- freely available
Sensors 2019, 19(13), 2940; https://doi.org/10.3390/s19132940
2. Materials and Methods
- AirVisual API: is a free tool that collects data from the largest network of ground-based sensors worldwide, providing air quality information. Air quality data based on the user GPS location from the smartphone were gathered using this API .
- Smartphones: these are the devices (i.e., iPhone and Samsung) used to validate the correct development of the application in both operating systems, iOS and Android. The minimum requirements of these devices are GPS, Bluetooth, and 4G connectivity in order to perform all the application functionalities. These devices are also the tool for citizen science data capture.
- City and Citizen Science Data: the participatory data collection approach, enabled through smartphones (by sending specific information provided by the user), wearable activity tracker devices, fixed and mobile environmental city stations, and satellite data.
2.2.1. Participation Criteria
- Older than 18 years.
- Living in the urban areas covered by the pilot.
- Agreeing to participate in the project by signing a consent form explaining the different aspects of the study.
- Agreeing to wear an activity tracker.
- Owning an Android or IOS smartphone.
2.2.2. Application System Design
- First phase: definition of the user needs and focus.In this phase, research regarding the effects of environmental pollution on the quality of life of people, as well as the effects of an unhealthy lifestyle, was carried out in order to define the metrics for a multidimensional evaluation [21,22]. Furthermore, a benchmark assessment was conducted to identify applications and solutions that raise awareness among users of the effects of environmental pollution on health. Gamification techniques were analysed in order to define, in the second phase, the gamification strategy, to enhance users’ motivation . Finally, focus groups were conducted with PHOs and users to define their needs and requirements to improve citizens’ quality of life.
- Second phase: creation of a non-functional prototype and gamification strategy.The first version of the system is a non-functional prototype that was built as a set of mock-ups that described the appearance and functionalities of the system. Mock-ups were revised and improved later considering the feedback received from end users. This approach allows to simulate/anticipate the possible user experience through two activities: (1) the definition of the most relevant screens of the application to specify the minimum viable set of functionalities; (2) the definition of the user interaction, which describes the workflow of the screens and its elements to perform a specific task. The non-functional prototype was conceived together with a gamification strategy. The gamification strategy defined is based on the application of game-design elements and game principles in non-gamified contexts  in order to produce positive effects on individuals . Concretely, the main goal of the gamification process was to get a positive behaviour change regarding healthy and green habits.
- Third phase: development of the prototype.In this phase, the different elements of the application were developed, based on the feedback received of the non-functional prototype from phase two. These elements are as follows: (1) interfaces, from home screen to the different modules of the application; (2) communication and interaction with external services such as activity tracker devices and APIs; (3) application workflow, which includes trigger activities, feedback to user, educational content, monitor activities, gamification strategy, and so on. The development of the prototype followed a cross platform application approach, supporting Android and iOS devices, in order to cover all kinds of smartphone users.
2.2.3. Consumption and Production Data Flow Process
- Citizen science data gathering: this allows data gathering from the world of IoT and wearable devices that provide health status, presence, and use of a specific service. This includes a data acquisition phase binding the data streams to a common representational scheme to prepare the data for the sensor fusion and to counter some conditions with some pre-processing and elimination procedures (avoiding overloading of data), thus harvesting the data from the sensor units, tagging them with location and time information and storing data in real time. This supports data production and leverages data science.
- Data sampling and collection: this process gathers environmental data from public data sources; mainly air quality data coming from satellites, fixed and mobile stations, as well as through the city sensors and citizen science mobile devices (smartphones and wearables). The collected information enables data production to be handled by the Environmental Context within the data ecosystem.
- Data set fusion: this process brings together data sampling and collection with high-quality pre-existing datasets (i.e., environment and health) to provide a comprehensive view of the environmental domain (e.g., risks, potential exposure) and integration with health information (e.g., asthma related symptoms). The collected information enables the Big Data Context, which is the operating environment that allows data fusion and spatio-temporal modelling in order to provide, for example, health risk estimates or pollution estimated exposure real-time information. This data layer also provides the urban data lake (including cloud-based servers), which stores the data and applies interpolation models to allow an interactive data consumption process, providing valuable insights and data to enable information-oriented services.
- Data interaction: this supports interactive data consumption through the mobile context and application. The collected information enables the Mobile Context. It provides a tool that hides the complexity of the data flows and provides the information in one coherent integrated visualization. It is a robust, user-friendly, and appealing interactive data layer that aims at providing data value and empowerment to citizens.
2.2.4. Pilots Plan
- Stage 1, training: municipal leaders, community leaders, NGOs, and local businesses received training, covering the objectives of the study and the PulsAir application functioning, in order to execute the recruitment of users.
- Stage 2, deployment: included the recruitment of users, the distribution of the wearable trackers, installation of PulsAir app in the smartphones, sign up with individual user access codes, and synchronization of the wearable trackers with PulsAir app.
- Stage 3, follow-up: during this phase, interactions with the pilot organisations are being maintained in order to perform refinements on the application according to users’ feedbacks and cultural requirements of each pilot city (e.g., language, educative systems, ethnic groups, religions, and metric systems among others).
- Stage 4, evaluation: includes the technical validation (e.g., performance evaluation, safety, and reliability assessment) and the assessment of data quality, user experience and usability, as well as social acceptance and effectiveness of the PulsAir application. The evaluation criteria includes the following measures: (a) social acceptance and effectiveness determined taking into account the working conditions (e.g., citizen science environment), attitude towards the outcome, reciprocity, and performance of the tools; (b) usability, which measures the effectiveness efficiency, learnability, robustness, and utility of the application and ecosystem; and (c) user experience taking into account embodiment, emotion, feeling of trust, and satisfaction. This phase is currently being implemented in the different cities involved in PULSE project deployment.
3.1. User Needs and Requirements Driving the Citizen Science-Based Application
- Mobile information and generation of higher density of data: there is a need to use heterogeneous mobile technologies (different brands and operating systems), commercial health trackers (e.g., Fitbit, Garmin), and the integration of new fixed IoT-based sensors in cities, thus engaging citizens for integrating citizen science. This is driven by the need for increasing the continuous gathering of quantitative and dynamic health and environmental perceptual data, in order to generate a higher density of health data and air quality measurements to provide a more accurate spatial and temporal data for health and environmental risk prediction and estimation.
- Privacy: the need of privacy requirements that ensure the system compliance with the General Data Protection Regulation (GDPR) to grant full control of the shared information to the users and a secure process of data gathering that can confer anonymization of the information.
- Air quality status and information: citizens reported the need to receive information and increase the awareness of pollution levels and exposure in the area where they live and perform activities. Also, it was reported as crucial to understand the pollutants and the status of the air pollution in the city. The city can leverage on the community’s air quality stations and on other networks of environmental devices available in the market that can be installed at home in balconies, gardens, rooftops, and so on. Furthermore, users would like to be able to see contributions to improve environmental actions in the city, and reported the need of having prospective routes, with alternative low polluted paths, to reach a destination in the mapping.
- Health-related assessment and recommendations: users reported the need to receive tailored feedback and information that will show areas and parameters that indicate health risk for their health situation. Also, recommendations, guidelines, and information that will impact on their health-oriented behavioural habits; showing somehow the contribution of the actions regarding health and community improvements.
- An appealing, easy to use, and robust application: users reported the need for an easy to use interface that will provide an enjoyable application. This meant to have a user-friendly and intuitive setting that could easily manage processes, data sharing, and participation with robust real-time processing and feedback.
3.2. Non-Functional Prototype and Gamification Approach
- Avatar: the visual representation of the user’s status in terms of health and green habits; it is the main character of the application. With this element, it is expected that users have a much higher chance of developing personal ownership.
- Points: used as status indicators and to reward users for their different behaviours or objectives achieved in the application, such as use of wearable activity tracker devices or filling in questionnaires and getting extra points depending on the answer, oriented to promote or to follow healthy and green habits checked with the data collected. Points are used to enable a reward mechanism that drives motivational and engagement behaviours through citizen science.
- Levels: Five levels were defined within the gamification strategy. The Level-up is achieved through the accumulation of points from the activities completed by the user. The levels represent the air quality conditions of the city in which the Avatar lives. User’s interactions with the application and accumulated points improve the conditions of the city. Level 1 is a highly polluted city, and Level 5 represents a clean and sustainable city.
- Rewards: tangible recompense (either physically or virtually) for the achievement of an objective through an action or series of actions proposed.
- Leaderboard: an ordered list with the user’s score (achieved points). Two categories are implemented, per city and worldwide.
3.3. The System Components
3.3.1. PulsAir Mobile Application
- “Me” module contains information about the citizen. It gathers all the specific information regarding the user, from questionnaires and from the wearable devices (e.g., Fitbit band). It also provides specific educational content to promote healthy lifestyles and feedback about the health risks using state of the art models for cardiovascular risk (Framingham, ), TD2M (FindRisk, ), and asthma (Verlato, ). The “Me” module shows the daily physical activity (steps) of the user and historical data of total activity from the previous days. The wearable device information is integrated in the app. The user can also access the “Health tips” to learn how to improve lifestyle based on the results of the answered questionnaires.
- “My city” module contains all the data related to the city and neighbourhood of the user. Through this module, the user has access to a page that summarizes the status of the city (i.e., pollution levels), specific contents about the city, the pollutants, and possible more sustainable mobility and household habits that can contribute to the reduction of polluted emissions. The module provides a detailed map to show pollution levels in different areas of the city. In addition, it allows the user to tag or input data through questionnaires to inform about the status of the neighbourhood. The information is personalized for each user, aiming at empowering the citizens with personal tools that estimate and manage their pollution exposure, and thus take healthier actions (i.e., less polluted routes when walking).
Mobile Application Interface
- Homepage, final design follows a card layout style with a simple and clean background. The cards give access to the different modules of the application, as shown in Figure 3a. From top to bottom, the cards show the following elements:
- “Me” card, shows the information about the number of questionnaires completed and the risk of developing cardiovascular disease, type 2 diabetes, or asthma, based on the results of the questionnaires completed.
- “My City” card, presents a summary of the air quality and weather conditions at the current location of the user.
- “Weekly Activity” card, presents the summary of the activities performed in terms of calories burnt, steps, and kilometres travelled. This information is collected through the activity tracker device.
- The “Me” module screen shows the information gathered from the users; the wearable data and the health risks (Figure 3b). The menu, at the bottom of the screen, is used to access the answered questionnaires and suggested educational content and recommendations.
- The “My City” module shows the air quality and weather information about the city and the current user’s GPS position (Figure 3c). The menu, at the button on the screen, gives access to a pollution map and content related to air pollution.
- The usage of the application triggers the gamification engine, allowing users to earn points to level up through the five levels available (Figure 3d). The application starts with a sad avatar living in a dirty city; if the users perform the suggested activity within the application (i.e., questionnaire, educational contents, changes in habits), they will be taken to the next level until they reach the final level that shows a happy avatar living in a clean city (Figure 3e). The Leaderboard screen shows the user’s collected points (Figure 3f) in order to boost competitions among the citizens.
Mobile Application Workflow
- Monitor: the application tracks GPS position and integrates data of wellness sensor (e.g., Fitbit) and air quality. From time to time, some questionnaire are triggered to the users. The application also tracks the degree of use of the application.
- Assess: the gathered information is used to assess the citizens in terms of health risks (risk of the onset of T2D, asthma, and cardiovascular disease).
- Personalize: according to the assessment, the application triggers specific contents and recommendations to the users. The application can identify healthy or green behaviour that needs to be improved and prepare a set of tips to help the user to be aware and understand the importance of modifying a specific behaviour.
- Act: the application promotes specific healthy habits by proposing a specific goal to the users (e.g., 8000 daily steps). According to this goal, the application creates a follow-up procedure to check if the goal is achieved in the proposed time (e.g., one day).
- Support and reward: when the user finish the proposed activity, the application triggers a mechanism of reward and supports the user to be motivated.
3.3.2. Data Acquisition
3.3.3. Big Data-Oriented Ecosystem
- Graphical User Interfaces (GUI): they represent all the screens as described in Section 3.3.1. They refer to four main activities for the users: the Citizen module that refers to the individual information and ways to promote healthy habits. The City module contains all the screens related to the health, green behavior, and pollution maps. “Preferences” contains all the screens for user’s preferences, and the “Home” page is the initial interaction flow.
- Intervention Engine (IE): this is a mechanism to provide the intervention in the application. It is based on a scheduler that executes a questionnaire engine able to process the intervention by proposing a set of activities to the user to promote green and healthy habits.
- Feedback Engine (FE): this is the logic that triggers the proper feedback to the users. There are multi modalities of feedbacks depending on the context and nature of the data. Feedback can be in form of charts (e.g., evolution of the daily steps from the health tracker), educational content (in form of texts, infographics, web pages, or videos), personalized feedbacks (messages build based on rules of a decision support system), or notifications or reminders that are triggered to push users to perform an activity.
- Sensing Service (SE): the artefact abstracts the complexity of the sensor connection and provides access to the internal sensor of the mobile phone (currently the GPS), sensors that are available on the WiFi network of the users, and third-party services.
- Synchronization Service (SS): this service communicates with the big data context to transfer the citizen data to the citizen database; the position of the user is then used (if available) to assess the environmental context and enrich the feedback to the users (e.g., pollution maps).
- App Database (AD): the service offers full data persistence inside the smartphone. The database is securely encrypted and stored in a non-accessible area of the smartphone.
- Air Quality (AQ): Air quality data of the area where the user is located, coming from fixed and mobile sensors or measurement stations deployed throughout the city or satellites, among others.
- Weather: updated information of the weather in the user’s location.
- Mobility: public transport stops, pedestrian routes, bike lanes, public rental bicycles, and other types of green transportation.
- Public Services: information of police and firemen stations, public health organizations, hospitals, and schools, among others.
Big Data Context
4. Discussion and Conclusions
Conflicts of Interest
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|Profile||Questionnaire that allows defining the socio-demographic profile of the user including age, gender, and level of education among others.|
|Basic Socio-Demographics||Questionnaire focused on socio-demographic status in terms of occupation, household composition, and specific questions to define if the users have been previously diagnosed with asthma or diabetes.|
|Neighbourhood Environment||This questionnaire seeks to study the neighbourhood of the respondents by asking questions about traffic, violence, mobility, and green areas among others .|
|Health Behaviours and Habits||Eating and smoking habits are measured with this questionnaire. Alcohol abuse is also measure based on the Alcohol Use Disorders Identification Test (AUDIT-C) questionnaire .|
|International Physical Activity (IPAQ-SF)||This questionnaire assesses the types of intensity of physical activity and sitting time that people do as part of their daily lives to estimate total physical activity [28,29].|
|EuroQol-5D||This questionnaire measures the health-related quality of life of the respondents through a single index value that can be used in the clinical and economic evaluation of health care and in population health surveys .|
|European Social Survey (ESS)||The ESS measures the attitudes, beliefs, and behaviour patterns of diverse populations .|
|Generalized Anxiety Disorder (GAD)||It is a tool for screening of GAD and assessing its severity in clinical practice and research .|
|Patient Health Questionnaire-9 (PHQ-9)||Used for detection of depression .|
|Type of Information||Scope||Data Gathering Step|
|Questionnaire||To gather subjective information according to the questionnaires presented in Table 1||Data interaction|
|GPS||To track user localization and estimate activities and cross-matching with air quality data.||Data interaction|
|Log App’s usage||To track system adherence and acceptance||Data interaction|
|Activity||To track physical activity. To monitor mobility||Citizen science data gathering|
|Sleep||To assess quality of sleep||Citizen science data gathering|
|Heart Rate||To assess cardiovascular health and fitness level||Citizen science data gathering|
|Air quality||To collect pollutants data||Data sampling and collection|
|Mobility||To provide information about public transport (i.e., bus, metro, train) or any other transport system (e.g., car sharing, bike renting) available next to the user’s location||Data sampling and collection|
|City services||To facilitate the list of available services in a certain location (e.g., tourism attractions, shops, restaurants, public services)||Data sampling and collection|
|Epidemiological||To provide the data of population health aggregated by district including demographic, disease, and mortality information||Data fusion|
|City||To inform about global statistics of the city (e.g., crime ratio, number of schools, universities, number of tourists/years)||Data fusion|
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