Influence of light on exercise performance in athletes and overweight individuals

Background Athletes often need to compete at times of the day that do not meet their time of peak performance. To comply with television prime time finals often take place in the late evening, although at this time of the day there seems to be a fast decrease in performance. Bright light exposure increases alertness, reduces sleepiness and suppresses the hormone melatonin. These mechanisms induced by light exposure may prevent the time of the day related decrease in performance. In contrast to sport competitions, many exercise interventions in clinics or rehabilitation centers often take place in the morning to comply with the staffs working schedules. However, especially in the morning lower exercise intensities are chosen by most people. Light exposure has been show to increase mood and alertness, which may lead to higher training intensity and in mid- and long term to body mass reduction. Aims: The aims of this PhD project were: (1) to evaluate a possible positive dose-response relationship between bright light exposure and maximum cycling performance in athletes, (2) to investigate the effects of evening bright and blue light exposures on maximum cycling performance and (3) acoustic reaction time and handgrip strength in elite athletes and (4) to investigate the effect of morning bright light exposure on self-chosen exercise intensity and mood in overweight individuals. Methods: During this PhD project three studies were performed. In study 1 data from previous studies conducted by Prof. Schmidt-Trucksäss were analyzed to evaluate a possible dose-response relationship between bright light exposure and maximum cycling performance. In the analyzed studies participants were exposed to bright light and a control light condition in a cross-over design only prior to or prior to and during a 40-minute time trial on a bicycle ergometer. The intensity of bright light and control light was identical in all studies but the studies differed in exposure durations. To compare the differences in the work performed (kJ) during the time trial within one group (i.e. one duration of light exposure) a paired t-test was used. Differences between the groups were tested with analyses of variance with the dose of light exposure (high, medium and low) and the difference in the work performed after bright and control light exposure. Based on the results of study 1 two further studies (randomized controlled trials) were planned and conducted. In study 2 (first trial; Clinicaltrials.gov ID: NCT02203539) male elite athletes performed a cardiopulmonary exercise test to assess maximum oxygen uptake (V̇O2max) which determines the level of fitness. One week later participants performed a reaction time task and maximum handgrip strength test before they were exposed to either bright, monochromatic blue or a control light condition in the evening for 60 minutes. The light exposure started 17 hours after each individuals’ midpoint of sleep to test all participants at the same internal time. Immediately after the light exposure participants performed the reaction time task and handgrip strength test again and then a 12-minute time trial on a bicycle ergometer. An analysis of covariance with adjustment for V̇O2max from the baseline test was run to compare the differences in the work performed (kJ) between the three groups. Additionally, linear regression analyses were used to estimate the effect of melanopic light exposure on melatonin suppression and end-spurt performance, which was defined as the ratio of the performance during the first and last minute of the time trial. Analyses of covariance with adjustment for the values before the light exposure were used to compare acoustic reaction time and maximum handgrip strength after the light exposure between the three groups. In study 3 (second trial; Clinicaltrials.gov ID: NCT02636335) overweight individuals performed a cardiopulmonary exercise test to assess V̇O2max. Two days later participants performed a 30-minute exercise session with self-chosen exercise intensity for familiarization. Three to seven days later participants were exposed to either bright or a control light condition in the morning for 30 minutes prior to and during a 30-minute exercise session with self-chosen exercise intensity on a bicycle ergometer starting at 08:00. Participants also filled out a multidimensional mood questionnaire including the three domains “good-bad”, “awake-tired”, and “calm-nervous” all of which are bipolar scales. This questionnaire was answered prior to the light exposure, after the light exposure but prior to the exercise session and after the exercise session with persisting light exposure. Analyses of covariance with adjustment for V̇O2max were used to compare the difference in mean power output (W) during the exercise session between the two groups. Multivariate analyses were used to test for differences in mood before the light exposure, after the light exposure and after the exercise session between the groups. Results: Publication 1: Dose-response relationship between light exposure and cycling performance in athletes [1] In athletes there was no significant difference in the work performed (kJ) during the time trial between bright light and control light in those participants that were exposed to light for only 60 minutes prior to the time trial or those participants exposed to 60 minutes prior to and during the time trial. In contrast athletes exposed to light for 120 minutes prior to and during the time trial performed significantly more work after bright light exposure. Further, there was a significant positive dose-response relationship between the duration of light exposure and the work performed over the three doses. Publication 2: Effects of bright and blue light exposure on maximum cycling performance in elite athletes [2] In elite athletes evening bright or blue light exposure for 60 minutes in duration immediately before a 12-minute time trial did not significantly increase the work performed (kJ) compared to a control condition. Athletes exposed to high doses of melanopic light showed a significantly higher performance gain during the time trial, defined as the ratio of the work performed in the first and last minute of the time trial. This was associated with a stronger decrease in melatonin. However, there were no significant changes in sleepiness, motivation or mood through the light exposure between bright or blue light compared to control light. No severe adverse events occurred in any group and minor adverse events (e.g. glare, headache) were reported as often in the bright light group as reported in the control group. Publication 3: Effects of bright and blue light exposure on simple acoustic reaction time and maximum handgrip strength in elite athletes [3] In elite athletes evening bright or blue light exposure for 60 minutes in duration immediately before a simple acoustic reaction time task and a maximum handgrip strength test did not significantly reduce reaction time (ms) or increase handgrip strength (kg) compared to a control condition. Further, the actual light intensities reaching the participants’ eyes were lower than intended according to the protocol and showed a high variation between participants. Publication 4: Effect of light exposure on self-chosen exercise intensity and mood in overweight individuals [4] In overweight individuals morning bright light exposure for 30 minutes in duration prior to and during a 30-minute exercise session did not increase self-chosen exercise intensity (mean power output in Watts) compared to a control condition. None of the three domains of the multidimensional mood questionnaire was significantly altered by light exposure. Conclusions: There is a positive dose-response relationship between the duration athletes are exposed to bright light and the maximum cycling performance in a subsequent 40-minute time trial. To increase maximum performance significantly compared to control condition a pre-exercise exposure of 120 minutes seemed to be necessary, because participants with shorter exposure durations showed no higher performance. Exposure to high doses of melanopic light in the evening improved end-spurt performance in elite athletes resulting in a potentially meaningful enhancement of performance. Although bright light did not significantly increase maximum performance further studies are recommended, because the reported difference between bright and control represents a relevant advantage in sports competitions. Acoustic reaction time and maximum handgrip strength were not improved by light exposure in elite athletes. Likewise, in overweight individuals bright light exposure in the morning did neither increase self-chosen exercise intensity in a 30-minute exercise session nor improve mood compared to exposure to control light. Athletes and overweight individuals exposed to bright light showed not more adverse events than participants in the control condition

Low Cardiorespiratory Fitness Post-COVID-19: A Narrative Review

Patients recovering from COVID-19 often report symptoms of exhaustion, fatigue and dyspnoea and present with exercise intolerance persisting for months post-infection. Numerous studies investigated these sequelae and their possible underlying mechanisms using cardiopulmonary exercise testing. We aimed to provide an in-depth discussion as well as an overview of the contribution of selected organ systems to exercise intolerance based on the Wasserman gears. The gears represent the pulmonary system, cardiovascular system, and periphery/musculature and mitochondria. Thirty-two studies that examined adult patients post-COVID-19 via cardiopulmonary exercise testing were included. In 22 of 26 studies reporting cardiorespiratory fitness (herein defined as peak oxygen uptake-VO2peak), VO2peak was < 90% of predicted value in patients. VO2peak was notably below normal even in the long-term. Given the available evidence, the contribution of respiratory function to low VO2peak seems to be only minor except for lung diffusion capacity. The prevalence of low lung diffusion capacity was high in the included studies. The cardiovascular system might contribute to low VO2peak via subnormal cardiac output due to chronotropic incompetence and reduced stroke volume, especially in the first months post-infection. Chronotropic incompetence was similarly present in the moderate- and long-term follow-up. However, contrary findings exist. Peripheral factors such as muscle mass, strength and perfusion, mitochondrial function, or arteriovenous oxygen difference may also contribute to low VO2peak. More data are required, however. The findings of this review do not support deconditioning as the primary mechanism of low VO2peak post-COVID-19. Post-COVID-19 sequelae are multifaceted and require individual diagnosis and treatment

In Athletes, the Diurnal Variations in Maximum Oxygen Uptake Are More Than Twice as Large as the Day-to-Day Variations

In competitive sports any substantial individual differences in diurnal variations in maximal performance are highly relevant. Previous studies have exclusively focused on how the time of day affects performance and disregarded the maximal individual diurnal variation of performance. Thus, the aims of this study were (1) to investigate the maximum diurnal variation in maximum oxygen uptake (VO2max), (2) to compare the diurnal variation of VO2max during the day to the day-to-day variation in VO2max, and (3) to investigate if there is a time-of-day effect on VO2max. Ten male and seven female athletes (mean VO2max: 58.2 ± 6.9 ml/kg/min) performed six maximal cardiopulmonary exercise tests including a verification-phase at six different times of the day (i.e., diurnal variation) and a seventh test at the same time the sixth test took place (i.e., day-to-day variation). The test times were 7:00, 10:00, 13:00, 16:00, 19:00, and 21:00. The order of exercise tests was the same for all participants to ensure sufficient recovery but the time of day of the first exercise test was randomized. We used paired t-tests to compare the nadir and peak of diurnal variations, day-to-day variations and the difference between diurnal and day-to-day variations. The mean difference in VO2max was 5.0 ± 1.9 ml/kg/min (95% CI: 4.1, 6.0) for the diurnal variation and 2.0 ± 1.0 ml/kg/min (95% CI: 1.5, 2.5) for the day-to-day variation. The diurnal variation was significantly higher than the day-to-day variation with a mean difference of 3.0 ± 2.1 ml/kg/min (95% CI: 1.9, 4.1). The linear mixed effects model revealed no significant differences in VO2max for any pairwise comparison between the different times of the day (all p &gt; 0.11). This absence of a time-of-day effect is explained by the fact that peak VO2max was achieved at different times of the day by different athletes. The diurnal variations have meaningful implications for competitive sports and need to be considered by athletes. However, the results are also relevant to research. To increase signal-to-noise-ratio in intervention studies it is necessary to conduct cardiopulmonary exercise testing at the same time of the day for pre- and post-intervention exercise tests

Normative data and standard operating procedures for static and dynamic retinal vessel analysis as biomarker for cardiovascular risk

Retinal vessel phenotype is predictive for cardiovascular outcome. This cross-sectional population-based study aimed to quantify normative data and standard operating procedures for static and dynamic retinal vessel analysis. We analysed central retinal arteriolar (CRAE) and venular (CRVE) diameter equivalents, as well as retinal endothelial function, measured by flicker light‐induced maximal arteriolar (aFID) and venular (vFID) dilatation. Measurements were performed in 277 healthy individuals aged 20 to 82 years of the COmPLETE study. The mean range from the youngest compared to the oldest decade was 196 ± 13 to 166 ± 17 µm for CRAE, 220 ± 15 to 199 ± 16 µm for CRVE, 3.74 ± 2.17 to 3.79 ± 2.43% for aFID and 4.64 ± 1.85 to 3.86 ± 1.56% for vFID. Lower CRAE [estimate (95% CI): - 0.52 (- 0.61 to - 0.43)], CRVE [- 0.33 (- 0.43 to - 0.24)] and vFID [- 0.01 (- 0.26 to - 0.00)], but not aFID, were significantly associated with older age. Interestingly, higher blood pressure was associated with narrower CRAE [- 0.82 (- 1.00 to - 0.63)] but higher aFID [0.05 (0.03 to 0.07)]. Likewise, narrower CRAE were associated with a higher predicted aFID [- 0.02 (- 0.37 to - 0.01)]. We recommend use of defined standardized operating procedures and cardiovascular risk stratification based on normative data to allow for clinical implementation of retinal vessel analysis in a personalized medicine approach

The Oxygen Uptake Plateau—A Critical Review of the Frequently Misunderstood Phenomenon

A flattening of the oxygen uptake-work rate relationship at severe exercise indicates the achievement of maximum oxygen uptake [Formula: see text]. Unfortunately, a distinct plateau [Formula: see text] at [Formula: see text]is not found in all participants. The aim of this investigation was to critically review the influence of research methods and physiological factors on the [Formula: see text] incidence. It is shown that many studies used inappropriate definitions or methodical approaches to check for the occurrence of a [Formula: see text]. In contrast to the widespread assumptions it is unclear whether there is higher [Formula: see text] incidence in (uphill) running compared to cycling exercise or in discontinuous compared to continuous incremental exercise tests. Furthermore, most studies that evaluated the validity of supramaximal verification phases, reported verification bout durations, which are too short to ensure that [Formula: see text] have been achieved by all participants. As a result, there is little evidence for a higher [Formula: see text] incidence and a corresponding advantage for the diagnoses of [Formula: see text] when incremental tests are supplemented by supramaximal verification bouts. Preliminary evidence suggests that the occurrence of a [Formula: see text] in continuous incremental tests is determined by physiological factors like anaerobic capacity, [Formula: see text]-kinetics and accumulation of metabolites in the submaximal intensity domain. Subsequent studies should take more attention to the use of valid [Formula: see text] definitions, which require a cut-off at ~ 50% of the submaximal [Formula: see text] increase and rather large sampling intervals. Furthermore, if verification bouts are used to verify the achievement of [Formula: see text]/[Formula: see text], it should be ensured that they can be sustained for sufficient durations

Methodological aspects for accelerometer-based assessment of physical activity in heart failure and health

For valid accelerometer-assessed physical activity (PA) data, several methodological aspects should be considered. We aimed to 1) visualize the applicability of absolute accelerometer cut-offs to classify PA intensity, 2) verify recommendations to measure PA over 7 days by examining inter-day variability and reactivity, 3) examine seasonal differences in PA, and 4) recommend during which 10 h day period accelerometers should be worn to capture the most PA in patients with heart failure (HEART) and healthy individuals (HEALTH).; Fifty-six HEART (23% female; mean age 66 ± 13 years) and 299 HEALTH (51% female; mean age 54 ± 19 years) of the COmPLETE study wore accelerometers for 14 days. Aim 1 was analyzed descriptively. Key analyses were performed using linear mixed models.; The results yielded poor applicability of absolute cut-offs. The day of the week significantly affected PA in both groups. PA-reactivity was not present in either group. A seasonal influence on PA was only found in HEALTH. Large inter-individual variability in PA timing was present.; Our data indicated that absolute cut-offs foster inaccuracies in both populations. In HEART, Sunday and four other days included in the analyses seem sufficient to estimate PA and the consideration of seasonal differences and reactivity seems not necessary. For healthy individuals, both weekend days plus four other days should be integrated into the analyses and seasonal differences should be considered. Due to substantial inter-individual variability in PA timing, accelerometers should be worn throughout waking time. These findings may improve future PA assessment.; The COmPLETE study was registered at clinicaltrials.gov ( NCT03986892 )

Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X+

Abstract Background Wrist-worn accelerometers are increasingly used in epidemiological studies to record physical activity. The accelerometer data are usually only analyzed if the convention for compliant wear time is met (i.e. ≥ 10 h per day) but the algorithms to detect wear time have been developed based on data from hip-worn devices only and have not been tested in a free-living setting. The aim of this study was to validate the automatic wear time detection algorithms of one of the most frequently used devices in a free-living setting. Methods Sixty-eight adults wore one ActiGraph GT3X+ accelerometer on the wrist and one on the hip and additionally recorded wear times for each device separately in a diary. Monitoring phase was during three consecutive days in a free-living setting. Wear time was computed by the algorithms of Troiano and Choi and compared to the diary recordings. Results Mean wear time was over 1420 min per day for both devices on all days. Lin’s concordance correlation coefficient for the wrist-worn wear time was 0.73 (0.60; 0.82) when comparing the diary with Troiano and 0.78 (0.67; 0.86) when comparing the diary with Choi. For hip-worn devices the respective values were 0.23 (0.13; 0.33) for Troiano and 0.92 (0.88; 0.95) for Choi. Mean and standard deviation values for absolute percentage errors for wrist-worn devices were − 1.3 ± 8.1% in Troiano and 0.9 ± 7.7% in Choi. The respective values for hip-worn devices were − 17.5 ± 10% in Troiano and − 0.8 ± 4.6% in Choi. Conclusions Hip worn devices may be preferred due to their higher accuracy in physical activity measurement. Automatic wear-time detection can show high errors in individuals, but on a group level, type I, type II, and total errors are generally low when the Choi algorithm is used. In a real-life setting and participants with a high compliance, the algorithm by Choi is sufficient to distinguish wear time from non-wear time on a group level

Comparison of V̇O2-Kinetic Parameters for the Management of Heart Failure

Objective:; The aim of this study was to analyze whether V̇O; 2; -kinetics during cardiopulmonary exercise testing (CPET) is a useful marker for the diagnosis of heart failure (HF) and to determine which V̇O; 2; -kinetic parameter distinguishes healthy participants and patients with HF.; Methods:; A total of 526 healthy participants and 79 patients with HF between 20 and 90 years of age performed a CPET. The CPET was preceded by a 3-min low-intensity warm-up and followed by a 3-min recovery bout. V̇O; 2; -kinetics was calculated from the rest to exercise transition of the warm-up bout (on-kinetics), from the exercise to recovery transition following ramp test termination (off-kinetics) and from the initial delay of V̇O; 2; during the warm-up to ramp test transition (ramp-kinetics).; Results:; V̇O; 2; off-kinetics showed the highest; z; -score differences between healthy participants and patients with HF. Furthermore, off-kinetics was strongly associated with V̇O; 2peak; . In contrast, ramp-kinetics and on-kinetics showed only minimal; z; -score differences between healthy participants and patients with HF. The best on- and off-kinetic parameters significantly improved a model to predict the disease severity. However, there was no relevant additional value of V̇O; 2; -kinetics when V̇O; 2peak; was part of the model.; Conclusion:; V̇O; 2; off-kinetics appears to be superior for distinguishing patients with HF and healthy participants compared with V̇O; 2; on-kinetics and ramp-kinetics. If V̇O; 2peak; cannot be determined, V̇O; 2; off-kinetics provides an acceptable substitute. However, the additional value beyond that of V̇O; 2peak; cannot be provided by V̇O; 2; -kinetics

Microvascular endothelial dysfunction in heart failure patients: An indication for exercise treatment?

Endothelial dysfunction represents a diagnostic marker to differentiate disease severity in chronic heart failure (CHF) patients. Retinal vessel phenotyping was applied in CHF patients as it has been acknowledged as a sensitive diagnostic tool to quantify microvascular health and overall cardiovascular risk.; The central retinal arteriolar (CRAE) and venular diameter equivalents (CRVE) as well as the retinal microvascular function, quantified by arteriolar (aFID) and venular flicker-light induced dilatation (vFID), were analyzed in 26 CHF patients. These data were compared with 26 age- and sex-matched healthy peers. The effects of an exercise intervention on retinal microvascular health in one CHF patient were investigated to demonstrate potentially beneficial effects of exercise treatment in a case report format as proof of concept.; CHF patients showed narrower CRAE (170 ± 16 μm vs. 176 ± 16 μm, p = 0.237) and wider CRVE (217 ± 20 μm vs. 210 ± 17 μm, p = 0.152), resulting in a significantly lower arteriolar-to-venular diameter ratio (AVR, 0.79 ± 0.07 vs. 0.84 ± 0.06, p = 0.004) compared to controls. More strikingly, CHF patients showed significantly lower mean aFID (1.24 ± 1.14% vs. 3.78 ± 1.85%, p < 0.001) and vFID (2.89 ± 1.33% vs. 3.88 ± 1.83%, p = 0.033). Twelve weeks of exercise therapy induced wider CRAE (143 ± 1.0 μm vs. 153 ± 0.9 μm), narrower CRVE (183 ± 3.1 μm vs. 180 ± 2.4 μm) and improved aFID (0.67% vs. 1.25%) in a male 78 years old CHF patient.; aFID is a sensitive diagnostic tool to quantify microvascular impairments in CHF patients. Exercise treatment in CHF patients has high potential to improve retinal microvascular health as a marker for vascular regeneration and overall risk reduction, which warrants further examination by randomized controlled trials

Reference values for accelerometer metrics and associations with cardiorespiratory fitness: a prospective cohort study of healthy adults and patients with heart failure

Background Accelerometry has gained increasing popularity and yields numerous physical activity (PA) outcomes (Rowlands et al., 2019). These include traditional cut-point-based (i.e. light, moderate, and vigorous PA) and cut-point-free metrics (i.e. intensity gradient [IG] and average acceleration [AvAcc]). IG reflects the intensity distribution of PA across the day (Rowlands et al., 2018; Fairclough et al., 2019). AvAcc is a proxy for the daily volume of PA ( Rowlands et al., 2018; Fairclough et al., 2019). Cut-point-based metrics are commonly expressed in minutes per day, making their interpretation simple (Troiano et al., 2014). Yet, the measured acceleration needs to be categorised by setting population- and device-dependent cut-points to obtain these metrics (Troiano et al., 2014). Cut-point-free metrics, on the other hand, are comparable across studies, accelerometer brands (Migueles et al., 2022), and diverse populations (Rowlands et al., 2018). However, their interpretation is not easy. Besides, it is unknown how cut-point-free metrics are associated with cardiorespiratory fitness (CRF), an important health indicator in healthy individuals and patient populations with impaired CRF (Kodama et al., 2009). We thus aimed to 1) compare the association of CRF with cut-point-free metrics to that with cut-point-based metrics in a prospective cohort of healthy adults aged 20 to 89 years and patients with heart failure, and 2) provide age-, sex-, and CRF-related reference values for healthy adults. Methods The COmPLETE study was cross-sectional. Healthy individuals were recruited via unaddressed letters sent to randomly selected postal districts in the Basel area (Wagner et al., 2019). Patients with heart failure were approached as described elsewhere (Wagner et al., 2019). Subjects were asked to wear GENEActiv accelerometers on their non-dominant wrist for up to 14 days and undergo cardiopulmonary exercise testing on a cycle ergometer to determine CRF. Raw accelerometer data were processed using the R-package GGIR (Migueles et al., 2019; van Hees et al., 2013). Associations between CRF and accelerometer metrics were examined using multiple linear regression models adjusted for sex, age, and body mass index. Percentile curves were generated with Generalised Additive Models for Location, Scale, and Shape (Stasinopoulos &amp; Rigby, 2008). Results Four hundred and sixty-three healthy adults and 67 patients with heart failure were included in the analyses. IG and AvAcc provide complementary information on PA. Both metrics were independently associated with CRF in healthy individuals. The best cut-point-free regression model (AvAcc+IG) performed similar to the best cut-point-based model (vigorous activity) and explained 73.9% and 74.2% of the variance in CRF, respectively. In patients with heart failure, IG was associated with CRF, independent of AvAcc. Cut-point-free models (IG+AvAcc, IG alone) had comparable predictive value for CRF as the best cut-point-based metric (moderate-to-vigorous activity). We produced age-, sex-, and CRF-related reference values for IG, AvAcc, moderate-to-vigorous, and vigorous activity for healthy adults. Moreover, we developed a web-based application (rawacceleration) facilitating the interpretation of cut-point-free metrics. Conclusions Cut-point-free metrics are not only more robust than cut-point-based metrics, but also have similar predictive value for CRF and, in turn, indirectly for the risk of mortality and longevity (Kodama et al., 2009; Mok et al., 2019). This may be the case in both healthy individuals and patients with heart failure. Our findings together with those of previous studies (Rowlands et al., 2018; Fairclough et al., 2019), therefore, provide a rationale that cut-point-free metrics facilitate the capture of the volume and intensity distribution of the PA profile across populations, and thus may be a viable alternative to cut-point-based metrics in describing PA. Our reference values will enhance the utility of IG and AvAcc and facilitate their interpretation. Finally, our web-based application will simplify this process and also support the translation of cut-point-free metrics into meaningful outcomes. References Fairclough, S. J., Taylor, S., Rowlands, A. V., Boddy, L. M., &amp; Noonan, R. J. (2019) Average acceleration and intensity gradient of primary school children and associations with indicators of health and well-being. 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PloS one, 8(4), Article e61691. https://doi.org/10.1371/journal.pone.0061691 Wagner, J., Knaier, R., Infanger, D., Arbeev, K., Briel, M., Dieterle, T., Hanssen, H., Faude, O., Roth, R., Hinrichs, T., &amp; Schmidt-Trucksäss, A. (2019). Functional aging in health and heart failure: The COmPLETE Study. BMC Cardiovascular Disorders, 19, Article 180. https://doi.org/10.1186/s12872-019-1164-