From Social Network to Peer Support Network: Opportunities to Explore Mechanisms of Online Peer Support for Mental Health.

An increasing number of psychological interventions are shifting to online modes of delivery. One such intervention is peer-to-peer support, which in this context may provide internet users living with mental health disorders an opportunity to connect with and support others living with similar conditions. This paper presents a call for further research into how platforms such as Facebook could be used as channels for peer support and the mechanisms that may underlie their effectiveness. We discuss the background of peer support, how it has transitioned online, and consider theories and models that may have relevance. We also consider the importance of moderation within online peer support and the development of specific social network-based online interventions. We conclude that for social network sites to be used as peer-to-peer support interventions, more research is needed to understand their effectiveness, the role of moderation in these communities, and the mechanisms that produce the benefits experienced by users.

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.

Technology for Healthy Aging and Wellbeing: Co-producing Solutions.

Methods to facilitate co-production in mental health are important for engaging end users. As part of the Technology for Healthy Aging and Wellbeing (THAW) initiative we organized two interactive co-production workshops, to bring together older adults, health and social care professionals, non-governmental organizations, and researchers. In the first workshop, we used two activities: Technology Interaction and Scavenger Hunt, to explore the potential for different stakeholders to discuss late life mental health and existing technology. In the second workshop, we used Vignettes, Scavenger Hunt, and Invention Test to examine how older adults and other stakeholders might co-produce solutions to support mental wellbeing in later life using new and emerging technologies. In this paper, we share the interactive materials and activities and consider their value for co-production. Overall, the interactive methods were successful in engaging stakeholders with a broad range of technologies to support mental health and wellbeing and in co-producing ideas for how they could be leveraged and incorporated into older people's lives and support services. We offer this example of using interactive methods to facilitate co-production to encourage greater involvement of older adults and other under-represented groups in co-producing mental health technologies and services.

Informing the Development of a Digital Health Platform Through Universal Points of Care: Qualitative Survey Study.

BACKGROUND: Epilepsy, multiple sclerosis (MS), and depression are chronic conditions where technology holds potential in clinical monitoring and self-management. Over 5 years, the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) consortium has explored the application of remote measurement technology (RMT) to the management and self-management of patients in these clinical areas. The consortium is large and includes clinical and nonclinical researchers as well as a patient advisory board. OBJECTIVE: This formative development study aimed to understand how consortium members viewed the potential of RMT in epilepsy, MS, and depression. METHODS: In this qualitative survey study, we developed a methodological tool, universal points of care (UPOC), to gather views on the potential use, acceptance, and value of a novel RMT platform across 3 chronic conditions (MS, epilepsy, and depression). UPOC builds upon use case scenario methodology, using expert elicitation and analysis of care pathways to develop scenarios applicable across multiple conditions. After developing scenarios, we elicited views on the potential of RMT in these different scenarios through a survey administered to 28 subject matter experts, consisting of 16 health care practitioners; 5 health care services researchers; and 7 people with lived experience of MS, epilepsy, or depression. Survey results were analyzed thematically and using an existing framework of factors describing links between design and context. RESULTS: The survey elicited potential beneficial applications of the RADAR-CNS RMT system as well as patient, clinical, and nonclinical requirements of RMT across the 3 conditions of interest. Potential applications included recognition of early warning signs of relapse from subclinical signals for MS, seizure precipitant signals for epilepsy, and behavior change in depression. RMT was also thought to have the potential to overcome the problem of underreporting, which is especially problematic in epilepsy, and to allow the capture of secondary symptoms that are not generally collected in MS, such as mood. CONCLUSIONS: Respondents suggested novel and unanticipated uses of RMT, including the use of RMT to detect emerging side effects of treatment, enable behavior change for sleep regulation and activity, and offer a way to include family and other carers in a care network, which could assist with goal setting. These suggestions, together with others from this and related work, will inform the development of the system for its eventual application in research and clinical practice. The UPOC methodology was effective in directing respondents to consider the value of health care technologies in condition-specific experiences of everyday life and working practice.

Healthcare practitioner views and experiences of patients self-monitoring blood pressure: a vignette study.

BACKGROUND: Home self-monitoring of blood pressure is widely used in primary care to assist in the diagnosis of hypertension, as well as to improve clinical outcomes and support adherence to medication. The National Institute for Health and Care Excellence (NICE) care pathways for hypertension recommend specific guidelines, although they lack detail on supporting patients to self-monitor. AIM: To elicit primary care practitioners' experiences of managing patients' home blood pressure self-monitoring, across surgeries located in different socioeconomic areas. DESIGN & SETTING: A qualitative focus group study was conducted with a total of 21 primary care professionals. METHOD: Participants were GPs and practice nurses (PNs), purposively recruited from surgeries in areas of low and high deprivation, according to the English indices of multiple deprivation. Six vignettes were developed featuring data from interviews with people who self-monitor and these were used in five focus groups. Results were thematically analysed. RESULTS: Themes derived in the thematic analysis largely reflected topics covered by the vignettes. These included: advice on purchase of a device; supporting home monitoring; mitigating patient anxiety experienced as a result of home monitoring; valuing patients' data; and effect of socioeconomic factors. CONCLUSION: The work provides an account of methods used by primary care practitioners in the management of home blood pressure self-monitoring, where guidance may be lacking and primary care practitioners act on their own judgement. Findings complement recent policy documentation, which recognises the need to adopt new ways of working to empower patients (for example, additional support from healthcare assistants), but lacks detail on how this should be done.

Health Care Professionals' Views on Using Remote Measurement Technology in Managing Central Nervous System Disorders: Qualitative Interview Study.

BACKGROUND: Remote measurement technologies (RMT) can be used to collect data on a variety of bio-behavioral variables, which may improve the care of patients with central nervous system disorders. Although various studies have explored their potential, prior work has highlighted a knowledge gap in health care professionals' (HCPs) perceptions of the value of RMT in clinical practice. OBJECTIVE: This study aims to understand HCPs' perspectives on using RMT in health care practice for the care of patients with depression, epilepsy, or multiple sclerosis (MS). METHODS: Semistructured interviews were conducted with 26 multidisciplinary primary and secondary care HCPs who care for patients with epilepsy, depression, or MS. Interviews were transcribed verbatim and analyzed using thematic analysis. RESULTS: A total of 8 main themes emerged from the analysis: (1) potential clinical value of RMT data; (2) when to use RMT in care pathways; (3) roles of health care staff who may use RMT data; (4) presentation and accessibility of data; (5) obstacles to successful use of RMT; (6) limits to the role of RMT; (7) empowering patients; and (8) considerations around alert-based systems. CONCLUSIONS: RMT could add value to the system of care for patients with central nervous system disorders by providing clinicians with graphic summaries of data in the patient record. Barriers of both technical and human nature should be considered when using these technologies, as should the limits to the benefits they can offer.

Older Adults' Perspectives on Using Digital Technology to Maintain Good Mental Health: Interactive Group Study.

BACKGROUND: A growing number of apps to support good mental health and well-being are available on digital platforms. However, very few studies have examined older adults' attitudes toward the use of these apps, despite increasing uptake of digital technologies by this demographic. OBJECTIVE: This study sought to explore older adults' perspectives on technology to support good mental health. METHODS: A total of 15 older adults aged 50 years or older, in two groups, participated in sessions to explore the use of digital technologies to support mental health. Interactive activities were designed to capture participants' immediate reactions to apps and websites designed to support mental health and to explore their experiences of using technology for these purposes in their own lives. Template analysis was used to analyze transcripts of the group discussions. RESULTS: Older adults were motivated to turn to technology to improve mood through mechanisms of distraction, normalization, and facilitated expression of mental states, while aiming to reduce burden on others. Perceived barriers to use included fear of consequences and the impact of low mood on readiness to engage with technology, as well as a lack of prior knowledge applicable to digital technologies. Participants were aware of websites available to support mental health, but awareness alone did not motivate use. CONCLUSIONS: Older adults are motivated to use digital technologies to improve their mental health, but barriers remain that developers need to address for this population to access them.

Technology for Early Detection of Depression and Anxiety in Older People.

Under-diagnosis of depression and anxiety is common in older adults. This project took a mixed methods approach to explore the application of machine learning and technology for early detection of these conditions. Mood measures collected with digital technologies were used to predict depression and anxiety status according to the Geriatric Depression Scale (GDS) and the Hospital Anxiety and Depression Scale (HADS). Interactive group activities and interviews were used to explore views of older adults and healthcare professionals on this approach respectively. The results show good potential for using a machine learning approach with mood data to predict later depression, though prospective results are preliminary. Qualitative findings highlight motivators and barriers to use of mental health technologies, as well as usability issues. If consideration is given to these issues, this approach could allow alerts to be provided to healthcare staff to draw attention to service users who may go on to experience depression.