The Effect of Hearing-Protection Devices on Auditory Situational Awareness and Listening Effort.

OBJECTIVES: Hearing-protection devices (HPDs) are made available, and often are required, for industrial use as well as military training exercises and operational duties. However, these devices often are disliked, and consequently not worn, in part because they compromise situational awareness through reduced sound detection and localization performance as well as degraded speech intelligibility. In this study, we carried out a series of tests, involving normal-hearing subjects and multiple background-noise conditions, designed to evaluate the performance of four HPDs in terms of their modifications of auditory-detection thresholds, sound-localization accuracy, and speech intelligibility. In addition, we assessed their impact on listening effort to understand how the additional effort required to perceive and process auditory signals while wearing an HPD reduces available cognitive resources for other tasks. DESIGN: Thirteen normal-hearing subjects participated in a protocol, which included auditory tasks designed to measure detection and localization performance, speech intelligibility, and cognitive load. Each participant repeated the battery of tests with unoccluded ears and four hearing protectors, two active (electronic) and two passive. The tasks were performed both in quiet and in background noise. RESULTS: Our findings indicate that, in variable degrees, all of the tested HPDs induce performance degradation on most of the conducted tasks as compared to the open ear. Of particular note in this study is the finding of increased cognitive load or listening effort, as measured by visual reaction time, for some hearing protectors during a dual-task, which added working-memory demands to the speech-intelligibility task. CONCLUSIONS: These results indicate that situational awareness can vary greatly across the spectrum of HPDs, and that listening effort is another aspect of performance that should be considered in future studies. The increased listening effort induced by hearing protectors may lead to earlier cognitive fatigue in noisy environments. Further study is required to characterize how auditory performance is limited by the combination of hearing impairment and the use of HPDs, and how the effects of such limitations can be linked to safe and effective use of hearing protection to maximize job performance.

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.

Noise dosimetry for tactical environments.

Noise exposure and the subsequent hearing loss are well documented aspects of military life. Numerous studies have indicated high rates of noise-induced hearing injury (NIHI) in active-duty service men and women, and recent statistics from the U.S. Department of Veterans Affairs indicate a population of veterans with hearing loss that is growing at an increasing rate. In an effort to minimize hearing loss, the U.S. Department of Defense (DoD) updated its Hearing Conservation Program in 2010, and also has recently revised the DoD Design Criteria Standard Noise Limits (MIL-STD-1474E) which defines allowable noise levels in the design of all military acquisitions including weapons and vehicles. Even with such mandates, it remains a challenge to accurately quantify the noise exposure experienced by a Warfighter over the course of a mission or training exercise, or even in a standard work day. Noise dosimeters are intended for exactly this purpose, but variations in device placement (e.g., free-field, on-body, in/near-ear), hardware (e.g., microphone, analog-to-digital converter), measurement time (e.g., work day, 24-h), and dose metric calculations (e.g., time-weighted energy, peak levels, Auditory Risk Units), as well as noise types (e.g., continuous, intermittent, impulsive) can cause exposure measurements to be incomplete, inaccurate, or inappropriate for a given situation. This paper describes the design of a noise dosimeter capable of acquiring exposure data across tactical environments. Two generations of prototypes have been built at MIT Lincoln Laboratory with funding from the U.S. Army, Navy, and Marine Corps. Details related to hardware, signal processing, and testing efforts are provided, along with example tactical military noise data and lessons learned from early fieldings. Finally, we discuss the continued need to prioritize personalized dosimetry in order to improve models that predict or characterize the risk of auditory damage, to integrate dosimeters with hearing-protection devices, and to inform strategies and metrics for reducing NIHI.