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Wearable-Measured Sleep and Resting Heart Rate Variability as an Outcome of and Predictor for Subjective Stress Measures

The effects of stress may be alleviated when its impact or a decreased stress-resilience are detected early. This study explores whether wearable-measured sleep and resting HRV in police officers can be predicted by stress-related Ecological Momentary Assessment (EMA) measures in preceding days and predict stress-related EMA outcomes in subsequent days. Eight police officers used an Oura ring to collect daily Total Sleep Time (TST) and resting Heart Rate Variability (HRV) and an EMA app for measuring demands, stress, mental exhaustion, and vigor during 15-55 weeks. Vector Autoregression (VAR) models were created and complemented by Granger causation tests and Impulse Response Function visualizations. Demands negatively predicted TST and HRV in one participant. TST negatively predicted demands, stress, and mental exhaustion in two, three, and five participants, respectively, and positively predicted vigor in five participants. HRV negatively predicted demands in two participants, and stress and mental exhaustion in one participant. Changes in HRV lasted longer than those in TST. Bidirectional associations of TST and resting HRV with stress-related outcomes were observed at a weak-to-moderate strength, but not consistently across participants. TST and resting HRV are more consistent predictors of stress-resilience in upcoming days than indicators of stress-related measures in prior days.

Using the apple watch to teach and learn about heart rate variability while vacationing.

Does Wearable-Measured Heart Rate Variability During Sleep Predict Perceived Morning Mental and Physical Fitness?

The emergence of wearable sensor technology may provide opportunities for automated measurement of psychophysiological markers of mental and physical fitness, which can be used for personalized feedback. This study explores to what extent within-subject changes in resting heart rate variability (HRV) during sleep predict the perceived mental and physical fitness of military personnel on the subsequent morning. Participants wore a Garmin wrist-worn wearable and filled in a short morning questionnaire on their perceived mental and physical fitness during a period of up to 46 days. A custom-built smartphone app was used to directly retrieve heart rate and accelerometer data from the wearable, on which open-source algorithms for sleep detection and artefact filtering were applied. A sample of 571 complete observations in 63 participants were analyzed using linear mixed models. Resting HRV during sleep was a small predictor of perceived physical fitness (marginal R 2  = .031), but not of mental fitness. The items on perceived mental and physical fitness were strongly correlated (r = .77). Based on the current findings, resting HRV during sleep appears to be more related to the physical component of perceived fitness than its mental component. Recommendations for future studies include improvements in the measurement of sleep and resting HRV, as well as further investigation of the potential impact of resting HRV as a buffer on stress-related outcomes.

Influence of respiration frequency on heart rate variability parameters:

BACKGROUND:Many patients visiting physiotherapists for musculoskeletal disorders face psychosocial challenges which may form a large barrier to recover. There are only a limited number of evidence based psychosocial therapies, but they are mainly based on breathing exercises. OBJECTIVE: to study which respiration frequency would lead to the highest relaxation, reflected in vagal tone derived from the heart rate variability (HRV) in healthy subjects. METHODS: A randomized controlled cross sectional study was performed. Respiration cycles of four, five, six, seven and eight breaths per minute (BPM) were delivered in randomized order for two minutes each. HRV metrics were measured during the sessions with electrocardiogram (ECG). Repeated Measures ANOVA’s were performed to analyze differences between breathing frequencies. RESULTS: 100 healthy volunteers were included (40 male). Standard Deviation of inter beat intervals (SDNN) values were significantly highest at 5 BPM, whereas the Root Mean Square of Successive Differences (RMSSD) values appeared highest at 7 breaths per minute (p< 0.01). High Frequency (HF) power was lowest at 4 BPM, whereas Low Frequency (LF) power was not significantly influenced by respiration frequency. CONCLUSIONS: Breathing at a frequency of 5 to 7 breaths per minute leads to highest HRV values, but there is no single respiration ratio that maximizes all metrics. Physiotherapists may use five to seven BPM as guidance to determine ideal breathing frequencies

Measuring heart rate with optical sensor

The problem addressed in this report is to verify the possibility of using an optical sensor in the SaxShirt in order to extract the heart rate. There are specifically three questions that we try to address. 1) How is it possible to extract heart rate (BPM) from the optical sensor? 2) Is it possible to use the sensor for extracting BPM during movement? 3) Is the heart rate measured in this way useful for measuring other higher-level parameters such as heart rate coherence and heart rate variability? For this purpose, we have performed tests with the sensor placed on different spots and the data was analyzed to see if heart rate can be extracted from the sensor measurements.

Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App:

Background: The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. Objective: This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. Methods: In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. Results: Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). Conclusions: To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes.