Improving Peer Online Forums (iPOF): protocol for a realist evaluation of peer online mental health forums to inform practice and policy.

INTRODUCTION: Peer online mental health forums are commonly used and offer accessible support. Positive and negative impacts have been reported by forum members and moderators, but it is unclear why these impacts occur, for whom and in which forums. This multiple method realist study explores underlying mechanisms to understand how forums work for different people. The findings will inform codesign of best practice guidance and policy tools to enhance the uptake and effectiveness of peer online mental health forums. METHODS AND ANALYSIS: In workstream 1, we will conduct a realist synthesis, based on existing literature and interviews with approximately 20 stakeholders, to generate initial programme theories about the impacts of forums on members and moderators and mechanisms driving these. Initial theories that are relevant for forum design and implementation will be prioritised for testing in workstream 2.Workstream 2 is a multiple case study design with mixed methods with several online mental health forums differing in contextual features. Quantitative surveys of forum members, qualitative interviews and Corpus-based Discourse Analysis and Natural Language Processing of forum posts will be used to test and refine programme theories. Final programme theories will be developed through novel triangulation of the data.Workstream 3 will run alongside workstreams 1 and 2. Key stakeholders from participating forums, including members and moderators, will be recruited to a Codesign group. They will inform the study design and materials, refine and prioritise theories, and codesign best policy and practice guidance. ETHICS AND DISSEMINATION: Ethical approval was granted by Solihull Research Ethics Committee (IRAS 314029). Findings will be reported in accordance with RAMESES (Realist And MEta-narrative Evidence Syntheses: Evolving Standards) guidelines, published as open access and shared widely, along with codesigned tools. TRIAL REGISTRATION NUMBER: ISRCTN 62469166; the protocol for the realist synthesis in workstream one is prospectively registered at PROSPERO CRD42022352528.

Developing an Automated Assessment of In-session Patient Activation for Psychological Therapy: Codevelopment Approach.

BACKGROUND: Patient activation is defined as a patient's confidence and perceived ability to manage their own health. Patient activation has been a consistent predictor of long-term health and care costs, particularly for people with multiple long-term health conditions. However, there is currently no means of measuring patient activation from what is said in health care consultations. This may be particularly important for psychological therapy because most current methods for evaluating therapy content cannot be used routinely due to time and cost restraints. Natural language processing (NLP) has been used increasingly to classify and evaluate the contents of psychological therapy. This aims to make the routine, systematic evaluation of psychological therapy contents more accessible in terms of time and cost restraints. However, comparatively little attention has been paid to algorithmic trust and interpretability, with few studies in the field involving end users or stakeholders in algorithm development. OBJECTIVE: This study applied a responsible design to use NLP in the development of an artificial intelligence model to automate the ratings assigned by a psychological therapy process measure: the consultation interactions coding scheme (CICS). The CICS assesses the level of patient activation observable from turn-by-turn psychological therapy interactions. METHODS: With consent, 128 sessions of remotely delivered cognitive behavioral therapy from 53 participants experiencing multiple physical and mental health problems were anonymously transcribed and rated by trained human CICS coders. Using participatory methodology, a multidisciplinary team proposed candidate language features that they thought would discriminate between high and low patient activation. The team included service-user researchers, psychological therapists, applied linguists, digital research experts, artificial intelligence ethics researchers, and NLP researchers. Identified language features were extracted from the transcripts alongside demographic features, and machine learning was applied using k-nearest neighbors and bagged trees algorithms to assess whether in-session patient activation and interaction types could be accurately classified. RESULTS: The k-nearest neighbors classifier obtained 73% accuracy (82% precision and 80% recall) in a test data set. The bagged trees classifier obtained 81% accuracy for test data (87% precision and 75% recall) in differentiating between interactions rated high in patient activation and those rated low or neutral. CONCLUSIONS: Coproduced language features identified through a multidisciplinary collaboration can be used to discriminate among psychological therapy session contents based on patient activation among patients experiencing multiple long-term physical and mental health conditions.

Measuring Online Wellbeing: A Scoping Review of Subjective Wellbeing Measures.

With the increasing importance of the internet to our everyday lives, questions are rightly being asked about how its' use affects our wellbeing. It is important to be able to effectively measure the effects of the online context, as it allows us to assess the impact of specific online contexts on wellbeing that may not apply to offline wellbeing. This paper describes a scoping review of English language, peer-reviewed articles published in MEDLINE, EMBASE, and PsychInfo between 1st January 2015 and 31st December 2019 to identify what measures are used to assess subjective wellbeing and in particular to identify any measures used in the online context. Two hundred forty studies were identified; 160 studies were removed by abstract screening, and 17 studies were removed by full-text screening, leaving 63 included studies. Fifty-six subjective wellbeing scales were identified with 18 excluded and 38 included for further analysis. Only one study was identified researching online wellbeing, and no specific online wellbeing scale was found. Therefore, common features of the existing scales, such as the number and type of questions, are compared to offer recommendations for building an online wellbeing scale. Such a scale is recommended to be between 3 and 20 questions, using mainly 5-point Likert or Likert-like scales to measure at least positive and negative affect, and ideally life satisfaction, and to use mainly subjective evaluation. Further research is needed to establish how these findings for the offline world effectively translate into an online measure of wellbeing.

Potential of using visual imagery to revolutionise measurement of emotional health.

Appropriate measurement of emotional health by all those working with children and young people is an increasing focus for professional practice. Most of the tools used for assessment or self-assessment of emotional health were designed in the mid-20th century using language and technology derived from pen and paper written texts. However, are they fit for purpose in an age of pervasive computing with increasingly rich audiovisual media devices being in the hands of young people? This thought piece explores how the increased use of visual imagery, especially forms that can be viewed or created on digital devices, might provide a way forward for more effective measuring of emotional health, including smiley faces, other emojis and other potential forms of visual imagery. The authors bring together perspectives from healthcare, counselling, youth advocacy, academic research, primary care and school-based mental health support to explore these issues.

Identifying research priorities for digital technology in mental health care: results of the James Lind Alliance Priority Setting Partnership.

Digital technology, including the internet, smartphones, and wearables, provides the possibility to bridge the mental health treatment gap by offering flexible and tailored approaches to mental health care that are more accessible and potentially less stigmatising than those currently available. However, the evidence base for digital mental health interventions, including demonstration of clinical effectiveness and cost-effectiveness in real-world settings, remains inadequate. The James Lind Alliance Priority Setting Partnership for digital technology in mental health care was established to identify research priorities that reflect the perspectives and unmet needs of people with lived experience of mental health problems and use of mental health services, their carers, and health-care practitioners. 644 participants contributed 1369 separate questions, which were reduced by qualitative thematic analysis into six overarching themes. Following removal of out-of-scope questions and a comprehensive search of existing evidence, 134 questions were verified as uncertainties suitable for research. These questions were then ranked online and in workshops by 628 participants to produce a shortlist of 26. The top ten research priorities, which were identified by consensus at a stakeholder workshop, should inform research policy and funding in this field. Identified priorities primarily relate to the safety and efficacy of digital technology interventions in comparison with face-to-face interventions, evidence of population reach, mechanisms of therapeutic change, and the ways in which the effectiveness of digital interventions in combination with human support might be optimised.

Direct to Public Peer Support and e-Therapy Program Versus Information to Aid Self-Management of Depression and Anxiety: Protocol for a Randomized Controlled Trial.

BACKGROUND: Regardless of geography or income, effective help for depression and anxiety only reaches a small proportion of those who might benefit from it. The scale of the problem suggests a role for effective, safe, anonymized public health-driven Web-based services such as Big White Wall (BWW), which offer immediate peer support at low cost. OBJECTIVE: Using Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) methodology, the aim of this study was to determine the population reach, effectiveness, cost-effectiveness, and barriers and drivers to implementation of BWW compared with Web-based information compiled by UK's National Health Service (NHS, NHS Choices Moodzone) in people with probable mild to moderate depression and anxiety disorder. METHODS: A pragmatic, parallel-group, single-blind randomized controlled trial (RCT) is being conducted using a fully automated trial website in which eligible participants are randomized to receive either 6 months access to BWW or signposted to the NHS Moodzone site. The recruitment of 2200 people to the study will be facilitated by a public health engagement campaign involving general marketing and social media, primary care clinical champions, health care staff, large employers, and third sector groups. People will refer themselves to the study and will be eligible if they are older than 16 years, have probable mild to moderate depression or anxiety disorders, and have access to the Internet. RESULTS: The primary outcome will be the Warwick-Edinburgh Mental Well-Being Scale at 6 weeks. We will also explore the reach, maintenance, cost-effectiveness, and barriers and drivers to implementation and possible mechanisms of actions using a range of qualitative and quantitative methods. CONCLUSIONS: This will be the first fully digital trial of a direct to public online peer support program for common mental disorders. The potential advantages of adding this to current NHS mental health services and the challenges of designing a public health campaign and RCT of two digital interventions using a fully automated digital enrollment and data collection process are considered for people with depression and anxiety. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 12673428; http://www.controlled-trials.com/ISRCTN12673428/12673428 (Archived by WebCite at http://www.webcitation.org/6uw6ZJk5a).