e-Health & e-epidemiology: the E4N cohort study

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








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








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!

Ne pas toucher dias ci-dessousI am here because I love to give presentations.

You can find me at @username

The E4N prospective study

A unique family cohort study!

Selected as an “Investment for the Future” by the French

National Research Agency (ANR)

HELLO!



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



Thanks to

The E4N project team

Questions?

My Twitter: @GFaghe

Health & Medicine

e-Health & e-epidemiology: the E4N cohort study