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