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Use of mobile technology paired with heart rate monitor to remotely quantify behavioral health markers among military reservists and first responders
Davila, M. I., Kizakevich, P. N., Eckhoff, R., Morgan, J., Meleth, S., Ramirez, D., Morgan, T., Strange, L. B., Lane, M., Weimer, B., Lewis, A., Lewis, G. F., & Hourani, L. I. (2021). Use of mobile technology paired with heart rate monitor to remotely quantify behavioral health markers among military reservists and first responders. Military Medicine, 186(Suppl 1), 17-24. https://doi.org/10.1093/milmed/usaa395
INTRODUCTION: Heart rate variability (HRV) is a biological marker that reflects an individual's autonomic nervous system regulation. Psychological resilience is an individual's ability to recover from an adverse event and return to physiological homeostasis and mental well-being, indicated by higher resting HRV. The Biofeedback Assisted Resilience Training (BART) study evaluates a resilience-building intervention, with or without HRV biofeedback. This article evaluates the feasibility of remote psychophysiological research by validating the HRV data collected.
MATERIALS AND METHODS: The BART platform consists of a mobile health application (BART app) paired to a wearable heart rate monitor. The BART app is installed on the participant's personal phone/tablet to track and collect self-report psychological and physiological data. The platform collects raw heart rate data and processes HRV to server as online biofeedback. The raw data is processed offline to derive HRV for statistical analysis. The following HRV parameters are validated: inter-beat interval, respiratory sinus arrhythmia, low-frequency HRV, biofeedback HRV, and heart period. Bland-Altman and scatter plots are used to compare and contrast online and offline HRV measures. Repeated-measures ANOVA are used to compared means across tasks during the stress (rest, stress, and recovery) and training (rest and paced breathing) sessions in order to validate autonomic nervous system changes to physiological challenges.
RESULTS: The analyses included 245 participants. Bland-Altman plots showed excellent agreement and minimal bias between online and offline unedited inter-beat interval data during the stress session. RMANOVA during the training session indicated a significant strong effect on biofeedback HRV, F(11,390) = 967.96, P < .01. During the stress session, RMANOVA showed significant strong effect on respiratory sinus arrhythmia and low-frequency HRV, and a significant but weak effect on heart period.
CONCLUSIONS: The BART digital health platform supports remote behavioral and physiological data collection, intervention delivery, and online HRV biofeedback.
