Medical encounters (including injury and illness) at mass community-based endurance sports events: an international consensus statement on definitions and methods of data recording and reporting.

Mass participation endurance sports events are popular but a large number of participants are older and may be at risk of medical complications during events. Medical encounters (defined fully in the statement) include those traditionally considered 'musculoskeletal' (eg, strains) and those due to 'illness' (eg, cardiac, respiratory, endocrine). The rate of sudden death during mass endurance events (running, cycling and triathlon) is between 0.4 and 3.3 per 100 000 entrants. The rate of other serious medical encounters (eg, exertional heat stroke, hyponatraemia) is rarely reported; in runners it can be up to 100 times higher than that of sudden death, that is, between 16 and 155 per 100 000 race entrants. This consensus statement has two goals. It (1) defines terms for injury and illness-related medical encounters, severity and timing of medical encounters, and diagnostic categories of medical encounters, and (2) describes the methods for recording data at mass participation endurance sports events and reporting results to authorities and for publication. This unifying consensus statement will allow data from various events to be compared and aggregated. This will inform athlete/patient management, and thus make endurance events safer.

Participation in fitness-related activities of an incentive-based health promotion program and hospital costs: a retrospective longitudinal study.

PURPOSE: A retrospective, longitudinal study examined changes in participation in fitness-related activities and hospital claims over 5 years amongst members of an incentivized health promotion program offered by a private health insurer. DESIGN: A 3-year retrospective observational analysis measuring gym visits and participation in documented fitness-related activities, probability of hospital admission, and associated costs of admission. SETTING: A South African private health plan, Discovery Health and the Vitality health promotion program. PARTICIPANTS: 304,054 adult members of the Discovery medical plan, 192,467 of whom registered for the health promotion program and 111,587 members who were not on the program. INTERVENTION: Members were incentivised for fitness-related activities on the basis of the frequency of gym visits. MEASURES: Changes in electronically documented gym visits and registered participation in fitness-related activities over 3 years and measures of association between changes in participation (years 1-3) and subsequent probability and costs of hospital admission (years 4-5). Hospital admissions and associated costs are based on claims extracted from the health insurer database. ANALYSIS: The probability of a claim modeled by using linear logistic regression and costs of claims examined by using general linear models. Propensity scores were estimated and included age, gender, registration for chronic disease benefits, plan type, and the presence of a claim during the transition period, and these were used as covariates in the final model. RESULTS: There was a significant decrease in the prevalence of inactive members (76% to 68%) over 5 years. Members who remained highly active (years 1-3) had a lower probability (p < .05) of hospital admission in years 4 to 5 (20.7%) compared with those who remained inactive (22.2%). The odds of admission were 13% lower for two additional gym visits per week (odds ratio, .87; 95% confidence interval [CI], .801-.949). CONCLUSION: We observed an increase in fitness-related activities over time amongst members of this incentive-based health promotion program, which was associated with a lower probability of hospital admission and lower hospital costs in the subsequent 2 years.

Measuring training load in sports.

The principle of training can be reduced to a simple "dose-response" relationship. The "response" in this relationship can be measured as a change in performance or the adaptation of a physiological system. The "dose" of training, or physiological stress associated with the training load, is more difficult to measure as there is no absolute "gold standard" which can be used in the field, making it difficult to validate procedures. Attempts have been made to use heart rate as a marker of intensity during training, but the theoretical attractiveness of this method is not supported by the accuracy and the practicality of using this method during training or competition. The session RPE, based on the product of training duration and perceived intensity is more practical and can be used in a variety of sports. However, the score depends on a subjective assessment, and the intersubject comparisons may be inaccurate. The demands of different sports vary and therefore the methods of assessing training need to vary accordingly. The time has come to reach consensus on assessing training accurately in different sports. There is a precedent for this consensus approach with scientists having already done so for the assessment of physical activity, and for defining injuries in rugby, football and cricket. Standardizing these methods has resulted in the quality of research in these areas increasing exponentially.

The quantification of training load, the training response and the effect on performance.

Historically, the ability of coaches to prescribe training to achieve optimal athletic performance can be attributed to many years of personal experience. A more modern approach is to adopt scientific methods in the development of optimal training programmes. However, there is not much research in this area, particularly into the quantification of training programmes and their effects on physiological adaptation and subsequent performance. Several methods have been used to quantify training load, including questionnaires, diaries, physiological monitoring and direct observation. More recently, indices of training stress have been proposed, including the training impulse, which uses heart rate measurements and training load, and session rating of perceived exertion measurements, which utilizes subjective perception of effort scores and duration of exercise. Although physiological adaptations to training are well documented, their influence on performance has not been accurately quantified. To date, no single physiological marker has been identified that can measure the fitness and fatigue responses to exercise or accurately predict performance. Models attempting to quantify the relationship between training and performance have been proposed, many of which consider the athlete as a system in which the training load is the input and performance the system output. Although attractive in concept, the accuracy of these theoretical models has proven poor. A possible reason may be the absence of a measure of individuality in each athlete's response to training. Thus, in the future more attention should be directed towards measurements that reflect individual capacity to respond or adapt to exercise training rather than an absolute measure of changes in physiological variables that occur with training.

Autonomic control of heart rate during and after exercise : measurements and implications for monitoring training status.

Endurance training decreases resting and submaximal heart rate, while maximum heart rate may decrease slightly or remain unchanged after training. The effect of endurance training on various indices of heart rate variability remains inconclusive. This may be due to the use of inconsistent analysis methodologies and different training programmes that make it difficult to compare the results of various studies and thus reach a consensus on the specific training effects on heart rate variability. Heart rate recovery after exercise involves a coordinated interaction of parasympathetic re-activation and sympathetic withdrawal. It has been shown that a delayed heart rate recovery is a strong predictor of mortality. Conversely, endurance-trained athletes have an accelerated heart rate recovery after exercise. Since the autonomic nervous system is interlinked with many other physiological systems, the responsiveness of the autonomic nervous system in maintaining homeostasis may provide useful information about the functional adaptations of the body. This review investigates the potential of using heart rate recovery as a measure of training-induced disturbances in autonomic control, which may provide useful information for training prescription.

Quantifying training load: a comparison of subjective and objective methods.

PURPOSE: To establish the relationship between a subjective (session rating of perceived exertion [RPE]) and 2 objective (training impulse [TRIMP]) and summatedheart- rate-zone (SHRZ) methods of quantifying training load and explain characteristics of the variance not accounted for in these relationships. METHODS: Thirty-three participants trained ad libitum for 2 wk, and their heart rate (HR) and RPE were recorded to calculate training load. Subjects were divided into groups based on whether the regression equations over- (OVER), under- (UNDER), or accurately predicted (ACCURATE) the relationship between objective and subjective methods. RESULTS: A correlation of r = .76 (95% CI: .56 to .88) occurred between TRIMP and session-RPE training load. OVER spent a greater percentage of training time in zone 4 of SHRZ (ie, 80% to 90% HRmax) than UNDER (46% +/- 8% vs 25% +/- 10% [mean +/- SD], P = .008). UNDER spent a greater percentage of training time in zone 1 of SHRZ (ie, 50% to 60% HRmax) than OVER (15%+/- 8% vs 3% +/- 3%, P = .005) and ACCURATE (5% +/- 3%, P = .020) and more time in zone 2 of SHRZ (ie, 60% to 70%HRmax) than OVER (17% +/- 6% vs 7% +/- 6%, P = .039). A correlation of r = .84 (.70 to .92) occurred between SHRZ and session-RPE training load. OVER spent proportionally more time in Zone 4 than UNDER (45% +/- 8% vs 25% +/- 10%, P = .018). UNDER had a lower training HR than ACCURATE (132 +/- 10 vs 148 +/- 12 beats/min, P = .048) and spent more time in zone 1 than OVER (15% +/- 8% vs 4% +/- 3%, P = .013) and ACCURATE (5% +/- 3%, P = .015). CONCLUSIONS: The session-RPE method provides reasonably accurate assessments of training load compared with HR-based methods, but they deviate in accuracy when proportionally more time is spent training at low or high intensity.

Changes in heart rate recovery in response to acute changes in training load.

Heart rate recovery is an indirect marker of autonomic function and changes therein may offer a practical way of quantifying the physiological effects of training. We assessed whether per cent heart rate recovery (HRr%) after a standardized sub-maximal running (Heart rate Interval Monitoring System: HIMS) test, changed with acute changes in training load. A total of 28 men and women (mean age 30+/-5 years) trained ad libitum for 2 weeks during which their heart rate (HR) was recorded. Training load was quantified using Training Impulse (TRIMPs). The participants were grouped based on whether they increased (Group I, n=9), decreased (Group D, n=8) or kept their training load constant (Group S, n=11) from week 1 to week 2. Each week, the subjects completed a HIMS test. Changes between weeks in HR at the end of the test and HRr% were compared between groups. Mean per cent change in TRIMPs from week 1 to week 2 was significantly different among the groups (Group I, 55+/-21% vs Group S, -6+/-6% vs Group D, -42+/-16%; P<0.05). Group I had a slower HRr% and Group D tended to have a slightly faster HRr% after HIMS 2 than after HIMS 1 (mean per cent change 5.6+/-8.7 vs -2.6+/-3.9; P=0.03). Thus a negative effect on HRr was observed with increases in training load. Sub-maximal HR was not affected by acute changes in training load. Whereas HR during exercise measures cardiac load, HRr may reflect the state of the autonomic nervous system, indicating the body's capacity to respond to exercise.