scholarly journals Positive affect state is a good predictor of movement and stress: combining data from ESM/EMA, mobile HRV measurements and trait questionnaires

2020 ◽  
Author(s):  
Ilmari Määttänen ◽  
Pentti Henttonen ◽  
Julius Väliaho ◽  
Jussi Palomäki ◽  
Maisa Thibault ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Negative Affect ◽  
Positive Affect ◽  
Physiological Stress ◽  
Self Report ◽  
Accelerometer Data ◽  
Combining Data ◽  
Affect States ◽  
Affect State

Personality describes the average behaviour and responses of individuals across situations; but personality traits are often poor predictors of behaviour in specific situations. This is known as the “personality paradox”.We evaluated the interrelations between various trait and state variables in participants’ everyday lives. As state measures, we used 1) experience sampling methodology (ESM/EMA) to measure perceived affect, stress, and presence of social company; and 2) heart-rate variability and real-time movement (accelerometer data) to indicate physiological stress and physical movement. These data were linked with self-report measures of personality and personality-like traits.Trait variables predicted affect states and multiple associations were found: neuroticism and rumination decreased positive affect and increased negative affect. Positive affect state, in turn, was the strongest predictor of observed movement. Positive affect was also associated with heart rate and heart rate variability (HRV). Negative affect, in turn, was not associated with neither movement, HR or HRV.The study provides evidence on the influence of personality-like traits and social context to affect states, and, in turn, their influence to movement and stress variables.

scholarly journals Impact of Himalayan Singing Bowls Meditation Session on Mood and Heart Rate Variability – An Observational Study

2020 ◽  
Vol 1 (4) ◽  
pp. 20-30
Author(s):  
Saharsh Panchal ◽  
Fariburz Irani ◽  
Gunjan Y Trivedi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Negative Affect ◽  
Positive Affect ◽  
Physiological Parameters ◽  
Stress Index ◽  
Positive And Negative Affect ◽  
Physiological Benefits ◽  
The Impact ◽  
Singing Bowls

Introduction Scientific evidence has demonstrated the psychological and physiological benefits of meditation. Sound vibrations also improve emotional wellbeing while enhancing the physiological parameters. There is an opportunity to explore the psychological and physiological benefits of Himalayan Singing Bowls (HSB) sound bath meditation, i.e. meditation assisted with sound vibrations. Aim of the Study The study explored changes in mood and Heart Rate Variability (HRV) parameters after a HSB Sound Bath Meditation on healthy individuals. The primary objectives of the study were to understand if a 40 minute long seated HSB Sound Bath Meditation results in significant improvement (a) in positive affect and negative affect, as measured by Positive And Negative Affect Scale (PANAS) and (b) in physiological parameters, as measured by Heart Rate Variability. The secondary objective of the study was to understand the impact on various moods as measured by Profile Of Mood States (POMS) Survey. Methods The psychological parameters included changes in Positive and Negative Affect (measured on 77 individuals using PANAS) and changes in specific, positive and negative moods (measured on 17 individuals using POMS) before and after the meditation session. The physiological parameters included HRV parameters such as Heart Rate (HR), Stress Index (SI) and Root Mean Square of Standard Deviation (RMSSD) measuring during the entire session on 15 individuals using the EmWave Pro device. HRV data analysis was conducted with Kubios HRV Premium and all the data was analyzed using paired T-Test. Results All the subjects after meditation showed statistically significant improvement in Positive Affect (mood) and a reduction in Negative Affect (mood). The HRV parameters showed a trend demonstrating overall relaxation with a statistically significant reduction in HR, Stress Index and an increase in RMSSD in the last 5 minutes as compared to the first 5 minutes. Consistent with changes in positive, negative mood and HRV, all the participants showed statistically significant reduction in tension, anger, fatigue, depression and confusion. In terms of positive mood, there was a statistically significant improvement in esteem related affect and an increase (but not statistically significant) in vigor. Conclusion The findings demonstrate that seated HSB Sound Bath Meditation session has a positive impact on the mood related measures. The physiological changes measured during the meditation using HRV parameters indicated a consistent reduction in Heart Rate throughout the meditation and a reduction in overall sympathetic tone and an increase in parasympathetic tone. Thus, HSB can be used to improve both psychological and physiological parameters even after one 40 min session. Future work in this area could explore comparison with a control group and a longer study duration consisting of multiple sessions.

scholarly journals Predictive Value of Subacute Heart Rate Variability for Determining Outcome Following Adolescent Concussion

2021 ◽  
Vol 10 (1) ◽  
pp. 161
Author(s):  
Colt A. Coffman ◽  
Jacob J. M. Kay ◽  
Kat M. Saba ◽  
Adam T. Harrison ◽  
Jeffrey P. Holloway ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Cognitive Performance ◽  
Predictive Value ◽  
System Integration ◽  
Sports Medicine ◽  
Clinical Symptoms ◽  
Physiological Stress ◽  
Neurological Dysfunction ◽  
Neurobehavioral Function

Objective assessments of concussion recovery are crucial for facilitating effective clinical management. However, predictive tools for determining adolescent concussion outcomes are currently limited. Research suggests that heart rate variability (HRV) represents an indirect and objective marker of central and peripheral nervous system integration. Therefore, it may effectively identify underlying deficits and reliably predict the symptomology following concussion. Thus, the present study sought to evaluate the relationship between HRV and adolescent concussion outcomes. Furthermore, we sought to examine its predictive value for assessing outcomes. Fifty-five concussed adolescents (12–17 years old) recruited from a local sports medicine clinic were assessed during the initial subacute evaluation (within 15 days postinjury) and instructed to follow up for a post-acute evaluation. Self-reported clinical and depressive symptoms, neurobehavioral function, and cognitive performance were collected at each timepoint. Short-term HRV metrics via photoplethysmography were obtained under resting conditions and physiological stress. Regression analyses demonstrated significant associations between HRV metrics, clinical symptoms, neurobehavioral function, and cognitive performance at the subacute evaluation. Importantly, the analyses illustrated that subacute HRV metrics significantly predicted diminished post-acute neurobehavioral function and cognitive performance. These findings indicate that subacute HRV metrics may serve as a viable predictive biomarker for identifying underlying neurological dysfunction following concussion and predict late cognitive outcomes.

249 Overnight heart rate variability depends on age, gender, and day of the week: a field observation using a smart bed platform

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A100-A101
Author(s):  
Shawn Barr ◽  
Kwanghyun Sohn ◽  
Gary Garcia
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Physiological Stress ◽  
Large Population ◽  
Population Level ◽  
Large Set ◽  
Sleep State ◽  
Cross Sectional ◽  
Day Of The Week ◽  
Broad Interest

Abstract Introduction Heart rate variability (HRV) is commonly used to assess the activity of the autonomic nervous system (ANS). ANS function changes, reflected in HRV, result from factors including lifestyle, aging, cardiorespiratory illnesses, sleep state, and physiological stress. Despite broad interest in HRV, few studies have established normative overnight HRV values for a large population. To better understand population level HRV changes, ecologically-valid, overnight sleep SDNN (standard deviation of all normal heartbeat intervals, lower HRV is reflected by lower SDNN) values have been analyzed for a large sample of Sleep Number 360 smart bed users. Methods Overnight SDNN values were obtained over the course of 18.2M sleep sessions from 379,225 sleepers (48 ± 14.7 sessions/user). 50.9 percent of sleepers were female. The age was normally distributed with mean ± SD of 52.8 ± 12.7 years (range 21 to 84). Heartbeat intervals used to compute SDNN were extracted from a ballistocardiogram (BCG). BCG-based HRV estimation during sleep has previously been validated against ECG-based HRV with an R-square of 0.5. Results Using a Generalized Linear Model, significant cross-sectional associations with SDNN were observed for three variables of interest: age, gender, and day-of-the-week. For sleepers under 50, SDNN declined at a rate of about 2.1 ms/year, then leveled off for sleepers aged 50-65, and increased slightly thereafter. Women under 50 displayed lower, more slowly declining, SDNN values than men, but this trend reversed for sleepers over 50. Throughout the week, SDNN values followed a U-shaped (women) or L-shaped (men) pattern, where values were highest during the weekend and lowest at mid-week. Conclusion Using a smart bed to unobtrusively measure overnight SDNN values for a large set of sleepers in an ecologically valid environment, reveals significant effects of age, gender, and day of the week on overnight SDNN. Support (if any):

scholarly journals Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

10.2196/16875 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16875 ◽  
Author(s):  
Nicholas C Jacobson ◽  
Berta Summers ◽  
Sabine Wilhelm
Keyword(s):  
Social Anxiety ◽  
Negative Affect ◽  
Positive Affect ◽  
Symptom Severity ◽  
Anxiety Symptom ◽  
Self Report ◽  
Sensor Data ◽  
Digital Phenotyping ◽  
Passive Sensor ◽  
Digital Biomarkers

Background Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. Objective This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. Methods In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants’ social anxiety symptom severity. Results The results suggested that these passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. Conclusions These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

scholarly journals Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors (Preprint)

2019 ◽  
Author(s):  
Nicholas C Jacobson ◽  
Berta Summers ◽  
Sabine Wilhelm
Keyword(s):  
Social Anxiety ◽  
Negative Affect ◽  
Positive Affect ◽  
Symptom Severity ◽  
Anxiety Symptom ◽  
Self Report ◽  
Sensor Data ◽  
Digital Phenotyping ◽  
Passive Sensor ◽  
Digital Biomarkers

BACKGROUND Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. OBJECTIVE This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. METHODS In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants’ social anxiety symptom severity. RESULTS The results suggested that these passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (<i>r</i>=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. CONCLUSIONS These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

Autonomic and Perceptual Responses to Induction of a Ketogenic Diet in Free-Living Endurance Athletes: A Randomized, Crossover Trial

2020 ◽  
pp. 1-7
Author(s):  
Ed Maunder ◽  
Deborah K. Dulson ◽  
David M. Shaw
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Exercise Capacity ◽  
Ketogenic Diet ◽  
Physiological Stress ◽  
Endurance Performance ◽  
Endurance Athletes ◽  
Recovery Stress ◽  
Individual Level ◽  
Dietary Shift

Purpose: Considerable interindividual heterogeneity has been observed in endurance performance responses following induction of a ketogenic diet (KD). It is plausible that a physiological stress response in the period following the dramatic dietary shift associated with transition to a KD may explain this heterogeneity. Methods: In a randomized, crossover study design, 8 trained male runners completed an incremental exercise test and ran to exhaustion at 70%VO2max before and after a 31-day rigorously controlled habitual diet or KD intervention, and recorded heart rate variability (root mean square of the sum of successive differences in R–R intervals [rMSSD]) upon waking each morning along with the recovery–stress questionnaire for athletes each week. Data were analyzed using linear mixed models. Results: A significant reduction in rMSSD was observed in the KD (−9.77 [4.03] ms, P = .02), along with an increase in day-to-day variability in rMSSD (2.1% [1.0%], P = .03). The reduction in rMSSD in the KD for the subgroup of individuals exhibiting impaired exercise capacity following induction of the KD approached significance (Δ −22 [15] ms, P = .06, N = 4); whereas no effect was observed in those who exhibited unchanged exercise capacity (Δ 5 [18] ms, P = .61, N = 4). No main effects were observed for recovery–stress questionnaire for athletes. Conclusions: Our data suggest those working with endurance athletes transitioning onto a KD may consider using noninvasive, inexpensive resting heart rate variability measures to gain individual-level insights into the likely short-term effects on exercise capacity.

Sign in / Sign up

Export Citation Format

Share Document