Nov 19, 2015 - http://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidan cedocuments/ucm446680.pdf. Accessed November 25 ...
Online: http://www.bmj.com/content/351/bmj.h5627/rr-8 Searching for the Holy Grail: Patient Engagement in Computerized Therapy Riva G., Università Cattolica del Sacro Cuore, Milan, Italy and Istituto Auxologico Italiano, Milan, Italy Graffigna G., Barello S., Triberti S. Università Cattolica del Sacro Cuore, Milan, Italy Cite this as: BMJ 2015;351:h5627/rr-8, online: http://www.bmj.com/content/351/bmj.h5627/rr-8 (accessed on X)
Patient engagement in healthcare is considered as a key strategy to improve patients' adherence, clinical outcomes, and satisfaction toward the received care (1-2). This is true also for CBT. As demonstrated by Gleen and colleagues (3) patient engagement in CBT is a stable predictor of greater reductions in depression symptoms and functional disability. In our view, the key question raised by the Gilbody et al. (4) results is this: is the real-world usage pattern of eCBT able to provide a level of patient engagement comparable to the one of a GP consultation? Apparently not. But this is not a new outcome. Barazzone and colleagues (5), in their assessment of three eCBT programs for depression, including the two ones – Beating the Blues and MoodGym - used in the pragmatic trial by Gilbody et al. (3) conclude in this way: “Results have highlighted that there are issues and limitations involved in translating features implicit to a therapeutic alliance to a computer program” (p. 413). First, all users receive more or less the same intervention. More, they are not able to repeat the modules according to their needs and preferences. In other words these programs are lacking in both empathy - the therapist's capacity to identify with the client's thoughts and feelings – and responsiveness - the therapist's ability to modify therapy to meet the client's needs and requirements – that are usually provided by a GP in his/her consultation and are an important part of a therapeutic relationship (6). This outcome opens a new critical question for assessing the effectiveness of eCBT: is symptom reduction enough to evaluate the real life efficacy of these interventions? Again, apparently not. Both the two eCBT programs used Gilbody et al. (3) were assessed previoulsy in different randomized controlled trials and the collected evidence supports their effectiveness for the treatment of depression. So, what is changed now? The answer may come from a systematic review discussing these trials. In their conclusions Kaltenthaler and colleagues note (7): “All studies were associated with considerable drop-out rates and little evidence was presented regarding participants’ preferences and the acceptability of the therapy.” (p. 181).
To put it simply, eCBT programs are effective in real world settings - where there are no constraints on usual GP care - when their ability of reducing clinical symptoms is associated to patient engagement. On one side, the design of the empirically supported eCBT programs should include specific features designed to develop and maintain a therapeutic alliance (5). On the other side, assessing this variable (8) in randomized eCBT trials may improve the transferability of their results. The draft US Food and Drug Administration Draft guidance on patient preference (9) presented this week by a JAMA viewpoint (10) follow this path: “FDA believes that patients can and should bring their own experiences to bear in helping the Agency evaluate the benefit-risk profile of certain devices. This kind of input can be important to consider during regulatory decision-making for certain devices.” (p.1). References 1. Forbat L, Cayless S, Knighting K, et al. Engaging patients in health care: an empirical study of the role of engagement on attitudes and action. Patient Educ Couns 2009;74:84–90. 2. Graffigna G, Barello S,Riva G. How to make health information technology effective: the challenge of patient engagement. Arch Phys Med Rehabil 2013;94:2034–5. 3. Glenn D, Golinelli D, Rose RD, et al. Who gets the most out of cognitive behavioral therapy for anxiety disorders? The role of treatment dose and patient engagement. J of Consult Clinical Psych, 2013, 81:639-649. 4. Gilbody S, Littlewood E, Hewitt C, et al. Computerised cognitive behaviour therapy (cCBT) as treatment for depression in primary care (REEACT trial): large scale pragmatic randomised controlled trial BMJ 2015; 351 :h5627 5. Barazzone N, Cavanagh K and Richards DA. Computerized cognitive behavioural therapy and the therapeutic alliance: A qualitative enquiry. Br J Clinic Psych, 51: 396–417. 6. Cahill J, Barkham M, Hardy G, et al. A review of critical appraisal of measures of therapist-patient interactions in mental health settings. Health Tech Assess 2008, 12(24), 1–58. doi:10.3310/hta12240 7. Kaltenthaler E, Parry G, Beverley C. et al. Computerised cognitive–behavioural therapy for depression: systematic review. Brit J of Psych 2008, 193 (3) 181-184. 8. Graffigna G, Barello S, Bonanomi A et al. Measuring patient engagement: development and psychometric properties of the Patient Health Engagement (PHE) Scale. Front Psychol 2015, http://dx.doi.org/10.3389/fpsyg.2015.00274 9. US Food and Drug Administration. Draft guidance on patient preference information. http://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidan cedocuments/ucm446680.pdf. Accessed November 25 , 2015. 10. Hunter NL, O’Callaghan K, Califf RM, Engaging Patients Across the Spectrum of Medical Product DevelopmentView From the US Food and Drug Administration. JAMA. Published online November 19, 2015. doi:10.1001/jama.2015.15818
