In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs. Non Destructive Testing (NDT) is a wide group of analysis techniques employed to assess the quality and to evaluate the safety of materials in building or transport industry . These na priori knowledge and their difficulty to be automated. On the other hand, Machine Learning (ML) algorithms are another data fusion approach that overcomes these issues by learning directly from data , , 8], whel method , by meanl method . Even inl defined by  [D the transducer diameter and The flaw profiles were then extracted by estimating the Time of Flight at each sensor position, by looking at the maximum envelope of each Amplitude Scan (A-scan) between the emission and the background echo. The amplitude corresponding to detected Time of Flight was also recorded. As mentioned in the Introduction, one of the limitations of US inspections is the presence of the near field ed by  , with D ANNs are employed in this work with the aim to implement a data fusion technique able to estimate flaws parameters using simultaneously EC and US signals. Generally speaking, an ANNs is composed of simple computing units, interconnected by means of coefficients called weights, and able to perform predictions in regression and/or classification contexts, after being trained on an exhaustive data set. The ANNs implemented in this study has been trained using four inputs. The first two are represented by the simulated real and imaginary parts of the complex impedances and are related to the EC testing. The latter ones correspond to the Time of flight and echoes amplitudes derived during the US simulations. The ANNs inputs were then connected to one hidden layer of 20 neurons. This value, being set empirically, assures accurate estimations without risking over-fitting. In our case, deeper architectures with more than one hidden layer and more neurons in hidden layers have been tried without improving the ANN parameters estimations, and even deteriorating them due to over-fitting. The more neurons the more parameters have be optimized, so the more training samples are needed. It is a good practice with ANNs to keep the architecture as small as possible while reaching enough accuracy, as bigger ANNs tend to over-fit. The final layer is composed of two outputs neurons representing the radius and depths estimations as depicted in orted in  were choT over the training samples [The ANNs training consisted of minimizing the chosen error function between the network outputs  samples . To do slgorithm  has beenEC and US measurements are performed on an Aluminum block containing three cylindrical defects with radius of 2.5 mm and located at depths of 1, 3 and 7.5 mm as shown in The experimental set-up used for EC testing is composed by a NORTEC 600 connected to a low-frequency Olympus probe, a data acquisition device . The EC probe is placed in contact with the test material, perpendicular to its surface, and it can move along the inspection direction thank to a step motor. For every measured point, the NORTEC 600 induced an electromagnetic field at the frequency of 1 kHz through the probe and measured the resulting impedance. Active and reactive impedances were then recorded after an averaging process (number of averages = 100) and sent to the PC station via USB connection. The entire experimental set-up is depicted in For illustrative purposes, the corresponding measured and simulated signals are compared in the The experimental device for US measurements is composed by a pulse-echo acquisition system, equipped with a 6.35 mm diameter US probe , a data acquisition device , a step motor and a PC station, as depicted in the schematic diagram of A comparison between the simulated Time of Flight in function of the position and the measured ones is given in This paragraph presents the results obtained using EC and US data using the ANNs architecture reported in The results are summarized in To better monitor the accuracy of the flaw parameters estimations, the MAEs for estimated radii and depths were averaged by zones, as depicted in To validate the proposed data fusion approach, the EC and US data measured on the Aluminum test sample containing the three SDHs see , are useIn this paper, it has been shown that use of ANNs allowed us to implement a data fusion algorithm for NDT applications using simultaneously EC and US data, with the aim to exploit the complementary advantages of these two different techniques.At first, the developed method was tested with simulated signals, estimating radii and depths of SDHs in an Aluminum block presenting a wide variety of radius and depth. In addition, a statistical study of the ANNs performance has been effectuated, providing a confidence index for each estimation. Eventually, experimental measurements were performed to validate the entire procedure. The experimental results confirmed that proposed data fusion algorithm is able to properly estimate SDHs depths and radii, even for flaws located in the EC and US blind zone. Despite the simplicity of the method presented in this paper in terms of ANNs architecture, test material and the flaw geometries, we believe that the approach presented here could represent a promising tool for future employments of data fusion algorithms in NDT. Further improvements could be tied to more accurate numerical models and multi-layers ANNs, with the aim to investigate more complex materials presenting different flaw geometries. This would reduce estimations errors tied to different levels of uncertainty and inaccuracy in the data set. The ultimate goal of this study could be the design of an NDT smart sensor, able to provide accurate estimations of flaws eventually present in the structure, combing EC and US inspections.


The role of micronutrients in health and disease has increased the curiosity and interest among researchers. The prime focus of this review is the significance of trace elements- calcium, vitamin D, selenium and zinc with cardiovascular health. WHO identified cardiovascular diseases (CVD) as the leading cause of deaths globally. Identifying the risk factors that could be modified and creating new treatment strategies remains to be the main concern for CVD prevention. The data that showed the relationship between trace elements and various ways in which they may contribute to cardiovascular health and disease from clinical trials and observational studies were collected from databases such as PubMed and Embase. Based on these collected data, it shows that either high or low circulating serum levels can be associated with the development of cardiovascular diseases. Micronutrients through diet contribute to improved cardiac health. However, due to our current lifestyle, there is a huge dependency on dietary supplements. Based on the observational studies, it is evident that supplements cause sudden increase in the circulating levels of the nutrients and result in cardiovascular damage. Thus, it is advisable to restrict the use of supplements, owing to the potent risks it may cause. In order to understand the exact mechanism between micronutrients and cardiac health, more clinical studies are required. A staggering 31% of mortality worldwide can be primarily due to cardiovascular disease. Nearly 85% of these deaths are due to heart attacks and strokes. A combination of risk factors like unhealthy diet and obesity, physical inactivity, hyperlipidemia, stress, and hereditary factors contribute to developing cardiovascular diseases (CVD) . IdentifCalcium plays an important role in the biological system but is a pivotal mineral in bone health and cardiovascular system . The daiOn the contrary, increase in the levels of serum calcium is correlated with a potential risk of CVD like hypertension, myocardial infarction (MI) etc. and there are several meta-analysis reports in support . StudiesSince there are studies reporting the downside of increased calcium levels, especially on cardiovascular health, there is some concern regarding calcium supplements. In view of the increasing concerns, it would be advisable to rely on the dietary sources of calcium to meet the daily recommended levels and limit the usage of supplements.Cholecalciferol or vitamin D3 is a steroid hormone naturally available, and it can also be obtained from consumption of seafood especially salmon, tuna and eel, cow milk, and eggs which are rich sources of vitamin D. Vitamin D is absorbed in the small intestine. The skin naturally synthesizes cholecalciferol from 7-dehydrocholesterol when exposed to ultraviolet B rays (UVB). Cholecalciferol is hydroxylated to 25-hydroxyvitamin D [25(OH)D], the major metabolite that circulates in blood. It is transformed into 125-dihydroxyvitamin D  the active form in the kidneys by the 1α-hydroxylase enzyme.Deficiency of vitamin D is a global problem and some of the causes included is diminishing synthesis in the skin due to aging, hyperpigmentation, low dietary intake, limited exposure to sun, pollution, smoking, obesity . The daiLower levels of vitamin D in the blood has been linked with higher cardiovascular risk in numerous observational studies, epidemiological investigations, and laboratory studies . SeveralREN-1C represses renin expression, and aids in lowering the probability of emerging hypertension. In addition, the renal arteries of individuals with diminished levels of vitamin D have low expressing angiotensin-l receptors  and calcium metabolism is also suggested as one of the mechanisms which alter the blood pressure. Minimal levels of vitamin D will increase PTH which in turn constricts the vasculature and leads to hypertension by increasing intracellular calcium levels . SupplemMany epidemiological studies have correlated lower serum vitamin D levels to increased incidence of type 2 diabetes mellitus. Diminished levels of vitamin D are assumed to increase insulin resistance, cause damage to the beta-cells of the pancreas, and consequently decrease secretion of insulin . There iVitamin D has shown some protective influence on vessel walls as observed in some research papers. Vessel walls are shielded against damage from inflammation by enhancing the expression of anti-inflammatory cytokines like IL-10 and by reducing the expression of pro-inflammatory proteins like, TNF-α and IL-6 . But theAlthough vitamin D is dynamically involved in several pathways in the homeostasis of the cardiovascular health and low vitamin D levels are recognized as a menace for chief cardiovascular diseases including hypertension, atherosclerosis, strokes, heart failure etc. the supporting evidence available is limited. It is essential to prolong studying the outcomes of vitamin D on cardiovascular health with more emphasis on the pathology before advocating the use of vitamin D supplements for improving cardiovascular health to the public.Zinc has numerous functions in the human body and therefore is recognized as an important trace element. It is a vital antioxidant that prevents the formation and reactive response of free radicals which may damage the cells and lead to degenerative diseases . Daily aDeficiency of zinc is correlated with the development of cardiovascular diseases, mainly atherosclerosis . ImpaireRole of zinc in regulating blood pressure may be because of its antioxidant property and its role in regulating the flow of calcium ions. In a study conducted on mice, it was seen that zinc deficient diet increased the blood pressure in mice. Reduced zinc levels lead to hypertension, hypercholesterolemia and a higher BMI in a study conducted on human subjects. The association was statistically significant . PossiblOxidative stress can trigger cellular and molecular destruction and aids in the development of insulin resistance which will lead to type 2 diabetes mellitus, which is a predetermined risk factor of cardiovascular disorders . Zinc isAlthough there is some evidence showing the role and benefits of zinc in cardiovascular health, it is not enough to rationalize its supplementation. More research must be done, and more steadfast and consistent experimental data would be desired to arrive at a decision.Selenium is another important trace element which influences cardiovascular health. It has a vital role in the formation of selenoproteins, so far about 30 of them are identified . The recDeficiency of selenium has been associated with CVD from a long time and an example is the Keshan disease a type of cardiomyopathy seen in some parts of China that has soil low in selenium . The undA meta- analysis report by Owing to the ill effects supplements have on the health of the individual, it is better to rely on naturally available sources. A summary of the daily requirement and dietary sources has been depicted in the Based on the large observational studies and available data, we conclude that, there definitely is an important role of the micronutrient’s calcium, zinc, vitamin D, and selenium on cardiovascular health. However, high circulating levels of these micronutrients have a detrimental effect on the heart health as evidenced in few of the studies, although the underlying mechanism is not very clearly understood. Micronutrients taken in the form of supplements cause an immediate surge in the serum levels for a prolonged period and the increment is sustained for longer hours. Further clinical trials and studies are needed to establish the benefits of supplements and it is important to weigh the risks before taking them. It is, therefore, advisable to depend on dietary sources to meet the daily requirement of micronutrients.HN and SC equally contributed in terms of conception of the work. HN performed database search, drafting of the manuscript. HN, SC, and SS edited and reviewed the manuscript. SC procured the grant for the publication. All the authors have given their consent for submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Meniere's disease (MD) is a clinical syndrome characterized by spontaneous recurrent vertigo, usually accompanied by hearing loss, tinnitus, and aural fullness. The cause of MD remains unclear and is generally considered to be associated with endolymphatic hydrops. Studies showed that patients with MD could have eustachian tube dysfunction (ETD). ETD can disrupt the pressure balance between the middle and inner ear and impair the inner ear's function. In recent years, several studies have attempted to identify MD by using wideband tympanometry (WBT). However, there are limited studies in this area. There is no consensus on how to use WBT to diagnose Meniere's disease. Therefore, we endeavored to conduct a narrative review in this aspect based on the latest research findings. Reduction in resonance frequency and absorbance are characteristic of MD and can identify Meniere's disease. The use of an increase in the integrated area of absorbance as an indicator for identifying MD is controversial. WBT seems to be ineffective as a diagnostic tool during the acute episodes of Meniere's disease. Patients with MD may benefit from WBT. WBT has excellent potential for future use in Meniere's disease. However, further large sample sizes, multicenter studies are needed. Meniere's disease (MD) is a clinical syndrome characterized by episodes of recurrent spontaneous vertigo, usually accompanied by fluctuating sensorineural hearing loss, tinnitus, and aural fullness . This syIn recent years, the possibility of multifrequency tympanometry (MFT) as a new diagnostic tool for MD has been evaluated. The use of MFT to investigate experimentally induced cricoid ligament alterations in guinea pigs revealed significant 2 kHz tympanic conduction curves, which could imply IE pressure . AlthougTo further the understanding of WBT for diagnosing MD, we have endeavored to produce a narrative review based on the most recent research findings. We searched a variety of literature databases, including PubMed, Scopus, Web of Science, Embase, and Cochrane, using variations of the descriptors WBT, MD, and EH. The inclusion criteria were literature on the use of WBT for identifying MD, which was published in English or Chinese. Non-relevant topic studies, non-English or non-Chinese language articles were excluded. Ultimately, 7 studies met the criteria for inclusion. In this mini-review, we will highlight the role of WBT in the diagnosis of MD.Traditional tympanometry assesses the impedance of the ME at a frequency of 226 Hz. Still, this traditional measurement method yields different results depending on the anatomical characteristics of the ME cavity, which may influence the test results . WBT empIn a WBT test, a microphone-generated sound signal is sent through the external ear canal to the ME, where a portion of it is absorbed, referred to as absorbed sound energy; the remaining portion is reflected by the tympanic membrane, which is referred to as reflected sound energy. The microphone can collect reflected sound energy in the external ear canal, and the absorbed sound energy can be exhibited indirectly via reflected sound energy. The ratio of absorbed sound energy to total sound energy is known as absorbance. The following formula calculates the absorbance. Absorbance (energy) = absorbed power/incident power = 1 – energy reflectance . The detThere may be a link between MD and eustachian tube dysfunction (ETD). The eustachian tube is a natural pressure regulating system that balances the pressure in the ME to the external pressure. It plays a vital role in the ventilation and drainage of the tympanic cavity and the mastoid air cell system. ETD can disrupt the pressure balance between the ME and the IE, impairing the function of the IE , 22. ThePatients with MD may be associated with ETD. According to a study by Kitajima et al., ~25% of patients with MD exhibit ETD . HoweverVT can affect the pressure mechanism in the IE through the round window membrane. Several studies have demonstrated that inserting a VT is beneficial in treating some patients who have MD. According to a prospective, randomized study, patients with MD who underwent endolymphatic sac shunts and patients who received VT inserted into the tympanic membrane had significantly fewer vertigo episodes at 6 and 12 months post-operatively with no difference between the two treatments . The autWBT can be applied to the diagnosis of MD based on the assumption that the stapes footplate and the annular ligament serve as the loudspeaker of the IE . EH causThe resonance frequency (RF) of the affected side of MD patients was significantly lower than that of the asymptomatic ear of the unaffected side of MD patients and the control group . The cauRecently, Miehe et al. found that patients with MD had significantly lower absorbance obtained by broadband tympanometry in the frequency range of 2,000–4,000 Hz compared to normal subjects . WBT canWhen applying WBT detection, 107 absorbance values from 226 to 8,000 Hz can be obtained, and then the integral area of the absorbance (IAA) of the subject is calculated. Two studies from China compared the difference in IAA between the affected ear and the unaffected ear in MD patients, yielding different results , 29. AccG-width is defined as the bimodal width of the waveform obtained when the conductance is measured at 2,000 Hz . SeveralWBT is a more comprehensive and relatively new indicator that may provide valuable information for patients than traditional tympanometry or MFT. It has the advantages of being objective, practical, minimally invasive, and rapid . HoweverCurrently, WBT has shown significant advantages in the diagnosis of ME diseases. The absorbance in the WBT systematically decreases with increasing ME effusion accumulation, which is a powerful and sensitive indicator of the volume of ME effusion in children with secretory otitis media . HoweverOther tests have some advantages in diagnosing MD. The VEMP test is used to evaluate the saccule and utricle bilaterally by monitoring the sternocleidomastoid and inferior oblique muscles . The intAlthough WBT has some value in the diagnosis of MD, it is essential to note that WBT measurements should be used as part of a test battery and interpreted in conjunction with the patient's history, physical examination, and other objective tests rather than relying solely on WBT results.The wideband acoustic immittance database (WAI), funded by the National Institutes of Health, has facilitated the development of research in this area, intending to allow audiological researchers to share WAI measurement data and perform comprehensive over multiple datasets . OtolaryIn this review, we would like to emphasize the following points:Patients with MD may benefit from WBT, a more comprehensive and relatively new indicator that provides helpful information.Patients with MD are likely to have ETD, theoretically allowing for identifying the condition using WBT.The reduced resonance frequency and absorbance are characteristic of MD and can identify MD.WBT can detect between asymptomatic and symptomatic patients with MD.Using an increase in the integral area of absorbance as an indicator for identifying MD is controversial. During acute episodes of MD, WBT appears to be ineffective as a diagnostic tool.More research is needed to realize the potential of this technology in clinical applications.XM conceptualized and drafted the manuscript. KZ, JY, and CH critically reviewed the literature and revised the draft manuscript. All authors contributed to the article and approved the submitted version.This study was funded by the Science and Technology Development Project of the Bureau of Science and Technology of Wuxi, China (Grant number CSZ0N1622).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Discovery of biomarkers is a continuous activity of the research community in the clinical domain that recently shifted its focus toward digital, non-traditional biomarkers that often use physiological, psychological, social, and environmental data to derive an intermediate biomarker. Such biomarkers, by triggering smart services, can be used in a clinical trial framework and eHealth or digital therapeutic services. In this work, we discuss the APACHE trial for determining the quality of life (QoL) of cervical cancer patients and demonstrate how we are discovering a biomarker for this therapeutic area that predicts significant QoL variations. To this extent, we present how real-world data can unfold a big potential for detecting the cervical cancer QoL biomarker and how it can be used for novel treatments. The presented methodology, derived in APACHE, is introduced by Healthentia eClinical solution, and it is beginning to be used in several clinical studies. The field of clinical research is undergoing a “data revolution.” The transformation of large volumes of medical records to an electronic format, and the remarkable growth in the data collected by health registries and during clinical studies provide opportunities to make risk prediction and intervention selection more precise. This increasing availability of the so-called “Big Data” has brought about a growing interest in machine learning (ML) algorithms for extracting knowledge from observations, typically conceptualized as datasets, and for constructing personalized risk prediction models.The concepts of real-world data (RWD) and real-world evidence (RWE) have come to be fashionable, along with those that describe outcome and experience from the perspective of the patient . The advent of wearable technologies has made the objective measurement of lifestyle possible to an unprecedented scale of dimensionality, and the collection of subjective information about outcome and experience as PROMs and PREMs.However, in spite of the use of RWD/RWE (and PROMs/PREMs) one important shortcoming in clinical research is that the actual outcome of trials is usually different from the expected one, and often not reproducible , 2. EvidFactors like trial-protocol adherence and compliance, dropout rate, and surveillance of adverse effects have been traditionally outlined as the most significant reasons behind this outcome difference, and several types of interventions aimed at reducing their impact have been put into place .To mitigate unexpected results from clinical trials, along with adherence and compliance issues, digital solutions as clinical diaries have flourished and are vastly used in the running of clinical trials to collect information on how patients are coping through the trial itself by focusing on PROMs. These digital solutions are termed ePRO.ePROs generally do not take into account the impact of the lifestyle and habits of the patient that can be measured using wearables (the RWD as objectively measured) on the effectiveness of the intervention and focus, instead, on the PROMs and PREMs. In addition to that, lifestyle has not yet been aggregated into actionable predictors, or used to generate simulated models to derive predictors, on outcomes and experiences.The processing of patient-centered RWD represents an innovative challenge for modern personalized medicine. Today, various patient-well-being dimensions can be satisfactorily met, using merely a multidimensional data collection approach. Data collection platforms, able to collect, manage, and interpret RWD of the patients, eventually supported by artificial intelligence (AI), are fundamental.In this study, we attempt to establish patient lifestyle and behavior as the driving force of an effective treatment, by addressing the following hypotheses:Objectively-measured RWD correlate to PROMs and PREMs and thus can predict them.The impact of behavior/lifestyle, as expressed by measured RWD, on PROMs and PREMs can be simulated by utilizing biomarkers in models of patients and testing intervention on them.via simulation of trials lead to behavior/lifestyle phenotypes that can be used as clinical endpoints and eligibility criteria in clinical trials.Groups of biomarkers identified Coaching on behavior/lifestyle can complete traditional interventions in everyday practice.This paper is organized around seven sections. Following the paper, after the abstract and introduction, the subjective and objective RWD as clinical outcomes are discussed in section Subjective and Objective RWD as Clinical Outcomes, including definitions and the role of RWD, RWE, and ePROs. section Lifestyle Behavior as a Biomarker With Clinical Value and Types of RWD presents the concept of lifestyle as a biomarker with clinical value, and in section AI Technologies for Defining, Modeling, and Simulating Lifestyle we present how AI can support the discovery and extraction of such biomarkers during clinical studies. Furthermore, in section Pilot Study to Evaluate the Hypothesis, we present elements from a series of clinical studies that utilize the Healthentia eClinical platform  to captuT is an indirect biomarker for flu if:A biomarker  is the vh and mass m of a person as:Is body temperature the only biomarker for the flu? No, but it certainly is a good standalone one. Is body mass a biomarker for obesity? Consider a person with a body mass of 120 kg. That person is obese only if their height is <2 m, based on their body mass index . Any stax and the biomarker non-linearly combines them into F(x). Discovering the non-linear combination function F is not done manually, resulting in an equation. Instead, it is done using an ML algorithm that learns F for the measurements x, yielding the metric to be evaluated for disease diagnosis or outcome prediction. In ML terminology, x is the feature vector, and F is the discriminant function of the classifier  and insurance claims data for medical research in the United States, required the Food and Drug Administration (FDA) to develop guidance on the use of RWE in the studies of medical product safety and outcomes for both postapproval studies and studies of new indications of approved drugs. Hence, FDA has issued the following definition: RWD are “evices.” . The Eurevices.” .the usage, and potential benefits or risks, of a medical product derived from analysis of RWD.” , etc., but they are not good for measuring disease severity, biomarkers and, in general, detailed clinical information. In this setting of increasing dimensionality and heterogeneity of data, ML methods are gaining traction as tools to analyze massive and complex datasets . When RWIn general, the value of RWE has been well-understood, to the point that many initiatives  are amassing insurance claims and EHR curated data.Real-world evidence derived from observational clinical trials is traditionally collected, objectively as such as data stemming from experimental trials. However, in the observational trials, the setting is entirely uncontrolled and this makes the advancing of “causal learning” to identify direct causes of a certain outcome more difficult. This uncertainty can be overcome by means of modern AI techniques that have proven to be effective, as well, in the setting of synthetic data created by simulation . Last buTo the best of our knowledge, RWD stemming from lifestyle have never been used to train AI-powered simulators alone or along with RWE.any report of the status of the patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else” (The FDA deffinition of Patient Reported Outomes (PROs) is “ne else” . Indeed,ne else” .PROs are currently used as clinical trial endpoints following a constant increase of their recognition as such over the last two decades. Along with PROs, other measurements related to the patient–reported experience and patient-reported behavior are being used more and more as endpoints in clinical trials.via open-end questions. In this setting, the value of PROs is not only recognized by the regulator and competent authorities but, as well, by many scientific societies.Structured and validated PROs reduce significantly the heterogeneity of the responses of patients making possible, to a higher extent, the understanding of the real differences in the perception of the outcome, as compared with the information collected PROs can be used as primary, secondary, or even exploratory/tertiary endpoints for the hypothesis generation. Interventional trials experimenting with a new medicinal product do have PROs, normally, as secondary endpoints, whereas palliative care trials or rehabilitation ones can have PROs as primary endpoints.Better understanding of the cost/benefits of a treatment;Better understanding of the patients' experience beyond the biomedical outcomes, especially in the domains of pain, fatigue and inconvenience from any other symptom;Better tools to improve methodologies of trials.Following the lines of simplification and according to , the benFrom the regulatory perspectives, the strength of the methodology PROs used in the trial could allow the achievement of the status of “PRO labeling” for approved products, which, in turn, allows PRO-supported claims.We consider PROs, thus defined and characterized as very reliable endpoints, to measure, in conjunction with the impact on the PROs themselves, the variations in the lifestyle behaviors; these are discussed in the following paragraph under the assumption that they may be considered as biomarkers with clinical value.Lifestyle behavior includes all features that characterize the daily living of people, without considering possible diseases in a direct manner. The importance of observing lifestyle behavior lies upon the evidence that lifestyle is a health determinant and has a two-way link with the disease. A patient with a chronic disease can see the health deterioration mapped into their lifestyle behavior, whereas changes in daily living can have a significant contribution to health, besides any intervention provided.There are several studies that provide solid evidence about the relation of lifestyle with health. A study related to diabetes prevention  suggestsAnother study  shows thWe quantify lifestyle by obtaining RWD in four fields: physiological, psychological, social, and environmental. Information in all these fields can be objectively measured using devices, or can be subjectively reported by people by answering questionnaires. This information will form the constituents of our proprietary composite biomarker, making up the feature vector to be used as an input to the underlying ML algorithm implementing this biomarker. Not everything we are discussing in the following subsections will be used in the final biomarker. As it is presented in section Composite Lifestyle Biomarker Discovery, domain-knowledge, and feature-importance analysis of the biomarker design process will drive the selection on a per-case basis, but here we give the extensive list.via composite tests like the 6-min walk, the frailty test, or games specifically designed to measure muscular responses . All these tests are scripted and hence can be measured using sensors and audiovisual instructions to the people on their smartphones. Heart-related features include the continuous measurements of the heart rate variability, the time spent in different heart rate zones, and the daily resting heart rate measurement. Sleep-related features include continuous measurements on the time spent in the different sleep stages . More physiological aspects are reported. We collected reported symptoms , including their intensity. We regularly collected reported weight and height also. Nutrition is paramount, starting at a higher level with the number of meals in the day and the main ingredient of the meal , but a more detailed analysis can also be used when available. Water, coffee, and alcohol intake are reported, and so are toilet visits. Finally, the menstrual cycle is also of importance.We have identified several types of RWD that can be grouped into four categories. In the physiological category of RWD, we mainly encounter RWD that are mostly measured using activity trackers and/or smartphones. Activity-related features are steps walked, distance, elevation, energy dissipation, time spent in different activity intensity zones  and exercise activities , and their distribution in the day. Presence indoors or outdoors is also of importance. Specialized physical activity is also measured Most RWD types in the psychological category are reported and include a high-level simple emotional state self-assessment or the 11 aspects of the OECD better life index , but wheReal-world data in the social category can be measured indirectly from the usage of the phone  and social media . More direct information can be reported using questionnaires on activities with friends, family, or co-workers.environmental category include environmental indicators for the assessment of the quality of life (QoL) that can be reported by the patients using questionnaires. Precise measurements of living or working environment quality can be obtained by integrating relevant commercial devices .Real-world data in the Risk and outcome predictions in clinical medicine have become more precise due to the remarkable growth in the data collected, and with RWE and the growing interest in AI techniques, the construction of personalized risk and outcome prediction models is now more robust.Our digital biomarkers are composite contextual ones, in the sense, that they comprise numerous diverse  indirect measurements, including environmental aspects . In Guthbiomarker definition stage, the domain experts select the clinically significant outcomes that need to be predicted by the biomarker(s). In most cases, the investigators are interested in whether these outcomes are reached or not by the patients. Then the biomarker is implemented as a binary classifier that predicts success or failure in reaching the outcome  do not consistently push toward a positive or negative prediction.The core of our biomarker discovery workflow is the biomarker iterative design stage. It involves the iterative retraining of the classifier or regressor implementing the biomarker. In this loop, the ML algorithm is used to train the biomarker using the current version of the feature vector. After training, the results are analyzed to refine the feature vector and repeat the process as long as the validation results are improved. The initial training happens when the first outcomes are collected after the end of the training period. Such outcomes can be intermediate ones, or even the final ones, meaning that the biomarker discovery cannot enter phase three before the end of the trial. At the end of the process, the feature vectors of all people are collected, together with the actual outcomes for the duration of the trial. During the iterations of this design stage, the training set is used to train the ML algorithm of choice. The choice depends on the problem at hand, the most determining factors being the size of the training set and the dimensionality of the feature vector. Classifiers that are able to uncover nonlinear decision surfaces are preferred, namely subclass linear methods –25, KernCases of SHAP values are shown as rows of point clouds in The performance of the biomarker is assessed at the biomarker performance assessment stage that follows after the iterative process. The RWD test set is used at this stage. It needs to be noted that for biomarkers predicting final trial outcomes, there are only as many feature vectors as there are patients in the trial. When the number of participants is low, then the validation stage is done using the test set itself, or in more tight cases the process is run repeatedly employing the “leave one out for testing” method, where all features are individually used to test classifiers or estimators trained and tuned using all the rest. The three biomarker-discovery phases and the resulting biomarker assessment are summarized in our proposed workflow for biomarker discovery trials, shown in By employing AI discovered biomarkers that successfully predict clinically significant outcomes, it is possible to drive decisions in digital therapeutics (DTx) , 34, 35.Digital therapeutics is also applied in the more general, disease agnostic, and well-being areas. In this area, a biomarker is used to drive behavioral change in a virtual coaching setup. The explainable AI elements already discussed in the previous section determine the elements of the feature vector of the particular patient who had the most positive and negative influence on the probability of a successful outcome. Then the person is coached in these elements. The virtual coach selects the feature vector elements of the strong influence that are related to the behavior  and to the environment. If they have a strong negative influence, it coaches the person to change behavior. If they have a strong positive influence, it encourages the person to keep up the good lifestyle in those aspects.While recently the use of ePRO and digital monitoring devices in clinical trials is ever-increasing, and many of those aim at deriving digital biomarkers , to the The study APACHE, which is an advanced patient monitoring and AI-supported outcomes assessment in cervical cancer using Internet of things technologies, is a cofinanced monocentric observational study using a remote monitoring device for patients affected by locally advanced cervical cancer. Patients are considered as such if staged larger than or equal to IB2 according to the FIGO staging system , with primary lesions larger than 4 cm. The patients undergo chemoradiotherapy (CRT) followed by either radical surgery or brachytherapy boost and are treated in Fondazione Policlinico Universitario “A. Gemelli” IRCCS of Rome, Italy. The foreseen study duration is 24 months. The study protocol foresees inclusion and exclusion criteria. The inclusion criteria require the patients to be younger than 70 years, be clinically able to use portable technologies, and be able to understand and sign informed consent. The exclusion criteria involve a major psychiatric disorder, inadequate performance status , and ongoing pregnancy or breastfeeding. Patient enrolment began in October 2020 and continues to date. A total of 50 patients are foreseen for this exploratory study. The selection procedure of patients adhering to the study protocol criteria foresees only a brief interview during the first visit to the advanced radiation therapy center of Gemelli for the initial radiotherapy treatment. During the interview, the informed consent is acquired by the attending physician, and papers describing the trial and expected role of the patient are provided. If the patient is motivated and computer literate, the whole procedure does not take more than 15–20 min. Please note that the initial inclusion criteria on cervical cancer has been widened to include other pelvic cancers, as discussed in section Expected Benefits, Early Findings, and Next Steps.The primary objective of the study is to assess the experience of patients using Healthentia  and a wearable tracker during the multimodal oncological therapies and follow-up period. The study has three secondary objectives. Firstly, to compare PROs with corresponding clinical records about toxicity, instrumental activities of daily living (IADLs), and stress/coping levels. Secondly, to profile patients based on their scores and activity, and lastly, to train models using ML on the patient-reported and monitored data.Patients have received a state-of -the-art wearable device (Fitbit INSPIRE) during their first visit prior to CRT start, that collects at a daily basis RWD like activity , sleep, and vital signs. During the whole observation period, patients are asked to report their weekly well-being by completing dedicated questionnaires sent to the ePRO App on their phone. A dedicated research nurse will flank the patients enrolled in the study in filling the e-questionnaires in case of need and follow up the correct flow of the questionnaires.Early and late toxicity will be assessed using the NCI-PRO-CTCAE™ ITEMS-ITALIAN Version 1.0 for the cutaneous, gastro-intestinal, and genito-urinary sectionsTherapy impact on instrumental daily activities will be assessed using the Lawton Brody IADLQoL will be assessed using the EORTC QLQ C30Nutritional status will be assessed using the malnutrition screening toolPsychological status will be assessed through self-administered tests, namely the distress thermometer, DT6 for distress evaluation and the Mental Adjustment to Cancer Scale, MINI-MAC 7, Italian version for coping evaluationUser experience and technology acceptance will be assessed using a customized questionnaire on the Healthentia App on enjoyment, aesthetics, control, trust in technology, perceived usefulness, and intention to use.The following scoring systems are selected to assess specific aspects of the experience of the patient during the multimodal treatment period:via the Healthentia portal web application. More information on the Healthentia eClinical solution is given in section Healthentia Platform.The collected data are transferred through the Healthentia app on the smartphone of the patient to Healthentia platform. Clinicians can monitor lifestyle behavioral patterns, detect changes in the health status, and be informed on clinical endpoints The collected RWD, i.e., objective data from wearable devices and subjective data collected from questionnaires  are fused together and define a multidimensional vector for each patient that consists of steps, resting heart-rate, sleep, etc., which characterizes their behavior throughout the day. After the models are trained from the RWD collected over an initial period, it is possible to predict outcomes and system scoring of the above scoring systems i.e., IADLs or QLQ, by feeding the system with the automatically collected vectors.via a set of parameters (different measurements of questions). The parameters are concatenated into a set of scores, as described in the section on the manual RWD selection stage. It is through these scores that the five aspects are quantified.The RWD that is being collected is currently grouped into five lifestyle aspects described in Each of the five lifestyle aspects see  combinesThe independent lifestyle aspect is reported on a weekly basis using the IADL questionnaire. There are eight questions covering telephone use, shopping, food preparation, housekeeping, laundry, transportation, medication adherence, and finances management. Each of these parameters contributes equally to the overall independence score.The QoL lifestyle aspect is assessed using two questionnaires. The EORTC QLQ assesses parameters on symptom experience , body image , sexual/vaginal functioning , and sexual worry/activity/enjoyment . These four groups are the four scores from EORTC QLQ, three of them obtained weekly and one monthly.The NCI/DRO/CTCAE questionnaire collects 45 parameters on a weekly basis, all having to do with different symptoms . They are grouped into five categories, the gastrointestinal (16 parameters), the skin (13 parameters), the neuro (2 parameters), the sexual (2 parameters), and the urinary (8 parameters). There are also two parameters that cannot be classified in the above groups of interest and are ignored by our scoring of the APACHE outcomes. They have not been removed from the data collected to maintain the integrity of the questionnaire used. These five categories are the five scores from NCI/DRO/CTCAE, obtained weekly.The psychological lifestyle aspect is also assessed using two questionnaires. The Mini Mac Scale assesses (every 3 months) parameters that are aggregated into scores  on the fighting spirit (16 parameters), helplessness/hopelessness (six parameters), anxious preoccupation (nine parameters), fatalism (eight parameters), and denial/avoidance (one parameter).The distress thermometer comprises a single parameter forming a score on a weekly basis. Finally, the nutrition lifestyle aspect comprises a single parameter on malnutrition score collected on a weekly basis.Summarizing, the biomarkers discovered in APACHE utilize 66 parameters and/or their grouping into 12 scores as a feature vector. They are being trained to predict significant variations in the three scores quantifying QoL from the EORTC QLQ questionnaire. Training is done on anonymized APACHE data using proprietary scripts built on top of well-established implementations of ML algorithms found in the Scikit Learn and Tensorflow libraries.Healthentia  is an eCThe Healthentia solution extends the use of a traditional ePRO/eCOA application by adding behavioral and health-related data collected from Internet of Things (IoT) devices. Utilizing ML on this data, it is possible to discover behavioral biomarkers and cluster patients into behavioral phenotypes, which allows the activation of smart services to predict clinical outcomes, generate prevention alarms, and link phenotypes with drug/intervention efficacy. In addition, based on reported outcomes, the AI module generates automatic alerts in case of adverse events. These automatic and prevention alarms support decision making by the investigator during the clinical trial, for the benefit of the health of the individual patient. For the AI module to do so online, the biomarker needs to be trained first as discussed in section Manual RWD Selection Stage.in vivo clinical studies, Healthentia allows the running of in silico trials that use the deep phenotyping outcomes together with legacy data, to create synthetic control arms and support pharmaceutical companies to design optimized studies.On top of Healthentia is available for clinical studies, under a strict regulatory framework, and a SaaS environment, which is open to the wider community. The SaaS version includes further features, such as eRecruitment, eConsent, and Virtual Coaching. Healthentia is already in use in APACHE, and we have received ethical clearance for its use in more studies with a top pharmaceutical company and a hospital. Results from its AI module (for biomarker training) have been published , albeit The APACHE study has been running for a few weeks now, albeit with lower enrolment rates than expected. The first RWD are being collected, as shown in In the APACHE study, we have introduced new patient-centered variables for risk stratification , allowing the prospective setup of rapid and fully personalized therapeutic approaches. The integration of such variables into clinical nomograms and multidimensional predictive models is contributing to realizing decisional support systems. The study contributes to the active monitoring of toxicity and therapy-related side effects, aiming at their reduction, and in the optimization of monitoring and follow-up strategies of the patients. Finally, the use of Healthentia enhances self-awareness of the patients about global clinical status and participated clinical decision making.Clinicians expect different benefits from the use of such advanced monitoring techniques. First of all, the reduction of toxicities may hamper QoL of the patient and their compliance to the oncological multimodal treatments, thanks to the identification of alert signs and early symptoms that may be overlooked during the visits or considered negligible by the patients themselves and not properly reported to the attending physician. This may be translated into an active personalization of the ongoing treatments  with significant expected advantages in terms of treatment quality and overall clinical outcomes. Furthermore, the use of this monitoring approach may represent a key resource for coping strategies enhancement, especially in hospitals where a psychoncology service is not available.As the volume of collected RWD increases, our next steps in the analysis side have to do with modeling the different scores and discovering a biomarker for the predictions of the QoL ones. Due to conflicting studies that do not allow to run multiple trials on the same cohort of patients, the enrollment has been slow, and therefore an amendment has been proposed to enlarge the cohort and grant the success of the trial. At the time being, patients affected by other pelvic cancer undergoing CRT are allowed in the study . This expansion of the inclusion criteria is justified by the similarities of cervical to the other pelvic cancers in terms of treatment (there is a radiotherapy does overlap) and most importantly the common types of possible toxicity  that allow the reuse of the same questionnaires and eliminate differences in data analysis.The limitation of the APACHE study lies with the automatic measurements. These need the compliance of the study participant, whose familiarity with activity trackers significantly declines with their age. The participant needs to understand the importance of the physiological data being collected and make sure their activity tracker is worn and is charged. Working with the unavoidable gaps in the collected physiological data is something we are investigating.In section Study Description we presented the study design, which is complemented by the scoring mechanisms that we have been prepared prior to the launch of the study. The physiological scores combine diverse parameters, and their combination weights will be investigated to obtain meaningful results. All the other scores combine parameters that carry similar weights and hence there are three combination options:A linear combination does not discriminate between number of events or their severity. As an example, consider four parameters with values 0–4, 4 indicating maximum severity. An average score of 1 is obtained with four 1's or three 0's and one 4. There are cases where the latter is more alarming than the former. Some of the questionnaires used in APACHE do have formal scoring suggestions –41, follxn, each with a value of zero if there is no symptom and integer values larger than zero indicating increased severity of the symptom. The events are combined into a single score s using:Non-linear combination, on the other hand, allows the investigator to put more weight on either the number of events or their severity. Consider a set of possible events a>1 the investigator has a score that puts emphasis on event severity vs. event count. On the other hand, selecting a < 1 puts the emphasis on the event count. Selecting a = 1 leads to the linear case.Selecting As already mentioned, the formal scoring of most of the questionnaires is linear. This is followed to facilitate clinical research. But to facilitate ML, we also experiment with non-linear scoring in APACHE. We are selecting the value of the exponent for each combination leading to the different scores, based on what the investigators need to emphasize with each one of them.The iterative design phase has recently started with an initial algorithm selection. Since there are 50 patients in the trial, there are 50 feature vector instances to train and evaluate the biomarkers. For this reason, algorithms like neural networks are not expected to be used. The biomarkers will most probably be based on a decision-tree classifier or random forests. Linear methods (with careful feature engineering) will be used as a baseline, together with their multiclass variants, since the decision boundaries are not expected to be linear.The biomarkers will be trained using the leave-one-out method, each time keeping one patient for testing, training with 45 and using the remaining four for hyper-parameter tuning during validation. The performance will be reported as the number of correctly identified patients out of the 50 leave-one-out experiments.At the definition stage of the APACHE biomarkers discovery, the clinically significant outcomes that need to be predicted by the biomarker are selected. These are dictated by the goal of APACHE, that is, the QoL in terms of low toxicity adverse effects, as is quantified by the three scores of the lifestyle aspect. As APACHE trial starts producing RWD, we will be training our biomarker to predict significant variations of these scores. At some milestone of the trial (currently planned for its end on the 52nd week) the predictors of the biomarker will be able to determine if a significant improvement of the 3 scores associated with the QoL of the patients is to be expected.Having trained the biomarker, we will be exploiting the explainable AI techniques described in section Composite Lifestyle Biomarker Discovery to determine the aspects in the life of the patient to coach in favor or against. This way, as discussed in section Using the Biomarkers for Digital Therapeutics, we will be driving DTx in this therapeutic area.Although the RWD collection and scoring presented in this study is customized to the needs of the APACHE trial for cervical cancer, the methodology for capturing and combining data, and most importantly, that for discovering biomarkers, is applicable to other conditions as well. Preliminary results are available for the application of this methodology on RWD for obesity, where the discovered biomarker predicts significant short-term weight variations and general well-being, where the biomarker classifies the health outlook of general population, aiming at using it for risk assessment and the analysis of its decisions in virtual coaching. We have published these early results in Pnevmatikakis et al. , and ourThe APACHE study addresses a very important milestone, that is represented by the clinical validation of AI technologies when creating models based on PROMs capable of predicting any outcome of clinical value. In this modeling, endeavor the clinical validation still represents a bottleneck. In particular, the complexity of promoting RWE from basic clinical decision support  to a fully validated  digital biomarker RWD-from-lifestyle-raw-material based is quite significant. The challenge is represented by the robust regulatory framework set to qualify a classical biomarker, herein adopted to evaluate a digital one vs. strictly technological standards, requirements, and credentials. If we consider the privacy endeavor (related to innovative data capture and handling solutions) and the cyber-sec one, these alone represent entirely new dimensions entering the ethical/regulatory dialog.In our perspective, extracting evidence with predictive values from lifestyle in a very homogeneous cohort  of subjects undergoing state-of-the-art treatment magnifies its value by offsetting this RWE toward a very stable, and to a certain extent expected, clinical outcome progression . In other words, a study like this creates the idea sandbox to evaluate the training of an ML algorithm in a low noise setting.Clinically, extracting such RWE has significant implications. Lifestyle-driven and outcome-connected digital biomarkers with the predictive value could enrich the diagnostic tools with agile (and relatively inexpensive) indicators (easy to collect in a continuous fashion), for example, of the onset of significant toxicity from an oncological treatment.Training cycle by training cycle, moreover, these digital biomarkers could pave the way to smart coaching that, in turn, could be promoted toward validated digital content as an active ingredient in a DTx perspective.The clinical study APACHE and its raw data are property of Gemelli and cannot be shared.The studies involving human participants were reviewed and approved by Gemelli. The patients/participants provided their written informed consent to participate in this study.SK, AP, and AC were the main editors. KK has contributed to the customization of Healthentia to support the APACHE trial. SK, AP, and AC have contributed to the ML of Healthentia. LB, VV, and GS have contributed to the study design and faciliating the APACHE trial. All authors contributed to the article and approved the submitted version.SK, AP, AC, and KK were employed by company Innovation Sprint Sprl. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The Covid-19 pandemic has put policy systems to the test. In this paper, we unmask the institutionalized resilience of the Dutch health care system to pandemic crisis. Building on logics of crisis decision-making and on the notion of ‘tact’, we reveal how the Dutch government initially succeeded in orchestrating collective action through aligning public health purposes and installing socio-economic policies to soften societal impact. However, when the crisis evolved into a more enduring one, a more contested policy arena emerged in which decision-makers had a hard time composing and defending a united decision-making strategy. Measures have become increasingly debated on all policy levels as well as among experts, and conflicts are widely covered in the Dutch media. With the 2021 elections ahead, this means an additional test of the resilience of the Dutch socio-political and health care systems. The acute crisis of the virus outbreak in the late winter of 2020, which resulted in an overflow of severely ill patients in hospitals suffering from yet unknown illnesses, has evolved into a more enduring crisis that challenges the sustainability of national political-economic and health care systems  legal requirements and both traditional and new institutionalized arrangements of governing health care and public health. In the empirical sections that follow, we deepen the analysis through the discussion of three topics that were key to crisis decision-making in the Netherlands: the alignment of hospital care; the negotiation of scarcity and the contested role of experts and expertise in policy-making.2.et al., et al., The Netherlands more or less followed the pace of the Covid-19 outbreak in other European countries. The first wave started late Winter 2020, followed by a second one in September which, after a brief decline, worsened in the course of December. The first Covid-patient (‘Patient Zero’) was confirmed at the end of February during a live talk show in which the Minister of Health answered questions about the virus outbreak in Europe. The virus was brought in by people returning from skiing trips in Northern-Italy and Austria. Carnival parties in the Southern regions of the country  induced a quick spread of the virus. The outbreak first restricted to the South, but mid-March also people in other parts of the country turned sick. Hospitals soon got overwhelmed with Covid-patients, particularly in the most contaminated regions. In those first weeks, the crisis (and policy-making) was framed by scarcity: there was a lack of personal protection equipment , specialized nurses, intensive care unit (ICU)-beds and ventilators  disputed their effectiveness.Policy-making was largely framed by the very acute hospital crisis – also drawing attention away from nursing homes and mental care  reopened, just like contact facilities. Measures of physical distancing however remained in place, and workers were still urged to work from home. Economic measures to soften economic consequences of the crisis were prolonged. The government furthermore enlarged test capacity, advising people to get tested (and go in quarantine) when developing symptoms. However, enlargement of test capacity appeared to be a difficult and highly contested undertaking due to limited test capacity and lacking national coordination (see below).In the course of the summer, infection rates gradually increased again, particularly among young adults and migrant populations see . The cenBelow, we discuss how policy issues were framed in the unfolding crisis, and how this opened (and closed) policy venues for decision-making and controlling the socio-political consequences of the pandemic. Pandemic policymaking however happens against the backdrop of institutionalized national health care systems. To that purpose, we first provide a brief overview of the Dutch health care system and zoom in on the role of local and national, and public and private authorities in public health crises.3.et al., Dutch health care decision-making is fragmented due to its layered and decentralized nature  stipulates authority in case of public health crisis at the national, regional and local levels. Already in January 2020, Covid-19 was classified as a category-A infectious disease. This provided the RIVM with a coordinating role and enabled regional authorities – of which there are 25 – to take measures to protect public health, like prohibiting group gatherings and closing bars, shops and sport facilities. Regional measures are worked out further in local emergency degrees. The A-classification furthermore obligates general practitioners and laboratories to report positive test results in order to foster national surveillance  has a central role in setting the conditions for regulation and hospital reimbursement to protect the public good  for a contract with health insurers, and health insurers in their turn compete for the insured  for the coordination of acute care delivery and also have a coordinating role in times of incidents or crisis, for instance about the distribution of patients and bed capacity. There are 11 acute care regions who collaborate in a national network  to coordinate patient care. In the following, we will further discuss the importance of this developing institutional crisis infrastructure for the framing of the Covid-crisis and how policy-decision making ensued. Rather than a chronological account of the Dutch response to Covid-19, we examine three topics that were key to the national debate and that enable us to analyse the resilience of the Dutch health care system; that is, its ability to cope with unanticipated dangers and to ‘bounce back’ . This more or less happened overnight. The LCPS took over the distribution of patients. Hospitals were required to provide their admittance rates twice a day, and if needed patients were transferred to other hospitals ‘with empty beds’. When the curve flattened early summer, the LCPS was dismantled. It got back into force half October when hospitals faced a second wave of patients and regular care was again scaled down. However, this time the focus was much more on an equal distribution (‘fair share’) of patients in order to keep up with regular care provision as much as possible. The LCPS, that had quickly grown out from an improvised call centre into a well-equipped and well-organized agency, was provided with the mandate to force unwilling hospitals to accept Covid-patients, showing the increased centralization of decision-making, albeit coordinated through the LNAZ and thus largely under the control of the acute care regions. For example, the LNAZ, in close contact with the medical associations, set criteria for which patients could be transferred.In the Dutch health care system, hospitals do not have an institutionalized public role in times of health crisis. Hospitals have their own emergency plans and are loosely organized in regional and national acute care networks. Individual crisis management however soon appeared insufficient during the Covid crisis. Hospitals were confronted with a large increase of severely ill patients, many of them needing ICU care. Regular care was scaled down to accommodate the growing group of Covid-patients. In the second week of March, hospitals in the South started to get overwhelmed . Physicians contacted colleagues in Northern hospitals to take over patients. This mechanism however soon failed; too many patients were in need of immediate medical support and intensive care treatment. This was the point that a new acute care infrastructure was set up, initiated by LNAZ who did not have a decisive role before. In close contact with the Ministry of Health, it created a centre for the (re)distribution of patients, supported by the Dutch army  for which no financial agreements existed, and the loss of regular care on which their financial position was based. Moreover, they were afraid to lose patient groups to competitive hospitals if they would turn into ‘Covid-hospitals’. Hospitals anxiously looked at neighbouring hospitals and whether they were scaling down or up and might ‘steal away patients’ . Also, when some hospitals started to use their private clinics to offer regular care, this was seen as a breach of the gentlemen's agreement of ‘being in this together’. The same goes for ICU capacity. As this has largely been centralized in Dutch health care (see below) the Covid crisis opened up possibilities for smaller hospitals to increase their ICU capacity, thereby also strengthening their acute care provision. Such competitive logics had to be negotiated constantly in ROAZ and LNAZ contexts. Disputes over extra costs were however mostly settled when health insurers offered generous settlements. Moreover, larger hospitals – mostly university medical centres – have generally gained more in ICU capacity than smaller ones.The need to ‘act as a whole’, which fits in with the public health crisis logic, and the private interests to safeguard a hospital's competitive position following the logic of the health care market, exemplifies the clash of two institutional layers of the Dutch health care system; that of the regulated market and that of the crisis organization. However, and especially in the first weeks, old traditions of collective action relived as national agencies, the acute care network, health insurers and hospitals tactfully drew up new collective infrastructures like the LCPS, while the NZa and the health insurance agency negotiated about reimbursement rules to keep Covid care going and soften the pressure of market competition. The NZa negotiated the tariffs and conditions for reimbursement, as well as the compensation of financial losses due to the scaling down of regular care provision. Similar initiatives were taken to compensate primary care providers (e.g. dentists and physiotherapists) for the sudden decline in their service delivery. Also, reimbursement rules were adjusted to enable alternative care provision like e-health and Covid after-care.5.The first months of the Covid-crisis were dominated by discussions on scarcity and collective attempts to meet urgent needs. Scarcity was multiple; a shortage of ICU-capacity , specialized workforce, personal protective equipment (PPE) and test capacity. Scarcity was both due to the pandemic  and institutionalized features of the Dutch health care system, like a limited number of ICU-beds and restricted test capacity.The scarcity problems were widely covered in the media, and the Minister of Health regularly had to report to parliament about the shortages and solutions to solve these. With regards to PPE, a problem that was particularly threatening  in the first weeks, the Ministry of Health took up a central coordinating role. Together with the LNAZ, PPEs were imported from all over the word – although the media also reported on costly and failed attempt to get hold on batches of face masks – and distributed among hospitals (nursing homes were only included later on). The government not only coordinated the purchase of equipment, but also took care of its finance, which conflicts with the institutionalized private responsibility of hospitals, but also illustrates the central approach of the first months.et al., Another main problem in the first phase was the shortage of ICU-beds and ventilators. Compared to other OECD countries, the Netherlands has a low number of hospital beds and particularly ICU-beds NVZ, . It was et al., Testing for Covid-19, a central element in international policies, however appeared an even more complicated and conflictuous undertaking that got more and more politicized. Scarcity of tests and laboratory capacity was due to both a worldwide high demand for tests, and an institutionalized restricted and highly regulated use of laboratory capacity for public health. In the Netherlands, only a few laboratories have been certified to conduct tests in case of public health problems. It became apparent that Dutch microbiologists had been successful in restricting lab capacity, arguing that highly specialized knowledge is needed and cannot be offered by private services  expected control and leadership from the central government.6.Gezondheidsraad) in case of public health crisis. The RIVM closely monitors infection and hospital admission rates and uses data models to analyse the impact of policy measures on the spread of the virus. These models have become key instruments for policy decision-making. The Health Council advises the Ministry of Health (amongst others) about vaccination policies and ethical issues. The role of experts and scientific knowledge is furthermore anchored in the national OMT that advises the cabinet about policy measures. The OMT is chaired by the director of the infection prevention unit at the RIVM and includes various scientific experts; all medical doctors, epidemiologists and virologists. This rather narrow focus on medical-epidemiological knowledge has been criticized, however, especially when the societal impact of Corona measures became clear (e.g. loneliness among elderly and children in unsafe home situations that could no longer go to school or day care because of the lock down). Social and economic scientists were hardly involved at the start, nor was the ‘normal’ advisory structure of the state, which however responded by setting up collaborative projects in the context of the Socioeconomic Council. Also, the RIVM set up a behavioural unit, making use of experts across the country. Social science input into decision-making however remained small.The reliance on experts is institutionalized in the Public Health Act which provides a leading role to the RIVM and the Health Council  acted reluctantly in issuing rules, which also resulted in the initial ‘intelligent’ lock down instead of a ‘full’ one. They argued that schools should remain open. As children were not the main sources of contamination, they should keep their social lives, and parents should be able to work from home. This more careful approach was soon thwarted by the medical specialist association that argued that not closing schools immediately would be irresponsible. Referring to ‘the Italian situation’ they appealed to broader worries among teachers and parents about safety at school. During a press conference of Prime Minister Rutte 2 days later, it was indeed announced that all day care, schools and universities would close that same week. This occurrence also illustrates the role of the Dutch media in national policy-making, which has sharply increased in the past few months. The media has had a striking role in the sharpening of restrictions as ample room was given to critical experts striving for more stringent measures, yet the media was much more reluctant to express criticism on restrictions.In the course of the summer a more contested and dispersed picture emerged. Infection and mortality rates and hospital admissions had sharply decreased, and restrictions were lifted. Mayors and other local authorities, at the same time, increasingly felt responsible to keep transmission rates low while also recognizing that people were out more often and violated the Corona-rules (e.g. keeping 1.5 m distance). Local authorities and (self-announced) experts and public figures increasingly started to criticize policy measures and national political leaders in talk shows, on social media and in the newspapers. Striking example is the so-called ‘Red Team’ consisting of a wide variety of scientists, (retired) physicians and opinion makers who argued for a much more stringent policy approach like the obligation to wear face masks and to close schools in case of infections. These ‘critical actors’ actively approached mayors, policy-makers from the Ministry of Health and journalists, pointing at more stringent policies in other countries that might be more successful in curbing the virus. They got support from (amongst others) associations of teachers and also some experts from the OMT openly supported these ideas. As a result, a new policy venue for a more ‘law-and-order’ approach was created that not only conflicted with the more moderate policy approach of the first wave, but also embarked on the ‘tactful’ approach of collective action and decision-making in the first phase of the crisis. This law-and-order approach was played out full midst December when infection rates went up and hospital and nursing home directors warned for an overwhelmed health care system with Christmas parties ahead. Prompted by an OMT advise, as well as publicly expressed worries of the mayors of the safety regions who did not manage to keep shopping areas quiet and signalled many violations of the 1.5 m distance norm, the government issued an immediate and full lock down. Although most citizens were caught by surprise, opinion surveys revealed that the measures were supported by a majority of the population.et al., Experts and ‘science’ play a central role in Dutch crisis policy making. Covid-19 policies are presented as evidence-based (following the logic of consequence), although the government and especially Prime-Minister Mark Rutte have frequently underscored the deep uncertainty in which decisions are taken, also revealing the ‘probe and learn strategy’ of the government (Ansell and Boin, 7.et al., et al., Public health crises, like the Covid-19 outbreak, require decisive intervention at both national and local levels. How such interventions are structured and play out in policy and health care practice depends on the institutional infrastructure of health care systems, revealing its institutionalized resilience capacity (Ansell


Existing measures to operationalize the constructs were adapted to the present research context. The data were analyzed using structural equation modeling. The results show that the theoretical assumptions of the theory of planned behavior were largely supported by the data, implying that the antecedents of environmentally friendly behavioral intentions can be applied to club members. Furthermore, gender- and sports-specific differences in the antecedents–intention relationship were detected. This study is among the first to examine environmentally friendly behavioral intentions in community sports clubs. It adds to an increasing body of research investigating environmental sustainability in sports.Environmentally friendly behavior has become increasingly important in recent years to reduce the speed of climate change and its negative impacts. Individual behavior, including environmentally friendly behavior, is largely formed by behavioral intentions. This study draws on the theory of planned behavior to examine the effects of attitudes toward the behavior, subjective norms, and perceived behavioral control on intentions of environmentally friendly behavior. It also investigates differences between genders and among sports. The study is based on data from a nationwide online survey of community sports club members in Germany in five team/racket sports ( The United Nations (UN) have developed 17 sustainable development goals which are indicative of the most pressing issues worldwide UN, . Goal 13Sports participation is one of the activities that contribute to individuals' health and well-being . This theory has been widely used to examine behavioral intentions in a number of contexts and is applied to the research context of grassroots sports clubs in this study. The empirical analysis is based on a cross-sectional survey of sports club members in five team/racket sports in Germany  which are typically characterized by possessing club houses and formal training facilities where all members have the opportunity to perform environmentally sustainable behavior or not . This study advances the following two research questions: (1) to what extent can the antecedents of environmentally friendly behavioral intentions as suggested by the TPB be applied to sports club members? And (2) are there differences in the antecedents–intention relationship between both genders and among sports? Such an examination enhances our understanding of the drivers behind behavioral intentions in relation to environmentally sustainable behavior and the potential of grassroots sports clubs to contribute to the environmental sustainability of the sports sector. The study contributes to the increasing body of research on sports ecology.Sports environmental research emerged early in the 1980s, but until 2008, only a few occasional and qualitative studies examining the role of the natural environment in the sports context were published  are slightly younger than their male counterparts .In this study, environmentally friendly behavior was understood as “behavior that consciously seeks to minimize the negative impact of one's actions on the natural and built world”  and experiential  items but can measure attitude as a unidimensional construct. Therefore, the items of a semantic differential are meant to correlate and load on a single factor in a confirmatory factor analysis (CFA) for valid and consistent measurement of attitude as a latent construct. Items that do not load on the latent attitude factor should be removed as they do not represent the latent construct , but low with the other items . The corrected item-total correlations were moderate (0.48 and 0.50) . The instrumental item (att3) was highly correlated with one  and moderately correlated with the other experiential item . As the experiential items were only moderately correlated and the instrumental item measured an additional part of the attitude, the items represent attitudes as a latent construct without being redundant. Since there were only three indicators left, no global or incremental model fit could be investigated, but the local model fit , and the local model fit was good  and the local model fit of the scale were good  indicated invariance across genders. Regarding sports, the poor model fit of the scalar model  indicated non-invariance across sports.For the comparisons of the latent means between women and men as well as between the different sports, the MG-CFAs for evaluating the measurement invariance were used. More precisely, the scalar model provided latent means and additional statistics for a comparison with a reference group.M = 0.084; p = 0.048; gHedges = 0.054) as well as a significantly higher PBC over acting environmentally friendly in sports clubs . The subjective norms related to acting environmentally friendly in sports clubs were perceived significantly weaker by men . Men also had significantly weaker intentions to act environmentally friendly in sports clubs in the next 2 weeks .The latent mean differences between genders and sports are shown in In the latent mean differences between sports, the MG-CFAs for the evaluation of the measurement invariance could only be used for the comparisons with the tennis sample, as this group was the reference group in these analyses. For the other comparisons, further MG-CFAs with other reference groups were conducted. As scalar invariance did not hold for the intentions scale, the means of intentions were compared using the alignment method  for all differences.According to the predictions of the TPB, a structural equation model was estimated for the full sample and the gender- and sport-specific subsamples. The intention to act environmentally friendly in the sports club was set to be predicted by attitudes, subjective norms, and PBC, which were free to correlate. A few gender- and sports-specific differences emerged in certain subsamples and parameters which are presented next. In the gender-specific subsamples , 3, 26.8The purpose of this study was to enhance the knowledge about drivers behind behavioral intentions in relation to environmentally sustainable behavior and the resulting potential of grassroots sports clubs to contribute to the environmental sustainability of the sports sector. While previous studies focused on recycling intentions of sports spectators . Since grassroots sports clubs are characterized by strong social cohesion of members, a general awareness of club members for the importance of environmental topics can contribute to increasing environmentally friendly intentions of all members. Since club members are part of each other's social network, spillover effects can occur. Furthermore, handball and football club managers should give their club members enough opportunities to perform environmentally friendly behavior. Such opportunities might yield to higher PBC which has a positive effect on intentions and, later on, also on actual behavior  studies. Second, the study only investigates environmentally friendly intentions, not actual behavior. This limitation comes with the nature of the survey, which was anonymous. Thus, it is not possible to collect further data about actual behaviors and link these data with the existing dataset to examine how behavioral intentions have affected actual behavior a few weeks later. Future research wishing to compromise respondents' anonymity should investigate the intention–behavior relationship of community sports club members. Third, the study only investigated the antecedents–intention relationship based on the TPB and its constructs. Other context-specific variables, such as awareness of environmental issues, should be investigated in future studies.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.MB drafted parts of the methods, conducted the data analysis, designed the graphical representation of the findings, and wrote parts of the results. TT drafted the theoretical framework and literature review, as well as parts of the methods, the discussion, and the conclusion. PW oversaw the data collection process and conducted parts of the data cleaning, drafted the introduction and parts of the results, discussion, conclusion, checked the overall manuscript for coherence, consistency, and format. All the authors contributed to the conception and design of the work, drafted it, revised it critically for important intellectual content, approved the final version of the manuscript, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.