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

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Exploratory Grants (P20)
Project #
5P20MH090318-03
Application #
8511830
Study Section
Special Emphasis Panel (ZMH1-ERB-B)
Project Start
Project End
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
3
Fiscal Year
2013
Total Cost
$226,270
Indirect Cost
$75,466
Name
Northwestern University at Chicago
Department
Type
DUNS #
005436803
City
Chicago
State
IL
Country
United States
Zip Code
60611
Pisani, Anthony R; Wyman, Peter A; Mohr, David C et al. (2016) Human Subjects Protection and Technology in Prevention Science: Selected Opportunities and Challenges. Prev Sci 17:765-78
Vittengl, Jeffrey R; Jarrett, Robin B; Weitz, Erica et al. (2016) Divergent Outcomes in Cognitive-Behavioral Therapy and Pharmacotherapy for Adult Depression. Am J Psychiatry 173:481-90
Ho, Joyce; Corden, Marya E; Caccamo, Lauren et al. (2016) Design and evaluation of a peer network to support adherence to a web-based intervention for adolescents. Internet Interv 6:50-56
Saeb, Sohrab; Zhang, Mi; Karr, Christopher J et al. (2015) Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study. J Med Internet Res 17:e175
Miner, Adam S; Schueller, Stephen M; Lattie, Emily G et al. (2015) Creation and validation of the Cognitive and Behavioral Response to Stress Scale in a depression trial. Psychiatry Res 230:819-25
Saeb, Sohrab; Zhang, Mi; Kwasny, Mary M et al. (2015) The Relationship between Clinical, Momentary, and Sensor-based Assessment of Depression. Int Conf Pervasive Comput Technol Healthc 2015:
Saeb, Sohrab; Körding, Konrad; Mohr, David C (2015) Making Activity Recognition Robust against Deceptive Behavior. PLoS One 10:e0144795
Schueller, Stephen M; Mohr, David C (2015) Initial Field Trial of a Coach-Supported Web-Based Depression Treatment. Int Conf Pervasive Comput Technol Healthc 2015:
Ben-Zeev, Dror; Schueller, Stephen M; Begale, Mark et al. (2015) Strategies for mHealth research: lessons from 3 mobile intervention studies. Adm Policy Ment Health 42:157-67
Mohr, David C; Schueller, Stephen M; Riley, William T et al. (2015) Trials of Intervention Principles: Evaluation Methods for Evolving Behavioral Intervention Technologies. J Med Internet Res 17:e166

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