disposed frequently. Here we report an approach that can continuously power wearable electronics only by human motion, realized through a triboelectric nanogenerator (TENG) with optimized materials and structural design. Fabricated by elastomeric materials and a helix inner electrode sticking on a tube with the dielectric layer and outer electrode, the TENG has desirable features including flexibility, stretchability, isotropy, weavability, water-resistance and a high surface charge density of 250 μC m
. With only the energy extracted from walking or jogging by the TENG that is built in outsoles, wearable electronics such as an electronic watch and fitness tracker can be immediately and continuously powered.
Ferroelectret nanogenerators were recently introduced as a promising alternative technology for harvesting kinetic energy. Here we report the device's intrinsic properties that allow for the ...bidirectional conversion of energy between electrical and mechanical domains; thus extending its potential use in wearable electronics beyond the power generation realm. This electromechanical coupling, combined with their flexibility and thin film-like form, bestows dual-functional transducing capabilities to the device that are used in this work to demonstrate its use as a thin, wearable and self-powered loudspeaker or microphone patch. To determine the device's performance and applicability, sound pressure level is characterized in both space and frequency domains for three different configurations. The confirmed device's high performance is further validated through its integration in three different systems: a music-playing flag, a sound recording film and a flexible microphone for security applications.
Abstract
Introduction:
Actigraphs are portable wrist-worn devices that record tri-axial accelerometry data. The data can then be used to approximate amount and timing of sleep and wake. Actigraphs ...are used both clinically and in research studies. The expense of such devices, however, limit their utility. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost effective alternative to the more expensive devices for sleep research. The lack of independent validation of minute-to-minute accelerometer data for these consumer wearable devices has hindered their utility and acceptance.
Methods:
We studied a new consumer-grade wearable device, Arc ($50, Huami Inc., Mountain View CA) for which minute-to-minute tri-axial accelerometer data (vector magnitude) were made available. Twelve healthy participants wore on their non-dominant wrist both an Arc and a clinical actigraph (Actiwatch Spectrum, Philips, Bend OR) continuously over a period of 48 hours in free-living conditions. Time-stamped data from each participants were aligned and the Cole-Kripke algorithm was used to determine sleep or wake for each 60s epoch (automatic thresholding was used for scoring). Receiver operating characteristic curves were plotted to optimize the relationship between the two devices.
Results:
Treating the Actiwatch as a gold-standard for determination of “sleep” and “wake”, Arc has an average sensitivity TP/(TP+FN) of 99.8 ± 0.05% (SEM), specificity TN/(TN+FP) of 85.1 ± 2.9% and precision TP/(TP+FP) of 99.5 ± 0.2% for the determination of sleep. For wake detection, Arc has a sensitivity of 85.1 ± 2.7%, specificity of 99.8 ± 0.05% and precision of 94.5 ± 1.3%.
Conclusion:
Preliminary results indicate that high degrees of agreement in minute-to-minute data scoring for sleep and wake periods were found between a consumer-grade and research-grade actigraph. Concomitant validation of the Actiwatch and of the Arc consumer-grade device against overnight polysomnography will be an important next step.
Support (If Any):
NHLBI T32.
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