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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.
The present review examines retrospective analyses of training intensity distribution (TID), i.e., the proportion of training at moderate (Zone 1, Z1), heavy (Z2) and severe (Z3) intensity by elite-to-world-class endurance athletes during different phases of the season. In addition, we discuss potential implications of our findings for research in this field, as well as for training by these athletes. Altogether, we included 175 TIDs, of which 120 quantified exercise intensity on the basis of heart rate and measured time-in-zone or employed variations of the session goal approach, with demarcation of zones of exercise intensity based on physiological parameters. Notably, 49% of the TIDs were single-case studies, predominantly concerning cross-country skiing and/or the biathlon. Eighty-nine TIDs were pyramidal (Z1 > Z2 > Z3), 65 polarized (Z1 > Z3 > Z2) and 8 “threshold” (Z2 > Z1 = Z3). However, these relative numbers varied between sports and the particular phases of the season. In 91% (n = 160) of the TIDs >60% of the endurance exercise was of low intensity. Regardless of the approach to quantification or phase of the season, cyclists and swimmers were found to perform a lower proportion of exercise in Z1 (<72%) and higher proportion in Z2 (>16%) than athletes involved in the triathlon, speed skating, rowing, running, cross-country skiing or biathlon (>80% in Z1 and <12% in Z2 in all these cases). For most of the athletes their proportion of heavy-to-severe exercise was higher during the period of competition than during the preparatory phase, although with considerable variability between sports. In conclusion, the existing literature in this area does not allow general conclusions to be drawn. The methods utilized for quantification vary widely and, moreover, contextual information concerning the mode of exercise, environmental conditions, and biomechanical aspects of the exercise is often lacking. Therefore, we recommend a more comprehensive approach in connection with future investigations on the TIDs of athletes involved in different endurance sports.
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.
Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to sports research, coaching and optimization of athletic performance. The strength of AI with regards to applied sports research, coaching and athletic performance involve the automation of time-consuming tasks, processing and analysis of large amounts of data, and recognition of complex patterns and relationships. However, it is also essential to be aware of the weaknesses associated with the integration of AI into this field. For instance, it is imperative that the data employed to train the AI system be both diverse and complete, in addition to as unbiased as possible with respect to factors such as the gender, level of performance, and experience of an athlete. Other challenges include e.g., limited adaptability to novel situations and the cost and other resources required. Opportunities include the possibility to monitor athletes both long-term and in real-time, the potential discovery of novel indicators of performance, and prediction of risk for future injury. Leveraging these opportunities can transform athletic development and the practice of sports science in general. Threats include over-dependence on technology, less involvement of human expertise, risks with respect to data privacy, breaching of the integrity and manipulation of data, and resistance to adopting such new technology. Understanding and addressing these SWOT factors is essential for maximizing the benefits of AI while mitigating its risks, thereby paving the way for its successful integration into sport science research, coaching, and optimization of athletic performance.
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.