Post on 23-Mar-2018
Understanding Social Determinants
of Health via Novel Technologies
Mohammad Hashemian, M.Sc.
Nathaniel Osgood, Ph.D.
Kevin Stanley, Ph.D.
Dept. of Computer Science,
Dept. of Community Health and Epidemiology
University of Saskatchewan 1
Outline
Using sensors to record human behavioral
patterns.
Understand social determinants of health using
smartphones
Challenges in automated data collection
systems.
Conclusion 2
Automated Data Collection Systems
Automatically recording human behavioral
patterns started at 2006 (Reality Mining).
The collected data mainly used in Computer
Networking.
Data offered potential for other sciences, such
as urban planning.
3
Applications in Health Sciences
Flunet project during 2009 H1N1 pandemic,
recorded contacts between 36 participants, and
their visit to certain public locations.
Minute-resolution contact data could help
investigate the role of contact duration in
infection transmission.
4
Moving to Smartphones
Hardware used in the first
experiment caused some
difficulties.
Smartphones as the next
generation provided:
More compliance.
Potential for large scale deployment.
5
iEpi: App for Data Collection
Data collection system tested with 39
participants for 5 weeks.
Collected data included:
Accelerometer samples
Bluetooth devices in proximity
GPS locations
WiFi networks in range
Battery state
Pilot resulted more than 45 million
records on human movement and activity. 6
Pilot Results
GPS and WiFi data can
help to find an individual’s
location.
Bluetooth scanning
shows the density of
people in proximity.
7
Social Determinants of Health*
1. Social Support
2. Physical Environment
3. Food
4. Transportation
5. Work
6. Unemployment
7. Addiction
8. Stress
8
*Social Determinants of Health, The Solid Facts, R. Wilkinson, M. Marmot, 2003
1. Social Support
Belonging to a social network, having strong
friendship, and similar conditions can improve
health situations.
Although quality of a relation cannot be
measured by sensors, there are indicators.
One measure is social interactions after work
hours.
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Social Interactions During and After Work
Sample Participant 1
Sample Participant 2
Density of people visited by two sample participants per location:
a) during the experiment, b) during working hours (9am-6pm), c) after working hours
a) b) c)
a) b) c) 10
High
Low
Other Types of Social Interactions
Distance and online socialization, e.g. phone
communication, online social networks.
Quantitative variables for this type of
communication can be readily measured by
smartphones.
11
2. Physical Environment
Physical environment can be divided to two
groups:
Natural factors, such as water or air quality
Built environment, such as housing or access to parks
Combination of geographical data and
localization information (e.g. GPS, WiFi), can
improve our understanding of the character of
the local physical environment.
12
Differences between two Neighborhoods
Participant 1 (east neighborhood): Participant 2 (west neighborhood):
13
High Low
Amenities in Surroundings
0
10
20
30
40
50
60
70
Num
ber
of
Busi
nes
ses
Participant 1 (east neighborhood)
Participant 2 (west neighborhood)
14
Amenities in Surroundings
0
2
4
6
8
10
12
14
16
18
Num
ber
of
Busi
nes
ses
Participant 1 (east neighborhood)
Participant 2 (west neighborhood)
15
Physical Environment
The selection bias in sampled population
(university students) affects the results.
The tool can be extended to extract people’s habit
in using different facilities.
Similar approach can be used for gaining insights
on natural factors of physical environment.
16
3. Food
People’s access to healthy food, and their
frequency of utilizing and selection of
restaurants can play an important role.
Analysis similar to the ones presented can
provide insight into people’s eating habits.
17
4. Transportation
Use of biking and public transport can affect air
pollution, activity level, social interactions, etc.
Smartphones can help understanding people’s
transportation habits.
18
Activity Level and Location
Location information via GPS and WiFi networks,
and activity data via accelerometer can indicate
transportation habits.
Accelerometer data can be classified into
movement types, such as walking, running,
biking, etc.
Collected 30 million accelerometer records are a
rich source of human activity patterns. 19
Activity Level per Location
Participant 1: Participant 2:
20
High
Low
Classifying Transportation Types
Speed can differentiate between vehicle and
non-vehicle transportation.
Movement pattern can differentiate between
public transport and personal car.
Activity can differentiate between walking,
running, and biking.
21
Other Determinants of Health
Complexities in measuring work conditions,
addiction, stress, etc. are considerably higher.
We can use indicators, for example we can
measure some physical manifestations of stress.
22
Smartphones or Surveys?
Despite the potential emphasized for automated
collection systems, they can’t replace surveys.
Combination of both offers the most
comprehensive results.
New types of surveys can be designed using
smartphones, e.g. context-based surveys.
23
Improving the Analysis Toolset
More advanced analysis toolsets are required to
convert raw data to meaningful information.
Analysis toolsets should:
Provide deeper analysis, such as stronger pattern
recognition system for accelerometer data.
Extend the analysis by combining multiple patterns,
for example people’s social network together with
their food or activity habits.
24
Is the System Scalable?
Do we have to provide a smartphone for each
participant?
One possibility is to have the data collection tool
as a normal smartphone app. in the app-market.
Risk evaluation is required.
25
Summary
Automated data collection systems can improve
understanding of social determinants of health by
providing higher resolution data on a larger scale.
Combining surveys with sensor-based
approaches can provide insights into qualitative
factors.
Improving the analysis toolsets is a key to gaining
more insights from raw sensed data.
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Thank you.
Acknowledgment: Dylan Knowles
Dept. of Computer Science
University of Saskatchewan
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