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Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity.
Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa).
Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE.
Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively.
Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested."
Virtual online training has emerged as one of the top 20 worldwide fitness trends for 2021 and continues to develop rapidly. Although this allows the cycling community to engage in virtual training and competition, critical evaluation of virtual training platforms is limited. Here, we discuss the strengths, weaknesses, opportunities and threats associated with virtual training technology and cycling in an attempt to enhance awareness of such aspects. Strengths include immersive worlds, innovative drafting mechanics, and versatility. Weaknesses include questionable data accuracy, inadequate strength and reliability of power-speed algorithms. Opportunities exist for expanding strategic partnerships with major cycling races, brands, and sponsors and improving user experience with the addition of video capture and “e-coaching.” Threats are present in the form of cheating during competition, and a lack of uptake and acceptance by a broader community.
Background
Physical activity (PA) guidelines acknowledge the health benefits of regular moderate-to-vigorous physical activity (MVPA) regardless of bout duration. However, little knowledge exists concerning the type and intensity distribution of structured and incidental lifestyle PA of students and office workers. The present study aimed to i) assess the duration and distribution of intensity of MVPAs during waking hours ≥50% of heart rate reserve (HRR), ii) to identify the type of PA through diary assessment, iii) to assign these activities into structured and lifestyle incidental PA, and iv) to compare this information between students and office workers.
Methods
Twenty-three healthy participants (11 students, 12 office workers) recorded heart rate (HR) with a wrist-worn HR monitor (Polar M600) and filled out a PA diary throughout seven consecutive days (i.e. ≥ 8 waking h/day). Relative HR zones were calculated, and PA diary information was coded using the Compendium of PA. We matched HR data with the reported PA and identified PA bouts during waking time ≥ 50% HRR concerning duration, HRR zone, type of PA, and assigned each activity to incidental and structured PA. Descriptive measures for time spend in different HRR zones and differences between students and office workers were calculated.
Results
In total, we analyzed 276.894 s (76 h 54 min 54 s) of waking time in HRR zones ≥50% and identified 169 different types of PA. The participants spend 31.9 ± 27.1 min/day or 3.9 ± 3.2% of their waking time in zones of ≥50% HRR with no difference between students and office workers (p > 0.01). The proportion of assigned incidental lifestyle PA was 76.9 ± 22.5%.
Conclusions
The present study provides initial insights regarding the type, amount, and distribution of intensity of structured and incidental lifestyle PA ≥ 50% HRR. Findings show a substantial amount of incidental lifestyle PA during waking hours and display the importance of promoting a physically active lifestyle. Future research could employ ambulatory assessments with integrated electronic diaries to detect information on the type and context of MVPA during the day.
Researchers have retrospectively analyzed the training intensity distribution (TID) of nationally and internationally competitive athletes in different endurance disciplines to determine the optimal volume and intensity for maximal adaptation. The majority of studies present a "pyramidal" TID with a high proportion of high volume, low intensity training (HVLIT). Some world-class athletes appear to adopt a so-called "polarized" TID (i.e., significant % of HVLIT and high intensity training) during certain phases of the season. However, emerging prospective randomized controlled studies have demonstrated superior responses of variables related to endurance when applying a polarized TID in well-trained and recreational individuals when compared with a TID that emphasizes HVLIT or threshold training. The aims of the present review are to: (1) summarize the main responses of retrospective and prospective studies exploring TID; (2) provide a systematic overview on TIDs during preparation, pre-competition, and competition phases in different endurance disciplines and performance levels; (3) address whether one TID has demonstrated greater efficacy than another; and (4) highlight research gaps in an effort to direct future scientific studies.
Purpose:
The aim of the study was to evaluate the mucosal immune function and circadian variation of salivary cortisol, Immunoglobin-A (sIgA) secretion rate and mood during a period of high-intensity interval training (HIIT) compared to long-slow distance training (LSD).
Methods:
Recreational male runners (n = 28) completed nine sessions of either HIIT or LSD within 3 weeks. The HIIT involved 4 × 4 min of running at 90–95% of maximum heart rate interspersed with 3 min of active recovery while the LSD comprised of continuous running at 70–75% of maximum heart rate for 60–80 min. The psycho-immunological stress-response was investigated with a full daily profile of salivary cortisol and immunoglobin-A (sIgA) secretion rate along with the mood state on a baseline day, the first and last day of training and at follow-up 4 days after the last day of training. Before and after the training period, each athlete's running performance and peak oxygen uptake (V·O\(_{2peak}\)) was determined with an incremental exercise test.
Results:
The HIIT resulted in a longer time-to-exhaustion (P = 0.02) and increased V·O\(_{2peak}\) compared to LSD (P = 0.01). The circadian variation of sIgA secretion rate showed highest values in the morning immediately after waking up followed by a decrease throughout the day in both groups (P < 0.05). With HIIT, the wake-up response of sIgA secretion rate was higher on the last day of training (P < 0.01) as well as the area under the curve (AUC\(_{G}\)) higher on the first and last day of training and follow-up compared to the LSD (P = 0.01). Also the AUC\(_{G}\) for the sIgA secretion rate correlated with the increase in V·O\(_{2peak}\) and running performance. The AUC\(_{G}\) for cortisol remained unaffected on the first and last day of training but increased on the follow-up day with both, HIIT and LSD (P < 0.01).
Conclusion:
The increased sIgA secretion rate with the HIIT indicates no compromised mucosal immune function compared to LSD and shows the functional adaptation of the mucosal immune system in response to the increased stress and training load of nine sessions of HIIT.
The purpose of this study was threefold: 1) to assess the eggbeater kick and throwing performance using a number of water polo specific tests, 2) to explore the relation between the eggbeater kick and throwing performance, and 3) to investigate the relation between the eggbeater kick in the water and strength tests performed in a controlled laboratory setting in elite water polo players. Fifteen male water polo players of the German National Team completed dynamic and isometric strength tests for muscle groups (adductor, abductor, abdominal, pectoralis) frequently used during water polo. After these laboratory strength tests, six water polo specific in-water tests were conducted. The eggbeater kick assessed leg endurance and agility, maximal throwing velocity and jump height. A 400 m test and a sprint test examined aerobic and anaerobic performance. The strongest correlation was found between jump height and arm length (p < 0.001, r = 0.89). The laboratory diagnostics of important muscles showed positive correlations with the results of the in-water tests (p < 0.05, r = 0.52-0.70). Muscular strength of the adductor, abdominal and pectoralis muscles was positively related to in-water endurance agility as assessed by the eggbeater kick (p < 0.05; r = 0.53-0.66). Findings from the current study emphasize the need to assess indices of water polo performance both in and out of the water as well as the relation among these parameters to best assess the complex profile of water polo players.
Speckle tracking-derived bi-atrial strain before and after eleven weeks of training in elite rowers
(2018)
The left (LA) and right (RA) atria undergo adaptive remodeling in response to hemodynamic stress not only induced by endurance exercise but also as part of several cardiovascular diseases thereby confounding differential diagnosis. Echocardiographic assessment of the atria with novel speckle tracking (STE)-derived variables broadens the diagnostic spectrum compared to conventional analyses and has the potential to differentiate physiologic from pathologic changes. The purpose of this study was to assess and categorize baseline values of bi-atrial structure and function in elite rowers according to recommended cutoffs, and to assess the cardiac changes occurring with endurance training. Therefore, fifteen elite rowers underwent 2D-echocardiographic analysis of established variables of cardiac structure and function as well as STE-derived variables of bi-atrial function. Measurements were performed at baseline and after eleven weeks of extensive training. 40% of athletes displayed mildly enlarged LA and 47% mildly enlarged RA at baseline, whereas no athlete fell below the lower reference values of LA and RA reservoir strain. Average power during a 2000 m ergometer rowing test (P2000 m) improved from 426 +/- 39 W to 442 +/- 34 W (p = 0.010) but there were no changes of echocardiographic variables following training. In elite rowers, longitudinal bi-atrial strain assessment indicates normal resting function of structurally enlarged atria and thereby may assist to differentiate between exercise-induced versus disease-associated structural cardiac changes in which function is commonly impaired.
The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.