Proof-of-concept model for instantaneous heart rate-drift correction during low and high exercise exertion.

Introduction: This study aimed to model below and above anaerobic threshold exercise-induced heart rate (HR) drift, so that the corrected HR could better represent V˙O2 kinetics during and after the exercise itself. Methods: Fifteen healthy subjects (age: 28 ± 5 years; V˙O2Max: 50 ± 8 mL/kg/min; 5 females) underwent a maximal and a 30-min submaximal (80% of the anaerobic threshold) running exercises. A five-stage computational (i.e., delay block, new training impulse-calculation block, Sigmoid correction block, increase block, and decrease block) model was built to account for instantaneous HR, fitness, and age and to onset, increase, and decrease according to the exercise intensity and duration. Results: The area under the curve (AUC) of the hysteresis function, which described the differences in the maximal and submaximal exercise-induced V˙O2 and HR kinetics, was significantly reduced for both maximal (26%) and submaximal (77%) exercises and consequent recoveries. Discussion: In conclusion, this model allowed HR drift instantaneous correction, which could be exploited in the future for more accurate V˙O2 estimations.

The influence of low resistance respiratory muscle training on pulmonary function and high intensity exercise performance.

Background/objectives: Respiratory muscle training (RMT) was recognized as an effective means to improve respiratory muscle (RM) strength and enhance exercise performance. The purpose of this study was to examine the effect of low-intensity RMT on RM strength, pulmonary function, and performance. Methods: Fourteen healthy active adults were assigned randomly to either a training or placebo group. The training group completed six weeks of RMT, which consisted of a first week, 1 set of 15 min/d, 5 d/wk at 10-25% of maximal inspiratory pressure (PImax), and the remaining 5 weeks, 2 sets of 15 min/d, 5 d/wk, at 30% PImax. The placebo group followed the same protocol but with almost no additional ventilatory resistance. Measurement of RM strength and endurance, spirometry, and endurance exercise performance were obtained before and after the RMT program. Results: In the training group, PImax (+14%) and maximal expiratory pressure (PEmax, +27%), forced vital capacity (FVC, +3.6%), maximal oxygen uptake (VO2max, +11%), and time to exhaustion (Tlim90%, +25%) increased significantly from baseline values (P < 0.05). No significant changes were observed in the placebo group. Also, no significant interaction in maximum voluntary ventilation (MVV12), minute ventilation (VE), and respiratory rate (RR) were detected. Conclusions: These data suggest that low-intensity RMT is an effective tool to improve RM strength, pulmonary elastic properties and endurance exercise performance.

Failure of Digital Device Performance in Monitoring Physical Exercise in a Pilot Study in Sedentary Persons with HIV.

Digital devices have gained popularity in the last 10 years as a tool for exercise prescription, the monitoring of daily physical activity, and nutrition for the management of a health-related parameter. Therefore, the aim of this study was to assess the effectiveness of the use of digital devices to monitor exercise data in sedentary persons with HIV who exercise following an individualized activity pacing (AP) protocol on cardiorespiratory fitness body composition, blood lipid profile, and psychological parameters. Twenty-four PLWH were enrolled in an 18-week randomized, open-label, pilot AP exercise protocol. All participants were monitored by a Health Band connected to a mobile app that transmitted the data to a server. At week 3, they were randomized either in an experimental group (EG), in which an open device configuration enabled them to receive training data feedback (n = 12), or continued with no data feedback (control group, n = 12). The primary endpoint was improvement from the baseline of 15% of steady-state oxygen consumption (V˙O2) during a 6-min walking test. Technical issues occurred when pairing the health band with the app, which prevented EG participants from regularly receiving data feedback, and with data transmission to the server, which enabled only 40% monitoring of the total training days. Consequently, the study outcomes could not be compared between the two groups, and participants also lost confidence in the study. However, 19 out of 24 participants completed the AP program. Overall, only 6 (32%) improved steady-state V˙O2, with no significant changes at W18 from the baseline. Significant reductions were observed of BMI (p = 0.040), hip circumference (p = 0.027), and total-(p = 0.049) and HDL-cholesterol (p = 0.045). The failure of digital device performance substantially affected study procedures, monitoring, and participants' engagement, and likely limited the potential benefits of the AP exercise program.

Plasma Interleukin-10 and Cholesterol Levels May Inform about Interdependences between Fitness and Fatness in Healthy Individuals.

Relationships between demographic, anthropometric, inflammatory, lipid and glucose tolerance markers in connection with the fat but fit paradigm were investigated by supervised and unsupervised learning. Data from 81 apparently healthy participants (87% females) were used to generate four classes of fatness and fitness. Principal Component Analysis (PCA) revealed that the principal component was preponderantly composed of glucose tolerance parameters. IL-10 and high-density lipoprotein, low-density lipoprotein (LDL), and total cholesterol, along with body mass index (BMI), were the most important features according to Random Forest based recursive feature elimination. Decision Tree classification showed that these play a key role into assigning each individual in one of the four classes, with 70% accuracy, and acceptable classification agreement, κ = 0.54. However, the best classifier with 88% accuracy and κ = 0.79 was the Naïve Bayes. LDL and BMI partially mediated the relationship between fitness and fatness. Although unsupervised learning showed that the glucose tolerance cluster explains the highest quote of the variance, supervised learning revealed that the importance of IL-10, cholesterol levels and BMI was greater than the glucose tolerance PCA cluster. These results suggest that fitness and fatness may be interconnected by anti-inflammatory responses and cholesterol levels. Randomized controlled trials are needed to confirm these preliminary outcomes.

Osteogenic Properties of 3D-Printed Silica-Carbon-Calcite Composite Scaffolds: Novel Approach for Personalized Bone Tissue Regeneration.

Carbon enriched bioceramic (C-Bio) scaffolds have recently shown exceptional results in terms of their biological and mechanical properties. The present study aims at assessing the ability of the C-Bio scaffolds to affect the commitment of canine adipose-derived mesenchymal stem cells (cAD-MSCs) and investigating the influence of carbon on cell proliferation and osteogenic differentiation of cAD-MSCs in vitro. The commitment of cAD-MSCs to an osteoblastic phenotype has been evaluated by expression of several osteogenic markers using real-time PCR. Biocompatibility analyses through 3-(4,5-dimethyl- thiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), lactate dehydrogenase (LDH) activity, hemolysis assay, and Ames test demonstrated excellent biocompatibility of both materials. A significant increase in the extracellular alkaline phosphatase (ALP) activity and expression of runt-related transcription factor (RUNX), ALP, osterix (OSX), and receptor activator of nuclear factor kappa-Β ligand (RANKL) genes was observed in C-Bio scaffolds compared to those without carbon (Bio). Scanning electron microscopy (SEM) demonstrated excellent cell attachment on both material surfaces; however, the cellular layer on C-Bio fibers exhibited an apparent secretome activity. Based on our findings, graphene can improve cell adhesion, growth, and osteogenic differentiation of cAD-MSCs in vitro. This study proposed carbon as an additive for a novel three-dimensional (3D)-printable biocompatible scaffold which could become the key structural material for bone tissue reconstruction.

Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence.

BACKGROUND: Monitoring and interfacing technologies may increase physical activity (PA) program adherence in older adults, but they should account for aspects influencing older adults' PA behavior. This study aimed at gathering preliminary wrist-based PA adherence data in free-living and relate these to the influencing factors. METHODS: Ten healthy older adults (4 females, aged 70-78 years) provided health, fatigue, activity levels, attitude towards pacing, and self-efficacy information and performed a 6 min-walk test to assess their fitness. After a baseline week they followed a two-week walking and exercise intervention. Participants saw their progress via a purposely designed mobile application. RESULTS: Walking and exercise adherence did not increase during the intervention (p = 0.38, p = 0.65). Self-efficacy decreased (p = 0.024). The baseline physical component of the Short Form Health Survey was the most predictive variable of walking adherence. Baseline perceived risk of over-activity and resting heart rate (HRrest) were the most predictive variables of exercise adherence. When the latter two were used to cluster participants according to their exercise adherence, the fitness gap between exercise-adherent and non-adherent increased after the intervention (p = 0.004). CONCLUSIONS: Risk of over-activity and HRrest profiled short-term exercise adherence in older adults. If confirmed in a larger and longer study, these could personalize interventions aimed at increasing adherence.

Assisting Home-Based Resistance Training for Normotensive and Prehypertensive Individuals Using Ambient Lighting and Sonification Feedback: Sensor-Based System Evaluation.

BACKGROUND: Physical exercise is an effective lifestyle intervention to improve blood pressure. Although aerobic sports can be performed anywhere, resistance exercises are traditionally performed at the gym; extending the latter to the home setting may promote an increase in the number of practitioners. OBJECTIVE: This study aims to evaluate a sensor-based system that guides resistance exercises through ambient lighting and sonification (A/S) feedback in a home setting in 34 study participants who were normotensive and prehypertensive. METHODS: Participants took part in a 1.5-hour exercise session in which they experienced the A/S feedback (ie, experimental condition) as well as a control condition (ie, no feedback) and a reference condition (ie, verbal feedback through a human remote coach). The system was evaluated for improving exercise form (range of motion, timing, and breathing patterns) as well as psychophysiological experience (perceived exertion, attentional focus, competence, and motivation). RESULTS: A/S feedback was significantly better than the control for concentric (mean 2.48, SD 0.75 seconds; P<.001) and eccentric (mean 2.92, SD 1.05 seconds; P<.001) contraction times, concentric range of motion consistency (mean 15.64, SD 8.31 cm vs mean 17.94, SD 9.75 cm; P<.001), and perceived exertion (mean 3.37, SD 0.78 vs mean 3.64, SD 0.76; P<.001). However, A/S feedback did not outperform verbal feedback on any of these measures. The breathing technique was best in the control condition (ie, without any feedback). Participants did not show more positive changes in perceived competence with A/S feedback or verbal feedback. CONCLUSIONS: The system seemed to improve resistance exercise execution and perception in comparison with the control, but did not outperform a human tele-coach. Further research is warranted to improve the breathing technique.

Cardiorespiratory fitness estimation from heart rate and body movement in daily life.

Low cardiorespiratory fitness (CRF) increases risk of all-cause mortality and cardiovascular events. Periodic CRF assessment can have an important preventive function. The objective of this study was to develop a protocol-free method to estimate CRF in daily life based on heart rate (HR) and body acceleration measurements. Acceleration and HR data were collected from 37 subjects (men = 49%) while they performed a standardized laboratory activity protocol (sitting, walking, running, cycling) and during a 5-day free-living monitoring period. CRF was determined by oxygen uptake (V̇o2max) during maximal exercise testing. A doubly labeled water-validated equation was used to predict total energy expenditure (TEE) from acceleration data. A fitness index was defined as the ratio between TEE and HR (TEE-pulse). Activity recognition techniques were used to process acceleration features and classify sedentary, ambulatory, and other activity types. Regression equations based on TEE-pulse data from each activity type were developed to predict V̇o2max. TEE-pulse measured within each activity type of the laboratory protocol was highly correlated with V̇o2max (r from 0.74-0.91). Averaging the outcome of each activity-type specific equation based on TEE-pulse from the laboratory data led to accurate estimates of V̇o2max [root mean square error (RMSE): 300 mL O2/min, or 10%]. The difference between laboratory and free-living determined TEE-pulse was 3.7 ± 11% (r = 0.85). The prediction method preserved the prediction accuracy when applied to free-living data (RMSE: 367 mL O2/min, or 12%). Measurements of body acceleration and HR can be used to predict V̇o2max in daily life. Activity-specific prediction equations are needed to achieve highly accurate estimates of CRF.NEW & NOTEWORTHY This is among the very few studies validating, in free-living conditions, a method to estimate cardiorespiratory fitness using heart rate and body acceleration data. A novel parameter called TEE-pulse, which was defined as the ratio between accelerometer-determined energy expenditure and heart rate, was highly correlated with maximal oxygen uptake (V̇o2max). Activity classification and the use of activity-selective prediction equations outperformed previously published methods for estimating V̇o2max from heart rate and acceleration data.

The Role of Technology in Adherence to Physical Activity Programs in Patients with Chronic Diseases Experiencing Fatigue: a Systematic Review.

BACKGROUND: The beneficial role of physical activity (PA) to manage the health condition of patients with chronic diseases is well known. However, adherence to PA guidelines in this group is still low. Monitoring and user-interface technology could represent a significant tool to increase exercise adherence to those particular groups who experience difficulties in adhering to regular and substantial physical activity, and could be supportive in increasing the success of PA programs and interventions. This systematic review aimed at evaluating the effect of physical activity monitoring technology in improving adherence to a PA program in patients with chronic diseases experiencing fatigue. METHODS: This systematic review was conducted according to PRISMA guidelines. The literature search was performed in Embase, Medline, Biosis, Scopus, and SPORTDiscus. We filtered the literature according to the question: "Does monitoring technology affect adherence to physical activity and exercise programs in patients with chronic diseases perceiving fatigue?". RESULTS: The search resulted in 1790 hits; finally, eight studies were included, with a total number of 205 patients. Study quality was moderate except for one study of high quality. Only three disease types emerged, COPD, HF, and cancer. PA programs were rather short (from 8 to 13 weeks) except for one 3-year-long study. Five studies employed pedometers and two an activity monitor. Three studies based their adherence on steps, the remaining studies focused on active minutes. Adherence was explicitly reported in two studies, and otherwise derived. Four studies showed high adherence levels (85% week-10, 89% week-8, 81% week-13, 105% week-13, 83% average week-1-12) and three low levels (56% week-12, 41% year-2, 14 year-3). CONCLUSION: The small number of studies identified did not allow to establish whether the use of monitoring technology could improve adherence to PA programs in patients with chronic diseases experiencing fatigue, but the current evidence seems to suggest that this is a field warranting further study, particularly into how monitoring technology can help to engage patients to adhere to PA programs.

Quarter-mile walk test sensitive to training-induced fitness changes.

BACKGROUND: Cardiorespiratory fitness (CRF) is an important aspect of the overall health of an individual and its monitoring must be promoted in the general population. Thus, the aim of the study was to cross-validate and improve CRF estimation based on quarter-mile Rockport Fitness Walking Test. METHODS: Thirty participants (31.4±7.99 years) were randomized in either a four-week aerobic training group (10 men and 10 women) or a control group (eight men and two women). CRF was assessed via VO2max test and estimated via quarter-mile Rockport Fitness and Ebbeling treadmill tests, before and after the training intervention. The original quarter-mile Rockport VO2max estimation was found to greatly overestimate CRF by 22 mL/kg/min. When its coefficient was updated according to our data, it largely improved (by 6.8 mL/kg/min). Furthermore, a new algorithm for predicting VO2max was designed using multi-linear regression analysis. RESULTS: The original quarter-mile Rockport Fitness Walking Test was not sensitive to CRF changes. It showed changes in VO2max, which were significantly different from the actual observed changes (-1.1±4.08 vs. 1.61±2.84, P=0.02, respectively). The Ebbeling treadmill test appeared to systematically overestimate CRF changes. Our new algorithm showed improved sensitivity for detecting CRF changes and stability. CONCLUSIONS: The original quarter-mile Rockport Fitness Walking Test equation for predicting VO2max was neither accurate nor sensitive to changes in CRF, most likely due to cardiovascular drift. Our new algorithm, based on the same brisk walking test, can provide a more accurate estimate of CRF, which is also sensitive to VO2max changes, in a broad age range (18 to 50 years).

Evaluation of an activity monitor for use in pregnancy to help reduce excessive gestational weight gain.

BACKGROUND: Excessive weight gain during pregnancy increases the risk for negative effects on mother and child during pregnancy, delivery, and also postnatally. Excessive weight gain can be partially compensated by being sufficiently physically active, which can be measured using activity trackers. Modern activity trackers often use accelerometer data as well as heart rate data to estimate energy expenditure. Because pregnancy affects the metabolism and cardiac output, it is not evident that activity trackers that are calibrated to the general population can be reliably used during pregnancy. We evaluated whether an activity monitor designed for the general population is sufficiently accurate for estimating energy expenditure in pregnant women. METHODS: Forty pregnant women (age: 30.8 ± 4.7 years, BMI: 25.0 ± 4.0) from all three trimesters performed a 1-h protocol including paced and self-paced exercise activities as well as household activities. We tracked reference energy expenditure using indirect calorimetry and used equivalence testing to determine whether the estimated energy expenditure from the activity monitor was within the limits of equivalence. RESULTS: Overall we found an averaged underestimation of 10 kcal (estimated energy expenditure was 97% of the reference measurement). The 90% CI for the cumulative total energy expenditure was 94-100%. The activities of self-paced cycling, household activities, stair-walking, and yoga had one of their equivalence boundaries outside a 80-125% range of equivalence; for exercise on a cross-trainer, for self-paced and fixed-pace walking, fixed-paced cycling, and resting, the estimations were within the limits of equivalence. CONCLUSIONS: We conclude that the activity monitor is sufficiently accurate for every-day use during pregnancy. The observed deviations can be accounted for and are acceptable from a statistical and an applied perspective because the positive and negative deviations that we observed cancel out to an accurate average energy expenditure over a day, and estimations during exercise are sufficiently accurate to enable coaching on physical activity. The positive and negative deviations themselves were relatively small. Therefore, the activity monitor can be used to help in preventing excessive weight gain during pregnancy by accurately tracking physical activity.

Methodological Shortcomings of Wrist-Worn Heart Rate Monitors Validations.

Wearable sensor technology could have an important role for clinical research and in delivering health care. Accordingly, such technology should undergo rigorous evaluation prior to market launch, and its performance should be supported by evidence-based marketing claims. Many studies have been published attempting to validate wrist-worn photoplethysmography (PPG)-based heart rate monitoring devices, but their contrasting results question the utility of this technology. The reason why many validations did not provide conclusive evidence of the validity of wrist-worn PPG-based heart rate monitoring devices is mostly methodological. The validation strategy should consider the nature of data provided by both the investigational and reference devices. There should be uniformity in the statistical approach to the analyses employed in these validation studies. The investigators should test the technology in the population of interest and in a setting appropriate for intended use. Device industries and the scientific community require robust standards for the validation of new wearable sensor technology.

Wrist-worn optical and chest strap heart rate comparison in a heterogeneous sample of healthy individuals and in coronary artery disease patients.

BACKGROUND: The need for unobtrusive HR (heart rate) monitoring has led to the development of a new generation of strapless HR monitors. The aim of this study was to determine whether such an unobtrusive, wrist-worn optical HR monitor (OHRM) could be equivalent and therefore a valid alternative to a traditional chest strap during a broad range of activities in a heterogeneous healthy population and coronary artery disease (CAD) patients. METHODS: One hundred ninety-nine healthy volunteers, 84 males and 115 females, including 35 overweight-obese subjects, 53 pregnant women, and 20 CAD patients were tested in the present study. Second-by-second HR measured by the OHRM was concurrently evaluated against an ECG-based chest strap monitor during a broad range of activities (i.e., walking, running, cycling, gym, household, and sedentary activities). RESULTS: Data coverage, percentage of time the OHRM provides a HR not larger than 10 bpm from the reference, went from a minimum of 92% of the time in the least periodic activity (i.e., gym), to 95% during the most intense activity (i.e., running), and to a maximum of 98% for sedentary activities. The limits of agreement of the difference between the OHRM and the chest strap HR were within the range of ±15 bpm. The OHRM showed a concordance correlation coefficient of 0.98. Overall, the mean absolute error was not larger than 3 bpm, which can be considered clinically acceptable for a number of applications. A similar performance was found for CAD (94.2% coverage, 2.4 bpm error), but the small sample size does not allow any quantitative comparison. CONCLUSION: Heart rate measured by OHRM at the wrist and ECG-based HR measured via a traditional chest strap are acceptably close in a broad range of activities in a heterogeneous, healthy population, and showed initial promising results also in CAD patients.

Ecological momentary assessment of food perceptions and eating behavior using a novel phone application in adults with or without obesity.

We developed a smart phone application to measure participants' food-reward perceptions and eating behavior in their naturalistic environment. Intensity ratings (0 - not at all to 10 - very strongly) of perceived anticipation of food (wanting) and food enjoyment at endpoint of intake (liking) were recorded as they occurred over a period of 14 days. Moreover, food craving trait, implicit and explicit attitude towards healthy food, and body composition were assessed. 53 participants provided complete data. Participants were classified by percentage of body fat; 33 participants with lower body fat (L-group) and 20 with higher body fat (H-group; ≥25% body fat for males and ≥32% for females). L-group participants reported 6.34 (2.00) food wanting events per day, whereas H-group participants recorded significantly fewer food wanting events (5.07 (1.42)); both groups resisted about the same percentage of wanting events (L-group: 29.2 (15.5)%; H-group 27.3 (12.8)%). Perceived intensity ratings were significantly different within the L-group in the order liking (7.65 (0.81)) > un-resisted wanting (leading to eating) (7.00 (1.01)) > resisted wanting (not leading to eating) (6.02 (1.72)) but not in the H-group. Liking scores (L-group: 7.65 (0.81); H-group: 7.14 (1.04)) were significantly higher in L-group than in H-group after controlling for age. Our results show that individuals with higher percentage of body fat show less food enjoyment after intake and reveal no differentiation in intensity ratings of perceived anticipatory and consummatory food reward. These results are consistent with a hypothesized reward deficiency among individuals with higher percentage of body fat.

Proof of concept of a 45-second cardiorespiratory fitness self-test for coronary artery disease patients based on accelerometry.

Cardiorespiratory fitness (CRF) provides important diagnostic and prognostic information. It is measured directly via laboratory maximal testing or indirectly via submaximal protocols making use of predictor parameters such as submaximal [Formula: see text], heart rate, workload, and perceived exertion. We have established an innovative methodology, which can provide CRF prediction based only on body motion during a periodic movement. Thirty healthy subjects (40% females, 31.3 ± 7.8 yrs, 25.1 ± 3.2 BMI) and eighteen male coronary artery disease (CAD) (56.6 ± 7.4 yrs, 28.7 ± 4.0 BMI) patients performed a [Formula: see text] test on a cycle ergometer as well as a 45 second squatting protocol at a fixed tempo (80 bpm). A tri-axial accelerometer was used to monitor movements during the squat exercise test. Three regression models were developed to predict CRF based on subject characteristics and a new accelerometer-derived feature describing motion decay. For each model, the Pearson correlation coefficient and the root mean squared error percentage were calculated using the leave-one-subject-out cross-validation method (rcv, RMSEcv). The model built with all healthy individuals' data showed an rcv = 0.68 and an RMSEcv = 16.7%. The CRF prediction improved when only healthy individuals with normal to lower fitness (CRF<40 ml/min/kg) were included, showing an rcv = 0.91 and RMSEcv = 8.7%. Finally, our accelerometry-based CRF prediction CAD patients, the majority of whom taking ß-blockers, still showed high accuracy (rcv = 0.91; RMSEcv = 9.6%). In conclusion, motion decay and subject characteristics could be used to predict CRF in healthy people as well as in CAD patients taking ß-blockers, accurately. This method could represent a valid alternative for patients taking ß-blockers, but needs to be further validated in a larger population.

Influence of Competition Day on Cognitive Control and HRV in Young Male Gymnasts.

Sartor, F, Capuzzoni, S, Rospo, G, La Torre, A, Vailati, F, and Vailati, E. Influence of competition day on cognitive control and HRV in young male gymnasts. J Strength Cond Res 31(7): 1982-1993, 2017-In gymnastics, high levels of executive attention and physical and coordinative capacities are required. However, training planning does not usually account for dynamic alterations in cognitive capacity. This study investigated whether cognitive capacity was altered by the approach of a competition. Ten elite male gymnasts (16 ± 2 years, 57.3 ± 16.1 kg, 1.64 ± 1.27 m) were monitored for sleep, life demands, rating of perceived exertion (RPE), and pain, starting 5 days before the competition and up to 5 days after it. Stroop task performance and concomitant heart rate variability (HRV) were monitored 5 days and 1 day before the competition and then 1 and 5 days after. Sleep and life demands were not affected by the competition. Localized pain ranged from mild to moderate levels throughout the observation period. It weakly correlated with RPE (r = 0.241, p = 0.010) and moderately with number of errors (NoEs) (r = 0.639, p = 0.047). The RPE was higher for the competition day (p = 0.002). Median reaction times during the Stroop task were higher in the period preceding the competition (p < 0.001) for similar NoEs. The HRV during the congruent stimuli task showed higher root mean square differences of successive beats, portion of normal to normal intervals exceeding 50 milliseconds, and high frequency after the competition (p ≤ 0.05). Poincare plot SD2 showed a positive correlation with Stroop task NoEs (r = 0.590, p < 0.001). In conclusion, the period preceding an important competition affected cognitive performance and HRV in young male gymnasts. A practical implication of this study is that training loads could be adjusted according to mental stress before a competition.

A 45-Second Self-Test for Cardiorespiratory Fitness: Heart Rate-Based Estimation in Healthy Individuals.

Cardio-respiratory fitness (CRF) is a widespread essential indicator in Sports Science as well as in Sports Medicine. This study aimed to develop and validate a prediction model for CRF based on a 45 second self-test, which can be conducted anywhere. Criterion validity, test re-test study was set up to accomplish our objectives. Data from 81 healthy volunteers (age: 29 ± 8 years, BMI: 24.0 ± 2.9), 18 of whom females, were used to validate this test against gold standard. Nineteen volunteers repeated this test twice in order to evaluate its repeatability. CRF estimation models were developed using heart rate (HR) features extracted from the resting, exercise, and the recovery phase. The most predictive HR feature was the intercept of the linear equation fitting the HR values during the recovery phase normalized for the height2 (r2 = 0.30). The Ruffier-Dickson Index (RDI), which was originally developed for this squat test, showed a negative significant correlation with CRF (r = -0.40), but explained only 15% of the variability in CRF. A multivariate model based on RDI and sex, age and height increased the explained variability up to 53% with a cross validation (CV) error of 0.532 L ∙ min-1 and substantial repeatability (ICC = 0.91). The best predictive multivariate model made use of the linear intercept of HR at the beginning of the recovery normalized for height2 and age2; this had an adjusted r2 = 0. 59, a CV error of 0.495 L·min-1 and substantial repeatability (ICC = 0.93). It also had a higher agreement in classifying CRF levels (κ = 0.42) than RDI-based model (κ = 0.29). In conclusion, this simple 45 s self-test can be used to estimate and classify CRF in healthy individuals with moderate accuracy and large repeatability when HR recovery features are included.

Energy expenditure estimation in beta-blocker-medicated cardiac patients by combining heart rate and body movement data.

BACKGROUND: Accurate assessment of energy expenditure provides an opportunity to monitor physical activity during cardiac rehabilitation. However, the available assessment methods, based on the combination of heart rate (HR) and body movement data, are not applicable for patients using beta-blocker medication. Therefore, we developed an energy expenditure prediction model for beta-blocker-medicated cardiac rehabilitation patients. METHODS: Sixteen male cardiac rehabilitation patients (age: 55.8 ± 7.3 years, weight: 93.1 ± 11.8 kg) underwent a physical activity protocol with 11 low- to moderate-intensity common daily life activities. Energy expenditure was assessed using a portable indirect calorimeter. HR and body movement data were recorded during the protocol using unobtrusive wearable devices. In addition, patients underwent a symptom-limited exercise test and resting metabolic rate assessment. Energy expenditure estimation models were developed using multivariate regression analyses based on HR and body movement data and/or patient characteristics. In addition, a HR-flex model was developed. RESULTS: The model combining HR and body movement data and patient characteristics showed the highest correlation and lowest error (r2 = 0.84, root mean squared error = 0.834 kcal/minute) with total energy expenditure. The method based on individual calibration data (HR-flex) showed lower accuracy (i2 = 0.83, root mean squared error = 0.992 kcal/minute). CONCLUSIONS: Our results show that combining HR and body movement data improves the accuracy of energy expenditure prediction models in cardiac patients, similar to methods that have been developed for healthy subjects. The proposed methodology does not require individual calibration and is based on the data that are available in clinical practice.

Cardiorespiratory Improvements Achieved by American College of Sports Medicine's Exercise Prescription Implemented on a Mobile App.

BACKGROUND: Strong evidence shows that an increase in cardiorespiratory fitness (CRF) and physical activity (PA) reduces cardiovascular disease risk. OBJECTIVE: To test whether a scientifically endorsed program to increase CRF and PA, implemented on an easy-to-use, always-accessible mobile app would be effective in improving CRF. METHODS: Of 63 healthy volunteers participating, 18 tested the user interface of the Cardio-Fitness App (CF-App); and 45 underwent a 2-week intervention period, of whom 33 eventually concluded it. These were assigned into three groups. The Step-based App (Step-App) group (n=8), followed 10,000 steps/day prescription, the CF-App group (n=13), and the Supervised Cardio-Fitness (Super-CF) group (n=12), both followed a heart rate (HR)-based program according to American College of Sports Medicine (ACSM) guidelines, but either implemented on the app, or at the gym, respectively. Participants were tested for CRF, PA, resting systolic and diastolic blood pressures (SBP, DBP), resting, exercise, and recovery HR. RESULTS: CRF increased in all groups (+4.9%; P<.001). SBP decreased in all groups (-2.6 mm Hg; P=.03). DBP decrease was higher in the Super-CF group (-3.5 mm Hg) than in the Step-App group (-2.1 mm Hg; P<.001). Posttest exercise HR decreased in all groups (-3.4 bpm; P=.02). Posttest recovery HR was lower in the Super-CF group (-10.1 bpm) than in the other two groups (CF-App: -4.9 bpm, Step-App: -3.3 bpm; P<.001). The CF-App group, however, achieved these improvements with more training heart beats (P<.01). CONCLUSIONS: A 10,000 steps/day target-based app improved CRF similar to an ACSM guideline-based program whether it was implemented on a mobile app or in supervised gym sessions.