Computer and mobile technology-based interventions for substance use disorders: An organizing framework

Addictive Behaviors 38 (2013) 1747–1756

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Addictive Behaviors

Computer and mobile technology-based interventions for substance use disorders: An organizing framework Erika B. Litvin ⁎, Ana M. Abrantes, Richard A. Brown Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Butler Hospital, 345 Blackstone Blvd., Providence, RI 02906, United States

H I G H L I G H T S ► ► ► ► ►

Interest in technology-based interventions (TBIs) for drug use is increasing. We summarize previous reviews of TBIs, focusing on moderators of efficacy. We present an organizing framework of TBI design considerations. The framework covers Accessibility, Usage, Human Contact, and Intervention Content. We offer suggestions for future research within these framework elements.

a r t i c l e Keywords: Drug abuse Internet Web Online Treatment

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a b s t r a c t Research devoted to the development of therapeutic, behavioral interventions for substance use disorders (SUDs) that can be accessed and delivered via computer and mobile technologies has increased rapidly during the past decade. Numerous recent reviews of this literature have supported the efficacy of technology-based interventions (TBIs), but have also revealed their great heterogeneity and a limited understanding of treatment mechanisms. We conducted a “review of reviews” focused on summarizing findings of previous reviews with respect to moderators of TBIs' efficacy, and present an organizing framework of considerations involved in designing and evaluating TBIs for SUDs. The four primary elements that comprise our framework are Accessibility, Usage, Human Contact, and Intervention Content, with several sub-elements within each category. We offer some suggested directions for future research grouped within these four primary considerations. We believe that technology affords unique opportunities to improve, support, and supplement therapeutic and peer relationships via dynamic applications that adapt to individuals' constantly changing motivation and treatment needs. We hope that our framework will aid in guiding programmatic progress in this exciting field. © 2012 Elsevier Ltd. All rights reserved.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Defining “technology-based intervention” (TBI) . . . . . . . . . “Review of reviews” . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Literature search procedure . . . . . . . . . . . . . . . . . . 2.2. Identification of review articles . . . . . . . . . . . . . . . . 2.3. Results: moderators of TBI efficacy identified by previous reviews 2.3.1. Methodological variables . . . . . . . . . . . . . . . 2.3.2. Participant variables . . . . . . . . . . . . . . . . . 2.3.3. Intervention variables . . . . . . . . . . . . . . . . TBIs for substance use disorders: an organizing framework . . . . . . . 3.1. Accessibility . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1. Setting/location . . . . . . . . . . . . . . . . . . . 3.1.2. Type of technology . . . . . . . . . . . . . . . . . .

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⁎ Corresponding author. Tel.: +1 401 455 6577; fax: +1 401 455 6424. E-mail addresses: [email protected] (E.B. Litvin), [email protected] (A.M. Abrantes), [email protected] (R.A. Brown). 0306-4603/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.addbeh.2012.09.003

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3.2.

Usage . . . . . . . . . . . . . . . . . 3.2.1. Duration vs exposure . . . . . 3.2.2. Paradata . . . . . . . . . . . 3.2.3. Prompts . . . . . . . . . . . 3.2.4. Attrition . . . . . . . . . . . 3.3. Human contact . . . . . . . . . . . . 3.3.1. Contact with clinician . . . . . 3.3.2. Contact with peers . . . . . . 3.4. Intervention content . . . . . . . . . . 3.4.1. Static vs. dynamic and degree of 3.4.2. Theory/orientation . . . . . . 4. Summary and directions for future research . . 4.1. Accessibility . . . . . . . . . . . . . . 4.2. Usage . . . . . . . . . . . . . . . . . 4.3. Human contact . . . . . . . . . . . . 4.4. Intervention content . . . . . . . . . . 5. Conclusion . . . . . . . . . . . . . . . . . . Role of funding sources . . . . . . . . . . . . . . Contributors . . . . . . . . . . . . . . . . . . . Conflict of interest . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . .

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1. Introduction In the last two decades we have witnessed unprecedented growth in computer and mobile technologies. Worldwide, Internet access increased 480% from 2000 to 2011, and now exceeds 50% of the population in North America, Europe, and Australia (Internet World Stats, 2011). Approximately 90% of the world's population now lives in an area where mobile phone service is available, and the total number of subscribers is equivalent to 77% of the population (International Telecommunication Union, 2010). In addition to information gathering and communication, uses of these technologies have expanded to incorporate various aspects of daily living, including entertainment, social networking, shopping, photography, organizational tools, and banking. Given these trends, it is not surprising that there has been significant recent interest and work devoted to the development of therapeutic, behavioral interventions that can be accessed and delivered through these technologies. Our primary area of focus, substance use disorders (SUDs), is no exception. Relative to face-to-face interventions for SUDs, technology-based interventions (TBIs) may afford numerous advantages (Ondersma, Chase, Svikis, & Schuster, 2005). Primary among these is TBIs' portability, flexibility, and therefore, disseminability. These qualities give them potential to reach a far greater number of people who need treatment, and therefore ultimately to have a larger overall impact, than face-to-face interventions (Glasgow, Vogt, & Boles, 1999). TBIs require less clinician training and availability and as such they are generally more cost-effective than face-to-face treatments (McCrone et al., 2004; Olmstead, Ostrow, & Carroll, 2010). TBIs may be especially appealing in substance-abusing populations, for whom cost, privacy, and anonymity are of particular importance and represent barriers to treatment entry (SAMHSA, 2011). Finally, TBIs can provide automated and tailored information with a degree of standardization not always found in face-to-face interventions, which makes them attractive from a research perspective. Nevertheless, Barak, Klein, and Proudfoot (2009) caution that the field of TBIs “has suffered from a lack of clarity and consistency” (p. 4) and may be described as “diffuse, incoherent, and sometimes even perplexing” (p. 5) with regard to defining what qualifies as a TBI, terminology, and methodology. Therefore, given the above listed advantageous and appealing characteristics of TBIs for SUDs, further exploration of the opportunities afforded by technology-based approaches through the development of an organizing framework would make an important contribution to this rapidly growing area

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of research. In presenting our framework, we do not provide a meta-analysis or systematic review of TBIs for SUDs outcomes because numerous such reviews have recently been published. Furthermore, we agree with Strecher (2007) that evaluating TBIs as a single “class of intervention,” especially as they continue to proliferate and diversify, is becoming akin to asking “‘Do movies entertain?’ Clearly, some movies do and some do not.” (p. 69). However, before presenting our framework, we will define what types of substance use TBIs our framework encompasses, and provide a brief “review of reviews” that summarizes findings in existing reviews with regard to moderators of outcomes.

1.1. Defining “technology-based intervention” (TBI) Barak et al. (2009) proposed to standardize the classification of Internet-based interventions for health behavior change into four categories: web-based interventions (WBIs), online counseling and therapy (OCT), Internet-operated therapeutic software (IOTS), or other online activities (e.g., blogs, support groups) (OOA). WBIs are “primarily self-guided” (p. 5) programs further subdivided into education interventions (largely static websites that aim to educate about a specific problem), self-guided interventions (structured, “modularized,” dynamic programs often modeled on a face-to-face intervention), and human-supported interventions (similar to self-guided but with adjunctive, tailored human support). OCT refers to synchronous (i.e., real time) or asynchronous (i.e., not in real time, such as email) communication between a therapist and client(s) via the Internet. Such communication may be text-, audio-, or video-based (e.g., King et al., 2009). IOTS encompasses a variety of technologies such as artificial intelligence (“robot simulation of therapists” p. 11), “expert systems” (provides assessment and feedback, e.g., Drinker's Check-Up, Squires & Hester, 2004), and virtual reality environments (e.g., Woodruff, Conway, Edwards, Elliott, & Crittenden, 2007). As the last category, OOA, consists of material generated and maintained by patients rather than clinicians, and we are interested in the development of TBIs from the perspective of clinical researchers, our framework will not be applicable to OOA except as such features are sometimes are included within a larger TBI. Although Barak et al. (2009) state that these categories are mutually exclusive and “significantly different” from one other, they recognize that their proposed taxonomy is more practical than empirical or theoretical. Indeed, existing interventions often incorporate aspects of multiple categories.

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We have chosen not to limit our framework to a particular platform (e.g., Internet) because we believe that other variables, for example, the amount of human contact and the theory or therapy orientation that informs the intervention's content, likely account for more variance in users' experiences and outcomes of TBIs than specific technologies per se. Furthermore, the field is evolving quickly. Already we have witnessed a progression from offline computer-based interventions that run from a CD or DVD or are installed on a local hard drive to Internet-based interventions to mobile technology-based interventions (MTBIs, e.g., text messaging interventions) delivered via handheld portable devices (Riley et al., 2011), although new interventions are still being developed for each of these technologies. Therefore, we will define a TBI as content intended to promote change in substance use behavior or antecedents to behavior that is transmitted or communicated in the absence of direct human contact or facilitation via an interface between the intervention recipient(s) and a technological device (stationary computer or mobile device). Thus our framework is not intended to apply to live face-to-face communication between a patient and therapist in separate locations via a web camera. This form of treatment is functionally equivalent to traditional face-to-face therapy, albeit with unique challenges (for a review, see Barak et al., 2009), and our primary interest lies in how technology has been used to develop novel forms of intervention for substance use that do not require a live human therapist or can be used as adjuncts to traditional face-to-face therapy. We also acknowledge that our organizing framework isn't intended to incorporate creative uses of technology to accomplish clinically relevant tasks that were once only possible face-to-face. For example, contingency management interventions have been developed in which abstinence is documented via video (e.g., Stoops et al., 2009). Other applications of technology to psychosocial and behavioral interventions we do not address are therapist training programs and programs that therapists can use to guide their live sessions with clients (see Cucciare, Weingardt, & Humphreys, 2009). 2. “Review of reviews” 2.1. Literature search procedure We searched the PubMed and PsycInfo databases for reviews of substance use-related TBIs published through August 2011 using a combination of the following terms in the title of the manuscript: (electronic or computer* or web* or online* or internet* or cyber* or “virtual reality” or “e-health” or “m-health” or mobile or “cell phone” or cellular or texting or “text messag*” or “sms” or “short message service”) AND (substance* or drug* or smok* or nicotine or tobacco or cigarette* or drink* or alcohol* or opioid* or opiate* or polydrug or marijuana or cannabis or cocaine or detox* or methadone or buprenorphine or addict* or chemical or dependen* or behavior* or “health promotion”) AND (review* or meta-analy*). We also searched the reference sections of identified reviews for additional articles. 2.2. Identification of review articles We located 9 meta-analytic reviews (Carey, Scott-Sheldon, Elliott, Bolles, & Carey, 2009; Khadjesari, Murray, Hewitt, Hartley, & Godfrey, 2011; Myung, McDonnell, Kazinets, Seo, & Moskowitz, 2009; Portnoy, Scott-Sheldon, Johnson, & Carey, 2008; Riper et al., 2011; Rooke, Thorsteinsson, Karpin, Copeland, & Allsop, 2010; Tait & Christensen, 2010; Webb, Joseph, Yardley, & Michie, 2010; Whittaker et al., 2009) and 13 systematic or qualitative reviews (Bewick et al., 2008; Bickel, Christensen, & Marsch, 2011; Civljak, Sheikh, Stead, & Car, 2010; Copeland & Martin, 2004; Elliott, Carey, & Bolles, 2008; Gainsbury & Blaszczynski, 2011; Hutton et al., 2011; Moore, Fazzino, Garnet, Cutter, & Barry, 2011; Newman, Szkodny, Llera, & Przeworski, 2011; Shahab & McEwen, 2009; Vernon, 2010; Walters, Wright, &

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Shegog, 2006; White et al., 2010) of TBIs for substance use. These 22 reviews vary in how they define a TBI, which classes of substances and technologies they encompass, and potential moderators considered. For example, interventions consisting of tailored written materials that were generated by a computer program, printed, and mailed to participants were included in some reviews, although participants had no direct contact with the computer (e.g., Copeland & Martin, 2004; Myung et al., 2009; Walters et al., 2006). Many were restricted to a particular technology, usually the Internet (e.g., Bewick et al., 2008), or a particular substance(s) (e.g., tobacco, Civljak et al., 2010). The reviews emphasize that caution is necessary in interpretation of meta-analytic results because these interventions are characterized by great heterogeneity, and often lack a theoretical basis (Hutton et al., 2011; Riley et al., 2011) or their dynamic nature may transcend older, more static theories (Riley et al., 2011). Indeed, a number of authors explicitly state that they opted not to conduct a meta-analysis because the degree of heterogeneity rendered meta-analytic methods inappropriate (e.g., Civljak et al., 2010). Despite significant variation in inclusion criteria and methodology, as a whole they reach a common conclusion—TBIs for substance use problems are efficacious, but effect sizes are generally small to medium at best and treatment mechanisms remain largely unknown. Potential moderators they consider include various features, duration, settings, or populations, sometimes via formal statistical analyses and sometimes qualitatively. However, any particular potential moderator has only been evaluated in a handful of reviews at most, limiting conclusions and supporting a need for a framework to organize the myriad decision choices involved in developing a TBI. 2.3. Results: moderators of TBI efficacy identified by previous reviews 2.3.1. Methodological variables By far the most consistent finding is that TBIs are associated with significantly better outcomes than minimal control conditions (e.g., no treatment, wait list, self-help booklet) but not other active, legitimate, relevant interventions (e.g., another active TBI or a non-TBI such as face-to-face therapy) (Carey et al., 2009; Elliott et al., 2008; Hutton et al., 2011; Khadjesari et al., 2011; Portnoy et al., 2008; Rooke et al., 2010; Shahab & McEwen, 2009). Methodological quality (e.g., indicators such as randomization, blinding of therapists and assessment staff, statistical approach) (Myung et al., 2009; Rooke et al., 2010) and duration of follow-up (Myung et al., 2009; Portnoy et al., 2008; Rooke et al., 2010) have not been consistently associated with effect size. One review reported that year of publication significantly moderated effect size for TBIs targeting alcohol use in college students, such that studies published earlier had greater effect sizes (Carey et al., 2009). 2.3.2. Participant variables With respect to participant characteristics, several reviews have reported that the evidence for the efficacy of TBIs is stronger for adults relative to college students and adolescents (Hutton et al., 2011; Khadjesari et al., 2011; Myung et al., 2009). However, this finding may be confounded by other variables often associated with age such as duration and intensity (e.g., college students have often received brief, single session interventions); also, there have been fewer studies in adolescents relative to college students and adults. A consistent relationship between gender and outcomes has not been found (Elliott et al., 2008; Gainsbury & Blaszczynski, 2011; Portnoy et al., 2008). Finally, tobacco TBIs seem to be more efficacious for treatment-seeking smokers (Shahab & McEwen, 2009; Walters et al., 2006), and alcohol TBIs for college students have been efficacious only in students who are already regular drinkers when the intervention is administered (Elliott et al., 2008). 2.3.3. Intervention variables Regarding intervention structure and content, there is some evidence that more intensive (e.g., greater number of minutes or

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sessions) TBIs are more efficacious than briefer TBIs (Portnoy et al., 2008; Riper et al., 2011), although two meta-analyses did not find a relationship between treatment duration and effect size (Rooke et al., 2010; Shahab & McEwen, 2009). Whether and how much contact intervention recipients have with a human therapist may influence outcomes, with one review reporting that some contact was better than no contact (Carey et al., 2009), but others reporting mixed results (Newman et al., 2011; Rooke et al., 2010). Several reviews have described a positive correlation between exposure to or engagement with the intervention (e.g., number of log-ins, duration of time spent on the website) and outcomes (Gainsbury & Blaszczynski, 2011; Hutton et al., 2011; Shahab & McEwen, 2009). Two reviews found that offline interventions were associated with greater effect sizes than online, Internet-based interventions (Portnoy et al., 2008; Rooke et al., 2010), but another reported no significant differences (Myung et al., 2009). Offline interventions are more likely to be administered in supervised (i.e., research or clinical) settings as opposed to in recipients' homes and this greater supervision, along with other possible differences (e.g., greater motivation of participants in treatment settings) could perhaps account for the difference (Portnoy et al., 2008). Indeed, Portnoy et al. (2008) also reported that tobacco interventions were more efficacious if they were administered “on-site,” although Riper et al. (2011) reported that location was not a significant moderator of outcomes. With respect to intervention content, few elements have been found to affect outcomes in a consistent manner. One meta-analysis found that greater use of theory in developing the intervention's content was associated with larger effect sizes (Webb et al., 2010). Finally, tobacco interventions that have content tailored to recipients appear to be more efficacious than those with non-tailored content (Civljak et al., 2010; Hutton et al., 2011).

Home

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3. TBIs for substance use disorders: an organizing framework As already noted, TBIs are uniquely suited for studying treatment mechanisms given that they are more easily manipulated and afford a great degree of experimental control and standardization (e.g., Danaher & Seeley, 2009). As such they represent a primary vehicle to address recent criticism that research on SUD interventions has focused too much on outcomes to the neglect of process (Doss, 2004; Morgenstern & McKay, 2007; Orford, 2008). Unfortunately, few researchers have thus far taken advantage of this potential (for a good exception, see Strecher et al., 2008). The majority of TBIs are still multicomponent interventions constructed “from scratch,” (Vinson et al., 2011) and the same “black box” problem—the difficulty in identifying active treatment components in a typical clinical trial of a multicomponent intervention (the “black box”) versus a control group—that has characterized psychotherapy research as a whole applies to the TBIs literature as well. The process of TBI development is frequently glossed over in publications of RCT results, despite the great potential value of this information (Barretto, Bingham, Goh, & Shope, 2011; Brendryen, Kraft, & Schaalma, 2010). Largely absent from existing reviews have been efforts to categorize systematically the considerations involved in designing and evaluating TBIs. We hope that our proposed framework, illustrated with examples of frequently cited TBI studies, helps fill this gap and offers a path for more programmatic progress in the field. Many of our examples are derived from smoking cessation interventions, as smoking has been one of our primary interests and has also received a great deal of attention with respect to quantity and variety of existing TBIs; however, we believe our examples apply to SUDs broadly. We also acknowledge that our model may apply more broadly than SUDs (i.e., TBIs targeting other health-related behaviors or psychological disorders); nevertheless, our model was constructed with TBIs for SUDs in mind.

Mobile

Stationary

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Accessibility

Synchronous Clinician

Dynamic

Intervention Content

Human Contact

Tailoring

Usage

Peers

Duration

Indefinite

Limited

Exposure

Paradata

Tunneling

Prompts

Theory/orientation

Attrition

Logarithmic

Sigmoid

L-shaped

Fig. 1. Organizing framework for technology-based interventions for substance use disorders.

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Our framework complements and extends certain elements of a general model for Internet-based behavior change interventions developed by Ritterband, Thorndike, Cox, Kovatchev, and Gonder-Frederick (2009) (see also Persuasive Systems Design, another general model of intervention elements relevant to TBIs, Lehto & Oinas-Kukkonen, 2011). Ritterband et al.'s model includes 9 primary components and their nonlinear relationships to each other (environment, user characteristics, website use, support, website, mechanisms of change, behavior change, symptom improvement, treatment maintenance). Whereas their model attempts to explain how Internet-based interventions produce positive outcomes, defined as symptom improvement, and is largely descriptive in its treatment of intervention design choices, our framework complements their excellent work to target more specifically how and why developers of substance use interventions might make specific choices for what Ritterband et al. term the environment, website use, support, and website (i.e., the intervention itself). Our framework (see Fig. 1) consists of four primary TBI design considerations: accessibility (setting/location, type of technology), usage (duration, exposure, attrition), amount of human contact (asynchronous, synchronous, amount of clinician contact, amount of contact with peers), and intervention content (static vs. dynamic, degree of tailoring, theory/orientation). Design choices in each of these categories must balance concerns related to generalizability and dissemination, target population (e.g., age, socioeconomic status, literacy level, substance(s) used, problem severity), treatment goals (e.g., abstinence vs. harm reduction, change in substance use behavior vs. antecedents to behavior), and cost. 3.1. Accessibility 3.1.1. Setting/location Individuals have accessed TBIs for substance use from their homes or dorm rooms using their personal computers (e.g., An et al., 2008; Brendryen & Kraft, 2008; Chiauzzi, Green, Lord, Thum, & Goldstein, 2005; Finfgeld-Connett & Madsen, 2008; Hester, Delaney, Campbell, & Handmaker, 2009; Lenert, Munoz, Perez, & Bansod, 2004; McKay, Danaher, Seeley, Lichtenstein, & Gau, 2008; Strecher, Shiffman, & West, 2005; Strecher et al., 2008; Swartz, Noell, Schroeder, & Ary, 2006) or computers provided by the research team (e.g., Japuntich et al., 2006; Patten et al., 2006). TBIs have also been accessed in the workplace (e.g., Doumas & Hannah, 2008), in research clinic settings (e.g., Budney et al., 2011; Hester, Squires, & Delaney, 2005; Kay-Lambkin, Baker, Lewin, & Carr, 2009; Lewis & Neighbors, 2007), schools (e.g., Fritzler et al., 2008), hospitals and other medical settings (e.g., Cunningham et al., 2009a; Kypri et al., 2004; Maio et al., 2005; Ondersma et al., 2005), and mental health or substance abuse treatment settings (e.g., Brooks, Ryder, Carise, & Kirby, 2010; Carroll et al., 2008). Indeed, as noted previously, a primary advantage of TBIs is their flexibility for use in a variety of settings, especially as devices have gotten smaller and more portable (i.e., progression from desktop towers to laptops to even smaller machines such as “netbooks,” touch-screen tablet devices, and “smartphones.”). Each setting has advantages and disadvantages relative to the study's goals. For example, TBIs that individuals may access from home without the supervision of research or clinical staff have the advantage of convenience for both researchers and intervention recipients and wide potential reach. They can be accessed by those who are unable or unwilling to engage in traditional face-to-face treatment due to cost, lack of transportation or childcare, lack of availability (e.g., rural area), perceived stigma associated with substance use, privacy concerns, or low problem severity. They also may also be more easily disseminated to the public as in some cases dissemination could be as simple as making the website or mobile application publicly available. However, when users are not supervised, ability to monitor access, usage, and outcomes is more limited. For example, it is challenging to verify with certainty that the

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intervention is being used by the intended person and what types of distractions may be present. It is also more difficult to assist with resolving technical difficulties in a timely manner, especially if use of the TBI is dependent upon successful installation of special software programs to the computer or mobile device or a minimum Internet connection speed. More specific to substance use, TBIs that are administered in research or clinical settings may have lower reach and generalizability, but afford tighter control over individuals' access and use and allow for monitoring of adverse physiological and psychological reactions, which is especially a concern in populations with more severe substance use problems, as well as biochemical verification of abstinence. 3.1.2. Type of technology Researchers must decide whether the intervention will be delivered on a stationary computer that is offline (e.g., Carroll et al., 2008) or online (e.g., Brendryen, Drozd, & Kraft, 2008), or a mobile device (e.g., phone, tablet computer) (e.g., Ondersma et al., 2005; Riley, Obermayer, & Jean-Mary, 2008), and whether special software or hardware (e.g., Ondersma et al., 2005) is required. Again, researchers must consider their target population and whether study participants will access the TBI using personal devices they already own or whether the device will be provided to the participant. If participants will be using devices they already own, researchers must consider compatibility with a variety of device brands and service providers, and the complexity of the procedure required for installation and use. A related consideration is the pace of technological advancement. The degree to which an intervention takes advantage of the latest technology to enhance sophistication and appeal (i.e., what Ritterband et al. term “appearance” and “delivery”) must be balanced against the degree to which it will be compatible with older devices, as well as the ease with which it can be updated or made compatible with future technologies or device models when its original form inevitably becomes outdated. 3.2. Usage 3.2.1. Duration vs exposure Unlike face-to-face interventions, in which intervention duration or “dose” is often tightly controlled (e.g., 12 weekly 1-hour sessions), TBIs have varied greatly in the duration of time that access to the intervention is provided and how much exposure to the intervention content individuals received relative to how much was available. Some studies have provided limited access (e.g., the website was discontinued at end of the treatment or follow-up period, or participants could only access the intervention at the research site or clinic), whereas other TBIs (generally, Internet-based TBIs accessed from participants' homes) were continuously available throughout the follow-up phase of the study and for the indefinite future. Some studies limit the available content more selectively during the active treatment phase of the study, periodically unveiling new content and directing participants to specific intervention activities at specific times (e.g., Brendryen & Kraft, 2008). These more controlled, directive interventions that “tunnel” participants to selected content matching their state or stage within the process of change (see Baker et al., 2011) may be more efficacious than free “user-guided” access models (Kraft, Drozd, & Olsen, 2009; Strecher, 2007). 3.2.2. Paradata However, providing access does not ensure exposure and use of all possible content. Exposure is commonly measured via the use of paradata, defined as “auxiliary data that capture details about the process of interaction with the online intervention” (Couper et al., 2010), for example, tracking login frequency and duration of time spent on specific portions of the intervention. These data can be collected unobtrusively using technology such as cookies that is incorporated into Internet-based interventions. Numerous research

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questions may be answered via paradata, such as patterns of use over time and the relationship between usage patterns and treatment outcomes (Couper et al., 2010). For example, Patten et al. (2006, 2007) found no differences in tobacco abstinence rates between a homebased Internet smoking cessation program (“Stomp Out Smokes,” SOS) for which adolescents were provided access for 24 weeks (168 days), and a brief face-to-face office intervention (BOI), although SOS did outperform the BOI with regard to reduction in average number of days smoked. However, paradata revealed that use of SOS declined rapidly over time, as just over half of all logins to SOS occurred during the first 3 weeks, and less than one-third of participants were still using the site by week 3. The median number of days that participants logged in was only 5, with a range of 0 (7% never logged in SOS) to 33. Also, some pages within the SOS website (e.g., interactive components including a discussion support group and quit plan) were viewed much more often than others (e.g., static informational pages). The authors report that their intention was to “emulate a home-based, real-world, adolescent use of an Internet site” (Patten et al., 2007, p.438) and therefore they did not provide any prompts (e.g., emails) to remind or encourage use of SOS. Even in TBIs designed to be completed in a single session, paradata may still provide valuable information such as the duration of time spent on various portions of the intervention. 3.2.3. Prompts In contrast to the procedure of Patten et al. described above, many studies of TBIs do periodically remind participants to use the intervention and/or offer various incentives to do so, which can effectively increase usage (Fry & Neff, 2009). For example, An et al. (2008), who compared a web-based TBI (“RealU”) to a minimal control intervention for smoking cessation in college students, asked participants in the RealU condition to visit the website 20 times over a 30-week period. Each week, RealU participants were prompted via email and offered $10 if they completed certain activities within the website. Paradata indicated that 88% of participants completed the activities for at least 18 of the 20 weeks. As with many other design considerations, decisions about whether and how frequently to prompt or incentivize TBI use may depend on the study's aim (e.g., efficacy vs. effectiveness trial). Furthermore, prompts and incentives may have different effects on research participants' behavior versus individuals who access the intervention outside of a research context (i.e., “open access”) (Tate & Zabinski, 2004). 3.2.4. Attrition Somewhat related to TBI duration and exposure is attrition, which has been recognized as a “fundamental” issue in TBI controlled trials, especially for home-based Internet interventions in which participants never meet the researchers face-to-face (Eysenbach, 2005). It is not unusual to see much higher attrition rates in Internet intervention trials relative to traditional face-to-face intervention trials. Eysenbach (2005) describes a general model of adoption of technological innovation that may be applied to usage of TBIs (Rogers, 2003) as well as other factors influencing attrition in TBI studies including: participants' expectations, ease of enrollment (in terms of strictness of inclusion/exclusion criteria as well as baseline measures required prior to receiving access to the TBI), ease of drop out, usability, amount of phone or in-person contact with the research team versus virtual contact, problem severity, expense, availability of similar alternative interventions, and comfort and experience with technology. Eysenbach (2005) identifies two different types of attrition that are often but not always highly correlated: “dropout attrition,” which refers to failing to complete study follow-up assessments, and “non-usage attrition” in which participants stop or greatly reduce their usage of the intervention but nonetheless continue to complete follow-up assessments. Eysenbach predicts that dropout attrition curves generally follow non-usage attrition curves. Attrition curves

may be logarithmic (steady attrition rate over time), sigmoid (low attrition initially, then a period of high attrition, followed by another period of low attrition in which “hardcore users” continue to use the TBI), or L-shaped (high attrition initially followed by low attrition as a stable user base develops). Eysenbach offers suggestions for how to deal with these different attrition types and curves with regard to data analysis, as traditional intent-to-treat (ITT) analysis, in which drop-outs are assumed to be treatment failures, may not be ideal or most appropriate (Eysenbach, 2005), given that TBI non-usage attrition may be as likely to reflect successful change in substance use behavior as lack of success (Strecher, 2007). 3.3. Human contact 3.3.1. Contact with clinician Some TBIs are designed as self-contained, fully automated standalone treatments, meaning that the TBI is the primary or only treatment administered. This is the case for many web-based smoking cessation TBIs (e.g., Brendryen et al., 2008; Swartz et al., 2006) and brief alcohol TBIs (e.g., Cunningham, Wild, Cordingley, van Mierlo, & Humphreys, 2009b; Walters, Vader, & Harris, 2007). However, as described previously, sometimes TBIs have been used as adjuncts or supplements to another form of treatment that involves human contact such as traditional face-to-face psychotherapy or pharmacotherapy (e.g., Carroll et al., 2008; Japuntich et al., 2006; Strecher et al., 2005). A related issue is the degree of automation of the TBI itself, which may be defined as the amount of time that a live human must spend on administering the TBI, regardless of the amount of contact with intervention recipients. For example, recipients may have no face-to-face contact with a clinician but receive feedback or instruction from a distant clinician that has reviewed information submitted electronically (e.g., ask an “expert,” Chiauzzi et al., 2005; McKay et al., 2008; Patten et al., 2006). Issues that may influence intervention design choices in this area include problem severity and imminence of risk (e.g., in high imminent risk populations such an adjunctive TBI may have more benefits) and reach and dissemination goals (Tate & Zabinski, 2004). 3.3.2. Contact with peers Similar to individual vs. group face-to-face treatment, TBIs can also be provided in simulated individual or group settings. For example, many web-based TBIs offer features such as discussion boards (e.g., McKay et al., 2008; Riper et al., 2008) or chat rooms in which participants can converse with each other. Tate and Zabinski (2004) review the advantages and disadvantages of what they term asynchronous (e.g., email and other discussion boards in which participants generate and read messages independently) and synchronous (e.g., chatrooms in which participants are online and respond to each other simultaneously in real-time) communication. With regard to TBIs for substance use, there remains limited research on the effects of communication among participants in isolation from the myriad other components that typically comprise TBIs (Lehto & Oinas-Kukkonen, 2011; Strecher, 2007). 3.4. Intervention content 3.4.1. Static vs. dynamic and degree of tailoring Some TBIs consist primarily of participants passively absorbing static content—reading text, listening to audio narration, or watching video, whereas others require participants to input information and interact with the program more actively and/or incorporate varying degrees of tailoring or personalization (see Strecher et al., 2008). Strecher (2007) reviews the evolution of Internet-based TBIs for health behavior change, noting that in the late 1990s there were numerous digital “pamphlet racks” (p. 59) of questionable accuracy and validity that have since been discontinued. He notes that

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unfortunately, many of the TBIs evaluated in contemporary research continue to rely on “simple information-transfer models” (p. 59) and do not take advantage of the Internet's potential for interactivity. 3.4.2. Theory/orientation The same health behavior theories and therapy orientations that have informed the development of efficacious face-to-face interventions for SUDs, such as social learning theories, cognitive–behavioral therapy, and motivational interviewing have also informed the development of TBIs. In some cases, existing manuals for face-to-face treatments have been adapted to a TBI format, preserving as much of the original intervention content as possible (e.g., Carroll et al., 2008). Yet, modeling TBIs on efficacious face-to-face interventions and attempting to replicate and translate their structure and content as closely as possible into an electronic format, while an obvious, justified initial step, does not capitalize on what is perhaps the most significant potential advantage of TBIs relative to face-to-face interventions, particularly with respect to substance use disorders and other behavior change goals specifically. That is, TBIs afford great potential for highly tailored, unlimited, constantly available content that adapts and responds in a dynamic, iterative manner to within-day fluctuations in individuals' environmental and social contexts and emotional and physiological states (Kraft et al., 2009; Riley et al., 2011), as well as to sudden, dramatic, unpredictable, non-linear (“quantum”) shifts in motivation to change that characterize the course of substance use and other risk behaviors (Resnicow & Page, 2008; Resnicow & Vaughan, 2006). Indeed, prospective research confirms that substance use is a complex behavior that does not follow a simple linear pattern of regular use, abstinence, and relapse regardless of the individuals' intentions for abstinence (Hufford, Witkiewitz, Shields, Kodya, & Caruso, 2003; Peters & Hughes, 2009; Witkiewitz & Marlatt, 2007). Such fluctuations have demonstrated strong influences on substance use behavior and are difficult to detect by traditional means of data collection at widely spaced follow-ups (e.g., Berkman, Dickenson, Falk, & Lieberman, 2011; Gwaltney, Shiffman, & Sayette, 2005). For example, tobacco craving induced by just two hours of abstinence significantly impacts self-efficacy for cessation and intentions for future smoking (Nordgren, van der Pligt, & van Harreveld, 2008). Additionally, several retrospective studies have shown that, contrary to conventional wisdom and clinical lore, about half of attempts to quit smoking are unplanned, and these abrupt attempts, which are often spurred by a sudden surge of motivation that is sometimes but not always triggered by a discrete, salient, unpredictable event (e.g., a heart attack, ultimatum from family, experience of social stigma, see Larabie, 2005), are somewhat more likely to result in long-term abstinence than planned attempts (Ferguson, Shiffman, Gitchell, Sembower, & West, 2009; Larabie, 2005; West & Sohal, 2006). In contrast to the chronic, unpredictable course of substance use, empirically-validated face-to-face interventions for substance use disorders, especially as they are tested in the context of randomized controlled trials (RCTs), typically are focused on a single, pre-planned and pre-determined attempt at abstinence and are highly structured and time-limited (e.g., 12 weekly manual-guided sessions). This type of design presumes a linear pattern of change that will persist after the intervention ends. Therapists are limited in the degree to which they can tailor the treatment to a specific participant's fluctuating needs due to the constraints and tight experimental control required to draw valid conclusions within the RCT paradigm. For example, in a typical smoking cessation intervention, therapists assist smokers in early sessions with setting and planning for their quit date (e.g., identifying triggers and generating alternative coping strategies), which is generally pre-determined by the researchers; following the quit date, later sessions emphasize relapse prevention skills (e.g., planning ahead for high-risk situations) and framing occasional slips as learning experiences and opportunities to generate new coping skills. Less

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guidance is provided for how to intervene when the patient fails to quit at all, or returns to baseline smoking levels (i.e., complete relapse) during active treatment. In contrast, a TBI could be pre-programmed to adapt automatically to a patients' current smoking status and to various contingencies in an infinite variety of combinations, such that each patient receives a different intervention tailored to their needs, but also a perfectly standardized intervention. 4. Summary and directions for future research The young field of technology-based interventions (TBIs), and TBIs for SUDs more specifically, has expanded rapidly in recent years, and will continue to grow exponentially. However, as explicated by Barak et al. (2009), in the current moment the field lacks a coherent vocabulary, organization, and structure. “Black box” multicomponent interventions are being designed in isolation, “from scratch,” without the benefit of a rich empirical literature to guide design and content choices. Thus, at this time comparisons among TBIs are difficult, it remains entirely unclear how TBIs exert their efficacy, and it is impossible to recommend the inclusion or exclusion of particular features or components. Nevertheless, in this section we offer some ideas for fruitful research directions, grouped within our organizing framework, aimed at generating answers to these questions. Our suggestions are informed by the 28 “propositions” for TBIs suggested by Kraft et al. (2009). Kraft et al. (2009) provide a strong rationale for why the field of TBIs needs to progress beyond direct adaptations of existing face-to-face interventions and better harness the dynamic potential of technology for intervening at the exact moments when individuals are maximally motivated and receptive and for identifying mechanisms of treatment via systematic and creative manipulation of intervention components (e.g., Strecher et al., 2008). 4.1. Accessibility As technology continues to increase in sophistication and becomes ever more ubiquitous in both homes and public spaces, new and exciting opportunities to intervene in non-traditional settings will continue to arise. We predict that new TBIs will offer increasing flexibility in treatment options and settings, and motivate engagement in treatment by being available immediately and constantly, in everyday environments, to take advantage of sudden motivation surges. 4.2. Usage Previous research has revealed that unless incentivized, individuals are apt to “drop out” or discontinue using TBIs that are not highly engaging and at least somewhat tunneled with clear therapeutic goals, even more so than traditional treatments, and especially when the TBI is accessed outside of a clinical setting and in the absence of supervision. TBIs that lack tunneling (i.e., those based on an information-transfer model that allows users to explore and access freely all available content in any order and at any time they wish) are probably best described as technology-based self-help. Such interventions incorporate multimedia and interactive components and therefore may be more novel and engaging than traditional printed self-help materials. A priority for future research will be to determine the optimal degree of “tunneling” to maximize engagement, retention, and efficacy in the absence of research participation incentives. 4.3. Human contact Currently there exist completely automated TBIs that require no human contact (e.g., Brendryen et al., 2008) as well as TBIs that incorporate either asynchronous or synchronous clinician contact (e.g., e-mail contact). However, one relatively unexplored area is hybrid treatments that combine aspects of traditional face-to-face

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therapy with supplemental technology-based components that encourage continued engagement outside of the therapist's office. For example, many therapists consider homework an integral component of treatment. In particular, psychologists believe that homework assignments are even more important in the treatment of SUDs relative to many other types of disorders (Kazantzis & Dattilio, 2010). Homework assignments typically consist of asking clients to monitor and document the occurrence of specific types of cognitions, behavior, and/or their use of coping strategies discussed in therapy. Completion of homework is associated with positive outcomes (Mausbach, Moore, Roesch, Cardenas, & Patterson, 2010) and patients generally have positive attitudes toward receiving homework (Fehm & Mrose, 2008). Technology has great potential to improve clients' compliance with and benefit from homework, by facilitating not only the assignment process (e.g., electronic version rather than paper that is easily lost or forgotten) but compliance (i.e., clients could complete homework on a mobile device that provides reminders to encourage completion at appropriate times and records when completion occurs). Finally, another relatively unexplored and yet great potential for TBIs is to engage individuals not only with obtaining extended support from their therapists but also the ongoing social support of their peers via social networking, discussion, and chat functions. Some TBIs have included peer contact features, but it remains unclear how best to incorporate this feature. 4.4. Intervention content Empirically-supported face-to-face treatments (e.g., cognitive– behavioral therapy) have been adapted successfully to technologybased formats (e.g., Carroll et al., 2008) and, in some particularly impressive cases, with little or no loss of efficacy (e.g., Kay-Lambkin et al., 2009). However, as noted previously, such direct adaptations do not take full advantage of the possibilities for technology to facilitate constantly available intervention that responds to an individual's changing environment and physiological and emotional states. If, as we predict, TBIs begin to move more rapidly in this direction, we are likely to require a whole new language and literature on “empirically-supported” TBIs. It is likely that the criteria currently used to define face-to-face treatments as efficacious (Chambless & Ollendick, 2001) will prove inadequate or inappropriate, and an entirely new set of standards will need to be developed. The RE-AIM (reach, efficacy/effectiveness, adoption, implementation, and maintenance) framework for evaluating the overall impact of an intervention (Glasgow et al., 1999) may represent a helpful starting point in the development of such standards, as many TBIs are likely to be less efficacious than intensive face-to-face treatments, but have far greater reach. As such, TBIs represent an important vehicle to speed the dissemination and implementation of empirically supported treatments into clinical practice. 5. Conclusion Interest in and research on technology-based interventions (TBIs) for substance use disorders is increasing at an exponential rate. Completely automated interventions that require no live therapist contact have the potential to provide valuable assistance to individuals whose substance use problems do not require medical detoxification and who for whatever reason are unable or are unwilling to seek traditional treatment. For others, TBIs should not and cannot completely replace traditional treatment, but we believe that technology affords exciting opportunities to improve, support, and supplement strong therapeutic and peer relationships via constantly available, tailored, dynamic applications that increase and maintain engagement and motivation. In this way, TBIs will come to represent a valuable set of tools that therapists may have at their disposal to extend and augment treatment.

Given the exponential growth of TBI interventions for substance use disorders over the past decade, this is a very clearly an exciting time in the history of intervention research. We look forward to the tremendous progress that will inevitably take place over the coming years, and we hope that the organizing framework we have proposed will aid in guiding programmatic, creative lines of research that take full advantage of the opportunities afforded by technology to enhance understanding of how best to help people reduce and cease problematic substance use. Role of funding sources The authors have no funding sources to declare. Contributors EBL and RAB collaborated in conceptualizing the idea for this manuscript. EBL conducted the literature search and designed the framework. EBL wrote the first draft of the manuscript. AMA and RAB made significant contributions to the content of the manuscript. All authors edited the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest All authors declare that they have no conflicts of interest.

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