eHealth, electronic data capture & citizen science

in healthcare: implications for clinical trials

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Jeremy Wyatt DM FRCP ACMI Fellow

Leadership chair in eHealth research,

Leeds university

With thanks to Dr Shiva Sathanandam, Institute

of Digital Healthcare, Warwick University

Agenda

Definition of eHealth

Three challenges:

1. Studying eH as an intervention: some examples

of eH studies and potential risks associated with

eH interventions

2. Using eH to support clinical trials

3. How might patients use these methods to

conduct their own clinical trials ?

Summary and conclusions

2/23

eHealth / health informatics

Aim: to improve use of information in health, social & self care

Related terms:

• mHealth, Connected health

• Telehealth, telemedicine

• Digital healthcare

• Cybermedicine, cyberhealth

Focus is on:

• Information,

• Communication, and

• Decisions of clinicians, patients, public

Cluster RCT of GP teledermatology to prevent

unnecessary referrals in 560 patients

With Depts. of Medical Informatics and Primary Care, AMC Amsterdam

Instant messaging triage by NHS Direct

nurses for deaf people

The range of eH interventions

Remote consultation by phone, Skype,

videoconference

Interactive programmes / DVDs. eg for CBT

Automated phone / SMS reminders

Tailored msgs or emails for health promotion

Web sites:

• Standard information eg. NHS Choices

• Personalised information eg. My Diabetes; stroke risk

• Interactive “serious” games

• Immersive environments – Second Life

• Virtual / augmented reality

Social media & forums, eg. SharpTalk

Apps and devices including some or all of the above 6/23

Medicom medication monitor

“Persuasive technology” – an RCT

Persuasive features:

1. Domain name uses https security, .dundee.ac.uk domain vs .com

2. University Logo

3. No advertising

4. References

5. Address & contact details

6. Privacy Statement

7. Articles are all dated

8. Site is certified (W3C and Health on the Net

RCT results - joining the NHS organ

register

889/25000 students recruited in 5 days to internet trial

Same joining rate (38%) in persuasive & control groups

Overall, 336 joined NHS organ register, including:

• 126 (49%) of 260 blood donors (HR 1.46, p=0.02)

• 65 (38%) of 173 who know a donor/recipient (NS)

• 68 (23%) of 296 who initially said “maybe”

• 22 (10%) of 232 who initially said “no” !

Interpretation:

• Fogg’s guidelines do not apply in this setting ?

• NHS should target young blood donors

Funded by NHS Chief Scientist Scotland Nind et al, JMIR 2012 (submitted)

Neuromarketing

Methods include: eye tracking, galvanic skin response, functional

MRI, facial recognition via web cam

Old

design

New

design

Telehealth devices

Implanted CardioMEMS sensor & transmitter in distal branch of descending PA

External device sending data to call centre; on screen questions and chart

Tele-health for people with

long term conditions

37 RCTs measuring impact of TH on heart

failure mortality: average 18% lower

Potential adverse effects of

using the internet

Internet phenomenon Adverse effect

Misleading / biased

website, risk calculator

Poor self care decisions; misplaced pessimism / optimism

Viruses Loss of privacy; deletion of data; threat of blackmailing

Trojans eg. keystroke

loggers

Fraudulent access to internet banking details

Addiction to eg. Second

Life

Debt (Linden Labs dollars); cyber widowhood

Flaming by trolls Bullying, anxiety, depression, harm to internet persona;

suicide

Social media eg. Friends

Reunited

Marital disharmony / break up

Forums on anorexia,

suicide

Distorted perspective, self harm

Forums on alternative

therapies

Exposure to poisons (oil of wormwood); delays in seeking

allopathic medicine

12/23

Remote robotic surgery ?

13/23

eH and the research process

Define sample

Screen Recruit Consent Baseline

data Intervene

14/23

Online

consent

Online

intervention

Remote

monitoring

Online

ADR report

Online

recruitment

Data / text

mining

Email / SMS

reminders

Is SMS data capture reliable & valid for

research in young mothers ?

Theory: young mothers are digital natives but very busy

Sent msgs to 350 young mothers in Tayside on infant

feeding every 2 weeks; free SMS responses

Reliability: compared SMS responses to:

• Duplicate msgs in 48 women 1 day later

• Phone calls to 62 women

Validity: compared SMS responses to:

• Health visitor records at 2 weeks

• Other factors correlated / not correlated with feeding method

Funded by NHS Scotland Chief Scientist

Whitford H et al, JAMIA 2012

Some confounders in

internet-based trials

These can lead to Type 1 or Type 2 errors:

• Checklist effect

• Data collection biases

• Hawthorne Effect

• Contamination

• Placebo effects

See chapter 8 in F&W 2nd edition

16/30

Consequences of internet based

studies for researchers

17/39

Pros Cons

Novel, zero cost, personalised

interventions

(Pilot first face to face to test safety)

Do they generalise to real life behaviour ?

Wider reach to participants Risk of cyberdivide (Apps, Smart TVs...)

Who regulates outside UK ? (EMEA)

Reduced opportunity for FTF counselling

Faster study accrual Unknown denominator (clickable link in email ?)

Reliable replication of intervention Unknown participant characteristics (FTF visit)

Collect data more frequently,

check data as it is collected

Unverifiable participant outcomes (home device,

FTF visit)

Study data ready to analyse Possible to falsify data (electronic signatures)

Allows rapid cycle research eg.

MOST-SMART

Allows anyone to research and publish anything

(an advantage ?)

Data ownership unclear; processing may occur

outside EU (specify EU server farm, not Cloud)

Health Citizen science &

“participatory medicine”

27% of Americans tracked own health data online, 18%

sought others with same condition [Pew Internet 2011]

Health social networking: MedHelp [12M visits / month],

PatientsLikeMe, TuDiabetes, CureTogether, Asthmapolis,

DailyStrength, 23andMe, QuantifiedSelf, DIYGenomics...

Insights:

• Near immediate speed at which findings can be tested and applied

• Researcher- and participant-organised research; diverse intended

outcomes, levels of rigor

• “Large-scale parameter-stratified cohorts” - great potential for

research beyond disease to personal ised prevention

• Buzz words: bio-citizenry, crowd funding, individual experimentation,

Health Camp, community computing...

See: Swan M. Crowd sourced health research studies. JMIR 2012; 14(6): e46

18/23

Quantified Self studies

QS: 28 founder members in SF 2008, 5524 in 42 groups

early 2012

Butter Mind study – RCT with 45 people, showed that

eating 60g of butter / day improved speed of calculation.

Methods unclear, no controlling for IQ / education etc.

Reported in blog 2010

Blueberry study – running since 1999, hundreds of

participants, said to show 1% increase in online word

recall exercises. Pub online / conf posters.

Use Genomera study platform – eBay for studies – post on

it & seek participants. Althea similar.

19/23

Patient-led Citizen science

A group of people with asthma “meet” via FaceBook, decide

to answer the question: “What happens if we take

prednisolone 40mg for 15 days instead of 5 ?”

They can:

• Define eligibility criteria

• Randomise themselves

• Purchase drugs from online pharmacies

• Measure & record end points (PEFR, admissions) using

Google Docs spreadsheet / Survey Monkey

• Collaborate on data analysis

• Publish their results online

20/23

Patients Like Me lithium study

Background: promising Italian study (16 ALS cases, 28 controls)

PLM member with ALS recruited 348 other members to try Li off

label, with doctor’s support

• No difference at study end: 149 took Li for 2m, 78 for 12m

• Confirmed in later researcher-organised comparison of 149 pts vs. 447

controls & in later RCT

Why citizen science ? Pts decided on & designed study, sought

drug, made own measures; then prompted researchers to carry

out case control study & RCT

Other PLM studies on eg.:

• Improved measures for ALS, MS

• ALS aetiology (limb use)

• Reasons for low participation in ALS studies

• Pathological gambling in ALS 21/23

Citizen Scientist accuracy & biases

Identification of non-native plant species [Jordan, Envir Man 2012]:

• 119 volunteers over 3 years, trained to count / press samples

• 97% accurate overall on 13 species – but only 50% for 3 rarer species

• Accuracy per volunteer 54%; 70% for professionals not trained on this task

Crowd sourced water level data from 17 sites via SMS [Lowry 2013]:

• 130 msgs sent by 68 observers: 85% sent just 1 SMS

• “Dr Smith Effect” - one phone sent 60 SMS relating to same meter

• 113 of 130 (87%) readings usable

• RMS error in 19 observations compared to telemetry: 0.016 feet [!]

• Location bias – most obs. nr beaver dam

Weekend bias – migratory bird arrival reports 32% more likely on

weekend days [Courter, Int J Biometerology 2012]

Geographical differences in butterfly reporting – more species

per volunteer, per day in NY vs. Chicago [Matteson 2012]

Biased detection of bird mortality by site – deaths at bird

feeders / in nest boxes overestimated 3X [Cooper 2012]

22/23

Potential pros and cons of

patient-run trials

Trial aspect Pros Cons

RCT hypothesis Real patient questions May not be original [“Us too” studies ?]

Likely to be pragmatic

cf. explanatory

May not be theory-based

Participant

recruitment, follow up

Faster wider reach ?

FU more complete ?

Volunteer effect: generalises to others?

Unknown denominator

Intervention uptake Enthusiastic - if rand.

to intervention group

Is intervention reliably replicated ?

Risk of contamination [Zelen design]

Outcome measures Real PROMs; data

often open, identified

May not be reliable, valid

Analysis of results Likely to be rapid,

focused

Risk of data dredging [pre-pub protocol,

triple blinding]

May ignore threats to validity

Trial results Ownership = uptake ? NIH effect in other groups

Other aspects Less expensive

Public engagement

Tackle new questions

May not be publishable

Risk of patient-initiated fraud ?

Risk of publication bias 23/23

Summary & conclusions

1. eHealth = healthcare at a distance

2. Benefits of eH: more frequent interaction, tailoring,

multimedia, social support – but has side effects

3. We need eH RCTs to help develop a theoretical base, study

impact on quality, safety, equity of access, resource use etc...

4. We can use eH methods to support & improve drug trials

5. Patient-designed and run RCTs are emerging: we should

engage with & help improve this health citizen science

6. Let us embrace eH and regulate proportionately, where

risks outweigh benefits [UK Better Regulation Task Force]

24/23