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BackgroundPhysical 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. ObjectiveOur 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). MethodsWhile 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. ResultsWhile 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. ConclusionsThe 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.

Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies.

Objectives: Aims of the present investigation encompassed: (i) the quantification of training and match loads experienced by starters and non-starters within a professional soccer team; (ii) the identification of variations in these loads across different training modalities, namely, Preparation Training (PT), Match and Match Compensation Training (M&MCT), and their cumulative sum (Total Load; TL); and (iii) the formulation of guidelines aimed at harmonizing the weekly workloads between the groups. Methods: Internal and external load of training sessions (n = 97), competitive matches (n = 21) and running based sessions (n = 4) were recorded for 21 weeks to investigate possible heterogeneity between starters and non-starters across PT, M&MCT and TL. Results: During PT, time spent in heart rate zone 5 (HRZ5) was increased for non-starters (+46.1%). During M&MCT, lower loads for non-starters were found in the rate of perceived exertion (sRPE) (-45.6%), HRZ4 (-54%) and HRZ5 (-77.8%), total distance (TD) (-37%), number of sprints (-58.1%), distance in speed zone 1 (-51.1%), zone 3 (-61.5%) and zone 4 (-59.8%) (SZ1, SZ3&4) and all acceleration and deceleration zones (Acc1-4; Dec1-4) (Acc1: -53.1%; Acc2: -56.3%; Acc3: -59.2%; Acc4: 57.8%) (Dec1: -45.9%; Dec2: -55.2%; Dec3: -63.2%; Dec4: -67.7%). Regarding TL, the non-starters' loads remain lower compared to starters for sRPE (-19.2%), HRZ4 (-21.6%) and HRZ5 (-41.4%), number of sprints (-26.7%), SZ3 (-34.2%), Acc3 (-24.4%), Acc4 (-26.1%), Dec2 (-18.7%), Dec3 (-24%) and Dec4 (-31.2%). Conclusions: By implementing a running-based regimen on matchday and MCT the day after (MD+1), TD, distances in SZ1, SZ2, SZ4, SZ5, and counts of accelerations in Acc1&2, as well as Dec1, were effectively replicated for non-starters. All other variables remained unaligned for the non-starters. Given the prevalent emphasis on Small-Sided Games (SSGs) during MCT at MD+1, the incorporation of an additional running-based session for non-starters on MD is advised to address gaps in TD, sprint counts, and high-intensity load variables, such as HRZ4&5 and SZ 3 to 5. [ABSTRACT FROM AUTHOR]

Introduction: The effect of displayed step count in smartwatches on the accuracy of daily step-count estimation and the potential underlying psychological factors have not been revealed. The study aimed for the following: (i) To investigate whether the counting and reporting of daily steps by a smartwatch increases the daily step-count estimation accuracy and (ii) to elucidating underlying psychological factors. Methods: A total of 34 healthy men and women participants wore smartwatches for 4 weeks. In week 1 (baseline), 3 (follow-up 1), and 8 (followup 2), the number of smartwatch displayed steps was blinded for each participant. In week 2 (Intervention), the number of steps was not blinded. During baseline and follow-ups 1 and 2, the participants were instructed to estimate their number of steps four times per day. During the 4-week washout period between follow-ups 1 and 2, no feedback was provided. The Body Awareness Questionnaire and the Body Responsiveness Questionnaire (BRQ) were used to elucidate the psychological facets of the assumed estimation accuracy. Results: The mean absolute percentage error between the participants' steps count estimations and measured steps counts were 29.49% (at baseline), 0.54% (intervention), 11.89% (follow-up 1), and 15.14% (follow-up 2), respectively. There was a significant effect between baseline and follow-up 1 [t (61.7) = 3.433, p < 0.001] but not between follow-up 1 and followup 2 [t (60.3) = -0.288, p = 0.774]. Only the BRQ subscale "Suppression of Bodily Sensations" appeared to be significant at the Baseline (p = 0.012; Bonferroni adjusted p = 0.048) as a factor influencing step-count estimation accuracy. Conclusion: The counting and reporting of daily steps with a smartwatch allows improving the subjective estimation accuracy of daily step counts, with a stabilizing effect for at least 6 weeks. Especially individuals who tend to suppress their bodily sensations are less accurate in their daily step-count estimation before the intervention. [ABSTRACT FROM AUTHOR]

Exercise scientists (especially in the field of biomolecular research) frequently classify athletic cohorts into categories such as endurance, strength, or mixed, and create a practical framework for studying diverse athletic populations between seemingly similar groups. It is crucial to recognize the limitations and complexities of these classifications, as they may oversimplify the multidimensional characteristics of each sport. If so, the validity of studies dealing with such approaches may become compromised and the comparability across different studies challenging or impossible. This perspective critically examines and highlights the issues associated with current sports typologies, critiques existing sports classification systems, and emphasizes the imperative for a universally accepted classification model to enhance the quality of biomolecular research of sports in the future. [ABSTRACT FROM AUTHOR]

Purpose: Replicating the studies of Gibson et al and Brownstein et al to assess performance, and physiological, and perceived variables during a repeated sprint protocol (RSP) with standardized versus self-selected recovery in youth soccer players. Methods: Nineteen male soccer players (age 13.1 [1.3] y) completed 2 separate RSPs. RSP1: 10 × 30-m sprints with 30-second recovery and RSP2: 10 × 30-m sprints interspersed with self-selected recovery periods. Mean time of both 10 × 30-m RSPs and self-selected recovery periods of RSP2 were assessed. Heart rate, blood lactate concentration, and rates of perceived exertion were measured following RSPs. Results: RSP2 revealed longer recovery periods (RSP1: 30.0 [0.0] s; RSP2: 39.0 [7.7] s; P <.001; effect size d = 1.648) with shorter repeated sprint time (mean 30-m sprint time: RSP1: 4.965 [0.256] s; RSP2: 4.865 [0.227] s; P =.014; d = 0.414). Blood lactate concentration (P =.002–.005; d = 0.730–0.958), heart rate (P <.001; d = 1.353), and rates of perceived exertion (RSP1: 14.9 [1.9]; RSP2: 12.9 [2.1]; P =.016; d = 1.046) were higher following RSP1. Conclusion: In contrast to the original studies, the present replication study demonstrated that self-selected recovery periods during a RSP leads to better repeated sprint performance compared with standardized recovery periods in youth soccer players. The better repeated sprint performance with individual recovery durations in RSP2 was achieved with less physiological and perceived effort. [ABSTRACT FROM AUTHOR]

ChatGPT may be used by runners to generate training plans to enhance performance or health aspects. However, the quality of ChatGPT generated training plans based on different input information is unknown. The objective of the study was to evaluate ChatGPT-generated six-week training plans for runners based on different input information granularity. Three training plans were generated by ChatGPT using different input information granularity. 22 quality criteria for training plans were drawn from the literature and used to evaluate training plans by coaching experts on a 1-5 Likert Scale. A Friedmann test assessed significant differences in quality between training plans. For training plans 1, 2 and 3, a median rating of <3 was given 19, 11, and 1 times, a median rating of 3 was given 3, 5, and 8 times and a median rating of >3 was given 0, 6, 13 times, respectively. Training plan 1 received significantly lower ratings compared to training plan 2 for 3 criteria, and 15 times significantly lower ratings compared to training plan 3 (p < 0.05). Training plan 2 received significantly lower ratings (p < 0.05) compared to plan 3 for 9 criteria. ChatGPT generated plans are ranked sub-optimally by coaching experts, although the quality increases when more input information are provided. An understanding of aspects relevant to programming distance running training is important, and we advise avoiding the use of ChatGPT generated training plans without an expert coach's feedback. [ABSTRACT FROM AUTHOR]

Purpose: (1) To compare various physiological indicators of performance during a 5 × 1500-m incremental kayak test performed on an ergometer and on-water and (2) to analyze the relationships between these indicators and the actual competition performance of elite sprint kayakers, aiming to provide information to coaches for evaluating and planning training on-water. Methods: A total of 14 male and female German elite sprint kayakers performed an incremental test both on an ergometer and on-water. The tissue saturation index of the musculus (m.) biceps brachii, oxygen consumption, ratings of perceived exertion, and levels of blood lactate were measured and compared with actual racing times. In addition, power output was monitored during ergometer testing only. Results: Oxygen consumption during the fourth (P =.02; d = 0.32) and final (fifth; P <.001; d = 0.32) steps of incremental testing was higher on-water than on the ergometer. The tissue saturation index of the m. biceps brachii was approximately 21% higher at the end of the ergometer test (P =.002; d = 1.14). During the second (P =.01; d = 0.78), third (P =.005; d = 0.93), and fourth stages (P =.005; d = 1.02), the ratings of perceived exertion for ergometer kayaking was higher. During the final step, power output was most closely correlated to 200- (r =.88), 500- (r =.93), and 1000-m (r =.86) racing times (all Ps <.01). Conclusions: During high-intensity kayaking on an ergometer or on-water, the oxygen consumption and tissue saturation index of the m. biceps brachii differ. Furthermore, at moderate to submaximal intensities, the ratings of perceived exertion were higher for ergometer than for on-water kayaking. Finally, of all parameters assessed, the power output during ergometer kayaking exhibited the strongest correlation with actual racing performance. [ABSTRACT FROM AUTHOR]