BACHELOR THESIS. Body composition during fasting and non-fasting conditions measured with bioelectrical impedance analysis.

Bachelor's Programme in Exercise Biomedicine, 180 credits BACHELOR THESIS Body composition during fasting and non-fasting conditions measured with bioelectrical impedance analysis Frida Svedin Bachelor's Thesis in Exercise Biomedicine, 15 credits Halmstad 2017-05-24

Body composition during fasting and non-fasting conditions measured with bioelectrical impedance analysis Frida Svedin 2017-05-24 Bachelor Thesis 15 credits in Exercise Biomedicine Halmstad University School of Business, Engineering and Science Thesis supervisor: Åsa Andersson Thesis examiner: Eva Strandell

Acknowledgments I would like to thank my supervisor Åsa Andersson, assistant professor at Halmstad University and Copenhagen University, for all the help and support. Also, I would like to thank my study colleague, Frida Eriksson.

Abstract Background: In 2014, there were nearly 2 billon overweight people around the world. This causes excessive costs for the society and is also a threat to the human condition. In recent time, there has been an increase of understanding the individual parts of the body composition. One method to measure body composition is using a bioelectrical impedance analyzer. The current recommendation is to measure during fasting conditions. However, there are few studies that have investigated a meal s effect on body composition measured with bioelectrical impedance analysis, and those studies have presented varying results. If a bioelectrical impedance analyzer could be used without previous fasting, it would increase the use and utility of bioelectrical impedance analyzers. This could in turn, for example, reduce waiting lists in hospitals where bioelectrical impedance analyzers are used. Aim: The main aim of this study was to investigate a meal s effect on body composition when measured with a bioelectrical impedance analyzer. The secondary aim was to investigate the correlation between skeletal muscle mass and hand grip strength when using a bioelectrical impedance analysis and a hand-held dynamometer respectively, during fasting conditions. Methods: In this present study, 27 subjects in the age of 21-59 years old participated. The subjects arrived at the laboratory in the morning during fasting conditions. Firstly, a bioelectrical impedance analyze and a hand grip strength test were completed. Thereafter, all subjects ate a meal containing at least 500 kcal. The following bioelectrical impedance analysis were completed 60, 90 and 120 minutes post meal intake. The data was then analyzed in SPSS version 20 through a paired T-test and a Pearson correlation test respectively. Results: The results showed that all body composition parameters investigated in this present study, except for minerals, not were statistically different 90 minutes after a meal intake containing at least 500 kcal, compared to the fasting condition, when measured with a bioelectrical impedance analyzer. Furthermore, a moderate correlation was found between hand grip strength and skeletal muscle mass for women. The same correlation was found weak for men. Conclusion: The results from this present study indicates that it is possible to measure body composition with a bioelectrical impedance analyzer 90 minutes post meal intake, except for minerals. Also, it indicates that a hand grip strength test is not a valid test for measuring skeletal muscle mass.

Abstrakt Bakgrund: År 2014 var nästan 2 biljoner personer överviktiga i världen. Detta kostar enorma summor pengar för samhället och är ett hot mot människans hälsa. Nyligen publicerad forskning har visat en ökad förståelse av individuella delar av kroppssammansättningen. En metod för att mäta kroppssammansättningen är med en bioelektrisk impedans analys. Den nuvarande rekommendationen är att genomföra detta test är under fastande mage, men det är få studier som har undersökt en måltids effekt på kroppssammansättningen när den mäts med bioelektrisk impedans analys. Dessa studier har uppvisat varierande resultat. Om det inte är nödvändigt att genomföra en bioelektrisk impedans analys under fastande mage skulle användandet och tillgängligheten av bioelektrisk impedans analyser kunna öka. Det skulle då exempelvis kunna minska vårdköerna på de sjukhus där dessa maskiner används. Syfte: Det primära syftet med denna studie var att undersöka hur en måltid påverkade kroppsammansättningen mätt med en bioelektrisk impedans analys. Det sekundära syftet var att undersöka hur stark korrelationen var mellan skelettmuskelmassa och handgreppsstyrka på fastande mage mätt med en bioelektrisk impedans analys och handdynamometer respektive. Metod: I denna studie deltog 27 försökspersoner mellan 21 59 år. Försökspersonerna anlände på morgonen till laboratoriet på fastande mage. Inledningsvis genomfördes en bioelektrisk impedans analys och ett handstyrketest. Efter detta åt försökspersonerna en måltid som innehöll minst 500 kcal. Resterande bioelektriska impedans analyser genomfördes 60, 90 och 120 minuter efter den intagna måltiden. Datan analyserades sedan i SPSS version 20 genom ett parat T-test och ett Pearson korrelations test respektive. Resultat: Resultaten från denna studie visade att alla kroppssammansättningsvariabler som undersöktes i denna studie, förutom mineraler, inte hade någon statistik signifikant skillnad 90 minuter efter måltid jämfört med fastande mage, mätt med bioelektrisk impedans analys. Dessutom visade resultatet att korrelationen mellan skelettmuskelmassa och handgreppsstyrka var moderat för kvinnor och svag för män. Konklusion: Resultaten från denna studie indikerar på att det är möjligt att mäta kroppssammansättningen med en bioelektrisk impedans analys 90 minuter efter en måltid, förutom mineraler som inte är möjligt. Därtill visade resultaten från denna studie att ett handgreppsstyrketest inte är en valid mätmetod för skelettmuskelmassa.

Table of Contents 1. Background... 1 1.1 Body composition... 1 1.2 Bioelectrical impedance analysis... 1 1.3 Food digestion... 2 1.4 Bioelectrical impedance analysis and food intake... 2 1.5 Hand grip strength... 3 1.6 Muscle mass... 4 1.7 Aim... 4 1.7.1 Research questions... 5 2. Methods... 5 2.1 Subjects... 5 2.2 Study design... 5 2.3 Testing procedure... 6 2.4 Validity and reliability... 7 2.4.1 Bioelectrical impedance analyzer... 7 2.4.2 Hand-held dynamometer... 8 2.5 Ethical and social considerations... 8 2.5.1 Ethics... 8 2.5.2 Social considerations... 8 2.6 Statistics... 9 3. Results... 9 3.1 Body composition before and after a meal intake... 9 3.1.1 All subjects... 9 3.1.2 Men... 10 3.1.3 Women... 11 3.2 Correlation between hand grip strength and skeletal muscle mass... 12 4. Discussion... 14 4.1 Result discussion... 14 4.1.1 Body composition before and after a meal intake... 14 4.1.2 Correlation between hand grip strength and skeletal muscle mass... 17 4.2 Method discussion... 18 5. Conclusions... 19 6. References... 20 Appendices... 25 Appendices 1... 25 Appendices 2... 26 Appendices 3... 28 Appendices 4... 30

1. Background 1.1 Body composition In 2014, over 1.9 billon adult people were overweight and out of these were 600 million obese. This is equivalent to 39% overweight and 19% obese people of the world s adult population. The terms overweight and obese are defined by excessive or abnormal fat accumulation and a Body Mass Index (BMI) equal or superior than 25 and 30 respectively (WHO, 2016). Unfortunately, obesity is an increasing problem in both adults and children worldwide. This is a threat to the human condition (Katz, 2016) and causes excessive costs for the society (Hammond & Levine, 2010). In recent time, there has been an increase of understanding the importance of the body composition. Evidence shows that individual components in body composition has influence on chronic disease debut, treatment responses and health (Seabolt, Welch & Silver, 2015). For example, abdominal obesity is associated with a significantly increased risk of developing cardiovascular diseases and type 2 diabetes (Denton & Karpe, 2016). Besides, additional visceral fat is significantly associated with the metabolic syndrome (Zhu & Wang, 2011). The muscle mass, on the other hand, can reduce the risk developing osteoporosis through the contraction forces that it puts on the bones. An increase in muscle mass can also facilitate weight loss, since the increased muscles mass could have a significant effect on the resting energy expenditure. Additionally, the muscle mass plays an important role in recovering from both severe illness and traumas, and for the basal metabolic rate, whereas protein breakdown and synthesis is the main energy expenditure in a resting muscle (Wolfe, 2006). 1.2 Bioelectrical impedance analysis One method to investigate the body composition is using a bioelectrical impedance analyzer. This machine uses electrical conductivity to measure the impedance in the body. Fat free mass has a low impendence due to its high water and electrolyte content. Fat mass on the contrary, contains a small amount of water and has therefore a higher impedance. The analyzer with pre-programmed predication equations automatically calculate the body composition due to the subject s impedance, height, age and gender (Dixon, Masteller & Andreacci, 2013). The analysis shows fat free mass (FFM), fat mass (FM), total body water (TBW), extracellular water (ECW) and intracellular water (ICW) in both healthy and sick individuals (Kyle et al., 2004). Bioelectrical impedance analysis is a quick, simple and 1

relatively inexpensive method which is preferable in several situations (Lingwood, 2013; Lukaski, 1999; Kyle et al., 2004). 1.3 Food digestion The human body consist of the nutrients water, lipids, proteins, carbohydrates, minerals and vitamins. Since those nutrients continuously are converted in the body, new nutrients must be provided by food intake. When food is processed in the body it is called digestion and it is both neural and hormonally controlled. The digestive tract contains of the oral cavity, throat, esophagus, stomach, small intestine, colon and rectum. Also, the liver, gallbladder and pancreas belongs to the digestive organs since they secrete different fluids who are essential in food breakdown and absorption. With the term digestion, it means that larger molecules are broken down to smaller ones, so that the body eventually can absorb those and be utilized by the cells (Abrahamsson, Andersson & Nilsson, 2013, p.17-25). This approach involves both mechanical digestion, for example chewing, and chemical digestion. Chemical digestion means that, for instance, enzymes are breaking down the food molecules to smaller molecules, for example the enzymes sucrase and lactase that breaks disaccharides into monosaccharides. Also, different nutrients start their breakdown in different parts of the digestion tract. Carbohydrates starts their breakdown as early as in the oral cavity, protein in the stomach and lipids not until in the small intestine (Marieb, 2012, p.477-480). It has also been shown that different types of protein are absorbed at different rates (Koopman et al., 2009), as well as different types of carbohydrates are absorbed at different rates (Cori, 1925). Furthermore, water can partially be absorbed in the stomach. However, carbohydrates, fat and proteins absorbs in the small intestine. It takes about 0.5 2 days from a finished food intake to the non-absorbent food leaving the body as feces (Abrahamsson et al., 2013, p.28-29). 1.4 Bioelectrical impedance analysis and food intake The general recommendations for measuring body composition when using a bioelectrical impedance analyzer is during fasting conditions. However, few studies have investigated a meal s effect on body composition with bioelectrical impedance analysis. If fasting is not necessary, the use and utility of bioelectrical impedance analyzers would increase (Dixon et al., 2013). One study found that breakfast significantly lowered the body impedance after a meal intake. The decline continued until four hours after the meal intake. A lower impedance could, in turn, lead to a significantly underestimation of body fat mass (Gallagher, Walker & 2

O'Dea, 1998). Another study showed similar results with a consistent decline in body impedance after a meal intake (Slinde & Rossander-Hulthén, 2001). On the other hand, one study showed opposite results. This study showed a significant increase in body impedance after a meal intake for all measurements the following two hours. It also showed a significant increase in estimated body fat percentage (Androutsos, Gerasimidis, Karanikolou, Reilly & Edwards, 2015). To my knowledge, only one study has investigated a meal s impact on body composition with a contact-electrode foot-to-hand bioelectrical impedance analyzer. However, this study only measured body composition at baseline and 20, 40 and 60 minutes after a meal intake (Dixon et al., 2013). Therefore, the main purpose of this present study was to investigate body composition during fasting and non-fasting conditions in up until 2 hours after a meal with bioelectrical impedance analysis. 1.5 Hand grip strength A hand grip strength test measured with a hand-held dynamometer is an easy, portable inexpensive and quick method to investigate muscle strength (Guerra & Amaral, 2009). Research has showed that a hand grip strength test is the most reliable and valid field-based muscular fitness test (Cadenas-Sanchez et al., 2016). Research has also showed that hand grip strength has a strong correlation with the total muscle strength in the body. Even though, the correlation was only moderate when controlled for weight (Wind, Takken, Helders & Engelbert, 2010). It has also been shown a strong correlation between hand grip strength and muscle mass. This study was using forearm circumference and creatinine excretion to estimate the muscle mass (Kallman, Plato & Tobin, 1990). The subject can stand up, lay down or sit while doing the hand grip strength test and is therefore an option for both healthy and ill subjects. A hand grip strength test can investigate short-term changes in nutritional status and is therefore a good tool in health care (Hillman et al., 2005). A hand grip strength test is also a tool for prediction of mortality, disability and post-surgery complications (Bohannon, 2008). Moreover, a hand grip strength test may help identify individuals who has a risk for work-related musculoskeletal disorders in the hands and forearms. For example, disorders as carpal tunnel syndrome, muscle strains and nerve impeachments (Nicolay & Walker, 2005). 3

1.6 Muscle mass In the human body, there are over 660 muscles and as stated earlier, skeletal muscles play an important role for the metabolism. Besides that, they also play a vital role in locomotion, heat production and support of soft tissues. The muscle mass consists of 75% water, 20% protein and 5% of different substances such as carbohydrates, minerals, salts and enzymes. The most important proteins in the muscles are tropomyosin, troponin, myosin and actin. A skeletal muscle is built up by muscle fibers, which in turn is built up by myofibrils. Myofibrils consists of the two myofilaments called actin and myosin. Actin, considered as the thin filament, and myosin, considered as the thick filament, are responsible for the muscle contraction. These myofilaments are found organized in the sarcomeres, which represent the smallest functional unit in a muscle fiber and is a unit between two Z-lines (Cardinale, Newton & Nosaka, 2011, p.3-4). Since muscle mass has great effects on human health (Wolfe, 2006) and since a hand-held dynamometer is a valid and reliable field-based test (Cadenas-Sanchez et al., 2016), I found it interesting to investigated if the hand-held dynamometer is a valid tool for measuring the muscle mass in the body. Therefore, the secondary purpose of this study was to investigate the correlation between hand grip strength using a hand-held dynamometer and total skeletal muscle mass using a bioelectrical impedance analyzer during fasting conditions. A study of exanimated this correlation. However, they investigated the muscle mass with a different method (Kallman et al., 1990). 1.7 Aim The main aim of this study was to investigate a meal s impact on body composition compared to the fasting condition, when using a bioelectrical impedance analyzer. The secondary aim was to investigate the correlation between muscle mass and handgrip strength when using a bioelectrical impedance analysis and a hand-held dynamometer respectively during fasting conditions. My hypothesis was that there would not be a statistical significance difference in body composition before and after a meal, and that there would be a strong correlation between a hand grip strength test and the body s skeletal muscle mass. 4

1.7.1 Research questions Is it a statistically significant difference in body composition between fasting and nonfasting conditions when using a bioelectrical impedance analyzer in men and women? If not, how long after a meal intake is the measurement possible to complete? How strong is the correlation between hand grip strength and total muscle mass in the body during fasting conditions? 2. Methods 2.1 Subjects In this study, 35 adults volunteered to participate. However, five of them got sick and since all subjects were required to be healthy from infections at least one week before the test session as one of the inclusion criteria, these five were not allowed to participate. One subject did not want to reveal the reason for dropping out. Moreover, two subjects who participated did not ate a meal containing at least 500 kcal and were therefore excluded. In total, 27 adults (17 women and 10 men) with a mean age at 38 ± 12 years old participated and conducted all test sessions in accordance with the study s inclusion criteria and requirements. All subjects were healthy from infections at minimum one week before the test session, they were free from injuries at minimum one month before the test session and did not have a chronic disease. Furthermore, individuals who have or have had an eating disorder were excluded because of risk of relapse. Subjects who had a pacemaker or were pregnant were also excluded. All students and workers that fit in the study s inclusion criteria at Halmstad University were permitted to participate in this study. Requests was sent by e-mail and as announcements in several information boards at Halmstad University, see in Appendices 1. 2.2 Study design The present study design is experimental. The main aim was to investigate potential differences in the body composition before and after a meal using a pared sample T-test. The secondary aim was to investigate the correlation between skeletal muscle mass and hand grip strength during fasting conditions using Pearson correlation test. 5

2.3 Testing procedure All subjects arrived at the Human Performance Laboratory at Halmstad University in the morning between 8 pm and 9.30 pm at one of the seven test occasion days. If the distance was below 2 kilometers the subjects either walked slowly or rode the bike to Halmstad University, otherwise the subjects traveled by public transport or by car. The general guidelines told that subjects should not have eaten or drunken within 4 hours of the test (Dixon et al., 2013). However, since our test sessions took place in the morning, the subjects were fasting since yesterday evening at latest from 10 pm. Furthermore, the subjects had not been drinking alcohol within 48 hours of the test, not exercise within 12 hours of the test and empty the bladder within 30 minutes of the test (Dixon et al., 2013). The subjects had received written instructions and the requirements for participating in the study beforehand through email, see Appendices 3. At arrival, the subject s height was measured to the nearest 0.5 cm using a stadiometer (SECA-217, Seca Ltd, Hamburg, Germany). The first bioelectrical impedance analysis was measured in underwear using InBody 770 (Biospace, Seoul, Korea). The bioelectrical impedance analyzer measured five body segments (left arm, right arm, trunk, left leg and right leg) with 8-point tactile electrodes at six different frequencies during the analysis (1, 5, 50, 250, 500 and 1000 khz) and is therefore a segmental multi frequency bioelectrical impedance analyzer. After the first bioelectrical impedance analysis, all subjects did a maximum hand grip strength test with their dominant hand three times with a hand-held dynamometer T.K.K. 5401 GRIP-D (Takel Scientific Instruments CO, Tokyo, Japan). Subjects were sitting down at a chair with the elbow joint at 90 degrees and the forearm along the table when doing the test. The best attempt out of the three efforts was selected for further analyzing (Metter, Talbot, Schrager & Conwit, 2002). The hand grip strength tests were performed during fasting conditions due to the investigation of the correlation with skeletal muscle mass which was measured with a BIA, where the recommendation is to measure during fasting conditions. Therefore, the conditions of these two tests was determined to be the same when analyzing the correlation between them. Directly afterwards, the subjects were eating a meal containing at least 500 kcal which they had brought to the laboratory by themselves, they had also received suggestions at suitable meals beforehand. The limit of >500 kcal was determinate by what a normal Swedish breakfast contains (two eggs, two sandwiches with butter and cheese and one apple). The subjects had no liquid intake limitation. All subjects were writing a food diary which included 6

the food and the quantity of it. After the subject s food and drink intake immediately after the first test session they were not allowed to eat or drink until the remaining test sessions were completed. The food diaries were then collected and analyzed in Dietist Net (version 17.02.03) to investigate that the subject had eaten at least 500 kcal. The second, third and fourth bioelectrical impedance analysis were done in underwear 60, 90 and 120 minutes after the meal intake with InBody 770. Subjects were to empty the bladder at least 30 minutes prior to these three test sessions (Dixon et al., 2013). During all test sessions, the data was collected and stored into an USB memory and all personal data were replaced by a code. The data could only be seen by the study s test supervisor and the test leaders. 2.4 Validity and reliability 2.4.1 Bioelectrical impedance analyzer To my knowledge, no study has examined the validity and reliability of InBody 770 to this date. However, many studies have investigated the validity and reliability of other similar models of the InBody. One study showed an Intraclass and Pearson correlation >0.92 when testing InBody 720 bioelectrical impedance analyzer to Dual-energy X-ray absorptiometry (DEXA). However, the InBody 720 bioelectrical impedance analyzer overestimated lean mass (LM) and percentage lean mass (%LM) and underestimated fat mass (FM) and percentage fat mass (%FM) (Tompuri et al., 2015). These results were supported by another study who showed similar results (Völgyi et al., 2008). Another study showed a Pearson correlation (r) at 0.91 in FM between InBody 3.0 and hydrostatic weighting which often is mentioned as the golden standard measurement in body composition, and 0.94 between InBody 3.0 and DEXA. In %BF it showed a Pearson correlation (r) at 0.81 between InBody 3.0 and hydrostatic weighting, and 0.88 between InBody 3.0 and DEXA. The study also showed a Pearson correlation (r) at 0.83 and 0.88 in FFM between InBody 3.0 and hydrostatic weighting and InBody 3.0 and DEXA respectively (Jukka, 2003). An additional study showed a strong correlation (r=0.96) in fat free mass (FFM) between InBody 520 bioelectrical impedance analyzer and hydrostatic weighting (Utter & Lambeth, 2010). In conclusion, all three studies showed a strong correlation between bioelectrical impedance analyzer and DEXA and/or hydrostatic weighting (Jukka, 2003; Tompuri et al., 2015; Utter & Lambeth, 2010). Furthermore, InBody 320 has showed a great test-retest reliability with a 1.8% difference between measurements (Jensky-Squires, Dieli-Conwright, Rossuello, Erceg, McCauley & Schroeder, 2008). However, it should be notified that bioelectrical impedance analyzers are not able to 7

measure mineral mass. Even though, a BIA can estimate a value for the minerals, since the bone mineral mass is closely related to the fat free mass (InBody, 2017). 2.4.2 Hand-held dynamometer Newly published research showed that the hand-held dynamometer T.K.K 5401 GRIP-D used in this present study had excellent test-retest reliability with a mean difference at 0.04-0.25 kilograms. The T.K.K. dynamometer also showed a good validity (Cadenas-Sanchez et al., 2016). 2.5 Ethical and social considerations 2.5.1 Ethics In human research three main principles applies. First, it is mandatory to minimize potential harm for all subjects. Second, it is voluntary to participate and the subjects should be well informed about the study. Third, the subjects can withdraw from the study at any time (Laake, Benestad & Olsen, 2007, p.55). In this study, all ethical principles mentioned above was applied and prevailed. The study was completed in accordance to the Declaration of Helsinki. All subjects were well informed about the study and signed the consent for participation. The study was reviewed by the study s supervisor beforehand it started, see Appendices 2. 2.5.2 Social considerations There may be some social implications due to this present study. If the results show that body composition can be used during both fasting and non-fasting conditions the availability for body composition measurements will increase. Then, there may be an increase in anxiety around body composition and an increase in comparing individual body composition measurements to each other. This in turn could lead to a better awareness of your health, diet and training, which is a positive perspective. On the other hand, it could also lead to an increased risk for falling into a fixation to training and diet or an eating disorder. However, due to the possibility that if the study s result shows that bioelectrical impedance analyzers can be used even during non-fasting conditions, the study is considered as a larger benefit than damage. 8

2.6 Statistics The data was collected and stored into IBM SPSS statistics version 20 for Apple. Thereafter the data was investigated whether it was normally distributed or not with a Schapiro-Wilk test. The data was then analyzed for difference in body composition between fasting and nonfasting conditions if measured with bioelectrical impedance analysis. The following variables in body composition were analyzed with a paired T-test: minerals, total body water, intracellular water, extracellular water, fat mass, fat free mass, soft lean mass and skeletal muscle mass. Thereafter, a Pearson correlation test was used to investigate how strong the correlation was between total skeletal muscle mass and handgrip strength during fasting conditions. A correlation coefficient between ± 0 to ± 0.4 was viewed as a weak correlation, ± 0.4 to ± 0.6 as moderate correlation and ± 0.6 to ± 1.0 as strong correlation (Thomas, Nelson & Silverman, 2011, p.100). For all analyses, the statistical significance level used was 0.05 (Thomas et al., 2011, p.132). 3. Results In this study, 27 subjects with a mean age at 38 ± 12 years participated. 10 of them were men and 17 were women. Subjects characteristics is presented in Table 1. The Schapiro-Wilk test showed that all variables were normally distributed. Table 1. Subject characteristics All subjects Men Women N = 27 N = 10 N = 17 Age (years) 38 ± 12 a 33 ± 12 41 ± 12 Length (cm) 174.0 ± 8.0 181.7 ± 5.5 169.4 ± 5.3 Body mass (kg) 72.6 ± 12.3 83.4 ± 8.2 66.2 ± 9.7 a Data presented as mean ± SD. 3.1 Body composition before and after a meal intake 3.1.1 All subjects Results showed that there was no statistical difference 60, 90 or 120 minutes after a meal intake (>500 kcal) in fat mass compared to the fasting condition values, when using a bioelectrical impedance analyzer. Total body water, intracellular water, extracellular water, fat free mass, soft lean mass and skeletal muscle mass showed no statistical difference 90 or 9

120 minutes after a meal intake (>500 kcal) when comparing to the fasting conditions values, when using a bioelectrical impedance analyzer. However, minerals did show a statistical difference at all post meal (60, 90 and 120 minutes) bioelectrical impedance analyzer measurements when comparing to the fasting conditions values. More detailed data is presented in Table 2 and in Appendices 4 Table 1. Table 2. Paired T-test for eight body composition parameters during fasting conditions, 60, 90 and 120 minutes after a meal intake in all subjects (n=27). Minerals (kg) Total body water (L) Mean SD p-value Mean SD p-value Fasting 4.04 0.87 Fasting 41.83 8.84 60 min 4.11 0.85 0.001* 60 min 42.12 8.85 0.001* 90 min 4.11 0.83 0.003* 90 min 41.99 8.78 0.242 120 min 4.09 0.84 0.017* 120 min 41.97 8.79 0.292 Extracellular water (L) Intracellular water (L) Mean SD p-value Mean SD p-value Fasting 15.70 3.24 Fasting 26.90 5.67 60 min 15.79 3.24 0.043* 60 min 26.29 5.60 0.007* 90 min 15.80 3.22 0.071 90 min 26.19 5.58 0.337 120 min 15.74 3.23 0.075 120 min 26.17 5.58 0.411 Fat mass (kg) Fat free mass (kg) Mean SD p-value Mean SD p-value Fasting 15.42 6.38 Fasting 57.17 12.12 60 min 15.57 6.39 0.127 60 min 57.61 12.13 <0.001* 90 min 15.70 6.26 0.113 90 min 57.43 12.01 0.167 120 min 15.67 6.23 0.142 120 min 57.37 12.02 0.269 Lean body mass (kg) Skeletal muscle mass (kg) Mean SD p-value Mean SD p-value Fasting 53.81 11.41 Fasting 31.91 7.10 60 min 54.19 11.43 0.001* 60 min 32.29 7.32 0.043* 90 min 53.97 11.34 0.291 90 min 32.11 7.29 0.280 120 min 53.97 11.34 0.352 120 min 32.09 7.29 0.301 * = Significantly different from fasting conditions p <0.05 3.1.2 Men Results showed that there was no statistical difference 60, 90 or 120 minutes after a meal intake (>500 kcal) in intracellular water, extracellular water, fat mass and skeletal muscle mass compared to the fasting condition values in men, when using a bioelectrical impedance analyzer. Total body water, minerals, fat free mass and soft lean mass showed no statistical 10

difference 90 or 120 minutes after a meal intake (>500 kcal) when comparing to the fasting conditions values in men, when using a bioelectrical impedance analyzer. More detailed data is presented in Table 3 and in Appendices 4 Table 2. Table 3. Paired T-test for eight body composition variables during fasting conditions, 60, 90 and 120 minutes after a meal intake in men (n=10). Minerals (kg) Total body water (L) Mean SD p-value Mean SD p-value Fasting 4.97 0.60 Fasting 51.51 5.16 60 min 5.04 0.54 0.039* 60 min 51.85 5.13 0.012* 90 min 5.01 0.54 0.193 90 min 51.63 5.07 0.601 120 min 4.99 0.54 0.441 120 min 51.63 5.03 0.623 Extracellular water (L) Intracellular water (L) Mean SD p-value Mean SD p-value Fasting 19.19 2.04 Fasting 32.32 3.16 60 min 19.29 2.07 0.293 60 min 32.46 3.18 0.061 90 min 19.29 2.00 0.221 90 min 32.34 3.13 0.898 120 min 19.30 1.99 0.227 120 min 32.33 3.10 0.951 Fat mass (kg) Fat free mass (kg) Mean SD p-value Mean SD p-value Fasting 12.96 5.02 Fasting 70.44 7.09 60 min 13.06 5.07 0.204 60 min 70.95 7.04 0.006* 90 min 13.32 4.68 0.192 90 min 70.64 6.94 0.530 120 min 13.27 4.43 0.317 120 min 70.59 6.88 0.656 Lean body mass (kg) Skeletal muscle mass (kg) Mean SD p-value Mean SD p-value Fasting 66.32 6.60 Fasting 39.66 4.03 60 min 66.76 6.60 0.011* 60 min 40.36 4.14 0.165 90 min 66.47 6.50 0.614 90 min 40.15 4.07 0.269 120 min 66.44 6.44 0.711 120 min 40.14 4.03 0.272 * = Significantly different from fasting conditions p <0.05 3.1.3 Women Results showed that there was no statistical difference 60, 90 or 120 minutes after a meal intake (>500 kcal) in extracellular water and fat mass compared to the fasting condition values in women, when using a bioelectrical impedance analyzer. Total body water, intracellular water, fat free mass, soft lean mass and skeletal muscle mass showed no statistical difference 90 or 120 minutes after a meal intake (>500 kcal) when comparing to the fasting conditions values in women, when using a bioelectrical impedance analyzer. However, 11

minerals did show a statistical difference at all post meal (60, 90 and 120 minutes) bioelectrical impedance analyzer measurements when comparing to the fasting conditions values in women. More detailed data is presented in Table 4 and in Appendices 4 Table 3. Table 4. Paired T-test for eight body composition variables during fasting conditions, 60, 90 and 120 minutes after a meal intake in women (n=17). Minerals (kg) Total body water (L) Mean SD p-value Mean SD p-value Fasting 3.50 0.43 Fasting 36.14 4.36 60 min 3.56 0.42 0.016* 60 min 36.40 4.31 0.023* 90 min 3.58 0.40 0.009* 90 min 36.32 4.30 0.304 120 min 3.56 0.41 0.023* 120 min 36.28 4.32 0.351 Extracellular water (L) Intracellular water (L) Mean SD p-value Mean SD p-value Fasting 13.65 1.63 Fasting 22.42 2.83 60 min 13.74 1.59 0.074 60 min 22.67 2.74 0.036* 90 min 13.74 1.59 0.199 90 min 22.58 2.74 0.307 120 min 13.73 1.60 0.214 120 min 22.55 2.74 0.353 Fat mass (kg) Fat free mass (kg) Mean SD p-value Mean SD p-value Fasting 16.86 6.78 Fasting 49.37 5.97 60 min 17.05 6.77 0.231 60 min 49.77 5.89 0.017* 90 min 17.11 6.77 0.320 90 min 49.66 5.85 0.227 120 min 17.08 6.80 0.307 120 min 49.59 5.90 0.299 Lean body mass (kg) Skeletal muscle mass (kg) Mean SD p-value Mean SD p-value Fasting 46.45 5.63 Fasting 27.35 3.62 60 min 46.79 5.57 0.025* 60 min 27.55 3.57 0.043* 90 min 46.66 5.55 0.367 90 min 27.38 3.59 0.830 120 min 46.63 5.58 0.377 120 min 27.39 3.60 0.932 * = Significantly different from fasting conditions p <0.05 3.2 Correlation between hand grip strength and skeletal muscle mass A hand grip strength test was performed to investigate the correlation with skeletal muscle mass, measured with a hand-held dynamometer and a bioelectrical impedance analyzer respectively. The Pearson correlation test showed a weak positive correlation in men between hand grip strength and skeletal muscle mass during fasting conditions (linear regression p= 12

0.77, r= 0.11, r 2 = 0.01), see Table 5 and Figure 1. In women, the Pearson correlation test showed a moderate significant positive correlation between hand grip strength and skeletal muscle mass during fasting conditions (linear regression p= 0.04, r= 0.50, r 2 = 0.25), see Table 5 and Figure 2. Table 5. The correlation between skeletal muscle mass and hand grip strength during fasting conditions. Skeletal muscle mass (kg) Hand grip strength (kg) N Pearson correlation test Men 39.66 ± 4.03 53.96 ± 7.27 10 0.106 0.770 Women 27.35 ± 3.62 34.09 ± 6.26 17 0.501* 0.040 a Data presented as mean ± SD. *. Correlation is significant at the 0.05 level (2-tailed). p Correlation between skeletal muscle mass and hand grip strength during fasting conditions for men Skeletal muscle mass (kg) 50 45 R² = 0,01 40 35 30 30 40 50 60 70 Hand grip strength (kg) Figure 1. The correlation between skeletal muscle mass and hand grip strength during fasting conditions in men (n=10). 13

Skeletal muscle mass (kg) 45 40 35 30 25 Correlation between skeletal muscle mass and hand grip strength during fasting conditions in women R² = 0,25 20 15 20 25 30 35 40 45 50 Hand grip strength (kg) Figure 2. The correlation between skeletal muscle mass and hand grip strength during fasting conditions in women (n=17). 4. Discussion The results of this present study shows that total body water, intracellular water, extracellular water, fat free mass, soft lean mass, skeletal muscle mass and fat mass are not significantly different 90 minutes after intake of a meal containing above 500 kcal compared to the fasting condition, when measured with a bioelectrical impedance analyzer. It has also been shown a moderate correlation between skeletal muscle mass and hand grip strength during fasting conditions for women. For men, the correlation was weak. 4.1 Result discussion 4.1.1 Body composition before and after a meal intake When measuring body composition with bioelectrical impedance analysis (BIA) the recommendation is to do it under fasting conditions. However, as stated earlier in this study, there are not many studies that have investigated a meal s impact on the body composition when measured with BIA. These studies have presented varying results. In one study the subjects (n=43) had a meal containing at average 919 ± 215 kcal. The study investigated the meal s impact on the body composition 20, 40 and 60 minutes after then meal intake with a leg-to-leg BIA, a segmental BIA and a multi-frequency BIA. All three bioelectrical impedance analyzers showed an increased in %body fat, body mass and body impedance during all measurements after the meal intake compared to the baseline set before the meal 14

intake. The difference between the fasting and non-fasting condition was statistical significantly (p <0.05) (Dixon et al., 2013). Compared to the present study, the study of Dixon et al. (2013) probably gave their subjects a greater intake of energy. Some of this present study s subject only ate slightly above 500 kcal compared to the average energy intake of 919 ± 215 kcal. This could be one of explanations why the results differed. Furthermore, Androutsos et al. (2015) studied the impact of a meal on body composition measured with a bioelectrical impedance analyzer. The subjects were eating a high carbohydrate meal (n=35) on the first day and a high fat meal (n=33) on the second day. The results showed a significantly increased (p <0.05) body impedance, fat mass and %body fat for both the high carbohydrates and the high fat meals at 30, 60, 90 and 120 minutes after the meal ingestion compared to the fasting condition. These results were similar to the results presented in a study by Dixon et al. (2013) who also showed an increase in these variables after a meal intake. Also, both Androutsos et al. (2015) and Dixon et al. (2013) claims that it may not always be necessary for all groups to measure body composition with a bioelectrical impedance analysis during fasting conditions. One limitation with the study of Androutsos et al. (2015) may be the choice of bioelectrical impedance analyzer where they used a leg-to-leg BIA. This type of BIA is not as accurate and valid as foot-to-hand BIA when validated against DEXA and magnetic resonance imaging (Bosy-Westphal et al., 2008). Both Androutsos et al. (2015) and our present study measured BIA until 120 minutes post meal. However, our results showed that fat mass had no statistical significant difference 60 minutes after a meal intake compared to the fasting values, where the study of Androutsos et al. (2015) showed the opposite result. A study of Gallagher et al. (1998) also examined a meal s impact on the body composition measured with BIA. However, this study used another type of BIA where the subject lays down in a supine position, and the electrodes are attached to the skin through electrode tape and electrode cream. In this study, the subjects ate a low fat (4%) (n=10) breakfast at one occasion and on another occasion a breakfast containing 28% fat (n=29), both meals containing 550 kcal. In contrary to Dixon et al. (2013), this study showed a significant decreased impedance for both meals 120 minutes post meal ingestion. The lowest body impedance was measured 4 hours after the meal intake for both meals. There were no differences in body impedance between the low fat and the normal fat breakfast. The authors suggest that the ingestion of fluid and electrolytes could be a reason why the body impedance 15

decreased (Gallagher et al., 1998). Furthermore, a similar study to the Gallagher et al. (1998) was done by Slinde and Rossander-Hulthén (2001). Both these studies used the type of BIA where the subject lays down in a supine position and the electrodes are attached to the skin. Another common dominator between these studies is the relative equal amount of the meal s energy content. The subjects (n=18) in the study of Slinde and Rossander-Hulthén (2001) ate an average meal containing 652 ± 77 kcal three times in one day. Moreover, the results were also similar the study of Gallagher et al. (1998); the body impedance decreased consistently 120 minutes post meal intake after the first meal. After the second and third meal, the impedance decreased for 240 minutes and then started do increase again. The decrease after the second and third meal was twice as much as the decrease after the first meal intake. The decrease was significant (p <0.05) ~100 minutes after the first meal intake and continued as a significant decrease in body impedance for all following BIA measurements (until 195 minutes post the third meal). When measured the following morning the body impedance was back at baseline level (Slinde & Rossander-Hulthén, 2001). A possible reason to these studies opposite results compared to the study of Dixon et al. (2013) might be due to the different types of bioelectrical impedance analyzers or due to the relative large difference in energy intake for the subjects. Both Slinde and Rossander-Hulthén (2001) and Gallagher et al. (1998) claims that a bioelectrical impedance analysis must be done during fasting conditions to receive the accurate results. Regarding the results of the minerals before and after a meal intake, it should be noticed that bioelectrical impedance analyzers not are able to measure mineral mass. The minerals are an estimated value by the BIA, since the bone mineral mass is closely related to the fat free mass (InBody, 2017). The clinical standard for measuring bone mineral density is through Dual- Energy X-ray Absorptiometry (DEXA) (Jeukendrup & Gleeson, 2014, p.358). A recommendation for more accurate results before and after a meal intake regarding the bone mineral mass is to measure this with DEXA. Therefore, the results from this present study concerning the minerals could therefore be questioned. Lastly, one possible explanation to this present study s different results compared to other similar studies discussed earlier (Androutsos et al., 2015; Dixon et al., 2013; Gallagher et al., 1998; Slinde & Rossander-Hulthén, 2001) is that our subjects did not have any restriction according the portions of the macronutrients. Therefore, the meals in this present study could have contained widely different portions of carbohydrates, lipids and protein. As stated 16

earlier, the macronutrients start their breakdown in different parts of the digestion tract (Marieb, 2012, p.477-480) and have different absorption rates (Cori, 1925; Koopman et al., 2009). Therefore, the possibility of different macronutrients portions in our study may have affected the results. 4.1.2 Correlation between hand grip strength and skeletal muscle mass Previous research of Wind et al. (2010) has showed a strong correlation between hand grip strength and total body strength when 384 children, adolescents and young adults in the age of 8-20 years old participated (r=0.736-0.890, p <0.01). The correlation was moderate when controlled for weight (r=0.485-0.564, p <0.01). The another s suggest that the hand grip strength test can be used in clinical settings as a general indicator of general muscle strength. However, that it can lead to some inaccuracies on individual level (Wind et al., 2010). When researchers are including control variables, it is because reduce error terms and increase the statistical power or exclude alternative explanations for the findings (Becker, 2005). The reason why this present study did not control for weight was because the fact that it could affect the result was discovered after the statistical analyses were completed. Furthermore, it is the skeletal muscle mass who produce the force and body movement when, for example, lifting a weight (Marieb, 2012, p.183-185). Therefore, the results from this present study may be slightly surprising, since it indicates that there is only a weak correlation for the men, and a moderate for the women, between hand grip strength and skeletal muscle mass. But, where Wind et al. (2010) found a strong correlation between hand grip strength and total body strength. Moreover, a study of Kallman et al. (1990) found a strong correlation between hand grip strength and muscle mass (r 2 = 0.6, p <0.0001). One possible reason why this present study s results differed compared to the study of Kallman et al. (1990) might be due to the different methods for investigating the muscle mass. In this present study, a BIA was used to examine the muscle mass, where Kallman et al. (1990) used forearm circumference and creatinine excretion to estimate the muscle mass. Furthermore, according to Hillman et al. (2005) a hand grip test can investigate short-term changes in nutritional status. Since the subjects in this present study did the test during fasting conditions, this could have affected the results. However, it might not have shown a big difference if doing the test after the breakfast, since the subjects only were fasting since the night before. One other possible explanation that could have affected the results in this present study is due so that the subjects did not got any verbal encouragement during the hand grip strength test. This has been shown 17

to significantly (p<0.05) increase the performance with 5 % due to the increased motivation in a study by McNair, Depledge, Brettkelly & Stanley (1996). 4.2 Method discussion One limitation with this present study is the fact that the subjects did not eat the same meal. If the subjects instead would have eaten a standardized meal, I could have been counting the portion of carbohydrates, lipids and protein. This in turn, would make it easier to reproduce the study and due to this fact, it may also affect the study s result. The reason why not standardize the meal was due to practical reasons. I thought it would sound complicated when requiring subjects, and therefore, not get enough number of subjects participating in the study. Moreover, a major limitation with this study closely related to the food is the liquid intake at the meal that neither was standardized. Hence, the amount of water or other liquids intake is highly possible to vary among the subjects. Further limitations with this study may be some parts of the test procedure. A few times the test leaders forgot to verbally remind the subjects to empty their bladder within 30 minutes of all measurements with bioelectrical impedance analysis. On the other hand, all subjects (except for one) had received written instructions where this was stated beforehand the test session. Moreover, the subjects were not supervised while eating their meal. Therefore, the test leaders could not be certain that the subjects ate everything they claimed or that the subjects did eat at the right time in accordance to the following BIA. The subjects most probably did not eat exactly at the right time prior to the following BIA tests 60, 90 and 120 minutes after the meal intake. Also, the subjects were not supervised between the BIA tests. Hence, it is possible that they ate or drunk when they were not allowed to. Regarding the hand grip strength test, the position of shoulder, elbow and wrist may have influenced the results. In this present study, the only requirement for the subject was to hold the elbow in 90 against the table. However, a study of Kattel, Fredericks, Fernandez & Lee (1996) presented that peak hand grip strength occurred when the shoulder and wrist were in neutral posture (0 ) and the elbow in 135. Therefore, this upper body posture may have altered the results in this study if completed this way instead. 18

5. Conclusions In conclusion, the results of this study showed that body composition did not have a statistically difference 90 minutes after a meal intake compared to the fasting conditions when measured with a bioelectrical impedance analyzer, except for minerals who did show a statistically difference. Hence, the results of this present study indicate that body composition can be measured with a bioelectrical impedance analyzer both during fasting conditions and 90 minutes post a meal intake, without receiving statistically different results. This could in turn, lead to an increased use and utility for bioelectrical impedance analyzers if it is not necessary to measure during fasting conditions. There are few studies that have investigated a meal s impact on body composition measured with bioelectrical impedance analysis. Those studies have presented varying results. Therefore, more research is provided to determine whether bioelectrical impedance analysis must be done during fasting condition or not. Also, in this present study, a moderate correlation was found between hand grip strength and skeletal muscle mass for women. The same correlation was found weak for men, suggesting that a hand grip strength test is not a valid test for measuring skeletal muscle mass. 19

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