Context Aware Mobile Agent for Reducing Stress and Obesity by Motivating Physical Activity: A Design Approach Saurav Gupta
Sanjay Sood
CDAC Mohali, INDIA Email Id:
[email protected]
Department of Information Technology Chandigarh Administration, INDIA Email Id:
[email protected]
Abstract--- Context aware computing, an emerging form of ubiquitous computing, has enabled the way computers interact with humans. With the ability to sense and adapt to the physical environment, its use provides immense potential in improving one’s own health. This paper highlights the design of a context aware mobile agent named, ‘Let’s Exercise’, which is aimed at users suffering from both stress and obesity. Based on the survey, 33 users (n=33) were identified and contextual messages were sent which motivated them to do physical activity in order to reduce both stress and obesity. The impact of the study, which shows high level of satisfaction and acceptance of the technology, is also discussed in the paper.
environments. The sensors usually include temperature sensors, motion sensors, gyro meters, accelerometers, global positioning systems, etc. These sensors capture the location, activity, identity and time. • Thinking Subsystem: As part of the Thinking sub-system, the preprocessing or reasoning of the raw data is done along with other information to derive knowledge. The sensors provide the contextual information, which is reasoned to derive a suitable action plan for the present context. This contextual information could be used for data analysis and knowledge discovery. • Acting Subsystem: Upon the recognition of situation, based upon contextual information, action is triggered. This triggering of actions is done by the application designed as part of the Acting Subsystem. In contextaware ubiquitous systems, these applications are usually designed for mobiles and smartphones. Context aware computing, is emerging as a new age solution for improving healthcare and providing personalized healthcare services [4-8]. With the developing countries facing a shortage of healthcare professionals and lack of adequate medical infrastructure, there is a growing need of creating an ecosystem where an individual could manage and monitor their health in a real time basis with the use of context aware data. [2,9]. This paper highlights the design of a context aware mobile application and a study conducted on the impact of this system on subjects (n=33) suffering from both obesity and stress. Scientific studies having proven that there exists a direct co-relation between stress and obesity [10], increasing or initiating a physical activity program has been found to be an important aspect in managing obesity and reducing stress [11]. Hence, the mobile agent based on the physical environment, sends context related messages encouraging them to undertake physical activity. Section II of the paper highlights the design and key components of the application, ‘Let’s Exercise’. Section III, discusses the outcome of the study conducted and Section IV concludes it.
Keywords- context awareness, human-computer interaction, computer-physical environment interaction, ubiquitous computing, mobile application, mobile health I. INTRODUCTION Recent technological advancements have led to a paradigm shift in ICT systems; the conventional ICT modular and centralized systems have evolved into systems powered by ubiquitous computing. The conventional ICT systems had a restricted physical environment interaction and had a constant human intervention in the loop. These limitations were overshadowed by the use of ubiquitous computing which led to the increase in computer-to-human interaction [1]. An emerging form of ubiquitous computing is context aware computing, in which the mobile system senses, recognizes and adapts to the changing physical environment and creates a personalized setting to the user [2]. According to Gartner, ‘Context-aware computing is a style of computing in which situational and environmental information about people, places and things is used to anticipate immediate needs and proactively offer enriched, situation-aware and usable content, functions and experiences’ [3]. As depicted in Figure 1, context aware system architecture typically consists of 03 components: • Sensing Subsystem: The sensing subsystem comprises of sensors, which acquire raw data from the physical
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The alert is received on the users’ smart phone as a notification. • Upon the action selected by the user for the given alert, the response is recorded. The mobile app, ‘Let’s Exercise’ uses internet connectivity to determine the contextual environment of the user and thereby send relevant alerts. The key components designed as part of the ‘Let’s Exercise’ system architecture as depicted in figure 1 are: A. Sensors and APIs • Fig. 1. Architecture of Context Aware Systems (source: Baldauf and Dustdar)
II. LET’S EXERCISE ‘Let’s Exercise’ is a context aware mobile application designed for Android based mobile phones supporting operating system version 4.1 and above. The mobile app uses contextual information for sending alerts to the users, motivating them to do physical activity. The backend database of the app is developed on MS-SQL technology. The exchange of communication between the mobile app ‘Lets Exercise’ and the database occurs using web services. The application utilizes smartphone’s in-built sensors and integrates open source web APIs for sensing and adapting to the desired physical environment. The process flow of designed system is depicted in Figure 2 and is explained as follows:
Weather API: The weather information helps identify the type of activity, weather indoor or outdoor, can be performed. To determine the weather information from the user location, the ‘Open Weather API’ [12] was integrated with the ‘Lets Exercise’ application. Weather information is retrieved in form of 03 parameters, namely, temperature, humidity level, and the weather forecast. Based on these parameters, classification of favorable temperature and extreme temperature is done. This is depicted in table 1. Favorable temperature is the combination of parameters suggesting a suitable environment for doing a physical activity outdoors. Extreme temperature suggests unsuitable environment for doing physical activity outdoors and hence the alerts are sent for indoor activity.
TABLE I. CLASSIFICATION OF TEMPERATURE VALUES
Temperature
Favorable Range 18ºC - 35ºC
Humidity Level
upto 90%
>90%
Clear Skies Sunny Cloudy
Windy Rain Thunderstorm Hailstorm
Forecast
• Fig.2. System Architecture
• •
•
Based on the user configuration, a background service in the smartphone gets triggered at the scheduled time to determine the location and the weather type The current location of the user is fetched and compared to identify location as either ‘Home’ or ‘Office’ or ‘Outside’. The weather for that location is analyzed on the basis of temperature, humidity level and forecast. Based on the determination of location and weather type, a pattern is generated. This pattern along with the user’s ID is sent to the web server using a web service. The pattern received is analyzed to determine the alert message.
•
Extreme Range < 17ºC & >35ºC
Location: for determining the user’s location, Google fused location API was integrated with ‘Let’s exercise’ application. The API determines the location by either using the GPS sensor on the phone device or the Wi-Fi network to which the device is connected with or the cellular network of the service provider [13]. The API provides option between ‘high accuracy’ and ‘low power’. For the application, an accuracy upto 50 meters was chosen. The location is identified and mapped for 03 location types: Home, office or other. Time: The time on the user’s mobile device is used to differentiate between the acceptable and unacceptable time zone. The acceptable time zone is the duration in which the user opts to receive contextual alerts. The acceptable time zone is user configured and based on this, the number of alerts are equally divided across the time duration.
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Activity Tracker: In order to track the user movements, such as walking, running, cycling, the API, ‘Google fused location’ was integrated with ‘Let’s Exercise’. With this API, real time mobility of the person is tracked and recorded to calculate the calories burnt and time taken.
B. User Interface •
Alert Configuration The mobile application, ‘Let’s Exercise’ provides an interface to the users to configure the application. The user can define: a) the locations for ‘work’ and ‘home’. All other locations identified would be termed as being ‘outside’. b) number of alerts to be received in a day and c) the time frame (acceptable time zone) in which the user would want to receive alerts. This makes the application customized to the user settings and hence makes it more adaptable. Location based services: The application determines the user location and suggests the nearby gyms and parks to the user. The user can select the desired option and based on the selection, the activity tracker is initiated which determines the type of activity initiated (walking, jogging, running, cycling) by the user. The application calculates the distance travelled, calories burnt and the time taken by the user to undertake the particular activity. It also records the track take by the user. Recommended Exercises: This section provides various exercises, which the user can read and follow.
•
•
Fig.3. Rule Aggregator
The motivational messages and the physical activity recommendations are stored in the Pattern Database. Depending on the user’s contextual environment, the messages are chosen on a round robin basis and delivered as a ‘dialog box type’ message on the users’ smartphone. The flow of sending messages is shown in figure 04. With the option to receive upto 10 messages in a day, the users could also define a time range for receiving the alerts.
C. Rule Aggregator The information retrieved from the sensors and the APIs about the location, weather and the time, is transmitted to the ‘Rule Aggregator’ for generating an information pattern. With the smartphones having good computational abilities, the pattern is generated on the device itself. The ‘Rule Aggregator’ groups the location type (home, office and outside), weather (ideal or adverse) and time (acceptable and unacceptable) together. For example, if the code generated is H-I-AT, this implies that the user is at his home (H) where the weather is ideal (I) and he can receive messages as per the defined time zone (AT). This pattern generated is then sent to the pattern message database for triggering the relevant alert. The rule aggregator is depicted in figure 3. D. Alerts Researchers have proved that sending positive motivational messages about a defined problem to the user yields high adherence rates [14-19]. Hence while sending contextual alerts to the users, it was preceded by a positive motivational message about physical activity. The message format was as follows: Alert = Positive and motivational message + Context based physical activity recommendation
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Fig 4. Sending Context aware Alerts
The configuration of alerts is done in the following manner: • Calculates the total time available from time window. • Divides total time by number of alerts in a day to get time interval between alerts. • Sets the alerts at calculated time intervals. E. Response Collector At the time of receiving an alert, the user is prompted to select a response. The response has 04 options, these are: 1) Let’s do it, 2) Will do, but later, 3) Too busy to do it, 4) No, thanks. The responses are collected and stored in the response collector. The responses are stored at the server with the use of web services and are mapped to the user. These responses help in generating user trends. III. RESULTS In a randomized controlled trial, the mobile application, ‘Let’s Exercise’ was installed on 33 users’ smart phone. The age group of the users was between 24-30 years, and all of them were overweight/ obese and had high levels of stress. The users were asked to use the application during September in the year 2014 for a period of 04 weeks. Post the evaluation
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period, the users were asked to fill a questionnaire. The questionnaire comprised of 04 sections having 20 questions. Section 01 comprised of the demographic details comprising of 07 questions. For the remaining 13 questions, the users responded based on the 5-point likert scale. The responses were ‘strongly agree’, ‘agree’, ‘neutral’, ‘disagree’ and ‘strongly disagree’. The outcome of this survey is highlighted in table 2.
TABLE II. SURVEY FEEBACK BASED ON EFFECTIVENESS, USEFULNESS AND SATISFACTION (ALL FIGURES IN %) EFFECTIVENESS
AA
A
N
D
DD
60.6
30.3
9.1
0
0
57.6
30.3
12.1
0
0
48.5
36.4
15.1
0
0
66.6
27.3
6.1
0
0
The technology has motivated me to do physical activities
69.7
27.3
3
0
0
USEFULNESS
AA
A
N
D
DD
The technology provided is useful
54.6
36.3
9.1
0
0
The technology provided is informative
48.5
45.5
6
0
0
I would use this app frequently
48.5
39.4
9.1
3
0
The technology is convenient to use
63.6
30.3
6.1
0
0
The technology was effective in understanding the user context The technology accurately determines my location The technology accurately determines the weather The technology was efficient in understanding the user context
IV. CONCLUSION With the developing countries facing a shortage of healthcare professionals and inadequate medical infrastructure, there is a growing need for the people to be self-reliant and hence focus on ‘self-care’. Here, context aware computing assumes significance as it enhances the computer to human interaction by adapting to the physical environment. The contextual information, once available, enables the user to make an informed decision and helps in better managing one’s own health. This paper brings out the successful use of context aware mobile agent for the people suffering from both stress and obesity. With both being lifestyle oriented diseases, contextual alerts were sent to the users to motivate them to do physical activity and to reduce the impact of both the diseases. REFERENCES [1] [2]
[3] [4]
[5] SATISFACTION I am satisfied with the technology developed The technology performed as expected I would adopt this app as part of my daily routine The prompts/ alerts were apt and appropriate
AA
A
N
D
DD
60.6
24.2
15.2
0
0
51.5
42.4
6.1
0
0
24.2
36.4
24.2
15.2
0
48.5
45.4
6.1
0
0
[6]
[7]
AA= Strongly Agree, A= Agree, N= Neutral, D=Disagree, DD= Strongly Disagree
As observed, the users expressed that the technology developed is effective and useful, as it has helped motivated them to do physical activity. With 84.8% of the users being satisfied with the technology, 60.6% of the users have responded saying that they would continue to use ‘Let’s Exercise’ mobile app. Apart from the survey, users were asked to give their opinion to improve the application. Suggestions included, a) to include meditation exercises and food suggestions also as part of the alerts, as this would provide a more balanced approach. b) Because of the connectivity issues, a standalone/ offline version of the application should also be designed.
[8]
[9]
[10]
W. Dargie - Context Aware Computing and Self-Managing Systems; CRC Press 2009. M. Vrbaski and D. Petriu, “Toward a Context Awareness Framework for Healthcare Applications”, IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA), 978-1-4673-5197-3/13, May 2013. Gartner, link: http://www.gartner.com/it-glossary/contextaware-computing-2; last accessed on 22 January 2015 L. M. Gardini, R. Braga, J. Bringel, C. Oliveira, R. Andrade, H. Martin, L. O. M. Andrade and M. Oliveira, “Clariisa, a Context-Aware Framework Based on Geolocation for a Health Care Governance System”, IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom), 10.1109/HealthCom.2013.6720696, Oct 2013. '37REy1, T. H. Falk and M. M. Inrs, “Context awareness in WBANs: a survey on medical and non-medical applications”, IEEE Wireless Communications, Volume: 20, Issue: 4, August 2013. Y. Lin, J. Jessurun, B. Vries and H. Timmermans, “Motivate: towards Context-Aware Recommendation Mobile System for Healthy Living”, 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops 2011 (IEEE), pg 250-253, May 2011. Y. Lin, A. J. Jessurun, B. de Vries and H.J.P. Timmermans, “A context- aware persuasive system for active living simulator system design”, Conference proceeding of 10th International Conference on Design & Decision Support Systems in Architecture and Urban Planning, pp.1-12. 2010. A. M. Khan and S. Lee, “Need for a Context-Aware Personalized Health Intervention System to Ensure LongTerm Behavior Change to Prevent Obesity”, 5th International Workshop on Software Engineering in Health Care (SEHC), doi: 10.1109/SEHC.2013.6602481, May 2013. H. Viswanathan, B. Chen, and D. Pompili, “Research Challenges in Computation Communication, and Context Awareness for Ubiquitous Healthcare”, IEEE Communications Magazine, Volume: 50, Issue:5, May 2012. S.J. Torres and C. A. Nowson, “Relationship between stress, eating behavior, and obesity”, Nutrition. 2007 Nov-Dec, 23(11-12):887-94. Epub 2007 Sep 17.
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[11]
[12] [13]
[14]
[15]
[16]
[17]
[18]
[19]
966
Anjana et al, “Physical activity and inactivity patterns in India – results from the ICMR-INDIAB study (Phase-1)”, Int J Behav Nutr Phys Act. 2014, 11:26, Feb 2014. Open weather API, link: http://openweathermap.org/api, last accessed on: 18-1-2015. Google developers, Google fused location API, link: https://developer.android.com/google/playservices/location.html, last accessed by: 18-1-2015. G. L. Krepsemail et al, “Development and validation of motivational messages to improve prescription medication adherence for patients with chronic health problems”, Patient Education and Counseling, Volume 83, Issue 3, Pages 375– 381, June 2011. Kim and J. M. Keller, “Effects of motivational and volitional email messages(MVEM) with personal messages on undergraduate students’ motivation, study habits and achievement”, British Journal of Educational Technology, Vol 39 No 1 2008, doi:10.1111/j.1467-8535.2007.00701.x, 2008. J. Visser and J. M. Keller, “The clinical use of motivational messages: an inquiry into the validity of the ARCS model of motivational design”, Instructional Science, 1990, Volume 19, Issue 6, pp 467-500, 1990. A. E. Latimer-Cheung et al, “Evidence-informed recommendations for constructing and disseminating messages supplementing the new Canadian Physical Activity Guidelines”, BMC Public Health 2013, 13:419 doi:10.1186/1471-2458-13-419, 2013. L. Mbuagbaw et al, “Mobile phone text messages for improving adherence to antiretroviral therapy (ART): an individual patient data meta-analysis of randomised trials”, BMJ Open 2013, doi:10.1136/bmjopen-2013-003950, 2013. P. K. Mulgund, et al, “Personalized Medication Adherence Motivating and Reminding System (PMAMRS)”, Advancing the Impact of Design Science: Moving from Theory to Practice Lecture Notes in Computer Science, Volume 8463, pp 448452, 2014.
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