In this paper, we propose a framework for studying the AGGIR (Autonomie Gérontologique et Groupe Iso Ressources—Autonomy Gerontology Iso-Resources Groups) grid model, with the aim of assessing the level of independence of elderly people in accordance with their capabilities of performing daily activities as well as interacting with their environments. In order to model the Activities of Daily Living (ADL), we extend a previously proposed Domain Specific Language (DSL), by defining new operators to deal with constraints related to time and location of activities and event recognition. The proposed framework aims at providing an analysis tool regarding the performance of elderly/disabled people within a home environment by means of data recovered from sensors using a smart-home simulator environment. We perform an evaluation of our framework in several scenarios, considering five of the AGGIR variables  as well as health-care devices for tracking the occurrence of elderly activities. The results demonstrate the accuracy of the proposed framework for managing the tracked records correctly and, thus, generate the appropriate event information related to the ADL. Due to this increase, reducing medical costs and improving quality of care service [According the United Nations Department of Economic and Social Affairs (UN DESA)  that require monitoring ,10,11,12In this context, in a previous work , we presIn this paper, we extend our previous works by introducing the identification of complex activities and providing support for simulation and visualization. In order perform this, the DSL is extended by defining operators that deal with constraints related to time, location, and event recognition. Moreover, this DSL is able to categorise a set of activities based on the constants of the AGGIR grid, which classifies autonomy levels to various environmental factors affecting the activities and social life of a person. Thus, more AGGIR activities can be detected, such as alimentation, dressing, toileting, elimination, and transfers. The framework is improved by adding support for more types of sensors and the capacity of performing configuration on the environments and visual simulations of the behaviour of people in such environments. Thus, the framework provides an analysis tool regarding the performance of elderly/disabled people within a home environment by means of data recovered from sensors using the iCASA simulator. In turn, the framework provides a general approach to detect ADL, relate them with the AGGIR variables, and to determine the independence of elder people in their homes. In order to evaluate our approach, we pick five of the AGGIR variables  and evaluate their testability in many scenarios by means of records representing the occurrence of activities or unexpected behavior of the elderly. Furthermore, a health-care device use case concerning the employment of medical equipment for tracking blood glucose is presented. The results demonstrate the accuracy of our framework in managing the obtained records correctly and, thus, generating the appropriate event information.In summary, our main contribution in this work is a framework aimed at providing an evaluation tool based on the AGGIR model regarding the performance of ADL by elderly/disabled people within a home environment by processing data recovered from sensors.The remainder of this paper is organized as follows. The AGGIR grid model as well as the definition and characteristics regarding different types of DSL are explained in In this section, the AGGIR grid model, a tool introduced in January 2002 for expanding senior benefits, is briefly described. Additionally, a review of the different types of DSL in any context  is presented.The AGGIR grid is an autonomy assessment tool used in France to measure the independency levels of elderly people. The AGGIR grid model is a six-level dependence scale (GIR1 to GIR6) that can be defined based on a set of seventeen three-state variables. Each variable can have one of these values: (A) for complete dependency; (B) for partial dependency; and (C) for complete independency. The variables are classified into the following two groups: discriminatory and illustrative variables, as shown in Since DSLs are recognized as effective tools for increasing the productivity and quality of software development ,17,18, iA DSL is a programming language that helps developers in defining concepts in terminology belonging to a particular domain ,21,22,23DSLs are conceived as small textual languages that are able to take certain narrow parts of programming and make them “easier to understand and therefore quicker to write, quicker to modify, and less likely to breed bugs”. Their main goal is to improve developers’ productivity as well as reducing the communication gap in software development between programmers and domain experts . MoreoveDSLs are separated into two categories based on their design: internal DSL and external DSL . An inteMoreover, patterns  and desiSeveral DSLs have been developed for different areas of application, e.g., expert rules, business rules, and configuration rules. A systematic mapping study is presented in .The conceptual DSL described in  expresseIoTDSL, a prototype DSL meant to allow end users to drive the IoT devices installed in their homes relying on a high-level rule-based language is presented in  in orderA framework is proposed in  that useA DSL called ReqDL for describing requirements and capturing bidirectional traceability data concerning system modeling elements is introduced in . AdditioA graphical DSL provides domain experts with an intuitive and user-friendly tool for defining the complex event processing domains of interest for which critical situations in real time need to be detected . Some reDolphin, an extensible programming language implemented as a Groovy DSL for autonomous vehicle networks, is described in . It is dThe authors of  introducA DSL called ALPH for ubiquitous healthcare is presented in . It aimsIn order to assist pathway-supporting health information systems, the authors of ,41 propoAll these works demonstrate the utility of different types of DSL in several domains. With the purpose to support the identification of ADL performed at home by elderly/disabled people, many works have proposed the development of DSL. In the following section, we dedicate the discussion on the revision of existing DSL in the context of both description and recognition of ADL. Furthermore, a brief review of operators regarding time, location, and event constraints is introduced.BODY_STATE_START/ BODY_STATE_STOP for indicating the change of a particular body state of the subject ; and POI_ENTER/POI_EXIT for managing the location type and/or the GPS coordinates. Furthermore, the user-environment interaction is described by activities such as FURNITURE_OPEN/ CLOSED for sensing contact, motion, and vibration on furniture and tools. Moreover, state_type and location_type are defined in order to identify the performance of activities.The authors of  introducThe authors of  proposedAn ambient assisted living architecture for the monitoring of elderly people was presented in . The sysstart and stop moving actions is presented. It includes START_MOVING and STOP_MOVING. Furthermore, it is possible to calculate the duration of body state status along with the STILL_TIME value. Such a duration can be computed and treated as a measure.The authors of  presenteA DSL for processing online events targeting binary sensors is presented in . The appA tool-based methodology aimed to track the ADL of elder adults supporting replicable research is proposed in ; it permAn approach with the objective to evaluate a DSL focused on assistive services by professional caregivers is presented in . Such anThe research in  addresseAll these works consider the correct identification of the activities that require analysis of ADL. However, there is still a lack of ADL to be detected while others only focus on a subset of specific activities. However, most importantly, they are not based on a specific tool, such as the AGGIR grid.In this section, we present a review of different operators used to describe time and location relations among events. Actions and activities that can be identified in a space are called events. These events can be tagged with time and location that, in turn, render events related according to the moment and location in which they occur.ANDlater and ANDsim in Event-Condition-Action rules introduced in [According to the study presented in , in ordeduced in . The autduced in  applied duced in   is to detect a user’s vital signs, activities, and surroundings. In order to achieve this goal, a set of sensors is needed to measure vital signs from the inhabitant. Moreover, smart home sensors need to be deployed to monitor the surroundings in a home environment; thus, it is possible to identify the activities associated with the AGGIR constants by considering physiological contexts and environmental contexts.Thus, for developing the proposed DSL, it is necessary to acknowledge which sensors must be employed for this matter. With regard to the identification of ADL by means of several sensors for providing non intrusive monitoring, a group of sensors that can describe such activities are proposed. The aforementioned sensors are listed in Moreover, relevant data for each performed task identified through the data gathered by sensors are considered in As previously mentioned, health care issues within the aging population represent a problem that needs to be addressed in the proposed DSL. Regarding this aim, a group of commercial medical sensors are suggested; such sensors are usually considered non-intrusive, replaceable, and most of them are low cost. Medical sensors are mainly used in the medical field for the objective of pervasive healthcare. Some of the conventional medical sensors for physiological measurements are listed in Before, During, and After. To this end, the representation of the DSL relies on a GUI in order to describe complex events.Due to the fact that we need to understand the process under which the proposed DSL works, it is relevant to highlight its most remarkable characteristics: (i) expressing situations related to the AGGIR variables that respond to the activities performed by the elderly/handicapped people at home; (ii) the representation of attribute-based event conditions; (iii) the representation of spatio-temporal event conditions. These are defined by temporal operators, such as DSL interface. We propose a graphical DSL rather than textual language in order to make it easy to handle since the user might not necessarily be a person who is acknowledged in the programming field. Data recovery from sensors is crucial for the detection of ADL. For this matter, the proposed DSL plays an important role by representing the gathering of information related to the sensed environments, as well as returning graphical results by means of the aforementioned GUI. Additionally, the information present in the results can be saved as textual specifications in order to be reused and, thus, facilitates finding analysed data without performing any manipulation once again. This functionality allows its reusability by different context-aware applications and, thus, is not limited only to the AGGIR variables.DSL operations. The textual specifications generated by DSL GUI can be used to describe events from an operation perspective, such as an operation in charge of measuring one of the following options: a value, a maximum or minimum value, an average of a set of values, or graphically displaying measured values within a time domain. These operations are exposed as a programming interface as follows: (i) the Value operation returns the value (s) of the device; (ii) the Maximum operation returns the maximum value of the value (s) of the device; (iii) the Minimum operation returns the minimum of the value (s) of the device; (iv) the Average operation returns the average of the value (s) of the device; and (v) the Graphic operation returns an icon with the value (s) of the device.Value operation can be calculated for the following values: (i) on a specific date; (ii) between two hours; (iii) between two dates; (iv) from a specific date. The operations Maximum, Minimum, Average, and Graphic are calculated for the following values: (i) between two dates; (ii) from a date; and the (iii) total . After selecting the operation as well as the desired calculation means, it only remains to specify the date or the time according to what has been previously selected.Due to the fact that events within a smart home environment are sensed during a specific time range, multiple possibilities for describing time using the DSL are considered. This includes determining a specific date or description of a time range starting from a particular date or between two-time points representing the beginning and end of the event. The specified time points can be either a date for a day or hours within a day. The Since, in the proposed DSL, the need to represent activities performed by the elderly or handicapped people within a home environment is present, it is imperative to be aware of when such an activity takes place, as well as if there exists repetition or periodicity during the interval of its performance. By implementing such a methodology, the detection of anomalies on the behaviour of the inhabitants is intended; thus, the coherence of the ADL can be ensured.Spatio-temporal event conditions. In order to deal with temporal constraints, spatio-temporal event conditions are also represented by the DSL. Those are defined by temporal operators such as “Before, During, After, Begin, and End” and are defined using spatial operators such as “Inside, Outside, and Joint”. The proposed criteria for determining the parameters for the example of the preceding paragraph include the following: time and space and within the time dimension. Moreover, it is relevant to consider the following:(i)Concurrency: For recognizing activities that take place simultaneously, but they do not necessarily require the user’s interaction at the same time. That is, activities that have been started but not yet ended by the inhabitant .(ii)Precedence: For establishing a logical order of the activities, e.g., going to the bathroom and then washing hands.(iii)Simultaneity: For identifying the activity that takes the most amount of the time from the user when multitasking capabilities might be present, e.g., preparing meals and calling on the phone or watching television while eating.(iv)Recurrence: For determining a logical sequences of situations. In the case where there is a recurrence of an activity, it is essential to define what this activity is and whether the activity iscarried out regularly.Some examples of these four are presented in So far, the main characteristics of the proposed DSL have been introduced. In this section, the explanation regarding the orchestration for both atomic and complex event detection is provided.Activity, and each Activity represents one of different tasks composing every AGGIR variable. Activity is associated with data obtained from sensors located within the smart-home environment concerning three of the AGGIR variables: toileting, dressing, and transfers. For the toileting AGGIR variable, three atomic events extracted from sensor readings are considered: the bathroom door sensor identifies the Activity regarding opening/closing the door; the toilet flush sensor indicates the Activity of activating the toilet flush; and the washbasin proximity sensor describes the Activity of washing hands.In order to recognize an atomic event, an evaluation of a given sensor reading against a predefined condition from a simple activity is required. In the proposed approach, an atomic event is simply defined as an Situation.Following the description of atomic event detection, it is important to emphasize how complex event detection takes place in the proposed approach. For this subject and parting from the readings from sensors defining a simple activity, a complex event can be considered as a sum of multiple events connected together representing each of the AGGIR grid variables. That is, an event that summarizes, represents, or denotes a set of atomic events (Activities). In the proposed approach, a complex event is stated clearly as a Situation, Situation is composed of the following Activities: open bathroom door, use of toilet flush, wash hands. The (ii) dressing Situation is composed of the following Activities: open wardrobe door, spend time changing clothes, close wardrobe door. The (iii) transfers Situation is composed of the following Activities: lying down, sitting down, getting up. Furthermore, concerning the achievement of each one of the aforementioned variables, the activities conforming to such variables must be detected by following a specific sequence: One activity must be finished before the next one can be performed. That is, the precedence criteria must be considered in order for the variable to be completed, as pictured in In order to illustrate the composition of a After describing the mechanism for orchestrating complex events, the next section describes the proposed operators for representing complex events in the DSL.Allen’s temporal operators. In order to deal with the constraints regarding the time dimension, it is necessary to express conditions regarding temporal relations between thesensor states that are relevant for describing activities. To this end, the DSL takes advantage of the criteria over the time dimension mentioned in X MEETS [t∞) Y implies that event Y should be met by event X, that the start time of X must occur between X should occur right before the beginning of Y.With the aim to establish finite time intervals, temporal bounds relative to Allen’s temporal relations were applied, e.g., the constraint Location based operators. Such operators are in charge of helping to determine the position of the inhabitant within a home environment, as well as the description of ADL through sensor readings. To this effect, the proposed approach considers location based operators, “Inside”, “Outside”, and “Joint”, in order to locate the ADL performed for the inhabitant in space. The descriptions regarding each one of the proposed location based operators are presented in Event based operators. Additionally, with regard to the determination of planned tasks that should be carried out by the inhabitant, such as taking medications, event based operators are important for this matter. These operators are presented in The next section is dedicated to the presentation of the framework proposal for the validation and experimentation of the DSL.Following the proposed DSL, a framework is introduced to fulfill the requirements for processing complex events regarding the AGGIR grid model generated by data recovered from sensors within a smart home environment in order to evaluate the level of independency of elderly people, according to their capabilities in performing activities and interacting with their environments over time.The main purpose of the presented framework is to encourage users who are not necessarily acknowledged in the programming field to be able to define events according to the AGGIR grid variables by providing high-level abstraction, which makes it easy and intuitive by means of concepts that are close to the final users.The proposed framework is composed by three main modules, as shown in Simulator module: In order to describe the elderly’s ADL, smart-home environment scenarios need to be parameterized. For this purpose, the activities performed by the subject are carried out and information is recovered from sensors located within the smart home environment. As the Simulator module, iCASA [e, iCASA  was intee, iCASA , as listDescriptor module: For the purpose of helping users to interact with the framework, the Descriptor module provides a GUI in order to describe all the criteria over the time dimension, location, and events, as well as the required scenarios for the simulation. Moreover, sensors for identifying the ADL have to be specified through the GUI. A group of sensors that can describe such activities are introduced in Moreover, with regard to the scenario specification concerning a certain period of time, the descriptor module is in charge of the definition of the parameters with respect to location of both ADL and sensors, type of sensors, time, and events that need to be identified: (i) Location Map, which is applied to refer to the representation of the environment by means of the house plan where the implementation of the sensor network takes place; (ii) Sensor network, which is focused on defining information related to the sensor network environment infrastructure, and to this end relevant data should be managed, such as the inventory of sensors located within the smart-home environment, as well as the location where each sensor is implemented; (iii) Sensor reading, due to the fact that the information retrieved from sensors is in raw format, such data need to be organized for the purpose of easing their retrieval and interpretation during the event detection process; (iv) Event condition, since conditions are established for triggering the detection of events, such conditions must be defined by the user; and (v) Event occurrence, where once the event conditions have been provided, the event occurrence must be managed in accordance with such aforementioned conditions.Analyzer module: In order to recover data from the Simulator module and the Descriptor module, the Analyzer module is proposed to carry out such a task. Once all the necessary data are collected, the analyzer module analyzes them in order to classify them and to evaluate if the AGGIR variables of the case study have been carried out to completion. The Analyzer module consists of the following: (i) a record filter; (ii) an event detector; and (iii) a variable calculator. Such components are described hereafter.Record Filter: Once the simulation has been conducted, in order to manage the obtained data, the record filter of the Analyzer module separates the recovered data into a series of records. Each record is responsible for the description of actions collected by the sensor network located within the smart-home environment. Therefore, each register is conformed by data files, i.e., the time when the action takes place (corresponding to the simulator clock), the sensor ID, and the sensed value. Furthermore, because the retrieval of such values is generated in raw data format, it is necessary to translate them by means of the Analyzer module with the aim of achieving the identification of events, as well as calculation of values related to the AGGIR grid variables.Event detector is presented.With the aim of performing a filtering of all entries, the first task of the analyzer module is to classify such inputs in a correct manner. For this matter, the generation of “filters” was modeled in the present approach as classes. These classes were introduced in order to determine the type of elements that integrate the analyzed set of instructions by giving each element the appropriate attributes for the modeling of raw data. To this extent, the proposed generated classes for data classification are enlisted in Event detector: After all relevant data from PSN and BSN have been gathered, the identification of events must be carried out. To this extent, the Event detector relies on the introduced DSL to achieve such identification, by managing information grouped in multiple records which are extracted from several sensors.Aiming to separate each reading detected by several devices, the Event detector retrieves information in terms of the scripting language. Such events are identified as low-level actions instances, e.g., if a presence sensor is off in room X and after a period of time another presence sensor is on in room Y, it can be inferred that the action “walk” was carried out.Regarding the recognition of malfunctions, the detection of abnormal situations is carried out at the level of identification of events and situations. For this matter, throughout the analysis of low-level events  collected from device readings from the simulator module script, it was possible to extract a set of anomalies to be detected by the aforementioned module. Such anomalies are enlisted hereafter in Each one of the aforementioned anomalies is related to the AGGIR grid variables. The detection of any of these issues may return a negative value for the associated variable, which is the inability to satisfactorily carry out the AGGIR variable in question.Variable calculator: Regarding the achievement of the AGGIR variables, the criteria consisting of accomplishing a determined number of events during a specific time lapse related to the evaluated AGGIR variable must be fulfilled, e.g., in order to validate if the dressing AGGIR variable is consummated, the AGGIR dressing event conditions such as moving towards the wardrobe at least twice a day must be met. For this purpose, every AGGIR variable is based on three major states that identify whether the elderly inhabitant possesses the ability to perform the ADL conforming to a specific variable, whether it is conforming completely, partially, or not existent at all. This study covers a proposal which only relies on two cases out of the three above-mentioned options, meaning that either the inhabitant is in complete possession of the skills concerning the performance of the ADL composing the evaluated AGGIR variable or simply not.Provided the main components of the aforementioned framework proposal, the description of the process for achieving the detection of complex events is then referred.Due to the fact that ADL takes place within a domestic surroundings, the sensors for detecting such ADL are placed within a smart-home environment. For this matter, such an environment is defined and configured through the Simulator module. Then, the detection process is triggered by the readings obtained from the sensors located within the smart home environment simulator.startTime and endTime,as shown in In order to obtain information to achieve the identification of the AGGIR variables after every activity has been carried out to completion, some relevant data have to be considered, e.g., As a result, the history log file is necessary for deducting the possible activities that will be performed in the future and to find a relation among them to assure coherent behaviour. Such a file will permit obtaining information on whether it is from long or short periods of time, making possible the identification of complex situations inside the home environment, such as feeding, toileting, and transfers, where data collected through the timeline of activities are useful for determining if the behaviour of the inhabitant can be considered as normal.In order to demonstrate the efficacy and suitability of our proposed approach, a set of experiments with regard to the detection of the AGGIR grid variables is performed. For the purpose of evaluating the introduced DSL, four of the AGGIR variables  are picked with the aim of determining their testability in many scenarios by means of records representing the occurrence of ADL performed by the elderly inhabitant within a one week period. In order to detect either the achievement or absence of the AGGIR variables by means of the DSL, we follow a methodological process for the experimental simulations consisting on the following steps, as depicted in Step 1. In order to prepare the scenario for an elderly indoor daily routine over the course of one week, the first step consists of the specification of the criteria over the time and location of activities, type and location of sensors, and the events that have to be detected; in this specific case, all activities related to dressing, toileting, feeding, and transfer AGGIR variables. For this matter, the aforementioned actions are specified and described through the GUI of the Descriptor module. To this end, the primary source of information used to generate the scenario is the schedule proposed by the work of [ work of  where thStep 2. Once the information provided by the Descriptor module is established; the Simulator module, i.e., the iCASA framework, executes the simulation according to such precise information.Step 3. After the simulation is performed, the record filter component of the Analyzer module organizes the resulting data into a set of records, each of which represents an action captured by a sensor within the smart home environment .In what follows, the main characteristics of the simulation scenarios concerning the identification of ADL as well as the AGGIR grid variables are described. In addition, since evaluating the ability of the proposed approach to deal with larger time domains is required, as well as several records and events, the scalability of the introduced framework is also illustrated.Conditions regarding the achievement of a certain number of events within a period of time must be accomplished for the evaluation of the AGGIR variables. In order to detect the toileting AGGIR variable, the following conditions must be met: the inhabitant must use the toilet and wash his hands after eating or using the toilet for at least three times a day. To calculate the transfer variable, namely the ability of a person to perform the basic movements of his daily routine such as rising from bed, sitting down, and standing up from a chair, there must be at least three sitting events a day, either taking place in the living room or in the kitchen, and at least one event of rising from the bed one time per day, while the verification of the dressing variable relies on dressing events such as approaching the wardrobe at least twice a day.In this sense, we represent all these variables as follows. The conditions for the achievement of the toileting AGGIR variable are as follows: (i) the inhabitant must use the toilet; (ii) after, the chain of the toilet flush must be pulled; (iii) after using the toilet, the inhabitant should have his hands washed. In order to analyze such a variable, the records created by the presence sensor located within the bathroom area, alongside the records issued from the use of both the toilet flush sensor and the washbasin proximity sensor, must be examined. To identify the AGGIR dressing variable, the rules for the detection of ADLs composing such a variable are as follows: (i) the inhabitant must be close to the wardrobe area; and (ii) the inhabitant must spend time changing clothes. To this end, data originating from the wardrobe door sensor, as well as the wardrobe proximity sensor, must be gathered. For the purpose of recognizing the AGGIR transfer variable, the following constituent events regarding the aforementioned variable must occur: (i) getting up from bed; (ii) taking a seat; and (iii) standing up from a chair, but not necessarily in this order. In this matter, capacitive sensors located at both the bed and chair/armchair are responsible for the data collection.In order to illustrate the three AGGIR variables outlined above, With a view to perform the evaluation of the AGGIR variables, all the simulating and processing operations are performed by means of two sets of inputs for the introduced framework in order to generate two different scenarios. For this matter, the week is separated into seven days, each of which is represented by means of one simulation file within the smart home environment simulator.To this effect, the first scenario is simulated of an ideal week case scenario, meaning that all the ADL were performed with no impediment by the inhabitant throughout the entire week by means of a simulated sensor network within a house environment for monitoring the ADL carried out by the elderly resident. The results obtained from the simulation are presented in For the purpose of generating a malfunctioning on the developed criteria, some of the ADL related to the evaluated AGGIR variables were randomly excluded in the second scenario during the seven-day period of analysis. Once the simulation was conducted, the obtained results demonstrate that the detection of complex events regarding either the achievement or the absence of ADL were carried out successfully, according to the pre-established conditions, as listed in Furthermore, the proposed method succeeded in identifying three simulated problems within a week. The days were randomly chosen. With the aim of evaluating the alimentation AGGIR variable, for the first scenario, the ADL related to cooking must be performed by the inhabitant before the alimentation AGGIR variable takes place. That is to say, the accomplishment of preconditioned events conforming to cooking activity is necessary, particularly the following: (i) open/close the refrigerator door; (ii) open/close the kitchen cabinet door; (iii) use of the stove/burners; (iv) use of the traditional/microwave oven and not necessarily in this order. On the other hand, for the second scenario, some of the above-mentioned events concerning the cooking task were dropped. For this matter, information collected from sensors located within the kitchen area must be analyzed. To this end, data originated by both the refrigerator and kitchen cabinet door sensors, the stove magnetic sensor, and the oven/microwave oven sensor must be gathered.Nonetheless, it is important to remark that according to the AGGIR grid, cooking is not conceived as a discriminatory variable and not as an evaluation criterion for the alimentation AGGIR variable, which only requires the elderly individual to be capable of serving and eating a prepared meal. For this reason, in our approach, cooking is considered as an ADL that can trigger the Alimentation AGGIR variable with the intention of not contradicting the pre-established evaluation criteria, which can also be used for detecting the aforementioned variable in this work.Once the cooking task is achieved, in addition to the first scenario, events defining the alimentation AGGIR variable must be identified, such as the following: (i) sitting within the dining room area; (ii) placing meal on the table; and (iii) spending time consuming food. Moreover, with the aim of identifying the occurrence of the above-mentioned events, data from sensors located within the dining room area must be collected, such as the following: chair capacitive sensor, table capacitive sensor, and dinning room presence sensor. With respect to the second scenario, some of the ADL conforming to the alimentation AGGIR variable were arbitrarily left out. Additionally, in order to assure a coherent self-feeding behaviour, both scenarios must occur at least three times a day. The orchestration of activities regarding the alimentation AGGIR variable is illustrated in Having completed the simulations of both scenarios, the results concerning the first scenario proves that, indeed, the detection of complex events conforming the cooking tasks, as well as those from the alimentation AGGIR variable, were carried out to completion successfully and in accordance with the predetermined input conditions, as listed in Moreover, as for the second scenario, due to the fact that some of the activities were not taken into account for the analysis of both the cooking activity and the alimentation AGGIR variable; the proposed framework also succeeded in identifying whether or not the concerned ADL was performed according to initial parameters, as presented in Furthermore, in order to illustrate the number of events related to the cooking task, as well as for the alimentation AGGIR variable, In order to prove the flexibility of the proposed framework, a health-care device use case is introduced. This case considers a resident in charge of taking glucose level measurements by means of a glucometer, performing a type of measurement other than those listed in the AGGIR grid. We design two scenarios where, to ensure continuous measuring, it is necessary for the house resident to perform such measurements at least three times a day. To this extent, the first simulation scenario consists of a week during which the person was completely responsible of employing a glucometer device for measuring his own glucose levels, meaning that the completion of a planned medical task was achieved. The second scenario was thought to randomly exclude some of the measurements throughout several days of the week. All the simulations and operations are carried out by the proposed framework by means of records corresponding to each scenario. The results obtained from the simulations corresponding to both scenarios are presented in With regard to the simulation results, it can be observed in The latter can be interpreted as the efficiency of the framework to perform the recognition of planned tasks concerning measurements carried out by the user by means of medical devices.We also performed experiments to evaluate the framework scalability and performance In what follows, we describe the results regarding these experiments.To validate the execution of the framework under event processing conditions regarding both larger time domains and higher workload cases, the scalability of the framework for a period of time longer than one week was tested. For this matter, a time lapse of three months was simulated in order to generate the corresponding records. Next, the proposed criteria were applied with the intent to create the events and, thus, to calculate the values of the three following AGGIR variables: toileting, transfers, and dressing. In addition, with a view to allow flexibility when processing records and generating events, the assumption that each month is conformed by thirty days was considered. By assuming so, there was a possibility to perform the required simulations by employing an approximate number of days per month, i.e., 28, 29, 30, and 31 days .Finally, to make sure that the algorithm is scalable, the elapsed time during the calculation of the three above-mentioned AGGIR variables for simulation time periods for one day, one week, two weeks, three weeks, a month, two months, and three months were monitored. O(n), where n defines the number of events, implying that the framework is scalable as a whole.In order to present the results thus achieved, With the aim of applying the above-mentioned steps, several use cases are provided, namely the alimentation, toileting, cooking, and transfer AGGIR variables, as well as a description regarding the use case of a medical device for measuring blood glucose level. Moreover, the results obtained show that the proposed framework succeeded on detecting the events; therefore, the AGGIR variables according to pre-established conditions are present in the experimentation scenarios.In recent years, it has been reported that the population sector targeted as the elderly is increasing more rapidly than in the previous decades. In addition to that, age-related health issues anddifferent chronic diseases are starting to become apparent at this stage of life. Moreover, various surveys indicate that elderly people are willing to live independently in their own homes for as long as possible. In order to assess the above-mentioned problem, PSNs and BSNs in smart environments represent an option regarding healthcare solutions for monitoring elderly people at home. However, despite their popularity, the absence of a monitoring approach with regard to a dependency evaluation model such as the AGGIR grid variables has never been treated. In this context, the core contributions of this research work are as follows: (i) a state-of-the-art related to the proposed subject and future circumstances with regard to aging population, as well as the capabilities of technology for assisting maintenance and monitoring the health needs of people are reviewed; (ii) a DSL relying on the AGGIR grid evaluation model for assessing the performance of ADLs of the elderly/handicapped at home; (iii) a framework tool for validating the proposed DSL in terms of its capacity for processing complex events; this framework offers a smart-home simulator called iCASA for carrying out evaluation and experimentation and a parser in charge of processing the data issued from the DSL aimed to create the appropriate instructions for the iCASA platform.We are currently working on proposing the identification of a three-state variable approach for the AGGIR grid. Due to fact that the current version of the introduced DSL is only able to recognise two of the three values (A for complete dependency and C for complete independency) of the AGGIR grid variables, the proposal of an improved version of the DSL comprehending the three states of identification  is one of the possible directions for future work. For the purpose of assessing AGGIR variables that can be considered as more elaborate, such as coherence, further experimentation is required for evaluating crucial activities describing the concerned variable. Even though the presented approach considers the evaluation of the AGGIR discriminatory variables, with the goal of covering the whole spectrum of the AGGIR variables, the integration of the AGGIR illustrative variables is one of the next steps to be taken. Currently, the perspective introduced in this research study relies on the AGGIR grid variables. In order to expand the coverage of the suggested approach, the inclusion of other dependency evaluation models  represents a field of opportunity for broadening the scope of the present work. In addition to the presence of the house residents, visitors can represent a factor for change regarding household dynamics. For this matter, the adaptability of the proposed approach must be determined by tests concerning unknown users relative to the smart-home environment, as well as the impact that their visit may cause to the actual inhabitants. Despite the fact of having employed a smart-home simulator, as well as a schedule describing the actual activities that were carried out by the user, the application of the present work for real-time use cases is recommended. To this extent, the monitoring of ADL within an actual smart-home environment would be ideal for testing the present approach in a real-time scenario.


Aspergillus fumigatus possesses five putative siderophore transporters. Here, we demonstrate that the siderophore transporters Sit1 and Sit2 have overlapping, as well as unique, substrate specificities. With respect to ferrichrome-type siderophores, the utilization of ferrirhodin and ferrirubin depended exclusively on Sit2, use of ferrichrome A depended mainly on Sit1, and utilization of ferrichrome, ferricrocin, and ferrichrysin was mediated by both transporters. Moreover, both Sit1 and Sit2 mediated use of the coprogen-type siderophores coprogen and coprogen B, while only Sit1 transported the bacterial ferrioxamine-type xenosiderophores ferrioxamines B, G, and E. Neither Sit1 nor Sit2 were important for the utilization of the endogenous siderophores fusarinine C and triacetylfusarinine C. Furthermore, A. fumigatus was found to lack utilization of the xenosiderophores schizokinen, basidiochrome, rhizoferrin, ornibactin, rhodotorulic acid, and enterobactin. Taken together, this study characterized siderophore use by A. fumigatus and substrate characteristics of Sit1 and Sit2.Siderophore-mediated acquisition of iron has been shown to be indispensable for the virulence of several fungal pathogens, the siderophore transporter Sit1 was found to mediate uptake of the novel antifungal drug VL-2397, and siderophores were shown to be useful as biomarkers as well as for imaging of fungal infections. However, siderophore uptake in filamentous fungi is poorly characterized. The opportunistic human pathogen  Therefore, microorganisms evolved different strategies for iron acquisition. The mold Aspergillus fumigatus is the most common and life-threatening opportunistic airborne fungal pathogen in humans [A. fumigatus employs two high-affinity iron acquisition mechanisms, reductive iron assimilation (RIA) and siderophore-mediated iron acquisition, as well as low-affinity ferrous (Fe2+) iron uptake [3+ by membrane-bound metalloreductases such as FreB into Fe2+ followed by reoxidation and cellular uptake of iron by a protein complex consisting of the ferroxidase FetC and the iron permease FtrA [A. fumigatus produce exclusively hydroxamate-class siderophores. A. fumigatus secretes two fusarinine-type siderophores, triacetylfusarinine C (TAFC) and fusarinine C, to solubilize and sequester environmental iron and employs two ferrichrome-type siderophores for intracellular handling of iron, hyphal ferricrocin and conidial hydroxyferricrocin [A. fumigatus. After the secretion and chelation of iron, the siderophore-iron complexes are taken up by specific transporters, which belong to the “Siderophore-Iron-Transporter” (SIT)-subfamily of the major facilitator protein superfamily [Saccharomyces cerevisiae, Candida albicans, Candida glabrata, or Cryptococcus neoformans, possess SITs for the uptake of xenosiderophores, i.e., non-self-produced siderophores [+) gradient across the membrane as the source of energy [S. cerevisiae, which possesses four SITs [S. cerevisiae SITs are more similar to each other than they are to SITs from other fungal species [A. fumigatus SITs cannot be predicted on the basis of sequence similarity to S. cerevisiae SITs. A. fumigatus possesses five potential SITs, termed MirB, MirC, MirD, Sit1 and Sit2, which are transcriptionally repressed by iron indicating a role in iron homeostasis [Erwinia spp. [A. fumigatus has been shown to take up ferrioxamine E and coprogen but the respective SITs have not been characterized [Iron is an essential element for almost all organisms since it serves as a cofactor of numerous cellular processes. In excess, however, this metal can be highly toxic by promoting the production of reactive oxygen species . Consequn humans . In addin humans . A. fumiase FtrA ,5. Siderase FtrA . Within ase FtrA . Within ricrocin . The bioricrocin ,9; i.e.,erfamily . The prerophores . SITs arrophores  and mostf energy ,12,13. Tour SITs . However species , which ieostasis . MirB waeostasis ,17, Sit1eostasis  and Sit2eostasis . Ferrichnia spp. ,20. The nia spp. . Furthercterized .A. fumigatus [A. fumigatus Sit1 was found to mediate uptake of the novel antifungal drug VL-2397, also termed ASP2397 [A. fumigatus SITs are not only of scientific but also of translational interest as SITs might serve as a target for therapy and/or diagnosis of fungal infections. The analysis of substrate specificities of Sit1 and Sit2 was previously characterized by heterologous expression in S. cerevisiae and short-term uptake studies in A. fumigatus mutants lacking Sit1 and/or Sit2 focusing exclusively on ferrichrome and ferrioxamine B [A. fumigatus and particularly of Sit1 and Sit2, we applied an alternative approach. We generated mutants lacking Sit1 and/or Sit2 in a genetic background avoiding interference with endogenous siderophores and RIA to enable characterization of siderophore uptake by growth studies. This strategy allows analysis of a variety of siderophores without radiolabeling and to identify exclusivity of substrate specificity. Taken together, this study revealed that A. fumigatus is able to utilize a wide spectrum of siderophores with different efficiency and identified several siderophores that cannot be used by this mold. Moreover, the overlapping and specific substrate specificities of Sit1 and Sit2 characterized here provide structural information of the respective substrates. Furthermore, phylogenetic analysis revealed insights into (i) the evolutionary conservation of Sit1 and Sit2, (ii) the relationship of Sit1 and Sit2, and (iii) the value of phylogenetic analysis for substrate prediction of SITs.Siderophore-mediated iron assimilation is found in fungi and bacteria. Fungal siderophore biosynthesis raised considerable interest because it was found to be essential for the virulence of several animal- and plant-pathogenic fungal species including umigatus . Moreoveumigatus ,24,25, fumigatus ,27,28, aumigatus . Further ASP2397 . Therefoxamine B . To compA. fumigatus strains were grown on Aspergillus minimal medium (AMM) [w/v) glucose and 20 mM glutamine as carbon and nitrogen sources, respectively. For iron-replete conditions, FeSO4 was added to a final concentration of 0.03 mM; for iron starvation, iron addition was omitted. For spore production of A. fumigatus ΔsidAΔftrA strains, an iron concentration of 5 mM was used. For point-inoculation on plates, 104 spores were used; for inoculation of liquid media, 106 spores per mL of medium were used; incubation of solid and liquid cultures was performed at 37 °C. Supplementation with siderophores was performed during pouring the plates at 65 °C.For spore production and plate growth assays, um (AMM)  containiA. fumigatus strain AfS77, a derivative of A. fumigatus ATCC46645 lacking non-homologous recombination (akuA::loxP), which facilitates genetic manipulation [sidA/∆ftrA A. fumigatus mutant strain, sidA (AFUA_2G07680) and ftrA (AFUA_5G03800) were replaced in a PEG-mediated transformation approach by a self-excising hygromycin resistance cassette (hph) containing the β-rec/six site-specific recombination system under the control of the xylose-inducible promoter [sidA and ftrA were amplified by PCR using the primer pairs TO16/TO17 (sidA 5′-NCR), TO18/TO19 (sidA 3′-NCR), TO20/TO21 (ftrA 5′-NCR) and TO22/TO23 (ftrA 3′-NCR). The self-excising hph resistance cassette was excised from plasmid pSK529 by digestion with the restriction enzyme FspI. This fragment, together with the respective NCR fragments, were assembled by a NEBuilder reaction  in a pUC19L  backbone, resulting in the plasmids p∆sidA-rec and p∆ftrA-rec. These plasmids were used as templates to amplify the deletion constructs with a size of 6.8 kb for transformation by PCR using the primer pairs TO56/TO57 for sidA or TO60/61 for ftrA, respectively. Selection for sidA deletion transformants was performed on AMM containing 0.2 mg/mL hygromycin B , the resistance cassette was excised from ΔsidA mutants by cultivation on AMM containing 1% xylose. Subsequently, ftrA was deleted with the same strategy. Correct genotypes were confirmed by Southern blot analysis. The resulting ΔsidAΔftrA strain was then used as the background strain for further genetic manipulations. For all other genetic manipulations, non-self-excising selection markers were employed.All studies were carried out in pulation ,34. For promoter ,35. Thersit1 (AFUA_7G06060) deletion in the ∆sidA∆ftrA background, genomic DNA from a previously generated ∆sit1 strain [sit1 deletion cassette by using oligonucleotides TO102/TO105. This fragment contains 5’- and 3´-NCR of sit1 and an interjacent hph cassette.For 1 strain  was usedsit1 encoding sequence followed by the GFP-derivative mVenus-encoding gene amplified with oligonucleotides MA07/MA12 from plasmid pMMHL69 [hph cassette amplified with MA13/MA14 from pMMHL69 and a fragment containing the 3′ -NCR of sit1 amplified with oligonucleotides MA15/MA16 from genomic DNA. For N-terminal Venus-tagging of Sit1, the plasmid pMA05 was generated including sit1 5′-NCR amplified with MA07/MA17 from genomic DNA, a Venus-encoding fragment with MA18/MA19 from pMMHL69, the sit1 region with MA12/MA20 from genomic DNA, an hph cassette with MA13/MA14 from pMMHL69 and the 3′ -NCR of sit1 with oligonucleotides MA15/16 from genomic DNA. Fragments for pMA04 or pMA05, respectively, were assembled by a NEBuilder reaction  in a pUC19L (Thermo Fisher) backbone. Fragments amplified from pMA04 or pMA05 with primers MA20/MA16 and MA07/MA16, respectively, were used in the A. fumigatus transformation rounds into wild-type.For C-terminal Venus-tagging of Sit1, plasmid pMA04 was generated by including the  pMMHL69 , an hph sit2 (AFU_7G04730) the plasmid pMA01 containing the Δsit2 deletion cassette was designed with a 5′-NCR of sit2, a pyrithiamine resistance cassette (ptrA) and a 3′-NCR of sit2, individually amplified by PCR with oligonucleotides MA01/02, MA03/04 and MA05/06, respectively, using genomic DNA as a template for the NCRs and the plasmid pSK275 [ptrA. Amplified fragments were assembled by a NEBuilder reaction  in a pUC19L (Thermo Fisher) backbone. For transformation of A. fumigatus, the deletion construct was PCR-amplified from plasmid pMA01 with primers MA01/MA06.For deletion of d pSK275  for ptrAsit1 and ∆sit2 mutants in ∆sidA∆ftrA background, sit1 and sit2 genes including 1.5 kb 5´- and 3´-NCRs were integrated into the fcyB locus, which allows selection for 5-flucytosine resistance without the need of another selection marker [sit1 and sit2 genes were PCR-amplified from genomic DNA using primers MA53/54 and MA55/56, respectively. The resulted fragments were then assembled in a NEBuilder reaction  into a pUC19L-fcyB vector containing the 5′ and 3′ flanking regions of fcyB locus [NotI-digestion and the resulting fragments were used for transformation in A. fumigatus. The transformation of A. fumigatus AfS77 was performed according to Tilburn et al. 1983 [For the complementation of ∆n marker . TherefoyB locus . The genal. 1983 . Selecti® ; rhodotorulic acid and basidiochrome were gifts from Günther Winkelmann and Kurt Haselwandter, respectively. All other siderophores were purchased from EMC Microcollections, Germany. Triacetylfusarinine C, fusarinine C, and ferricrocin were produced and isolated in-house from iron-starved A. fumigatus liquid cultures as described previously [Ferrichrome and ferrioxamine B were purchased from Sigmaeviously ,39.For microscopy, spores of fluorescent-tagged fungal strains were grown in coverslips with AMM at 37 °C, under iron starvation or iron sufficiency. Mycelia were observed with a Zeiss Axioplan fluorescence microscope  equipped with an Axiocam 503 mono microscope camera  and excitation/emission filters at 428/536 nm for mVenus detection. Image processing and editing were made with ZEN 2 (Blue Edition) microscope software, Adobe Photoshop CS6 (v.13), and Microsoft Power Point (v.16).Saccharomyces Genome Database [Candida Genome Database [The phylogenetic tree was constructed from the multiple protein sequence alignment using the neighbor-joining method based on 100 replicates, also in Geneious Prime. The GenBank protein accession number follows each sequence name. Protter was used for domain organization prediction [Protein sequences of respective membrane transporters were obtained from FungiDB, the Database , the CanDatabase , and NCBDatabase . MultiplDatabase  algorithediction .A. fumigatus is hampered by interference with the endogenous high-affinity iron acquisition systems RIA and endogenous siderophore production. To avoid this and to allow the characterization of siderophore uptake in A. fumigatus by simple growth studies, a selection marker-free mutant lacking siderophore biosynthesis  and reductive iron assimilation  was generated using an excisable hygromycin (hph) selection marker [A. fumigatus ∆sidA∆ftrA mutant strain [hph. As the previously described mutant, the new ∆sidA∆ftrA double mutant is able to grow only in the presence of ≥3 mM Fe2+ concentrations via the low-affinity iron uptake system or when supplemented with low concentrations (µM) of utilizable siderophores via high-affinity uptake by SITs (sidA∆ftrA∆sit1) or Sit2 (∆sidA∆ftrA∆sit2) or both SITs (∆sidA∆ftrA∆sit1∆sit2) and assayed their growth on solid media supplemented with iron or siderophores (sit1∆sit2 and this was not affected by inactivation of Sit1 and/or Sit2 (A. fumigatus [A. fumigatus is poorer compared to the other siderophores as even higher concentrations (5 µM) still led to lower growth promotion  and alternatively C-terminally (Sit1C-Venus) with the yellow fluorescence protein derivative Venus with expression of the tagged sit1 alleles under control of the endogenous promoter as described in Materials and Methods. In agreement with the reported transcriptional regulation, epifluorescence microscopy demonstrated that the production of both Sit1N-Venus and Sit1C-Venus was repressed by iron , as the experimental set-up is difficult due to the interference with endogenous siderophores.A. fumigatus. To exclude interference with endogenous siderophore production and to allow maximal sensitivity, substrate specificity was analyzed by growth assays using mutants lacking Sit1 and/or Sit2 in a genetic A. fumigatus background lacking siderophore biosynthesis and RIA to avoid interference with endogenous siderophores and SIT-independent high-affinity uptake of iron from the supplemented ferric siderophores. These studies revealed that Sit1 is the sole transporter of A. fumigatus for ferrioxamine-type siderophores including cyclic ferrioxamine E as well as linear ferrioxamine G and ferrioxamine B and previously described ferrioxamine B derivatives, of which the terminal amino group was acetylated or succinylated [A. fumigatus depends exclusively on Sit1 and Sit2 with the exception of low-efficacy use of ferricrocin by an unknown transporter independent of Sit1 or Sit2. In this respect, it is interesting to note that A. fumigatus MirB was previously shown to mediate the utilization of ferricrocin when expressed heterologously in S. cerevisiae [A. fumigatus resistant to this antifungal drug [5-acylated N5-hydroxyornithine residues (positions R4–R6), which provide the hydroxamate groups for iron chelation, and three additional amino acids (positions R1–R3) [4–R6 and the amino acid residues present in positions R1–R3; whereby glycine is present in position R3 in most ferrichrome-types with VL-2397 being an exception. 1 and R2; (ii) both Sit1 and Sit2 accept acetyl as acyl-group in R4–R6; (iii) Sit2 but not Sit1 accepts anhydromevalonyl as acyl-group in positions R4–R6; (iv) Sit2 does not distinguish between cis- and trans- anhydromevalonyl as acyl group in positions R4–R6; (v) Sit1, and to a lesser extent Sit2, accept methylglutaconyl as acyl group in positions R4–R6, (vi) methylglutaconyl as acyl-group in R4–R6 significantly decreases uptake efficacy in comparison to anhydromevalonyl as seen from the significant differences in growth promotion, and (vii) Sit1 accepts asparagine, leucine, and D-phenylalanine in positions R1, R2, and R3, while at least one of these amino acid residues disturbs recognition by Sit2. These results demonstrate that both the amino acid residues in positions R1–R3 as well as the acyl-groups impact recognition of ferrichrome-type siderophores by SITs. The data provide structural insights in the substrate specificity of Sit1 and Sit2, which will help to understand substrate recognition. Similar to ferrichrome A, the utilization efficacy of both coprogen-type siderophores tested, coprogen and coprogen B, was low despite the fact that these siderophores were accepted by both Sit1 and Sit2. Neither Sit1 nor Sit2 were important for the utilization of the endogenous siderophores fusarinine C and TAFC, which does not exclude a role in the transport of these siderophores but excludes a dominant role. Moreover, heterologous expression in S. cerevisiae suggested that MirB transports TAFC [A. fumigatus was found to lack utilization of the xenosiderophores basidiochrome, rhodotorulic acid, enterobactin, rhizoferrin, ornibactin and schizokinen–which belong to hydroxamate-, catecholate-, carboxylate- and mixed-class siderophores  located in the terminal extracellular loop was found to significantly impair ferrichrome utilization [A. fumigatus Sit1) with exception of the Sit2 clade (A. fumigatus Sit1, an N479K mutation located in the 13th transmembrane domain (A. fumigatus resistant to VL-2397 [Previously, C-terminally Venus-tagged Sit1 expressed under the control of a xylose-inducible promoter was shown to be localized in the plasma membrane . Here weanalysis . Remarkaanalysis . These ranalysis . In C. glization . As showt2 clade  in whiche domain  was foun VL-2397 . Accordi VL-2397  this mutA. fumigatus belong to different subclades and that despite overlapping substrate specificities, Sit1 and Sit2 are only distantly related. Sit2 is closer related to MirD and the TAFC transporter MirB than to Sit1 (S. cerevisiae Gex2 and S. pombe Str3 have been reported to transport glutathione and heme, respectively [Saccharomycotina species S. cerevisiae, C. albicans and C. glabrata cluster closely, which indicates a common origin despite having in part different substrates (A. fumigatus Sit1 is the closest related to the “Saccharomycotina subclade” again indicating a common origin. Within the A. fumigatus Sit1 subclade, F. graminearum Sit1 has been shown to transport ferrichrome and ferrioxamine B and C. neoformans Sit1 was found to transport ferrioxamine B (Phylogenetic analysis indicated that the five SITs of  to Sit1 . These r to Sit1  have beeectively ,60. As pectively , all SITbstrates . Notablyxamine B . This ovA. fumigatus infection by positron emission tomography [A. fumigatus [A. fumigatus lacks the production of coprogen- and ferrioxamine-type siderophores and utilizes ferrichrome-type siderophores only intracellularly [The genes encoding Sit1 and Sit2 are expressed during infection as shown in a murine aspergillosis model , which imography , which uumigatus , which mumigatus , displayumigatus . The disllularly . In thisllularly ,63. Therllularly . The imp


P < 0.05). This training improved the parents’ perception of ADHD knowledge, treatment options, and drug side effects awareness (P < 0.05). Our study aims to underscore the suitability of such programs in the local nuances of the Chinese context, show application feasibility to pediatricians and psychiatrists, and provide supporting evidence for their utilization within the country’s health and educational systems.Attention-Deficit/Hyperactivity Disorder (ADHD) is the most common chronic neurodevelopmental disorder in childhood, placing a heavy burden on family and society. The treatment of school-aged children with ADHD emphasizes multimodal interventions, but most current research focuses solely on parent training and family functioning. The aim of this study was to examine the effect of parent-teacher training on academic performance and parental anxiety. In an open-label cluster randomized controlled trial from January 2018 to January 2019, 14 primary schools in Shanghai were randomly assigned into the intervention group and the control group, and ADHD screening was conducted for students from grades one to five. Children in both groups received medication as prescribe by their pediatricians. In the intervention group, families and teachers also received parent-teacher training. The training included ADHD behavioral interventions for parents, as well as classroom management skills for teachers. This study screened 9,295 students, 99 children in the control group and 105 children in the intervention group were included in the analysis. The intervention group demonstrated significant improvement in ADHD symptoms and academic performance and decreases in parent stress compared to that in the control group ( Attention-defici/hyperactivity disorder (ADHD) is one of the most common chronic neurological diseases in early childhood . NumerouADHD brings a heavy burden and induces a variety of undesirable effects to children, parent and society. Children with ADHD have poor academic performance, quality of life, and high incidence of conflicts with parents or teachers . ParentsCurrently, evidence-based treatment for school-aged children with ADHD is medical and behavioral treatment, but both therapies have limitations . StimulaDespite shown efficacy, quite often medical treatment suffers from poor compliance , due to Parent-teacher training linked family and school systems to provide support for school-aged children with ADHD . This trOur study is a cluster randomized controlled trial (RCT) to investigate the effect and acceptability of parent-teacher training, and to the best of our knowledge, it is one of the earliest attempts to evaluate the effectiveness and acceptability of pediatrician-guided parent-teacher training in China. This study aims to underscore the suitability of such programs in the local nuances of the Chinese context, show application feasibility to pediatricians and psychiatrists, and provide supporting evidence for their utilization within the country’s health and educational systems.We aimed to explore whether the pediatrician-guided parent-teacher training could deliver a benefit for children with ADHD, compared with only received medication treatment. We hypothesis that compared with the children in the control group, the children in the intervention group would reduce ADHD symptoms, parental stress and improve academic performance after the intervention.This study was a cluster RCT to investigate the effect and acceptability of the parent-teacher training after 4 and 10 month. It consists of three parts: ADHD screening, parent-teacher training implementation, and evaluation the effect and acceptability of the parent-teacher training.The RCT was carried out at 14 primary schools (children aged 6–11 years) located in Jiading District, Shanghai, China. A meeting convened by professional pediatricians aims to give the teachers and the parents a better understanding of the goal of the research and the specific pattern of intervention. Parents and teachers who agreed to participate in the program must sign an informed consent. This study was conducted from January 2018 to December 2018. Parents of children who screened positive were notified by phone or e-mail to take their children to Shanghai Children’s Hospital for specialized examination. Eligible patients were included after obtaining written informed consent.Inclusion criteria: (1) Meet the diagnosis of ADHD according to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) . (2) BetExclusion criteria: (1) Intellectual disability (IQ < 70). (2) excluded autistic spectrum disorder (ASD), epilepsy, schizophrenia, cerebral palsy and other nervous system diseases and mental disorders. (3) Excluded children with severe heart, brain, kidney, and other organ dysfunction. (4) Children with hearing and visual impairment.The randomization sequence was created by a third-party statistician using SAS statistical software version 9.1 . A total of 14 schools were included in this study and were randomly divided into the intervention group (seven schools) and the control group (seven schools). Parents, teachers, and pediatricians were aware of the allocated arm. A senior statistician who was blinded to the group allocation carried out the primary and secondary analysis.This study provided a multimodal treatment for teachers and parents in the intervention group, which integrates family, school, and medical department to address the aims of a holistic ADHD management strategy. The parent-teacher training herein was 16-week training for parents and teachers, the three components: teacher training, family training, and ADHD knowledge improvement; and applied medical treatment were described below. Details describing the treatment components were provided in Children in both the intervention and control groups were treated with medication. All children started stimulant medication as prescribed by their pediatricians, referring to the clinical practice guidelines for ADHD children published by the The intervention was implemented by hospital pediatricians targeting to improve the knowledge of ADHD, behavioral intervention and teachers’ classroom management. Hospital pediatricians conducted 1 group meeting with participating school teachers  and 1 school-based consultation with parents and school teachers (30 min communication between parents and teachers about the performance of children). Teachers completed the daily behavior report card and homework plan .The intervention began with an overview of the study and behavioral intervention for ADHD children conducted by hospital pediatricians. Hospital pediatricians conducted 2 group parent trainings sessions  and 2 individualized family therapy sessions . The training included the use of commands, rewards, discipline, and stress management. There were two conferences calls between the hospital pediatricians and the parents (approximately for 10 min) to monitor the children’s treatment and to improve the intervention if needed.This study established a WeChat messaging platform-based  official account, which was followed by the parents. ADHD information was published to parents and teachers with the goal to inform about ADHD and gain disease-related knowledge through pictures, texts, and videos. In addition, this study distributed two training manuals to introduce the symptoms, causes, diagnosis, treatment, and intervention of ADHD to the children in the intervention group .This training was conducted by multi-disciplinary research team whose participants are hospital pediatricians, psychologist, statistician, postgraduates, and teachers in 14 primary schools in Jiading District. All teacher and parent trainings were held in the classrooms of primary schools and the intervention was conducted by the hospital pediatricians. Ninety five percentage teachers participated in teacher training, and 80% parents participated in family training. This study contacted with parents by telephone every month and inquired about treatment status, medication status, and drug side effects.1 as ChiCTR1800014945. All participants signed the informed consent.This study has been reviewed and approved by the Research Ethics Committee of Shanghai Children’s Hospital (2018R003) and registered at the Chinese Clinical Trial RegistryResearchers use Conners Parent Symptom Questionnaire (PSQ) and Conners Teacher Rating Scale (TRS) to assess the symptoms of ADHD in primary school children. There are 48 items in PSQ, including six factors including conduct problems, learning problems, psychosomatic, hyperactivity-impulsivity, anxiety, and ADHD index . The higSwanson Nolan and Pelham’s fourth scale (SNAP-IV) contains 26 items, including three factors of inattention, hyperactivity, and impulsivity. The higher the score, the more severe the ADHD symptoms. Researchers use it to assess the severity of core symptoms of ADHD. The whole consistency Cronbach α = 0.94, the subscales for attention deficit, hyperactivity – impulsivity, and oppositional disobedience were 0.90, 0.79, and 0.89, respectively.The secondary outcomes consisted of academic performance and parent stress, all questionnaires were measured at baseline (t1), the end of the treatment (t2), and 10-month (t3) see .Academic Performance Questionnaire (APQ) was developed based on the Olivero Bruni’s Teacher school achievement form and school-teacher can APQ to assess students’ academic performance. The questionnaire consists of 15 items using 5-point scale ranging from 1 to 5, with higher scores indicating better performance and four subscales . The internal consistency Cronbach α = 0.962 .Parenting Stress Index (PSI) is used to assess the difficulties, anxiety, nervousness, and the level of parenting stress which aims to identify various difficulties in children’s growth and development . It consThe Treatment Acceptability Questionnaire (TAQ) scale were rated on a 6-point Likert-type scale, ranging from 1 (strongly disagree) to 6 (strongly agree) with higher scores indicating the greater acceptability. TAQ is 8-item measure and is widely used in the treatment acceptability assessment of ADHD children .This study collected the child’s gender, child’s age, child’s grade, primary caregivers, socioeconomic status, family structure, parents’ age, parents’ education degree, and child’s ADHD medication status.n = 176) is needed. In order to adjust the drop out and loss of 10%, 194 participants will be needed.We used PASS 16 software  to calculate the sample size. The sample size was based on the primary outcome of this study; the SNAP-IV scores at 10 months between the intervention group and the control group. In this study, we assumed the difference of mean scores in the intervention group and the control group was 0.2 with the standard deviation (SD) 0.48 . Since tt-test for continuous variables and Chi-square tests for categorical variables.In this study, 105 children were eventually in the intervention group and 99 children were included in the control group. Multiple imputation methods were used to replace the missing data in the primary efficacy analysis to reduce potential confounding bias. The baseline variables of the two groups were compared by analysis of a Comparisons between the intervention group and the control group for the continuous outcome variables were performed with the use of linear mixed effect model to repeated measures. We had planned to use a linear mixed-effect model for the primary and secondary outcomes analysis. The model included group, time, the interaction of group with time as fixed effects and child-specific random intercepts, child age and mother age as covariates.2 to evaluate intervention effects on the primary outcomes and secondary outcomes.All data were performed with SAS software version 9.2  and R version 4.0.2A total of 9,295 children completed the screening questionnaire, 334 (3.59%) were positive for the screening. Of those 334 children screening positive children, 243 (72.75%) were diagnosed ADHD. However, 21 children were excluded because 5 children have developmental delay, 4 students drop out of school, and 12 declined to participate this study. Information on the flow diagram of participant enrollment in this study is shown in P > 0.05). As shown in In this study, 64.8% of the parents in the intervention group hold the opinion that this treatment would help their children. We first analyzed the mean and 95% CI for the outcome variables at different time point and change from baseline between the two groups see . SNAP-IVF = 8.27, P = 0.005), academic performance  and parent stress  in the intervention group as compared to the control group. The mixed model showed significant difference in the ADHD performance, academic performance, and parent stress over time (P < 0.05). There was no significant time×group effect on all outcome variables (P > 0.05)  see .P < 0.05). Before training, there were only 28.1% believed that ADHD was a neurological disorder, 14.9% believed medication was the first-line treatment and 13.2% parents drug effects answered correctly. While after training, 44.7% believed that ADHD was a neurological disorder, 27.2% believed medication was the first-line treatment and 42.1% parents answered drug effects correctly . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.LS and YW designed this research. JC and YT coordinated the implementation of this research. LS wrote the manuscript. LS and CW contributed to the analysis of this research. All authors have read and approved the final 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Attention-deficit/hyperactivity disorder (ADHD), the most diagnosed emerging neurodevelopmental disorder in children, is a growing health crisis in the United States. Due to the potential increase in ADHD severity during and post the COVID-19 pandemic, we analyzed recent national and two state-specific ADHD data distribution among U.S. children and adolescents by investigating a broad range of socioeconomic status (SES) factors. Child and adolescent ADHD diagnosis and treatment data were parent-reported via National Survey of Children’s Health (NSCH). The nationwide childhood prevalence of ADHD is 8.7%, and 62.1% of diagnosed children are taking medication. Louisiana (15.7%) has the highest percentage of children diagnosed with ADHD and California (5.6%) has the lowest, followed by Nevada (5.9%). Multiple correspondence analysis  examining 30 factors highlights four areas of interest at the national and state level: race/ethnicity, financial status, family structure, and neighborhood characteristics. Positive correlations between ADHD diagnosis and unsafe school, unsafe neighborhood, and economic hardship are evident nationally and statewide, while the association between a lack of ADHD diagnosis and higher urban neighborhood amenities are evident nationally, but not in two opposing outlier states—Louisiana or Nevada. National and state-specific hierarchical analyses demonstrate significant correlations between the various SES factors and ADHD outcomes. Since the national analysis does not account for the demographic heterogeneity within regions or individual states, the U.S. should rely on comprehensive, county-specific, near real-time data reporting to effectively model and mitigate the ADHD epidemic and similar national health crises. Moreover, childhood ADHD prevalence has been increasing since the late 1990s and early 2000s9, resulting in an estimated 8.7% (5.3 million) of U.S. children aged 3–17 years diagnosed with ADHD in 2018/2019 [National Survey of Children’s Health (NSCH) 2018–19]. Symptoms in diagnosed adolescents and young adults have worsened during the COVID-19 pandemic, but it is not known if this trend will continue post-COVID-1913. ADHD outcome data should be promptly reported and analyzed to recognize any potential need for population-specific interventions. Notably, ADHD diagnosis is difficult, and the symptoms are nonspecific16. Therefore, while current trends indicate a growing number of ADHD cases during the pandemic13, possible false positive ADHD diagnosis due to increased severity of ADHD-associated symptoms stipulates effective diagnosis and treatment of ADHD.Attention-deficit/hyperactivity disorder (ADHD), the most common childhood neurodevelopmental disorder, is a growing health crisis in the United States. ADHD is characterized by long-lasting negative consequences, including increased risks for substance abuse and addiction, anti-social and criminal behavior, lower academic attainment, and significantly greater personal and overall economic adversity; an overall estimated incremental median cost of $949.24 per year is associated with ADHD cases, with the average direct expenditure increasing in younger age groups up to preschool age21. The combined effect of several newly approved ADHD drugs, the 5th edition revision of the Diagnostic and Statistical Manual of Mental Disorders manual (DSM-5), and updated ADHD guidelines for children, teens, and adults may have contributed to the growing number of ADHD cases24. The DSM-5 and American Academy of Pediatrics (AAP) guidelines revised the ADHD diagnosis age criteria by raising the age of symptom onset from seven to twelve years old and outlining pediatric guidelines for children aged 4–18 years24. Furthermore, the updated DSM-5 guidelines require adolescents to display only five, instead of six, ADHD symptoms22.Based on representative samples of U.S. physician office visits, rates of ADHD diagnosis and medication treatment have been significantly increasing25. The AAP recommends preschool-age children (4–5-year-old) receive behavioral therapy administered by a parent or teacher as the first treatment option, with medication prescribed only if behavioral therapy does not reduce impairment, and that elementary-school-age children (6–11-year-old) and adolescents (12–18-year-old) receive a combination of medication and behavioral therapy24. Consistent with the AAP guidelines, the Healthy People 2020 developmental objectives include increasing the proportion of ADHD-diagnosed preschool-age children who receive behavioral therapy, and children and adolescents aged 6–17 years who receive medication, behavioral therapy, or a combination of the two26. To evaluate the success of AAP treatment guidelines and Healthy People 2020 goals and to inform effective responses, especially during the potential escalation of ADHD severity during the COVID-19 pandemic, trends related to the treatment of ADHD should be monitored13.National 2016 prevalence estimates indicate that 62.0% of children 2–17-year-old diagnosed with ADHD take medication, accounting for 5.1% of all U.S. children within that age range27. National data could mask within-state variance relevant to interactions of multiple factors, including state demographics, socioeconomic status (SES) factors, and ADHD diagnosis and medication treatment rates. Targeted studies have identified SES patterns related to ADHD outcomes across counties and greater variation of ADHD outcomes have been reported among counties30. However, the U.S. lacks a comprehensive system to collect and report ADHD data at the county-level, and the existing data is subject to misrepresentation and discrepancies in sampling and data collection methodology31.While regional patterns of ADHD diagnosis and medication prescription vary, the U.S. Southern region generally has higher rates of diagnosis and medication, while the Western region has lower rates of both36. Factors associated with a higher likelihood of taking ADHD medication included being non-Hispanic, living in homes with English as the primary language, living in the South, and having a currently co-occurring condition(s)25. These associations have not been reexamined collectively since trends were identified in the 2016 NSCH25. While previous studies identified variables associated with a higher likelihood of ADHD diagnosis and medication prescription, many important SES indicators from the NSCH were grouped and not individually assessed in relation to each other, potentially confounding critical trends and associations. Thus, when evaluating available data, such variation within broad SES categories should be considered and identified. This approach likely represents a more equitable survey-based epidemiological data analysis, minimizing generalization biases and potential misrepresentation of individuals’ SES designation.ADHD diagnosis among children has been correlated to family income, gender, race/ethnicity, primary household language, and insurance status39. This comprehensive analysis of both national and representative state-level data differentiates national and state-specific associations.In this study, we quantify more recent associations between ADHD diagnosis and a broad range of SES factors including income, insurance, area of residence, neighborhood characteristics, race/ethnicity, sex, family structure, household language, parent education, parental nativity, and school environment, utilizing data from the NSCH 2018–2019 dataset. The inclusion of available factors associated with mental health disorders, such as family structure, highlights important associations to childhood ADHD diagnosis in the U.S11, an analysis of the most recent pre-COVID-19 ADHD data and SES factors is necessary as a basis to better understand during and post COVID-19 ADHD trends40. Our national and state-specific hierarchical analysis, encompassing relevant SES factors, demonstrates the correlations between the various factors and ADHD outcomes. Since national data does not represent the demographic heterogeneity among states and local regions, we compared the data of two outlier states with opposing ADHD rates to national averages to better understand the effect of SES factors on ADHD diagnosis among children and adolescents in the U.S.Since the COVID-19 pandemic is projected to significantly exacerbate the effects of ADHD, specifically in childrenPrevalence of parent-reported ADHD diagnosis and medication among noninstitutionalized children 3–17-years-old for 2018–2019 are presented by state and region in Fig. Within each region, the oldest age group (12–17-year-old) are the largest group of children diagnosed with ADHD and receiving medication  and the youngest age group (3–5-year-old) is the smallest in each region . This pattern is consistent among children diagnosed but not receiving medication, with 12–17-year-old being the largest group  and 3–5-year-old being the smallest group . In all regions, more children diagnosed with ADHD are receiving medication than not within the age groups of 6–11 and 12–17. The opposite trend is evident within the youngest age group of 3–5-year-old  has the highest percentage of children diagnosed with ADHD and California  has the lowest, followed by Nevada , within LA (n = 1255), and within NV (n = 1154). Of all analyzed individual response data, 6.7% were missing and imputed for Multiple Correspondence Analysis , metropolitan statistical area, or metropolitan principal city], which are not evident for LA or NV Fig. a,d. NeigChi-square and Cramér’s V analyses reveal levels of significance and effect sizes for each response dataset Figs. ; Table 1There are no statistically significant correlations to ADHD diagnosis in the LA dataset Fig. b-I. BesiFor the NV dataset, only economic hardship is significant relative to ADHD diagnosis Fig. c-I. Besi27. While a more comprehensive approach is to analyze the greater variation between and within counties, the data collection systems in the U.S. that report county-specific childhood ADHD diagnosis are limited30. This analysis is not focused on sex-specific correlations since our chi-square results  to analyze a broad range of socioeconomic status (SES) factors relative to childhood ADHD and to each other. An analysis of SES factor interactions can influence future data collection strategies and help develop new hypotheses. We selected two opposing outlier states, Louisiana (LA), and Nevada (NV), based on high and low ADHD diagnosis and medication rates, respectively, to compare national to state-specific associations, since the state-level variation of ADHD diagnostic prevalence has been establishedlts Fig. a-I are itus Fig.  and limi9. ADHD medication treatment rates follow a similar pattern like ADHD diagnosis, but are less concentrated regionally, with evident state-specific variations , South Dakota (SD), Kansas (KS), Massachusetts (MA), and West Virginia (WV) represent in-between state-specific prevalence  guidelines24. The youngest age group (3–5-year-old) has the lowest diagnosis and medication treatment rates across all regions  having higher diagnosis rates, regional trends indicate that ADHD-diagnosed adolescents more often take medication than not  are correlated to divorce or separation of parents, which is strongly associated to ADHD diagnosis39 , including economic adversity and household violence, are reportedly significantly associated with childhood ADHD diagnosis, even when controlling for known parental and child-risk factors of ADHD60. Hence, family structure is important to investigate in relation to other SES factors and ADHD outcomes. Nationally, grandparent households, the death of a guardian, and divorce or separation are associated with parent-reported ADHD diagnosis , metropolitan statistical area, or metropolitan principal city, are positively correlated with a lack of ADHD diagnosis, which is expected since urban areas have greater access to resources57. Neighborhood amenities, including the presence of parks, sidewalks, and recreation centers, promote physical activity which is associated with improved cognition and behavior and could benefit childhood ADHD outcomes70. Lack of neighborhood amenities is strongly associated with ADHD diagnosis . NSCH is a United States population-based, cross-sectional survey sponsored by the Health Resources and Services Administration’s Maternal and Child Health Bureau. The NSCH is designed to collect data on health markers of noninstitutionalized children aged 0–17, which has been distributed via an online/mail-based format annually since 2016, previously conducted as a telephone survey every four years. Households that are more likely to contain children are oversampled but are otherwise randomly selected for an invitation household screen. Households with children, as indicated by the screen, receive a questionnaire regarding the health of one randomly selected child. A full explanation of the NSCH methods can be found at https://mchb.hrsa.gov/data/national-surveys/data-user.All data used represent portions of aggregate parent-reported ADHD diagnosis and treatment data for non-institutionalized children 3–17 years of age, disaggregated into three categorizations: 3–5-year-old, 6–11-year-old, and 12–17-year-old. Data are collected from the annual National Survey of Children’s Health (NSCH) 2018–2019 combined dataset, nationally and by state https://www.childhealthdata.org/learn-about-the-nsch/nsch-codebooks). Approximately 71,000 screens were completed for the 2018 NSCH, with 30,530 of the 38,140 qualified households completing the child-specific questionnaire. Approximately 68,500 screens were completed for the 2019 NSCH, with 29,433 of the 36,196 qualified households completing the child-specific questionnaire . Unrelated survey questions were removed before analysis. For multiple correspondence analysis (MCA), missing responses were imputed using the regularized iterative MCA algorithm of the missMDA R package73. Missing responses were excluded for chi-square analyses and associated Cramér’s V calculations.Individual household survey responses from the NSCH 2018 and 2019 Topical Data and Input were combined and used to analyze national and statewide associations in ADHD diagnosis and SES factors. Each respondent was weighted to account for the survey year in addition to the same factors used to weight population estimates. The NSCH 2018 Topical SAS Codebook was used to code the questionnaire questions and parent-reported responses . If the parent or guardian responded “yes”, they were directed to answer the following questions:ADHD outcomes and SES factors were analyzed at the national, regional, and state levels. Local physician population and policies regarding schools’ roles in ADHD treatment were not included in the NSCH and could not be quantified. Percentages of children with a reported ADHD diagnosis currently taking medication in each state were calculated from weighted population estimates of parent-reported diagnosis and medication use. Percentages of diagnosis and medication were mapped onto the U.S. states using the map function of the graph builder in JMP 14.3.0.To determine ADHD prevalence within each age group by region, weighted population estimates of each category by the state were used to calculate a percentage out of the total estimated population and then normalized to the total weighted percentage of children with ADHD in that state. Regional percentages are the averages of that category across all states in that region, normalized to the average total weighted percentage of children with ADHD in that region. States selected for further analysis reflect a combination of relatively low or high ADHD diagnosis and medication rates. The two states represent either end of both spectrums, and the states in between represent high diagnosis and low medication rates, low diagnosis and high medication rates, or median medication and diagnosis rates. These data were visualized using the bar graph Fig. c and cel74, then plotted using the Factoextra package in R75. Associated bar plots were created using the ggplot2 package, with proximity values calculated as (distance to ADHD_N–distance to ADHD_Y) / (distance between ADHD_N and ADHD_Y). ADHD diagnosis was compared to qualitative survey items related to the family’s SES via Pearson’s chi-square analyses with Bonferroni correction and an adjusted p-value cut-off of p < 0.05 to quantify significance. To quantify the strength of association, Cramér’s V values were calculated for each chi-square value. For items with four possible ordinal responses , to increase the counts in the contingency tables, the two most positive and two most negative responses were combined to yield two categories of responses (e.g. Definitely/somewhat agree or Definitely/somewhat disagree). To examine similarities in significance between variables, hierarchical clustering based on p-values using the complete linkage agglomerative method was performed in R 3.6.372. Associated matrices were created using the ggplot2 package, software version 3.3.076.To analyze associations among ADHD diagnosis and SES factors, related items from individual household survey responses were analyzed via multiple correspondence analysis (MCA) using the FactoMineR packageNo protocol approval was needed for this study because it uses only publicly available, de-identified data. The author’s institutional review board does not consider the secondary analysis of publicly available data as research on human subjects. All methods were performed in accordance with relevant guidelines and regulations.