Disease-related malnutrition is a common challenge among hospitalized patients. There seems to be a lack of an effective system to follow-up nutritional monitoring and treatment of patients at nutritional risk after risk assessment. We identify a need for a more standardized system to prevent and treat disease-related malnutrition.We aimed to develop a dietary assessment app for tablets for use in a hospital setting and to evaluate the app’s ability to measure individual intake of energy, protein, liquid, and food and beverage items among hospitalized patients for two days. We also aimed to measure patients’ experiences using the app.We have developed the MyFood app, which consists of three modules: 1) collection of information about the patient, 2) dietary assessment function, and 3) evaluation of recorded intake compared to individual needs. We used observations from digital photography of the meals, combined with partial weighing of the meal components, as a reference method to evaluate the app’s dietary assessment system for two days. Differences in the intake estimations of energy, protein, liquid, and food and beverage items between MyFood and the photograph method were analyzed on both group and individual level.Thirty-two patients hospitalized at Oslo University Hospital were included in the study. The data collection period ran from March to May 2017. About half of the patients had ≥90% agreement between MyFood and the photograph method for energy, protein, and liquid intake on both recording days. Dinner was the meal with the lowest percent agreement between methods. MyFood overestimated patients’ intake of bread and cereals and underestimated fruit consumption. Agreement between methods increased from day 1 to day 2 for bread and cereals, spreads, egg, yogurt, soup, hot dishes, and desserts. Ninety percent of participants reported that MyFood was easy to use, and 97% found the app easy to navigate.We developed the MyFood app as a tool to monitor dietary intake among hospitalized patients at nutritional risk. The recorded intake of energy, protein, and liquid using MyFood showed good agreement with the photograph method for the majority of participants. The app’s ability to estimate intake within food groups was good, except for bread and cereals which were overestimated and fruits which were underestimated. The app was well accepted among study participants and has the potential to be a dietary assessment tool for use among patients in clinical practice. Disease-related malnutrition is a common challenge in patients with chronic or severe diseases  with a pAccording to the Norwegian “National guidelines for prevention and treatment of malnutrition”  and EuroEuropean data from the nutritionDay survey indicates that dietary assessment is only performed for a small number of patients at nutritional risk and that documentation of food intake is rarely done . NorwegiIn the next decade, the need for healthcare will increase and, there will be a shortage of labor. This should be met with more effective, less people-demanding services and increased use of welfare technology . There sWe developed an app, MyFood (MinMat), for mini tablet computers as part of a decision support system to prevent and treat disease-related malnutrition. Assessment in the app is based on self-reported dietary intake where the patient (or a nurse) records consumption of food and beverages. The memory of intake, ability to estimate portion sizes, and perceptions of socially desirable responses are well-known challenges associated with self-reported dietary intake . Self-reThe aim of this study was to develop a dietary assessment app for tablets for use in a hospital setting and to evaluate the app’s ability to measure individual intake of energy, protein, liquid, and food and beverages for two days compared to photograph observations combined with partial weighing as the reference method. We also aimed to measure the patients’ experiences using the app.My Food was developed by researchers at the University of Oslo and Oslo University Hospital (OUH) and by interaction designers and developers at the University Center for Information Technology (USIT).Nurses and patients were involved in the design process. Paper sketches of MyFood were developed and explored with three nurses and three patients at the Department of Gastrointestinal Surgery at OUH, Rikshospitalet. The feedback we received was used to modify the design and content of the app before the technical development process began. A prototype of MyFood was then developed and tested by four patients and two nurses. Their feedback was used for additional modifications of MyFood before the evaluation study was performed.MyFood consisted of the following three modules:In the first module, the nurse, or other healthcare professional, recorded information about the patient. This information included: Norwegian patient registry (NPR) number, gender, date of birth, height (in centimeters), weight (in kilograms), whether the patient had a fever . The app included prompting questions . Hot dishes were recorded by selecting an icon depicting the portion consumed , protein (grams), and liquid (milliliters). Nutritional data were retrieved from an in-house data program (KBS version 7.0), based on the Norwegian food composition table , and froThe third module automatically compared dietary intake with individual requirements for energy, protein, and liquids. This module was developed by including several algorithms in the app. The algorithms estimated the patients’ daily requirements for energy, protein, and liquids and were based on recommendations from the Norwegian Directorate of Health ,27.The data flow in the app used a Web form and secure storage in “Services for sensitive data”   hosted bThe Mobile Device Management System, AirWatch, was used to control the iPads during the data collection period. If tablets disappeared, we were able to clean the disappeared tablet remotely and make it impossible to use until reopened via AirWatch. It was possible to maintain total control of sensitive data stored on the tablets using this system.The evaluation study was performed at OUH, Rikshospitalet in the Departments of Gastrointestinal Surgery and Hematology.Inclusion criteria were:≥18 years of age≥2 days of expected stayExclusion criteria were:PregnancySpecial infection precautionsPsychiatric patientsCritically ill patientsPatients not able to read the Norwegian languageP=.05 and a calculated standardized difference of 1.0, 32 patients were included in the evaluation study.Even though MyFood is designed to be used by patients at nutritional risk, this was not an inclusion criterion as we wanted to include patients eating various amounts of food to evaluate the app. Based on a power of 0.8, a significance level of The study was performed in accordance with the Helsinki declaration and was acknowledged by the Norwegian Regional ethical committee (2016/1464), the Data protection officer at OUH, and the Chief Information Security Officer at the University of Oslo. Informed written consent was collected from all participating patients.Information about the study, including the nurses’ responsibilities in the data collection period, was sent to all nurses via e-mail. In the Department of Hematology, a ten-minute presentation by the project workers was held for the nurses during the morning meetings of the first two days of the data collection period. The responsible nurse in each department identified patients who met all of the inclusion criteria and none of the exclusion criteria.The patients were registered in the app by a nurse or by one of the project workers before breakfast. Information including the NPR number, height, weight, presence of fever, special diet, or special preferences was registered in the app before patient use.Written instructions on how to record dietary intake in MyFood were given to the patients and the nurses. Once included in the study, patients answered a form with information about education, living conditions, and level of experience with apps and tablets or smartphones.Included patients were given a tablet (iPad mini 32GB) and were asked to use MyFood for two days to record their intake of food and beverages for the breakfast, lunch, and dinner meals. If patients were not able to or did not want to record information themselves, a nurse performed the dietary recording for them. The patients were instructed to record dietary intake as soon as possible after the meals in order to get as precise recordings as possible. They were also informed both verbally and in writing to record the intake for the breakfast, lunch, and dinner meals only, not snacks or beverages consumed between the respective meals. If patients did not find exactly what they had consumed in the app, they were instructed to record something similar.After two days of using MyFood, participants were asked to answer a form regarding assumptions about comprehension, content, and perceived value and usability of the app.We used observations from digital photography of the meals combined with partial weighing of meal components as the reference method to evaluate the dietary assessment function in MyFood. The reference method is further described as the photograph method. A digital system camera (Sony A500/16-50mm PZ objective) was mounted to a removable trolley (85 cm * 50 cm) on an adjustable and pivotable tripod. The camera lens was approximately 0.6 m above the trolley. A researcher photographed the trays with the patients’ meal before and after consumption. The trays were marked with the study participant number. The numbered trays were placed on a marked area on the trolley and a 30-cm ruler was placed on the tray as a reference size. The photographs were taken at an angle of 45° to the trays so that in-depth images could be taken for more convenient meal content estimation. In addition to the observations from photographs, partial weighing of meal components was performed by the researchers. Plates, glasses, cups, and food items in separate packaging were weighed on an electronic scale before and after the meal. In cases where determining the type of food or beverage from the photographs were challenging , the patients were asked about what specific type of foods or beverages they included in the meal.Two project workers underwent practice in photographing and estimating portion sizes before the data collection, to secure a standardized method and higher level of agreement. Thirteen meals  were prepared by a third person. The meals were prepared to illustrate the portion size before consumption and after consumption, by removing all or parts of the food. The meals were photographed before and after some or all the food were removed from the tray. Glasses, plates, cups, and food items in separate packaging were weighed. Both project workers observed the photographs and calculated the weights to estimate the consumption of food and beverages. The interobserver reliability (IOR) between the two project workers was calculated to be 0.92 for energy content. The project workers’ estimations of energy content matched with the known energy content by 0.94. This was considered satisfactory, based on criteria in other studies .The food and beverage intake observed from the photographs and estimated from partly weighing in the evaluation study were compared with the intake recorded in MyFood. Observed and weighed intake was estimated separately by the two project workers, before recording the data in an in-house diet calculation system (KBS version 7.0). The project workers estimations were compared with the requirement of an IOR above 0.85 for energy, protein, and liquids in each meal. If IOR was <0.85 the calculations were repeated and recompared. In cases with obvious typing mistakes, this was corrected by the respective project worker. If the project workers had estimated different amounts, the pictures were re-evaluated, and the project workers agreed on where to adjust the estimated amounts (in grams). After corrections, the total IOR was 0.97 for energy, 0.98 for protein, and 0.98 for liquid. A final data file with estimated consumption based on the photograph method was created by averaging the estimations of the two project workers.Statistical analyses were performed using the statistical software package IBM SPSS Statistics 24. All tests were two-sided with a 5% level of significance. The data were analyzed on both group and individual level. Differences in the intake estimations of energy, protein, liquid, and food groups, between MyFood and the photograph method, were analyzed with Wilcoxon Signed Ranks Test due to nonnormally distributed variables. Multiple scatter plots of consumption of energy, protein, liquid, and selected food groups were used to illustrate the difference between the estimated intake in MyFood and from the photograph method for each individual subject. The differences between the methods were assessed in total and divided into the breakfast, lunch, and dinner meals for recording days 1 and 2 separately. To calculate omitted food items, one omission was counted as an item observed from photographs in a meal but not recorded in MyFood.The study sample consisted of 32 patients at OUH, Rikshospitalet; 18 from the Department of Gastrointestinal Surgery and 14 from the Department of Hematology. The data collection period ran from March to May 2017, and the participants were recruited continuously during the period. A flowchart describing the recruitment process is illustrated in Characteristics of the study participants are illustrated in The median intake of energy was not significantly different between the methods the first day, except for lunch where median recorded intake in MyFood was significantly higher compared to the photograph method. The second day a significantly lower median total energy intake was found in MyFood compared to the photograph method. The opposite was observed for the lunch and dinner meal.The recorded median protein intake in MyFood was significantly lower for total intake, breakfast, and lunch, compared to the photograph method on day 1. The second day the median intake of protein for breakfast was significantly lower in MyFood, compared to the photograph method. No other statistically significant differences were found for median protein intake on day 2.The median liquid intake showed relatively good agreement between the methods on the group level. Only for breakfast the first day the median recorded intake was significantly lower in MyFood compared to the photograph method.About half of the patients had ≥90% agreement in total for energy, protein, and liquid intake, somewhat lower for protein and higher for liquids both recording days. The breakfast meal had the highest proportion of participants with ≥80% agreement between the methods for all nutrient components both days, except for protein intake the first recording day. The agreement between the methods was lowest for the dinner meal.Recorded individual energy intake in MyFood and intake estimated from the photograph method are illustrated in MyFood estimated the energy consumption relatively accurate for the majority of the patients. On average for the two days, approximately 70% of the participants had less than 20% disagreement between the two methods, and approximately 50% had less than 10% disagreement . For somThe individual protein consumption recorded in MyFood, compared to the photograph method showed relatively coinciding agreement. The agreement was most coinciding on day 2 . On averThe agreement between the methods for low and medium liquid intake was good, with a tendency to increased deviations for higher intakes. This was seen on both recording days . On averThe consumption (grams) within food groups are shown in Estimated bread and cereal consumption was, in most cases, higher in MyFood compared to estimations from the photograph method . On averRecordings of spreads tended to be lower in MyFood compared to the photograph method when the intake increased . About 7The food group with the largest deviations between the methods was hot dishes. The discrepancies were highest the first day . About 3On average for the two days, about 70% of the participants had less than 20% disagreement in estimated intake of cold beverages between the two methods, and about 50% had less than 10% disagreement . No partThe number of food and beverage items recorded in MyFood and observed from photographs was calculated  and 7. TNinety percent of the participants reported that MyFood was easy to use. All but one (97%) of the participating patients found the app easy to navigate in. Most of the patients (87%) experienced to record correct amount of foods and beverages. Thirteen percent had to acquire new knowledge to use the app. Seventy-one percent reported to be become more aware of the amount of foods and beverages needed, after using MyFood.The MyFood app is developed for use among hospitalized patients at nutritional risk. According to the Norwegian Safety Program: “In Safe Hands”  all patiTo our knowledge, no similar study has been conducted in patients to allow direct comparison of results.Even though the main objective of the study was to evaluate recorded intake in MyFood on the individual level, estimated intake on the group level was analyzed to investigate if overall disagreement was present. The median total energy intake was not different between the methods the first recording day, however, a lower median intake in MyFood compared to the photograph method was found the second day. Underestimation of energy intake is often seen in validation studies of self-reporting dietary assessment tools among healthy adults  and amonMedian recorded protein intake was lower in MyFood compared to estimations from the photograph method in total, for breakfast, and for lunch recording day 1, and for breakfast recording day 2. This deviates from results in other validation studies on electronic dietary assessment tools. Raatz and coworkers  and FukuRecorded liquid intake in MyFood showed generally good agreement with the photograph method. However, recorded intake to the breakfast meal the first day was significantly lower compared to the photograph method. Several of the participants consumed both cold and hot beverages for the breakfast meal. This may have increased the chance of not remember to record all types of beverages consumed.The main aim of the present study was to evaluate MyFood’s ability to estimate the patients’ dietary intake on an individual level. This contrasts most other validation studies which focus on mean intake on group level and cross-classification, but not on absolute intakes. We evaluated MyFood compared to the photograph method for two separate days. A comparison of one-day and three-day calorie counts to estimate dietary intake by Breslow and Sorkin  suggesteThe individual drop-plots presented in the present study showed an overestimation of energy intake in MyFood, compared to the photograph method for some participants and underestimation for others. An explanation may be that both duplicate recordings and omissions of food items were observed. Five participants had duplicate recordings of some meal components the first day and one participant the second day. The largest discrepancies in energy intake between the methods on the individual level were found for the dinner meal the first day. This may be explained by inaccurate estimation of portion sizes for hot dishes. We found that several participants selected a full portion, even though not consuming a whole plate. The discrepancies in individual energy intake between the methods were wider the first recording day, and more coinciding the second day. We also observed fewer duplicate recordings in the app the second, compared to the first recording day. This may be due to a learning effect where the patients became more familiar with the app after one day of recording. A tendency to such a learning effect was observed in general in the evaluation study. A potential learning effect with the repeated use of a computerized dietary assessment tool was also found in a validation study among 41 adults with type 2 diabetes mellitus. The authors argued that the patients became more familiar with the website with repeated use .We found a tendency to lower recorded protein intake in MyFood compared to the photograph method for participants with higher protein intake. This may be explained by the omission of typical protein-rich spreads with higher intakes due to recall bias. Cheese was omitted by three participants, ham by two participants, and egg by one participant. The second recording day the recorded protein intake in MyFood was more coinciding with the estimated protein intake in the photograph method.Liquid consumption on individual level showed a tendency to increased deviations between MyFood and the photograph method among participants with a higher liquid intake. The first day the patient with the largest deviation had omitted both coffee and milk from the app recordings. The second day the intake recorded in MyFood was higher compared to the photograph method for some of the participants due to the recording of a full glass in the app, even though only consuming half or three-quarters of a full glass size.The proportion of participants having less than 20% disagreement in MyFood and the photograph method was 69% for energy intake, 66% for protein intake, and 63% for liquid intake the first day, whereas the corresponding proportions were 76% for energy, 83% for protein and 72% for liquid intake the second day. This may be due to a learning effect as discussed above.The majority of the food groups showed good agreement between MyFood and the photograph method on the group level. Good agreement in recording of food groups is consistent with findings from a validation study on a dietary assessment app for smartphones compared to repeated 24 h recall interviews  and FoodIn the present study, some of the standard portion sizes of hot dishes included in MyFood seemed to be too large compared to actual size served to the patients. A full portion size in MyFood was based on information from the hospital kitchen at OUH, Rikshospitalet on how standard portion sizes should be constituted when served. The visibility and description of what constitutes a full portion size in MyFood  may not Twelve percent of foods and beverages were omitted in MyFood the first day, and 11% the second day. The food group most often omitted both recording days was butter, margarine, and mayonnaise. When recording several types of spreads, butter and margarine are typically easy to forget. Spreads were found to be among the food items most often omitted in a validation study of a Web-based dietary assessment tool among 117 school children . The omiThe majority of the patients found MyFood easy to use and more than 70% became more aware of own nutritional needs. Electronic dietary assessment tools are generally well accepted and preferred over conventional methods among healthy subjects . Our stuThe development process of the MyFood app involved nurses and patients, which is considered an important strength. The evaluation of recorded dietary intake in MyFood was compared to observations from meal photographs. The photograph method is a validated tool for assessment of dietary intake, compared to weighed records ,49. In aA limitation of the study is that only dietary intake to breakfast, lunch, and dinner was evaluated. Energy consumption for the breakfast, lunch, and dinner meals together have been reported to account for 85% of patients’ total intake . By not Based on the results in the present evaluation study, we consider MyFood as having good potential for use as a dietary assessment tool among hospitalized patients at nutritional risk. MyFood may provide support to health care workers in their tasks related to the nutritional treatment of patients at nutritional risk. This support may contribute to prevent development of disease-related malnutrition among at-risk patients. Corrections of some of the portion sizes in the app and prompting related to use of butter/margarine and portion size of dinner may increase the accuracy of the app further. An evaluation study among other patient groups may be valuable to amplify the potential for use of MyFood in the hospital setting.We have developed an app for tablets for use among hospitalized patients at nutritional risk. The app includes dietary assessment functionality for evaluation of patients’ dietary intake compared to individual needs of energy, protein, and liquids. The recorded intake of energy, protein, and liquids in MyFood showed good agreement with the photograph method for the majority of the participants. The app’s ability to estimate intake within food groups was good, except for bread and cereals which were overestimated, and fruit which was underestimated. MyFood was well accepted among the study participant and has the potential to be a dietary assessment tool for use among patients in clinical practice.


The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system . Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming. This isThe vast amount of data in IoT environments, gathered from a global-scale deployment of smart-things, is the basis for making intelligent decisions and providing better services . In othAlso, outside of the IoT research area, DQ has been broadly studied during last years, and it has become a mature research area capturing the growing interest of the industry due to the different types of values that companies can extract from data . This faDue to the youth of IoT, and despite DQ standards, frameworks, management techniques and tools proposed in the literature, DQ for IoT has not been yet widely studied. However, and prior to this research line, it is possible to cite some works that had addressed some DQ concerns in sensor wireless networks ,14, or iThe remainder of this paper is organized as follows: This section introduces some general ideas about Smart, Connected Products (SCPs) operations, as an essential part of IoT. In addition, some challenges related to DQ in SCP environments are also introduced.According to Cook et al. , a smartSCPs have three main core components: physical, smart, and connectivity components. Smart components extend the capabilities and value of the physical components, while connectivity extends the capabilities and value of the smart components. This enables some smart components to exist outside the physical product itself, with a cycle of value improvement .IoT and SCP can be confused in some contexts. However, IoT simply reflects the growing number of SCPs and highlights the new opportunities they can represent. IoT, which can involve people or things, is a mean for interchanging information. What makes SCPs essentially different is not the Internet, but the changing nature of the “things” . A produSCPs include processors, sensors, software and connectivity that allow data to be exchanged between the product and its environment. The data collected by sensors of these SCPs can be then analysed to inform decision-making, enable operational efficiencies and continuously improve the performance of the product. This paper focuses on the data produced by such sensors, and how inadequate levels of data quality may affect the processing of the data, while smart and connectivity parts of SCPs are outside of the scope of this paper.SCPs can be connected in large, complex networks throughout three different layers : acquisiAcquisition layer refers to the sensor data collection system where sensors, raw (or sensed) and pre-processed data are managed. This is the main focus of this paper.Processing layer involves data resulting from data processing and management centre where energy, storage and analyse capabilities are more significant.Utilization layer concerns delivered data (or post-processed data) exploited, for example, over a GIS or combined with other services or applications.As previously stated, the scope of the paper is limited to the data produced by SCPs’ sensors. Hence, the proposal is mainly intended to be applied in the context of the acquisition layer. Nevertheless, the management of the data quality in sensors can impact on how data is processed (processing layer) and how data may be used later (utilization layer).Networking and management of SCP operations can generate the business intelligence needed to deliver smart services. Smart services are delivered to or via smart objects that feature awareness and connectivity . SCP canSCP operations enable new capabilities for companies, although also arising new problems and challenges must be taken into account. On one hand, SCP operations require companies to build and support an entirely new technology infrastructure . TechnolSensor data: data that is generated by sensors and digitalized in a computer-readable format .Device data: It is integrated by sensor data; observed metadata ; and device meta data , so device data, for example, can be data coming from the camera (device).General data: data related to/or coming from devices which has been modified or computed to derive different data plus business data .IoT data: general data plus device data.According to the different sources of data in these SCP environments, we can distinguish different types of aggregated data:A reduction in the levels of quality of these data due to different problems in SCP operations can threaten the success factors of SCP environments . The quaTilak et al.  provide DQ characteristics to assess data quality in use within a specific context. This aspect considers selected criteria to estimate the quality of raw sensor data at the acquisition and processing layer. There are some DQ characteristics considered, which make it possible to estimate the quality on data sources, their context of acquisition and their transmission to the data management and processing. These DQ characteristics are accuracy and completeness according to ISO/IEC 25012 . It is aDQ Characteristics aimed at managing internal data quality. The main goal of managing internal data quality is to avoid inconsistent data and maintain the temporality of sensor data at the processing layer. These characteristics are consistency and currency according to ISO/IEC 25012 .All the mentioned SCP devices/sensor errors will lead to different DQ problems in the three layers depicted in The goal of this section is twofold. First, There are some published works in the literature that address the concerns related to data quality management in SCP and IoT environments. Karkouch et al.  presenteAnother research subarea of DQ in sensor networks is the DQ management in sensor data streams. Klein et al.  presenteOther works focus on data management from different viewpoints. For example, Al-Ruithe et al.  detailedThere are some methodologies that can be used as drivers for assessing and managing DQ. First, Lee et al.  proposedBatini et al.  providedThe recent standard ISO 8000-61  providesReviewing the literature, it is possible to find that the concept of data quality has been defined in different ways. The widest used definitions are aligned with the concept of “fitness for use”. In , it is dIn our case, and according to our philosophy of aligning with international standards, the DQ Model proposed, is a specialization of the DQ Model introduced in ISO/IEC 25012 [The DQ Model focuses on the quality of the data as part of an information system and defines quality characteristics for target data used by humans and systems . This model categorizes quality attributes into fifteen characteristics and considers three perspectives: inherent, system-dependent and both jointly : the reason for making this distinction is that fixing errors due to dysfunctional sensors requires first fixing the sensors; on the other hand, if one can assure that the root cause is not grounded on a dysfunctional sensor, but on the data itself, then, to fix data quality errors, then data quality management techniques should be used since it should not be ignored what data means. The description of the data quality characteristics can be found in the following paragraphs:Although our DQ Model considers initially all the DQ characteristics defined in ISO/IEC 25012, it could be necessary to customize the DQ characteristics chosen to adapt them into the specific SCP context. This customization might depend on the concrete organization and how it applies the methodology to specific SCP contexts. The customized model will conform the data quality model for an organization with a specific SCP environment.This section presents DAQUA-MASS, an ISO 8000-61-based Data Quality Management methodology for data sensors. Steps provided in this methodology are based in some of the processes introduced by ISO 8000-61 , which, The plan phase establishes the strategy and the data quality improvement implementation plan as necessary to deliver results in accordance with data requirements;In the do phase the data quality improvement implementation plan is executed;During the check phase, it is conducted the monitorization of data quality and process performance against the strategy and data requirements and report the results to validate the efficiency of the corrective actions; and finallyThe act phase takes actions to continually improve process performance.Steps of DAQUA-MASS are grouped in four phases according to the Plan-Do-Check-Act cycle, as it is done in 8000-61. PDCA is implicitly a closed loop, signifying that the process is iterative and last phase of every iteration provides feedback on starting a new iteration. Many acknowledged models, such as the in IoT information fusion  or the JWe consider the following list of stakeholders to be involved in the application of this methodology. It should be notice that there are alternative, similar (even overlapped) role definitions in the context of data quality management and SCP environments. However, we consider these are the most common and well-known stakeholders, and other that are not mentioned can be easily assimilated to one of the proposed.Chief Information Officer (CIO). It is the most senior executive in an enterprise responsible for the traditional information technology and computer systems that support enterprise goal.Chief Data Officer (CDO). It is a corporate officer responsible for enterprise wide data governance and utilization of data as an asset, via data processing, analysis, data mining, information trading and other means. CIO and CDO are at the executive level.Data Governance Manager (DGM). He or she is overseeing enterprise data governance program development and is responsible for architecting certain solutions and frameworks. It is at the strategic level.Data Quality Steward for SCP domain (SCP DQ Steward). It is a DQ steward at tactical level for the area of SCP environments by considering its implications in DQ.Data Quality Steward (DQ Steward). It is responsible for utilizing an organization’s data quality governance processes to ensure fitness of data elements—both the content and metadata. Data stewards have a specialist role that incorporates processes, policies, guidelines and responsibilities for administering organizations’ entire data in compliance with DQ policies and procedures. This is at operational level.SCP Technical Architect (SCP Arch). A SCP architect is a SCP  expert who makes high-level design choices and dictates technical standards, including SCP technology standards, tools, and platforms.The data quality planning phase establishes data requirements and objectives for data quality, creating plans to achieve the objectives and evaluating the performance of the plans. These plans balance current data quality levels, cost, resources and capabilities across the organization for the assessment of data quality of sensor data. This phase is initiated based on needs and expectations of stakeholders or the feedback of the process improvements performed during data quality improvement (the act phase). The expected result of the execution of this phase is the complete planification for adequately collect sensor data. This includes requirements management, definition of policies for data, devices lifecycle management, and it is important to highlight the definition of policies to retain the original unmanipulated data and all versions of the input data. P1-1. Sensor and sensor data lifecycle management specification. The lifecycle of sensors  and sensP1-2. Management of quality policies, standards and procedures for data quality management. Specify fundamental intentions and rules for data quality management. Ensure the data quality policies, standards and procedures are appropriate for the data quality strategy, comply with data requirements and establish the foundation for continual improvement of the effectiveness and efficiency of data quality management in all the key SCP operations. The purpose of data quality policy/standards/procedures management is to capture rules that apply to performing data quality control, data quality assurance, data quality improvement, data-related support and resource provision consistently. Before the implementation and definition of the complete plan, it should be defined the policies, standards and procedures in order to define the implementation plan based on them.P1-3. Provision of sensor data and work specifications. Developing specifications that describe characteristics of data and work instructions for smart connected products enables Data processing and data quality monitoring and control. To support the description of the provision of the sensor data work specifications some metadata must be provided. Metadata can help with one of the biggest problems of SCP: interoperability. Interoperability refers to the ability for one or more smart connected products to communicate and exchange .P1-4. Identification, prioritization and validation of sensor data requirements. Collect the needs and expectations related to sensor data from devices and stakeholders. Then, it is translated by identification, prioritization and validation of data requirements. The purpose of Requirements Management is to establish the basis for creating or for refining a data quality strategy for SCP environments aligned to the needs and expectations of stakeholders. It is important to have well defined and implemented good sensor data requirements to avoid problems since the start and to facilitate the collection and integration of sensor data.This first step is devoted to characterizing the current state of the SCP environments and all the specific sensor data contexts. This step has the following activities:P2-1. Identification of the data quality characteristics representing quality requirements and determination and development of metrics and measurement methods. Develop or select the measurement indicators, corresponding metrics and measurement methods used to measure the quality levels of data produced by devices during all the SCP operations.P2-2. Measurement and analysis of data quality levels. Measure the data quality levels by implementing the measurement plans and determining the measurement results. This means the application of the measurement algorithms defined and developed on the previous step (P2-1). Such algorithms strongly depend on the IT infrastructure landscape of every organization. Thereby, every organization often develop their own algorithms or use commercial solutions which are compliant with their own infrastructure. Merino et al.  presentsThis step is aimed to evaluate the current state of the data quality levels in all the surrounding SCP environments. This step has the following activities: P3-1. Analysis of root causes of data nonconformities. Analysing the root causes of each data quality issue and assess the effect of the issue on business processes. The purpose of root cause analysis and solution development of non-solved data quality non-conformities is to establish, in accordance with the data quality strategy and with the priorities identified by Data Quality Assurance, the basis on which to perform data cleansing and/or process improvement for data nonconformity prevention.P3-2. Data quality risk assessment. Identify risks throughout the data life cycle, analyse the impact if each risk was to occur and determine risk priorities to establish the basis for monitoring and control of processes and data. The purpose of data quality monitoring and control is, by following applicable work instructions, to identify and respond when Data Processing fails to deliver data that meet the requirements in the corresponding data specification. It allows to control what is happening on data or even on the SCP environment. For example, with the control of sensor data quality, it is possible to identify certain issues on devices. Data is often the most important thing to protect, because although the direct cost of losing it may be small compared with research data or intellectual property. If a sensor is not functioning any more due to some reason, it can be replaced, but if a data loss is produced is very difficult to recover it or even impossible to recover. This can bring not only a data loss but can also be the root cause of other problems. In conclusion, data quality risks should be identified in order to avoid them. As Karkouch et al. highlight in , the maiP3-3. Development of improvement solutions to eliminate the root causes. Propose solutions to eliminate the root causes and prevent recurrence of nonconformities. Evaluate the feasibility of the proposed improvements through cost benefit analysis.P3-4. Definition of improvement targets. The purpose of this activity is to analyse possible improvements areas according to the business processes, risk catalogue and the data quality strategy; and then it selects those that are more aligned with the data quality strategy and/or are able to lead to greater data quality enhancement regarding previous detected risks. Specific areas or sub-nets of devices in the organization’s SCP environments could also serve as a criterion to determine specific improvement targets.P3-5. Establishment of the data quality enhancement plan. Define the scope and target of data quality and prepare detailed implementation plans, defining and allocating the resources needed.Data Quality implementation planning identifies the resources and sequencing by which to perform DQ control, DQ Assurance, DQ Improvement, data-related support and resource provision across the organization. At this point, after the collection of sensor data requirements and the definition of standards and policies, it is important to configure the devices to implement the plan and place each node following a defined strategy. Optimal node placement allows to obtain the optimal value through data. This step considers the following activities:This phase consists of the data quality control that is carried out based on the implementation plan established in data quality planning (see the Plan phase). The phase, when completed, delivers data that meet the specified requirements. This phase involves creating, using and updating data according to specified work instructions and monitoring quality by checking whether the data conform to predetermined specifications.In this phase, it is important to address aspects such as the provision of complete metadata, the use of flags to convey information about data, the documentation of all sensor data processing, monitoring and control of data, maintain appropriate levels of human inspection, perform range, domain and slope checks, implement an automated alert system for sensor network issues or automate sensor data quality procedures. D1-1. Establish flags to convey information about the sensor data. Flags or qualifiers convey information about individual data values, typically using codes that are stored in a separate field to correspond with each value. Flags can be highly specific to individual studies and data sets or standardized across all data.D1-2. Definition of the optimal node placement plan. It is a very challenging problem that has been proven to be NP-hard  for most of the formulations of sensor deployment ,41. To tD1-3. Redesign the software or hardware that includes sensors to eliminate root causes. It is an alternative process to the optimal re-placements of sensors. For example, redesign and reimplement a fragment of a SCP’s firmware could improve fault tolerance.D1-4. Data Cleansing. The purpose of Data Cleansing is to ensure, in response to the results of root cause analysis and solution development, the organization can access data sets that contain no nonconformities capable of causing unacceptable disruption to the effectiveness and efficiency of decision making using those data. Also, the nonconformities are corrected implementing developed solutions and make a record of the corrections.D1-5. Force an appropriate level of human inspection. If performed by trained and experienced technicians, visual inspection is used to monitor the state of industrial equipment and to identify necessary repairs. There are also technologies used to assist with fault recognition or even to automate inspections. Shop floor workers are made responsible for basic maintenance including cleaning machines, visual inspection and initial detection of machine degradation.D1-6. Implement an automated alert system to warn about potential sensor data quality issues. Having only human inspection can be a complex task for maintenance staff. It is necessary to implement a system in which some key indicators are constantly reading if the sensors status is correct or not. If a sensor is not working properly, the root cause can be due to a hardware, network or software failure and affects to data quality. Furthermore, an automated data quality procedure might identify anomalous spikes in the data and flag them. Even though, it is almost always necessary the human supervision, intervention and inspection as stated in ,46; the D1-7. Schedule sensor maintenance to minimize data quality issues. Sensors require routine maintenance and scheduled calibration that, in some cases, can be done only by the manufacturer. Ideally, maintenance and repairs are scheduled to minimize data loss  or staggered in such a way that data from a nearby sensor can be used to fill gaps. In cases in which unscheduled maintenance is required, stocking replacement parts on site ensures that any part of the network can be replaced immediately.The purpose of Provision of Data Specifications and Work Instructions is to establish the basis on which to perform Data Processing and Data Quality Monitoring and Control, taking account of the outcomes of the Data Quality Planning, the Data-Related Support and the Resource Provision processes. Having the plan obtained for the first phase, the first step is to provide data and work specification to collect data from devices on the defined way. This step considers the following activities:The check phase consists of the data quality assurance, which measures data quality levels and the methodology steps performance related to data nonconformities or other issues that have arisen as a result of the data quality planning (see the Plan phase) or the data quality control (see the Do phase). This measurement provides evidence by which to evaluate the impact of any identified poor levels of data quality on the effectiveness and efficiency of business processes. On this phase is important to address aspects like schedule sensor maintenance and repairs to minimize data loss, measurement and evaluation of sensor data quality. C1-1. Monitoring and control of the enhanced data. According to the identified risk priorities, monitor and measure conformity of data to the applicable specification. Monitoring and measuring takes place either at intervals or continuously and in accordance with applicable work instructions. This work instructions can be: perform range checks on numerical data, perform domain checks on categorical data and perform slope and persistence checks on continuous data streams. If data nonconformities are found, then correct the data when viable and distribute to stakeholders a record of the viability and degree of success for each corrective action.C1-2. Definition of an interstice comparison plan. Create policies for comparing data with data from related sensors. If no replicate sensors exist, interstice comparisons are useful, whereby data from one location are compared with data from nearby identical sensors.The purpose of Measurement of Data Quality is the Provision of Measurement Criteria, to generate input for the Evaluation of Measurement Results. This step considers the following activities:C2-1. Issue analysis. Review non-solved nonconformities arising from Data Processing to identify those that are possibly connected to the reported issue that has triggered the need for Data Quality Assurance. This review creates a set of related nonconformities. This set is the basis for further investigation through the measurement of data quality levels in SCP environments. Respond to the reporting of unresolved data nonconformities from within Data Quality Control, indications of the recurrence of types of nonconformity or other issues raised against the results of Data Quality Planning or Data Quality Control.The purpose of Review of Data Quality Issues is to identify the starting point for deciding to measure data quality levels and process performance with the potential to generate opportunities to improve data quality. The results of measurement and evaluation of data quality are analysed and possible issues are identified. This step considers the following activities:The Act phase consists of the data quality improvement that involves analysing the root causes of data quality issues based on the assessment results derived from data quality assurance (see the Check phase). To prevent future data nonconformities, the steps in this phase try to correct all the existing nonconformities and also transforms SCP operations as appropriate. On this phase, it is important to address aspects as analysis of root causes of sensor data problems, management of sensor data problems, correction and prevention, make available ready access to replacement parts and anticipate common repairs and maintain inventory replacement parts. A1-1. Make available ready access to replacement parts. Schedule routine calibration of instruments and sensors based on manufacturer specifications. Maintaining additional calibrated sensors of the same make/model can allow immediate replacement of sensors removed for calibration to avoid data loss. Otherwise, sensor calibrations can be scheduled at non-critical times or staggered such that a nearby sensor can be used as a proxy to fill gaps.A1-2. Update the strategy for node replacement. Controlled replacement is often pursued for only a selected subset of the employed nodes with the goal of structuring the network topology in a way that achieves the desired application requirements. In addition to coverage, the nodes’ positions affect numerous network performance metrics such as energy consumption, delay and throughput. For example, large distances between nodes weaken the communication links, lower the throughput and increase energy consumption. Additionally, it can anticipate some common repairs and maintain inventory replacement parts. This means that sensors could be replaced before failure where sensor lifetimes are known or can be estimated.The purpose of Data Quality issues prevention results of Root Cause Analysis and Solution Development is to increase the extent to which the organization achieves a systematic and systemic approach to achieving data quality. This step tries to prevent issues on devices or, for example, to avoid data loss when it can be predicted the repair or the maintenance of devices of the SCP environment. In order to prevent the recurrence of each actual or the occurrence of each potential data nonconformity or similar data nonconformities by refining and applying guidelines, rules and procedures. To achieve this, the following activities should be conducted: In this paper, we have tackled the challenges of data quality problems in SCP environments. Many research and standardization efforts have been made in the DQ area over the last years, and some interesting results have been already transferred to IoT as well. However, the approaches presented in the literature have two main drawbacks. On the one hand, these proposals do not take into account the nature of SCP environments and the concrete factors that affect the way in which DQ must be treated in such context. On the other hand, these approaches do not consider existing DQ management standards that have not been tailored yet for IoT and, more specifically, to SCP contexts. Given the importance of institutionalizing best practices in SCP for DQ management, we consider it of paramount importance to provide practitioners and organizations with techniques aligned with standards, reducing their adaptation efforts and favouring systematic and holistic approaches to the problem. As a main contribution, we have provided in this paper a Data Quality Management Methodology for Sensor Data, named DAQUA-MASS, based on ISO 8000-61. The methodology is structured according to the PDCA Cycle of continuous improvement. The methodology is composed by 7 steps divided in several activities. Input and output products are already identified for each activity in the methodology. It is noteworthy to highlight the identification of the various roles involved in the management of data quality in sensor data.So, the data quality model along with the methodology offer a unique framework to enable designers of IoT projects including sensor networks and practitioners in charge of exploiting IoT systems to assure that the business processes working over these systems can manage data with adequate levels of quality. Once discarded data quality problems or identified data quality problems root causes, it will be easier to focus the attention exclusively on sensor networks of IoT systems. Working aligned to international open standards will enable organizations to speak the same language and devote the required efforts only to the proper working of the IoT systems, preventing to be aware of some other data quality concerns.Future research lines are planned to be primarily focused on the empirical validation of this methodology. It will be used by different organization in various SCP environments in order to ensure its applicability in large scale. The mentioned case studies will allow us to provide different specializations of the data quality model (or recommendation on how to tailor it) to different organization depending on the domain. In parallel, other future research lines are in accordance with some limitations of the scope considered in this paper. Thus, it is expected to be considered data quality management issues and its implications in the processing and utilization layers apart from the acquisition layer see . While s


This paper applies the concept of cultural ecosystem services (CES) to reveal the diverse benefits the Baltic Sea provides to human well-being. The study identifies and defines relevant CES for marine and coastal environments and applies them in a survey with 4800 respondents from Germany, Finland and Latvia. The relative importance of various CES was determined by asking respondents to allocate 100 points between CES related to recreation, landscape, inspiration, learning and education, spiritual experiences and belonging, historically and culturally important places and the existence of habitats. The results reveal significant differences in the importance of various CES across countries, users and nonusers of the Baltic Sea, as well as respondents with different human–nature relationships. The results emphasize the importance of considering recreation, landscapes and habitats in conservation policies, while acknowledging that all CES are perceived as important by some population groups.The online version of this article (10.1007/s13280-019-01239-1) contains supplementary material, which is available to authorized users. The cultural ecosystem services (CES) concept relates to services provided by ecosystems in terms of their life-enriching and life-affirming contributions to human well-being  develop a measure that covers the entire CES range. Using this measure in the three geographically, socio-economically and culturally different Baltic Sea countries Finland, Germany and Latvia, (2) we identify the relative importance of various CES for the Baltic Sea region. Then, (3) we identify the determinants that explain the individually-perceived importance of different CES using compositional statistical analysis. The determinants we focus on include sociodemographic, attitudinal, geographic and recreation-related factors. We further group individuals based on their perceptions of CES. We (4) identify categories of CES that are perceived as separate and those that cluster with each other, in order to evaluate the feasibility of CES classification. Finally, (5) we discuss the implications of the relative importance of CES for the management of the Baltic Sea.In developing a CES measure we take into account critical discussions concerning the CES concept. CES often occur in bundles   and the benefits that result from the processes and entities that people actively create and express through interactions with ecosystems and that affect their identities, experiences and capabilities  developed from the work on environmental accounting undertaken by the European Environment Agency  and a pilot survey in each country in June–July 2016.The final survey was implemented between November 2016 and February 2017. It was targeted at residents of each country using stratified random sampling that was representative of the population in each country. Stratifying, for instance, according to age, gender, location and educational level ensured representative samples of the national populations. For Germany, coastal regions were oversampled to increase the share of Baltic Sea visitors in the final sample. The primary data collection method used in Finland and Germany was computer-assisted web interviews (CAWI) with internet panels . The parameter corresponding to the male gender, for example, was interpreted as the increment (in the sense of perturbation) on the average response from the baseline  to the level ‘male’. In the interpretation, one may compare the estimated composition parameters with the 8-part neutral element /8. That is, any component with an estimated coefficient greater than 0.125 (1/8) can be seen as having an increasing effect and any component with a coefficient smaller than 0.125 can be seen as having a decreasing effect on the dependent variable, i.e. the importance of the CES subcategory.Table For Finland, there was an increase with respect to CES related to recreation, education, spiritual, historic and habitat services compared to Germany, meaning that these five CES were more important in Finland than in Germany. Landscape and inspiration were less important. For Latvia, recreation, inspiration and spiritual were more important than in Germany, but landscape, education, historic and habitat less important.sumnep increased, that is, when a respondent had a more biocentric worldview, the importance of education, historic and habitat services increased, whereas the other categories decreased.For nonusers, there was an increase with respect to the CES related to education, spiritual and habitat compared to those who had visited the Baltic Sea. Thus, these services appeared more important to nonusers, which seems intuitive since they are not necessarily associated with direct use of the Baltic Sea. Male respondents seemed to have larger values especially for recreation than women and smaller values for habitat. As country, nonuser, male and sumnep were of interest. The first bar corresponds to the baseline values of the predictors country, nonuser and male  and the median value for sumnep. Every other bar is compared to this bar while changing the level of one predictor at a time.Figure sumnep was less straightforward. The largest estimated positive effect for sumnep was with respect to habitat. We see an increase for habitat when comparing the first and last bars in Fig. rd quartile sumnep values, respectively.The estimated coefficients with respect to each predictor and composition show an increase or a decrease compared to the baseline prediction. For example, the relative weight of recreation was much larger for Latvia than for Germany. Components for inspiration, education, spiritual, historic and other services all had relatively small values. Therefore, we could have combined or dropped these parts of the response composition since they contribute little to the predictions. The effect of We also performed hierarchical cluster analysis in order to examine the closeness of the response compositions in Euclidean distance, as well as the closeness of individuals.Figure Table This paper presents results of an international study on the relative importance of CES in the Baltic Sea environment for the residents of the three coastal countries: Finland, Germany and Latvia. Relative importance was elicited in a survey question where respondents allocated 100 points between seven categories of CES conforming to the CICES classification of ecosystem services: recreation, landscape, inspiration, learning and education, spiritual experiences and belonging, historically and culturally important places and the existence of habitats. With this compositional measure we avoided the problem often found in importance measures with 5 or 7 point scales, that is, high scores for every item without differences among items.The results indicate that recreation, habitats and landscapes are considered particularly important CES in the three Baltic Sea countries. Differences in the importance of CES among respondents were explained primarily by the respondents’ country of residence, gender and recreational use of the Baltic Sea. Recreation is the most important CES in all three countries, but habitats are almost as important in Finland and Germany. These country-specific differences are likely related to the sizes, shapes and population densities of the three countries as well as cultural factors. For example, the German Baltic Sea coastline is short compared to its land area and the country stretches further away from the Baltic Sea than the other countries, leading to more emphasis on CES that are less tangible and not associated with the use of coastal and marine areas, including education, spiritual and habitat services. Similar to Van Riper and Kyle , our resCluster analyses of CES reveal clear groupings. Interestingly, the existence of habitats forms its own cluster, which implies that people consider the existence of habitats as separate from other CES. Likewise, clustering of respondents indicated that half of them emphasize the importance of habitats over other CES. These two results suggest a distinct status for the existence of habitats among CES for the general public.Spiritual experiences, inspiration, education and historic and cultural places form a cluster of CES, and are on average seen as less important than the other CES. These notions may help in further developing and operationalizing the ES classification, and indicate how the categories may be combined in future research.Qualitative and deliberative valuation methods for CES are often suggested to capture the contextuality, subjectivity and plurality of values , which requires economic analyses of the marine environment with a focus on ES as one of the recommended approaches. The most important CES , can be prioritized in conservation policies while acknowledging that all CES are considered important by some groups of people. The differences in the importance of CES across countries may create challenges for regional policies since countries may place varying emphases on the goals of protection efforts.Future policy oriented research should focus on the spatial distribution of marine CES and the subsequent benefits from their improved provision. In addition, future research might focus on CES other than recreation, which is the most studied in the Baltic Sea area. This information would also serve the EU Maritime Spatial Planning Directive (2014/89/EU) where assessments of ES can support the planning of maritime activities to ensure sustainable use of the marine environment.Supplementary material 1 (PDF 55 kb)Below is the link to the electronic supplementary material.


Requested data will be provided after approval from the data service review board. However, the family tree DB can be limitedly provided for studies with high public value in order to maximize personal information protection.We constructed the family tree database (DB) by using a new family code system that can logically express interpersonal family relationships and by comparing and complementing health insurance eligibility data and resident register data of the National Health Information Database (NHID). In the family tree DB, Parents and grandparents are matched for more than 95% of those who were born between 2010 and 2017. Codes for inverse relationships and extended relationships are generated using sequences of the three-digit basic family codes. The family tree DB contains variables such as sex, birth year, family relations, and degree of kinship (maximum of 4) between subjects and family members. Using the family tree DB, we find that prevalence rates of hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and cancer are higher for those with family history. The family tree DB may omit some relationships due to incomplete past data, and some family relations cannot be uniquely determined because the source data only contain relationships between head and members of the household. The family tree DB is a part of the NHID, and researchers can submit requests for data on the website at  The National Health Insurance (NHI) is a social health insurance system in Korea, which achieved universal coverage of all Korean residents in 1989. Also, all healthcare providers are mandatorily enrolled in the NHI. The National Health Insurance Service (NHIS) is a single insurer, and fee-for-service is the main payment system [The NHIS has demographic information , socioeconomic information , disability registration information, in order to manage the eligibility of all residents and impose insurance contributions. Also, the NHIS has a wide range of health risk factors, as it operates the national health screening programs such as infant/adult general check-ups, cancer screening, etc. In addition, it also has detailed information on health care utilization  submitted by medical providers for reimbursement. To meet the demands for the data for various research purposes, the NHIS created the National Health Information Database (NHID) in 2012 as a research database (DB), excluding identification information such as resident registration number [Family relation is known as a relevant factor in the biological aspects of disease and family history . RecentlHowever, there have been few source data that provide accurate family relationships. Although there are some survey data having information on family relations, most of them only include relationships to the heads of household, and thus, it is hard to determine the relationships between other household members. The health insurance eligibility data and resident registration data owned by NHIS also have similar limitations, having only codes for relationships to heads of household. To overcome such limitations, we develop a code system to logically express interpersonal relationships within families and establish a DB of interpersonal family relationships of the entire population.The family tree DB is created by comparing and complementing health insurance eligibility data and resident register data in the NHID to obtain more accurate family relationships. The family tree DB basically covers the same population as the NHID, but it should be noted that the health insurance eligibility data and the resident register data are missing some data before 2002 and 2004, respectively, because of incomplete electronization. The family tree DB does not have a time variable like reference year; instead, it only shows the relationship between two individuals as long as they have ever had a kinship relationship. For example, the family tree DB only shows the relationship that person A is person B’s parent. Therefore, when examining matching rates of the family tree DB, we restrict ourselves to only those who lived in Korea in 2017 to set the denominators. We develop a new family code system to identify family relationships between individuals from the limited information on relationships to head of household. The family code is a sequence of three-digit basic codes that are required to connect an individual to their kin. The first digit of a three-digit basic code indicates the degree of kinship, which can take values 0, 1, or 2. The second digit is a character that differentiates self, spouse, parent, child, older sibling, and younger sibling among the same degree of kinship. The third digit indicates gender, which takes values M, W, or X . In otheMajor variables of the family tree DB are shown in This dataset was drawn from a retrospective cohort based on administrative data, and separate patient recruitment procedures were not carried out. As the data were de-identified, the consent of the subject and direct contact were not applicable.Since the family tree DB was only recently established in 2018, and access to the DB is limited even for research purposes, very few studies have been conducted using this DB thus far. The family tree DB will be most effective when used with the NHID rather than used independently. An example of this is the systematic analysis of family history of disease shown in The family tree DB is a unique DB in Korea that contains family relations up to the fourth degree of kinship for the entire population using health insurance eligibility and resident register data. In addition, it allows more complete identification of family relationships between two individuals even if their relationship is not identified from the source data in which there are only relationships between head and a member of household. This new DB is expected to serve as a fundamental source to provide empirical evidence for solving various social problems through its application to medical research as well as social policies. The limitations of this project are as follows. Due to restrictions of available data , family relations among the elderly or those who passed away before the source data were created cannot be clearly determined. In addition, due to limitations of the source data based on the head of household-members of household relationship, non-determinable codes may occur when extended relationship codes are created. Finally, as there is a lack of studies using this DB, additional comparison studies should be conducted to validate its accuracy.http://nhiss.nhis.or.kr. When a researcher makes an online request for data based on a research proposal approved by institutional review board, the data are provided after approval by the data service review board. However, the family tree DB can be limitedly provided for studies with high public value in order to maximize personal information protection.The family tree DB is part of the NHID and the general principles regarding access to the NHID are noted in the health insurance data share service information at


Early diagnosis is a critical component of the global response to the human immunodeficiency virus (HIV). In Australia, more than two-thirds of women from Southeast Asia are diagnosed late with HIV. There is limited evidence regarding the barriers to HIV testing and which interventions work to increase an uptake among migrants living in high-income countries. This participatory action research (PAR) project will work with women from Indonesia to co-design an intervention to increase HIV testing uptake in Western Australia. The project will involve trained community researchers, representatives from relevant organizations, and community women born in Indonesia. We will conduct three PAR cycles. Phase one will use focus groups to understand enablers for HIV testing among community members. In phase two, data will be presented back to members of the participating communities who will be invited to co-design an intervention to increase HIV testing. The final cycle will focus on implementing and evaluating the resulting intervention. This project will add to the small body of literature on pathways and enablers to HIV testing, and to new insights regarding interventions that work for women from migrant communities and why.  In the last decade, notifications of human immunodeficiency virus (HIV) in Australia have increased among people born overseas, predominately from Northeast Asia (NEA) and Southeast Asia (SEA) ,2. This Global migration has been rapidly increasing, from 173 million in 2000 to over 258 international migrants in 2017 . In AustMigration can influence health status, with migrants facing significant intersecting health and social inequities ,9. CircuThis vulnerability to HIV is linked to broader structural influences, such as poverty, gender inequality, and access to health services ,13. For For women from SEA living with HIV in Australia, almost two thirds (63%) are diagnosed late (measured by a CD4 cell count of fewer than 350 cells/µl at diagnosis), meaning they have lived with the virus for four or more years before diagnosis . In compCulturally and linguistically diverse (CaLD) people from high HIV prevalence countries , are a Despite being recognized as a priority population for Australia’s HIV response, investment in health and support services for migrant populations, nationally, has been described as a ‘one size fits all’ approach . Little The involvement of migrant communities in both research and health interventions may be challenging , particuThis project will work with Indonesian women in Western Australia to develop a multi-strategic intervention to increase HIV testing, and further explore the intersection between migration and HIV ,18,41,42A co-design process will be used to develop the intervention and evaluation. Co-design is a collaborative process, in which participants take an active role in the design of the intervention alongside academics and other relevant stakeholders . The aimIdentify and assess the pathways and enablers to HIV testing among women from IndonesiaRecord and synthesize the use of a co-design process in developing an interventionDetermine the critical elements of a co-designed intervention to increase HIV testing uptake among women from IndonesiaImplement and evaluate a co-designed intervention to increase HIV testing uptake among women from IndonesiaReview the utility of a participatory action research approach in addressing HIV with women from IndonesiaSynthesize findings of the intervention and provide recommendations on appropriate strategies to prevent and manage HIV among women from Indonesia.This research will use participatory action research methodology to co-design an intervention to increase HIV testing uptake among women from Indonesia in Perth, Western Australia (WA). The objectives of this study are to:There is no fixed process for the design and implementation of PAR studies, nor is there a singular theoretical framework . InsteadThree cycles of PAR will be conducted . PAR is There will be three groups of participants in this study: (1) Peer community researchers, (2) representatives from relevant organizations, and (3) women from Indonesia (referred to as community members). Involving both community researchers and organizational representatives will improve understanding of the issue, increase ownership at both a community and organizational level, as well as integrate knowledge into organizations to improve practice and increase the sustainability of the intervention after the project has finished ,56. Organizations represented will include the state Department of Health and the state peak organization for HIV. It may include additional organizations relevant to Indonesian women identified in phase one. Organizations will assist to establish relationships with community members, inform the development of the intervention and collaborate in the implementation and evaluation of the intervention. Between five and eight Indonesian women will be trained as community researchers for the life of the project. Recruitment will occur through existing networks of organizations, with community researchers purposively selected based on their English proficiency, and connectedness to community. Representation will be sought from individuals from different age groups, relationship status, educational backgrounds and length of time in Australia. This will ensure that community researchers involved will be able to connect with people from a range of backgrounds. The Eighth National HIV Strategy  outlinesCommon to PAR, community researchers will be involved throughout the project . CommuniTraining will be provided to community researchers by the research team on HIV and sexual health, project governance, and relevant research skills. This will help build the research capacity of the group and facilitate community ownership of the project . The groThis phase will include establishing community researchers, conducting a review of HIV resources, and focus groups with community members. An extended time-period (approximately 12 months) has been planned to allow for the further development of relationships between the lead researcher and community, critical to the process of PAR. www.allgood.org.au—a national website housing information on sexually transmitted infections (STIs) and bloodborne viruses (BBVs) for both culturally and linguistically diverse and Aboriginal and Torres Strait Islander communities. Further online searches will be conducted to identify relevant resources. During phase one, HIV resources from both Australia and Indonesia (when available in English) will be reviewed. An online search for available resources will be initially conducted via The Health Literacy INDEX tool will be used to evaluate the health literacy demands of identified resources . INDEX iPurposive sampling is the deliberate selection of individuals who can provide in-depth relevant information as well as broader views . OrganizA short survey will collect demographic data from focus group participants. Informed by literature, the focus group guide will be developed in collaboration with community researchers and organizational representatives. Relevant theoretical sources (selected based on discussions with community researchers) will inform question development and data analysis ,21,41,64Focus group discussions were chosen for their strengths in directly reflecting the voices of marginalized groups and the ability to encourage participants to share, build on, and disagree on sensitive topics . Focus gCommunity members will describe and draw maps of their interactions with health services and other sources of help-seeking , and perceived pathways and enablers to testing for HIV ,70. CommAudio recordings of the focus groups will be transcribed verbatim by the lead author enabling data immersion . QualitaCommunity maps will be retained as physical copies, as well as captured electronically. This will involve describing the images used and linking it to the participant’s descriptor of the image. Data will be analysed thematically , using tDue to the nature of PAR, the complete scope of the second and third cycle will be unknown prior to the completion of phase one. The second stage will be to plan an intervention to increase uptake of HIV testing. It will be community-driven through co-design workshops ,56,76 anSelected strategies that can be implemented within the time of the project will be identified in initial discussions . These strategies may aim to develop relevant skills within the community to access services, reduce HIV-related stigma, and encourage uptake of HIV testing. Where possible, strategies will build on the existing strengths and opportunities within the community, such as religious leaders or existing community events, and within organizations. This will also involve determining the expected process, impact, and outcome evaluation indicators of a successful intervention with community and organization representatives.Previous examples of strategies arising through PAR include: Educational workshops delivered by peers , advocatWorkshops will be conducted with community members to ensure a variety of ideas are heard and to bolster ownership from community. Relevant organisational representatives and the research team will be invited to provide feedback on the identified strategies. Community researchers will co-facilitate all events. Workshops will be ongoing until final strategies have been decided on. Notes will be taken during workshops, and in subsequent meetings with community researchers, organizational representatives, and the research team to provide an audit trail of decision-making. Data will be analysed thematically , though In partnership with community and organisational representatives, selected intervention strategies (identified in phase two) will be implemented and evaluated. It is likely that strategies will only reach proof of concept, or be tested for feasibility ,83 amongOver the course of the PAR project, a number of methods will be used to record and review the utility of both the PAR approach and co-design element ,87, the The intervention will target community members, and a subset (not involved in the co-design) may be involved in the feasibility testing of the intervention. Evaluation of the process of the research will include community researchers, community members involved in the co-design process and organisational representatives. This project has ethical approval from the Curtin University Human Research Ethics Committee (HRE2018-0790). Informed consent will be obtained from participants prior to taking part in focus groups in phase one, and potentially for evaluation in phase three . Informed consent will also be obtained from community researchers. To address ethical concerns in working with culturally and linguistically diverse populations, the “4 Ps” framework to ensure culturally safe research will be applied: Partnership, participation, protection and power . This frCommunity researchers will facilitate the inclusion of non-English speaking participants, including translating participant information sheets and consent forms (where relevant), to ensure participants understand the nature of the research. We anticipate that most participants will speak English. The results of the project will further add to the literature on what interventions work with migrant communities and why. It is likely to have relevance to other jurisdictions, and other community groups, as a process of working with community. This work is in line with the Australian Government’s goal of virtual elimination of new HIV transmissions by 2022 , with a


Several scientific areas have benefited significantly from the introduction of nanotechnology and the respective evolution. This is especially noteworthy in the development of new drug substances and products. This review focuses on the introduction of nanomedicines in the pharmaceutical market, and all the controversy associated to basic concepts related to these nanosystems, and the numerous methodologies applied for enhanced knowledge. Due to the properties conferred by the nanoscale, the challenges for nanotechnology implementation, specifically in the pharmaceutical development of new drug products and respective regulatory issues are critically discussed, mainly focused on the European Union context. Finally, issues pertaining to the current applications and future developments are presented. Over the last years, nanotechnology has been introduced in our daily routine. This revolutionary technology has been applied in multiple fields through an integrated approach. An increasing number of applications and products containing nanomaterials or at least with nano-based claims have become available. This also happens in pharmaceutical research. The use of nanotechnology in the development of new medicines is now part of our research and in the European Union (EU) it has been recognized as a Key Enabling Technology, capable of providing new and innovative medical solution to address unmet medical needs  created a definition based on the European Commission Joint Research Center and on the Scientific Committee on Emerging and Newly Identified Health Risks. This definition is only used as a reference to determine whether a material is considered a nanomaterial or not; however, it is not classified as hazardous or safe. The EC claims that it should be used as a reference for additional regulatory and policy frameworks related to quality, safety, efficacy, and risks assessment , associated with the expected clinical advantages of nano-engineering  has not established its own definition for “nanotechnology,” “nanomaterial,” “nanoscale,” or other related terms, instead adopting the meanings commonly employed in relation to the engineering of materials that have at least one dimension in the size range of approximately 1 nanometer (nm) to 100 nm. Based on the current scientific and technical understanding of nanomaterials and their characteristics, FDA advises that evaluations of safety, effectiveness, public health impact, or regulatory status of nanotechnology products should consider any unique properties and behaviors that the application of nanotechnology may impart  and surface area :The pharmaceutical manufacturing of nanomaterials involves two different approaches: top down and bottom down. The top down process involves the breakdown of a bulk material into a smaller one or smaller pieces by mechanical or chemical energy. Conversely, the bottom down process starts with atomic or molecular species allowing the precursor particles to increase in size through chemical reaction Luther, . These t“particle is a minute piece of matter with defined physical boundaries” , controlled drug delivery (nanotherapy), and regenerative medicine. A new area which combines diagnostics and therapy termed theranostics is emerging and is a promising approach which holds in the same system both the diagnosis/imaging agent and the medicine. Nanomedicine is holding promising changes in clinical practice by the introduction of novel medicines for both diagnosis and treatment, having enabled to address unmet medical needs, by (i) integrating effective molecules that otherwise could not be used because of their high toxicity , (ii) exploiting multiple mechanisms of action , (iii) maximizing efficacy  and reducing dose and toxicity, (iv) providing drug targeting, controlled and site specific release, favoring a preferential distribution within the body  and improved transport across biological barriers Chan, .This is a result of intrinsic properties of nanomaterials that have brought many advantages in the pharmaceutical development. Due to their small size, nanomaterials have a high specific surface area in relation to the volume. Consequently, the particle surface energy is increased, making the nanomaterials much more reactive. Nanomaterials have a tendency to adsorb biomolecules, e.g., proteins, lipids, among others, when in contact with the biological fluids. One of the most important interactions with the living matter relies on the plasma/serum biomoleculeadsorption layer, known as “corona,” that forms on the surface of colloidal nanoparticles , High-Resolution TEM, Scanning Electron Microscopy (SEM), cryo-SEM, Atomic Force Microscopy and Particle Tracking Analysis are just some of the examples. The main disadvantage of these methods is the operation under high-vacuum, although recently with the development of cryo-SEM sample dehydration has been prevented under high-vacuum conditions , Analytical Centrifugation (AC) and Differential Electrical Mobility Analysis are some of the techniques that can be applied. The FFF techniques include different methods which separate the particles according to the force field applied. AC separates the particles through centrifugal sedimentation , Small-angle X-ray Scattering (SAXS) and X-ray Diffraction (XRD) are some of the examples. DLS and QELS are based on the Brownian motion of the sample. XRD is a good technique to obtain information about the chemical composition, crystal structure and physical properties  used to determine zeta potential, which is a parameter related to the overall charge a particle acquires in a particular medium. ELS measures the electrophoretic mobility of particles in dispersion, based on the principle of electrophoresis , supported by Process Analytical Technologies (PAT) is one of the pharmaceutical development approaches that were recognized for the systematic evaluation and control of nanomedicines FDA, .Note that some of the physicochemical characteristics of nanomaterials can change during the manufacturing process, which compromises the quality and safety of the final nanomedicine. The basis of QbD relies on the identification of the Quality Attributes (QA), which refers to the chemical, physical or biological properties or another relevant characteristic of the nanomaterial. Some of them may be modified by the manufacturing and should be within a specific range for quality control purposes. In this situation, these characteristics are considered Critical Quality Attributes (CQA). The variability of the CQA can be caused by the critical material attributes and process parameters  of critical quality and performance attributes of raw and in-process materials and processes, with the goal of ensuring final product quality” FDA, . The PATBiocompatibility is another essential property in the design of drug delivery systems. One very general and brief definition of a biocompatible surface is that it cannot trigger an undesired' response from the organism. Biocompatibility is alternatively defined as “the ability of a material to perform with an appropriate response in a specific application”  specifically address nanomaterials safety  located in the liver, spleen, and bone marrow . ROS comprise hydroxyl radicals, superoxide anion and hydrogen peroxide. Under normal conditions, the cells produce these reactive species as a result of the metabolism. However, when exposed to nanomaterials the production of ROS increases. Cells have the capacity to defend itself through reduced glutathione, superoxide dismutase, glutathione peroxidase and catalase mechanisms. The superoxide dismutase converts superoxide anion into hydrogen peroxide and catalase, in contrast, converts it into water and molecular oxygen  were one the first applications of computational tools applied in toxicology. QSAR models are based on the hypothesis that the toxicity of nanomaterials and their cellular fate in the body can be predicted by their characteristics, and different biological reactions are the result of physicochemical characteristics, such as size, shape, zeta potential, or surface charge, etc., gathered as a set of descriptors. QSAR aims at identifying the physicochemical characteristics which lead to toxicity, so as to provide alterations to reduce toxicology. A mathematical model is created, which allows liking descriptors and the biological activity  Figure  that ranDue to the size effects, this parameter is assumed as truly necessary, because when nanomaterials are getting smaller and smaller there is an increase in solubility, which is more evident in poorly soluble nanomaterials than in soluble ones. The adherence to the surface of membranes increases with the decrease of the size. Another important aspect related to size that must be considered is the phagocytosis by macrophages. Above 100 nm, nanomaterials can only be internalized by macrophages, a specific cell population, while nanomaterials below 100 nm can be internalized by any cell due to endocytosis. Thus, nanomaterials below 100 nm are associated to higher toxicity risks in comparison with nanomaterials above 100 nm  can activate the immune system by adsorption to proteins like opsonins, even if the particle belongs to the class I of the NCS Figure . The bioNCS is a simple guide to the evaluation of the risk of nanoparticles, but there are many other parameters playing a relevant role in nanotoxicity determination , where the active principle consists of different synthetic structures in vivo activity is dependent on the manufacturing process and, consequently, the comparability needs to establish throughout the life cycle, as happens to the biological nanomedicines. Moreover, for some NBCDs groups like liposomes, glatiramoids, and iron carbohydrate complexes, there are draft regulatory approaches, which help the regulatory bodies to create a final framework for the different NBCDs families , the bio-distribution of the product and its intracellular fate are specifically detailed.EMA already released some reflection papers regarding nanomedicines with surface coating, intravenous liposomal, block copolymer micelle, and iron-based nano-colloidal nanomedicines , is being developed. Through HTA, information about medicine safety, effectiveness and cost-effectiveness is generated so as support health and political decision-makers  in Europe.”The reformulation of pre-existing medicines or the development of new ones has been largely boosted by the increasing research in nanomedicine. Changes in toxicity, solubility and bioavailability profile are some of the modifications that nanotechnology introduces in medicines.In the last decades, we have assisted to the translation of several applications of nanomedicine in the clinical practice, ranging from medical devices to nanopharmaceuticals. However, there is still a long way toward the complete regulation of nanomedicines, from the creation of harmonized definitions in all Europe to the development of protocols for the characterization, evaluation and process control of nanomedicines. A universally accepted definition for nanomedicines still does not exist, and may even not be feasible at all or useful. The medicinal products span a large range in terms of type and structure, and have been used in a multitude of indications for acute and chronic diseases. Also, ongoing research is rapidly leading to the emergence of more sophisticated nanostructured designs that requires careful understanding of pharmacokinetic and pharmacodynamic properties of nanomedicines, determined by the respective chemical composition and physicochemical properties, which thus poses additional challenges in regulatory terms.EMA has recognized the importance of the establishment of recommendations for nanomedicines to guide their development and approval. In turn, the nanotechnology methods for the development of nanomedicines bring new challenges for the current regulatory framework used.EMA have already created an expert group on nanomedicines, gathering members from academia and European regulatory network. The main goal of this group is to provide scientific information about nanomedicines in order to develop or review guidelines. The expert group also helps EMA in discussions with international partners about nanomedicines. For the developer an early advice provided from the regulators for the required data is highly recommended.The equivalence of complex drug products is another topic that brings scientific and regulatory challenges. Evidence for sufficient similarity must be gathered using a careful stepwise, hopefully consensual, procedure. In the coming years, through all the innovation in science and technology, it is expected an increasingly higher number of medicines based on nanotechnology. For a common understanding among different stakeholders the development of guidelines for the development and evaluation of nanomedicines is mandatory, in order to approve new and innovative nanomedicines in the pharmaceutical market. This process must be also carried out along with interagency harmonization efforts, to support rational decisions pertaining to scientific and regulatory aspects, financing and market access.CV conceived the original idea and directed the work. SS took the lead in writing the manuscript. AP and JS helped supervise the manuscript. All authors provided critical feedback and helped shape the research, analysis and revision of the manuscript.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.