Purpose of this study was to test utility of heart rate variability (HRV) in daily endurance exercise prescriptions. Twenty-six healthy, moderately fit males were randomized into predefined training group (TRA, n = 8), HRV-guided training group (HRV, n = 9), and control group (n = 9). Four-week training period consisted of running sessions lasting 40 min each at either low- or high-intensity level. TRA group trained on 6 days a week, with two sessions at low and four at high intensity. Individual training program for HRV group was based on individual changes in high-frequency R-R interval oscillations measured every morning. Increase or no change in HRV resulted in high-intensity training on that day. If there was significant decrease in HRV (below reference value [10-day mean-SD] or decreasing trend for 2 days), low-intensity training or rest was prescribed. Peak oxygen consumption (VO(2peak)) and maximal running velocity (Load(max)) were measured in maximal treadmill test before and after the training. In TRA group, Load(max) increased from 15.1 +/- 1.3 to 15.7 +/- 1.2 km h(-1) (P = 0.004), whereas VO(2peak) did not change significantly (54 +/- 4 pre and 55 +/- 3 ml kg(-1) min(-1) post, P = 0.224). In HRV group, significant increases were observed in both Load(max) (from 15.5 +/- 1.0 to 16.4 +/- 1.0 km h(-1), P < 0.001) and VO(2peak) (from 56 +/- 4 to 60 +/- 5 ml kg(-1) min(-1), P = 0.002). The change in Load(max) was significantly greater in HRV group compared to TRA group (0.5 +/- 0.4 vs. 0.9 +/- 0.2 km h(-1), P = 0.048, adjusted for baseline values). No significant differences were observed in the changes of VO(2peak) between the groups. We concluded that cardiorespiratory fitness can be improved effectively by using HRV for daily training prescription.

Fractal HR dynamics were improved more by combining strength training with endurance training in our older men compared with endurance training alone, although strength training alone produced no changes in fractal HR behavior. The synergistic effect in fractal HR behavior occurred regardless of changes in aerobic capacity.

HRV measurements are beneficial in exercise training prescription in moderately active men and women. Women benefit from HRV guidance by achieving significant improvement in fitness with a lower training load.

Large individual differences in the responsiveness of cardiorespiratory fitness (VO2peak) to endurance training have been observed in healthy subjects. We tested the hypothesis that subjects with a poor responsiveness to endurance training might benefit from resistance training in terms of aerobic fitness. The study population consisted of sedentary healthy male and female subjects (n=91, 42+/-5 year) assigned to either a training (n=73) or a control group (n=18). The randomized cross-over study design included a 2-week laboratory-controlled endurance or resistance training period with a 2-month detraining period between the interventions. Large individual differences were observed in the changes of VO2peak (DeltaVO2peak) after both the endurance (average 8+/-6 %, P<0.001, range -5 to +22%) and resistance training (average 4+/-5%, P<0.001, range -8 to +16%). The average increase in DeltaVO2peak between genders was similar after both the endurance (8+/-6% for both genders, P=ns) and resistance training (3+/-5% for males and 5+/-6% for females, P=ns). There was no linear relationship between the changes in VO2peak after each training intervention (r=-.09, P=ns). On the contrary, when the study group was divided into quartiles according to the endurance training response (1+/-3, 6+/-1, 9+/-1, and 16+/-3% increase in VO2peak), the group with the lowest response to endurance training increased VO2peak after the resistance training intervention (DeltaVO2peak 7+/-5%, P<0.001). The individual responsiveness of VO2peak to exercise training is related to the mode of training. The healthy males and females whose training response is low after endurance training seem to result in a marked improvement in their cardiorespiratory fitness by resistance training.

Objective: To validate the accuracy of the Oura ring in the quantification of resting heart rate (HR) and heart rate variability (HRV). Background: Wearable devices have become comfortable, lightweight, and technologically advanced for assessing health behavior. As an example, the novel Oura ring integrates daily physical activity and nocturnal cardiovascular measurements. Ring users can follow their autonomic nervous system responses to their daily behavior based on nightly changes in HR and HRV, and adjust their behavior accordingly after self-reflection. As wearable photoplethysmogram (PPG) can be disrupted by several confounding influences, it is crucial to demonstrate the accuracy of ring measurements. Approach: Nocturnal HR and HRV were assessed in 49 adults with simultaneous measurements from the Oura ring and the gold standard ECG measurement. Female and male participants with a wide age range (15–72 years) and physical activity status were included. Regression analysis between ECG and the ring outcomes was performed. Main results: Very high agreement between the ring and ECG was observed for nightly average HR and HRV (r2 = 0.996 and 0.980, respectively) with a mean bias of −0.63 bpm and −1.2 ms. High agreement was also observed across 5 min segments within individual nights in (r2 = 0.869 ± 0.098 and 0.765 ± 0.178 in HR and HRV, respectively). Significance: Present findings indicate high validity of the Oura ring in the assessment of nocturnal HR and HRV in healthy adults. The results show the utility of this miniaturised device as a lifestyle management tool in long-term settings. High quality PPG signal results prompt future studies utilizing ring PPG towards clinically relevant health outcomes.

Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished.

High intensity cycling training increases oxidative capacity in skeletal muscles and improves insulin sensitivity. The present study compared the effect of eight weeks of sprint interval running (SIT) and continuous running at moderate intensity (CT) on insulin sensitivity and cholesterol profile in young healthy subjects (age 25.2 ± 0.7; VO(2max) 49.3 ± 1.2 ml·kg(-1)·min(-1)). SIT and CT increased maximal oxygen uptake by 5.3 ± 1.8 and 3.8 ± 1.6%, respectively (p < 0.05 for both). Oral glucose tolerance test (OGTT) was performed before and 60 h after the last training session. SIT, but not CT, reduced glucose area under curve and improved HOMA β-cell index (p < 0.05). Insulin area under curve did not decrease significantly in any group. SIT, but not CT, reduced LDL and total cholesterol. In conclusion, sprint interval running improves insulin sensitivity and cholesterol profile in healthy subjects, and sprint interval running may be more effective to improve insulin sensitivity than continuous running at moderate intensity.

BackgroundBody temperature is a common method in menstrual cycle phase tracking because of its biphasic form. In ambulatory studies, different skin temperatures have proven to follow a similar pattern. The aim of this pilot study was to assess the applicability of nocturnal finger skin temperature based on a wearable Oura ring to monitor menstrual cycle and predict menstruations and ovulations in real life.MethodsVolunteer women (n = 22) wore the Oura ring, measured ovulation through urine tests, and kept diaries on menstruations at an average of 114.7 days (SD 20.6), of which oral temperature was measured immediately after wake-up at an average of 1.9 cycles (SD 1.2). Skin and oral temperatures were compared by assessing daily values using repeated measures correlation and phase mean values and differences between phases using dependent t-test. Developed algorithms using skin temperature were tested to predict the start of menstruation and ovulation. The performance of algorithms was assessed with sensitivity and positive predictive values (true positive defined with different windows around the reported day).ResultsNocturnal skin temperatures and oral temperatures differed between follicular and luteal phases with higher temperatures in the luteal phase, with a difference of 0.30 °C (SD 0.12) for skin and 0.23 °C (SD 0.09) for oral temperature (p < 0.001). Correlation between skin and oral temperatures was found using daily temperatures (r = 0.563, p < 0.001) and differences between phases (r = 0.589, p = 0.004). Menstruations were detected with a sensitivity of 71.9–86.5% in window lengths of ±2 to ±4 days. Ovulations were detected with the best-performing algorithm with a sensitivity of 83.3% in fertile window from − 3 to + 2 days around the verified ovulation. Positive predictive values had similar percentages to those of sensitivities. The mean offset for estimations were 0.4 days (SD 1.8) for menstruations and 0.6 days (SD 1.5) for ovulations with the best-performing algorithm.ConclusionsNocturnal skin temperature based on wearable ring showed potential for menstrual cycle monitoring in real life conditions.