Introduction

In the June issue of Psychotherapy, Newman, Przeworski, Consoli, and Taylor present a study on the use of a palmtop computer-assisted therapy for Generalized Anxiety Disorder (GAD) (Newman et al., 2014). This novel evaluation of the efficiency of coupling a computer program with face-to-face Cognitive-Behavioral Therapy (CBT) was the culmination of questions that began percolating in the early 1990s: How can we deliver therapy in a more cost-effective and accessible way?

In 1995, a typical course of individual therapy for GAD was about $2,181.00 per client and required 23.2 billable hours of therapist contact (Turner et al., 1995). Such a price tag created a significant barrier to receiving treatment. One proposed solution was computer-delivered treatments, which were estimated to save up to a thousand dollars per client in costs (Newman et al., 1999; Newman et al., 1996). By programming portions of therapy onto a device, many barriers to receiving treatment might be lowered (e.g., scheduling conflicts during 9-5 business days, over-booked clinicians, and physical distance).

Although the benefits of therapist-free or therapist-reduced therapy are manifold, there is a significant caveat: As therapist contact decreases, so too may patient engagement and positive outcomes. Although the content programmed into self-guided programs is evidence-based, it appears that efficacious therapy may involve more than valuable content presentation. Below we review our work on computer-assisted therapy and the evolution of Guided Self-Help (GSH) interventions. We also highlight some considerations for developing the role of a program “coach.”

Our Work on Computer-Assisted Psychotherapy

Motivated by previous research demonstrating utility in the use of computers for psychological treatment based on tasks ranging from assessment and self-monitoring to progressive relaxation, Newman et al. (1996) developed a computer program to complement CBT for panic disorder. Recognizing that CBT involved a structured therapy protocol that could be programmed into computers interactively (Selmi et al., 1990) and that most therapeutic change occurs in response to “homework” outside of therapy sessions, Newman and colleagues designed a treatment protocol for panic that coupled a computer program with a condensed therapy component (4 weeks versus traditional 12 weeks), a design meant to provide all the content but in a shorter period of time. The computer program delivered daily anxiety assessments as well as interactive exercises for cognitive restructuring, exposure, and breathing retraining tasks.

As had been previously proposed (Taylor et al., 1991), they observed that the key advantage of computer-assisted CBT—beyond obvious cost-savings—was facilitation of homework and compliance with ongoing assessments. Furthermore, increased patient self-management and practice could improve the experience for both patient and therapist, as less in-person time was dedicated to repetitive instructions.

Following additional case studies, Newman et al. (2014) conducted an RCT to evaluate the efficacy of a similar computer-assisted treatment for GAD compared to treatment as usual. Newman and colleagues demonstrated that a 6-session computer-assisted Group CBT for GAD was more efficacious than 6-session Group alone, confirming hypotheses about the benefits of this medium of therapy (e.g., increased assessment compliance). They also demonstrated that the computer-assisted intervention was as efficacious as traditional 12-session Group CBT, suggesting that the integration of technology can reduce therapist time necessary to achieve results.

Newman and colleagues’ research over the years has effectively demonstrated that components of therapy are well delivered via a computer platform (Newman et al., 1996; Newman et al., 1997; Newman, 1999; Newman et al., 1999; Newman et al., 2011). Our early research focused on using computers as an adjunct to face-to-face treatment rather than as the sole treatment (Taylor et al., 1991). However, many studies have demonstrated the benefits of guided self-help, and a next step in our research is to evaluate guided, Smartphone-led treatments based on our previous work. In this model, the Smartphone application is the central component of therapy versus a supplement to therapy. However, the element of human touch that is lost in purely self-help programs is retained. Ideally, this model will allow for both cost-effectiveness and efficacy in a manner purely self-help has not been able to achieve.

The Motivation for Guided Self-Help Programs

There is a clear need for more cost-effective and accessible routes to therapy. It is also important that clients have a variety of ways to access treatment. This is true regardless of whether the goal is to accommodate individual preferences or to serve a population for which there are not enough resources.

One solution to resource constraints is to train more therapists. However, the level of scaling up of therapist training and on-going supervision that is required to meet individual needs is neither possible given existing resources nor sustainable. Although solutions such as “train the trainer” models and “web-centered training” alleviate some constraints, therapist-centered approaches will always struggle to meet the growing need for psychological treatment (Fairburn & Patel, 2014).

Fairburn and Patel (2014) proposed a model for increasing the availability of psychological treatments by moving from therapist-led to program-led treatments. Enacting such a model would involve shifting focus from therapists to programs. If greater emphasis is placed on developing educational and interactive programs, opportunities to reach more people may increase. In this model, the majority of the user’s time is spent using the program while the therapist serves as a “coach” who provides instruction on using the program effectively and encourages the user through check-ins and motivational feedback. Beyond increasing the efficiency of providing therapy through standardized treatment and reduced variable costs, program-led interventions may also reach individuals who would not seek treatment through traditional routes.

The Evolution of Coaching in Psychotherapy

The idea of alternative methods of delivering healthcare treatment has been around for a long while, motivated by a need to make the most of limited resources. Perhaps the most popular example is the community health care manual, Where There Is No Doctor, which has been translated into over 100 languages since it was written in 1970 (Werner et al., 1995). This manual provides vital yet simple information on diagnosing, treating, and preventing common medical problems, particularly relevant to areas in which trained medical professionals are scarce and paraprofessionals must lead.

More recently, alternative psychotherapy delivery models have become increasingly possible with the evolution of technology. A 1992 study found that the demand for behavioral psychotherapy exceeded its supply in the United Kingdom and that “care could be cloned by the dissemination of effective self-help technology” (Marks, 1992). In anxiety disorder treatments, for example, exposure therapy provides the greatest effect size (Marks, 1987) and is better executed via self-exposure (Marks et al., 1988). During session, both clinician and patient devise the exposure protocol, which the patient can carry out between sessions aided by a computer program. In the next session, both review logged homework, discuss any problems, and negotiate further tasks. In that way, time with a clinician is drastically reduced and treatments are far more accessible. Given recent technological developments (e.g., more powerful mobile computing, stronger network coverage, cheaper mobile devices), Marks’ vision is not only more feasible but also scalable.

The Parameters of Coaching in GSH

When designing a coached intervention, a number of components must be considered. First, traditional program content must be organized within a self-help framework, adapting content, tone, and format (e.g., paper manual, online program, or app). Second, program delivery must be determined, specifying the dose and length, modality (e.g., face-to-face or via telephone), and structure of coaching sessions. Third, and most importantly, program guidance must be outlined.

Program content

In a study developing a GSH program for PTSD treatment, researchers and pilot users outlined important program themes of tailoring, choice, and simplicity (Lewis et al., 2013). GSH programs should be adaptable per user rather than one-size-fits-all, customizable based on user preference, and simple. These authors also stipulated multimedia delivery and mandatory components of psychoeducation, relaxation techniques, exposure guidance, and maintenance and relapse prevention. Another qualitative study, which leveraged research on the patient experience in primary care to define the framework for GSH, emphasized highlighting the intervention as a method of regaining control and defining the individual as the agent of change (Khan et al., 2007). 

Program delivery

The program delivery should involve six to eight 20- to 30-minute sessions over three to four months. Though most sessions can be conducted via telephone or even through messaging, the initial session should be more intimate (e.g., face-to-face) to establish the relationship and discuss the program plan. Each session should follow a similar format, involving reviewing patient’s progress by evaluating a representative sample of monitoring records, identifying problems and discussing potential solutions, and providing encouragement to continue engagement with the self-help program (Carter & Fairburn, 1995).

Program guidance

The first step in outlining guidance is defining a set of coach criteria (e.g., necessary interpersonal and therapeutic competencies and educational training). Although there is debate around the level of professionalism of the coach, most researchers are in agreement that trained paraprofessionals can facilitate GSH programs (Bennett-Levy et al., 2010).

Second, develop a coaching manual to aid treatment delivery. For online programs, design a coaching “dashboard.” This should display an overview of user(s) progress, provide tools to facilitate coaching, and—most critically—be easy to use. The dashboard should align with key coach responsibilities. The coach must set the program pace by ensuring users do not progress too quickly or slowly through the content. The coach must also motivate the user to continue the program by identifying and praising progress and troubleshooting difficulties. Finally, the coach should keep the users focused on their goals for using the program (Carter & Fairburn, 1995).

Third, create a protocol for quality assurance of coaching, ensuring appropriate training and supervision as well as coach accountability. In addition to the coaching manual, coaches should receive a limited amount of training (e.g., treating two to three pilot users while supervised by a clinical psychologist) (Carter & Fairburn, 1998).

Finally, devise a system to optimize coaching by monitoring outcomes and identifying moderators of GSH treatment effect. Beyond designing the structural components of a coaching system, a set of principles should guide coaches’ interactions with users to maximize likelihood of positive outcomes. For example, research suggests a strong predictor of patient success in anxiety programs is consistent homework completion (Mausbach, 2010). Therefore, a key role of an anxiety program coach is to monitor homework completions and intervene with motivational feedback when necessary. Similarly, a set of client predictors of positive outcomes should be identified. These could include the user’s belief in the program, motivation to use it, treatment goals and expectations, and satisfaction with program and coach. Based on these predictors, coaches can guide treatment and motivate users accordingly.

Evaluating the Efficacy of GSH Interventions

GSH has potential to be not only a more accessible but also an effective route of treatment. A meta-analysis of 21 RCTs with 810 participants found the effect size of the difference between GSH and face-to-face psychotherapies for depression and anxiety disorders at post-test to be d = -0.02, in favor of GSH (Cuijpers et al., 2010).

Although we can say with confidence that GSH can be as efficacious as face-to-face therapy, there are still a number of unknowns. The research on GSH interventions is scattered across disorder types and often does not follow similar development protocols. Resultantly, there is not enough evidence from studies in routine clinical practice to deem GSH effective for those accessing primary care services (Coull & Morris, 2011). Overall, more research is necessary to define the parameters of GSH and how it can be incorporated into a system that must treat a variety of patients with different needs and preferences.

To evaluate GSH, we must understand the ecosystem in which it will be delivered, specifically how it can be initiated, evaluated, and sustained. The British government provides the best example of implementing GSH within an ecosystem. In 2009, they launched the biggest expansion of mental health services in the world, the Improving Access for Psychological Therapy (IAPT) initiative. The program aimed to improve access to National Institute for Health and Care Excellence (NICE)-recommended evidence-based psychological therapies and close the resource gap by training new therapists in evidence-based therapies. For example, Psychological Well-Being Practitioners (PWPs) could be trained in one year to support GSH interventions (Department of Health, 2012).

Using universal outcome monitoring within the IAPT system, GSH can be introduced and evaluated in a stepped care model of treatment. Assessing metrics such as step-up rates and treatment compliance, GSH was designated as Step 2 in a 5-step system, meant for treating and monitoring mild mental health problems (Evans, 2013).

In addition to monitoring clinical outcomes, assessing patient satisfaction and treatment accessibility is necessary for program evaluation. Process measures such as therapeutic alliance and compliance with treatment can provide information about patient experience and inform therapy improvements (Newman et al., 2011).

Finally, attention must be given to those delivering the treatment. To use computerized self-help treatments with clients, practitioners reported wanting more research on treatment efficacy, training, and lower technology costs (Whitfield & Williams, 2004). Similarly, PWPs in the IAPT system have expressed concern over insufficient training for difficult cases and limited career progression (Evans, 2013). Monitoring these concerns are just as important as monitoring outcomes to sustain a GSH program.

New Frontiers for Program-Led Interventions

The opportunities for GSH interventions and, more broadly, program-led interventions and the tools that support them are boundless. Beyond solving a problem of resource constraint, program-led solutions may actually prove more effective than traditional face-to-face routes for certain treatments. A research team at the University of Southern California explored the use of virtual humans (VHs) in clinical interviews; they found that subjects who believed they were interacting with a computer were more willing to disclose information than those who believed the VH was controlled by a human operator (Lucas et al., 2014). Using this alternative approach, VHs can partner with clinicians to help overcome the significant issue of limited disclosure.

Another group of researchers in Australia evaluated an automated interactive telephone system aiming to improve the uptake and maintenance of diabetes self-care behaviors (Williams et al., 2012). In this GSH model, the “guide” was both the facilitator and the telephone program. The Telephone-Linked Care (TLC) Diabetes system, which consists of a computer equipped with speech recognition and voice processing software, tracked blood glucose monitoring and patient activity and provided tailored feedback and encouragement. The TLC Coordinator instructed the patient on program usage, programmed individual self-care clinical targets, and responded to atypical program use alerts as necessary. Compared to treatment as usual, the TLC program intervention demonstrated a significant reduction in average blood glucose levels and a significant improvement in mental health-related quality of life, suggesting this computer-supported GSH program could be an accessible and cost-effective intervention for individuals with diabetes.

Researchers at the Massachusetts Institute of Technology founded GeriJoy, which provides “cost-effective 24/7 dementia oversight and companionship” (Gerijoy, 2014). Virtual talking pets, which are monitored and controlled by GeriJoy’s team of remote care staff, keep older adults company and improve health through pet therapy. By leveraging a combination of human and program, GeriJoy can deliver natural and compassionate company to seniors at scale.

Once new GSH treatment modalities become standardized, researchers can focus on how to improve them. For example, researchers at Stanford have demonstrated that patients who are matched to a physician who mirrors their reported ideal affect (i.e., how they want to feel) show greater physician preference (Sims et al., 2013). Imagine assessing whether affect-matching in virtual humans (or pets) or automated phone systems improves upon non-affect matching equivalents.

Bringing It All Together

In order to make novel GSH interventions accessible routes of treatment, they must be integrated into existing treatment delivery systems. For example, these interventions must be a step in a stepped care model like IAPT. They must be supported by either adapting existing resources or creating new resources (e.g., PWPs in IAPT). And, they must be monitored for continued improvement. When integrating a new treatment, ongoing studies must ensure that patients are faring just as well or better in comparison to treatment as usual.

By adding this variable factor of coaching, we are exponentially increasing the ways in which treatment can be delivered. However, with increased options comes a greater chance of not providing the most appropriate or evidence-based treatment. Therefore, we must integrate new interventions into systems, monitor outcomes, and determine inflection points based on meeting benchmarks of expected outcomes. In this way, we can give GSH interventions a fair opportunity to become standardized and prescribed treatments.

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Nitya Kanuri, B.A.