Establishing Digital Biomarkers for Occupational Health Assessment in Commercial Salmon Fishermen: Protocol for a Mixed-Methods Study

Relationship of personal, situational, and environmental factors to injury experience in commercial fishing

INTRODUCTION

Commercial fishing work involves a variety of activities and is hazardous. While much is understood to mitigate fatalities in this industry, research must further explore nonfatal injury characteristics, factors related to injury, and potential injury prevention strategies. This paper determines if fishing experience is associated with injury risk and explores common work activities associated with injury.

METHOD

Key informant interviews and a survey of fishermen were conducted to refine work activity codes and collect injury experiences. Independent sample t-tests compared the means of the years fishing by injury incident for all crab fishermen then stratified by position. Descriptive statistics explored the nature of injury in relation to work activity.

RESULTS

The level of experience was significantly lower for injured fishermen compared to fishermen who reported no injuries, but when stratified by position at the time of the injury, the association of injury to experience was only significant for owners. This stratified result demonstrates that the work activity, rather than experience, drives the apparent relationship of experience to injury. Being tired (24%) and weather (26%) were indicated as contributing factors at the time of injury.

CONCLUSION

Modifying the work environment to better control hazards would benefit all fishermen, regardless of their experience, age, or position. Further work into effective interventions that fishermen would adopt is needed to reduce injury risk. Any formal or informal training of new fishermen should focus on the most hazardous activities, but more experienced fishermen would also benefit. Additionally, effective training or interventions for fatigue management, and decision support tools for weather- and navigation-related decisions would further reduce risk of at sea injuries.

PRACTICAL APPLICATIONS

Injury prevention training, for all fishermen, regardless of their position and years of experience, should cover the most hazardous tasks, fatigue risk management strategies, and weather decisions.

Occupational Stress Monitoring Using Biomarkers and Smartwatches: A Systematic Review

This article presents a systematic review of the literature concerning scientific publications on wrist wearables that can help to identify stress levels. The study is part of a research project aimed at modeling a stress surveillance system and providing coping recommendations. The investigation followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In total, 38 articles were selected for full reading, and 10 articles were selected owing to their alignment with the study proposal. The types of technologies used in the research stand out amongst our main results after analyzing the articles. It is noteworthy that stress assessments are still based on standardized questionnaires, completed by the participants. The main biomarkers collected by the devices used in the selected works included: heart rate variation, cortisol analysis, skin conductance, body temperature, and blood volume at the wrist. This study concludes that developing a wrist wearable for stress identification using physiological and chemical sensors is challenging but possible and applicable.

Applications of deep learning methods in digital biomarker research using noninvasive sensing data

Introduction: Noninvasive digital biomarkers are critical elements in digital healthcare in terms of not only the ease of measurement but also their use of raw data. In recent years, deep learning methods have been put to use to analyze these diverse heterogeneous data; these methods include representation learning for feature extraction and supervised learning for the prediction of these biomarkers. Methods: We introduce clinical cases of digital biomarkers and various deep-learning methods applied according to each data type. In addition, deep learning methods for the integrated analysis of multidimensional heterogeneous data are introduced, and the utility of these data as an integrated digital biomarker is presented. The current status of digital biomarker research is examined by surveying research cases applied to various types of data as well as modeling methods. Results: We present a future research direction for using data from heterogeneous sources together by introducing deep learning methods for dimensionality reduction and mode integration from multimodal digital biomarker studies covering related domains. The integration of multimodality has led to advances in research through the improvement of performance and complementarity between modes. Discussion: The integrative digital biomarker will be more useful for research on diseases that require data from multiple sources to be treated together. Since delicate signals from patients are not missed and the interaction effects between signals are also considered, it will be helpful for immediate detection and more accurate prediction of symptoms.

Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review

PURPOSE

The study aimed to identify which digital biomarkers are collected and which specific devices are used according to vulnerable and susceptible individual characteristics in a living-lab setting.

MATERIALS AND METHODS

A literature search, screening, and appraisal process was implemented using the Web of Science, Pubmed, and Embase databases. The search query included a combination of terms related to "digital biomarkers," "devices that collect digital biomarkers," and "vulnerable and susceptible groups." After the screening and appraisal process, a total of 37 relevant articles were obtained.

RESULTS

In elderly people, the main digital biomarkers measured were values related to physical activity. Most of the studies used sensors. The articles targeting children aimed to predict diseases, and most of them used devices that are simple and can induce some interest, such as wearable device-based smart toys. In those who were disabled, digital biomarkers that measured location-based movement for the purpose of diagnosing disabilities were widely used, and most were measured by easy-to-use devices that did not require detailed explanations. In the disadvantaged, digital biomarkers related to health promotion were measured, and various wearable devices, such as smart bands and headbands were used depending on the purpose and target.

CONCLUSION

As the digital biomarkers and devices that collect them vary depending on the characteristics of study subjects, researchers should pay attention not only to the purpose of the study but also the characteristics of study subjects when collecting and analyzing digital biomarkers from living labs.