Internet- and mobile phone based, automated programs for patients

Internet- and mobile phone based, automated programs for patients Jean-François ETTER, PhD IMSP, Faculty of Medicine, University of Geneva, Switzerland E-mail: Jean-Francois.Etter@unige.ch SSMI, Lausanne, May 13, 2011

Internet or mobile phone interventions Re-usable interventions, marginal cost = 0 (vs. consumables such as in-person visit, drugs) Low threshold : - some prefer to avoid face-to-face contact - rural medical desert - low cost Screening, early detection: medical care seeked earlier Ageing, chronic diseases, costs - more patients, fewer health professionals - treat more people with less money and fewer staff - prevention better than cure SPAN: Smoking, Nutrition, Alcohol, Physical activity

Content of interventions Automated programs, with tailored feedback and follow-up - virtual coach, virtual therapist Tests + feedback, screening, early detection: - e.g. BMI, depression, alcohol abuse, tobacco dependence Share, provide + obtain support - personal stories, blogs Support from real people (peers or professionals): - support groups (my problem => the problem I share with others) - discussion forums - chat - 1 to 1 counseling by health professional Mobile, timely interaction (assessment and feedback) - e.g. smoking lapse, pill taking

Automated programs: aims Inform, educate Change attitudes, self-confidence Skills training Emotional support, encouragement Change behavior - adherence, use of medications - participation in medical care - smoking cessation, alcohol use, diet, etc. Maintain change over time

Automated online programs: principles Should be based on theory, e.g. - transtheoretical model of behavior change - CBT, motivational interviewing Evaluation - validated questionnaires Automated, individually-tailored feedback: - written report, pictures, videos, audio files - personal action plan, exercises Follow-up - tailored e-mails, SMS Personal page accessed with password: - progress reports and graphs

Automated online programs: applications Addictions (tobacco, alcohol) Mental health - Depression, anxiety Health promotion, prevention: - Physical activity, weight loss, diet Chronic diseases self-management: - Asthma, diabetes, chronic pain Participation in healthcare, adherence Patient education Patient empowerment (ability to influence + understand own health) «health management» vs. treatment Etc

Impact = Reach * Efficacy RE-AIM framework for health promotion (Glasgow et al., Am J Public Health 1999;89:1322) Reach Efficacy Adoption by health care settings, workplaces Implementation whether patients use it as intended, adherence Maintenance over time

Reach Switzerland:. 75% of population have access to Internet. >90% have a mobile phone Mobile phones: high usage even in low-income people / countries 24 / 7 / 365 Low cost for users, once equipped Everywhere, even in remote, rural areas (medical desert), or for patients with limited access to healthcare system (e.g. mothers of young children, older people, handicap) Many people with mental health problems do not seek treatment e.g. online screening for alcohol: early detection + treatment Translation: worldwide impact

Reach: retain visitors, obtain several visits Chronic, relapsing conditions Long term treatment Support for several attempts to change, over several years Challenges: - Retain participants over many years - Obtain high exposure among visitors. Number of pages seen. Time spent on website / smart phone app. Obtain several visits per visitor

Hard-to-reach audiences People not motivated to change, or ambivalent, or unaware Illiteracy, low SES, immigrants (if no translation) Older people (may change over time as more retired people used Internet professionally)

How to reach smokers who are not motivated to quit? What specific features should be developed for them? 17 9 47 Problemignor Ambivalent Precont Contempl 25 2 Prepar Source: Tabakmonitoring 2010

Switzerland: % smokers (Tabakmonitoring) 16-19 years 20-69 years

Switzerland: increasing social gap By impacting only high SES, current smoking prevention interventions / policies inadvertently increased health inequalities

How to reach the low SES, the illiterate? Prevalence of illiteracy = 10-15% Involve target audience in the development of programs / apps Work with specialized social / healthcare providers Develop specific contents / supports - Video - Audio (podcasts) - Pictures, comics Add TV, radio component to intervention

Efficacy of automated, online systems Many RCTS have been published in recent years + several meta-analyses Smoking cessation: 24 years of RCTs of online interventions 1 st RCT on Compuserve was conducted in 1987 * Next slides: reviews and meta-analyses only * Schneider SJ, Walter R, O Donnell R. Computerized communication as a medium for behavioral smoking cessation treatment: controlled evaluation. Comp Hum Behav 1990;6(2):141-151. * Schneider, 1986. S.J. Schneider, Trial of an on-line behavioral smoking cessation program. Computers in Human Behavior 2 (1986), pp. 277 286

Smoking: 9 RCTs using the Web: OR=1.40 Myung SK, McDonnell DD, Kazinets G, Seo HG, Moskowitz JM. Effects of Web- and computer-based smoking cessation programs: meta-analysis of randomized controlled trials. Arch Intern Med. 2009;169:929-37.

Smoking, meta-analysis: 11 RCTs on Web: RR=1.80 Web-based, tailored, interactive smoking cessation interventions were effective compared with untailored booklets or e-mail interventions [rate ratio (RR) 1.8; 95% confidence interval (CI) 1.4 2.3], increasing 6-month abstinence by 17% (95% CI 12 21%). Fully automated interventions increased smoking cessation rates (RR 1.4, 95% CI 1.0 2.0), but evidence was less clear-cut for nonautomated interventions. Shahab L, McEwen A. Online support for smoking cessation: a systematic review of the literature. Addiction. 2009;104:1792-804.

Smoking: Cochrane review 20 RCTs Heterogeneity, little pooling Conclusions: Some Internet-based interventions can assist smoking cessation, especially if - the information is appropriately tailored to the users and - frequent automated contacts with the users are ensured, however trials did not show consistent effects. Civljak et al. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev. 2010;9:CD007078.

Cochrane: Tailored interactive internet versus non tailored / non internet, smoking abstinence at short term follow-up.

Efficacy: online alcohol interventions Meta-analysis, 17 RCTs Median effect size = 0.54 (medium effect size) White A., et al. Online Alcohol Interventions: A Systematic Review. J Med Internet Res 2010;12:e62

Efficacy: online CBT for depression and anxiety Meta-analysis, 26 RCTs CBT, self-help Effect size 0.42 to 0.65 for depression (medium effect size) 0.29 to 1.74 for anxiety (medium to large effect size) Griffiths et al. The efficacy of internet interventions for depression and anxiety disorders: a review of randomised controlled trials. Med J Australia 2010;192:S4

Efficacy: online interventions for depression Meta-analysis, 12 RCTs Total N=2446 Effect size = 0.41 (medium effect size) Andersson et al. Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cogn Behav Ther. 2009;38:196-205.

Efficacy: online interventions for anxiety Meta-analysis, 19 RCTs Effect size = 0.49-1.14 (medium to large effect sizes) Similar to effect sizes for therapist-delivered treatment Reger et al. A meta-analysis of the effects of internet- and computer-based cognitive-behavioral treatments for anxiety. J Clin Psychol. 2009 Jan;65(1):53-75.

Efficacy: online interventions for weight loss Review: 18 studies Results: heterogeneity Half the studies showed effectiveness Neve et al. Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obes Rev. 2010;11:306-21.

Efficacy: online interventions for chronic pain Meta-analysis: 11 studies CBT Effect size = 0.29 (small effect) Macea et al. The efficacy of web-based cognitive behavioral interventions for chronic pain: a systematic review and meta-analysis. J Pain. 2010;11:917-29.

Efficacy: education of patients with breast cancer Review: 14 articles (incl. 9 RCTs) N=2374 participants Interactive, Internet-based programs Positive effects on patients knowledge Ryhänen et al. The effects of Internet or interactive computer-based patient education in the field of breast cancer: a systematic literature review. Patient Educ Couns. 2010;79:5-13.

Efficacy: mobile phone intervention for diabetes / glycaemic control Meta-analysis: 22 trials 1657 participants Most interventions = mobile phone + Internet Median follow-up duration = 6 months Reduced glycated hemoglobin values [ HbA(1c) ] by: - 0.5%, 95% confidence interval, 0.3-0.7% - 6 mmol/mol; 95% confidence interval 4-8 mmol/mol Conclusion: statistically significant improvement in glycaemic control and self-management in diabetes care Liang et al. Effect of mobile phone intervention for diabetes on glycaemic control: a meta-analysis. Diabetic Medicine. 2011 Apr;28:455-63.

Efficacy: summary Many studies, large N, several meta-analysis Proven efficacy across various health problems and behaviors RCTs mainly of automated interventions Fewer RCTs of other features / services (e.g. peer groups, discussion forums, mobile phone interv.) Large variability of effects Usually, follow-up <12 months And several studies show that the effect of online, automated interventions is similar to the effect of face-to-face counseling

Efficacy: open questions Which service is best suited to each category (age, sex, education, motivation, severity of disease) Participant s characteristics that predict outcome? Moderators / mediators Assess unintended effects - substitute for face-to-face counseling? Effect of web / mobile phone interventions, over and above traditional interventions, in intergrated programs (self-help materials, helplines, clinics)

Quality Too often, low quality of programs / interventions / apps Depth of coverage for key topics is often minimal Potential adverse consequences of low quality programs: - interventions perceived as ineffective - missed opportunities - decreased self-efficacy if attempt to change behavior fails - misleading information on treatments (recommendations to avoid effective treatments or to use ineffective ones) on the nature of disease Online social support: - for whom is it effective? - adverse outcomes (conflicts + e.g. pro-anorexia websites)

USA: smoking cessation websites most cited by smokers 120 100 80 60 40 20 0 112 71 39 37 31 29 28 26 N answers 22 20 18 17 13 PM Quit Assist QuitNet.com RJ Reynolds Lungusa.com WebMD.com Committed Quitters cancer.org Stopsmoking.com Quitsmoking.com Quitsmoking.About.com Anti-smoking.org Nicorette.com Smokefree.gov Web survey, 2005, 706 participants Etter JF. Nicotine & Tobacco Research, 2006;8:S27

USA: highest quality websites (1-10 score) 10 9 8 7 6 5 4 3 2 1 6.7 6.7 6.4 6.5 6.2 5.9 6.1 5.5 6 7 7.2 6.1 7.4 PM Quit Assist QuitNet.com RJ Reynolds Lungusa.com WebMD.com Committed Quitters cancer.org Stopsmoking.com Quitsmoking.com Quitsmoking.About.com Anti-smoking.org Nicorette.com Smokefree.gov 0 Quality 1-10

Social media: adverse outcomes Conflicts, mobbing on discussion forums Normality by numbers: e.g. heavy drinking = normal in groups of heavy drinkers E.g. pro-anorexia, pro-suicide websites

Adoption, implemention, maintenance Adoption - by target audience - by health care settings - at the workplace Implementation - do patients use these interventions as intended? - adherence Maintenance over time - viability, durability of web sites / mobile phone apps - many websites / programs disappear after a few years - many of the interventions tested were experimental and are no longer available - multiple sources of funding

Adoption: integration virtual + real world Collaborations with: Doctors, pharmacists, dentists, hospitals Smoking cessation clinics Helplines Schools, workplaces Government agencies NGOs Pharma companies Large websites (not just health-related websites)

Conclusions High reach Efficacy of fully-automated programs, various fields Efficacy of other Internet / mobile phone features / services? Complementary to clinic visit Potential for development: - integration in healthcare systems - health disparities: develop interventions that reach + are effective in low SES - translate + export to low-income countries Who should pay for the products and services provided by this new industry?