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Frédéric Chagué, Marjolaine Georges, Aurélie Gudjoncik, Thierry Hermerel, Gilles Garet, Jean Israël, Jean-Philippe Hager, Patrick Aviat, Yves Laurent, Vincent Gremeaux, Yves Cottin, Marianne Zeller, Cardiovascular prevention and at-risk behaviours in a large population of amateur rugby players, European Journal of Preventive Cardiology, Volume 26, Issue 14, 1 September 2019, Pages 1522–1530, https://doi.org/10.1177/2047487319837066
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Abstract
We aimed to investigate cardiovascular risk factors and health behaviours prospectively in a large population of French amateur rugby players.
An anonymous questionnaire was displayed to rugby players aged over 12 years enrolled in the 2014–2015 French amateur rugby championship from the Burgundy region (n = 5140). Questions addressed awareness on: (a) cardiovascular prevention; (b) tobacco, alcohol and highly caffeinated beverages consumption; and (c) adherence to prevention guidelines (ECG checks, training in basic life support, avoidance of sports practice during fever/infectious episodes).
Among the 640 participants who completed the questionnaires, most were male (90%) and were aged under 35 years (80%). Almost half had basic life support training (42%), but only a minority attended an ECG check-up before licensing (37%), and only a few were aware of the cardiovascular prevention information campaign (17%), similarly across the age groups. Surprisingly, playing rugby with fever was commonly reported (44%) and was even more frequent in young women (55%). A high number of respondents were current smokers (35%), of whom most reported consumption less than 2 hours before/after a rugby session. Alcohol drinkers were frequent (69%), of whom most (79%) drank alcohol less than 2 hours before/after a match. Highly caffeinated beverages consumption (34%) was high, particularly in younger players (39%). Half highly caffeinated beverages consumption was in the setting of a rugby session, even greater in women and mainly motivated by performance enhancement (34%).
Our findings from a representative regional cohort may help to identify targets for cardiovascular prevention through the development of educational programmes aiming to improve the knowledge and behaviour of amateur rugby players.
Introduction
The impact of sports on health is often considered a double-edged sword.1 Indeed, the benefits of physical activity have been clearly demonstrated for the prevention of cardiovascular diseases and peak exercise oxygen pulse at maximal exercise is inversely associated with cardiovascular mortality.2 On the other hand, heavy exertion can trigger acute coronary syndrome (ACS) or sudden cardiac death (SCD).1,3
Coronary atherothrombosis is the leading cause of cardiovascular events, especially after 35 years of age, although it can occur earlier; the other main causes include inherited cardiomyopathies and channelopathies, anomalous origin of coronary arteries and myocarditis.4,5 Consumption of tobacco, performance-enhancing substances, alcohol or caffeine is frequently associated with physical activity, even although they are known to increase the risk of cardiovascular events.6–9
SCD events are often reported in the media, particularly when it occurs in elite athletes.10 However, the absolute number of events is dramatically greater in amateur sportsmen or during leisure activity.3,11 The incidence of sports-related SCD in young competitive athletes has been estimated at 9.8/million/year in France. However, the annual number of events might be higher in amateur athletes, which represent the majority of the sports population.3,11 Various programmes such as public information campaigns about at-risk behaviours, ECG check-ups before obtaining a sports licence and public training in basic life support (BLS) have been suggested as preventive actions to reduce the risk of cardiovascular events during sports activity.1,4 However, available data on the current level of adherence to SCD prevention guidelines in routine sports activity are scarce.
We aimed to investigate prospectively the prevalence of risk factors and consumption habits in a large population of French amateur rugby players.
Subjects and methods
The French Rugby Federation (FRF) in 2011 organised a nationwide campaign for awareness of cardiovascular risk prevention in licensed rugby players. In each rugby club, a FRF poster summarising the 10 rules for improving cardiovascular safety during rugby practice was displayed in the rugby clubs (see Supplementary data). In order to assess the impact of the campaign, an anonymous questionnaire was distributed to all rugby players aged 12 years or more and enrolled in the 2014–2015 French amateur rugby championship in the French region of Burgundy (n = 5140) (see Supplementary data). The accuracy of this type of questionnaire has been validated, especially in adolescents.12
Statistical analysis
Continuous data are presented as median (interquartile range; IQR) or mean ± SD, as appropriate, or as a proportion. For continuous variables, a Kolmogorov–Smirnov analysis was performed to test for normality. Non-normal variables were log-transformed before being added to the analysis. The Mann–Whitney rank sum test or Student’s t-test was used to compare two groups and a one-way analysis of variance (ANOVA) or Kruskal–Wallis one-way analysis, as appropriate, was performed for comparisons of more than two groups. Dichotomous variables were compared with chi square tests. All analyses were performed using the SPSS 12.0 software package (IBM Inc., USA).
Results
Among the 5140 distributed questionnaires, 640 (12.5%) were completed and fit for analysis. Most respondents (574 (90%)) were male and 508 (80%) were aged under 35 years. Demographic data are summarised in Table 1.
. | Total . | <35 Years . | ≥35 Years . | ||||||
---|---|---|---|---|---|---|---|---|---|
. | M + F . | M . | F . | M + F . | M . | F . | M + F . | M . | F . |
N (%) | 640 | 574 (90%) | 66 (10%) | 508 | 448 (88%) | 60 (12%) | 132 | 126 (95%) | 6 (5%) |
Age, years | |||||||||
Mean ± SD | 27 11 | 27 11 | 25 6 | 22 6 | 22 6 | 23 5 | 44 7 | 44 7 | 38 4 |
Median | 25 | 25 | 24 | 22 | 22 | 23 | 43 | 43 | 37 |
IQR | 18–33 | 18–33 | 20–28 | 17–27 | 17–28 | 19–26 | 38–48 | 39–48 | 36–41 |
Min–max | 12–65 | 12–65 | 14–46 | 12–35 | 12–35 | 14–33 | 35–65 | 35–65 | 36–46 |
. | Total . | <35 Years . | ≥35 Years . | ||||||
---|---|---|---|---|---|---|---|---|---|
. | M + F . | M . | F . | M + F . | M . | F . | M + F . | M . | F . |
N (%) | 640 | 574 (90%) | 66 (10%) | 508 | 448 (88%) | 60 (12%) | 132 | 126 (95%) | 6 (5%) |
Age, years | |||||||||
Mean ± SD | 27 11 | 27 11 | 25 6 | 22 6 | 22 6 | 23 5 | 44 7 | 44 7 | 38 4 |
Median | 25 | 25 | 24 | 22 | 22 | 23 | 43 | 43 | 37 |
IQR | 18–33 | 18–33 | 20–28 | 17–27 | 17–28 | 19–26 | 38–48 | 39–48 | 36–41 |
Min–max | 12–65 | 12–65 | 14–46 | 12–35 | 12–35 | 14–33 | 35–65 | 35–65 | 36–46 |
M: male; F: female; IQR: interquartile range; SD: standard deviation
. | Total . | <35 Years . | ≥35 Years . | ||||||
---|---|---|---|---|---|---|---|---|---|
. | M + F . | M . | F . | M + F . | M . | F . | M + F . | M . | F . |
N (%) | 640 | 574 (90%) | 66 (10%) | 508 | 448 (88%) | 60 (12%) | 132 | 126 (95%) | 6 (5%) |
Age, years | |||||||||
Mean ± SD | 27 11 | 27 11 | 25 6 | 22 6 | 22 6 | 23 5 | 44 7 | 44 7 | 38 4 |
Median | 25 | 25 | 24 | 22 | 22 | 23 | 43 | 43 | 37 |
IQR | 18–33 | 18–33 | 20–28 | 17–27 | 17–28 | 19–26 | 38–48 | 39–48 | 36–41 |
Min–max | 12–65 | 12–65 | 14–46 | 12–35 | 12–35 | 14–33 | 35–65 | 35–65 | 36–46 |
. | Total . | <35 Years . | ≥35 Years . | ||||||
---|---|---|---|---|---|---|---|---|---|
. | M + F . | M . | F . | M + F . | M . | F . | M + F . | M . | F . |
N (%) | 640 | 574 (90%) | 66 (10%) | 508 | 448 (88%) | 60 (12%) | 132 | 126 (95%) | 6 (5%) |
Age, years | |||||||||
Mean ± SD | 27 11 | 27 11 | 25 6 | 22 6 | 22 6 | 23 5 | 44 7 | 44 7 | 38 4 |
Median | 25 | 25 | 24 | 22 | 22 | 23 | 43 | 43 | 37 |
IQR | 18–33 | 18–33 | 20–28 | 17–27 | 17–28 | 19–26 | 38–48 | 39–48 | 36–41 |
Min–max | 12–65 | 12–65 | 14–46 | 12–35 | 12–35 | 14–33 | 35–65 | 35–65 | 36–46 |
M: male; F: female; IQR: interquartile range; SD: standard deviation
Adherence to guidelines and level of information on cardiovascular prevention
The findings are summarised in Table 2.
Adherence to guidelines and level of information on cardiovascular prevention (n (%)).
. | Number . | Poster awareness . | ECG check-up . | BLS training . |
---|---|---|---|---|
Total | 640 | 108 (16.9%) | 237 (37.0%) | 299 (46.7%) |
M | 574 | 100 (17.4%) | 217 (3.8%) | 271 (47.2%) |
F | 66 | 8 (1.2%) | 20 (30.3%) | 28 (42.4%) |
<35 Years | 508 | 84 (16.6%) | 156 (30.7%) | 227 (44.7%) |
M | 448 | 77 (17.2%) | 140 (31.3%) | 201 (44.9%) |
F | 60 | 7 (11.7%) | 16 (26.7%) | 26 (44.3%) |
≥35 Years | 132 | 24 (18.2%) | 81 (61.4%) | 72 (54.5%) |
M | 126 | 23 (18.3%) | 77 (61.1%) | 70 (55.6%) |
F | 6 | 1 (16.7%) | 4 (66.7%) | 2 (33.3%) |
. | Number . | Poster awareness . | ECG check-up . | BLS training . |
---|---|---|---|---|
Total | 640 | 108 (16.9%) | 237 (37.0%) | 299 (46.7%) |
M | 574 | 100 (17.4%) | 217 (3.8%) | 271 (47.2%) |
F | 66 | 8 (1.2%) | 20 (30.3%) | 28 (42.4%) |
<35 Years | 508 | 84 (16.6%) | 156 (30.7%) | 227 (44.7%) |
M | 448 | 77 (17.2%) | 140 (31.3%) | 201 (44.9%) |
F | 60 | 7 (11.7%) | 16 (26.7%) | 26 (44.3%) |
≥35 Years | 132 | 24 (18.2%) | 81 (61.4%) | 72 (54.5%) |
M | 126 | 23 (18.3%) | 77 (61.1%) | 70 (55.6%) |
F | 6 | 1 (16.7%) | 4 (66.7%) | 2 (33.3%) |
M: male; F: female; BLS: basic life support.
Adherence to guidelines and level of information on cardiovascular prevention (n (%)).
. | Number . | Poster awareness . | ECG check-up . | BLS training . |
---|---|---|---|---|
Total | 640 | 108 (16.9%) | 237 (37.0%) | 299 (46.7%) |
M | 574 | 100 (17.4%) | 217 (3.8%) | 271 (47.2%) |
F | 66 | 8 (1.2%) | 20 (30.3%) | 28 (42.4%) |
<35 Years | 508 | 84 (16.6%) | 156 (30.7%) | 227 (44.7%) |
M | 448 | 77 (17.2%) | 140 (31.3%) | 201 (44.9%) |
F | 60 | 7 (11.7%) | 16 (26.7%) | 26 (44.3%) |
≥35 Years | 132 | 24 (18.2%) | 81 (61.4%) | 72 (54.5%) |
M | 126 | 23 (18.3%) | 77 (61.1%) | 70 (55.6%) |
F | 6 | 1 (16.7%) | 4 (66.7%) | 2 (33.3%) |
. | Number . | Poster awareness . | ECG check-up . | BLS training . |
---|---|---|---|---|
Total | 640 | 108 (16.9%) | 237 (37.0%) | 299 (46.7%) |
M | 574 | 100 (17.4%) | 217 (3.8%) | 271 (47.2%) |
F | 66 | 8 (1.2%) | 20 (30.3%) | 28 (42.4%) |
<35 Years | 508 | 84 (16.6%) | 156 (30.7%) | 227 (44.7%) |
M | 448 | 77 (17.2%) | 140 (31.3%) | 201 (44.9%) |
F | 60 | 7 (11.7%) | 16 (26.7%) | 26 (44.3%) |
≥35 Years | 132 | 24 (18.2%) | 81 (61.4%) | 72 (54.5%) |
M | 126 | 23 (18.3%) | 77 (61.1%) | 70 (55.6%) |
F | 6 | 1 (16.7%) | 4 (66.7%) | 2 (33.3%) |
M: male; F: female; BLS: basic life support.
Use of poster to transmit information about at-risk behaviour
A minority (17%) of the respondents were aware of the official poster displaying information on cardiovascular safety during sports, and this level of information was similar across age groups (16.6% for <35 years vs. 18.2% for ≥35 years, P = 0.69). The information was most commonly displayed within the rugby club facility (41%).
Previously performed ECG
Only 37% reported attending an ECG check-up, and the rate was lower in younger players (31% for <35 years) than in older players (61% for ≥35 years) (P < 0.001) (Figure 1). The median (IQR) age for the first ECG check-up was 21 (15–30) years.

At-risk behaviours according to age. BLS: basic life support; HCB: highly caffeinated beverage.
Training in BLS
Almost half (46.7%) of the respondents declared they had attended at least one session of BLS training. This level of training was not significantly lower in younger players (44.7% vs. 54.5%, P = 0.050, respectively) (Figure 1).
At-risk behaviour
The findings are summarised in Table 3.
. | Number . | Fever M/T . | Fever M . | Fever T . |
---|---|---|---|---|
Whole | 640 | 284 (44.4%) | 193 (30.2%) | 273 (42.7%) |
M | 574 | 248 (43.2%) | 173 (30.1%) | 237 (41.3%) |
F | 66 | 36 (54.5%) | 20 (30.3%) | 36 (54.5%) |
<35 Years | 508 | 241 (47.4%) | 166 (32.7%) | 233 (45.9%) |
M | 448 | 208 (46.4%) | 146 (32.6%) | 200 (44.6%) |
F | 60 | 33 (55.0%) | 20 (33.3%) | 33 (55.0%) |
≥35 Years | 132 | 43 (32.6%) | 27 (20.5%) | 40 (30.3%) |
M | 126 | 40 (31.7%) | 27 (21.4%) | 37 (29.4%) |
F | 6 | 3 (50.0%) | 0 (0.0%) | 3 (50.0%) |
. | Number . | Fever M/T . | Fever M . | Fever T . |
---|---|---|---|---|
Whole | 640 | 284 (44.4%) | 193 (30.2%) | 273 (42.7%) |
M | 574 | 248 (43.2%) | 173 (30.1%) | 237 (41.3%) |
F | 66 | 36 (54.5%) | 20 (30.3%) | 36 (54.5%) |
<35 Years | 508 | 241 (47.4%) | 166 (32.7%) | 233 (45.9%) |
M | 448 | 208 (46.4%) | 146 (32.6%) | 200 (44.6%) |
F | 60 | 33 (55.0%) | 20 (33.3%) | 33 (55.0%) |
≥35 Years | 132 | 43 (32.6%) | 27 (20.5%) | 40 (30.3%) |
M | 126 | 40 (31.7%) | 27 (21.4%) | 37 (29.4%) |
F | 6 | 3 (50.0%) | 0 (0.0%) | 3 (50.0%) |
M: male; F: female; M: during match; T: during training.
. | Number . | Fever M/T . | Fever M . | Fever T . |
---|---|---|---|---|
Whole | 640 | 284 (44.4%) | 193 (30.2%) | 273 (42.7%) |
M | 574 | 248 (43.2%) | 173 (30.1%) | 237 (41.3%) |
F | 66 | 36 (54.5%) | 20 (30.3%) | 36 (54.5%) |
<35 Years | 508 | 241 (47.4%) | 166 (32.7%) | 233 (45.9%) |
M | 448 | 208 (46.4%) | 146 (32.6%) | 200 (44.6%) |
F | 60 | 33 (55.0%) | 20 (33.3%) | 33 (55.0%) |
≥35 Years | 132 | 43 (32.6%) | 27 (20.5%) | 40 (30.3%) |
M | 126 | 40 (31.7%) | 27 (21.4%) | 37 (29.4%) |
F | 6 | 3 (50.0%) | 0 (0.0%) | 3 (50.0%) |
. | Number . | Fever M/T . | Fever M . | Fever T . |
---|---|---|---|---|
Whole | 640 | 284 (44.4%) | 193 (30.2%) | 273 (42.7%) |
M | 574 | 248 (43.2%) | 173 (30.1%) | 237 (41.3%) |
F | 66 | 36 (54.5%) | 20 (30.3%) | 36 (54.5%) |
<35 Years | 508 | 241 (47.4%) | 166 (32.7%) | 233 (45.9%) |
M | 448 | 208 (46.4%) | 146 (32.6%) | 200 (44.6%) |
F | 60 | 33 (55.0%) | 20 (33.3%) | 33 (55.0%) |
≥35 Years | 132 | 43 (32.6%) | 27 (20.5%) | 40 (30.3%) |
M | 126 | 40 (31.7%) | 27 (21.4%) | 37 (29.4%) |
F | 6 | 3 (50.0%) | 0 (0.0%) | 3 (50.0%) |
M: male; F: female; M: during match; T: during training.
Fever and sport
Surprisingly, a high rate (44.4%) of rugby players reported having played rugby with a fever during the last season. It was more frequent in the younger than in the older age group (47.4 vs. 32.6%, P = 0.002) (Figure 1). Overall, 30.2% of players reported having played at least a match while being feverish (P = 0.0088 between younger and older groups) and 42.7% reported having trained at least once while being feverish (P = 0.0015 for younger vs. older groups).
Consumption of non-illicit substances
The findings are reported in Table 4.
Tobacco . | Alcohol . | HCBs . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Total . | Daily consumers . | 2 Hours B/A . | Total . | Daily consumers . | 2 Hours B/A . | 2 Hours before M . | Total . | Daily consumers . | 2 Hours B/A . | 2 Hours B/A M . | 2 Hours before M . |
Whole | 222 (34.7%) | 144 (64.9%) | 173 (77.9%) | 444 (69.4%) | 16 (3.6%) | 352 (79.3%) | 23 (5.2%) | 219 (34.2%) | 12 (5.5%) | 113 (51.6%) | 101 (46.1%) | 87 (39.7%) |
M | 196 (34.1%) | 128 (65.3%) | 152 (77.6%) | 400 (69.7%) | 16 (4.0%) | 320 (80.0%) | 23 (5.8%) | 199 (34.7%) | 11 (5.5%) | 101 (50.8%) | 90 (45.2%) | 76 (38.2%) |
F | 26 (39.4%) | 16 (61.5%) | 21 (80.8%) | 40 (66.7%) | 0 (0%) | 32 (72.7%) | 0 (0%) | 20 (30.3%) | 1 (5.0%) | 12 (60.0%) | 11 (55.0%) | 11 (55.0%) |
<35 Years | 170 (33.5%) | 109 (64.1%) | 130 (76.5%) | 337 (66.1%) | 13 (0.9%) | 255 (75.7%) | 14 (4.2%) | 198 (39.0%) | 10 (5.1%) | 101 (51.0%) | 90 (45.5%) | 76 (38.4%) |
M | 148 (33.0%) | 96 (64.9%) | 113 (76.4%) | 297 (66.3%) | 13 (4.4%) | 225 (75.8%) | 14 (4.7%) | 180 (40.2%) | 9 (5.0%) | 90 (50.0%) | 80 (44.4%) | 66 (36.7%) |
F | 22 (36.7%) | 13 (59.1%) | 17 (77.3%) | 40 (66.7%) | 0 (0%) | 30 (75.0%) | 0 (0%) | 18 (30.0%) | 1 (5.6%) | 11 (61.1%) | 10 (55.6%) | 10 (55.6%) |
≥35 Years | 52 (39.4%) | 35 (67.3%) | 43 (82.7%) | 107 (81.1%) | 3 (12.1%) | 97 (80.7%) | 9 (8.4%) | 21 (15.9%) | 2 (9.5%) | 12 (57.1%) | 11 (52.4%) | 11 (52.4%) |
M | 48 (38.1%) | 32 (66.7%) | 39 (81.3%) | 103 (81.7%) | 3 (2.9%) | 95 (92.2%) | 9 (8.7%) | 19 (15.1%) | 2 (10.5%) | 11 (57.9%) | 10 (52.6%) | 10 (52.6%) |
F | 4 (66.7%) | 3 (75.0%) | 4 (100%) | 4 (66.7%) | 0 (0%) | 2 (50.0%) | 0 (0%) | 2 (33.3%) | 0 (0%) | 1 (50.0%) | 1 (50.0%) | 1 (50.0%) |
Tobacco . | Alcohol . | HCBs . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Total . | Daily consumers . | 2 Hours B/A . | Total . | Daily consumers . | 2 Hours B/A . | 2 Hours before M . | Total . | Daily consumers . | 2 Hours B/A . | 2 Hours B/A M . | 2 Hours before M . |
Whole | 222 (34.7%) | 144 (64.9%) | 173 (77.9%) | 444 (69.4%) | 16 (3.6%) | 352 (79.3%) | 23 (5.2%) | 219 (34.2%) | 12 (5.5%) | 113 (51.6%) | 101 (46.1%) | 87 (39.7%) |
M | 196 (34.1%) | 128 (65.3%) | 152 (77.6%) | 400 (69.7%) | 16 (4.0%) | 320 (80.0%) | 23 (5.8%) | 199 (34.7%) | 11 (5.5%) | 101 (50.8%) | 90 (45.2%) | 76 (38.2%) |
F | 26 (39.4%) | 16 (61.5%) | 21 (80.8%) | 40 (66.7%) | 0 (0%) | 32 (72.7%) | 0 (0%) | 20 (30.3%) | 1 (5.0%) | 12 (60.0%) | 11 (55.0%) | 11 (55.0%) |
<35 Years | 170 (33.5%) | 109 (64.1%) | 130 (76.5%) | 337 (66.1%) | 13 (0.9%) | 255 (75.7%) | 14 (4.2%) | 198 (39.0%) | 10 (5.1%) | 101 (51.0%) | 90 (45.5%) | 76 (38.4%) |
M | 148 (33.0%) | 96 (64.9%) | 113 (76.4%) | 297 (66.3%) | 13 (4.4%) | 225 (75.8%) | 14 (4.7%) | 180 (40.2%) | 9 (5.0%) | 90 (50.0%) | 80 (44.4%) | 66 (36.7%) |
F | 22 (36.7%) | 13 (59.1%) | 17 (77.3%) | 40 (66.7%) | 0 (0%) | 30 (75.0%) | 0 (0%) | 18 (30.0%) | 1 (5.6%) | 11 (61.1%) | 10 (55.6%) | 10 (55.6%) |
≥35 Years | 52 (39.4%) | 35 (67.3%) | 43 (82.7%) | 107 (81.1%) | 3 (12.1%) | 97 (80.7%) | 9 (8.4%) | 21 (15.9%) | 2 (9.5%) | 12 (57.1%) | 11 (52.4%) | 11 (52.4%) |
M | 48 (38.1%) | 32 (66.7%) | 39 (81.3%) | 103 (81.7%) | 3 (2.9%) | 95 (92.2%) | 9 (8.7%) | 19 (15.1%) | 2 (10.5%) | 11 (57.9%) | 10 (52.6%) | 10 (52.6%) |
F | 4 (66.7%) | 3 (75.0%) | 4 (100%) | 4 (66.7%) | 0 (0%) | 2 (50.0%) | 0 (0%) | 2 (33.3%) | 0 (0%) | 1 (50.0%) | 1 (50.0%) | 1 (50.0%) |
B/A: before or after a rugby session; B/A M: before or after playing a match; 2 Hours before M: within 2 hours before playing a match; HCB: highly caffeinated beverage.
Daily consumers (% of total consumers).
Tobacco . | Alcohol . | HCBs . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Total . | Daily consumers . | 2 Hours B/A . | Total . | Daily consumers . | 2 Hours B/A . | 2 Hours before M . | Total . | Daily consumers . | 2 Hours B/A . | 2 Hours B/A M . | 2 Hours before M . |
Whole | 222 (34.7%) | 144 (64.9%) | 173 (77.9%) | 444 (69.4%) | 16 (3.6%) | 352 (79.3%) | 23 (5.2%) | 219 (34.2%) | 12 (5.5%) | 113 (51.6%) | 101 (46.1%) | 87 (39.7%) |
M | 196 (34.1%) | 128 (65.3%) | 152 (77.6%) | 400 (69.7%) | 16 (4.0%) | 320 (80.0%) | 23 (5.8%) | 199 (34.7%) | 11 (5.5%) | 101 (50.8%) | 90 (45.2%) | 76 (38.2%) |
F | 26 (39.4%) | 16 (61.5%) | 21 (80.8%) | 40 (66.7%) | 0 (0%) | 32 (72.7%) | 0 (0%) | 20 (30.3%) | 1 (5.0%) | 12 (60.0%) | 11 (55.0%) | 11 (55.0%) |
<35 Years | 170 (33.5%) | 109 (64.1%) | 130 (76.5%) | 337 (66.1%) | 13 (0.9%) | 255 (75.7%) | 14 (4.2%) | 198 (39.0%) | 10 (5.1%) | 101 (51.0%) | 90 (45.5%) | 76 (38.4%) |
M | 148 (33.0%) | 96 (64.9%) | 113 (76.4%) | 297 (66.3%) | 13 (4.4%) | 225 (75.8%) | 14 (4.7%) | 180 (40.2%) | 9 (5.0%) | 90 (50.0%) | 80 (44.4%) | 66 (36.7%) |
F | 22 (36.7%) | 13 (59.1%) | 17 (77.3%) | 40 (66.7%) | 0 (0%) | 30 (75.0%) | 0 (0%) | 18 (30.0%) | 1 (5.6%) | 11 (61.1%) | 10 (55.6%) | 10 (55.6%) |
≥35 Years | 52 (39.4%) | 35 (67.3%) | 43 (82.7%) | 107 (81.1%) | 3 (12.1%) | 97 (80.7%) | 9 (8.4%) | 21 (15.9%) | 2 (9.5%) | 12 (57.1%) | 11 (52.4%) | 11 (52.4%) |
M | 48 (38.1%) | 32 (66.7%) | 39 (81.3%) | 103 (81.7%) | 3 (2.9%) | 95 (92.2%) | 9 (8.7%) | 19 (15.1%) | 2 (10.5%) | 11 (57.9%) | 10 (52.6%) | 10 (52.6%) |
F | 4 (66.7%) | 3 (75.0%) | 4 (100%) | 4 (66.7%) | 0 (0%) | 2 (50.0%) | 0 (0%) | 2 (33.3%) | 0 (0%) | 1 (50.0%) | 1 (50.0%) | 1 (50.0%) |
Tobacco . | Alcohol . | HCBs . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Total . | Daily consumers . | 2 Hours B/A . | Total . | Daily consumers . | 2 Hours B/A . | 2 Hours before M . | Total . | Daily consumers . | 2 Hours B/A . | 2 Hours B/A M . | 2 Hours before M . |
Whole | 222 (34.7%) | 144 (64.9%) | 173 (77.9%) | 444 (69.4%) | 16 (3.6%) | 352 (79.3%) | 23 (5.2%) | 219 (34.2%) | 12 (5.5%) | 113 (51.6%) | 101 (46.1%) | 87 (39.7%) |
M | 196 (34.1%) | 128 (65.3%) | 152 (77.6%) | 400 (69.7%) | 16 (4.0%) | 320 (80.0%) | 23 (5.8%) | 199 (34.7%) | 11 (5.5%) | 101 (50.8%) | 90 (45.2%) | 76 (38.2%) |
F | 26 (39.4%) | 16 (61.5%) | 21 (80.8%) | 40 (66.7%) | 0 (0%) | 32 (72.7%) | 0 (0%) | 20 (30.3%) | 1 (5.0%) | 12 (60.0%) | 11 (55.0%) | 11 (55.0%) |
<35 Years | 170 (33.5%) | 109 (64.1%) | 130 (76.5%) | 337 (66.1%) | 13 (0.9%) | 255 (75.7%) | 14 (4.2%) | 198 (39.0%) | 10 (5.1%) | 101 (51.0%) | 90 (45.5%) | 76 (38.4%) |
M | 148 (33.0%) | 96 (64.9%) | 113 (76.4%) | 297 (66.3%) | 13 (4.4%) | 225 (75.8%) | 14 (4.7%) | 180 (40.2%) | 9 (5.0%) | 90 (50.0%) | 80 (44.4%) | 66 (36.7%) |
F | 22 (36.7%) | 13 (59.1%) | 17 (77.3%) | 40 (66.7%) | 0 (0%) | 30 (75.0%) | 0 (0%) | 18 (30.0%) | 1 (5.6%) | 11 (61.1%) | 10 (55.6%) | 10 (55.6%) |
≥35 Years | 52 (39.4%) | 35 (67.3%) | 43 (82.7%) | 107 (81.1%) | 3 (12.1%) | 97 (80.7%) | 9 (8.4%) | 21 (15.9%) | 2 (9.5%) | 12 (57.1%) | 11 (52.4%) | 11 (52.4%) |
M | 48 (38.1%) | 32 (66.7%) | 39 (81.3%) | 103 (81.7%) | 3 (2.9%) | 95 (92.2%) | 9 (8.7%) | 19 (15.1%) | 2 (10.5%) | 11 (57.9%) | 10 (52.6%) | 10 (52.6%) |
F | 4 (66.7%) | 3 (75.0%) | 4 (100%) | 4 (66.7%) | 0 (0%) | 2 (50.0%) | 0 (0%) | 2 (33.3%) | 0 (0%) | 1 (50.0%) | 1 (50.0%) | 1 (50.0%) |
B/A: before or after a rugby session; B/A M: before or after playing a match; 2 Hours before M: within 2 hours before playing a match; HCB: highly caffeinated beverage.
Daily consumers (% of total consumers).
Tobacco
More than a third (34.7%) of the respondents reported smoking during the last season, and most of these (64.9%) were daily smokers. This consumption was similar for both age groups (33.5% in the younger vs. 39.4% in the older group, respectively, P = 0.2185). Surprisingly, more than three-quarters (77.9%) of the smokers reported consuming tobacco within 2 hours before or after the rugby session. Only 2.3% reported other kinds of nicotine consumption, such as electronic cigarettes, nicotine patches, chewing-gum or chicha. None reported using dipping smokeless tobacco.
Alcohol
A majority (69.4%) of the players had drunk alcohol over the course of the last season, and this rate was slightly lower in the under 35 years group (66.1% vs. 81.1%, P = 0.001). More precisely, 53% drank alcohol once a week and only 3.6% declared daily alcohol intake. Strikingly, most (79.3%) of the alcohol drinkers consumed alcohol within 2 hours either before or after the match, and 5.2% of the drinkers consumed alcohol within 2 hours before a match.
Highly caffeinated beverages
A high rate of respondents (34.2%) reported highly caffeinated beverage (HCB) consumption, particularly for the younger group (39.0 vs. 15.9%, respectively, P < 0.0001) (Figure 1). In half (51.6%) of the consumers, the beverage was drunk within 2 hours before or after a sports session. The most common (34.3%) reason for HCB intake was fighting fatigue and/or performance enhancement. This consumption was mostly within the 2 hours before playing a match (39.7%), and tended to be higher in female respondents than male respondents (55.0% vs. 38.2%, P = 0.1568).
Multiple consumption patterns
A subgroup of subjects (n = 29; 4.5%) reported consumption of the three substances (tobacco, alcohol and HCBs) within the 2 hours before or after a rugby session. Others reported the consumption of associations of tobacco and alcohol (11.7%), tobacco and HCBs (5.8%) and alcohol and HCBs (1.1%). The difference between age groups was only significant for tobacco and alcohol (9.6% <35 years vs. 19.7% ≥ 35 years, P = 0.006). There was no difference between male and female respondents for any of these patterns.
Discussion
Our large contemporary sport-based survey shows that in current amateur rugby practice, a significant prevalence of conditions, including at-risk behaviours and unawareness of risk factors, could impair the cardiovascular health of players.
Adherence to recommended prevention guidelines
In France, a yearly licence is mandatory to play rugby in competition, and this licence is obtained after a medical examination which may include an ECG screening (recommended). Less than 40% of our subjects declared that they had undergone prior ECG assessment. Moreover, ECG screening had been done in less than one third of the younger (<35 years) players, who are the priority target population, and in whom the benefit of screening has been reported. In these players, cardiomyopathies, pre-excitation and channelopathies, which are major underlying causes of SCD, are identifiable with ECG screening.5 Systematic screening including ECG has been associated with a reduction of SCD and is recommended by French guidelines.13 Similar rates of ECG screening have been reported in athletes.14 In our study, although no information was available on the reason for ECG screening, sports medicine is expected to be a major cause leading to ECG checks. However, multiple other undetermined situations may have driven the ECG checks, including working medicine supervision, national insurance programmes, preoperative ECG checks, etc. Overall, these unknown conditions are probably more frequent in older individuals.
Approximately half of the rugby players declared they had had prior training in BLS, and 17% had attended training provided by the FRF and the remainder at school or ‘at work’. Moreover, BLS training is mandatory for obtaining rugby coach, educator and referee diplomas. This type of training, along with the installation of automatic defibrillators in public places, has been associated with a better prognosis after ACS during sports.15 In our region, where the survival rate after sports-related ACS is high, 75% of the amateur rugby stadiums are supplied with an external defibrillator, similar to the rate reported in Ireland.16 French data report that a significant number of rugby players (32%) live in areas, where bystander cardiopulmonary resuscitation is performed in more than 90% of sports-related cardiac arrests, with a success rate of approximately 50%.3,15
At-risk behaviours
More than 40% of our players declared they played at least one match or training session when suffering from a febrile illness during the previous season. Since acute myocarditis is a frequent cause of SCD during sports, abstaining from heavy exercise during febrile episodes is highly recommended.4 A poster summarising cardiovascular recommendations, including how to proceed during febrile episodes, was handed out to each rugby club in the region. However, very few individuals (17%) reported the information displayed by the poster, and only 40.7% of these saw the information at their rugby facilities.
In our study, the tobacco consumption rate (>35%) in the previous rugby season, with a similar sex ratio, was comparable to French and European studies addressing young individuals.14,17–19 In our region, the prevalence of daily tobacco consumption was estimated in 2016 to be 24.6% in the 15–24 year age group, 35.6% in the 25–34 year group and 32.6% in the 35–44 year group (see Supplementary data). Among 1034 French students (20.8 ± 2.3 years, sex ratio M/F 0.45), the prevalence of smokers was 24.3%.18 These rates of tobacco use were close to those of our rugby players, for a similar age group (i.e. 33.5% in those <35 years). In 2014, the prevalence of 17-year-old French daily smokers was estimated at 39% and the prevalence of regular alcohol consumers at 15% (see Supplementary data).
Smoking prevalence diminishes with an increase in exercise practice19 and is less frequent in elite student athletes.20,21 In 458 French elite student athletes (16–24 years, sex ratio M/F 0.65), 25.1% and 20.5% of women and men smoked (10.3 and 9.4% daily), respectively; alcohol drinking was found in 62.8% and 70.4% (at least 10 times a month in 0.6% and 5.6%), respectively.20 In 602 elite young Norwegian athletes (16.5 ± 0.3 years, sex ratio M/F 0.55), the prevalence of alcohol drinking was 36.5%, less than sedentary controls.21
Surprisingly, alternative patterns of nicotine intake were rarely reported in our survey, in contrast to studies conducted in Nordic sports or in French students.22 Nearly 80% of respondents had smoked within the 2 hours before or after a rugby session, irrespective of current recommendations,8 which indicates that tobacco smoking is a major risk factor for ACS and SCD. Vascular functional impairment relevant to atherosclerosis progression can occur early in smoking adolescents, even for low levels of consumption, but stopping during adolescence could restore arterial health.23 Underlying mechanisms include endothelial dysfunction, inflammation, platelet activation and coronary spasm. This risk is largely amplified during exercise and recovery, through catecholamine release and hydroelectrolytic depletion.1,6
Regular alcohol drinking was common, even in women, and especially in the older respondents (≥35 years). However, the daily consumption of alcohol was under 4%. Alcohol consumption was frequently reported within 2 hours after a game, suggestive of the cultural ‘third-halftime’ consumption pattern. A positive correlation between alcohol intake and sports activity has been described.19 A lower rate of alcohol consumption has been observed in elite young athletes when compared to other athletes or non-athletes.20,21,24 In our study, players compete at a regional, i.e. non-elite level. Alcohol consumption is more common in ball, team and field sports, such as rugby, than in other sports.20,24,25 In our survey, no questions addressed binge drinking, which is a common pattern of consumption in athletes, especially in rugby and other team or contact sports.26 Notably, more than 5% of consumers reported alcohol intake before a match. Such a feature is common in rugby players25 and suggests an intention to dope. Acute alcohol intake promotes the risk of traumatic injury, in addition to ventricular arrhythmia and ACS.9,27 High-intensity drinking has recently been shown to increase arterial stiffness significantly, which is a marker of vascular damage that predicted later cardiovascular disease and events in a British cohort of teenagers (up to 17 years).23 Alcohol also impairs performance, increases the risk of injury and alters recovery.25,26 Moreover, consumption in the young may lead to addiction and related chronic complications.25 The consumption patterns of these substances are complex and may differ between populations and the type of physical activity.19,21 The smoking rate has been shown to be lower in teenagers participating in sports activities,28 while alcohol consumption was more frequent in sports participants.29 Furthermore, tobacco and alcohol consumption patterns are also different according to the level of practice (i.e. elite or amateur), type of sports (i.e. individual or team sports). For example, smoking was found to be lower in individual when compared with team sports,30 elite rather than amateur athletes; alcohol seems to be more common in team sports.31 However, to date, no specific data are available in French amateur rugby players.
HCB intake was reported by over two-thirds of the players, and was more than two times as frequent for those under 35 years of age (39% vs. 15.9%, P < 0.001). High HCB consumption has previously been reported in American or European adolescents and young adults.32,33,34 In our survey, almost 50% of consumers had drunk HCBs within the 2 hours before or after a match. In this particular group, consumption was clearly associated with a ‘festive’ atmosphere. More than one-third of consumers declared performance-based motivation, and approximately 40% drank HCBs before playing a match. In the north of France, HCB consumption has been evaluated in young sports participants with a prevalence of 65% for experimentation and 15% for regular use. Consumption was more frequent in the younger sports participants and their motivation was more often festive and socialising than in the older participants (see Supplementary data).
Surprisingly, a high proportion of players consuming HCBs close to a rugby session often also drank before playing a match. These findings are highly suggestive of doping behaviour. In a population of Italian students, the reasons for consuming HCBs involved performance.33 HCB composition includes water, carbohydrates, caffeine, taurine and guarana (which also contains substantial amounts of caffeine).35–37 Caffeine or taurine in HCBs may improve some aspects of physical performance,37 and in rugby players the beneficial effect of HCBs has been attributed to caffeine.38 However, in a meta-analysis, Souza et al. found that increased performance was related to taurine rather than caffeine.39 Moreover, some work found that sugar-free HCBs had no beneficial effect,40 therefore suggesting that the supply of carbohydrate yielded a positive effect. Overall, these studies are associated with potential bias such as placebo effect or conflicting interests of authorship.41,42 Severe deleterious acute events caused by HCBs have been largely reported, including neurological and cardiovascular events such as ACS, life-threatening arrhythmia and SCD.36,43 The pathophysiological mechanisms for cardiovascular effects are mainly caused by the significant amounts of caffeine contained in HCBs, and the major effects are mediated through stimulation of adenosine pathways.36 HCBs may promote endothelial dysfunction, affect coagulability, platelet function, impair coronary vasodilation and decrease myocardial blood flow during exercise when ingested before.36 HCBs have also been shown to impair myocardial electrophysiological properties, associated with an increase in heart rate, and characterised by QTc interval prolongation, and to induce severe arrhythmias, including supraventricular and life-threatening ventricular arrhythmias.43 This effect could interfere with exercise-induced ventricular arrhythmias, although its burden in athletes remains controversial.44 Regarding the relation between HCBs and BLS and advanced life support, experimental findings in animals have shown that alcohol intake elevates the ventricular defibrillation threshold.45 However, in humans, no data are available supporting such an effect associated with alcohol or HCB consumption.
Moreover, the large amounts of sugar contained in HCBs promote haemoconcentration, and HCB intake commonly leads to dehydration and loss of electrolytes because of its diuretic effect and increased sweating.35,42 Overall, these effects are reinforced by the impact of exercise on these rhythmic, vascular and haemorheological disorders.1 We must point out that the association of HCBs and alcohol consumption, which is a common pattern, enhances neurological, cardiovascular and traumatic risk.35 Among the HCB brands used by the responders, none of them included substances from the World Anti-Doping Agency prohibited list. Before each rugby season, players were discouraged by the coaches and medical resources to buy any supplements and other substances available on the web.
In our study, HCB intake was often associated with alcohol and tobacco consumption. Joint HCB consumption with substance use and other at-risk behaviours such as binge drinking, sweetened beverage intake, junk food, steroid and performance-enhancing substances intake or the number of hours playing video games is often reported in children and young adults.32,42,46 HCB consumption has also been positively linked to body mass index (BMI), due to sugar contained in HCBs and the associated eating behaviours that promote obesity and diabetes.35,46 However, BMI is difficult to interpret in rugby players, regarding the wide range of morphologies in amateur practicing and the specific impact of sport-related elevation in muscle mass on BMI. Recent data has shown that measure of body composition using body fat mass index (BFMI) and fat-free mass index (FFMI) body BFMI are needed to avoid misclassifications in rugby players.47
Conclusion
The present work shows that some common behaviours exhibited by amateur rugby players could negatively interfere with their cardiovascular health. Although nearly 50% are trained in BLS, ECG has been performed in a limited number of players, especially in those under 35 years of age. Moreover, our survey highlights that a high proportion of rugby players exercise even when they are feverish. The consumption of non-illicit substances surrounding the exercise period is frequent, thus potentially promoting acute cardiac events. Our data highlight that alcohol and especially HCBs are often consumed before a match, suggesting doping behaviour. Our findings in a large regional cohort may help to identify targets for the improvement of cardiovascular prevention through the development of educational programmes aiming to improve the knowledge and behaviour of amateur rugby players.
Acknowledgements
The authors would like to thank Julien Petit and Jean-Christophe Dincher for collecting data and providing technical assistance and Maud Maza for completing the statistical analyses. They would also like to thank Suzanne Rankin for proofreading the document.
Author contribution
VG, YC, MG, FC, TH, GG, JI, JPH, PA and YL contributed to the conception or design of the work. FC and AG contributed to the acquisition, analysis, or interpretation of data for the work. MG and FC drafted the manuscript. MZ, YC, FC, AG, TH, GG, JI, JPH, PA, YL and VG critically revised the manuscript. MG, MZ, FC, AG, TH, GG, JI, JPH, PA, YL and VG finally approved the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work ensuring integrity and accuracy.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work was supported by the Dijon University Hospital, the French Federation of Cardiology, the Association de Cardiology de Bourgogne, the Burgundy Rugby Committee and the French Federation of Rugby, and by grants from the Agence Régionale de Santé (ARS) de Bourgogne-Franche-Comté and from the Regional Council of Burgundy-Franche-Comté.
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