Background: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized.
Objective: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non-care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only.
Methods: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care-seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes.
Results: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes.
Conclusions: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non-care-seeking populations, which may have future application in health care resource planning.
Trial Registration: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800.
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http://dx.doi.org/10.2196/41050 | DOI Listing |
Background: Interest in use of digital technology to advance AD/ADRD research has been growing exponentially over the last few years. This acceleration is fueled in part by growing awareness that both well used research methods as well as newer biomarker approaches are 1) inadequate for clinical symptom detection in the earliest stages of an insidious onset disease and 2) have resulted in inaccurate as well as biased data that is generating treatment and prevention solutions that are insufficiently relevant to some and potentially not relevant to many.
Methods: Sensors embedded in mobile devices such as smartphones and wearables deliver a high penetration, low-cost solution for overcoming previous limitations of early detection sensitivity and limited representative reach.
ACS Appl Mater Interfaces
January 2025
Key Laboratory of MEMS of the Ministry of Education, Southeast University, Nanjing 210096, China.
As one of the core parts of the Internet-of-things (IOTs), multimodal sensors have exhibited great advantages in fields such as human-machine interaction, electronic skin, and environmental monitoring. However, current multimodal sensors substantially introduce a bloated equipment architecture and a complicated decoupling mechanism. In this work we propose a multimodal fusion sensing platform based on a power-dependent piecewise linear decoupling mechanism, allowing four parameters to be perceived and decoded from the passive wireless single component, which greatly broadens the configurable freedom of a sensor in the IOT.
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January 2025
College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610068, China.
Hydrogels are promising materials for wearable electronics, artificial skins and biomedical engineering, but their limited stretchability, self-recovery and crack resistance restrict their performance in demanding applications. Despite efforts to enhance these properties using micelle cross-links, nanofillers and dynamic interactions, it remains a challenge to fabricate hydrogels that combine high stretchability, self-healing and strong adhesion. Herein, we report a novel hydrogel synthesized the copolymerization of acrylamide (AM), maleic acid (MA) and acrylonitrile (AN), designed to address these limitations.
View Article and Find Full Text PDFACS Sens
January 2025
School of Chemistry, Australian Centre for Nanomedicine, The University of New South Wales, Sydney, NSW 2052, Australia.
Achieving sensors that can sensitively and selectively quantify levels of analytes in complex biofluids such as blood remains a significant challenge. To address this, we synthesized an array of isolated carbon nanochannels on a flat gold electrode that function as molecular sieves to prevent protein fouling and eliminate the need for antifouling layers. Utilizing a two-step pulsed technique, a reductive pulse expels negative interferences and fouling molecules followed by an oxidative pulse that oxidizes glucose at the bottom of the channel and on the gold surface.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Key Laboratory of Optoelectronic Technology & Systems of Ministry of Education, International R&D Center of Micro-Nano Systems and New Materials Technology, Chongqing University, Chongqing, 400044, China.
Sound signals not only serve as the primary communication medium but also find application in fields such as medical diagnosis and fault detection. With public healthcare resources increasingly under pressure, and challenges faced by disabled individuals on a daily basis, solutions that facilitate low-cost private healthcare hold considerable promise. Acoustic methods have been widely studied because of their lower technical complexity compared to other medical solutions, as well as the high safety threshold of the human body to acoustic energy.
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