Predicting Office Workers' Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

Risk Prediction Models as an Emerging Trend for Managing Cancer-Related Fatigue: A Systematic Review

AIM

To systematically identify, describe and evaluate the existing risk prediction models for cancer-related fatigue.

DESIGN

Systematic review.

DATA SOURCES

Seven databases (EMBASE, Cochrane Database, MEDLINE, CINAHL, CNKI, SinoMed and Wanfang) were conducted from inception to August 14, 2023 and updated in September 15, 2024.

REVIEW METHODS

A systematic search was conducted to identify studies that reported the development of risk prediction models for cancer-related fatigue. Two researchers independently performed a comprehensive assessment of the included studies. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability.

RESULTS

Eighteen studies were included in this review. These models predicted cancer-related fatigue in various cancers, including breast cancer, prostate cancer, gynaecological tumours and lung cancer. The most commonly included predictors were anxiety and depression, age, chemotherapy status, sleep quality and pain. Thirteen studies assessed the model performance by using the receiver operating characteristic curve. Although most models exhibited good predictive performance, a higher risk of bias was observed because of inappropriate handling of missing data methods and an imbalance in events per variable.

CONCLUSION

Prediction models show promise for cancer-related fatigue management and precision care, but few are ready for clinical application due to methodological limitations.

IMPLICATIONS FOR THE PROFESSION

Future research should focus on improving the clinical utility of cancer-related fatigue models while balancing predictive accuracy with cost-effectiveness to promote equitable care.

IMPACT

This study critically systematically evaluated the prediction models of cancer-related fatigue. The existing prediction models have a weak methodological foundation, with only a few having the potential to be implemented in clinical practice.

REPORTING METHOD

The review is reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis in Systematic Reviews and Meta-Analyses checklist (TRIPOD-SRMA).

PUBLIC CONTRIBUTION

No patient or public contribution.

Experimental Study on Emergency Psychophysiological and Behavioral Reactions to Coal Mining Accidents

Effective emergency responses are crucial for preventing coal mine accidents and mitigating injuries. This paper aims to investigate the characteristics of emergency psychophysiological reactions to coal mine accidents and to explore the potential of key indicators for identifying emergency behavioral patterns. Initially, virtual reality technology facilitated a simulation experiment for emergency escape during coal mine accidents. Subsequently, the characteristics of emergency reactions were analyzed through correlation analysis, hypothesis testing, and analysis of variance. The significant changes in physiological indicators were then taken as input features and fed into the three classifiers of machine learning algorithms. These classifications ultimately led to the identification of behavioral patterns, including agility, defensiveness, panic, and rigidity, that individuals may exhibit during a coal mine accident emergency. The study results revealed an intricate relationship between the mental activities induced by accident stimuli and the resulting physiological changes and behavioral performances. During the virtual reality simulation of a coal mine accident, subjects were observed to experience significant physiological changes in electrodermal activity, heart rate variability, electromyogram, respiration, and skin temperature. The random forest classification model, based on SCR + RANGE + IBI + SDNN + LF/HF, outperformed all other models, achieving accuracies of up to 92%. These findings hold promising implications for early warning systems targeting abnormal psychophysiological and behavioral reactions to emergency accidents, potentially serving as a life-saving measure in perilous situations and fostering the sustainable growth of the coal mining industry.

Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

Previous studies have primarily focused on predicting stress arousal, encompassing physiological, behavioral, and psychological responses to stressors, while neglecting the examination of stress appraisal. Stress appraisal involves the cognitive evaluation of a situation as stressful or non-stressful, and as a threat/pressure or a challenge/opportunity. In this study, we investigated several research questions related to the association between states of stress appraisal (i.e., boredom, eustress, coexisting eustress-distress, distress) and various factors such as stress levels, mood, productivity, physiological and behavioral responses, as well as the most effective ML algorithms and data signals for predicting stress appraisal. The results support the Yerkes-Dodson law, showing that a moderate stress level is associated with increased productivity and positive mood, while low and high levels of stress are related to decreased productivity and negative mood, with distress overpowering eustress when they coexist. Changes in stress appraisal relative to physiological and behavioral features were examined through the lenses of stress arousal, activity engagement, and performance. An XGBOOST model achieved the best prediction accuracies of stress appraisal, reaching 82.78% when combining physiological and behavioral features and 79.55% using only the physiological dataset. The small accuracy difference of 3% indicates that physiological data alone may be adequate to accurately predict stress appraisal, and the feature importance results identified electrodermal activity, skin temperature, and blood volume pulse as the most useful physiologic features. Implementing these models within work environments can serve as a foundation for designing workplace policies, practices, and stress management strategies that prioritize the promotion of eustress while reducing distress and boredom. Such efforts can foster a supportive work environment to enhance employee well-being and productivity.