mMamee: A mHealth Platform for Monitoring and Assessing Maternal Environmental Exposure

Katerina Karagiannaki, Stavros Chonianakis

Computer Science Dept., University of Crete, Greece & Institute of Computer Science, Foundation for Research and

Technology-Hellas {karayan,chonian}@csd.uoc.gr

Evridiki Patelarou Department of Postgraduate

Research, Florence Nightingale Faculty of

Nursing and Midwifery, King’s College, London, UK evridiki.patelarou@kcl.ac.uk

Athanasia Panousopoulou

Institute of Computer Science, Foundation for

Research and Technology-Hellas

apanouso@ics.forth.gr

Maria Papadopouli Computer Science Dept.,

University of Crete, Greece & Institute of Computer Science,

Foundation for Research and Technology-Hellas

mgp@ics.forth.gr

Abstract—Over the last years significant efforts have been made to prevent and/or minimize exposure to a wide range of environmental risks (e.g. air pollution and nutrition) that adverse health effects especially among vulnerable populations including pregnant women. Towards this direction, mHealth approaches can provide the means for remotely capturing the environmental factors that affect maternal health, and replace traditional, tedious ways of logging, e.g. face-to-face interviews. This work presents mMamee, a mHealth platform for monitoring and assessing maternal environmental exposure. mMamee employs a client-server architecture and addresses the integration of sensing data and descriptive input on maternal daily habits. The future application of this platform to monitor environmental exposure during pregnancy is outlined. The conclusions derived highlight the feasibility of mMamee for the realization of long-term epidemiological studies.

Keywords- mHealth, Mobile Computing, Client-server architectures, Environmental Exposure, Maternal Health.

I. INTRODUCTION During the last decade mobile communication and

computing services have penetrated our daily culture. The potential of mobile computing has also been shifted towards the delivery of health care solutions, yielding mHealth, a rapidly expanding area of research and practice [1]. mHealth changes the face of modern healthcare by providing a wide range of functions, for improving the level of confidence and satisfaction of patients, reducing the healthcare costs [2], and modernizing data acquisition and analysis for clinical trials [3].

One of the latest mHealth trends is related to the assessment of the environmental exposure, i.e. the environmental pollutants and lifestyle factors that affect individuals’ health status in urban environments. Recent research [4] has emphasized the importance of the integration of location-based services into mHealth platforms for evaluating the human long-term exposure air pollutants. Nevertheless in order to obtain a better understanding of environmental exposure, different sensing parameters (e.g. air pollution, noise, ultra violet

radiation, temperature, humidity, biosignals) should be combined. The authors in [5] proposed a standalone mobile platform, which can be carried in a backpack and monitors environmental pollutants while people are moving within a city. Such approaches yield short insights on exposure factors and acute health responses, and can be essential, especially for vulnerable populations, such as pregnant women. However these multi-sensing mobile platforms are cumbersome, and as such, are neither suitable for long-term studies, nor appropriate for women during maternity. In addition, such sensor-oriented solutions often focus on absolute metrics, without considering qualitative aspects, e.g. dietary habits, working/living conditions, and lifestyle choices, which are important for evaluating maternal and fetal status in urban environments. Thus there is a demand to broad personalized mHealth systems for assessing the maternal environment exposure.

In this paper, we present the conceptual design and prototype development of the mMamee platform, a mHealth solution for monitoring and assessing daily environmental exposures that affect the health of women during pregnancy. The mMamee platform encompasses three distinct characteristics, namely (a) a smartphone application for self-reporting and monitoring of the medical condition, environmental and lifestyle habits of pregnant women, in the form of easy-to-follow questionnaires via a user-friendly GUI, (b) streams of urban sensing data which measure various ambient conditions, and (c) a backbone architecture for integrating these two heterogeneous information sources. The proposed platform relies on the combination of user descriptive input and urban sensing measurements, over a client-server architecture that aims to provide reliability in terms of data collection and analysis. Our ultimate objective is to evaluate the long-term impact of various exposure factors on the maternal and fetal health. A feasibility field study will help us establish the benefits of such systems comparatively to established methods (e.g., using laptops, paper questionnaires), and extract measurable indices in terms of participation, response and completion rates. Thus, the herein proposed platform will be implemented in large-scale epidemiological studies,

aiming to analyze the effect of exposure to a wide range of environmental hazards during pregnancy.

II. RELATED WORK The mHealth architectures have significantly advanced

the practices of explicit recording of vital signs for daily activity monitoring [6] and self-management of chronic health conditions, such as diabetes [7]. Nevertheless, systematic medical reviews [8] on mobile applications and accompanying architectures highlight the lack of sophisticated mHealth services for maternal and fetal health, as related efforts are limited to ethnographical studies [9]. The following paragraphs highlight the related ICT systems, which are focusing on either static Decision Support Systems [10], or standalone Body Sensor Network (BSN) architectures [11] for monitoring vital signs for both the mother and the fetus [12]-[17].

In order to prevent pregnancy complications and related later childhood disease due to the insufficient access to healthcare services in rural and remote areas, the authors in [10] have designed a Decision Support System which enables the remote monitoring of pregnant women. The approach therein adopted relies on face-to-face interviews with pregnant women regarding the use of healthcare services, dietary and exercise habits, and medical history. The collected data has been subject of a rigorous statistical analysis and distilled into the significant variables, which were evaluated by gynecologists and employed for exporting the rules of the Decision Support System. The resulting platform allows screening for patients who need physician and provides advice for those that do not need medical assistance.

Shifting towards distributed and personalized care models for maternal and fetal health, current trends rely on vital signs monitoring at home, integrated with mobile and or / internet services for remote access by healthcare professionals. As such, the authors in [12] propose a mHealth architecture for monitoring the high-blood pressure of pregnant women that suffer from Gestational Diabetes Mellitus (GDM). The proposed three-tier architecture is comprised of a mobile application used by the patients, a distributed agent environment, and a patient management system for medical professionals. The individuals are responsible for the manual entry of their physiologic data and symptoms associated to GDM and follow their weekly objectives, whilst the healthcare professionals can remotely observe the status of the patient by the means of HTTP services. While such an approach requires the active participation of the individual, parallel efforts concentrate on (semi-) automated body-centric architectures. Typically, they combine short (e.g. Bluetooth and IEEE 802.15.4) and long-range communications (e.g. WiFi and Cellular Networks) with wearable sensing technologies for monitoring the fetal movements with flex sensors [13], the fetal heart rate with ECG [14] and sound sensors [15]. In a similar direction, the MAMICare platform [16] integrates different types of medical devices into a specialized, wearable, smart medical kit for assisting health volunteers and social

workers in rural areas to efficiently monitor the health status of pregnant women. Going one step further than monitoring raw vital signs and accordingly notifying medical professionals, the authors in [17] extend the standard body-centric architecture towards an intelligent electronic system that allows predicting the evolution of specific pregnancy disorders. In this scheme, raw vital sign collected by wearable sensors, user entries, and medical records are fed to a Bayesian model that allows predicting pre-eclampsia. The result platform, called e-MomCare, besides for updating the health records of the patient, is additionally capable of providing advice to the patient on whether an action should be taken or not.

The discussion thus far highlights the emphasis on providing the necessary means for remote monitoring and interpretation of essential biometric measurements. While this is an important aspect, limitations are identified in the ability of these systems to grasp and identify correlations between daily habits, modern lifestyle choices, and potential sources of pollution and examine their long-term effect on maternal and fetal health. As such, the herein proposed architecture attempts to overcome these constrains, by offering medical expects access to modern mobile technologies for an in-depth, understanding of the potential factors than can compromise maternal health.

III. SYSTEM ARCHITECTURE The mMamee platform follows a client-server

architecture, employing smart-phones and a sensing infrastructure over a cloud (presented in Figure 1).

Figure 1. The mMamee architecture.

The sensing infrastructure is deployed at the urban

environment for recording ambient conditions, such as temperature, humidity, CO2 in the atmosphere, and level of city noise, as well as specific indices of pollution in

residential environments, such as the quality of potable water and percentage of dust in the atmosphere.

This information is collected in a self-organized and automated manner over wireless networks (IEEE 802.11, IEEE 802.15.4/ZigBee, Bluetooth), and is periodically uploaded in the Urban Sensing database system. The management of the Urban Sensing database system relies on cloud services, which adopt the design principles of OpenStack cloud operating system [18] and the Open Cloud Computing Interface (OCCI) [19]. The mMamee client is deployed on conventional smartphones and is capable of capturing the daily activity of maternal women, by: (a) occasionally recording their location within the urban environment using either conventional sensors (i.e., GPS) or IP-based localization [20]; (b) allowing the individuals to complete a properly formed questionnaire, related to their daily habits, at a time of their own convenience. These sources of heterogeneous information are combined at a cloud-based server, comprised of sophisticated components, responsible for handling security and privacy issues, as well as for extracting, analyzing and storing potential sources of pollution, ambient conditions, and high-level semantics on daily habits. Specifically, multivariate analysis on urban and residential data is employed for extracting potential sources of pollution. These results are correlated to users daily activity, based on time and location information. The distilled information is available to the healthcare professionals, over HTTPs services, for further classification and generation of long-term profiles of environmental exposure to maternal women.

The mMamee platform adopts a structured design approach, based on which the sensing technology is decoupled from the remaining of the platform. As such, the essential aspect for the architecture of mMamee is the availability of streams of urban sensing data, and not the hardware details of underlying sensing infrastructure; provided that urban data are available on a remote database system, the platform invests on its own intelligence, and software components for conventional smartphones for the integration of raw streams of urban data with descriptive input provided by the users.

The descriptive input is in the form of responses to an on-line questionnaire, accessible at any time over smartphones. The contents of the questionnaire, go well beyond the dietary habits of the subjects and cover aspects related to the conditions of residential pollution (e.g. living in industrial zones or suburb areas), lifestyle choices (e.g. alcohol consumption and smoking), quality of potable and cooking water, and quality of cosmetics used during pregnancy. The existence of such a questionnaire allows the system to record in a simple manner the daily routine of women in maternity, without violating their privacy by non-stop monitoring of their activities.

The adopted client-server architecture ensures the robust operation of the proposed platform, offering functionality for addressing security and privacy aspects that cannot be offered with peer-to-peer approaches, as well as for employing sophisticated methods for metadata

analysis and manipulation. More specifically, the core of mMamee relies on the client-server architecture of the u-map system [21]-[22]. The u-map is a user-centric crowd-sourcing-based recommendation system that enables a client device running on a smartphone to monitor the infrastructure of various services (e.g., telecom services), and collect opinion scores/feedback from users about their perceived quality of experience for that service.

Figure 2. The mMamee client-server architecture is based on the u-map

[21]-[22]. White boxes indicate components that are finalized and implemented on the prototype.

The proposed architecture (presented in Figure 2) adopts the design principles of [22], regarding the data representation, management, and access control. On the client side, the Back-end interface is responsible for the on-demand connection of the client with the server, for handling queries and retrieval upon abrupt disconnection. The Monitor component records the time-stamped position information of the device (i.e. GPS coordinates). The Questionnaire Handler is responsible for loading the questionnaire on the GUI, processing the user responses, and accordingly redirecting the user to the appropriate next question. Users can go back and change a response to a question, pause the process of the questionnaire, and complete it at their own time and availability. It is considered worthwhile to mention that all information collected at the client side is stored in a local database, and is automatically uploaded to the server, upon the establishment of the client-server connection. Therefore,

Figure 3. Screenshots from the mMamee client GUI and on-line questionnaire. the client-server communication is not a prerequisite for filling in the questionnaire, thereby increasing the usage flexibility of the platform.

On the server side, the Urban Sensing Data Handler is responsible for parsing the streams of urban data, synchronizing between different points of sensing, extracting spatio-temporal correlations and higher-level semantics. The system maintains a distributed spatio-temporal geo-database that stores and processes the uploaded information provided by users and the Urban Sensing Data Handler. The Access Control component enables a user-centric access control module, allowing users to control which privacy-sensitive information can be revealed to third parties, through a fine-grained discretionary approach. As such, user-defined rules determine “who has access to what data” and “what information can be queried by the database of the system”. The latter “injects” the access control rules into the database request, thereby keeping inaccessible data out of reach. Finally, the Security and Privacy component, safeguards the collected data from unauthorized access and intrusions, relying on standard technologies (e.g., public-private key pairs, TLS) for secure connection to the system’s database and data anonymization.

Figure 4. The activities of the mMamee client front-end.

A. Prototype Development The main functionality of the mMamee, indicated by the white boxes in Figure 2, has been implemented. Its server runs an Apache HTTP server on a Linux machine. The spatio-temporal database is configured on top, allowing clients to register their access, login, and connect on

demand. The client is implemented over Android OS, and incorporates the Monitor component, the Back-end interface, the Questionnaire Handler, the local database, and the GUI. The Back-end interface includes a HTTPs client, implementing the connection with the server, and using JSON data-interchange format for communication. The mMamee client allows maternal women to register to the platform and securely login to the provided services, which entail occasional recording of their GPS coordinates, and access to the questionnaire. Figure 3 presents some screenshots from the GUI and the on-line questionnaire running on the mMamee client.A diagram of the activities running at the front-end of the implemented client prototype is shown in Figure 4. Our efforts have so far been concentrated on the components of the client-server architecture that are essential for the interaction with the end-user. The extension of the platform’s functionalities targets towards two main directions, namely (a) the implementation of the server functionalities that are responsible for the acquisition and analysis of environmental data, (b) the design of a feasibility study for evaluating the efficacy of the proposed platform. The following Section discusses the strengths and limitations of the proposed platform.

IV. DISCUSSION The conceptual design and prototype development of mMamee exploits the current state of the art in mobile computing for modernizing the epidemiological studies on assessing the exposome, i.e. the human, non-genetic environmental exposures of future mothers. Opposed to the current state-of-the-art, its key advantage is the potential to guarantee the free-living conditions of the target population, i.e. monitoring their daily habits and exposome, without restricting their activities. The mMamee combines the background monitoring of ambient conditions with mobile services, accessible from conventional smartphones. As such, it eliminates the necessity of specialized wearable equipment that can compromise the daily activities and behavior of women in maternity. In addition, the modular design of the proposed system safeguards its interoperability and adaptation to different cases of sensing technology employed for monitoring urban conditions. Similarly, the attributes (e.g. language) and the content of the on-line questionnaire can be easily customized and personalized to address the cultural background of different target populations and requirements of the medical protocol study. The cloud-based architecture satisfies the requirement of suitable storage and data maintenance during long-term and large-scale studies in a cost-effective manner. Moreover the database services at the server- as well as client- level address the data availability and fault-tolerance requirements. Finally, the access control and data

anonymization functionalities at the server side safeguard the privacy of the individuals. Despite the plethora of advantages, mMamee faces several challenges. The user engagement is a key aspect that can compromise its adoption from the target population. As such, the formulation of the specific questions as well as the GUI should guarantee that the interest of the user will not fade over time. To avoid such risk, the customization of the graphical interface to meet the subjective aesthetics of the user, is considered as an extension of the herein presented prototype. Another limitation of the system is its dependency on urban sensing data. In order to thoroughly assess the exposure for women in maternity, the existence of the appropriate urban sensing information is a prerequisite for the system. While the ad-hoc deployment of the respective platforms is considered trivial for small-scale studies, a wider feasibility study would require access to public infrastructures. As such, the adoption of mMamee for large-scale studies should consider the involvement and support of city authorities. Driven by the importance of the ultimate objective of the proposed platform, we elaborate on the anticipated implications for epidemiological studies in the following paragraphs.

V. IMPLICATIONS FOR EPIDEMIOLOGICAL RESEARCH The mMamee platform can potentially replace the traditional cumbersome, often expensive, and prone to errors and bias face-to-face or telephone interviews or participant−recorded activity logs which have been used so far to collect habits−related data. Furthermore, mMamee allows the recruitment of participants from a large and diverse population, without increasing the cost, enhancing the scope of the epidemiological study. As such the mMamee would reduce the total staff cost, resulting to significant reduction of the total budget needed for performing an epidemiological study on maternal environmental exposure, without compromising the objectives of the study. Driven by the anticipated technical and economic benefits, we plan to employ mMamee in a feasibility study among pregnant women with the following objectives: (1) to evaluate the efficacy of the application to accurately assess pregnant women's environmental exposure against established methods, such as laptops, paper- questionnaires, (2) to quantify response, participation and completion rates from participation to the study, which are considered valuable aspects for future pregnancy related epidemiological studies, (3) to assess pregnant women’s views and experience from participating in the study, and (4) to test acceptability of study procedures to women. The results of this feasibility study will shape a larger-scale study on the effect of a wide range of environmental exposures on pregnancy outcome (low birth weight,

stillbirth, preterm) and furthermore to develop future environmental pregnancy interventions, which will aim to reduce exposure and prevent risk of adverse effects.

VI. CONCLUSIONS This paper presented the conceptual design and prototype development of mMamee, a mHealth platform for monitoring and assessing the environmental exposures of women in maternity. Our approach is motivated by the necessity of modernizing long-term epidemiological studies on the factors that compromise maternal and fetal health. The ultimate objective of mMamee is to provide to healthcare professionals the means for extracting correlations on the vectors that affect maternal and fetal health in urban environments. In parallel, mMamee can yield monitoring and alert services for the benefits of the patients. Towards this twofold direction, our current focus is on the implementation of the server functionalities that are responsible for the aggregation of urban sensing data, and the integration of the herein presented prototype with small-scale sensing platforms for monitoring residential pollution (e.g. water quality monitoring, [23]). Finally, an important next step is the design of the respective medical protocol and analysis plan that will allow the evaluation of the feasibility of the proposed platform over the targeted population.

ACKNOWLEDGMENT The authors would like to thank Nikolaos Rapousis and

Michalis Katsarakis for their contributions in prototype development. This work has been supported by a Google Faculty Award (2013) and an investigator-driven grant based on scientific excellence from the General Secretariat for Research and Technology (GSRT) in Greece (PI Maria Papadopouli). Corresponding author: Maria Papadopouli, mgp@ics.forth.gr.

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