Using Topic Modelling to Personalise a Digital Self-compassion Training

Lubbe, Laura M. van der; Groot, Nina F de; Gerritsen, Charlotte

doi:10.1007/978-3-030-99194-4_32

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Virtual proximity is also recognized as a benefit of interventions for mental health that use ecological momentary interventions (EMIS), a specific type of mobile health that enables patients to access interventions in each moment and context of daily life called a “therapist in your pocket approach” ( Schick et al, 2021 ) such as encourage physical activity in blue-green spaces (HEART by BioAsssist) ( Gallos et al, 2022 ). Automated topic modeling is recognized as being useful for personalized digital self-compassion interventions and overcoming barriers to traditional care ( van der Lubbe et al, 2022 ). eHealth coaching for older people’s self-management may have benefits for addressing unmet need in mental health services ( Bevilacqua et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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BackgroundAdvances 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.ObjectivesThe 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 methodsA 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.ResultsSearches 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.ConclusionThere 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.ImplicationsIn 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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Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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“…Based on the topics discussed by pilot participants, the following list of topics was created for exercise situations: social media, body image, social anxiety, relationships, job, school, and emotions. With the use of a specifically designed topic modelling algorithm [23], the topics discussed by users were detected in the gratitude and self-compassion journals and the 'practise self-compassion'-exercise. For the two other exercises, the user was able to choose to either practice with a familiar, unfamiliar or random topic.…”

Section: Self-compassion Training Websitementioning

confidence: 99%

Experiences of Users with an Online Self-Guided Mental Health Training Program Using Gamification

Lubbe

Gerritsen

Klein

et al. 2023

J Healthc Inform Res

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Young adulthood is a period of high risk for the development of mental health concerns. Increasing well-being among young adults is important to prevent mental health concerns and their consequences. Self-compassion has been identified as a modifiable trait with the potential to protect against mental health concerns. An online self-guided mental health training program using gamification was developed and the user experience was evaluated in a 6-week experimental design. During this period, 294 participants were allocated to use the online training program via a website. User experience was assessed via self-report questionnaires, and interaction data for the training program were also collected. Results showed that those who completed the intervention (n= 47) visited the website on average 3.2 days a week, with a mean of 45.8 interactions during the 6 weeks. Participants report positive user experiences of the online training, on average a System Usability Scale Brooke (1) score of 79.1 (out of 100) at the end-point. Participants showed positive engagement with story elements of the training, based on an average score of 4.1 (out of 5) in the evaluation of the story at the end-point. This study found the online self-compassion intervention for youth to be acceptable, although some features seem preferred by users as compared to others. Gamification in the form of a guiding story and a reward structure seemed to be a promising element for successfully motivating participants and serving as a guiding metaphor for self-compassion.

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Using Topic Modelling to Personalise a Digital Self-compassion Training

Cited by 2 publications

References 9 publications

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

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

Experiences of Users with an Online Self-Guided Mental Health Training Program Using Gamification

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