Amir Aly
email: amir.aly@ensta-paristech.fr

Adriana Tapus
email: adriana.tapus@ensta-paristech.fr

A Model for Synthesizing a Combined Verbal and NonVerbal Behavior Based on Personality Traits in Human-Robot Interaction

Robots are more and more present in our daily life; they have to move into human-centered environments, to interact with humans, and to obey some social rules so as to produce an appropriate social behavior in accordance with human profile (i.e., personality, state of mood, and preferences). Recent research studies discussed the effect of personality traits on the verbal and nonverbal production, which plays a major role in transferring and understanding messages in a social interaction between a human and a robot. The characteristics of the generated gestures (e.g., amplitude, direction, rate, and speed) during the nonverbal communication can differ according to the personality trait, which, similarly, influences the verbal content of the human speech in terms of verbosity, repetitions, etc. Therefore, our research tries to map human verbal behavior to a corresponding combined robot verbalnonverbal behavior based on the personality dimensions of the interacting human. The system estimates -first -human personality traits through a psycholinguistic analysis of the spoken language, then it uses PERSONAGE natural language generator that tries to generate a corresponding verbal language to the estimated personality traits. Gestures are generated by using BEAT toolkit, which performs a linguistic and contextual analysis of the generated language relying on rules derived from extensive research into human conversational behavior. We explored the human-robot personality matching aspect and the differences of the adapted combined robot behavior (gesture and speech) over the adapted speech only robot behavior in interaction. Our model validated that the participants preferred more to interact with a robot that has a similar personality. Besides, it validated that the adapted robot behavior expressed through combined gestures and speech, is more engaging and attractive than the adapted robot behavior expressed only through speech. Our experiments were validated using Nao robot.

I. Introduction

Creating a socially-intelligent robot able to interact with humans in a natural manner and to synthesize appropriately comprehensible multimodal behaviors in a wide range of interaction contexts, is a highly complicated task. This requires a high level of multimodal perception, so that the robot should understand the internal states, intentions, and personality dimensions of the human in order to be capable of generating an appropriate verbal and nonverbal combined behavior.

The related literature reveals hard efforts aiming to support the natural human-robot conversational interaction. The authors in [START_REF] Grosz | Team: A transportable natural language interface system[END_REF], tried to create a limited verbal natural language interface to access information in a database. An interesting theoretical study on the Natural Language (NL) was discussed in [START_REF] Finin | Natural language interactions with artificial experts[END_REF], in which the authors tried to study the effect of using natural language interaction of rich functionality (e.g. paraphrasing, correcting misconceptions, etc.) on the effective use of expert systems. Another interesting theoretical study was discussed in [START_REF] Wahlster | User models in dialog systems[END_REF] and [START_REF] Zukerman | Natural language processing and user modeling: Synergies and limitations[END_REF], where the authors focused on the field of user modeling (i.e. understanding user beliefs, goals and plans) in artificial intelligence dialog systems, and illustrated the importance of such modeling on interaction. Later on, some research studies tried to illustrate how the dialogue systems that are adapted to the user's model (including the ability to explicitly and dynamically change the aspects of the relationship with the interacting human through the use of social talks, in the same way as humans behave) will be more believable [START_REF] Andre | The automated design of believable dialogues for animated presentation teams[END_REF], [START_REF] Cassell | Negotiated collusion: Modeling social language and its relationship effects in intelligent agents[END_REF], [START_REF] Forbes-Riley | Investigating human tutor responses to student uncertainty for adaptive system development[END_REF], [START_REF] Forbes-Riley | Responding to student uncertainty during computer tutoring: An experimental evaluation[END_REF]. Some efforts were driven towards generating synchronized verbal and nonverbal behaviors as discussed in [START_REF] Ng-Thow-Hing | Synchronized gesture and speech production for humanoid robots[END_REF]. The authors presented a model that is capable of synchronizing expressive gestures with speech. The model was implemented on Honda humanoid robot and was able to generate a full range of gesture types, such as emblems, iconic and metaphoric gestures, deictic pointing and beat gestures. Similarly, virtual agents had received much attention concerning generating expressive behaviors. The authors in [START_REF] Kopp | Multimodal communication from multimodal thinking -towards an integrated model of speech and gesture production[END_REF] tried to simulate the natural speech-gestures production model that humans have on the 3D agent MAX. They proposed an architecture for generating synchronized speech and gestures in a free and spontaneous way. For example, it is sufficient to support the system with some preliminary information about an object to describe and the system will generate itself an expressive combined verbal and nonverbal behavior exactly as humans do. Another interesting approach was discussed in [START_REF] Bevacqua | Speaking with emotions[END_REF], [START_REF] Hartmann | Formational parameters and adaptive prototype instantiation for mpeg-4 compliant gesture synthesis[END_REF]. The authors developed the embodied conversational agent GRETA. It can express its emotional states and intentions through verbal and nonverbal behaviors, and be socially aware. It can endorse different roles; e.g. be a dialog companion, a storyteller, a virtual tutor, or even an actor in different games. Despite the rich literature of generating expressive behaviors with humanoid robots and 3D agents, and to the best of our knowledge, no research work discussed the importance of generating combined verbal and nonverbal behaviors based on the personality traits of the interacting human.

Personality is a key determinant in human social interactions. In the literature, there are different models of personality (e.g., Big5 [START_REF] Goldberg | An alternative description of personality: The big-five factor structure[END_REF], Eysenck Model of Personality (PEN) [START_REF] Eysenck | Dimensions of personality: 16, 5 or 3? criteria for a taxonomic paradigm[END_REF], Meyers-Briggs [START_REF] Briggs | Gifts differing: Understanding personality type[END_REF]). The personality has a long term effect on the generated behavior, which gives more reliability to the personality dimensions for characterizing the generated verbal and nonverbal behavior, to the contrary of other short-term characteristics like estimating human emotions through prosodic features. In HRI and HCI, a direct relationship between personality and behavior has long been recognized [START_REF] Christensen | Embodied social interaction for robots[END_REF], [START_REF] Eriksson | Hands-off assistive robotics for post-stroke arm rehabilitation[END_REF], [START_REF] Nass | Does computer-synthesized speech manifest personality? experimental tests of recognition, similarity-attraction, and consistency attraction[END_REF], [START_REF] Woods | Are robots like people? -relationships between participant and robot personality traits in human-robot interaction studies[END_REF]. In the context of human modeling and adapting the dialog of a machine (i.e. a humanoid robot or a computer) to the personality of the interacting human, the authors in [START_REF] Tapus | Socially assistive robots: The link between personality, empathy, physiological signals, and task performance[END_REF], [START_REF] Reeves | The media equation[END_REF], [START_REF] Nass | Does computer-synthesized speech manifest personality? experimental tests of recognition, similarity-attraction, and consistency attraction[END_REF] proved empirically that the human interacting with a dialog machine will spend more time on the assigned task if the generated behavior matches with human personality, which validates the similarity attraction principle stating that individuals are more attracted by others with the same personality traits.

Based on these findings, we posit that the personality is a key factor in human-robot interactions (HRI). In this research, we try to develop an adapted customized verbal-nonverbal robot behavior based on the personality dimensions of the interacting human. The work described in this paper validates that individuals prefer more to interact with a robot that has the same personality with theirs and that a combined (gesture and speech) robot behavior is more engaging than a speech only robot behavior.

The rest of the paper is structured as following: Section II presents a general overview of the architecture of the system, Section III describes the nonverbal behavior's knowledge base extension, Section IV illustrates how we realized the synchronized verbal and nonverbal behaviors on the robot, Section V illustrates the design, the hypotheses, and the scenario of interaction, Section VI provides a description of the experimental results, and finally, Section VII concludes the paper.

II. SYSTEM ARCHITECTURE

Our system is a coordination between many subsystems: (1) Dragon Naturally Speaking toolkit that translates the spoken language of the interacting human into a text; (2) Personality Recognizer that tries to estimate the personality traits of the interacting human [START_REF] Mairesse | Using linguistic cues for the automatic recognition of personality in conversation and text[END_REF]; (3) PERSONAGE natural language generator that adapts the generated text to the personality dimensions of the interacting human [START_REF] Mairesse | Controlling user perceptions of linguistic style: Trainable generation of personality traits[END_REF]; (4) BEAT toolkit that translates the generated text into gestures [START_REF] Cassell | Beat: The behavior expression animation toolkit[END_REF]; (5) Nao robot as the test-bed platform. An overview of our system architecture is presented in Figure 1.

A. Personality Recognizer

Personality markers in language had received lot of interest from psycholinguistic studies. The authors in [START_REF] Furnham | Language and Personality[END_REF], [START_REF] Dewaele | Extraversion: the unloved variable in applied linguistic research[END_REF], and [START_REF] Scherer | Language and Personality[END_REF] described how linguistic features linked the extraversion and the introversion traits to speech production. They stated that extraverts talked more, louder, and more repetitively with less pauses and hesitations, with respect to the introverts. Moreover, extraverts have higher speech rates, a higher verbal output, and a less formal language, while introverts use a broader vocabulary. On the other hand, the authors in [START_REF] Pennebaker | Linguistic styles: Language use as an individual difference[END_REF], stated that extraverts use more positive emotion words, and show more agreements and compliments than introverts.

A general approach for characterizing the majority of personality traits was discussed in [START_REF] Pennebaker | Linguistic styles: Language use as an individual difference[END_REF], in which the authors used the Linguistic Inquiry and Word Count (LIWC) tool to count word categories of 2479 essays (containing 1.9 million words) written by different persons covering the five personality traits described in Big Five Framework (Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). This dictionary enabled them to state general relationships and characteristics of the five personality traits, such as: Conscientious people avoid negations, negative emotion words, and words reflecting discrepancies (e.g., should and would). Similarly, the authors in [START_REF] Mehl | Personality in its natural habitat: Manifestations and implicit folk theories of personality in daily life[END_REF] created a spoken data corpus (97468 words and 15269 utterances) beside their transcripts, covering different personality traits. This corpus was sub-divided into several word categories using the LIWC tool.

The findings of the previous two data corpora were the basic body of the research conducted by the authors in [START_REF] Mairesse | Using linguistic cues for the automatic recognition of personality in conversation and text[END_REF]. They created a huge database including LIWC psycholinguistic features, such as: Anger words (e.g. hate), Metaphysical issues (e.g. God), Family members (e.g. mom, brother), etc., in addition to other features from the MRC psycholinguistic database [START_REF] Coltheart | The mrc psycholinguistic database[END_REF], such as: Frequency of use (e.g. Low: duly, nudity, High: he, the), Concreteness (e.g. Low: patience, High: ship), etc., in addition to the Utterance type features, such as: Command (e.g. must, have to), Prompt (e.g. Yeah, Ok), Question, and Assertion (which is any utterance out of the previous categories). The relationship between the utterance type features and the personality traits was discussed in [START_REF] Gill | Taking care of the linguistic features of extraversion[END_REF], [START_REF] Vogel | L'interlangue et la personnalite de l'apprenant[END_REF], in which for example, extraverts are more assertive when writing emails. Afterwards, the system was trained on the previously stated data corpora using the support vector machines (SVM) algorithm, and was cross validated so as to approve its performance.

B. PERSONAGE Generator

PERSONAGE is a natural language generator that can model findings from psychological studies to express various personality traits through language. The architecture of the generator is illustrated in Figure [START_REF] Finin | Natural language interactions with artificial experts[END_REF], which is based on the traditional pipelined natural language generation (NLG) architecture [START_REF] Reiter | Building Natural Language Generation Systems[END_REF]. The input consists of personality traits' scores besides a selection of restaurants (recommendation and/or comparison) in New York City, with associated scalar values representing the ratings of six attributes: food quality, cuisine, service, location, price and atmosphere (PERSONAGE is previously supported by a database containing the six attributes ratings of more than 700 restaurants in New York City, the data is collected from real surveys and opinions of people that visited these restaurants). In addition to other generation parameters ∈ [0, 1], like the verbosity parameter which is set to 1, in order to maximize the wordy content in the utterance. The content planner is responsible for selecting and structuring (in a tree format) the information to be processed by the sentence planner in terms of the values of some parameters such as the verbosity, polarity, repetitions (i.e. the content planer decides what to say).

The sentence planner deals with phrasing the information structured by the content planner. It searches in the generation dictionary for the set of syntactic elementary structures stored for each proposition in the content plan (e.g. if the content planner structured a recommendation, the sentence planner would precise the syntactic parts of the recommendation like: verb, noun, etc.). Afterwards, it aggregates the obtained syntactic templates in order to produce the utterance's full syntactic structure [START_REF] Stent | Trainable sentence planning for complex information presentation in spoken dialog systems[END_REF].

The pragmatic marker insertion process in the sentence planner modifies the aggregated syntactic structure in order to produce various pragmatic effects like the hedge you know, the question tags, etc. The lexical choice process selects the most appropriate lexeme (from many different lexemes expressed by PERSONAGE) for each content word in terms of the lexeme's length, frequency of use, and strength [START_REF] Fellbaum | WordNet: An Electronic Lexical Database[END_REF], [START_REF] Chklovski | Verbocean: Mining the web for fine-grained semantic verb relations[END_REF]. Last but not least, the realization process, which follows the sentence planner, converts the final syntactic structure into a string by applying surface grammatical rules, such as morphological inflection and Fig. 2. PERSONAGE Architecture [START_REF] Mairesse | Controlling user perceptions of linguistic style: Trainable generation of personality traits[END_REF] Fig. 3. Beat Architecture [START_REF] Cassell | Beat: The behavior expression animation toolkit[END_REF] function word insertion [START_REF] Lavoie | A fast and portable realizer for text generation systems[END_REF].

C. BEAT Toolkit

BEAT is the Behavior Expression Animation Toolkit that takes a text as input and generates a corresponding synchronized set of gestures. It uses linguistic and contextual information contained in the text to control body and face gestures, besides the intonation of the voice. The mapping from text to facial, intonational, and bodily gestures is contained in a set of rules derived from the state of the art in nonverbal conversational behavior research [START_REF] Cassell | Beat: The behavior expression animation toolkit[END_REF].

BEAT pipeline is composed of different XML-based modules as illustrated in Figure [START_REF] Wahlster | User models in dialog systems[END_REF]. The language tagging module receives an XML tagged text generated from PERSONAGE, and converts it into a parse tree with different discourse annotations (e.g. theme and rheme). The behavior generation module uses the output tags of the language module and suggests all possible gestures, then the behavior filtering module selects the most appropriate set of gestures (using the gestures conflict and priority threshold filters). The user-definable data structures, such as: the generator and filter sets (indicated in dotted lines) provide the generation and filtering rules and conditions for the behavior generation and selection processes. Meanwhile, the knowledge base adds some contextual information and definitions that are important in generating relevant and precise non verbal behaviors, such as: T ype which attributes features with their values to different object types (e.g., the object "Home", which belongs to the class "Place" with type features attributes as "House, Apartment"), Instance, which describes specific cases of recognizable objects (e.g., the "Spiral" shape could be considered as a shape instance of the object "Stairs"), Scene, which groups all instances of the same environment into scenes, and Gesture, which specifies different kinds of gestures and their proposed trajectories and hand shapes. The behavior scheduling module converts the input XML tree into a set of synchronized speech and gestures. It includes a TTS (text-to-speech) engine that calculates the duration of words and phonemes, which helps in constructing an animation schedule for the aligned gestures with words. The script compilation module compiles the animation script into some executive instructions that can be used in animating a 3D agent or a humanoid robot.

III. NONVERBAL BEHAVIOR'S KNOWLEDGE BASE EXTENSION

The purpose of the extension we performed on BEAT toolkit is to add necessary information about the generated text by PERSONAGE comparing (and/or recommending) between different restaurants in New York City. The object type "Restaurant" is defined as an object in the class "Place" with some information about the location, price category, and cuisine, which have been used in the interaction scenarios. Some instances were also added to the knowledge base describing some attached places to the object "Restaurant", such as: "Basement", and "Dining Room", in terms of the size, lightening, and painting. The new added scenes to the knowledge base define the name of restaurants containing the previously defined instances. The precised gestures' characteristics in the knowledge base concern different types of iconic gestures (beside deictic gestures) with their hand shapes and arm trajectories. Some new added linguistic keywords were aligned to specific iconic gestures with their geometrical hand/arm shapes' characteristics, such as: the adjective "narrow" was aligned to the hand shape "Hands in Front" and the arm trajectory "Span" in order to refer to a small span separating between the two hands which match the meaning of the adjective "narrow".

IV. MODELING THE SYNCHRONIZED VERBAL AND NON VERBAL BEHAVIORS ON THE ROBOT

Despite the fact that BEAT toolkit was built as a customizable gestures generator, so that some other categories of gestures can be added to the generation system, we found that the already built in gestures are sufficient for the short verbal context generated by PERSONAGE. Therefore, in this research we are interested in four kinds of gestures: Iconic, Posture Shift, Metaphoric, and Gaze gestures. The animation script (generated by BEAT toolkit) described in Figure [START_REF] Zukerman | Natural language processing and user modeling: Synergies and limitations[END_REF], indicates the proposed synchrony between the verbal content and the corresponding allocated gestures of the following sentence: The first restaurant was calm and not far from downtown but expensive though. The second restaurant had a narrow dining room but also had a better quality and was little cheaper. The system divides the sentence into chunks, where each chunk contains a group of words with a specific allocated gesture. The symbol "WI" indicates the index of words (31 words in total), while the symbol "SRT" defines the estimated duration of each group of words with the allocated gesture. The animation script reveals also that the adjective word "narrow" was attributed to an iconic gesture where the two hands will be used to depict the gesture "Gesture Both", which proves the importance of customizing the knowledge base in order to generate the most appropriate nonverbal behavior.

Metaphoric gestures (which are not present in the animation script in Figure 4) are used frequently in order to represent the narrated speech but not in a physical way like the iconic gestures. They could take the form of a general arm/hand shaking or even a specific shape, like when we want to express time sequences, we use the word "after" associated with a specific arm/hand motion symbolizing this idea. Therefore, this word (and other similar new words) is added and allocated in the knowledge base to the corresponding specific arm/hand motion trajectory similarly to iconic gestures. On the other hand, the generation of general metaphoric gestures doesn't follow a specific linguistic rule, which makes it a virtual generation of gestures. Our approach associates the generation of general metaphoric gestures to some prosodic rules as discussed in [START_REF] Aly | Speech to head gesture mapping in multimodal human-robot interaction[END_REF], [START_REF]An integrated model of speech to arm gestures mapping in human-robot interaction[END_REF], so as to integrate the para-verbal modality into the generation of a nonverbal behavior. Consequently, for an introverted speaker who doesn't speak a lot, he will have a corresponding limited pitch contour which will lead to a corresponding limited set of generated gestures, and vise versa for the extraverted individuals.

The mapping of the gaze, posture-shift, iconic, and specific-shape metaphoric gestures to the robot from the animation script necessitates that the robot processes each line of the script indicating the duration of each chunk containing a synchronized verbal content with a nonverbal behavior. Kendon in [START_REF] Kendon | Gesticulation and speech: two aspects of the process of utterance[END_REF] defined the gesture phrases as the units of gestural movement that contain one or more subsequent movement phases, which are: preparation, stroke, and retraction beside some intermediate holds.

The problem that may appear when modeling a combined verbal and nonverbal behavior on a robot (in case of iconic and specific-shape metaphoric gestures), is the required high temporal synchronization between the stroke (the expressive phase) and the affiliate (the affiliated word or sub-phrase) in order to express an idea accurately. The time estimation indicated in the animation script reveals the estimated time for the stroke phase (in case of a generated iconic gesture), consequently an additional time estimation for the preparation phase should be assumed so that the hands/arms leave their initial position and get ready for the stroke phase. Therefore, the gesture stroke is set to precede the affiliate's onset by a given offset (one syllable's approximate duration of 0.3 s).

V. EXPERIMENTAL SETUP

We begin this section by introducing the robot system that was used in our experiments and continue with the overview of the experiments we conducted.

A. Robot test-bed

The experimental test-bed used in this study is the humanoid Nao robot developed by Aldebaran Robotics 1 . Nao is a 25 degrees of freedom robot, equipped with an inertial sensor, two cameras, eyes eight full-color RGB LEDs, and many other sensors, including a sonar which allows it to comprehend its environment with stability and precision.

B. Hypotheses

The presented work aimed to test the following hypotheses:

Hypothesis 1: The robot behavior that matches the user's personality expressed through speech and gestures will be preferred by the user. Hypothesis 2: The robot personality expressed through adapted combined speech and gestures will be perceived more expressive by the user than the robot personality expressed only through adapted speech.

C. Experimental Design

In order to test and validate the first hypothesis, the user was exposed to 2 robot personalities:

• the robot uses introverted cues (expressed through gestures and speech) to communicate with the user.

• the robot uses extraverted cues (expressed through gestures and speech) to communicate with the user. Similarly, in order to validate the second hypothesis, the users tested 2 different conditions:

• the robot communicates with the user only through speech (the robot-user personality matches). We call it: adapted speech only robot behavior.

• the robot communicates both through gestures and speech with the user (the robot-user personality matches), We call it: adapted combined robot behavior. All the previous four conditions were randomly ordered during the experiments. For the second hypothesis, we excluded the condition of interaction through gestures 1 http://www.aldebaran-robotics.com/ Fig. 5.

Introverted and Extraverted robot conditions (In the introverted condition, the robot's head was looking down with a low gesture rate. Meanwhile in the extraverted condition, the robot's head was looking up with a high gesture rate).

only, as it doesn't fit in the normal context of non-mute human-human interaction. Before the experiments, each participant was asked to complete two questionnaires: one for determining personal details such as gender, age, occupation, and educational background, and another for establishing the subject's personality traits based on the Big 5 Inventory Test [START_REF] Matthews | Personality traits[END_REF]. For our experiments, we focused only on the extraversion factor that indicates the level of sociability of an individual. An extraverted individual tends to be sociable, friendly, fun loving, and talkative, while an introverted individual tends to be reserved, inhibited, and quiet (Figure 5).

The theme of the interaction is restaurants in New York city (USA). The robot has a list of restaurants from New York city and its role is to give advice regarding the food quality, the service, and the price of the restaurants.

Our interaction scenario is as following:

• The robot introduces itself and asks the participant to say some things he/she knows about New York city. This first step is used by the robot to automatically identify the user's personality based on the linguistic cues (the Personality Recognizer tool is used).

• Afterwards, the robot has a list of restaurants and asks the participant to choose a particular restaurant so as to find out more details about it.

• The robot waits for the participant's input and produces an appropriate utterance and gesture based on the personality traits (PERSONAGE generator and BEAT toolkit are employed).

• The participant can ask details about other restaurants.

• The interaction ends when the user doesn't want to know more information about other restaurants.

At the end of each experiment, the experimenter presented a short debriefing. The average time of a single interaction in a given condition was varying between 3 and 4 minutes. This variation depends on the fact that some participants asked more questions than the others during the free interaction experiments, without any relation to the personality condition.

The system evaluation was performed based on user introspection (questionnaires). After each experiment, the participant completed one questionnaire designed to evaluate the impression of the robot personality, the interaction with the robot, the robot speech and gestures synchronization and matching, etc. All questions (24 question in total) were presented on a 7-point Likert scale.

VI. EXPERIMENTAL RESULTS

The subject pool for this experiment consisted of 21 participants (14 male, 7 female; 12 introverted and 9 extraverted). Approximately 47% were 21-25 years old and 53% were 26-30 years old. The recruited participants were ENSTA-ParisTech undergraduate and graduate students, diverse in undergraduate major. We found that there is a reasonable correlation between the measured online and offline 5 Inventory Test [START_REF] Matthews | Personality traits[END_REF]) personality traits of the participants in 14 results. For the 7 uncorrelated results, 4 results were considered as introverted in the offline test and became extraverted in the online test, and vise versa for the remaining uncorrelated 3 results. However, the online test is believed to be more trusted than the offline test, because people may show different aspects of their personality in an imprecise manner through the offline test.

In order to test the first hypothesis all the participants were exposed to two robot conditions: introverted robot and extraverted robot. The robot personality was expressed through speech and gestures as discussed in Section II. In the introverted robot condition, the robot gestures were narrow, slow and at a low rate. Contrarily, in the extraverted robot condition, the robot gestures were broad, quick, and at a high rate (Figure 5). The speech content is also based on personality traits; the robot gives more details in the extraverted condition than in the introverted condition. After each condition, the participants were asked to answer the following question: "Do you find a match between the robot personality (expressed through speech and gestures) and your personality?".

Our ANOVA analyses showed that the extraverted individuals perceived the extraverted robot as significantly more close to their personality than the introverted robot (F [1, 17] = 40.5, p = 0.0). A similar tendency was observed for the introverted individuals who preferred the introverted robot to the extraverted robot (F [1, 23] = 7.76, p = 0.0108) (Figure 6). All participants (introverted and extraverted together) considered that the robot speech and gestures were semantically matched (content), significantly more in the extraverted condition than in the introverted condition (F [1, 41] = 9.29, p = 0.0041). However, when the personality trait is included in the analysis, this aspect is significant only for the extraverted individuals (F [1, 17] = 6.87, p = 0.0185). For the second hypothesis, the participants tested two other conditions: adapted combined robot behavior (gestures and speech adapted to the user's profile and personality) and adapted speech only robot behavior. Participants found the interaction with the adapted combined robot behavior more engaging than the adapted speech only robot behavior (F [1, 41] = 13.16, p = 0.0008) (Figure 7). Participants were also asked if the robot behavior was appropriate. Through ANOVA, we found that the adapted speech only robot behavior was significantly considered less appropriate than the adapted combined robot behavior (F [1, 41] = 20.16, p = 0.0). Furthermore, this result was also observed when the analysis was performed separately on the introverted participants (F [1, 23] = 9.5422, p = 0.005361) and on the extraverted participants (F [1, 17] = 10.5625, p = 0.005).

Our results showed that personality plays an important role in the interaction and that there is a difference of perception and preference for the robot as a function of its personality. The introverted individuals preferred interacting with the introverted robot more than with the extraverted robot. The same tendency of user-robot personality matching was also observed for the extraverted individuals. Moreover, we emphasized that a robot be-havior that mixes gestures and speech is more engaging and considered more appropriate and natural by the participants than a robot behavior based only on speech.

The reported effect of the experiments under different personality conditions is related to the variation in the verbal content and gestural characteristics generated by the robot. For example in the introverted condition, the robot didn't give lot of details about the restaurants, and the frequency of performing gestures was low, beside the low energy of gestures (this is due to PERSONAGE generator which receives a previously set personality score as introverted unlike the second hypothesis's conditions, where the Personality Recognizer toolkit is employed online in order to calculate the score of human personality (i.e., extraversion trait), regardless of any prescribed personality scores as in the first hypothesis conditions, then generates a verbal content corresponding to that personality trait's score. In parallel, BEAT toolkit generates a set of gestures corresponding to the generated verbal content by PERSONAGE generator. The control parameters of the generated gestures, like: Speed, Amplitude, etc. were fixed experimentally through the range of the personality score from 10% (maximum introversion) to 100% (maximum extraversion). So that the robot implements the generated gestures by BEAT toolkit in a corresponding manner to the desired personality to show). The effect of this introverted behavior gave the extraverted participants, for example, the feeling that the reflected robot behavior doesn't tend a lot to be social or active like them when they speak or act, and that was the evaluated effect at the end through the questionnaires.

VII. Conclusion

The paper describes a complete architecture for generating a combined verbal and nonverbal robot behavior based on the interacting human's personality traits. The personality dimensions of the interacting human are estimated through a psycholinguistic analysis of speech content. Furthermore,PERSONAGE generator uses the calculated personality scores in order to generate a corresponding text adapted to the interacting human's personality. Afterwards, BEAT toolkit is used in order to generate different kinds of gestures corresponding to the input text (in parallel with our developed general metaphoric gesture generator, which generates gestures based on the human's speech). Our work proves the important role of humanrobot personality matching in creating a more appropriate interaction, and shows that the adapted combined robot behavior expressed through gestures and speech is more engaging and natural than the adapted speech-only robot behavior. Besides, this paper proves that extraverts prefer high speed robot movements contrarily to introverts, and that the perceived semantic matching between the generated robot speech and gestures, was higher in the extraverted condition than in the introverted condition. For the future work, we are interested in realizing a more dynamic synchronization between the affiliate and the stroke phase. Besides, we are interested in extending PER-SONAGE language generator to include other domains than tourism and restaurants.

Fig. 1 .

 1 Fig. 1. General Overview of the System Architecture

Fig. 4 .

 4 Fig. 4. Synchronized Verbal and Non Verbal XML Animation Script

Fig. 6 .Fig. 7 .

 67 Fig. 6. Personality Matching for Robot and Extraverted Robot Conditions

Acknowledgment

This work was supported by the French National Research Agency (ANR) through Chaire D'Excellence program 2009 (Human Robot Interaction for Assistive Applications). The project website is: http://www.enstaparistech.fr/˜tapus/HRIAA/.


M Aide Nicolas

M Allouche

M Alves

Arnaud Chirurgie

M Aouba

M Babin

Emmanuel Oto-Rhino-Laryngologie

M Bénateau

Hervé Chirurgie

M Benoist

Guillaume Gynécologie -Obstétrique

M Berger

Ludovic Chirurgie

M Bergot

Emmanuel Pneumologie

M Bibeau Frédéric

Mme Brazo

Perrine Psychiatrie D'adultes

M Brouard

Jacques Pédiatrie

M Bustany

Pierre Pharmacologie

Mme Chapon

Françoise Histologie

Embryologie Mme

Clin-Godard Bénédicte

M Coquerel

Antoine Pharmacologie

M Dao

Manh Thông

Hépatologie-Gastro-Entérologie M Damaj

Ghandi Laurent

Hématologie M Defer

Gilles Neurologie

M Delamillieure

Pascal Psychiatrie D'adultes

M Denise

Pierre Physiologie

M Derlon

Jean-Michel Éméritat

Mme Dollfus

Sonia Psychiatrie D'adultes

M Dreyfus

Michel Gynécologie -Obstétrique

M Du

Cheyron Damien

Mme Émery

Evelyne Neurochirurgie

M Esmail-Beygui

Farzin Cardiologie

Mme Fauvet

Raffaèle Gynécologie -Obstétrique

M Fischer

Marc-Olivier Anesthésiologie

Péri-Opératoire M Gérard

Jean-Louis Anesthésiologie

Péri-Opératoire M Guillois

Bernard Pédiatrie

Mme Guittet-Baud

M Habrand

M Hamon Martial

Cardiologie Mme

Hamon Michèle

M Hanouz

Jean-Luc Anesthésiologie

Péri-Opératoire M Hulet

Ligny Bruno

M Icard Philippe

Lambert Olivier

Bactériologie -Virologie Mme

Joly-Lobbedez Florence

Cancérologie M Joubert

Michael Endocrinologie

Mme Kottler

M Launoy Guy

M Le Hello

Simon Bactériologie-Virologie

Mme Le Mauff

Brigitte Immunologie

Mme Leporrier

Michel Éméritat

M Leroy

François Rééducation

M Lobbedez

Thierry Néphrologie

M Manrique

M Marcélli

Christian Rhumatologie

M Martinaud

Olivier Neurologie

M Maurel

M Milliez

Paul Cardiologie

M Moreau

Sylvain Anatomie

/ Oto-Rhino-Laryngologie

M Moutel Grégoire

M Normand

Hervé Physiologie

M Parienti

M Pelage

Mme Piquet

Marie-Astrid Nutrition

M Quintyn

Jean-Claude Ophtalmologie

M Ravasse

Philippe Chirurgie

M Reznik

Yves Endocrinologie

M Rod

Julien Chirurgie

M Roupie

Mme Thariat

Juliette Radiothérapie

M Tillou

Xavier Urologie

M Touzé

Emmanuel Neurologie

M Troussard

Xavier Hématologie

Mme Vabret

Astrid Bactériologie -Virologie

M Verdon

Renaud Maladies

Mme Verneuil

Laurence Dermatologie

M Viader

Fausto Neurologie

M Vivien Denis

Professeur Associé

Des Universités

A Temps

Plein M Vabret

François Addictologie

Professeurs Associés

A Mi-Temps M De

La Sayette

Vincent Neurologie

Mme Dompmartin-Blanchère

Anne Dermatologie

Mme Lescure

M Sabatier Rémi

Mme Leleu Solveig

Doyen Professeur

Emmanuel Touzé

Assesseurs Professeur

Paul Milliez

Guy Launoy

Professeur Sonia Dollfus

Evelyne Professeur

Emery

Sarah Madame

Chemtob

M Alexandre

Joachim Pharmacologie

Mme Benhaïm

Annie Biologie

M Besnard

Stéphane Physiologie

Mme Bonhomme

Julie Parasitologie

M Bouvier

Nicolas Néphrologie

M Coulbault

M Creveuil

M De Boysson

Mme Debruyne

Danièle Éméritat

Mme Derlon-Borel

Annie Éméritat

Mme Dina

Julia Bactériologie -Virologie

Mme Dupont

Claire Pédiatrie

M Étard

Olivier Physiologie

M Gaberel

Thomas Neurochirurgie

M Gruchy

Nicolas Génétique

M Guénolé

Fabian Pédopsychiatrie

M Hitier

M Isnard

Christophe Bactériologie

Virologie M Legallois

Damien Cardiologie

Mme Lelong-Boulouard Véronique

Nathalie Éméritat

M Lubrano

M Mittre

Hervé Biologie

M Repessé

Yohann Hématologie

M Sesboüé

Bruno Physiologie

M Toutirais

Olivier Immunologie

M Veyssiere

Alexis Chirurgie

Maitres De

Associés Des

Mi-Temps Mme

Abbate-Leray Pascale

M Couette

M Sainmont

Noel Mme

Jaegher De

Sophie

maxillo-faciale et stomatologie

A Monsieur le Professeur Paul-Usmar MILLIEZ,

Merci de me faire l'honneur de présider cette thèse. Merci pour la qualité de votre enseignement durant mon internat qui me sera précieux dans l'exercice de ma profession. J'ai été très fier de faire parti de votre service. Veuillez trouver ici l'expression de mon profond respect et de ma sincère gratitude.

A Monsieur le Docteur Damien LEGALLOIS,

Merci pour ton investissement dans ce travail et de m'avoir permis de le mener à bien. J'espère que le résultat de ce travail de thèse sera à la hauteur de tes espérances. Merci pour ton enseignement, tes précieux conseils et ta disponibilité durant mon internat. Je tiens à te témoigner ma profonde et chaleureuse gratitude.

A Monsieur Professeur Rémi SABATIER,

Merci pour l'honneur que tu me fais en participant à ce jury. Merci pour le partage de ton expertise et de tes conseils qui ont participé à l'enrichissement de mon enseignement au cours de ces années d'internat.

A Madame le Docteur Katrien BLANCHART,

Merci pour l'honneur que tu me fais en participant à ce jury. Merci de ton enseignement et de tes conseils durant mon internat m'ayant fait progresser dans la gestion de l'insuffisance cardiaque.

A Madame le Docteur Laure CHAUFOURIER

Merci pour l'honneur que tu me fais en participant à ce jury. Merci de m'avoir conseillé durant mon internat et donné goût à la réadaptation cardiaque ainsi qu'à l'insuffisance cardiaque lors de mon passage à Criqueboeuf.

A Madame le Docteur Annette BELIN,

Merci de m'avoir fait partager la richesse de tes connaissances et de ton enseignement dans le domaine de l'insuffisance cardiaque au cours de ces années.

Aux équipes médicales et paramédicales de cardiologie du CHU de Caen, du CH de Saint Lô, de la réadaptation de Criqueboeuf et de la réadaptation de la Miséricorde.

Qui par leurs précieux conseils, m'ont aidé à forger ma pratique médicale et ont contribué à former le médecin que je deviens.

A mes parents,

Qui m'ont toujours soutenu dans la réussite de mes entreprises et ont toujours été là pour moi.

A mon frère Julien, sa femme Sri et à ma soeur Gaëlle Qui par leurs encouragements et leur bienveillance m'ont poussé vers la voie de la réussite du cursus d'études médicales.

A ma grand-mère, Qui j'espère est fière du médecin que je deviens.

Aux autres membres de ma famille,

Qui m'ont toujours soutenu tout au long de mes années d'études.

A mes co-internes et néanmoins amis de cardio,

Merci pour les cafés (et surtout les soirées) ainsi que les SIRC partagés. Merci d'avoir toujours été là pour passer de bons moments ensemble. Merci à ma promotion de m'avoir supporté pendant 4 ans, Maud (2 semestres la pauvre), Lin, Thibaut, Laurine, Camille, Margaux, Sophie, Idir et surtout Patrick mon fwewe qui n'arrive toujours pas à m'appeler par mon prénom. Merci également aux plus jeunes pour leur bonne humeur perpétuelle (Mémé, Cynthia et tous les autres !). Merci également aux plus anciens qui sont devenus nos mentors.

A mes amis de la fac,

Notamment à Hubert, Baptiste et Jo avec qui les années sont passées beaucoup trop vite et sur qui je sais que je pourrais toujours compter. Merci également à tous les autres pour les supers moments passés avec vous.

I. Introduction

Rehospitalizations are common in patients with heart failure (HF). The rate of readmission after discharge is estimated to 15% at 30 days (1, 2) and up to 50% at 6 months (3) with significant medicoeconomic impact and poor prognosis. It is estimated that most of the rehospitalizations that occuring in the vulnerability period (i.e. post-discharge) could be avoided [START_REF] Desai | Intensive management to reduce hospitalizations in patients with heart failure[END_REF]. Some checklist (CL) have been suggested as a tool to assist physicians in the management of discharge of HF patients in order to reduce early readmissions [START_REF]Target: Hf strategies and clinical tools. discharge checklist[END_REF][START_REF] Mr Cowie | The optimize heart failure care program: Initial lessons from global implementation[END_REF][START_REF]Discharge checklist of the "groupe insuffisance cardiaque et cardiomyopathies" group of the french society of cardiology[END_REF][START_REF] Mebazaa | Acute heart failure and cardiogenic shock: a multidisciplinary practical guidance[END_REF][START_REF] Basoor | Decreased readmissions and improved quality of care with the use of an inexpensive checklist in heart failure[END_REF][START_REF] Legallois | Improving quality of care in patients with decompensated acute heart failure using a discharge checklist[END_REF]. It has been recently demonstrated that the use of a discharge CL yielded a better uptitration of evidence-based medications, a higher referral to telemedecine and a higher proportion of patients with planned medical appointment at discharge [START_REF] Legallois | Improving quality of care in patients with decompensated acute heart failure using a discharge checklist[END_REF]. There was no difference regarding mortality and HF readmission rate at 6 months of follow-up. The CL used was the same for all HF patients. However, HF is a very heterogeneous syndrome. Patients with preserved or mid-range left ventricular ejection fraction (LVEF) tend to be older and to have more comorbidities. In addition, the benefit of evidence-based medications has been demonstrated only in patients with altered LVEF [START_REF] Ponikowski | ESC guidelines for the diagnosis and treatment of acute and chronic heart failure[END_REF]. As a consequence, it is likely that a same discharge CL may not have the same usefulness in all the broad spectrum of patients with HF, leading to the concept of personalized checklist (PCL). We aimed to design an automated personalized discharge CL, depending on major baseline characteristics of HF patients, to help physicians in therapeutic optimization and planning discharge coordination. Then, we evaluate its usefulness in improving quality of care and reducing rehospitalization and mortality rates in this population.

II. Methods

A. Checklist.

We designed an algorithm according to HF ESC Guidelines (11) (HF-CHECK software, APP, January 2019). Three simple clinical and paraclinical parameters (i.e. age, LVEF and history of HF [de novo, chronic ischaemic HF or chronic non-ischaemic HF]) were computed in order to generate PCL for each patient. The aim of these PCL is to help initial work-up, promote treatment optimization and development of a specific careplan before discharge in patients with decompensated HF. A corrective action may be suggested if a point was not optimized. In addition, the PCL was dynamic as the algorithm was able to add or remove specific points during the completion of the data. As an example, if the rhythm was documented as atrial fibrillation, the PCL suggests to consider to restore sinus rhythm and add the term anticoagulation in the evidence-based medications at discharge. Two examples of algorithm-generated personalized CLs are provided in Figure 1. We sought to prospectively include all patients hospitalized for acute decompensated HF from May 2018 to October 2018 in our institution (PCL cohort, Figure 2). Patients were included only if they were discharge at home or in a cardiac rehabilitation center, whatever the LVEF. Exclusion criteria included transfert to a non-capable cardiac rehabilitation hospitalization unit, patients already included in the control group (see below) and patients younger than 18 years. During this period, recommendations on how to use the PCL were regularly provided to medical staff : PCL had to be fullfilled regularly during the hospitalization and before the planned discharge in order to allow therapeutic optimization and follow-up planning. PCL was not intended to replace individual medical judgment or individual patient needs and the attending physician was not enforced to use or follow it for all HF patients. A control cohort was retrospectively included in our institution from May 2017 to October 2017 with the same inclusion and exclusion criteria (Figure 2). For each patient of this control cohort, a PCL was retrospectively fulfilled using medical recordings with the latest available data at the time of discharge.

If a patient was hospitalized more than once, he was included at the first hospitalization and the others were counted as readmissions. A patient included in the control cohort and rehospitalized during the PCL period was included in the control group, not in the PCL group.

C. Endpoints.

The primary endpoints were mortality, readmission for HF and a composite criteria of both within 6 months of discharge. Secondary endpoints were defined as quality of care rendered as measured by evidence-based medications, appropriate medication uptitration, correction of deficiencies and planned careplan.

D. Statistical Analysis.

Quantitative variables were described as mean ± standard deviation and were compared using Student ttest or Wilcoxon rank sum test when appropriated. Qualitative variables were described as number and percentage and were compared using Fisher exact test. Rates of adverse events were compared between groups using Fisher exact test. A p value<0.05 was considered significant. All analyses were performed using R (R Foundation for Statistical Computing, version 3.4.4, Vienna, Austria).

III. Results

A. Population characteristics.

During the inclusion period of the prospective cohort, there was 198 admissions for acute HF. Nine admissions were rehospitalizations of patients already included in the study. Among the remaining 189 patients, 22 were excluded and PCL was not completely fulfilled in 27 patients (Figure 2). The PCL cohort included 140 patients. We retrospectively included 183 patients in the control cohort. One patient was lost to follow-up in each group. Baseline characteristics are depicted in Table 1. The mean age was 78.1±12.2 years in the PCL group vs. 79.0±12.5 years in the control group (p=0.46). Fifty-two patients (28.6%) were hospitalized for de novo HF, as compared to 35 patients (25.2%) in the control group (p=0. [START_REF] Ponikowski | Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency[END_REF]). There was no difference regarding LVEF between the groups (43.5±13.0% vs. 41.3±14.8%, p=0.18) or triggering factors among patients with chronic decompensated HF.

B. Checklist data.

The results of the CL data are depicted in Table 2. There was no difference among groups regarding the proportion of patients with systolic blood pressure or heart rate in the target ranges. There is less patients with a significant worsening in kidney function (i.e. >20% increase in plasma creatinin levels) in the PCL group (16 patients [11.5%] vs. 39 patients [21.4%], p=0.03). Natriuretic peptides measurements were available in 128 patients (92.1%) in the PCL group vs. 150 patients (82.4%) in the control group (p=0.02) without significant difference among natriuretic peptides levels when available.

C. Outcomes.

During the 6-month follow-up period, 32 patients died in the PCL group (23%) vs. 43 patients in the control group (23.2%, p=0.94, Table 3). Among patients who died during the 6-month follow-up, 8 patients had palliative care needs at inclusion. Five patients had a severe valvular heart disease (e.g. aortic stenosis with severly depressed LVEF) and two had cardiac amyloidosis. There was no difference regarding HF readmission rate (38 patients in the PCL group [27.3%] vs. 64 patients in the control group [35.2%], p=0.12). The survival freedom from readmission forHF at 6 months was 57.6% in the PCL group vs. 49.5% in the control group (Figure 3, p=0.14).

D. Quality of care.

The prevalence of iron deficiency was about two-thirds in the two cohorts but the measurements of both ferritin and total saturation of transferrin was performed in 137 patients (98.6%) in the PCL group vs. 123 patients (67.6%) in the control group (p<0.001, Table 2), leading to a higher proportion of patients treated with iron (either per os or intravenous): 74 patients (53.2% of the PCL cohort) vs. 65 patients (35.7% of the control cohort), p < 0.01. Vitamine D deficiency was better screened and treated in the PCL cohort (102 patients [73.4%] vs. 54 patients [29.7%], p<0.001). Albumin was available in all patients in the PCL cohort and in 168 patients (92.3%) in the control group. However, 41/44 patients (93.2%) with an albumine < 35g/L in the PCL group had a prescription of oral nutritional supplement (93.2%) vs. 18/45 (40%) in the control group (p<0.001). There was no difference regarding the proportion of patients with a HF indication of β-blockers or renin-angiotensin-aldosterone system (RAAS) blockers (angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB) or angiotensin receptorneprilysin inhibitor (ARNI)): 31.7% in the PCL group vs. 29.7% in the control group (p=0.80) and 38.1% vs. 33.5% (p=0.46), respectively. There was also no difference regarding the baseline and discharge dose of these evidence-based medications or diuretics dose among groups. The post discharge follow-up was better planned in the PCL group as demonstrated by a higher referral to heart failure management, telemedecine or cardiac rehabilitation programs (72 patients [51.8%] vs. 65 patients [35.7%], p<0.01). Table 3. Outcomes at 6 months. One patient was lost to follow-up in each group.

IV. Discussion

We did not demonstrate a reduction of mortality or HF readmissions using our discharge PCL. There was no difference regarding the use of evidence-based medications such as βblockers or RAAS blockers in the subset of patients with altered LVEF. However, the use of personalized algorithm-based checklists yielded a significant improvement in the screening p programs. In the present study, we found a readmission rate for HF of 27.3% in the PCL cohort and 35.2% in the control cohort at 6 months. This is similar to the rate we observed in our previous study [START_REF] Legallois | Improving quality of care in patients with decompensated acute heart failure using a discharge checklist[END_REF]. At 3 months, the mortality rate we observed was 12.9% and 13.8% in the PCL cohort and the control cohort, respectively. The mortality rate that has been reported in OPTIMIZE-HF [START_REF] Gc Fonarow | Day of admission and clinical outcomes for patients hospitalized for heart failure[END_REF] and GREAT (13) registries were lower (6.7% and 7% respectively at 3 months) but our patients were older (78.6 years vs. 73 years and 72 years, respectively). A 3-month postdischarge mortality of 14.7% has been reported in a population of HF patients ≥ 75 years old [START_REF] Formiga | Basal functional status predicts three-month mortality after a heart failure hospitalization in elderly patients -the prospective RICA study[END_REF]. At last, the mortality rate was also lower in our previous study [START_REF] Legallois | Improving quality of care in patients with decompensated acute heart failure using a discharge checklist[END_REF] (12.1% at 6 months) but the population was different since we include in the present work a significant proportion of patients denied for surgical or transcatheter aortic valve replacement, palliative care needs or cardiac amyloidosis that are conditions associated with poor outcomes.

The present study showed that the use of a personalized algorithm-generated discharge checklist did not improve outcomes in patients hospitalized for acute HF. In our pilot study, the use of the same standardized checklist for all HF patients was neither associated with an improvement in mortality or HF readmission rate [START_REF] Legallois | Improving quality of care in patients with decompensated acute heart failure using a discharge checklist[END_REF]. Some explanations could be advanced. First, it has been demonstrated that the administration of HF oral therapies, including β-blockers and RAAS inhibitors is associated with better survival of patients hospitalized for acute HF [START_REF] Gayat | Heart failure oral therapies at discharge are associated with better outcome in acute heart failure: a propensity-score matched study[END_REF]. In our study, some patients with altered LVEF were not treated with β-blockers or RAAS inhibitors at discharge because of contraindications. Overall, only a third of patients included in the two cohorts had an indication for both β-blockers and RAAS inhibitors. As a consequence, it was not possible to prescribe evidence-based medications in about 70% of patients. Second, the uptitration of the aforesaid therapies was probably not optimal as there was only a slight increase of the dose of medications at discharge. In addition, 9 patients (17.0%) in the PCL cohort were not treated with a RAAS blocker despite being indicated, mostly because of the worsening of the kidney function with diuretic therapy, despite a better kidney function in the latter group (p=0.03 vs. control group). It has been demonstrated that shorter length of stay is associated with an increase in readmission rates in patients hospitalized for acute HF [START_REF] Cerlinskait | Finding the balance ˙ between costs and quality in heart failure: a global challenge[END_REF] and that administration of RAAS inhibitors is associated with better outcomes [START_REF] Gayat | Heart failure oral therapies at discharge are associated with better outcome in acute heart failure: a propensity-score matched study[END_REF]. In case of a transient worsening of kidney function, it may be preferred to postpone discharge in order to introduce or resume RAAS blockers. At last, patients were included in a single center which had already published about the usefulness of a discharge checklist [START_REF] Legallois | Improving quality of care in patients with decompensated acute heart failure using a discharge checklist[END_REF]. As a consequence, it may decrease the benefit of our discharge PCL as our control cohort was included by practicians that were used to perform discharge checklist in patients with decompensated HF in a daily setting.

The algorithm was built according to HF ESC guidelines 2016 [START_REF] Ponikowski | ESC guidelines for the diagnosis and treatment of acute and chronic heart failure[END_REF]. Because it was based on the prevention of cardiovascular triggering factors of acute HF and the uptitration of evidence-based therapies, our personalized discharge CL may be less effective in patients with heart failure with preserved ejection fraction (HFpEF). We included items about the screening and the treatment of comorbidities and frailty like iron deficiency, vitamin D deficiency or malnutrition, that are common findings in patients with heart failure, including older patients with HFpEF [START_REF] Opasich | Blunted erythropoietin production and defective iron supply for erythropoiesis as major causes of anaemia in patients with chronic heart failure[END_REF][START_REF] Cm Porto | Association between vitamin d deficiency and heart failure risk in the elderly[END_REF][START_REF] Nakamura | Prognostic impact of malnutrition assessed using geriatric nutritional risk index in patients aged ≥80 years with heart failure[END_REF]. The use of our algorithm yielded a significant improvement of the treatment of iron deficiency, vitamin D deficiency and malnutrition in the PCL cohort when compared to the control cohort. The correction of an iron deficiency with ferric carboxymaltose in symptomatic HF patients with a LVEF≤45% led to an improvement in quality of life from week 12 onwards in the CONFIRM-HF trial [START_REF] Ponikowski | Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency[END_REF]. There is also growing evidence that vitamin D is a potent regulator of the hepcidin-ferroportin axis and may have a role in iron homeostasis [START_REF] Bacchetta | Suppression of iron-regulatory hepcidin by vitamin d[END_REF]. Vitamin D deficiency may be implicated in the pathophysiology of HF with altered LVEF through activation of the RAAS, impaired calcium handling, exaggerated inflammation, secondary hyperparathyroidism, pro-fibrotic properties, and proatherogenic potential [START_REF] Wu | Role of vitamin d in patients with heart failure with reduced ejection fraction[END_REF]. Lower vitamin D levels were associated with reduced functional capacity in patients with diastolic dysfunction or HFpEF and were predictive for an increased rate of cardiovascular hospitalizations [START_REF] Nolte | Vitamin d deficiency in patients with diastolic dysfunction or heart failure with preserved ejection fraction[END_REF]. Liu et al. described that low vitamin D levels were associated with activation of RAAS, inflammation and poor prognosis [START_REF] Liu | Vitamin d status and outcomes in heart failure patients[END_REF]. However, there is controversial data about the usefulness of supplemental vitamin D in patients with symptomatic HF. A daily vitamin D dose of 4000 IU did not reduce mortality in patients with advanced HF in the EVITA trial [START_REF] Zittermann | Effect of vitamin d on all-cause mortality in heart failure (EVITA): a 3-year randomized clinical trial with 4000 IU vitamin d daily[END_REF] and was associated with a greater need for mechanical circulatory support. Conversely, two recent meta-analysis showed that vitamin D treatment could potentially improve cardiac function in patients with HF [START_REF] Zhao | Effect of vitamin d on ventricular remodelling in heart failure: a meta-analysis of randomised controlled trials[END_REF][START_REF] Chunbin | Efficacy of vitamin d on chronic heart failure among adults[END_REF]. Cachexia and sarcopenia are common in patients with HF and are associated with pro-inflammatory immune activation, neurohormonal dysfunction, poor nutrition and malabsorption, impaired calorie and protein balance, anabolic hormone resistance, reduced anabolic drive, prolonged immobilization and physical deconditioning, together characterized by catabolic/anabolic imbalance [START_REF] Sd Anker | Hormonal changes and catabolic/anabolic imbalance in chronic heart failure and their importance for cardiac cachexia[END_REF]. It has been recently demonstrated that malnutrition is a common finding in very elderly patients hospitalized for HF and is an independant predictor of mortality [START_REF] Nakamura | Prognostic impact of malnutrition assessed using geriatric nutritional risk index in patients aged ≥80 years with heart failure[END_REF]. However, there is no clear data about the benefit of nutritional supplements in patients with HF.

The use of our algorithm-based discharge CL was not associated with a significant decrease regarding HF readmission rate at 6 months (38 patients [27.3%] vs. 64 patients [35.2%], HR = 0.69, 95%CI [0.43-1.12], p=0.12) but the curves tend to separate at the second month after the discharge. It could be hypothesized that the significant correction of deficiencies may improve patients general condition.

Further larger studies investigating the usefulness of the systematic screening and correction of deficiencies on outcomes are warranted, particularly in older patients with HFpEF. The use of PCL was associated with a high referral to follow-up programs, including telemedicine or cardiac rehabilitation programs. It has been previously demonstrated that lower risk-standardized 30-day readmission rates were associated with strategies including partnering with community physicians and nurses and arranging follow-up appointments before dicharge [START_REF] Bradley | Hospital strategies associated with 30-day readmission rates for patients with heart failure[END_REF]. In addition, cardiac rehabilitation was associated with modest significant reductions for both all-cause mortality or hospitalization and cardiovascular mortality or heart failure hospitalization after adjustment for highly prognostic predictors of the primary end point in the HF-ACTION randomized trial [START_REF] Cm O'connor | Efficacy and safety of exercise training in patients with chronic heart failure[END_REF].

Our study has some limits. First, it was a single center study with a relatively small number of patients.

Second, our center already published about the usefulness of discharge checklist in patients with decompensated HF and the careplan of the patients included in the control cohort may be already partially optimized. Third, we did not assess if the use of the PCL lead to specific modification in therapies or careplan for a given patient. Fourth, CL was not used in all patients admitted for decompensated acute HF as physicians were not enforced to use it. Despite being simple and taking approximately only 5 minutes per patient discharge, the use of such a tool may be too long in a daily setting.

V. Conclusion

The use of an algorithm-based personalized discharge checklist was not associated with an improvement in mortality rate at 6 months in patients with decompensated heart failure. However, there was a non-significant trend towards a reduction regarding HF readmissions, a significant higher referral to follow-up programs and a better screening and treatment of malnutrition, iron and vitamin D deficiencies. A multicenter study is warranted to assess the usefulness of a simple costless personalized checklist in the setting of decompensated heart failure in a large HF patients population.

VU, le

RESUME DE LA THESE EN ANGLAIS :

Background. The use of a discharge checklist in patients with acute heart failure (HF) yields a better quality of care, but a single tool may not be suitable in all HF patients. Aims. Evaluate the usefulness of an algorithm-generated personalized discharge checklist (PCL) based on simple baseline characteristics (age, left ventricular ejection fraction (LVEF) and history of HF) on mortality, readmission for HF and quality of care. Methods. We prospectively included 139 patients hospitalized for HF from May 2018 to October 2018. A PCL was created for each patient and had to be fulfilled before discharge. A control cohort of 182 patients was retrospectively included from precedent year. The primary endpoint was mortality and readmission for HF at 6 months. Results. There was no difference among baseline characteristics between PCL and control cohorts regarding mean age and LVEF. During the 6-month follow-up period, 32 patients (23%) died in the PCL cohort vs. 43 patients (23,2%) in the control cohort (p=0.94). 38 patients (27.3%) were hospitalized for HF in the PCL cohort vs. 64 (35.2%) in the control cohort with curves divergence at 2 months (p=0.12). A higher proportion of patients were screened and treated for iron and vitamin D deficiencies as malnutrition supplemented in the PCL group. The post discharge follow-up was better planned in the PCL group were enrolled in a cardiac rehabilitation, telemedicine or PRADO program.

Conclusion.

There was no difference regarding mortality and HF rehospitalization rate using a PCL in HF patients. However, an improvement in the management of HF comorbidities and a better referral to follow-up programs were observed. KEY WORDS : heart failure, discharge checklist, rehospitalisation

Fig. 1 .

 1 Fig. 1. Two examples of personalized discharge checklist. Upper panel : a 42-year old man with de novo heart failure, LVEF=25%. Lower panel : a 82-year old man with hypertension, hospitalized for a new episode of decompensated heart failure without clear precipitating factor. No coronary artery disease. LVEF=60%.

Fig. 2 .

 2 Fig. 2. Flow chart of enrollment and 6-months outcomes. AHF : acute heart failure, PCL : personalized checklist

Table 2 .Fig. 3 .

 23 Fig. 3. Survival freedom from hospitalization for heart failure (HF).

  MOTS CLES : insuffisance cardiaque, check-list de sortie, réhospitalisation TITRE DE LA THESE EN ANGLAIS : A personalized algorithm-based discharge checklist in patients with acute heart failure.

Table 1 . Baseline characteristics. Data were expressed as mean ± SD or n (%). HF: acute heart failure, LVEF: left ventricular ejection fraction.

 1

Checklist de sortie basée sur un algorithme personnalisé chez les patients admis pour décompensation cardiaque.

  Doyen de la Faculté RESUME DE LA THESE EN FRANÇAIS : Introduction. L'utilisation d'une checklist de sortie chez les patients admis pour insuffisance cardiaque (IC) a montré une amélioration de la qualité de vie mais n'est pas applicable pour toutes les IC. But. Evaluer l'intérêt d'une checklist de sortie personnalisée (CLP) basée sur des caractéristiques simples (âge, fraction d'éjection ventriculaire gauche (FEVG) et type d'IC) sur la mortalité, les réadmissions pour IC et la qualité de vie. Méthodes. Nous avons prospectivement inclus 139 patients hospitalisés pour IC entre Mai 2018 et Octobre 2018. Une CLP était créée pour chaque patient et remplie avant la sortie. Un groupe contrôle de 182 patients était rétrospectivement inclus l'année précédente. Le critère primaire était la mortalité et les réadmissions à 6 mois pour IC. Résultats. Les cohortes CLP et contrôle étaient comparables sur les caractéristiques de la population comme l'âge moyen et la FEVG. Durant les 6 mois de suivi, 32 patients (23%) étaient décédés dans le groupe CLP contre 43 patients (23.2%) dans le groupe contrôle (p=0.94). 38 patients (27.3%) étaient réadmis pour IC dans le groupe CLP contre 64 (35.2%) dans le groupe contrôle avec une divergence des courbes à 2 mois (p=0.12). On observait dans le groupe CLP un plus grand taux de patients pris en charge pour les carences en fer, vitamine D et la dénutrition ainsi qu'un plus grand recours aux programmes de suivi (réadaptation cardiaque, télémédecine et PRADO). Conclusion. L'utilisation d'une CLP chez les patients IC ne montre pas de différence sur la mortalité et les réadmissions pour IC. Cependant, une meilleure gestion des comorbidités et un plus grand recours aux programmes de suivis sont observés.

	ANNEE DE SOUTENANCE : 2019
	NOM ET PRENOM DE L'AUTEUR : ALLAIN FLORENT
	TITRE DE LA THESE :
	VU et permis d'imprimer
	en référence à la délibération
	du Conseil d'Université
	en date du 14 Décembre 1973
	Pour le Président
	de l'Université de CAEN et P.O
	Le Doyen

Remerciements

Tables :

Table 1 : Baseline characteristics.