Assessment of Maximum Oxygen Uptake in Elite Youth Soccer Players: A Comparative Analysis of Smartwatch Technology, Yoyo Intermittent Recovery Test 2, and Respiratory Gas Analysis.

The maximum oxygen uptake (VO2max) is a critical factor for endurance performance in soccer. Novel wearable technology may allow frequent assessment of V̇O2max during non-fatiguing warm-up runs of soccer players with minimal interference to soccer practice. The aim of this study was to assess the validity of VO2max provided by a consumer grade smartwatch (Garmin Forerunner 245, Garmin, Olathe, USA, Software:13.00) and the YoYo Intermittent Recovery Run 2 (YYIR2) by comparing it with respiratory gas analysis. 24 trained male youth soccer players performed different tests to assess VO2max: i) a treadmill test employing respiratory gas analysis, ii) YYIR2 and iii) during a non-fatiguing warm-up run of 10 min wearing a smartwatch as recommended by the device-manufacturer on 3 different days within 2 weeks. As the device-manufacturer indicates that validity of smartwatch-derived VO2max may differ with an increase in runs, 16 players performed a second run with the smartwatch to test this claim. The main evidence revealed that the smartwatch showed an ICC of 0.37 [95% CI: -0.25; 0.71] a mean absolute percentage error (MAPE) of 5.58% after one run, as well as an ICC of 0.54 [95% CI: -0.3; 8.4] and a MAPE of 1.06% after the second run with the smartwatch. The YYIR2 showed an ICC of 0.17 [95% CI: -5.7; 0.6]; and MAPE of 4.2%. When using the smartwatch for VO2max assessment in a non-fatiguing run as a warm-up, as suggested by the device manufacturer before soccer practice, the MAPE diminishes after two runs. Therefore, for more accurate VO2max assessment with the smartwatch, we recommend to perform at least two runs to reduce the MAPE and enhance the validity of the findings.

Acute physiological, biomechanical, and perceptual responses of runners wearing downward-curved carbon fiber insoles.

In a randomized controlled cross-over study ten male runners (26.7 ± 4.9 years; recent 5-km time: 18:37 ± 1:07 min:s) performed an incremental treadmill test (ITT) and a 3-km time trial (3-km TT) on a treadmill while wearing either carbon fiber insoles with downwards curvature or insoles made of butyl rubber (control condition) in light road racing shoes (Saucony Fastwitch 9). Oxygen uptake, respiratory exchange ratio, heart rate, blood lactate concentration, stride frequency, stride length and time to exhaustion were assessed during ITT. After ITT, all runners rated their perceived exertion, perceived shoe comfort and perceived shoe performance. Running time, heart rate, blood lactate levels, stride frequency and stride length were recorded during, and shoe comfort and shoe performance after, the 3-km TT. All parameters obtained during or after the ITT did not differ between the two conditions [range: p = 0.188 to 0.948 (alpha value: 0.05); Cohen's d = 0.021 to 0.479] despite the rating of shoe comfort showing better scores for the control insoles (p = 0.001; d = -1.646). All parameters during and after the 3-km TT showed no differences (p = 0.200 to 1.000; d = 0.000 to 0.501) between both conditions except for shoe comfort showing better scores for control insoles (p = 0.017; d = -0.919). Running with carbon fiber insoles with downwards curvature did not change running performance or any submaximal or maximal physiological or biomechanical parameter and perceived exertion compared to control condition. Shoe comfort is impaired while running with carbon fiber insoles. Wearing carbon fiber insoles with downwards curvature during treadmill running is not beneficial when compared to running with control insoles.

ChatGPT Generated Training Plans for Runners are not Rated Optimal by Coaching Experts, but Increase in Quality with Additional Input Information.

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.

Assessing anaerobic speed reserve: A systematic review on the validity and reliability of methods to determine maximal aerobic speed and maximal sprinting speed in running-based sports.

PURPOSE: Locomotor profiling using anaerobic speed reserve (ASR) enables insights into athletes' physiological and neuromuscular contributing factors and prescription of high-intensity training beyond maximal aerobic speed (MAS). This systematic review aimed to determine the validity and reliability of different methods to assess the characteristics of ASR, i.e., MAS and maximal sprinting speed (MSS). METHODS: A comprehensive search of the PubMed and Web of Science databases was conducted according to the PRISMA guidelines. Studies were included if they reported data on validity and/or reliability for methods to assess MAS or MSS. RESULTS: 58 studies were included with 28 studies referring to MAS and 30 studies to MSS. Regarding MAS, different methods for cardiopulmonary exercise testing yielded different values (four out of seven studies) of MAS (Cohen's d (ES) = 0.83-2.8; Pearson's r/intraclass correlation coefficient (r/ICC) = 0.46-0.85). Criterion validity of different field tests showed heterogeneous results (ES = 0-3.57; r/ICC = 0.40-0.96). Intraday and interday reliability was mostly acceptable for the investigated methods (ICC/r>0.76; CV<16.9%). Regarding MSS, radar and laser measurements (one out of one studies), timing gates (two out of two studies), and video analysis showed mostly good criterion validity (two out of two studies) (ES = 0.02-0.53; r/ICC = 0.93-0.98) and reliability (r/ICC>0.83; CV<2.43%). Criterion validity (ES = 0.02-7.11) and reliability (r/ICC = 0.14-0.97; CV = 0.7-9.77%) for global or local positioning systems (seven out of nine studies) and treadmill sprinting (one out of one studies) was not acceptable in most studies. CONCLUSION: The criterion validity of incremental field tests or shuttle runs to examine MAS cannot be confirmed. Results on time trials indicate that distances adapted to the participants' sporting background, fitness, or sex might be suitable to estimate MAS. Regarding MSS, only sprints with radar or laser measures, timing gates, or video analysis provide valid and reliable results for linear sprints of 20 to 70 m.

Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis.

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.

Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis.

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.

The magnitude and time-course of physiological responses to 9 weeks of incremental ramp testing.

PURPOSE: The aims of this study were to assess (1) the day-to-day variability in, and (2) the magnitude and time-course of adaptation of physiological parameters (i.e., maximal oxygen uptake [VO2 max], heart rate [HR], blood lactate concentration, respiratory exchange ratio [RER], ratings of perceived exertion [RPE], and time-to-exhaustion [TTE]) in response to an intervention involving three incremental ramp tests per week for 9 weeks. METHODS: Twelve participants (25 ± 4 yrs, VO2 max, 47.8 ± 5.2 mL∙min-1 ∙kg-1 (means ± SD)) completed the entire experimental procedure. The tests comprised a 5-min constant workload to obtain submaximal parameters followed by an incremental protocol until exhaustion. RESULTS: The mean day-to-day variability for the maximal value of VO2 was 2.8%, 1.1% for HR, 18.1% for blood lactate concentration, 2.1% for RER, 1.1% for RPE, and 5.0% for TTE. The values for the corresponding submaximal variables were 3.8% for VO2 , 2.1% for HR, 15.6% for blood lactate concentration, 2.6% for RER and 6.0% for RPE. VO2 max (+4.7% ± 3.5%), TTE (+17.9% ± 8.6%), and submaximal HR (-3.2 ± 3.5%) improved significantly. Except for RPE (p < 0.01), there were no alterations in the coefficient of variation for any parameter. On the group level, the first changes greater than the day-to-day variability in VO2 max, TTE, and submaximal HR were observed after 21, 12, and 9 training sessions, respectively. CONCLUSION: Based on our findings, we recommend that training studies include assessment of the reliability of the measurements, for example, the CVs in the specific laboratory to be able to judge if the changes detected are actually physiological.

Physiological response and physical performance after 40 min and 90 min daytime nap opportunities.

This study aimed to examine the impact of 40-min and 90-min naps on performance in the 5-m shuttle run test (5mSRT) and on various physiological and perceptual measures. Sixteen male athletes (20 ± 3 years, 173 ± 7 cm, 67 ± 7 kg) performed the 5mSRT after a 40-min nap (N40), after a 90-min nap (N90), and in a no-nap, control condition (N0). The 5mSRT involves six repetitions of 30 s of all-out exercise. Total distance (in the six repetitions) and highest distance (in a single repetition) in the 5mSRT were greater after naps than in N0 (p < 0.001), and the total distance, which reflects the anaerobic capacity, was greater in N90 than in N40 (p < 0.05). Physiological and perceptual responses were favourable in both nap conditions (p < 0.01), more so in N90 than in N40 (p < 0.05). Together, the results support the contention that physiological/perceptual responses after napping contribute to improved exercise performance and that longer naps are more effective.

Differences in Physical Match Performance and Injury Occurrence Before and After the COVID-19 Break in Professional European Soccer Leagues: A Systematic Review.

BACKGROUND: Due to the COVID-19 pandemic, matches and soccer-specific training were suspended for several weeks, matches after resumption were congested, and substitutions per team and game increased from three to five. OBJECTIVE: The aim of this review was to examine possible differences in physical match performance and injuries between before and after the COVID-19 induced break of matches and training in professional male European soccer leagues during the 2019/2020 season. METHODS: A systematic search identified all scientifically peer-reviewed publications involving elite male soccer players competing in the European leagues which reported physical match performance variables such as total running distance and running distance at different speed zones and/or injury parameters pre- and post-COVID-19 induced break. RESULTS: In total, 11 articles were included, which were coming from German Bundesliga, Polish Ekstraklasa, Croatian HNL, Spanish La Liga, and Italian Serie A. In all studies investigating the German Bundesliga, most parameters of physical match performance remained unaffected (0.08 ≤ p ≤ 0.82; - 0.15 ≤ ES 0.15), while studies investigating the Polish Ekstraklasa (p ≤ 0.03; - 0.27 ≤ ES - 0.18), Croatian HNL (p ≤ 0.04; - 1.42 ≤ ES ≤ 1.44), Spanish La Liga (p ≤ 0.017; - 0.32 ≤ ES ≤ 5.5), and Italian Serie A (p ≤ 0.014; - 1.01 ≤ ES 0.24) showed a decrease in most parameters of physical match performance after the COVID-19 break. Injury rates were only investigated by studies targeting the German Bundesliga and Italian Serie A. In the majority of studies (3 out of 4 studies), there occurred no difference in injuries between pre- and post-COVID-19 break (p > 0.05; ES = N/A). CONCLUSION: Results indicate that Bundesliga teams maintained physical match performance during the 9-weeks break in matches and 3-weeks break in group training, whereas a longer match and group training interruption up to 15 weeks and 8 weeks, respectively, in the other leagues appeared to lead to a decreased physical match performance. Regarding injuries, we speculate that the increase in substitutions from 3 to 5 substitutions per game might prevent an increase in injury occurrence during matches. The underlying studies' results provide hints for possible upcoming unexpected interruptions with respect to optimal physical preparations for the resumption of matches and a congested schedule to maintain physical match performance, or for possible rule changes such as 5 instead of 3 substitutions to avoid physical overload during congested match schedules.

Type and intensity distribution of structured and incidental lifestyle physical activity of students and office workers: a retrospective content analysis.

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.

Assessment of Peak Oxygen Uptake with a Smartwatch and its Usefulness for Training of Runners.

Peak oxygen uptake (˙VO2peak) is an important factor contributing to running performance. Wearable technology may allow the assessment of ˙VO2peak more frequently and on a larger scale. We aim to i) validate the ˙VO2peak assessed by a smartwatch (Garmin Forerunner 245), and ii) discuss how this parameter may assist to evaluate and guide training procedures. A total of 23 runners (12 female, 11 male; ˙VO2peak: 48.6±6.8 ml∙min-1∙kg-1) visited the laboratory twice to determine their ˙VO2peak during a treadmill ramp test. Between laboratory visits, participants wore a smartwatch and performed three outdoor runs to obtain ˙VO2peak values provided by the smartwatch. The ˙VO2peak obtained by the criterion measure ranged from 38 to 61 ml∙min-1∙kg-1. The mean absolute percentage error (MAPE) between the smartwatch and the criterion ˙VO2peak was 5.7%. The criterion measure revealed a coefficient of variation of 4.0% over the VO2peak range from 38-61 ml∙min-1∙kg-1. MAPE between the smartwatch and criterion measure was 7.1, 4.1 and -6.2% when analyzing ˙VO2peak ranging from 39-45 ml∙min-1∙kg-1, 45-55 ml∙min-1∙kg-1 or 55-61 ml∙min-1∙kg-1, respectively.

Proof-of-concept and concurrent validity of a prototype headset to assess peak oxygen uptake without a face mask.

OBJECTIVE: Portable gas exchange instruments allow the assessment of peak oxygen uptake (V̇O2peak) but are often bulky, expensive and require wearing a face mask thereby limiting their routine application. A newly developed miniaturized headset (VitaScale, Nuremberg, Germany) may overcome these barriers and allow measuring V̇O2peak without applying a face mask. Here we aimed (i) to disclose the technical setup of a headset incorporating a gas and volume sensor to measure volume flow and expired oxygen concentration and (ii) to assess the concurrent criterion-validity of the headset to measure V̇O2peak in 44 individuals exercising on a stationary cycle ergometer in consideration of the test-retest reliability of the criterion measure. RESULTS: The coefficient of variation (CV%) while measuring V̇O2peak during incremental cycling with the headset was 6.8%. The CV% for reliability of the criterion measure was 4.0% for V̇O2peak. Based on the present data, the headset might offer a new technology for V̇O2peak measurement due to its low-cost and mask-free design.

Establishing a cardiac training group for patients with heart failure: the "HIP-in-Würzburg" study.

BACKGROUND: Exercise training in heart failure (HF) is recommended but not routinely offered, because of logistic and safety-related reasons. In 2020, the German Society for Prevention&Rehabilitation and the German Society for Cardiology requested establishing dedicated "HF training groups." Here, we aimed to implement and evaluate the feasibility and safety of one of the first HF training groups in Germany. METHODS: Twelve patients (three women) with symptomatic HF (NYHA class II/III) and an ejection fraction ≤ 45% participated and were offered weekly, physician-supervised exercise training for 1 year. Patients received a wrist-worn pedometer (M430 Polar) and underwent the following assessments at baseline and after 4, 8 and 12 months: cardiopulmonary exercise test, 6-min walk test, echocardiography (blinded reading), and quality of life assessment (Kansas City Cardiomyopathy Questionnaire, KCCQ). RESULTS: All patients (median age [quartiles] 64 [49; 64] years) completed the study and participated in 76% of the offered 36 training sessions. The pedometer was worn ≥ 1000 min per day over 86% of the time. No cardiovascular events occurred during training. Across 12 months, NT-proBNP dropped from 986 pg/ml [455; 1937] to 483 pg/ml [247; 2322], and LVEF increased from 36% [29;41] to 41% [32;46]%, (p for trend = 0.01). We observed no changes in exercise capacity except for a subtle increase in peak VO2% predicted, from 66.5 [49; 77] to 67 [52; 78]; p for trend = 0.03. The physical function and social limitation domains of the KCCQ improved from 60 [54; 82] to 71 [58; 95, and from 63 [39; 83] to 78 [64; 92]; p for trend = 0.04 and = 0.01, respectively. Positive trends were further seen for the clinical and overall summary scores. CONCLUSION: This pilot study showed that the implementation of a supervised HF-exercise program is feasible, safe, and has the potential to improve both quality of life and surrogate markers of HF severity. This first exercise experiment should facilitate the design of risk-adopted training programs for patients with HF.

Moving Together While Staying Apart: Practical Recommendations for 24-Hour Home-Based Movement Behaviours for Those With Cardiovascular Disease.

The novel coronavirus disease 2019 is a global public health crisis that disproportionately affects those with preexisting conditions. Cardiovascular disease (CVD) is the leading cause of death worldwide and many key CVD risk factors are modifiable (e.g., physical inactivity, sedentary behaviour, obesity). To limit the spread of coronavirus 2019, most governments have implemented restrictions and recommended staying at home, reducing social contact to a select and exclusive few, and limiting large gatherings. Such public health constraints may have unintended, negative health consequences on 24-hour movement behaviours. The primary purpose of this review is to provide practical at-home recommendations for sedentary time, sleep, and physical activity in those living with CVD. Adults with CVD will benefit from practical recommendations to reduce sedentary time, increase purposeful exercise, and maintain optimal sleep patterns while being at home and adhering to public health restrictions. Our recommendations include the following: (i) self-monitoring sitting time; (ii) engaging in 2-3 days per week of purposeful exercise for those with low exercise capacity and > 3 days per week for those with moderate-to-high exercise capacity; (iii) self-monitoring exercise intensity through the use of scales or wearable devices; (iv) maintaining a regular sleep schedule; and (v) moving daily. Clinicians should be aware that clear communication of the importance of limiting prolonged sedentary time, engaging in regular physical activity and exercise, and ensuring optimal sleep in association with the provision of clear, comprehensible, and practical advice is fundamental to ensuring that those living with CVD respond optimally to the challenges posed by the pandemic.

La nouvelle maladie à coronavirus 2019 représente une crise de santé publique mondiale qui touche de manière disproportionnée les personnes présentant des pathologies préexistantes. Les maladies cardiovasculaires (MCV) constituent la principale cause de décès dans le monde et de nombreux facteurs de risque majeurs de MCV sont modifiables (par exemple, le manque d'activité physique, un comportement sédentaire, l'obésité). Pour limiter la propagation du coronavirus 2019, la plupart des gouvernements ont mis en place des restrictions et recommandé de rester à la maison, de réduire les contacts sociaux à un nombre de personnes restreint et exclusif, et de limiter les grands rassemblements. Ces contraintes de santé publique peuvent involontairement entraîner des conséquences sanitaires négatives sur les habitudes de déplacement sur 24 heures. L'objectif principal de cette étude est de fournir des recommandations pratiques, réalisables à domicile, en rapport avec le temps de sédentarité, le sommeil et l'activité physique chez les personnes atteintes de MCV. Les adultes atteints de MCV tireront avantage de recommandations pratiques dans le but de réduire le temps de sédentarité, augmenter l'activité physique volontaire et maintenir des habitudes de sommeil optimales, tout en restant à la maison et en respectant les restrictions sanitaires. Nos recommandations sont les suivantes: (i) autosurveillance du temps passé assis; (ii) pratique d'une activité physique volontaire 2 à 3 jours par semaine pour les personnes ayant une faible capacité d'exercice et > 3 jours par semaine pour les personnes ayant une capacité d'exercice modérée à élevée; (iii) autosurveillance de l'intensité de l'activité par l'utilisation de barème ou de dispositifs portatifs; (iv) maintien d'horaires de sommeil réguliers; et (v) action de bouger quotidiennement. Les cliniciens doivent être conscients qu'une communication claire quant à l'importance de limiter le temps de sédentarité prolongée, de pratiquer régulièrement de l'exercice ou une activité physique, et d'assurer un sommeil optimal, associée à la préconisation de conseils clairs, compréhensibles et pratiques, est fondamentale pour garantir une réponse optimale de la part des personnes vivant avec des MCV quant aux défis posés par la pandémie.

Monitoring and adapting endurance training on the basis of heart rate variability monitored by wearable technologies: A systematic review with meta-analysis.

OBJECTIVES: To systematically perform a meta-analysis of the scientific literature to determine whether the outcomes of endurance training based on heart rate variability (HRV) are more favorable than those of predefined training. DESIGN: Systematic review and meta-analysis. METHODS: PubMed and Web of Science were searched systematically in March of 2020 using keywords related to endurance, the ANS, and training. To compare the outcomes of HRV-guided and predefined training, Hedges' g effect size and associated 95% confidence intervals were calculated. RESULTS: A total of 8 studies (198 participants) were identified comprising 9 interventions involving a variety of approaches. Compared to predefined training, most HRV-guided interventions included fewer moderate- and/or high-intensity training sessions. Fixed effects meta-analysis revealed a significant medium-sized positive effect of HRV-guided training on submaximal physiological parameters (g = 0.296, 95% CI 0.031 to 0.562, p = 0.028), but its effects on performance (g = 0.079, 95% CI -0.050 to 0.393, p = 0.597) and V̇O2peak (g = 0.171, 95% CI -0.213 to 0.371, p = 0.130) were small and not statistically significant. Moreover, with regards to performance, HRV-guided training was associated with fewer non-responders and more positive responders. CONCLUSIONS: In comparison to predefined training, HRV-guided endurance training had a medium-sized effect on submaximal physiological parameters, but only a small and non-significant influence on performance and V̇O2peak. There were fewer non-responders regarding performance with HRV-based training.

Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis.

BACKGROUND: Decreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA. OBJECTIVE: The aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users' PA behavior. METHODS: The BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin Vívoactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature. RESULTS: The most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin Vívoactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA. CONCLUSIONS: Wearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.

Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study.

PURPOSE: Pronounced differences in individual physiological adaptation may occur following various training mesocycles in runners. Here we aimed to assess the individual changes in performance and physiological adaptation of recreational runners performing mesocycles with different intensity, duration and frequency. METHODS: Employing a randomized cross-over design, the intra-individual physiological responses [i.e., peak ([Formula: see text]) and submaximal ([Formula: see text]) oxygen uptake, velocity at lactate thresholds (V2, V4)] and performance (time-to-exhaustion (TTE)) of 13 recreational runners who performed three 3-week sessions of high-intensity interval training (HIIT), high-volume low-intensity training (HVLIT) or more but shorter sessions of HVLIT (high-frequency training; HFT) were assessed. RESULTS: [Formula: see text], V2, V4 and TTE were not altered by HIIT, HVLIT or HFT (p > 0.05). [Formula: see text] improved to the same extent following HVLIT (p = 0.045) and HFT (p = 0.02). The number of moderately negative responders was higher following HIIT (15.4%); and HFT (15.4%) than HVLIT (7.6%). The number of very positive responders was higher following HVLIT (38.5%) than HFT (23%) or HIIT (7.7%). 46% of the runners responded positively to two mesocycles, while 23% did not respond to any. CONCLUSION: On a group level, none of the interventions altered [Formula: see text], V2, V4 or TTE, while HVLIT and HFT improved [Formula: see text]. The mean adaptation index indicated similar numbers of positive, negative and non-responders to HIIT, HVLIT and HFT, but more very positive responders to HVLIT than HFT or HIIT. 46% responded positively to two mesocycles, while 23% did not respond to any. These findings indicate that the magnitude of responses to HIIT, HVLIT and HFT is highly individual and no pattern was apparent.

Predefined vs data-guided training prescription based on autonomic nervous system variation: A systematic review.

Monitoring variations in the functioning of the autonomic nervous system may help personalize training of runners and provide more pronounced physiological adaptations and performance improvements. We systematically reviewed the scientific literature comparing physiological adaptations and/or improvements in performance following training based on responses of the autonomic nervous system (ie, changes in heart rate variability) and predefined training. PubMed, SPORTDiscus, and Web of Science were searched systematically in July 2019. Keywords related to endurance, running, autonomic nervous system, and training. Studies were included if they (a) involved interventions consisting predominantly of running training; (b) lasted at least 3 weeks; (c) reported pre- and post-intervention assessment of running performance and/or physiological parameters; (d) included an experimental group performing training adjusted continuously on the basis of alterations in HRV and a control group; and (e) involved healthy runners. Five studies involving six interventions and 166 participants fulfilled our inclusion criteria. Four HRV-based interventions reduced the amount of moderate- and/or high-intensity training significantly. In five interventions, improvements in performance parameters (3000 m, 5000 m, Loadmax, Tlim) were more pronounced following HRV-based training. Peak oxygen uptake ( V ˙ O 2 peak ) and submaximal running parameters (eg, LT1, LT2) improved following both HRV-based and predefined training, with no clear difference in the extent of improvement in V ˙ O 2 peak . Submaximal running parameters tended to improve more following HRV-based training. Research findings to date have been limited and inconsistent. Both HRV-based and predefined training improve running performance and certain submaximal physiological adaptations, with effects of the former training tending to be greater.

Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study.

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 . 82 for the trained runners, and 86 . 08 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.