AndWellness:%20An%20Open%20Mobile%20System%20for%20Activity%20and%20Experience%20Sampling

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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

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Outline

• Introduction
• Related works
• System overview
• Implementation
• Evaluation
• Discussion
• Conclusion

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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.

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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

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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

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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 participants timely adherence to the
  process
• configure when and why a participant is queried

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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

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Related works

• Can be divided into two classes
• Experience sampling studies
• Other software systems

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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

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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.
• Dont incorporate reminders, triggers etc.

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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.

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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.

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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.

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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.

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System overview

• Dashboard
• view a summary of upload statistics
• view participants current progress
• visualize currently uploaded data different
  visualizations
• a map view find relations between time,
  location, and surveys responses

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System overview

• Example Dashboard view

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System overview

• Design has to meet a number of requirements on
• Usability
• Power
• Privacy
• Transparency

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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 participants
  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

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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

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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.

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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.

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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

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Implementation

• Server

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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

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Implementation

• Application on the phone

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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

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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

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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

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Evaluation

• Network and Storage again each of the activity
  sampling rates were selected

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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

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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.

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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.

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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

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Discussion

• Transparency
• Participants want to know how data is being
  collected, where the data is going, and the
  ability to visualize the data
• boost participants adherence, participant trust
  and engagement

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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

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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

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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

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Question?

• Thanks for listening!