The organization and aims of the Center are guided by our conceptual framework. As displayed in Fig. 1, two critical goals for TABIs remain the enhancement of efficacy and adherence. Our Center Model is built upon models proposed by Eysenbach (35) Ritterband (36) and Mohr (37) . We propose three factors that affect efficacy and adherence in internet interventions. (38). 1) Human Support: There is growing evidence that support from clinicians can improve adherence to internet interventions far more than can non-human support (e.g. automated e-mails) (37,39-42). Human beings appear to have a unique ability to elicit a sense of accountability in adhering to behavioral treatments from their fellow humans (43). Web 2.0 allows a care system to harness networked users (i.e. peers), and to engineer interactions that support both adherence to treatment (e.g. logging in) as well as the quality of use (44). 2) Connectedness: This factor reflects the continuity of contact between the user and the TABI care system. Telecommunications technologies (e-mail, telephone, mobile phones) permit more continuous contact with users, which can extend more fully into the user's environment. Connectedness adds two potential improvements over simple internet interventions. First, they allow "push" functions that can contact patients, rather than relying on patients to contact the website. Second, they can potentially permit more frequent monitoring (e.g. ecological momentary assessment;EMA) of the user in his/her environment and interventions that are timed at critical moments. 3) Presentation Characteristics: This factor typically refers to features of technology (e.g. use of audio and video, or degree of tailoring in a website) that may affect how the user interacts with it (36,38). Technologies are becoming available that permit much more interactive, simulated environments that have the potential to provide unique avenues for learning. Cost and cost-effectiveness are included in the model. While cost savings is frequently discussed as an advantage of internet and technology assisted interventions, little work has actually examined this assumption. Indeed, there is no model as to how to determine costs of these interventions. As cost and efficacy are precursors to conducting cost-effectiveness analysis "*, a cost and cost-effectiveness model will be developed under the Methods Core (the dotted lines indicated the exploratory nature of this section). Figure 1 also displays the hypothesized relationships among these factors. Solid arrows represent the expected relationships among the constructs. Dashed arrows represent effects that are potentially mediated by other constructs. Presentation Characteristics are expected to contribute uniquely to both efficacy and adherence. Connectedness and Support are expected to increase adherence. They are also expected to improve efficacy, however, the effect on efficacy is hypothesized to be mediated by the effects on adherence. Dotted lines reflect pilot measurement projects. We recognize that many other factors likely affect efficacy and adherence, including user and environmental characteristics. This Center will attend to user characteristics (in particular, underserved populations including ethnic minorities, chronically ill, and the elderly), and environmental characteristics such as structural barriers to accessing F2F or online care. Data will be collected in these areas and investigators will be involved in supporting these efforts with the aim of developing future projects in these areas. However, the primary aims of the proposed projects will focus on developing Tl translation with the aim of improving adherence and efficacy in TABIs

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory Grants (P20)
Project #
Application #
Study Section
Special Emphasis Panel (ZMH1-ERB-B)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Northwestern University at Chicago
United States
Zip Code
Schueller, Stephen M; Begale, Mark; Penedo, Frank J et al. (2014) Purple: a modular system for developing and deploying behavioral intervention technologies. J Med Internet Res 16:e181
Kalapatapu, Raj K; Ho, Joyce; Cai, Xuan et al. (2014) Cognitive-behavioral therapy in depressed primary care patients with co-occurring problematic alcohol use: effect of telephone-administered vs. face-to-face treatment-a secondary analysis. J Psychoactive Drugs 46:85-92
Mohr, David C; Schueller, Stephen M; Montague, Enid et al. (2014) The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. J Med Internet Res 16:e146
Mohr, David C; Cheung, Ken; Schueller, Stephen M et al. (2013) Continuous evaluation of evolving behavioral intervention technologies. Am J Prev Med 45:517-23
Mohr, David C; Burns, Michelle Nicole; Schueller, Stephen M et al. (2013) Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry 35:332-8
Duffecy, Jennifer; Sanford, Stacy; Wagner, Lynne et al. (2013) Project onward: an innovative e-health intervention for cancer survivors. Psychooncology 22:947-51
Brown, C Hendricks; Mohr, David C; Gallo, Carlos G et al. (2013) A computational future for preventing HIV in minority communities: how advanced technology can improve implementation of effective programs. J Acquir Immune Defic Syndr 63 Suppl 1:S72-84