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opencc-by-4.0Sep 2020View details →

Effects of COVID-19 lockdown on heart rate variability

Introduction: Strict lockdown rules were imposed to the French population from 17 March to 11 May 2020, which may result in limited possibilities of physical activity, modified psychological and health states. This report is focused on HRV parameters kinetics before, during and after this lockdown period. Methods: 95 participants were included in this study (27 women, 68 men, 37 ± 11 years, 176 ± 8 cm, 71 ± 12 kg), who underwent regular orthostatic tests (a 5-minute supine followed by a 5-minute standing recording of heart rate (HR)) on a regular basis before (BSL), during (CFN) and after (RCV) the lockdown. HR, power in low- and high-frequency bands (LF, HF, respectively) and root mean square of the successive differences (RMSSD) were computed for each orthostatic test, and for each position. Subjective well-being was assessed on a 0-10 visual analogic scale (VAS). The participants were split in two groups, those who reported an improved well-being (WB+, increase >2 in VAS score) and those who did not (WB-) during CFN. Results: Out of the 95 participants, 19 were classified WB+ and 76 WB-. There was an increase in HR and a decrease in RMSSD when measured supine in CFN and RCV, compared to BSL in WB-, whilst opposite results were found in WB+ (i.e. decrease in HR and increase in RMSSD in CFN and RCV; increase in LF and HF in RCV). When pooling data of the three phases, there was a moderate significant correlation between VAS and HR, RMSSD, HF, respectively, in the supine position; the higher the VAS score (i.e., subjective well-being), the higher the RMSSD and HF and the lower the HR. In standing position, HRV parameters were not modified during CFN. Conclusion: Our results suggest that the strict COVID-19 lockdown likely had opposite effects on French population as 20% of participants improved parasympathetic activation (RMSSD, HF) and rated positively this period, whilst 80% showed altered responses and deteriorated well-being.  The changes in HRV parameters during and after the lockdown period were in line with subjective well-being responses. The observed recordings may reflect a large variety of responses (anxiety, anticipatory stress, change on physical activity…) beyond the scope of the present study. However, these results confirmed the usefulness of HRV as a non-invasive means for monitoring well-being and health in the general population.

opencc-by-4.0Jun 2022View details →

Effects of severe anthropogenic disturbance on the heart rate and body temperature in free-living greylag geese (Anser anser)

Anthropogenic disturbances are a major concern for the welfare and conservation of wildlife. We recorded heart rate and body temperature of 20 free-living greylag geese in response to a major regularly re-occurring anthropogenic disturbance, New Year’s Eve fireworks. Heart rate and body temperature were significantly higher in the first and second hour of the new year, compared to the same hour on the 31st of December, the average during December and the average during January. Heart rate and body temperature was not significantly affected by sex or age. From 0200-0300 onwards, 1st of January heart rates did not significantly differ from the other periods, however body temperatures were significantly increased until 0300-0400. From 0400-0500, heart rate was not affected by any of the investigated factors, whereas body temperature was significantly increased on the 1st of January compared 31st of December and the December average but not compared to the January average. To conclude, our results show that New Year’s Eve fireworks cause a substantial physiological response, indicative of a stress response in greylag geese, which is costly in terms of energy expenditure.

opencc-by-4.0May 2024View details →

Dataset for study: Adaptive Hip Exoskeleton Control using Heart Rate Feedback Reduces Oxygen Cost during Ecological Locomotion

This record contains the dataset for a manuscript currently under preparation and submission. See the description PDF file for more details. The information here will be updated according to the progress in the peer review and publication procedure.

Figure 2 in Heart rate response and bimodal gas eXchange in three developmental stages of the bullfrog Lithobates catesbeianus (Anura: Ranidae)

Figure 2. Representative data recording of electrocardiogram (A) and aerial ventilation (B) in a premetamorphic Lithobates catesbeianus at 25°C. In B the signals show a ventilatory event where the tadpole renewed the air in its lungs, resulting in a marked drop in PO2 and an increase in PCO2. Following the ventilatory event, the expired air was mixed with the remaining air within the closed respirometry system, resulting in a PO2 slightly lower, and a PCO2 slightly greater, than before ventilation.

Figure 1 in Heart rate response and bimodal gas eXchange in three developmental stages of the bullfrog Lithobates catesbeianus (Anura: Ranidae)

Figure 1. Scheme of non-invasive apparatus to measure gas exchange in water (A) and air (B), and heart rate (C).

Figure 4 in Heart rate response and bimodal gas eXchange in three developmental stages of the bullfrog Lithobates catesbeianus (Anura: Ranidae)

Figure 4. Mass-specific oxygen consumption (A) and carbon dioxide released (B) for aerial (red lines and points) and aquatic (blue lines and points) gas exchange during development of Lithobates catesbeianus.

Figure 5 in Heart rate response and bimodal gas eXchange in three developmental stages of the bullfrog Lithobates catesbeianus (Anura: Ranidae)

Figure 5. Relationship between Log whole-body oxygen consumption (A) and carbon dioxide release (B) (µmol h-1) in 10 air (filled symbols) and water (open symbols), and Log10 body mass (g) in larval (blue triangles), premetamorphic (orange squares) and metamorphic (green circles) stages of Lithobates catesbeianus. Each point represents a measurement from a single animal. The regression lines correspond to aerial (red) and aquatic (blue) gas exchange. Dotted lines represent no significant correlation.

opencc-by-4.0Jul 2021View details →

Validation of heart rate measurement of Fitbit Charge 4 and Xiaomi Mi Band 5

Database containig data from heart rate validation study of 2 wristbands: Fitbit Charge 4 and Xiaomi Mi Band 5.

opencc-by-4.0May 2023View details →

Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'

Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.

opencc-by-sa-4.0Jun 2023View details →

Wrist-worn sensor validation for heart rate variability and electrodermal activity detection in a stressful driving environment

The current dataset contributes to assess the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Heart Rate Variability (HRV) and ElectroDermal Activity (EDA) signals were recorded over six experimental conditions (i.e., Baseline, Video Clip, Scream, No Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The raw quality of the physiological signals was enhanced by means of robust semi-automatic reconstruction algorithms. Heart Rate Variability time-domain parameters showed high accuracy in motion-free experimental conditions, while Heart Rate Variability frequency-domain parameters reported sufficient accuracy in almost every experimental condition.

ClinicalTrials.gov40/100

Perceived Social Support, Heart Rate Variability, and Hopelessness in Patients With Ischemic Heart Disease

ClinicalTrials.gov study NCT05003791. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad36/100

Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?

Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.

opencc-zeroDec 2020View details →

opencc-by-4.0Dec 2020View details →

Data for "Does the Preferred Walk-Run Transition Speed on Steep Inclines Minimize Energetic Cost, Heart Rate or Neither?"

Abstract Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis. Methods Subjects walked and ran on a classic Quinton 18-60 motorized treadmill with a rigid steel deck (Quinton Instrument Company, Bothell, WA). Determination of PTS: The average of the walk-to-run transition speed (WRTS) and run-to-walk transition speed (RWTS) defined the PTS as per Hreljac et. al. (2007). We first determined the WRTS in the walk-first group and then their RWTS and vice versa for the run-first group. Based on pilot experiments, we selected starting speeds such that there was no doubt which gait would be preferred at the initial speed. Once the speed of the treadmill was correctly set, subjects mounted the treadmill and chose their gait ad libitum. After we determined the preferred gait at the particular speed, the subject straddled the treadmill belt while we changed the speed by 0.1 m/s (increased during WRTS trials, decreased during RWTS trials). The process repeated until a gait transition occurred and was sustained for 30 seconds. Determination of EOTS and HROTS: For the energetics and heart rate trials, we set the initial speed based on pilot experiments that indicated it would be near the EOTS. Subjects in the walk-first group walked at the incline-specific initial speed for 5 min, rested for &sim;5 min and then ran at that speed for 5 min. Subjects in the run-first group did the opposite. During the rest periods, we re-weighed the subject and they drank just enough water to compensate for the weight loss due mostly to sweating. Thus, each subject maintained a nearly constant weight throughout all the trials. To measure metabolic rate during walking and running, we used an open-circuit, expired gas analysis system (TrueOne 2400; ParvoMedics, Sandy, UT). Subjects wore a mouthpiece with a one-way breathing valve and a nose clip allowing us to collect their expired air. The ParvoMedics software calculated the STPD rates of oxygen consumption (V□O2) and carbon dioxide production (V□CO2) and we averaged the last 2 minutes of each 5-minute trial. We then calculated metabolic power using the equation of Péronnet and Massicotte (1991) equation, as clarified by Kipp et al. (2018). We only included trials with respiratory exchange ratios (RER) <1.0 to ensure that metabolic energy was predominantly being provided from oxidative pathways. We used an R7 Polar iWL (Polar Electro Oy, Kempele, Finland) to measure heart rate in beats per minute (bpm) and averaged the values for the last 2 min of each trial. Immediately after both gait trials were completed for the initial speed, we calculated and compared the metabolic power required for walking and running. If walking was the more economical gait, we increased the treadmill speed by 0.1 m/s, and the process repeated. If running was the more economical gait, we decreased the treadmill speed by 0.1 m/s, and the process repeated. Each subject performed three speeds, both walking and running at each incline. However, some subjects needed to complete walking and running trials at a fourth speed so that we could obtain energetics data for one speed faster and one speed slower than their EOTS. For the three speeds at which the differences between metabolic rates between walking and running were least, we calculated linear regression equations for both metabolic power and heart rate as functions of speed for both walking and running for each subject and incline. The speeds at which the two equations intersected defined the EOTS and HROTS for each subject. Overall, we analyzed ten subjects at four different inclines, i.e. 40 determinations of EOTS and HROTS. Of those 80 linear regression analyses, the walking vs. running regressions intersected at a speed < 3 m/sec for all but two subjects (one subject for EOTS at 15° and a different subject for HROTS at 10°). Essentially, those individuals’ regression lines were nearly parallel. We chose to exclude those two conditions from further statistical analysis and aggregate data compilation. Usage Notes There are two missing values, as noted in the methods: HROTS for subject 5 at 10 degrees and EOTS for subject 4 at 15 degrees.

opencc-zeroFeb 2015View details →

Dataset for article: Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88

Dataset for article: Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88

opencc-zeroAug 2013View details →

Breathing frequency bias in fractal analysis of heart rate variability (datasets)

data form the article: Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Bi- ological Psychology, 82(1), pp. 82-88

opencc-by-4.0Nov 2023View details →

Energy management strategies for ectothermic vertebrates: temperature and heart rate as independent proxies

Filtered data used for analysis of energy management strategies in three different species. R script provides an example of the across- and within-individual analysis.

dryad36/100

High heart rates in hunting porpoises

The impressive breath-hold capabilities of marine mammals are facilitated by both enhanced O2 stores and reductions in the rate of O2 consumption via peripheral vasoconstriction and bradycardia, coined the dive response. Many studies have focused on the extreme role of the dive response in maximizing dive duration in marine mammals, but few have addressed how these adjustments may compromise the capability to hunt, digest and thermoregulate during routine dives. Here we use DTAGs which record heart rate together with foraging and movement behaviour to investigate how O2 management is balanced between the need to dive and forage in five wild harbour porpoises that hunt thousands of small prey daily during continuous shallow diving. Dive heart rate was moderate (median minimum 47-69 bpm) and relatively stable across dive types, dive duration (0.5-3.3 min), and activity. A moderate dive response, allowing for some perfusion of peripheral tissues, may be essential for fuelling the high field metabolic rates required to maintain body temperature and support digestion during diving in these small, continuously-feeding cetaceans. Thus, despite having the capacity to prolong dives via a strong dive response, for these shallow-diving cetaceans, it appears to be more efficient to maintain circulation while diving: extreme heart-rate gymnastics are for deep dives and emergencies, not everyday use.

opencc-zeroNov 2021View details →