Objective Suicide is the 2nd leading cause of death in adolescence, and acute pediatric mental health emergency department (ED) visits have doubled in the past decade. The objective of this study was to evaluate physiologic parameters relationship to suicide severity.Methods This was a prospective, observational study from April 2018 thru November 2019 in a tertiary care pediatric emergency department (ED) and inpatient pediatric psychiatric unit enrolling acutely suicidal adolescent patients. Patients wore a wrist device that used photoplethysmography for 7 days during their acute hospitalization to measure heart rate variability (HRV). During that time, Columbia Suicide Severity Scores (CSSRS) were assessed at 3 time points.Results There was complete device data and follow-up for 51 patients. There was an increase in the high frequency (HF) component of HRV in patients that had a 25% or greater decrease in their CSSRS (mean difference 11.89 ms/ Hz ; p-value 0.005). Patients with a CSSRS≥15 on day of enrollment had a lower, although not statistically significant, HF component (mean difference -8.34 ms/ Hz; p-value 0.071).Conclusion We found an inverse correlation between parasympathetic activity measured through the HF component and suicidality in an acutely suicidal population of adolescents. Wearable technology may have the ability to improve outpatient monitoring for earlier detection and intervention.

Objective Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology.Methods This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject’s HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered.Results Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed.Conclusion Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.

Heart rate variability (HRV) evaluates beat-to-beat interval (BBI) differences and is a suggested marker of the autonomic nervous system with diagnostic/monitoring capabilities in mental health; especially parasympathetic measures. The standard duration for short-term HRV analysis ranges from 24 h down to 5-min. However, wearable technology, mainly wrist devices, have large amounts of motion at times resulting in need for shorter duration of monitoring. The objective of this study was to evaluate the correlation between 1 and 5 min segments of continuous HRV data collected simultaneously on the same patient. Subjects wore a patch electrocardiograph (Cardea Solo, Inc.) over a 1–7 day period. For every consecutive hour the patch was worn, we selected a 5-min, artifact-free electrocardiogram segment. HRV metric calculation was performed to the entire 5-min segment and the first 1-min from this same 5-min segment. There were 492 h of electrocardiogram data collected allowing calculation of 492 5 min and 1 min segments. 1 min segments of data showed good correlation to 5 min segments in both time and frequency domains: root mean square of successive difference (RMSSD) (R = 0.92), high frequency component (HF) (R = 0.90), low frequency component (LF) (R = 0.71), and standard deviation of NN intervals (SDNN) (R = 0.63). Mental health research focused on parasympathetic HRV metrics, HF and RMSSD, may be accomplished through smaller time windows of recording, making wearable technology possible for monitoring.