This review is the result of a virtual, collaborative live review discussion organized and hosted by PREreview and JMIR Publications on January 16, 2025. The discussion was joined by 16 people: 2 facilitators, 1 member of the JMIR Publications team, and 13 live review participants including 3 who agreed to be named but have not contributed to composing this review into its final form: Uday Kumar Chalwadi, Killivalavan Solai, Prasakthi Venkatesan. The authors of this review have dedicated additional asynchronous time over the course of two weeks to help compose this final report using the notes from the Live Review. We thank all participants who contributed to the discussion and made it possible for us to provide feedback on this preprint.

Summary

Anxiety, particularly state anxiety (s-anxiety), is increasingly recognized as a health concern linked to mental and physical issues, including adverse cardiovascular and long-term health outcomes. This study leverages non-invasive wearable technology to identify interpretable biomarkers resulting from s-anxiety using electrooculography (EOG) and electrodermal activity (EDA). Two datasets were developed: BLINKEO, focusing on blink-related EOG features, and EMOCOLD, analyzing EOG and EDA responses during a Cold Pressor Test (CPT). The authors then used both datasets and applied statistical analysis (e.g., F1 scoring, SHAP analysis) to identify biomarkers of anxiety. Results revealed that using EOG data (blink duration, peak height, and opening integral), in tandem with EDA data (mean signal, permutation, entropy, and Hjorth activity) led to the identification of novel biomarkers that reveal nuanced emotional and stress responses. Moreover, it was found that SHapley Additive exPlanations (SHAP) analysis can more accurately determine which features are relevant to enhancing model performance. The findings highlight the potential of combining EOG and EDA biomarker data to create robust, real-time models for anxiety detection. Combinations of physiological features (as sets) were more effective as measures of stress response than individual features alone. This research underscores the transformative role of non-invasive wearable technology in personalized mental health monitoring and intervention strategies.

List of major concerns and feedback:

Concerns with methods

Concerns with analysis

Concerns with ethics

List of minor concerns and feedback

Minor concerns with methods

Minor concerns with analysis and presentation

Suggestions

References

Concluding remarks

We thank the authors of the preprint for posting their work openly for feedback. We also thank all participants of the Live Review call for their time and for engaging in the lively discussion that generated this review.

Competing interests

Daniela Saderi was a facilitator of this call and one of the organizers. No other competing interests were declared by the reviewers.