Detección de eventos run Ͳ up y run Ͳ down.

Common factors across therapies may be responsible for up to 30% of

therapeutic change (Lambert & Barley, 2001), and research on these common factors has often focused on qualities of the relationship between therapist and client. For instance, therapeutic alliance theory has included concepts such as empathy, perceptions of therapist credibility, and patient empowerment (Elvins & Green, 2008).One of the most widely used is Bordin’s (1979) working alliance theory, concerned with the client and therapist’s joint involvement in collaborative, purposive work within therapy (Hatcher & Barends, 2006). There are three fundamental aspects to this collaborative work: agreement between client and therapist on therapeutic goals; agreement upon therapeutic tasks needed to achieve the goals; and the quality of the client and therapist’s interpersonal bond (Bordin, 1979). The therapeutic alliance is a pantheoretical concept, which applies regardless of the therapeutic approach (Horvath & Luborsky, 1993).

Meta-analyses indicate a modest but reliable relationship between the quality of therapeutic alliance and outcomes of therapy, with aggregated r

values ranging from .22 – 2.75 (for example, Horvath et al., 2011; Martin et al., 2000). Although concerns have been raised about the correlational nature of the majority of alliance research, recent investigations using instrumental variable modelling support a causal role of therapeutic alliance for therapy outcomes; engaging in therapy when a strong alliance has been established is beneficial, but when alliance levels are poor, engaging in therapy is detrimental (Goldsmith et al., 2015).

It seems clear that the therapeutic alliance is critical for the success of face-to-face mental health treatment. But what about treatments that might not actively involve a human therapist? There has been a growth in the

development of therapies which are delivered by digital technology and the internet (Barak et al., 2009). However, there have also been concerns

expressed about these technology-based approaches, often around the lack of therapeutic relationship, or perceived difficulties in establishing an alliance (Fleming & Merry, 2013; Stallard et al., 2010).

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While there are a range of approaches which incorporate digital

technology in some way, this review covers a particular category of treatment. Web-based therapeutic interventions often take the form of structured

treatment packages that deliver content in a modular format, make use of multimedia and interactivity, and may provide automated, tailored feedback (Barak et al., 2009).Although primarily self-guided, they may feature support from a therapist or other helper (Barak et al., 2009). The terminology used for this review is “technology-based intervention” (TBI; Kiluk et al., 2014) to also cover interventions which meet the criteria above, but are delivered without the use of the internet (for example, a computer program or a mobile phone).

Meta-analyses and systematic reviews have found that: computerised treatment can reduce symptoms and improve recovery in depression (Richards & Richardson, 2012); mobile phone applications can reduce depression, stress, and substance use (Donker et al., 2013); and internet and mobile-based

interventions appear to be feasible and acceptable for psychosis treatment (Alvarez-Jimenez et al., 2014). While many interventions are cognitive

behavioural therapy(CBT)-focused (Barak et al., 2009), other approaches have also been used. For example, online mindfulness has demonstrated

improvement in stress and symptoms of depression and anxiety (Cavanagh et al., 2013). In terms of efficiency, evidence suggests that TBIs are potentially highly cost-effective (Hedman et al., 2012; Ramsey, 2015).

Although a therapeutic relationship between client and therapist may not be present, can qualities of the relationship between user and technology be influential instead? Technology-based approaches may still provide a channel for the common factors of therapy (Peck, 2010), and some TBIs evidence attempts to promote therapeutic relationship features (Proudfoot, 2004). As an example, anthropomorphic “agents” (on-screen entities; Beale & Creed, 2009) may be incorporated into health change interventions, which might add more interpersonal dimensions and improve the human-technology relationship (for example, Bickmore & Picard, 2005).

Without using such agents, TBIs might mimic features of the therapeutic relationship in other ways, perhaps by the provision of corrective feedback via

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automated algorithms (Helgadóttir, Menzies, Onslow, Packman, & O'Brian, 2009). Evidence of this in action is provided by a study which qualitatively analysed computerised treatments targeting depression (Barazzone et al., 2012). It was found that numerous strategies were used to create a

collaborative relationship, such as providing feedback, as well as the formation of agreed goals between the user and the intervention. This clearly relates to Bordin’s (1979) conceptualisation of the alliance in therapy as a collaborative process, and indicates that the alliance may remain relevant to TBIs, albeit in a different way.

Other studies have adapted measures to reflect the alliance between user and the technology itself, rather than the alliance between user and human therapist. To illustrate with an example, Kiluk et al. (2014) adapted the Working Alliance Inventory (WAI; Horvath & Greenberg, 1989), which is based on Bordin’s (1979) model of the alliance. This resulted in the creation of the WAI-Tech, which was adapted by substituting the word “therapist” for the name of the computerised therapy program. A systematic review of papers that have taken similar approaches to Kiluk et al. (2014) will allow these findings to be synthesised, to produce an understanding of the current state of the field.

The present review will also include TBIs delivered by smartphone, since it is suspected that smartphones present unique features which may facilitate a human-technology relationship. Smartphones are commonly used in everyday life and often allow for a continuous internet connection, meaning that an intervention could be accessed in a wide range of locations or circumstances (Donker et al., 2013; Gravenhorst et al., 2014; Ramsey, 2015). This increased availability as well as the familiarity of an everyday device may support the development of an alliance with a smartphone-delivered intervention.

Considering all of the above, it seems that the nature of the human- technology relationship in TBIs is a worthy subject for detailed investigation. An understanding of this could help us design better, more engaging TBIs, and to understand why some TBIs may be more effective for improving mental health than others. It could also help us extend and build upon theories of therapeutic alliance,

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which need exploration in non-traditional therapeutic settings (Elvins & Green, 2008).

3.2.1 Research aims

The overall aim of this systematic review is to provide an understanding of the nature of the relationship between human and digital technology in the context of TBIs for mental health problems, particularly concerning the relevance of the

therapeutic alliance.

Specifically, the primary aims of this systematic review are:

• To identify the terms and concepts used regarding the relationship between human and technology in the context of TBIs for mental health problems. • To ascertain whether the working alliance model of the therapeutic alliance

remains valid in a TBI context.

• To provide an understanding of the factors which influence the human- technology relationship.

The secondary aims of the review are:

• To identify the measures used to assess the human-technology relationship within TBIs.

• To review research regarding the ability of the human-technology relationship to predict the outcomes of TBIs for mental health problems.