Josie Bernicot

Olga Volckaert-Legrier

Antonine Goumi

Alain Bert-Erboul

R Lenhart

S Ling

K Campbell

Purcell

C Thurlow

M Poff

C Tosell

P Kortum

C Shepard

L Barg-Walkow

SMS Experience and Textisms in Young Adolescents: Presentation of a Longitudinally Collected Corpus

2010) and in France (CREDOC, L. Bigot and P. Croutte 2011) confirm this feeling, emphasizing the young age of the texters who use SMSes as a core part of their social relations. In France in 2011, 82% of 12 to 17 year-olds had a mobile phone, and 99% used their mobile phone to send SMSes. Over the past 10 years, the number of studies focusing on this new mode of communication has continued to grow. Consistent information in several languages is now becoming available concerning message length, speaking turns, openings, closings, spelling changes with regard to the traditional written code (textisms), gender differences (cf.

Introduction

Text messages (SMS) have 'taken over' daily life, bringing to the fore written forms which would have been unimaginable 20 years ago (for example, "the gr8 db8" or, in in a longitudinal manner over a period of 12 months. The objective was to show how beginning texters became able to master the structural characteristics of SMSes and, in particular, their orthographic forms (textisms). The summary of research already carried out in this area is presented below.

Orthographic forms of SMSes (textisms) in young adolescents

Early research took into account the existence of abbreviations in SMSes and calculated the percentage of SMSes with abbreviations or the percentage of participants using abbreviations. In R. [START_REF] Ling | The socio-linguistics of SMS: An analysis of SMS use by a random sample of Norwegians[END_REF] corpus, 6% of the messages were written with abbreviations: girls between the ages of 13 and 15 were the greatest users of abbreviations (20% of their messages contained them).

More recent research has taken into account the density of textisms as a reference index. A textism is defined as a change in a word's orthographic form as compared to traditional writing. For each message, the density of textisms is equal to the number of changes divided by the total number of words in the message. Studies of children or young adolescents (9-12 years old) are rare and do not point out any specific forms of textisms for this age group. The classifications used are derived from those of C. [START_REF] Thurlow | Generation Txt? The sociolinguistics of young people's textmessaging[END_REF] which include the following 10 categories for the English language.

-Shortenings (bro for brother) -Contractions (gd for good) -G-clippings (goin for going) -Other clippings (hav for have) -Acronyms (BFPO for British Forces Posted Overseas) -Initialisms (V for very) -Letter/number homophones (2moro for tomorrow) -Misspellings (cuming for coming) -Non-conventional spellings (fone for phone) -Accent stylizations (afta for after) B. Plester, C. Wood and P. Joshi (2009) asked 88 British children (mean age: 10 years and 7 months) to write out the messages that they had written on their mobile phones in 10 daily life situations (e.g.: telling a friend in class about being late due to a bus not stopping, or letting a mother know that she forgot to buy dog food). The average age at which the first mobile phone was acquired was 9 and the participants therefore had approximately one and a half years of experience. The proportion of textisms was .34, with a difference between girls and boys (respectively .38 for girls and .28 for boys). The authors also studied the kinds of textisms and showed that two major categories stood out: simplifications (accent stylizations) which conserve the pronunciation of the spoken language (ex.: afta for after) and replacements of letters with a number homophone (e.g.: 2moro for tomorrow). P. Plester, M. K. Lerkkanen, L. Linjama, H. Rasku-Puttonen and K. Littleton (2011) analysed the SMSes of 65 young Finnish adolescents between the ages of 9 and 11 (mean age: 10 years and 7 months). The children reported having received their first mobile phone at the age of 7 years and 3 months, and therefore they are considered as having more than 3 years of texting experience. The participants were asked to copy down the SMSes they had sent over the weekend and give them to the researcher when they arrived back at school. The average proportion of textisms was .48 and simplifications (accent stylizations) conserving the pronunciation of spoken Finnish dominated all the other categories. The replacements of letters with a number homophone were never used. The most-used types of textisms therefore vary from one language to another.

N. Kemp and C. Bushnell (2011) asked 86 children between the ages of 10 and 12 (mean age: 11 years and 6 months) to write a message in SMS language on a mobile phone. The message was dictated by a researcher and the participants were to imagine that they were sending it to a friend (ex: When will we see you tonight? Because someone left a message about your friend being sick. Are you sick too?). When the T9 key was deactivated (without a dictionary), the proportion of textisms was .48. The children in the sample reported that they sent on average 24 SMSes per day and had been using a mobile phone for almost two years.

C. Bushnell, N. Kemp and F.H. Martin (2011) carried out a study with 227 Australian children between the ages of 10 and 12 (mean age: 11 years and 5 months). The children were asked to write down 30 conventional words just as they would if they were using the words in a message sent to a friend. Since the children were not allowed to use their telephone at school, the test was done with paper and pencil. The results indicated that the proportion of textisms in writing task was .53.

C. Wood, E. Jackson, L. Hart, B. Plester and L. Wilde (2011a) collected, in a longitudinal fashion over 10 weekends and half-term breaks, the SMSes of 56 children between the ages of 9 and 10 (mean age: 9 years and 10 months). The participants were from the Midlands region of the United Kingdom. The children had never used a mobile phone before the beginning of the study. Depending on the weekend, the number of messages sent varied between 6 and 45, and the proportion of textisms between .12 and .16. Over the 10 weeks, no evolution in usage was shown.

C. Wood, S. Meachmen, S. Bowyer, E. Jackson, M.L. Tarczynski-Bowles and B. Plester (2011b) carried out a longitudinal study of 1,019 children between the ages of 8 and 12 (mean age: 10 years and 4 months). The participants were from the West Midlands region of the United Kingdom. The children reported owning their mobile phone since the age of 8 years and 1 month. The children were asked to provide a sample of the messages they had sent at two different time periods: at the beginning of the school year and at the end of the school year. The results showed that the average ratio of textisms went from .33 to .40 between the beginning and the end of the school year. This slight increase masks the decreases at 8 to 9 years of age and at 11 to 12 years of age which remain unexplained. Both at the beginning and at the end of the year, the textism ratio was greater for the 11 to 12 year-olds than for the 8 to 9 year-olds (.42/.27 and .33/.074, respectively).

Overall, the studies carried out with children and young adolescents between the ages of 9 and 12 made use of a variety of methods ranging from the collection of natural data to the simulation of writing words in SMS language in a paper-and-pencil situation. The density of textisms revealed itself to also be highly variable: the values ranged from .074 to .53. Longitudinal studies of novice participants, such as those of Wood et al. (2011a), did not show an increase in the density of textisms with practice. This might be explained by the fact that a collection period limited to only 10 weekends was too short for an evolution to be demonstrated. It is therefore necessary to augment the existing data in order to have clear information regarding the orthographic forms of SMSes for the 9 to 12 year-old age group.

Research objectives

The existing research is limited and did not make use of the longitudinal method necessary for understanding the process of acquisition, with the exception of the studies carried out by C. Wood et al. (2011a) and C. Wood et al. (2011b). It should be noted that, in the first case, the study period was 10 weeks in length and, in the second case, the study period was one school year, but only one sample of SMSes is available for the beginning and the end of the year. To analyse the textisms, most of the studies either used scenarios (N. Kemp and C. Bushnell 2011;B. Plester, C. Wood and P. Joshi 2009) or were not based on an SMS collection method in an ecological condition. The methodology used in the present study enabled the collection of SMSes in natural writing conditions. Furthermore, the categories of textisms could certainly be refined so as to be adapted to young texters. Finally, the existing studies did not take into account the gender variable (girls/boys) even though some research (J. Bernicot, O. Volckaert-Legrier, A. [START_REF] Bernicot | Forms and Functions of SMS Messages: A Study of Variations in a Corpus Written by Adolescents[END_REF]A. Goumi, O. Volckaert-Legrier, A. Bert-Erboul and J. Bernicot 2011) show differences in SMS1 length, dialogical structure and function in participants between the ages of 15 and 16. The objective of the present study was to fill in these gaps by examining, by means of a longitudinal study over a long period (12 months), how the orthographic characteristics of SMSes evolve (month by month) in young adolescents (11-12 years of age), by categorizing the textisms in terms of their distance from traditional written code.

Method

Participants

Nineteen young adolescents participated in the study: 10 girls and 9 boys (average age = 11.79 years, SD2 =.59 of a year). They were recruited in a public secondary school in a town located in the Poitou-Charentes region of France (6 th and 7 th years). The pupils who had never owned or used a mobile phone were invited to participate in the study. The offer was as follows: to be equipped, free of charge, with a mobile phone for one year and to agree to "donate" to the research team at least 20 SMSes (written by the students themselves) per month. The research team guaranteed the students' anonymity at all stages of the study. The students as well as their parents provided their written consent and agreement. The participants were all from middle class families, of legal school age, and native French speakers. The participants' academic results in French class showed that they were able to write texts according to traditional rules, although they made some mistakes depending on their level.

Materials

The materials consisted of a Sony Ericsson model J132 or Alcatel model OT-303 mobile phones (French models). These two open-face (non-flip style) mobile phone models with a nine-key keyboard are similar. The T9 key, which enabled predictive text and access to a dictionary, was deactivated. The cards which served to recharge the mobile phones were, on the one hand, cards worth €15 (equivalent to 30 minutes of voice communication or 150 SMSes) and, on the other hand, cards worth €5 and valid for five days with unlimited SMSes. A 3G key and the Vodafone Mobile Connect software (a screenshot of this software is provided in Appendix A) installed on a computer allowed the research team to receive the SMSes which were 'donated' each month by the participants.

Procedure

The procedure for collecting the SMSes is summarized in Table 1. At the beginning of each month, the participants' mobile phones were automatically credited with a sum of €15, the equivalent of 30 minutes of voice communication or 150 SMSes. Once per month, the participants' mobile phones were also credited with the sum of €5, allowing an unlimited number of SMSes to be sent over a period of five days. It was during this period that the participants were to send at least 20 SMSes to the research team, freely chosen from the SMSes that they had sent throughout the month and that they had written themselves. These SMSes, with the help of the 3G key, were received onto a computer by means of the Vodafone SMS software. Using this software, a "cut and paste" procedure enabled the SMSes to be entered into the cells of a Microsoft Excel workbook. This procedure was repeated for 12 months. The participants respected their agreement by providing a total of 4,524 messages3 : on average, per participant, 19.84 SMS (SD = 3.02) per month and 238.10 SMS (SD = 36.24) for the year. The data collection took place throughout the 2009-2010 academic year. As an example, Table 2 presents the SMSes (with their original form in French and their transcribed form in traditional French) of participant No 10 for month 1.

Coding

Two indexes were taken into consideration: the type of textisms and the density of textisms. A textism is defined as a change in the orthographic form of a word as compared to traditional writing. For each message, the density of textisms was equal to the number of words with changes divided by the total number of words in the message.

The coding of the textisms was based, on the one hand, on the analytical grids of the English language (R. Grinter and M.A. Eldridge 2003;B. Plester, C. Wood and P. Joshi 2009;C. Thurlow and A. Brown 2003) and on the grids dedicated to the French language (J. Anis 2007; R. [START_REF] Panckhurst | Short Message Service (SMS) : typologie et problématiques futures[END_REF]. R. [START_REF] Panckhurst | Texting in three European languages: does the linguistic typology differ?[END_REF] showed the particularities of French (in SMS language) as compared to Italian and Spanish. E. [START_REF] Stark | La morphosyntaxe dans les SMS suisses francophones: Le marquage de l'accord sujetverbe conjugué[END_REF] studied the morphosyntax in SMSes written in Swiss French, and examined SMSes in the three languages (French, German, Italian) spoken in Switzerland (E. Stark and C. Dürscheid 2011). In this study, it was deemed important to distinguish between the two large types of textisms with regard to their accordance with, or rupture from, traditional written code. From a cognitive point of view, the first case involves a different application of the same rules, while in the second case; there is an application or invention of different rules.

a)

The textisms which were consistent with the traditional code of grapheme-phoneme correspondence: the orthographic changes did not modify the phonology (pronunciation) of the words and were carried out with graphic forms which exist in traditional writing. b)

b) The textisms which broke with the traditional code of graphemephoneme correspondence: the orthographic changes modified the phonology (pronunciation) of the words and/or were carried out with graphic forms which do not exist in traditional writing.

Table 3 presents the different sub-categories together with examples.

All of the categories of C. [START_REF] Thurlow | Generation Txt? The sociolinguistics of young people's textmessaging[END_REF] were found in this study, except for "g-clippings", which are specific to the English language. The category of "agglutinations", made up of words placed one after the other without a space ("patavoir" [nothaveyou] instead of "pas t'avoir" [not have you], or "jcroyé" [ithot] instead of "je croyais" [i thought]), was highlighted, and this has no equivalent in the classification of C. [START_REF] Thurlow | Generation Txt? The sociolinguistics of young people's textmessaging[END_REF].

With regard to the coding of the different types of textisms with the categories of this study, a very high intercoder agreement of 99.68% was found, using 250 messages chosen at random and containing 940 textisms.

Results

Different types of textisms

Table 4 provides an overall view of the distribution of the different types of textisms. In the corpus used in this study, the average proportion of textisms was .52, which meant that slightly more than half of the words produced by the participants contained a change with regard to traditional spelling and that slightly less than half contained no changes.

Proportion of words with textisms

Proportion of words without textisms .52

.48

Table 4. Proportions of words with textisms for each category of textisms Among the words with textisms, half (.26) were consistent with traditional code and the other half (.26) broke with this code. For the textisms which were consistent with the traditional code, simplifications made up the largest proportion (.23). For the textisms which broke with the traditional code, graphemes which led to a modification of the phonology made up the largest proportion (.13).

The density of textisms

First, with the participants as the random variable, the results were processed by means of a one-way ANOVA (analysis of variance): SMS experience (months 1 to 12). The dependent variable was the density of textisms: the total number of textisms divided by the number of words per message.

T extisms for all the categories combined The effect of SMS experience was significant, F(11,198) = 3.90, p < .00004, ² =.18: on the whole, the density of textisms rose from month 1 (average = .44) to month 12 (average = .57) (cf. Figure 1).

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 MONTHS 0,

Density of textisms

For the textisms which were consistent with the traditional code, the results were processed by means of a two-factor ANOVA: SMS experience (months 1 to 12) x gender (female/male). The dependent variable was the density of textisms: the number of textisms consistent with the traditional code divided by the number of words per message.

The interaction effect between SMS experience and gender was significant, F(11,187) = 2.54, p < .005, ² =.13. For girls, this density (cf. Figure 2) remained stable over the months, while for boys, the increase between month 1 and month 12 was significant, F(1,17) = 6.00, p < .02. Furthermore, for months 7 to 12 grouped together, boys produced significantly more textisms which were consistent with the traditional code than girls did F(1,17) = 6.40, p < .02. For the textisms which broke with the traditional code, the results were also processed by means of a two-factor ANOVA: SMS experience (months 1 to 12) x gender (female/male). The dependent variable was the density of textisms: the number of textisms breaking with the traditional code divided by the number of words per message.

The effect of SMS experience was significant F(11,187) = 2.40, p < .008, ² =.12. No significant interaction effect for SMS experience x gender was found. For both girls and boys, there was an increase from month 1 to month 12 (cf. Figure 3).

Conclusions

The first objective of this study was to compile a corpus of SMSes written by young adolescents between the ages of 11 and 12. This goal was achieved: 4,524 SMSes produced by participants who had no experience at the start of the collection which lasted 12 months and included one collection per month. The technique which was used guaranteed that the SMSes were those written in daily life situations by the participants. The corpus, in French, added to that of C. Fairon, J.R. Klein and S. Paumier ( 2006) et E. [START_REF] Stark | SMS4science: An international corpus-based texting project and the specific challenges for multilingual Switzerland[END_REF] ("sms4science") by providing data from younger texters (11 to 12 years of age), and whose productions were observed over a one-year period.

The first result was that the density of textisms increased with SMS experience (between month 1 and month 12 of the collection). In one year, the average grew from .44 to .57 with large variances: For example, in month 12, the smallest density value was .21 and the greatest value was .79. Differences, then, were observed from one participant to the other, but a general trend existed of a high density from the very first month and an increase in this density of textisms with SMS experience.

How might the high density of textisms produced by the novice users in the first month be explained? An initial explanation might point to a particularly weak level of spelling in traditional writing. Yet, as was mentioned earlier in this paper when the participants were presented, this was not the case: where spelling was concerned, all of these participants met the expectations of the academic system. A second explanation might be an immediate adaptation to the new writing context of the mobile phone: even if they had never used one themselves, the participants 'knew' by cultural transmission that SMS rules were not the same as classroom writing rules. This second hypothesis needs to be verified by means of a survey of novice users.

The average density of textisms that was observed (.57 after one year of experience) was greater than those found in comparable collections carried out with the longitudinal method: .16 for C. Wood et al. (2011a) and .40 for C. Wood et al. (2011b). Additional research will make it possible to determine whether this difference in results was due to the language (French/English) or the collection techniques. In the study carried out by C. Wood et al. (2011b), the density of textisms was .33 in the first month of the collection with participants who had, on average, two years of SMS experience; in the present study, it was .44 for participants who had no experience at all. This study aimed to go further than an analysis of the density of textisms by considering the type of textisms and the gender of the participants. The distinction between the two types of textisms (consistent with or breaking with the traditional written code) was based upon the different cognitive processes which make a deeper analysis possible. On the whole, the density of textisms was .52, with a density of .26 for textisms which were consistent with the traditional written code, and the same density of .26 for those which broke with this code.

For the textisms which were consistent with the traditional code, the same rules were applied in a different way: for example, by simplifying "donner [to give]" and making it "donné", the phoneme-grapheme code is respected "differently". Instead of transcribing the "é" by the grapheme "er" (which is pronounced the same way) which is expected in terms of morphosyntax, it is transcribed by using another existing and phonologically possible grapheme, the letter "é". The evolution with use is not the same for girls as for boys: for girls, the density remains stable, while for boys, it increases with use. For the last months (7 to 12), the density of textisms which were consistent with the traditional written code was higher for boys than for girls.

For textisms which broke with the traditional code, a new rule was applied or invented: For example, by writing "jtdgdt" for "je t'adore grave de trop" [I love you way too much], a message is produced which cannot be decrypted (or pronounced) using the traditional phoneme-grapheme traditional written code. A new rule is used, which leads to a new convention being used by those who have mastered the SMS register. The evolution with use is the same for girls as for boys: the density increases with use. At month 12, girls produced 57% of textisms which broke with the traditional code, while boys only made 47%. For this new variable, the results are analogous with the data obtained in other areas: girls tend to master the characteristics of an SMS register earlier than boys (J. Bernicot, O. Volckaert-Legrier, A. [START_REF] Bernicot | Forms and Functions of SMS Messages: A Study of Variations in a Corpus Written by Adolescents[END_REF]A. Goumi, O. Volckaert-Legrier, A. Bert-Erboul and J. Bernicot 2011;C. Thurlow and M. Poff 2012, in press).

This study demonstrated the importance of carrying out an in-depth analysis of textisms in terms of the cognitive processes involved by going further than the description of the orthographic changes with regard to the traditional written code. Continuing in this direction will make it possible to show and provide a convincing argument for the fact that SMSes are not an example of the deterioration of traditional writing, but instead constitute a register of their own which belongs to a very precise situation of communication (D. Crystal 2001;D. Ravid and L. Tolchinsky 2002): written correspondence with close friends and relatives by means of a mobile phone.

Figure 1 .

 1 Figure 1. Evolution of textism density over one year by months (M) of SMS experience The vertical bars represent the standard deviations

Figure 2 .

 2 Figure 2. Evolution over one year of the density of textisms which were consistent with the traditional code by months (M) of SMS experience and gender (F: female, M: male) The vertical bars represent the standard deviations

TFigure 3 .

 3 Figure 3. Evolution over one year of the density of textisms which broke with the traditional code by months (M) of SMS experience and gender (F: female, M: male) The vertical bars represent the standard deviations

Table 2 .

 2 SMSes from participant No 10 for month 1 The participant was a girl who was 12 years and 7 months old Cc J'ai une mauvaise nouvelle pour toi marine veu pa sortir avec toi Dsl Hey I have some bad news for you Marine does not want to go out with you sorry Ouai ta raison Tu me dira si ya des nouvel

	Text message No	Original SMSes in French	English translations into traditional French of SMSes in transcribed form
		Es ke jordan a un portable	Does Jordan have a mobile phone
		Jlui et di je sai pa	I told him I don't know
		Cc c megane Tu y sera ver kel heur au	Hey it's Mégane around what time will you
		stade mercredi?	be at the stadium Wednesday?
		3 ou 4	3 or 4
		Chui ché des gen et je menui	I'm at some people's house and I'm bored
		Ouai dmin et c ke laprèm Si tu vien fo	Yeah t'morrow and it's that in the
		ke tu révise pars ky a un control en	afternoon if you come you have to study
		histoir	because there's a history test
		Jai resu ton mésage mai pourquoi tu ma	I got your message but why did you send
		envoyé ça ?	me that?
			Yeah you're right tell me if you hear
			anything
		Non mai jvai à crepigny ché mon	No but I'm going to Crépigny to my
		tonton et je vai mennuyé à mourir	uncle's place and I'm going to die of
		Pourkoi t au stade?	boredom Why are you at the stadium?
		A ok Ta fé té maths?	Ah ok did you do your maths?
		Ladresse de ché ele?	Her home address?
		Dsl jui pa a crepigny Jui en train de me	Sorry I'm not in Crépigny I'm swimming
		baigné Mé normalemen jiré au foot	but I should be going to football next
		vendredi prochain	Friday
		A ok Et tu fé koi Ya un match	Ah ok and what are you up to is there a
			match
		Nou on sor just de la piscine	We just got out of the pool
		Cc dsl si je t pa répondu hier soir C	Hey sorry I didn't get back to you last
		parce ke jai étin mon portable	night it's because I turned off my mobile
		Es ke demain tu poura amené ta trouce	Tomorrow can you bring your case where
		ou ya tou t badge stp	you keep all your cards please
		Pk tu rép pa a ana	Why won't you answer Ana
		Cc c vrai ke tu sor avec marine	Hey is it true that you're going out with
			Marine
		Ba parce ke jaime bien tenbêté	Ha because I really like messing with you

Textisms which were consistent with the traditional grapheme-phoneme correspondence code

	Categories	Examples	Traditional transcriptions in
			French
	Simplifications	é	et
		donné	donner
		koi	quoi
		biz	bise
		tro	trop
		concer	concert
	Complexifications	lessons	leçon
		j'ai male	j'ai mal
	Substitutions	chanbre	chambre
		es	ai
		fais	fait
	Textisms breaking with the traditional grapheme-phoneme correspondence code
	Categories	Examples	Traditional transcriptions in
			French
	New grapheme-phoneme	mwa	moi
	correspondences without	qi	qui
	modification of phonology	dem1	demain
		ct	c'était
	Agglutinations	entrain	en train
		jcroyé	je croyais
	Modifications of phonology	pk	pourquoi
		jtdgdt	je t'adore grave de trop
		couz	cousine
		chpa	je ne sais pas
	Words or graphic forms which do	mdr	mort de rire
	not exist in traditional French	jtkife	je t'aime
		ouiiiiiiiiiiiiiiiiiiii	Repeated letters as an expression of
			urgency
		pfff	New onomatopoeia
			Smiley expressing sadness

Table 3 .

 3 Examples of different categories of textisms which were consistent with or breaking with the traditional grapheme-phoneme correspondence code

The girls' messages were longer than the boys' messages and were often more relational than informational. The girls sent fewer messages which were lacking an opening and a closing (just the message) than the boys.

SD: Standard Deviation

All of the text messages sent by the participants were taken into consideration, except for the text messages which were 'chain letters' or 'spam' (by definition not written by the participants themselves).

Acknowledgments

This research was supported by the French National Research Agency project ANR-08-COMM-011-01 entitled "Adolescents and Computer-Mediated Writing: Prerequisites, Uses, and Learning". Special thanks are extended to Martha Randy for translating this paper. Paper accepted by Lingvisticae Investigationes (November 2012, revised version).

Summary

The aim of this paper was to study the characteristics of SMSes in a population for which there is currently only limited data: young adolescents (girls and boys) between 11 and 12 years of age. The analysis focused on a corpus of 4,524 SMSes sent by 19 informants in everyday real-life situations over a one-year period. At the beginning of the study, the participants were complete novices. This study sets forth a new analysis grid which distinguishes between two categories of textisms which were defined based on the following cognitive processes: a) textisms which are consistent with the traditional written code of grapheme-phoneme correspondence and b) textisms which break with this traditional code. On the whole, the density of textisms was .52 and .26, respectively, for each kind of textism. The results showed an increase in the density of textisms with SMS experience (from month 1 to month 12), but also a variation depending upon the type of textism and the gender of the texter. For boys, the density of both types of textisms increased with SMS experience, while for girls, the density of textisms only increased for textisms which broke with the code. The results were interpreted in terms of the construction of an SMS register with specific linguistic markers resulting from a different use of traditional writing rules or the use of inventions as compared to traditional writing.

Authors' addresses


Ronan Thibault
email: ronan.thibault@chu-rennes.fr

Anne-Marie Makhlouf

Aurélien Mulliez

M Cristina Gonzalez

Gintautas Kekstas

Nada Rotovnik Kozjek

Jean-Charles Preiser

Isabel Ceniceros Rozalen

Sylvain Dadet

Zeljko Krznaric

Kinga Kupczyk

Fabienne Tamion

Noël Cano

Claude Pichard

Phase Angle

Séverine Graf

Claudia P Heidegger

Marcio Guerreiro

Pelotas Silvana Orlandi

Andrius Klimasauskas

Gintare Sostakaite

Kristina Gorjup

Katja Kogovšek

Kopriva Katja

Pirtovšek

Michelle Norrenberg

Karen Rizkallah

François Wéry

Ali Ait Hssain

Bruno Pereira

Clermont- Bertrand Souweine

Steven Grange

Antoine Marchalot

Pauline Bernier-Enguerrand

Marlena Jakubczyk

Aleksandra Różowicz

Stanisław Kłęk

Skawina Bydgoszcz

Ana Kunovic

Prof Ronan Thibault

Fat-free mass at

Keywords: bioelectrical impedance analysis, severity of illness index, body composition, critical illness, critical care outcomes

INTRODUCTION

Intensive care unit (ICU) patients feature a loss of fat-free mass up to 440 g/day, which is associated with increased morbidity and prolonged recovery [START_REF] Kyle | Bioelectrical impedance analysis-part I: review of principles and methods[END_REF]. In several clinical conditions, fat-free mass [START_REF] Kyle | Bioelectrical impedance analysispart II: utilization in clinical practice[END_REF] derived from bioelectrical impedance analysis have been associated with worse clinical outcome [START_REF] Thibault | Body composition: why, when and for who?[END_REF][START_REF] Pichard | Nutritional assessment: lean body mass depletion at hospital admission is associated with an increased length of stay[END_REF][START_REF] Gupta | Bioelectrical impedance phase angle in clinical practice: implications for prognosis in advanced colorectal cancer[END_REF][START_REF] Schwenk | Phase angle from bioelectrical impedance analysis remains an independent predictive marker in HIV-infected patients in the era of highly active antiretroviral treatment[END_REF][7]. However, solid data to support this association in ICU patients are lacking. Only a single retrospective study of 51 ICU patients with acute respiratory failure correlated loss of active cell mass with mortality [START_REF] Faisy | Bioelectrical impedance analysis in estimating nutritional status and outcome of patients with chronic obstructive pulmonary disease and acute respiratory failure[END_REF]. In a pilot study published in an abstract form performed in 55 ICU patients, we observed that phase angle measured at 5 kHz was significantly related to the Sequential Organ Failure Assessment (SOFA) (r=0.38, P=0.03) [START_REF] Thibault | relationship between bioelectrical phase angle se angle, sofa score and mortality in intensive care unit (ICU) patients[END_REF]. These studies suggest that phase angle, that reflects fat-free mass, may be useful in predicting ICU morbidity and mortality (Figure 1).

Bioelectrical impedance analysis is a well validated, non-invasive and relatively

inexpensive technique to measure body composition [START_REF] Kyle | Bioelectrical impedance analysis-part I: review of principles and methods[END_REF]. In the ICU, bioelectrical impedance analysis is frequently described as a non-reliable technique to assess nutritional status through body composition, i.e. fat-free mass or fat mass, because of rapid fluid shifts. This is related to the equations used that have all been validated in clinical situations associated with fluid homeostasis and constant water content. Phase angle is easily, quickly, and repeatedly measured at bedside by bioelectrical impedance analysis. Phase angle is reflecting intracellular status and fat-free mass (Figure 1): altered intracellular water to extracellular water distribution is suggested by low phase angle [START_REF] Thibault | Body composition: why, when and for who?[END_REF].

Phase angle measurement does not require recalled parameters, body weight, and lab tests.

Classic ICU validated severity scores (e.g. Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), SOFA) and recent nutritional scores have been developed to foresee the clinical outcome of ICU patients [START_REF] Heyland | Identifying critically ill patients who benefit the most from nutrition therapy: the development and initial validation of a novel risk assessment tool[END_REF]. Most of these scores are time consuming and suffer some degree of discriminative power (i.e. APACHE II and SAPS II are not validated in cardiovascular surgery patients [START_REF] Hekmat | Prediction of mortality in intensive care unit cardiac surgical patients[END_REF][START_REF] Gall | A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study[END_REF]).

We conducted an international multicentre prospective observational study in ICU patients to assess the prognosis value of fat-free mass at admission (day 1) measured by bioelectrical impedance analysis-derived phase angle. The primary aim was to determine the association between day 1 phase angle and 28-day mortality. The secondary aim was to determine the relation between day 1 phase angle and disease severity scores (APACHE II and SAPS II).

METHODS

Study design

The Phase angle Project is an international multicentre prospective observational study involving the intensive care departments and nutrition units of ten centres in nine countries: Geneva (Switzerland), Pelotas (Brazil), Brussels (Belgium), Vilnius (Lithuania), Clermont-Ferrand (France), Ljubljana (Slovenia), Palma de Majorque (Spain), Bydgoszcz (Poland), Zagreb (Croatia), and Rouen (France). The project was coordinated by the Nutrition unit, Geneva University Hospital, Geneva, Switzerland, with RT as the international study coordinator. The research protocol was approved by the Geneva University Hospitals Ethics Committee, then by the Ethical Committee of each participating hospital. The total number of beds among the participatings ICUs was 320, including 70% of medical and 30% of surgical beds.

Patients' recruitment

Each participating centre had to include 100 consecutive patients. This means that during the inclusion period, each centre should include all admitted patients meeting the inclusion criteria until the goal of 100 included patients was reached. For one single patient, the study duration was one month. The overall inclusion period took place from September 1 st 2012 and April 30, 2015. The inclusion criteria were: age >18 years, expected length of stay >48 hours, absence of pacemaker or heart defibrillator implant, absence of known pregnancy or lactation, affiliation to an health insurance, and written informed consent by the patient or her/his next of kin in case of inability to consent. Patients were not eligible if they were readmitted 48 hours after their discharge to avoid that a single patient was included twice.

Study endpoints

The primary endpoint was 28-day mortality, i.e. mortality during the 28 consecutive days following ICU admission. If patients were discharged from the hospital or transferred in another health care centres, the information of 28-day mortality was collected by examination of medical records, phone call to the hospital department or to the patient's general physician.

The secondary endpoints were APACHE II and SAPS II collected at day 1. Day 1 was defined as the day of ICU admission if patients were admitted at or before noon, or as the day following ICU admission if patients were admitted after noon.

Phase angle measurement by bioelectrical impedance analysis

50 kHz phase angle was measured by bioelectrical impedance analysis in every patient at day 1 (day 1 phase angle), and repeated at day 5 for patients still hospitalized in the ICU.

After skin cleaning with 70% ethanol, four adhesive electrodes (3M Red Dot T, 3M Health Care, Borken, Germany) were placed on the dorsal side of the left hand, left wrist, left foot, and left ankle (Figure 1) while the patient was lying supine with hand palms facing inwards. If the left side was not accessible (e.g. wounds, fracture,…), the right side was chosen. Phase angle measurement at day 5 was performed with respect to day 1 electrodes position. An electrical current of 0.8 mAmp was produced at 50 KHz by a generator/analyser (Nutriguard M, DataInput, Gmbh, Darmstadt, Germany) during approximately two minutes to measure whole-body resistance (R) and reactance (Xc) [START_REF] Kyle | Bioelectrical impedance analysis-part I: review of principles and methods[END_REF].

Phase angle was calculated according to the following equation: phase angle (degree) = arctan(Xc/R)*(180/) (Figure 1).

Data collection

Weight and height were measured or obtained by the patient's or their next of kin's interview the day of phase angle measurement by bioelectrical impedance analysis. Body mass index was calculated as weight (kg) divided by squared height (m 2 ). Clinical information (primary and secondary admission diagnoses, ICU severity scores, outcomes) was retrieved by each investigator from the hospital electronic medical record. In Geneva University Hospitals, the ICU dedicated computerised information data management system (CliniSoft 6.2, General Electric) was used.

Statistics

Sample size calculation

The sample size calculation was based on the main endpoint, the 28-day mortality, the recruitment capacity of each centre and the estimation of statistical power. Based on other recent prospective randomized controlled trials [START_REF] Heidegger | Optimization of energy provision with supplemental parenteral nutrition (SPN) in critically ill patients: a randomised controlled clinical trial[END_REF][START_REF] Doig | Early parenteral nutrition in critically ill patients with short-term relative contraindications to early enteral nutrition: a randomized controlled trial[END_REF], and statistics from the ICU department of the Geneva University Hospitals, we expected a 28-day mortality of 15%.

To obtain a area under the receiver operating characteristic (ROC) curve assessing 28-day mortality prediction by phase angle of 0.85, and given bilateral  and  risks of 5 and 10%, respectively, 1000 patients had to be recruited [START_REF] Hanley | The meaning and use of the area under a receiver operating characteristic (ROC) curve[END_REF]. We therefore planned to include 100 patients per centre.

Statistical analysis

Descriptive statistics are expressed as mean ± standard deviation or median and interquartile range for continuous data, according to their distribution, and number of patients (percent, %, followed immediately after by the counts for the numerator/denominator) for categorical data. Receiver Operating Characteristic curves were used to analyse the association of each variable with 28-day mortality, and to find the best cut-off associated with 28-day mortality according to the Youden method.

Receiver Operating Characteristic curves are analysed with their area under the curve (AUC) and its 95% confidence interval. Discriminative power of AUC was determined according to the following classification: 0.90≤AUC≤1.0, excellent; 0.80≤AUC<0.90, good; 0.70≤AUC<0.80, fair; 0.60≤AUC<0.70, poor; 0.50≤AUC<0.60, fail [START_REF] Swets | Measuring the accuracy of diagnostic systems[END_REF]. We analyzed 28-day mortality with the χ2 tests (or Fisher's exact test when appropriate) for categorical data, and the Student's t test (or Mann-Whitney-Wilcoxon test when appropriate) for continuous data. A multivariable logistic regression adjusted for the factors with a P-value<0.2 in the univariate analysis was performed with a backward stepwise elimination process eliminating all variables that did not contribute (Pvalue ≥0.05). For continuous variables, the adjusted odd ratio was expressed for 1-point increase. From this model we computed a risk-score of 28-day mortality. Results are expressed as adjusted odds ratio and their 95% confidence interval. The risk-score was used in a Receiver Operating Characteristic curves analysis to assess its discriminative power towards 28-day mortality. Sensitivity, specificity, positive predictive value and negative predictive value, and their 95% confidence interval were calculated according to the Youden method. Statistics were computed with STATA V12 (Stata Corp, College Station, Texas, USA) and two-sided P-value<0.05 was considered statistically significant.

RESULTS

Patients' selection, characteristics and outcomes

Among 3605 screened patients, 1009 patients were first included according to the inclusion criteria, and after secondary exclusions (8%, 77/1009), 931/3605 patients were analyzed (26% of screened patients) (Figure 2). Sixty-four percent (49/77) of secondary exclusions occurred mainly because bioelectrical impedance analysis was not performed at admission. Analyzed patients were included from the 10 participating centres as follow: Geneva (n=170), Ljubljana (n=107), Pelotas (n=103), Brussels (n=100), Vilnius (n=100), Clermont-Ferrand (n=99), Palma de Majorque (n=96), Rouen (n=56), Bydgoszcz (n=51), and Zagreb (n=49). Patients' characteristics and diagnoses at admission are detailed in Table 1. Mean day 1 phase angle was 4.5±1.85° (range: 1.24 -21.0). Day 1 phase angle was measured in 932 patients; one patient was secondly excluded because of aberrant result (day 1 phase angle =0). As expected, day 1 phase angle was lower in older patients, female and patients with lower body mass index (data not shown). SAPS II was collected in 657 patients since it was not collected in routine clinical practice in two centres. Patients' clinical outcomes during follow up are shown in Table 1.

Relation between day 1 fat-free mass and 28-day mortality

28-day mortality was 19.3 % (180/931) (range 9.6-38.8%), close to the 15% mortality predicted for the sample size calculation. Day 1 phase angle was lower in non-survivors than in survivors (Table 1 and Figure 3A). This difference failed to reach statistical significance at day 5 in the 527/931 patients (57%) remaining hospitalized in the ICU.

The area under the receiver operating characteristic curve assessing 28-day mortality prediction by day 1 phase angle was: 0.63 [95% confidence interval, 0.58-0.67] (Figure 3B), indicating a poor discriminative power of day 1 phase angle. [START_REF] Wischmeyer | Winning the war against ICU-acquired weakness: new innovations in nutrition and exercise physiology[END_REF] respectively. The area under the Receiver Operating Characteristic curve of this riskscore was: 0.79 [95% confidence interval, 0.75-0.82] (Figure 4), improving the discriminative power either values of day 1 phase angle, or APACHE II or SAPS II alone.

Relation between day 1 phase angle and ICU severity scores

There was a correlation between day 1 fat-free mass and ICU severity scores. APACHE II and SAPS II were significantly higher in patients with day 1 phase angle <3.49 than in patients with day 1 phase angle ≥3.49 (APACHE II, 21.8 ± 9.2 vs. 17.7 ± 8.7, P<0.001; SAPS II, 48.0 ± 19.2 vs. 40.5 ± 18.3, P<0.001).

DISCUSSION

In this international multicentre prospective observational study, a low fat-free mass measured by bioelectrical impedance analysis-derived phase angle at ICU admission was associated with increased 28-day mortality. A multivariable combined score (including day 1 phase angle, APACHE II, and other parameters) improves the discriminative power of 28-day mortality compared to either values of day 1 phase angle, or APACHE II or SAPS II alone. This study suggests that phase angle could be used as part of prognosis markers at ICU admission.

In the non-ICU patients, bioelectrical impedance analysis is a well validated and easy-touse method to measure at bedside body composition: fat-free mass, fat mass, and total, intra-and/or extracellular body water, as well as an easy and quick measurement of phase angle that is a reflect of fat-free mass. A low phase angle is reflecting fat-free mass loss and cell dysfunction whereas a higher value (>6 in normal subjects [START_REF] Bosy-Westphase Angle L A | Phase angle from bioelectrical impedance analysis: population reference values by age, sex, and body mass index[END_REF]) is reflecting good cell health or nutritional status (Figure 1). Low phase angle has been correlated with a worse disease prognosis in oncology [START_REF] Gupta | Bioelectrical impedance phase angle in clinical practice: implications for prognosis in advanced colorectal cancer[END_REF][START_REF] Gupta | Bioelectrical impedance phase angle in clinical practice: implications for prognosis in stage IIIB and IV non-small cell lung cancer[END_REF][START_REF] Gupta | Bioelectrical impedance phase angle in clinical practice: implications for prognosis in advanced colorectal cancer[END_REF][START_REF] Gupta | Bioelectrical impedance phase angle as a prognostic indicator in advanced pancreatic cancer[END_REF][START_REF] Paiva | Standardized phase angle from bioelectrical impedance analysis as prognostic factor for survival in patients with cancer[END_REF], human immunodeficiency virus infection [START_REF] Schwenk | Phase angle from bioelectrical impedance analysis remains an independent predictive marker in HIV-infected patients in the era of highly active antiretroviral treatment[END_REF], amyotrophic lateral sclerosis [START_REF] Desport | Phase angle is a prognostic factor for survival in amyotrophic lateral sclerosis[END_REF], geriatric [START_REF] Wirth | Bioelectric impedance phase angle is associated with hospital mortality of geriatric patients[END_REF], renal dialysis in chronic renal failure [START_REF] Mushnick | Relationship of bioelectrical impedance parameters to nutrition and survival in peritoneal dialysis patients[END_REF], liver cirrhosis [START_REF] Selberg | Norms and correlates of bioimpedance phase angle in healthy human subjects, hospitalized patients, and patients with liver cirrhosis[END_REF], acquired immune deficiency syndrome [START_REF] Schwenk | Phase angle from bioelectrical impedance analysis remains an independent predictive marker in HIV-infected patients in the era of highly active antiretroviral treatment[END_REF], and gastrointestinal surgical patients [START_REF] Barbosa-Silva | Bioelectric impedance and individual characteristics as prognostic factors for post-operative complications[END_REF]. The phase angle cut-off associated with increased mortality is variable: <2.5° in amyotrophic lateral sclerosis [START_REF] Desport | Phase angle is a prognostic factor for survival in amyotrophic lateral sclerosis[END_REF], <3.5° in geriatrics [START_REF] Wirth | Bioelectric impedance phase angle is associated with hospital mortality of geriatric patients[END_REF], from <1.65 to <5.6° in oncology [START_REF] Gupta | Bioelectrical impedance phase angle in clinical practice: implications for prognosis in stage IIIB and IV non-small cell lung cancer[END_REF][START_REF] Gupta | Bioelectrical impedance phase angle in clinical practice: implications for prognosis in advanced colorectal cancer[END_REF][START_REF] Gupta | Bioelectrical impedance phase angle as a prognostic indicator in advanced pancreatic cancer[END_REF][START_REF] Paiva | Standardized phase angle from bioelectrical impedance analysis as prognostic factor for survival in patients with cancer[END_REF], <5.4° in cirrhosis [START_REF] Selberg | Norms and correlates of bioimpedance phase angle in healthy human subjects, hospitalized patients, and patients with liver cirrhosis[END_REF], and, in our study, <3.49° at ICU admission. These differences among cut-offs could be explained by the lack of homogeneity between these different study populations. The relation of fat-free mass loss, defined as a low phase angle, with prognosis and illness severity reinforces the interest in using bioelectrical impedance analysis for the clinical management of patients with acute (e.g. ICU) or chronic diseases.

The relationship between fat-free mass and mortality failed to reach statistical significance at day 5. No single explanation is available. This could be related to the reduction of the study sample at day 5 because of early discharge. However, one could have expected a significant relationship between day 5 fat-free mass and mortality. Indeed, at the early phase of ICU stay (i.e. the first 96 hours), critically ill patients feature a loss of fat-free mass up to 440 g/day, which is associated with increased morbidity and prolonged recovery [START_REF] Kyle | Bioelectrical impedance analysis-part I: review of principles and methods[END_REF]. One could hypothesize that patients with fat-free mass loss at day 5 should more suffer from functional impairment or ICU-acquired weakness [START_REF] Wischmeyer | Winning the war against ICU-acquired weakness: new innovations in nutrition and exercise physiology[END_REF][START_REF] Pichard | Energy deficit is clinically relevant for critically ill patients: yes[END_REF][START_REF] Hoffer | Energy deficit is clinically relevant for critically ill patients: no[END_REF], or could be at increased risk of late mortality (post-28-day).

In our study, APACHE II or SAPS II has a poor/fair discriminative value to predict 28day mortality. Areas under the receiver operating characteristic curves are much below the values reported in the validation studies of these ICU severity scores [START_REF] Hekmat | Prediction of mortality in intensive care unit cardiac surgical patients[END_REF][START_REF] Gall | A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study[END_REF]. This could be explained by the lower sample size or by patients' selection: in the APACHE II validation study, were included only cardiac surgery patients [START_REF] Hekmat | Prediction of mortality in intensive care unit cardiac surgical patients[END_REF], in the SAPS II validation study, were excluded patients with coronary care or cardiac surgery [START_REF] Gall | A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study[END_REF], whereas our ICU population was mixed with six percent of cardiac patients. Interestingly, in our study, the mortality predictability of either day 1 phase angle, SAPS II or APACHE II alone was much improved by a multivariable model including day 1 phase angle and APACHE II, among other variables. This indicates that phase angle, a marker of fat-free mass, illness severity and cell health improves mortality predictability of ICU severity scores. These ICU severity scores are built from clinical and biological data that could not reflect strictly an alteration of cell functions, as does the phase angle. Moreover, phase angle values are dependent on gender, age, and nutritional status: phase angle is lower in female, older, or patients with loss of fat-free mass, i.e. malnourished [START_REF] Bosy-Westphase Angle L A | Phase angle from bioelectrical impedance analysis: population reference values by age, sex, and body mass index[END_REF]. Therefore by combining ICU severity scores with a marker of fat-free mass loss, whatever its aetiology (disease severity, chronic diseases, ageing,…), the prognostic value could be improved.

Phase angle is calculated only from two clinical parameters, reactance and resistance, reflecting the electrical properties of body cells. By this fact, phase angle may be less dependent on fluid variations than usual body composition equations. However one could speculate that, in the ICU, rapid fluid shifts, overhydration or dehydration status could contribute to cell damage and therefore be associated with a decreased phase angle.

Further studies are needed to better assess the relation between body mass index, body composition including water compartments, and phase angle, in ICU patients.

Another applicability of bioelectrical impedance analysis in the ICU could be the bioelectrical impedance vector analysis (BIVA). Indeed Jones et al showed that repeated BIVA hydration measurements could detect fluid accumulation or fluid balance >2 litres in ICU patients [START_REF] Jones | Bioelectrical impedance vector analysis in critically ill patients: a prospective, clinician-blinded investigation[END_REF]. However studies are needed to better define bioelectrical impedance analysis-derived phase angle and BIVA place in the ICU patients' management.

In our study, patients with pacemaker or implantable cardioverter defibrillators were excluded from the study according to the latest European Society for Clinical Nutrition and Metabolism guidelines [START_REF] Kyle | Bioelectrical impedance analysispart II: utilization in clinical practice[END_REF]. However, two studies [START_REF] Buch | Effect of bioimpedance body composition analysis on function of implanted cardiac devices[END_REF]33] have proven that bioelectrical impedance analysis can be safely performed in patients equipped with implantable cardioverter defibrillators.

Study limitations. We planned to analyze 1000 patients on a basis of 100 patients per centre, but, due to secondary exclusions, 931 patients were analyzed, and due to 3.9% of missing data, multivariable analysis was performed in 895 patients. Phase angle was shown to be predictive of 28-day mortality in ICU population, known to have rapid fluid shifts. The exact role of these fluid shifts on the observed findings remains to be determined. As previously reported [START_REF] Jones | Bioelectrical impedance vector analysis in critically ill patients: a prospective, clinician-blinded investigation[END_REF], bioelectrical impedance analysis could not be systematically performed in the best conditions: in patients who could not be positioned in a full supine position due to clinical constraints (e.g., those with head injury and intracranial pressure monitoring); on occasion the positioning of the electrodes had to be modified slightly because of the presence of other devices (e.g., intravenous cannulas and soft restraints).

In conclusion, fat-free mass loss, measured at ICU admission by bioelectrical impedance analysis-derived phase angle, is associated with 28-day mortality. Day 1 phase angle, APACHE II, and SAPSII, alone, have a poor discriminative power. A multivariable combined score improves 28-day mortality predictability. Bioelectrical impedance analysis-derived phase angle could be used as a marker of illness severity in the ICU patients. As fat-free mass loss, i.e. low phase angle, defines malnutrition, future studies may be designed to assess whether nutritional interventions targeted to phase angle values could change patients' outcome.

Preiser, Nada Rotovnik Kozjek, Isabel Ceniceros Rozalen, Kinga Kupczyk, Zeljko Krznaric, and Fabienne Tamion recruited patients and collected the data. Noël Cano conceived the study and obtained funding. Claude Pichard conceived the study, participated in its design, obtained funding, participated in the interpretation of the data, and drafted the manuscript.

Figures legendsFigure 1 -

 1 Figures legends

Figure 2 -

 2 Figure 2 -Study flow chart.

Figure 3 -

 3 Figure 3 -Predictive value of fat-free mass, measured by bioelectrical impedance

Figure 4 -

 4 Figure 4 -Predictive value of the multivariable composite score for 28-day mortality (n=895). The score was determined by multivariable logistic regression and calculated as: risk-score = -3.55 [95% confidence interval, -4.67 ; -2.43]-0.67 [-1.11 ; -0.24] (if surgical admission diagnosis) + 0.014 [0.002 ; 0.027]* age -0.146 [-0.254; -0.039]* day 1 phase angle + 0.0784 * APACHE II -0.94 [0.054 ; 0.102] (if admission diagnosis is another one of those listed)]. Area under the receiver operating characteristic curve = 0.79 [95% confidence interval, 0.75-0.82]. For a score ≥0.1716, sensitivity, specificity, positive and negative predictive values were respectively: 77%, 132/172,

Table 1 .

 1 -day mortality was higher in patients with day 1 phase angle <3.49 (31%, 86/275) than with Day 1 phase angle ≥3.49 (14%, 94/656), P<0.001. The areas under the Receiver Operating Characteristic curve assessing 28-day mortality prediction by APACHE II (n=901) or SAPS II (n=657) indicated also a poor / fair discriminative power of APACHE II and SAPS II: 0.69 [95% confidence interval, 0.65-0.73] and 0.72 [95% confidence interval, 0.67-0.77], respectively. Univariate analysis of factors associated with 28-day mortality is shown in For the multivariable analysis (Table 2), APACHE II and body mass index were missing in 30 and six patients, respectively, representing 3.9% (36/931), of missing data. As indicated in Table 2, variables significantly associated with increased 28-day mortality in multivariable logistic regression were: day 1 phase angle, APACHE II, surgical admission diagnosis, admission for another diagnosis that one of those listed, and age. A multivariable composite risk-score improved 28-day mortality predictability. The score was determined by multivariable logistic regression and calculated as: riskscore = -3.55 [95% confidence interval, -4.67 ; -2.43]-0.67 [-1.11 ; -0.24] (if surgical

	admission diagnosis) + 0.014 [0.002 ; 0.027]* age -0.146 [-0.254; -0.039]* Day 1 phase
	angle + 0.0784 * APACHE II -0.94 [0.054 ; 0.102] (if admission diagnosis is another
	one of those listed)]. For a score ≥0.1716, sensitivity, specificity, positive predictive
	value, and negative predictive value, were 77%, 132/172, [95% confidence interval, 70-
	83]; 66%, 477/723, [62-69]; 35%, 132/378, [30-40]; and 92%, 477/517, [90-94],

Table 1 -Patients' characteristics, diagnoses at intensive care unit admission, and univariate analysis of variables associated with 28-day mortality (n=931).

 1 33. Meyer P, Makhlouf AM, Mondouagne Engkolo LP, et al (2016) Safety of bioelectrical impedance analysis in patients equipped with implantable cardioverter defibrillators. JPEN J Parenter Enteral Nutr; Feb 22. pii: 0148607116633823 Areas under the receiver operating characteristic curve (AUC) are shown for all variables. Mortality were collected at day 28 even after intensive care unit discharge. The other variables were only assessed during the intensive care unit

	1
	2
	3
	4	stay.
			Missing	All	AUC	Alive at 28-	Dead at 28-	OR [95% CI]	P-value
			(n)	n=931	[95% CI]	DAY	DAY
						n=751	n=180
		Male / female -n (%)	0	561 (60) /	0.50	453 (60) /	108 (60) /	0.99 [0.71 -1.38]	0.94
				370 (40)	[0.46 -0.54]	298 (40)	72 (40)
		Age (year) -mean ± SD	0	61.2 ± 16.3 0.60	60.2 ± 16.4	65.5 ± 15.3	1.02 [1.01 -1.03]	<0.001
					[0.55 -0.64]
		Age <65 / ≥65 years	0	463 (50) /	0.59	400 (53) /	63 (35) /	2.12 [1.51 -2.97]	<0.001
		-n (%)		468 (50)	[0.55 -0.63]	351 (47)	117 (65)
		Body mass index	6	26.4 ± 5.6	0.53	26.5 ± 5.7	25.9 ± 5.2	0.98 [0.95 -1.01]	0.17
		-mean ± SD			[0.48 -0.57]
		Body mass index (BMI)	6		0.53				0.42
		-n (%)			[0.49 -0.57]
		<18.5		31 (3)		22 (3)	9 (5)	1.66 [0.74 -3.75]	0.21
		18.5 ≤ BMI <25		409 (44)		329 (44)	80 (45)	Reference	-
		25 ≤ BMI < 30		274 (30)		222 (30)	52 (29)	0.95 [0.65 -1.40]	0.85
		30 ≤ BMI < 35		140 (15)		113 (15)	27 (15)	0.97 [0.60 -1.58]	0.94
		≥35		71 (8)		62 (8)	9 (5)	0.59 [0.28 -1.24]	0.17
		Day 1 phase angle (degree)	0	4.50 ± 1.85 0.63	4.59 ± 1.79	4.10 ± 2.04	0.84 [0.75 -0.93]	0.001
		-mean ± SD			[0.58 -0.67]
		Day 1 phase angle	0	275 (30) /	0.61	189 (25) /	86 (48) /	2.72 [1.94 -3.81]	<0.001
		<3.49 / ≥3.49 -n (%)		656 (70)	[0.57 -0.65]	562 (75)	94 (52)

Table 2 -Logistic regression multivariable analysis of factors associated with 28- day mortality (n=895).

 2 For continuous variables (age, day 1 phase angle, and APACHE II), the adjusted odds ratio (aOR) are expressed for 1-point increase.

	Variables	aOR	95% confidence interval P-value
	Age	1.014	1.0016 -1.0271	0.03
	Day 1 phase angle	0.86	0.78 -0.96	0.008
	APACHE II	1.08	1.06 -1.11	<0.001
	Admission diagnosis :
	surgery vs. Medicine	0.51	0.33 -0.79	0.002
	Other diagnosis	0.39	0.21 -0.72	0.003
	APACHE, Acute Physiology and Chronic Health Evaluation.

Acknowledgements

The authors gratefully thank Gaëlle Emvalomenos and Thora Theodorsdottir for their help to recruit patients at Geneva University Hospital and database management and all the nurse staff and physicians in all the investigating centres. We especially thank Dr Fabrice Kwiatkowski, biostatistician, at Jean Perrin's Cancer Centre, Clermont-Ferrand, France, for his careful review of all statistical analyses.

Funding: The Phase angle Project was supported by the European Society for Clinical Nutrition and Metabolism (ESPEN) as a fellowship grant 2012 allocated to Ronan Thibault. Additional research grants (five percent of the main funding by ESPEN) were obtained from Bioparhom, Le Bourget-du-Lac, France and the public Foundation Nutrition 2000Plus, Switzerland.

Conflict of Interest disclosures:

Research grant from Bioparhom, Le Bourget-du-Lac, France.

Authors' contributions

Ronan Thibault conceived the study, participated in its design, obtained funding, coordinated the study, recruited patients, collected, analyzed and interpreted the data, managed the database, and drafted the manuscript. Anne-Marie Makhlouf recruited patients, collected the data, and managed the database. Aurélien Mulliez performed the statistical analysis. Cristina Gonzalez, Sylvain Dadet, Gintautas Kekstas, Jean-Charles