The Digital Therapeutic Alliance and Human-Computer Interaction

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

BACKGROUND

There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables.

OBJECTIVE

We aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data.

METHODS

We developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model.

RESULTS

For the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F1-score of 91.71%; our participant-inclusive generalized model achieved an average accuracy of 66.95% and an F1-score of 42.50%; and our participant-exclusive generalized model achieved an average accuracy of 67.65% and an F1-score of 43.05%.

CONCLUSIONS

Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.

Acceptability and experience of a smartphone symptom monitoring app for people with psychosis in China (YouXin): a qualitative study

BACKGROUND

Access to high-quality mental healthcare remains challenging for people with psychosis globally, including China. Smartphone-based symptom monitoring has the potential to support scalable mental healthcare. However, no such tool, until now, has been developed and evaluated for people with psychosis in China. This study investigated the acceptability and the experience of using a symptom self-monitoring smartphone app (YouXin) specifically developed for people with psychosis in China.

METHODS

Semi-structured interviews were conducted with 10 participants with psychosis to explore the acceptability of YouXin. Participants were recruited from the non-randomised feasibility study that tested the validity, feasibility, acceptability and safety of the YouXin app. Data analysis was guided by the theoretical framework of acceptability.

RESULTS

Most participants felt the app was acceptable and easy to use, and no unbearable burdens or opportunity costs were reported. Participants found completing the self-monitoring app rewarding and experienced a sense of achievement. Privacy and data security were not major concerns for participants, largely due to trust in their treating hospital around data protection. Participants found the app easy to use and attributed this to the training provided at the beginning of the study. A few participants said they had built some form of relationship with the app and would miss the app when the study finished.

CONCLUSIONS

The YouXin app is acceptable for symptom self-monitoring in people with experience of psychosis in China. Participants gained greater insights about their symptoms by using the YouXin app. As we only collected retrospective acceptability in this study, future studies are warranted to assess hypothetical acceptability before the commencement of study to provide a more comprehensive understanding of implementation.

Longitudinal outcomes of a therapist-supported digital mental health intervention for depression and anxiety symptoms: A retrospective cohort study

PURPOSE

This study examined treatment outcomes (depression and anxiety symptoms) up to 24 months after completion of a therapist-supported digital mental health intervention (DMHI).

METHODS

The sample consisted of 380 participants who participated in an eight-week DMHI from February 6, 2017 to May 20, 2019. Participants reported depression and anxiety symptoms at eight timepoints from baseline to 24 months. Mixed-effects modelling was used to investigate symptom changes over time. The proportion of participants meeting criteria for treatment response, clinically significant change, and remission of depression and anxiety symptoms were calculated, including proportions demonstrating each outcome sustained up to each timepoint.

RESULTS

Multivariate analyses yielded statistically significant reductions in depression (β = -5.40) and anxiety (β = -3.31) symptoms from baseline to end of treatment (8 weeks). Symptom levels remained significantly reduced from baseline through 24 months. The proportion of participants meeting criteria for clinical treatment outcomes remained constant over 24 months, although there were linear decreases in the proportions experiencing sustained clinical outcomes.

CONCLUSIONS

Treatment gains were made for depression and anxiety symptoms at the end of treatment and up to 24 months. Future studies should determine the feasibility of integrating post-treatment programmes into DMHIs to address symptom deterioration.

German Version of the Mobile Agnew Relationship Measure: Translation and Validation Study

BACKGROUND

The mobile Agnew Relationship Measure (mARM) is a self-report questionnaire for the evaluation of digital mental health interventions and their interactions with users. With the global increase in digital mental health intervention research, translated measures are required to conduct research with local populations.

OBJECTIVE

The aim of this study was to translate and validate the original English version of the mARM into a German version (mARM-G).

METHODS

A total of 2 native German speakers who spoke English as their second language conducted forward translation of the original items. This version was then back translated by 2 native German speakers with a fluent knowledge of English. An independent bilingual reviewer then compared these drafts and created a final German version. The mARM-G was validated by 15 experts in the field of mobile app development and 15 nonexperts for content validity and face validity; 144 participants were recruited to conduct reliability testing as well as confirmatory factor analysis.

RESULTS

The content validity index of the mARM-G was 0.90 (expert ratings) and 0.79 (nonexperts). The face validity index was 0.89 (experts) and 0.86 (nonexperts). Internal consistency for the entire scale was Cronbach α=.91. Confirmatory factor analysis results were as follows: the chi-square statistic to df ratio was 1.66. Comparative Fit Index was 0.87 and the Tucker-Lewis Index was 0.86. The root mean square error of approximation was 0.07.

CONCLUSIONS

The mARM-G is a valid and reliable tool that can be used for future studies in German-speaking countries.

The connection still matters: Therapeutic alliance with digital treatment for alcohol use disorder

BACKGROUND

A strong cooperative bond between the patient and provider ("therapeutic alliance") is robustly associated with better alcohol use disorder (AUD) treatment outcomes. Although digital treatments for AUD have significant potential, the function of the alliance during digital programs is unclear. We compared the validity of patient-reported measures of the alliance with a digital treatment ("digital alliance") for AUD and the alliance with their clinician ("clinician alliance").

METHODS

We used data from an 8-week, randomized clinical trial of a computerized cognitive behavioral therapy program (CBT4CBT) during outpatient AUD treatment. Treatment conditions included CBT4CBT with minimal clinical monitoring (CBT4CBT + monitor) or with treatment as usual (CBT4CBT + TAU). The digital alliance and clinician alliance were measured with similar versions of the Working Alliance Inventory (WAI). The WAI ratings were completed at the 2nd and 6th treatment sessions. A timeline followback calendar assessed daily alcohol use. Bayesian multilevel models compared the strength of the alliances and tested their associations with future alcohol use.

RESULTS

Data from 43 participants were included (age M = 44; 65% male; 51% Black, 40% White, 9% other; 14% Hispanic). The digital alliance ratings had similar internal reliability as the clinician alliance ratings (ω's > 0.90). Differences between digital alliance and clinician alliance ratings were negligible in both treatment conditions (BF01  = 9 and 31). During treatment, within-person increases in the digital alliance and the clinician alliance predicted modest decreases in future drinking to a similar degree (BF01  = 15). Alliance ratings were not associated with future drinking when posttreatment follow-up drinking data were included (BF10  < 3).

CONCLUSIONS

The digital alliance with CBT4CBT was comparable to the clinician alliance. The digital alliance and clinician alliance had similar, albeit very small, associations with abstinence during treatment. Future research can explore how the digital alliance develops to improve AUD treatment efficacy.

Conceptualizing the digital therapeutic alliance in the context of fully automated mental health apps: A thematic analysis

Fully automated mental health apps provide a promising opportunity for increasing access to mental health care and resources. Given this opportunity, continued research into the utility and effectiveness of mental health apps is crucial. Therapeutic alliance (TA) refers to the relationship between a client and a healthcare professional, and has been shown to be an important predictor of clinical outcomes in face-to-face therapy. Given the significance of TA in traditional therapy, it is important to explore whether the notion of a digital therapeutic alliance (DTA) in the context of fully automated mental health apps also plays an important role in clinical outcomes. Current evidence shows that the conceptualization of DTA in the context of fully automated mental health apps can be potentially different to TA in face-to-face therapy. Thus, a new DTA conceptual model is necessary for comprehensively understanding the mechanisms underpinning DTA for fully automated mental health apps. To the best of our knowledge, this is the first study that qualitatively explored the dimensions of a DTA in the context of fully automated mental health apps. We conducted interviews with 20 users of mental health apps to explore the key dimensions comprising DTA in the context of fully automated mental health apps. We found that although conceptualizations of DTA and TA have shared dimensions, flexibility and emotional experiences are unique domains in DTA. On the other hand, although agreement on goals between a therapist and a client is important in face to face therapy, we found that users can have an alliance with an app without a goal. The importance of goal needs further investigations.

Developing a Game (Inner Dragon) Within a Leading Smartphone App for Smoking Cessation: Design and Feasibility Evaluation Study

BACKGROUND

Several stand-alone smartphone apps have used serious games to provide an engaging approach to quitting smoking. So far, the uptake of these games has been modest, and the evidence base for their efficacy in promoting smoking cessation is still evolving. The feasibility of integrating a game into a popular smoking cessation app is unclear.

OBJECTIVE

The aim of this paper was to describe the design and iterative development of the Inner Dragon game within Smoke Free, a smartphone app with proven efficacy, and the results of a single-arm feasibility trial as part of a broad program that seeks to assess the effectiveness of the gamified app for smoking cessation.

METHODS

In phase 1, the study team undertook a multistep process to design and develop the game, including web-based focus group discussions with end users (n=15). In phase 2, a single-arm study of Smoke Free users who were trying to quit (n=30) was conducted to assess the feasibility and acceptability of the integrated game and to establish the feasibility of the planned procedures for a randomized pilot trial.

RESULTS

Phase 1 led to the final design of Inner Dragon, informed by principles from psychology and behavioral economics and incorporating several game mechanics designed to increase user engagement and retention. Inner Dragon users maintain an evolving pet dragon that serves as a virtual avatar for the users' progress in quitting. The phase-2 study established the feasibility of the study methods. The mean number of app sessions completed per user was 13.8 (SD 13.1; median 8; range 1-46), with a mean duration per session of 5.8 (median 1.1; range 0-81.1) minutes. Overall, three-fourths (18/24, 75%) of the participants entered the Inner Dragon game at least once and had a mean of 2.4 (SD 2.4) sessions of game use. The use of Inner Dragon was positively associated with the total number of app sessions (correlation 0.57). The mean satisfaction score of participants who provided ratings (11/24, 46%) was 4.2 (SD 0.6) on a 5-point scale; however, satisfaction ratings for Inner Dragon were only completed by 13% (3/24) of the participants.

CONCLUSIONS

Findings supported further development and evaluation of Inner Dragon as a beneficial feature of Smoke Free. The next step of this study is to conduct a randomized pilot trial to determine whether the gamified version of the app increases user engagement over a standard version of the app.

Interplay Between Innovation and Intersubjectivity: Therapists Perceptions of Phantom Motor Execution Therapy and Its Effect on Phantom Limb Pain

Purpose

Interpersonal processes, including therapeutic alliance, may modulate the impact of interventions on pain experience. However, the role of interpersonal context on the effects of technology-enhanced interventions remains underexplored. This study elicited therapists' perspectives on how a novel rehabilitative process, involving Phantom Motor Execution (PME), may impact phantom limb pain. The mediating role of therapeutic alliance, and the way PME influenced its formation, was investigated.

Methods

A qualitative descriptive design, using a framework method, was used to explore therapists' (n=11) experiences of delivering PME treatment. Semi-structured online-based interviews were conducted.

Results

A 3-way interaction between therapist, patient, and the PME device was an overarching construct tying four themes together. It formed the context for change in phantom limb experience. The perceived therapeutic effects (theme 1) extended beyond those initially hypothesised and highlighted the mediating role of the key actors and context (theme 2). The therapeutic relationship was perceived as a transformative journey (theme 3), creating an opportunity for communication, collaboration, and bonding. It was seen as a cause and a consequence of therapeutic effects. Future directions, including the role of expertise-informed adaptations and enabling aspects of customised solutions, were indicated (theme 4).

Conclusion

This study pointed to intrapersonal, interpersonal, and contextual factors that should be considered in clinical implementation of novel rehabilitative tools. The results demonstrated that therapists have unique insights and a crucial role in facilitating PME treatment. The study highlighted the need to consider the biopsychosocial model of pain in designing, evaluating, and implementing technology-supported interventions.

Trajectories of depression symptoms in a therapist-supported digital mental health intervention: a repeated measures latent profile analysis

PURPOSE

Major depression affects 10% of the US adult population annually, contributing to significant burden and impairment. Research indicates treatment response is a non-linear process characterized by combinations of gradual changes and abrupt shifts in depression symptoms, although less is known about differential trajectories of depression symptoms in therapist-supported digital mental health interventions (DMHI).

METHODS

Repeated measures latent profile analysis was used to empirically identify differential trajectories based upon biweekly depression scores on the Patient Health Questionnaire-9 (PHQ-9) among patients engaging in a therapist-supported DMHI from January 2020 to July 2021. Multivariate associations between symptom trajectories with sociodemographics and clinical characteristics were examined with multinomial logistic regression. Minimal clinically important differences (MCID) were defined as a five-point change on the PHQ-9 from baseline to week 12.

RESULTS

The final sample included 2192 patients aged 18 to 82 (mean = 39.1). Four distinct trajectories emerged that differed by symptom severity and trajectory of depression symptoms over 12 weeks. All trajectories demonstrated reductions in symptoms. Despite meeting MCID criteria, evidence of treatment resistance was found among the trajectory with the highest symptom severity. Chronicity of major depressive episodes and lifetime trauma exposures were ubiquitous across the trajectories in a multinomial logistic regression model.

CONCLUSIONS

These data indicate that changes in depression symptoms during DMHI are heterogenous and non-linear, suggesting a need for precision care strategies to address treatment resistance and increase engagement. Future efforts should examine the effectiveness of trauma-informed treatment modules for DMHIs as well as protocols for continuation treatment and relapse prevention.

Factors Affecting Digital Tool Use in Client Interaction According to Mental Health Professionals: Interview Study

BACKGROUND

Digital tools and interventions are being increasingly developed in response to the growing mental health crisis, and mental health professionals (MHPs) considerably influence their adoption in client practice. However, how MHPs use digital tools in client interaction is yet to be sufficiently understood, which poses challenges to their design, development, and implementation.

OBJECTIVE

This study aimed to create a contextual understanding of how MHPs use different digital tools in clinical client practice and what characterizes the use across tools.

METHODS

A total of 19 Finnish MHPs participated in semistructured interviews, and the data were transcribed, coded, and inductively analyzed.

RESULTS

We found that MHP digital tool use was characterized by 3 distinct functions: communication, diagnosis and evaluation, and facilitating therapeutic change. The functions were addressed using analog tools, digitized tools that mimic their analog counterparts, and digital tools that use the possibilities native to digital. The MHP-client communication included various media alongside face-to-face meetings, the MHPs increasingly used digitized tools in client evaluation, and the MHPs actively used digitized materials to facilitate therapeutic change. MHP tool use was generally characterized by adaptability-it was negotiated in client interactions. However, there was considerable variance in the breadth of MHPs' digital toolbox. The existing clinical practices emphasized MHP-client interaction and invited incremental rather than radical developments, which challenged the achievement of the scalability benefits expected from digital tools.

CONCLUSIONS

MHPs use digitized and digital tools in client practice. Our results contribute to the user-centered research, development, and implementation of new digital solutions in mental health care by classifying them according to their function and medium and describing how MHPs use and do not use them.

Waiting for a digital therapist: three challenges on the path to psychotherapy delivered by artificial intelligence

Growing demand for broadly accessible mental health care, together with the rapid development of new technologies, trigger discussions about the feasibility of psychotherapeutic interventions based on interactions with Conversational Artificial Intelligence (CAI). Many authors argue that while currently available CAI can be a useful supplement for human-delivered psychotherapy, it is not yet capable of delivering fully fledged psychotherapy on its own. The goal of this paper is to investigate what are the most important obstacles on our way to developing CAI systems capable of delivering psychotherapy in the future. To this end, we formulate and discuss three challenges central to this quest. Firstly, we might not be able to develop effective AI-based psychotherapy unless we deepen our understanding of what makes human-delivered psychotherapy effective. Secondly, assuming that it requires building a therapeutic relationship, it is not clear whether psychotherapy can be delivered by non-human agents. Thirdly, conducting psychotherapy might be a problem too complicated for narrow AI, i.e., AI proficient in dealing with only relatively simple and well-delineated tasks. If this is the case, we should not expect CAI to be capable of delivering fully-fledged psychotherapy until the so-called "general" or "human-like" AI is developed. While we believe that all these challenges can ultimately be overcome, we think that being mindful of them is crucial to ensure well-balanced and steady progress on our path to AI-based psychotherapy.

To chat or bot to chat: Ethical issues with using chatbots in mental health

This paper presents a critical review of key ethical issues raised by the emergence of mental health chatbots. Chatbots use varying degrees of artificial intelligence and are increasingly deployed in many different domains including mental health. The technology may sometimes be beneficial, such as when it promotes access to mental health information and services. Yet, chatbots raise a variety of ethical concerns that are often magnified in people experiencing mental ill-health. These ethical challenges need to be appreciated and addressed throughout the technology pipeline. After identifying and examining four important ethical issues by means of a recognised ethical framework comprised of five key principles, the paper offers recommendations to guide chatbot designers, purveyers, researchers and mental health practitioners in the ethical creation and deployment of chatbots for mental health.

The service user experience of SlowMo therapy: A co-produced thematic analysis of service users' subjective experience

OBJECTIVES

SlowMo is the first blended digital therapy for paranoia, showing significant small-moderate reductions in paranoia in a recent large-scale randomized controlled trial (RCT). This study explored the subjective service-user experience of the SlowMo therapy content and design; the experience of the blended therapy approach, including the triangle of the therapeutic alliance; and the experience of the digital aspects of the intervention.

DESIGN

Qualitative co-produced sub-study of an RCT.

METHODS

Participants were 22 adult service users with schizophrenia-spectrum psychosis and persistent distressing paranoia, who completed at least one SlowMo therapy session and a 24-week follow-up, at one of 3 sites in Oxford, London, and Sussex, UK. They were interviewed by peer researchers, using a topic guide co-produced by the Patient and Public Involvement (PPI) team. The transcribed data were analysed thematically. Multiple coding and triangulation, and lay peer researcher validation were used to reach a consensus on the final theme structure.

RESULTS

Six core themes were identified: (i) starting the SlowMo journey; (ii) the central role of the supportive therapist; (iii) slowing things down; (iv) value and learning from social connections; (v) approaches and challenges of technology; and (vi) improvements in paranoia and well-being.

CONCLUSIONS

For these service users, slowing down for a moment was helpful, and integrated into thinking over time. Learning from social connections reflected reduced isolation, and enhanced learning through videos, vignettes, and peers. The central role of the supportive therapist and the triangle of alliance between service user, therapist, and digital platform were effective in promoting positive therapeutic outcomes.

Developing and evaluating an educational web-based tool for health professions education: the Flipped Classroom Navigator

BACKGROUND

Flipped classroom pedagogy is a blended learning approach applied in undergraduate health professions education. However, teachers and students may require training to effectively engage in flipped classroom pedagogy. Thus, this study aimed to design, develop, and evaluate a web-based tool for fostering flipped classroom pedagogy in undergraduate health professions education.

METHODS

This is an educational design-based research with a descriptive evaluation component which was conducted in two steps: (i) design & development and (ii) evaluation of an educational website. An expert panel was formed to evaluate the website by using a website evaluation questionnaire (WEQ). Descriptive statistics were employed to calculate the experts' agreement level.

RESULTS

An innovative website design was used to provide access to a range of digital devices. The development process occurred concurrently in two steps: (i) website development and (ii) learning content development. The educational website was branded as the Flipped Classroom Navigator (FCN). Based on WEQ scores, the FCN obtained a good level of agreement (≥ 80%) for its' ease of use, hyperlinks, structure, relevance, comprehension, completeness, and layout.

CONCLUSIONS

The FCN is an effective method for providing training to promote flipped classroom pedagogy in health professions education. The FCN achieved good evaluation scores and comments from experts. However, it is also necessary to obtain acceptance from the end-users, which could be the focus of future research. Nonetheless, the expert panel pinpointed areas for further development before introducing the FCN to end-users.

Can chatbots help to motivate smoking cessation? A study on the effectiveness of motivational interviewing on engagement and therapeutic alliance

Background

Cigarette smoking poses a major threat to public health. While cessation support provided by healthcare professionals is effective, its use remains low. Chatbots have the potential to serve as a useful addition. The objective of this study is to explore the possibility of using a motivational interviewing style chatbot to enhance engagement, therapeutic alliance, and perceived empathy in the context of smoking cessation.

Methods

A preregistered web-based experiment was conducted in which smokers (n = 153) were randomly assigned to either the motivational interviewing (MI)-style chatbot condition (n = 78) or the neutral chatbot condition (n = 75) and interacted with the chatbot in two sessions. In the assessment session, typical intake questions in smoking cessation interventions were administered by the chatbot, such as smoking history, nicotine dependence level, and intention to quit. In the feedback session, the chatbot provided personalized normative feedback and discussed with participants potential reasons to quit. Engagement with the chatbot, therapeutic alliance, and perceived empathy were the primary outcomes and were assessed after both sessions. Secondary outcomes were motivation to quit and perceived communication competence and were assessed after the two sessions.

Results

No significant effects of the experimental manipulation (MI-style or neutral chatbot) were found on engagement, therapeutic alliance, or perceived empathy. A significant increase in therapeutic alliance over two sessions emerged in both conditions, with participants reporting significantly increased motivation to quit. The chatbot was perceived as highly competent, and communication competence was positively associated with engagement, therapeutic alliance, and perceived empathy.

Conclusion

The results of this preregistered study suggest that talking with a chatbot about smoking cessation can help to motivate smokers to quit and that the effect of conversation has the potential to build up over time. We did not find support for an extra motivating effect of the MI-style chatbot, for which we discuss possible reasons. These findings highlight the promise of using chatbots to motivate smoking cessation. Implications for future research are discussed.

Human-Computer Interaction in Digital Mental Health

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.

Recommender systems for mental health apps: advantages and ethical challenges

Recommender systems assist users in receiving preferred or relevant services and information. Using such technology could be instrumental in addressing the lack of relevance digital mental health apps have to the user, a leading cause of low engagement. However, the use of recommender systems for digital mental health apps, particularly those driven by personal data and artificial intelligence, presents a range of ethical considerations. This paper focuses on considerations particular to the juncture of recommender systems and digital mental health technologies. While separate bodies of work have focused on these two areas, to our knowledge, the intersection presented in this paper has not yet been examined. This paper identifies and discusses a set of advantages and ethical concerns related to incorporating recommender systems into the digital mental health (DMH) ecosystem. Advantages of incorporating recommender systems into DMH apps are identified as (1) a reduction in choice overload, (2) improvement to the digital therapeutic alliance, and (3) increased access to personal data & self-management. Ethical challenges identified are (1) lack of explainability, (2) complexities pertaining to the privacy/personalization trade-off and recommendation quality, and (3) the control of app usage history data. These novel considerations will provide a greater understanding of how DMH apps can effectively and ethically implement recommender systems.

Digital Therapeutic Alliance With Fully Automated Mental Health Smartphone Apps: A Narrative Review

Fully automated mental health smartphone apps show strong promise in increasing access to psychological support. Therefore, it is crucial to understand how to make these apps effective. The therapeutic alliance (TA), or the relationship between healthcare professionals and clients, is considered fundamental to successful treatment outcomes in face-to-face therapy. Thus, understanding the TA in the context of fully automated apps would bring us insights into building effective smartphone apps which engage users. However, the concept of a digital therapeutic alliance (DTA) in the context of fully automated mental health smartphone apps is nascent and under-researched, and only a handful of studies have been published in this area. In particular, no published review paper examined the DTA in the context of fully automated apps. The objective of this review was to integrate the extant literature to identify research gaps and future directions in the investigation of DTA in relation to fully automated mental health smartphone apps. Our findings suggest that the DTA in relation to fully automated smartphone apps needs to be conceptualized differently to traditional face-to-face TA. First, the role of bond in the context of fully automated apps is unclear. Second, human components of face-to-face TA, such as empathy, are hard to achieve in the digital context. Third, some users may perceive apps as more non-judgmental and flexible, which may further influence DTA formation. Subdisciplines of computer science, such as affective computing and positive computing, and some human-computer interaction (HCI) theories, such as those of persuasive technology and human-app attachment, can potentially help to foster a sense of empathy, build tasks and goals and develop bond or an attachment between users and apps, which may further contribute to DTA formation in fully automated smartphone apps. Whilst the review produced a relatively limited quantity of literature, this reflects the novelty of the topic and the need for further research.

The Digital Therapeutic Alliance: Prospects and Considerations

The growing prevalence of digital approaches to mental health care raises a range of questions and considerations. A notion that has recently emerged is that of the digital therapeutic alliance, prompting consideration of whether and how the concept of therapeutic alliance, which has proven to be a central ingredient of successful traditional psychotherapy, could translate to mental health care via digital technologies. This special issue editorial article outlines the topic of digital therapeutic alliance and introduces the five articles that comprise the special issue.

Impact of Jointly Using an e-Mental Health Resource (Self-Management And Recovery Technology) on Interactions Between Service Users Experiencing Severe Mental Illness and Community Mental Health Workers: Grounded Theory Study

BACKGROUND

e-Mental health resources are increasingly available for people who experience severe mental illness, including those who are users of community mental health services. However, the potential for service users (SUs) living with severe mental illness to use e-mental health resources together with their community mental health workers (MHWs) has received little attention.

OBJECTIVE

This study aims to identify how jointly using an interactive website called Self-Management And Recovery Technology (SMART) in a community mental health context influenced therapeutic processes and interactions between SUs and MHWs from their perspective.

METHODS

We conducted a qualitative study using a constructivist grounded theory methodology. Data were collected through individual semistructured interviews with 37 SUs and 15 MHWs who used the SMART website together for 2 to 6 months. Data analysis involved iterative phases of coding, constant comparison, memo writing, theoretical sampling, and consultation with stakeholders to support the study's credibility.

RESULTS

A substantive grounded theory, discovering ways to keep life on track, was developed, which portrays a shared discovery process arising from the SU-worker-SMART website interactions. The discovery process included choosing to use the website, revealing SUs' experiences, exploring these experiences, and gaining new perspectives on how SUs did and could keep their lives on track. SUs and MHWs perceived that their three-way interactions were enjoyable, beneficial, and recovery focused when using the website together. They experienced the shared discovery process as relationship building-their interactions when using the website together were more engaging and equal.

CONCLUSIONS

Jointly using an e-mental health resource elicited recovery-oriented interactions and processes between SUs and MHWs that strengthened their therapeutic relationship in real-world community mental health services. Further work to develop and integrate this novel use of e-mental health in community mental health practice is warranted.