Research Overview

The Brief Inventory of Perceived Stress is a nine-item measure that is multidimensional, psychometrically sound and longitudinally stable. It was developed from items for the Perceived Stress Scale and the Perceived Stress Questionnaire and contains three factors:

• Lack of Control
• Pushed
• Conflict and Imposition

The measure and scoring instructions are available at Brief Inventory of Perceived Stress.

Reference: Lehman KA, Burns MN, Gagen EC, Mohr DC. Development of the brief inventory of perceived stress. J Clin Psychol. 2012;68(6):631-644.

The initial items in the Perceived Barriers to Psychological Treatments were derived from 260 participants. These items were then were administered to 658 primary care patients. Exploratory factor analysis on half the sample resulted in eight factors, which were supported by confirmatory factor analysis conducted on the other half. Factors include:

• Stigma
• Lack of motivation
• Emotional concerns
• Negative perception of therapy
• Misfit of therapy to needs
• Time constraints
• Participation restrictions
• Availability of services
• Cost

The measure and scoring instructions are available at Perceived Barriers to Care.

Reference: Mohr DC, Ho J, Duffecy J, Duffecy, J., Baron, K.G., Lehman, K.A., Jin, L., Reifler, D. Perceived barriers to psychological treatments and their relationship to depression. J Clin Psychol. 2010;66(4):394-409.

The Center for Behavioral Intervention Technologies is 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 settings interventions. 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 at the bottom of this page. The overarching framework is our Accelerated Creation-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 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 that can produce rapid results in one trial.  Effectiveness and implementation are evaluated simultaneously. Using our Trials of Intervention Principles 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. 

References

• 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.

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 worldwide.

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 the translation of skills into the patient's daily life.

Coaching manuals are further described and provided under the Workbooks & Protocols tab.

References

• 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.
• Lattie, E.G., Graham, A.K., Hadjistavropoulous, H.D., Dear, B.F., Titov, N., & Mohr, D.C. (2019). Guidance on defining the scope and development of text-based coaching protocols for digital mental health interventions. Digital Health, DOI: 10.1177/2055207619896145

The failure of many digital mental health tools can be tied to the lack of detailed stakeholder engagement by the research team. At the Center for Behavioral Intervention Technologies, we are committed to including the perspectives of stakeholders who are using or are affected by the tools, services and processes we are developing in the design process.

Furthermore, we will engage with stakeholders throughout the design process to ensure that our designs are responsive to their feedback. Our design work incorporates theories and practices from user-centered design. We will continuously engage with stakeholders through an iterative design and evaluation process to ensure that our designs not only deliver the needed technologies and services but also meet the needs of the stakeholders.

Publications

• Meyerhoff J, Kornfield R, Lattie EG, Knapp AA, Kruzan KP, Jacobs M, Stamatis CA, Taple BJ, Beltzer ML, Berry ABL, Reddy M, Mohr DC, Graham AK. (2023). From formative design to service-ready therapeutic: A pragmatic approach to designing digital mental health interventions across domains. Internet Interventions; 34: 100677.
• Kornfield, R.+, Zhang, A*, Nicholas, J, Schueller, S., Cambo, S., Mohr, D. Reddy, M.,(2020). "Energy is a Finite Resource": Designing Technology to Support Individuals across Fluctuating Symptoms of Depression" In Proceedings of ACM Conf on Human Factors in Computing (CHI'20). Honolulu, Hawaii. April 27-30, 2020.
• Ng, A., Kornfield, R, Zalta, A., Schueller, S., Brennan, M., Reddy, M. (2019). ProviderPerspectives on Integrating Sensor-Captured Patient-Generated Data in Mental Health Care. In Proceedings of the 22ndACM Conf on Computer Supported Cooperative Work and Social Computing (CSCW'19). Austin, TX
• Graham, A., Wildes, J., Reddy, M., Munson, S. Taylor, C.B., Mohr, D. (2019). "User-centered design for technology-enabled services for eating disorders". International Journal of Eating Disorders.

Experimental therapeutics is a framework focused on measuring whether experimentally manipulated, hypothesized targets of an intervention produce changes in clinical outcomes. The process is defined as:

1. Identify a hypothesized target.
2. Examine if an intervention can engage the target.
3. Examine if, by engaging the target, the intervention leads to changes in clinical outcomes. 

To date, experimental therapeutics for mental health interventions have focused on individual-level targets. We have proposed that experimental therapeutics should be expanded to include engagement as a target for digital mental health interventions.

Our conceptual model is shown in the Figure. Engagement can be defined and measured by subjective and objective units of analysis. These metrics can be evaluated as mediating factors that influence users' response to digital interventions. Such work is important for understanding how to design digital mental health interventions that lead to improvements in clinical outcomes. 

Review our paper for more information.

Publications

• Graham AK, Lattie EG, Mohr DC. Experimental Therapeutics for Digital Mental Health. JAMA Psychiatry. 2019 Dec 1;76(12):1223-1224. doi: 10.1001/jamapsychiatry.2019.2075. PMCID: PMC7271442.