To optimize performance, coaches and athletes are always looking for the right balance between training load and recovery. Therefore, closely monitoring of athletes is important. Heart rate recovery (HRR) after standardized sub maximal exercise has been proposed as a useful variable to monitor (Lamberts et al., 2004). However, it is well known that heart rate, next to biological variability, is influenced by several factors such as training load and psychosocial stress. So, the purpose was to look at individual variability in HRR from one week to another using the heart rate interval monitoring system (HIMS). Methods Eight elite Dutch female indoor hockey players (age: 23.9±3.91yr, length: 155.0±7.01cm, weight: 56.6±6.16kg) completed the HIMS two weeks in a row (Lamberts et al., 2004). The heart rate at the end of the last stage (HRend) was determined and the HRR was calculated one minute after the end of the last stage. Furthermore, training load and psychosocial stress and recovery were monitored using the Foster-method (1998) and the RESTQ-Sport (Nederhof et al., 2008), respectively. Results A strong correlation was found between the HRend from one week to the other (r=0.984 p.
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BackgroundRoutine outpatient care of patients with coronary artery disease (CAD) lacks a simple measure of physical fitness and risk of mortality. Heart rate recovery (HRR) is noninvasive and easily obtainable in outpatient settings. Prior studies have suggested that delayed postexercise HRR in the first minutes is associated withmortality in several types of populations. However, a comprehensive overview of the prognostic value of delayed HRR for time to mortality specifically in CAD patients is not available. The purpose of the current meta-analysis is to evaluate the prognostic value of delayed HRR in CAD patients.MethodsWe conducted a systematic search in OVID MEDLINE and OVID EMBASE to identify studies reporting on HRR and risk of incident cardiovascular events or mortality in CAD patients. Hazard ratios for delayed versus nondelayed HRR were pooled using random-effects meta-analysis.Results Four studies were included, comprising 2,428 CAD patients. The study quality of the included studies was rated moderate (n = 2) to high (n = 2). Delayed HRR was defined by ≤12 to ≤21 beat/min in the recovery period. During follow-up (range 2.0-9.8 years), 151 patients died (6.2% [range 2.5%-19.5%]). Only data on mortality could be pooled. Heterogeneity was limited (I² = 32%; P = .23); pooled unadjusted hazard ratio for mortality, based on 3 studies, was 5.8 (95% CI 3.2-10.4).CoclusionsIn CAD patients, delayed HRR is significantly associated with all-cause mortality. As exercise testing is performed routinely in CAD patients, HRR can be considered in monitoring exercise; still, further research must investigate the addition of HRR in current risk scores.
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The aim of the present study was to find early markers for overreaching that are applicable in sport practice. In a group of elite soccer players aged 15–18, the stress–recovery balance and reaction times before and after exercise were assessed. Overreaching was indicated by an elevated submaximal heart rate during a sport-specific field test. Submaximal changes in heart rate were prospectively monitored by means of monthly Interval Shuttle Run Tests during two competitive seasons. Out of 94 players, seven players with an elevated heart rate of at least one month could be included in the study, together with seven controls, matched for age, body composition, training and performance level. The stress–recovery balance was assessed with the Dutch version of the Recovery Stress Questionnaire (RESTQ-Sport). The soccer players with an elevated heart rate reported a disturbed stress–recovery balance (Mann–Whitney test, P<0.05). An ANOVA for repeated measures of reaction times revealed a significant main effect of time (F 1,12=13.87, P<0.01) indicating an improvement of psychomotor speed. No differences between groups were found. We conclude that soccer players with an elevated submaximal heart rate of at least one month share a disturbed stress–recovery balance, but they could not be distinguished from controls based on reaction time after strenuous exercise.
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The purpose was to investigate perceived stress and recovery related to cycling performance of female athletes over one full year. 20 female athletes (age, 27±8 years; E2max, 50.3±4.6 mL·kg-1·min-1) were measured 8 times in one year to determine perceived stress and recovery (RESTQ-Sport) in relation to cycling performance (Lamberts and Lambert Submaximal Cycle Test (LSCT)). All 19 RESTQ-Sport scales were calculated and scores of the 4 main categories were determined (i. e., general stress, general recovery, sport-specific stress and sport-specific recovery). A balance score of total stress and recovery was calculated by recovery-stress. Power at the second stage (P80), third stage (P90) and heart rate recovery (HRR60 s) of the LSCT were determined as performance parameters. 110 RESTQ-Sports and LSCTs were analysed using a multilevel approach (random intercepts model). Higher self-efficacy was related to improvement of all performance parameters. Higher total recovery stress, and lower emotional stress were related to improvement of P90 and HRR60 s. Higher sport-specific recovery was related to P80, higher general stress, fatigue and physical complaints were related to decreased P90 and higher social stress and injury were related to decreased HRR60 s. Improved perceived recovery and stress contributed to an improved performance. Relevant information could be provided by monitoring changes in perceived stress and recovery of female athletes.
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Physical and psychosocial stress and recovery are important performance determinants. A holistic approach that monitors these performance determinants over a longer period of time is lacking. Therefore this study aims to investigate the effect of a player’s physical and psychosocial stress and recovery on field-test performance. In a prospective non-experimental cohort design 10 female Dutch floorball players were monitored over 6 months. To monitor physical and psychosocial stress and recovery, daily training-logs and three-weekly the Recovery-Stress Questionnaire for Athletes (RESTQ-Sport) were filled out respectively. To determine field-test performance 6 Heart rate Interval Monitoring System (HIMS) and 4 Repeated Modified Agility T-test (RMAT) measurements were performed. Multilevel prediction models were applied to account for within-players and between-players field-test performance changes. The results show that more psychosocial stress and less psychosocial recovery over 3 to 6 weeks before testing decrease HIMS performance (p≤0.05). More physical stress over 6 weeks before testing improves RMAT performance (p≤0.05). In conclusion, physical and psychosocial stress and recovery affect submaximal interval-based running performance and agility up to 6 weeks before testing. Therefore both physical and psychosocial stress and recovery should be monitored in daily routines to optimize performance.
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Background: The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. Objective: This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. Methods: In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. Results: Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). Conclusions: To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes.
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The emergence of wearable sensor technology may provide opportunities for automated measurement of psychophysiological markers of mental and physical fitness, which can be used for personalized feedback. This study explores to what extent within-subject changes in resting heart rate variability (HRV) during sleep predict the perceived mental and physical fitness of military personnel on the subsequent morning. Participants wore a Garmin wrist-worn wearable and filled in a short morning questionnaire on their perceived mental and physical fitness during a period of up to 46 days. A custom-built smartphone app was used to directly retrieve heart rate and accelerometer data from the wearable, on which open-source algorithms for sleep detection and artefact filtering were applied. A sample of 571 complete observations in 63 participants were analyzed using linear mixed models. Resting HRV during sleep was a small predictor of perceived physical fitness (marginal R 2 = .031), but not of mental fitness. The items on perceived mental and physical fitness were strongly correlated (r = .77). Based on the current findings, resting HRV during sleep appears to be more related to the physical component of perceived fitness than its mental component. Recommendations for future studies include improvements in the measurement of sleep and resting HRV, as well as further investigation of the potential impact of resting HRV as a buffer on stress-related outcomes.
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Low heart rate variability (HRV) is related to health problems that are known reasons for sick-leave or early retirement. A 1-minute-protocol could allow large scale HRV measurement for screening of health problems and, potentially, sustained employability. Our objectives were to explore the association of HRV with measures of health. Cross-sectional design with 877 Dutch employees assessed during a Workers’ Health Assessment. Personal and job characteristics, workability, psychological and mental problems, and lifestyle were measured with questionnaires. Biometry was measured (BMI, waist circumference, blood pressure, glucose, cholesterol). HRV was assessed with a 1-minute paced deep-breathing protocol and expressed as mean heart rate range (MHRR). A low MHRR indicates a higher health risk. Groups were classified age adjusted for HRV and compared. Spearman correlations between raw MHRR and the other measures were calculated. Significant univariable correlations (p < 0.05) were entered in a linear regression model to explore the multivariable association with MHRR. Age, years of employment, BMI and waist circumference differed significantly between HRV groups. Significant correlations were found between MHRR and age, workability, BMI, waist circumference, cholesterol, systolic and diastolic blood-pressure and reported physical activity and alcohol consumption. In the multivariable analyses 21.1% of variance was explained: a low HRV correlates with aging, higher BMI and higher levels of reported physically activity. HRV was significantly associated with age, measures of obesity (BMI, waist circumference), and with reported physical activity, which provides a first glance of the utility of a 1-minute paced deep-breathing HRV protocol as part of a comprehensive preventive Workers’ Health Assessment.Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat ivecommons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate redit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Consumer wearables health data may reflect the impact of pancreatic cancer and its treatment on cardiorespiratory fitness and the subsequent recovery after treatment. The patient is a 65-year-old male treated for borderline resectable pancreatic cancer. Treatment consisted of four courses of FOLFIRINOX neoadjuvant chemotherapy, a Whipple procedure with a right hemicolectomy and venous segment resection, and eight courses of adjuvant FOLFIRINOX chemotherapy. Physical activity and moderate to vigorous physical activity declined after the onset of symptoms, increased in the weeks before surgery, declined after surgery and then gradually recovered during and after adjuvant chemotherapy. Estimated VO 2max remained stable during neoadjuvant chemotherapy, sharply decreased after surgery and then gradually recovered. Heart rate at rest increased and heart rate variability decreased after the onset of symptoms reaching their highest and lowest values after surgery. Both gradually returned to baseline seven months after the last course of chemotherapy. The physical impact of pancreatic cancer and its treatment and recovery was in this case reflected on consumer wearable health data. Seven months after the last chemotherapy recovery was close to baseline values.
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