Research Methodology

CBITs has been a pioneer in the development of research and evaluation methods that fit the digital mental health context.  Current research methods have resulted in hundreds of trials that have demonstrated efficacy, yet this enormous evidence base has not translated into successful or sustainable implementation in real-world healthcare settings.  Indeed, many value-based healthcare organizations have attempted to implement commercially available digital mental health settingsinterventions. Unfortunately, these implementations have failed because patients do not use them and they are not well-accepted by providers.

Our Solution Focused approach has been articulated in a number of papers listed on this page. The overarching framework is our Accelerated Create-to-Sustainment (ACTS) model.  In contrast to traditional research methods, which aim to generate generalizable evidence of efficacy or effectiveness, the end goal of solution focused research is to create a sustainable implementation in one setting.  From there, the intervention can be scaled out to other settings.

There are three stages in the ACTs model:.

  • Create Stage: This phase employs user- centered design design (UCD) methods to deeply engage with all stakeholders to understand their needs, preferences, and limitations in an iterative design process.  Importantly, we begin simultaneously designing the service protocol (the goals and specific actions on a provider), the technologies that support that service, and the implementation plan. Therefore, we don’t just focus first on the technology design but rather view all components as equally important.
  • Optimization-Effectiveness-Implementation (OEI) Hybrid Trial Stage: We move away from a phase-based research model in which interventions are moved sequentially through pilot, efficacy, effectiveness, and implementation trials, to a model can produce rapid results in one trial.  Effectiveness and implementation are evaluated simultaneously. Using our Trials of Intervention Principles (TIPS) method, we integrate optimization into the trial. Thus, we encourage learning and improvement in service protocol, technologies, and implementation plan throughout the course of the trial.
  • Sustainment Stage: To ensure sustainment, we continue past the OEI Hybrid Trial, turning over essential implementation functions normally provided by research staff to staff in the healthcare system.  These functions include training, supervision, and continuous quality improvement methods.


Mohr DC, Lyon AR, Lattie EG, Reddy M, Schueller SM. Accelerating Digital Mental Health Research From Early Design and Creation to Successful Implementation and Sustainment. 2017;19(5):e153.

Mohr DC, Schueller SM, Riley WT, et al. Trials of Intervention Principles: Evaluation Methods for Evolving Behavioral Intervention Technologies. J Med Internet Res. 2015;17(7):e166.

Mohr DC, Cheung K, Schueller SM, Hendricks Brown C, Duan N. Continuous evaluation of evolving behavioral intervention technologies. Am J Prev Med. 2013;45(4):517-523.

Mohr DC, Schueller SM, Montague E, Burns MN, Rashidi P. The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. J Med Internet Res. 2014;16(6):e146.

Coaching and Care Mangement Models

Many meta-analyses of digital mental health intervention trials have shown that human coaching improves both patient engagement and outcomes.  Yet relatively little literature has addressed what the essential elements of coaching are. We have proposed two broad models. First, our Supportive Accountability model posits that a core function of a coach is to keep the user engaged.  This is achieved through accountability, which is defined as the patient knowing that they will communicate with a coach about whether they have or have not completed the required activities with the digital tools. This process is wrapped in a supportive relationship, ensuring a warm, empathic bond in which the patient knows that the coach has the best interests of the patient at heart, and reassurance that coach is credible and competent. This model is simple to implement and is being used by a growing number of digital mental health programs world-wide.

This model has been extended to the Efficiency Model, which identifies additional potential failure points of digital mental health interventions and the coach’s role in overcoming those failure points.  This expands the Supportive Accountability model to consider not just that a patient has used an intervention tool, but also how the patient is using it.  For example, tools may fail because the patient does not know how to use them, in which case the coach can assist the patient.  The tool may not match the needs or preferences of the patient, in which case the coach can direct the patient to more appropriate tools.  Finally, the end goal of treatment is not that patients use digital tools, rather it is that they make changes in their lives, which may not be visible to the coach through tool use. This requires the coach to inquire about translation of skills into the patient’s daily life.


Mohr DC, Cuijpers P, Lehman K. Supportive Accountability: A Model for Providing Human Support to Enhance Adherence to eHealth Interventions. J Med Internet Res. 2011;13(1):e30.

Schueller SM, Tomasino KN, Mohr DC. Integrating Human Support into Behavioral Intervention Technologies: The Efficiency Model of Support. Clinical Psychology: Science and Practice. 2016(24):27-45.