This study aims to identify significant differences between the countries of the European Union, follow the course of achievement of the convergence objectives, assess developments against specific common characteristics of the countries, and propose possible measures that could improve the state of health in the EU as a whole by implementing standard cohesion policies. To compare efficiency and productivity among the states of the European Union, we used data envelopment analysis (DEA) and the Malmquist productivity index (MPI). On the basis of our findings, even countries that joined the EU later achieve high technical efficiency values. However, it should be noted that it is in these countries that technical efficiency values tend to decline. The values of the Malmquist productivity index broadly indicate stagnation in western countries and productivity decline in central and eastern European countries. This decline is mainly due to a negative shift in the technological frontier in these countries. The efficiency of healthcare systems and the use of financial and other resources are very often discussed in not only the scientific but also the practical and political spheres. This is also indicated by literature surveys regularly published in various journals. Therefore, measuring efficiency in healthcare is linked to the search for variables that can plausibly reflect the level and quality of healthcare in countries, regions, or health service providers.Since health is one of the most crucial sectors of the public sector, drawing the attention of national governments and political groups to improve their health is an inevitable phenomenon. Healthcare has also found a place in sustainable development goals and recovery plans. Today, people are even more aware of the importance of this sector, the imperfections of which have been revealed by the COVID-19 pandemic [Research suggests that inequalities between countries are significant and see sustainable economic growth as one of the options for improving health . HealthcFor this reason, financial stability and sustainability are often mentioned, given many factors—population aging, rising investment prices, and pressure on salary growth ,7. Thus,As we have already indicated above, healthcare efficiency is a discussed topic, and there are several approaches to measuring efficiency. Efficiency can be measured on the part of healthcare providers ,13,14, cIn this study, addressing the research gaps identified above, we analyze developments, changes, and reasons for efficiency changes with productivity in selected European countries during the period under review. In addition, this study looks for significant differences between the countries of the European Union, follows the direction toward the convergence objectives, assesses developments against specific common country characteristics, and proposes possible measures that could improve the state of health in the EU as a whole by implementing standard cohesion policies.This study is divided into five sections. In On the basis of research and other literature surveys ,27,28,29Its theoretical foundations were laid by Farrell  and devet and t + 1.x is an input vector, y is an output vector, and I indicates the model orientation (input). The geometric mean of the MPI from Equations (3) and (4) can then be calculated as shown in Equation (5).The Malmquist productivity index (MPI) measures productivity changes along with time changes and can be broken down into efficiency changes and changes in technology using a DEA nonparametric approach. MPI can be expressed using the distance function (E) as Equations (3) and (4) using observations at time This geometric mean can then be divided into so-called “technological change” (TECH)—change in technological efficiency (TE) and change in efficiency (EFFCH)—change in management efficiency (ME); see Equation (6).Technological change is due to technological changes (investments in new machinery and buildings). Management decisions cause a change in efficiency. If the indicator value is greater than 1, it means that there has been an efficiency gain; if the value is less than 1, it means that there has been a decrease in efficiency .The object examined in this study constituted the countries of the European Union. Today, the European Union consists of 27 countries. In this research, we omitted Malta, Cyprus, and Luxembourg because of the unavailability of data. We examined the remaining 24 countries for the period 2013–2019. This is how we created a data panel with a size of 216 observations—decision-making units (DMU).We obtained data for our research from the databases of the European Statistical Office , as theyThe highest average spending in the reporting period was in Germany, The Netherlands, and Sweden, along with others mostly in western and northern Europe. By contrast, the lowest expenditure expressed in PPS per capita was in Romania, Bulgaria, and Latvia. Differences in minimum and maximum levels were not significant, but it must be noted that most countries’ spending on the country’s health systems has increased.Germany, Austria, and Bulgaria had the highest number of hospital beds per 100,000 inhabitants. The lowest average bed counts were in Denmark, Sweden, and Spain. Although a high number of beds does not necessarily mean quality healthcare, the number of beds should be reduced to such levels that healthcare is provided for all who need it. In most countries, there has been a significant decrease in the number of beds.We consider three variables as outputs in this research. We can consider the number of outpatients and inpatients as output. A higher number of treated persons must not indicate healthcare efficiency; it must also be due to low prophylaxis levels. There are indicators which reflect the quality of the healthcare system according to the health status of its inhabitants. The first variable is the number of years in full health at age 65 ; it measures the number of years that a person at age 65 is still expected to live in a healthy condition. It is a health expectancy indicator which combines information on mortality and morbidity. This variable indicates the state of the health system in earlier periods. It is an output that faithfully indicates the previous development of the health system’s quality. It also indicates the state of health of the country’s population. The second and third output variables are the life expectancy of men (Life_Expect_M) and women (Life_Expect_F) at birth ,18,19,37The highest number of healthy years at the age of 65 was recorded in Sweden, Ireland, and Denmark. The lowest number of years in full health at the age of 65 was recorded is Latvia, Slovakia, and Croatia. Life expectancy at birth was the longest for both men and women in Spain, Italy, and Sweden. The lowest values were recorded in Bulgaria, Romania, and Hungary. For all three output variables, an increase in absolute values can be observed over the period under review, which is a positive fact. Individual measurements were processed through the R program.In this section, we characterize the results of efficiency modeling using the DEA method and productivity changes using MPI. For the needs of this research and clarity of results and context, we divide the results of the technical efficiency modeling into countries that joined the EU before and after 2014 inclusive. Efficiency was measured for countries together, where we took the window approach. The efficiency of using capacity and financial resources, taking into account the quality of the health of the country’s population, has been largely stagnant in these countries. The least efficient countries were Austria and Germany, which spent relatively high resources on healthcare. On the other hand, Greece, Spain, and Sweden were among the most efficient countries, with the most efficient being Finland and The Netherlands. By contrast, the most significant decline in efficiency was observed in Portugal. The situation was slightly different in the countries that joined the EU at a later stage, where most countries had declined technical efficiency. This suggests that extensive spending was not translated into the quality of the population’s health quickly enough or sufficiently efficiently in these countries. Croatia, Estonia, and Latvia achieved the highest efficiencies. The Czech Republic and Lithuania were among the least efficient countries. However, they were more efficient than the weakest countries that joined the EU before 2004. In general, it can be concluded that the efficiency of the countries that joined the EU later was not so significant compared to the original EU countries. However, there is a significant difference in efficiency decline.In this summary table, it can be observed that the efficiency values of the countries that joined the EU in recent years were on average (0.8525) higher than the averages of countries that joined the EU before 2004 (0.7799). Estonia, Croatia, Sweden, Spain, Greece, Estonia, Croatia, Sweden, Spain, and Greece had the highest average efficiency values over the reporting period, but other countries were not far behind.Standard deviations indicated low variability in technical efficiency values for most countries surveyed. Many facts can be established on the basis of the presented results of measuring the changes in productivity in EU countries over time. First, productivity growth over the whole period was observed only in Sweden. On the contrary, some countries, such as Estonia and Romania, experienced a steady decline in productivity. For countries that joined the EU before 2004, the geometric mean change in productivity (MPI) was higher throughout the reporting period. Another interesting fact is the trend of higher values of geometric mean changes caused by technology efficiency change (TechCh) in countries that joined the EU later. Conversely, countries that joined the EU before 2004 had higher average changes in efficiency caused by changes in technical efficiency (EffCh). In general, in addition to 2015/2016, there was a decline in EU countries’ productivity in financing health systems during the reporting period.To better understand the context, we examined productivity changes directly between 2019 and 2013, i.e., we omitted 2014–2018 in the calculations. The resulting MPI values, technical efficiency changes, and changes in technology efficiency are shown in MPI values were used to arrange countries in descending order. The highest increases in MPI were recorded in Sweden (1.2805), Finland (1.2177), and Germany (1.1227). The most significant decreases in productivity were recorded in Romania (0.6883), Latvia (0.7738), and Bulgaria (0.7823). The turning point in the increase/decrease in productivity was between Italy and Greece, where MPI values decreased. Our previous findings are corroborated by the fact that the decline in productivity in countries that joined the EU after 2004 was mainly driven by a significant reduction in technology efficiency (TechCh). In countries such as Slovakia, Hungary, and Poland, the decline in the efficiency of technology was limited by the increase in technical efficiency. To further investigate the effects of TechCh and EffCh, we decided to push up clusters of countries and look for common causes of this situation. Using the so-called elbow method, we identified the possibility to create four clusters with the countries we surveyed. A separate cluster consisted of Finland, which achieved the highest productivity gains due to the increase in brick efficiency. This was followed by a cluster of five countries belonging to the original EU countries and those that joined the EU in 2004. There were also four countries in the third cluster. Lastly, the fourth cluster, where up to 14 countries can be found, was extensive. In these countries, the change in EffCh was around 1.0. In most cases, productivity dropped due to decreased technical efficiency. Using the so-called elbow method, we identified the possibility to create four clusters with the countries we surveyed. The first cluster consisted of Sweden, while the second cluster of countries with lower technology efficiency changes consisted of seven countries. It should be noted that some of them achieved a negative change, i.e., a decrease in TechCh values. It is in this cluster that only indigenous EU countries were still represented. Another cluster comprised a mix of countries that joined the EU before and after 2004. Only countries that joined the EU in 2004 or later were in the last cluster. The countries of this cluster experienced the highest decrease in TechCh.Both previous research and our research have confirmed that cohesion policy’s convergence objectives in healthcare do not fulfill their potential to a sufficient degree . We takeOn the contrary, technical efficiency did not change significantly in the countries surveyed and showed a growing trend rather than declining. This can be due to the excellent quality of the services provided by the financial sources. We are, therefore, faced with a significant problem, particularly evident in the countries that joined the EU later. The problem is that we have quality pearls, but we do not have or do not use capital efficiently enough for capital investments.Countries such as Germany and Austria, which achieved relatively low efficiencies, cannot be considered countries with poor healthcare quality. In their case, inputs are only transferred to a lesser extent to the outputs. Our research did not include, for example, patient satisfaction and the quality of healthcare facilities, as these indicators are relatively difficult to measure and unavailable for all countries on a unified basis.Efficiencies in countries that joined the EU in 2004 may also be due to the outflow of medical staff in better conditions abroad. Some countries are still unable to compete with the more developed countries, especially regarding salaries and working conditions. However, as other authors recommended, these factors need to be analyzed at the national level . We mustOn the basis of the results and discussion, many conclusions can be drawn, which may also be recommendations for policymakers.As for efficiency in the countries that joined the EU in 2004 and later declined, it is necessary to rethink cohesion policy and the direction of inventiveness from the structural funds to those countries. These countries are still actual beneficiaries of structural funds resources. Investments should be aimed at maintaining a high-quality workforce in those countries, which firmly holds the efficiency of these countries at acceptable levels. Encourage investment in buildings, infrastructure, and equipment, as these countries are still lagging behind western European countries in this area. These efforts can also be seen in the current recovery plans, where some countries want to have completely new hospitals and medical facilities through structured funds.Furthermore, the various post-pandemic recovery support plans need to be geared toward specific regions. Our research has also shown still significant differences in countries, which may also result from different ways health systems are financed even in last years, not only in the years we researched. Countries should not be allowed to use relatively costly resources in an address manner.This study dealt with some limitations. The first and most relevant was the absence of human capital input. Many studies use the labor input  but we were unable to find consistent data for this indicator on the EU level. We can only exclude some countries or periods, which would, in our opinion, lead to inconsistent conclusions on the EU policies level. Many countries must deal with low numbers of medical staff; hence, excluding this indicator as input did not excessively harm the results.It can be observed that health spending in countries grew relatively sharply every year. This guides our recommendation to improve the quality of datasets, which are often incomplete and sometimes lack countrywide data. This prevents a more detailed analysis of the causes of the inequalities that have arisen. Similarly, we could analyze how efficiently they are handled with the structural funds. Future research could also include the measurement of scale changes in both CRS and VRS models using individual clusters, which could help describe the differences more thoroughly. This could help both the scientific sphere and the makers of standard cohesion policies.


Infographics have become an increasingly popular method to present research findings and increase the attention research receives. As many scientific journals now use infographics to boost the visibility and uptake of the research they publish, infographics have become an important tool for medical education. It is unknown whether such infographics convey the key characteristics that are needed to make useful interpretations of the data such as an adequate description of the study population, interventions, comparators and outcomes; methodological limitations; and numerical estimates of benefits and harms. This study described whether infographics published in peer-reviewed health and medical research journals contain key characteristics that are needed to make useful interpretations of clinical research.In this cross-sectional study, we identified peer-reviewed journals listed in the top quintile of 35 unique fields of medicine and health research listed in the Journal Citation Reports database. Two researchers screened journals for the presence of infographics. We defined an infographic as a graphical visual representation of research findings. We extracted data from a sample of two of the most recent infographics from each journal. Outcomes were the proportion of infographics that reported key characteristics such as study population, interventions, comparators and outcomes, benefits, harms, effect estimates with measures of precision, between-group differences and conflicts of interest; acknowledged risk of bias, certainty of evidence and study limitations; and based their conclusions on the study’s primary outcome.We included 129 infographics from 69 journals. Most infographics described the population (81%), intervention (96%), comparator (91%) and outcomes (94%), but fewer contained enough information on the population (26%), intervention (45%), comparator (20%) and outcomes (55%) for those components of the study to be understood without referring to the main paper. Risk of bias was acknowledged in only 2% of infographics, and none of the 69 studies that had declared a conflict of interest disclosed it in the infographics.Most infographics do not report sufficient information to allow readers to interpret study findings, including the study characteristics, results, and sources of bias. Our results can inform initiatives to improve the quality of the information presented in infographics.The online version contains supplementary material available at 10.1186/s12909-022-03404-9. They generally use images and data visualisations  to foster knowledge translation through increasing attention, comprehension and recall; and are considered aesthetically appealing and useful to communicate research findings among peers, the media and the public , 2. InfoThere is limited guidance on how to appropriately report research findings within infographics. To the best of our knowledge, only one guideline has been developed to inform the design of infographics (7-item GRAPHIC guidelines) . HoweverInfographics that summarise the results of clinical research  could improve knowledge translation and increase uptake of new evidence in clinical practice. However, it is unknown whether such infographics convey the key characteristics that are needed to make useful interpretations of the data. Such characteristics include but are not limited to: an adequate description of the study population, interventions, comparators and outcomes; methodological limitations; and numerical estimates of benefits and harms.describe the study population, interventions, comparators and outcomes (and do so well enough for the infographic to be understood independently of the main paper);report the benefits and harms of an intervention, effect estimates with measures of precision, between-group differences, the relationship of the effect estimates to known thresholds of clinical importance, and clear summary statistics for dichotomous outcomes;acknowledge risk of bias, the certainty of evidence (if applicable), and study limitations;acknowledge risk of bias/certainty of evidence in their conclusion, base conclusions on the correct populations, interventions or outcomes (i.e. no issue with indirectness), and base conclusions on the primary outcome; andreport conflicts of interest.There is yet to be a systematic assessment of the reporting of key research characteristics in infographics. The aim of this study was to describe the proportion of infographics of clinical research that:We reported this cross-sectional study following the STROBE guidelines . We defiquintile based on journal impact factor using data from the 2019 journal impact factor index were selected .Step 1: One researcher selected the 35 unique fields related to health and medical research from the Journal Citation Reports database  for infographics. This was done by searching terms synonymous with “infographic” in the journal’s search box , and by manually checking articles published ahead of print and in all issues from August 2018 to October 2020. We did not consider issues designated to conference proceedings, special issues, or supplements. We also searched for special sections within the journal’s website  for the analyses.Study design ;Whether the infographic described the study population, interventions, comparators, and outcomes, and whether the description was adequate for the infographic to be understood independently from the article. We considered these descriptions to be adequate when investigators did not need to check the original study report to understand key details. For example, the population needed to include some demographic characteristics . The intervention and comparison needed to include some information on the intervention parameters . The outcome needed to be specific about the measure used .Whether the infographic reported benefits and harms , effect estimates and measures of precision, and between-group differences; and presented effect estimates in relation to known thresholds of clinical importance, and a clear summary statistic for dichotomous outcomes , number needed to treat (NTT), or charts commonly used to communicate absolute risk ) .Whether the infographic acknowledged risk of bias, the certainty of evidence (if applicable), and study limitations.Whether the infographic had a conclusion, acknowledged limitations/risk of bias/certainty of evidence in their conclusion, based their conclusions on the correct populations, interventions or outcomes (i.e. no issue with indirectness), and based their conclusions on the primary outcome (i.e. no ‘spin’) .Whether the infographic reported conflicts of interest.Wherever “not applicable” was used, that infographic was not counted in the denominator for that item. The following data were extracted:Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.Within the 35 unique fields related to medicine and health research listed in the Journal Citation Reports database, we identified 597 journals that were listed in the top quintile of these fields. Of these, we identified 69 journals from 18 fields that contained infographics that met our eligibility criteria , followed by Cardiac & Cardiovascular Systems and Surgery , Gastroenterology & Hepatology and Urology & Nephrology . The other 14 fields contributed fewer journals, ranging from 1 to 5 per field , intervention (96%), comparator (91%) and outcomes (94%) of the study. However, fewer infographics contained enough information on the population (26%), intervention (45%), comparator (50%) and outcomes (55%) for these components of the study to be understood without referring to the main paper.Fewer infographics reported harms (26%) compared to benefits (84%). Only 67% and 22% reported an effect estimate and measures of imprecision around an effect estimate, respectively. Risk of bias was acknowledged in only 2% of infographics, and certainty of evidence was only mentioned by 10% of infographics of systematic reviews. Of the 63 infographics that contained a conclusion, most (92%) did not have issues with indirectness, and most were based on findings from the primary outcome (86%). Only 5% of these conclusions considered risk of bias. None of the 69 studies that declared a conflict of interest disclosed it in the infographics.There were some differences in some of the outcomes when data were stratified by study design. These data are displayed in Table Infographics typically presented information on patients, interventions, comparators and outcomes. However, fewer reported sufficient information to allow readers to understand them without reference to the main paper. Critical aspects of results such as reporting measures of imprecision around the effect estimate or clearly labelling the statistic used to summarise dichotomous outcomes were seldom reported. Sources of bias, certainty of evidence, and study limitations were rarely acknowledged. No infographic disclosed conflicts of interest even though more than half of the original studies in our sample had originally disclosed at least one source of conflict of interest.Infographics have been shown to increase measures of research attention such as engagement on social media and Altmetric scores , 12, 13,Most infographics that had a conclusion reported findings for the primary outcome of the study (86%). In other words, only 14% of infographics were considered to have some form of spin. The proportion of spin in our sample was much lower than spin in other samples (26% to 85%) depending on the study design . This coNone of the infographics included in our study disclosed conflicts of interest, although more than half of the studies from our sample had some form of conflict of interest declared. Conflicts of interest are an important source of bias in clinical research , so thisAnyone creating an infographic could consider the items that were assessed in our study and ensure that any items that are relevant to the study design being summarised are included in the infographic. Reporting checklists for infographics for individual study designs would simplify that process and our group has commenced preparing these. In proposing such reporting checklists, we acknowledge that some items may be more or less relevant depending on the purpose of the infographic, such as notifying the general public about the existence of a new study versus informing clinicians about the evidence generated by that study. Depending on the purpose and on the format in which the infographic will be distributed , all relevant items may not be incorporated in every infographic but, in general, the more items on the checklist that are incorporated in the infographic the more informative it will be.One item that we choose to highlight here is risk of bias. This was achieved by only 2% of the infographics we analysed, but it can be succinctly summarised, as shown in the infographic for the study by van de Leemkolk et al. .The infographics that satisfied more relevant items than most infographics were the ones produced by JAMA , 21. ApaOur team has recently completed a survey (submitted for publication – data not available yet) conducted with consumers of infographics summarising health or medical research  and found that 41% used infographics as a substitute for the full-text at least half of the time, 55% thought infographics should be detailed enough so they do not have to read the full text, and 64% viewed infographics as tools to reduce the time burden of reading the full text.n = 597) across 35 research fields. Our definition of infographics was broad, which allowed us to capture a broad range of different types of infographics . A potential limitation of this approach is that different types of infographics might have been lumped together. However it is worth mentioning that the terms currently used by different journals to describe the various types of infographics they produce is not standardised. Future research could develop a classification system for different types of infographics and repeat our analysis.Searching for infographics is challenging because nomenclatures vary by journal  and journal policies are unclear about whether infographics are routinely produced for all published papers. Furthermore, there are currently thousands of medical journals, so searching for infographics across all journals is not feasible. Most infographics cannot be located by searching indexing databases like PubMed. To overcome these limitations, we designed a comprehensive multi-step search that allowed us to search a large number of journals (We limited searches to journals in the top quintile of each research field, which could be considered a limitation. Although such journals typically enforce use of reporting checklists in their published papers , the infMost infographics do not report sufficient information to allow readers interpret study findings, including the study characteristics, results, and sources of bias. Our results can inform initiatives to improve the quality of the information presented in infographics. While infographics could be made more informative, clinicians and other users of clinical research should not only rely on infographics for decision-making. These decisions should only be made after reading the full-text publication.Additional file 1: Appendix A. Fields ofmedicine and health research. Appendix B. List ofincluded journals and number of infographics included from each journal. Appendix C. Coding instructions.


Cardiac rehabilitation is an individualized outpatient program of physical exercises and medical education designed to accelerate recovery and improve health status in heart disease patients. In this study, we aimed for assessment of patients’ perception of the involvement of technology and remote monitoring devices in cardiac recovery. During the Living Lab Phase of the Virtual Coaching Activities for Rehabilitation in Elderly (vCare) project, we evaluated eleven patients (five heart failure patients and six ischemic heart disease patients). Patient admission in the UMFCD cardiology clinical department served as a shared inclusion criterion for both study groups. In addition, the presence of II or III heart failure NYHA stage status was considered an inclusion criterion for the heart failure study group and patients diagnosed with ischemic heart disease for the second one. We conducted a system usability survey to assess the patients’ perception of the system’s technical and medical functions. The survey had excellent preliminary results in the heart failure study group and good results in the ischemic heart disease group. The limited access of patients to cardiac rehabilitation in Romania has led to increased interest and motivation in this study. The final version of the product is designed to adapt to patient needs and necessities; therefore, patient perception is necessary.  Cardiac rehabilitation is an individualized outpatient program of physical exercises and medical education designed to accelerate recovery and improve health status in heart disease patients . A cardiac rehabilitation program is structured with three stages: the acute phase , the subacute phase (center-based), and outpatient therapy (home-based). Unfortunately, the second and third phases of a cardiac rehabilitation program face many barriers of implementation with an overall participation of under 50%, despite international guidelines prescribing recommendations ,3. The main factors leading to low adherence of patients to cardiac rehabilitation can be classified into person-related  and aspect-related  . The 21st century is characterized by a longer life expectancy as the number of people aged over 65 years is increasing. It is expected that by 2030 the percentage of people over 65 years old will grow from 17.4% to 25.6%. The ageing of the population translates into a high prevalence of cardiac diseases and loss of cognitive and physical autonomy. It represents a medical and social issue, which should be addressed sooner rather than later in order to secure the future well-being of this fragile group of patients [Heart disease has been the leading cause of death worldwide for the past 20 years, and the number is steadily rising, from 2 million deaths per year in 2000 to nearly 9 million deaths in 2019 . Clinical and epidemiological studies developed short- and long-term risk prediction algorithms for identifying patients at high-risk of developing cardiovascular disease. These patients are the main beneficiaries of reducing cardiovascular risk factors through primary prevention programs .Globally, the costs of hospitalization, drug or interventional treatment, and monitoring visits of cardiovascular disease patients are huge and increasing alongside patient life expectancy. As an example, in the United States of America in 2010, medical costs were approximately USD 863 billion, with an estimated increase of up to USD 1 trillion by the year 2030 . Home-based cardiac rehabilitation is a way for patients to enjoy all the benefits of clinician-supervised cardiac recovery from the comfort of their homes, under medical supervision. In addition, virtual communication, the development of digital modules, and the monitoring of vital signs are all possible with wearable devices . Although it represented an alternative for many years, there were no premises of the rapid implementation of online cardiac rehabilitation. The COVID-19 pandemic raised the opportunity for rapid integration among the services offered by healthcare providers. These services were translated into telemedicine and the use of remote assistance devices . The switch from classic to online cardiac rehabilitation means waiting time reduction, flexible participation schedules, no need for the patient to travel, and benefits in terms of morbidity, mortality, and quality of life ,13.Beyond the COVID-19 pandemic, adopting technology as the standard practice looks promising and might be the best alternative for specific cardiac patients in terms of efficiency in secondary prevention management of cardiovascular disease . Although ambulatory cardiac rehabilitation programs prove to be efficient, they are rarely prescribed to patients due to uneven access to health services .Telerehabilitation could be the answer to this problem. Current literature supports the use of digital technology to address health inequalities and create compelling and constant changes in medical services offered. The ability of telerehabilitation to overcome temporal and spatial barriers is highlighted even more in the current COVID-19 pandemic situation .Romania and many other European Countries face health inequalities, as significant numbers of patients are not admitted to rehabilitation programs, mostly due to lack of financial resources and lack of reimbursement. A recent study investigating key barriers in cardiac rehabilitation in East-Central Europe highlighted demographic issues as one of the most significant drawbacks for rehabilitation adherence ,18. In a successful implementation, rural communities would be the main beneficiaries of these services. Despite these limitations, the overall image is auspicious. Digital health technology could answer many of the challenges faced by on-site cardiac rehabilitation. Virtual coaches, also known as e-coaches, can vary, from classic rule-based smartphone apps that guide the patient, to more advanced machine learning algorithms, that intelligently support patient rehabilitation with automatic adaptation of clinical pathways. Virtual coaches can provide medical follow-up in the community and can reach out to demographically remote areas that are difficult to access by health care providers ,20.Apart from improving patient quality of life, the virtual coach optimizes the economics of medical and social care treatments . One example of a virtual assistant under development is the virtual coach for rehabilitation in the elderly patients developed in the vCare project. Under the funding of the European Union’s Horizon 2020 Research and Innovation Programme grant no 769807, the vCARE project has the overall objective to support the recovery of active and independent life at home . The development of the vCARE project involves twelve partners from seven European countries with a multidisciplinary consortium of researchers, healthcare providers, and industry experts. The virtual coach must be used in different national systems for various pathologies and rehabilitation settings to ensure a multi-site and multi-setting approach .The chosen pathologies are stroke, Parkinson’s disease, heart failure, and ischemic heart disease, divided among the healthcare providers participating in the project .One of the project sub-goals is evaluating patient perception of the virtual assistant as a constant in their lives, an essential parameter for patient’s motivation and further participation in the system testing, and a vital evaluation of the public receptivity of virtual coaches . Recent article reviews of patient perception of telemedicine state a 95–100% patient satisfaction rate compared with classic medical appointments .Decreased time travel, the presence of administrative support, reliable and easy-to-use technology, and adequate reimbursement of medical services are a few of the positive arguments patients cite in favor of telemedicine . In the center of the active development of telemedicine and remote medical assistance, one should see patient perception as essential for technology’s future growth and implementation ,25. Telemedicine represents a healthy mix of medical care and technology. Therefore, it should be instituted in all urban medical centers to support rural, isolated areas, acting as a bridge for broader accessibility of patients to medical services and a good strategy in the second prevention management of cardiovascular disease and more . The main purpose of this study was to assess patient perceptions of virtual assistant use in the process of remote cardiac recovery. Given the novelty of the technology, we simultaneously analyzed the profile of the patient who would benefit from this service, the degree of technology used at home, and the desire in accessing such service. We consider all these key parameters when evaluating the opportunity to launch a product on the medical market.In the active development of innovative healthcare technology, both medical staff and patients should evaluate its acceptability and usability throughout the entire project.The Living Lab Phase of the vCARE project was oriented towards three significant parameters: utility, efficacy, and adaptability, to be able to quantify the adoptability of the virtual solution. This phase’s primary goal was to validate the functionality of the digital platform, its interactions with patients, and its adaptive capacities according to individual patient opinion. The Heart Failure (HF) and Ischemic Heart Disease (IHD) Living Lab Phase took place at the Department of Cardiology of “Carol Davila” University of Medicine and Pharmacy (UMFCD) in Bucharest, Romania for a period of 11 months.The Living Lab field of study consisted in testing the physical therapy and risk factor modification pathways pre-established by the healthcare providers according to the needs and necessities of the patients, using the virtual assistant, with constant monitoring of specific key performance indicators .Five patients were enrolled in the heart failure study group and six in the ischemic heart disease study group. Patient admission in the UMFCD cardiology clinical department served as a shared inclusion criterion. In addition, the presence of a II or III Heart Failure New York Heart Association Functional Classification (NYHA) Stage status was considered an inclusion criterion for the heart failure study group. We selected the patients with stable or unstable angina pectoris, myocardial infarction, ischemic heart disease with medical therapy, history of percutaneous coronary intervention, or coronary bypass surgery for inclusion in the ischemic heart disease study group. The exclusion criteria were age (which had to be over 18 years old) and patients with movement disabilities.Patient participation in the study was voluntary. All patients received information about the study beforehand. We could not collect patient data completely anonymously, so the pseudo anonymization alternative was chosen. Every participant in the study received an identifier, which separated the personal information from the study’s data collection. Every participant was informed about their right to privacy and the private storage and use of their data. The study conducted did not present any potential physical, social, psychological, or legal harm to patients. Every patient signed to confirm informed consent.The initial assessment of patients consisted of an analysis of demographic indices, cardiovascular risk factors, personal medical history, current cardiovascular health status, and level of home technology use.Each patient in the two study groups followed a cardiac rehabilitation program with 2–3 times a week, for a total of 6 weeks. During each session, they performed specific pathways of physical therapy and risk factor modification using virtual assistance with all wearable devices at their disposal: a blood pressure monitor, smart bracelet, environmental sensors, body-tracking sensors, and an intelligent weight scale. At the end of each patient’s program, we conducted a system usability survey to assess the patient’s perception of the system’s technical and medical functions . The System Usability Scale (SUS) is a measuring tool initially created in 1986, which allows users to evaluate a wide variety of technological products and services. Already an industry-standard tool, the SUS is an efficient and easy-to-use scale, reliable for small sample sizes, and is able to differentiate a usable system from an unusable one.The scale has a 10-item design; each participant scores every item with one of five responses from “strongly agree” to “strongly disagree”. Then, the responses are converted to numbers, added, and multiplied by 2.5 to reach the final scores of 0–40 to 0–100 . Relying on research, SUS scores over 68 are above average, and anything less than 68 is below average. However, the correct way of interpreting the results would be to normalize the score to produce a percentile ranking . The HF study group had five patients (four men and one woman), and the IHD group had six patients (five men and one woman). The small sample size of our study group was due to the COVID-19 pandemic, and also to the Living Lab pilot test conditions. It was impossible to perform cardiac rehabilitation in safe conditions with more than one patient per session. The primary purpose of this phase was to test the product and effectively integrate all components. The number of patients involved in this stage was desired to be low, precisely because of the possible errors that could occur with the first use of the system. Given the excellent results obtained, it was decided to analyze them, considering the possibility of conforming or refuting them in the subsequent phases of the project currently underway.Microsoft Excel was used to collect and analyze demographic data, cardiovascular risk factors, and the use of home technology. For the system usability evaluation, the SUS results were entered into an SPSS database for analysis, with a representative creation of the results in the form of box plot figures.We believe the results obtained are relevant despite the small patient study groups, considering the measuring tool used. The SUS is a reliable tool designed to be used for small sample sizes, and to be able to differentiate a usable system from an unusable one. Study participants’ demographic characteristics and cardiovascular risk factors are presented in At study initiation, an evaluation of the technology used at home by the enrolled patients was also performed. Ten items were evaluated, nine of which can be found in The System Usability Survey for the Heart Failure study group had excellent preliminary results: five out of five patients reached good or excellent scores .The results were weaker in the Ischemic Heart Disease group, with only three out of six patients reaching good or excellent scores. However, the results were over 63, which means that they exceeded the average .Along with the significant results obtained from evaluating patients’ perceptions, their interest and motivation were the key elements that crowned the success of the Living Lab Phase of the vCARE project. Some of their objective opinions can be found in One of the key parameters in the successful implementation of medical technology is the perception of the user, who in our case, is the patient. Not only is technology research expensive, but so is its implementation, especially in countries with poor infrastructure. The unfortunate context of the new coronavirus pandemic we are currently experiencing has shown us the importance of technology as a key medical resource, able to streamline the work of healthcare professionals, reduce costs, and expand access to health care in less-favored areas .In addition to testing the product we are currently developing, we had as a sub-goal, the evaluation of patients’ perceptions of the long-term use of technology in cardiac recovery, a medical area as disadvantaged as it is essential in the secondary prevention of cardiovascular disease.One of the most important findings we encountered is the cardiac patients’ motivations and interest in participating in the study when given the opportunity of cardiac rehabilitation. We believe the reason for this positive attitude is the low availability of cardiac rehabilitation programs in Romania. In a recent study, Nabutovsky I. et al., 2020, evaluated through a cross-sectional study, the Israeli cardiovascular patients perception, attitude, and behavioral intention toward remote digital cardiac rehabilitation. The study concluded with positive results, so that more than 80% of the participants were interested in telerehabilitation services if they had been made available to them. These results were not influenced by demographic factors such as age, level of technology used, or gender .Although pleased with our study results, they are only preliminary; we need further studies to certify these outcomes. At first, we thought that the favorable results were based on the young age of the enrolled patients; we therefore analyzed the possibility that the scores could differ in older patients. The SUS results in the ischemic heart disease group slightly contradicted this theory because the lower mean age was not associated with better results. This fact indicates that no matter the age, even little knowledge of technology can come in handy when interacting with a virtual coach. We correlated our system’s usability scale results with the few other results available in the literature. Joao Balsa, Isa Felix et al., 2020, conducted a similar study of an intelligent virtual assistant promoting behavior change and self-care in older people with type 2 diabetes; they obtained an aggregated mean SUS score of 73.75, corresponding to borderline excellent .Haggerty T., Brabson L. et al., 2021, used the System Usability Scale in a two-cycle approach for testing a weight management tool, mWRAPPED. They obtained the first cycle average patient score of 76.5 and an after-revisal average score of 80.5. The study concluded the initial usability of the obesity tool for primary care in patients .The evaluation of the opinion of both medical staff and patients is relevant in many studies. Maciej Banach, Dan Gaita et al., 2020, evaluated medical staff opinion through anonymous surveys in order to highlight implementation barriers of the ADA/EASD (American Diabetes Association/European Association for the Study of Diabetes) guidelines and the optimal measures to overcome them . A pilot study conducted by Brewer C. et al., 2017, assessed the virtual word technology of the feasibility and acceptability of cardiac rehabilitation programs. The small study group  were unanimously satisfied by the method novelty, accessibility, and social connectivity. They reported important improvement in health knowledge and habits secondary to following remote cardiac rehabilitation .Acceptability among patients also emerges from the study results of Banner D. et al., 2015, which strongly support home implementation of virtual cardiac rehabilitation programs for patients recovering from a cardiac event. They highlight benefits such as increased accessibility in disadvantaged environments and a cost-effective method of secondary prevention of cardiovascular disease. As a future certification strategy of cardiac rehabilitation long-term benefits, authors consider mandatory the multiple environmental evaluation (from developed to underdeveloped countries) on more medical complex patient groups .According to our results and those valid in the current literature, the perception evaluated in enrolled patients is statistically significant. The fact that the cardiovascular patient is eager to use technology as a mean of remote cardiac rehabilitation makes development in this area both medically and economically profitable in the future. Given the unfavorable epidemiological context of cardiovascular disease, there should be found effective and inexpensive ways to provide patients with adequate secondary prevention therapies and to not further suffocate the overburdened medical system in the future.The virtual assistant we are currently developing has the underlying base of a machine learning approach. The final version of the product must adapt to patient needs and necessities to provide efficient secondary prevention management of their cardiovascular disease. Imagined as the locum tenens of cardiac rehabilitation medical staff, vCARE is the human appearance of post-discharge continued medical care for patients. The following steps for the vCARE project are to enroll more patients to test all system functionalities, such as pharmacological intervention, and emotional and social rehabilitation. After the Living Lab, the Pilot Test phase initiates with moving the intelligent system to patient home environments. Finally, we shall compare these collected data with a similar group of patients following conventional cardiac rehabilitation. We identified several limitations to our results. First, the study sample was small, with a prevalence of male patients, so our population might not fully characterize the average Romanian patient. However, the evaluated patient sample provided efficient feedback with a usable system result. Additionally, the presence of the medical staff might have facilitated, in some cases, the use of the system by the patients, further testing being needed in the home environment to certify the actual capacity of the patient in using the techniques and to reevaluate their perception.


The increasing presence of documented and undocumented migrants increases the commitment of the Italian National Health Service to their health needs, following its founding principle of equity. In particular, chronic diseases, such as diabetes, represent a crucial area where patients’ health is affected by their adherence to care pathways, for which the recent literature has reported alarming low levels. In the case of migrants, obstacles to adherence, such as language or organizational barriers, could be overcome thanks also to charitable organizations providing healthcare services. In this study, we aimed to compare the adherence among documented and undocumented migrants who received healthcare services in Milan, Italy, either from the National Health Service (NHS) or from a charitable organization. We identified a cohort of newly taken into care diabetic patients composed of two groups: (i) documented migrants that attend the NHS; and (ii) undocumented migrants that attend a charity. Information was tracked by merging two datasets: the regional healthcare information system of Lombardy, and a unique dataset that collects data on specialistic visits and pharmaceutical prescriptions for all people visiting one of the most prominent charitable organizations in Italy. The annual diabetologist visit was used as the measure of adherence. The probability of being adherent was compared among the two groups by using a multivariate log-binomial regression model, considering a set of personal characteristics that may impact health behaviors. The cohort comprised 6429 subjects. The percentage of adherence was 52% among the documented migrants, and 74% among the undocumented. Regression results confirmed this pattern: undocumented patients have an increased probability of being adherent by 1.19 times (95% CI: 1.12 to 1.26) compared to documented ones. Our study revealed the potentiality of charitable organizations in guaranteeing continuity of care to undocumented migrants. We argue that this mechanism would benefit from central coordination by the government. The population trends of the last decades reveal a widespread phenomenon of migration that concerns all countries worldwide, with the number of international migrants estimated to be almost 281 million globally in 2020 . In ItalThe last decade has seen an exponential increase in the role played by chronic diseases within the healthcare context. Among them, diabetes has received great attention due to several significant aspects, primarily related to its magnitude and impact: the last IDF Diabetes Atlas estimates diabetes in 2021 to be responsible for 537 million diagnoses (people aged 20–79 years), 6.7 million deaths, and a total health expenditure of at least 966 billion dollars . In ItalRegarding the assistance required by diabetic patients, the concept that is vital for their health is adherence. The World Health Organization defines adherence as “the extent to which a person’s behaviour—taking medication, following a diet, and/or executing lifestyle changes—corresponds with agreed recommendations from a health care provider” . Even ifNonetheless, several concerns arise about the low levels of adherence among diabetic patients ,20. SeveIn this panorama, a strategic role could be played by charitable organizations, which have been recognized as having the potential to provide health care services at prevention, treatment, and rehabilitation levels . In ItalAll this considered, in this paper, we investigated whether migrant populations are managed differently by the NHS (documented migrants) or charitable organizations (undocumented migrants). Our findings explored whether charities could represent a strategical gatekeeper of the healthcare system for undocumented migrants, contributing to setting the steppingstone that allows them access to the universalistic NHS. We approached this issue by focusing on the concept of adherence in diabetes. In order to deepen this point, we identified a cohort of newly taken into care diabetic patients of foreign citizenship composed of two groups, which differ because they include (i) documented migrants that receive healthcare services from the NHS; and (ii) undocumented migrants that make use of healthcare services within a charitable organization. More specifically, we aimed to evaluate which factors affect adherence, intended as receiving at least one diabetologist visit during the year following the inclusion into the study. In particular, among the factors considered, we aimed to highlight the difference in adherence that exists among migrants that attend NHS services and those who do not. We focused the discussion on the possible explanations of such dynamics. Results will help to shed some light on the integration mechanisms that may enable migrants of any type to access the NHS freely.Our study is a retrospective cohort study. The study cohort was composed of two groups of diabetic patients, all with not-Italian citizenship, whose information has been tracked by using two distinct datasets. In particular, the first group was composed of documented migrants that are resident in Milan and have access to the national health service (NHS) through the regional health service of Lombardy. We shortened this to “Documented NHS” (information coming from the first dataset). For the second group (that was shortened to “Undocumented OSF), we considered all the undocumented migrants that visited a charitable organization in Milan, i.e., Opera San Francesco (OSF).OSF in one of Italy’s most prominent charitable organizations, which has guaranteed free assistance and shelter to the needy in Milan since 1959. OSF delivers medical assistance through outpatient clinics for almost all specialties, and the generous donations of its supporters allow medicines to be dispensed for free to patients according to prescriptions after each consultation. Moreover, OSF pursues a mapping effort by keeping track of all the information of all patients seen during the last decade. This wealth of data has enabled researchers to enlighten aspects of migrants’ health that are of strategical importance for the Italian healthcare system, such as risk factors and the epidemiology of diseases .The two datasets we referred to consisted of: (1) the regional healthcare information system of Lombardy, which contains data on the healthcare services provided through the NHS to the ten million inhabitants of the region over the last few decades; and (2) a dataset that collects information on all people visiting OSF, which owns data on specialistic visits since 2009 and on pharmaceutical prescriptions from June 2011 .Out of all the populations considered in the two datasets, we considered only patients with not-Italian citizenship living in Milan. Among them, for both groups, we selected diabetic patients by following the literature of case-identification algorithms based on Italian healthcare administrative databases . In the The date of the first contact with either the NHS or the OSF for diabetes was labelled “index date”, i.e., the documented date of diagnosis. In order to include into the study cohort only patients newly taken into care for diabetes, we excluded patients who had either a diagnostic code of diabetes or an antidiabetic medication in the two years before the index date. Patients were followed up from the index date to the next year (one-year follow-up). To be able to map the health episodes during the one-year follow-up, we excluded from the study people that have migrated or died during the one-year of follow-up, based on the available information: for the Documented NHS, we included only those subjects that were beneficiaries of the NHS and resident in Milan during the one-year follow-up; for the Undocumented OSF, since no information on migrations or death is available in OSF databases, we included only those subjects that made at least one access of any type in OSF during the period from the end of the follow up to the following year. Given that (i) complete information on Undocumented OSF as for both visits and drugs is available from 2012, (ii) we decided to stop our observation period at the beginning of 2020 to avoid the consideration of biases related to COVID-19, and (iii) the enrolment process reported in Other than the exposure of interest , we retrieved for the whole cohort the main demographic data: age, sex, and country of birth. We then recorded all pharmacological treatments and characterized patients by the type of therapy followed during the follow-up, classified into four levels: none , oral antidiabetic drugs only , insulin , the combination of insulin and oral antidiabetic drugs .The dependent variable for the study is the indicator of adherence: a boolean variable that has value 1 if the subject has made at least one diabetologist visit during the one-year follow-up, zero otherwise. In this way, we used one of the indicators specified in the national guidelines, of which all the practitioners are well informed . We tracWe performed a set of descriptive statistics to report the main characteristics of our cohort and the existing differences in adherence levels as depending on our set of independent variables, which we outlined graphically. We ultimately ran a log-binomial regression to enlighten the direction and magnitude of the association of the independent variables on the probability of being adherent. Stratified analyses by sex were also performed. Estimates were reported as risk ratios (RR), along with 95% confidence intervals (CI). An additional check of the results was made by performing an analysis with the use of the Propensity Score Matching between Documented NHS and Undocumented OSF, based on the same independent variables used for the main analysis. The results from the log-binomial regression performed on the matched dataset confirmed the main results, thus are not shown in the following, although available upon request. All analyses were performed with R and SAS.The cohort is composed by 6429 subjects, of which 274 belong to the group of Undocumented OSF and 6155 to the group of Documented NHS. The number of subjects enrolled by year varies between 44 and 73 (Undocumented OSF) and between 1152 and 1280 (Documented NHS). Even if the adherence seems confirmed to be higher for the Undocumented OSF, the presence of multiple differences among the groups calls for a regression to observe the adherence levels ceteris paribus.p = 0.002). Being aged over 69 years was associated to a reduction in the probability of being adherent of 8% (2% to 15%), with respect to those aged less than 40 years. As compared to people coming from Africa, those coming from America, Asia and Europe were associated to an increased probability of being adherent of 5% (0% to 10%), 5% (1% to 9%) and 6% (1% to 12%), respectively. Finally, significant results come out for the role played by the pharmaceutical treatment. The higher the complexity of the treatment, the higher the probability of being adherent. In particular, patients treated with both oral and insulin therapy were associated to an increased probability of being adherent of 31% (24% to 37%), as compared to those untreated.Regression results in The provision of healthcare services to immigrants is a crucial issue, especially when focusing on the need for chronic patients of adherence and continuity of care. The risk of inappropriateness, which is a significant theme in terms of healthcare expenditure, emerges alongside the risk of not accessing the NHS. The literature has shown that the immigration status of an individual is a major factor that negatively affects that person’s ability to seek and experience healthcare services ,34. UndoThere are mainly two streams of access to healthcare services for undocumented migrants, i.e., conventional healthcare facilities and informal systems, such as charitable organizations. On one side, several previous studies, and also a recent Italian work focusing on diabetic undocumented migrants, have reported different usage of conventional healthcare services, represented by more urgent hospital admissions, more preventable complications, and a higher recurrence in terms of access and costs to hospital services rather than drugs . On the Following this need, our study documents that, taking into account personal characteristics that may impact on healthcare behaviours , attending a charitable organization as OSF, compared to receiving healthcare services provided by the NHS, increases the probability of being adherent to the diabetologist visit prescribed annually to diabetic patients. Anecdotal evidence collected during our work suggests that what enables charities to reach this achievement is their capacity to reach undocumented immigrants with a closer and person-oriented approach that creates a strong bond between the patient and OSF—which are defined as “migrant-oriented practices”. OSF offers a holistic service composed of general practitioners, drug delivery and specialistic visits, and cultural mediators and interpreters that enable overcoming linguistic barriers. Moreover, people attending OSF may receive not only medical services but other services, such as food, social cohesion, and leisure, which contribute to decreasing their opportunity cost of attending, and is different to what happens with NHS healthcare facilities. Similar features have also been reported by another previous Italian work that documented the higher propensity of undocumented migrants to use the services offered by volunteers .Our study supports reflections on health policies directed to the care of undocumented migrants, which recall previous findings on the potential role of the NHS to tackle inequities in health . With unOur study presents strengths and limitations, mostly coming from the data used. In fact, the main characteristic of our study is that of an observational study with a retrospective approach. Consequently, we do not have the possibility of choosing the people included in the study, nor their number. This fact resulted in the sample size of Undocumented OSF being smaller than the one of Documented NHS. However, this aspect comes with all the advantages of a real-world-based study. Another challenge is represented by dealing with undocumented migrants’ data, which raises some issues related to the difficulty of tracking their stories. In our case, we assumed identifying newly taken into care diabetic cases among undocumented migrants through their first contact for diabetes received in OSF. However, those patients could have moved from other countries/cities where they had already received a diagnosis, thus being prevalent cases with different adherence characteristics. Despite this limitation, OSF provides a unique dataset with information on health services used by undocumented migrants for a considerable amount of time. This characteristic, which represents a great value added given the difficulty that research on migration faces with data availability , enabledIn this paper, we addressed the crucial topic of adherence among diabetic migrant patients, focusing on undocumented migrants. Our results show that taking into account personal characteristics that may impact healthcare behaviours, receiving healthcare services within a charitable organization rather than from the NHS increases the probability of being adherent by 1.19 times (95% CI: 1.12 to 1.26). On one side, this result echoes with previous studies that have encouraged to value the migrant-oriented practices adopted by charities, which make healthcare services more sensitive to cultural specificities. On the other side, this result enables a quantification of the potential impact of charitable organizations, and thus strengthens the need for central coordination by the government.


Healthcare workers need to be at work 24 h a day to ensure continuity of care in hospitals. However, shift work - particularly night shifts - can have negative acute and long-term effects on health and productivity due to disturbances in the circadian rhythm. Shift work is also associated with unhealthy lifestyle behaviors such as poor sleep hygiene and diet. The PerfectFit@Night intervention aims to improve sleep and recovery, and reduce fatigue, and therewith contribute to sustainable employability of healthcare workers. The current study describes the intervention and the evaluation and implementation.The study population will consist of healthcare workers, nurses and physicians, with night shifts in a large Dutch academic hospital. The intervention consists of individual and environmental intervention elements: i) an e-learning for healthcare workers to increase knowledge and awareness on a healthy lifestyle during night shifts, ii) a powernap bed to take powernaps during night shifts, iii) the availability of healthy food at the department during night shifts, iv) a workshop on healthy rostering at the level of the department, and v) individual sleep coaching among the high risk group. In a longitudinal prospective study, data will be collected 1 month before the start of the intervention, in the week before the start of the intervention, and three and 6 months after the start of the intervention. The primary outcomes are sleep, fatigue, and need for recovery. The implementation process will be evaluated using the framework of Steckler and Linnan. Cost-benefit analyses from the employers perspective will be conducted to understand the possible financial consequences or benefits of the implementation of PerfectFit@Night.The feasibility and effectiveness of this workplace health promotion program will be investigated by means of an effect, process and economic evaluation. If proven effective, PerfectFit@Night can be implemented on a larger scale within the healthcare sector.NL9224. Registered 17 January 2021.Netherlands Trial Register trial number  To ensure continuity of care in hospitals, healthcare workers need to be at work 24 h a day. Consequently, the healthcare sector has one of the highest proportions of night workers. In the Netherlands, one out of five workers in the healthcare sector works night shifts . HoweverShift work is also associated with unhealthy behaviors, such as poor sleep hygiene , and chaBecause of the high proportion of healthcare workers with night work, interventions specifically aimed at this group of workers are needed. Especially since it is not possible to avoid night shifts in the healthcare sector. Recent reviews of health promotion interventions among night workers conclude that the effectiveness of these interventions is inconclusive and based on heterogeneous interventions, with some interventions showing modest positive effects –21. The To contribute to an improvement of health and sustainable employability among night workers in the healthcare sector, the PerfectFit@Night intervention is developed to improve sleep and recovery, and to reduce fatigue. We will evaluate the effectiveness of the intervention on sleep, fatigue, and recovery, as well as the implementation. The current paper describes the PerfectFit@Night intervention and the design of the effect, process, and economic evaluation.The PerfectFit@Night intervention is developed by a needs assessment combining existing literature with a participatory approach to ensure that individuals’ needs and preferences, such as individual characteristics  and the social and physical context , are also taken into account.Based on the needs assessment, the following program objectives emerged: i) to increase the knowledge and awareness on working healthy during night shifts, ii) to accept taking powernaps and actually take powernaps during night shifts, iii) to have insight in and availability of healthy food/snacks during night shifts, iv) to have knowledge on healthy rostering, and, additionally, v) to provide sleep coaching for night workers with existing sleep problems and fatigue.Based on the needs assessment and program objectives, the intervention consists of the following individual and environmental elements see Fig. : i) an ePerfectFit@Night consists of individual and environmental intervention elements see Fig. . This seTo increase knowledge and awareness on working healthy during night shifts, an interactive e-learning is developed for healthcare workers with night shifts. This e-learning focuses on the effects and possible risks of night work, and it proposes additional information and advice on how to decrease these risks. The e-learning consists of three short modules of approximately ten to fifteen minutes each, of which each on a specific topic, namely the impact of night work on health, sleep and powernaps, and nutrition and physical activity. The e-learning concludes with a test for self-assessment. The night workers receive credits  to motivate them to start and complete the e-learning.In line with the ASE model, the e-learning targets i) beliefs about, for example, the working mechanisms of powernaps to change attitude, ii) requirements in the social setting  to change social influence, and iii) challenging night workers to set goals and experiment to enhance self-efficacy.In order to accept and encourage healthcare workers to take a nap during night shifts, powernap beds were purchased. Following the ANGELO framework, installing a powernap bed at the department, which is placed in a quiet and suitable room , is paid by the participating departments or hospital , and it is allowed by the management to take a powernap . At the individual level, sociocultural factors are role models, social support from colleagues and the management, and social norms regarding powernaps. It is known that attitude towards and acceptance of taking naps during night shifts is challenging. Therefore, the e-learning provides information about the working mechanisms of powernaps and encourages the intention to use powernaps. Besides, examples of good practices in other departments and hospitals are given, and workers of the department are involved as ambassadors (role models).To encourage healthy nutrition, healthcare workers with night shifts should have insight in healthy nutrition, and healthy food needs to be offered by the employer. The night workers are informed about the best timing to consume specific nutritional elements through the e-learning module, but also by flyers and a webinar. This information is established in a collaboration with a dietician who specializes in chrono nutrition, based on the latest insights from research and experiences. Following the ANGELO-framework, healthy food is made available during night shifts by providing products  that is paid by the department . The heads of the departments agreed to provide and pay for the food . At the individual level, sociocultural factors that are a condition for change in diet to occur are role models (ambassadors), social support from colleagues and the management, and social norms.Having insight in healthy rostering at the level of the department is targeted by a workshop that focuses mainly on staff members, team leaders, and managers dealing with the rosters. The workshop will be tailored to the department. Even though research shows that forward rotating rosters have the best effects, the optimal roster for shift workers remains unknown. The workshop aims to motivate roster makers to have conversations with their workers about the rosters. In line with the ANGELO-framework, the workshop aims to start a conversation at the workplace about changing the rosters , and motivate to actually make changes to the rosters . Also important is the acceptance from the management and the workers that the rosters change , and a sufficient amount of workers to change the rosters . The social context with financial and human resources consequences will also be addressed.The previously described intervention elements are mainly focused on universal prevention of risks due to night work. Additionally, night workers with existing large sleep problems and fatigue are offered sessions with a sleep coach to reduce these problems (indicated prevention). Night workers that score low on subjective global sleep quality  ) and higThe intervention will be implemented throughout a period of 3 months, and thereafter may continue at the discretion of participating individuals and departments within the hospital.A prospective study design is used to investigate the effect of the PerfectFit@Night intervention on sleep, fatigue, and recovery. The intervention is implemented at different hospital departments, making randomization at individual level not possible. Randomization at the work department was not possible, because whether and when the interested departments start PerfectFit@Night is highly dependent on the developments regarding the COVID-19 pandemic. Since randomization at individual level nor work department is possible, the intervention is implemented across at least six departments over a 3 months period, whereby departments move from the control condition to the intervention condition. In this phased pre-post approach within each department there are two periods of data collection before the start of the intervention (1 month and right before the start of the intervention) and two periods of data collection thereafter (3 months and 6 months after the start of the intervention).The study has been approved by the Medical Ethical Review Committee of the Erasmus University Medical Center (Erasmus MC) and registered in the Netherlands Trial Register under trial number NL9224.The study population consists of all healthcare workers working night shifts from at least six different departments of the Erasmus University Medical Center in the Netherlands. The participants that completed the baseline questionnaires were asked to fill out a short questionnaire before the start of the intervention period, and follow-up questionnaires after three and six months after the start of the intervention .A power calculation was conducted to determine the sample size. The calculation is based on the number of night workers needed to identify a medium effect-size (at least 0.27) in sleep quality, as measured with the Bergen Shift Work Sleep Questionnaire (BSWSQ) . A previThe outcomes are assessed using a questionnaire which is sent to the night workers of the participating departments both digitally and on paper.The primary outcome measures are sleep, fatigue, and need for recovery, which are measured at all four time points.r = 0.69, which shows a good construct validity [Sleep is measured using the Bergen Shift Work Sleep Questionnaire (BSWSQ), which is developed to assess discrete sleep problems related to different work shifts and rest days in shift workers . The quevalidity .To assess fatigue, the Short Fatigue Questionnaire (SFQ)  is used,α = 0.86–0.88), construct validity, and sensitivity to change in The Netherlands [To measure the participants’ need for recovery, the Need for Recovery Scale of the Dutch Questionnaire on the Experience and Evaluation of Work (Dutch abbreviation: VBBA 2.0) is used . The Neeherlands –32.Secondary outcomes are assessed three times, at the first baseline measurement and at three and six months follow-up.Six self-constructed questions are asked regarding sleep hygiene, e.g. about taking naps during nights shifts, and taking into account what to eat and drink during and after night shifts. The items can be answered on a four-point scale ranging from “never” to “always”.Sustainable employability is measured using three items of the Work Ability Index (WAI) . The subAssessment of the dietary intake is focused on the consumption of beverages and snacks during night shifts generally over the past month. Participants are asked to indicate the amount of water, caffeinated coffee, caffeinated tea, and energy drinks they consumed, and whether they ate pre-specified snacks. The snacks are categorized, based on the revised version of the Fat List . For eacis measured subjectively using a single item of the RAND-36 Health Survey on self-perceived health status . The ansA number of covariates are measured once at baseline.Demographic characteristics such as age, gender, and household composition are collected, as well as work factors such as job title, number of years in night work, frequency of night shifts, and working hours.To assess whether de participants are morning, evening, or intermediate type persons, the night workers are asked whether they consider themselves “definitely a morning person”, “more a morning than evening person”, “more an evening than morning person”, or “definitely an evening person”. A single question regarding self-reported chronotype has shown to be in excellent agreement with chronotype assessment based on sleep times .The participants are asked to state their height and weight. BMI will be calculated by dividing weight (in kilograms) by the square of height (in meters).Physical activity is assessed by two questions regarding the amount of days a week an individual spends more than 30 moderate and high intensity active minutes in leisure time in general.Three items of the Job Content Questionnaire (JCQ) are used to assess psychological job demands. The items measure how fast, much, and hard an individual’s work is . The queAnother five items of the JCQ are used to assess autonomy at work . These iTo assess social support at work, four items of the Copenhagen Psychosocial Questionnaire (COPSOQ) are used . The iteThree items of the COPSOQ are used to assess emotional strain of the job . These iAnother three items of the COPSOQ are used to assess appreciation, recognition, and respect of the management, as well as fair treatment at work . The iteFive self-constructed items are used to get insight in the rosters of the night workers and how satisfied they are with the rostering process. Three items concern the roster process , which can be answered on a four-point scale ranging from “never” to “always”. Two items concern the questions whether complaints about the rosters are taken seriously, and whether the night worker wants to have more say in the rosters. These two items can be answered with “yes”, “no”, or “not applicable”.To be able to test for differences in outcomes between departments, information on the department the employee works at and the amount of night workers at that department are collected.Baseline characteristics of the participants and of the departments will be summarized using descriptive statistics to detect possible selection bias and lack of balance between departments. To evaluate the effectiveness of the intervention, linear mixed effects models will be applied for scale-scores, and generalized linear mixed models for dichotomous outcomes. These statistical methods take into account the repeated measures of the study design. The analyses will be adjusted for important confounders based on the individual participants and the department, and both statistical tests and confidence intervals will be calculated (with a significance level of 5%). The difference in starting point across the departments and the multilevel structure of the departments will be taken into account. In order to analyze whether the (in)effectiveness of the intervention is moderated by the implementation factors, the linear and generalized mixed models will be expanded with the process factors that emerge from the process evaluation to evaluate whether higher uptake and quality of implementation is associated with higher effect size.The implementation process will be evaluated after the intervention period by assessing the seven components of the framework of Steckler and Linnan . These aReach, dose received, and dose delivered will be assessed based on descriptive statistics of the quantitative data. Questionnaires, interviews, and logbooks will be used to study fidelity, satisfaction, and context.To help employers and managers to understand the possible financial consequences or benefits of the implementation of PerfectFit@Night, cost-benefit analyses from the employers perspective will be conducted if the intervention has been proven effective. To perform the economic analysis, three measures are included in the study.These costs are related to the development and implementation of the intervention. They also include labor costs of the intervention staff. Some intervention elements, such as the powernap beds, have already been purchased by the departments. Therefore, these elements are not taken into account in the economic evaluation.Productivity costs is measured using costs due to absenteeism (sickness) and due to presenteeism (loss of productivity at work). Absenteeism is measured using three questions regarding the sickness absence of the past month. To measure productivity, two questions of the Productivity and Disease Questionnaire (PRODIQ) are asked . These qDifferent studies on interventions to enhance health and sustainable employability of healthcare workers with night shifts have already been conducted, but research on the effectiveness of these measures is rare. The current study described the integrated intervention PerfectFit@Night, consisting of a set of elements focusing on individual behavior with respect to lifestyle and working conditions. Both the effectiveness of the intervention on sleep, fatigue, and recovery, and the implementation process will be evaluated. Cost-benefit analyses from the employers perspective will be conducted to understand the possible financial consequences or benefits of the implementation of PerfectFit@Night.The first strength of this intervention is its integrative character. The intervention is a combination of universal prevention and indicated prevention, and it targets both individuals and the work environment. Second, the evaluation of both effectiveness and implementation makes it possible to distinguish between theory and program failure.The study also has some limitations. First, the integrated nature of the intervention makes it impossible to make conclusions about the effects of each individual intervention element. Second, it depends on the willingness of the department heads to invest in the powernap beds and healthy nutrition. Third, whether and when the interested departments start the PerfectFit@Night is highly dependent on the developments regarding the COVID-19 pandemic, making randomization at the individual and work department not possible. Last, the time frame of the intervention leads to the inability to draw conclusions on the long-term effects.The PerfectFit@Night intervention aims to improve sleep, fatigue, and recovery of night workers in the healthcare sector by introducing an intervention program which combines universal and indicated prevention targeting both individuals at the workplace as well as the work environment. Both the effectiveness, implementation process, and cost-benefit of the PerfectFit@Night intervention will be evaluated.