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Goldberg SB, Jiwani Z, Bolt DM, Riordan KM, Davidson RJ, Hirshberg MJ. Evidence for Bidirectional, Cross-Lagged Associations Between Alliance and Psychological Distress in an Unguided Mobile-Health Intervention. Clin Psychol Sci 2024; 12:517-525. [PMID: 38863442 PMCID: PMC11164554 DOI: 10.1177/21677026231184890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
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.
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Affiliation(s)
- Simon B Goldberg
- Department of Counseling Psychology, UW-Madison, Madison, WI, USA
- Center for Healthy Minds, UW-Madison, Madison, WI, USA
| | - Zishan Jiwani
- Department of Counseling Psychology, UW-Madison, Madison, WI, USA
- Center for Healthy Minds, UW-Madison, Madison, WI, USA
| | - Daniel M Bolt
- Department of Educational Psychology, UW-Madison, Madison, WI, USA
| | - Kevin M Riordan
- Department of Counseling Psychology, UW-Madison, Madison, WI, USA
- Center for Healthy Minds, UW-Madison, Madison, WI, USA
| | - Richard J Davidson
- Center for Healthy Minds, UW-Madison, Madison, WI, USA
- Department of Psychology, UW-Madison, Madison, WI, USA
- Department of Psychiatry, UW-Madison, Madison, WI, USA
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Quayle E, Larkin A, Schwannauer M, Varese F, Cartwright K, Chitsabesan P, Green V, Radford G, Richards C, Shafi S, Whelan P, Chan C, Hewins W, Newton A, Niebauer E, Sandys M, Ward J, Bucci S. Experiences of a digital health intervention for young people exposed to technology assisted sexual abuse: a qualitative study. BMC Psychiatry 2024; 24:237. [PMID: 38549096 PMCID: PMC10979588 DOI: 10.1186/s12888-024-05605-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 02/10/2024] [Indexed: 04/01/2024] Open
Abstract
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).
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Affiliation(s)
- Ethel Quayle
- Department of Clinical and Health Psychology, School of Health in Social Science, University of Edinburgh, Edinburgh, UK
| | - Amanda Larkin
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Matthias Schwannauer
- Department of Clinical and Health Psychology, School of Health in Social Science, University of Edinburgh, Edinburgh, UK
- NHS Lothian, Edinburgh, UK
| | - Filippo Varese
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science, The University of Manchester, Manchester, UK
| | - Kim Cartwright
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | | | | | | | | | | | - Pauline Whelan
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science, The University of Manchester, Manchester, UK
| | - Cindy Chan
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - William Hewins
- Department of Clinical and Health Psychology, School of Health in Social Science, University of Edinburgh, Edinburgh, UK
- NHS Lothian, Edinburgh, UK
| | - Alice Newton
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Erica Niebauer
- Department of Clinical and Health Psychology, School of Health in Social Science, University of Edinburgh, Edinburgh, UK
- NHS Lothian, Edinburgh, UK
| | - Marina Sandys
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Jennifer Ward
- Department of Clinical and Health Psychology, School of Health in Social Science, University of Edinburgh, Edinburgh, UK
- NHS Lothian, Edinburgh, UK
| | - Sandra Bucci
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK.
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science, The University of Manchester, Manchester, UK.
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Rogan J, Bucci S, Firth J. Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis. JMIR Ment Health 2024; 11:e49577. [PMID: 38261403 PMCID: PMC10848143 DOI: 10.2196/49577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 01/24/2024] Open
Abstract
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.
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Affiliation(s)
- Jessica Rogan
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Joseph Firth
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
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AshaRani PV, Tan YWB, Samari E, Wang P, Cetty L, Satghare P, Verma SK, Tang C, Subramaniam M. The relationship between therapeutic alliance, frequency of consultation and uptake of telemedicine among patients seeking treatment for early psychosis: A moderated mediation model. Digit Health 2024; 10:20552076241247194. [PMID: 38698830 PMCID: PMC11064748 DOI: 10.1177/20552076241247194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2024] [Indexed: 05/05/2024] Open
Abstract
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.
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Affiliation(s)
- PV AshaRani
- Research Division, Institute of Mental Health, Singapore, Singapore
| | | | - Ellaisha Samari
- Research Division, Institute of Mental Health, Singapore, Singapore
| | - Peizhi Wang
- Research Division, Institute of Mental Health, Singapore, Singapore
| | - Laxman Cetty
- Research Division, Institute of Mental Health, Singapore, Singapore
| | - Pratika Satghare
- Research Division, Institute of Mental Health, Singapore, Singapore
| | - Swapna K Verma
- Department of Psychosis, Duke-NUS Medical School, Singapore, Singapore
- Early Psychosis Intervention Programme, Department of Psychosis, Institute of Mental Health, Singapore, Singapore
| | - Charmaine Tang
- Early Psychosis Intervention Programme, Department of Psychosis, Institute of Mental Health, Singapore, Singapore
| | - Mythily Subramaniam
- Research Division, Institute of Mental Health, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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von Wulffen C, Marciniak MA, Rohde J, Kalisch R, Binder H, Tuescher O, Kleim B. German Version of the Mobile Agnew Relationship Measure: Translation and Validation Study. J Med Internet Res 2023; 25:e43368. [PMID: 37955952 PMCID: PMC10682917 DOI: 10.2196/43368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 07/03/2023] [Accepted: 07/27/2023] [Indexed: 11/14/2023] Open
Abstract
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.
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Affiliation(s)
- Clemens von Wulffen
- Department of Psychology, University of Zurich, Zürich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Zurich, Switzerland
| | - Marta Anna Marciniak
- Department of Psychology, University of Zurich, Zürich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Zurich, Switzerland
| | - Judith Rohde
- Department of Psychology, University of Zurich, Zürich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Zurich, Switzerland
| | - Raffael Kalisch
- Leibniz Institute for Resilience Research, Mainz, Germany
- Neuroimaging Center, Focus Program Translational Neuroscience, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Oliver Tuescher
- Leibniz Institute for Resilience Research, Mainz, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center, University Johannes Gutenberg University, Mainz, Germany
| | - Birgit Kleim
- Department of Psychology, University of Zurich, Zürich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Zurich, Switzerland
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Hassan L, Eisner E, Berry K, Emsley R, Ainsworth J, Lewis S, Haddock G, Edge D, Bucci S. User engagement in a randomised controlled trial for a digital health intervention for early psychosis (Actissist 2.0 trial). Psychiatry Res 2023; 329:115536. [PMID: 37857132 DOI: 10.1016/j.psychres.2023.115536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 10/02/2023] [Accepted: 10/07/2023] [Indexed: 10/21/2023]
Abstract
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.
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Affiliation(s)
- Lamiece Hassan
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Emily Eisner
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK; Research and Innovation, Greater Manchester Mental Health Foundation NHS Trust, Manchester, UK
| | - Katherine Berry
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK; Research and Innovation, Greater Manchester Mental Health Foundation NHS Trust, Manchester, UK
| | - Richard Emsley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - John Ainsworth
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Shôn Lewis
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK; Research and Innovation, Greater Manchester Mental Health Foundation NHS Trust, Manchester, UK
| | - Gillian Haddock
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK; Research and Innovation, Greater Manchester Mental Health Foundation NHS Trust, Manchester, UK
| | - Dawn Edge
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK; Research and Innovation, Greater Manchester Mental Health Foundation NHS Trust, Manchester, UK
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK; Research and Innovation, Greater Manchester Mental Health Foundation NHS Trust, Manchester, UK.
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Tong F, Lederman R, D'Alfonso S, Berry K, Bucci S. Conceptualizing the digital therapeutic alliance in the context of fully automated mental health apps: A thematic analysis. Clin Psychol Psychother 2023; 30:998-1012. [PMID: 37042076 DOI: 10.1002/cpp.2851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/13/2023]
Abstract
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.
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Affiliation(s)
- Fangziyun Tong
- School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, USA
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
| | - Reeva Lederman
- School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, USA
| | - Simon D'Alfonso
- School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, USA
| | - Katherine Berry
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
- Complex Trauma and Resilience Research Unit, Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
- Complex Trauma and Resilience Research Unit, Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
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Ribba B, Peck R, Hutchinson L, Bousnina I, Motti D. Digital Therapeutics as a New Therapeutic Modality: A Review from the Perspective of Clinical Pharmacology. Clin Pharmacol Ther 2023; 114:578-590. [PMID: 37392464 DOI: 10.1002/cpt.2989] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/24/2023] [Indexed: 07/03/2023]
Abstract
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.
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Affiliation(s)
- Benjamin Ribba
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Richard Peck
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Lucy Hutchinson
- Roche Information Solutions, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Imein Bousnina
- Genentech, A Member of the Roche Group, Washington, DC, USA
| | - Dario Motti
- Roche Information Solutions, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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Senathirajah Y, Solomonides A. Human Factors and Organizational Issues: Contributions from 2022. Yearb Med Inform 2023; 32:210-214. [PMID: 38147862 PMCID: PMC10751143 DOI: 10.1055/s-0043-1768750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Abstract
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.
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Etter JF, Vera Cruz G, Khazaal Y. Predicting smoking cessation, reduction and relapse six months after using the Stop-Tabac app for smartphones: a machine learning analysis. BMC Public Health 2023; 23:1076. [PMID: 37277740 DOI: 10.1186/s12889-023-15859-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 05/10/2023] [Indexed: 06/07/2023] Open
Abstract
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 .
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Affiliation(s)
- Jean-François Etter
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Germano Vera Cruz
- Department of Psychology, UR 7273 CRP-CPO, University of Picardie Jules Verne, Chemin du Thil, Amiens, 80025, France.
- Département de Psychiatrie, Service de médecine des addictions, Lausanne University Hospital, Rue du Bugnon 23, Lausanne, 1011, Switzerland.
| | - Yasser Khazaal
- Addiction Medicine, Lausanne University Hospital, Lausanne, Switzerland.
- Lausanne University, Lausanne, Switzerland.
- Department of Psychiatry and Addictology, Montreal University, Montreal, Canada.
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11
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Morrow E, Zidaru T, Ross F, Mason C, Patel KD, Ream M, Stockley R. Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Front Psychol 2023; 13:971044. [PMID: 36733854 PMCID: PMC9887144 DOI: 10.3389/fpsyg.2022.971044] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/05/2022] [Indexed: 01/18/2023] Open
Abstract
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.
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Affiliation(s)
| | - Teodor Zidaru
- Department of Anthropology, London School of Economics and Political Sciences, London, United Kingdom
| | - Fiona Ross
- Faculty of Health, Science, Social Care and Education, Kingston University London, London, United Kingdom
| | - Cindy Mason
- Artificial Intelligence Researcher (Independent), Palo Alto, CA, United States
| | | | - Melissa Ream
- Kent Surrey Sussex Academic Health Science Network (AHSN) and the National AHSN Network Artificial Intelligence (AI) Initiative, Surrey, United Kingdom
| | - Rich Stockley
- Head of Research and Engagement, Surrey Heartlands Health and Care Partnership, Surrey, United Kingdom
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Wang Q, Peng S, Zha Z, Han X, Deng C, Hu L, Hu P. Enhancing the conversational agent with an emotional support system for mental health digital therapeutics. Front Psychiatry 2023; 14:1148534. [PMID: 37139323 PMCID: PMC10149869 DOI: 10.3389/fpsyt.2023.1148534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/22/2023] [Indexed: 05/05/2023] Open
Abstract
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.
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Affiliation(s)
- Qing Wang
- China Mobile Research Institute, Beijing, China
| | | | - Zhiyuan Zha
- School of Information, Renmin University of China, Beijing, China
| | - Xue Han
- China Mobile Research Institute, Beijing, China
| | - Chao Deng
- China Mobile Research Institute, Beijing, China
- Chao Deng
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
| | - Pengwei Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- *Correspondence: Pengwei Hu
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