Nutritional plan running athlete

Inhalt

1. Personal information:
Personal details:
Social details
Medical details:
Anthropometrics:
Assistance requested
Athlete´s own ideas and questions:
Nutrition based complaints:
Client´s phase of behaviour change:

2. Exercise history
Summary and intensity of sport:
Sports energy systems described:
Sport´s energy use (TDEE) described:
Relative distribution of substrate use:
Absolute distribution:

3. Nutritional history
Method:

4. Nutritional analysis and diagnosis
Absolute and relative nutrition analysis:
Timing of most relevant nutrients has been reviewed in depth:
Diagnosis proposed regarding the clients points of concern:
Goal:

5. Nutritional plan
Advice for energy and macronutrients is properly substantiated
Timing of nutrient intake is properly substantiated
Clients questions addressed
Nutritional plan is viable in practice and offers practical tips for products
Follow-up consultations to meet goals

6. Appendix

7. References

1. Personal information:

Personal details:

Age: 34-year

Gender: female

Type of sports: marathon running

Weight: 61 kg

Height: 174 cm

BMI: 20,4 is according to the WHO in the normal field (WHO, n.d.).

Social details:

The person is single, lives in her own flat and works as an employee with working times from 5am to 14pm- the most time sitting- in an office. She is member of a running sports club and has one younger brother.

Medical details:

A light iron deficiency was measured two years ago, since these measurements she is in treatment and takes iron supplements to avoid deficiency (100 mg/Tag oral intake). She is rarely sick and takes no medication at the time and has no previous or present injuries.

Anthropometrics:

In bioelectrical analysis (BIA) a small alternating current flow faster through fat-free body (more hydrated) mass and extracellular water than through fat mass or bone tissues. The relationships of resistance, volume and current can evaluate the body water volume, the fat-free mass (FFM) and the percentage of body fat (McArdle, Katch & Katch, 2010, p. 748-750).

Pros and cons of the method:

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Taking the pros and cons in consideration, a whole-body BIA measurement (four measuring points) instead of only the lower part of body was used. Because I have no experience with using a caliper I used the BIA scale for a relatively reliable result. Another reason why I choose this method is that it provides an easy administered and valid tool to assess body composition.

Following results of the athlete were measured in the morning, after a toilette visit, without clothes at a room temperature of 22 degrees:

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(Appendix 1)

Biometrics:

To measure VO2max use during a standardized submaximal exercise a treadmill test was used. The aerobe threshold (LTP 1) was at 150 HR and the anaerobe threshold (LTP 2) was at 180 HR (Appendix 2). The date of the test was one month ago, so the values are valid.

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The athlete did the test because she was interested in her IAT to increase performance while training at the right HF values and she was aware of the cons. A warm-up before the test was done.

Assistance requested:

The athletes goal is to reduce ~5% fat mass and increase muscle mass that she can perform better in a (running) marathon next year in autumn. So, the focus is on energy balance.

Athlete´s own ideas and questions:

The athlete’s questions concern the quality and macronutrient contribution of canteen food she is consuming during workdays form Monday until Friday.

Nutrition based complaints:

Asking about nutritional complaints she answered that there are no nutritional or gastrointestinal problems so far.

Client´s phase of behaviour change:

The athlete wants to support endurance performance with an improved basic nutrition and compete with other female athletes. To determine the phase of behaviour change I used the transtheoretical model (Prochaska, DiClemente & Norcross, 1992). The athlete has already take action in the last months, she has already changed her eating habits a little and eats mainly whole grain products. These changes show that she is willing to change eating habits for better performance, but now she don´t know what she should exactly change.

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Image 1: Transtheoretical model of change

2. Exercise history

Marathon running is a 42,195km long event which takes place on roads or trails. In- prolonged aerobic- marathon running the median time for a finish is about 4:45:30 according to Running USA (2016).

Summary and intensity of sport:

Week training schedule

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The heartrate was measured with the Garmin Forerunner 920XT and a breast belt. The validity of heart rate measurements during running and walking has 2,89% (3 bpm) of error values and of 0.30%-0.74% according distance (Claes, Buys, Avila, Finlay, Kennedy, Guldenring, Budts & Cornelissen, 2017).

Sports energy systems described:

The physical activities range from 1-1 ½ hours, this training schedule is continued during winter (5 days/week), in spring one hiking activity with lower intensity will be replaced with running at higher intensity. On 4 days, she trains endurance capacity which is between 60-69% of HFmax, on one day she trains speed-endurance at 70-80% of HFmax (scienceintraining, n.d.). According to Gastin, 90% of energy are from aerobic and 10% from anaerobic energy sources during a 10km-run (2001, 725-741).

Sport´s energy use (TDEE) described:

The athlete trains most of the time in aerobic area- which is under or at 150HF- so she burns mainly fat and CHO (Mc Ardle, Katch & Katch, 2010, p. 212). The BMR of 1317 kcal was calculated with the new FFM based Cunningham formula, this method is an accurate equation for endurance athletes and can be used for sport nutritional advices (Ten Haaf & Peter Weijs, 2014).

The FFM was calculated (Appendix 1) and body fat % was measured with the BIA scale, there can be some small deviations according to the value of measurement.

TEF was calculated with 10% because it´s the most common value according to Mc Ardle, Katch & Katch (2010).

Including PAL and TEF the calculated TEE is: 1883kcal (Appendix 4)

According to the compendium of MET values (Ainsworth, Haskell, Herrmann, Meckes, Bassett, Tudor-Locke & Leon, 2011) the athlete trains at two different MET values:

- 11 MET: running (5,3min/km) 615kcal for 1h
- 8,9 MET: running (6min/km) 498kcal for 1h and 747kcal for 1 ½ hours

Total energy expenditure on training days with different intensities and durance:

- 8,9MET: Training day with 1h: = 2303kcal
- 8,9MET Training day with 1 ½ h: = 2439kcal
- 11MET: Training day with 1h= 2420kcal

Relative distribution of substrate use:

During a training at 8,9MET the athlete will burn in one hour 498kcal, according to Astrand, Rodahl & Dahl (2003) the distribution at exercise intensity of 55-65% is:

- ~50% of total energy comes from muscle glycogen and plasma glucose
- ~50% comes from fat

At ~75%, which is the intensity of 11MET following fuels are used:

- ~65-75% of the total energy comes from muscle glycogen and plasma glucose
- 25% plasma FFA and another fat source (Astrand, Rodahl & Dahl, 2003).

Absolute distribution:

At ~55-65% of exercise intensity following substrates will be burned during 1h running:

- 273-373kcal (93g) muscle glycogen and from plasma glucose
- 125kcal (14g) from FFA and other fat sources

At 75% exercise intensity (11MET) following fuels are used during 1h running:

- 339-400kcal (~100g) from muscle glycogen and from plasma glucose
- 153kcal (17g) kcal from FFA and other fat sources (Astrand, Rodahl & Dahl, 2003) (Appendix 5).

3. Nutritional history

Method:

To analyze the athlete´s nutrition history a food diary was used, it is a prospective method accumulating data about consumed foods, beverages and supplements over one week. I will mainly analyze macronutrient intake (protein, CHO, fat and EE total) because she wants to lose fat mass and gain muscle mass. Data can be used to compute current diet of individuals and show inadequate intakes of special nutrients.

The method was used because practical knowledge is available and it´s the best method to analyze nutritional history (Basiotis et al., 1987), following pros and cons must be considered:

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Under- or overestimations of meal sizes can lead to inaccuracies, do avoid these athletes were advised to weight portions with a scale if possible. Some days the athlete eats lunch in a canteen, so only estimation is possible.

4. Nutritional analysis and diagnosis

Absolute and relative nutrition analysis:

General quantitative analysis

The food intake according to the diary was calculated with the app MyFitnessPal. It offers a variety of different amounts, brands and controlled food choices according to the kcal and macronutrient values (Appendix 7). To control the results most of the values where compared with the tables of food composition in Whitney & Rolfes (2010). Maintenance, calorie awareness, and food variety of the app were graded very good by users (PR newswire, 2013) which says less about the reliability of the app. Experience with using the app was given by the athlete.

Macronutrient intake, calculation of g/kg BW, distribution of kcal total and norm values of each day:

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Analysis:

CHO: On 2 of 7 days 50% of the total kcal were from CHO, which were the highest values. The recommendations are 60-70%, she consumed way to less CHO.

Protein: requirements are in average adequate, on resting days the intake should be lower, especially if related to request.

Fat: Intakes are in average slightly higher than the general recommendations of ~30% of total energy, but related to the request the intake need to be lower to lose body fat mass (Potgieter, 2013).

Related to request “lose fat mass and built muscle mass” the focus is on energy balance. The % of CHO intake should be higher and the fat % should be lower, due to the higher kcal value of fat. On 4 days she is already in a negative balance, only on 3 days she is in a positive energy balance.

Norm values:

For days with the same intensity and durance the same recommendations were given, for resting days the same.

Carbohydrate intake: 5g/kg BW was chosen because the athlete is training on a medium exercise level and trains ~7hours a week (Pramuková, Szabadosová, & Šoltésová, 2011), for training days with no extra CHO are advised because the intensity is ~65%(Mc Ardle, Katch & Katch, 2010).

Protein intake: 1,3g/kg BW was chosen because running increases muscle soreness and more protein is needed by the athlete (Lemon, 1991).

Fat intake: 0,7g/kg BW is recommended because the athlete wants to reduce body fat mass (Potgieter, 2013), because fat has the highest energy density.

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¹ topendsports.com

² Mc Ardle, Katch & Katch, 2010, p. 750

¹ (Röcker, Schotte, Niess, Horstmann & Dickhuth, 1998, p. 1552-1557).

¹ (Ortega, Perez-Rodrigo & Lopez-Sobaler, 2015)

¹ Mc Ardle, Katch & Katch, 2010

² Potgieter, 2013

³ Cox, 2000, S. 656-671; ADA, DC & ACSM, 2009, S. 510

⁴ Pramuková, Szabadosová, & Šoltésová, 2011