Epidemiology 3.0-
How to integrate connected objects and the Internet of Things (IoT) in
modern epidemiological research?
Guy Fagherazzi, PhD@GFaghe
Challenges in epidemiologyTrue for any past, present or future epidemiological study
➢ Maximize recruitment & minimize attrition➢ Collect high-quality data on factors and
outcomes of interest➢ Optimize logistics
○ costs of acquisition○ processing○ data analysis
Gold standard = Cohort studies
➢ Prospective studies
➢ No selection and recall bias
40s-50s: First cohort studies
- The Framingham Heart Study (1948)- The study of the Japanese atomic bomb survivors (1950)
70s: First modern large cohort studies
- The Nurses' Health Study (1976): risks of oral contraceptives
90s: A new dimension in the design and the data collection
- The Million Women Study UK (1996): women’s health- The E3N cohort study (1990): risk factors for breast cancer
But first, a look in the
mirror
E3N: a great research tool
Cancer Diabetes Nutrition Reproductive factors and hormones
729 peer-reviewed publications in
international journals
The largest study on women’s health in France
- 98 995 women- 26 years of follow-up- Self-reported questionnaires sent
every 2-3 years since 1990
E3N: a great research tool
Cancer Diabetes Nutrition Reproductive factors and hormones
729 peer-reviewed publications in
international journals
The largest study on women’s health in France
- 98 995 women- 26 years of follow-up- Self-reported questionnaires sent
every 2-3 years since 1990
Pioneer on the data acquisition in 1990
- Optical scan of paper questionnaires (600 pages of questionnaires per hour)
- Systems of Automatic Document Reading & Character Recognition
- Video-coding and data checking
Ability to handle a large amount of data on many volunteers
E3N: a great research tool
Cancer Diabetes Nutrition Reproductive factors and hormones
729 peer-reviewed publications in
international journals
The largest study on women’s health in France
- 98 995 women- 26 years of follow-up- Self-reported questionnaires sent
every 2-3 years since 1990
Pioneer on the data acquisition in 1990
- Optical scan of paper questionnaires (600 pages of questionnaires per hour)
- Systems of Automatic Document Reading & Character Recognition
- Video-coding and data checking
Ability to handle a large amount of data on many volunteers
Knowledge production in epidemiology is a long process
It requires:
- Time (≃3 years to deliver the data to researchers)
- Large human resources
- Budget
How to do more, better and faster?
Today & tomorrow
- Epidemiology 3.0 -The example of the E4N study
HELLO!
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The E4N prospective study
A unique family cohort study!
Selected as an “Investment for the Future” by the French
National Research Agency (ANR)
HELLO!
Ne pas toucher dias ci-dessousI am here because I love to give presentations.
You can find me at @username
3 generations200 000 participants
100 000 E3N women
20 000 fathers of E3N women’s children
50 000 children (in 2017)
20 000 grand-children (in 2018)
1st generation: paper questionnaires
2nd & 3rd generations: e-cohort
Saliva samples for all the participants
Trans-generational
➢ Heredity and transmission of health determinants
➢ Genetics and epigenetics of chronic diseases (cancer, diabetes,...)
Expertise on exposures
➢ Epigenetics on lifestyle (smoking, physical activity, diet)
➢ Lifestyle and microbiota
➢ Socioeconomic mobility through generations and its impact on lifestyle and health
e-epidemiology
➢ Integrate new technologies and the Internet of Things in modern epidemiology to collect high-quality data
Some research axes
Social Networks &
Internet➢ Website: e4n.fr➢ Twitter: @EtudeE4N➢ Facebook: facebook.com/EtudeE4N
➢ 2nd and the 3rd generations: already 9 500 online preregistrations!
The Web Platform
Platform
Designed as a “Data Hub”
Questionnaire SMS Smartphone Biobank SNIIRAM(M-A database)
The E4N Platform
➢ Short online questionnaires➢ Answer from a laptop, smartphone or a tablet➢ Questionnaires sent by SMS➢ Sync with connected objects➢ Automatic feedback and statistical
dashboards for the participants➢ Awards and badges (gamification)
E4N and the Internet of ThingsConnected objects for epidemiological research
1st part (ongoing)Work on Withings data from 30,000 customers, over a 6 month period➢ Age➢ Sex➢ Number of steps➢ Weight➢ Blood pressure➢ Heart rate➢ Sleep (available on ⅓ of the data) Objectives
➢ Manipulate data from connected objects
➢ Study the associations between lifestyle and sleep (quantity and quality)
➢ Evaluate the predictive capacity of the data
2nd part (2017)Equip 400 E4N participants with an activity tracker (Withings Go)
➢ Collect passive & continuous data on our participants
➢ Identify the leverages and breaks to a long-term use
➢ Correlate with data from traditional questionnaires on physical activity and sleep
➢ Validation study vs. data from an ActiGraph accelerometer (gold standard) on 50 participants
Ultimately
➢ Follow a large E4N “connected” sub-cohort
○ Either equipped by us or spontaneously (smartphones, connected objects,...)
○ Different types of connected objects, different brands
➢ Continuous and passive data collection in addition to traditional questionnaires
➢ We are open to test new devices in a real-life setting
➢ In the end: this will help us to better understand the relationships between lifestyle and health
Tomorrow for E4N...
➢ Intervention studies nested in the E4N cohort on connected devices
➢ Try to connect the seniors (ie. the 1st generation of the E4N study)
➢ Connected devices to track specific diseases for tertiary prevention (diabetes, cancer)
➢ Evaluate diet accurately and effortless is one of the next big challenges!
The next steps?
Many areas to explore!
PredictivePersonalizedPreventiveParticipatory
P4 Epidemiology
Epidemiology 4P
Ne pas toucher dias ci-dessousI am here because I love to give presentations.
You can find me at @username
Thanks to
The E4N project team
Questions?
My Twitter: @GFaghe