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.

9

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.

26

Thank you.

Acknowledgment: Dylan Knowles

Dept. of Computer Science

University of Saskatchewan

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