International Journal of Sport Nutrition and Exercise Metabolism, 2015, 25, 594 -602 http://dx.doi.org/10.1123/ijsnem.2015-0073 © 2015 Human Kinetics, Inc.
ORIGINAL RESEARCH
Energy Availability and Dietary Patterns of Adult Male and Female Competitive Cyclists With Lower Than Expected Bone Mineral Density Rebecca T. Viner, Margaret Harris, Jackie R. Berning, and Nanna L. Meyer The purpose of this study was to assess energy availability (EA) and dietary patterns of 10 adult (29–49 years) male (n = 6) and female (n = 4) competitive (USA Cycling Category: Pro, n = 2; 1–4, n = 8) endurance cyclists (5 road, 5 off-road), with lower than expected bone mineral density (BMD; Z score < 0) across a season. Energy intake (EI) and exercise energy expenditure during preseason (PS), competition (C), and off-season (OS) were estimated from 3-day dietary records, completed once per month, across a cycling season. BMD was measured by DXA at 0 months/5 months/10 months. The Three-Factor Eating Questionnaire (TFEQ) was used to assess cognitive dietary restraint. Seventy percent of participants had low EA [(LEA); < 30 kcal·kg fat-free mass (FFM) –1·day–1] during PS, 90% during C, and 80% during OS (range: 3–37 kcal·kg FFM–1·day–1). Ninety percent of cyclists had LEA during ≥ 1 training period, and 70% had LEA across the season. Seventy percent of cyclists were identified as restrained eaters who consciously restrict EI as a means of weight control. Mean daily carbohydrate intake was below sport nutrition recommendations during each training period (PS: 3.9 ± 1.1 g·kg–1·day–1, p < .001; C: 4.3 ± 1.4 g·kg–1·day–1, p = .005; OS: 3.7 ± 1.4 g·kg–1·day–1, p = .01). There were no differences in EA and EI·kg–1 between male and female cyclists and road and off-road cyclists. Low EI, and specifically low carbohydrate intake, appears to be the main contributor to chronic LEA in these cyclists. Adult male and female competitive road and off-road cyclists in the United States may be at risk for long-term LEA. Further studies are needed to explore strategies to prevent and monitor long-term LEA in these athletes. Keywords: energy balance, relative energy deficiency, athletes, nutrition Evidence suggests that cyclists may be at risk for low bone mass, particularly at the lumbar spine (Nagle & Brooks, 2011; Scofield & Hecht, 2012). The nonweight bearing nature of cycling and the repetitive pattern of lowamplitude and evenly distributed skeletal loads during cycling may not adequately stimulate bone formation (Nichols et al., 2003). In addition, nutrient and hormonal imbalances, due to sweat calcium losses and inadequate energy intake (EI), may decrease bone formation and increase bone resorption (Lombardi et al., 2012; Nattiv et al., 2007; Oosthuyse et al., 2014). Energy availability (EA) is calculated as EI minus energy expended during exercise (EEE) relative to fatfree mass (FFM). Brief periods of low energy availability [(LEA); 35%EI) during C and OS. PRO, fat, and fiber intake did not change significantly across the season. The ED of intake changed throughout the season, χ2(2) = 7.2 (p = .03). ED increased significantly (p = .02) from C (0.8 ± 0.4 kcal·g–1·day–1) to OS (0.9 ± 0.3 kcal·g–1·day–1). Figure 2 displays the distribution of cyclists with inadequate intakes of micronutrients (Institute of Medicine, Food and Nutrition Board, 2001, 2011) from food alone (excluding supplements). Multivitamins (40% of participants) and fish oil (30%) were supplements used regularly (>3 days·week–1). Per protocol of the original
Results Participant descriptive characteristics are presented in Table 1. There were no significant changes in mean BM, percentage of body fat (BF), or FFM across the season for any group of cyclists. There were no significant changes in the group mean BMD measurements at any site across the season. Forty percent of participants had low BMD at the lumbar spine (n = 2/5 RC, 2/5 MB) and 10% (n = 1/5 MB) had low BMD at the femoral neck. Participants exercised an average of 1.4 ± 0.5 hr·day–1. All participants performed strength training, and 40% of participants performed running exercise in addition to cycling (Table 1).
Table 1 Descriptive Characteristics of Cyclists by Gender and Specialty (n = 10), M ± SD Male (n = 6)
Female (n = 4)
pa
Age (years)
42.0 ± 7.7
38.4 ± 10.3
.54
Height (cm)
177.9 ± 4.2
165.4 ± 6.4
.01
72.4 ± 6.8
62.8 ± 12.2
.14
11.9 ± 4.5
24.9 ± 8.4
63.8 ± 3.8 1.6 ± 0.6
Body
Massd
Body
Fatd
(kg)
(%)
Fat-Free
Massd
Exercise
(hr·day-1)
(kg)
MBb (n = 5)
RCc (n = 5)
pa
35.8 ± 9.0
45.3 ± 5.0
.07
171.4 ± 7.6
174.4 ± 9.1
.59
65.4 ± 6.5
71.7 ± 12.6
.35
.01
15.9 ± 7.0
18.3 ± 11.2
.70
46.5 ± 4.5
< .001
55.4 ± 9.2
58.3 ± 11.1
.66
1.2 ± 0.4
.29
1.4 ± 0.5
1.4 ± 0.7
.84
Cycling only exercise
(hr·day-1)
1.4 ± 0.6
0.8 ± 0.4
.07
1.2 ± 0.6
1.2 ± 0.6
1.0
Non-cycling exercise
(hr·day-1)
0.2 ± 0.1
0.3 ± 0.2
.72
0.2 ± 0.2
0.3 ±0.1
.15
13 ± 5
5±1
.02
12 ± 7
8±3
.26
Pro, 2-4
1-3
-
Pro, 1-3
2-4
-
Racing experience (years) USA cycling category (range) aIndependent-samples
t test or MannWhitney U test, difference is significant at p < .05.
bMountain
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bike cyclists.
cRoad
cyclists.
dAssessed
by DXA.
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Energy Availability of Competitive Cyclists 597
Figure 1 — Percentage distribution of adult competitive cyclists with low energy availability across the cycling season.
study, 90% of participants were taking calcium (500– 1,000 mg·day–1) and vitamin D supplements (400–5,000 IU·day–1) because of insufficiencies.
Dietary Patterns Cyclists consumed an average of 5 ± 1 meals·day–1, including 1 ± 1 exercise meals·day–1 across the season. EA was not correlated with meals·day –1 or exercise meals·day–1. Overall, the majority of daily energy (27%), PRO (32%), and fat (29%) was consumed during dinner, but CHO intake was greatest at breakfast (24%). Exercise meals accounted for 15% of daily CHO intake. Most participants consumed food/fluids postexercise (70%) rather than pre- (30%) or during (40%) exercise. Ten percent of participants regularly consumed food/fluids pre-, during, and postexercise. Average CHO intakes pre-, during, and postexercise were 0.2 ± 0.0 g·kg–1·hr–1, 7.9 ± 2.5 g·hr–1, and 0.3 ± 0.2 g·kg–1·hr–1, respectively. The total CHO content (in grams) of exercise meals changed significantly across the season, χ2(2) = 11.13 (p = .004), increasing from PS (43 ± 41 g·day–1) to C (68 ± 49 g·day–1) and decreasing significantly (p = .004) from C to OS (35 ± 42 g·day–1). A fairly strong negative correlation was revealed between EEE and CHO intake during PS, rs(8) = –.61 (p = .06), but a fairly strong positive correlation was found during C, r(8) = .67 (p = .03), and there was no correlation during OS. Average TFEQ scores (10.1 ± 3.6) did not change from 0–10 months. There were no differences in scores between male and female cyclists. Road cyclists (12.2 ± 2.4) scored higher than MB (8.2 ± 4.5), although not
significantly because of large variances (p = .31). There were no significant differences in EA, EI·kg–1, EEE, macronutrient intake, ED, or fiber intake between RE and non-RE. The majority of cyclists were identified as RE (70% all; 100% RC, 40% MB, 67% male cyclists, and 75% female cyclists).
Discussion This study is the first to examine EA across the season in cyclists. We found that EA did not change across the season and remained below the threshold (30 kcal·kg FFM–1·day–1) commonly associated with negative effects on bone health (Ihle & Loucks, 2004). Low EI, and specifically low CHO intake, appears to be the main contributor to chronic LEA in these cyclists. Mean CHO intake (3.9 g·kg–1·day–1) was lower than that previously reported of cyclists (5.5–10.0 g·kg– 1·day–1; Burke et al., 2001; Martin et al., 2002; Penteado et al., 2010; Vogt et al., 2005). Unlike past studies, our participants, rather than a team manager or a kitchen staff, were responsible for their own food provisions. Their CHO selections were likely influenced by current fad diets, which promote restriction of CHO sources (grains, legumes, tubers), as these foods were scarce in their dietary records and gluten-free food choices were popular. Furthermore, two participants admitted they were following the Paleo diet and/or a gluten-free diet. Notably, the only participants with mean EA > 30 kcal·kg FFM–1·day–1 (n = 2) also had the highest daily CHO intakes.
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31.2 ± 8.9
795 ± 424
29.3 ± 6.8
1043 ± 718
EIb
EEEc
RC
949 ± 855
33.6 ± 7.6
27.1 ± 14.3
MB
938 ± 291
26.5 ± 5.4
16.4 ± 9.4
M
1424 ± 491*
34.7 ± 6.0
19.5 ± 8.5
697 ± 244*
29.8 ± 8.9
25.5 ± 3.1
F
RC
1189 ± 527
34.9 ± 8.9
24.5 ± 6.1
Competition MB
1076 ± 615
30.6 ± 5.3
19.3 ± 8.2
M
1030 ± 539
31.8 ± 7.5
21.7 ± 9.2
27 ± 8
15.0 ± 3.6
13.2 ± 2.7
cyclists. bike cyclists. *Significant difference between RC and MB at p < .05.
bMountain
Fiber (g·1000
aRoad
kcal-1)
1.0 ± 0.3
1.1 ± 0.3
Fat
1.3 ± 0.4
1.4 ± 0.4
Protein
3.7 ± 1.2
3.9 ± 1.2
Carbohydrate
Macronutrients relative to body mass (g·kg-1day-1)
Fiber
61 ± 7 26 ± 10
72 ± 19
Fat
(g·day-1)
94 ± 29
Protein
77 ± 15
267 ± 84
Carbohydrate
Macronutrients 223 ± 43
0.8 ± 0.2
(g·day-1)
0.8 ± 0.4
of food
1741 ± 265
2083 ± 155
Female
kcal·g-1
All
kcal·day-1
Energy
Variable
Gender
12.0 ± 1.1
1.1 ± 0.3
1.5 ± 0.4
4.1 ± 1.3
27 ± 8
78 ± 21
106 ± 30
296 ± 94
0.9 ± 0.5
2311 ± 485
Male
13.8 ± 3.0
1.1 ± 0.4
1.6 ± 0.4
4.1 ± 1.6
31 ± 8
80 ± 22
112 ± 25*
288 ± 95
0.8 ± 0.4
2323 ± 471
RCa
Specialty
Table 3 Cyclists’ Average Daily Intake of Energy, Macronutrients, and Fiber by Gender and Specialty, M ± SD
aEnergy Availability (kcal·kgFFM-1·day-1). bEnergy Intake (kcal·kgBM-1·day-1). cExercise Energy Expenditure (kcal·day-1) *Significant difference between M and F, independent samples t test; t(8) = –2.71, p = .03.
F
26.2 ± 14.1
M
18.8 ± 12.1
EAa
Pre-Season F
12.6 ± 2.6
1.0 ± 0.1
1.2 ± 0.2
3.7 ± 0.9
23 ± 7
63 ± 10
76 ± 19*
246 ± 74
0.9 ± 0.4
1843 ± 420
MBb
482 ± 59
25.4 ± 9.1
23.8 ± 8.9
840 ± 561
31.2 ± 9.7
24.9 ± 10.9
RC
Off-Season
Table 2 A Comparison of Energy Availability and Its Components Between Male (M) and Female (F) Cyclists and Road Cyclists (RC) and Mountain Bike Cyclists (MB), M ± SD
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MB
781 ± 479
27.3 ± 7.3
20.1 ± 5.8
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Energy Availability of Competitive Cyclists 599
Figure 2 — Distribution of cyclists with inadequate micronutrient intake ( 30 kcal·kg FFM–1·day–1 across the season. It is also generally accepted that CHO intake before and during exercise improves performance, and frequency of eating occasions·day–1 has been positively associated with EI (Burke et al., 2003; Rodriguez et al., 2009). Contrary to CHO intake, our participants appeared to consume greater amounts of fat than cyclists of previous studies (31% vs. 17–21% daily EI; Martin et al., 2002; Vogt et al., 2005). However, no differences were evident when fat intake was expressed relative to BM (1.1 g·kg–1·day–1). Expressing fat intake as a percentage of EI is misleading in populations with low EI; thus, absolute (g·day–1) and relative intakes (g·kg BM–1·day–1) are more useful. Long-term LEA increases an athlete’s risk of nutrient deficiencies (Rodriguez et al., 2009; Sundgot-Borgen et al., 2013). Indeed, we found both male and female cyclists had poor dietary intakes of several micronutrients
essential for bone health (Figure 2). Cyclists should be encouraged to consume a wide variety of foods to meet their micronutrient needs. Foods that are not tolerable on high-intensity training or competition days should be encouraged on other days, rather than eliminated from the diet (e.g., whole grains, legumes, vegetables). Despite chronic LEA and poor micronutrient intakes, our cyclists’ BMD was maintained over 10 months. This contradicts previous findings of significant decreases in BMD at the hip and spine of male and female cyclists over a competitive season (Barry & Kohrt, 2008; Beshgetoor et al., 2000; Sherk et al., 2014). It could be that 10 months was too short of a period to detect decreases in the group BMD of our cyclists. It is also possible the jumping, calcium, and vitamin D supplement interventions of the original study stimulated bone mineralization sufficiently to maintain the BMD of our cyclists. It is recommended that cyclists consume 1,500 mg·day–1 calcium and 1,500– 2,000 IU·day–1 vitamin D while performing programs of high-impact loading and/or resistance training at least 2–3 days·week–1 to promote bone health (Mountjoy et al., 2014). Our cyclists consumed ~1,400–1,900 mg of calcium and 500–1,000 IU of vitamin D·day–1 and performed 10 maximal vertical jumps·day–1, 5 days·week–1. In addition, all participants performed regular strength training exercise, and 40% engaged in regular running exercise. Participation in these activities has previously been reported to preserve bone in the hip and the spine of adult men (Nichols & Rauh, 2011).
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600 Viner et al.
Previously, Warner et al. (2002) observed that MB have higher hip and spine BMD than RC and controls. The greater ground surface loads induced by the rough terrain inherent to MB may impose an osteogenic stimulus (De Lorenzo & Hull, 1999). In addition, MB spend more time than RC out of their seats with just two points in bicycle contact, which causes greater loading of the appendicular skeleton (Impellizzeri & Marcora, 2007). We may not have found significant decreases in the group mean BMD of our cyclists because 50% were primarily MB cyclists. However, there was still a high prevalence (40%) of lower than normal lumbar spine BMD among the cyclists in the current study, including 2/5 of MB. And contrary to Warner et al. (2002), we found no significant differences between the BMD of RC and MB. Prevalence of LEA in this study may be overestimated because of errors associated with using selfreported food intake and dietary analysis software to estimate EI. Athletes may undereat during periods of recording, and/or underreport their food intake by as much as 20% (Burke et al., 2001). Our participants were motivated to keep accurate food records to benefit their own athletic performance, and they were contacted within one week of recording to clarify any voids in their records. In addition, the same researcher entered all food intakes into Food Processor and manually entered nutrition information for any food not already in the database. In addition, the previous literature suggests that we had more than enough days of food records to estimate average group data for EI and CHO (Burke et al., 2001). Group mean EI and EA may have been low in this study because 70% of the participants were identified as RE, and RE consciously restrict food intake as a means of weight control (Stunkard & Messick, 1985). Other studies have found that male and female competitive cyclists restrict EI and/or increase training loads to reduce BM, and they suggest that there is a high prevalence of disordered eating behaviors and eating disorders among cyclists (Filaire et al., 2007; Haakonssen et al., 2015; Riebl et al., 2007; Sundgot-Borgen et al., 2013). During pre- and posttesting interviews, all of our cyclists stated that they were either trying to maintain their current weight or trying to lose weight. Although mean BM did not change across the season, there may have been intervals when short-term decreases in BM occurred. The small sample size of this study limited our power to detect changes in EA across time, associations between EA and EI, and differences in EA between cyclists of different genders and specialties. The lack of a control group with Z scores > 0 and with mean EA > 30 limited our abilities to analyze the effects of chronic LEA on bone health. In addition, we were unable to use power output or HR data to quantify EEE.
Conclusion According to USA Cycling, 72% of competitive cyclists in the United States are between the ages of 25 and 54
years and 51% of competitive cyclists are age 35 years or older (USA Cycling & The USA Cycling Development Foundation, 2013). Our findings suggest that there may be a high prevalence of chronic LEA and dietary restraint among this population of competitive cyclists. More studies are needed to accurately estimate the prevalence of LEA among competitive cyclists of different age groups, disciplines, and genders. It is also important to identify eating behaviors and dietary patterns that increase the risk of LEA in this population so we can effectively prevent, identify, and treat LEA. Cyclists should focus on eating frequently throughout the day, especially pre-, during, and postexercise, to achieve adequate EI to support bone health and optimize athletic performance. Acknowledgments This study was designed and conducted by RTV and NLM; data were collected by RTV and analyzed by RTV and MH. All authors undertook data interpretation and manuscript preparation, and all authors approved the final version of the paper.
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ORIGINAL RESEARCH
Energy Availability and Dietary Patterns of Adult Male and Female Competitive Cyclists With Lower Than Expected Bone Mineral Density Rebecca T. Viner, Margaret Harris, Jackie R. Berning, and Nanna L. Meyer The purpose of this study was to assess energy availability (EA) and dietary patterns of 10 adult (29–49 years) male (n = 6) and female (n = 4) competitive (USA Cycling Category: Pro, n = 2; 1–4, n = 8) endurance cyclists (5 road, 5 off-road), with lower than expected bone mineral density (BMD; Z score < 0) across a season. Energy intake (EI) and exercise energy expenditure during preseason (PS), competition (C), and off-season (OS) were estimated from 3-day dietary records, completed once per month, across a cycling season. BMD was measured by DXA at 0 months/5 months/10 months. The Three-Factor Eating Questionnaire (TFEQ) was used to assess cognitive dietary restraint. Seventy percent of participants had low EA [(LEA); < 30 kcal·kg fat-free mass (FFM) –1·day–1] during PS, 90% during C, and 80% during OS (range: 3–37 kcal·kg FFM–1·day–1). Ninety percent of cyclists had LEA during ≥ 1 training period, and 70% had LEA across the season. Seventy percent of cyclists were identified as restrained eaters who consciously restrict EI as a means of weight control. Mean daily carbohydrate intake was below sport nutrition recommendations during each training period (PS: 3.9 ± 1.1 g·kg–1·day–1, p < .001; C: 4.3 ± 1.4 g·kg–1·day–1, p = .005; OS: 3.7 ± 1.4 g·kg–1·day–1, p = .01). There were no differences in EA and EI·kg–1 between male and female cyclists and road and off-road cyclists. Low EI, and specifically low carbohydrate intake, appears to be the main contributor to chronic LEA in these cyclists. Adult male and female competitive road and off-road cyclists in the United States may be at risk for long-term LEA. Further studies are needed to explore strategies to prevent and monitor long-term LEA in these athletes. Keywords: energy balance, relative energy deficiency, athletes, nutrition Evidence suggests that cyclists may be at risk for low bone mass, particularly at the lumbar spine (Nagle & Brooks, 2011; Scofield & Hecht, 2012). The nonweight bearing nature of cycling and the repetitive pattern of lowamplitude and evenly distributed skeletal loads during cycling may not adequately stimulate bone formation (Nichols et al., 2003). In addition, nutrient and hormonal imbalances, due to sweat calcium losses and inadequate energy intake (EI), may decrease bone formation and increase bone resorption (Lombardi et al., 2012; Nattiv et al., 2007; Oosthuyse et al., 2014). Energy availability (EA) is calculated as EI minus energy expended during exercise (EEE) relative to fatfree mass (FFM). Brief periods of low energy availability [(LEA); 35%EI) during C and OS. PRO, fat, and fiber intake did not change significantly across the season. The ED of intake changed throughout the season, χ2(2) = 7.2 (p = .03). ED increased significantly (p = .02) from C (0.8 ± 0.4 kcal·g–1·day–1) to OS (0.9 ± 0.3 kcal·g–1·day–1). Figure 2 displays the distribution of cyclists with inadequate intakes of micronutrients (Institute of Medicine, Food and Nutrition Board, 2001, 2011) from food alone (excluding supplements). Multivitamins (40% of participants) and fish oil (30%) were supplements used regularly (>3 days·week–1). Per protocol of the original
Results Participant descriptive characteristics are presented in Table 1. There were no significant changes in mean BM, percentage of body fat (BF), or FFM across the season for any group of cyclists. There were no significant changes in the group mean BMD measurements at any site across the season. Forty percent of participants had low BMD at the lumbar spine (n = 2/5 RC, 2/5 MB) and 10% (n = 1/5 MB) had low BMD at the femoral neck. Participants exercised an average of 1.4 ± 0.5 hr·day–1. All participants performed strength training, and 40% of participants performed running exercise in addition to cycling (Table 1).
Table 1 Descriptive Characteristics of Cyclists by Gender and Specialty (n = 10), M ± SD Male (n = 6)
Female (n = 4)
pa
Age (years)
42.0 ± 7.7
38.4 ± 10.3
.54
Height (cm)
177.9 ± 4.2
165.4 ± 6.4
.01
72.4 ± 6.8
62.8 ± 12.2
.14
11.9 ± 4.5
24.9 ± 8.4
63.8 ± 3.8 1.6 ± 0.6
Body
Massd
Body
Fatd
(kg)
(%)
Fat-Free
Massd
Exercise
(hr·day-1)
(kg)
MBb (n = 5)
RCc (n = 5)
pa
35.8 ± 9.0
45.3 ± 5.0
.07
171.4 ± 7.6
174.4 ± 9.1
.59
65.4 ± 6.5
71.7 ± 12.6
.35
.01
15.9 ± 7.0
18.3 ± 11.2
.70
46.5 ± 4.5
< .001
55.4 ± 9.2
58.3 ± 11.1
.66
1.2 ± 0.4
.29
1.4 ± 0.5
1.4 ± 0.7
.84
Cycling only exercise
(hr·day-1)
1.4 ± 0.6
0.8 ± 0.4
.07
1.2 ± 0.6
1.2 ± 0.6
1.0
Non-cycling exercise
(hr·day-1)
0.2 ± 0.1
0.3 ± 0.2
.72
0.2 ± 0.2
0.3 ±0.1
.15
13 ± 5
5±1
.02
12 ± 7
8±3
.26
Pro, 2-4
1-3
-
Pro, 1-3
2-4
-
Racing experience (years) USA cycling category (range) aIndependent-samples
t test or MannWhitney U test, difference is significant at p < .05.
bMountain
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bike cyclists.
cRoad
cyclists.
dAssessed
by DXA.
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Energy Availability of Competitive Cyclists 597
Figure 1 — Percentage distribution of adult competitive cyclists with low energy availability across the cycling season.
study, 90% of participants were taking calcium (500– 1,000 mg·day–1) and vitamin D supplements (400–5,000 IU·day–1) because of insufficiencies.
Dietary Patterns Cyclists consumed an average of 5 ± 1 meals·day–1, including 1 ± 1 exercise meals·day–1 across the season. EA was not correlated with meals·day –1 or exercise meals·day–1. Overall, the majority of daily energy (27%), PRO (32%), and fat (29%) was consumed during dinner, but CHO intake was greatest at breakfast (24%). Exercise meals accounted for 15% of daily CHO intake. Most participants consumed food/fluids postexercise (70%) rather than pre- (30%) or during (40%) exercise. Ten percent of participants regularly consumed food/fluids pre-, during, and postexercise. Average CHO intakes pre-, during, and postexercise were 0.2 ± 0.0 g·kg–1·hr–1, 7.9 ± 2.5 g·hr–1, and 0.3 ± 0.2 g·kg–1·hr–1, respectively. The total CHO content (in grams) of exercise meals changed significantly across the season, χ2(2) = 11.13 (p = .004), increasing from PS (43 ± 41 g·day–1) to C (68 ± 49 g·day–1) and decreasing significantly (p = .004) from C to OS (35 ± 42 g·day–1). A fairly strong negative correlation was revealed between EEE and CHO intake during PS, rs(8) = –.61 (p = .06), but a fairly strong positive correlation was found during C, r(8) = .67 (p = .03), and there was no correlation during OS. Average TFEQ scores (10.1 ± 3.6) did not change from 0–10 months. There were no differences in scores between male and female cyclists. Road cyclists (12.2 ± 2.4) scored higher than MB (8.2 ± 4.5), although not
significantly because of large variances (p = .31). There were no significant differences in EA, EI·kg–1, EEE, macronutrient intake, ED, or fiber intake between RE and non-RE. The majority of cyclists were identified as RE (70% all; 100% RC, 40% MB, 67% male cyclists, and 75% female cyclists).
Discussion This study is the first to examine EA across the season in cyclists. We found that EA did not change across the season and remained below the threshold (30 kcal·kg FFM–1·day–1) commonly associated with negative effects on bone health (Ihle & Loucks, 2004). Low EI, and specifically low CHO intake, appears to be the main contributor to chronic LEA in these cyclists. Mean CHO intake (3.9 g·kg–1·day–1) was lower than that previously reported of cyclists (5.5–10.0 g·kg– 1·day–1; Burke et al., 2001; Martin et al., 2002; Penteado et al., 2010; Vogt et al., 2005). Unlike past studies, our participants, rather than a team manager or a kitchen staff, were responsible for their own food provisions. Their CHO selections were likely influenced by current fad diets, which promote restriction of CHO sources (grains, legumes, tubers), as these foods were scarce in their dietary records and gluten-free food choices were popular. Furthermore, two participants admitted they were following the Paleo diet and/or a gluten-free diet. Notably, the only participants with mean EA > 30 kcal·kg FFM–1·day–1 (n = 2) also had the highest daily CHO intakes.
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31.2 ± 8.9
795 ± 424
29.3 ± 6.8
1043 ± 718
EIb
EEEc
RC
949 ± 855
33.6 ± 7.6
27.1 ± 14.3
MB
938 ± 291
26.5 ± 5.4
16.4 ± 9.4
M
1424 ± 491*
34.7 ± 6.0
19.5 ± 8.5
697 ± 244*
29.8 ± 8.9
25.5 ± 3.1
F
RC
1189 ± 527
34.9 ± 8.9
24.5 ± 6.1
Competition MB
1076 ± 615
30.6 ± 5.3
19.3 ± 8.2
M
1030 ± 539
31.8 ± 7.5
21.7 ± 9.2
27 ± 8
15.0 ± 3.6
13.2 ± 2.7
cyclists. bike cyclists. *Significant difference between RC and MB at p < .05.
bMountain
Fiber (g·1000
aRoad
kcal-1)
1.0 ± 0.3
1.1 ± 0.3
Fat
1.3 ± 0.4
1.4 ± 0.4
Protein
3.7 ± 1.2
3.9 ± 1.2
Carbohydrate
Macronutrients relative to body mass (g·kg-1day-1)
Fiber
61 ± 7 26 ± 10
72 ± 19
Fat
(g·day-1)
94 ± 29
Protein
77 ± 15
267 ± 84
Carbohydrate
Macronutrients 223 ± 43
0.8 ± 0.2
(g·day-1)
0.8 ± 0.4
of food
1741 ± 265
2083 ± 155
Female
kcal·g-1
All
kcal·day-1
Energy
Variable
Gender
12.0 ± 1.1
1.1 ± 0.3
1.5 ± 0.4
4.1 ± 1.3
27 ± 8
78 ± 21
106 ± 30
296 ± 94
0.9 ± 0.5
2311 ± 485
Male
13.8 ± 3.0
1.1 ± 0.4
1.6 ± 0.4
4.1 ± 1.6
31 ± 8
80 ± 22
112 ± 25*
288 ± 95
0.8 ± 0.4
2323 ± 471
RCa
Specialty
Table 3 Cyclists’ Average Daily Intake of Energy, Macronutrients, and Fiber by Gender and Specialty, M ± SD
aEnergy Availability (kcal·kgFFM-1·day-1). bEnergy Intake (kcal·kgBM-1·day-1). cExercise Energy Expenditure (kcal·day-1) *Significant difference between M and F, independent samples t test; t(8) = –2.71, p = .03.
F
26.2 ± 14.1
M
18.8 ± 12.1
EAa
Pre-Season F
12.6 ± 2.6
1.0 ± 0.1
1.2 ± 0.2
3.7 ± 0.9
23 ± 7
63 ± 10
76 ± 19*
246 ± 74
0.9 ± 0.4
1843 ± 420
MBb
482 ± 59
25.4 ± 9.1
23.8 ± 8.9
840 ± 561
31.2 ± 9.7
24.9 ± 10.9
RC
Off-Season
Table 2 A Comparison of Energy Availability and Its Components Between Male (M) and Female (F) Cyclists and Road Cyclists (RC) and Mountain Bike Cyclists (MB), M ± SD
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MB
781 ± 479
27.3 ± 7.3
20.1 ± 5.8
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Energy Availability of Competitive Cyclists 599
Figure 2 — Distribution of cyclists with inadequate micronutrient intake ( 30 kcal·kg FFM–1·day–1 across the season. It is also generally accepted that CHO intake before and during exercise improves performance, and frequency of eating occasions·day–1 has been positively associated with EI (Burke et al., 2003; Rodriguez et al., 2009). Contrary to CHO intake, our participants appeared to consume greater amounts of fat than cyclists of previous studies (31% vs. 17–21% daily EI; Martin et al., 2002; Vogt et al., 2005). However, no differences were evident when fat intake was expressed relative to BM (1.1 g·kg–1·day–1). Expressing fat intake as a percentage of EI is misleading in populations with low EI; thus, absolute (g·day–1) and relative intakes (g·kg BM–1·day–1) are more useful. Long-term LEA increases an athlete’s risk of nutrient deficiencies (Rodriguez et al., 2009; Sundgot-Borgen et al., 2013). Indeed, we found both male and female cyclists had poor dietary intakes of several micronutrients
essential for bone health (Figure 2). Cyclists should be encouraged to consume a wide variety of foods to meet their micronutrient needs. Foods that are not tolerable on high-intensity training or competition days should be encouraged on other days, rather than eliminated from the diet (e.g., whole grains, legumes, vegetables). Despite chronic LEA and poor micronutrient intakes, our cyclists’ BMD was maintained over 10 months. This contradicts previous findings of significant decreases in BMD at the hip and spine of male and female cyclists over a competitive season (Barry & Kohrt, 2008; Beshgetoor et al., 2000; Sherk et al., 2014). It could be that 10 months was too short of a period to detect decreases in the group BMD of our cyclists. It is also possible the jumping, calcium, and vitamin D supplement interventions of the original study stimulated bone mineralization sufficiently to maintain the BMD of our cyclists. It is recommended that cyclists consume 1,500 mg·day–1 calcium and 1,500– 2,000 IU·day–1 vitamin D while performing programs of high-impact loading and/or resistance training at least 2–3 days·week–1 to promote bone health (Mountjoy et al., 2014). Our cyclists consumed ~1,400–1,900 mg of calcium and 500–1,000 IU of vitamin D·day–1 and performed 10 maximal vertical jumps·day–1, 5 days·week–1. In addition, all participants performed regular strength training exercise, and 40% engaged in regular running exercise. Participation in these activities has previously been reported to preserve bone in the hip and the spine of adult men (Nichols & Rauh, 2011).
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600 Viner et al.
Previously, Warner et al. (2002) observed that MB have higher hip and spine BMD than RC and controls. The greater ground surface loads induced by the rough terrain inherent to MB may impose an osteogenic stimulus (De Lorenzo & Hull, 1999). In addition, MB spend more time than RC out of their seats with just two points in bicycle contact, which causes greater loading of the appendicular skeleton (Impellizzeri & Marcora, 2007). We may not have found significant decreases in the group mean BMD of our cyclists because 50% were primarily MB cyclists. However, there was still a high prevalence (40%) of lower than normal lumbar spine BMD among the cyclists in the current study, including 2/5 of MB. And contrary to Warner et al. (2002), we found no significant differences between the BMD of RC and MB. Prevalence of LEA in this study may be overestimated because of errors associated with using selfreported food intake and dietary analysis software to estimate EI. Athletes may undereat during periods of recording, and/or underreport their food intake by as much as 20% (Burke et al., 2001). Our participants were motivated to keep accurate food records to benefit their own athletic performance, and they were contacted within one week of recording to clarify any voids in their records. In addition, the same researcher entered all food intakes into Food Processor and manually entered nutrition information for any food not already in the database. In addition, the previous literature suggests that we had more than enough days of food records to estimate average group data for EI and CHO (Burke et al., 2001). Group mean EI and EA may have been low in this study because 70% of the participants were identified as RE, and RE consciously restrict food intake as a means of weight control (Stunkard & Messick, 1985). Other studies have found that male and female competitive cyclists restrict EI and/or increase training loads to reduce BM, and they suggest that there is a high prevalence of disordered eating behaviors and eating disorders among cyclists (Filaire et al., 2007; Haakonssen et al., 2015; Riebl et al., 2007; Sundgot-Borgen et al., 2013). During pre- and posttesting interviews, all of our cyclists stated that they were either trying to maintain their current weight or trying to lose weight. Although mean BM did not change across the season, there may have been intervals when short-term decreases in BM occurred. The small sample size of this study limited our power to detect changes in EA across time, associations between EA and EI, and differences in EA between cyclists of different genders and specialties. The lack of a control group with Z scores > 0 and with mean EA > 30 limited our abilities to analyze the effects of chronic LEA on bone health. In addition, we were unable to use power output or HR data to quantify EEE.
Conclusion According to USA Cycling, 72% of competitive cyclists in the United States are between the ages of 25 and 54
years and 51% of competitive cyclists are age 35 years or older (USA Cycling & The USA Cycling Development Foundation, 2013). Our findings suggest that there may be a high prevalence of chronic LEA and dietary restraint among this population of competitive cyclists. More studies are needed to accurately estimate the prevalence of LEA among competitive cyclists of different age groups, disciplines, and genders. It is also important to identify eating behaviors and dietary patterns that increase the risk of LEA in this population so we can effectively prevent, identify, and treat LEA. Cyclists should focus on eating frequently throughout the day, especially pre-, during, and postexercise, to achieve adequate EI to support bone health and optimize athletic performance. Acknowledgments This study was designed and conducted by RTV and NLM; data were collected by RTV and analyzed by RTV and MH. All authors undertook data interpretation and manuscript preparation, and all authors approved the final version of the paper.
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