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This paper reports on the findings of a technical expert panel convened by the Agency for Healthcare Research and Quality and the National Institute of Mental Health, charged with reviewing the state of research on behavioral intervention technologies (BITs) in mental health and identifying the top research priorities. BITs is the comprehensive term used to refer to behavioral and psychological interventions that use information and communication technology features to address behavioral and mental health outcomes. Mental health BITs using videoconferencing and standard telephone technologies to deliver psychotherapy have been wellvalidated. Web-based interventions have shown efficacy across a broad range of mental health outcomes, although outcomes vary widely. Social media such as online support groups have produced generally disappointing outcomes when used alone. Mobile technologies have received limited attention for mental health outcomes, although findings from behavioral health suggest they are promising. Virtual reality has shown good efficacy for anxiety and pediatric disorders. Serious gaming has received relatively little work in mental health. Recommendations for next step research in each of these are made. Research focused on understanding of reach, adherence, barriers and cost is recommended. As BITs can generate large amounts of data, improvements in the collection, storage, analysis, and visualization of big data will be required. Traditional psychological and behavioral theories have proven insufficient to understand how BITs produce behavioral change. Thus new theoretical models, as well as new evaluation strategies, will be required. Finally, for BITs to have a public health impact, research on implementation and application to prevention will be required.
A Technical Expert Panel entitled “Health Information Technology and Mental Health: the Way Forward” was convened by the Agency for Healthcare Research and Quality (AHRQ) and the National Institute of Mental Health (NIMH) and met several times over 2010, culminating in a multidisciplinary advisory meeting on November 16–17, 2010. The focus of the panel was to review the state of research on health information technology in mental health, and identify the top research priorities in the field. This paper reports on the findings of the subgroup charged more specifically with behavioral intervention technologies (BITs).
This paper will provide a brief definition, explain why BITs research is high priority, present an overview of the research to date, and outline the research challenges for the field.
BITs are the application of behavioral and psychological intervention strategies through the use of technology features to address behavioral, cognitive and affective targets that support physical, behavioral and mental health (1). Thus far, BITs have been used to implement change strategies that include self-assessment and self-monitoring, psychoeducation, goal-setting, skillbuilding, and feedback. To do so, BITs have employed a number of technology media, including but not limited to telephone and videoconferencing, web-based interventions (also called internet interventions), interventions using mobile devices (also called mHealth interventions), the integration of sensors for patient monitoring, social media, virtual reality, and gaming.
BITs should be distinguished as a subset of the far broader field of eHealth, which refers to the use of information and communications technology to support healthcare more generally. Some eHealth technologies not included in the BIT category would include electronic health records, clinical decision support programs, use of telemedicine for purely medical care deliver, and so on.
BITs have the potential to reach populations who otherwise would not be able to access care. Even in urban areas, 75% of primary care patients with depression can identify one or more structural or psychological barriers that substantially interfere with or prevent access to behavioral treatment; these rates are far higher in rural areas (2). BITs offer the potential to overcome many of those barriers to access, although the objective evidence for this is still largely unavailable (see Clarke et al., this issue).
BITs not only provide new delivery media for mental health treatments, they also open the possibility for entirely new interventions. For example, mobile technologies can harness sensors and ubiquitous computing to provide continuous monitoring and/or intervention in the patient’s environment. Virtual reality creates simulated environments that afford a high degree of control in engineering the provision of therapeutic experiences. Gaming may provide teaching methods that are more engaging. These opportunities may also challenge and expand the limits of our knowledge regarding human behavior and behavior change processes.
Industry has begun developing and marketing BITs for a wide variety of mental health problems. These are frequently brought to market with little or no evaluation of their efficacy or utility. Thus, there is a growing need to evaluate these interventions and thereby protect the stakeholders (patients, payers, providers, and families), set industry-wide standards of proof of benefit and quality, provide information to stakeholders to guide treatment choice, and support the development of new policies and guidelines for the use, development, and evaluation of BITs.
We will review here BITs that include the telephone, web-based intervention, mobile technologies, social media, virtual reality, virtual humans, and gaming. These technologies are mediums for both therapist-delivered and machine-powered interventions that expand the capacity to deliver interventions across space and time. Each section will identify the next steps in that area of research.
Despite the proliferation of evidence-based psychotherapies for numerous mental disorders, the delivery of those treatments to those who require them is a perennial challenge, given the shortage and uneven geographic distribution of trained providers (3). A growing number of trials have evaluated using BITs to connect providers and patients in different geographic locations. Current technologies in this subset include delivering psychotherapy via videoconferencing, telephone, instant messaging, and email. These resources require professionals to conduct the psychotherapy but overcome access barriers such as limited transportation, medical disabilities, or absence of available local services. These interventions generally maintain the standard structure of psychotherapy, but the delivery media reduce the “band-width” of information communicated between the therapist and patient (4, 5). Videoconferencing eliminates immediate presence and some cues, the telephone eliminates visual cues, instant messaging eliminates voice quality, and email eliminates synchronous communication. While these communications media can extend care geographically and temporally in the case of asynchronous communication such as email, concerns have been raised that the lack of presence may diminish therapeutic relationships, reduce efficacy, increase risks to privacy, and lead to difficulties in managing emergencies and crises (6).
A growing number of studies have examined videoconferencing used to extend specialized care to remote clinics, while newer studies are evaluating the use of videoconferencing with patients in their own homes. These studies have generally found that videoconferencing is effective and acceptable to patients (7). A large number of studies found that the telephone is an effective medium for delivering psychological care (8–10), and is as efficacious as face-to-face treatment, and can reduce dropout (9, 11, 83). Psychotherapy has also been provided effectively and with reasonable effect sizes via instant messaging (12). There is no evidence that these delivery media increase risk, as emergent mental health situations for outpatients are often managed at a distance, even in the context of traditional services.
Evidence is emerging that psychotherapy delivered by videoconferencing and telephone may be equivalent in efficacy to face-to-face delivered treatment. While these delivery modalities likely extend care geographically, they continue to rely on humans to deliver the content and are therefore limited by the pool of available professionals and the costs associated with their time. These interventions could substantially increase the pool of patients. Accordingly, research evaluating cost-effectiveness and implementation models will be required.
Psychoeducational or intervention content can also be delivered using primarily machine-powered systems with limited (or no) requirement for a professional driving the interaction. Older studies used preloaded desktop computer programs or CD-ROMs that either required patients to come to a provider’s office or providers to distribute content to potential users (84). These interventions are similar to face-to-face therapy where skills and content are taught in sessions with the expectation to be applied in other setting and situations. The predominant mode of dissemination of these treatments currently is through a website. These web-based interventions have often used evidence based psychological treatments as frameworks that are then operationalized and programmed for web delivery of skills training, other tutorials, and promotion of real-life generalization of new skills (13). The efficacy of web-based intervention has been demonstrated by a large number of trials for a number of mental health problems, including depression, anxiety disorders, alcohol and substance abuse, and insomnia (14–18). In addition, there are promising results for bipolar disorder (19) and schizophrenia (20). However, within this growing number of studies, the range of outcomes can vary from virtually no effect to effect sizes that are similar to face-to-face interventions.
Web-based interventions can include a variety of features. Most provide text-based didactic information, much like printed self-help materials (21, 22). These didactic materials can be supplemented by audio, video and animation. Many web-based interventions also include interactive tools to support learning, such as activity monitoring, thought records or distress ratings. Feedback tools can provide graphic visualizations of the patient’s progress. One disadvantage of web-based interventions, however, is their status as “pull” technologies that rely on the patient’s initiative to access the intervention. Thus, many developers include features that “push” information to the user, such as automated email reminders to encourage patients to return to the website.
Many web-based interventions are designed to be used over a period of time, requiring decisions about how new content and tools are rolled out. New material is typically rolled out based on time (e.g., weekly or semi-weekly), task completion (e.g., after the patient completes specific tasks) or a combination of the two, although there are also websites that allow access to all material from the beginning. Web-based interventions vary considerably on the degree to which presentation of lessons and tools is tailored and personalized to meet individual patient needs or characteristics. Some websites provide the same information and tools to all patients, while some provide specific modules based on presenting problems (23). Sites can vary considerably in their look and feel, as well as how difficult they are to use and navigate. It is believed that the quality and design of websites can influence adherence and outcomes (24), although this belief has generally not been rigorously tested.
Web-based interventions can stand alone, or they can be supported by a coach or therapist. Standalone interventions have produced a wide range of outcomes, from small or nonsignificant (e.g. many depression sites, substance abuse) to moderate (some anxiety disorders) to fairly large (insomnia) (14, 15, 18, 25). Attrition and non-adherence can also range widely, from extremely high (>95% for standalone depression sites), modest (many anxiety sites), to minimal (insomnia), depending on the disorder and how participants were sampled or screened.
In general, human support via periodic phone or e-mail contact improves adherence and outcomes when using internet-based treatments, although this may vary depending on the patient population, disorder, or the quality, usability, and utility of the program. Outcomes of coach or therapist-supported internet treatments are moderate to large for depression, and have found to be equivalent to face-to-face group intervention for some anxiety disorders (16, 17). One model to explain these coaching effects is supportive accountability (4), which suggests that the mechanism by which coach presence increases adherence is the accountability created by clear adherence goals, monitoring, and scheduled contacts in which progress will be reviewed with a supportive coach or therapist. If supportive accountability or other such models are supported, they can provide a framework for developing new and more cost-effective methods of enhancing adherence, as will be described in the Social Media section below.
While the potential for web-based interventions to be effective has been established, little is known about how to optimize their design and implementation. Basic design questions should be investigated, such as the blend of didactic information and interactive tools, effective methods of conveying information, and how best to allow patients to progress through interventions. Personalization and tailoring, which have received some attention in web-based interventions outside of mental health (26, 27), remain largely unexamined in mental health. With the growing capacity to collect large amounts of user data from large samples, data-mining could be used to identify user preferences and provide patients with content and tools that they are likely to find useful and to use. Adherence to web-based interventions has been widely acknowledged to be a problem (25, 28) and should constitute a standard outcome measure and a focus of new research. As interventions demonstrate efficacy, models for implementation should be examined.
Other technologies are pervasive, moving intervention into the real-world. Thus interventions are applied “in-the-moment” using technologies that people carry with them through their days. This offers the potential to provide a ubiquitous connection between a care system and the patient. Older studies used personal digital assistants and handheld computers, while more recent work has shifted to cell phones and smartphones. Trials of mobile BITs have found some positive short-term benefits for anxiety disorders (39), eating disorders (40), bipolar disorder (41), and schizophrenia (42), although others have not been effective at treating depression (43). Mobile phone interventions have also been used to enhance adherence to psychotropic medications (42) and prevent depression in adolescents (44). Given that this literature is limited and of variable quality, it is premature to make any definitive conclusions regarding the efficacy of mobile BITs (39).
Most mobile BITs employ short message service (SMS or text) messages, although some use voice messaging or video (45). Some interventions deliver reminders or informational messages, while other mobile BITs use SMS dialogues that are often automated to result in provision of tailored feedback or information. The intervention can initiate such SMS dialogues, but some interventions emphasize or allow patient-initiated dialogue. Most commonly, messaging is only one component of the intervention, which can include web-based components, brief telephone support by coaches, and/or contact with a clinician. The frequency of SMS messaging can range from once weekly to as often as 5+ daily, and is commonly linked to the anticipated frequency of the target behavior. Timing of SMS messages may also be tied to the times of day when the patient is particularly vulnerable to difficulties. Another dimension along which mobile BITs vary is personalization and tailoring. Some mobile BITs use highly personalized messages, while others employ generic tips or messages.
A recent trend in mobile BITs is the use of passive data collection to work around patients’ reluctance to log information (46). This allows mobile BITs to detect times at which intervention is needed, without necessitating the patient to log their current state. In passive data collection, sensors automatically collect data that can help to infer the patient state. These sensors can be located within the mobile device itself (e.g., GPS, Bluetooth, accelerometer, etc.), or be external devices available via a wireless connection (e.g., heart rate or galvanic skin response sensors). Intervention delivery can be triggered by the sensor data in two ways. One approach develops algorithms that predict patient states by using predefined algorithms derived a priori from scientific or clinical knowledge (47, 48). The algorithms then make inferences regarding the patient’s states by using the sensor data, and the inferred states consequently determine the need for intervention. The second approach uses machine learning techniques (49) that are applied to simultaneously collected sensor data and patient self-reports. In this approach, a model is developed to describe the relationship between the sensor values and the patient’s self-reported states. Once reliable models are achieved, the models can be applied to new sensor data, and predict patient states in the absence of any self-reported data. The advantage to this second approach is that the models are automatically generated and personalized to each individual patient. This application of machine learning is a new and challenging strategy that has been used to detect physical activity, affective state, and social context with differing degrees of accuracy (e.g., 50).
There have also been steps taken to integrate the real-time data capture capabilities of mobile devices with social networks. For example, the Virtual Environments for Raised Awareness (VERA) mobile phone app enables users to take photos representing their health decisions and share them with other VERA users who can then post comments (51). Although not designed for healthcare purposes, the CenceMe mobile phone application uses passively collected phone sensor data to make inferences regarding physical activity (e.g., whether the user is sitting or walking) and social states, and then these inferred states are shared with the user’s social network via the user’s status on Facebook, Twitter, or other networking tools(52).
Mobile BITs research for mental health remains at an early developmental stage. Considerable work is needed to better understand how patients use mobile phones, and how interventions can be integrated into these existing use patterns as seamlessly as possible. Well-designed efficacy trials are required for interventions that have demonstrated initial promise. Continued work developing mobile social media and context sensing systems that harness sensors to evaluate patient states has the potential to create tools that can be weaved into the flow of patients’ lives.
Social media refers to the use of web-based and mobile technologies to allow for the exchange of user-created content between peers. Social media has been used in the context of mental health BITs primarily in the form of internet support groups (ISGs). ISGs are often used by patients as a source of information and support (29, 30), however the quality of many of these studies remains poor. In general, ISGs that are moderated by a clinician tend to show benefits (31). However, findings of unmoderated support groups have tended to show either no benefits relative to control arms (32, 33) or some worsening of symptoms (34, 35). The potential for negative outcomes with ISGs may reflect the effects of social contagion – that negative affective states can be transmitted through online social networks (36, 37).
The disappointing effects of unmoderated ISGs to date do not necessarily mean that online peer networks have no role in our treatment armamentarium, particularly given their popularity among some patient groups (e.g., cancer survivors) (30). One emerging area is the embedding of web-based intervention tools into social networks that are engineered to encourage peer interactions that align with principals of supportive accountability (4). Preliminary results, while limited due to small sample sizes, suggest harnessing peer social networks to encourage collaborative learning and adherence to web-based interventions may prove useful (38).
The use of ISGs has not been rigorously investigated. Understanding the structure of peer interactions and the effects that such interactions have on target outcomes may lead to better engineered websites. Embedding online collaborative learning tools in networks may improve outcomes both by teaching skills and by encouraging communication that is more likely to be therapeutically beneficial.
A growing body of research indicates that immersive virtual reality exposure therapy may be effective for anxiety disorders (53), as well as pediatric disorders such as attention deficit disorder (54, 55). However, the cost of immersive 3-D systems has been a barrier to widespread implementation.
Online virtual worlds such as Second Life have also been proposed as a potential vehicle for delivering mental health treatment (58). Online virtual worlds have been used primarily to provide informational libraries, interactive educational tools, and social media such as chat rooms for health related issues such as healthy lifestyle, HIV, and pediatric disorders. (59). Little mental health research has been conducted in this area to date.
Virtual humans, or conversational agents, are images of humans that are programmed to interact with a user. These characters can be implemented in the context of immersive, virtual reality interventions, or as 2-dimensional figures on a flat screen. The use of conversational agents in mental health interventions to date is very limited; however, patients rate these agents positively on measures of empathy and alliance (56). In one example, patients reported high satisfaction with a conversational agent that delivered hospital discharge instructions, and patients with depression and/or low health literacy reported stronger therapeutic alliance with the agent. The agent also elicited more questions from those with lower health literacy, suggesting such agents may have the potential to provide health information in a supportive, non-threatening manner (57).
More cost-effective methods of delivering virtual reality therapy will be required before it can be widely implemented. The use of conversational agents is just beginning and still requires initial developmental work and evaluation.
Gaming can include both offline video games as well as web-based games that can use elaborate online worlds that support exploration and therapeutic role-playing. Games can be very engaging and thus have the potential to increase patient motivation, to initiate and continue treatment and support successful behavior change. Early work on using games for mental health examined the use of off-the-shelf games within treatment sessions to increase therapeutic alliance and reinforce lessons.(60) A recent shift is the creation of entertaining games with nonentertainment related goals (such as promoting mental health) encompassed by the serious gaming movement. Games have been examined as treatments for attention deficit hyperactivity disorder, autism spectrum disorders, and reduction of aggression in children and for improving behavioral outcomes related to medical issues such as diabetes and cancer. Much of this work remains in early stages (61).
Greater attention has been devoted to using games to support physical health including the promotion of physical activity and adherence to medications. For example, Re-Mission, a computer videogame designed to promote adherence to a treatment regime for cancer, users blast cancer cells and positive self-care practices to achieve mission objectives (62). This game increased adherence to prescribed oral chemotherapy medication and enhanced psychological resilience such as cancer self-efficacy. Games for mental health that embody therapeutic principles have been developed but lack formal evaluations of their efficacy. For example, Personal Investigator is a computer game designed for children to play alongside their therapist and requires users to hunt for clues to solve a personal problem (63). In a gaming version of cognitive-behavioral therapy, children solve a mystery by hunting unhelpful thoughts and replacing them with helpful thoughts (64).
Investigations indicate that gaming may do more than just teach people what to do; the interactivity aspect of playing games might enhance the uptake of interventions. In a study of Re- Mission, only those playing the game experienced activation in reward-related neural circuits when compared with participants who were yoked to watching the same game play (65). Participants who received a virtual game plus exercise cycling experienced increased cognitive benefits over those who did cycling alone (66). These findings suggest that a game-play setting can enhance the benefits that people receive from therapeutic interventions.
Most games have been developed for children and adolescents and few trials have evaluated the efficacy of these games for mental health outcomes. More work needs to address whether gaming is a reasonable approach for adults and older populations. Trials investigating games also need to control for comparable paradigms to determine if games are merely shells that help teach well-established behavior change principles (i.e., goal setting, competition) or if interactivity improves outcome through different mechanisms.
BITs vary considerably in terms of their level of development and evaluation. Some of the next-step research for specific BITs has been mentioned above. Below, we will discuss larger, cross-cutting research themes that this panel identified as priorities.
Many BITs, such as web-based interventions and in some instances virtual reality interventions, have high dropout and low utilization rates. Research in this area has been plagued by a lack of a shared definition of what constitutes adherence (25). For example, webbased interventions have used number of logins, time on site, and number of modules completed to name a few. The relationship of these different metrics to each other and to clinical outcomes can vary considerably, with logins generally being unrelated or negatively associated with improvement, and number of modules typically being positively associated with improvement (67). Login rate may reflect self-titration of intervention use based on the patients recognition of their own need (68) while greater use of modules may produce a dose-related response, or may simply reflect user characteristics associated with improvement such as conscientiousness. Improved measurement and understanding of the meaning and implication of adherence metrics is required.
A variety of methods of improving adherence have been proposed and investigated, including improving the quality of the technology (e.g. tailoring content to the user’s needs, enhancing the usability, simplifying the interface, etc.) (24, 69), adding human support and adherence promotion via phone, email or other media (4), using incentives (70, 71), and making resources more engaging through gaming elements (72). Continued research aimed at testing and uncovering new methods of improving adherence will be important to optimize the effectiveness of these interventions, as there is evidence that greater adherence and engagement is often associated with better outcomes. At the same time, however, we should avoid overgeneralized assumptions that increased engagement is necessarily better under all circumstances or with all BITs (68, 73). Some patients may stop using a BIT because they lose interest, but others may stop using because they have received the assistance they sought, because the BIT does not meet their needs, or a variety of other reasons. Research is needed to better understand patterns of use and engagement, as well as the determinants and consequences of engagement.
Many BITs justify their development in part by claiming that they increase the reach of interventions and reduce barriers to care by decreasing cost (e.g., via reduced time burdens on clinicians), minimizing problems related to time constraints and transportation, overcoming problems in the availability of care, increasing the efficiency of care, and/or reducing discomfort associated with traditional face-to-face services (74). However, researchers have also acknowledged that BITs may create barriers. For example, many patients may be uncomfortable with, or unable to use or access, the technologies employed by the interventions. It is also possible that BITs may increase costs by delaying the provision of traditional treatments with greater evidence of efficacy, or by increasing the numbers of patients receiving services. Very little work has examined assumptions about the capacity for BITs to reduce barriers and costs. Laying the groundwork for eventual implementation requires an understanding of where and how reach is extended and barriers are overcome or created.
Beyond integration into existing delivery systems, BITs provide promising avenues for creating new streams to deliver resources for the prevention of mental health problems. BITs can improve both the screening and early delivery of interventions to reduce risk factors and build psychological resources. Sensor technology and automated data collection allows for passive monitoring of psychological states that can alert users and providers to acute and chronic stress states that might predispose someone to later clinical disorders. Thus, low intensity interventions can be provided via BITs prior to the need for clinical intervention as a cost-effective approach for prevention (75). The capability of BITs to reach a vast population allows for the dissemination of universal prevention interventions that are too costly to provide using other resources (44). Lastly, assistive technologies, those that provide support for better decision making, emotional regulation, or interpersonal interactions can provide skills necessary to ensure that psychological, emotional, and social deficits do not spiral into clinical disorder. For example, advances in computational approaches for measuring affect through video data can support the creation of wearable technologies to improve recognition of affective cues. This could improve social interactions as quality social relationships are an important protective factor against mental disorders (76). Thus, BITs should not only be geared toward treatment, but should also be implemented in prevention efforts to reduce the overall burden of mental disorders.
Many forms of BITs, such as web-based and mobile interventions, permit the aggregation of large amounts of data across large numbers of users (e.g., usage data, patient workflow, sensor readings). These data offer the opportunity to improve the quality of interventions and develop deeper tailoring and personalization. However, these datasets, sometimes referred to as “big data,” create difficulties in data capture, storage, cleaning, analytics, visualization and sharing. It is critical to conduct research aimed at developing the capacity to manage big data (e.g. data acquisition, storage and cleaning), identify features that are informative and useful, apply analytic techniques such data mining and machine learning, and create visualization methods that allow us to understand the data. Harnessing big data to allow BITs systems to continuously learn from users has the potential to substantially improve the effectiveness of these interventions.
Much of the development of BITs to date has either attempted to adapt accepted intervention paradigms such as cognitive behavioral therapy (CBT) through new media, such as web-based CBT, or has adopted new technologies such as SMS to engage patients based on common sense approaches. While such strategies have yielded benefits, the field is also encountering the limitations of these paradigms. Behavioral and psychological theory may continue to be relevant at the larger level of how behavioral, affective, environmental, and cognitive factors interact to effect improvements in mental health. However, a growing number of investigators have recognized that current theoretical models are inadequate to inform the development of BITs that can increasingly interact dynamically and adaptively with users (77). To enhance the design of BITs, theoretical models are necessary that can explain how users interact with the technologies, and how such interactions can lead to behavior change and improved clinical outcomes. One possible example is control systems engineering, which operationalize inputs, decision rules, and outputs. Integrating knowledge from this field into behavioral science could potentially help explain how people use and can benefit from BITs (78). The adaptation and/or development of such theories will allow larger principals to be tested, thereby allowing the field to move forward more rapidly.
BIT research requires new models for development, optimization, and evaluation. Developing early BIT prototypes requires a clear understanding of how patients interact with BITs, that can guide the numerous decisions regarding the structure and interface of the technology. Because this emerging area of research lies at the intersection of behavioral science, engineering, and clinical science, existing developmental models from engineering can be borrowed and adapted. For example, user centered design (79) and usability testing (80) are common methods used in product development. These strategies involve systematic problem analysis, design, and an iterative process of evaluation to ensure that the BIT is understandable, attractive, and usable for the targeted patient population. Human factors and human-computer interaction are disciplines with well-developed methodologies for design and usability testing (80) that could be adapted for BITs research.
The properties of usability and attractiveness, while necessary for engagement, are not necessarily sufficient for efficacy. Early BIT development often results in multiple features that may or may not contribute to efficacy. Research frameworks to optimize BITs’ efficacy will be required. One example of how principles of optimization have been adapted from engineering into a BITs context is the methodological framework called Multiphase Optimization Strategy (MOST) (81). MOST uses randomized experimentation in a phased approach to identify specific components or features of BITs that contribute to outcomes.
Traditional intervention evaluation models typically require years and can be costly. The rapidity with which new technologies are emerging and developing means that such traditional evaluation methods will result in final validation long after the technology underlying the BIT is obsolete. Furthermore, the continuously evolving nature of technologies and the BITs that use those technologies will require strategies that can continuously evaluate efficacy as new versions and changes are introduced. The traditional differentiation between initial evaluation and implementation will become increasingly arcane due to the ability of many forms of BITs to collect data on use and outcomes, as well as the rapid expansion and availability of these programs at a public health scale across the U.S. or even the world within months after initial program launch. Thus, the field requires methods that can rapidly validate the efficacy of BITs, while at the same time continuously evaluating the incorporation of new developments and innovations for the protection of patients and stakeholders.
BITs can only begin to reach their public health potential if they are integrated into existing healthcare delivery systems. This integration will have to take place on at least four levels: 1) Research should identify where BITs fit into the armamentarium of treatment options. Examining stepped care models that incorporate BITs into a care system is an example of this type of research (82); 2) Technological integration is also likely to enhance the impact of BITs. If BITs are integrated into electronic medical records and patient portals, then referrals, treatment monitoring, appropriate follow-up, and fuller integration into the patient’s overall treatment are more likely to occur; 3) BITs will also have to be integrated economically into our healthcare system. Developing models of costing BITs will support this; 4) BITs will have to be accepted and adopted by healthcare delivery teams as well as patients. The literature on practice adoption of behavioral treatments in general is limited, and this is particularly true for BITs.
Behavioral Intervention Technologies (BITs) encompass diverse approaches, including remote provision of psychotherapy, web-based and mobile interventions, social media, virtual worlds and humans, and gaming. These intervention areas are currently in widely varying stages of evaluation and maturity, requiring research that ranges from basic development and evaluation to implementation studies. As a new and evolving field, BITs research is encountering challenges that no single field or specialty can address alone. BITs research will require integration of theoretical and research paradigms from many different disciplines, including behavioral and psychological sciences, engineering, computer science, human-computer interaction, communications, and design to name a few. It is critically important to refine our development and evaluation methodologies, as well as develop testable BITs implementation models.
Work on this paper was supported in part by NIMH grants R01-MH059708 and P20-MH090318.
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