Background:Ambulatory assessment and active and passive monitoring all offer a real-time, flexible approach to assessing mood and behavior in mood disorders. Despite their potential, concerns remain regarding the performance, usability, adherence, and potential safety of these tools. Objective:This study synthesizes the findings from 7 systematic reviews, integrating quantitative and qualitative data from randomized trials, observational studies, and user experience research to evaluate the performance, feasibility, acceptability, and clinical impact of ambulatory assessment and mood monitoring in people with depression and bipolar disorder. We assessed studies over the medium or long term (3 months or more). Methods:A summary of a series of systematic reviews was carried out by the authors-including meta-analyses (for quantitative data) and meta-syntheses (for qualitative data). Eight electronic databases were searched, and mixed methods studies were included. Studies were assessed for risk of bias. The results were checked for coherence, and recommendations were made by individuals with lived experience, methodologists, and psychiatrists. GRADE (Grading of Recommendations Assessment, Development, and Evaluation) was used to assess the quality and strength of the evidence. Results:The 111 included studies included 19,945 participants and used 69 different ambulatory assessment protocols or mood-monitoring interventions. Key barriers to implementation were identified, including performance inconsistency, adverse effects, and user disengagement. Evidence-based recommendations are provided to guide future clinical and research applications. Conclusions:Ambulatory assessment and mood monitoring hold promise in research and clinical practice, yet their implementation requires more rigorous evaluation, greater personalization, and responsible, user-centered design. Crucially, these measures can add granularity and confirmation, but additional context is often required, and none of these measures are robust enough yet to replace current outcomes.

The preferences and opinions of individuals with depression will likely be fundamental for the success of mood monitoring interventions, or for ambulatory assessment approaches as methods of data collection. Concerns have been raised regarding negative psychological effects of repeated mood assessment. This systematic review and meta-synthesis of qualitative studies assessed the user experience of mood monitoring and ambulatory assessment procedures. This included: barriers and facilitators to use for people with depression and for clinicians, negative psychological effects and the intended purpose of use. Eight electronic databases were searched and mixed-methods studies were included. Qualitative studies were rated for risk of bias. Fourteen studies were identified. We identified seven overarching concepts: negative psychological effects, perceived effectiveness, difficulties in completing questionnaires, sharing with others, desired features, purpose of mood monitoring, and clinician barriers/facilitators. While many participants found the mood monitoring/ambulatory assessment therapeutic and positive, many participants reported negative consequences from ambulatory assessment/mood monitoring. Future protocols should monitor negative psychological effects, whether they are long-lasting and consider testing the incorporation of additional therapeutic elements to manage them. We report additional key concepts that are likely to improve the user experience, engagement, attrition, usability and acceptability of ambulatory assessment/mood monitoring protocols for people with depression.

IntroductionPeer 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 analysisIn 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 disseminationEthical 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 numberISRCTN 62469166; the protocol for the realist synthesis in workstream one is prospectively registered at PROSPERO CRD42022352528.

This paper presents a new methodological approach, TrustScapes, an open access tool designed to identify and visualise stakeholders’ concerns and policy recommendations on data protection, algorithmic bias, and online safety for a fairer and more trustworthy online world. We first describe how the tool was co-created with young people and other stakeholders through a series of workshops. We then present two sets of TrustScapes focus groups to illustrate how the tool can be used, and the data analysed. The paper then provides the methodological insights, including the strengths of the TrustScapes and the lessons for future research using TrustScapes. A key strength of this method is that it allows people to visualise their ideas and thoughts on the worksheet, using the keywords and sketches provided. The flexibility in the mode of delivery is another strength of the TrustScapes method. The TrustScapes focus groups can be conducted in a relatively short time (1.5–2 hours), either in person or online depending on the participants’ needs, geological locations, and practicality. Our experience with the TrustScapes offers some lessons (related to the data collection and analysis) for researchers who wish to use this method in the future. Finally, we describe how the outcomes from the TrustScapes focus groups should help to inform future policy decisions.

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.

Understanding stakeholders' views on novel autonomous systems in healthcare is essential to ensure these are not abandoned after substantial investment has been made. The ExTRAPPOLATE project applied the principles of Responsible Research and Innovation (RRI) in the development of an automated feedback system for psychological therapists, 'AutoCICS'. A Patient and Practitioner Reference Group (PPRG) was convened over three online workshops to inform the system's development. Iterative workshops allowed proposed changes to the system (based on stakeholder comments) to be scrutinized. The PPRG reference group provided valuable insights, differentiated by role, including concerns and suggestions related to the applicability and acceptability of the system to different patients, as well as ethical considerations. The RRI approach enabled the anticipation of barriers to use, reflection on stakeholders' views, effective engagement with stakeholders, and action to revise the design and proposed use of the system prior to testing in future planned feasibility and effectiveness studies. Many best practices and learnings can be taken from the application of RRI in the development of the AutoCICS system.

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.

In this paper we report progress on a novel explainable artificial intelligence (XAI) initiative applying Natural Language Processing (NLP) with elements of codesign to develop a text classifier for application in psychotherapy training. The task is to produce a tool that will facilitate therapists to review their sessions by automatically labelling transcript text with levels of interaction for patient activation in known psychological processes, using XAI to increase their trust in the model’s suggestions and client trajectory predictions. After pre-processing of the language features extracted from professionally annotated therapy session transcripts, we apply a supervised machine learning approach (CHAID) to classify interaction labels (negative, neutral, positive). Weighted samples are used to overcome class imbalanced data. The results show this initial model can make useful distinctions among the three labels of patient activation with 74% accuracy and provide insight into its reasoning. This ongoing project will additionally evaluate which XAI approaches can be used to increase the transparency of the tool to end users, exploring whether direct involvement of stakeholders improves usability of the XAI interface and therefore trust in the solution.

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.

Aims: A 10-month project funded by the NewMind network sought to develop the specification of a visualisation toolbox that could be applied on digital platforms (web- or app-based) to support adults with lived experience of mental health difficulties to present and track their personal wellbeing in a multi-media format. Methods: A participant co-design methodology, Double Diamond from the Design Council (Great Britain), was used consisting of four phases: Discover - a set of literature and app searches of wellbeing and health visualisation material; Define - an initial workshop with participants with lived experience of mental health problems to discuss wellbeing and visualisation techniques and to share personal visualisations; Develop - a second workshop to add detail to personal visualisations, for example, forms of media to be employed, degree of control over sharing; and Deliver - to disseminate the learning from the exercise. Results: Two design workshops were held in December 2017 and April 2018 with 13 and 12 experts-by-experience involved, respectively, including two peer researchers (co-authors) and two individual-carer dyads in each workshop, with over 50% of those being present in both workshops. A total of 20 detailed visualisations were produced, the majority focusing on highly personal and detailed presentations of wellbeing. Discussion: While participants concurred on a range of typical dimensions of wellbeing, the individual visualisations generated were in contrast to the techniques currently employed by existing digital wellbeing apps and there was a great diversity in preference for different visualisation types. Participants considered personal visualisations to be useful as self-administered interventions or as a step towards seeking help, as well as being tools for self-appraisal. Conclusion: The results suggest that an authoring approach using existing apps may provide the high degree of flexibility required. Training on such tools, delivered via a module on a recovery college course, could be offered.

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.

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. 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. 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. 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. 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. International Standard Randomized Controlled Trial Number (ISRCTN): 12673428; http://www.controlled-trials.com/ISRCTN12673428/12673428 (Archived by WebCite at http://www.webcitation.org/6uw6ZJk5a)