PhD Guy Fagherazzi
email: guy.fagherazzi@gmail.com

MD, PhD Philippe Ravaud

Digital diabetes: perspectives for diabetes prevention, management and research

Keywords: Artificial intelligence, Big Data, Connected devices, Diabetes online community, Digitosome, Management, Prevention, Research, Social media, Technology, Type 1 diabetes, Type 2 diabetes

Digital medicine, digital research and artificial intelligence (AI) have the power to transform the field of diabetes with continuous and no-burden remote monitoring of patients' symptoms, physiological data, behaviours, and social and environmental contexts through the use of wearables, sensors and smartphone technologies. Moreover, data generated online and by digital technologies-which the authors suggest be grouped under the term 'digitosome'-constitute, through the quantity and variety of information they represent, a powerful potential for identifying new digital markers and patterns of risk that, ultimately, when combined with clinical data, can improve diabetes management and quality of life, and also prevent diabetes-related complications. Moving from a world in which patients are characterized by only a few recent measurements of fasting glucose levels and glycated haemoglobin to a world where patients, healthcare professionals and research scientists can consider various key parameters at thousands of time points simultaneously will profoundly change the way diabetes is prevented, managed and characterized in patients living with diabetes, as well as how it is scientifically researched. Indeed, the present review looks at how the digitization of diabetes can impact all fields of diabetes-its prevention, management, technology and research-and how it can complement, but not replace, what is usually done in traditional clinical settings. Such a profound shift is a genuine game changer that should be embraced by all, as it can provide solid research results transferable to patients, improve general health literacy, and provide tools to facilitate the everyday decision-making process by both healthcare professionals and patients living with diabetes.

Introduction

A total of 422 million adults worldwide were living with diabetes in 2014, which was four times more than in 1980 [START_REF] Ravaud | NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants[END_REF]. Diabetes imposes a heavy economic burden on both the global healthcare system and wider global economy. The global cost is estimated to be 825 billion US dollars per year, and is mostly driven by the costs of diabetes complications [2]. Yet, so far, there is still an unmet need for research into the health of people living with diabetes to improve their quality of life (QoL) and disease management, and prevent them from developing diabetes-related complications and premature mortality. Future research should now be driven by the fact that optimal diabetes control means both feeling and functioning better, which goes far beyond simply maintaining glycated haemoglobin (HbA1c) levels within the normal range. Therefore, digital technologies and electronic healthcare (ehealthcare), along with the Big Data they generate and the artificial intelligence (AI) methods that analyze them, represent a major opportunity to rethink diabetes, as they are expected to have a major impact on all aspects of diabetes, from prevention to research, including diabetes care and management. However, these are still early days in the delivery of health services and information using the Internet and related technologies, such as smartphone apps and connected devices (often referred to as 'mHealth') [START_REF] Cameron | An ontology of and roadmap for mHealth research[END_REF], but also for social media, telemedicine [START_REF] Boogerd | What Is eHealth": Time for An Update?[END_REF] and e-health records that can completely change the diabetes landscape (Table I).

The digitosome, an opportunity to fight diabetes

Data generated online and by digital technologies used by individuals-which may be grouped under the concept of 'digitosome'-constitute, by the quantity and variety of information they represent, a major potential to modify the way people with diabetes are monitored to identify new digital markers and patterns of risk that, ultimately, when combined with clinical data, can improve diabetes management and QoL, and also prevent complications. Compared with those who have other chronic conditions, patients with diabetes (whether type 1 or 2) self-monitor themselves the most, and are also the ones most willing to do so [START_REF] Huygens | Self-monitoring of health data by patients with a chronic disease: does disease controllability matter?[END_REF]. Indeed, people with diabetes, and especially those treated by insulin, regularly monitor their glucose levels and usually (or should) follow strict daily protocols.

Such routines generate a strong potential for the use of e-health tools to efficiently track patterns of lifestyle factors and biomarkers of interest in everyday life. Digital medicine, digital research and artificial intelligence (AI) have the power to transform the field of diabetes through continuous and no-burden remote monitoring of patients' symptoms, physiological data and behaviours in social and environmental contexts [START_REF] Collins | NIH's transformative opportunities for the behavioral and social sciences[END_REF][START_REF] Mcconnell | Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study[END_REF], and the use of wearables, sensors and smartphone technologies.

Primary and tertiary diabetes prevention in the digital era

Self-tracking and e-coaching for healthy lifestyle changes

Type 1 diabetes (T1D) represents < 1 in 10 diabetes cases, and effective methods of prevention are still sought. Type 2 diabetes (T2D) and its complications are preventable by changes in lifestyle and adequate disease management. In fact, changing lifestyle habits like physical activity and sleep is seen today as a priority for influencing trends in the evolution of chronic diseases as well as improving health-related QoL [START_REF] Block | Development of Alive! (A Lifestyle Intervention Via Email), and its effect on health-related quality of life, presenteeism, and other behavioral outcomes: randomized controlled trial[END_REF]. A landmark paper published in 2015 described wearable devices as "facilitators, not drivers, of health behaviour change" [START_REF] Patel | Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change[END_REF], and the currently available digital health technologies provide meaningful contributions to the design of healthy lifestyle interventions [START_REF] Dallery | A New Frontier: Integrating Behavioral and Digital Technology to Promote Health Behavior[END_REF][START_REF] Lentferink | Key Components in eHealth Interventions Combining Self-Tracking and Persuasive eCoaching to Promote a Healthier Lifestyle: A Scoping Review[END_REF]. A recent meta-analysis calculated an average benefit of 0.49% in glycated haemoglobin (HbA1c) levels for those using mobile apps for T2D management vs controls [START_REF] Hou | Do Mobile Phone Applications Improve Glycemic Control (HbA 1c) in the Self-management of Diabetes? A Systematic Review, Metaanalysis, and GRADE of 14 Randomized Trials[END_REF]. However, it is important to bear in mind that these interventions were implemented in underpowered small-to-moderate sample populations [START_REF] Shah | Managing diabetes in the digital age[END_REF]. In addition, no evidence-based predictive or system-alert tools to identify at-risk patterns through data from connected devices have been either implemented or independently validated so far. In fact, several barriers to the optimal use of digital tools for diabetes management have been identified: cost; insufficient scientific evidence; benefits limited to only some populations; data protection and security concerns; and regulatory legality issues [START_REF] Shah | Managing diabetes in the digital age[END_REF].

More optimistically, personalized nutrition plans adapted to patients' glycaemic and microbiota profiles will soon be validated and incorporated into smartphone apps and other connected devices, and tested in real-life settings [START_REF] Zeevi | Personalized Nutrition by Prediction of Glycemic Responses[END_REF]. It is believed that such personalized AI-based lifestyle interventions will, one day, make it possible to help people prevent or efficiently delay the onset of T2D, and also improve everyday diabetes management while reducing the risk of long-term complications (Table I).

Diabetes technology

Connected devices for management and monitoring

As 'smart' glucose and blood-pressure monitors, activity trackers and weighing scales become the most commonly used connected devices in the world of diabetes, more sophisticated tools are coming onto the market [START_REF] Son | An integrated self-healable electronic skin system fabricated via dynamic reconstruction of a nanostructured conducting network[END_REF]. From 'smart' socks, which are supposed to monitor foot temperature to prevent inflammation and foot ulcers, to connected portable mini-electrocardiography (ECG) to track cardiovascular health, the increasing variety of devices for people living with diabetes should transform the way the disease is managed (Fig. 1). However, to make these tools accessible to the wider population, large-scale reallife studies are needed to assess their safety and usefulness, and to quantify their benefits compared with standard care. In addition, the multitude of smartphone apps to manage connected devices, but the lack of interoperability between them, may be a potent barrier to efficient diabetes management.

Flash and continuous glucose monitoring

It has increasingly been suggested that monitoring not only glycaemic levels, but also glycaemic variability, is of major interest [START_REF] Petrie | Improving the clinical value and utility of CGM systems: issues and recommendations : A joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group[END_REF]. Flash glucose monitors (FGMs) and continuous glucose monitors (CGMs) are recently available devices that can help patients manage their disease more effectively [START_REF] Joubert | Effectiveness of continuous glucose monitoring in dialysis patients with diabetes: the DIALYDIAB pilot study[END_REF]. Originally used by people with T1D or insulinrequiring T2D [START_REF] Beck | Continuous Glucose Monitoring Versus Usual Care in Patients With Type 2 Diabetes Receiving Multiple Daily Insulin Injections: A Randomized Trial[END_REF], their use is expected to increase. Recent studies of connected CGM and/or FGM [START_REF] Heinemann | CGM Versus FGM; or, Continuous Glucose Monitoring Is Not Flash Glucose Monitoring[END_REF] such as the FreeStyle Libre (Abbott Diabetes Care, Alameda, CA, USA), a device that provides data on qualitative and quantitative changes in blood glucose over a 14day period [START_REF] Leelarathna | Flash forward: a review of flash glucose monitoring[END_REF], have shown that their use for real-time evaluation of glycaemic fluctuations can help to better control short-term oscillations in blood glucose levels and also improve glycometabolic control, especially in people treated with multiple daily insulin injections [START_REF] Feig | Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial[END_REF].

Glucose fluctuations, as evaluated by mean amplitude of glycaemic excursions (MAGE) and postprandial incremental area under the curve (AUC), have already been associated with endothelial dysfunction, and greater risks of macrovascular complications [START_REF] Costantino | Impact of Glycemic Variability on Chromatin Remodeling, Oxidative Stress and Endothelial Dysfunction in Type 2 Diabetic Patients with Target HbA1c Levels[END_REF] and overall mortality [START_REF] Mccoy | Increased mortality of patients with diabetes reporting severe hypoglycemia[END_REF]. Also, people with higher rates of FGM per day experienced more time within range and less time in hyper-and hypoglycaemia [START_REF] Dunn | Real-world flash glucose monitoring patterns and associations between self-monitoring frequency and glycaemic measures: A European analysis of over 60 million glucose tests[END_REF].

CGM has been found to perform even better than FGM in preventing hypoglycaemia and patients' fear of it [START_REF] Quemerais | Preliminary Evaluation of a New Semi-Closed-Loop Insulin Therapy System Over the Prandial Period in Adult Patients With Type 1 Diabetes[END_REF][START_REF] Franc | O50 Premiers tests de l'algorithme de régulation «Diabeloop » chez des diabétiques de type 1 traités par pompe, en situation d'activité physique, qu'elle soit anticipée ou non[END_REF]. In addition, the option of coupling a CGM system with an insulin pump now offers the possibility of a closed-loop insulin system such as Diabeloop technology (Diabeloop SAS, Grenoble, France) [START_REF] Quemerais | Preliminary Evaluation of a New Semi-Closed-Loop Insulin Therapy System Over the Prandial Period in Adult Patients With Type 1 Diabetes[END_REF], which is currently being tested as the future of T1D and insulin-treated T2D management [START_REF] Heinemann | CGM Versus FGM; or, Continuous Glucose Monitoring Is Not Flash Glucose Monitoring[END_REF]. CGM also has the potential to reduce healthcare costs [START_REF] Wood | Continuous Glucose Monitoring: a review of the evidence, opportunities for future use and ongoing challenges[END_REF].

Because devices like the FreeStyle Libre are now reimbursed in some countries (including France) and have even received US Food and Drug Administration (FDA) approval, they will profoundly affect the way diabetes patients track their disease and the way healthcare professionals (HCPs) address key issues of diabetes management, given the increasing amount of data that must be taken into account. Also, in terms of research, there is now a window of opportunity for large-scale prospective studies with long-term follow-ups to use these devices to explore patterns of glycaemic variability, hypoglycaemia episodes and time spent within range in relation to QoL, diabetes distress and other psychological factors, and incidence of diabetes complications [START_REF] Valencia | How to prevent the microvascular complications of type 2 diabetes beyond glucose control[END_REF]. Such studies will soon offer an even better understanding of the role of glycaemia and blood-pressure variability and their determinants.

The open-source movement

As the community of people with diabetes is eager to access recent innovations as quickly and as affordably as possible [START_REF] Guillot | Diabetes and connected objects, appropriation or rejection?[END_REF] (see #WeAreNotWaiting on Twitter), it is of interest to observe open-source initiatives from communities such as the Open Artificial Pancreas System (OpenAPS) project [START_REF] Openaps | The Open Artificial Pancreas System Project n[END_REF]. In an effort to make safe and effective basic artificial pancreas system technology more widely available, thereby disrupting the usual lengthy marketing and authorization process for medical devices, this project has most notably designed tools that automatically adjust basal insulin pump delivery to keep blood glucose within range overnight and between meals, based on currently available insulin pumps and CGMs already in the marketplace. While these tools are not FDA-approved and not for sale, they are open-source designed systems that people with T1D can choose to 'build' for themselves. Thus, the paradigm shift comes from the fact that the OpenAPS reference Besides this open-source initiative for diabetes technology, there are similar initiatives involving the data generated by diabetes medical devices. The most charismatic initiative so far is the Tidepool project [START_REF]Tidepool n[END_REF], a non-profit open-source effort that has developed a hub for diabetes data, where patients with diabetes can gather and visualize, in one place, all their data from commonly used medical devices (insulin pumps, CGMs, blood glucose monitors, mobile devices) and also add their contextual information in real time to help them manage their disease. Moreover, the Tidepool platform uses a health crowdsourcing approach, encouraging patients to donate their data to create large databases for research purposes and Big Data analyses.

Artificial intelligence and diabetes

AI has-and will continue to have-many applications in the field of diabetes. Already, AI has gained significant traction in recent years in the field of medical imaging [START_REF] De Fauw | Clinically applicable deep learning for diagnosis and referral in retinal disease[END_REF]. Automated diagnosis of diabetic retinopathy (DR) and cardiovascular risk factor monitoring are now possible, thanks to deep learning algorithms based on large retinal fundus imaging datasets [START_REF] Gulshan | Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs[END_REF][START_REF] Ting | Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes[END_REF]. Other AI algorithms will soon be integrated into smart telemedicine devices and be increasingly used to provide personalized preventative programmes, as well as personalized diabetes management adapted to patients' lifestyles, treatments, genetic backgrounds and environments [START_REF] Kavakiotis | Machine Learning and Data Mining Methods in Diabetes Research[END_REF]. With constant improvement in models, closed-loop systems will also benefit from more powerful and accurate algorithms, thereby enabling a future artificial pancreas to take more and more parameters into consideration to predict the correct amount of insulin to administer [START_REF] Rigla | Artificial Intelligence Methodologies and Their Application to Diabetes[END_REF]. AI-based decisions will also be made by HCPs to choose the optimal line of treatment [START_REF] Zimmermann | Use of Big-Data Algorithms to Characterize Patients with T2D on Basal Insulin (BI) Who Add a Glucagon-Like Peptide-1 Receptor Agonist (GLP-1 RA) and Predict Their A1C Response[END_REF], based on deep or machine-learning algorithms trained on large medico-administrative or e-health record datasets [START_REF] Mei | Deep Diabetologist: Learning to Prescribe Hypoglycemic Medications with Recurrent Neural Networks[END_REF].

The use of AI will allow the processing, in real time, of massive datasets into actionable information for both patients and clinicians [START_REF] Elliott | Sustainability of a Real-Time Informatics Alert to Prevent Inpatient Severe Hypoglycemia[END_REF], including the implementation of large-scale interventions personalized as per patients' characteristics and adapted, over time, to prevent events such as hypoglycaemia and diabetes complications. Nevertheless, so far and surprisingly enough, and despite the increasing offer of digital solutions, relatively little scientific and independent evidence is available to guide patients, their HCPs or even researchers on how to make the best use of all such generated data to improve our understanding of diabetes and its complications.

Digital tools and sensors allow transition from the occasional assessment of disease to prospective real-time, continuous, high-throughput and no-burden remote monitoring of patients' symptoms, physiological data, behaviours, and social and environmental settings [START_REF] Collins | NIH's transformative opportunities for the behavioral and social sciences[END_REF][START_REF] Mcconnell | Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study[END_REF]. Furthermore, the causes of frequent hospitalizations related to chronic disease may be amenable to digital strategies that will eventually forego inpatient facilities [START_REF] Topol | The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care[END_REF]. Impressive progress in data science techniques, natural language processing (such as learning from unstructured texts and speech recognition) and AI also enables the processing of heterogeneous and massive datasets collected by digital tools to help manage care, monitor patients and predict their outcomes [START_REF] Kvedar | Digital medicine's march on chronic disease[END_REF][START_REF] Beam | Translating Artificial Intelligence Into Clinical Care[END_REF]. Indeed, digital medicine could allow large-scale dissemination of interventions for remote and continuous behavioural changes at low cost and initially personalized, but adapted over the course of treatment on the basis of context, timing and previous responses [START_REF] Collins | NIH's transformative opportunities for the behavioral and social sciences[END_REF].

However, while the combination of digital technologies and AI has the potential to revolutionize healthcare for people with chronic diseases like T1D and T2D, it will also challenge current processes and organization of care, the nature and role of HCPs and their relationships with patients [START_REF] Verghese | What This Computer Needs Is a Physician: Humanism and Artificial Intelligence[END_REF], information systems, the structure of healthcare facilities, funding of healthcare and the entire traditional chain of healthcare values [START_REF] Kvedar | Digital medicine's march on chronic disease[END_REF]. Indeed, considerable effort is now required to exploit the full potential of these technologies and to overcome barriers to their widespread implementation, while nonetheless preserving the core values of the relationship between patients and their caregivers.

Social media and diabetes online communities

There is an active online diabetes community around the world [START_REF] Bevan | T1D Exchange: An Online Community for People Touched by Type 1 Diabetes[END_REF][START_REF] Balkhi | The diabetes online community: the importance of forum use in parents of children with type 1 diabetes[END_REF]. These include a strong Twitter diabetes community that frequently shares feelings, everyday struggles and anxieties about the disease via specific hashtags such as #t1d (T1D) [START_REF] Bevan | T1D Exchange: An Online Community for People Touched by Type 1 Diabetes[END_REF], #t2d (T2D) and #doc (diabetes online community) [START_REF] Balkhi | The diabetes online community: the importance of forum use in parents of children with type 1 diabetes[END_REF]. Patients with diabetes regularly share how they feel and how they behave because of their disease. Other dedicated social-media platforms, such as PatientsLikeMe and Carenity, also offer ideal environments for exchanges about diabetes and tips to improve diabetes management while also sharing the fears, anxiety and stress. These are digital spaces with no hierarchy for information-sharing or the development of online communities [START_REF] Roland | Preliminary Evidence for the Emergence of a Health Care Online Community of Practice: Using a Netnographic Framework for Twitter Hashtag Analytics[END_REF].

The use of social media as part of diabetes interventions has been convincingly associated with HbA1c reduction and potentially with benefits for health-related QoL, and diabetes empowerment and awareness [START_REF] Gabarron | Social Media Use in Interventions for Diabetes: Rapid Evidence-Based Review[END_REF]. These platforms also offer research scientists valuable information to better identify key determinants of good diabetes management and of tertiary prevention. Platforms such as Twitter are being increasingly used and has led to the publication of > 1000 academic papers [START_REF] Williams | What do people study when they study Twitter? Classifying Twitter related academic papers[END_REF], albeit so far mostly for descriptive purposes such as cross-sectional associations with disorders like obesity [START_REF] Gore | You Are What You Tweet: Connecting the Geographic Variation in America's Obesity Rate to Twitter Content[END_REF] and for disease surveillance [START_REF] Signorini | The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic[END_REF]. It has even been suggested that Twitter might also be used for analyzing disease-related symptoms and medication use [START_REF] Paul | You Are What You Tweet: Analyzing Twitter for Public Health[END_REF][START_REF] Adrover | Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter[END_REF]. In fact, psychological characteristics collected through social media have been associated with cardiovascular mortality at the community level [START_REF] Eichstaedt | Psychological language on Twitter predicts county-level heart disease mortality[END_REF].

Thus, in terms of research, combining unstructured textual data like Tweets with clinical data would be a novel approach towards what is now available in clinical settings [START_REF] Fagherazzi | Étude mondiale de la détresse liée au diabète : le potentiel du réseau social Twitter pour la recherche médicale[END_REF], making this new form of research a promising arena, perched at the interface between computer science, epidemiology and medical research [START_REF] Signorini | The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic[END_REF][START_REF] Ranard | Crowdsourcing--harnessing the masses to advance health and medicine, a systematic review[END_REF], in which to study signals for pharmacovigilance, patient-reported outcomes, psychological well-being, diabetes-related distress patterns and, more broadly, everyday diabetes management in a real-life setting.

Evolution of diabetes research

Digitization of the diabetes world has had a major impact on care, but will also have a considerable effect on diabetes research (Fig. 2). The field of diabetes e-epidemiology is growing rapidly, with new technologies collecting vast amounts of data while allowing access to powerful AI methods to analyze them. Traditional cohort studies are now more and more being replaced by e-cohorts-large populations followed online through e-questionnaires and monitored passively via connected devices and e-health records. This will profoundly change the paradigm of research recruitment and data collection, as patients will be recruited once, but able to participate in multiple projects, and their data also collected only once, but used for multiple purposes [START_REF]Nous Patients -Communauté pour la Recherche[END_REF].

These hybrid research materials, based on a variety of data sources [START_REF]Etude Auprès Des Enfants Des Femmes E3N n.d[END_REF], will ultimately help research scientists identify innovative risk markers of diabetes and diabetes-related complications, analyze new biological pathways, evaluate multiple morbidity more easily and nest clinical trials ('trials within cohorts', or TWICs) [START_REF] Bibby | Commentary: considerations for using the "Trials within Cohorts" design in a clinical trial of an investigational medicinal product[END_REF], along with providing more accurate participant selection. Privately funded e-cohorts, such as Project Baseline [START_REF]Project Baseline -Verily n[END_REF], have been specifically initiated to create new AI algorithms and to validate models. These new forms of research will, by design, place participants at the centre of the research process.

Participants could even suggest research ideas directly to researchers through dedicated platforms [START_REF]INSPIRE : Identification, Narration et Sélection par les Patients d'Idées de Recherche[END_REF], which might help investigators address key issues for people living with diabetes.

Future challenges

Defining new metrics for clinical practice

The identification of new digital biomarkers, based on data generated by CGMs/FGMs, activity trackers or other connected devices, is likely to profoundly change clinical practice by moving from an era in which controlling HbA1c is the gold standard [START_REF] Draznin | Guidelines Versus Guidelines: What's Best for the Patient?[END_REF] to an era where an individualized approach towards HbA1c monitoring can be combined with parameters derived from such devices, including time spent in range, glycaemic exposure, glycaemic variability, and hypo-and hyperglycaemia-with more to come soon. Ultimately, this will help clinicians and HCPs design personalized diabetes management [START_REF] Wright | Metrics Beyond Hemoglobin A1C in Diabetes Management: Time in Range, Hypoglycemia, and Other Parameters[END_REF]. Moreover, with more and more data available from personal connected devices and e-health records [START_REF] Miotto | Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records[END_REF], AI methods will also help to devise more accurate risk prediction models for diabetes and diabetes-related complications that will, in turn, help to personalize treatment, care, surveillance and management strategies, thereby boosting our advance towards precision medicine in diabetes [START_REF] Dagliati | Machine Learning Methods to Predict Diabetes Complications[END_REF]. However, two conditions are necessary for such 'augmented diabetes clinical practice' to happen: (i) the development of tools to routinely extract parameters and risk scores from raw data and visualize them in a descriptive manner, in parallel with (ii) evidence-based guidelines provided by learnèd diabetes societies [START_REF] Joubert | Indication, organization, practical implementation and interpretation guidelines for retrospective CGM recording: A French position statement[END_REF].

Challenges for patient-healthcare provider relationships

As it is estimated that 50-70% of routine follow-up clinical consultations could be replaced by remote monitoring, use of digital health records and virtual house calls [START_REF] Topol | The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care[END_REF] in combination with the huge amount of data continuously generated by patients themselves, making them 'experts' in their own health, it is evident that new technologies will also modify patient-HCP relationships. In addition, these new technologies are likely to have a major impact on the economic models currently used for HCP standard care.

In any case, patient empowerment evoked by digitization of care will certainly place patients living with diabetes at the centre of disease management and research. Digital technologies can therefore create tremendous opportunities to revisit patient-HCP relationships through empowering patients by giving them access to their own health data, thereby increasing their health literacy and understanding of their disease. Consequently, there is a need to train patients as much as caregivers on how to understand and take advantage of the digital transformation of healthcare. However, differential training of HCPs and patients on how to interpret data is a necessary condition for optimal use of data generated through digital means, thereby also implying a change in the way diabetes medicine is taught. Developing massive open online courses (MOOC) on the digital transformation of medicine for HCPs and patients with diabetes is one promising perspective [START_REF] Atique | Lessons learnt from a MOOC about social media for digital health literacy[END_REF]. Digital solutions can also increase the efficiency of the healthcare system from all perspectives: for instance, in the complex context of multiple morbidity where only 14% of T2D patients have no other comorbidities [START_REF] Suh | Impact of comorbid conditions and race/ethnicity on glycemic control among the US population with type 2 diabetes, 1988-1994 to 1999-2004[END_REF], it could improve patients' behaviours, avoid flare-ups and unscheduled hospitalizations, and prevent worsening of preexisting conditions and/or the onset of new diabetes-related complications. This would also reduce the time spent in healthcare facilities and avoid unnecessary travel for patients. For medical staff, it would enable more flexible follow-ups whereas, for hospitals, the result would be more efficient use of resources. It has been estimated that, in the US alone, remote monitoring of people with diabetes could save up to 25% of the cost of their care [START_REF] Atique | Lessons learnt from a MOOC about social media for digital health literacy[END_REF][START_REF]) Roundtable on Evidence-Based Medicine. The Healthcare Imperative: Lowering Costs and Improving Outcomes: Workshop Series Summary[END_REF].

Challenges for diabetes research

The development of data science methods and AI adapted for health data (development of methods to train new algorithms without breaching data privacy, transferability of algorithms in different settings) has led to the development of ecosystems of digital tools and sensors to remotely monitor patients, methods to co-construct, with patients, digital interventions for behavioural changes tailored to patient preferences and characteristics, but adapted over time, and methods for rigorous evaluation of the efficacy and efficiency of these new digital technologies. Nevertheless, thoughtful consideration of the philosophical and ethical issues underlying the use of digital tools and AI in diabetes (and chronic diseases more generally) is essential when dealing with various other related aspects, such as individually predicted prognoses, the 'Orwellian' nature of continuous remote monitoring and real-time alert systems, the balance between benefits and risk of data privacy exploitation, and the impact of digital medicine on patients, caregivers, care organizations and society in general. Indeed, patients' informed consent to participate in medical research, one of the fundamental rights of patients, will soon be radically transformed by new technologies involving block-chain tools [START_REF] Benchoufi | Blockchain technology for improving clinical research quality[END_REF][START_REF] Benchoufi | Blockchain protocols in clinical trials: Transparency and traceability of consent[END_REF].

Although digital solutions have a huge potential to modify the diabetes ecosystem, many barriers and challenges persist. However, the next stage in the production of diabetes knowledge will be reached once patients can be characterized by combining their clinical and epidemiological data with their genome and other -omics into exposomes and digitisomes, thus allowing the identification of innovative digital biomarkers correlating with established clinical outcomes. However, so far, there are no standard protocols for data exchanges and interoperability to do this. Limiting the risk of data or technological hacking and maintaining the trust of users for new technologies are also key issues in ensuring their long-term use and safety. Accelerating the open-data movement in diabetes by liberating huge amounts of de-identified datasets is also necessary to ensure breakthroughs in diabetes research, although yet another fundamental requirement is that the validity of the data collected for research purposes should prevail over their 'bigness' [START_REF] Saracci | Epidemiology in wonderland: Big Data and precision medicine[END_REF]. In addition, it is important to remember that the availability of new technology does not equate to acceptability by all. This means that ensuring adequate provision of evidence-based and informed education is now crucial, as is also co-designing services hand-in-hand with patients to maximize their utility. Similarly, there is a growing need for technical and psychological support of patients, which still needs to be addressed with the use of both conventional and new technologies. Doing so should create an opportunity for the expected benefits of new technologies to be realized [START_REF] Choudhary | Hypoglycaemia in type 1 diabetes: technological treatments, their limitations and the place of psychology[END_REF].

Finally, the use of digital tools in diabetes should serve to reduce social inequities, while taking great care not to increase the already existing digital barriers to healthcare and treatment innovations by socially disadvantaged populations [START_REF] Huxley | Digital communication between clinician and patient and the impact on marginalised groups: a realist review in general practice[END_REF]. Indeed, there is a risk that not adequately considering access or the expertise needed for digital innovations may inadvertently widen the gap in healthcare socioeconomic and age-related inequalities [START_REF] Buch | Artificial intelligence in diabetes care[END_REF]. In an effort to limit biases, discrimination against and underrepresentation of specific groups of populations by AI algorithms, it is essential to expand the diversity of participants in epidemiological and clinical studies [START_REF] Char | Implementing Machine Learning in Health Care -Addressing Ethical Challenges[END_REF]. This would allow tools like social media, online communities and smartphone apps to provide new opportunities for reaching the so-called hard-to-reach populations [START_REF] Shaghaghi | Approaches to Recruiting "Hard-To-Reach" Populations into Re-search: A Review of the Literature[END_REF][START_REF] Weiner | Recruiting Hard-to-Reach Populations: The Utility of Facebook for Recruiting Qualitative In-Depth Interviewees[END_REF][START_REF] Preece | Online Communities: researching sociability and usability in hard to reach populations[END_REF].

Conclusion

All forms of diabetes can be seen as e-diseases, as the world shifts from a place where patients are characterized by only a few recent measurements of fasting glucose levels and glycated haemoglobin to a place where patients, HCPs and research scientists are able to consider simultaneously thousands of data points of various key parameters. This will profoundly change the way in which diabetes is prevented and managed, and how people living with diabetes are characterized in medical research. However, given these digitally based data and technologies, there is also now an opportunity to design modern medical research paradigms, to promote open-data research to increase transparency and reproducibility, and to include diabetes patients and their online communities more effectively into all research efforts. Ultimately, new digital technologies, Big-Data-driven analyses and AI applied to diabetes data will change the way diabetes and diabetes-related complications, and their prevention and management, are dealt with. They should complement, but not replace, what is usually done in traditional clinical settings. Indeed, they truly constitute a game changer that should be embraced by all, as they can provide solid research results transferable to patients, improve general health literacy, and offer tools to facilitate everyday decision-making processes for both HCPs and people living with diabetes.

Figure legends

  design, toolset and implementation are all open-source and freely applicable to other opensource projects and research, and may also be included in proprietary products by the industry. Such initiatives should accelerate the pace of innovation in diabetes management.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Recent and future medical innovations to help people living with diabetes. ECG: electrocardiography. Fig. 2. The digitosome comprises new technologies, datasets and artificial intelligence available in the service of diabetes prevention, management, care and research. HCP: healthcare professional/provider.

Beverley Balkau for her diligent proofreading of this review. Guy Fagherazzi takes full responsibility for the work as a whole, and for the decision to submit and publish the manuscript. Guy Fagherazzi is supported by the French Research Agency (Agence Nationale de la Recherche) by an Investissement d'Avenir (Investment for the Future) grant (ANR-10-COHO-0006) that supports the E4N study and the IDEX Paris Saclay Nutriperso project.

DISCLOSURES

The authors have no conflicts of interest to declare for this work.


Amine Dakkoune

Lamiae Vernières-Hassimi

Sébastien Leveneur

Dimitri Lefebvre

Lionel Estel

ANALYSIS OF THERMAL RUNAWAY EVENTS IN FRENCH CHEMICAL INDUSTRY

1

Introduction

Thermal runaway represents a serious threat in the chemical industry. This dreaded phenomenon can lead to dangerous consequences to humans, environment and equipment (Jiang et al., 2016a). From a chemical engineering point of view, thermal runaway occurs when the heat-flow rate released by reactions exceeds the heat-flow rate exchanged with the surroundings. Hence, thermal accumulation increases and the reaction temperature keeps rising, which speeds up the heat-flow rate released (Jiang et al., 2016b).

Thermal runaway, well known event by chemists, can result in an explosion or a high quantity of gas and/or vapor emission that can be flammable and/or toxic. The outbreak of the reactor and the explosive combustion of the gases emitted, may lead to the destruction of buildings and the formation of secondary fires, which can aggravate the overall consequences via the domino effect [START_REF] Hemmatian | The significance of domino effect in chemical accidents[END_REF].

The cause of the biggest industrial disasters in history like Seveso (1976) and Bhopal (1984) was thermal runaway. In the Seveso disaster, the reactor safety disk broke because of an increase in temperature and pressure in the reactor due to an exothermic side reaction. The bursting of the container resulted in the release of a large quantity of dioxins into the atmosphere. The Bhopal disaster leads to a discharge of 40 tons of toxic gases due to a runaway reaction in a pesticide storage facility [START_REF] Aria | ARIA : Retour d'expérience sur accidents technologiques[END_REF].

The French chemical industry has experienced many events due to thermal runaway, such as the Saint Vulbas mishap in 1994 where 324 kg of hydrogen peroxide was released due to a runaway reaction in a fine chemistry unit. In addition, the Wingles mishap in 2010 involved a situation of overpressure in a reactor due to a thermal runaway reaction that burst the rupture disc of the reactor and released 3 tons of styrene. Furthermore, the incident at the lubricant additive manufacturing plant in Rouen (2013) involved a prolonged release of a high concentration of mercaptans, a highly odorous and toxic gas, because of a runaway reaction in a storage tank [START_REF] Aria | ARIA : Retour d'expérience sur accidents technologiques[END_REF]. In our previous study (Dakkoune et al., 2018a), we found that 25% of the events in the chemical industry in France were caused by thermal runaway between 1974 and 2014. Therefore, it is important to examine how these events can be avoided.

The analysis of past events enables a continuous improvement of process safety. Based on an examination of the scientific literature, we found that several research groups have studied the kinetics and the risk analysis of thermal runaway reactions [START_REF] Liu | Comprehensive runaway kinetic analysis and validation of three azo compounds using calorimetric approach and simulation[END_REF][START_REF] Ni | Semi-batch reactors: Thermal runaway risk[END_REF][START_REF] Rakotondramaro | Cooling and stirring failure for semi-batch reactor: Application to exothermic reactions in multiphase reactor[END_REF][START_REF] Vernières-Hassimi | Zero-Order Versus Intrinsic Kinetics for the Determination of the Time to Maximum Rate under Ind[END_REF][START_REF] Vernières-Hassimi | Alternative method to prevent thermal runaway in case of error on operating conditions continuous reactor[END_REF][START_REF] Wu | Experimental study on the thermal runaway of hydrogen peroxide with in-/organic impurities by a batch reactor[END_REF]. However, we noted an absence of work related to thermal runaway events in the French chemical industry despite the significant presence of this risk. This lack of information urged us to look into this problem. In this study, we analyze the events involving thermal runaway reactions in the French chemical industry between 1988 and 2013, as well as their causes and consequences. We focus on data contained in the ARIA database. The results obtained were compared with a similar study carried out in the same period in the United Kingdom about thermal runaway events [START_REF] Saada | Causes and consequences of thermal runaway incidents-Will they ever be avoided? Process[END_REF]. Finally, based on experience feedback, lessons were learned and recommendations were given.

The rest of this paper is organized as follows: Section 2 gives an overview on some means of prevention from process events. In Section 3, we introduce the working methodology that includes the chosen data and event selection. Section 4 is divided in 3 parts. The first one deals with the causes and consequences of thermal runaway events. The second part is dedicated to lessons learned from experience feedback. In the third part, we compare the current results with the ones obtained from the United Kingdom study. The conclusion is presented in Section 5.

Prevention of process events.

The prevention of major risks is a social safety necessity. The protection of process plants from unexpected failures and maintaining their continuity is becoming a priority for industries and countries. Theses preventions aim to avoid severe societal and economic crises that may happen. To remedy this problem, stringent industrial risk regulations have been developed, and scientific research effort increases [START_REF] Khan | Methods and models in process safety and risk management: Past, present and future[END_REF].

Regulation aspects

From a political point of view, different laws have been voted upon by the French and European Union parliaments to prevent major chemical risks such as the "Bachelot law" Act No 2003-699 of July 30 th , 2003 that was created in response to the AZF plant explosion in September 2001 [START_REF] Aria | ARIA : Retour d'expérience sur accidents technologiques[END_REF]. This law includes a technological risk prevention plan. This prevention plan should reduce risks at the source, redefine urban and building plans, and reinforce buildings or expropriate the most exposed residences. Alternatively, the Seveso directive, the most well-known European directive on industrial plant risk management, was originally adopted in 1982 following the Seveso disaster in Italy in 1976. It requires that the EU Member States define a policy for the prevention of major industrial risks. These laws require that industries realize risk analysis, in order to demonstrate to authorities and civil society that they are able to control and reduce the risks associated with their field of activity and reinforce their safety.

Risk analysis aspects

The role of risk analysis is to identify the sources and the degree of risk that can cause damage to humans, environment or property, and to add adequate preventive measures in order to eliminate or to control those risks. The study of accident risk analysis started in the 1970s according to [START_REF] Kletz | The Origins and History of Loss Prevention[END_REF] following the succession of major accidents and the development of the chemical industry [START_REF] Planas | Historical evolution of process safety and major-accident hazards prevention in Spain. Contribution of the pioneer Joaquim Casal[END_REF]. graphs, bow-tie methods, failure mode analysis, effects and criticality analysis (FMECA), and hazard and operability analysis (HAZOP). However, these methods have limitations that make their use critical [START_REF] Baybutt | A critique of the Hazard and Operability (HAZOP) study[END_REF][START_REF] Baybutt | The use of risk matrices and risk graphs for SIL determination[END_REF].

Educational aspects

From an educational point of view, integrating process safety into chemical university programs may be part of the solution. [START_REF] Perrin | Promoting safety teaching: An essential requirement for the chemical engineering education in the French universities[END_REF] demonstrated the importance of integrating safety principles into the educational courses of chemical engineering. [START_REF] Leveneur | Mass & energy balances coupling in chemical reactors for a better understanding of thermal safety[END_REF] and [START_REF] Mkpat | Process safety education: A literature review[END_REF] proposed pedagogical education models about this subject. This approach can also be applied to operators and professional training in process safety [START_REF] Thomas | The safety and chemical engineering education committee-broadening the reach of chemical engineering process safety education[END_REF].

Material and methods

The database

The exploitation of one or more databases is essential to searching for past events and identifying the information necessary to conduct risk analysis. Several databases exist on industrial chemical events such as: The ARIA database is one of the available European databases. It has been used in several studies to describe different technological accidents [START_REF] Casson Moreno | Analysis of accidents in biogas production and upgrading[END_REF][START_REF] Casson Moreno | Major accident hazard in bioenergy production[END_REF][START_REF] Okoh | A study of maintenance-related major accident cases in the 21st century[END_REF][START_REF] Ramírez-Camacho | Assessing the consequences of pipeline accidents to support land-use planning[END_REF]Kirchsteiger, 1999;[START_REF] Trávníček | Quantitative analyses of biogas plant accidents in Europe[END_REF].

The ARIA database, managed by the French Ministry of Ecology since 1992, is an open database that compiles an inventory of past technological and industrial events. It includes information and experience feedback related to these events. This database contains a search engine that can extract events according to specific criteria such as the type of events or the area of activity. In addition, the public can access the data in the ARIA database by visiting the ARIA website [START_REF] Aria | ARIA : Retour d'expérience sur accidents technologiques[END_REF].

The ARIA database describes roughly 42,000 events occurring in France and roughly 6,000 events occurring abroad. About 71% of these events concern classified installations. The other events concern the transport of hazardous materials by roads, rail, waterways or pipeline; the distribution and domestic use of gas, mines and quarries; and hydraulic structures.

Data selection

In the ARIA database, there are two ways to record events: -Summary form that presents key information.

-Detailed fact sheets that provide more information about the events, their circumstances, consequences, measures taken over the short or medium term, proven or suspected causes, and follow-up or lessons learned.

In our study, to properly analyze the causes and consequences of thermal runaway events, we worked with the detailed fact sheets. Fig. 1 shows the procedure that we followed to select thermal runaway events. Events occurred in the French chemical sector. Among these 4,000 events, 169 are sufficiently documented and are associated with very precise fact sheets. Among these detailed fact sheets, 43 events due to thermal runaway occurred between 1988 and 2013. This study focuses on these 43 detailed events.

Using the definition provided by [START_REF] Rathnayaka | SHIPP methodology: Predictive accident modeling approach. Part I: Methodology and model description[END_REF], the events illustrated in this work were classified into five categories :

-Near miss: an event with no consequences observed on health, damage for environment or property and loss of production. It has the potential to result in a loss but does not usually happen.

-Mishap: an event that could cause a minor impact on health, minor damages to property and environment, and loss of production.

-Incident: an event that could cause major injuries, localized damage to property and environment, and a considerable loss of production.

-Accident: an event that may cause massive human fatalities or permanent disabilities, considerable damage to property and environment, production and huge financial loss.

-Disaster: an event that may cause massive fatalities, extensive damage to property and environment, massive loss of production and temporary or permanent plant shutdown.

These definitions are summarized in the Table 1.

Report in international media

Considering the 43 events, we didn't find any disaster or near miss events due to thermal runaway.

Fig. 2 shows the repartition of the events due to thermal runaway, in this study more than half of these events were incidents.

Experience feedback results and discussion

Evolution of events over time

The 43 chemical events in France involving thermal runaway reactions between 1988 and 2013 were grouped and distributed over successive periods of four-year (Fig. 3). The number of past events increased from six over the period 1988-1993 to eleven over the period 1999-2003; the "accident" category disappeared after 1998. On the other hand, the number of incidents and mishaps increased from 1988 to 2003. These increases took place despite the presence of safety laws like:

-The French law of 1976 relating to classified installations in terms of industrial risks.

-The European regulations such as the Seveso 1 directive of 1982, which requires that member states identify industrial sites presenting risks of major events, and maintain a high level of prevention in these sites. In 1996, the new Seveso 2 directive was promulgated in order to introduce some novelties like the notion of prevention and the classification of installations according to two thresholds: "high threshold Seveso" and "low threshold Seveso". In addition, the new directive reinforces the control of urbanization around highrisk sites and the need to inform the public.

After 2003, the number of events decreased slightly to eight during the period 2004-2009. The number of events then increased to 10 over the last period (2009)(2010)(2011)(2012)(2013). In this period, a new

French law has been promulgated, known as the "Bachelot law", whose goal is to reduce the risks of Seveso high-threshold sites. Furthermore, the enactment of the Seveso Directive 2 into French law in 2000 establishes a correspondence between the Facilities Classified for Environmental Protection (ICPE) and the Seveso Directive.

The risk law was modified in 2012 to reinforce the management of major industrial risks by introducing the development of technological risk prevention plans and a more detailed approach to the study of hazards. The third Seveso Directive, which reinforces the requirements already imposed on industrialists to control major events involving chemical products, was entered into force on 2015.

These laws can intervene to reduce these risks. However, it remains necessary to look for the main causes that lead to this critical scenario, and find technical solutions to prevent or reduce the occurrence of similar events.

Reactions and industries

As shown in Fig. 4, the thermal runaway events occurred in three main kinds of reactions. We found that 34.9% of the reported events concerned polymerization reactions as the first responsible reaction. Decomposition reactions represented 18.6% of the reported events, and nitration reactions represented 9.3% of the reported events. We also noted several other types of exothermic reactions that were lower than 5% (e.g., hydrogenation, oxidation, etc.). Most of these reactions are characterized by a large quantity of heat and gaseous products.

For the events due to decomposition reactions, we identified two different types:

-Decomposition in purpose: the goal of the process was the separation of a single chemical compound into other desired chemical compounds. During this process, the temperature was not controlled and thermal runaway occurred. For example, an incident took place in a paint manufacturing plant in France in 1998. The weakly exothermic reaction at this plant was typically controlled. However, due to a lack of process control, a succession of exothermic chemical reactions produced in the reactor caused an explosion and a fire in the plant (ARIA N°17740).

-Decomposition by accident: the decomposition reaction is not the goal of the chemical process. However, a failure results in the temperature not being controlled and thermal runaway occurs. For example, in a fine chemical unit in France, a runaway reaction took place in a reactor in 1994. This accident was due to an operator who stopped the agitator of the reactor without noticing (ARIA N°5900).

Polymerization and decomposition reactions are more significant in terms of damage, and they are responsible for more than half of the thermal runaway events recorded in France. These observations were also found in the works of [START_REF] Saada | Causes and consequences of thermal runaway incidents-Will they ever be avoided? Process[END_REF] in the United Kingdom. This type of reactions often produces a rapid increase in heat and pressure that produces large amounts of energy and can cause fires, releases of gas and/or vapor or explosions if the reactor cooling system sectors shown in Fig. 5. Fig. 5 shows the categories of industries affected by thermal runaway events. Manufacturing of paints and adhesives accounted for 23.3% of the total number of reported events. Organic chemical industries accounted for 20.9% of the total number of reported events. Plastics and rubber products industries accounted for 18.6% of the total number of reported events. Pharmaceuticals industry followed with 16.3% and inorganic chemicals industry accounted for 13.9% of the total number of reported events. Refinery industry accounted for 2.3% of the total number of reported events. Other chemical industry categories include industrial activities such as manufacture of fertilizers and manufacture of explosives. Together, these industries accounted for 4.6% of the total number of reported events.

Human consequences of thermal runaway

The consequences of thermal runaway events vary from one event to another. In one case, thermal runaway caused an explosion and a fire of a hydrogenation reactor in a plant dedicated to toluene di-amine (TDA) (ARIA N°7956). This event resulted in the death of one employee and injuries to three other employees. In addition, several substances were released during the explosion (hydrogen, isopropanol, nickel, TDA, etc.). In another case, an exothermic runaway reaction caused an explosion and a fire in a reactor in a pharmaceutical plant (ARIA N°7069). This event was responsible for the death of an operator and the emission of toxic gases. Injuries of six operators and property damages estimated at roughly 2.13 M€ resulted from another event (ARIA N°4708).

In an event, thermal runaway caused the release of 690 kg of formaldehyde and 36 kg of phenol from a polymerization reactor (ARIA N°7135). In another case, thermal runaway caused the release of 58.5 tons of styrene in a copolymerization reactor (ARIA N°17740).

In order to illustrate the degree of severity of these events, we have opted to focus on human consequences. We found that 40% of the runaway events in this study caused injuries or fatalities to operators or the general population. Table 2 lists the number of injuries and deaths due to thermal runaway events in each chemical industry. According to our data, the paints and adhesives industry experienced the majority of reported injuries (25 people) followed by the inorganics industry (17 injuries), the pharmaceuticals industry (15 injuries) and the organic manufacturing industry (12 injuries). These last three industries also suffered one death each.

Deaths constitute almost 4% of people affected by runaway events. The deaths of three people were reported due to an explosion following a thermal runaway reaction. Roughly, half of injuries (53%)

were caused by projectiles or toxic releases and 41% of injuries were caused by explosions. The remaining injuries (6%) were caused by fire.

The largest number of injuries were reported for two events: the first one was an extended release of Mercaptan at a paints and adhesives chemical plant in 2013 (ARIA N°43616). Individuals displaying symptoms of nausea, headaches and irritated upper respiratory tracts required 20 medical consultations. The other event was an explosion at an inorganic chemical plant in 2003 (ARIA N°24819), a technician sustained a throat injury due to shattered glass; 13 other employees experienced shock.

Causes of thermal runaway events

Based on the experience feedback events, 17 different causes related to the thermal runaway events were found in the French chemical industry between 1988 and 2013. These causes were classified into three major groups: technical and physical causes, human and organizational causes, and natural causes.

Table 3 lists the different causes and their appearance rates for the 43 thermal runaway events in the French chemical industry studied here. Because most of the events recorded in this study have multiple causes, it was difficult to distinguish the initial cause from secondary causes. For this reason, the causes identified in this Table regroup all initial, secondary, etc. causes.

Causes of thermal runaway events in the United Kingdom chemical industry will be compared with causes of thermal runaway events in French chemical industry in the section 4.6.

Table 3.

Possible causes responsible of thermal runaway events in French and United Kingdom chemical industries.

Possible causes of events % of cause in each country

France United Kingdom

Technical and physical causes

Stirring / Cooling 8.5

9.1 Quality: impurity, particle size 7.7

3.4

Technical failure: mechanical / electrical 6.9

12.5

Detector malfunctioned 6.9 N°13520) and leakages of cooling water (ARIA N°17740).

The second most important factor was the quality of the reactants introduced into the reactor (10 events; 7.7%). For example, the accidental addition of water (an impurity) in the reaction mixture caused SOCl2 hydrolysis with the production of SO2 and HCl and led to an increase in the pressure in the reactor (ARIA N°7069, N°4708). In addition, the presence of impurities favors decomposition reactions and initiates thermal runaway (ARIA N°30323, N°40496, N°29752, N°29082, N°16213, N°2375). Finally, the size of particles also plays an important role in the reaction stability (ARIA N°24819).

The third factor is shared between technical failures and detector malfunctions (9 events for each;

6.9%). For example, fault indication of the actual temperature within a tank due to poor positioning of the temperature sensor (ARIA N°43616), a communication fault between the temperature sensor of the and the controller regulating the temperature (ARIA N°41305) or an incorrect value provided by the detector (ARIA N°21994). Furthermore, technical failures like the failure of the automated control mechanism subsequent to a short circuit (ARIA N°16424), the failure of the control of a pneumatic drainage valve (ARIA N°18339), or the failure of a safety card (ARIA N°27001, N°3536) can also cause thermal runaway events.

Another major factor is the problems associated with the accidental presence of an exothermic reaction (7 events; 5.4%). For instance, the slow decomposition of a chemical species in the presence of air (ARIA N°22459) or heat and light (ARIA N°44335) and contact with another chemical species (ARIA N°4460, N°7135). The others technical and physical causes are less prevalent.

Many human and organizational causes are responsible for a large number of thermal runaway events. According to Table 3, operator errors are the most common causes of thermal runaway (28 events; 21.5%). For example, the presence of a contaminant in the reactor (ARIA N°30323, N°7069), an error manipulation due to a lack of focus (ARIA N°5900, N°38617) or a manipulation by an operator for the first time (ARIA N°4708), can also cause thermal runaway. In addition, a lack of respect for reagent quantities or handling steps (ARIA N°40496, N°36630) and poor interventions by the operators on the system (ARIA N°25952, N°33561, N°3536), also contribute to thermal runaway events. Poor risk analysis (e.g., inadequate or absent devices or safety instructions (ARIA N°7135, N°4708, N°8056, N°30323, N°29082, N°22693) and insufficient risk analysis (ARIA N°44071, N°22693)) was the cause of 13 events (10.0%). Additionally, reactor charging contributed to eight runaway events (6.1%). For example, overcharge of chemicals in the reactor (ARIA N°40328) and the rapid introduction of reactants in the reactor (ARIA N°36794, N°7135, N°36630) favors the reaction drift.

Other human and organizational causes play minor roles in thermal runaway compared with the three factors noted above. A lack of communication between employees, insufficient operator training, poor response to emergency or training, leaking, poor control of maintenance operations, inadequate procedures, and the use of unsuitable devices are all contributing causes.

Natural phenomena such as storms and environmental conditions such as high temperatures have been directly responsible for four thermal runaway events. For example, a storm that causes a reactor to lose power (thereby ceasing stirring and cooling) resulted in thermal runaway by domino effect (ARIA N°38617). Also, an extremely high outside temperature contributed to thermal runaway by transferring heat through the reactor, which was poorly insulated (ARIA N°25952).

Through these results, we noted a strong presence of human errors involved in thermal runaway events in the French chemical industry. This finding was also reported by [START_REF] Saada | Causes and consequences of thermal runaway incidents-Will they ever be avoided? Process[END_REF] for the United Kingdom. [START_REF] Cacciabue | Human factors impact on risk analysis of complex systems[END_REF] confirmed that human errors contribute to an increase in accidents. However, it is difficult to estimate human reactions to an accident because human behavior changes from one human to another. In addition, interaction between humans, and between humans and machines/organizational structure make the problem more complex. [START_REF] Nivolianitou | Comparison of techniques for accident scenario analysis in hazardous systems[END_REF] confirmed that the pronounced complexity of mechanical and electronic systems is one of the factors that complicates the role of humans in a plant.

Future research that focuses on events related to thermal runaway must take into consideration problems related to the human factor. This issue must be studied further in order to help staff working in chemical industries determine the best solution to reduce thermal runaway risks. The information resulting from experience feedback by using ARIA database can subsequently provide lessons and recommendations to reduce the frequency of similar thermal runaway events in the future.

Lessons learned

The study of past events (experience feedback) has an important role in preventing the same event or an event with the same causes. This importance is reflected in the lessons learned from each event that occurred before. The analysis of thermal runaway events in France indicates that these events are mainly caused by the lack of knowledge of the reaction system, technical and maintenance problems, operator errors as well as insufficient risk management.

-Lack of knowledge of the reaction system: in order to prevent thermal runaway events, firstly, it is essential to have a deep knowledge on thermal behavior of the reaction medium such as thermodynamic and kinetic constants. This knowledge can be obtained through various laboratory techniques such as differential scanning calorimetry and adiabatic calorimetry. Although the theories go back at least 50 years [START_REF] Aris | Elementary Chemical Reactor Analysis[END_REF] and events have been recorded at least as long ago [START_REF] Lees | Loss prevention in the process industries hazard identification assessment and control[END_REF], basic safety precautions such as adiabatic calorimetry measurement or analysis of potential side reactions are not taken into account.

Thus, it is important to use modeling tools to evaluate the risks of thermal runaway. The models must be reliable, explicit and simple to obtain key parameters of safety according to the operating conditions. These models may make the intervention more effective in the case of a malfunction [START_REF] Vernieres-Hassimi | New Analytical Method for Maximum Temperature Assessment in an Exothermic Tubular Chemical Reactor[END_REF]) by combining them with detection methods, and / or control methods (Vernières-Hassimi and [START_REF] Vernières-Hassimi | Alternative method to prevent thermal runaway in case of error on operating conditions continuous reactor[END_REF]Dakkoune et al., 2018b;[START_REF] Dakkoune | Model-based fault detection and isolation for chemical processes: Application to the prevention of thermal runaway[END_REF].

-Technical and maintenance problems: in order to prevent technical and maintenance failures, a study based on experience feedback can be used to identify critical safety equipment.

This study would guide the financial investment to improve the resistance of this equipment. For example, in the case of corrosion, an analysis must be performed regarding the compatibility of the substances used with materials of process conception. This analysis can avoid problems of corrosion leading to leaks, incompatibilities between the reaction medium and reactor container / storage etc.

-Operator errors: the chemical industry needs to be more vigilant about risks that are mainly related to human factors, due to the complexity of their behavior. In addition to that, the operator interacts with other external factors such as hardware, management, another operator, etc. In order to reduce the events related to the human factor, a search for methods to improve employee behavior is essential. In fact, it can be interesting to work on improving the ergonomics of several equipment, and the training of operators for process safety. An investigation can also be proposed to improve the intervention of operators. However, it would be difficult to completely eliminate all human errors during an operation.

-Insufficient risk management: a risk management system related to the prevention of runaway is essential. The analysis of past events has shown a deficiency in the application of this system, which is mainly related to the assumptions made in the identification of the events feared and / or the selection of dangerous phenomena. This analysis also showed that in some cases the risk management systems were either non-existent or not updated after a modification of the process or the operating mode.

Comparison of French, the United States and the U.K industries

In our previous study of events in the chemical industry in France, we found that 25% of these events were caused by thermal runaway (Dakkoune et al., 2018a). In another study conducted in the United States, and related to the causes of major chemical industry events, [START_REF] Balasubramanian | Study of major accidents and lessons learned[END_REF] found that 26% of major events were due to thermal runaway. According to these two references, France has experienced the same percentage of runaway events as the United

States. [START_REF] Saada | Causes and consequences of thermal runaway incidents-Will they ever be avoided? Process[END_REF] also declared that the incidence of events due to thermal runaway was significant in the United Kingdom.

In the following, a comparison involving thermal runaway was carried out between the current study and the study elaborated by [START_REF] Saada | Causes and consequences of thermal runaway incidents-Will they ever be avoided? Process[END_REF]. This comparison was interested in causes and consequences of events related to thermal runaway in the United Kingdom in the same period of our study .

This comparison is necessary to situate the position and evaluate the performance and capabilities of the French chemical industry in terms of safety compared to those who are similar economically and geographically. Nevertheless, these comparisons will never be 100% reliable because no system is identical to another.

The results show that the main reactions responsible for thermal runaway events in UK and France were polymerization and decomposition reactions (Fig. 6). These two reactions were responsible for almost half of runaway events in UK. On the other hand, industries affected by the highest number of thermal runaway events were also similar with the current study. These industries are the organic chemical industry at first followed by plastics and rubber industries. More details are presented in Fig. 7. In regards to thermal runaway causes, similar results have been observed for the current study and the UK study (Table 3). The main causes were related to operator errors, technical and mechanical failures, unsuitable procedures and devices, stirring and cooling problems.

Conclusion

Risk analysis was carried out on 43 selected past events involving thermal runaway in the French chemical industry between 1988 and 2013. Detailed information about these events was obtained from the ARIA database.

In this study, we found that thermal runaway events were primarily associated with polymerization and decomposition reactions. Operator errors were the primary causes of the thermal runaway events and the number of victims was significant. The current events were compared with the runaway events in UK, the comparison confirms some common points like: operator error was the major cause of runaway events, the polymerization and decomposition reactions were the main reactions responsible for this runaway. Based on experience feedback, lessons were learned and recommendations were provided to prevent thermal runaway events in the future.

Thermal runaway events are a significant problem that is still occurring in chemical industry, more than half of the studied events were classified as incidents. From the period of 1999-2003, the number of thermal runaway events increased and these events have remained more or less stable over the last 10 years. This situation raises the question of the real impact of laws related to risk on industries. One could also note that reaction thermodynamic study is a difficult subject. Although it is difficult to eliminate the risk of thermal runaway, and as a result it is not likely that runaway reaction events will disappear. The only remaining measure is to isolate the reactor from the rest of the world and / or to remove employers and residents from the reactor environment in order to avoid the human consequences (death, injury...) and equipment damage.

  ARIA: Analyse, Recherche et Information sur les Accidents (France); CSB: Chemical Safety Board (United States); FACTS: Failure and ACcidents Technical information System (Netherlands); MARS: Major Accident Reporting System (European Union); MHIDAS: Major Hazard Incident Data Service (United Kingdom); RISCAD: Relief Information System for Chemical Accidents Database (Japan); ZEMA: Zentrale Melde-und Auswertestelle für Störfälle und Störungen in verfahrenstechnischen Anlagen (Germany).

Fig. 1 .

 1 Fig. 1. Structure of events selection in ARIA database.

Fig. 2 .

 2 Fig.2. Repartition of the 43 events due to thermal runaway in France

Fig. 3 .

 3 Fig. 3. Distribution of events related to thermal runaway for chemical industry in France from 1988 to 2013.

Fig. 4 .

 4 Fig. 4. Repartition of reactions responsible of thermal runaway events in France

Fig. 5 .

 5 Fig. 5. Percentage of events in the various chemical industries in France

Fig. 6 .

 6 Fig. 6. Comparison and repartition of reactions responsible of thermal runaway events in France (Fr) and the United Kingdom (UK).

Fig. 7 .

 7 Fig. 7. Comparison of the percentage of thermal runaway events in the various chemical industries between France and the United Kingdom.

Table 1 .

 1 Definition for each event class.

	Events	Human	Consequences Environment Production	Property	Reputation
	Near miss	No injuy	No impact	No loss	No effet	No impact
	Mishap	Minor health effects	Minor impacts	Production loss / work hours loss	Minor impacts	Minor impacts
	Incident	A major health effect or injury	Localized damage	Considerable loss / work days loss	Localized damage	Considerable impact
		One or
	Accident	more fatalities or permanent	Considerable effects	Heavy financial loss.	Considerable damage	Report in national media
		major
		disabilities
				Extensive
	Catastrophic accident or disaster	Multiple fatalities	Massive environmental effects	damage / may cause a shutdown of	Extensive damage
				the plant

Table 2 .

 2 Number of injuries and deaths due to thermal runaway events in France for each chemical industry sector from 1988 to 2013.

	Chemical Industry	Number of injuries Number of deaths
	Paints and adhesives	25	0
	Inorganics	17	1
	Pharmaceuticals	15	1
	Organics	12	1
	Plastics and rubbers	7	0
	Other chemical industries	1	0
	Refinery	0	0
	Total	77	3

Acknowledgements

This project AMED has been funded with the support from the European Union with the European Regional Development Fund (ERDF) and from the Regional Council of Normandie.