Assessing the Usefulness of Mobile Apps for Noise Management in Occupational Health and Safety: Quantitative Measurement and Expert Elicitation Study

Background

Overexposure to occupational noise can lead to hearing loss. Occupational noise mapping is conventionally performed with a calibrated sound level meter (SLM). With the rise of mobile apps, there is a growing number of SLM apps available on mobile phones. However, few studies have evaluated such apps for accuracy and usefulness to guide those with occupational noise detection needs in selecting a quality app.

Objective

The purpose of this study was to evaluate the accuracy and usefulness of SLM mobile apps to guide workplace health and safety professionals in determining these apps' suitability for assessing occupational noise exposure.

Methods

The following three iOS apps were assessed: the NIOSH (National Institute for Occupational Safety and Health) Sound Level Meter, Decibel X, and SoundMeter X apps. The selected apps were evaluated for their accuracy in measuring sound levels in low-, moderate-, and high-noise settings within both simulated environments and real-world environments by comparing them to a conventional SLM. The usefulness of the apps was then assessed by occupational health specialists using the Mobile App Rating Scale (MARS).

Results

The NIOSH Sound Level Meter app accurately measured noise across a range of sound levels in both simulated settings and real-world settings. However, considerable variation was observed between readings. In comparison, the Decibel X and SoundMeter X apps showed more consistent readings but consistently underestimated noise levels, suggesting that they may pose a risk for workers. Nevertheless, none of the differences in sound measurements between the three apps and the conventional SLM were statistically significant (NIOSH Sound Level Meter: P=.78; Decibel X: P=.38; SoundMeter X: P=.40). The MARS scores for the three apps were all above 3.0, indicating the usefulness of these apps.

Conclusions

Under the conditions of this study, the NIOSH Sound Level Meter app had equivalent accuracy to the calibrated SLM and a degree of usefulness according to the MARS. This suggests that the NIOSH Sound Level Meter app may be suitable for mapping noise levels as part of a monitoring strategy in workplaces. However, it is important to understand its limitations. Mobile apps should complement but not replace conventional SLMs when trying to assess occupational noise exposure risk. Our outcomes also suggest that the MARS tool may have limited applicability to measurement-based apps and may be more suited to information-based apps that collect, record, and store information.

Noise survey of neonatal intensive care unit at a government tertiary-care centre

BACKGROUND

With technological advancement, Neonatal Intensive Care Units (NICU) have become noisier than ever. Studies have shown the detrimental effects of increasing noise in NICU on growing pre-term and sick neonates. The present study aimed to survey the amount of noise in one of the NICU blocks of a government tertiary care centre and explore ways to control it when dealing with these sick babies.

METHODS

A detailed noise survey was carried out, for February 2023, in one of the two blocks of NICU in a government tertiary-care centre. The noise measurements were performed using two "Sound Ear 3" noise meters. The analyses were done in Leq (equivalent continuous sound levels) A-weighted decibels (dBA).

RESULTS

The extracted data analysis revealed that the NICU block was exposed to a mean Leq of 67.78 dBA noise with a maximum of 89.0 dBA. There was a significant difference between the values noted in devices at different locations and across different periods. There were certain instances (57 and 42 for two devices) when there were sudden spikes in the noise levels beyond 80 dBA. It was also seen that noise was more than 65 dBA most of the time (72% and 66% for the two devices).

CONCLUSION

The noise survey carried out over one month revealed a considerable amount of noise in the NICU of a government tertiary-care centre. The study also explored ways such as environmental modification, human behavior modification, awareness programs, and neonatal-centered modifications to reduce the noise and lower its detrimental effects on the growth of neonates.

The use of ecological momentary assessment to evaluate real-world aided outcomes with children

BACKGROUND

Ecological momentary assessment (EMA) methods allow for real-time, real-world survey data collection. Studies with adults have reported EMA as a feasible and valid tool in the measurement of real-world listening experience. Research is needed to investigate the use of EMA with children who wear hearing aids.

OBJECTIVES

This study explored the implementation of EMA with children using a single-blinded repeated measures design to evaluate real-world aided outcome.

METHODS

Twenty-nine children, aged 7-17, used manual program switching to access hearing aid programs, fitted according to Desired Sensation Level (DSL) version 5.0 child quiet and noise prescriptive targets. Aided outcome was measured using participant-triggered twice-daily EMA entries, across listening situations and hearing dimensions.

RESULTS

Adherence to the EMA protocol by the children was high (82.4% compliance rate). Speech loudness, understanding and preference results were found to relate to both the hearing aid program and the listening situation. Aided outcomes related to prescription-based noise management were found to be highest in noisy situations.

CONCLUSIONS

Mobile device-based EMA methods can be used to inform daily life listening experience with children. Prescription-based noise management was found to decrease perceived loudness in noisy, non-school environments; this should be evaluated in combination with hearing aid noise reductions features.

The physics of bacterial decision making

The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network: (1) A stochastic switch whose transition probability is controlled by two order parameters-population density and internal/external stress. (2) An adaptable timer whose clock rate is normalized by the same two previous order parameters. (3) Sensing units which measure population density and external stress. (4) A communication module that exchanges information about the cells' internal stress levels. (5) An oscillating gate of the stochastic switch which is regulated by the timer. The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.