AndWellness: An Open Mobile System for Activity and Experience Sampling

1. AndWellness: An Open Mobile System for Activity andExperience Sampling John Hicks, Nithya Ramanathan, Donnie Kim, Mohamad Monibi, Joshua Selsky, Mark Hansen, Deborah Estrin Presented by Hien Nguyen

2. Outline • Introduction • Related works • System overview • Implementation • Evaluation • Discussion • Conclusion

3. Introduction • AndWellness: a personal data collection system for activity and experience sampling. • Area of usage: health and behavior monitoring • Example: Cancer Survivor Study by UCLA measures the behaviors and emotions of young breast cancer survivors. Collect daily information on: • nights sleep, various emotional feedback, behaviors • remind users to take simple saliva sample to measure various biomarkers.

4. Introduction • In the past: • asked participants to recall events • used paper diaries to log events • employed automated telephone systems to record data • recently used PDA or wireless mobile devices to log data

5. Introduction • The trend: moving towards a real time assessment of human behavior • Because factors can and do affect the memory of participants recalling past experiences (Recall bias): • current emotional state • length of time asked to recall • participant sitting in a foreign environment • Alleviate the issue by having participants record events as they happen or immediately thereafter

6. Introduction • Challenges with experiences sampling studies: • Time and resources for developing robust data collection systems from scratch • Data collection systems should allow researchers enough control to: • measure a participant’s timely adherence to the process • configure when and why a participant is queried

7. Introduction • AndWellness: a personal data collection system, uses mobile phones to collect and analyze data from: • active, triggered user experience samples: survey responses • passive logging of onboard environmental sensors

8. Related works • Can be divided into two classes: • Experience sampling studies • Other software systems

9. Related works • Experience Sampling Studies • Paper diary: very low upfront cost but costly and labor intensive post-study analysis, can not verify adherence, low motivation to adherence Reminders can help • Automated phone system: eliminates problems with data entry (response stored as integer), time stamped responses ensuring adherence • Recently, various handheld devices: prompt triggers to remind participants, compliance checks via time stamps

10. Related Works • Other Software Systems, some examples: • Pendragon Forms, Frontline Forms, Nokia Data Gathering: provide tools to organize surveys and collect data, but closed source and closed standards. • JavaRosa, RapidSMS, FrontlineSMS, EpiHandy: more flexible to tune for particular uses, but are primary focused on collecting textual data. • Don’t incorporate reminders, triggers etc.

11. Related Works • AndWellness takes advantage of the flexibility of phones to reduce participant burden: • contextual triggers: avoid bothering participants at inopportune times • survey configuration: branching to avoid asking redundant or useless questions • sampling onboard sensors: i.e, GPS to collect continuous data w/o interrupting participants. • Focus on adherence and quality of responses from non-technical participants: different from other systems.

12. System overview • Three subsystems: • An application to collect data on an Android mobile device • A server to configure studies and store collected data • A dashboard to display participants’ statistics and data.

13. System overview • Paradigm: • prompting participants on their mobile device to answer surveys at configured intervals • uploading the responses wirelessly to a central server • responses are parsed into a central database • and can be viewed by both the researchers and participants in real-time.

14. System overview • The end-to-end data collection system contains three main components: • Campaigns: contain surveys and other continuous data collection types or sensors. • Sensors: location traces (GPS) and activity inference (still, walking, running, biking, driving) by GPS and accelerometer and uses clustering techniques • Triggers: launching surveys based on time, location, or other contextual clues, can be configured.

15. System overview • Dashboard • view a summary of upload statistics • view participant’s current progress • visualize currently uploaded data: different visualizations • a map view: find relations between time, location, and surveys responses

16. System overview • Example Dashboard view:

17. System overview • Design: has to meet a number of requirements on • Usability • Power • Privacy • Transparency

18. System overview • Usability: building an unobtrusive application on the mobile device • avoids interfering with standard phone operation • does not drain the phone battery too quickly • does not notify or require the participant’s attention more than necessary • nor exhibit high latency during participant interaction. • easy to learn and use: simple clicks • conditional prompts: allow branching and reducing number of prompts to be completed • automatically upload pending data • Overall: minimize interaction and interference with the user

19. System overview • Power: avoid having to constantly recharge the phone • continuous location and activity sensor have been optimized to balance battery drain with accuracy • tunable to allow the researcher to adjust the balance between resolution and power drain

20. System overview • Privacy: personal or private information to be kept securely • transported using end-to-end encryption • user names are created using randomized dictionary strings to preserve participant anonyminity • informaion can be made only accessible by authorized individuals • balance between usability and privacy: how often to make the user login, to validate and to handle multiple users sharing the same phone.

21. System overview • Transparency: • data are uploaded in near real time • use dashboard to view feedback about data collection process and to know if the device is working correctly • Data collection process becomes clear and meaningful adds motivation to the participant to continue to collect more data.

22. Implementation • Server • implemented in Java 1.6, Spring framework • hosted in the Apache Tomcat 6.0 environment • uses MySQL 5.1 database • HTTP based APIs control access to upload and download

23. Implementation • Server

24. Implementation • Application on the phone • implemented using standard Android development framework in Java programming language • after reading in the configuration, automatically generates the survey questions and response inputs • user can open the settings menu to adjust the trigger times for daily triggered surveys • securely transmits the data to the server with end to end reliability, ensuring no data is lost

25. Implementation • Application on the phone

26. Implementation • Visualizations • online data visualizations have been implemented in JavaScript (data can be viewed w/o any custom software) • the system constantly pre-aggregates any collected data into much lower resolutions, grab only the necessary resolution of data • uses gzip before transport to minimize latency

27. Evaluation • Phone Performance: experiment using • Android based mobile device: Qualcomm MSM 7201A 528 MHz processor, 192 MB RAM, 1150 mAh lithium ion battery. • SystemSens: records CPU utilization, network usage, and current battery percentage

28. Evaluation • CPU Utilization: the application was set to sample activity 1/1 sec, 1/30 secs, 1/1min, and not at all • As we increase the inference rate, CPU utilization only increases a couple percent inference is quick and efficient

29. Evaluation • Network and Storage: again each of the activity sampling rates were selected

30. Evaluation • Battery Life: run the sampling activity at the above rates and measure the rate of battery drain for each • the entire battery is drained in about 7.6 hours with activity inference running unfortunately fast • main battery drains: GPS and accelerometer plan to adjust the activity inference module to better duty cycles those sensors

31. Discussion • Discuss various issues implementing & designing AndWellness: • data quality • privacy • transparency • Review feedback from: • In lab testing • initial testing for two planned studies • several focus groups.

32. Discussion • Data Quality: Bias stems from two main sources • user returning tainted data: only allow participant to respond to survey when it triggers and not other times and no review previous survey. AndWellness has these features and new ones can be easily added. • incomplete data: due to difficulty of the question, question being triggered at inopportune times. AndWellness includes per user configuration of triggers.

33. Discussion • Privacy: important to allow users to trust the data collection process • User login names are randomly assigned by a word generator • data linking user name to personally identifying information is quarantined • data linking can be made accessible to only specific people, or even destroyed making the data permanently anonymous. • But too much emphasis on privacy could frustrate participant and reduced adherence rates • AndWellness can fit either need

34. Discussion • Transparency: • Participants want to know how data is being collected, where the data is going, and the ability to visualize the data • boost participant‘s adherence, participant trust and engagement

35. Discussion • Preliminary Feedback • the system of triggers and reminders is almost mandatory for continued participation • reminders and response feedback were enough to help participant feel engaged • however, the vibration pattern and ringtones used for the notifications were too annoying

36. Future directions • Allowing researchers to use our system to define their own campaigns, survey prompts, and data types without programmer assistance • Ability to remotely change prompts and triggers manually from the central server and even automatically based on characteristics of returned data from participants

37. Conclusions • AndWellness: an end-to-end data collection system designed to monitor participants’ daily habits and behaviors • collects in situ behavorial and contextual data from participants • brings together the advantages from similar systems to meet the requirements set by researchers

38. Question? • Thanks for listening!