Using Mobile Phones to Deliver Person-centred Population Health Prograames

Dr Robyn Whittaker Clinical Trials Research Unit Using mobile phones to deliver person-centred population health programmes

Mobile phones in health
Appointment reminders
Reporting test results
Text medication reminders
Support self-management
Self-testing & adjustment medications
Telemedicine
Remote sensors
Access to personal health information

Population health programmes
Stand-alone
Person-centred/individualised
But at less cost
With the reach of mass media
Behaviour change support to reduce the risk of disease

Examples
Txt messaging stop smoking support (00/01)
Video messaging stop smoking support (06/09)
Multimedia messaging depression prevention
(2008/11)

STOMP programme
Voice or web registration
smoking history, triggers etc
0 weeks
Quit Day
free text messaging begins
1-3 weeks Daily text message countdown + action plan Quit calendar web page Personalised text message tips Introduction to CellPall (“quit buddy”) Text your friends “ TXT crave” 4 personalised text message tips per day Quiz or poll eg “top 5 reasons to quit” Web page Quit Meter (eg $ saved), profiles Target Date Quit Relapse 6 weeks 26 weeks

Why mobile phones?
Wide reach - more mobile phone accounts than people
Delivery via method that is already integrated into daily life
Always with people regardless of location
Less ‘digital divide’ than with other technological methods

Household Economic Survey
Less socio-economic gradient than access to internet
ACNielsen Survey 04
No difference between Maori & non-Maori % using mobile phones
Regular txt messaging higher in Maori and Pacific than others

STUB IT participants 24% Maori 10% Asian 7.5% Pacific

STOMP: % quit at 6 weeks Age Gender Ethnicity Income Region
Overall relative risk 2.8, 95% CI 2.1-3.5, p <0.0001

Functionality meets theory
Benefits/barriers
Outcome expectations
Self-efficacy
Positive reinforcement
Observational learning
Preparation cues/triggers
Social support
State behavioural intentions
Tailoring
Engagement

Functionality meets theory
Benefits/barriers
Outcome expectations
Self-efficacy
Positive reinforcement
Observational learning
Preparation cues/triggers
Social support
State behavioural intentions
Tailoring
Engagement
Provide information
Proactive & timely messages
Video messaging
Ask for help on demand
Social network/contacts
Long-term
Direct & individual
Interactive

Proactive & timely
STUB IT
select two message time periods
automatically sends out randomly within those time periods
e.g. first cigarette in the morning, lunch break, after work
“ Good reminders of what I’m trying to do”

Ask for help when/where needed
Text ‘CRAVE’
Get immediate txt or video in response
Context-specific (drinking, stressed, boredom)
Text RELAPSE
Get 3 messages over next 2 hours
Motivation to keep going
Go to website & increase messages

Provide observational learning
Role models
‘ Ordinary’ kiwis going through quitting or same issues as them
Talk about what difficulties they have experienced & what they did to get through it or how they faced it
Feel good cos got through another day/ sorted a problem

Promote social support
STOMP: Txting friends & family on quit day in order to get messages of support
STOMP: Quit buddy

What they liked about STUB IT
“ The constant/daily support”
“ Felt like I was quitting as a group”
“ Real people doing it”
“ That people were sharing their own experiences so I was not feeling alone”

Provide long-term support
MEMO:
Mobile website for summary info & how to get help out to 12 months

Tailored
Select Quit Date
Select role model or topics of interest
Select appropriate time periods
Use nickname
Relapse programme

Provide interactivity
Data collection questions
How many cigs have u smkd in past 7 days?
CRAVE
Polls & surveys in STOMP
How long does it take nicotine 2 leave yr body? 2hrs, 2days or 2 wks
Submit video messages in STUB IT
Participants website

Results - STOMP
1705 participant RCTrial
Doubled short-term quit rates
28% cf. 13% (RR2.20, 95% CI 1.79-2.70)
Equally effective for all sub-groups
As effective for Māori as non-Māori

Results – STUB IT
Failed to recruit sufficient participants to show statistically significant difference
36.7% have not gone back to daily smoking at 6 mths cf.25.8%
Why?
Readiness to quit of target popn
‘ Ahead of the curve’ wrt mobile technology
Competition/Incentives/Low coverage

Acknowledgements
Prof A.Rodgers, Dr S.Merry, RB.Lin,M.Wills, M.Jones, T.Corbett, Dr D.Bramley, Dr T.Riddell, Dr S.Denny, R.Maddison, C.Bullen, H.MacRobbie, P.Salmon, V.Parag, Dr K.Stasiak, M.Shepherd, Dr H.McDowell, I.Doherty, Dr S.Ameratunga
E.Dorey, J.Strydom, J.vanRooyen, S.Chua, M.Malik
Funders:
Health Research Council of NZ
National Heart Foundation
Cancer Society of NZ Ministry of Health
Auckland UniServices Ltd Digital Strategy
Vodafone NZ Ltd Oakley Foundation
Alcatel

Publications
STOMP
Rodgers et al. Tobacco Control 2005
Bramley et al. NZMJ 2006
STUB IT
Whittaker et al. JMIR 2008
[email_address]
www.ctru.auckland.ac.nz

Using Mobile Phones to Deliver Person-centred Population Health Prograames

by Health Informatics New Zealand on Oct 08, 2009

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Using Mobile Phones to Deliver Person-centred Population Health Prograames — Presentation Transcript