Hoogtepunten SRNT ehealth & mhealth

Improving Mental Health by Sharing Knowledge

Hoogtepunten SRNT eHealth & mHealth Anouk de Gee 17 september 2015

De “e-health worst” • Groot(/groter) bereik – ook onder moeilijk bereikbare groepen (?)

• Groot gebruiksgemak – altijd en overal toegankelijk

• Goede mogelijkheden voor personalisatie – individualized health care

• Maar… wat weten we over effecten? 2

Wat cijfers… • • • •

4,5 miljoen mensen hebben een mobiele telefoon 2 miljoen mensen hebben een smartphone Gemiddeld gebruik per dag: 120 minuten 80% gebruikt zijn telefoon binnen 15 minuten na het opstaan

Percent (N) graduate Percent (N) male Access to smartphone Look for health info online

US sample 84.6 (121)

Saudi sample 66.9 (109)

93.0 (133) 99.3 (142) 57.0 (81)

95.7 (156) 98.2 (160) 56.3 (90)

Abdallaziz Alzahrane, Robert West, University College London 3

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E-health voor stoppen met roken  Vorm

 Inhoud  Sneak preview (lopend onderzoek)

Met dank aan de sprekers voor toestemming voor gebruik van (de inhoud van) hun dia’s

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Computer Tailoring • Computer-tailoring (De Vries & Brug, 1999) = adaptation of health communication messages to individual characteristics and needs using a largely computerized process Theoretical Model

• Advantage: One can make highly personalized programs that are tailored to the needs of ONE person • Effective and cost-effective

Questionnaire

Feedback library

Data file Tailoring program

The TailorBuilder

Dr. Hein de Vries Feedback

Maastricht University

3 Randomized Controlled Trials 1) RCT: Tailored vs. non tailored Web based computer advise (N = 195) – Attitudes, Social influences, Self-efficacy, Action planning – Results after 6 months: OR = 3.21, p<.05 (1.16-8.90) | ES = .37 – No condition x education interaction • te Poel et al., 2009, Health Ed Res 25%

20,40%

20% 15%

7,80% tailored

10% non-tailored

5% 0% 7 day point prevalence

3 Randomized Controlled Trials 1) RCT: Tailored vs. non tailored Web based computer advise (N = 195) – Attitudes, Social influences, Self-efficacy, Action planning – Results after 6 months: OR = 3.21, p<.05 (1.16-8.90) | ES = .37 – No condition x education interaction • te Poel et al., 2009, Health Ed Res

2) RCT: Action planning (AP) program vs. no intervention to prevent relapse – Tailored advice, 3 preparatory and 3 coping planning sessions – Results after 12 months: AP significantly better than Control – No condition x education interaction • Elfeddali et al., 2012, JMIR

3 Randomized Controlled Trials 1) RCT: Tailored vs. non tailored Web based computer advise (N = 195) – Attitudes, Social influences, Self-efficacy, Action planning – Results after 6 months: OR = 3.21, p<.05 (1.16-8.90) | ES = .37 – No condition x education interaction • te Poel et al., 2009, Health Ed Res

2) RCT: Action planning (AP) program vs. no intervention to prevent relapse – Tailored advice, 3 preparatory and 3 coping planning sessions – Results after 12 months: AP significantly better than Control – No condition x education interaction • Elfeddali et al., 2012, JMIR

3) RCT (3-arm): Control Condition vs. Video messages vs. Text messages (N = 2106) – Tailored messages on smoking behaviour, attitude, perceived social influence, perceived self-efficacy, action plans (content the same for video and text messages) • Stanczyk et al., 2014, JMIR

Video

Text

Control

6 months

30,6%

22.6%

14.6%

Video & tekst both effective

12 months

20.2%

13.5%

12.0%

Video > control

Conclusions

• Computer Tailored eHealth – – – –

Can be used to reach the lower educated Videos are equally effective for the LSES and HSES Good appreciation Good effectiveness and cost effective

Dr. Hein de Vries Maastricht University

Recruitment via GP or Mass media • GP: flyers and posters • Massmedia: regional newspapers, internet

€4640 (€32 per smokerr) 144 smokers More lower educated More chronically ill

€2920 (€4 per smoker) 688 smokers Higher educated Less chronic ill persons

Overall: more lower educated reached by mass media

Dr. Hein de Vries Maastricht University

- QuitCoach - QuitonQ - RealEQuit

Prof. Ron Borland Cancer Council Victoria

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QuitCoach (computer) vs. onQ (sms)

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RealEQuit (app) vs. onQ (sms)

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Stoppen met roken apps Characterising stop-smoking smartphone apps in terms of inclusion of • behaviour change techniques • engagement features • ease-of-use features 2012 & 2014 Harveen Kaur Ubhi Maastricht University / University College London

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Selection: only iPhone apps and only free available. 19

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Conclusion: no evidence for improvement from 2012 to 2014 24

0.50

0.75

1.00

Survival curve by group - medication users

0.00

0.25

• Focus on post-quit strategies after quitting

0

30

60

1.00

Survival curve by

90 120 150 analysis time group - non-medication users

180

0.75

Not structured planning Structured planning

• Immediate implementation intervention had no impact

0.25

0.50

• Structured planning was significantly better than the Base QuitCoach

0.00

Prof. Ron Borland 0

30

60

90 analysis time

120

Not structured planning Structured planning

150

180

Cancer Council Victoria




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Bupa Quit Developing a strategy for evaluating stop-smoking smartphone apps in randomised controlled trials: the example of BupaQuit Aleksandra Herbec University College London

GLobal Institute for Digital Health Excellence (www.glidhe.org) collaborative project between Bupa Digital and University College London 27

Bupa Quit - development Adaptation & further development of an existing application SmokeFree28 (http://www.sf28.co.uk/): • 28-day Challenge to be Smoke Free • 18% self-reported abstinence rates at 4 week follow-up (see Ubhi et al., 2015)

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Basic version

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Bupa Quit research • Evaluate the effectiveness, usage, & acceptability • RCT, target sample N=816 adult daily smokers

• Global dissemination, comparative data analysis – available in Spain, Poland, Australia (future: China)

• Further development: focus om personalisation 30

SmokeFree Baby Factorial experiment for the optimization phase of a smartphone app to aid smoking cessation in pregnancy

Ildiko Tombor University College London

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• • • • •

Identity Stress Relief Health Effects Face-to-Face Behaviour

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eHealth = complex interventions • • • •

Various interacting components Potentially high degree of flexibility and tailoring Different delivery strategies How to determine the optimal content of an app?

Multiphase Optimization Strategy (MOST) • Test the effects of individual components • Test the optimal level of each component Goal: To maximise the overall effect of an intervention Collins et al., 2011

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SmokeBeat

“World's first smoking cessation app using wearables”

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Stoppen met roken app voor Nederlandse jongeren

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Stoppen met roken app voor Nederlandse jongeren

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Highlights • e-Health kan effectief zijn (tailored) – Computer, video, sms, app, …

• Bereik van laag opgeleide doelgroep is mogelijk • Wie wat wanneer krijgt maakt uit – Informatie-zoekers vs. gerekruteerden

• Nog veel ruimte voor optimalisatie Knelpunten • Implementatie: bereik & betrokkenheid • Technologische ontwikkelingen gaan snel; onderzoek niet

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Dank voor uw aandacht! Anouk de Gee [email protected]

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Hoogtepunten SRNT ehealth & mhealth

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