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

Chinese Expert Consensus on Home Nutrition Administration for Older Patients (2024)

Families constitute important settings for the nutritional management of older adults, and there is a growing demand for continuous nutritional management services extending from hospital care to home-based care. Since the publication of the Chinese Expert Consensus on Home Nutrition Administration for the Elderly (2017 Edition) 7 years ago, accumulating research evidence and practical experience in home-based nutritional management has highlighted an urgent need for updating this consensus. In this revised edition, a total of 35 recommendations and expert consensus statements have been proposed to guide community-based health professionals in delivering scientifically sound, rational, and standardized home nutritional management services to elderly populations residing at home or within community settings.

Exploring the implementation of a novel, transdiagnostic, hybrid ecological momentary intervention for improving resilience in youth (EMIcompass): A process evaluation in the realist framework

OBJECTIVES

Previous research indicates that the hybrid compassion-focused intervention EMIcompass may reduce stress reactivity and improve quality of life. Our aim was to investigate what components of the intervention work for whom, how, and under which circumstances to contribute to the enhancement of resilience.

METHODS

We conducted a process evaluation using a realist framework. First, we developed initial programme theories using compassion-focused interventions and ecological momentary intervention documents, in addition to conducting a focus group and an interview with an individual expert. Second, we tested the initial programme theories based on qualitative data from 20 participants. Third, we refined the programme theories by analysing and interpreting the data.

RESULTS

We identified four programme components experienced as enhancing the activation of the soothing system and the application of compassion-focused principles. EMIcompass was perceived as lowering the burden of and barriers to treatment and facilitating the translation into daily life. Intra- and interpersonal context factors interacted with the mechanisms, leading to improvement in well-being, which was identified as a main outcome.

DISCUSSION

The intervention worked by strengthening participants' soothing system and facilitating ecological translation leading to improved well-being. Adaptions to improve the intervention may allow for more flexibility in individual intervention trajectories acknowledging different needs and preferences.

Chatting your way to quitting: A longitudinal exploration of smokers' interaction with a cessation chatbot

Background

Cigarette smoking poses a major public health risk, requiring scalable and accessible interventions. Chatbots offer a promising solution, given their potential in providing personalized, long-term interactions. Despite their promise, limited research has examined their efficacy and the intertwined relationship between user experience and effectiveness over an extended period of time.

Methods

In this prospective, single-arm study, we developed and evaluated Roby, a 5-session chatbot intervention incorporating motivational interviewing and cognitive behavioral therapy to help smokers quit. Roby engaged Dutch adult smokers (N = 102) in conversations covering topics such as setting a quit date, managing withdrawal and cravings, and relapse prevention. The primary outcome was the continuous abstinence rate at the end of the intervention, and secondary outcomes included 7-day point prevalence abstinence, self-efficacy, and cravings. User engagement, therapeutic alliance, and interaction satisfaction were measured weekly, and the trajectory was analyzed using Linear Mixed Models.

Results

Following an intention-to-treat principle, 18.6 % of participants achieved continuous abstinence, and 37.3 % achieved 7-day point prevalence abstinence. Self-efficacy significantly improved over the intervention, and cravings decreased over time. A slight decreasing trend was observed in engagement and satisfaction, likely due to a novelty effect. However, the decrease did not affect the intervention's outcomes.

Conclusion

This study demonstrates the feasibility and initial usefulness of Roby, highlighting the potential for chatbots in long-term cessation support. Future research should further validate these findings with randomized controlled trials. Additional efforts should focus on monitoring and maintaining user experience in the long term to enhance effectiveness.

Patients prefer human psychiatrists over chatbots: a cross-sectional study

AIM

To rate the level of patients' satisfaction with responses on questions regarding mental health provided by human psychiatrists, pharmacists, and chatbot platforms.

METHODS

This cross-sectional study enrolled 89 patients who were pharmacologically treated for their mental disorder in one institution in Croatia and one in Bosnia and Herzegovina during October 2023. They asked psychiatrists, pharmacists, ChatGPT, and one Croatian chatbot questions about their mental disorder and medications and rated the satisfaction with the responses.

RESULTS

Almost half of the patients had used ChatGPT before the study, and only 12.4% had used the Croatian platform. The patients were most satisfied with the information provided by psychiatrists (4.67 out of 5 about mental disorder and 4.51 about medications), followed by pharmacists (3.94 about medications), ChatGPT (3.66 about mental disorder and 3.45 about medications), and the Croatian platform (3.66 about mental disorder and 3.44 about medications). Almost half of the participants believed it was easier for them to put a question to a psychiatrist than to a chatbot, and only 10% claimed it was easier to ask ChatGPT.

CONCLUSION

Patients with mental health disorders were more satisfied with responses from their psychiatrists than from chatbots, and satisfaction with chatbots' knowledge on mental disorders and medications was still too low to justify their usage in these patients.

Patterns of Skills Review in Smartphone Cognitive Behavioral Therapy for Depression: Observational Study of Intervention Content Use

BACKGROUND

Smartphones could enhance access to effective cognitive behavioral therapy (CBT). Users may frequently and flexibly access bite-size CBT content on personal devices, review and practice skills, and thereby achieve better outcomes.

OBJECTIVE

We explored the distribution of actual interactions participants had with therapeutic content in a trial of smartphone CBT for depression and whether interactions were within assigned treatment modules or revisits to prior module content (ie, between-module interactions).

METHODS

We examined the association between the number of within- and between-module interactions and baseline and end-of-treatment symptom severity during an 8-week, single-arm open trial of a therapist-guided CBT for depression mobile health app.

RESULTS

Interactions were more frequent early in treatment and modestly declined in later stages. Within modules, most participants consistently made more interactions than required to progress to the next module and tended to return to all types of content rather than focus on 1 skill. By contrast, only 15 of 26 participants ever revisited prior module content (median number of revisits=1, mode=0, IQR 0-4). More revisits were associated with more severe end-of-treatment symptom severity after controlling for pretreatment symptom severity (P<.05).

CONCLUSIONS

The results suggest that the frequency of use is an insufficient metric of engagement, lacking the nuance of what users are engaging with and when during treatment. This lens is essential for developing personalized recommendations and yielding better treatment outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05386329; https://clinicaltrials.gov/study/NCT05386329?term=NCT05386329.

The Safety of Digital Mental Health Interventions: Findings and Recommendations From a Qualitative Study Exploring Users' Experiences, Concerns, and Suggestions

BACKGROUND

The literature around the safety of digital mental health interventions (DMHIs) is growing. However, the user/patient perspective is still absent from it. Understanding the user/patient perspective can ensure that professionals address issues that are significant to users/patients and help direct future research in the field.

OBJECTIVE

This qualitative study aims to explore DMHI users' experiences, views, concerns, and suggestions regarding the safety of DMHIs.

METHODS

We included individuals aged 18 years old or older, having experience in using a DMHI, and can speak and understand English without the need for a translator. Fifteen individual interviews were conducted. Deductive thematic analysis was used to analyze the data.

RESULTS

The analysis of the interview transcripts yielded 3 main themes: Nonresponse: A Concern, a Risk, and How Users Mitigate It, Symptom Deterioration and Its Management, and Concerns Around Data Privacy and How to Mitigate Them.

CONCLUSIONS

The results of this study led to 7 recommendations on how the safety of DMHIs can be improved: provide "easy access" versions of key information, use "approved by..." badges, anticipate and support deterioration, provide real-time feedback, acknowledge the lack of personalization, responsibly manage access, and provide genuine crisis support. These recommendations arose from users' experiences and suggestions. If implemented, these recommendations can improve the safety of DMHIs and enhance users' experience.

Establishment and Maintenance of a Digital Therapeutic Alliance in People Living With Negative Symptoms of Schizophrenia: Two Exploratory Single-Arm Studies

BACKGROUND

Evidence-based digital therapeutics represent a new treatment modality in mental health, potentially providing cost-efficient, accessible means of augmenting existing treatments for chronic mental illnesses. CT-155/BI 3972080 is a prescription digital therapeutic under development as an adjunct to standard of care treatments for patients 18 years of age and older with experiential negative symptoms (ENS) of schizophrenia. Individual components of CT-155/BI 3972080 are designed based on the underlying principles of face-to-face treatment. A positive therapeutic alliance between patients and health care providers is linked with improved clinical outcomes in mental health. Likewise, establishing a similar therapeutic alliance with a digital therapeutic (ie, digital working alliance [DWA]) may be important for engagement and treatment effectiveness of this modality.

OBJECTIVE

This study aimed to investigate the establishment and maintenance of a DWA between a beta version of CT-155/BI 3972080 (CT-155 beta) and adults with ENS of schizophrenia.

METHODS

Two multicenter, exploratory, single-arm studies (study 1: CT-155-C-001 and study 2: CT-155-C-002) enrolled adults with schizophrenia and ENS receiving stable antipsychotic medication (≥12 weeks). Participants had access to CT-155 beta and were presented with daily in-app activities during a 3-week orientation phase that included lessons designed to facilitate building of a DWA. In study 2, the 3-week orientation phase was followed by an abbreviated active 4-week phase. Digital literacy at baseline was evaluated using the Mobile Device Proficiency Questionnaire (MDPQ). The mobile Agnew Relationship Measure (mARM) was used to assess DWA establishment after 3 weeks in both studies, and after 7 weeks in study 2 to assess DWA maintenance. Participant safety, digital literacy, and correlations between negative symptom severity and DWA were assessed in both studies.

RESULTS

Of the enrolled participants, 94% (46/49) and 86% (43/50) completed studies 1 and 2, respectively. Most were male (study 1: 71%, 35/49; study 2: 80%, 40/50). The baseline digital literacy assessed through MDPQ score was comparable in both studies (study 1: mean 30.56, SD 8.06; study 2: mean 28.69, SD 8.31) indicating proficiency in mobile device use. After 3 weeks, mARM scores (study 1: mean 5.16, SD 0.8; study 2: mean 5.36, SD 1.06) indicated that a positive DWA was established in both studies. In study 2, the positive DWA established at week 3 was maintained at week 7 (mARM: mean 5.48, SD 0.97). There were no adverse events (AEs) in study 1, and 3 nonserious and nontreatment-related AEs in study 2.

CONCLUSIONS

A positive DWA was established between participants and CT-155 beta within 3 weeks. The second 7-week study showed maintenance of the DWA to the end of the study. Results support the establishment and maintenance of a DWA between adults with ENS of schizophrenia and a beta version of CT-155/BI 3972080, a prescription digital therapeutic under development to target these symptoms.

TRIAL REGISTRATION

Clinicaltrials.gov NCT05486312; https://clinicaltrials.gov/study/NCT05486312.

Developing an Evidence- and Theory-Informed Mother-Daughter mHealth Intervention Prototype Targeting Physical Activity in Preteen Girls of Low Socioeconomic Position: Multiphase Co-Design Study

BACKGROUND

Preteen girls of lower socioeconomic position are at increased risk of physical inactivity. Parental support, particularly from mothers, is positively correlated with girls' physical activity levels. Consequently, family-based interventions are recognized as a promising approach to improve young people's physical activity. However, the effects of these interventions on girls' physical activity are often inconsistent, with calls for more rigorous, theory-informed, and co-designed family-based interventions to promote physical activity in this cohort.

OBJECTIVE

This study aimed to use co-design methods to develop an evidence- and theory-informed mother-daughter mobile health intervention prototype targeting physical activity in preteen girls.

METHODS

The intervention prototype was developed in accordance with the United Kingdom Medical Research Council framework, the Behaviour Change Wheel, the Theoretical Domains Framework, and the Behaviour Change Techniques Ontology. The Behaviour Change Intervention Ontology was also used to annotate the intervention characteristics. The co-design process incorporated three phases: (1) behavioral analysis, (2) the selection of intervention components, and (3) refinement of the intervention prototype. Throughout these phases, workshops were conducted with preteen girls (n=10), mothers of preteen girls (n=9), and primary school teachers (n=6), with additional input from an academic advisory panel.

RESULTS

This 3-phase co-design process resulted in the development of a theory-informed intervention that targeted two behaviors: (1) mothers' engagement in a range of supportive behaviors for their daughters' physical activity and (2) daughters' physical activity behavior. Formative research identified 11 theoretical domains to be targeted as part of the intervention (eg, knowledge, skills, and beliefs about capabilities). These were to be targeted by 6 intervention functions (eg, education, persuasion, and modeling) and 27 behavior change techniques (eg, goal setting and self-monitoring). The co-design process resulted in a mobile app being chosen as the mode of delivery for the intervention.

CONCLUSIONS

This paper offers a comprehensive description and analysis of using co-design methods to develop a mother-daughter mobile health intervention prototype that is ready for feasibility and acceptability testing. The Behaviour Change Wheel, Theoretical Domains Framework, and Behaviour Change Techniques Ontology provided a systematic and transparent theoretical foundation for developing the prototype by enabling the identification of potential pathways for behavior change. Annotating the Behaviour Change Intervention Ontology entities represents the intervention characteristics in a detailed and structured way that supports improved communication, replication, and implementation of interventions.

Tales of hope and hesitation: Smoking cessation experts' views on the opportunities and risks of digital behaviour change interventions

Objective

Smoking remains a global health challenge, with 1.14 billion active smokers worldwide. Many of these smokers seek cessation support. The rise of mHealth offers novel intervention methods, providing monitoring and tailored feedback. This study aimed to map the opportunities and challenges of integrating digital behaviour change interventions (mHealth) into smoking cessation practices by understanding professionals' perceptions of these tools.

Methods

A qualitative study was conducted involving semi-structured interviews with 11 experienced smoking cessation professionals in Flanders, Belgium. Data collection occurred between January and April 2023. Inductive thematic analysis was performed to identify key patterns and themes in the experts' views regarding mHealth interventions.

Results

The analysis revealed four primary themes: (1) The Inexorable March of Technology - experts acknowledged the inevitability of technology in smoking cessation but varied in enthusiasm; (2) The Shimmering Mirage of Possibility - technology was viewed as supplementary, offering efficiency and support but limited in depth; (3) The Footnotes to Enthusiasm - experts expressed concerns over privacy, inclusivity, and the potential for technology to displace human care; and (4) The Human Anchor - the irreplaceable role of human connection and therapeutic alliance, which digital tools might not be able to replicate.

Conclusion

Experts believe mHealth interventions can augment smoking cessation support but should not replace human-driven care. A blended approach, integrating digital tools with traditional therapeutic relationships, offers the most promise. Addressing concerns about privacy, inclusivity, and most importantly the limits of digital therapeutic alliances is essential for successful mHealth implementation in smoking cessation.

Emotion Regulation Self-Efficacy as a Mechanism of Alliance and Outcomes in a Brief, Transdiagnostic Digital Mental Health Intervention: L'auto-efficacité de la régulation des émotions en tant que mécanisme d'alliance et de résultats dans une brève intervention transdiagnostique numérique en santé mentale

OBJECTIVES

Digital mental health interventions have shown promise for alleviating various forms of psychopathology, including depression and anxiety. However, the mechanisms of such interventions remain largely unexplored. The purpose of this study was to investigate a potential mechanistic process through which one hybrid digital mental health intervention (i.e., the Digital Clinic) might operate. We hypothesized that emotion regulation (ER) self-efficacy at the treatment midpoint may mediate the relationship between alliance (i.e., therapeutic alliance and digital alliance) and outcome (i.e., co-morbid symptoms of depression and anxiety) at the treatment endpoint.

METHODS

Data used in this study came from the Digital Clinic, a brief transdiagnostic telehealth treatment program augmented by a dual-purpose digital phenotyping and intervention smartphone app. Recruited primarily from primary care, participants were 82 adults (73% White, 64% cisgender women, mean age 41) receiving outpatient treatment in the northeastern United States. All constructs were measured with validated scales, including The Working Alliance Inventory-Short Revised (WAI-SR) for therapeutic alliance, the Digital Working Alliance Inventory (DWAI) for digital alliance, the PROMIS Self-Efficacy for Managing Emotions Short Form scale for ER self-efficacy, and the Patient Health Questionnaire Anxiety-Depression Scale (PHQ-ADS) for co-morbid symptoms of depression and anxiety.

RESULTS

Significant reductions in co-morbid symptoms of depression and anxiety and significant increases in ER self-efficacy were found from baseline to treatment endpoint. Therapeutic and digital alliance at the midpoint each predicted reductions in co-morbid symptoms of depression and anxiety at the endpoint through ER self-efficacy, controlling for baseline scores.

CONCLUSIONS

Findings suggest that ER self-efficacy may be a proximal predictor of clinical improvement that may be enhanced by therapeutic and digital alliance. Future controlled research is essential to improve knowledge of the mechanisms of digital mental health interventions and to enhance their effectiveness.

User perceptions and utilisation of features of an AI-enabled workplace digital mental wellness platform 'mindline at work'

BACKGROUND

The working population encounters unique work-related stressors. Despite these challenges, accessibility to mental healthcare remains limited. Digital technology-enabled mental wellness tools can offer much-needed access to mental healthcare. However, existing literature has given limited attention to their relevance and user engagement, particularly for the working population.

AIM

This study aims to assess user perceptions and feature utilisation of mindline at work, a nationally developed AI-enabled digital platform designed to improve mental wellness in the working population.

METHODS

This study adopted a mixed-methods design comprising a survey (n=399) and semistructured interviews (n=40) with office-based working adults. Participants were asked to use mindline at work for 4 weeks. We collected data about utilisation of the platform features, intention for sustained use and perceptions of specific features.

RESULTS

Participants under 5 years of work experience reported lower utilisation of multimedia resources but higher utilisation of emotion self-assessment tools and the AI chatbot compared with their counterparts (p<0.001). The platform received a moderate level of satisfaction (57%) and positive intention for sustained use (58%). Participants regarded mindline at work as an 'essential' safeguard against workplace stress, valuing its secure and non-judgmental space and user anonymity. However, they wanted greater institutional support for office workers' mental wellness to enhance the uptake. The AI chatbot was perceived as useful for self-reflection and problem-solving, despite limited maturity.

CONCLUSION

Identifying the unique benefits of specific features for different segments of working adults can foster a personalised user experience and promote mental well-being. Increasing workplace awareness is essential for platform adoption.

War, emotions, mental health, and artificial intelligence

During the war time dysregulation of negative emotions such as fear, anger, hatred, frustration, sadness, humiliation, and hopelessness can overrule normal societal values, culture, and endanger global peace and security, and mental health in affected societies. Therefore, it is understandable that the range and power of negative emotions may play important roles in consideration of human behavior in any armed conflict. The estimation and assessment of dominant negative emotions during war time are crucial but are challenged by the complexity of emotions' neuro-psycho-physiology. Currently available natural language processing (NLP) tools have comprehensive computational methods to analyze and understand the emotional content of related textual data in war-inflicted societies. Innovative AI-driven technologies incorporating machine learning, neuro-linguistic programming, cloud infrastructure, and novel digital therapeutic tools and applications present an immense potential to enhance mental health care worldwide. This advancement could make mental health services more cost-effective and readily accessible. Due to the inadequate number of psychiatrists and limited psychiatric resources in coping with mental health consequences of war and traumas, new digital therapeutic wearable devices supported by AI tools and means might be promising approach in psychiatry of future. Transformation of negative dominant emotional maps might be undertaken by the simultaneous combination of online cognitive behavioral therapy (CBT) on individual level, as well as usage of emotionally based strategic communications (EBSC) on a public level. The proposed positive emotional transformation by means of CBT and EBSC may provide important leverage in efforts to protect mental health of civil population in war-inflicted societies. AI-based tools that can be applied in design of EBSC stimuli, like Open AI Chat GPT or Google Gemini may have great potential to significantly enhance emotionally based strategic communications by more comprehensive understanding of semantic and linguistic analysis of available text datasets of war-traumatized society. Human in the loop enhanced by Chat GPT and Gemini can aid in design and development of emotionally annotated messages that resonate among targeted population, amplifying the impact of strategic communications in shaping human dominant emotional maps into a more positive by CBT and EBCS.

Evidence for Bidirectional, Cross-Lagged Associations Between Alliance and Psychological Distress in an Unguided Mobile-Health Intervention

Bidirectional associations between changes in symptoms and alliance are established for in-person psychotherapy. Alliance may play an important role in promoting engagement and effectiveness within unguided mobile health (mHealth) interventions. Using models disaggregating alliance and psychological distress into within- and between-person components (random intercept cross-lagged panel model), we report bidirectional associations between alliance and distress over the course of a 4-week smartphone-based meditation intervention (n=302, 80.0% elevated depression/anxiety). Associations were stable across time with effect sizes similar to those observed for psychotherapy (βs=-.13 to -.14 and -.09 to -.10, for distress to alliance and alliance to distress, respectively). Alliance may be worth measuring to improve the acceptability and effectiveness of mHealth tools. Further empirical and theoretical work characterizing the role and meaning of alliance in unguided mHealth is warranted.

Experiences of a digital health intervention for young people exposed to technology assisted sexual abuse: a qualitative study

BACKGROUND

There is growing evidence that Technology Assisted Sexual Abuse (TASA) represents a serious problem for large numbers of children. To date, there are very few evidence-based interventions available to young people (YP) after they have been exposed to this form of abuse, and access to support services remains a challenge. Digital tools such as smartphones have the potential to increase access to mental health support and may provide an opportunity for YP to both manage their distress and reduce the possibility of further victimization. The current study explores the acceptability of a digital health intervention (DHI; the i-Minds app) which is a theory-driven, co-produced, mentalization-based DHI designed for YP aged 12-18 who have experienced TASA.

METHODS

Semi-structured interviews were conducted with 15 YP recruited through Child and Adolescent Mental Health Services, a Sexual Assault Referral Centre and an e-therapy provider who had access to the i-Minds app as part of a feasibility clinical trial. Interviews focused on the acceptability and usability of i-Minds and were coded to themes based on the Acceptability of Healthcare Interventions framework.

RESULTS

All participants found the i-Minds app acceptable. Many aspects of the app were seen as enjoyable and useful in helping YP understand their abuse, manage feelings, and change behavior. The app was seen as usable and easy to navigate, but for some participants the level of text was problematic and aspects of the content was, at times, emotionally distressing at times.

CONCLUSIONS

The i-Minds app is useful in the management of TASA and helping change some risk-related vulnerabilities. The app was designed, developed and evaluated with YP who had experienced TASA and this may account for the high levels of acceptability seen.

TRIAL REGISTRATION

The trial was registered on the ISRCTN registry on the 12/04/2022 as i-Minds: a digital intervention for young people exposed to online sexual abuse (ISRCTN43130832).

Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis

BACKGROUND

Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation.

OBJECTIVE

This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care.

METHODS

A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework.

RESULTS

Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues.

CONCLUSIONS

Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.

A qualitative study exploring the digital therapeutic alliance with fully automated smartphone apps

Background

Digital mental health interventions are increasingly used to scale up access to mental health support, yet very few mental health apps are empirically supported, with high attrition rates. The therapeutic alliance between therapists and clients is a key factor in predicting psychological therapy retention and outcomes. Understanding how this concept translates to the digital context, the so-called digital therapeutic alliance (DTA), may help enhance retention and outcomes in digital mental health.

Objectives

To explore and conceptualise the DTA with mental health interventions delivered by standalone apps. Specifically: (1) whether people with mental health difficulties form an alliance with mental health apps; (2) what constitutes the DTA; and (3) the similarities and differences across the traditional therapeutic alliance and DTA.

Methods

Qualitative semi-structured interviews were conducted with 17 individuals utilising mental health apps. Interview topic examples included rationale for app support, goals around app use and perceived connection. Interviews were analysed using thematic analysis.

Results

Five core themes were identified: (1) connection with 'an other'; (2) accessibility; (3) choice and empowerment; (4) goals and expectations: having lower expectations than in-person support; and (5) safe place: non-judgemental, loyalty, trust.

Conclusions

The study strengthens support for the concept of the DTA but adds to existing knowledge by suggesting that the DTA needs to be conceived differently to the traditional therapeutic alliance. App developers can enhance alliance by paying particular attention to the use of human voice features, validating statements, personalised interfaces, and multiple options for activities and strategies.

Exploring the potential of digital therapeutics: An assessment of progress and promise

Digital therapeutics (DTx), a burgeoning subset of digital health solutions, has garnered considerable attention in recent times. These cutting-edge therapeutic interventions employ diverse technologies, powered by software algorithms, to treat, manage, and prevent a wide array of diseases and disorders. Although DTx shows significant promise as an integral component of medical care, its widespread integration is still in the preliminary stages. This limited adoption can be largely attributed to the scarcity of comprehensive research that delves into DTx's scope, including its technological underpinnings, potential application areas, and challenges-namely, regulatory hurdles and modest physician uptake. This review aims to bridge this knowledge gap by offering an in-depth overview of DTx products' value to both patients and clinicians. It evaluates the current state of maturity of DTx applications driven by digital technologies and investigates the obstacles that developers and regulators encounter in the market introduction phase.

The relationship between therapeutic alliance, frequency of consultation and uptake of telemedicine among patients seeking treatment for early psychosis: A moderated mediation model

Background

Telehealth services ensure the delivery of healthcare services to a wider range of consumers through online platforms. Nonetheless, the acceptance and uptake of telehealth remain elusive. This study aims to understand the (a) uptake and (b) acceptability of telemedicine, (c) if therapeutic alliance mediates the relationship between the frequency of consultations with clinicians and the uptake of telemedicine in patients with early psychosis, and (d) role of education in moderating the relationship between therapeutic alliance and the uptake of telemedicine for their mental healthcare.

Methods

A convenience sample of outpatients (n = 109) seeking treatment for early psychosis and their care providers (n = 106) were recruited from a tertiary psychiatric care centre. Sociodemographic and clinical characteristics, therapeutic alliance (Working Alliance Inventory), and telemedicine use were captured through self-administered surveys. The moderated mediation analysis was performed using PROCESS macro 3.4.1 with therapeutic alliance and level of education as the mediating and moderating factors, respectively.

Results

The acceptance of telemedicine was high (possibly will use: 47.7%; definitely will use: 26.6%) whilst the uptake was low (11%). Therapeutic alliance mediated the relationship between the frequency of consultation and the uptake of telemedicine (β: 0.326; CI: 0.042, 0.637). This effect was moderated by the level of education (β: -0.058; p < 0.05).

Conclusion

Therapeutic alliance mediates the relationship between the frequency of consultations and the uptake of telemedicine services with the level of education moderating this mediation. Focusing on the patients with lower education to improve their telemedicine knowledge and therapeutic alliance might increase the uptake.

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.

User engagement in a randomised controlled trial for a digital health intervention for early psychosis (Actissist 2.0 trial)

Digital Health Interventions (DHIs) can help support people with mental health problems. Achieving satisfactory levels of patient engagement is a crucial, yet often underexplored, pre-requisite for health improvement. Actissist is a co-produced DHI delivered via a smartphone app for people with early psychosis, based on Cognitive Behaviour Therapy principles. This study describes and compares engagement patterns among participants in the two arms of the Actissist 2.0 randomised controlled trial. Engagement frequency and duration were measured among participants using the Actissist app in the intervention arm (n = 87) and the ClinTouch symptom monitoring only app used as the control condition (n = 81). Overall, 47.1 % of Actissist and 45.7 % of ClinTouch users completed at least a third of scheduled alerts while active in the study. The mean frequency (77.1 versus 60.2 total responses) and the median duration (80 versus 75 days until last response) of engagement were not significantly higher among Actissist users compared to ClinTouch users. Older age, White ethnicity, using their own smartphone device and, among Actissist users, an increased sense of therapeutic alliance were significantly associated with increased engagement. Through exploiting detailed usage data, this study identifies possible participant-level and DHI-level predictors of engagement to inform the practical implementation of future DHIs.

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.

Digital Therapeutics as a New Therapeutic Modality: A Review from the Perspective of Clinical Pharmacology

The promise of transforming digital technologies into treatments is what drives the development of digital therapeutics (DTx), generally known as software applications embedded within accessible technologies-such as smartphones-to treat, manage, or prevent a pathological condition. Whereas DTx solutions that successfully demonstrate effectiveness and safety could drastically improve the life of patients in multiple therapeutic areas, there is a general consensus that generating therapeutic evidence for DTx presents challenges and open questions. We believe there are three main areas where the application of clinical pharmacology principles from the drug development field could benefit DTx development: the characterization of the mechanism of action, the optimization of the intervention, and, finally, its dosing. We reviewed DTx studies to explore how the field is approaching these topics and to better characterize the challenges associated with them. This leads us to emphasize the role that the application of clinical pharmacology principles could play in the development of DTx and to advocate for a development approach that merges such principles from development of traditional therapeutics with important considerations from the highly attractive and fast-paced world of digital solutions.

Human Factors and Organizational Issues: Contributions from 2022

OBJECTIVES

To review publications in the field of Human Factors and Organisational Issues (HF&OI) in the year 2022 and to assess major contributions to the subject.

METHOD

A bibliographic search was conducted following refinement of standardized queries used in previous years. Sources used were PubMed, Web of Science, and referral via references from other papers. The search was carried out in January 2023, and (using the PubMed article type inclusion functionality) included clinical trials, meta-analyses, randomized controlled trials, reviews, case reports, classical articles, clinical studies, observational studies (including veterinary), comparative studies, and pragmatic clinical trials.

RESULTS

Among the 520 returned papers published in 2022 in the various areas of HF&OI, the full review process selected two best papers from among 10 finalists. As in previous years, topics showed development including increased use of Artificial Intelligence (AI) and digital health tools, advancement of methodological frameworks for implementation and evaluation as well as design, and trials of specific digital tools.

CONCLUSIONS

Recent literature in HF&OI continues to focus on both theoretical advances and practical deployment, with focus on areas of patient-facing digital health, methods for design and evaluation, and attention to implementation barriers.

Predicting smoking cessation, reduction and relapse six months after using the Stop-Tabac app for smartphones: a machine learning analysis

BACKGROUND

An analysis of predictors of smoking behaviour among users of smoking cessation apps can provide useful information beyond what is already known about predictors in other contexts. Therefore, the aim of the present study was to identify the best predictors of smoking cessation, smoking reduction and relapse six months after starting to use the smartphone app Stop-Tabac.

METHOD

Secondary analysis of 5293 daily smokers from Switzerland and France who participated in a randomised trial testing the effectiveness of this app in 2020, with follow-up at one and six months. Machine learning algorithms were used to analyse the data. The analyses for smoking cessation included only the 1407 participants who responded after six months; the analysis for smoking reduction included only the 673 smokers at 6-month follow-up; and the analysis for relapse at 6 months included only the 502 individuals who had quit smoking after one month.

RESULTS

Smoking cessation after 6 months was predicted by the following factors (in this order): tobacco dependence, motivation to quit smoking, frequency of app use and its perceived usefulness, and nicotine medication use. Among those who were still smoking at follow-up, reduction in cigarettes/day was predicted by tobacco dependence, nicotine medication use, frequency of app use and its perceived usefulness, and e-cigarette use. Among those who had quit smoking after one month, relapse after six months was predicted by intention to quit, frequency of app use, perceived usefulness of the app, level of dependence and nicotine medication use.

CONCLUSION

Using machine learning algorithms, we identified independent predictors of smoking cessation, smoking reduction and relapse. Studies on the predictors of smoking behavior among users of smoking cessation apps may provide useful insights for the future development of these apps and future experimental studies.

CLINICAL TRIAL REGISTRATION

ISRCTN Registry: ISRCTN11318024, 17 May 2018. http://www.isrctn.com/ISRCTN11318024 .

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Background

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.

Objectives

The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?

Materials and methods

A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.

Results

Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.

Conclusion

There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.

Implications

In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

Enhancing the conversational agent with an emotional support system for mental health digital therapeutics

As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines.