Hoogtepunten SRNT ehealth & mhealth

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1 Improving Mental Health by Sharing Knowledge Hoogtepunten SRNT ehealth & mhealth Anouk de Gee 17 september 2015

2 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

3 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 US sample Saudi sample Percent (N) graduate 84.6 (121) 66.9 (109) Percent (N) male 93.0 (133) 95.7 (156) Access to smartphone 99.3 (142) 98.2 (160) Look for health info online 57.0 (81) 56.3 (90) Abdallaziz Alzahrane, Robert West, University College London 3

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6 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 6

7 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 7

8 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 Data file Feedback library Tailoring program Feedback The TailorBuilder Dr. Hein de Vries Maastricht University

9 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 ( ) ES =.37 No condition x education interaction te Poel et al., 2009, Health Ed Res 25% 20,40% 20% 15% 10% 5% 7,80% tailored non-tailored 0% 7 day point prevalence

10 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 ( ) 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

11 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 ( ) 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

12 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

13 GP: flyers and posters Recruitment via GP or Mass media 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

14 - QuitCoach - QuitonQ - RealEQuit Prof. Ron Borland Cancer Council Victoria 14

15 QuitCoach (computer) vs. onq (sms) 15

16 RealEQuit (app) vs. onq (sms) 16

17 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 17

18 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 18

19 Selection: only iphone apps and only free available. 19

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24 Conclusion: no evidence for improvement from 2012 to

25 Survival curve by group - medication users analysis time Survival curve by group - non-medication users Not structured planning Structured planning analysis time Focus on post-quit strategies after quitting Immediate implementation intervention had no impact Structured planning was significantly better than the Base QuitCoach Prof. Ron Borland Cancer Council Victoria Not structured planning Structured planning

26 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 26

27 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 ( collaborative project between Bupa Digital and University College London 27

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

29 Basic version 29

30 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

31 SmokeFree Baby Factorial experiment for the optimization phase of a smartphone app to aid smoking cessation in pregnancy Ildiko Tombor University College London 31

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33 Identity Stress Relief Health Effects Face-to-Face Behaviour 33

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36 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.,

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39 SmokeBeat World's first smoking cessation app using wearables 39

40 Stoppen met roken app voor Nederlandse jongeren 40

41 Stoppen met roken app voor Nederlandse jongeren 41

42 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 42

43 Dank voor uw aandacht! Anouk de Gee 43