Hannu Kinnunen

Heart Rate Dynamics after Combined Endurance and Strength Training in Older Men

PURPOSE Aging alters cardiac autonomic function, which may contribute to a higher risk of cardiac events. Spectral measures of HR variability (HRV) and fractal-like behavior of HR are considered as markers of a healthy heart. The present study examined the effects of combining endurance and strength training compared with endurance or strength training alone on HR dynamics and physical fitness in older previously untrained men aged 40-67 yr. METHODS Subjects were randomized into endurance training (E, n = 23), strength training (S, n = 25), combined endurance and strength training (ES, n = 29), or control group (C, n = 16). Short-term fractal scaling exponent (alpha1) and spectral HRV were analyzed from maximal aerobic cycling tests and during supine rest, and leg extension one repetition maximum strength was measured. RESULTS Aerobic capacity and maximal strength increased in the training groups performing endurance and/or strength training, respectively. Only ES showed a decrease after training in fractal HR behavior during exercise, and the difference was significant between groups (P = 0.019). During supine rest, alpha1 only decreased significantly (P = 0.039) in ES from 1.18 (SD = 0.20) to 1.11 (SD = 0.21). The decrease in alpha1 at rest from 1.21 (SD = 0.19) to 1.11 (SD = 0.22) also approached significance (P = 0.061) in E. Changes in spectral measures of HRV were minor during the study period and only occurred during exercise. CONCLUSION 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.

Physical activity and heart rate variability measured simultaneously during waking hours

Heart rate (HR) variability (HRV) during ambulatory recordings may be affected by individual differences in daily physical activity (PA). However, the influence of various levels of PA on different measures of HRV is not exactly known. We examined the association between simultaneously measured HRV and objective PA data obtained with an accelerometer during waking hours among 45 healthy adults. Bouts of PA were identified from minute-by-minute accelerometer data as metabolic equivalent (METs) values and calculated as mean METs for 30 min. HRV was analyzed concurrently. Within-individual correlation analyses and sign tests were performed to study the relationships between various HRV indexes and PA. The mean PA time was 15:44 +/- 1:01 h, and the mean MET was 1.91 +/- 0.14. HR and sample entropy, but not the other measures of HRV, had a significant relationship with PA, as shown by both correlation analyses (r = 0.64, P = 0.021, and r = -0.55, P = 0.022, respectively) and sign tests (P < 0.0001 for both). Beat-to-beat R-R interval fluctuation expressed as SD1 also demonstrated a significant relation to PA according to the sign test (P = 0.037) and a trend of association according to the correlation analysis (r = -0.40, P = 0.129). The complexity measure of HRV, in addition to average HR and the short-term index of HRV (SD1), is significantly influenced by the level of PA during ambulatory conditions. Long-term HRV indexes remained relatively stable at various activity levels, making them the most robust indexes for the assessment of cardiac autonomic function during free-running ambulatory conditions.

Serum sex hormone-binding globulin and cortisol concentrations are associated with overreaching during strenuous military training.

Tanskanen, MM, Kyröläinen, H, Uusitalo, AL, Huovinen, J, Nissilä, J, Kinnunen, H, Atalay, M, and Häkkinen, K. Serum sex hormone-binding globulin and cortisol concentrations are associated with overreaching during strenuous military training. J Strength Cond Res 25(3): 787-797, 2011-The purpose was (a) to study the effect of an 8-week Finnish military basic training period (BT) on physical fitness, body composition, mood state, and serum biochemical parameters among new conscripts; (b) to determine the incidence of overreaching (OR); and (c) to evaluate whether initial levels or training responses differ between OR and noOR subjects. Fifty-seven males (19.7 ± 0.3 years) were evaluated before and during BT. Overreaching subjects had to fulfill 3 of 5 criteria: decreased aerobic physical fitness (&OV0312;O2max), increased rating of perceived exertion (RPE) in 45-minute submaximal test at 70% of &OV0312;O2max or sick absence from these tests, increased somatic or emotional symptoms of OR, and high incidence of sick absence from daily service. &OV0312;O2max improved during the first 4 weeks of BT. During the second half of BT, a stagnation of increase in &OV0312;O2max was observed, basal serum sex hormone-binding globulin (SHBG) increased, and insulin-like growth factor-1 and cortisol decreased. Furthermore, submaximal exercise-induced increases in cortisol, maximum heart rate, and postexercise increase in blood lactate were blunted. Of 57 subjects, 33% were classified as OR. They had higher basal SHBG before and after 4 and 7 weeks of training and higher basal serum cortisol at the end of BT than noOR subjects. In addition, in contrast to noOR, OR subjects exhibited no increase in basal testosterone/cortisol ratio but a decrease in maximal La/RPE ratio during BT. As one-third of the conscripts were overreached, training after BT should involve recovery training to prevent overtraining syndrome from developing. The results confirm that serum SHBG, cortisol, and testosterone/cortisol and maximal La/RPE ratios could be useful tools to indicate whether training is too strenuous.

Sprint interval running increases insulin sensitivity in young healthy subjects.

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; VO2max 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.

Efficient accelerometer-based swimming exercise tracking

The study concentrates on tracking swimming exercises based on the data of 3D accelerometer and shows that human activities can be tracked accurately using low sampling rates. The tracking of swimming exercise is done in three phases: first the swimming style and turns are recognized, secondly the number of strokes are counted and thirdly the intensity of swimming is estimated. Tracking is done using efficient methods because the methods presented in the study are designed for light applications which do not allow heavy computing. To keep tracking as light as possible it is studied what is the lowest sampling frequency that can be used and still obtain accurate results. Moreover, two different sensor placements (wrist and upper back) are compared. The results of the study show that tracking can be done with high accuracy using simple methods that are fast to calculate and with a really low sampling frequency. It is shown that an upper back-worn sensor is more accurate than a wrist-worn one when the swimming style is recognized, but when the number of strokes is counted and intensity estimated, the sensors give approximately equally accurate results.

Association of Military Training with Oxidative Stress and Overreaching

UNLABELLED We hypothesized that increased oxidative stress and disrupted redox balance may be predisposing factors and markers for overreaching (OR). PURPOSE The studys purpose was to examine whether oxidative stress markers and antioxidant status and physical fitness are related to OR during an 8-wk military basic training (BT) period. METHODS Oxidative stress and antioxidant status were evaluated in the beginning and after 4 and 7 wk of training in 35 males (age = 19.7 ± 0.3 yr) at rest and immediately after a 45-min submaximal exercise. Physical activity (PA) was monitored by an accelerometer throughout BT. Indicators of OR were also examined. RESULTS From baseline to week 4, increased daytime moderate to vigorous PA led to concomitant decreases in the ratio of oxidized to total glutathione (GSSG/TGSH) and GSSG. After 4 wk of BT, GSSG/TGSH and GSSG returned to the baseline values at rest, whereas PA remained unchanged. At every time point, acute exercise decreased TGSH and increased GSSG and GSSG/TGSH, whereas a decrease was observed in antioxidant capacity after 4 wk of training. In the beginning of BT, OR subjects (11 of the 35 males) had higher GSSG, GSSG/TGSH, and malondialdehyde (a marker of lipid peroxidation) at rest (P < 0.01-0.05) and lower response of GSSG and GSSG/TGSH ratio (P < 0.01) to exercise than non-OR subjects. Moreover, OR subjects had higher PA during BT than non-OR (P < 0.05). CONCLUSIONS The sustained training load during the last 4 wk of BT led to oxidative stress observable both at rest and after submaximal exercise. Increased oxidative stress may be a marker of insufficient recovery leading possibly to OR.

Clustering-based activity classification with a wrist-worn accelerometer using basic features

Automatic recognition of activities using time series data collected from exercise can facilitate development of applications that motivate people to exercise more frequently and actively. This article presents a method for recognizing nine different everyday sport activities, such as running, walking, aerobics and Nordic walking, using only two-dimensional wrist-worn accelerometer. The suggested method is based on clustering the data by first using an EM -algorithm to form homogeneous groups and then applying C4.5-based decision trees inside these groups. The features extracted for classification process are simple features, such as variance and mean, which are calculated from compressed signals that contain only such points of the original time series where the derivative is equal to zero. The data were collected by ten subjects and they contained nine different sports. Using the presented method, the data were classified with an accuracy of 85%, whereas the accuracy using an automatically generated decision tree was 80%. The purpose of this method is to recognize activities in order to form an activity diary.

Wrist-worn accelerometers in assessment of energy expenditure during intensive training

We assessed the ability of the Polar activity recorder (AR) to measure energy expenditure (EE) during military training. Twenty-four voluntary male conscripts participated in the study and wore an AR on the non-dominant wrist 24 h a day for 7 d. The AR analyzed and stored the frequency of hand movements (f_hand) into memory at 1 min intervals. The relationship between f_hand and EE was studied over a 7 d period of military training using the doubly labeled water (DLW) technique. In addition, the relationship between f_hand and EE was analyzed during walking and running on a treadmill with an indirect calorimeter (IC), and f_hand was measured during a supervised 45 min field march test where the conscripts carried combat gear. EE was expressed as physical activity level (PAL), total energy expenditure (TEE), and activity-induced energy expenditure adjusted for body mass (AEE/BM). Over the 7 d period, f_hand alone explained 46% of inter-individual variation in PAL(DLW). After inclusion of body height and mass in the model used to predict PAL(DLW) from f_hand, a very high positive correlation and a low standard error of estimate (SEE) were observed between the AR and DLW techniques: for TEE r = 0.86 (p < 0.001), the SEE was 6.3%, and for AEE/BM r = 0.84 (p < 0.001), the SEE was 12.8%. In the treadmill exercise, f_hand correlated highly with PAL(IC) (r = 0.97 ± 0.02). In the 45 min field march test, the AR measured similar f_hand as on the treadmill at the same speed. In conclusion, the wrist-worn AR can be regarded as a reliable and valid method for assessing EE during intensive training.

Effects of habitual physical activity on response to endurance training

Abstract We hypothesised that habitual physical activity (PA) together with progressive endurance training contributes to the differences in training response (Δ[Vdot]O2max) in healthy and physically active male participants. Twenty volunteers (age 30±3 years and [Vdot]O2max 54±7 ml·kg−1·min−1) participated in an eight-week training program which included four to six heart rate-guided exercise sessions weekly. PA data over the whole period were collected by an accelerometer-equipped wristwatch. Individual relative intensities of endurance training and PA were separately determined by adjusting to [Vdot]O2max reserve and calculated as mean daily duration (min) of training and PA at light, moderate, high and very high intensity levels. [Vdot]O2max increased 6.4±4.1% (p < 0.0001) during the training period. Δ[Vdot]O2max correlated with the amount of habitual PA that was mainly of light intensity (r = 0.53, p = 0.016), but not with the duration of moderate, high or very high intensity PA (p = ns for all). Age, body mass index, and daily amount of training at any intensity level of exercise were not related to Δ[Vdot]O2max (p = ns for all). In conclusion, a high amount of habitual PA together with prescribed endurance training was associated with good training response in physically active males.

Estimation of Exercise Energy Expenditure Using a Wrist-Worn Accelerometer: A Linear Mixed Model Approach with Fixed-Effect Variable Selection

This article presents an approach to estimating exercise energy expenditure based on acceleration measurements from a wrist-worn biaxial sensor. The method uses the linear mixed model that makes it possible to model both between-subject and within-subject variation in energy expenditure. More precisely, a random-intercepts model is used. The variance and mean of the acceleration signals at 15-second intervals as well as subject demographics (height, weight, body mass index, age and VO2max) are used. Energy expenditure is modelled in four different activities: walking, running, Nordic walking and bicycling. This study introduces an effective backward model selection procedure for selecting the fixed-effect variables in the model. The procedure uses leave-one-out cross-validation to be able to effectively exploit the available data set and to ensure the robustness of the model. Estimation accuracy in test sets is used as a criterion of model performance. The model selection procedure proposed notably improves estimation accuracy. In walking, running, Nordic walking and bicycling, average estimation errors of 3.9, 3.6, 1.9 and 13.5 percent are reached. The respective Person correlations for these activities are 0.91, 0.98, 0.97, and 0.81. These results are also compared to the performance of the general linear model. It is discovered that the linear mixed model outperforms the model that does not take the individual levels of energy expenditure of the subjects into account.