Monitoring Training and Recovery during a Period of Increased Intensity or Volume in Recreational Endurance Athletes

Sarcopenia interventions in long-term care facilities targeting sedentary behaviour and physical inactivity: A systematic review

BACKGROUND

Sedentary behaviour and physical inactivity are independent risk factors for sarcopenia for long-term care facility residents. Understanding the components, mechanisms and context of interventions that target change in these risk factors can help optimize sarcopenia management approaches. This study aimed to identify, appraise and synthesize the interventions targeting sedentary behaviour and physical inactivity, construct a Theory of Change logic model, inform complex sarcopenia intervention development and identify areas for improvement.

METHODS

Eight electronic databases, including Embase and Web of Science, were searched for eligible interventional studies from inception until February 2024. Narrative synthesis was used. The Theory of Change was applied to develop a logic model presenting the synthesized results. A Cochrane risk of bias assessment tool was used for quality appraisal.

RESULTS

The study included 21 articles involving 1014 participants, with mean ages ranging from 72.5 to 90.4 years. The proportion of female participants ranged from 8.0% to 100.0%. The applied sarcopenia diagnosis criteria varied, including those of the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People. The overall risk of bias in the included studies was moderate. Interventions primarily targeted physical inactivity, with resistance training being the most common intervention type. The reporting of intervention adherence was insufficient (only 11 out of 21 included studies provided adherence reports), and adherence overall and by intervention type was not possible to discern due to inconsistent criteria for high adherence across these studies. Four categories of intervention input were identified: educational resources; exercise equipment and accessories; monitoring and tailoring tools; and motivational strategies. Intervention activities fell into five categories: determining the intervention plan; educating; tailoring; organizing, supervising, assisting and motivating; and monitoring. While sarcopenia-related indicators were commonly used as desired outcomes, intermediate outcomes (i.e., sedentary time and physical activity level) and other long-term outcomes (i.e., economic outcomes) were less considered. Contextual factors affecting intervention use included participant characteristics (i.e., medical condition and education level) and intervention provider characteristics (i.e., trustworthiness).

CONCLUSIONS

The findings led to the development of a novel logic model detailing essential components for interventions aimed at managing sarcopenia in long-term care facilities, with a focus on addressing sedentary behaviour and physical inactivity. Future sarcopenia interventions in long-term care facilities should fully attend to sedentary behaviour, enhance adherence to interventions through improved education, monitoring, tailoring and motivation and establish an agreed standard set of outcome measures.

Comparison of Polarized Versus Other Types of Endurance Training Intensity Distribution on Athletes' Endurance Performance: A Systematic Review with Meta-analysis

BACKGROUND

Polarized training intensity distribution (POL) was recently suggested to be superior to other training intensity distribution (TID) regimens for endurance performance improvement.

OBJECTIVE

We aimed to systematically review and meta-analyze evidence comparing POL to other TIDs on endurance performance.

METHODS

PRISMA guidelines were followed. The protocol was registered at PROSPERO (CRD42022365117). PubMed, Scopus, and Web of Science were searched up to 20 October 2022 for studies in adults and young adults for ≥ 4 weeks comparing POL with other TID interventions regarding VO2peak, time-trial (TT), time to exhaustion (TTE) or speed or power at the second ventilatory or lactate threshold (V/P at VT2/LT2). Risk of bias was assessed with RoB-2 and ROBINS-I. Certainty of evidence was assessed with GRADE. Results were analyzed by random effects meta-analysis using standardized mean differences.

RESULTS

Seventeen studies met the inclusion criteria (n = 437 subjects). Pooled effect estimates suggest POL superiority for improving VO2peak (SMD = 0.24 [95% CI 0.01, 0.48]; z = 2.02 (p = 0.040); 11 studies, n = 284; I2 = 0%; high certainty of evidence). Superiority, however, only occurred in shorter interventions (< 12 weeks) (SMD = 0.40 [95% CI 0.08, 0.71; z = 2.49 (p = 0.01); n = 163; I2 = 0%) and for highly trained athletes (SMD = 0.46 [95% CI 0.10, 0.82]; z = 2.51 (p = 0.01); n = 125; I2 = 0%). The remaining endurance performance surrogates were similarly affected by POL and other TIDs: TT (SMD = - 0.01 [95% CI -0.28, 0.25]; z =  - 0.10 (p = 0.92); n = 221; I2 = 0%), TTE (SMD = 0.30 [95% CI - 0.20, 0.79]; z = 1.18 (p = 0.24); n = 66; I2 = 0%) and V/P VT2/LT2 (SMD = 0.04 [95% CI -0.21, 0.29]; z = 0.32 (p = 0.75); n = 253; I2 = 0%). Risk of bias for randomized controlled trials was rated as of some concern and for non-randomized controlled trials as low risk of bias (two studies) and some concerns (one study).

CONCLUSIONS

POL is superior to other TIDs for improving VO2peak, particularly in shorter duration interventions and highly trained athletes. However, the effect of POL was similar to that of other TIDs on the remaining surrogates of endurance performance. The results suggest that POL more effectively improves aerobic power but is similar to other TIDs for improving aerobic capacity.

Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes

Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.

Individualized Endurance Training Based on Recovery and Training Status in Recreational Runners

PURPOSE

Long-term development of endurance performance requires a proper balance between strain and recovery. Because responses and adaptations to training are highly individual, this study examined whether individually adjusted endurance training based on recovery and training status would lead to greater adaptations compared with a predefined program.

METHODS

Recreational runners were divided into predefined (PD; n = 14) or individualized (IND; n = 16) training groups. In IND, the training load was decreased, maintained, or increased twice a week based on nocturnal heart rate variability, perceived recovery, and heart rate-running speed index. Both groups performed 3-wk preparatory, 6-wk volume, and 6-wk interval periods. Incremental treadmill tests and 10-km running tests were performed before the preparatory period ( T0 ) and after the preparatory ( T1 ), volume ( T2 ), and interval ( T3 ) periods. The magnitude of training adaptations was defined based on the coefficient of variation between T0 and T1 tests (high >2×, low <0.5×).

RESULTS

Both groups improved ( P < 0.01) their maximal treadmill speed and 10-km time from T1 to T3 . The change in the 10-km time was greater in IND compared with PD (-6.2% ± 2.8% vs -2.9% ± 2.4%, P = 0.002). In addition, IND had more high responders (50% vs 29%) and fewer low responders (0% vs 21%) compared with PD in the change of maximal treadmill speed and 10-km performance (81% vs 23% and 13% vs 23%), respectively.

CONCLUSIONS

PD and IND induced positive training adaptations, but the individualized training seemed more beneficial in endurance performance. Moreover, IND increased the likelihood of high response and decreased the occurrence of low response to endurance training.

Physiological, Perceptual, and Performance Responses to the 2-Week Block of High- versus Low-Intensity Endurance Training

PURPOSE

This study examined the physiological, perceptual, and performance responses to a 2-wk block of increased training load and compared whether responses differ between high-intensity interval (HIIT) and low-intensity training (LIT).

METHODS

Thirty recreationally trained males and females performed a 2-wk block of 10 HIIT sessions (INT, n = 15) or 70% increased volume of LIT (VOL, n = 15). Running time in the 3000 m and basal serum and urine hormone concentrations were measured before (T1) and after the block (T2), and after a recovery week (T3). In addition, weekly averages of nocturnal heart rate variability (HRV) and perceived recovery were compared with the baseline.

RESULTS

Both groups improved their running time in the 3000 m from T1 to T2 (INT = -1.8% ± 1.6%, P = 0.003; VOL = -1.4% ± 1.7%, P = 0.017) and from T1 to T3 (INT = -2.5% ± 1.6%, P < 0.001; VOL = -2.2% ± 1.9%, P = 0.001). Resting norepinephrine concentration increased in INT from T1 to T2 (P = 0.01) and remained elevated at T3 (P = 0.018). The change in HRV from the baseline was different between the groups during the first week (INT = -1.0% ± 2.0% vs VOL = 1.8% ± 3.2%, P = 0.008). Muscle soreness increased only in INT (P < 0.001), and the change was different compared with VOL across the block and recovery weeks (P < 0.05).

CONCLUSIONS

HIIT and LIT blocks increased endurance performance in a short period. Although both protocols seemed to be tolerable for recreational athletes, a HIIT block may induce some negative responses such as increased muscle soreness and decreased parasympathetic activity.

Validity of the Wrist-Worn Polar Vantage V2 to Measure Heart Rate and Heart Rate Variability at Rest

Heart rate (HR) and heart rate variability (HRV) can be monitored with wearable devices throughout the day. Resting HRV in particular, reflecting cardiac parasympathetic activity, has been proposed to be a useful marker in the monitoring of health and recovery from training. This study examined the validity of the wrist-based photoplethysmography (PPG) method to measure HR and HRV at rest. Recreationally endurance-trained participants recorded pulse-to-pulse (PP) and RR intervals simultaneously with a PPG-based watch and reference heart rate sensor (HRS) at a laboratory in a supine position (n = 39; 5-min recording) and at home during sleep (n = 29; 4-h recording). In addition, analyses were performed from pooled laboratory data (n = 11344 PP and RR intervals). Differences and correlations were analyzed between the HRS- and PPG-derived HR and LnRMSSD (the natural logarithm of the root mean square of successive differences). A very good agreement was found between pooled PP and RR intervals with a mean bias of 0.17 ms and a correlation coefficient of 0.993 (p < 0.001). In the laboratory, HR did not differ between the devices (mean bias 0.0 bpm), but PPG slightly underestimated the nocturnal recordings (mean bias -0.7 bpm, p < 0.001). PPG overestimated LnRMSSD both in the laboratory (mean bias 0.20 ms, p < 0.001) and nocturnal recordings (mean bias 0.17 ms, p < 0.001). However, very strong intraclass correlations in the nocturnal recordings were found between the devices (HR: 0.998, p < 0.001; LnRMSSD: 0.931, p < 0.001). In conclusion, PPG was able to measure HR and HRV with adequate accuracy in recreational athletes. However, when strict absolute values are of importance, systematic overestimation, which seemed to especially concern participants with low LnRMSSD, should be acknowledged.

Effects of Increased Load of Low- Versus High-Intensity Endurance Training on Performance and Physiological Adaptations in Endurance Athletes

PURPOSE

To compare the effects of increased load of low- versus high-intensity endurance training on performance and physiological adaptations in well-trained endurance athletes.

METHODS

Following an 8-week preintervention period, 51 (36 men and 15 women) junior cross-country skiers and biathletes were randomly allocated into a low-intensity (LIG, n = 26) or high-intensity training group (HIG, n = 25) for an 8-week intervention period, load balanced using the overall training impulse score. Both groups performed an uphill running time trial and were assessed for laboratory performance and physiological profiling in treadmill running and roller-ski skating preintervention and postintervention.

RESULTS

Preintervention to postintervention changes in running time trial did not differ between groups (P = .44), with significant improvements in HIG (-2.3% [3.2%], P = .01) but not in LIG (-1.5% [2.9%], P = .20). There were no differences between groups in peak speed changes when incremental running and roller-ski skating to exhaustion (P = .30 and P = .20, respectively), with both modes being significantly improved in HIG (2.2% [3.1%] and 2.5% [3.4%], both P < .01) and in roller-ski skating for LIG (1.5% [2.4%], P < .01). There was a between-group difference in running maximal oxygen uptake changes (P = .04), tending to improve in HIG (3.0% [6.4%], P = .09) but not in LIG (-0.7% [4.6%], P = .25). Changes in roller-ski skating peak oxygen uptake differed between groups (P = .02), with significant improvements in HIG (3.6% [5.4%], P = .01) but not in LIG (-0.1% [0.17%], P = .62).

CONCLUSION

There was no significant difference in performance adaptations between increased load of low- versus high-intensity training in well-trained endurance athletes, although both methods improved performance. However, increased load of high-intensity training elicited better maximal oxygen uptake adaptations compared to increased load of low-intensity training.