Luisa Brinkschulte

Natascha Mariacher

María Stephan Schlögl

Inés Torres

Raquel Justo

Javier Mikel Olaso

Anna Esposito

Gennaro Cordasco

Gérard Chollet

Cornelius Glackin

María Inés Torres

Colin Pickard

Dijana Petrovska-Delacretaz

Mohamed Amine Hmani

Ayment Mtibaa

Anaïs Fernandez

Daria Kyslitska

Begoña Fernandez-Ruanova

Jofre Tenorio-Laranga

Mari Aksnes

Maria Stylianou Korsnes

Miriam Reiner

Fredrik Lindner

Olivier Deroo

Olga Gordeeva

The EMPATHIC Project: Building an Expressive, Advanced Virtual Coach to Improve Independent Healthy-Life-Years of the Elderly

come

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Introduction

Much of Europe's elder care is still provided by informal care givers (e.g. family members). Yet, predictions point to less availability for this type of care in the future, for which studies suggest paying more attention to the lifestyle of elderly, helping them remain independent [START_REF] Soleymani | A multimodal database for affect recognition and implicit tagging[END_REF]. Psychological and structural obstacles as well as lack of relevant knowledge may however, complicate intentions. Thus, the focus should be on elderly's internal and external difficulties, offering facilities and arrangements, which support active living. According to Kirkwood [START_REF] Kirkwood | Understanding the odd science of aging[END_REF], 75% of longevity may be explained by socio-behavioral conditions the remaining 25% by genetic factors. Based on this assumption, the variability found in environmental factors does to some extent explain variations found in aging; ranging from an active and positive life course to aging with illness and dependence. It is further highlighted that four principles promote active aging. Those four are: (1) dignity, which includes privacy, informed consent, autonomy, obtrusiveness and equal access; (2) autonomy, whose application requires respect for the self-determination of people and the recognition of heterogeneity in people and their preferences, essentially, in their individual approach to aging; (3) participation, which is highlighted by the World Health Organization as one of the pillars of active aging, together with health and safety; and finally (4) joint responsibility, which refers to the necessity to involve all generations in the construction of a welfare society supporting all ages. Information and Communication Technologies (ICT) are sought to become an enabler for these 4 principles, helping elderly remain autonomous and independent and consequently, allowing them to stay an active member of their societal community. The EMPATHIC (Empathic, Expressive, Advanced Virtual Coach to Improve Independent Healthy-Life-Years of the Elderly) 13 project aims to significantly contribute to these respective technological advancements.

Project Overview

Building on initial previous explorations (cf. [START_REF] Lee | First Time Encounters with Roberta: A Humanoid Assistant for Conversational Autobiography Creation[END_REF]), the goal of EMPATHIC is to research, innovate and validate new interaction paradigms, laying the foundation for future generations of Personalized Virtual Coaches that help elderly people live independently. The envisioned wellness coaching advice seeks to promote healthy habits and behavior, by challenging users to transform their personal goals and needs into actions. Furthermore, the EMPATHIC Virtual Coach (EMPATHIC-VC) aims to engage its healthy senior users so that they actively reduce their risks with respect to potential chronic diseases, that they maintain a healthy diet, have adequate physical activity, as well as encourage social engagement, thus contributing to their ability to maintain a satisfying and independent lifestyle. The EMPATHIC-VC shall motivate users through a number of project-defined metrics whose ambition is to create a personal, friendly and familiar environment, avoiding the threatening effects of unfamiliar new gadgets or an excessive focus on medical supervision. As such, the project seeks to set its focus beyond the basic medical and physical needs of elderly, and rather create a link between their body and emotional well- 13 https://www.cordis.europa.eu/project/rcn/212371_en.html being. The EMPATHIC-VC shall be capable of perceiving emotional and social states of people, learn and understand senior users' context dependent expectations and requirements, as well as their personal history, and thus respond adaptively to their needs. To achieve these goals, EMPATHIC works on innovative, multimodal face and speech analytics, adaptive spoken dialogue systems, intelligent computational models and natural human-computer interfaces, supporting the development of an emotionally-expressive virtual coach, designed to help aging users and their carers. Building upon neuroscience research, the project will use unobtrusive remote technologies so as to extract physiological markers of emotional states in real-time. That is, the EMPATHIC-VC shall monitor facial cues and speech style, which underpin users' basic neural functions, and from this formulate online adaptive responses, facilitating interaction through mimicking and in turn promoting empathy in the user. To develop the proposed EMPATHIC-VC, a number of scientific and technological challenges need to be addressed:

1. The virtual coach has to listen to users and correctly understand the information communicated by their face, speech and language, through non-intrusive technologies; 2. It has to be capable of deriving the personal needs of users from a history of usercoach interactions, as well as identify users' emotional status and concerns; 3. It has to be able to implement high level healthy-aging and well-being plans, and construct an effective motivational model, implemented by multimodal dialogues specifically tailored towards individual users.

In order to maximize the acceptability, use and impact of the EMPATHIC-VC, it will be designed so that it runs primarily on tablets and smartphones, but also PCs, laptops, and even TV screens may be supported if such is highlighted by user preferences. To ensure user-driven design, EMPATHIC will involve senior users throughout all phases of the project, considering their input with respect to functional and non-functional product requirements. All the developed technologies will respect data privacy in terms of secure storage, management and transmission according to EU and national regulations. They will be demonstrated and validated in a series of clearly-defined realistic use cases representing different coaching goals and user profiles. The respective test sample will consist of 200-250 senior users recruited in three European countries (i.e. Spain, Norway and France), representing different culture and lifestyle environments.

Project Objectives

The EMPAHIC research focuses on six objectives. The first objective deals with the design of the EMPATHIC-VC, which shall engage healthy-senior users and reach preset benefits, measured through project-defined metrics, to enhance well-being. We aim to achieve this by using the EMPATHIC-VC to increase awareness of one's personal physical status, to improve diet and nutritional habits, and to actively promote physical exercises and social activities. Objective two focuses on the continuous involvement of end-users, so as to optimally align the technology with the users' personalized needs and | 4

requirements, all of which shall be derived by the coach. The third objective aims to supply the coach with incorporated, non-intrusive, privacy-preserving, empathic, and expressive interaction technologies. Objective four centers around the validation of the coach's efficiency and effectiveness across three distinct European societies (i.e. Norway, Spain and France), evaluated by 200 to 250 end users.

Objective five evaluates and validates the effectiveness of the EMPATHIC-VC designs against users' very personalized acceptance and affordance criteria (such as the ability to adapt to users' underlying mood). Finally, objective six concerns the EMPATHIC-VS's industry acceptance and open-source access, identifying appropriate evaluation criteria to improve the 'specification-capture-design-implementation' software engineering process of implementing socially-centered ICT products. These six objectives will be tackled by the following research and development actions:  First, we will provide automatic, personalized advice guidance through advanced ICT. That is, we will investigate the identification and assessment of main cues related to physical, cognitive, mental and social well-being.  Second, we will define personalized, psychologically motivated and acceptable coach plans and strategies.  Moreover, the project will research the translation of professional coaching behavior into actions to be performed by the intelligent EMPATHIC-VC.  Also, research will investigate non-intrusive technologies to detect markers for individuals' emotional and health status. For this, emotional information from eyes, face, speech and language will be used to deliver a hypothesis and consequently assist the decisions to be taken by the EMPATHIC-VC.  A focus is also investigations into sudden shifts in users' emotions. Here, planned efforts span around the implementation of discrete health-coach goals and actions through an intelligent computational system, underlying a spoken dialogue system adapted to users' intentions, emotions and context. Consequently, research focuses on data-driven modelling of users and tasks, machine learning for understanding users, on learning policies and questionnaires to deal with coach goals, on statistical approaches for dialogue management driven by user and coach goals, and on online learning for adaptation.  Another concrete work action is to provide the EMPATHIC-VC with natural, empathic, personalized and expressive communication capabilities, which supports emotional bonds that result in engaged and effective relationships.

Three sets of KPIs will be used to route the fulfillment of these objectives. First, usefulness and effectiveness in quality of life for the coached elderly will be consistently analyzed. Second, task success both with respect to the VC's system decisions and the Spoken Dialogue System's understanding, will be measured. Finally, user acceptance and usability as well as cost-effectiveness will be evaluated.

Relevant Related Work

Research areas where the EMPATHIC project aims to offer advances include virtual health and wellness coaching, understanding users' speech, extracting emotions from spoken language, assessing user preferences when interacting with machines, making decisions in Spoken Dialogue Systems, defining natural, expressive verbal system interactions, and adapting talking-face agents to user preferences. The following sections provide an overview of these areas.

Virtual Health and Wellness Coaching

Due to a predicted future lack of informal caregivers, attention needs to be put on counteracting the consequent challenges for elderly. The idea to offer virtual health and wellness coaching draws on theories of human development, social psychology, organizational leadership, and adult learning theory, where the term "Virtual Coach" does refer to a coaching program or device aiming to guide users through tasks for the purpose of prompting positive behavior or assisting them with learning new skills. While from a pure utilitarian perspective the coach's challenge is to support users in improving their lifestyle, delivering relevant messages empathically, rather than simply stating facts, triggers emotions associated with reaching a goal and thus help promote a trustworthy relationship between a user and the system [START_REF] Ding | Virtual coach technology for supporting self-care[END_REF]. In contrast to, for example, a medication reminder, virtual coaches monitor how users perform activities, provide situational awareness and provide feedback and encouragement that matches users' cognitive states and circumstances [START_REF] Siewiorek | Architecture and applications of virtual coaches[END_REF].

Understanding Users' Speech

One recurring problem of Automatic Speech Recognition (ASR) concerns the dependence on transcribed speech data where phonemes, words and other speech units are accurately timed. Additionally, the production of an ASR model for a particular language requires the time-consuming development of language models. Consequently, the focus of developers has been on the development of ASR systems, which neither require explicit language models nor the accurate timing of speech units. One bottom-up approach to this is Automatic Language Independent Speech Processing (ALISP) [START_REF] Chollet | Toward ALISP: A proposal for automatic language independent speech processing[END_REF], where spectral features corresponding to speech units are extracted automatically from acoustic data. A more recent top-down approach relies on the availability of large amounts of speech data with text transcripts such as movie scripts and subtitles. Whilst these transcripts do not contain timing of speech units at the word or phoneme level, they can be aligned with spectrogram slices using connectionist temporal classification (CTC) [START_REF] Graves | Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks[END_REF].

The application of Recurrent Neural Networks (RNN) to language is facilitated by the recent appearance of vector representations of words, such as Word2Vec [START_REF] Mikolov | Distributed representations of words and phrases and their compositionality[END_REF] and GloVe [START_REF] Pennington | Glove: Global vectors for word representation[END_REF], trained using Skipgram auto-encoders and their inverse Continuous Bag of Words (CBOW) approaches. Traditional GMM-HMM based ASR systems are essentially models | 6

of sequences of acoustic states, with state probabilities optimized using statistical language models. However, for large models accurate estimation of probabilities of sequences of states becomes increasingly difficult. Hence, language models based on RNNs, which began with pioneering work by Bengio [START_REF] Bengio | A neural probabilistic language model[END_REF], are now becoming more mainstream [START_REF] Mikolov | Statistical language models based on neural networks[END_REF]. This is crucial for many temporal applications, but particularly for speech and natural language processing where long-term dependencies in characters or words need to be exploited for natural language parsing of sentence structure, context and meaning. Innovations of RNN network architectures such as the emergence of sequence-to-sequence models [START_REF] Cho | Learning phrase representations using RNN encoder-decoder for statistical machine translation[END_REF], which have encoder and decoder RNN components connected together using a so-called neural attention mechanism, were developed for machine translation applications but are now being used for a range of sequence-based tasks [START_REF] Yao | Sequence-to-sequence neural net models for graphemeto-phoneme conversion[END_REF]. These deep learning approaches are changing the landscape of understanding a user's speech.

Extracting Emotions from Spoken Language

The interaction between human beings and computers shall become more natural once computers are able to perceive and respond to humans' non-verbal communication cues, such as those triggered by emotions. Emotion recognition from speech, face images and video have a long history. In recent years, researchers began to exploit diverse signals to analyze human emotions. Busso [START_REF] Busso | Analysis of emotion recognition using facial expressions, speech and multimodal information[END_REF], for example, showed that emotion recognition improves considerably using facial expressions, speech and multimodal Information.

Soleymani [START_REF] Soleymani | A multimodal database for affect recognition and implicit tagging[END_REF] on the other hand, presented a user-independent emotion recognition method, which aims at recovering affective tags for videos using electroencephalogram (EEG), pupillary response and gaze distance. In general, eye tracking has become an optimal technology for studying cognition, and human emotion correlates. Thus, multimodal emotional databases begin to be available [START_REF] Mckeown | The semaine database: Annotated multimodal records of emotionally colored conversations between a person and a limited agent[END_REF], and new algorithmic methods, which exploit such multimodal emotional data with deep neural networks [START_REF] Khorrami | Ustekinumab for the treatment of refractory Crohn's disease: the Spanish experience in a large multicentre open-label cohort[END_REF] for emotion recognition, start to appear.

Assessing User Preferences when Interacting with Machines

Europe counts an ever-increasing number of senior citizens suffering from cognitive decline, different types of mental disorder, anxiety or depression 14 . Additionally, it is estimated that 47.5 million people worldwide are living with dementia -a number that is predicted to further increase in upcoming next years 15 . This places considerable burdens on national health care institutions in terms of medical, social, and care costs [START_REF] Mcdaid | Promoting mental well-being in the workplace: a European policy perspective[END_REF]. Information and Communication Technology systems, in the form of embodied conversational agents, have been proposed to reduce these burdens because they can provide automated on demand health assistance, thereby reducing the above-mentioned costs (cf. [START_REF] Hawley | A voice-input voice-output communication aid for people with severe speech impairment[END_REF], Parker for ageing [START_REF] Parker | Telecare for an ageing population?[END_REF], and Prescott for companionship [START_REF] Prescott | Robot companions for citizens: Roadmapping the potential for future robots in empowering older people[END_REF]). Thus, currently there is a huge demand for complex autonomous systems able to assist people with their needs, ranging from long term support of health states (including caring for elders with motor-skill restrictions) to communicative disorders. Provisions of support have been made through both the monitoring and detection of changes in the physical, and/or cognitive, and/or social daily functional activities, as well as in offering therapeutic interventions [START_REF] Rosser | Technologically-assisted behaviour change: a systematic review of studies of novel technologies for the management of chronic illness[END_REF]. Until now, however, there have been relatively few efforts assessing human interactional exchanges in context in order to develop systems that are able to establish a trusting relationship with users [START_REF] Cavanagh | Inter)personal computing: the role of the therapeutic relationship in e-mental health[END_REF], take appropriate and autonomous actions to provide long-term and meaningful help [START_REF] Desteno | Detecting the trustworthiness of novel partners in economic exchange[END_REF][START_REF] Buendia | From informative cooperative dialogues to longterm social relation with a robot[END_REF], and rise emphatic feelings so as to build up therapeutic relationships with users.

Making Decisions in Spoken Dialogue Systems

Spoken dialogue systems have been developed in various domains and dealing with different goals. Typically, they provide users with information on topics such as flight schedules [START_REF] Barzilai | Convex Multi-Task Learning by Clustering[END_REF], restaurant recommendations [START_REF] Williams | Partially observable Markov decision processes for spoken dialog systems[END_REF], tourist attractions [START_REF] Kim | The fourth dialog state tracking challenge[END_REF] or customer service [START_REF] Serras | Entropy-driven dialog for topic classification: detecting and tackling uncertainty[END_REF]. Particularly relevant to EMPATHIC are spoken dialogue systems supporting tele-medical and personal assistant applications for older people [START_REF] Olaso | A Multi-lingual Evaluation of the vAssist Spoken Dialog System. | 16 Comparing Disco and RavenClaw -Dialogues with Social Robots[END_REF]. The basic tasks of a dialog system is to interpret the user's input (Automatic Speech Recognition) in a given context (Natural Language Understanding), to decide what action the system should take (Dialogue Management), and consequently produce an appropriate response (Natural Language Generation). The input speech is transformed into text using Automatic Speech Recognition (ASR), and the output forwarded to a Text-to-Speech Synthesizer. The Natural Language Understanding (NLU) component identifies the intent of the speaker and the semantic content. This classification can be performed using either a data-driven statistical [START_REF] Mairesse | Spoken language understanding from unaligned data using discriminative classification models[END_REF] or a knowledge-based approach, (e.g. through handcrafted grammars). The major challenge is the identification of a semantic inference based on often ambiguous natural language input. Thus, the Dialogue Manager (DM) decides upon the action a system should perform at any given state in the dialogue, using the string of semantic units provided by the NLU as well as the (learned) history of the dialogue. The applied supervised learning is focused on making predictions about inputs [START_REF] Bishop | Pattern Recognition and Machine Learning[END_REF][START_REF] Hastie | The Elements of Statistical Learning[END_REF][START_REF] Ben-David | Understanding Machine Learning: From Theory to Algorithms[END_REF]. When building systems for virtual coaching, there is, however, a need to build personalized models for each user. One possible approach is to use data collected from all users and consequently build a general user model. Another approach focuses on building individual models per user, yet such often induces noise, due to insufficient data. A framework used to balance these two extremes is the so-called multi-task framework. Here, all data is used to build a number of models based on either clustering [START_REF] Barzilai | Convex Multi-Task Learning by Clustering[END_REF][START_REF] Crammer | Learning Multiple Tasks Using Shared Hypotheses[END_REF][START_REF] Jacob | Clustered Multi-Task Learning: A Convex Formulation[END_REF], sharing parameters [START_REF] Micchelli | Kernels for multi-task learning[END_REF][START_REF] Caponnetto | Universal multi-task kernels[END_REF][START_REF] Evgeniou | Regularized multi-task learning[END_REF], or features [START_REF] Kumar | Learning Task Grouping and Overlap in Multitask Learning[END_REF][START_REF] Argyriou | Multi-Task Feature Learning[END_REF].

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Defining Natural, Expressive Verbal System Interaction

Over the last 50 years, Text-to-Speech (TTS) technology has evolved, starting from producing rather robotic voices [START_REF] Klatt | Software for a cascade/parallel formant synthesizer[END_REF] to more humanoid voices in the 90s [START_REF] Charpentier | Diphone synthesis using an overlap-add technique for speech waveforms concatenation[END_REF], until at the beginning of the 21st century eventually more sophisticated and natural voice technology started to appear [START_REF] Hunt | Unit selection in a concatenative speech synthesis system using a large speech database[END_REF]. The next breakthrough in this domain should thus go beyond pure TTS optimization and rather start including human characteristics in dialog, such as for example relevant social cues in discussions. These types of advancements, however, require the technology to have the ability to render emotions, to use different prosody models, or even to understand the meaning of text and thus adapt the speech accordingly.

Adapting Talking-Face Agents to User Preferences

Virtual representations of humans in computer mediated interactions are usually categorized as avatars or agents. Avatars are controlled by humans, whereas agents are controlled by computer algorithms. Hence, interaction with an avatar qualifies as computermediated communication [START_REF] Morkes | Effects of humor in task-oriented humancomputer interaction and computer-mediated communication: A direct test of SRCT theory[END_REF], whereas interaction with an agent qualifies as humancomputer interaction. Several studies have emphasized the importance of building agents and avatars able to create a relationship with users while expressing emotions [START_REF] Bevacqua | Interacting with emotional virtual agents[END_REF]. Agents showing appropriate emotions lead to higher perceived believability and trust.

Agents have also been endowed with conversational skills able to display communicative gestures, and they can adapt their facial expressions depending on their social relationship with interlocutors [START_REF] Niewiadomski | On finity, countability, cardinalities, and cylindric extensions of type-2 fuzzy sets in linguistic summarization of databases[END_REF]. Yet, these models work only in specifically controlled contexts.

Proposed Methodology

The EMPATHIC technology development aims at being end-user driven from the very beginning. 200-250 seniors will be recruited in three European countries, i.e. Spain, Norway, and France, representing different culture and lifestyle environments. Developments will run along the following key stages:  User-driven requirements analysis and selection of use cases  Definition of architecture  Privacy-driven collection of initially required language resources  Mid-period prototypical proof-of-concept  Technology Integration  Privacy-driven end-user validation in 3 countries  Stakeholder inclusion, dissemination and exploitation planning, including operative recommendations

EMPATHIC-VC Outline

The EMPATHIC-VC aims to combine different functional aspects. The agent should represent an artificial entity with machine/deep learning capabilities, and online access to a database holding social, medical and administrative history information on users, which will be used to provide intelligent coaching. To build upon this, the coach should further be equipped with emotions that humans can read. Humans read emotions automatically, independently of education or intelligence. It is not sufficient that the EMPATHIC-VC only extracts and understands emotions. Rather it needs to have the intelligence to extract the event related facial-voice-eyes-posture states and generate emotions in a way that mirrors humans in expressing their emotions. To achieve such capabilities, it is planned to use an existing database of emotional faces, which go beyond the seven Ekman expressions [START_REF] Ekman | Facial expressions of emotion[END_REF], and couple this with voice frequencies and pitch to validate humans' emotional perception. While a set of personalized variables and parameters will represent the initial configuration of the EMPATHIC-VC, their values and influence will be consistently modified through learning algorithms driven by real interactions.

EMPATHIC-VC Architecture

The EMPATHIC-VC will be designed as a client-server architecture, where the client is running in the form of a (web-based) application on tablets, smartphones, laptops or PCs. The device's own camera and microphone will be used to sense a user, the display to present a talking face agent. Figure 1 shows the proposed EMPATHIC-VC architecture. The web frontend will relay audio-visual information between the client and server using WebRTC. The authentication and authorization service recognizes the user and provides | 10 application access. The expressive translation service is responsible for translating natural language text and emotional cues into an inbound semantic entity stream, and outbound semantic entity streams into text and emotions. The dialogue manager is responsible for constructing and dispatching queries to the intelligent coach system based on the current inbound semantic entity stream, i.e. the information obtained from the context provider service. The dialogue manager also compiles the information received from the intelligent coach into an outbound semantic entity stream, which is dispatched back to the translation service. The dialogue history service stores and supplies relevant information collected from previous dialogues with a user. The intelligent coach service is responsible for responding to queries from the dialogue manager and uses the user's knowledge bank and general knowledge bank services. The user's specific knowledge bank is a data store containing specific information about the current user. The general knowledge bank stores medical data, workflows and actions. The semantic entity stream is a stream of semantic information represented in a format that facilitates computational operations and queries. The context provider service persists and supplies contextual information about an ongoing dialogue. When the dialogue has ended this service extracts the relevant information and supplies contextual information to the dialogue history service, after which all contextual information is deleted.

As seen in the Figure 1 the application components are grouped into three distinct layers. The first layer is the interface layer, which identifies and authorizes users of the system, and selects suitable communication endpoints based on the location of the user's device, to minimize latency between the client and the server application. The next layer is the speech processing, translation and animation layer, which receives and submits audio and video using two-way translation into semantic entity streams to communicate with the intelligent coach layer. Finally, the intelligent coach layer uses the available knowledge and data to create dialogue strategies and actions. In order to simulate important system functions, which require ongoing development (e.g. speech recognition and language understanding), a Wizard of Oz (WOZ) component will be developed. The WOZ component will be used to explore user responses and the consequent handling of the dialogue, to test different dialogue strategies and to collect language and video resources needed to train technology components. Furthermore, the WOZ component will allow for collecting additional feedback and preference data related to the appearance of the agent, helping its ongoing augmentation.

EMPATHIC-VC Evaluations

The EMPATHIC-VC will be tested by representative, realistic use cases for different user profiles, in three different European countries, i.e. Spain, France and Norway (complemented by additional evaluations in Italy). A sample of 200-250 users will allow for the evaluation of various scenarios with respect to the earlier presented project KPIs. Two specific scenarios, including a communication and a daily care scenario, will be explored. The first scenario counts as a free scenario where the user may ask about the weather, restaurants, movies and other miscellaneous information. The second scenario is agentdriven and inquires on user feelings, health states, and dietaries. In cases where the technological progress of the EMPATHIC-VC is not ready to satisfy interactional requests, the WOZ component will take over. After each interaction, post-test interviews will be conducted, focusing on users' subjective judgements about the quality of their interaction with the system, the interaction's easiness/ difficulty, the quality of the lived experience, and the expected benefits when requiring the system's services in each scenario.

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The outputs of these evaluation will include (1) a database of audio-video recordings of user-system interactional exchanges; (2) a lexicon of quantitative and qualitative usability metrics; (3) parameters describing the appearance efficacy and trustworthiness of the system; (4) a set of qualitative and quantitative parameters describing users' needs; [START_REF] Bengio | A neural probabilistic language model[END_REF] expectations and requirements related to three different user-systems interaction modes; and (6) a mathematical model of multimodal interactional factors serving as tool for identifying explicit and implicit interactional preference features for establishing empathic therapeutic relationships between the EMPATHIC-VC and its users.

All interactions will be video-recorded so as to analyze user behavior, emotional, vocal, facial, and gestural expressions, as well as, the flow of the dialogues. Furthermore, participants will be asked to assess their individual impression of the system as well as their perceived ease of use and effectiveness, their expectations and their overall acceptance of the system. The recorded interactions will be assessed with respect to their degree of empathy and trust through the Interpersonal Reactivity Index (IRI) [START_REF] Davis | Measuring individual differences in empathy: Evidence for a multidimensional approach[END_REF] and the Barrett-Lennard Relationship Inventory (BLRI) [START_REF] Barrett-Lennard | The relationship inventory now: Issues and advances in theory, method and use[END_REF]. The Rotter scale [START_REF] Rotter | Interpersonal trust, trustworthiness, and gullibility[END_REF], Rempel and Holmes scale [START_REF] Rempel | Trust in close relation-ships[END_REF] will be used to measure participants' tendency to trust others and their trust level towards a specific conversant. This type of analysis and the mathematical modelling of the interactional parameters, through the combined processing and the cross-linguistic and cross-cultural comparisons of the three field-trial languages, shall define degrees of preference, acceptance and trustworthiness for the developed system. The result will be a mathematical model of multimodal interactional factors serving as a tool for identifying elder's explicit and implicit preferences for interface modalities. First, the model's validity will be assessed for each single factor (e.g. for the interaction levels) and successively extended to define different integrated parameter sets. The best fit will then be used as elders' system preference identification tool. Next, a Confirmatory Factor Analysis (CFA) [START_REF] Yang-Wallentin | Confirmatory Factor Analysis of Ordinal Variables With Misspecified Models[END_REF] will test whether the correlational structure of the identified factors is consistent with the proposed model. Furthermore, a two-phase validation study will collect caregiver's judgment with respect to the system's feasibility, effectiveness and usability.

Conclusion

The paper outlined the goals and methods proposed by the EMPATHIC Research & Innovation project. The envisioned EMPATHIC Virtual Coach for elderly aims to advance research related to emotion extraction and speech recognition as well as to dialog management, human-computer interaction and virtual health and wellness coaching. The project is strictly committed to an end-user driven, culture sensitive development approach, which focuses on realistic use case scenarios to be tested and evaluated with 200-250 seniors in three European countries (i.e. Spain, France and Norway). In doing so, EM-PATHIC seeks to lay the foundation for future generations of Personalized Virtual Coaches to assist elderly people living independently at and around their home.

Figure 1 :

 1 Figure 1: Proposed EMPATCHIC-VC Architecture

Figure 2

 2 Figure 2 illustrates the data flow in the EMPATHIC network presenting main hardware and software components. It shows where hardware servers should be located in relation to their software components in order to reflect the volume of computation or hardware specialism the servers should have.

Figure 2 :

 2 Figure 2: Data flow in the EMPATHIC network

Acknowledgment

The research presented in this paper is conducted as part of the project EMPATHIC that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 769872.


Rodney Platid
email: plat.rodney@gmail.com

Maria Vasile1☯

Franc¸ois Roubille3☯

Gre´goire Mercierid

Relationships between the COVID-19 lockdown, socioeconomic factors and acute coronary syndrome hospitalisations in France

Introduction

Worldwide, the COVID-19 pandemic has been associated with an overall drop in acute coronary syndrome (ACS) hospitalizations. Additionally, there is a well-known association between ACS and socioeconomic status. This study aims to assess the COVID-19 effect on ACS admissions in France during the first national lockdown and investigate the factors associated with its spatial heterogeneity.

Materials and methods

In this retrospective study, we used the French hospital discharge database (PMSI) to estimate ACS admission rates in all public and private hospitals in 2019 and 2020. A negative binomial regression explored the nationwide change in ACS admissions during lockdown compared with 2019. A multivariate analysis explored the factors associated with the ACS admission incidence rate ratio (IRR, 2020 incidence rate/2019 incidence rate) variation at the county level.

Results

We found a significant but geographically heterogeneous nationwide reduction in ACS admissions during lockdown ). After adjustment for cumulative COVID-19 admissions and the ageing index, a higher share of people on short-term working arrangements during lockdown at the county level was associated with a lower IRR, while a higher share of individuals with a high school degree and a higher density of acute care beds were associated with a higher ratio.

Conclusions

During the first national lockdown, there was an overall decrease in ACS admissions. Local provision of inpatient care and socioeconomic determinants linked to occupation were independently associated with the variation in hospitalizations.

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Introduction

The current coronavirus 2019 (COVID-19) pandemic poses unprecedented challenges to health care systems. Transitions in the organisation of hospital services and in the use of health care by the population have had a major impact on hospital activities, particularly in the sectors managing noncommunicable diseases [START_REF] Modesti | Indirect implications of COVID-19 prevention strategies on non-communicable diseases[END_REF]. During the first wave of the pandemic, when hospital overload was at its highest, significant drops in the volume of admissions worldwide ranging from 20.2% to 73% were observed for acute cardiovascular diseases [START_REF] Kiss | The impact of the COVID-19 pandemic on the care and management of patients with acute cardiovascular disease: a systematic review[END_REF]. In France, a 30% decrease in the volume of admissions for myocardial infarction (MI) was reported during the first month of the national lockdown, which was implemented from 17 March to 11 May 2020 [START_REF] Mesnier | Hospital admissions for acute myocardial infarction before and after lockdown according to regional prevalence of COVID-19 and patient profile in France: a registry study[END_REF].

However, there are notable territorial disparities in the burden of acute coronary syndromes (ACSs) in France as well as in the impact of the epidemic on the health care system [START_REF] Santos | Departmental disparities in potential years of life lost due to premature cardiovascular diseases in France (2013-2015)[END_REF][START_REF] Gaudart | Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study[END_REF]. Moreover, the crisis has had a differentiated impact on individuals according to their socioeconomic status, affecting the most disadvantaged individuals more frequently and more severely [START_REF] Hawkins | Socio-economic status and COVID-19-related cases and fatalities[END_REF][START_REF] Ginsburgh | COVID-19 and the role of inequality in French regional departments[END_REF]. Additionally, the independent link between social determinants and cardiovascular diseases is well established in developed countries [START_REF] Rosengren | Socioeconomic status and risk of cardiovascular disease in 20 low-income, middleincome, and highincome countries: the Prospective Urban Rural Epidemiologic (PURE) study[END_REF][START_REF] Manrique-Garcia | Socioeconomic position and incidence of acute myocardial infarction: a meta-analysis[END_REF][START_REF] Lecoffre | Hospitalizations for neuro-cardiovascular diseases and social deprivation in France in[END_REF]. These worrying findings highlight the collateral effects of the pandemic on hospital admissions for ACS and the role that health inequalities may have had. A better understanding of these events is essential to better anticipate them, enabling our health systems to adapt faster to future health crises and ensure the safety of individuals.

Objectives

We hypothesise that the first lockdown reduced hospital admissions for ACS in France and that this effect was partly dependent on contextual factors related to social inequalities in health. Our objectives were, first, to quantify the trend of admissions in 2019 and 2020 and more specifically during the first national lockdown period in comparison with the same period in 2019 and, second, to investigate the contribution of contextual factors to the variation of these admissions during this period.

Materials and methods

Study design and population

In this French retrospective nationwide study, we used data from the French hospital discharge database (PMSI) to estimate weekly rates of admission for ACS. The PMSI is a national database that includes standardised medical information on hospital admissions, such as diagnoses, medical procedures and health care use. We included every admission for adult patients to any public or private hospital for ACS in metropolitan France from 1 January 2019 to 31 December 2020.

Procedures

ACS admissions included hospitalisations for unstable angina (ICD-10 code I.20), ST segment elevation myocardial infarction (STEMI, ICD-10 code I.21 except I21.4), non-STsegment elevation myocardial infarction (NSTEMI, ICD-10 code I.21.4), and recurrent myocardial infarction (ICD-10 code I.22) [START_REF] Bezin | Choice of ICD-10 codes for the identification of acute coronary syndrome in the French hospitalization database[END_REF]. To take into account some iterative revisions of the PMSI coding rules published during the crisis and the coding errors that may have resulted, especially in the case of hospitalisation for an ACS associated with a COVID-19 infection, we also included stays with an associated secondary diagnosis code related to COVID-19 (ICD-10 codes U.07.10 to U.07.15) if the ACS ICD code was found at the onset of the hospitalisation. To avoid iatrogenic events and MI occurring during hospitalisation, we excluded patients with a main diagnosis of ACS transferred from another hospital or department (Fig 1).

To investigate the contribution of the factors involved in the variation of admissions during the first lockdown, the multivariate analyses used environmental data aggregated at the county level. The socioeconomic level was measured by the average income, education level, labourers and unemployment rates at the county level provided by the National Institute of Statistics and Economic Studies (INSEE) [12]. Social isolation was measured by the share of single-person households [12]. Socioeconomic determinants also included data on living conditions during the lockdown (decline of financial situation, short time working, teleworking) from the national Epicov study [START_REF] Givord | INSEE Première-Lockdown: unequal economic consequences for households[END_REF].

The supply of inpatient and outpatient care was measured by the density of general practitioners and the density of acute care beds in medicine. Hospital accessibility was also measured by the average travel time between the ZIP code of residence and the ZIP code of the nearest hospital for the patients included in this study, available in the PMSI. To adjust for the local burden of the epidemic, we used the cumulative incidence of admissions for COVID-19 during the first lockdown at the county level. We also included the precrisis global and cardiovascular health condition of populations with the ageing index (i.e., the ratio of the population aged 65 and over to the population under 20), the standardised mortality rate of people aged over 65 years, and the standardised rate of people hospitalised for ACS in 2018 at the county level.

Statistical analysis

The weekly incidences of admissions for the different types of ACS, as well as for hospital mortality, were estimated at the national level and over the years 2019 and 2020. We conducted a univariate analysis to quantify the first lockdown's effect on admissions for all types of ACS. Their national cumulative incidence were compared over identical periods, including the first French national lockdown, corresponding to weeks 12 to 19 of 2019 and 2020 (i.e., 18 March to 12 May 2019 and 16 March to 10 May 2020). To address the overdispersion found in the data, a negative binomial regression was used to assess the change in the number of cumulative admissions for the different types of ACS over these two periods (except for recurrent MI, for which there was insufficient data). This regression quantifies the impact of the first confinement on ACS admissions, which is represented by a national cumulative incidence rate ratio (IRR) [START_REF] Mesnier | Hospital admissions for acute myocardial infarction before and after lockdown according to regional prevalence of COVID-19 and patient profile in France: a registry study[END_REF]:

National cumulative incidence rate ratio (RRI)=

Cumulative incidence rate of admissions in France during the period of interest; in 2020 -----------------------------------------------------------------------------------------------------------

-Cumulative incidence rate of admissions in France during the period of interest; in 2019

Using ZIP codes attached to each stay in the PMSI, the IRR for any type of ACS was mapped at the county level, leading to a local cumulative incidence ratio that allowed us to report the impact of lockdown on ACS hospitalisations in each county:

Local cumulative incidence rate ratio (IRR)i = Cumulative incidence rate of admissions in country(i) during the period of interest; in 2020 -----------------------------------------------------------------------------------------------------------------Cumulative incidence rate of admissions in country(i) during the period of interest; in 2019 Using linear regression, we conducted univariate and multivariate analyses using counties as statistical units to explore the factors associated with the variation in the local cumulative IRR, which was our dependant variable. Each county-level aggregate environmental data listed in the procedure section was tested univariately with our dependant variable. Multicollinearity issues were investigated, and each variable associated with the local IRR with a p value< 0.20 was inserted into the multivariate model. Of the included variables that were no longer significant in the multivariate analysis, the ageing index and the cumulative county incidence of admissions for COVID-19 during lockdown were maintained in the model as they are adjustment variables, reflecting the local burden of COVID-19, as well as the overall and cardiovascular health condition of populations prior to the crisis. Descriptive analyses were performed, reporting continuous data as mean values with standard deviation (SD) and median values with range (quartile 1 and quartile 3).

As the data were spatially distributed, local and global spatial dependence phenomena were screened using Moran's I and LISA tests. Residual spatial interactions remaining in the linear regression model were searched using Moran's I test on residuals. Given the persistence of spatial autocorrelation in the errors of the model, a spatial error model (SEM) using queen contiguity was estimated [START_REF] Insee | Handbook of Spatial Analysis[END_REF]. Its validity was verified by the Hausman test. The analyses were carried out using R studio v4.0.3.

Ethics statement

This study was approved by the Institutional Review Board of the Montpellier university hospital (IORG number 0009525-I.R.B approval number: 202000600). Since all patientlevel data in the PMSI database are anonymized before third parties are given access, informed consent at an individual patient level was not required. The study posed no potential risks to individuals or individual privacy, and was conducted in accordance with relevant international and French regulatory requirements. During 2019, the average weekly incidence of admissions in France for all types of ACS was 4.68/100,000 inhabitants; 2.03/100,000 inhabitants for unstable angina; 1.30, 1.26 and 0.10/ 100,000 inhabitants for STEMI, NSTEMI and recurrent MI, respectively; and 0.13/100,000 inhabitants for in-hospital mortality. In 2020, they were equal to 4.34/100,000 inhabitants for all types of ACS; 1.79/100,000 inhabitants for unstable angina; 1.25, 1.22 and 0.09/100,000 inhabitants for STEMI, NSTEMI and recurrent MI, respectively; and 0.12/100,000 inhabitants for in-hospital mortality.

Results

Admissions and in-hospital mortality rates for ACS in 2019 and 2020

For all types of ACS, weekly admissions peaked in early and late 2019 and 2020, with weekly of approximately 5.0/100,000 inhabitants. During the first lockdown, the incidence ranged from 2.89 to 3.92 hospitalisations/100,000 inhabitants, while the corresponding period in 2019 recorded an incidence ranging from 4.55 to 5.11 hospitalisations/100,000 inhabitants. In the latter part of 2020, the weekly incidence remained, on average, lower than those observed in 2019. These trends are found for all types of ACS, except for recurrent MI, as well as for in hospital mortality, which appears to be stable overall during 2019 and 2020. The data additionally highlights the presence of a second drop in incidences from weeks 25 to 40 in 2019 and 2020, which could be linked to the well-known summer decrease in incident cases of ACS [START_REF] Spencer | Seasonal distribution of acute myocardial infarction in the second National Registry of Myocardial Infarction[END_REF].

The first national lockdown impact

In France, there was a significant drop in admissions for all types of ACS during the lockdown, accounting for 30% of the volume of hospitalisations recorded over the same time period in 2019 (IRR 0.70 [0.64-0.76], p value < 0.0001). This decline was most noticeable for unstable angina hospitalisations, with a 39% reduction in the number of admissions in 2020 (IRR 0.61 [0.54-0.70], p value < 0.0001). For myocardial infarction, hospitalisations were more modestly affected, with a similar decrease in volumes for NSTEMI (IRR 0.79 [0.71-0.88], p value < 0.0001) and STEMI (IRR 0.80 [0.74-0.86], p value < 0.0001). Inhospital mortality did not vary significantly during the first lockdown (IRR 0.91 [0.79-1.06], p value 0.22).

Fig 3 shows the county variation of the national admission IRR for all types of ACS, depicting variation in volumes from a decrease of 39% to an increase of 61%. Despite a national trend of a 30% drop in the total number of admissions, four counties recorded a higher number of admissions for ACS in 2020. In contrast, fourteen departments were the most severely affected by the crisis, with relative decreases exceeding 40% over the course of the lockdown.

Moran's I test revealed a positive spatial autocorrelation of county IRRs (Moran index 0.24, p value < 0.001). In addition, the local spatial autocorrelation tests LISA found statistically significant clusters of greater decrease or increase in hospital admissions for ACS in 2020. Fig 4 shows their distribution.

Table 1 presents the mean value and distribution by county of the variables used in this study. Each variable univariably associated with the variation of the IRR with a p value < 0.20 was inserted in the multivariate model. No interactions for the inserted variables were found. The assumptions of the linear model were respected, but a spatial interaction phenomenon was detected in the residuals of the linear model (Moran's test on the residuals, p value 0.001). The use of an SEM was considered valid (Hausman test p value 0.93) [START_REF] Insee | Handbook of Spatial Analysis[END_REF].

Tables 2 and3 presents the results of the univariate and multivariate analyses. At the county level, the regression models indicate an association between, on the one hand, the rate of individuals whose highest degree is a high school diploma (p 0.003) and the density of acute care beds in medicine (p 0.013) and, on the other hand, the positive variation in the IRR of admissions for ACS. Thus, for each 1% increase in the high school graduate rate, the IRR increased by 4.1%, while the provision of one additional acute care bed per 100,000 inhabitants was associated with a 0.07% ratio increase. Conversely, we found an association between, on the one hand, the rate of individuals who were on short time working during the crisis and, on the other hand, the negative variation in the IRR of hospitalisations for ACS (p 0.008). As a result, for each 1% increase in the short-term working individual rate, the IRR decreased by 1.5%. These results are adjusted by the cumulative incidences of hospital admissions for COVID-19 and the ageing index.

Discussion Admission trends in 2019-2020 and the impact of the lockdown

During the first lockdown, our study highlights a substantial drop for all types of ACS admissions in France. This decrease was heterogeneous over the French metropolitan territory and seemed to intensify following a line connecting southwestern France to northeastern France. It was also more pronounced for given types of ACS, such as unstable angina, and in the first half of the lockdown, as the rates then started to rise again within its second half. As the incidences of weekly hospitalisations registered in the latter part of 2020 did not exceed those of 2019, the curves do not highlight the presence of a compensatory rebound in hospitalisations.

Worldwide, several studies have explored the decrease in ACS admissions during the lockdown and have suggested possible hypothesis and explanations. The first hypothesis is that many ACS did not reach the hospital, a first explanation being that many patients may have forgone hospital care for various reasons, including the fear of being infected by COVID-19 at the hospital, the concern about overloading the health services, or the willingness to avoid long waiting times. In a crisis context where the messages spread by the authorities urged people to stay home [START_REF] Kiss | The impact of the COVID-19 pandemic on the care and management of patients with acute cardiovascular disease: a systematic review[END_REF]. In France, during the first month of lockdown, this hypothesis can be supported by a larger reduction in hospital admissions in elderly patients or patients with STEMI; the first being fragile and therefore cautious, the second experiencing less severe symptoms and therefore being more likely to minimise them [START_REF] Mesnier | Hospital admissions for acute myocardial infarction before and after lockdown according to regional prevalence of COVID-19 and patient profile in France: a registry study[END_REF]. Our data do not highlight differences in this reduction according to the type of myocardial infarction, this difference possibly having been rebalanced during the catch-up phenomenon observed in the second half of the lockdown. However our study reveals a larger reduction in hospitalisations for unstable angina, which is the least severe and least symptomatic ACS. This could be related to a phenomenon of avoidance of care, which is easier in the case of a minor ACS, than in the case of a severe presentation. A second explanation for this decrease in admissions could be linked to the overload and disorganisation of the health care system, leading to dysfunctions, particularly in the prehospital stage, and missed opportunities to provide care. For example, in Gironde county, emergency calls for chest pain increased fivefold during the lockdown, while our data revealed a 26% drop in admissions for ACS in this county [START_REF] Gil-Jardiné | Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification[END_REF]. This is echoed in one region of England where, although admissions for ACS were also significantly impacted, emergency calls for MI remained unchanged [START_REF] Mafham | COVID-19 pandemic and admission rates for and management of acute coronary syndromes in England[END_REF]. A French study also reported a doubling of the average time between the onset of ACS symptoms and the first medical contact, with similar findings in other countries [START_REF] Kiss | The impact of the COVID-19 pandemic on the care and management of patients with acute cardiovascular disease: a systematic review[END_REF][START_REF] Rangé | Incidence, delays, and outcomes of STEMI during COVID-19 outbreak: Analysis from the France PCI registry[END_REF]. Overall, whether related to care avoidance events or pre-hospital care overload issues, some circumstances may have led to a substantial decrease in ACS admissions during the lockdown period. Thus, a decline in admission does not automatically imply a decrease in ACS occurrence, and it can be hypothesised that a share of the missing patients in hospitals experienced an ACS, but were not willing or able to reach the hospital and receive the needed care for their condition.

The second hypothesis to explain the decrease in ACS admissions is that there was a real decrease in the incidence of ACS during lockdown [START_REF] Huet | One train may hide another: Acute cardiovascular diseases could be neglected because of the COVID-19 pandemic[END_REF]. The absence of a rebound in hospitalisations following the lockdown seems to support this hypothesis. This could be the consequence of a significant change in living conditions and behaviours influencing cardiovascular risk (e.g., professional and physical activity, diet and consumption, sleep, anxiety); or of a change in environmental conditions with, notably, a reduction in air pollution connected with the reduction in activity during confinement. Indeed, air pollution is a recognized risk factor and a known trigger for ACS, and numerous studies highlights the relationship between transient elevations in ambient air pollutants and an increased risk of ischemic myocardial infarction [START_REF] Claeys | Climate and environmental triggers of acute myocardial infarction[END_REF]. In addition, the concentration of most air pollutants decreased transiently during the lockdown in France, to a greater or lesser extent depending on the geographical area and its baseline pollution level [START_REF] Ikhlasse | Environmental impacts of pre/during and postlockdown periods on prominent air pollutants in France[END_REF]. But interestingly, a study specifically investigating the relation between the hospital management of ACS during the lockdown and air pollutants found no association between an short term air cleaning effect and a concomitant reduction in invasive procedures for ACS [START_REF] Arrivi | Association Between Air Pollution and Acute Coronary Syndromes During Lockdown for COVID-19: Results From the Terni Hub Center[END_REF]. These facts raise questions about the impact of a transient and geographically heterogeneous decrease in air pollutants on the occurrence of ACS, and how it may have interacted with the other factors considered. Finally, a last assumption to explain a potential decrease in the incidence of ACS would be the existence of a competitive risk on mortality between COVID-19 and MI [START_REF] Lantelme | Worrying decrease in hospital admissions for myocardial infarction during the COVID-19 pandemic[END_REF].

Regarding the impact of the crisis on ACS management once patients were admitted to an hospital, our study found no significant variation in inpatient mortality during the lockdown.

The role of determinants linked to social and territorial inequalities in health

Regarding the determinants of this decrease in admissions, our results suggest that, at the county level, a higher share of individuals on short-term working arrangements resulted in a larger decrease in admissions for ACS in 2020, i.e., a larger impact of the first lockdown on admissions. Some employment categories were more directly affected by part-time working than others; 24% of labourers, 18% of employees and 20% of shop keepers became part-time workers during the first lockdown, while the national mean was 15% [START_REF] Givord | INSEE Première-Lockdown: unequal economic consequences for households[END_REF]. Since we did not find other associations between the IRR variation and other determinants linked to occupational status or financial condition, we hypothesise that this result reflects a lower incidence of ACS among people placed on part-time working during the first lockdown. This could be explained first by the eviction from the workplace, as several studies documented the independent relationship between a high-demand occupation coupled with a low level of autonomy and the occurrence of MI [START_REF] Netterstrom | Relation between job strain and myocardial infarction: a case-control study[END_REF][START_REF] Kornitzer | Job stress and major coronary events: results from the Job Stress, Absenteeism and Coronary Heart Disease in Europe study[END_REF]. In addition, intensive outdoor physical activity is associated with an increased risk of atheromatous plaque rupture leading to ACS [START_REF] Kato | Physical exertion as a trigger of acute coronary syndrome caused by plaque erosion[END_REF]. A prolonged eviction from this environment could, thus, have reduced the excess risk connected with this exposure, as well as exposures to other occupational risks impacting health (e.g., tedious work, nightshifts, toxic substances). Once at home, partially employed individuals could have seized this opportunity to modify their lifestyles and improve their overall cardiovascular risk. However, this second interpretation must be taken with caution, as evolutions in modifiable cardiovascular risk factors during the lockdown have been observed in both favourable and unfavourable directions [START_REF] Guignard | Changes in smoking and alcohol consumption during COVID-19-related lockdown: A cross sectional study in France[END_REF][START_REF] Deschasaux-Tanguy | Diet and physical activity during the coronavirus disease 2019 (COVID 19) lockdown (March-May 2020): results from the French NutriNet-Sante´ cohort study[END_REF]. In contrast, at the county level, a high share of individuals with a high school degree increased ACS admissions in 2020, thus reducing the impact of confinement. This outcome can also be linked to occupation related exposure at the time of the lockdown. In effect, 62.8% of individuals with this degree are working as employees or in intermediate occupations [START_REF]Fact sheets: The integration of young people, Training and employment[END_REF]. During the first lockdown, a majority of individuals in intermediate occupations continued to work for the most part at the worksite, as teleworking was uncommon. This was also the case for 73% of the employees who kept working [START_REF] Lambert | How the COVID-19 epidemic changed working conditions in France[END_REF]. These individuals thus continued to be exposed to occupational risks on cardiovascular health; in working conditions that likely worsened, increasing the burden of these exposures. In addition, a French study found an association between a job involving contact with the public, which is fairly common in these professions, and a deterioration of cardiovascular risk during the lockdown [START_REF] Bérard | Lockdown-related factors associated with the worsening of cardiovascular risk and anxiety or depression during the COVID-19 pandemic[END_REF].

The last association in our study indicates that, at the county level, a higher density of acute care hospital equipment increased ACS admissions in 2020, thus reducing the impact of the first lockdown. Since our model is adjusted for the cumulative incidence of hospitalisations for COVID-19, this finding may reflect the effect of the resulting hospital overload. Indeed, counties with a lower level of acute care hospital resources were more likely to be overloaded with an equivalent number of COVID-19 patients, increasing dysfunctions in the prehospital care phase and complicating the admission of patients with ACS to the hospital. In a given county, greater hospital pressure may have also discouraged some patients from seeking care if they experienced ACS symptoms. This could have had the effect of exacerbating the foregoing of care, which was preexisting and probably more common in these territories due to the poorer availability of care.

Strengths and limits

This study has various strengths. First, it includes data from all public and private hospitals in metropolitan France before, during and after the lockdown. To our knowledge, this is the first study to examine the socioeconomic and health care supply-related determinants of the impact of lockdown on ACS admissions in France. The seasonality of ACS was considered, and spatial approaches allowed us to capture the processes involved more accurately. Our study also presents limitations. First, the study is a retrospective, descriptive, observational study of hospitalised ACS only, with a comparison to the year 2019 only. Prehospital care pathways and individual patient characteristics were not investigated, and the multivariate analysis of the impact of the health crisis focused only on the first lockdown. Another limitation is that the inpatient admission data may present coding heterogeneities according to the practices of the hospitals, inducing potential biases [START_REF] Bezin | Choice of ICD-10 codes for the identification of acute coronary syndrome in the French hospitalization database[END_REF]. Finally, as the data are aggregated at the county level, the results are subject to the modifiable area unit problem of scale and their interpretation to an ecological bias [START_REF] Insee | Handbook of Spatial Analysis[END_REF][START_REF] Lambert | How the COVID-19 epidemic changed working conditions in France[END_REF].

Conclusions

This study highlights the significant impact of lockdown on ACS admissions in France, as well as the spatial variation of this impact. This variation was explained by socioeconomic and health care determinants, highlighting the impact of occupational status and hospital care supply on the incidence of ACS admissions during the lockdown. This is likely to be the result of a real change in the incidence of ACS, as well as a decrease in the number of patients reaching the hospital, due to dysfunctions in prehospital care and the fact that some patients did not seek care. Consequently, we believe that clear and targeted messages from health authorities to high-risk communities, urging them to seek care when necessary, and increased attention to the integrity of all stages of the management of emergency care, will be essential to ensure that the health of individuals is better advised and protected and to limit the collateral damages of potential future public health crises.

Fig 2

 2 Fig 2 shows the annual trend of the respective incidences measured. A total of 304,467 stays were included, of which 42,706 occurred during the periods of interest.

Supporting information S1 Dataset. Minimal dataset.

Aggregate data used to perform figures and statistical analysis. (XLSX)

Author Contributions

Conceptualization:

François Roubille, Grégoire Mercier.

Data curation:

Maria Vasile.

Formal analysis:

Maria Vasile, Grégoire Mercier.

Investigation:

Rodney Plat, François Roubille, Grégoire Mercier.

Methodology:

Rodney Plat, Maria Vasile, Grégoire Mercier.

Project administration:

Grégoire Mercier.

Resources:

Rodney Plat, Maria Vasile.

Software:

Rodney Plat, Maria Vasile.

Supervision:

François Roubille, Grégoire Mercier.

Validation:

Rodney Plat, Maria Vasile, François Roubille, Grégoire Mercier.

Visualization:

Rodney Plat.

Writingoriginal draft:

Rodney Plat.

Writingreview & editing:

Rodney Plat, François Roubille, Grégoire Mercier.